,title,journal,DOI,date,collection,published,nauthors,keywords,topic.Drug discovery,topic.Genomics,topic.Imaging,topic.Epidemiology,topic.Healthcare,topic.Clinics,topic,pub_prob,pred_pub,auth.papers,auth.papers.score,auth.citations,auth.citations.score,art.citations,art.citations.score,art.views,art.views.score,final.score
0,Diagnostic accuracy estimates for COVID-19 RT-PCR and Lateral flow immunoassay tests with Bayesian latent class models.,Am J Epidemiol,33823529,4/7/21,pubmed,0,3,bayes,0.001751169,0.177449411,0.361510683,0.288213899,0.169323535,0.001751303,Epidemiology,0.8585707,TRUE,95.66666667,0.858185417,64,0.735549906,1,0.537564047,,,0.710433123
1,Recent efforts for drug identification from phytochemicals against SARS-CoV-2: Exploration of the chemical space to identify druggable leads.,Food Chem Toxicol,33823228,4/7/21,pubmed,0,7,in silico,0.831279909,0.001593546,0.001593534,0.162346,0.001593494,0.001593516,Drug discovery,0.5583956,TRUE,21,0.312016822,4.714285714,0.24719026,0,0.403234768,,,0.32081395
2,0,Eur J Pharmacol,33823185,4/7/21,pubmed,0,7,in silico,0.993543814,0.001291217,0.001291263,0.001291277,0.001291232,0.001291197,Drug discovery,0.695213,TRUE,9.714285714,0.145401695,2.428571429,0.175274284,0,0.403234768,,,0.241303582
3,Validation of the Thermo Scientific™ SARS-CoV-2 RT-PCR Detection Workflow for the Detection of SARS-CoV-2 from Stainless Steel Environmental Surface Swabs AOAC Performance Tested MethodSM 012103.,J AOAC Int,33822958,4/7/21,pubmed,0,8,"in silico, genomes",0.001254697,0.861367184,0.133614218,0.00125465,0.001254634,0.001254617,Genomics,0.91831005,TRUE,8.5,0.126662131,34.375,0.600816163,0,0.403234768,,,0.376904354
4,Choice of assemblers has a critical impact on de novo assembly of SARS-CoV-2 genome and characterizing variants.,Brief Bioinform,33822878,4/7/21,pubmed,0,7,"sequencing, metagenom, genomes",0.001943502,0.886135449,0.001943642,0.106090427,0.001943487,0.001943493,Genomics,0.9330338,TRUE,16.42857143,0.248067289,1.285714286,0.128512176,0,0.403234768,,,0.259938077
5,Magnitude and associated factors of poor medication adherence among diabetic and hypertensive patients visiting public health facilities in Ethiopia during the COVID-19 pandemic.,PLoS One,33822807,4/7/21,pubmed,0,8,logistic regression,0.001220003,0.001220019,0.001220029,0.00122005,0.625206756,0.369913143,Healthcare,0.84846973,TRUE,9.125,0.136062836,3.75,0.21982874,0,0.403234768,,,0.253042115
6,Assessing required SARS-CoV-2 blanket testing rates for possible control of the outbreak in the epicentre Lusaka province of Zambia with consideration for asymptomatic individuals: A simple mathematical modelling study.,PLoS One,33822785,4/7/21,pubmed,0,5,mathematical model,0.00139283,0.001392878,0.001392833,0.993035712,0.001392892,0.001392856,Epidemiology,0.16353792,FALSE,23.2,0.343496815,9.6,0.346735349,0,0.403234768,,,0.364488977
7,Natural mucosal barriers and COVID-19 in children.,JCI Insight,33822777,4/7/21,pubmed,0,8,sequencing,0.403570974,0.219302837,0.0011268,0.001126792,0.317000436,0.057872161,Drug discovery,0.78422785,TRUE,94,0.854598305,118.375,0.849478191,0,0.403234768,,,0.702437088
8,SARS-CoV-2 vaccines: a triumph of science and collaboration.,JCI Insight,33822773,4/7/21,pubmed,0,4,sequencing,0.577834969,0.067492991,0.001593597,0.303115231,0.04836971,0.001593501,Drug discovery,0.3279759,FALSE,166,0.955594038,121,0.853157613,0,0.403234768,,,0.737328806
9,Prediction models for clinical severity of COVID-19 patients using multi-center clinical data in Korea.,J Med Internet Res,33822738,4/7/21,pubmed,0,11,prediction model,0.001220045,0.001220004,0.180713803,0.073586129,0.001220069,0.742039949,Clinics,0.59240764,TRUE,28.81818182,0.415671965,,,0,0.403234768,,,0.409453366
10,Rise in Use of Digital Mental Health Tools and Technologies in the U.S. During the COVID-19 Pandemic.,J Med Internet Res,33822737,4/7/21,pubmed,0,11,logistic regression,0.000822936,0.000822912,0.000822926,0.188969835,0.794044185,0.014517206,Healthcare,0.25592723,FALSE,24.63636364,0.362731152,,,0,0.403234768,,,0.38298296
11,Patient Satisfaction and Trust in Telemedicine during the COVID-19 Pandemic.,JMIR Hum Factors,33822736,4/7/21,pubmed,0,4,logistic regression,0.000871536,0.000871544,0.000871595,0.059698769,0.74793505,0.189751506,Healthcare,0.9815074,TRUE,112.5,0.895479003,,,0,0.403234768,,,0.649356885
12,In Silico investigation of the viroporin E as a vaccine target against SARS-CoV-2.,Am J Physiol Lung Cell Mol Physiol,33822639,4/7/21,pubmed,0,3,"in silico, in-silico",0.993726762,0.001254685,0.001254622,0.001254613,0.001254653,0.001254665,Drug discovery,0.638792,TRUE,261.3333333,0.986270023,68,0.748193738,0,0.403234768,,,0.712566176
13,Chaga Medicinal Mushroom Inonotus obliquus (Agaricomycetes) Terpenoids May Interfere with SARS-CoV-2 Spike Protein Recognition of the Host Cell: A Molecular Docking Study.,Int J Med Mushrooms,33822495,4/7/21,pubmed,0,3,in silico,0.954878046,0.002183312,0.002183372,0.002183398,0.036388691,0.002183181,Drug discovery,0.57320607,TRUE,23.33333333,0.345723298,22.33333333,0.505753278,0,0.403234768,,,0.418237114
14,"Silibinin as potential tool against SARS-Cov-2: In silico spike receptor-binding domain and main protease molecular docking analysis, and in vitro endothelial protective effects.",Phytother Res,33822421,4/7/21,pubmed,0,6,"computational, in silico",0.993143587,0.001371265,0.00137129,0.001371267,0.001371282,0.00137131,Drug discovery,0.7746614,TRUE,62,0.715814212,41.66666667,0.643029168,0,0.403234768,,,0.587359383
15,Article title: Risk factors for bacterial infections in patients with moderate to severe COVID-19: A case control study.,J Med Virol,33822390,4/7/21,pubmed,0,3,logistic regression,0.001901792,0.056742302,0.001901796,0.001901952,0.001901829,0.935650329,Clinics,0.8457897,TRUE,4.333333333,0.057950399,0.333333333,0.073187048,0,0.403234768,,,0.178124072
16,Validation of the COVID-19 Indoor Test™ by Phylagen for Detection of SARS-CoV-2 Virus on Stainless Steel Surfaces AOAC Performance Tested MethodSM 122004.,J AOAC Int,33822087,4/7/21,pubmed,0,7,in silico,0.188191772,0.707913378,0.099509118,0.00146193,0.001461906,0.001461896,Genomics,0.83692384,TRUE,0.857142857,0.008411157,,,0,0.403234768,,,0.205822962
17,Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Related Death Among 7.6 Million US Veterans for Use in Vaccination Prioritization.,JAMA Netw Open,33822066,4/7/21,pubmed,0,11,logistic regression,0.000889065,0.000889065,0.000889103,0.213544004,0.225925064,0.5578637,Clinics,0.5787145,TRUE,87.81818182,0.836477209,134.7272727,0.869146374,0,0.403234768,,,0.702952784
18,Pan-selectin inhibitors as potential therapeutics for COVID-19 treatment: in silico screening study.,Glycobiology,33822042,4/7/21,pubmed,0,4,"virtual screening, in silico",0.87968055,0.001310324,0.001310414,0.001310396,0.001310358,0.115077958,Drug discovery,0.4176762,FALSE,57.5,0.685447461,7,0.299973241,0,0.403234768,,,0.462885156
19,High infection secondary attack rates of SARS-CoV-2 in Dutch households revealed by dense sampling.,Clin Infect Dis,33822007,4/7/21,pubmed,0,9,logistic regression,0.001350398,0.095653104,0.00135031,0.213601688,0.658426879,0.029617621,Healthcare,0.61770225,TRUE,102.1111111,0.873708949,145.2222222,0.879314959,0,0.403234768,,,0.718752892
20,Development and validation of a predictive model to predict and manage drug shortages.,Am J Health Syst Pharm,33821926,4/7/21,pubmed,0,7,"predictive model, dataset",0.307733131,0.00089808,0.414607841,0.086597514,0.115874651,0.074288784,Drug discovery,0.15039828,FALSE,12.85714286,0.192714454,1.142857143,0.123628579,0,0.403234768,,,0.239859267
21,The Effect of Telehealth Services on Provider Productivity.,Med Care,33821831,4/7/21,pubmed,0,6,dataset,0.001751183,0.001751228,0.001751252,0.345248069,0.597521099,0.05197717,Healthcare,0.5548254,TRUE,20.16666667,0.299585627,8.333333333,0.325662296,0,0.403234768,,,0.342827563
22,The impact of disruptions due to COVID-19 on HIV transmission and control among men who have sex with men in China.,J Int AIDS Soc,33821553,4/7/21,pubmed,0,10,mathematical model,0.001059365,0.060235393,0.001059381,0.813813063,0.001059413,0.122773386,Epidemiology,0.18817768,FALSE,124.4,0.913661946,,,0,0.403234768,,,0.658448357
23,Comparison of post-COVID depression and major depressive disorder.,medRxiv,33821286,4/7/21,pubmed,0,7,logistic regression,0.001751349,0.001751366,0.001751318,0.001751273,0.827663072,0.165331622,Healthcare,0.32281572,FALSE,164.7142857,0.95460449,,,0,0.403234768,,,0.678919629
24,COVID-19 vaccine impact on rates of SARS-CoV-2 cases and post vaccination strain sequences among healthcare workers at an urban academic medical center: a prospective cohort study.,medRxiv,33821283,4/7/21,pubmed,0,18,"genome sequences, genomes",0.00108534,0.281248336,0.00108534,0.001085371,0.508664045,0.206831568,Healthcare,0.19932768,FALSE,26.5,0.389325252,55,0.702167514,0,0.403234768,,,0.498242511
25,Structure and dynamics of SARS-CoV-2 proofreading exoribonuclease ExoN.,bioRxiv,33821277,4/7/21,pubmed,0,9,"molecular dynamics simulation, computational",0.871412128,0.121849769,0.001684489,0.001684569,0.001684516,0.001684529,Drug discovery,0.5341569,TRUE,67.77777778,0.752056404,,,0,0.403234768,,,0.577645586
26,"Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics.",bioRxiv,33821271,4/7/21,pubmed,0,21,"computational, sequencing",0.893065938,0.100195881,0.001684602,0.001684559,0.001684519,0.001684501,Drug discovery,0.15028578,FALSE,25.38095238,0.372131857,,,0,0.403234768,,,0.387683312
27,Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19.,Res Sq,33821262,4/7/21,pubmed,0,15,"computational, transcriptom",0.865649222,0.126576421,0.001943559,0.001943573,0.001943622,0.001943603,Drug discovery,0.27349645,FALSE,20.53333333,0.30502814,,,0,0.403234768,,,0.354131454
28,Research Methodology to Define the Introduction of the SARS-CoV-2 B.1.429 Variant in Hawaii.,Res Sq,33821261,4/7/21,pubmed,0,2,sequence alignment,0.001751163,0.879671511,0.001751309,0.113323709,0.001751161,0.001751146,Genomics,0.2162357,FALSE,72.5,0.77549632,82.5,0.78445277,0,0.403234768,,,0.654394619
29,Age-dependent pathogenic characteristics of SARS-CoV-2 infection in ferrets.,Res Sq,33821260,4/7/21,pubmed,0,19,transcriptom,0.364005746,0.09980607,0.001861686,0.001861737,0.001861827,0.530602934,Clinics,0.3821164,FALSE,9.105263158,0.135815449,,,1,0.537564047,,,0.336689748
30,Is sickle cell disease a risk factor for severe COVID-19 outcomes in hospitalized patients? A multicenter national retrospective cohort study.,EJHaem,33821258,4/7/21,pubmed,0,8,logistic regression,0.002032822,0.002032812,0.002032772,0.002032815,0.002032868,0.98983591,Clinics,0.78499556,TRUE,6.25,0.088564537,,,0,0.403234768,,,0.245899652
31,Safety of "hot" and "cold" site admissions within a high-volume urology department in the United Kingdom at the peak of the COVID-19 pandemic.,BJUI Compass,33821256,4/7/21,pubmed,0,60,logistic regression,0.001593589,0.100401908,0.001593513,0.001593524,0.105630966,0.7891865,Clinics,0.627082,TRUE,46.1,0.597315851,,,0,0.403234768,,,0.500275309
32,Construction of a risk map to understand the vulnerability of various types of cancer patients to COVID-19 infection.,MedComm (Beijing),33821252,4/7/21,pubmed,0,3,"sequencing, dataset",0.445793046,0.039667975,0.00162282,0.001622808,0.001622783,0.509670567,Clinics,0.5773276,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
33,Simultaneous Detection and Mutation Surveillance of SARS-CoV-2 and co-infections of multiple respiratory viruses by Rapid field-deployable sequencing.,Med (N Y),33821249,4/7/21,pubmed,0,15,"sequencing, genomes",0.001291283,0.772203521,0.132580801,0.001291306,0.091341812,0.001291277,Genomics,0.3302474,FALSE,35.73333333,0.494155483,,,0,0.403234768,,,0.448695125
34,Care of inflammatory bowel disease patients during coronavirus disease-19 pandemic using digital health-care technology.,JGH Open,33821221,4/7/21,pubmed,0,9,digital health,0.001350381,0.083434712,0.001350416,0.001350383,0.646334609,0.266179499,Healthcare,0.9950483,TRUE,56.88888889,0.680561568,,,0,0.403234768,,,0.541898168
35,Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks.,ArXiv,33821209,4/7/21,pubmed,0,25,"deep learning, lstm",0.000977434,0.000977432,0.995112719,0.000977456,0.000977462,0.000977496,Imaging,0.2940424,FALSE,94.64,0.855897087,,,0,0.403234768,,,0.629565927
36,Risk Factors and Outcomes of Acute Kidney Injury in Critically Ill Patients with Coronavirus Disease 2019.,Kidney Dis (Basel),33821208,4/7/21,pubmed,0,11,logistic regression,0.019173367,0.001098804,0.001098809,0.019178374,0.001098872,0.958351774,Clinics,0.9242139,TRUE,51.54545455,0.643329829,37.36363636,0.618276693,0,0.403234768,,,0.554947096
37,A simple mathematical model to predict and validate the spread of Covid-19 in India.,Mater Today Proc,33821202,4/7/21,pubmed,0,5,mathematical model,0.001901727,0.001901738,0.001901836,0.990491248,0.001901728,0.001901724,Epidemiology,0.37918752,FALSE,28,0.408312202,,,0,0.403234768,,,0.405773485
38,Predictive Analyses of COVID-19 Case Data to Estimate the Effectiveness of Nationwide Face Cover.,World Med Health Policy,33821197,4/7/21,pubmed,0,3,prediction model,0.001943448,0.001943465,0.001943497,0.99028259,0.001943529,0.001943472,Epidemiology,0.06668979,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
39,A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset.,Biomed Signal Process Control,33821166,4/7/21,pubmed,0,3,"deep learning, image processing, dataset",0.001171557,0.001171566,0.994142138,0.001171588,0.001171554,0.001171596,Imaging,0.042489648,FALSE,7.333333333,0.105572392,1.666666667,0.145036125,0,0.403234768,,,0.217947761
40,Are the concerns destroying mental health of college students?: A qualitative analysis portraying experiences amidst COVID-19 ambiguities.,Anal Soc Issues Public Policy,33821151,4/7/21,pubmed,0,2,data mining,0.001512029,0.001511844,0.146153867,0.50518196,0.344128469,0.001511829,Epidemiology,0.82601964,TRUE,12,0.183190055,,,0,0.403234768,,,0.293212411
41,Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases.,Neural Process Lett,33821142,4/7/21,pubmed,0,3,"bayes, neural network, forecasting model",0.002422254,0.002422243,0.613684597,0.376626283,0.002422305,0.002422318,Epidemiology,0.61683714,TRUE,26.66666667,0.390995114,0.666666667,0.096200161,0,0.403234768,,,0.296810014
42,Determination of Risk Groups for the Covid-19 Underlying Deseases.,Cybern Syst Anal,33821122,4/7/21,pubmed,0,3,"bayes, sequencing",0.003927611,0.65442063,0.00392772,0.187556822,0.003927537,0.14623968,Genomics,0.5602064,TRUE,30.66666667,0.440225122,2,0.164302917,0,0.403234768,,,0.335920936
43,The adverse impact of the Covid-19 pandemic on the labor market in Cameroon.,Afr Dev Rev,33821111,4/7/21,pubmed,0,1,logistic regression,0.002296565,0.002296606,0.002296633,0.146325344,0.844488276,0.002296575,Healthcare,0.40522057,FALSE,6,0.086028821,1,0.122023013,0,0.403234768,,,0.2037622
44,Characteristics of Imperial College London's COVID-19 research outputs.,Learn Publ,33821101,4/7/21,pubmed,0,2,dataset,0.001272673,0.001272689,0.001272698,0.844902288,0.150006892,0.001272759,Epidemiology,0.038994133,FALSE,4.5,0.061784897,0,0.055525823,0,0.403234768,,,0.173515162
45,An automated and fast system to identify COVID-19 from X-ray radiograph of the chest using image processing and machine learning.,Int J Imaging Syst Technol,33821097,4/7/21,pubmed,0,1,"machine learning, neural network, image processing, classifier, deep-learning, dataset",0.001141382,0.001141358,0.943880982,0.051553605,0.001141327,0.001141346,Imaging,0.5933586,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
46,COVID-19 vs influenza viruses: A cockroach optimized deep neural network classification approach.,Int J Imaging Syst Technol,33821096,4/7/21,pubmed,0,3,"neural network, genomes, dataset",0.194111307,0.244224045,0.556611096,0.001684557,0.001684504,0.001684491,Genomics,0.33403462,FALSE,333,0.993011318,110.3333333,0.838841317,0,0.403234768,,,0.745029134
47,Convolutional capsule network for COVID-19 detection using radiography images.,Int J Imaging Syst Technol,33821095,4/7/21,pubmed,0,2,"deep learning, neural network",0.001203902,0.001203458,0.993982071,0.001203703,0.001203421,0.001203444,Imaging,0.6651255,TRUE,23.5,0.348506401,,,0,0.403234768,,,0.375870584
48,A deep learning model for mass screening of COVID-19.,Int J Imaging Syst Technol,33821094,4/7/21,pubmed,0,5,"deep learning, neural network, network model, dataset",0.00127269,0.001272678,0.941257809,0.053651447,0.001272669,0.001272706,Imaging,0.3080264,FALSE,16.4,0.247572515,2,0.164302917,0,0.403234768,,,0.2717034
49,Automatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networks.,Int J Imaging Syst Technol,33821092,4/7/21,pubmed,0,4,neural network,0.001022622,0.00102268,0.943579021,0.052330375,0.001022644,0.001022657,Imaging,0.96097565,TRUE,39,0.530521368,9.75,0.349678887,0,0.403234768,,,0.427811674
50,An efficient primary screening of COVID-19 by serum Raman spectroscopy.,J Raman Spectrosc,33821082,4/7/21,pubmed,0,13,"machine learning, dataset",0.001085357,0.001085393,0.666583971,0.001085361,0.001085381,0.329074538,Clinics,0.3996294,FALSE,60.92307692,0.707650442,,,0,0.403234768,,,0.555442605
51,Classification of the social distance during the COVID-19 pandemic from electricity consumption using artificial intelligence.,Int J Energy Res,33821081,4/7/21,pubmed,0,7,"artificial intelligence, neural network",0.054618449,0.001461905,0.087167078,0.853828635,0.001461992,0.001461941,Epidemiology,0.81389564,TRUE,13,0.197352959,2,0.164302917,0,0.403234768,,,0.254963548
52,Fractional order mathematical modeling of novel corona virus (COVID-19).,Math Methods Appl Sci,33821069,4/7/21,pubmed,0,5,mathematical model,0.005047748,0.005047637,0.005048013,0.974761418,0.005047643,0.005047539,Epidemiology,0.63710225,TRUE,82.2,0.815263776,10.8,0.366871822,1,0.537564047,,,0.573233215
53,The Importance of Agriculture in the Economy: Impacts from COVID-19.,Am J Agric Econ,33821008,4/7/21,pubmed,0,2,simulation model,0.001901754,0.001901728,0.001901722,0.85000019,0.142392902,0.001901703,Epidemiology,0.023459107,FALSE,46,0.596882924,47,0.668718223,0,0.403234768,,,0.556278638
54,Clinical features and predictors of severity in COVID-19 patients with critical illness in Singapore.,Sci Rep,33820944,4/7/21,pubmed,0,81,logistic regression,0.001823369,0.001823325,0.001823347,0.001823372,0.001823325,0.990883262,Clinics,0.7188143,TRUE,33.7804878,0.473684211,,,0,0.403234768,,,0.438459489
55,Genomic surveillance of Nevada patients revealed prevalence of unique SARS-CoV-2 variants bearing mutations in the RdRp gene.,J Genet Genomics,33820739,4/7/21,pubmed,0,12,"sequencing, structural model",0.001371296,0.923040691,0.00137128,0.001371316,0.001371269,0.071474149,Genomics,0.6248499,TRUE,22.66666667,0.335580432,309.1666667,0.957184908,0,0.403234768,,,0.565333369
56,Risk estimation of SUDEP during COVID-19 pandemic era in a tertiary referral center.,Epilepsy Res,33819756,4/6/21,pubmed,0,4,logistic regression,0.031686101,0.001786626,0.001786589,0.215657532,0.257703184,0.491379968,Clinics,0.95548236,TRUE,18.75,0.28072237,5.25,0.260971367,0,0.403234768,,,0.314976168
57,No psychological vaccination: Vaccine hesitancy is associated with negative psychiatric outcomes among Israelis who received COVID-19 vaccination.,J Affect Disord,33819733,4/6/21,pubmed,0,4,logistic regression,0.00178666,0.001786629,0.00178655,0.001786655,0.991066915,0.001786591,Healthcare,0.238839,FALSE,91,0.846310842,53,0.693738293,0,0.403234768,,,0.647761301
58,Immunogenicity and antigenicity based T-cell and B-cell epitopes identification from conserved regions of 10664 SARS-CoV-2 genomes.,Infect Genet Evol,33819681,4/6/21,pubmed,0,3,"genome-wide, genomes, sequence alignment",0.484233338,0.440781149,0.072445791,0.000846578,0.000846591,0.000846552,Drug discovery,0.27592766,FALSE,38.33333333,0.522233904,,,0,0.403234768,,,0.462734336
59,Initial clinical characteristics of gravid SARS-CoV-2 positive patients and the risk of progression to severe COVID-19 disease.,Am J Obstet Gynecol MFM,33819676,4/6/21,pubmed,0,7,prediction model,0.001112631,0.001112641,0.186440606,0.001112643,0.001112654,0.809108825,Clinics,0.8079202,TRUE,14.28571429,0.215783289,,,0,0.403234768,,,0.309509028
60,Mediating effect of intolerance of uncertainty in the relationship between coping styles with stress during pandemic (COVID-19) process and compulsive buying behavior.,Prog Neuropsychopharmacol Biol Psychiatry,33819541,4/6/21,pubmed,0,2,structural model,0.002357982,0.002357882,0.002357747,0.662085358,0.328483344,0.002357687,Epidemiology,0.06233871,FALSE,5.5,0.077246583,0,0.055525823,0,0.403234768,,,0.178669058
61,Evaluation of the Abbott BinaxNOW rapid antigen test for SARS-CoV-2 infection in children: Implications for screening in a school setting.,PLoS One,33819311,4/6/21,pubmed,0,7,logistic regression,0.001085361,0.244805954,0.149303464,0.001085411,0.602634441,0.001085369,Healthcare,0.180466,FALSE,31.28571429,0.447461191,,,0,0.403234768,,,0.425347979
62,Emotions of COVID-19: A Study of Self-Reported Information and Emotions during the COVID-19 Pandemic using Artificial Intelligence.,J Med Internet Res,33819167,4/6/21,pubmed,0,6,artificial intelligence,0.100211897,0.000620891,0.047156291,0.489409777,0.361980309,0.000620835,Epidemiology,0.023700446,FALSE,11.33333333,0.170635166,0.5,0.087101953,0,0.403234768,,,0.220323962
63,Risk assessment of importation and local transmissions of COVID-19 in South Korea.,JMIR Public Health Surveill,33819165,4/6/21,pubmed,0,4,mathematical model,0.044567005,0.000838537,0.036955842,0.874348471,0.024591785,0.01869836,Epidemiology,0.6020402,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
64,The Rapid Development and Early Success of Covid 19 Vaccines Have Raised Hopes for Accelerating the Cancer Treatment Mechanism.,Arch Razi Inst,33818952,4/6/21,pubmed,0,1,artificial intelligence,0.619588243,0.000281353,0.056567521,0.214989547,0.000281354,0.108291982,Drug discovery,0.74319285,TRUE,96,0.858927577,13,0.400521809,0,0.403234768,,,0.554228051
65,Clinical and etiological analysis of co-infections and secondary infections in COVID-19 patients: an observational study.,Clin Respir J,33818909,4/6/21,pubmed,0,7,"sequencing, metagenom",0.001237077,0.265971542,0.042094893,0.001237065,0.001237101,0.688222322,Clinics,0.5430091,TRUE,6.857142857,0.097099388,,,0,0.403234768,,,0.250167078
66,Symptomatic reinfection of SARS-CoV-2 with spike protein variant N440K associated with immune escape.,J Med Virol,33818797,4/6/21,pubmed,0,14,"sequencing, whole genome",0.006539774,0.742945154,0.006539933,0.006540774,0.230894745,0.006539619,Genomics,0.48191786,FALSE,15.07142857,0.227657864,,,0,0.403234768,,,0.315446316
67,Safety of hydroxychloroquine in healthcare workers for COVID-19 prophylaxis.,Indian J Med Res,33818480,4/6/21,pubmed,0,24,logistic regression,0.105993689,0.001438124,0.001438113,0.001438153,0.418676971,0.47101495,Clinics,0.87690485,TRUE,35.375,0.490568372,,,0,0.403234768,,,0.44690157
68,Phylogenetic classification of the whole-genome sequences of SARS-CoV-2 from India & evolutionary trends.,Indian J Med Res,33818474,4/6/21,pubmed,0,14,"sequencing, whole-genome, whole genome, genome sequences, genomes",0.000956306,0.95051779,0.029662747,0.000956356,0.000956334,0.016950467,Genomics,0.5983943,TRUE,29.35714286,0.423217268,,,0,0.403234768,,,0.413226018
69,0,Indian J Med Res,33818470,4/6/21,pubmed,0,4,"computational, in silico",0.976693668,0.001046836,0.001046825,0.001046813,0.001046833,0.019119025,Drug discovery,0.9526908,TRUE,89,0.839384006,22,0.503746321,0,0.403234768,,,0.582121698
70,Using machine learning to investigate the public's emotional responses to work from home during the COVID-19 pandemic.,J Appl Psychol,33818121,4/6/21,pubmed,0,4,"machine learning, deep learning",0.001330061,0.001330063,0.156714608,0.837965095,0.00133009,0.001330083,Epidemiology,0.10456219,FALSE,13.75,0.208299833,,,0,0.403234768,,,0.3057673
71,Investigating the ACE2 polymorphisms in COVID-19 susceptibility: An in silico analysis.,Mol Genet Genomic Med,33818000,4/6/21,pubmed,0,2,in silico,0.559071059,0.173948545,0.001538185,0.04672316,0.001538161,0.217180891,Drug discovery,0.36260802,FALSE,31,0.445111015,4,0.231469093,0,0.403234768,,,0.359938292
72,Face-touching behavior as a possible correlate of mask-wearing: A video observational study of public place incidents during the COVID-19 pandemic.,Transbound Emerg Dis,33817991,4/6/21,pubmed,0,5,dataset,0.002183275,0.002183279,0.00218331,0.155946499,0.835320346,0.002183291,Healthcare,0.39631253,FALSE,30.6,0.439421114,,,0,0.403234768,,,0.421327941
73,Health workforce management in the context of the COVID-19 pandemic: A survey of physicians in Serbia.,Int J Health Plann Manage,33817831,4/6/21,pubmed,0,4,logistic regression,0.001684495,0.001684636,0.070182318,0.001684572,0.923079371,0.001684608,Healthcare,0.89731956,TRUE,21.75,0.321603068,21.75,0.50046829,0,0.403234768,,,0.408435375
74,An observational study to develop a scoring system and model to detect risk of hospital admission due to COVID-19.,J Am Coll Emerg Physicians Open,33817689,4/6/21,pubmed,0,5,"predictive model, logistic regression",0.00108534,0.018664503,0.227663916,0.001085387,0.001085393,0.750415462,Clinics,0.81027937,TRUE,23,0.34225988,,,0,0.403234768,,,0.372747324
75,Network-based Virus-Host Interaction Prediction with Application to SARS-CoV-2.,Patterns (N Y),33817672,4/6/21,pubmed,0,4,machine learning,0.87433322,0.002080645,0.117344358,0.002080625,0.002080621,0.002080531,Drug discovery,0.57546425,TRUE,1.75,0.017007855,,,0,0.403234768,,,0.210121311
76,"Outside-in induction of the IFITM3 trafficking system by infections, including SARS-CoV-2, in the pathobiology of Alzheimer's disease.",Brain Behav Immun Health,33817671,4/6/21,pubmed,0,7,"transcriptom, dataset",0.814144943,0.001156306,0.001156284,0.032935252,0.001156308,0.149450907,Drug discovery,0.56088316,TRUE,124.4285714,0.913723792,31.14285714,0.578806529,0,0.403234768,,,0.631921696
77,Network bioinformatics analysis provides insight into drug repurposing for COVID-19.,Med Drug Discov,33817623,4/6/21,pubmed,0,11,"bioinformatic, in silico, text mining",0.809104624,0.119451777,0.06682921,0.00153815,0.001538147,0.001538092,Drug discovery,0.5486256,TRUE,5.636363636,0.077926897,,,0,0.403234768,,,0.240580832
78,"Potential anti-COVID-19 agents, Cepharanthine and Nelfinavir, and their usage for combination treatment.",iScience,33817567,4/6/21,pubmed,0,30,mathematical model,0.898995482,0.001861728,0.001861678,0.093557649,0.001861707,0.001861757,Drug discovery,0.3433885,FALSE,151.8666667,0.945698559,,,0,0.403234768,,,0.674466663
79,Mink SARS-CoV-2 Infection in Poland - Short Communication.,J Vet Res,33817389,4/6/21,pubmed,0,12,"whole genome, genome sequences",0.00141516,0.837972279,0.024418161,0.001415157,0.099788395,0.034990848,Genomics,0.3701515,FALSE,41.16666667,0.549693859,13.91666667,0.410155205,0,0.403234768,,,0.454361277
80,Repurposing of approved drugs with potential to interact with SARS-CoV-2 receptor.,Biochem Biophys Rep,33817352,4/6/21,pubmed,0,2,"in silico, sequence alignment",0.861388394,0.089849942,0.044647889,0.001371282,0.001371242,0.001371251,Drug discovery,0.5956308,TRUE,19,0.285793803,1,0.122023013,0,0.403234768,,,0.270350528
81,Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images.,PeerJ Comput Sci,33817018,4/6/21,pubmed,0,4,"neural network, dataset",0.001171589,0.001171559,0.99414217,0.001171607,0.001171546,0.001171529,Imaging,0.3387968,FALSE,21.25,0.314614386,1.75,0.148381054,0,0.403234768,,,0.288743402
82,A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset.,PeerJ Comput Sci,33817014,4/6/21,pubmed,0,5,"deep learning, artificial intelligence, neural network, dataset",0.000838526,0.032065873,0.964579972,0.00083851,0.000838568,0.00083855,Imaging,0.27441692,FALSE,202.6,0.97303482,69.2,0.751003479,1,0.537564047,,,0.753867449
83,COVID-19: a new deep learning computer-aided model for classification.,PeerJ Comput Sci,33817008,4/6/21,pubmed,0,5,"deep learning, transfer learning, dataset",0.000898094,0.023701717,0.972705912,0.000898109,0.000898104,0.000898065,Imaging,0.1849252,FALSE,203,0.97322036,69,0.750535189,0,0.403234768,,,0.708996772
84,Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks.,PeerJ Comput Sci,33816964,4/6/21,pubmed,0,6,"machine learning, neural network, dataset",0.027598567,0.000716329,0.72494158,0.124686072,0.121341063,0.000716389,Imaging,0.72192883,TRUE,58.33333333,0.691941369,22.33333333,0.505753278,0,0.403234768,,,0.533643138
85,FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features.,PeerJ Comput Sci,33816957,4/6/21,pubmed,0,2,"artificial intelligence, neural network, dataset",0.001112632,0.001112628,0.994436826,0.001112647,0.001112623,0.001112645,Imaging,0.7514565,TRUE,17.5,0.263776362,3.5,0.213607172,2,0.618927094,,,0.365436876
86,A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans.,PeerJ Comput Sci,33816954,4/6/21,pubmed,0,3,"machine learning, computational, neural network, deep model, network model, dataset",0.000846525,0.000846529,0.995767318,0.000846567,0.000846528,0.000846532,Imaging,0.025357187,FALSE,23.66666667,0.34986703,2.333333333,0.173401124,1,0.537564047,,,0.353610734
87,"Public Knowledge, Attitudes, and Practices Behaviors Towards Coronavirus Disease 2019 (COVID-19) During a National Epidemic-China.",Front Public Health,33816423,4/6/21,pubmed,0,13,logistic regression,0.001291266,0.001291268,0.001291232,0.001291261,0.993543713,0.00129126,Healthcare,0.78044045,TRUE,6.076923077,0.086152514,,,0,0.403234768,,,0.244693641
88,"Psychological Stress Risk Factors, Concerns and Mental Health Support Among Health Care Workers in Vietnam During the Coronavirus Disease 2019 (COVID-19) Outbreak.",Front Public Health,33816419,4/6/21,pubmed,0,8,logistic regression,0.001272625,0.001272636,0.001272637,0.001272732,0.993636726,0.001272644,Healthcare,0.9520253,TRUE,4.125,0.055229142,,,0,0.403234768,,,0.229231955
89,Segmentation of COVID-19 pneumonia lesions: A deep learning approach.,Med J Islam Repub Iran,33816373,4/6/21,pubmed,0,10,"deep learning, artificial intelligence, neural network, dataset",0.001085367,0.001085365,0.937506574,0.001085358,0.00108535,0.058151986,Imaging,0.9123004,TRUE,64.4,0.731028511,31.5,0.581883864,0,0.403234768,,,0.572049048
90,0,Front Cell Infect Microbiol,33816347,4/6/21,pubmed,0,14,"sequencing, transcriptom",0.295972038,0.496962608,0.001786552,0.001786693,0.001786569,0.201705541,Genomics,0.84626985,TRUE,9.357142857,0.13940256,,,0,0.403234768,,,0.271318664
91,"Are nucleotide inhibitors, already used for treating hepatitis C virus infection, a potential option for the treatment of COVID-19 compared with standard of care? A literature review.",World J Virol,33816150,4/6/21,pubmed,0,1,in silico,0.565433836,0.059399435,0.002183313,0.002183375,0.002183327,0.368616713,Drug discovery,0.794569,TRUE,18,0.271569052,8,0.320511105,0,0.403234768,,,0.331771642
92,Derivation of a Contextually-Appropriate COVID-19 Mortality Scale for Low-Resource Settings.,Ann Glob Health,33816136,4/6/21,pubmed,0,3,machine learning,0.001171556,0.001171561,0.186861652,0.001171641,0.323006957,0.486616633,Clinics,0.39827406,FALSE,7.333333333,0.105572392,0.666666667,0.096200161,0,0.403234768,,,0.201669107
93,Non-standard finite difference scheme and analysis of smoking model with reversion class.,Results Phys,33816094,4/6/21,pubmed,0,2,mathematical model,0.004310327,0.004310058,0.256816088,0.725942244,0.004310373,0.00431091,Epidemiology,0.44593504,FALSE,29,0.41993939,6,0.280037463,0,0.403234768,,,0.367737207
94,A New Mathematical Model of COVID-19 Using Real Data from Pakistan.,Results Phys,33816093,4/6/21,pubmed,0,5,mathematical model,0.070951072,0.00208064,0.044899274,0.877907847,0.002080567,0.002080599,Epidemiology,0.7688105,TRUE,46.8,0.603686066,2.2,0.16838373,0,0.403234768,,,0.391768188
95,"Analysis, modeling and optimal control of COVID-19 outbreak with three forms of infection in Democratic Republic of the Congo.",Results Phys,33816092,4/6/21,pubmed,0,6,mathematical model,0.007061732,0.007061791,0.007061617,0.898962627,0.007061999,0.072790234,Epidemiology,0.55433166,TRUE,41.66666667,0.554703445,20,0.481000803,0,0.403234768,,,0.479646338
96,"A review of novel coronavirus disease (COVID-19): based on genomic structure, phylogeny, current shreds of evidence, candidate vaccines, and drug repurposing.",3 Biotech,33816047,4/6/21,pubmed,0,10,genomic structure,0.522393676,0.301250742,0.00125462,0.05959796,0.060970482,0.05453252,Drug discovery,0.51044846,TRUE,30.1,0.432617973,,,0,0.403234768,,,0.41792637
97,"Prevalence of Preterm Birth Rate During COVID-19 Lockdown in a Tertiary Care Hospital, Riyadh.",Cureus,33816033,4/6/21,pubmed,0,8,dataset,0.001823409,0.001823468,0.001823333,0.60126907,0.214654065,0.178606655,Epidemiology,0.6156781,TRUE,4.625,0.062774445,0.25,0.065493712,0,0.403234768,,,0.177167641
98,Computational Insights of phytochemical Driven Disruption of RNA dependent RNA polymerase Mediated replication of Coronavirus: A Strategic Treatment Plan against COVID-19.,New Microbes New Infect,33815808,4/6/21,pubmed,0,4,computational,0.969325753,0.001010959,0.001010951,0.026630472,0.001010946,0.00101092,Drug discovery,0.7243052,TRUE,58.75,0.694538933,18,0.46180091,0,0.403234768,,,0.519858203
99,Lung organoid simulations for modelling and predicting the effect of mutations on SARS-CoV-2 infectivity.,Comput Struct Biotechnol J,33815693,4/6/21,pubmed,0,2,machine learning,0.264747946,0.583251699,0.054279831,0.095246215,0.00123719,0.001237119,Genomics,0.23327762,FALSE,13.5,0.205393036,3.5,0.213607172,0,0.403234768,,,0.274078325
100,COVID-19 co-infection mathematical model as guided through signaling structural framework.,Comput Struct Biotechnol J,33815692,4/6/21,pubmed,0,2,mathematical model,0.674878216,0.002238545,0.002238589,0.201037939,0.002238596,0.117368115,Drug discovery,0.42882323,FALSE,4.5,0.061784897,,,0,0.403234768,,,0.232509832
101,Nanopore sequencing and its application to the study of microbial communities.,Comput Struct Biotechnol J,33815688,4/6/21,pubmed,0,3,"bioinformatic, sequencing, transcriptom, metagenom, genomes, genomic structure, metatranscriptom",0.001901853,0.591097266,0.117943517,0.285253948,0.001901733,0.001901683,Genomics,0.9220614,TRUE,7.333333333,0.105572392,0,0.055525823,0,0.403234768,,,0.188110994
102,Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network.,Evol Intell,33815622,4/6/21,pubmed,0,2,"deep learning, computational, neural network",0.001156281,0.001156294,0.589337782,0.406037096,0.001156317,0.001156231,Epidemiology,0.074520856,FALSE,12.5,0.189436576,,,0,0.403234768,,,0.296335672
103,"A Mathematical Model of COVID-19 Pandemic: A Case Study of Bangkok, Thailand.",Comput Math Methods Med,33815565,4/6/21,pubmed,0,3,mathematical model,0.057799148,0.057799148,0.057799148,0.711004258,0.057799148,0.057799148,Epidemiology,0.51474446,TRUE,6.666666667,0.094996598,1,0.122023013,0,0.403234768,,,0.20675146
104,Initial Insights Into the Genetic Epidemiology of SARS-CoV-2 Isolates From Kerala Suggest Local Spread From Limited Introductions.,Front Genet,33815467,4/6/21,pubmed,0,27,sequencing,0.05965551,0.906485307,0.002130787,0.002130762,0.002130705,0.027466929,Genomics,0.66807926,TRUE,10.37037037,0.15517348,,,0,0.403234768,,,0.279204124
105,Genomic Variations in SARS-CoV-2 Genomes From Gujarat: Underlying Role of Variants in Disease Epidemiology.,Front Genet,33815459,4/6/21,pubmed,0,15,"genomes, dataset",0.001022633,0.776701369,0.0010227,0.001022691,0.001022631,0.219207977,Genomics,0.40725005,FALSE,34.06666667,0.477889789,,,0,0.403234768,,,0.440562278
106,Nasopharyngeal Microbial Communities of Patients Infected With SARS-CoV-2 That Developed COVID-19.,Front Microbiol,33815323,4/6/21,pubmed,0,13,"sequencing, microbiom",0.001461881,0.376817505,0.078754254,0.001461933,0.001461981,0.540042447,Clinics,0.8709989,TRUE,32.69230769,0.462984724,,,0,0.403234768,,,0.433109746
107,Genomic Feature Analysis of Betacoronavirus Provides Insights Into SARS and COVID-19 Pandemics.,Front Microbiol,33815307,4/6/21,pubmed,0,10,genomes,0.001786625,0.991067035,0.001786664,0.001786569,0.001786536,0.00178657,Genomics,0.19926357,FALSE,14.8,0.222957511,,,0,0.403234768,,,0.313096139
108,Health Care Workers' Mental Health During the First Weeks of the SARS-CoV-2 Pandemic in Switzerland-A Cross-Sectional Study.,Front Psychiatry,33815162,4/6/21,pubmed,0,9,network analysis,0.001511884,0.001511854,0.001511991,0.001511929,0.943204129,0.050748213,Healthcare,0.53421706,TRUE,22.33333333,0.330261612,17.55555556,0.456382125,5,0.739490092,,,0.508711276
109,A High Percentage of Patients Recovered From COVID-19 but Discharged With Abnormal Liver Function Tests.,Front Physiol,33815147,4/6/21,pubmed,0,14,logistic regression,0.001461894,0.001461866,0.001461945,0.001461982,0.001461952,0.992690362,Clinics,0.9462251,TRUE,21.78571429,0.321788608,,,0,0.403234768,,,0.362511688
110,"Forecasting major impacts of COVID-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap.",Pers Ubiquitous Comput,33815032,4/6/21,pubmed,0,7,"mathematical model, prediction model",0.040586285,0.002032817,0.002032795,0.912141669,0.041173615,0.002032819,Epidemiology,0.0697476,FALSE,29.14285714,0.420310471,,,0,0.403234768,,,0.411772619
111,Computational simulation of the COVID-19 epidemic with the SEIR stochastic model.,Comput Math Organ Theory,33814968,4/6/21,pubmed,0,4,"computational, mathematical model",0.001511845,0.001511834,0.001511837,0.992440792,0.001511892,0.001511801,Epidemiology,0.105356455,FALSE,25.75,0.377698064,1.5,0.138747659,0,0.403234768,,,0.306560163
112,Helping Roles of Artificial Intelligence (AI) in the Screening and Evaluation of COVID-19 Based on the CT Images.,J Inflamm Res,33814922,4/6/21,pubmed,0,7,"artificial intelligence, neural network, logistic regression",0.001126833,0.001126859,0.617594452,0.001126827,0.001126878,0.377898152,Imaging,0.9167689,TRUE,2,0.022141134,,,0,0.403234768,,,0.212687951
113,Radiographic findings in COVID-19: Comparison between AI and radiologist.,Indian J Radiol Imaging,33814766,4/6/21,pubmed,0,6,artificial intelligence,0.001203402,0.001203411,0.886603131,0.001203445,0.108583103,0.001203508,Imaging,0.80135214,TRUE,9.5,0.143051518,0.833333333,0.102488627,0,0.403234768,,,0.216258304
114,The value of AI based CT severity scoring system in triage of patients with Covid-19 pneumonia as regards oxygen requirement and place of admission.,Indian J Radiol Imaging,33814763,4/6/21,pubmed,0,3,"deep learning, logistic regression",0.000786342,0.000786361,0.30709306,0.000786385,0.000786395,0.689761458,Clinics,0.7730243,TRUE,13.33333333,0.201558538,0.333333333,0.073187048,0,0.403234768,,,0.225993451
115,Comparing a deep learning model's diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs.,Indian J Radiol Imaging,33814762,4/6/21,pubmed,0,9,"deep learning, neural network, dataset",0.000988341,0.000988362,0.831174283,0.000988382,0.000988413,0.164872218,Imaging,0.31711602,FALSE,5.777777778,0.081204775,2.444444444,0.175408081,0,0.403234768,,,0.219949208
116,Financial impact of COVID-19 on radiology practice in India.,Indian J Radiol Imaging,33814759,4/6/21,pubmed,0,3,artificial intelligence,0.001565291,0.001565319,0.29602337,0.481255241,0.218025357,0.001565422,Epidemiology,0.96725595,TRUE,11.66666667,0.176510607,3.333333333,0.206515922,0,0.403234768,,,0.262087099
117,Artificial intelligence and radiology: Combating the COVID-19 conundrum.,Indian J Radiol Imaging,33814755,4/6/21,pubmed,0,1,artificial intelligence,0.001511817,0.001511887,0.790367433,0.203585185,0.001511852,0.001511827,Imaging,0.8547164,TRUE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
118,Uncertainty Quantification of a Mathematical Model of COVID-19 Transmission Dynamics with Mass Vaccination Strategy.,Chaos Solitons Fractals,33814733,4/6/21,pubmed,0,2,mathematical model,0.001272633,0.001272647,0.001272735,0.993636656,0.001272676,0.001272653,Epidemiology,0.083455026,FALSE,65.5,0.737646113,19.5,0.475983409,0,0.403234768,,,0.538954763
119,Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system.,Multimed Syst,33814730,4/6/21,pubmed,0,6,"machine learning, classifier, dataset",0.0019872,0.205286169,0.70827225,0.001987235,0.001987164,0.080479982,Genomics,0.2603446,FALSE,20.66666667,0.306512462,3.666666667,0.217621086,0,0.403234768,,,0.309122772
120,Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images.,Neural Comput Appl,33814729,4/6/21,pubmed,0,2,"neural network, classifier, dataset",0.035263935,0.001112651,0.323940678,0.637457471,0.001112652,0.001112613,Epidemiology,0.71420836,TRUE,141.5,0.936050467,96.5,0.816229596,0,0.403234768,,,0.718504943
121,Prevention schemes for future pandemic cases: mathematical model and experience of interurban multi-agent COVID-19 epidemic prevention.,Nonlinear Dyn,33814725,4/6/21,pubmed,0,2,mathematical model,0.074103793,0.00168453,0.001684551,0.919158018,0.001684596,0.001684512,Epidemiology,0.93233705,TRUE,16.5,0.249366071,,,0,0.403234768,,,0.326300419
122,Unmasking of crucial structural fragments for coronavirus protease inhibitors and its implications in COVID-19 drug discovery.,J Mol Struct,33814612,4/6/21,pubmed,0,4,"bayes, bayesian model",0.577849913,0.00194352,0.186142473,0.230177137,0.001943488,0.00194347,Drug discovery,0.90872043,TRUE,108.75,0.887315233,15.25,0.427682633,0,0.403234768,,,0.572744211
123,Correcting notification delay and forecasting of COVID-19 data.,J Math Anal Appl,33814611,4/6/21,pubmed,0,5,bayes,0.0021307,0.002130704,0.033838116,0.916704986,0.002130668,0.043064827,Epidemiology,0.40344375,FALSE,2.4,0.025419012,0.2,0.061145304,0,0.403234768,,,0.163266361
124,Public perceptions and disparities in access to telehealth orthopaedic services in the COVID-19 era.,J Natl Med Assoc,33814179,4/6/21,pubmed,0,7,logistic regression,0.001438134,0.001438149,0.001438184,0.001438296,0.971509205,0.022738031,Healthcare,0.94486403,TRUE,55,0.668686994,22.42857143,0.506221568,0,0.403234768,,,0.526047776
125,Role of Non-Thyroidal Illness Syndrome in Predicting Adverse Outcomes in COVID-19 Patients Predominantly of Mild to Moderate Severity.,Clin Endocrinol (Oxf),33813743,4/5/21,pubmed,0,14,logistic regression,0.00114143,0.184934584,0.040246204,0.001141334,0.001141347,0.771395102,Clinics,0.8641889,TRUE,72.14285714,0.773517224,,,0,0.403234768,,,0.588375996
126,Impact of the COVID-19 pandemic on the activity of the Radiological Emergency Department: the experience of the Maggiore della Carità Hospital in Novara.,Emerg Radiol,33813649,4/5/21,pubmed,0,9,dataset,0.0011563,0.001156349,0.299862995,0.394838872,0.151755547,0.151229937,Epidemiology,0.5665328,TRUE,37.55555556,0.514193828,10,0.355632861,0,0.403234768,,,0.424353819
127,Clinical features and risk factors associated with severe COVID-19 patients in China.,Chin Med J (Engl),33813510,4/5/21,pubmed,0,16,logistic regression,0.001684458,0.00168454,0.001684507,0.001684516,0.001684558,0.991577422,Clinics,0.9444215,TRUE,1.6875,0.016513081,,,0,0.403234768,,,0.209873924
128,A SARS-CoV-2 -human metalloproteome interaction map.,J Inorg Biochem,33813307,4/5/21,pubmed,0,5,proteom,0.89970664,0.002422522,0.002422394,0.002422404,0.090603299,0.002422742,Drug discovery,0.67661124,TRUE,134.2,0.926216835,26.4,0.540808135,1,0.537564047,,,0.668196339
129,"Analyzing Indian general public's perspective on anxiety, stress and trauma during Covid-19 - A machine learning study of 840,000 tweets.",Diabetes Metab Syndr,33813239,4/5/21,pubmed,0,3,machine learning,0.00178663,0.001786552,0.0412451,0.468481224,0.484913974,0.00178652,Healthcare,0.19943082,FALSE,18.66666667,0.279670975,0.666666667,0.096200161,0,0.403234768,,,0.259701968
130,Recommendations for accurate genotyping of SARS-CoV-2 using amplicon-based sequencing of clinical samples.,Clin Microbiol Infect,33813118,4/5/21,pubmed,0,29,"sequencing, genomic epidemiology, genome sequences, genomes",0.001187281,0.806252505,0.001187328,0.17378253,0.001187321,0.016403036,Genomics,0.52618814,TRUE,48.68965517,0.619456986,,,0,0.403234768,,,0.511345877
131,Determining which hospitalized COVID-19 patients require an urgent echocardiogram.,J Am Soc Echocardiogr,33812952,4/5/21,pubmed,0,4,logistic regression,0.00151196,0.00151188,0.085815613,0.056507824,0.001511858,0.853140865,Clinics,0.6070446,TRUE,187.5,0.967592306,,,0,0.403234768,,,0.685413537
132,The Metrics Matter: Improving Comparisons of COVID-19 Outbreaks in Nursing Homes.,J Am Med Dir Assoc,33812840,4/5/21,pubmed,0,4,dataset,0.001010935,0.001010933,0.001010989,0.448180982,0.547775178,0.001010983,Healthcare,0.31515953,FALSE,42.25,0.559898571,24.75,0.526491838,0,0.403234768,,,0.496541726
133,[Evaluation of social distancing measures on the transmissibility of COVID-19 in rural areas. Retrospective logitudinal study of posible cases].,Semergen,33812795,4/5/21,pubmed,0,4,predictive model,0.001943459,0.001943531,0.001943515,0.790891743,0.201334133,0.001943619,Epidemiology,0.738724,TRUE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
134,Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs.,Comput Biol Med,33812263,4/4/21,pubmed,0,7,"machine learning, classifier, dataset",0.001823351,0.001823437,0.867163319,0.001823515,0.001823363,0.125543016,Clinics,0.48302323,FALSE,2.571428571,0.027707341,,,0,0.403234768,,,0.215471054
135,The impact of reduced working on mental health in the early months of the COVID-19 pandemic: Results from the understanding society COVID-19 study.,J Affect Disord,33812244,4/4/21,pubmed,0,5,logistic regression,0.001622723,0.001622807,0.001622719,0.068641229,0.924867721,0.001622801,Healthcare,0.73097086,TRUE,78.6,0.800977179,78,0.773548301,0,0.403234768,,,0.659253416
136,Outcome of COVID-19 in hospitalized patients with chronic inflammatory diseases. A population based national register study in Denmark.,J Autoimmun,33812171,4/4/21,pubmed,0,8,logistic regression,0.00111261,0.001112613,0.001112603,0.001112658,0.001112639,0.994436878,Clinics,0.7497451,TRUE,92.75,0.850763807,,,0,0.403234768,,,0.626999287
137,COVID-19 Pandemic and the Cholecystitis Experience at a Major Urban Safety-Net Hospital.,J Surg Res,33812090,4/4/21,pubmed,0,5,logistic regression,0.001684485,0.001684504,0.001684498,0.001684565,0.205905301,0.787356647,Clinics,0.5776837,TRUE,24.2,0.357350485,20,0.481000803,0,0.403234768,,,0.413862019
138,The role of microRNAs in modulating SARS-CoV-2 infection in human cells: a systematic review.,Infect Genet Evol,33812037,4/4/21,pubmed,0,8,"computational, sequencing, in silico",0.748112167,0.200692949,0.00088024,0.000880279,0.000880221,0.048554144,Drug discovery,0.903191,TRUE,24.875,0.366256417,9.375,0.342721434,0,0.403234768,,,0.370737539
139,Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study.,Lancet Infect Dis,33811817,4/4/21,pubmed,0,3,mathematical model,0.000926341,0.000926294,0.000926261,0.819463115,0.123606699,0.054151291,Epidemiology,0.05177921,FALSE,132.3333333,0.924299586,125,0.857706717,1,0.537564047,,,0.773190116
140,BET inhibition blocks inflammation-induced cardiac dysfunction and SARS-CoV-2 infection.,Cell,33811809,4/4/21,pubmed,0,46,"sequencing, proteom, phosphoproteom",0.631502922,0.001438222,0.001438208,0.001438141,0.001438155,0.362744353,Drug discovery,0.22174513,FALSE,79.30434783,0.803574742,,,0,0.403234768,,,0.603404755
141,Exploring Large Community- and Clinically-Generated Datasets to Understand Resilience Before and During the COVID-19 Pandemic.,J Nurs Scholarsh,33811723,4/4/21,pubmed,0,7,dataset,0.000999535,0.063537681,0.192586429,0.332331133,0.409545623,0.000999599,Healthcare,0.15739787,FALSE,60.42857143,0.705300266,19.57142857,0.476317902,0,0.403234768,,,0.528284312
142,Impact of COVID-19 on an established physical activity and behaviour change support programme for cancer survivors: An exploratory survey of the Macmillan Move More service for Northern Ireland.,Support Care Cancer,33811517,4/4/21,pubmed,0,6,dataset,0.020416669,0.000916681,0.000916721,0.140211153,0.742310992,0.095227784,Healthcare,0.9884106,TRUE,15.5,0.234028078,,,0,0.403234768,,,0.318631423
143,"Resourcing, annotating and analysing synthetic peptides of SARS-CoV-2 for immunopeptidomics and other immunological studies.",Proteomics,33811468,4/4/21,pubmed,0,13,"proteom, immunopeptidom, dataset",0.745946072,0.001684552,0.001684632,0.247315595,0.001684664,0.001684485,Drug discovery,0.25358042,FALSE,59.23076923,0.697754963,,,0,0.403234768,,,0.550494865
144,Practical approach to prevent COVID-19 infection at breast cancer screening.,Breast Cancer,33811286,4/4/21,pubmed,0,8,"simulation model, computational",0.275845351,0.001653061,0.076740737,0.396396535,0.001653089,0.247711227,Epidemiology,0.9124614,TRUE,40.875,0.547281836,19.125,0.471902596,0,0.403234768,,,0.474139733
145,Genetic variability in COVID-19-related genes in the Brazilian population.,Hum Genome Var,33811212,4/4/21,pubmed,0,11,"in silico, exom, genomes",0.299358567,0.611649396,0.001272675,0.001272687,0.001272666,0.085174009,Genomics,0.55131906,TRUE,89.81818182,0.842043416,85.54545455,0.791477121,0,0.403234768,,,0.678918435
146,Pneumocystis pneumonia: An important consideration when investigating artificial intelligence-based methods in the radiological diagnosis of COVID-19.,Clin Imaging,33810937,4/4/21,pubmed,0,1,artificial intelligence,0.015998776,0.015998725,0.920005665,0.015999545,0.015998718,0.015998571,Epidemiology,0.7446681,TRUE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
147,"Virtualized clinical studies to assess the natural history and impact of gut microbiome modulation in non-hospitalized patients with mild to moderate COVID-19 a randomized, open-label, prospective study with a parallel group study evaluating the physiologic effects of KB109 on gut microbiota structure and function: a structured summary of a study protocol for a randomized controlled study.",Trials,33810796,4/4/21,pubmed,0,5,"sequencing, microbiom",0.044533459,0.100037283,0.004801664,0.055421678,0.28206837,0.513137546,Clinics,0.9728774,TRUE,,,,,0,0.403234768,,,0.403234768
148,0,Healthcare (Basel),33810572,4/4/21,pubmed,0,2,digital health,0.001272651,0.001272652,0.001272739,0.776712092,0.218197218,0.001272647,Epidemiology,0.1390143,FALSE,65.5,0.737646113,14.5,0.418450629,0,0.403234768,,,0.51977717
149,Synthesis and Characterization of a Minophosphonate Containing Chitosan Polymer Derivatives: Investigations of Cytotoxic Activity and in Silico Study of SARS-CoV-19.,Polymers (Basel),33810568,4/4/21,pubmed,0,7,"in silico, in-silico",0.935461716,0.001717174,0.057669509,0.001717182,0.001717183,0.001717236,Drug discovery,0.8137834,TRUE,30.57142857,0.438988187,14.14285714,0.413433235,0,0.403234768,,,0.418552063
150,Effect of Inactivation Methods on SARS-CoV-2 Virion Protein and Structure.,Viruses,33810401,4/4/21,pubmed,0,11,genomes,0.002422406,0.623013491,0.08642528,0.283294049,0.002422453,0.002422321,Genomics,0.22954378,FALSE,38.54545455,0.524274847,,,0,0.403234768,,,0.463754807
151,Study on Factors of People's Wearing Masks Based on Two Online Surveys: Cross-Sectional Evidence from China.,Int J Environ Res Public Health,33810355,4/4/21,pubmed,0,6,logistic regression,0.001538092,0.001538151,0.001538094,0.00153819,0.992309385,0.001538087,Healthcare,0.3347819,FALSE,9.5,0.143051518,,,0,0.403234768,,,0.273143143
152,"Multiple Early Introductions of SARS-CoV-2 to Cape Town, South Africa.",Viruses,33810168,4/4/21,pubmed,0,9,"bayes, sequencing, whole genome, genomes",0.001565317,0.831607501,0.001565338,0.068872237,0.001565448,0.094824159,Genomics,0.3684637,FALSE,40.11111111,0.54016946,19.44444444,0.474377843,0,0.403234768,,,0.472594024
153,An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies.,Int J Environ Res Public Health,33810086,4/4/21,pubmed,0,4,machine learning,0.001786568,0.00178658,0.49763466,0.405276255,0.001786597,0.09172934,Imaging,0.7446388,TRUE,64.5,0.731832519,54.25,0.698354295,0,0.403234768,,,0.611140527
154,A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images.,Sensors (Basel),33810066,4/4/21,pubmed,0,5,"supervised learning, deep learning, classifier, network model",0.001098832,0.001098871,0.977155618,0.001098884,0.001098901,0.018448895,Imaging,0.32552063,FALSE,191.4,0.969200322,43,0.649785925,0,0.403234768,,,0.674073671
155,Deep Learning for Novel Antimicrobial Peptide Design.,Biomolecules,33810011,4/4/21,pubmed,0,3,"bayes, deep learning, lstm",0.216287974,0.069627242,0.47608506,0.23363322,0.00218327,0.002183233,Epidemiology,0.8427869,TRUE,56.66666667,0.679757561,,,0,0.403234768,,,0.541496164
156,A Resource for the Network Representation of Cell Perturbations Caused by SARS-CoV-2 Infection.,Genes (Basel),33809949,4/4/21,pubmed,0,16,bioinformatic,0.768000536,0.001237124,0.05495147,0.173336721,0.001237084,0.001237065,Drug discovery,0.17537937,FALSE,46.1875,0.597686932,146.5,0.880519133,0,0.403234768,,,0.627146944
157,Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface.,Int J Mol Sci,33809918,4/4/21,pubmed,0,6,"classifier, in silico, dataset",0.473182977,0.001461958,0.492327113,0.001461953,0.001461969,0.03010403,Drug discovery,0.71424353,TRUE,19.33333333,0.288638753,11.33333333,0.375501739,0,0.403234768,,,0.355791753
158,Exploring the Attitudes of Health Professionals Providing Care to Patients Undergoing Treatment for Upper Gastrointestinal Cancers to Different Models of Nutrition Care Delivery: A Qualitative Investigation.,Nutrients,33809826,4/4/21,pubmed,0,4,digital health,0.001461877,0.001461925,0.001461976,0.212039577,0.574598349,0.208976296,Healthcare,0.9896058,TRUE,144.25,0.938709877,87.5,0.795959326,0,0.403234768,,,0.712634657
159,"Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19.",Int J Environ Res Public Health,33809665,4/4/21,pubmed,0,11,"neural network, network model, dataset",0.001291226,0.001291211,0.903840728,0.001291395,0.001291308,0.090994132,Imaging,0.44252026,FALSE,45.36363636,0.589708702,,,0,0.403234768,,,0.496471735
160,"Possible Roles of Permafrost Melting, Atmospheric Transport, and Solar Irradiance in the Development of Major Coronavirus and Influenza Pandemics.",Int J Environ Res Public Health,33809626,4/4/21,pubmed,0,3,microbiom,0.001438145,0.485442141,0.001438133,0.508805303,0.001438156,0.001438122,Epidemiology,0.36476478,FALSE,64,0.729173109,85.33333333,0.791075729,0,0.403234768,,,0.641161202
161,Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia.,Diagnostics (Basel),33809625,4/4/21,pubmed,0,13,"artificial intelligence, neural network, dataset",0.001237064,0.001237081,0.659294312,0.001237088,0.001237114,0.335757342,Imaging,0.4808448,FALSE,90.46153846,0.843960665,,,0,0.403234768,,,0.623597717
162,Fecal Microbiota Transplantation during and Post-COVID-19 Pandemic.,Int J Mol Sci,33809421,4/4/21,pubmed,0,3,microbiom,0.579414243,0.134952156,0.002562647,0.277945677,0.002562678,0.002562599,Drug discovery,0.9135361,TRUE,42.33333333,0.560888119,31.66666667,0.582686647,0,0.403234768,,,0.515603178
163,Indoor Model Simulation for COVID-19 Transport and Exposure.,Int J Environ Res Public Health,33809366,4/4/21,pubmed,0,10,model simulation,0.001684575,0.180492217,0.001684503,0.439271904,0.108244901,0.268621901,Epidemiology,0.09238714,FALSE,69.4,0.760529408,126.3,0.859713674,0,0.403234768,,,0.674492617
164,0,Cancers (Basel),33809063,4/4/21,pubmed,0,31,sequencing,0.823255498,0.001011027,0.001011028,0.001010947,0.001010944,0.172700556,Drug discovery,0.8849633,TRUE,229.1612903,0.980641969,,,0,0.403234768,,,0.691938368
165,White Blood Cells and Severe COVID-19: A Mendelian Randomization Study.,J Pers Med,33809027,4/4/21,pubmed,0,3,genome-wide,0.19303366,0.085591533,0.00156532,0.001565352,0.001565364,0.71667877,Clinics,0.5405426,TRUE,12.33333333,0.186467932,,,0,0.403234768,,,0.29485135
166,Willingness of Taiwan's Healthcare Workers and Outpatients to Vaccinate against COVID-19 during a Period without Community Outbreaks.,Vaccines (Basel),33808950,4/4/21,pubmed,0,8,logistic regression,0.001098795,0.001098832,0.001098805,0.001098843,0.9589509,0.036653825,Healthcare,0.8260234,TRUE,104.25,0.877976375,,,0,0.403234768,,,0.640605571
167,0,Pathogens,33808905,4/4/21,pubmed,0,4,sequencing,0.37785695,0.319807989,0.001538239,0.001538183,0.00153819,0.297720448,Drug discovery,0.9162526,TRUE,27,0.3960047,8.25,0.323789136,0,0.403234768,,,0.374342868
168,Mutations in Animal SARS-CoV-2 Induce Mismatches with the Diagnostic PCR Assays.,Pathogens,33808783,4/4/21,pubmed,0,2,"bioinformatic, genomes",0.037895365,0.95595214,0.00153816,0.001538138,0.001538124,0.001538074,Genomics,0.71527344,TRUE,46.5,0.601026656,13,0.400521809,0,0.403234768,,,0.468261077
169,Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model.,Int J Environ Res Public Health,33808764,4/4/21,pubmed,0,4,network analysis,0.002238464,0.002238538,0.040529416,0.950516552,0.002238547,0.002238483,Epidemiology,0.10783696,FALSE,14,0.213494959,,,0,0.403234768,,,0.308364864
170,Comprehensive Virtual Screening of the Antiviral Potentialities of Marine Polycyclic Guanidine Alkaloids against SARS-CoV-2 (COVID-19).,Biomolecules,33808721,4/4/21,pubmed,0,8,"virtual screening, in silico",0.94750025,0.000800584,0.000800581,0.000800608,0.000800599,0.049297379,Drug discovery,0.86583704,TRUE,37.75,0.516296617,,,0,0.403234768,,,0.459765692
171,Phylogenomic Evidence of Reinfection and Persistence of SARS-CoV-2: First Report from Colombia.,Vaccines (Basel),33808687,4/4/21,pubmed,0,14,"phylogenom, genomes",0.001901746,0.958139228,0.001901901,0.001901874,0.001901805,0.034253446,Genomics,0.40512323,FALSE,19.57142857,0.291421857,,,0,0.403234768,,,0.347328312
172,Information and Communications Technology (ICT) Usage during COVID-19: Motivating Factors and Implications.,Int J Environ Res Public Health,33808218,4/4/21,pubmed,0,3,logistic regression,0.00198723,0.001987265,0.001987168,0.350739592,0.641311634,0.00198711,Healthcare,0.83840823,TRUE,1,0.012307502,,,0,0.403234768,,,0.207771135
173,The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks.,Diagnostics (Basel),33807976,4/4/21,pubmed,0,9,neural network,0.001786505,0.001786567,0.991067278,0.001786574,0.00178657,0.001786507,Imaging,0.58610356,TRUE,50.44444444,0.634918672,12.22222222,0.387811078,0,0.403234768,,,0.475321506
174,Digital Health Transition in Rheumatology: A Qualitative Study.,Int J Environ Res Public Health,33807952,4/4/21,pubmed,0,11,digital health,0.002490493,0.002490588,0.002490526,0.643208831,0.201987806,0.147331755,Epidemiology,0.92880106,TRUE,82.36363636,0.815511163,54.18181818,0.698153599,0,0.403234768,,,0.63896651
175,SARS-CoV-2 Main Protease Active Site Ligands in the Human Metabolome.,Molecules,33807773,4/4/21,pubmed,0,3,"in silico, metabolom",0.990282344,0.001943631,0.001943507,0.001943522,0.001943533,0.001943462,Drug discovery,0.88192195,TRUE,39,0.530521368,21.66666667,0.499732406,0,0.403234768,,,0.477829514
176,Acceptability of a COVID-19 Vaccine among the Saudi Population.,Vaccines (Basel),33807732,4/4/21,pubmed,0,5,logistic regression,0.002080606,0.002080585,0.002080532,0.002080579,0.989597111,0.002080588,Healthcare,0.35252213,FALSE,10.2,0.153627312,2.8,0.188787798,0,0.403234768,,,0.248549959
177,Epidemiological Differences in the Impact of COVID-19 Vaccination in the United States and China.,Vaccines (Basel),33807647,4/4/21,pubmed,0,5,mathematical model,0.001751191,0.001751196,0.001751144,0.809887302,0.1831079,0.001751266,Epidemiology,0.1496709,FALSE,81.8,0.813964995,148.6,0.882392293,0,0.403234768,,,0.699864019
178,Molecular Analysis of SARS-CoV-2 Genetic Lineages in Jordan: Tracking the Introduction and Spread of COVID-19 UK Variant of Concern at a Country Level.,Pathogens,33807556,4/4/21,pubmed,0,2,bayes,0.000988361,0.995058134,0.000988346,0.000988421,0.000988373,0.000988365,Genomics,0.56354654,TRUE,17.5,0.263776362,3.5,0.213607172,0,0.403234768,,,0.293539434
179,Multifaceted Mechanisms of Action of Metformin Which Have Been Unraveled One after Another in the Long History.,Int J Mol Sci,33807522,4/4/21,pubmed,0,5,microbiom,0.554299743,0.070847298,0.001717207,0.070373346,0.001717278,0.301045127,Drug discovery,0.26978052,FALSE,172,0.95973777,,,0,0.403234768,,,0.681486269
180,Impact of COVID-19 on Out-of-Hospital Cardiac Arrest in Singapore.,Int J Environ Res Public Health,33807454,4/4/21,pubmed,0,9,logistic regression,0.002080522,0.002080572,0.002080522,0.002080659,0.628411629,0.363266095,Healthcare,0.95035017,TRUE,47.44444444,0.60931412,,,0,0.403234768,,,0.506274444
181,Bioinformatic Analysis of Genome-Predicted Bat Cathelicidins.,Molecules,33806967,4/4/21,pubmed,0,5,"bioinformatic, genomes",0.481776328,0.493219179,0.020826032,0.001392841,0.001392812,0.001392808,Genomics,0.7786002,TRUE,15.6,0.235450554,11.4,0.375970029,0,0.403234768,,,0.33821845
182,"Health Literacy, Digital Health Literacy, and COVID-19 Pandemic Attitudes and Behaviors in U.S. College Students: Implications for Interventions.",Int J Environ Res Public Health,33806763,4/4/21,pubmed,0,9,digital health,0.001751181,0.048286315,0.001751336,0.288922542,0.657537468,0.001751158,Healthcare,0.64184386,TRUE,32.88888889,0.464778279,33.22222222,0.593925609,0,0.403234768,,,0.487312885
183,"Relationship of Decrease in Frequency of Socialization to Daily Life, Social Life, and Physical Function in Community-Dwelling Adults Aged 60 and Over after the COVID-19 Pandemic.",Int J Environ Res Public Health,33806599,4/4/21,pubmed,0,8,logistic regression,0.001901741,0.001901714,0.001901683,0.001901825,0.99049123,0.001901807,Healthcare,0.8440727,TRUE,11.75,0.177562001,1.375,0.132927482,0,0.403234768,,,0.237908084
184,COVID-19 Confinement and Sexual Activity in Spain: A Cross-Sectional Study.,Int J Environ Res Public Health,33806553,4/4/21,pubmed,0,7,logistic regression,0.001717307,0.001717274,0.001717286,0.001717327,0.991413565,0.001717241,Healthcare,0.6312586,TRUE,65,0.734801163,,,0,0.403234768,,,0.569017965
185,COVID-19 Preventive Behaviours in Cameroon: A Six-Month Online National Survey.,Int J Environ Res Public Health,33806495,4/4/21,pubmed,0,29,logistic regression,0.001717183,0.001717267,0.001717188,0.086000475,0.907130656,0.001717231,Healthcare,0.6738989,TRUE,43.79310345,0.575669491,47.03448276,0.668785122,0,0.403234768,,,0.549229793
186,Repurposing of Some Natural Product Isolates as SARS-COV-2 Main Protease Inhibitors via In Vitro Cell Free and Cell-Based Antiviral Assessments and Molecular Modeling Approaches.,Pharmaceuticals (Basel),33806331,4/4/21,pubmed,0,10,virtual screening,0.993143317,0.00137135,0.001371315,0.001371303,0.001371324,0.001371391,Drug discovery,0.8774841,TRUE,62.5,0.718164389,20.1,0.481268397,0,0.403234768,,,0.534222518
187,Ribosome-Profiling Reveals Restricted Post Transcriptional Expression of Antiviral Cytokines and Transcription Factors during SARS-CoV-2 Infection.,Int J Mol Sci,33806254,4/4/21,pubmed,0,8,transcriptom,0.895501356,0.001823475,0.001823331,0.097205096,0.001823369,0.001823373,Drug discovery,0.5314392,TRUE,36.5,0.503123261,31.5,0.581883864,0,0.403234768,,,0.496080631
188,Site-Specific O-Glycosylation Analysis of SARS-CoV-2 Spike Protein Produced in Insect and Human Cells.,Viruses,33806155,4/4/21,pubmed,0,12,"proteom, glycoproteom",0.626500808,0.367007711,0.001622747,0.001622857,0.001622939,0.001622937,Drug discovery,0.24193245,FALSE,171.25,0.959119302,,,0,0.403234768,,,0.681177035
189,SARS-CoV-2 N501Y Introductions and Transmissions in Switzerland from Beginning of October 2020 to February 2021-Implementation of Swiss-Wide Diagnostic Screening and Whole Genome Sequencing.,Microorganisms,33806013,4/4/21,pubmed,0,46,"sequencing, whole genome, genomes",0.001751219,0.79505814,0.001751261,0.197936908,0.001751249,0.001751223,Genomics,0.38122347,FALSE,75.95652174,0.789968458,,,0,0.403234768,,,0.596601613
190,A Strong Seasonality Pattern for Covid-19 Incidence Rates Modulated by UV Radiation Levels.,Viruses,33805449,4/4/21,pubmed,0,7,machine learning,0.002080558,0.002080564,0.002080718,0.989596917,0.002080666,0.002080578,Epidemiology,0.11305243,FALSE,117.8571429,0.904199394,185.1428571,0.909486219,0,0.403234768,,,0.73897346
191,Accelerated Repurposing and Drug Development of Pulmonary Hypertension Therapies for COVID-19 Treatment Using an AI-Integrated Biosimulation Platform.,Molecules,33805419,4/4/21,pubmed,0,9,"in silico, dataset",0.94411791,0.001987124,0.001987171,0.001987228,0.001987102,0.047933464,Drug discovery,0.7412609,TRUE,4.333333333,0.057950399,,,0,0.403234768,,,0.230592583
192,Exosomes from COVID-19 Patients Carry Tenascin-C and Fibrinogen-β in Triggering Inflammatory Signals in Cells of Distant Organ.,Int J Mol Sci,33804769,4/4/21,pubmed,0,6,proteom,0.637231081,0.002422515,0.002422318,0.002422321,0.002422351,0.353079414,Drug discovery,0.76540494,TRUE,80.83333333,0.81025419,82.16666667,0.783315494,0,0.403234768,,,0.665601484
193,Endogenously Produced SARS-CoV-2 Specific IgG Antibodies May Have a Limited Impact on Clearing Nasal Shedding of Virus during Primary Infection in Humans.,Viruses,33804667,4/4/21,pubmed,0,5,mathematical model,0.092486658,0.586612642,0.001684506,0.200746743,0.001684577,0.116784873,Genomics,0.23567128,FALSE,101,0.871296926,,,0,0.403234768,,,0.637265847
194,Hyperparameter Optimization for COVID-19 Pneumonia Diagnosis Based on Chest CT.,Sensors (Basel),33804609,4/4/21,pubmed,0,4,neural network,0.001593499,0.001593484,0.992032617,0.001593483,0.001593454,0.001593463,Imaging,0.6822543,TRUE,88.25,0.837466757,37.25,0.617206315,0,0.403234768,,,0.619302613
195,SARS-CoV-2 Variant of Concern 202012/01 Has about Twofold Replicative Advantage and Acquires Concerning Mutations.,Viruses,33804556,4/4/21,pubmed,0,5,genomes,0.136401429,0.800424757,0.001593492,0.001593631,0.001593579,0.058393112,Genomics,0.12864223,FALSE,24.8,0.365328715,28.8,0.560543216,1,0.537564047,,,0.487811993
196,Work Engagement in Nurses during the Covid-19 Pandemic: A Cross-Sectional Study.,Healthcare (Basel),33804351,4/4/21,pubmed,0,6,probabilistic,0.002130663,0.002130694,0.002130803,0.002130696,0.805251585,0.186225558,Healthcare,0.9492488,TRUE,45.5,0.591440411,7.166666667,0.301311212,0,0.403234768,,,0.431995463
197,A Longitudinal Study of Subjective Daytime Sleepiness Changes in Elementary School Children Following a Temporary School Closure Due to COVID-19.,Children (Basel),33804339,4/4/21,pubmed,0,5,logistic regression,0.002032846,0.002032825,0.002032779,0.002032901,0.98983575,0.002032899,Healthcare,0.99341494,TRUE,26.2,0.383820892,14,0.412898047,0,0.403234768,,,0.399984569
198,"SARS-CoV-2 Is a Culprit for Some, but Not All Acute Ischemic Strokes: A Report from the Multinational COVID-19 Stroke Study Group.",J Clin Med,33804307,4/4/21,pubmed,0,17,"machine learning, logistic regression, dataset",0.000956325,0.000956378,0.309814574,0.000956394,0.000956342,0.686359988,Clinics,0.14789349,FALSE,83.88235294,0.820829983,,,0,0.403234768,,,0.612032375
199,Lessons from the COVID-19 Pandemic on the Use of Artificial Intelligence in Digital Radiology: The Submission of a Survey to Investigate the Opinion of Insiders.,Healthcare (Basel),33804195,4/4/21,pubmed,0,3,artificial intelligence,0.001203503,0.001203452,0.294583207,0.515563861,0.18624256,0.001203417,Epidemiology,0.88032174,TRUE,45,0.587049292,9.666666667,0.348274017,0,0.403234768,,,0.446186025
200,Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding.,Int J Environ Res Public Health,33804131,4/4/21,pubmed,0,2,"machine learning, artificial intelligence",0.163965542,0.001823499,0.260236869,0.570327301,0.001823485,0.001823304,Epidemiology,0.4004632,FALSE,5,0.070752675,,,0,0.403234768,,,0.236993721
201,"Metal-Bound Methisazone; Novel Drugs Targeting Prophylaxis and Treatment of SARS-CoV-2, a Molecular Docking Study.",Int J Mol Sci,33804129,4/4/21,pubmed,0,18,in silico,0.969974308,0.001565322,0.001565302,0.023764449,0.001565311,0.001565307,Drug discovery,0.7413268,TRUE,9.388888889,0.139588101,0.888888889,0.10422799,0,0.403234768,,,0.21568362
202,"Severe Acute Kidney Injury in Critically Ill Patients with COVID-19 Admitted to ICU: Incidence, Risk Factors, and Outcomes.",J Clin Med,33804100,4/4/21,pubmed,0,19,logistic regression,0.001237086,0.001237051,0.001237052,0.001237045,0.001237047,0.993814719,Clinics,0.7826101,TRUE,11.05263158,0.167233595,3.052631579,0.199558469,0,0.403234768,,,0.256675611
203,The Progression of COVID-19 and the Government Response in China.,Int J Environ Res Public Health,33804022,4/4/21,pubmed,0,4,model simulation,0.00146193,0.031892382,0.001461861,0.96226009,0.001461879,0.001461858,Epidemiology,0.56534946,TRUE,106,0.881192405,,,0,0.403234768,,,0.642213586
204,Metagenomic Snapshots of Viral Components in Guinean Bats.,Microorganisms,33803988,4/4/21,pubmed,0,16,"transcriptom, metagenom, genomes, metatranscriptom",0.002032798,0.989836068,0.002032757,0.002032871,0.00203276,0.002032746,Genomics,0.7916206,TRUE,62.75,0.719586864,101.9375,0.825595397,0,0.403234768,,,0.649472343
205,A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized COVID-19 Patients.,Infect Dis Rep,33803753,4/4/21,pubmed,0,5,"bayes, bayesian model",0.076486633,0.001203445,0.05099241,0.001203464,0.00120342,0.868910628,Clinics,0.21006775,FALSE,12.6,0.190302431,1.4,0.133864062,0,0.403234768,,,0.242467087
206,SARS-CoV-2 Molecular Transmission Clusters and Containment Measures in Ten European Regions during the First Pandemic Wave.,Life (Basel),33803490,4/4/21,pubmed,0,16,"bioinformatic, whole genome",0.00146195,0.540850866,0.00146188,0.342691732,0.112071654,0.001461919,Genomics,0.58610356,TRUE,58.25,0.691384749,44.0625,0.654468825,0,0.403234768,,,0.583029447
207,"Structure-Function Analyses of New SARS-CoV-2 Variants B.1.1.7, B.1.351 and B.1.1.28.1: Clinical, Diagnostic, Therapeutic and Public Health Implications.",Viruses,33803400,4/4/21,pubmed,0,10,molecular dynamics simulation,0.402562121,0.590946772,0.001622755,0.001622831,0.00162277,0.001622751,Genomics,0.51619023,TRUE,60.2,0.704125178,,,0,0.403234768,,,0.553679973
208,Detection and Genome Sequencing of SARS-CoV-2 in a Domestic Cat with Respiratory Signs in Switzerland.,Viruses,33802899,4/4/21,pubmed,0,15,sequencing,0.135040086,0.731085236,0.001593519,0.00159358,0.129094073,0.001593506,Genomics,0.31514728,FALSE,97.73333333,0.863937164,,,2,0.618927094,,,0.741432129
209,"Incidence and Death Rates from COVID-19 Are Not Always Coupled: An Analysis of Temporal Data on Local, Federal, and National Levels.",Healthcare (Basel),33802866,4/4/21,pubmed,0,5,prediction model,0.002562531,0.002562623,0.002562591,0.829324397,0.002562564,0.160425294,Epidemiology,0.19357568,FALSE,25.4,0.372626631,19,0.471367407,0,0.403234768,,,0.415742935
210,Is PF-00835231 a Pan-SARS-CoV-2 Mpro Inhibitor? A Comparative Study.,Molecules,33802860,4/4/21,pubmed,0,6,in silico,0.793705492,0.123008938,0.003214491,0.003214477,0.003214602,0.073642,Drug discovery,0.89299375,TRUE,10.33333333,0.155049787,,,0,0.403234768,,,0.279142277
211,SARS-CoV-2 Infection and Inflammatory Response in a Twin Pregnancy.,Int J Environ Res Public Health,33802696,4/4/21,pubmed,0,9,microbiom,0.254180008,0.323480746,0.001511843,0.00151191,0.001511893,0.417803599,Clinics,0.8085221,TRUE,51.66666667,0.644195683,19,0.471367407,0,0.403234768,,,0.506265953
212,mRNA-lncRNA Co-Expression Network Analysis Reveals the Role of lncRNAs in Immune Dysfunction during Severe SARS-CoV-2 Infection.,Viruses,33802569,4/4/21,pubmed,0,4,"sequencing, transcriptom, network analysis",0.86583385,0.052772162,0.001461903,0.001461873,0.00146188,0.077008332,Drug discovery,0.3470689,FALSE,31.25,0.447213804,,,0,0.403234768,,,0.425224286
213,Physical Activity during the First COVID-19-Related Lockdown in Italy.,Int J Environ Res Public Health,33802549,4/4/21,pubmed,0,7,"logistic regression, dataset",0.001392873,0.001392866,0.001392995,0.15937209,0.835056294,0.001392883,Healthcare,0.74977404,TRUE,51.28571429,0.640299338,23.28571429,0.514851485,0,0.403234768,,,0.519461864
214,Psychological Distress among Italian University Students Compared to General Workers during the COVID-19 Pandemic.,Int J Environ Res Public Health,33802514,4/4/21,pubmed,0,4,dataset,0.001392953,0.001392837,0.001392974,0.001392925,0.993035431,0.001392881,Healthcare,0.80320203,TRUE,50.75,0.636341147,26.25,0.539670859,0,0.403234768,,,0.526415591
215,The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks.,Medicina (Kaunas),33802471,4/4/21,pubmed,0,4,"neural network, data mining, dataset",0.001823339,0.001823393,0.051562731,0.941143709,0.001823448,0.00182338,Epidemiology,0.012810558,FALSE,39.75,0.536829736,12.75,0.395972705,0,0.403234768,,,0.445345736
216,COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases.,Sensors (Basel),33802428,4/4/21,pubmed,0,6,"deep learning, dataset",0.001310341,0.001310332,0.993448324,0.001310371,0.001310321,0.001310311,Imaging,0.14293504,FALSE,105.1666667,0.87939885,194.3333333,0.91463741,0,0.403234768,,,0.732423676
217,Acceptance of a COVID-19 Vaccine in Japan during the COVID-19 Pandemic.,Vaccines (Basel),33802285,4/4/21,pubmed,0,13,logistic regression,0.002238449,0.002238417,0.002238421,0.002238514,0.988807775,0.002238423,Healthcare,0.48682562,FALSE,102.1538462,0.873832643,,,2,0.618927094,,,0.746379868
218,"Measuring the Response Performance of U.S. States against COVID-19 Using an Integrated DEA, CART, and Logistic Regression Approach.",Healthcare (Basel),33802276,4/4/21,pubmed,0,3,"machine learning, logistic regression",0.00182337,0.001823345,0.472891006,0.519815358,0.001823437,0.001823485,Epidemiology,0.88438326,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
219,Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development.,Int J Environ Res Public Health,33802247,4/4/21,pubmed,0,5,"mathematical model, predictive model, network analysis",0.001653036,0.145218181,0.001653082,0.824334,0.025488633,0.001653068,Epidemiology,0.5430573,TRUE,62.6,0.718535469,11.6,0.379515654,0,0.403234768,,,0.50042863
220,A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19.,Int J Mol Sci,33802169,4/4/21,pubmed,0,5,computational,0.819782433,0.002357846,0.071700484,0.101443477,0.0023579,0.002357861,Drug discovery,0.72623694,TRUE,0.4,0.007421609,,,0,0.403234768,,,0.205328188
221,0,Pathogens,33802049,4/4/21,pubmed,0,10,genomes,0.001461932,0.650412889,0.311324296,0.033876979,0.001461898,0.001462006,Genomics,0.6983363,TRUE,65.7,0.738573814,40.8,0.638346267,0,0.403234768,,,0.59338495
222,The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces.,Int J Environ Res Public Health,33802001,4/4/21,pubmed,0,2,machine learning,0.00123706,0.001237107,0.060100825,0.934950829,0.001237107,0.001237072,Epidemiology,0.2921846,FALSE,3.5,0.044344115,0,0.055525823,0,0.403234768,,,0.167701569
223,Effects of Lifestyle Changes on the Mental Health of Healthcare Workers with Different Sense of Coherence Levels in the Era of COVID-19 Pandemic.,Int J Environ Res Public Health,33801912,4/4/21,pubmed,0,4,dataset,0.001901721,0.001901704,0.001901877,0.001901767,0.990491173,0.001901758,Healthcare,0.96919173,TRUE,2.25,0.023439916,,,0,0.403234768,,,0.213337342
224,miRCOVID-19: Potential Targets of Human miRNAs in SARS-CoV-2 for RNA-Based Drug Discovery.,Noncoding RNA,33801496,4/4/21,pubmed,0,2,"computational, transcriptom, genomes",0.729586861,0.266175651,0.001059381,0.001059389,0.001059367,0.001059351,Drug discovery,0.590514,TRUE,23.5,0.348506401,4.5,0.242708055,0,0.403234768,,,0.331483074
225,The Mutation Profile of SARS-CoV-2 Is Primarily Shaped by the Host Antiviral Defense.,Viruses,33801257,4/4/21,pubmed,0,5,genomes,0.06125112,0.865930455,0.001653338,0.001653095,0.001653011,0.067858981,Genomics,0.44493666,FALSE,7.8,0.113550622,2.2,0.16838373,0,0.403234768,,,0.228389706
226,0,Molecules,33801151,4/4/21,pubmed,0,9,"molecular dynamics simulation, in silico",0.993448094,0.001310364,0.001310402,0.001310372,0.001310347,0.001310421,Drug discovery,0.7913146,TRUE,46.44444444,0.600160802,7,0.299973241,0,0.403234768,,,0.43445627
227,COVID-19 Vaccination Willingness among Chinese Adults under the Free Vaccination Policy.,Vaccines (Basel),33801136,4/4/21,pubmed,0,6,logistic regression,0.001538239,0.001538101,0.001538103,0.170992851,0.822854584,0.001538123,Healthcare,0.41906607,FALSE,13.5,0.205393036,,,0,0.403234768,,,0.304313902
228,Comparison of Cancer Patients to Non-Cancer Patients among COVID-19 Inpatients at a National Level.,Cancers (Basel),33801131,4/4/21,pubmed,0,7,logistic regression,0.001126814,0.001126878,0.00112684,0.001126959,0.001126831,0.994365677,Clinics,0.58911407,TRUE,142.8571429,0.937225555,99.42857143,0.82124699,0,0.403234768,,,0.720569104
229,Fear of COVID-19 for Individuals and Family Members: Indications from the National Cross-Sectional Study of the EPICOVID19 Web-Based Survey.,Int J Environ Res Public Health,33801074,4/4/21,pubmed,0,9,logistic regression,0.001272669,0.044245379,0.047309015,0.001272748,0.797585545,0.108314644,Healthcare,0.6359441,TRUE,86.55555556,0.83171501,37.44444444,0.61887878,0,0.403234768,,,0.617942853
230,SEAHIR: A Specialized Compartmental Model for COVID-19.,Int J Environ Res Public Health,33800896,4/4/21,pubmed,0,6,dataset,0.001486447,0.036807871,0.001486473,0.935753921,0.001486455,0.022978832,Epidemiology,0.15274864,FALSE,82.33333333,0.815449317,44.5,0.656743377,0,0.403234768,,,0.625142487
231,Sleep and Awakening Quality during COVID-19 Confinement: Complexity and Relevance for Health and Behavior.,Int J Environ Res Public Health,33800607,4/4/21,pubmed,0,27,logistic regression,0.026524587,0.001392829,0.001392968,0.001392927,0.947493849,0.021802839,Healthcare,0.9632259,TRUE,31.7037037,0.452099697,,,0,0.403234768,,,0.427667232
232,Exposure to Perfluoroalkyl Substances and Mortality for COVID-19: A Spatial Ecological Analysis in the Veneto Region (Italy).,Int J Environ Res Public Health,33800362,4/4/21,pubmed,0,8,bayes,0.002358091,0.002357796,0.00235784,0.372150024,0.187840876,0.432935373,Clinics,0.2547953,FALSE,182.75,0.96561321,147.875,0.881723307,0,0.403234768,,,0.750190429
233,Trends of COVID-19 Admissions in an Italian Hub during the Pandemic Peak: Large Retrospective Study Focused on Older Subjects.,J Clin Med,33800020,4/4/21,pubmed,0,7,logistic regression,0.001237084,0.113461138,0.001237139,0.001237133,0.001237099,0.881590407,Clinics,0.9130659,TRUE,91.85714286,0.848475478,52.71428571,0.691865132,0,0.403234768,,,0.647858459
234,Structure-Based Virtual Screening Identifies Multiple Stable Binding Sites at the RecA Domains of SARS-CoV-2 Helicase Enzyme.,Molecules,33800013,4/4/21,pubmed,0,6,virtual screening,0.99488667,0.001022651,0.001022654,0.001022735,0.001022656,0.001022634,Drug discovery,0.92608875,TRUE,23.83333333,0.351598738,,,0,0.403234768,,,0.377416753
235,Association between Functional Inhibitors of Acid Sphingomyelinase (FIASMAs) and Reduced Risk of Death in COVID-19 Patients: A Retrospective Cohort Study.,Pharmaceuticals (Basel),33799977,4/4/21,pubmed,0,4,logistic regression,0.001272742,0.001272653,0.001272622,0.001272682,0.001272718,0.993636583,Clinics,0.9596395,TRUE,97.25,0.862452842,96,0.81509232,0,0.403234768,,,0.69359331
236,A Knowledge-Based Algorithm for Automatic Monitoring of Orthodontic Treatment: The Dental Monitoring System. Two Cases.,Sensors (Basel),33799930,4/4/21,pubmed,0,6,artificial intelligence,0.001203474,0.001203432,0.311522355,0.519848457,0.001203514,0.165018768,Epidemiology,0.5839491,TRUE,44.66666667,0.583462181,20.5,0.486218892,0,0.403234768,,,0.490971947
237,Spatiotemporal Analysis of COVID-19 Incidence Data.,Viruses,33799900,4/4/21,pubmed,0,3,dataset,0.001392864,0.001392864,0.04634431,0.911336621,0.001392841,0.0381405,Epidemiology,0.2661956,FALSE,266,0.987012184,244.3333333,0.935978057,0,0.403234768,,,0.775408336
238,0,Molecules,33799871,4/4/21,pubmed,0,4,virtual screening,0.991067102,0.001786544,0.001786665,0.001786624,0.001786525,0.00178654,Drug discovery,0.7611891,TRUE,26.75,0.391799122,,,0,0.403234768,,,0.397516945
239,"Transmission Dynamics of SARS-CoV-2 during an Outbreak in a Roma Community in Thessaly, Greece-Control Measures and Lessons Learned.",Int J Environ Res Public Health,33799791,4/4/21,pubmed,0,27,logistic regression,0.001786532,0.001786757,0.106951417,0.19217773,0.465681463,0.2316161,Healthcare,0.21575662,FALSE,32.40740741,0.46001608,17.66666667,0.457586299,0,0.403234768,,,0.440279049
240,Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment.,Int J Environ Res Public Health,33799509,4/4/21,pubmed,0,24,"neural network, dataset",0.001112625,0.001112647,0.841386242,0.042622202,0.001112675,0.11265361,Imaging,0.09805429,FALSE,38.875,0.527676418,28.16666667,0.555458924,0,0.403234768,,,0.495456703
241,Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories.,Epidemics,33799289,4/3/21,pubmed,0,12,bayes,0.001438118,0.001438135,0.001438093,0.93053911,0.063708317,0.001438226,Epidemiology,0.30929074,FALSE,91.83333333,0.848351784,88.16666667,0.797096602,0,0.403234768,,,0.682894384
242,Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images.,Comput Biol Med,33799220,4/3/21,pubmed,0,11,"neural network, dataset",0.000728107,0.000728104,0.996359472,0.000728112,0.000728104,0.0007281,Imaging,0.64648587,TRUE,48.45454545,0.61766343,40.18181818,0.634399251,1,0.537564047,,,0.596542243
243,An automated COVID-19 detection based on fused dynamic exemplar pyramid feature extraction and hybrid feature selection using deep learning.,Comput Biol Med,33799219,4/3/21,pubmed,0,3,"deep learning, neural network, classifier",0.000936116,0.000936105,0.995319514,0.000936125,0.000936065,0.000936074,Imaging,0.708391,TRUE,85.66666667,0.827509432,94.33333333,0.811412898,0,0.403234768,,,0.680719032
244,Recovery rate and factors associated with smell and taste disruption in patients with coronavirus disease 2019.,Am J Otolaryngol,33799139,4/3/21,pubmed,0,4,logistic regression,0.001461881,0.001462099,0.001461909,0.001461963,0.210962984,0.783189164,Clinics,0.96583056,TRUE,4.75,0.064506154,0,0.055525823,3,0.667819001,,,0.262616993
245,Rutin and flavone analogs as prospective SARS-CoV-2 main protease inhibitors: In silico drug discovery study.,J Mol Graph Model,33798836,4/3/21,pubmed,0,10,in silico,0.992924237,0.001415121,0.00141514,0.001415186,0.001415153,0.001415163,Drug discovery,0.97701454,TRUE,27.6,0.402374915,,,0,0.403234768,,,0.402804841
246,Molecular epidemiology analysis of early variants of SARS-CoV-2 reveals the potential impact of mutations P504L and Y541C (NSP13) in the clinical COVID-19 outcomes.,Infect Genet Evol,33798758,4/3/21,pubmed,0,8,"genomes, sequence alignment",0.001203425,0.840100134,0.001203418,0.001203483,0.001203429,0.15508611,Genomics,0.6418797,TRUE,3.625,0.045086276,,,0,0.403234768,,,0.224160522
247,COVID-19 Superspreading Suggests Mitigation by Social Network Modulation.,Phys Rev Lett,33798363,4/3/21,pubmed,0,3,mathematical model,0.005697618,0.005697705,0.0056976,0.971511504,0.005697992,0.005697581,Epidemiology,0.34988478,FALSE,205.3333333,0.973962521,287,0.950628847,5,0.739490092,,,0.888027153
248,Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques.,PLoS One,33798204,4/3/21,pubmed,0,3,"machine learning, active learning",0.001291264,0.001291306,0.520532177,0.001291265,0.474302758,0.001291231,Healthcare,0.47267622,FALSE,12.66666667,0.191044592,1.333333333,0.13252609,0,0.403234768,,,0.242268483
249,"Higher airborne pollen concentrations correlated with increased SARS-CoV-2 infection rates, as evidenced from 31 countries across the globe.",Proc Natl Acad Sci U S A,33798095,4/3/21,pubmed,0,154,dataset,0.164790482,0.001751346,0.001751176,0.828204509,0.00175129,0.001751197,Epidemiology,0.35257813,FALSE,49.42857143,0.627125982,44.35714286,0.655606101,1,0.537564047,,,0.606765377
250,Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia with Neural Networks.,IEEE Trans Ultrason Ferroelectr Freq Control,33798078,4/3/21,pubmed,0,10,neural network,0.00153816,0.001538171,0.894948571,0.098898842,0.001538106,0.00153815,Imaging,0.42410028,FALSE,0.4,0.007421609,,,0,0.403234768,,,0.205328188
251,Within-city Variation in Reactive Oxygen Species from Fine Particle Air Pollution and COVID-19.,Am J Respir Crit Care Med,33798018,4/3/21,pubmed,0,10,mathematical model,0.044401153,0.058501168,0.00133009,0.570363218,0.104629334,0.220775038,Epidemiology,0.49237454,FALSE,225.6,0.980023502,429.5,0.973508162,0,0.403234768,,,0.78558881
252,Inspecting the mechanism of fragment hit binding on SARS-CoV-2 Mpro by using supervised molecular dynamics (SuMD) simulations.,ChemMedChem,33797868,4/3/21,pubmed,0,6,"molecular dynamics simulation, computational, in-silico",0.69387505,0.002422367,0.296435676,0.002422366,0.002422291,0.00242225,Drug discovery,0.08319312,FALSE,74.83333333,0.785824726,18.16666667,0.462603693,0,0.403234768,,,0.550554396
253,Detectable respiratory SARS-CoV-2 RNA is associated with low vitamin D levels and high social deprivation.,Int J Clin Pract,33797849,4/3/21,pubmed,0,7,logistic regression,0.028543643,0.04407433,0.00186171,0.001861807,0.133064741,0.790593769,Clinics,0.5251745,TRUE,13,0.197352959,,,0,0.403234768,,,0.300293863
254,Novel mutations in NSP-1 and PLPro of SARS-CoV-2 NIB-1 genome mount for effective therapeutics.,J Genet Eng Biotechnol,33797663,4/3/21,pubmed,0,15,computational,0.565991099,0.429558364,0.001112623,0.001112641,0.001112653,0.00111262,Drug discovery,0.30778575,FALSE,17.6,0.264456676,,,0,0.403234768,,,0.333845722
255,COVID-19 and digital competencies among young physicians: are we (really) ready for the new era? A national survey of the Italian Young Medical Doctors Association.,Ann Ist Super Sanita,33797398,4/3/21,pubmed,0,10,"artificial intelligence, predictive model, digital health",0.046146069,0.001371259,0.246746515,0.1088248,0.595540096,0.001371261,Healthcare,0.86137676,TRUE,21.8,0.322283382,3,0.199424672,0,0.403234768,,,0.308314274
256,COVID19-inhibitory activity of withanolides involves targeting of the host cell surface receptor ACE2: insights from computational and biochemical assays.,J Biomol Struct Dyn,33797339,4/3/21,pubmed,0,8,computational,0.990882988,0.001823357,0.001823401,0.001823404,0.001823339,0.001823511,Drug discovery,0.84206045,TRUE,93.125,0.851691508,,,0,0.403234768,,,0.627463138
257,In silico characterization of mutations circulating in SARS-CoV-2 structural proteins.,J Biomol Struct Dyn,33797336,4/3/21,pubmed,0,9,"computational, in silico, genomes",0.440276978,0.491839685,0.041921218,0.001171576,0.001171584,0.023618958,Genomics,0.68626934,TRUE,23.44444444,0.347083926,,,0,0.403234768,,,0.375159347
258,The Association between ADHD and the Severity of COVID-19 Infection.,J Atten Disord,33797281,4/3/21,pubmed,0,12,logistic regression,0.002806445,0.002806437,0.002806404,0.002806444,0.082761017,0.906013252,Clinics,0.8549,TRUE,152.8333333,0.94662626,137.6666667,0.872491303,0,0.403234768,,,0.74078411
259,Discovery of Potent Covid-19 Main Protease Inhibitors using Integrated Drug Repurposing Strategy.,Biotechnol Appl Biochem,33797130,4/3/21,pubmed,0,5,machine learning,0.799129432,0.001565328,0.194609265,0.001565397,0.001565292,0.001565286,Drug discovery,0.9265354,TRUE,31,0.445111015,4.2,0.234211935,0,0.403234768,,,0.360852572
260,In silico approach of secondary metabolites from Brazilian herbal medicines to search for potential drugs against SARS-CoV-2.,Phytother Res,33797123,4/3/21,pubmed,0,8,in silico,0.989083611,0.002183302,0.002183227,0.002183354,0.00218321,0.002183297,Drug discovery,0.91535187,TRUE,13.875,0.209536768,3.75,0.21982874,0,0.403234768,,,0.277533425
261,A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing.,Nat Mach Intell,33796820,4/3/21,pubmed,0,5,"deep learning, neural network,  omics, predictive model, dataset",0.71200591,0.001220031,0.283113972,0.001220047,0.001220013,0.001220027,Drug discovery,0.055065155,FALSE,2.8,0.031047065,,,0,0.403234768,,,0.217140916
262,The Unique Impact of COVID-19 on Human Gut Microbiome Research.,Front Med (Lausanne),33796545,4/3/21,pubmed,0,7,microbiom,0.105927223,0.470039223,0.002296649,0.417143408,0.002296707,0.00229679,Genomics,0.9476583,TRUE,8.285714286,0.121590698,0.857142857,0.103224512,0,0.403234768,,,0.209349992
263,A Novel BrainHealth Index Prototype Improved by Telehealth-Delivered Training During COVID-19.,Front Public Health,33796498,4/3/21,pubmed,0,11,machine learning,0.000916715,0.000916708,0.369843012,0.10548259,0.521924287,0.000916689,Healthcare,0.84789634,TRUE,104.0909091,0.877728988,281.4545455,0.949090179,0,0.403234768,,,0.743351311
264,The Quixotic Task of Forecasting Peaks of COVID-19: Rather Focus on Forward and Backward Projections.,Front Public Health,33796495,4/3/21,pubmed,0,8,mathematical model,0.001072184,0.001072202,0.020114233,0.975597012,0.001072181,0.001072188,Epidemiology,0.39235017,FALSE,31.75,0.452408931,7,0.299973241,0,0.403234768,,,0.385205646
265,A Saliva-Based RNA Extraction-Free Workflow Integrated With Cas13a for SARS-CoV-2 Detection.,Front Cell Infect Microbiol,33796478,4/3/21,pubmed,0,13,image analysis,0.001371365,0.522188536,0.472326197,0.001371316,0.001371333,0.001371253,Genomics,0.51810086,TRUE,3.538461538,0.044467809,,,0,0.403234768,,,0.223851288
266,Seroprevalence of IgG against SARS-CoV-2 and its determinants among healthcare workers of a COVID-19 dedicated hospital of India.,Am J Blood Res,33796388,4/3/21,pubmed,0,6,logistic regression,0.001861724,0.16850204,0.001861759,0.123594271,0.702318421,0.001861786,Healthcare,0.6240062,TRUE,22,0.326056033,,,0,0.403234768,,,0.3646454
267,Network Analysis and Transcriptome Profiling Identify Autophagic and Mitochondrial Dysfunctions in SARS-CoV-2 Infection.,Front Genet,33796130,4/3/21,pubmed,0,9,"transcriptom, network analysis",0.861771148,0.113893295,0.00133003,0.00133006,0.001330058,0.020345408,Drug discovery,0.44746304,FALSE,61.55555556,0.712660028,,,0,0.403234768,,,0.557947398
268,Immune Interaction Map of Human SARS-CoV-2 Target Genes: Implications for Therapeutic Avenues.,Front Immunol,33796097,4/3/21,pubmed,0,5,bioinformatic,0.702911041,0.001438183,0.001438176,0.001438144,0.001438213,0.291336242,Drug discovery,0.71318805,TRUE,154.6,0.947615808,110.4,0.838975114,0,0.403234768,,,0.729941896
269,The Relevance of Bioinformatics Applications in the Discovery of Vaccine Candidates and Potential Drugs for COVID-19 Treatment.,Bioinform Biol Insights,33795932,4/3/21,pubmed,0,6,bioinformatic,0.659052284,0.334456554,0.001622811,0.001622856,0.001622765,0.00162273,Drug discovery,0.7147421,TRUE,53.33333333,0.656255798,4.666666667,0.246721969,0,0.403234768,,,0.435404178
270,"The Functional Classification of ORF8 in SARS-CoV-2 Replication, Immune Evasion, and Viral Pathogenesis Inferred through Phylogenetic Profiling.",Evol Bioinform Online,33795929,4/3/21,pubmed,0,5,genome-wide,0.601769911,0.276848096,0.001565388,0.001565321,0.001565377,0.116685908,Drug discovery,0.6098237,TRUE,8.2,0.120477457,,,0,0.403234768,,,0.261856112
271,0,Sci Rep,33795768,4/3/21,pubmed,0,8,logistic regression,0.001786538,0.001786496,0.056645693,0.001786522,0.030065515,0.907929236,Clinics,0.9770142,TRUE,33.125,0.467375843,,,0,0.403234768,,,0.435305305
272,Analysis of SARS-CoV-2 genomic epidemiology reveals disease transmission coupled to variant emergence and allelic variation.,Sci Rep,33795722,4/3/21,pubmed,0,2,"sequencing, whole genome, genomic epidemiology, genome sequences",0.000699353,0.418953826,0.000699366,0.500793525,0.000699357,0.078154573,Epidemiology,0.17610738,FALSE,104,0.877605294,157.5,0.890219427,0,0.403234768,,,0.723686496
273,Molecular dynamics and in silico mutagenesis on the reversible inhibitor-bound SARS-CoV-2 main protease complexes reveal the role of lateral pocket in enhancing the ligand affinity.,Sci Rep,33795718,4/3/21,pubmed,0,6,"computational, in silico",0.950469944,0.016392673,0.029920857,0.001072181,0.001072169,0.001072175,Drug discovery,0.878212,TRUE,13.66666667,0.207310285,4.333333333,0.237958255,0,0.403234768,,,0.282834436
274,Ontology-driven weak supervision for clinical entity classification in electronic health records.,Nat Commun,33795682,4/3/21,pubmed,0,7,machine learning,0.002183225,0.002183315,0.703074154,0.002183362,0.288192588,0.002183356,Healthcare,0.018834472,FALSE,21.85714286,0.322778156,,,0,0.403234768,,,0.363006462
275,Aging and monocyte immunometabolism in COVID-19.,Aging (Albany NY),33795524,4/3/21,pubmed,0,1,immunome,0.057800411,0.057800258,0.057799148,0.057799149,0.710997298,0.057803737,Healthcare,0.63715553,TRUE,80,0.807532933,278,0.948153599,0,0.403234768,,,0.719640433
276,Designed proteins assemble antibodies into modular nanocages.,Science,33795432,4/3/21,pubmed,0,30,computational,0.868900113,0.09392022,0.031474434,0.001901778,0.001901717,0.001901738,Drug discovery,0.13885528,FALSE,41.56666667,0.553590203,,,0,0.403234768,,,0.478412486
277,"Gene signature and immune cell profiling by high-dimensional, single-cell analysis in COVID-19 patients, presenting Low T3 syndrome and coexistent hematological malignancies.",J Transl Med,33794925,4/3/21,pubmed,0,23,multi-omics,0.331837251,0.001046859,0.001046812,0.001046836,0.001046819,0.663975423,Clinics,0.95624274,TRUE,85.04347826,0.825159255,66.47826087,0.743912229,0,0.403234768,,,0.657435417
278,The association between SARS-CoV-2 infection and preterm delivery: a prospective study with a multivariable analysis.,BMC Pregnancy Childbirth,33794829,4/3/21,pubmed,0,50,logistic regression,0.001565405,0.001565369,0.001565339,0.001565343,0.345050571,0.648687974,Clinics,0.7648575,TRUE,2.8,0.031047065,0.16,0.057599679,0,0.403234768,,,0.163960504
279,Risk Factors for Olfactory and Gustatory Dysfunctions in Patients with SARS-CoV-2 Infection.,Neuroepidemiology,33794531,4/2/21,pubmed,0,4,logistic regression,0.001987108,0.001987152,0.001987132,0.001987186,0.247450163,0.744601258,Clinics,0.8570802,TRUE,147.25,0.941554827,269,0.945544554,0,0.403234768,,,0.763444716
280,Virtual screening of plant-derived compounds against SARS-CoV-2 viral proteins using computational tools.,Sci Total Environ,33794459,4/2/21,pubmed,0,5,"virtual screening, computational",0.99303583,0.001392888,0.001392828,0.001392832,0.001392811,0.001392811,Drug discovery,0.963954,TRUE,22,0.326056033,22.8,0.509967889,0,0.403234768,,,0.41308623
281,When Planning Meets Reality: COVID-19 Inter-pandemic Survey of Michigan Nursing Homes.,Am J Infect Control,33794312,4/2/21,pubmed,0,9,logistic regression,0.001237174,0.001237139,0.001237149,0.001237137,0.920820168,0.074231234,Healthcare,0.7403638,TRUE,51.44444444,0.642216587,,,0,0.403234768,,,0.522725677
282,SARS-CoV2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential.,Biophys J,33794150,4/2/21,pubmed,0,7,molecular dynamics simulation,0.828689914,0.00139289,0.071925112,0.095206155,0.001393093,0.001392836,Drug discovery,0.23947892,FALSE,28,0.408312202,,,0,0.403234768,,,0.405773485
283,Digital Is Political: Why We Need a Feminist Conceptual Lens on Determinants of Digital Health.,OMICS,33794130,4/2/21,pubmed,0,1,"artificial intelligence, digital health",0.001098823,0.001098835,0.122745823,0.716622582,0.157335122,0.001098815,Epidemiology,0.35052466,FALSE,108,0.886078298,29,0.562884667,0,0.403234768,,,0.617399244
284,COVID-19 and Preparing Planetary Health for Future Ecological Crises: Hopes from Glycomics for Vaccine Innovation.,OMICS,33794117,4/2/21,pubmed,0,3," omics, glycomics",0.077699786,0.110838144,0.002238556,0.804746519,0.00223855,0.002238445,Epidemiology,0.71903527,TRUE,0.333333333,0.007174222,,,0,0.403234768,,,0.205204495
285,Patient access to chronic medications during the Covid-19 pandemic: Evidence from a comprehensive dataset of US insurance claims.,PLoS One,33793663,4/2/21,pubmed,0,3,dataset,0.056978076,0.001203428,0.001203464,0.132397666,0.243418457,0.56479891,Clinics,0.9212644,TRUE,22.33333333,0.330261612,24,0.521808938,0,0.403234768,,,0.418435106
286,Impact of Covid-19 pandemic on obstetric fistula repair program in Zimbabwe.,PLoS One,33793657,4/2/21,pubmed,0,4,dataset,0.001751225,0.001751234,0.049775653,0.444179108,0.500791527,0.001751252,Healthcare,0.96425045,TRUE,27,0.3960047,14.75,0.421327268,0,0.403234768,,,0.406855579
287,Semi-supervised learning for an improved diagnosis of COVID-19 in CT images.,PLoS One,33793650,4/2/21,pubmed,0,3,"supervised learning, unsupervised learning, neural network, dataset",0.001022608,0.001022616,0.994886933,0.001022614,0.001022605,0.001022624,Imaging,0.58075905,TRUE,20,0.298163152,,,0,0.403234768,,,0.35069896
288,Digital data-based strategies: A novel form of better understanding COVID-19 pandemic and international scientific collaboration.,PLoS One,33793613,4/2/21,pubmed,0,2,dataset,0.002032833,0.002032825,0.002032802,0.958139481,0.002032876,0.033729182,Epidemiology,0.75121254,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
289,Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19.,PLoS One,33793600,4/2/21,pubmed,0,7,machine learning,0.00115623,0.001156238,0.33038598,0.001156267,0.001156249,0.664989037,Clinics,0.95560277,TRUE,43.42857143,0.572144227,,,0,0.403234768,,,0.487689497
290,Baseline cardiometabolic profiles and SARS-CoV-2 infection in the UK Biobank.,PLoS One,33793566,4/2/21,pubmed,0,6,logistic regression,0.001350423,0.001350424,0.001350383,0.079356483,0.164010764,0.752581523,Clinics,0.4468034,FALSE,16.83333333,0.253509803,,,0,0.403234768,,,0.328372285
291,Computational epitope map of SARS-CoV-2 spike protein.,PLoS Comput Biol,33793546,4/2/21,pubmed,0,6,"molecular dynamics simulation, computational",0.85668663,0.137160747,0.001538113,0.001538206,0.001538154,0.00153815,Drug discovery,0.159356,FALSE,86.33333333,0.830354382,109.3333333,0.838038534,0,0.403234768,,,0.690542561
292,"Rapid Spread of SARS-CoV-2 in a State Prison After Introduction by Newly Transferred Incarcerated Persons - Wisconsin, August 14-October 22, 2020.",MMWR Morb Mortal Wkly Rep,33793462,4/2/21,pubmed,0,13,"sequencing, whole genome",0.001203393,0.174958939,0.001203507,0.349637713,0.377479641,0.095516807,Healthcare,0.7533383,TRUE,14.53846154,0.219741481,5.153846154,0.258094728,0,0.403234768,,,0.293690326
293,Machine Learning Applied to Spanish Clinical Laboratory Data for COVID-19 Outcome Prediction: Model Development and Validation.,J Med Internet Res,33793407,4/2/21,pubmed,0,4,"machine learning, classifier, predictive model",0.001171648,0.001171605,0.575196498,0.001171646,0.001171583,0.42011702,Clinics,0.6622827,TRUE,9.5,0.143051518,13.25,0.402997056,0,0.403234768,,,0.316427781
294,Development and validation of a predictive model to determine the level of care in patients confirmed with COVID-19.,Infect Dis (Lond),33793352,4/2/21,pubmed,0,7,"predictive model, logistic regression, prediction model, dataset",0.00131042,0.001310547,0.001310441,0.219737476,0.047862484,0.728468632,Clinics,0.9330127,TRUE,125.8571429,0.915084421,112,0.841450361,0,0.403234768,,,0.719923183
295,Nano-Enabled COVID-19 Vaccines: Meeting the Challenges of Durable Antibody Plus Cellular Immunity and Immune Escape.,ACS Nano,33793189,4/2/21,pubmed,0,2,genomes,0.402491337,0.293248016,0.001098885,0.300964031,0.001098874,0.001098857,Drug discovery,0.37264216,FALSE,149,0.943224689,,,0,0.403234768,,,0.673229728
296,Molecular profile of oral fluid in new coronavirus infection.,Klin Lab Diagn,33793111,4/2/21,pubmed,0,11,proteom,0.53890339,0.002720459,0.002720615,0.078745822,0.002720174,0.374189539,Drug discovery,0.8603411,TRUE,11,0.167171748,0.181818182,0.058803853,0,0.403234768,,,0.20973679
297,"Association of job loss, income loss, and financial burden with adverse mental health outcomes during coronavirus disease 2019 pandemic in Thailand: A nationwide cross-sectional study.",Depress Anxiety,33793028,4/2/21,pubmed,0,5,logistic regression,0.001141308,0.00114132,0.00114132,0.001141346,0.99429335,0.001141357,Healthcare,0.84178406,TRUE,58.2,0.690518894,30.2,0.571113192,0,0.403234768,,,0.554955618
298,Different incidences of acute kidney injury (AKI) and outcomes in COVID-19 patients with and without non-azithromycin antibiotics: a retrospective study.,J Med Virol,33792956,4/2/21,pubmed,0,8,logistic regression,0.102333043,0.001622716,0.001622698,0.056430998,0.001622801,0.836367744,Clinics,0.9745554,TRUE,6.875,0.097161234,1,0.122023013,0,0.403234768,,,0.207473005
299,Perspectives on Virtual (Remote) Clinical Trials as the 'New Normal' to Accelerate Drug Development.,Clin Pharmacol Ther,33792920,4/2/21,pubmed,0,4,"machine learning, artificial intelligence",0.295336514,0.001511844,0.167329518,0.499616936,0.00151193,0.034693257,Epidemiology,0.75477964,TRUE,30.5,0.438493413,28.5,0.558402462,0,0.403234768,,,0.466710214
300,Piece of the puzzle: Remdesivir disassembles the multimeric SARS-CoV-2 RNA-dependent RNA polymerase complex.,Cell Biochem Biophys,33792836,4/2/21,pubmed,0,3,computational,0.99106695,0.001786709,0.001786551,0.001786634,0.001786524,0.001786631,Drug discovery,0.5911077,TRUE,104.6666667,0.878594842,5,0.257024351,0,0.403234768,,,0.51295132
301,Toxicology and pharmacology of synthetic organoselenium compounds: an update.,Arch Toxicol,33792762,4/2/21,pubmed,0,3,computational,0.940071033,0.001486487,0.001486551,0.027029263,0.001486511,0.028440154,Drug discovery,0.9870404,TRUE,331.6666667,0.992640237,227.3333333,0.928886808,0,0.403234768,,,0.774920604
302,Sequencing of SARS-CoV-2 genome using different nanopore chemistries.,Appl Microbiol Biotechnol,33792750,4/2/21,pubmed,0,7,sequencing,0.048128426,0.868177115,0.001684577,0.078640821,0.001684509,0.001684551,Genomics,0.28603148,FALSE,30,0.432432432,38.85714286,0.626371421,0,0.403234768,,,0.487346207
303,Dental Pain and Worsened Socioeconomic Conditions Due to the COVID-19 Pandemic.,J Dent Res,33792422,4/2/21,pubmed,0,5,logistic regression,0.001565281,0.001565286,0.001565282,0.001565412,0.992173405,0.001565333,Healthcare,0.98638666,TRUE,89.6,0.841734183,33.8,0.597538132,0,0.403234768,,,0.614169027
304,Factors relating to working hours restriction that have impacted the professional identity of trainees in the last decade.,Br J Hosp Med (Lond),33792379,4/2/21,pubmed,0,4,supervised learning,0.002562602,0.002562725,0.281046433,0.225242962,0.486022644,0.002562635,Healthcare,0.2807225,FALSE,1.5,0.015523533,,,0,0.403234768,,,0.20937915
305,"The SARS-CoV-2 receptor angiotensin-converting enzyme 2 (ACE2) in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: analysis of high-throughput genetic, epigenetic, and gene expression studies.",medRxiv,33791744,4/2/21,pubmed,0,14,genome-wide,0.552015504,0.188156249,0.001593522,0.001593564,0.001593515,0.255047646,Drug discovery,0.27719754,FALSE,61.57142857,0.712907415,43.5,0.651725983,0,0.403234768,,,0.589289389
306,Disparities in SARS-CoV-2 seroprevalence among individuals presenting for care in central North Carolina over a six-month period.,medRxiv,33791743,4/2/21,pubmed,0,26,bayes,0.084093698,0.204403331,0.001415123,0.109741827,0.500476051,0.09986997,Healthcare,0.18568054,FALSE,39.30769231,0.532500464,,,0,0.403234768,,,0.467867616
307,The Impact of Vaccination to Control COVID-19 Burden in the United States: A Simulation Modeling Approach.,medRxiv,33791738,4/2/21,pubmed,0,7,simulation model,0.001220026,0.025028466,0.001220013,0.741928447,0.229383011,0.001220038,Epidemiology,0.03789127,FALSE,88.57142857,0.838085225,99.71428571,0.821982874,0,0.403234768,,,0.687767622
308,Development of a COVID-19 Application Ontology for the ACT Network.,medRxiv,33791734,4/2/21,pubmed,0,14,computational,0.099969243,0.002130737,0.389008266,0.254686302,0.002130701,0.25207475,Epidemiology,0.4630187,FALSE,125.4285714,0.914898881,204,0.919186513,0,0.403234768,,,0.745773387
309,Impaired antibacterial immune signaling and changes in the lung microbiome precede secondary bacterial pneumonia in COVID-19.,medRxiv,33791731,4/2/21,pubmed,0,33,"sequencing, transcriptom, microbiom, metatranscriptom",0.357947086,0.218810395,0.001010964,0.001011001,0.02359984,0.397620714,Clinics,0.07853821,FALSE,110.9090909,0.891582658,,,0,0.403234768,,,0.647408713
310,Case Study: Longitudinal immune profiling of a SARS-CoV-2 reinfection in a solid organ transplant recipient.,medRxiv,33791729,4/2/21,pubmed,0,31,sequencing,0.229982442,0.509245412,0.001254608,0.001254657,0.001254695,0.257008186,Genomics,0.2324161,FALSE,47,0.606407323,,,0,0.403234768,,,0.504821045
311,Transcriptome and Functions of Granulocytic Myeloid-Derived Suppressor Cells Determine their Association with Disease Severity of COVID-19.,medRxiv,33791717,4/2/21,pubmed,0,24,transcriptom,0.58272586,0.001291305,0.001291213,0.001291238,0.00129123,0.412109153,Drug discovery,0.38829607,FALSE,27.70833333,0.40379739,,,0,0.403234768,,,0.403516079
312,Age-related changes in the upper respiratory microbiome are associated with SARS-CoV-2 susceptibility and illness severity.,medRxiv,33791716,4/2/21,pubmed,0,15,"sequencing, microbiom",0.002183318,0.465124648,0.002183263,0.002183299,0.526142094,0.002183377,Healthcare,0.5600241,TRUE,29,0.41993939,,,0,0.403234768,,,0.411587079
313,Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Dynamics Tracking.,medRxiv,33791710,4/2/21,pubmed,0,25,"machine learning, image processing, classifier",0.000699383,0.451708699,0.287093099,0.000699387,0.000699376,0.259100057,Genomics,0.2819391,FALSE,27.36,0.399653658,,,0,0.403234768,,,0.401444213
314,Network medicine links SARS-CoV-2/COVID-19 infection to brain microvascular injury and neuroinflammation in dementia-like cognitive impairment.,bioRxiv,33791705,4/2/21,pubmed,0,11,"sequencing, transcriptom, interactom",0.601660312,0.112577673,0.001098828,0.0010989,0.001098919,0.282465368,Drug discovery,0.6632691,TRUE,86.63636364,0.831962397,,,0,0.403234768,,,0.617598582
315,ACE2 protein expression within isogenic cell lines is heterogeneous and associated with distinct transcriptomes.,bioRxiv,33791703,4/2/21,pubmed,0,2,transcriptom,0.87050414,0.123343318,0.00153819,0.001538157,0.001538094,0.001538101,Drug discovery,0.5206911,TRUE,17,0.257467994,0.5,0.087101953,0,0.403234768,,,0.249268238
316,Freely accessible ready to use global infrastructure for SARS-CoV-2 monitoring.,bioRxiv,33791701,4/2/21,pubmed,0,17,"computational, sequencing, deep sequencing, dataset",0.001438131,0.252662766,0.129268472,0.613754327,0.00143818,0.001438124,Epidemiology,0.177093,FALSE,36.88235294,0.507081452,,,0,0.403234768,,,0.45515811
317,Microbial signatures in the lower airways of mechanically ventilated COVID19 patients associated with poor clinical outcome.,Res Sq,33791687,4/2/21,pubmed,0,51,"transcriptom, metagenom, microbiom, metatranscriptom",0.180043133,0.392224888,0.001653041,0.001653067,0.001653075,0.422772797,Clinics,0.54593563,TRUE,59.03921569,0.696641722,,,0,0.403234768,,,0.549938245
318,The Avon Longitudinal Study of Parents and Children - A resource for COVID-19 research: Generation 2 questionnaire data capture May-July 2020.,Wellcome Open Res,33791441,4/2/21,pubmed,0,10,dataset,0.001220077,0.001220112,0.0012201,0.2463556,0.748764054,0.001220057,Healthcare,0.6287256,TRUE,47.5,0.610551054,,,84,0.974689796,,,0.792620425
319,Impact of Public Health Education Program on the Novel Coronavirus Outbreak in the United States.,Front Public Health,33791268,4/2/21,pubmed,0,13,mathematical model,0.002639057,0.002639045,0.002638972,0.768661966,0.146933284,0.076487676,Epidemiology,0.22019681,FALSE,10.15384615,0.152761457,6.384615385,0.285456248,0,0.403234768,,,0.280484158
320,COVID/HIV Co-Infection: A Syndemic Perspective on What to Ask and How to Answer.,Front Public Health,33791266,4/2/21,pubmed,0,5,digital health,0.050518877,0.044322512,0.169100649,0.508943627,0.225699102,0.001415232,Epidemiology,0.8576716,TRUE,94.6,0.8556497,55.6,0.704642762,0,0.403234768,,,0.654509076
321,SARS-CoV-2-Indigenous Microbiota Nexus: Does Gut Microbiota Contribute to Inflammation and Disease Severity in COVID-19?,Front Cell Infect Microbiol,33791231,4/2/21,pubmed,0,2,microbiom,0.465637934,0.268342356,0.002996513,0.002996693,0.118781769,0.141244735,Drug discovery,0.90893805,TRUE,90.5,0.844331746,44.5,0.656743377,0,0.403234768,,,0.634769964
322,A Study of a New Technique of the CT Scan View and Disease Classification Protocol Based on Level Challenges in Cases of Coronavirus Disease.,Radiol Res Pract,33791127,4/2/21,pubmed,0,3,"neural network, dataset",0.000988358,0.00098836,0.974112331,0.000988364,0.021934164,0.000988422,Imaging,0.5659342,TRUE,1.666666667,0.016451234,1,0.122023013,0,0.403234768,,,0.180569672
323,The Age-AST-D Dimer (AAD) Regression Model Predicts Severe COVID-19 Disease.,Dis Markers,33791045,4/2/21,pubmed,0,17,predictive model,0.001034609,0.001034589,0.001034638,0.001034619,0.001034602,0.994826943,Clinics,0.4769561,FALSE,14.23529412,0.214855588,0.764705882,0.099478191,0,0.403234768,,,0.239189515
324,0,Exp Ther Med,33790994,4/2/21,pubmed,0,4,bioinformatic,0.746747132,0.170934056,0.001717281,0.001717299,0.077166977,0.001717254,Drug discovery,0.85730714,TRUE,77,0.794668811,32.5,0.589376505,0,0.403234768,,,0.595760028
325,Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method.,Int J Endocrinol,33790965,4/2/21,pubmed,0,11,artificial intelligence,0.000988351,0.000988366,0.402585406,0.000988347,0.000988358,0.593461172,Clinics,0.85616565,TRUE,8.181818182,0.119920836,,,0,0.403234768,,,0.261577802
326,Detection of Microbial Agents in Oropharyngeal and Nasopharyngeal Samples of SARS-CoV-2 Patients.,Front Microbiol,33790878,4/2/21,pubmed,0,7,genomes,0.001823404,0.741165904,0.25154036,0.001823378,0.001823382,0.001823573,Genomics,0.5198128,TRUE,108,0.886078298,243.4285714,0.935576666,0,0.403234768,,,0.74162991
327,Anxiety and Its Associated Factors During the Initial Phase of the COVID-19 Pandemic in Indonesia.,Front Psychiatry,33790817,4/2/21,pubmed,0,13,logistic regression,0.000988461,0.000988436,0.000988367,0.055376154,0.940670146,0.000988436,Healthcare,0.99032116,TRUE,42.76923077,0.564660771,27.84615385,0.552314691,0,0.403234768,,,0.506736743
328,"Occupational Health Safety of Health Professionals and Associated Factors During COVID-19 Pandemics at North Showa Zone, Oromia Regional State, Ethiopia.",Risk Manag Healthc Policy,33790675,4/2/21,pubmed,0,11,logistic regression,0.024299509,0.001310393,0.001310386,0.071181497,0.90058782,0.001310395,Healthcare,0.6071547,TRUE,8.363636364,0.123755334,3,0.199424672,0,0.403234768,,,0.242138258
329,Characteristics and Behaviors Associated with Prevalent SARS-CoV-2 Infection.,Int J Gen Med,33790635,4/2/21,pubmed,0,7,logistic regression,0.001438121,0.001438177,0.001438199,0.184024156,0.576862088,0.234799258,Healthcare,0.39031428,FALSE,167,0.956274352,282.4285714,0.949558469,0,0.403234768,,,0.769689196
330,Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality.,J Inflamm Res,33790623,4/2/21,pubmed,0,5,logistic regression,0.000722165,0.000722169,0.262450586,0.000722181,0.014078916,0.721303982,Clinics,0.943581,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
331,Assessment of Psychological Distress and Associated Factors among Hospitalized Patients During the COVID-19 Pandemic at Selected Hospitals in Southwest Ethiopia.,Neuropsychiatr Dis Treat,33790557,4/2/21,pubmed,0,4,logistic regression,0.001187282,0.001187264,0.001187289,0.001187271,0.789030506,0.206220387,Healthcare,0.9436376,TRUE,6.25,0.088564537,0.5,0.087101953,0,0.403234768,,,0.192967086
332,Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study.,Sci Rep,33790365,4/2/21,pubmed,0,10,logistic regression,0.001203455,0.001203422,0.001203455,0.001203462,0.001203521,0.993982684,Clinics,0.7086615,TRUE,4.4,0.058321479,,,0,0.403234768,,,0.230778123
333,Computational drug repurposing study elucidating simultaneous inhibition of entry and replication of novel corona virus by Grazoprevir.,Sci Rep,33790352,4/2/21,pubmed,0,7,computational,0.993035815,0.001392856,0.001392819,0.001392846,0.001392851,0.001392813,Drug discovery,0.8580989,TRUE,29.57142857,0.426185911,,,0,0.403234768,,,0.414710339
334,Psychological Distress Reported by Primary-care Physicians in China During the COVID-19 Pandemic.,Psychosom Med,33790199,4/2/21,pubmed,0,8,logistic regression,0.001392829,0.001392871,0.001392832,0.001392913,0.9930357,0.001392855,Healthcare,0.9512304,TRUE,4.5,0.061784897,,,0,0.403234768,,,0.232509832
335,Findings from the first public COVID-19 temporary test centre in Hong Kong.,Hong Kong Med J,33790053,4/2/21,pubmed,0,7,logistic regression,0.001987118,0.320445865,0.00198718,0.001987251,0.351721429,0.321871156,Healthcare,0.8813145,TRUE,8.714285714,0.128764921,,,0,0.403234768,,,0.265999844
336,Task-specific information outperforms surveillance-style big data in predictive analytics.,Proc Natl Acad Sci U S A,33790010,4/2/21,pubmed,0,4,dataset,0.001565359,0.001565375,0.480004694,0.170225192,0.345074085,0.001565296,Healthcare,0.04532641,FALSE,53,0.654400396,128.75,0.862322719,0,0.403234768,,,0.639985961
337,Alterations in the human oral and gut microbiomes and lipidomics in COVID-19.,Gut,33789966,4/2/21,pubmed,0,25,"classifier, lipidom, microbiom",0.101536499,0.397156287,0.134559766,0.00118731,0.001187289,0.364372848,Genomics,0.54345715,TRUE,6.16,0.086647288,,,0,0.403234768,,,0.244941028
338,"Cytokeratin 18 cell death assays as biomarkers for quantification of apoptosis and necrosis in COVID-19: a prospective, observational study.",J Clin Pathol,33789919,4/2/21,pubmed,0,6,logistic regression,0.190775348,0.001392944,0.001392845,0.001392932,0.001392862,0.80365307,Clinics,0.44478977,FALSE,441.3333333,0.996412889,235.1666667,0.932231737,0,0.403234768,,,0.777293131
339,Household food insecurity and its association with self-reported male perpetration of intimate partner violence: a survey of two districts in central and western Uganda.,BMJ Open,33789856,4/2/21,pubmed,0,15,logistic regression,0.001538068,0.001538076,0.001538065,0.001538117,0.992309567,0.001538107,Healthcare,0.925109,TRUE,28.73333333,0.41480611,39.66666667,0.631790206,0,0.403234768,,,0.483277028
340,Which parameters support disposition decision in suspected COVID-19 cases in the emergency department (ED): a German clinical cohort study.,BMJ Open,33789854,4/2/21,pubmed,0,10,logistic regression,0.001254605,0.001254619,0.061898377,0.001254633,0.001254667,0.9330831,Clinics,0.9103272,TRUE,57.9,0.687983178,31.9,0.583757024,0,0.403234768,,,0.55832499
341,Prevalence and risk factors for chalazion in an older veteran population.,Br J Ophthalmol,33789846,4/2/21,pubmed,0,6,logistic regression,0.001987147,0.001987237,0.100196853,0.319670815,0.310121199,0.266036749,Epidemiology,0.9786014,TRUE,62.5,0.718164389,,,0,0.403234768,,,0.560699578
342,The relationship between SARS-COV-2 RNA positive duration and the risk of recurrent positive.,Infect Dis Poverty,33789752,4/2/21,pubmed,0,13,logistic regression,0.124555249,0.001538215,0.001538249,0.001538227,0.095826818,0.775003242,Clinics,0.2338996,FALSE,0.153846154,0.006617602,,,0,0.403234768,,,0.204926185
343,"Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification.",Scand J Trauma Resusc Emerg Med,33789721,4/2/21,pubmed,0,9,"artificial intelligence, neural network, network model",0.000752921,0.000752911,0.201895722,0.557729116,0.189785153,0.049084177,Epidemiology,0.6498538,TRUE,75,0.787061661,65.11111111,0.73916243,0,0.403234768,,,0.643152953
344,Scores based on neutrophil percentage and lactate dehydrogenase with or without oxygen saturation predict hospital mortality risk in severe COVID-19 patients.,Virol J,33789703,4/2/21,pubmed,0,15,"logistic regression, prediction model, dataset",0.001684488,0.001684518,0.533661298,0.001684531,0.001684504,0.45960066,Clinics,0.8210179,TRUE,47.2,0.607520564,,,0,0.403234768,,,0.505377666
345,Is Covid-19 community level testing effective in reaching at-risk populations? Evidence from spatial analysis of New Orleans patient data at walk-up sites.,BMC Public Health,33789647,4/2/21,pubmed,0,4,"logistic regression, dataset",0.000966801,0.292145742,0.000966806,0.433456824,0.246605804,0.025858024,Epidemiology,0.048977673,FALSE,10,0.15214299,,,0,0.403234768,,,0.277688879
346,The prognostic role of functional dependency in older inpatients with COVID-19.,BMC Geriatr,33789578,4/2/21,pubmed,0,6,logistic regression,0.000863092,0.000863068,0.000863034,0.000863066,0.024206926,0.972340814,Clinics,0.79232115,TRUE,21.16666667,0.31319191,3.666666667,0.217621086,0,0.403234768,,,0.311349255
347,SNAP Participants and High Levels of Food Insecurity in the Early Stages of the COVID-19 Pandemic.,Public Health Rep,33789530,4/2/21,pubmed,0,7,logistic regression,0.0015118,0.020990122,0.001511795,0.001511868,0.972962452,0.001511964,Healthcare,0.585118,TRUE,46,0.596882924,33,0.593256623,0,0.403234768,,,0.531124772
348,Estimation of secondary household attack rates for emergent spike L452R SARS-CoV-2 variants detected by genomic surveillance at a community-based testing site in San Francisco.,Clin Infect Dis,33788923,4/1/21,pubmed,0,29,"sequencing, genomes",0.001272641,0.608186297,0.001272648,0.128101395,0.241845182,0.019321836,Genomics,0.5112343,TRUE,46.55172414,0.601212196,,,0,0.403234768,,,0.502223482
349,Impact of the COVID-19 pandemic on skin cancer diagnosis: A population-based study.,PLoS One,33788858,4/1/21,pubmed,0,3,dataset,0.001415102,0.001415158,0.149957527,0.347295127,0.192033005,0.30788408,Epidemiology,0.8926786,TRUE,67,0.748160059,14.33333333,0.415975381,0,0.403234768,,,0.522456736
350,Socio-demographic correlate of knowledge and practice toward COVID-19 among people living in Mosul-Iraq: A cross-sectional study.,PLoS One,33788835,4/1/21,pubmed,0,3,logistic regression,0.00139287,0.001392863,0.001392833,0.036061595,0.958366984,0.001392855,Healthcare,0.36434168,FALSE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
351,Literature Review and Knowledge Distribution During an Outbreak: A Methodology for Managing Infodemics.,Acad Med,33788792,4/1/21,pubmed,0,9,artificial intelligence,0.000999546,0.000999538,0.323523514,0.623073144,0.05040466,0.000999598,Epidemiology,0.6716963,TRUE,17.66666667,0.266373925,24.88888889,0.527294621,0,0.403234768,,,0.398967771
352,Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns.,IEEE J Biomed Health Inform,33788696,4/1/21,pubmed,0,4,"machine learning, deep learning, dataset",0.001538182,0.001538188,0.820989814,0.146755487,0.001538154,0.027640175,Imaging,0.027165234,FALSE,217.25,0.977611479,156.75,0.889416644,0,0.403234768,,,0.756754297
353,A Novel Prediction Model for Long-Term SARS-CoV-2 RNA Shedding in Non-Severe Adult Hospitalized Patients with COVID-19: A Retrospective Cohort Study.,Infect Dis Ther,33788153,4/1/21,pubmed,0,7,prediction model,0.001085436,0.154811428,0.084705864,0.122786491,0.001085344,0.635525437,Clinics,0.32932258,FALSE,1,0.012307502,,,0,0.403234768,,,0.207771135
354,The association between vitamin D levels and the clinical severity and inflammation markers in pediatric COVID-19 patients: single-center experience from a pandemic hospital.,Eur J Pediatr,33788001,4/1/21,pubmed,0,7,logistic regression,0.134252471,0.001034573,0.001034562,0.001034566,0.034206546,0.828437282,Clinics,0.75803244,TRUE,30.14285714,0.433360134,6.142857143,0.281241638,0,0.403234768,,,0.37261218
355,"Risk Factors Associated With SARS-CoV-2 Infections, Hospitalization, and Mortality Among US Nursing Home Residents.",JAMA Netw Open,33787905,4/1/21,pubmed,0,3,logistic regression,0.000740313,0.000740318,0.015057033,0.036378808,0.509481806,0.437601721,Healthcare,0.9618137,TRUE,194.3333333,0.970870184,,,0,0.403234768,,,0.687052476
356,Drugmonizome and Drugmonizome-ML: integration and abstraction of small molecule attributes for drug enrichment analysis and machine learning.,Database (Oxford),33787872,4/1/21,pubmed,0,9,machine learning,0.737252138,0.000889075,0.259191569,0.00088908,0.000889057,0.000889081,Drug discovery,0.41118413,FALSE,42.88888889,0.566145092,207.8888889,0.921193471,0,0.403234768,,,0.63019111
357,Dental workers in front-line of COVID-19: an in silico evaluation targeting their prevention.,J Appl Oral Sci,33787730,4/1/21,pubmed,0,6,in silico,0.671342041,0.195698839,0.001622787,0.001622781,0.128090819,0.001622734,Drug discovery,0.8179254,TRUE,10.5,0.157338116,,,0,0.403234768,,,0.280286442
358,Modelling the impact of contact tracing of symptomatic individuals on the COVID-19 epidemic.,Clinics (Sao Paulo),33787657,4/1/21,pubmed,0,5,mathematical model,0.001511797,0.107776908,0.001511888,0.886175711,0.001511875,0.001511821,Epidemiology,0.1706821,FALSE,159.2,0.950831839,80.4,0.779970565,0,0.403234768,,,0.711345724
359,The Framing of COVID-19 in Italian Media and Its Relationship with Community Mobility: A Mixed-Method Approach.,J Health Commun,33787462,4/1/21,pubmed,0,8,computational,0.001461864,0.001461913,0.001461894,0.916912051,0.077240421,0.001461858,Epidemiology,0.08269003,FALSE,35.875,0.495516111,19.125,0.471902596,0,0.403234768,,,0.456884491
360,Applying Laplace Adomian decomposition method (LADM) for solving a model of Covid-19.,Comput Methods Biomech Biomed Engin,33787397,4/1/21,pubmed,0,3,mathematical model,0.0750632,0.002357717,0.287550455,0.630313179,0.002357726,0.002357723,Epidemiology,0.078446954,FALSE,17.33333333,0.261178799,1,0.122023013,0,0.403234768,,,0.262145527
361,"Experiences With COVID-19 Stressors and Parents' Use of Neglectful, Harsh, and Positive Parenting Practices in the Northeastern United States.",Child Maltreat,33787377,4/1/21,pubmed,0,2,logistic regression,0.001901687,0.001901693,0.001901684,0.001901749,0.990491473,0.001901714,Healthcare,0.93169105,TRUE,53.5,0.657245346,92,0.805726519,0,0.403234768,,,0.622068877
362,An Integrative in Silico Drug Repurposing Approach for Identification of Potential Inhibitors of SARS-CoV-2 Main Protease.,Mol Inform,33787066,4/1/21,pubmed,0,11,"data mining, in silico",0.991734629,0.00165305,0.001653105,0.001653096,0.00165307,0.00165305,Drug discovery,0.5274852,TRUE,39,0.530521368,11.27272727,0.374498261,0,0.403234768,,,0.436084799
363,Nurse's Achilles Heel: Using Big Data to Determine Workload Factors That Impact Near Misses.,J Nurs Scholarsh,33786985,4/1/21,pubmed,0,5,logistic regression,0.001156261,0.001156313,0.084421381,0.00115633,0.450508259,0.461601456,Clinics,0.4432932,FALSE,24,0.35574247,,,0,0.403234768,,,0.379488619
364,"Multiple Sites on SARS-CoV-2 Spike Protein are Susceptible to Proteolysis by Cathepsins B, K, L, S, and V.",Protein Sci,33786919,4/1/21,pubmed,0,8,computational,0.889325041,0.105273588,0.001350335,0.001350386,0.001350331,0.00135032,Drug discovery,0.575075,TRUE,14.875,0.223328592,,,0,0.403234768,,,0.31328168
365,Flavonoids as potential phytotherapeutics to combat cytokine storm in SARS-CoV-2.,Phytother Res,33786876,4/1/21,pubmed,0,5,in silico,0.703270597,0.001901762,0.001901778,0.001901827,0.153078951,0.137945085,Drug discovery,0.8787933,TRUE,20.2,0.300327788,0.8,0.101351351,0,0.403234768,,,0.268304636
366,Pharmacoinformatics-based identification of transmembrane protease serine-2 inhibitors from Morus Alba as SARS-CoV-2 cell entry inhibitors.,Mol Divers,33786727,4/1/21,pubmed,0,10,in-silico,0.993726918,0.00125459,0.001254623,0.001254672,0.001254599,0.001254598,Drug discovery,0.8089988,TRUE,17.4,0.26173542,,,0,0.403234768,,,0.332485094
367,Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia.,Eur Radiol,33786655,4/1/21,pubmed,0,7,"radiom, logistic regression",0.000907303,0.000907292,0.712485948,0.000907296,0.000907299,0.283884861,Imaging,0.78336596,TRUE,12.85714286,0.192714454,,,0,0.403234768,,,0.297974611
368,The Impact of Frailty on the Relationship between Life-Space Mobility and Quality of Life in Older Adults during the COVID-19 Pandemic.,J Nutr Health Aging,33786560,4/1/21,pubmed,0,12,logistic regression,0.00084653,0.000846522,0.000846529,0.348819442,0.625183578,0.0234574,Healthcare,0.7092394,TRUE,26.08333333,0.38270765,11.08333333,0.371487824,2,0.618927094,,,0.457707523
369,Computational Fluidic Modeling of a Low-Cost Fluidic Oscillator for Conversion of a CPAP Machine into an Emergency Use Mechanical Ventilator.,Adv Nanobiomed Res,33786536,4/1/21,pubmed,0,7,computational,0.045662347,0.001823528,0.001823515,0.757327804,0.001823478,0.191539329,Epidemiology,0.2683221,FALSE,12,0.183190055,5.428571429,0.264249398,0,0.403234768,,,0.283558074
370,An in silico analysis of effective siRNAs against COVID-19 by targeting the leader sequence of SARS-CoV-2.,Adv Cell Gene Ther,33786418,4/1/21,pubmed,0,2,"in silico, in-silico",0.702119444,0.221955804,0.001350439,0.001350398,0.071873518,0.001350397,Drug discovery,0.7216179,TRUE,16.5,0.249366071,,,0,0.403234768,,,0.326300419
371,COVID-19 Educational Innovation: Hybrid In-Person and Virtual Simulation for Emergency Medicine Trainees.,AEM Educ Train,33786409,4/1/21,pubmed,0,9,simulation model,0.001511905,0.001511826,0.001511878,0.427558481,0.566394084,0.001511826,Healthcare,0.62702304,TRUE,11.11111111,0.167419135,,,0,0.403234768,,,0.285326951
372,Cytosolic delivery of nucleic acids: The case of ionizable lipid nanoparticles.,Bioeng Transl Med,33786376,4/1/21,pubmed,0,7,in silico,0.749671446,0.194771674,0.05137814,0.001392942,0.001392923,0.001392874,Drug discovery,0.5564409,TRUE,67.85714286,0.752798565,39,0.62784319,0,0.403234768,,,0.594625507
373,Potent Noncovalent Inhibitors of the Main Protease of SARS-CoV-2 from Molecular Sculpting of the Drug Perampanel Guided by Free Energy Perturbation Calculations.,ACS Cent Sci,33786375,4/1/21,pubmed,0,16,computational,0.847178622,0.002562694,0.142570867,0.00256267,0.002562586,0.002562563,Drug discovery,0.6454146,TRUE,118.1875,0.904817861,253.9375,0.939523682,1,0.537564047,,,0.79396853
374,Sulfoglycodendrimer Therapeutics for HIV-1 and SARS-CoV-2.,Adv Ther (Weinh),33786368,4/1/21,pubmed,0,11,molecular dynamics simulation,0.990691421,0.00186181,0.001861699,0.001861743,0.001861668,0.001861659,Drug discovery,0.38139522,FALSE,58.45454545,0.69249799,,,0,0.403234768,,,0.547866379
375,Numerical analysis of COVID-19 model with constant fractional order and variable fractal dimension.,Results Phys,33786293,4/1/21,pubmed,0,2,mathematical model,0.004530841,0.004530751,0.004530888,0.97734591,0.004530761,0.004530849,Epidemiology,0.55743724,TRUE,47.5,0.610551054,2.5,0.180826866,2,0.618927094,,,0.470101672
376,In silico studies of selected multi-drug targeting against 3CLpro and nsp12 RNA-dependent RNA-polymerase proteins of SARS-CoV-2 and SARS-CoV.,Netw Model Anal Health Inform Bioinform,33786291,4/1/21,pubmed,0,5,in silico,0.967798631,0.00093612,0.000936106,0.000936098,0.028456942,0.000936103,Drug discovery,0.88822055,TRUE,11.6,0.175211825,1.4,0.133864062,0,0.403234768,,,0.237436885
377,Count-Valued Time Series Models for COVID-19 Daily Death Dynamics.,Stat (Int Stat Inst),33786170,4/1/21,pubmed,0,3,"bayes, model fit, predictive model",0.001901703,0.001901694,0.001901748,0.990491395,0.001901699,0.001901762,Epidemiology,0.11175719,FALSE,22.66666667,0.335580432,,,0,0.403234768,,,0.3694076
378,"Modeling, simulation, and case analysis of COVID-19 over network public opinion formation with individual internal factors and external information characteristics.",Concurr Comput,33786029,4/1/21,pubmed,0,4,simulation experiment,0.226255587,0.000846539,0.264147378,0.336765986,0.171137965,0.000846545,Epidemiology,0.38060826,FALSE,5.75,0.080957388,,,0,0.403234768,,,0.242096078
379,Coronavirus disease 2019 (COVID-19) and individuals with intellectual and developmental disabilities in Nigeria.,J Public Aff,33786015,4/1/21,pubmed,0,6,forecasting model,0.002806466,0.002806487,0.002806507,0.985967533,0.002806552,0.002806456,Epidemiology,0.8103391,TRUE,67.33333333,0.749396994,12.33333333,0.389550442,0,0.403234768,,,0.514060734
380,Metagenomics Approaches to Investigate the Gut Microbiome of COVID-19 Patients.,Bioinform Biol Insights,33786001,4/1/21,pubmed,0,9,"computational, bioinformatic, metagenom, microbiom",0.236869509,0.336551606,0.227064013,0.001987244,0.08867079,0.108856838,Genomics,0.879295,TRUE,48,0.614942173,27.33333333,0.54856837,0,0.403234768,,,0.522248437
381,Recovery preparedness of global air transport influenced by COVID-19 pandemic: Policy intervention analysis.,Transp Policy (Oxf),33785994,4/1/21,pubmed,0,6,bayes,0.001593544,0.001593511,0.001593604,0.992032284,0.001593555,0.001593502,Epidemiology,0.6175637,TRUE,15,0.227596017,,,0,0.403234768,,,0.315415392
382,COVID-19 Crisis Reduces Free Tropospheric Ozone Across the Northern Hemisphere.,Geophys Res Lett,33785974,4/1/21,pubmed,0,53,model simulation,0.003607347,0.003607595,0.003607177,0.981963209,0.003607342,0.003607329,Epidemiology,0.8393247,TRUE,67.24528302,0.748778527,,,0,0.403234768,,,0.576006647
383,Assessing the COVID-19 Impact on Air Quality: A Machine Learning Approach.,Geophys Res Lett,33785973,4/1/21,pubmed,0,2,machine learning,0.002720083,0.002720145,0.322038301,0.667081174,0.002720186,0.002720111,Epidemiology,0.58389735,TRUE,61.5,0.712350795,12,0.386740701,0,0.403234768,,,0.500775421
384,Machine learning is the key to diagnose COVID-19: a proof-of-concept study.,Sci Rep,33785852,4/1/21,pubmed,0,5,"machine learning, neural network, logistic regression",0.001156278,0.00115645,0.784831132,0.001156243,0.001156263,0.210543634,Imaging,0.9094331,TRUE,30.6,0.439421114,11.6,0.379515654,0,0.403234768,,,0.407390512
385,"Integrated characterization of SARS-CoV-2 genome, microbiome, antibiotic resistance and host response from single throat swabs.",Cell Discov,33785729,4/1/21,pubmed,0,16,"sequencing, transcriptom, microbiom, metatranscriptom",0.146590923,0.71747372,0.129836674,0.002032863,0.002032787,0.002033033,Genomics,0.6196822,TRUE,1.3125,0.013235203,,,0,0.403234768,,,0.208234985
386,Multidisciplinary Approaches Identify Compounds that Bind to Human ACE2 or SARS-CoV-2 Spike Protein as Candidates to Block SARS-CoV-2-ACE2 Receptor Interactions.,mBio,33785634,4/1/21,pubmed,0,10,in silico,0.996033044,0.000793405,0.00079338,0.000793396,0.000793385,0.00079339,Drug discovery,0.7678416,TRUE,74.9,0.786010267,77.9,0.772812416,0,0.403234768,,,0.65401915
387,Emergence and outcome of the SARS-CoV-2 "Marseille-4" variant.,Int J Infect Dis,33785459,4/1/21,pubmed,0,12,"sequencing, whole genome, genome sequences",0.048356663,0.847155137,0.001461924,0.001462009,0.001462025,0.100102242,Genomics,0.32171902,FALSE,500.1666667,0.997464284,650.8333333,0.986018196,1,0.537564047,,,0.840348842
388,Mobility and COVID-19 mortality across Scandinavia: A modeling study.,Travel Med Infect Dis,33785456,4/1/21,pubmed,0,2,dataset,0.001823365,0.001823351,0.001823411,0.846491566,0.00182339,0.146214918,Epidemiology,0.08917081,FALSE,17.5,0.263776362,,,0,0.403234768,,,0.333505565
389,Characteristics and Risk Factors Associated With Mortality in a Multicenter Spanish Cohort of Patients With COVID-19 Pneumonia.,Arch Bronconeumol,33785236,4/1/21,pubmed,0,7,logistic regression,0.001254618,0.001254664,0.001254659,0.05386213,0.074817355,0.867556574,Clinics,0.8493695,TRUE,15.85714286,0.238419197,9.714285714,0.348608509,0,0.403234768,,,0.330087491
390,Influence of COVID-19 on lifestyle behaviors in the Middle East and North Africa Region: a survey of 5896 individuals.,J Transl Med,33785043,4/1/21,pubmed,0,68,logistic regression,0.001371266,0.001371326,0.001371312,0.001371361,0.993143439,0.001371296,Healthcare,0.7450532,TRUE,3.043478261,0.037355433,,,0,0.403234768,,,0.2202951
391,The Early Impact of Social Distancing Measures on Drug Use.,Subst Use Misuse,33784957,4/1/21,pubmed,0,3,logistic regression,0.114822116,0.001565327,0.001565311,0.33575409,0.544727844,0.001565312,Healthcare,0.9260459,TRUE,22,0.326056033,58.33333333,0.71507894,0,0.403234768,,,0.48145658
392,Role of high-dose exposure in transmission hot zones as a driver of SARS-CoV-2 dynamics.,J R Soc Interface,33784886,4/1/21,pubmed,0,3,mathematical model,0.001254711,0.128033962,0.001254624,0.638606217,0.229595809,0.001254678,Epidemiology,0.16848058,FALSE,203.6666667,0.97359144,275.6666667,0.947083222,1,0.537564047,,,0.819412903
393,Clinical Features of the 60 Years and Older Patients Infected with 2019 Novel Coronavirus: Can We Predict Mortality Earlier?,Gerontology,33784699,3/31/21,pubmed,0,6,logistic regression,0.000916681,0.000916719,0.020343723,0.027035208,0.000916708,0.949870961,Clinics,0.8214854,TRUE,13.5,0.205393036,0.666666667,0.096200161,0,0.403234768,,,0.234942655
394,A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Diseases.,IEEE Rev Biomed Eng,33784625,3/31/21,pubmed,0,9,artificial intelligence,0.001511842,0.001512021,0.304751921,0.072470844,0.142398145,0.477355226,Clinics,0.51032555,TRUE,31.55555556,0.450244295,,,0,0.403234768,,,0.426739531
395,A Recombinant Fragment of Human Surfactant Protein D Binds Spike Protein and Inhibits Infectivity and Replication of SARS-CoV-2 in Clinical Samples.,Am J Respir Cell Mol Biol,33784482,3/31/21,pubmed,0,14,in-silico,0.732409143,0.183116727,0.001126799,0.001126862,0.001126838,0.081093632,Drug discovery,0.522805,TRUE,40.35714286,0.542952564,,,0,0.403234768,,,0.473093666
396,Thyroid function analysis in COVID-19: A retrospective study from a single center.,PLoS One,33784355,3/31/21,pubmed,0,6,logistic regression,0.002183485,0.002183465,0.002183322,0.116220501,0.002183284,0.875045943,Clinics,0.64152306,TRUE,19.33333333,0.288638753,,,0,0.403234768,,,0.34593676
397,The effectiveness of public health interventions against COVID-19: Lessons from the Singapore experience.,PLoS One,33784332,3/31/21,pubmed,0,9,simulation model,0.000786352,0.000786371,0.073540081,0.889533839,0.034566969,0.000786388,Epidemiology,0.18177125,FALSE,92.22222222,0.849403179,207.8888889,0.921193471,0,0.403234768,,,0.724610472
398,Spatial-temporal relationship between population mobility and COVID-19 outbreaks in South Carolina: A time series forecasting analysis.,J Med Internet Res,33784239,3/31/21,pubmed,0,7,predictive model,0.00118726,0.001187266,0.001187393,0.994063473,0.001187307,0.001187301,Epidemiology,0.7159746,TRUE,16,0.243552477,,,0,0.403234768,,,0.323393622
399,Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening.,J Chem Inf Model,33784094,3/31/21,pubmed,0,12,"virtual screening, computational, in silico",0.823205048,0.001486488,0.00148649,0.170848982,0.001486516,0.001486476,Drug discovery,0.8830955,TRUE,74.25,0.782856083,24,0.521808938,0,0.403234768,,,0.569299929
400,Accurate classification of COVID-19 patients with different severity via machine learning.,Clin Transl Med,33784017,3/31/21,pubmed,0,11,machine learning,0.013548751,0.013548861,0.932252811,0.013548804,0.013548832,0.013551941,Clinics,0.32398915,FALSE,30.54545455,0.4387408,15,0.42594327,0,0.403234768,,,0.422639613
401,Inferring UK COVID-19 fatal infection trajectories from daily mortality data: Were infections already in decline before the UK lockdowns?,Biometrics,33783826,3/31/21,pubmed,0,1,bayes,0.001392848,0.019638729,0.001392863,0.974789708,0.001392919,0.001392932,Epidemiology,0.20524675,FALSE,123,0.911806543,1272,0.995718491,0,0.403234768,,,0.770253267
402,Perspective: Nutritional Strategies Targeting the Gut Microbiome to Mitigate COVID-19 Outcomes.,Adv Nutr,33783468,3/31/21,pubmed,0,3,microbiom,0.16925833,0.041929893,0.001622726,0.268077698,0.001622818,0.517488535,Clinics,0.8063822,TRUE,124,0.912981632,214.3333333,0.924137008,0,0.403234768,,,0.746784469
403,"Comprehensive Comparative Genomic and Microsatellite Analysis of SARS, MERS, BAT-SARS and COVID-19 Coronaviruses.",J Med Virol,33782990,3/31/21,pubmed,0,6,"whole-genome, whole genome, genome sequences, genomes",0.001486472,0.992567597,0.001486475,0.001486527,0.001486492,0.001486438,Genomics,0.6618041,TRUE,34,0.477766096,,,0,0.403234768,,,0.440500432
404,Post-traumatic Stress Disorder Among COVID-19 Survivors at 3-Month Follow-up After Hospital Discharge.,J Gen Intern Med,33782888,3/31/21,pubmed,0,11,logistic regression,0.001203412,0.001203425,0.001203439,0.001203437,0.498975053,0.496211234,Healthcare,0.94456345,TRUE,90.54545455,0.844393593,27.27272727,0.547564892,0,0.403234768,,,0.598397751
405,Can COVID-19 and environmental research in developing countries support these countries to meet the environmental challenges induced by the pandemic?,Environ Sci Pollut Res Int,33782826,3/31/21,pubmed,0,2,"artificial intelligence, data mining",0.06124582,0.001392841,0.00139294,0.933182697,0.001392866,0.001392836,Epidemiology,0.6190899,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
406,Perspectives on plant flavonoid quercetin-based drugs for novel SARS-CoV-2.,Beni Suef Univ J Basic Appl Sci,33782651,3/31/21,pubmed,0,3,virtual screening,0.994886791,0.001022637,0.001022653,0.001022658,0.00102264,0.001022622,Drug discovery,0.9947767,TRUE,7.666666667,0.111633373,0.333333333,0.073187048,0,0.403234768,,,0.196018396
407,The potential of miRNA-based therapeutics in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection: a review.,J Pharm Anal,33782640,3/31/21,pubmed,0,2,genomes,0.878681988,0.054257177,0.002490601,0.002490528,0.059589204,0.002490502,Drug discovery,0.8061992,TRUE,40.5,0.544746119,5.5,0.267259834,0,0.403234768,,,0.40508024
408,Initial report of decreased SARS-CoV-2 viral load after inoculation with the BNT162b2 vaccine.,Nat Med,33782619,3/31/21,pubmed,0,14,dataset,0.004775512,0.976122286,0.004775736,0.004775491,0.004775509,0.004775466,Genomics,0.26279584,FALSE,53.57142857,0.657678273,61.64285714,0.726853091,0,0.403234768,,,0.595922044
409,Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.,NPJ Digit Med,33782526,3/31/21,pubmed,0,21,"machine learning, deep learning, artificial intelligence, dataset",0.001461906,0.00146191,0.850465682,0.001462003,0.001461946,0.143686552,Imaging,0.39830732,FALSE,101.8571429,0.872904942,,,0,0.403234768,,,0.638069855
410,Mass molecular testing for COVID19 using NGS-based technology and a highly scalable workflow.,Sci Rep,33782491,3/31/21,pubmed,0,15,"sequencing, whole genome",0.000946108,0.539296199,0.369526901,0.088338514,0.000946159,0.000946119,Genomics,0.14501745,FALSE,3.066666667,0.037540973,0.333333333,0.073187048,0,0.403234768,,,0.17132093
411,Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England.,Sci Rep,33782427,3/31/21,pubmed,0,47,dataset,0.104877259,0.001684509,0.001684578,0.65046794,0.161205698,0.080080017,Epidemiology,0.28225565,FALSE,82.10416667,0.814892696,,,0,0.403234768,,,0.609063732
412,"Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway.",Sci Rep,33782412,3/31/21,pubmed,0,10,transcriptom,0.885467376,0.043712917,0.002080561,0.002080699,0.002080561,0.064577885,Drug discovery,0.6122998,TRUE,107.3,0.883975509,143.5,0.877441798,0,0.403234768,,,0.721550692
413,"Diagnosis of SARS-CoV-2 infection with LamPORE, a high-throughput platform combining loop-mediated isothermal amplification and nanopore sequencing.",J Clin Microbiol,33782112,3/31/21,pubmed,0,22,sequencing,0.001717192,0.588059812,0.365434975,0.001717306,0.001717248,0.041353466,Genomics,0.5827234,TRUE,62.68181818,0.71909209,,,0,0.403234768,,,0.561163429
414,Cutting Edge: Reduced Adenosine-to-Inosine Editing of Endogenous Alu RNAs in Severe COVID-19 Disease.,J Immunol,33782089,3/31/21,pubmed,0,4,"sequencing, whole-genome",0.457778699,0.329798814,0.003335209,0.003335259,0.003335267,0.202416751,Drug discovery,0.35455164,FALSE,75.5,0.78860783,73.75,0.763312818,0,0.403234768,,,0.651718472
415,Effect Estimates of COVID-19 Non-Pharmaceutical Interventions are Non-Robust and Highly Model-Dependent.,J Clin Epidemiol,33781862,3/31/21,pubmed,0,4,bayes,0.002296534,0.002296583,0.002296555,0.988517265,0.002296533,0.00229653,Epidemiology,0.12256712,FALSE,49.75,0.630032779,140,0.874096869,0,0.403234768,,,0.635788138
416,Proteomic Analysis Identifies the RNA Helicase DDX3X as a Host Target Against SARS-CoV-2 Infection.,Antiviral Res,33781803,3/31/21,pubmed,0,14,"proteom, interactom",0.898605221,0.001392925,0.020699125,0.00139287,0.076517038,0.001392822,Drug discovery,0.2570299,FALSE,128.9285714,0.920217701,243.2857143,0.935442869,0,0.403234768,,,0.752965112
417,Genome-wide Analysis of 10664 SARS-CoV-2 Genomes to Identify Virus Strains in 73 Countries based on Single Nucleotide Polymorphism.,Virus Res,33781798,3/31/21,pubmed,0,5,"genome-wide, genomes, dataset",0.001059414,0.650851369,0.059445757,0.286524735,0.001059376,0.001059348,Genomics,0.21637493,FALSE,64.2,0.729482343,,,0,0.403234768,,,0.566358555
418,Atrial Fibrillation as a Predictor of Mortality in High Risk COVID-19 Patients: A Multicentre Study of 171 Patients.,Heart Lung Circ,33781697,3/31/21,pubmed,0,11,logistic regression,0.001861687,0.001861717,0.001861715,0.00186176,0.001861737,0.990691384,Clinics,0.6483235,TRUE,36.27272727,0.500216464,34.54545455,0.602287931,0,0.403234768,,,0.501913054
419,Psychosocial health of school-aged children during the initial COVID-19 safer-at-home school mandates in Florida: a cross-sectional study.,BMC Public Health,33781220,3/31/21,pubmed,0,7,logistic regression,0.001254635,0.001254604,0.001254617,0.001254667,0.993726871,0.001254606,Healthcare,0.9842353,TRUE,19.14285714,0.28622673,40.57142857,0.637409687,0,0.403234768,,,0.442290395
420,Protein-ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2.,Curr Med Chem,33781188,3/31/21,pubmed,0,6,"machine learning, computational",0.884788777,0.001156249,0.110586113,0.001156382,0.001156242,0.001156238,Drug discovery,0.42540625,FALSE,5.333333333,0.074339786,0.166666667,0.058736955,0,0.403234768,,,0.178770503
421,Escape of SARS-CoV-2 501Y.V2 from neutralization by convalescent plasma.,Nature,33780970,3/30/21,pubmed,0,21,sequencing,0.125700788,0.693503193,0.001511799,0.001511911,0.067304766,0.110467543,Genomics,0.18555203,FALSE,23.95238095,0.352588286,15.71428571,0.432833824,8,0.799987654,,,0.528469921
422,Risk Factors for Development of Acute Kidney Injury in COVID-19 Patients: A Retrospective Observational Cohort Study.,Nephron,33780937,3/30/21,pubmed,0,8,logistic regression,0.12199173,0.001717191,0.001717182,0.001717204,0.001717271,0.871139423,Clinics,0.9826069,TRUE,61,0.709196611,,,0,0.403234768,,,0.556215689
423,Deep learning for diagnosis of COVID-19 using 3D CT scans.,Comput Biol Med,33780867,3/30/21,pubmed,0,2,"deep learning, artificial intelligence",0.0015653,0.021888023,0.971850483,0.001565436,0.001565304,0.001565455,Imaging,0.593465,TRUE,104,0.877605294,136.5,0.871354027,0,0.403234768,,,0.71739803
424,"Viral genomic, metagenomic and human transcriptomic characterization and prediction of the clinical forms of COVID-19.",PLoS Pathog,33780519,3/30/21,pubmed,0,21,"transcriptom, metagenom",0.242271029,0.1901332,0.001486468,0.00148643,0.001486448,0.563136425,Clinics,0.80885446,TRUE,63.95238095,0.727812481,,,0,0.403234768,,,0.565523624
425,Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model.,PLoS Comput Biol,33780443,3/30/21,pubmed,0,10,"bayes, model fit, machine learning",0.001254597,0.001254606,0.202930364,0.79205119,0.001254598,0.001254645,Epidemiology,0.060774297,FALSE,57.4,0.684828994,,,0,0.403234768,,,0.544031881
426,Disparate temperature-dependent virus-host dynamics for SARS-CoV-2 and SARS-CoV in the human respiratory epithelium.,PLoS Biol,33780434,3/30/21,pubmed,0,22,transcriptom,0.624800389,0.368461374,0.001684539,0.001684611,0.001684541,0.001684546,Drug discovery,0.5896401,TRUE,36.09090909,0.498422908,,,0,0.403234768,,,0.450828838
427,Pandemic and its effect on professional environment on the Kingdom of Saudi Arabia.,Environ Sci Pollut Res Int,33779902,3/30/21,pubmed,0,4,logistic regression,0.00535312,0.005353039,0.005352939,0.326236799,0.652351283,0.00535282,Healthcare,0.5657525,TRUE,4,0.054734368,,,0,0.403234768,,,0.228984568
428,Digital Mental Health Challenges and the Horizon Ahead for Solutions.,JMIR Ment Health,33779570,3/30/21,pubmed,0,2,"machine learning, artificial intelligence",0.026963153,0.000672766,0.172393501,0.545811807,0.253485964,0.000672809,Epidemiology,0.8789302,TRUE,166,0.955594038,242.5,0.934773883,0,0.403234768,,,0.764534229
429,Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Clinical Blood Testing Data.,JMIR Med Inform,33779565,3/30/21,pubmed,0,12,"machine learning, deep learning, dataset",0.001126816,0.027429748,0.447694813,0.001126849,0.001126852,0.521494923,Clinics,0.5751082,TRUE,31.41666667,0.448883666,,,0,0.403234768,,,0.426059217
430,Why human factors science is demonstrably necessary: Historical and evolutionary foundations.,Ergonomics,33779512,3/30/21,pubmed,0,2,artificial intelligence,0.001593572,0.220003295,0.360546108,0.414670033,0.001593509,0.001593484,Epidemiology,0.097299814,FALSE,367.5,0.99486672,277,0.947819106,0,0.403234768,,,0.781973531
431,SARS-CoV-2 infection: molecular mechanisms of severe outcomes to suggest therapeutics.,Expert Rev Proteomics,33779460,3/30/21,pubmed,0,7," omics, dataset",0.501915538,0.064751077,0.001538238,0.170802512,0.001538158,0.259454476,Drug discovery,0.6072934,TRUE,35,0.488032655,11.42857143,0.37643832,0,0.403234768,,,0.422568581
432,"Effect of compliance with GOLD treatment recommendations on COPD health care resource utilization, cost, and exacerbations among patients with COPD on maintenance therapy.",J Manag Care Spec Pharm,33779246,3/30/21,pubmed,0,4,predictive model,0.020897819,0.020247685,0.06149741,0.131163776,0.180120745,0.586072565,Clinics,0.66542107,TRUE,32.25,0.458098831,,,0,0.403234768,,,0.430666799
433,A Cascade-SEME network for COVID-19 detection in chest c-ray images.,Med Phys,33778966,3/30/21,pubmed,0,7,dataset,0.000544462,0.027939402,0.926820024,0.043607207,0.000544459,0.000544446,Imaging,0.007599622,FALSE,1,0.012307502,,,0,0.403234768,,,0.207771135
434,"As a patient, we should embrace digital health tools available since COVID.",Cardiovasc Digit Health J,33778800,3/30/21,pubmed,0,1,digital health,0.034961874,0.03496188,0.034963142,0.430074603,0.03496287,0.430075632,Clinics,0.58692193,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
435,Phylogenomic tracing of asymptomatic transmission in a COVID-19 outbreak.,Innovation (N Y),33778799,3/30/21,pubmed,0,15,"sequencing, phylogenom",0.002898499,0.318673611,0.002898603,0.461969884,0.002898547,0.210660856,Epidemiology,0.5417695,TRUE,3.066666667,0.037540973,,,0,0.403234768,,,0.220387871
436,Phylogenomic analysis of COVID-19 summer and winter outbreaks in Hong Kong: An observational study.,Lancet Reg Health West Pac,33778795,3/30/21,pubmed,0,22,"phylogenom, genomes",0.001203408,0.931757144,0.001203406,0.001203471,0.001203471,0.063429099,Genomics,0.49441507,FALSE,92.81818182,0.850949348,,,0,0.403234768,,,0.627092058
437,Dynamics of SARS-CoV-2 neutralising antibody responses and duration of immunity: a longitudinal study.,Lancet Microbe,33778792,3/30/21,pubmed,0,22,machine learning,0.068798636,0.226253871,0.019321386,0.278334298,0.000710613,0.406581196,Clinics,0.5491056,TRUE,72.31818182,0.774444925,,,0,0.403234768,,,0.588839847
438,A comprehensive analysis and resource to use CRISPR-Cas13 for broad-spectrum targeting of RNA viruses.,Cell Rep Med,33778788,3/30/21,pubmed,0,6,"bioinformatic, in silico",0.46030925,0.4735807,0.001861801,0.001861803,0.06052442,0.001862026,Genomics,0.1781969,FALSE,18.16666667,0.272682293,,,0,0.403234768,,,0.33795853
439,Spatial mapping of SARS-CoV-2 and H1N1 Lung Injury Identifies Differential Transcriptional Signatures.,Cell Rep Med,33778787,3/30/21,pubmed,0,11,transcriptom,0.717092833,0.002032866,0.00203282,0.133012741,0.002032833,0.143795907,Drug discovery,0.16841951,FALSE,17.27272727,0.260312945,,,0,0.403234768,,,0.331773856
440,Natural Language Processing and Machine Learning for Detection of Respiratory Illness by Chest CT Imaging and Tracking of COVID-19 Pandemic in the US.,Radiol Cardiothorac Imaging,33778666,3/30/21,pubmed,0,9,machine learning,0.001085368,0.001085404,0.385933386,0.466902507,0.001085388,0.143907946,Epidemiology,0.25750405,FALSE,51.33333333,0.641288886,,,0,0.403234768,,,0.522261827
441,Perceived Impact of COVID-19 on Pediatric Radiology Departments Around the World: WFPI COVID-19 Task Force Survey Results from 6 Continents.,Radiol Cardiothorac Imaging,33778631,3/30/21,pubmed,0,11,logistic regression,0.001511827,0.001511933,0.112851836,0.00151195,0.881100565,0.001511888,Healthcare,0.96712285,TRUE,63.54545455,0.725029377,49.18181818,0.678084025,2,0.618927094,,,0.674013499
442,Quantitative Burden of COVID-19 Pneumonia on Chest CT Predicts Adverse Outcomes: A Post-Hoc Analysis of a Prospective International Registry.,Radiol Cardiothorac Imaging,33778629,3/30/21,pubmed,0,21,logistic regression,0.000889115,0.000889044,0.243879207,0.000889076,0.000889083,0.752564476,Clinics,0.875764,TRUE,174.0952381,0.961407632,104.3809524,0.829743109,4,0.707574542,,,0.832908428
443,Determinants of Chest X-Ray Sensitivity for COVID- 19: A Multi-Institutional Study in the United States.,Radiol Cardiothorac Imaging,33778628,3/30/21,pubmed,0,15,logistic regression,0.000936057,0.000936081,0.701744193,0.000936099,0.000936118,0.294511452,Imaging,0.70166916,TRUE,57.26666667,0.683530212,,,4,0.707574542,,,0.695552377
444,"CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia.",Radiol Cardiothorac Imaging,33778612,3/30/21,pubmed,0,9,"radiom, logistic regression",0.000871561,0.000871565,0.311564705,0.000871539,0.000871554,0.684949076,Clinics,0.6928574,TRUE,78.44444444,0.799987631,92.44444444,0.806863794,6,0.764429903,,,0.790427109
445,Regarding "Serial Quantitative Chest CT Assessment of COVID-19: Deep-Learning Approach".,Radiol Cardiothorac Imaging,33778590,3/30/21,pubmed,0,5,deep-learning,0.011749973,0.011749949,0.94124845,0.011750408,0.01175102,0.0117502,Imaging,0.5076034,TRUE,9.6,0.143608139,9,0.337904736,1,0.537564047,,,0.339692308
446,Accuracy and Reproducibility of Low-Dose Submillisievert Chest CT for the Diagnosis of COVID-19.,Radiol Cardiothorac Imaging,33778576,3/30/21,pubmed,0,11,image analysis,0.000793415,0.000793403,0.564078263,0.000793406,0.000793437,0.432748076,Imaging,0.7694209,TRUE,21.63636364,0.319562125,28.45454545,0.557465882,45,0.953206988,,,0.610078332
447,Extension of Coronavirus Disease 2019 on Chest CT and Implications for Chest Radiographic Interpretation.,Radiol Cardiothorac Imaging,33778565,3/30/21,pubmed,0,19,logistic regression,0.000988353,0.027925269,0.746693962,0.000988387,0.000988388,0.222415642,Imaging,0.6077492,TRUE,87.26315789,0.834436267,,,46,0.954009507,,,0.894222887
448,Longitudinal Assessment of COVID-19 Using a Deep Learning-based Quantitative CT Pipeline: Illustration of Two Cases.,Radiol Cardiothorac Imaging,33778563,3/30/21,pubmed,0,7,deep learning,0.011749744,0.011750234,0.941247038,0.011750143,0.011751798,0.011751043,Imaging,0.57085323,TRUE,46.71428571,0.602572825,62.57142857,0.731067701,27,0.92443978,,,0.752693435
449,Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach.,Radiol Cardiothorac Imaging,33778562,3/30/21,pubmed,0,7,deep learning,0.001022611,0.001022643,0.431219213,0.00102263,0.001022649,0.564690255,Clinics,0.9273813,TRUE,36.57142857,0.503432494,29.71428571,0.567366872,137,0.984690413,,,0.68516326
450,"Demographic, psychological, and experiential correlates of SARS-CoV-2 vaccination intentions in a sample of Canadian families.",Vaccine X,33778480,3/30/21,pubmed,0,2,logistic regression,0.001415153,0.001415198,0.001415121,0.284067035,0.710272369,0.001415124,Healthcare,0.69325626,TRUE,11,0.167171748,,,1,0.537564047,,,0.352367898
451,"Pre-existing conditions are associated with COVID-19 patients' hospitalization, despite confirmed clearance of SARS-CoV-2 virus.",EClinicalMedicine,33778434,3/30/21,pubmed,0,12,"neural network, network model",0.001653103,0.113204732,0.061182126,0.001653085,0.001653094,0.82065386,Clinics,0.5356786,TRUE,47.25,0.608015338,39.66666667,0.631790206,0,0.403234768,,,0.547680104
452,Racial Disparities in the Epidemiology of COVID-19 in Georgia: Trends Since State-Wide Reopening.,Health Equity,33778312,3/30/21,pubmed,0,5,logistic regression,0.001987093,0.001987123,0.001987123,0.363408906,0.433886261,0.196743495,Healthcare,0.9344363,TRUE,81.8,0.813964995,,,0,0.403234768,,,0.608599881
453,Proteomics Analysis of Serum from COVID-19 Patients.,ACS Omega,33778306,3/30/21,pubmed,0,7,"bioinformatic, proteom, network analysis",0.823799992,0.109735061,0.001046941,0.001046851,0.001046831,0.063324324,Drug discovery,0.7166538,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
454,Mutational analysis of structural proteins of SARS-CoV-2.,Heliyon,33778179,3/30/21,pubmed,0,7,sequence alignment,0.530742816,0.465166662,0.001022619,0.001022647,0.001022637,0.001022619,Drug discovery,0.5332645,TRUE,24.71428571,0.3637207,,,0,0.403234768,,,0.383477734
455,0,Virusdisease,33778129,3/30/21,pubmed,0,4,computational,0.948777736,0.001034623,0.001034634,0.001034636,0.047083685,0.001034686,Drug discovery,0.8434404,TRUE,25.5,0.37435834,0.5,0.087101953,0,0.403234768,,,0.288231687
456,"SARS-CoV-2 Testing of 11,884 Healthcare Workers at an Acute NHS Hospital Trust in England: A Retrospective Analysis.",Front Med (Lausanne),33777979,3/30/21,pubmed,0,24,"logistic regression, dataset",0.001565311,0.001565413,0.001565407,0.240760344,0.604582115,0.14996141,Healthcare,0.4074084,FALSE,33.83333333,0.474488218,50.91666667,0.684706984,0,0.403234768,,,0.52080999
457,Insights on the Structural Variations of the Furin-Like Cleavage Site Found Among the December 2019-July 2020 SARS-CoV-2 Spike Glycoprotein: A Computational Study Linking Viral Evolution and Infection.,Front Med (Lausanne),33777970,3/30/21,pubmed,0,10,"computational, genomic epidemiology",0.461591424,0.533783504,0.001156289,0.001156275,0.001156265,0.001156244,Genomics,0.5863167,TRUE,16.2,0.244974952,4.8,0.249331014,0,0.403234768,,,0.299180245
458,Pandemic Uncertainty and Socially Responsible Investments.,Front Public Health,33777890,3/30/21,pubmed,0,4,dataset,0.00431024,0.115125214,0.183209124,0.688735218,0.004310066,0.004310138,Epidemiology,0.58186483,TRUE,10.75,0.160244913,1,0.122023013,0,0.403234768,,,0.228500898
459,Gender Differences in Psychological and Behavioral Responses of Infected and Uninfected Health-Care Workers During the Early COVID-19 Outbreak.,Front Public Health,33777887,3/30/21,pubmed,0,5,logistic regression,0.00148645,0.001486434,0.001486523,0.001486454,0.992567674,0.001486464,Healthcare,0.9248216,TRUE,19.8,0.293957573,,,0,0.403234768,,,0.34859617
460,"The Epidemiology of COVID 19 in the Amazon and the Guianas: Similarities, Differences, and International Comparisons.",Front Public Health,33777876,3/30/21,pubmed,0,14,dataset,0.001219986,0.001220021,0.00122007,0.901357155,0.001220037,0.093762731,Epidemiology,0.1325899,FALSE,35.78571429,0.494712103,22.35714286,0.505820177,0,0.403234768,,,0.467922349
461,Next-Generation Sequencing Reveals the Progression of COVID-19.,Front Cell Infect Microbiol,33777844,3/30/21,pubmed,0,8,"sequencing, metagenom",0.45180825,0.418901886,0.122739729,0.002183317,0.002183254,0.002183564,Drug discovery,0.5798807,TRUE,25.25,0.370709382,17,0.451097137,0,0.403234768,,,0.408347095
462,Factor Analysis Approach to Classify COVID-19 Datasets in Several Regions.,Results Phys,33777669,3/30/21,pubmed,0,4,dataset,0.007673891,0.007674107,0.007673941,0.961629099,0.007674372,0.007674591,Epidemiology,0.8822961,TRUE,533.5,0.997959057,117,0.847002944,0,0.403234768,,,0.749398923
463,On nonlinear classical and fractional order dynamical system addressing COVID-19.,Results Phys,33777668,3/30/21,pubmed,0,5,mathematical model,0.151446165,0.002996477,0.002996636,0.836567605,0.002996552,0.002996565,Epidemiology,0.50574696,TRUE,247.2,0.984290927,64.4,0.736486486,0,0.403234768,,,0.70800406
464,A fractional order SITR mathematical model for forecasting of transmission of COVID-19 of India with lockdown effect.,Results Phys,33777667,3/30/21,pubmed,0,5,mathematical model,0.002032791,0.002032819,0.002032817,0.989836077,0.002032747,0.00203275,Epidemiology,0.60432243,TRUE,19.4,0.289257221,7.8,0.313286058,0,0.403234768,,,0.335259349
465,"Media consumption and mental health during COVID-19 lockdown: a UK cross-sectional study across England, Wales, Scotland and Northern Ireland.",Z Gesundh Wiss,33777650,3/30/21,pubmed,0,5,logistic regression,0.001461863,0.001461926,0.001461893,0.086352301,0.891526913,0.017735105,Healthcare,0.55414593,TRUE,64.2,0.729482343,78.2,0.773882794,0,0.403234768,,,0.635533301
466,A Retrospective Review of Outcomes in Intensive Care Unit Patients Infected With SARS-Cov2 in Correlation to Admission Acute Physiologic Assessment and Chronic Health Evaluation II Scores.,Cureus,33777587,3/30/21,pubmed,0,6,"predictive model, logistic regression",0.000699385,0.000699371,0.000699379,0.056504866,0.026268427,0.915128572,Clinics,0.9387882,TRUE,4.333333333,0.057950399,,,0,0.403234768,,,0.230592583
467,Risk prediction of COVID-19 incidence and mortality in a large multi-national hemodialysis cohort: implications for management of the pandemic in outpatient hemodialysis settings.,Clin Kidney J,33777363,3/30/21,pubmed,0,6,prediction model,0.022569792,0.001438128,0.001438176,0.173756981,0.001438268,0.799358654,Clinics,0.27806455,FALSE,53,0.654400396,,,0,0.403234768,,,0.528817582
468,Specific epitopes form extensive hydrogen-bonding networks to ensure efficient antibody binding of SARS-CoV-2: Implications for advanced antibody design.,Comput Struct Biotechnol J,33777333,3/30/21,pubmed,0,4,molecular dynamics simulation,0.787702625,0.188773824,0.001237087,0.001237075,0.001237047,0.019812343,Drug discovery,0.40955663,FALSE,9,0.135320675,,,0,0.403234768,,,0.269277721
469,A model to predict the risk of mortality in severely ill COVID-19 patients.,Comput Struct Biotechnol J,33777331,3/30/21,pubmed,0,12,"logistic regression, prediction model",0.001098829,0.001098807,0.06711737,0.001098814,0.001098827,0.928487353,Clinics,0.39947745,FALSE,6.166666667,0.087080215,,,0,0.403234768,,,0.245157491
470,PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation.,Comput Math Methods Med,33777167,3/30/21,pubmed,0,5,neural network,0.001987157,0.001987154,0.661582404,0.152043841,0.180412183,0.001987261,Imaging,0.6825668,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
471,Gut-Lung Axis in COVID-19.,Interdiscip Perspect Infect Dis,33777139,3/30/21,pubmed,0,4,microbiom,0.460420483,0.433937712,0.003760619,0.003760511,0.003760451,0.094360225,Drug discovery,0.72826767,TRUE,35.75,0.494217329,12,0.386740701,0,0.403234768,,,0.428064266
472,Caputo SIR model for COVID-19 under optimized fractional order.,Adv Differ Equ,33777127,3/30/21,pubmed,0,3,"model fit, mathematical model",0.001622717,0.001622713,0.001622747,0.99188639,0.001622732,0.001622701,Epidemiology,0.7778366,TRUE,600.3333333,0.998515678,152.3333333,0.885202034,0,0.403234768,,,0.762317493
473,Theoretical and numerical analysis for transmission dynamics of COVID-19 mathematical model involving Caputo-Fabrizio derivative.,Adv Differ Equ,33777126,3/30/21,pubmed,0,3,mathematical model,0.006089756,0.006089667,0.006090101,0.969551334,0.006089584,0.006089557,Epidemiology,0.7060129,TRUE,93.66666667,0.853794298,5.666666667,0.270203372,0,0.403234768,,,0.509077479
474,"T Cell Activation, Highly Armed Cytotoxic Cells and a Shift in Monocytes CD300 Receptors Expression Is Characteristic of Patients With Severe COVID-19.",Front Immunol,33777054,3/30/21,pubmed,0,11,correlation analysis,0.576624178,0.056672371,0.001717207,0.001717242,0.001717226,0.361551776,Drug discovery,0.64704037,TRUE,22.72727273,0.336075206,9.181818182,0.338908215,0,0.403234768,,,0.359406063
475,Exercise and Use of Enhancement Drugs at the Time of the COVID-19 Pandemic: A Multicultural Study on Coping Strategies During Self-Isolation and Related Risks.,Front Psychiatry,33776822,3/30/21,pubmed,0,23,logistic regression,0.06720524,0.001237098,0.104172543,0.001237129,0.824910922,0.001237069,Healthcare,0.70818657,TRUE,76,0.790586926,70.60869565,0.755418785,0,0.403234768,,,0.649746826
476,A Retrospective Study on the Use of Chinese Patent Medicine in 24 Medical Institutions for COVID-19 in China.,Front Pharmacol,33776751,3/30/21,pubmed,0,31,correlation analysis,0.296013712,0.000956342,0.000956333,0.481527236,0.000956369,0.219590008,Epidemiology,0.9639097,TRUE,3.903225806,0.047993073,,,0,0.403234768,,,0.22561392
477,Correction to: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.,Pers Ubiquitous Comput,33776615,3/30/21,pubmed,0,6,deep learning,0.057799812,0.057799601,0.057801681,0.711000481,0.057799162,0.057799264,Epidemiology,0.42690343,FALSE,6.666666667,0.094996598,,,0,0.403234768,,,0.249115683
478,Federated learning for COVID-19 screening from Chest X-ray images.,Appl Soft Comput,33776607,3/30/21,pubmed,0,4,"deep learning, dataset",0.026397565,0.00133007,0.968282076,0.001330094,0.001330141,0.001330055,Imaging,0.36880812,FALSE,75.25,0.787865669,32.75,0.590647578,0,0.403234768,,,0.593916005
479,"A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany.",Stoch Environ Res Risk Assess,33776559,3/30/21,pubmed,0,2,bayes,0.001987082,0.001987158,0.001987098,0.990064423,0.00198713,0.001987109,Epidemiology,0.38975757,FALSE,182.5,0.965365823,98,0.819106235,0,0.403234768,,,0.729235609
480,Tree parity machine guided patients' privileged based secure sharing of electronic medical record: cybersecurity for telehealth during COVID-19.,Multimed Tools Appl,33776546,3/30/21,pubmed,0,2,neural network,0.001438122,0.001438188,0.641770778,0.262674822,0.091239915,0.001438175,Epidemiology,0.11760783,FALSE,35,0.488032655,12.5,0.392761573,0,0.403234768,,,0.428009665
481,"Validation of the FCV-19 Scale and Assessment of Fear of COVID-19 in the Population of Mozambique, East Africa.",Psychol Res Behav Manag,33776494,3/30/21,pubmed,0,5,logistic regression,0.070566383,0.001438137,0.001438205,0.001438252,0.92368077,0.001438253,Healthcare,0.7690706,TRUE,13.8,0.208980147,2.6,0.18256623,0,0.403234768,,,0.264927048
482,Assessing Barriers Faced by Surgeons While Providing Surgical Care During the COVID-19 Pandemic in Pakistan: An Online Cross-Sectional Study.,J Multidiscip Healthc,33776444,3/30/21,pubmed,0,13,logistic regression,0.001565351,0.001565292,0.00156531,0.001565363,0.885953408,0.107785275,Healthcare,0.9907976,TRUE,30.69230769,0.440410662,,,0,0.403234768,,,0.421822715
483,"Forecasting Outbreak of COVID-19 in Turkey; Comparison of Box-Jenkins, Brown's Exponential Smoothing and Long Short-Term Memory Models.",Process Saf Environ Prot,33776248,3/30/21,pubmed,0,1,"prediction model, forecasting model, lstm",0.001901741,0.001901696,0.001901758,0.990491276,0.001901753,0.001901776,Epidemiology,0.41107136,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,,,0.165351392
484,0,J Appl Phycol,33776210,3/30/21,pubmed,0,3,"computational, in silico",0.791222749,0.001511901,0.00151192,0.001511929,0.172510671,0.03173083,Drug discovery,0.48905808,FALSE,23.33333333,0.345723298,37.33333333,0.618209794,0,0.403234768,,,0.45572262
485,Combat COVID-19 infodemic using explainable natural language processing models.,Inf Process Manag,33776192,3/30/21,pubmed,0,3,"machine learning, deep learning, dataset",0.001141447,0.001141366,0.67535619,0.185795127,0.135424505,0.001141364,Epidemiology,0.27905682,FALSE,33.66666667,0.47312759,28.66666667,0.559673535,0,0.403234768,,,0.478678631
486,"Complex network model for COVID-19: Human behavior, pseudo-periodic solutions and multiple epidemic waves.",J Math Anal Appl,33776143,3/30/21,pubmed,0,7,"mathematical model, network model",0.001538194,0.001538171,0.001538137,0.992309213,0.001538177,0.001538108,Epidemiology,0.2913724,FALSE,88,0.837095677,29.28571429,0.564824726,1,0.537564047,,,0.646494817
487,In silico studies on stilbenolignan analogues as SARS-CoV-2 Mpro inhibitors.,Chem Phys Lett,33776065,3/30/21,pubmed,0,1,in silico,0.991066869,0.001786656,0.001786579,0.001786688,0.001786581,0.001786627,Drug discovery,0.97953755,TRUE,30,0.432432432,0,0.055525823,0,0.403234768,,,0.297064341
488,In-silico screening for identification of potential inhibitors against SARS-CoV-2 transmembrane serine protease 2 (TMPRSS2).,Eur J Pharm Sci,33775827,3/30/21,pubmed,0,5,in-silico,0.922482417,0.000956349,0.000956315,0.073692241,0.000956342,0.000956335,Drug discovery,0.6792913,TRUE,13.4,0.202548086,1.6,0.140687717,0,0.403234768,,,0.248823524
489,"ELII: A Novel Inverted Index for Fast Temporal Query, with Application to a Large Covid-19 EHR Dataset.",J Biomed Inform,33775815,3/30/21,pubmed,0,3,dataset,0.001786558,0.001786573,0.411475685,0.540633472,0.001786533,0.042531178,Epidemiology,0.011380911,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
490,Environmental survival of SARS-CoV-2 - A solid waste perspective.,Environ Res,33775678,3/30/21,pubmed,0,22,artificial intelligence,0.002032831,0.098484398,0.002033018,0.669926694,0.22549031,0.00203275,Epidemiology,0.95723283,TRUE,15.36363636,0.231059435,,,0,0.403234768,,,0.317147101
491,"Associations Between Governor Political Affiliation and COVID-19 Cases, Deaths, and Testing in the U.S.",Am J Prev Med,33775513,3/30/21,pubmed,0,5,bayes,0.001717176,0.001717239,0.001717231,0.72327255,0.102096874,0.16947893,Epidemiology,0.31545478,FALSE,55.8,0.673201806,21.2,0.493644635,0,0.403234768,,,0.523360403
492,"Policy Interventions, Social Distancing, and SARS-CoV-2 Transmission in the United States: A Retrospective State-level Analysis.",Am J Med Sci,33775425,3/30/21,pubmed,0,12,logistic regression,0.001254581,0.001254606,0.001254597,0.805588984,0.189392552,0.00125468,Epidemiology,0.4234068,FALSE,40,0.539860226,12.25,0.388546963,0,0.403234768,,,0.443880652
493,"VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations.",Philos Trans A Math Phys Eng Sci,33775151,3/30/21,pubmed,0,26,"computational, in silico",0.103171483,0.001786611,0.231947671,0.659521126,0.001786537,0.001786572,Epidemiology,0.6180204,TRUE,70.69230769,0.766961469,33.80769231,0.597605031,2,0.618927094,,,0.661164532
494,Malaria Endemicity Effect On Covid-19 Pathophysiology: An In-Silico Protein-Protein Interaction Analysis.,J Ayub Med Coll Abbottabad,33774944,3/29/21,pubmed,0,12,in-silico,0.874698417,0.025060518,0.02506022,0.025060286,0.025060274,0.025060285,Drug discovery,0.5558988,TRUE,23.58333333,0.348877482,2.416666667,0.174939791,0,0.403234768,,,0.309017347
495,"Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases.",Comput Biol Med,33774272,3/29/21,pubmed,0,3,"deep learning, dataset",0.000871711,0.000871528,0.914423025,0.00087155,0.000871544,0.082090643,Imaging,0.50904185,TRUE,47.66666667,0.611787989,5,0.257024351,0,0.403234768,,,0.424015703
496,ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes.,J Biomed Inform,33774203,3/29/21,pubmed,0,10,logistic regression,0.001438161,0.116640062,0.360591254,0.001438251,0.23178991,0.288102362,Clinics,0.37194145,FALSE,22.9,0.338177995,,,0,0.403234768,,,0.370706381
497,Molecular characterization of interactions between the D614G variant of SARS-CoV-2 S-protein and neutralizing antibodies: A computational approach.,Infect Genet Evol,33774178,3/29/21,pubmed,0,6,"molecular dynamics simulation, computational",0.543951493,0.450000914,0.001511837,0.001511919,0.001512002,0.001511836,Drug discovery,0.28439385,FALSE,7.666666667,0.111633373,2.666666667,0.185442869,0,0.403234768,,,0.233437003
498,A novel single nucleotide polymorphism assay for the detection of N501Y SARS-CoV-2 variants.,J Virol Methods,33774075,3/29/21,pubmed,0,12,sequencing,0.00182331,0.690689994,0.001823515,0.001823399,0.001823449,0.302016333,Genomics,0.09077689,FALSE,3,0.037293586,0.083333333,0.055927214,0,0.403234768,,,0.165485189
499,Rapid whole-genome sequencing to inform COVID-19 outbreak response in Vietnam.,J Infect,33774018,3/29/21,pubmed,0,18,"sequencing, whole-genome",0.015999608,0.920004743,0.015998414,0.015999407,0.015999303,0.015998525,Genomics,0.28107572,FALSE,55.61111111,0.671903024,56.61111111,0.709526358,0,0.403234768,,,0.59488805
500,"Comparison of Biocartis IDYLLA ™ cartridge assay with Qiagen GeneReader NGS for detection of targetable mutations in EGFR, KRAS/NRAS, and BRAF genes.",Exp Mol Pathol,33773991,3/29/21,pubmed,0,7,sequencing,0.001461995,0.313537399,0.333083053,0.102122751,0.001461992,0.248332809,Genomics,0.7968861,TRUE,51.71428571,0.644319377,32.42857143,0.588774418,0,0.403234768,,,0.545442854
501,Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study.,Lancet Digit Health,33773969,3/29/21,pubmed,0,25,"artificial intelligence, neural network, deep-learning",0.000587829,0.000587839,0.590398613,0.000587856,0.00058786,0.407250002,Imaging,0.3314672,FALSE,16.08,0.243923557,,,0,0.403234768,,,0.323579163
502,Willingness to Vaccinate Against COVID-19 in the U.S.: Representative Longitudinal Evidence From April to October 2020.,Am J Prev Med,33773862,3/29/21,pubmed,0,2,logistic regression,0.00203277,0.002032821,0.002032746,0.002032836,0.989835994,0.002032833,Healthcare,0.24678531,FALSE,84,0.821881378,38,0.622223709,1,0.537564047,,,0.660556378
503,"Performance and operational feasibility of antigen and antibody rapid diagnostic tests for COVID-19 in symptomatic and asymptomatic patients in Cameroon: a clinical, prospective, diagnostic accuracy study.",Lancet Infect Dis,33773618,3/29/21,pubmed,0,29,simulation model,0.000765941,0.192397685,0.426764029,0.06308395,0.092542317,0.224446078,Clinics,0.32669628,FALSE,10.20689655,0.153689158,8.103448276,0.320979395,1,0.537564047,,,0.337410867
504,Economic Distress of Breast Cancer Patients Seeking Treatment at a Tertiary Cancer Center in Mumbai during COVID-19 Pandemic: A Cohort Study.,Asian Pac J Cancer Prev,33773543,3/29/21,pubmed,0,9,logistic regression,0.00118729,0.00118727,0.001187275,0.156862702,0.558792984,0.280782479,Healthcare,0.9362427,TRUE,69.77777778,0.762508504,,,0,0.403234768,,,0.582871636
505,SCENTinel 1.0: development of a rapid test to screen for smell loss.,Chem Senses,33773496,3/28/21,pubmed,0,7,bayes,0.001684519,0.001684632,0.68448633,0.001684725,0.308775107,0.001684688,Healthcare,0.30866766,FALSE,65.57142857,0.737831653,77,0.771139952,0,0.403234768,,,0.637402124
506,Estimation of the incubation period of COVID-19 using viral load data.,Epidemics,33773195,3/28/21,pubmed,0,12,mathematical model,0.002130675,0.212824123,0.002130678,0.592839282,0.002130744,0.1879445,Epidemiology,0.4483906,FALSE,174.0833333,0.961283938,136.25,0.871153332,0,0.403234768,,,0.745224013
507,Association between urinary per- and poly-fluoroalkyl substances and COVID-19 susceptibility.,Environ Int,33773143,3/28/21,pubmed,0,12,metabolom,0.290681648,0.001415177,0.001415128,0.076281246,0.108995617,0.521211185,Clinics,0.9583517,TRUE,0.75,0.008101923,,,0,0.403234768,,,0.205668346
508,Peptide microarray based analysis of antibody responses to SARS-CoV-2 identifies unique epitopes with potential for diagnostic test development.,Eur J Immunol,33772767,3/28/21,pubmed,0,16,proteom,0.348478998,0.5215293,0.001861739,0.001861773,0.001861817,0.124406373,Genomics,0.77782565,TRUE,48.25,0.616364648,61.9375,0.728124164,0,0.403234768,,,0.582574527
509,Implementation of a pharmacist-provided pharmacogenomics service in an executive health program.,Am J Health Syst Pharm,33772264,3/28/21,pubmed,0,5,pharmacogenom,0.07318063,0.036800439,0.106646488,0.26166225,0.457399701,0.064310493,Healthcare,0.92606664,TRUE,24,0.35574247,,,0,0.403234768,,,0.379488619
510,New machine learning model predicts who may benefit most from COVID-19 vaccination.,NPJ Digit Med,33772087,3/28/21,pubmed,0,2,machine learning,0.019530142,0.019531038,0.581890652,0.01953097,0.339987077,0.019530121,Healthcare,0.4323838,FALSE,105,0.879213309,72.5,0.760436179,0,0.403234768,,,0.680961419
511,"Machine Learning Research Towards Combating COVID-19: Virus Detection, Spread Prevention, and Medical Assistance.",J Biomed Inform,33771732,3/28/21,pubmed,0,9,machine learning,0.002357875,0.002357793,0.539524957,0.308257719,0.090361374,0.057140281,Epidemiology,0.6205546,TRUE,82.55555556,0.816315171,108.5555556,0.837101953,0,0.403234768,,,0.685550631
512,Transmission dynamics of COVID-19 in Nepal: Mathematical model uncovering effective controls.,J Theor Biol,33771611,3/28/21,pubmed,0,5,"mathematical model, predictive model",0.001392969,0.001392896,0.00139304,0.99303536,0.001392905,0.00139283,Epidemiology,0.90845114,TRUE,14,0.213494959,6.8,0.292881991,0,0.403234768,,,0.303203906
513,Increased HIV testing in people who use drugs hospitalized in the first wave of the COVID-19 pandemic.,J Subst Abuse Treat,33771274,3/28/21,pubmed,0,6,logistic regression,0.068002569,0.00143813,0.001438173,0.214087511,0.17583801,0.539195607,Clinics,0.6415423,TRUE,19.83333333,0.294266807,25.33333333,0.531576131,0,0.403234768,,,0.409692568
514,A novel banana fiber pad for menstrual hygiene in India: a feasibility and acceptability study.,BMC Womens Health,33771134,3/28/21,pubmed,0,6,logistic regression,0.000863066,0.028544611,0.030605529,0.511397417,0.427726329,0.000863048,Epidemiology,0.9977896,TRUE,32.33333333,0.459273919,7.666666667,0.310877709,0,0.403234768,,,0.391128799
515,Comparison of psychological symptoms between infected and non-infected COVID-19 health care workers.,BMC Psychiatry,33771122,3/28/21,pubmed,0,7,logistic regression,0.001684514,0.001684524,0.001684554,0.001684596,0.991577241,0.00168457,Healthcare,0.79468274,TRUE,129.7142857,0.921330942,55.42857143,0.703706181,0,0.403234768,,,0.67609063
516,"In silico comparative study of SARS-CoV-2 proteins and antigenic proteins in BCG, OPV, MMR and other vaccines: evidence of a possible putative protective effect.",BMC Bioinformatics,33771096,3/28/21,pubmed,0,9,in silico,0.632344377,0.177798223,0.182590274,0.00242237,0.002422377,0.002422379,Drug discovery,0.5756512,TRUE,26,0.382398417,20.44444444,0.484947819,0,0.403234768,,,0.423527001
517,Clinical characteristics and risk factors for SARS-CoV-2 infection in pregnant women attending a third level reference center in Mexico City.,J Matern Fetal Neonatal Med,33771080,3/28/21,pubmed,0,17,logistic regression,0.002422244,0.002422383,0.002422258,0.002422348,0.823213224,0.167097543,Healthcare,0.84445286,TRUE,8.882352941,0.130311089,1.705882353,0.14530372,0,0.403234768,,,0.226283192
518,Bronchoalveolar lavage in suspected COVID-19 cases with a negative nasopharyngeal swab: a retrospective cross-sectional study in a high-impact Northern Italy area.,Intern Emerg Med,33770367,3/27/21,pubmed,0,10,logistic regression,0.001203441,0.288445916,0.36317728,0.001203436,0.036364068,0.309605858,Imaging,0.5430882,TRUE,51.1,0.638876863,42.4,0.646641691,0,0.403234768,,,0.562917774
519,"Maternal health care services utilization amidstCOVID-19 pandemic in West Shoa zone, central Ethiopia.",PLoS One,33770120,3/27/21,pubmed,0,11,logistic regression,0.001254598,0.001254634,0.001254614,0.078961128,0.916020398,0.001254627,Healthcare,0.30878904,FALSE,6.090909091,0.086214361,2.909090909,0.190928552,0,0.403234768,,,0.22679256
520,Genomic variation and epidemiology of SARS-CoV-2 importation and early circulation in Israel.,PLoS One,33770098,3/27/21,pubmed,0,9,"whole genome, genomes",0.001684504,0.886721526,0.001684553,0.1065404,0.001684509,0.001684508,Genomics,0.42315337,FALSE,68.22222222,0.754715814,41,0.639751137,0,0.403234768,,,0.599233906
521,COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring.,IEEE J Biomed Health Inform,33769939,3/27/21,pubmed,0,5,deep learning,0.001987181,0.001987173,0.699323543,0.001987309,0.001987143,0.292727652,Imaging,0.43104988,FALSE,50.4,0.634547591,114.6,0.844193203,8,0.799987654,,,0.759576149
522,"A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis.",IEEE Rev Biomed Eng,33769936,3/27/21,pubmed,0,4,"machine learning, deep learning, mathematical model, deep-learning",0.105657482,0.001565337,0.489209482,0.400437092,0.001565319,0.001565288,Epidemiology,0.22423342,FALSE,6.5,0.093512277,0.25,0.065493712,0,0.403234768,,,0.187413585
523,Exploring the room for repurposed hydroxychloroquine to impede COVID-19: toxicities and multipronged combination approaches with pharmaceutical insights.,Expert Rev Clin Pharmacol,33769888,3/27/21,pubmed,0,5,computational,0.512067715,0.001565374,0.00156533,0.481670897,0.00156535,0.001565333,Drug discovery,0.8635526,TRUE,108.6,0.887005999,,,0,0.403234768,,,0.645120383
524,COVID-19-Induced Lockdowns Indicate the Short-Term Control Effect of Air Pollutant Emission in 174 Cities in China.,Environ Sci Technol,33769804,3/27/21,pubmed,0,9,"machine learning, prediction model, forecasting model",0.044841003,0.001565313,0.001565394,0.926718614,0.001565396,0.02374428,Epidemiology,0.537943,TRUE,40.77777778,0.546787062,,,0,0.403234768,,,0.475010915
525,SARS-CoV-2 biology and variants: anticipation of viral evolution and what needs to be done.,Environ Microbiol,33769683,3/27/21,pubmed,0,4,sequencing,0.136465061,0.642618878,0.00068316,0.2115127,0.000683185,0.008037016,Genomics,0.11048192,FALSE,302.25,0.990722988,1002,0.993577736,0,0.403234768,,,0.795845164
526,Predictors of UK healthcare worker burnout during the COVID-19 pandemic.,QJM,33769545,3/27/21,pubmed,0,4,logistic regression,0.001415137,0.001415109,0.001415127,0.001415124,0.89210713,0.102232373,Healthcare,0.868441,TRUE,138.25,0.930979034,192.75,0.91416912,0,0.403234768,,,0.749460974
527,Greatest changes in objective sleep architecture during COVID-19 lockdown in night-owls with increased REM sleep.,Sleep,33769511,3/27/21,pubmed,0,8,dataset,0.001330028,0.001330058,0.101820215,0.378141843,0.51604772,0.001330137,Healthcare,0.69085866,TRUE,236.75,0.982064444,179.875,0.906208188,0,0.403234768,,,0.7638358
528,The microbiome in atopic patients and potential modifications in the context of the severe acute respiratory syndrome coronavirus 2 pandemic.,Curr Opin Allergy Clin Immunol,33769313,3/27/21,pubmed,0,2,microbiom,0.339541972,0.392295941,0.001486432,0.001486494,0.00148645,0.263702712,Genomics,0.767523,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
529,"Detection of SARS-CoV-2 lineage P.1 in patients from a region with exponentially increasing hospitalisation rate, February 2021, Rio Grande do Sul, Southern Brazil.",Euro Surveill,33769251,3/27/21,pubmed,0,9,"sequencing, whole-genome",0.002996467,0.902361673,0.00299647,0.002996656,0.002996603,0.085652131,Genomics,0.76237607,TRUE,57,0.68204589,,,0,0.403234768,,,0.542640329
530,Conference report: 34th IUSTI Europe Congress.,Int J STD AIDS,33769154,3/27/21,pubmed,0,4,"sequencing, whole-genome",0.002130755,0.510197944,0.002130841,0.385634705,0.097774958,0.002130797,Genomics,0.79174256,TRUE,0.5,0.007668996,,,0,0.403234768,,,0.205451882
531,"Chlorogenic acid, a natural product as potential inhibitor of COVID-19: virtual screening experiment based on network pharmacology and molecular docking.",Nat Prod Res,33769143,3/27/21,pubmed,0,6,virtual screening,0.947033959,0.002638978,0.002639095,0.00263904,0.042409873,0.002639056,Drug discovery,0.97994614,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
532,The evolutionary dynamics of endemic human coronaviruses.,Virus Evol,33768964,3/27/21,pubmed,0,3,"bayes, dataset",0.30327374,0.556377853,0.000822931,0.064464219,0.000822958,0.074238299,Genomics,0.13453728,FALSE,267.6666667,0.987321418,881.6666667,0.99197217,0,0.403234768,,,0.794176118
533,Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units.,Health Care Manag Sci,33768389,3/27/21,pubmed,0,7,mathematical model,0.002898231,0.002898285,0.061468489,0.390614521,0.00289825,0.539222224,Clinics,0.26247922,FALSE,13.85714286,0.209351228,8.285714286,0.324324324,0,0.403234768,,,0.31230344
534,Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit.,IEEE Trans Big Data,33768136,3/27/21,pubmed,0,9,"machine learning, lstm",0.001538098,0.001538132,0.705248583,0.001538225,0.001538086,0.288598875,Clinics,0.24941313,FALSE,67,0.748160059,35.33333333,0.607439122,1,0.537564047,,,0.63105441
535,"Clinical, Serological, Whole Genome Sequence Analyses to Confirm SARS-CoV-2 Reinfection in Patients From Mumbai, India.",Front Med (Lausanne),33768104,3/27/21,pubmed,0,17,"sequencing, whole genome",0.019941594,0.841797543,0.021692402,0.001059362,0.001059352,0.114449747,Genomics,0.72256434,TRUE,33.70588235,0.473251283,7.823529412,0.313419855,0,0.403234768,,,0.396635302
536,How SARS-CoV-2 and Comparable Pathogens Can Be Defeated in a Single Day: Description and Mathematical Model of the Carrier Separation Plan (CSP).,Front Public Health,33768086,3/27/21,pubmed,0,3,mathematical model,0.041699126,0.001046874,0.001046869,0.792106642,0.163053658,0.00104683,Epidemiology,0.22253409,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
537,Structural and Drug Screening Analysis of the Non-structural Proteins of Severe Acute Respiratory Syndrome Coronavirus 2 Virus Extracted From Indian Coronavirus Disease 2019 Patients.,Front Genet,33767730,3/27/21,pubmed,0,8,"in silico, interactom",0.379633688,0.357279501,0.001126817,0.21348771,0.001126814,0.04734547,Drug discovery,0.38558125,FALSE,17.5,0.263776362,2.25,0.170925876,1,0.537564047,,,0.324088762
538,Depression and its relationship with quality of life in frontline psychiatric clinicians during the COVID-19 pandemic in China: a national survey.,Int J Biol Sci,33767580,3/27/21,pubmed,0,9,logistic regression,0.001861673,0.00186167,0.001861773,0.001861702,0.775120275,0.217432908,Healthcare,0.979688,TRUE,115.5555556,0.899870122,70.88888889,0.755820177,0,0.403234768,,,0.686308355
539,COVID-19 and Menstrual Status: Is Menopause an Independent Risk Factor for SARS Cov-2?,J Midlife Health,33767566,3/27/21,pubmed,0,7,logistic regression,0.001272639,0.017306893,0.001272631,0.001272649,0.001272702,0.977602486,Clinics,0.6352471,TRUE,38.42857143,0.523470839,8.142857143,0.321447685,0,0.403234768,,,0.416051097
540,Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England.,Nature,33767447,3/27/21,pubmed,0,34,whole genome,0.001593496,0.733959885,0.001593522,0.230180305,0.031079144,0.001593649,Genomics,0.31530383,FALSE,81.79411765,0.813779455,,,4,0.707574542,,,0.760676998
541,SARS-CoV-2 infection of the oral cavity and saliva.,Nat Med,33767405,3/27/21,pubmed,0,95,"sequencing, dataset",0.450548404,0.421857892,0.045490397,0.001565349,0.001565424,0.078972534,Drug discovery,0.20590451,FALSE,79.65384615,0.805491991,,,1,0.537564047,,,0.671528019
542,Ultra-fast proteomics with Scanning SWATH.,Nat Biotechnol,33767396,3/27/21,pubmed,0,19,proteom,0.41519804,0.001538191,0.352620152,0.153092813,0.001538139,0.076012665,Drug discovery,0.3752487,FALSE,67.10526316,0.748407446,,,0,0.403234768,,,0.575821107
543,In silico investigation of critical binding pattern in SARS-CoV-2 spike protein with angiotensin-converting enzyme 2.,Sci Rep,33767306,3/27/21,pubmed,0,3,in silico,0.919841296,0.076414435,0.000936065,0.000936071,0.000936064,0.000936069,Drug discovery,0.67563546,TRUE,10.66666667,0.159131672,16.33333333,0.440460262,0,0.403234768,,,0.334275567
544,Risk of QT prolongation through drug interactions between hydroxychloroquine and concomitant drugs prescribed in real world practice.,Sci Rep,33767276,3/27/21,pubmed,0,9,logistic regression,0.536342765,0.002806533,0.002806672,0.002806708,0.099492716,0.355744605,Drug discovery,0.9300661,TRUE,13.44444444,0.203104707,,,0,0.403234768,,,0.303169737
545,Generalized chest CT and lab curves throughout the course of COVID-19.,Sci Rep,33767213,3/27/21,pubmed,0,23,deep learning,0.001022665,0.001022658,0.473899835,0.084552754,0.001022661,0.438479426,Imaging,0.8915527,TRUE,122.8695652,0.911373616,,,0,0.403234768,,,0.657304192
546,AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19.,Sci Data,33767205,3/27/21,pubmed,0,26,dataset,0.001823374,0.001823355,0.08890542,0.903801101,0.001823441,0.00182331,Epidemiology,0.16022578,FALSE,15.38461538,0.231183128,13.84615385,0.409151726,0,0.403234768,,,0.347856541
547,Genome Sequences of Three SARS-CoV-2 P.1 Strains Identified from Patients Returning from Brazil to Italy.,Microbiol Resour Announc,33766902,3/27/21,pubmed,0,24,genome sequences,0.007674062,0.870372649,0.007673886,0.007674025,0.00767399,0.098931388,Genomics,0.50648725,TRUE,10.41666667,0.155730101,6.375,0.285322451,0,0.403234768,,,0.281429107
548,Safety of live attenuated herpes zoster vaccine in Australian adults 70-79 years of age: an observational study using active surveillance.,BMJ Open,33766842,3/27/21,pubmed,0,9,bayes,0.000889101,0.099929867,0.025863747,0.069529439,0.71224981,0.091538036,Healthcare,0.96585333,TRUE,100.2222222,0.869255984,62.11111111,0.728993845,0,0.403234768,,,0.667161532
549,A review on the interaction of nucleoside analogues with SARS-CoV-2 RNA dependent RNA polymerase.,Int J Biol Macromol,33766591,3/27/21,pubmed,0,8,computational,0.959830774,0.002080627,0.002080551,0.002080547,0.031846982,0.002080518,Drug discovery,0.95356476,TRUE,81.125,0.811676665,,,0,0.403234768,,,0.607455716
550,Functional analysis of SARS-CoV-2 proteins in Drosophila identifies Orf6-induced pathogenic effects with Selinexor as an effective treatment.,Cell Biosci,33766136,3/27/21,pubmed,0,5,bioinformatic,0.990491152,0.001901805,0.001901688,0.001901709,0.001901819,0.001901828,Drug discovery,0.6554196,TRUE,1.2,0.012740429,,,1,0.537564047,,,0.275152238
551,"Characterization of SARS-CoV-2 proteins reveals Orf6 pathogenicity, subcellular localization, host interactions and attenuation by Selinexor.",Cell Biosci,33766124,3/27/21,pubmed,0,6,proteom,0.973099805,0.021239871,0.001415074,0.001415079,0.00141508,0.001415091,Drug discovery,0.27948838,FALSE,30.33333333,0.436266931,,,1,0.537564047,,,0.486915489
552,"Association of the host genetic factors, hypercholesterolemia and diabetes with mild influenza in an Iranian population.",Virol J,33766078,3/27/21,pubmed,0,5,logistic regression,0.000880242,0.571113581,0.000880213,0.000880213,0.000880266,0.425365485,Genomics,0.70088077,TRUE,41.8,0.555692993,8.2,0.322785657,0,0.403234768,,,0.427237806
553,Psychological distress during the COVID-19 pandemic in Ethiopia: an online cross-sectional study to identify the need for equal attention of intervention.,Ann Gen Psychiatry,33766076,3/27/21,pubmed,0,8,logistic regression,0.000854704,0.000854691,0.000854693,0.000854705,0.995726503,0.000854705,Healthcare,0.51244456,TRUE,30,0.432432432,18.75,0.467621086,0,0.403234768,,,0.434429429
554,Vaccinomic approach for novel multi epitopes vaccine against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).,BMC Immunol,33765919,3/27/21,pubmed,0,3,vaccinom,0.883949514,0.000966833,0.000966861,0.112183172,0.000966788,0.000966831,Drug discovery,0.84362996,TRUE,15.66666667,0.236563795,2.333333333,0.173401124,0,0.403234768,,,0.271066562
555,Public awareness and anxiety during COVID-19 epidemic in China: A cross-sectional study.,Compr Psychiatry,33765493,3/26/21,pubmed,0,10,logistic regression,0.001653083,0.001653102,0.00165302,0.001653082,0.991734636,0.001653076,Healthcare,0.8632407,TRUE,13.3,0.200507143,2.1,0.164771207,0,0.403234768,,,0.256171039
556,Longitudinal profiling of respiratory and systemic immune responses reveals myeloid cell-driven lung inflammation in severe COVID-19.,Immunity,33765436,3/26/21,pubmed,0,24,transcriptom,0.588762359,0.001751332,0.001751225,0.001751184,0.119528957,0.286454944,Drug discovery,0.27806044,FALSE,39.58333333,0.534541406,44.875,0.658282044,1,0.537564047,,,0.576795833
557,Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses.,Immunity,33765435,3/26/21,pubmed,0,17,"sequencing, transcriptom",0.525231146,0.268855449,0.001622731,0.00162274,0.001622749,0.201045185,Drug discovery,0.38233802,FALSE,68.64705882,0.75706599,,,0,0.403234768,,,0.580150379
558,"In silico evaluation of potential inhibitory activity of remdesivir, favipiravir, ribavirin and galidesivir active forms on SARS-CoV-2 RNA polymerase.",Mol Divers,33765239,3/26/21,pubmed,0,3,"molecular dynamics simulation, in silico",0.990282681,0.00194351,0.00194346,0.001943464,0.001943442,0.001943443,Drug discovery,0.6912806,TRUE,26,0.382398417,2,0.164302917,0,0.403234768,,,0.316645367
559,"Sociodemographic characteristics and risk factors related to SARS-CoV-2 infection in Luanda, Angola.",PLoS One,33765102,3/26/21,pubmed,0,14,logistic regression,0.002898258,0.002898345,0.002898294,0.002898365,0.985508282,0.002898455,Healthcare,0.7363816,TRUE,5.285714286,0.073041004,0.857142857,0.103224512,0,0.403234768,,,0.193166761
560,Short-term effects of the COVID-19 state of emergency on contraceptive access and utilization in Mozambique.,PLoS One,33765080,3/26/21,pubmed,0,4,logistic regression,0.001786528,0.001786555,0.001786542,0.514295921,0.478557897,0.001786557,Epidemiology,0.6018919,TRUE,5.75,0.080957388,0.5,0.087101953,0,0.403234768,,,0.19043137
561,Remote assessment in adults with Autism or ADHD: A service user satisfaction survey.,PLoS One,33765076,3/26/21,pubmed,0,6,digital health,0.023074368,0.001684503,0.0016846,0.266667828,0.705204176,0.001684525,Healthcare,0.94653124,TRUE,9,0.135320675,,,0,0.403234768,,,0.269277721
562,App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning.,PLoS One,33765050,3/26/21,pubmed,0,10,"machine learning, predictive model",0.001751174,0.001751163,0.36430784,0.254544261,0.3758943,0.001751261,Healthcare,0.3511976,FALSE,32.4,0.45983054,27.1,0.546427616,0,0.403234768,,,0.469830974
563,"SARS-COV-2 comorbidity network and outcome in hospitalized patients in Crema, Italy.",PLoS One,33765013,3/26/21,pubmed,0,18,network analysis,0.105509674,0.076432693,0.24524618,0.000966759,0.000966752,0.570877942,Clinics,0.6311505,TRUE,55.27777778,0.669738388,66.33333333,0.743510838,0,0.403234768,,,0.605494664
564,Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: A Meta-Analysis.,JMIR Med Inform,33764884,3/26/21,pubmed,0,7,"deep learning, artificial intelligence",0.001203422,0.001203585,0.895761909,0.001203497,0.001203476,0.099424112,Imaging,0.32000324,FALSE,31.57142857,0.450553528,,,0,0.403234768,,,0.426894148
565,Prediction and feature importance analysis for severity of COVID-19 using artificial intelligence: A nationwide analysis in South Korea.,J Med Internet Res,33764883,3/26/21,pubmed,0,10,"artificial intelligence, neural network",0.000815353,0.000815328,0.52525078,0.075527376,0.000815411,0.396775752,Clinics,0.22685295,FALSE,5.2,0.071927763,,,0,0.403234768,,,0.237581265
566,"Thought I'd Share First": An Analysis of COVID-19 Conspiracy Theories and Misinformation Spread on Twitter.,JMIR Public Health Surveill,33764882,3/26/21,pubmed,0,8,"classifier, dataset",0.041015595,0.146288538,0.159449061,0.650934214,0.001156306,0.001156285,Epidemiology,0.002625287,FALSE,12.875,0.192961841,3.25,0.203572384,0,0.403234768,,,0.266589664
567,Probiotics reduce self-reported symptoms of upper respiratory tract infection in overweight and obese adults: should we be considering probiotics during viral pandemics?,Gut Microbes,33764850,3/26/21,pubmed,0,11,microbiom,0.18011374,0.186411711,0.001987198,0.001987291,0.487390135,0.142109926,Healthcare,0.8972463,TRUE,60,0.703444864,,,0,0.403234768,,,0.553339816
568,Revascularisation strategies in patients with significant left main coronary disease during the COVID-19 pandemic.,Catheter Cardiovasc Interv,33764676,3/26/21,pubmed,0,13,logistic regression,0.002357807,0.002357775,0.002357734,0.452360185,0.002357921,0.538208577,Clinics,0.91146517,TRUE,195.8461538,0.971550498,,,0,0.403234768,,,0.687392633
569,Dissemination and evolution of SARS-CoV-2 in the early pandemic phase in South America.,J Med Virol,33764553,3/26/21,pubmed,0,6,"bayes, whole-genome, genome sequences",0.00162269,0.758661963,0.001622686,0.234847175,0.001622775,0.001622711,Genomics,0.824126,TRUE,56.16666667,0.675861216,30.5,0.573187048,0,0.403234768,,,0.550761011
570,Adult PTSD symptoms and substance use during Wave 1 of the COVID-19 pandemic.,Addict Behav Rep,33763517,3/26/21,pubmed,0,1,logistic regression,0.001415085,0.00141511,0.00141517,0.001415131,0.99292436,0.001415144,Healthcare,0.433191,FALSE,48,0.614942173,53,0.693738293,0,0.403234768,,,0.570638411
571,"The association between prior physical fitness and depression in young adults during the COVID-19 pandemic-a cross-sectional, retrospective study.",PeerJ,33763311,3/26/21,pubmed,0,12,logistic regression,0.001310458,0.020679847,0.001310446,0.001310367,0.974078472,0.001310411,Healthcare,0.93800014,TRUE,21.33333333,0.31548024,,,0,0.403234768,,,0.359357504
572,Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images.,J Healthc Eng,33763196,3/26/21,pubmed,0,6,"deep learning, dataset",0.002183251,0.03344482,0.957822148,0.002183294,0.002183241,0.002183246,Imaging,0.46550214,FALSE,9.833333333,0.147628177,,,0,0.403234768,,,0.275431472
573,Predicting Lyme Disease From Patients' Peripheral Blood Mononuclear Cells Profiled With RNA-Sequencing.,Front Immunol,33763080,3/26/21,pubmed,0,16,"machine learning, sequencing, classifier",0.224623905,0.248061388,0.202670466,0.002130771,0.002130916,0.320382554,Clinics,0.25970292,FALSE,77.6875,0.797328221,155.375,0.888346267,0,0.403234768,,,0.696303085
574,Aripiprazole as a Candidate Treatment of COVID-19 Identified Through Genomic Analysis.,Front Pharmacol,33762960,3/26/21,pubmed,0,8,transcriptom,0.779565909,0.001254675,0.001254619,0.00125464,0.001254627,0.215415529,Drug discovery,0.9602576,TRUE,95.125,0.857072175,79,0.776023548,0,0.403234768,,,0.67877683
575,Revealing the mechanism of SARS-CoV-2 spike protein binding with ACE2.,Comput Sci Eng,33762895,3/26/21,pubmed,0,10,computational,0.885727837,0.002422307,0.104582817,0.002422371,0.002422291,0.002422377,Drug discovery,0.36333218,FALSE,33.9,0.475168532,15.5,0.430157881,0,0.403234768,,,0.43618706
576,COVID-19 Pandemic: Review of Contemporary and Forthcoming Detection Tools.,Infect Drug Resist,33762831,3/26/21,pubmed,0,14,"sequencing, metagenom",0.068294971,0.483350619,0.18319357,0.261437355,0.001861793,0.001861693,Genomics,0.78098345,TRUE,21.35714286,0.315727627,,,0,0.403234768,,,0.359481197
577,Spatio-temporal predictive modeling framework for infectious disease spread.,Sci Rep,33762613,3/26/21,pubmed,0,2,predictive model,0.001786582,0.001786553,0.001786727,0.889180067,0.001786515,0.103673555,Epidemiology,0.20859113,FALSE,49.5,0.628362917,48,0.673735617,2,0.618927094,,,0.640341876
578,College Students' Experiences of Race-Related Bias or Hatred in Their Lifetimes and COVID-19 Era.,J Public Health Manag Pract,33762541,3/26/21,pubmed,0,5,logistic regression,0.001141323,0.001141392,0.001141351,0.001141395,0.994293148,0.00114139,Healthcare,0.89573777,TRUE,94.2,0.854845692,26.2,0.539001873,0,0.403234768,,,0.599027444
579,Stigma against COVID-19 among health care workers in Indonesia.,Disaster Med Public Health Prep,33762053,3/26/21,pubmed,0,20,logistic regression,0.003335265,0.003335398,0.003335495,0.003335515,0.832869412,0.153788915,Healthcare,0.9814049,TRUE,21.8,0.322283382,7.15,0.300776024,0,0.403234768,,,0.342098058
580,A simple model for the total number of SARS-CoV-2 infections on a national level.,Epidemiol Infect,33762052,3/26/21,pubmed,0,6,mathematical model,0.002562542,0.002562652,0.002562656,0.987187002,0.002562564,0.002562584,Epidemiology,0.21367425,FALSE,135.6666667,0.927330076,145.8333333,0.880050843,0,0.403234768,,,0.736871896
581,Psychological experiences of healthcare professionals in Sri Lanka during COVID-19.,BMC Psychol,33761994,3/26/21,pubmed,0,4,logistic regression,0.001237045,0.001237048,0.001237074,0.00123706,0.932361936,0.062689839,Healthcare,0.99204504,TRUE,24.75,0.364710248,14.5,0.418450629,0,0.403234768,,,0.395465215
582,Mental health and preventive behaviour of pregnant women in China during the early phase of the COVID-19 period.,Infect Dis Poverty,33761984,3/26/21,pubmed,0,13,logistic regression,0.000936202,0.000936183,0.000936082,0.035880632,0.960374835,0.000936066,Healthcare,0.9732505,TRUE,17.30769231,0.260560331,,,0,0.403234768,,,0.33189755
583,Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.,BMC Pulm Med,33761909,3/26/21,pubmed,0,14,"deep learning, neural network, classifier",0.000889105,0.07094642,0.561278191,0.048497223,0.000889106,0.317499955,Clinics,0.7584921,TRUE,49.71428571,0.629476158,,,0,0.403234768,,,0.516355463
584,Prospective study of emergency medicine provider wellness across ten academic and community hospitals during the initial surge of the COVID-19 pandemic.,BMC Emerg Med,33761876,3/26/21,pubmed,0,10,logistic regression,0.000830646,0.013255617,0.000830642,0.000830667,0.983421752,0.000830676,Healthcare,0.9912739,TRUE,9.1,0.135753603,,,0,0.403234768,,,0.269494185
585,The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.,Medicine (Baltimore),33761733,3/26/21,pubmed,0,8,"machine learning, radiom",0.00117154,0.001171548,0.629627858,0.001171559,0.001171565,0.36568593,Imaging,0.8161603,TRUE,3,0.037293586,,,0,0.403234768,,,0.220264177
586,Combined clinical and imaging features better predict the critical outcomes of patients with SARS-COV-2.,Medicine (Baltimore),33761668,3/26/21,pubmed,0,9,logistic regression,0.001438128,0.001438108,0.202501477,0.001438135,0.00143813,0.791746022,Clinics,0.90869653,TRUE,0.333333333,0.007174222,,,0,0.403234768,,,0.205204495
587,Evaluation of kinetics and thermodynamics of interaction between immobilized SARS-CoV-2 nucleoprotein and specific antibodies by total internal reflection ellipsometry.,J Colloid Interface Sci,33761394,3/25/21,pubmed,0,10,mathematical model,0.556957224,0.212364347,0.002639227,0.222761055,0.002639053,0.002639095,Drug discovery,0.60830116,TRUE,10.2,0.153627312,1.2,0.126103827,1,0.537564047,,,0.272431728
588,Diverse SARS-CoV-2 variants preceded the initial COVID-19 outbreak in Croatia.,Arch Virol,33761008,3/25/21,pubmed,0,9,sequencing,0.099657704,0.698345155,0.003335282,0.00333535,0.003335298,0.191991211,Genomics,0.6168155,TRUE,16.55555556,0.249613458,8.444444444,0.327134065,0,0.403234768,,,0.326660763
589,Baseline clinical characteristics and prognostic factors in hospitalized COVID-19 patients aged ≤ 65 years: A retrospective observational study.,PLoS One,33760885,3/25/21,pubmed,0,13,logistic regression,0.00156533,0.00156532,0.001565287,0.001565338,0.001565328,0.992173397,Clinics,0.8836632,TRUE,21.46153846,0.316779022,6.153846154,0.281375435,0,0.403234768,,,0.333796408
590,Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States.,PLoS One,33760849,3/25/21,pubmed,0,5,bayes,0.001072164,0.001072204,0.001072218,0.762638379,0.233072812,0.001072224,Epidemiology,0.4171691,FALSE,70.2,0.76485868,41.2,0.640620819,0,0.403234768,,,0.602904756
591,Symptoms and symptom clusters associated with SARS-CoV-2 infection in community-based populations: Results from a statewide epidemiological study.,PLoS One,33760821,3/25/21,pubmed,0,7,logistic regression,0.085708853,0.001291379,0.115670972,0.001291264,0.604974745,0.191062788,Healthcare,0.19449592,FALSE,139.2857143,0.932339662,161.8571429,0.893698154,0,0.403234768,,,0.743090861
592,Model-free estimation of COVID-19 transmission dynamics from a complete outbreak.,PLoS One,33760817,3/25/21,pubmed,0,6,dataset,0.001684603,0.001684573,0.00168459,0.812074285,0.181187344,0.001684605,Epidemiology,0.23570001,FALSE,74.33333333,0.783227163,74,0.764182499,0,0.403234768,,,0.65021481
593,SARS-CoV-2 variants reveal features critical for replication in primary human cells.,PLoS Biol,33760807,3/25/21,pubmed,0,12,sequencing,0.368564542,0.584270187,0.001059374,0.001059361,0.001059377,0.043987161,Genomics,0.55347663,TRUE,22.08333333,0.32630342,42.5,0.647578271,0,0.403234768,,,0.45903882
594,Prescribing Phones to Address Health Equity Needs in the COVID-19 Era: The PHONE-CONNECT Program.,J Med Internet Res,33760753,3/25/21,pubmed,0,4,digital health,0.028756655,0.001684482,0.001684553,0.261356297,0.681956974,0.02456104,Healthcare,0.52443045,TRUE,11.75,0.177562001,4.5,0.242708055,0,0.403234768,,,0.274501608
595,Label-Free Segmentation of COVID-19 Lesions in Lung CT.,IEEE Trans Med Imaging,33760731,3/25/21,pubmed,0,4,dataset,0.001751331,0.001751226,0.991243808,0.001751289,0.001751181,0.001751166,Imaging,0.033635706,FALSE,84.25,0.822747232,81.75,0.782512711,4,0.707574542,,,0.770944828
596,0,J Phys Chem B,33760611,3/25/21,pubmed,0,4,computational,0.684340224,0.001141371,0.060714542,0.251520933,0.001141389,0.001141541,Drug discovery,0.16784582,FALSE,24.25,0.358092646,4.25,0.235750602,0,0.403234768,,,0.332359339
597,Membraneless organelles restructured and built by pandemic viruses: HIV-1 and SARS-CoV-2.,J Mol Cell Biol,33760045,3/25/21,pubmed,0,2,genomes,0.59695014,0.343437892,0.026593788,0.001565448,0.00156539,0.029887341,Drug discovery,0.37981895,FALSE,21,0.312016822,39,0.62784319,0,0.403234768,,,0.44769826
598,Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation.,JMIR Public Health Surveill,33759790,3/25/21,pubmed,0,11,"machine learning, computational",0.001112643,0.001112692,0.397164988,0.518175308,0.001112681,0.081321688,Epidemiology,0.34054846,FALSE,38.90909091,0.527861958,,,0,0.403234768,,,0.465548363
599,Factors associated with the willingness and acceptance of SARS-CoV-2 vaccine from adult subjects in China.,Hum Vaccin Immunother,33759691,3/25/21,pubmed,0,7,logistic regression,0.001901747,0.001901694,0.001901681,0.001901724,0.990491368,0.001901786,Healthcare,0.6241728,TRUE,2.714285714,0.029562744,,,0,0.403234768,,,0.216398756
600,0,J Biomol Struct Dyn,33759690,3/25/21,pubmed,0,5,molecular dynamics simulation,0.941384886,0.00139286,0.001392834,0.031342153,0.023094446,0.001392821,Drug discovery,0.9531867,TRUE,17.6,0.264456676,,,0,0.403234768,,,0.333845722
601,Cuckoo optimization algorithm in reverse logistics: A network design for COVID-19 waste management.,Waste Manag Res,33759639,3/25/21,pubmed,0,1,computational,0.001538158,0.001538124,0.208200263,0.785647172,0.001538172,0.00153811,Epidemiology,0.7947873,TRUE,17,0.257467994,0,0.055525823,0,0.403234768,,,0.238742862
602,Risk stratification by long non-coding RNAs profiling in COVID-19 patients.,J Cell Mol Med,33759345,3/25/21,pubmed,0,13,sequencing,0.002357995,0.327696304,0.081777389,0.002357837,0.002357769,0.583452706,Clinics,0.53862655,TRUE,3.384615385,0.040571464,,,0,0.403234768,,,0.221903116
603,"Psychological stress associated with the COVID-19 pandemic in postpartum women in Yokohama, Japan.",J Obstet Gynaecol Res,33759283,3/25/21,pubmed,0,5,logistic regression,0.00194349,0.001943445,0.001943503,0.001943478,0.812150503,0.180075582,Healthcare,0.9431429,TRUE,27.6,0.402374915,5,0.257024351,0,0.403234768,,,0.354211345
604,"Plant-derived chemicals as potential inhibitors of SARS-CoV-2 main protease (6LU7), a virtual screening study.",Phytother Res,33759279,3/25/21,pubmed,0,3,"virtual screening, dataset",0.944695346,0.001751195,0.001751196,0.048299961,0.001751141,0.001751161,Drug discovery,0.93627197,TRUE,9,0.135320675,0.666666667,0.096200161,0,0.403234768,,,0.211585201
605,"Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Juba, South Sudan: a population-based study.",medRxiv,33758900,3/25/21,pubmed,0,26,bayes,0.09795511,0.295652026,0.086253731,0.223534557,0.294742765,0.001861811,Genomics,0.4139184,FALSE,26.26923077,0.384872286,,,2,0.618927094,,,0.50189969
606,"Transmission, infectivity, and antibody neutralization of an emerging SARS-CoV-2 variant in California carrying a L452R spike protein mutation.",medRxiv,33758899,3/25/21,pubmed,0,46,"sequencing, whole-genome",0.093365525,0.835839181,0.001717206,0.02861776,0.00171722,0.038743108,Genomics,0.42588872,FALSE,24.76086957,0.364833941,,,8,0.799987654,,,0.582410798
607,Persistence of symptoms up to 10 months following acute COVID-19 illness.,medRxiv,33758896,3/25/21,pubmed,0,12,logistic regression,0.000838505,0.000838521,0.032092663,0.000838532,0.708437122,0.256954656,Healthcare,0.43044707,FALSE,99.08333333,0.867153194,,,0,0.403234768,,,0.635193981
608,All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting.,medRxiv,33758893,3/25/21,pubmed,0,8,"bayes, machine learning, probabilistic",0.001254631,0.001254622,0.293866056,0.70111535,0.001254697,0.001254644,Epidemiology,0.07867053,FALSE,93.375,0.852557363,99.25,0.821046294,0,0.403234768,,,0.692279475
609,High levels of common cold coronavirus antibodies in convalescent plasma are associated with improved survival in COVID-19 patients.,medRxiv,33758890,3/25/21,pubmed,0,21,bayes,0.129962855,0.309885058,0.001291234,0.001291322,0.00129127,0.556278261,Clinics,0.5294208,TRUE,107.6666667,0.885212444,77.57142857,0.772143431,0,0.403234768,,,0.686863547
610,Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data.,medRxiv,33758882,3/25/21,pubmed,0,4,"image processing, data mining",0.000880223,0.000880225,0.014321319,0.836975459,0.146062549,0.000880225,Epidemiology,0.0970473,FALSE,9.25,0.137918239,2.25,0.170925876,0,0.403234768,,,0.237359628
611,High-resolution epigenome analysis in nasal samples derived from children with respiratory viral infections reveals striking changes upon SARS-CoV-2 infection.,medRxiv,33758880,3/25/21,pubmed,0,7,"sequencing, genome-wide, whole-genome",0.335728778,0.618267405,0.001219995,0.001220047,0.001220023,0.042343752,Genomics,0.8370935,TRUE,31.57142857,0.450553528,31.42857143,0.580412095,0,0.403234768,,,0.478066797
612,Automated Production of Research Data Marts from a Canonical Fast Healthcare Interoperability Resource (FHIR) Data Repository: Applications to COVID-19 Research.,medRxiv,33758877,3/25/21,pubmed,0,7,dataset,0.001254663,0.001254708,0.391292838,0.457446041,0.001254699,0.147497051,Epidemiology,0.43047035,FALSE,34,0.477766096,28.71428571,0.559807332,0,0.403234768,,,0.480269398
613,Structural basis for backtracking by the SARS-CoV-2 replication-transcription complex.,bioRxiv,33758867,3/25/21,pubmed,0,14,molecular dynamics simulation,0.759734952,0.235316735,0.001237132,0.001237061,0.001237054,0.001237066,Drug discovery,0.3347282,FALSE,101.7142857,0.872719401,,,3,0.667819001,,,0.770269201
614,Acquisition of the L452R mutation in the ACE2-binding interface of Spike protein triggers recent massive expansion of SARS-Cov-2 variants.,bioRxiv,33758861,3/25/21,pubmed,0,17,genomes,0.183035425,0.813900844,0.00076592,0.00076594,0.000765939,0.000765931,Genomics,0.108713716,FALSE,40.58823529,0.545179046,62.35294118,0.730131121,3,0.667819001,,,0.647709723
615,Identification of ACE2 mutations that modulate SARS-CoV-2 spike binding across multiple mammalian species.,bioRxiv,33758860,3/25/21,pubmed,0,2,computational,0.453563501,0.477526931,0.001565387,0.001565365,0.064213481,0.001565335,Genomics,0.48787284,FALSE,26,0.382398417,4,0.231469093,0,0.403234768,,,0.339034092
616,DNA spike-ins enable confident interpretation of SARS-CoV-2 genomic data from amplicon-based sequencing.,bioRxiv,33758855,3/25/21,pubmed,0,19,sequencing,0.001823377,0.781464602,0.21124132,0.001823472,0.001823574,0.001823655,Genomics,0.5539181,TRUE,38.31578947,0.521244356,,,0,0.403234768,,,0.462239562
617,phastSim: efficient simulation of sequence evolution for pandemic-scale datasets.,bioRxiv,33758852,3/25/21,pubmed,0,6,"computational, bioinformatic, genomic epidemiology, genomes, dataset",0.001653087,0.590988906,0.269567982,0.134483941,0.001653042,0.001653042,Genomics,0.17499536,FALSE,40.5,0.544746119,299.5,0.954107573,0,0.403234768,,,0.634029487
618,Tiled-ClickSeq for targeted sequencing of complete coronavirus genomes with simultaneous capture of RNA recombination and minority variants.,bioRxiv,33758846,3/25/21,pubmed,0,16,"sequencing, whole genome, genomes",0.001156344,0.932090483,0.063284366,0.001156294,0.001156249,0.001156265,Genomics,0.2980792,FALSE,87.125,0.834065186,258.875,0.941129248,0,0.403234768,,,0.726143067
619,Single-cell immunophenotyping of the fetal immune response to maternal SARS-CoV-2 infection in late gestation.,Res Sq,33758834,3/25/21,pubmed,0,10,sequencing,0.602416354,0.30321968,0.002490415,0.002490503,0.086892459,0.002490589,Drug discovery,0.8340417,TRUE,34.9,0.485744326,,,0,0.403234768,,,0.444489547
620,Pan-India novel coronavirus SARS-CoV-2 genomics and global diversity analysis in spike protein.,Heliyon,33758785,3/25/21,pubmed,0,5,"molecular dynamics simulation, computational, genomes",0.256057714,0.710446735,0.001171535,0.001171619,0.001171579,0.029980818,Genomics,0.5113713,TRUE,16,0.243552477,5,0.257024351,0,0.403234768,,,0.301270532
621,"Recurrent neural network ensemble, a new instrument for the prediction of infectious diseases.",Eur Phys J Plus,33758734,3/25/21,pubmed,0,1,neural network,0.001653104,0.001653199,0.58368083,0.409706733,0.001653078,0.001653055,Epidemiology,0.066890776,FALSE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
622,Structural determinants driving the binding process between PDZ domain of wild type human PALS1 protein and SLiM sequences of SARS-CoV E proteins.,Comput Struct Biotechnol J,33758649,3/25/21,pubmed,0,8,"molecular dynamics simulation, computational",0.96861264,0.001272719,0.00127266,0.026296596,0.001272668,0.001272717,Drug discovery,0.21590531,FALSE,61.625,0.713092956,33.25,0.594193203,0,0.403234768,,,0.570173642
623,Enhanced BB84 quantum cryptography protocol for secure communication in wireless body sensor networks for medical applications.,Pers Ubiquitous Comput,33758585,3/25/21,pubmed,0,2,computational,0.000772653,0.000772639,0.134307508,0.789899344,0.000772657,0.073475199,Epidemiology,0.86449015,TRUE,1.5,0.015523533,0,0.055525823,0,0.403234768,,,0.158094708
624,A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings.,Appl Soft Comput,33758581,3/25/21,pubmed,0,3,"deep learning, neural network, classifier",0.002296528,0.033565225,0.787521312,0.04279798,0.002296699,0.131522256,Clinics,0.6789664,TRUE,8.666666667,0.12839384,0.666666667,0.096200161,0,0.403234768,,,0.209276256
625,0,Saudi J Biol Sci,33758570,3/25/21,pubmed,0,1,computational,0.960252098,0.001653096,0.001653058,0.001653118,0.033135619,0.001653011,Drug discovery,0.36964616,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
626,"Depression, Anxiety, and Stress Symptoms and Its Associated Factors Among Residents of Gondar Town During the Early Stage of COVID-19 Pandemic.",Risk Manag Healthc Policy,33758560,3/25/21,pubmed,0,9,model fit,0.001156232,0.001156251,0.001156265,0.001156271,0.978948009,0.016426972,Healthcare,0.84977067,TRUE,25.55555556,0.37472942,9.777777778,0.349946481,0,0.403234768,,,0.375970223
627,"Coronil, a Tri-Herbal Formulation, Attenuates Spike-Protein-Mediated SARS-CoV-2 Viral Entry into Human Alveolar Epithelial Cells and Pro-Inflammatory Cytokines Production by Inhibiting Spike Protein-ACE-2 Interaction.",J Inflamm Res,33758527,3/25/21,pubmed,0,5,"computational, genomes",0.903974324,0.091575099,0.001112604,0.001112618,0.00111267,0.001112684,Drug discovery,0.79526365,TRUE,70.4,0.765724535,,,0,0.403234768,,,0.584479651
628,Can CFD establish a connection to a milder COVID-19 disease in younger people? Aerosol deposition in lungs of different age groups based on Lagrangian particle tracking in turbulent flow.,Comput Mech,33758453,3/25/21,pubmed,0,5,computational,0.001622848,0.187834624,0.001622829,0.652528102,0.001622807,0.15476879,Epidemiology,0.7121624,TRUE,149.4,0.943657616,71,0.756556061,0,0.403234768,,,0.701149482
629,The effectiveness of contact tracing in mitigating COVID-19 outbreak: A model-based analysis in the context of India.,Appl Math Comput,33758439,3/25/21,pubmed,0,4,mathematical model,0.003214148,0.003214212,0.05676629,0.930376972,0.003214231,0.003214147,Epidemiology,0.5149286,TRUE,13.5,0.205393036,3,0.199424672,0,0.403234768,,,0.269350825
630,Computational characterization of inhaled droplet transport to the nasopharynx.,Sci Rep,33758241,3/25/21,pubmed,0,1,computational,0.002032908,0.153381978,0.067398572,0.636449781,0.002032837,0.138703924,Epidemiology,0.10813776,FALSE,31,0.445111015,11,0.371287129,1,0.537564047,,,0.45132073
631,Emergence of novel SARS-CoV-2 variants in the Netherlands.,Sci Rep,33758205,3/25/21,pubmed,0,2,"genome sequences, genomes",0.000977483,0.978679835,0.017410358,0.000977461,0.000977435,0.000977428,Genomics,0.15864623,FALSE,17.5,0.263776362,12.5,0.392761573,0,0.403234768,,,0.353257568
632,"Excess mortality during the COVID-19 pandemic: a geospatial and statistical analysis in Aden governorate, Yemen.",BMJ Glob Health,33758012,3/25/21,pubmed,0,9,mathematical model,0.001098842,0.001098824,0.001098887,0.931815665,0.001098855,0.063788927,Epidemiology,0.4231198,FALSE,38,0.519327107,197,0.916242976,0,0.403234768,,,0.61293495
633,The SARS-CoV-2 receptor and other key components of the Renin-Angiotensin-Aldosterone System related to COVID-19 are expressed in enterocytes in larval zebrafish.,Biol Open,33757938,3/25/21,pubmed,0,6,transcriptom,0.734487128,0.066066778,0.001371306,0.001371283,0.001371277,0.195332228,Drug discovery,0.82639146,TRUE,89,0.839384006,,,0,0.403234768,,,0.621309387
634,"Social Determinants of Health and Coronavirus Disease 2019 in Pregnancy: Condensation: Social determinants of health, including neighborhood characteristics such as household income and educational attainment, are associated with SARS-CoV-2 infection and severity of COVID-19 in pregnancy.",Am J Obstet Gynecol MFM,33757936,3/25/21,pubmed,0,10,logistic regression,0.000759364,0.049839327,0.0165401,0.000759407,0.484363356,0.447738446,Healthcare,0.8020479,TRUE,33.8,0.474117138,,,0,0.403234768,,,0.438675953
635,"Minimizing the impact of the triple burden of COVID-19, tuberculosis and HIV on health services in sub-Saharan Africa.",Int J Infect Dis,33757874,3/25/21,pubmed,0,12,predictive model,0.001823333,0.001823405,0.001823354,0.655388645,0.122456009,0.216685254,Epidemiology,0.5151749,TRUE,166.4166667,0.955717731,268.9166667,0.945410757,0,0.403234768,,,0.768121085
636,Increase in Frailty in Nursing Home Survivors of Coronavirus Disease 2019: Comparison With Noninfected Residents.,J Am Med Dir Assoc,33757725,3/25/21,pubmed,0,12,logistic regression,0.001330044,0.001330055,0.166202568,0.001330125,0.828477078,0.001330129,Healthcare,0.9328352,TRUE,67,0.748160059,41.83333333,0.643497458,0,0.403234768,,,0.598297428
637,Multinational dietary changes and anxiety during the coronavirus pandemic-findings from Israel.,Isr J Health Policy Res,33757598,3/25/21,pubmed,0,4,logistic regression,0.000643425,0.000643438,0.000643431,0.000643428,0.946958541,0.050467737,Healthcare,0.70859814,TRUE,98,0.864741171,69.5,0.752006957,0,0.403234768,,,0.673327632
638,Preparedness for self-isolation or quarantine and lockdown in South Africa: results from a rapid online survey.,BMC Public Health,33757461,3/25/21,pubmed,0,14,logistic regression,0.012461033,0.000800584,0.000800588,0.067606078,0.917531141,0.000800577,Healthcare,0.83093596,TRUE,33.21428571,0.468118004,47.28571429,0.669587905,0,0.403234768,,,0.513646892
639,"Human cell-dependent, directional, time-dependent changes in the mono- and oligonucleotide compositions of SARS-CoV-2 genomes.",BMC Microbiol,33757449,3/25/21,pubmed,0,3,genomes,0.061655929,0.8792202,0.001156227,0.055655126,0.001156265,0.001156253,Genomics,0.545679,TRUE,85,0.825035562,,,0,0.403234768,,,0.614135165
640,Computing infection distributions and longitudinal evolution patterns in lung CT images.,BMC Med Imaging,33757431,3/25/21,pubmed,0,19,dataset,0.000916684,0.000916726,0.536694896,0.273977242,0.000916688,0.186577764,Imaging,0.46006814,FALSE,97.21052632,0.862205455,,,0,0.403234768,,,0.632720111
641,SAveRUNNER: an R-based tool for drug repurposing.,BMC Bioinformatics,33757425,3/25/21,pubmed,0,2,interactom,0.69882161,0.001350395,0.295776748,0.001350449,0.001350458,0.00135034,Drug discovery,0.25816667,FALSE,39,0.530521368,34,0.5990768,0,0.403234768,,,0.510944312
642,COVID-19: Antiviral agents and enzyme inhibitors/receptor blockers in development.,Exp Biol Med (Maywood),33757336,3/25/21,pubmed,0,3,computational,0.738849503,0.002183336,0.002183284,0.252417361,0.002183274,0.002183242,Drug discovery,0.6730487,TRUE,4,0.054734368,,,0,0.403234768,,,0.228984568
643,"Identification and characterization of circRNAs encoded by MERS-CoV, SARS-CoV-1 and SARS-CoV-2.",Brief Bioinform,33757279,3/25/21,pubmed,0,12,"sequencing, data mining",0.653080039,0.34252453,0.001098825,0.001098833,0.001098841,0.001098933,Drug discovery,0.5955366,TRUE,27.83333333,0.405219865,27.5,0.549906342,0,0.403234768,,,0.452786992
644,How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles.,Brief Bioinform,33757278,3/25/21,pubmed,0,4,dataset,0.001011007,0.025524453,0.17832112,0.793121518,0.001010964,0.001010939,Epidemiology,0.2145023,FALSE,147.5,0.941678521,104.25,0.829475515,0,0.403234768,,,0.724796268
645,A deep learning-based medication behavior monitoring system.,Math Biosci Eng,33757196,3/25/21,pubmed,0,4,deep learning,0.001112669,0.001112669,0.582885458,0.001112686,0.343693772,0.070082746,Imaging,0.40746433,FALSE,0.25,0.006741295,,,0,0.403234768,,,0.204988031
646,Famotidine Repurposing for Novel Corona Virus Disease of 2019: A Systematic Review.,Drug Res (Stuttg),33757133,3/24/21,pubmed,0,3,computational,0.533683721,0.000746555,0.000746562,0.225705526,0.000746574,0.238371062,Drug discovery,0.781919,TRUE,20,0.298163152,0.666666667,0.096200161,0,0.403234768,,,0.265866027
647,Could Soluble Endothelial Protein C Receptor Levels Recognize Sars-Cov2-Positive Patients Requiring Hospitalization?,Shock,33756504,3/24/21,pubmed,0,11,logistic regression,0.179878953,0.001392875,0.033150304,0.001392847,0.001392842,0.782792179,Clinics,0.7187057,TRUE,44.63636364,0.582843713,26.54545455,0.542146107,0,0.403234768,,,0.509408196
648,Targeting Microbiome: An Alternative Strategy for Fighting SARS-CoV-2 Infection.,Chemotherapy,33756475,3/24/21,pubmed,0,14,microbiom,0.675154396,0.046549204,0.002806641,0.057906736,0.002806568,0.214776454,Drug discovery,0.7420303,TRUE,124.5,0.913785639,65.14285714,0.739363126,0,0.403234768,,,0.685461177
649,0,Environ Pollut,33756242,3/24/21,pubmed,0,10,model simulation,0.002296712,0.002296614,0.002296561,0.988516923,0.002296566,0.002296624,Epidemiology,0.7780279,TRUE,14,0.213494959,,,0,0.403234768,,,0.308364864
650,Risk factors for SARS-CoV-2 re-positivity in COVID-19 patients after discharge.,Int Immunopharmacol,33756229,3/24/21,pubmed,0,4,logistic regression,0.002080557,0.160299137,0.002080583,0.00208061,0.002080651,0.831378461,Clinics,0.5495919,TRUE,35.25,0.489331437,19.25,0.47310677,0,0.403234768,,,0.455224325
651,Co-morbidity and blood group type risk in coronavirus disease 2019 patients: A case-control study.,J Infect Public Health,33756193,3/24/21,pubmed,0,3,logistic regression,0.001330058,0.00133014,0.054987667,0.001330097,0.001330076,0.939691962,Clinics,0.7692076,TRUE,7.333333333,0.105572392,2.666666667,0.185442869,0,0.403234768,,,0.231416676
652,Impact of temporary closures of emergency departments during the COVID-19 outbreak on clinical outcomes for emergency patients in a metropolitan area.,Am J Emerg Med,33756131,3/24/21,pubmed,0,4,"logistic regression, dataset",0.001310336,0.001310338,0.001310384,0.196378713,0.289300382,0.510389846,Clinics,0.89720917,TRUE,58.75,0.694538933,,,0,0.403234768,,,0.54888685
653,Whole-genome sequencing of SARS-CoV-2 reveals the detection of G614 variant in Pakistan.,PLoS One,33755704,3/24/21,pubmed,0,12,"sequencing, whole-genome, whole genome, genome sequences, genomes",0.001219996,0.964403849,0.001220006,0.00122,0.001219992,0.030716158,Genomics,0.51935333,TRUE,44.66666667,0.583462181,,,0,0.403234768,,,0.493348474
654,Fatality and risk features for prognosis in COVID-19 according to the care approach - a retrospective cohort study.,PLoS One,33755683,3/24/21,pubmed,0,13,logistic regression,0.001622796,0.001622796,0.001622744,0.042984627,0.001622766,0.950524272,Clinics,0.8915386,TRUE,63.61538462,0.725524151,17.15384615,0.451766123,0,0.403234768,,,0.52684168
655,Are we there yet? Adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States.,J Med Internet Res,33755577,3/24/21,pubmed,0,4,computational,0.001486434,0.001486442,0.001486459,0.992567734,0.001486455,0.001486477,Epidemiology,0.050237894,FALSE,2.5,0.027459954,0,0.055525823,0,0.403234768,,,0.162073515
656,Mini-COVIDNet: Efficient Light Weight Deep Neural Network for Ultrasound based Point-of-Care Detection of COVID-19.,IEEE Trans Ultrason Ferroelectr Freq Control,33755565,3/24/21,pubmed,0,4,"deep learning, neural network, network model",0.001171565,0.001171563,0.994142059,0.001171631,0.001171639,0.001171543,Imaging,0.06051153,FALSE,38,0.519327107,18.25,0.463272679,0,0.403234768,,,0.461944851
657,Tinker-HP: Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields Using GPUs and Multi-GPU Systems.,J Chem Theory Comput,33755446,3/24/21,pubmed,0,12,molecular dynamics simulation,0.132578043,0.001187332,0.500423644,0.363436372,0.001187292,0.001187317,Epidemiology,0.07949546,FALSE,31.58333333,0.450677222,,,0,0.403234768,,,0.426955995
658,Teaching residents how to deliver active learning during didactic lectures.,Am J Health Syst Pharm,33755109,3/24/21,pubmed,0,4,active learning,0.007061571,0.007061516,0.007061752,0.96469178,0.007061783,0.007061598,Epidemiology,0.27254274,FALSE,17.75,0.267672707,,,0,0.403234768,,,0.335453737
659,A comparative online survey on the intention to get COVID-19 vaccine between Greek and Cypriot healthcare personnel: is the country a predictor?,Hum Vaccin Immunother,33754953,3/24/21,pubmed,0,5,logistic regression,0.002080549,0.002080627,0.002080573,0.105982605,0.810207939,0.077567707,Healthcare,0.89075255,TRUE,66.2,0.741851692,55.2,0.702635804,0,0.403234768,,,0.615907421
660,MR-proAdrenomedullin as a predictor of renal replacement therapy in a cohort of critically ill patients with COVID-19.,Biomarkers,33754916,3/24/21,pubmed,0,12,logistic regression,0.001751205,0.001751188,0.001751231,0.001751239,0.001751192,0.991243944,Clinics,0.84539926,TRUE,69.66666667,0.762075577,,,0,0.403234768,,,0.582655172
661,The mediating effect of media usage on the relationship between anxiety/fear and physician-patient trust during the COVID-19 pandemic.,Psychol Health,33754897,3/24/21,pubmed,0,6,machine learning,0.001392816,0.001392848,0.090504071,0.001392889,0.808699446,0.096617929,Healthcare,0.8345592,TRUE,15.16666667,0.228709258,,,0,0.403234768,,,0.315972013
662,"Neutrophil-to-Lymphocyte Ratio, derived Neutrophil-to-Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio and Lymphocyte-to-Monocyte Ratio as risk factors in critically ill COVID-19 patients, a single centered study.",J Ayub Med Coll Abbottabad,33754514,3/24/21,pubmed,0,8,logistic regression,0.001371254,0.001371294,0.062364069,0.001371267,0.001371283,0.932150833,Clinics,0.90278006,TRUE,5.875,0.081761395,,,0,0.403234768,,,0.242498081
663,CvDeep-COVID-19 Detection Model.,SN Comput Sci,33754141,3/24/21,pubmed,0,2,"machine learning, deep learning",0.001823339,0.001823363,0.915035073,0.001823419,0.001823432,0.077671374,Imaging,0.2847578,FALSE,9.5,0.143051518,8,0.320511105,0,0.403234768,,,0.288932464
664,COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking.,Inf Syst Front,33753967,3/24/21,pubmed,0,3,"machine learning, artificial intelligence, neural network, ensemble learning, transfer learning",0.001203434,0.001203482,0.835649021,0.159537149,0.001203448,0.001203466,Imaging,0.49908918,FALSE,51.33333333,0.641288886,39.66666667,0.631790206,0,0.403234768,,,0.558771287
665,MANet: A two-stage deep learning method for classification of COVID-19 from Chest X-ray images.,Neurocomputing,33753962,3/24/21,pubmed,0,3,deep learning,0.000800588,0.000800586,0.99599701,0.000800608,0.000800606,0.000800603,Imaging,0.17806005,FALSE,151.3333333,0.945451172,,,0,0.403234768,,,0.67434297
666,Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19.,Appl Energy,33753961,3/24/21,pubmed,0,5,prediction model,0.001684543,0.001684516,0.001684623,0.991577131,0.001684535,0.001684651,Epidemiology,0.3147802,FALSE,26.8,0.392417589,2.4,0.174872893,1,0.537564047,,,0.368284843
667,Risk profiles of severe illness in children with COVID-19: a meta-analysis of individual patients.,Pediatr Res,33753892,3/24/21,pubmed,0,7,prediction model,0.001371276,0.001371279,0.217470306,0.001371354,0.350362889,0.428052896,Clinics,0.46948388,FALSE,28.28571429,0.410291298,,,0,0.403234768,,,0.406763033
668,Characteristics and comparative clinical outcomes of prisoner versus non-prisoner populations hospitalized with COVID-19.,Sci Rep,33753786,3/24/21,pubmed,0,14,logistic regression,0.00156529,0.001565334,0.001565321,0.031182845,0.00156535,0.96255586,Clinics,0.86744076,TRUE,11.78571429,0.177685695,,,0,0.403234768,,,0.290460231
669,"Prediction of mortality by age and multi-morbidities among confirmed COVID-19 patients: Secondary analysis of surveillance data in Pune, Maharashtra, India.",Indian J Public Health,33753693,3/24/21,pubmed,0,4,logistic regression,0.002898265,0.002898533,0.002898252,0.057250278,0.002898404,0.931156268,Clinics,0.7011021,TRUE,12,0.183190055,2.75,0.187583623,0,0.403234768,,,0.258002815
670,Anti-SARS-CoV-2 antibody responses are attenuated in patients with IBD treated with infliximab.,Gut,33753421,3/24/21,pubmed,0,445,logistic regression,0.240877553,0.167790007,0.001511816,0.075631534,0.143464352,0.370724737,Clinics,0.6312457,TRUE,98.77419355,0.86628734,,,1,0.537564047,,,0.701925694
671,"New digital models of care in ophthalmology, during and beyond the COVID-19 pandemic.",Br J Ophthalmol,33753407,3/24/21,pubmed,0,9,digital health,0.00256267,0.002562601,0.002562748,0.767076437,0.173465683,0.051769861,Epidemiology,0.78042936,TRUE,202.5555556,0.972972973,,,0,0.403234768,,,0.68810387
672,"Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan.",J Biomed Inform,33753268,3/24/21,pubmed,0,6,"deep learning, neural network",0.001392879,0.00139286,0.319770593,0.604766127,0.071284669,0.001392871,Epidemiology,0.5660034,TRUE,86.16666667,0.829797761,30.66666667,0.574257426,0,0.403234768,,,0.602429985
673,Cardiac surgery outcome during the COVID-19 pandemic: a retrospective review of the early experience in nine UK centres.,J Cardiothorac Surg,33752706,3/24/21,pubmed,0,13,"logistic regression, dataset",0.001254628,0.001254662,0.001254686,0.276235493,0.00125467,0.718745861,Clinics,0.4706862,FALSE,43.61538462,0.574123322,,,0,0.403234768,,,0.488679045
674,"Changes in health status, workload, and lifestyle after starting the COVID-19 pandemic: a web-based survey of Japanese men and women.",Environ Health Prev Med,33752603,3/24/21,pubmed,0,3,logistic regression,0.00159352,0.001593512,0.001593471,0.001593561,0.992032406,0.00159353,Healthcare,0.6881783,TRUE,93,0.851567815,27.66666667,0.550909821,0,0.403234768,,,0.601904134
675,Effects of a DPP-4 Inhibitor and RAS Blockade on Clinical Outcomes of Patients with Diabetes and COVID-19.,Diabetes Metab J,33752274,3/23/21,pubmed,0,6,dataset,0.00194376,0.001943474,0.001943626,0.28862116,0.0019436,0.703604381,Clinics,0.98206055,TRUE,29.5,0.426000371,,,1,0.537564047,,,0.481782209
676,Variants in ACE2 and TMPRSS2 Genes Are Not Major Determinants of COVID-19 Severity in UK Biobank Subjects.,Hum Hered,33752217,3/23/21,pubmed,0,1,exom,0.002032888,0.670071858,0.002032839,0.002032884,0.0020329,0.321796631,Genomics,0.41604078,FALSE,18,0.271569052,22,0.503746321,2,0.618927094,,,0.464747489
677,"Structural insights on the interaction potential of natural leads against major protein targets of SARS-CoV-2: Molecular modelling, docking and dynamic simulation studies.",Comput Biol Med,33751995,3/23/21,pubmed,0,5,"virtual screening, computational",0.979430632,0.001098795,0.001098807,0.001098815,0.016174153,0.001098798,Drug discovery,0.8330055,TRUE,26,0.382398417,40,0.633395772,0,0.403234768,,,0.473009652
678,Bioinformatic analyses hinted at augmented T helper 17 cell differentiation and cytokine response as the central mechanism of COVID-19-associated Guillain-Barré syndrome.,Cell Prolif,33751722,3/23/21,pubmed,0,10,"bioinformatic, transcriptom, network analysis",0.840384613,0.002422672,0.00242228,0.002422264,0.002422283,0.149925889,Drug discovery,0.8208622,TRUE,16.9,0.253880883,,,0,0.403234768,,,0.328557825
679,Variable routes to genomic and host adaptation among coronaviruses.,J Evol Biol,33751699,3/23/21,pubmed,0,5,"genomes, structural model",0.130514543,0.865701002,0.000946109,0.000946125,0.000946094,0.000946127,Genomics,0.577128,TRUE,24.6,0.362483765,,,0,0.403234768,,,0.382859266
680,Acute kidney injury and mortality risk in older adults with COVID-19.,J Nephrol,33751497,3/23/21,pubmed,0,10,logistic regression,0.001511825,0.001511798,0.001511808,0.001511819,0.001511827,0.992440923,Clinics,0.80885124,TRUE,120.8,0.908281279,,,0,0.403234768,,,0.655758023
681,Factors Associated with Perceived Susceptibility to COVID-19 Among Urban and Rural Adults in Alabama.,J Community Health,33751308,3/23/21,pubmed,0,9,logistic regression,0.001350348,0.001350369,0.001350312,0.001350358,0.993248259,0.001350354,Healthcare,0.8024305,TRUE,37,0.508936854,,,0,0.403234768,,,0.456085811
682,Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe.,Eur J Health Econ,33751290,3/23/21,pubmed,0,3,dataset,0.001901721,0.001901786,0.001901765,0.894454804,0.09793805,0.001901873,Epidemiology,0.37439197,FALSE,61.33333333,0.710680933,2.333333333,0.173401124,0,0.403234768,,,0.429105608
683,The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE).,Health Care Manag Sci,33751281,3/23/21,pubmed,0,4,"computational, probabilistic",0.001511808,0.001511826,0.033805767,0.822136317,0.001511847,0.139522435,Epidemiology,0.084822476,FALSE,31.5,0.450058754,5.75,0.271742039,0,0.403234768,,,0.375011854
684,Risk and protective factors related to children's symptoms of emotional difficulties and hyperactivity/inattention during the COVID-19-related lockdown in France: results from a community sample.,Eur Child Adolesc Psychiatry,33751230,3/23/21,pubmed,0,8,logistic regression,0.001059339,0.00105936,0.00105936,0.001059412,0.994703185,0.001059344,Healthcare,0.81990474,TRUE,63.625,0.725647845,74.375,0.765119079,0,0.403234768,,,0.631333897
685,Negative vaccine attitudes and intentions to vaccinate against Covid-19 in relation to smoking status: a population survey of UK adults.,Nicotine Tob Res,33751125,3/23/21,pubmed,0,5,logistic regression,0.112556746,0.001350406,0.001350335,0.001350398,0.882041767,0.001350348,Healthcare,0.314875,FALSE,117.8,0.904013854,,,0,0.403234768,,,0.653624311
686,"Characteristics associated with household transmission of SARS-CoV-2 in Ontario, Canada: A cohort study.",Clin Infect Dis,33751026,3/23/21,pubmed,0,8,logistic regression,0.00125458,0.001254685,0.001254607,0.299133662,0.695847683,0.001254784,Healthcare,0.31405193,FALSE,37.875,0.517038778,,,2,0.618927094,,,0.567982936
687,Association of Vitamin D Status with SARS-CoV-2 Infection or COVID-19 Severity: A Systematic Review and Meta-analysis.,Adv Nutr,33751020,3/23/21,pubmed,0,6,logistic regression,0.001392848,0.001392843,0.001392853,0.104628042,0.001392903,0.889800511,Clinics,0.69299924,TRUE,20.83333333,0.307996784,,,1,0.537564047,,,0.422780416
688,"A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2.",PLoS Biol,33750978,3/23/21,pubmed,0,14,mathematical model,0.514604601,0.117203382,0.001085398,0.23036017,0.044294262,0.092452186,Drug discovery,0.28961122,FALSE,124.3571429,0.913538252,,,1,0.537564047,,,0.72555115
689,The impact of COVID-19 on mental health outcomes among hospital fever clinic attendants across Nepal: A cross-sectional study.,PLoS One,33750955,3/23/21,pubmed,0,6,logistic regression,0.001330025,0.001330035,0.001330107,0.001330081,0.993349644,0.001330107,Healthcare,0.9660062,TRUE,11,0.167171748,,,0,0.403234768,,,0.285203258
690,Comparison of public response to containment measures during the initial outbreak and resurgence of COVID-19 epidemic in China: an infodemiology study.,J Med Internet Res,33750739,3/23/21,pubmed,0,9,machine learning,0.00090737,0.000907352,0.00090733,0.995463311,0.000907337,0.000907299,Epidemiology,0.33337617,FALSE,41.88888889,0.55612592,,,0,0.403234768,,,0.479680344
691,Machine Learning Classification Models for COVID-19 Test Prioritization in Brazil.,J Med Internet Res,33750734,3/23/21,pubmed,0,8,"machine learning, supervised learning, logistic regression, dataset",0.001072183,0.001072205,0.656416869,0.116709993,0.223656485,0.001072265,Healthcare,0.9072781,TRUE,37.25,0.51035933,7.375,0.304923736,0,0.403234768,,,0.406172611
692,Regional Differences in Mortality Rates During the COVID-19 Epidemic in Italy.,Disaster Med Public Health Prep,33750493,3/23/21,pubmed,0,6,bayes,0.002183223,0.002183257,0.002183222,0.625666213,0.002183295,0.365600791,Epidemiology,0.80491257,TRUE,152.8333333,0.94662626,50.16666667,0.68209794,0,0.403234768,,,0.677319656
693,Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease.,BMC Cardiovasc Disord,33750304,3/23/21,pubmed,0,7,"machine learning, logistic regression, dataset",0.001371256,0.001371295,0.458770246,0.001371324,0.383386925,0.153728954,Healthcare,0.493233,FALSE,68.71428571,0.757684458,15.28571429,0.427749532,0,0.403234768,,,0.529556252
694,A region-specific clustering approach to investigate risk-factors in mortality rate during COVID-19: comprehensive statistical analysis from 208 countries.,J Med Eng Technol,33750249,3/23/21,pubmed,0,2,machine learning,0.001786546,0.001786614,0.148477461,0.64441667,0.001786592,0.201746117,Epidemiology,0.37612048,FALSE,30.5,0.438493413,0.5,0.087101953,0,0.403234768,,,0.309610045
695,"Perception of level of knowledge, skills, and safety before and after training to perform videolaryngoscopy with the Intubox barrier system for airway management in patients with COVID-19.",Emergencias,33750049,3/23/21,pubmed,0,8,logistic regression,0.000956301,0.000956345,0.00095631,0.000956324,0.046145282,0.950029438,Clinics,0.9966567,TRUE,7.5,0.108355495,0.875,0.103826599,0,0.403234768,,,0.205138954
696,Risk factors for severe outcomes in patients with systemic vasculitis & COVID-19: a bi-national registry-based cohort study.,Arthritis Rheumatol,33750043,3/23/21,pubmed,0,17,logistic regression,0.0018618,0.001861713,0.001861689,0.001861809,0.001861849,0.99069114,Clinics,0.96284467,TRUE,139.8823529,0.933391057,,,0,0.403234768,,,0.668312912
697,COVID-19: emergence and mutational diversification of SARS-CoV-2.,Microb Biotechnol,33750009,3/23/21,pubmed,0,1,"sequencing, genomes",0.002639064,0.986804544,0.00263899,0.002639085,0.002639065,0.002639251,Genomics,0.30392945,FALSE,233,0.981507824,527,0.980867006,0,0.403234768,,,0.788536532
698,Integrated intra- and intercellular signaling knowledge for multicellular omics analysis.,Mol Syst Biol,33749993,3/23/21,pubmed,0,12,"computational,  omics",0.730772487,0.001751242,0.001751263,0.154719958,0.109253888,0.001751163,Drug discovery,0.74998474,TRUE,85.66666667,0.827509432,,,0,0.403234768,,,0.6153721
699,A small molecule compound berberine as an orally active therapeutic candidate against COVID-19 and SARS: A computational and mechanistic study.,FASEB J,33749932,3/23/21,pubmed,0,11,"computational, in silico",0.89985378,0.000956316,0.028650756,0.000956365,0.000956389,0.068626395,Drug discovery,0.9086778,TRUE,71.45454545,0.770301194,,,0,0.403234768,,,0.586767981
700,"Predicting poor outcome in patients with suspected COVID-19 presenting to the Emergency Department (COVERED) - Development, internal and external validation of a prediction model.",Acute Med,33749689,3/23/21,pubmed,0,14,"logistic regression, prediction model",0.001310391,0.001310455,0.108261585,0.001310409,0.001310431,0.886496729,Clinics,0.9697252,TRUE,16.21428571,0.245036799,13.64285714,0.406542681,0,0.403234768,,,0.351604749
701,Genomic diversity of SARS-CoV-2 during early introduction into the Baltimore-Washington metropolitan area.,JCI Insight,33749660,3/23/21,pubmed,0,22,"sequencing, genomes",0.001987097,0.990064422,0.001987099,0.001987183,0.001987109,0.00198709,Genomics,0.5419083,TRUE,49.31818182,0.626198281,,,0,0.403234768,,,0.514716524
702,"Evaluation of the effects of chlorhexidine and several flavonoids as antiviral purposes on SARS-CoV-2 main protease: molecular docking, molecular dynamics simulation studies.",J Biomol Struct Dyn,33749547,3/23/21,pubmed,0,4,"virtual screening, molecular dynamics simulation",0.95978492,0.034367551,0.001461857,0.001461962,0.001461856,0.001461854,Drug discovery,0.9296108,TRUE,6.75,0.095862453,1.25,0.127776291,0,0.403234768,,,0.208957837
703,"On the search for COVID-19 therapeutics: identification of potential SARS-CoV-2 main protease inhibitors by virtual screening, pharmacophore modeling and molecular dynamics.",J Biomol Struct Dyn,33749545,3/23/21,pubmed,0,2,"virtual screening, molecular dynamics simulation, in-silico",0.993035591,0.001392853,0.001392912,0.00139286,0.001392881,0.001392902,Drug discovery,0.28924453,FALSE,22.5,0.333539489,,,0,0.403234768,,,0.368387128
704,Phylogenic analysis of coronavirus genome and molecular studies on potential anti-COVID-19 agents from selected FDA-approved drugs.,J Biomol Struct Dyn,33749538,3/23/21,pubmed,0,5,molecular dynamics simulation,0.865593131,0.129781735,0.001156268,0.001156268,0.001156269,0.00115633,Drug discovery,0.9482096,TRUE,16.2,0.244974952,2.2,0.16838373,0,0.403234768,,,0.272197817
705,Psychological impact of the coronavirus 2019 (COVID-19) pandemic on nurses.,Jpn J Nurs Sci,33749144,3/23/21,pubmed,0,2,logistic regression,0.00139282,0.00139283,0.001392829,0.001392833,0.97008891,0.024339777,Healthcare,0.9545224,TRUE,4,0.054734368,2,0.164302917,0,0.403234768,,,0.207424018
706,Lack of Molecular Mimicry between Nonhuman Primates and Infectious Pathogens: The Possible Genetic Bases.,Glob Med Genet,33748822,3/23/21,pubmed,0,1,proteom,0.391177594,0.597596097,0.002806489,0.002806568,0.002806583,0.002806669,Genomics,0.13790777,FALSE,188,0.96808708,65,0.739028633,0,0.403234768,,,0.70345016
707,"Prehospital hypoxemia, measured by pulse oximetry, predicts hospital outcomes during the New York City COVID-19 pandemic.",J Am Coll Emerg Physicians Open,33748809,3/23/21,pubmed,0,20,logistic regression,0.000889052,0.000889073,0.000889074,0.089202955,0.000889082,0.907240764,Clinics,0.78782094,TRUE,36.2,0.499412456,37.3,0.617607707,0,0.403234768,,,0.506751643
708,A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions.,Intell Based Med,33748802,3/23/21,pubmed,0,10,"machine learning, predictive model, logistic regression, dataset",0.001684496,0.001684536,0.38818076,0.001684521,0.00168454,0.605081147,Clinics,0.22677654,FALSE,12.9,0.193085534,,,0,0.403234768,,,0.298160151
709,Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound.,Front Big Data,33748752,3/23/21,pubmed,0,6,image processing,0.000988362,0.000988364,0.933323971,0.000988425,0.062722468,0.00098841,Imaging,0.9362415,TRUE,20.66666667,0.306512462,4.666666667,0.246721969,1,0.537564047,,,0.363599493
710,Machine Learning Approaches Reveal That the Number of Tests Do Not Matter to the Prediction of Global Confirmed COVID-19 Cases.,Front Artif Intell,33748745,3/23/21,pubmed,0,2,"machine learning, supervised learning, unsupervised learning",0.001272665,0.001272733,0.233617306,0.711661682,0.050902955,0.00127266,Epidemiology,0.3604011,FALSE,22.5,0.333539489,2,0.164302917,0,0.403234768,,,0.300359058
711,0,iScience,33748697,3/23/21,pubmed,0,21,genome-wide,0.250582441,0.459966793,0.002238442,0.002238453,0.002238497,0.282735373,Genomics,0.5714275,TRUE,74.71428571,0.785391799,37.23809524,0.617139417,0,0.403234768,,,0.601921994
712,Association between Chronic Statin Use and 30-Day Mortality in Hospitalized Patients with COVID-19.,Mayo Clin Proc Innov Qual Outcomes,33748678,3/23/21,pubmed,0,7,logistic regression,0.001511853,0.001511821,0.001511802,0.001511911,0.001511881,0.992440731,Clinics,0.5616291,TRUE,256.5714286,0.985280475,308.8571429,0.957051111,0,0.403234768,,,0.781855451
713,Investigating the Internalization and COVID-19 Antiviral Computational Analysis of Optimized Nanoscale Zinc Oxide.,ACS Omega,33748599,3/23/21,pubmed,0,6,"computational, in silico",0.903539828,0.088511436,0.001987206,0.001987292,0.001987119,0.001987119,Drug discovery,0.9040054,TRUE,20,0.298163152,13.66666667,0.407412363,0,0.403234768,,,0.369603427
714,SARS-CoV-2 Genome from the Khyber Pakhtunkhwa Province of Pakistan.,ACS Omega,33748571,3/23/21,pubmed,0,12,"computational, sequencing, whole genome, genome sequences, genomes",0.001330133,0.956753732,0.001330028,0.001330132,0.001330067,0.037925907,Genomics,0.64967746,TRUE,67.66666667,0.75137609,13.16666667,0.401792882,0,0.403234768,,,0.518801247
715,Evaluation of flavonoids as 2019-nCoV cell entry inhibitor through molecular docking and pharmacological analysis.,Heliyon,33748510,3/23/21,pubmed,0,4,molecular dynamics simulation,0.978874609,0.001010953,0.001010934,0.017081544,0.001010962,0.001010999,Drug discovery,0.9215618,TRUE,25.5,0.37435834,1.5,0.138747659,0,0.403234768,,,0.305446922
716,Projection of budgetary savings to US state Medicaid programs from reduced nursing home use due to an Alzheimer's disease treatment.,Alzheimers Dement (Amst),33748394,3/23/21,pubmed,0,5,simulation model,0.001943503,0.001943497,0.001943507,0.615997957,0.290589879,0.087581657,Epidemiology,0.26402307,FALSE,49.6,0.628733997,,,0,0.403234768,,,0.515984382
717,Data on the effects of COVID-19 pandemic on people's expectations about their future.,Data Brief,33748358,3/23/21,pubmed,0,10,dataset,0.001272677,0.001272653,0.001272704,0.523811708,0.471097618,0.00127264,Epidemiology,0.90033007,TRUE,35.4,0.490815759,12.6,0.394300241,0,0.403234768,,,0.429450256
718,"Level of Preparedness for COVID-19 and Its Associated Factors among Frontline Healthcare Providers in South Gondar Public Hospitals, Northwest Ethiopia, 2020: A Multicenter Cross-Sectional Study.",Biomed Res Int,33748271,3/23/21,pubmed,0,9,logistic regression,0.001371242,0.001371273,0.001371292,0.00137133,0.934324142,0.060190721,Healthcare,0.70453244,TRUE,6.888888889,0.097346775,0,0.055525823,0,0.403234768,,,0.185369122
719,Depressive and Anxiety Symptoms of Healthcare Workers in Intensive Care Unit Under the COVID-19 Epidemic: An Online Cross-Sectional Study in China.,Front Public Health,33748059,3/23/21,pubmed,0,13,logistic regression,0.001059362,0.001059365,0.001059342,0.001059369,0.84525435,0.150508213,Healthcare,0.8165883,TRUE,29.46153846,0.424206816,7.538461538,0.308067969,0,0.403234768,,,0.378503184
720,An Update on Advances in COVID-19 Laboratory Diagnosis and Testing Guidelines in India.,Front Public Health,33748054,3/23/21,pubmed,0,5,sequencing,0.001141356,0.413349114,0.465537134,0.117689687,0.001141347,0.001141362,Genomics,0.6140506,TRUE,23.4,0.346527305,1.8,0.150120417,0,0.403234768,,,0.29996083
721,Prediction of Sepsis in COVID-19 Using Laboratory Indicators.,Front Cell Infect Microbiol,33747973,3/23/21,pubmed,0,12,predictive model,0.001371317,0.001371406,0.316069083,0.001371277,0.001371312,0.678445605,Clinics,0.79394996,TRUE,150.1666667,0.94415239,118.5,0.849745785,0,0.403234768,,,0.732377648
722,Computational analysis of functional monomers used in molecular imprinting for promising COVID-19 detection.,Comput Theor Chem,33747754,3/23/21,pubmed,0,3,computational,0.73010341,0.080467817,0.181740974,0.002562605,0.002562651,0.002562543,Drug discovery,0.71041316,TRUE,2.666666667,0.029377203,0,0.055525823,0,0.403234768,,,0.162712598
723,Mathematical model for COVID-19 management in crowded settlements and high-activity areas.,Int J Dyn Control,33747709,3/23/21,pubmed,0,4,mathematical model,0.001987099,0.001987122,0.001987145,0.856458393,0.06808484,0.069495401,Epidemiology,0.28220004,FALSE,25,0.369286907,8.75,0.332218357,0,0.403234768,,,0.368246677
724,Genomic epidemiology of a densely sampled COVID-19 outbreak in China.,Virus Evol,33747543,3/23/21,pubmed,0,22,"bayes, genomic epidemiology, genomes, bayesian model",0.002357732,0.410461196,0.002357836,0.580107573,0.002357803,0.00235786,Epidemiology,0.18960041,FALSE,18.13636364,0.272187519,,,0,0.403234768,,,0.337711143
725,Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.,J Healthc Eng,33747419,3/23/21,pubmed,0,2,"deep learning, image processing",0.001171554,0.001171601,0.813851658,0.181462027,0.001171595,0.001171565,Imaging,0.7083582,TRUE,13,0.197352959,0.5,0.087101953,1,0.537564047,,,0.27400632
726,Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion.,J Healthc Eng,33747417,3/23/21,pubmed,0,8,"deep learning, dataset",0.001438168,0.046476147,0.947771358,0.0014381,0.001438106,0.001438121,Imaging,0.45567745,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
727,Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey.,Soc Netw Anal Min,33747252,3/23/21,pubmed,0,3,network analysis,0.001861711,0.001861741,0.001861869,0.661950054,0.330602951,0.001861674,Epidemiology,0.31132698,FALSE,9.666666667,0.144968767,6.666666667,0.290607439,0,0.403234768,,,0.279603658
728,Toward Accurate and Robust Environmental Surveillance Using Metagenomics.,Front Genet,33747038,3/23/21,pubmed,0,5,"sequencing, metagenom",0.001823367,0.213932537,0.39842663,0.382170763,0.001823367,0.001823337,Epidemiology,0.64062184,TRUE,114,0.897767333,179.4,0.905873695,0,0.403234768,,,0.735625265
729,Global Geographic and Temporal Analysis of SARS-CoV-2 Haplotypes Normalized by COVID-19 Cases During the Pandemic.,Front Microbiol,33746914,3/23/21,pubmed,0,8,genomes,0.00133004,0.866721614,0.001330113,0.001330118,0.001330146,0.12795797,Genomics,0.32644516,FALSE,2.375,0.025047931,0,0.055525823,1,0.537564047,,,0.206045934
730,Molecular Docking Reveals Ivermectin and Remdesivir as Potential Repurposed Drugs Against SARS-CoV-2.,Front Microbiol,33746908,3/23/21,pubmed,0,3,molecular dynamics simulation,0.970486723,0.001187297,0.001187259,0.001187293,0.001187319,0.024764108,Drug discovery,0.86192584,TRUE,35,0.488032655,18,0.46180091,1,0.537564047,,,0.495799204
731,In-Silico Drug Designing of Spike Receptor with Its ACE2 Receptor and Nsp10/Nsp16 MTase Complex Against SARS-CoV-2.,Int J Pept Res Ther,33746660,3/23/21,pubmed,0,6,"computational, in-silico",0.994505839,0.001098816,0.00109881,0.001098863,0.001098836,0.001098837,Drug discovery,0.7341218,TRUE,7.333333333,0.105572392,,,0,0.403234768,,,0.25440358
732,A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods.,Appl Soft Comput,33746657,3/23/21,pubmed,0,1,"machine learning, image processing, dataset",0.000765922,0.000765929,0.878145068,0.089140909,0.030416248,0.000765925,Imaging,0.7886491,TRUE,17,0.257467994,0,0.055525823,0,0.403234768,,,0.238742862
733,A network pharmacology based approach for predicting active ingredients and potential mechanism of Lianhuaqingwen capsule in treating COVID-19.,Int J Med Sci,33746604,3/23/21,pubmed,0,8,network analysis,0.943742931,0.001272658,0.001272681,0.001272646,0.001272678,0.051166406,Drug discovery,0.7918361,TRUE,17.875,0.268724102,,,0,0.403234768,,,0.335979435
734,"The Association between Obesity and Severity in Patients with Coronavirus Disease 2019: a Retrospective, Single-center Study, Wuhan.",Int J Med Sci,33746594,3/23/21,pubmed,0,18,logistic regression,0.000936117,0.000936078,0.000936068,0.000936076,0.000936075,0.995319586,Clinics,0.97891486,TRUE,27.66666667,0.403488156,,,0,0.403234768,,,0.403361462
735,Machining learning predicts the need for escalated care and mortality in COVID-19 patients from clinical variables.,Int J Med Sci,33746590,3/23/21,pubmed,0,5,"machine learning, predictive model",0.000988397,0.000988369,0.291605475,0.000988402,0.000988399,0.704440957,Clinics,0.70369774,TRUE,21.6,0.319438432,4,0.231469093,1,0.537564047,,,0.362823857
736,Systematic elucidation of the mechanism of Jingyin granule in the treatment of Novel Coronavirus (COVID-19) Pneumonia via Network Pharmacology.,Int J Med Sci,33746581,3/23/21,pubmed,0,4,genomes,0.938609581,0.020001814,0.001254621,0.00125461,0.037624763,0.001254611,Drug discovery,0.9974978,TRUE,33.5,0.471890655,29.25,0.564557131,0,0.403234768,,,0.479894185
737,Simulation-based study of COVID-19 outbreak associated with air-conditioning in a restaurant.,Phys Fluids (1994),33746488,3/23/21,pubmed,0,4,computational,0.001461962,0.001461961,0.001461918,0.992690335,0.001461933,0.001461891,Epidemiology,0.39127108,FALSE,0,0.006432061,,,1,0.537564047,,,0.271998054
738,Estimating COVID-19 exposure in a classroom setting: A comparison between mathematical and numerical models.,Phys Fluids (1994),33746487,3/23/21,pubmed,0,2,computational,0.001901788,0.001901761,0.001901722,0.82247382,0.030589061,0.141231848,Epidemiology,0.6509049,TRUE,58,0.689714887,9.5,0.345531175,2,0.618927094,,,0.551391052
739,Disease transmission through expiratory aerosols on an urban bus.,Phys Fluids (1994),33746484,3/23/21,pubmed,0,6,computational,0.001786626,0.00178655,0.001786563,0.932542749,0.001786576,0.060310937,Epidemiology,0.47121477,FALSE,112.5,0.895479003,101.8333333,0.825528499,2,0.618927094,,,0.779978199
740,"Working in lockdown: the relationship between COVID-19 induced work stressors, job performance, distress, and life satisfaction.",Curr Psychol,33746462,3/23/21,pubmed,0,4,structural model,0.001438211,0.001438144,0.103408961,0.137879645,0.754396916,0.001438123,Healthcare,0.92994124,TRUE,8.25,0.121343311,3.25,0.203572384,0,0.403234768,,,0.242716821
741,Clustering Patterns Connecting COVID-19 Dynamics and Human Mobility Using Optimal Transport.,Sankhya B (2008),33746458,3/23/21,pubmed,0,4,computational,0.002238531,0.002238651,0.002238495,0.98880732,0.002238512,0.002238491,Epidemiology,0.24324948,FALSE,118,0.904632321,109.25,0.837904736,3,0.667819001,,,0.80345202
742,Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells.,Chaos Solitons Fractals,33746373,3/23/21,pubmed,0,5,"deep learning, neural network, lstm",0.033590506,0.001684514,0.174573801,0.786782085,0.001684551,0.001684543,Epidemiology,0.5656667,TRUE,10.2,0.153627312,1.4,0.133864062,1,0.537564047,,,0.275018474
743,COVID-19: Automatic detection from X-ray images by utilizing deep learning methods.,Expert Syst Appl,33746370,3/23/21,pubmed,0,6,deep learning,0.00090729,0.036003653,0.786407087,0.000907352,0.000907323,0.174867295,Imaging,0.62425846,TRUE,12.5,0.189436576,,,0,0.403234768,,,0.296335672
744,Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images.,Expert Syst Appl,33746369,3/23/21,pubmed,0,6,"deep learning, dataset",0.00087159,0.000871582,0.995641994,0.000871624,0.000871611,0.000871599,Imaging,0.72028816,TRUE,39.66666667,0.536211268,40.5,0.637342788,0,0.403234768,,,0.525596275
745,COVID-19 dynamics considering the influence of hospital infrastructure: an investigation into Brazilian scenarios.,Nonlinear Dyn,33746362,3/23/21,pubmed,0,3,mathematical model,0.001538139,0.001538136,0.001538129,0.992309229,0.001538122,0.001538244,Epidemiology,0.35697848,FALSE,76.66666667,0.792689715,19,0.471367407,0,0.403234768,,,0.555763963
746,EMR2vec: Bridging the Gap Between Patient Data and Clinical Trial.,Comput Ind Eng,33746344,3/23/21,pubmed,0,4,"machine learning, neural network, dataset",0.265257254,0.001371382,0.729257373,0.001371338,0.001371288,0.001371364,Imaging,0.08715582,FALSE,40.25,0.541715629,6.25,0.283315494,0,0.403234768,,,0.409421963
747,"Structural investigations, quantum mechanical studies on proton and metal affinity and biological activity predictions of selpercatinib.",J Mol Liq,33746318,3/23/21,pubmed,0,5,computational,0.859328341,0.001653103,0.087280859,0.001653152,0.001653054,0.048431491,Drug discovery,0.83348733,TRUE,32.8,0.464221659,0,0.055525823,0,0.403234768,,,0.30766075
748,"In silico Nigellidine (N. sativa) bind to viral spike/active-sites of ACE1/2, AT1/2 to prevent COVID-19 induced vaso-tumult/vascular-damage/comorbidity.",Vascul Pharmacol,33746069,3/23/21,pubmed,0,3,in silico,0.869890992,0.001112635,0.001112627,0.001112651,0.001112636,0.125658458,Drug discovery,0.6711255,TRUE,43,0.567629414,17.33333333,0.45424137,0,0.403234768,,,0.475035184
749,The prevalence of malnutrition and analysis of related factors among adult patients with the Coronavirus Disease 2019 (COVID 19) in a tertiary government hospital: The MalnutriCoV study.,Clin Nutr ESPEN,33745628,3/23/21,pubmed,0,5,logistic regression,0.001511817,0.001511856,0.001511845,0.001511887,0.126211421,0.867741175,Clinics,0.5754305,TRUE,7.8,0.113550622,1,0.122023013,0,0.403234768,,,0.212936134
750,Are history of dietary intake and food habits of patients with clinical symptoms of COVID 19 different from healthy controls? A case-control study.,Clin Nutr ESPEN,33745593,3/23/21,pubmed,0,8,logistic regression,0.002130665,0.032843972,0.002130703,0.002130747,0.88930397,0.071459942,Healthcare,0.9752519,TRUE,12.25,0.185107304,1.375,0.132927482,0,0.403234768,,,0.240423185
751,"Health and Institutional Risk Factors of COVID-19 Mortality in Mexico, 2020.",Am J Prev Med,33745520,3/23/21,pubmed,0,2,bayes,0.001511924,0.001511901,0.001511842,0.422116749,0.001511855,0.571835729,Clinics,0.2134921,FALSE,5,0.070752675,1,0.122023013,1,0.537564047,,,0.243446578
752,The Psychological Impact of the COVID-19 Pandemic Affected Decision-Making Processes.,Span J Psychol,33745483,3/23/21,pubmed,0,2,machine learning,0.002422325,0.002422317,0.335010245,0.002422356,0.655300472,0.002422284,Healthcare,0.94764966,TRUE,8.5,0.126662131,2,0.164302917,0,0.403234768,,,0.231399939
753,Harnessing the natural pool of polyketide and non-ribosomal peptide family: A route map towards novel drug development.,Curr Mol Pharmacol,33745440,3/23/21,pubmed,0,7,in-silico,0.665352467,0.042161038,0.080972524,0.209039799,0.001237093,0.001237078,Drug discovery,0.82629156,TRUE,8.142857143,0.119426062,3.571428571,0.21414236,0,0.403234768,,,0.245601063
754,The emerging role of probiotics as a mitigation strategy against coronavirus disease 2019 (COVID-19).,Arch Virol,33745067,3/22/21,pubmed,0,15,microbiom,0.678710021,0.151924903,0.001943642,0.163534284,0.001943556,0.001943595,Drug discovery,0.8250996,TRUE,21.86666667,0.322840002,,,0,0.403234768,,,0.363037385
755,THE YEAR IN BASIC VASCULAR BIOLOGY RESEARCH: FROM MECHANORECEPTORS AND NETS TO SMARTPHONE DATA AND OMICS.,Cardiovasc Res,33744925,3/22/21,pubmed,0,7,"bioinformatic,  omics",0.302108867,0.003101711,0.003101541,0.513724563,0.003101524,0.174861793,Epidemiology,0.621424,TRUE,227.4285714,0.980518276,363.2857143,0.967888681,0,0.403234768,,,0.783880575
756,"TMBIM6, a potential virus target protein identified by integrated multiomics data analysis in SARS-CoV-2-infected host cells.",Aging (Albany NY),33744846,3/22/21,pubmed,0,8,"multiom, network analysis",0.907414721,0.084810789,0.001943617,0.00194369,0.00194367,0.001943513,Drug discovery,0.8124509,TRUE,0.375,0.007236069,,,0,0.403234768,,,0.205235418
757,COVID-19 in multiple sclerosis and neuromyelitis optica spectrum disorder patients in Latin America: COVID-19 in MS and NMOSD patients in LATAM.,Mult Scler Relat Disord,33744758,3/22/21,pubmed,0,54,logistic regression,0.001371249,0.001371326,0.001371257,0.034103739,0.001371313,0.960411116,Clinics,0.7374078,TRUE,17.37037037,0.261426186,6.962962963,0.294487557,0,0.403234768,,,0.31971617
758,Statistical methods for comparing test positivity rates between countries: Which method should be used and why?,Methods,33744396,3/22/21,pubmed,0,2,bayes,0.001622739,0.001622779,0.001622785,0.845807675,0.001622809,0.147701213,Epidemiology,0.23765594,FALSE,123,0.911806543,119,0.850280974,0,0.403234768,,,0.721774095
759,0,J Mol Biol,33744314,3/22/21,pubmed,0,2,sequence alignment,0.77467452,0.2195729,0.001438113,0.001438167,0.001438168,0.001438133,Drug discovery,0.43453017,FALSE,329.5,0.992516544,189.5,0.911760771,0,0.403234768,,,0.769170694
760,Findings from an online survey on the mental health effects of COVID-19 on Canadians with disabilities and chronic health conditions.,Disabil Health J,33744158,3/22/21,pubmed,0,4,logistic regression,0.001350361,0.001350357,0.001350477,0.183906971,0.81069148,0.001350354,Healthcare,0.28756976,FALSE,21.25,0.314614386,6.5,0.288132192,0,0.403234768,,,0.335327115
761,Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study.,Lancet Infect Dis,33743847,3/22/21,pubmed,0,5,mathematical model,0.00091673,0.000916687,0.000916673,0.65954616,0.311996731,0.025707019,Epidemiology,0.10972795,FALSE,89.4,0.840806482,211.8,0.922999732,5,0.739490092,,,0.834432102
762,Indels in SARS-CoV-2 occur at template-switching hotspots.,BioData Min,33743803,3/22/21,pubmed,0,10,transcriptom,0.001220093,0.993899877,0.001219995,0.001220023,0.001220001,0.001220011,Genomics,0.24002674,FALSE,30.9,0.442204218,,,0,0.403234768,,,0.422719493
763,The WSES: what do we see in the future?,World J Emerg Surg,33743715,3/22/21,pubmed,0,9,artificial intelligence,0.004775168,0.004775204,0.358645343,0.004775626,0.622253479,0.004775179,Healthcare,0.74329185,TRUE,209.6666667,0.975384996,171.2222222,0.900321113,0,0.403234768,,,0.759646959
764,Evaluation of lung involvement in COVID-19 pneumonia based on ultrasound images.,Biomed Eng Online,33743707,3/22/21,pubmed,0,13,deep learning,0.001861714,0.001861688,0.818557582,0.001861874,0.001861698,0.173995444,Clinics,0.76887953,TRUE,0.230769231,0.006679448,,,0,0.403234768,,,0.204957108
765,Workplace factors associated with mental health of healthcare workers during the COVID-19 pandemic: an international cross-sectional study.,BMC Health Serv Res,33743674,3/22/21,pubmed,0,8,logistic regression,0.000966747,0.00096677,0.000966774,0.086255288,0.734769868,0.176074553,Healthcare,0.5304134,TRUE,55,0.668686994,,,0,0.403234768,,,0.535960881
766,Combating COVID-19: The role of drug repurposing and medicinal plants.,J Infect Public Health,33743371,3/21/21,pubmed,0,2,in silico,0.759458551,0.000977477,0.000977461,0.193435292,0.044173732,0.000977487,Drug discovery,0.95340514,TRUE,157.5,0.949718597,91,0.803987155,1,0.537564047,,,0.7637566
767,Modelization of Covid-19 pandemic spreading: A machine learning forecasting with relaxation scenarios of countermeasures.,J Infect Public Health,33743367,3/21/21,pubmed,0,4,"machine learning, mathematical model",0.002996479,0.002996478,0.002996571,0.985017611,0.002996398,0.002996464,Epidemiology,0.7527531,TRUE,15.25,0.229884347,8.75,0.332218357,0,0.403234768,,,0.321779157
768,COVID-19 in Patients with Multiple Sclerosis: Associations with Disease-Modifying Therapies.,CNS Drugs,33743151,3/21/21,pubmed,0,14,logistic regression,0.251720854,0.000916716,0.000916727,0.000916741,0.000916736,0.744612226,Clinics,0.1885364,FALSE,68.28571429,0.755396128,,,0,0.403234768,,,0.579315448
769,Host-dependent molecular factors mediating SARS-CoV-2 infection to gain clinical insights for developing effective targeted therapy.,Mol Genet Genomics,33743061,3/21/21,pubmed,0,6,artificial intelligence,0.189761335,0.001156294,0.423095275,0.001156314,0.032311413,0.35251937,Imaging,0.747267,TRUE,21,0.312016822,4.333333333,0.237958255,0,0.403234768,,,0.317736615
770,[Analysis of Changes and Factors Influencing Air Pollutants in the Beijing-Tianjin-Hebei Region During the COVID-19 Pandemic].,Huan Jing Ke Xue,33742918,3/21/21,pubmed,0,6,dataset,0.001291234,0.001291238,0.001291224,0.993543789,0.001291272,0.001291243,Epidemiology,0.9239806,TRUE,0.833333333,0.008287464,,,0,0.403234768,,,0.205761116
771,The transition to teletherapy in marriage and family therapy training settings during COVID-19: What do the data tell us?,J Marital Fam Ther,33742728,3/21/21,pubmed,0,8,logistic regression,0.388783585,0.003101543,0.061255679,0.003101774,0.540655707,0.003101712,Healthcare,0.5804447,TRUE,9.25,0.137918239,,,0,0.403234768,,,0.270576503
772,"Mask up to keep it up": Preliminary evidence of the association between erectile dysfunction and COVID-19.,Andrology,33742540,3/21/21,pubmed,0,7,logistic regression,0.001371354,0.001371295,0.001371311,0.070469794,0.489733957,0.43568229,Healthcare,0.82791597,TRUE,115.4285714,0.899622735,75.42857143,0.76746053,0,0.403234768,,,0.690106011
773,Association between electrocardiographic features and mortality in COVID-19 patients.,Ann Noninvasive Electrocardiol,33742501,3/21/21,pubmed,0,12,logistic regression,0.173074424,0.001684559,0.00168481,0.001684573,0.037151022,0.784720612,Clinics,0.9004937,TRUE,17.25,0.260189251,,,0,0.403234768,,,0.331712009
774,Multicentre cohort study of acute cholecystitis management during the COVID-19 pandemic.,Eur J Trauma Emerg Surg,33742223,3/21/21,pubmed,0,25,logistic regression,0.000936064,0.00093607,0.000936071,0.000936093,0.000936099,0.995319603,Clinics,0.5357501,TRUE,5.24,0.07217515,0.6,0.09011239,0,0.403234768,,,0.188507436
775,Dietary changes and anxiety during the coronavirus pandemic: a multinational survey.,Eur J Clin Nutr,33742156,3/21/21,pubmed,0,4,logistic regression,0.001486411,0.001486431,0.001486417,0.001486443,0.936255191,0.057799107,Healthcare,0.63881564,TRUE,98,0.864741171,69.5,0.752006957,0,0.403234768,,,0.673327632
776,"Clinical characteristics and related risk factors of disease severity in 101 COVID-19 patients hospitalized in Wuhan, China.",Acta Pharmacol Sin,33742107,3/21/21,pubmed,0,7,logistic regression,0.001141331,0.001141341,0.001141488,0.05233134,0.001141336,0.943103163,Clinics,0.98391414,TRUE,3,0.037293586,,,0,0.403234768,,,0.220264177
777,Functional profiling of COVID-19 respiratory tract microbiomes.,Sci Rep,33742096,3/21/21,pubmed,0,4,"sequencing, transcriptom, microbiom, metatranscriptom",0.52056179,0.411598213,0.001272691,0.001272665,0.001272652,0.06402199,Drug discovery,0.8837071,TRUE,24.75,0.364710248,5,0.257024351,0,0.403234768,,,0.341656456
778,Combining initial chest CT with clinical variables in differentiating coronavirus disease 2019 (COVID-19) pneumonia from influenza pneumonia.,Sci Rep,33742041,3/21/21,pubmed,0,7,logistic regression,0.001171564,0.001171601,0.713451569,0.00117164,0.001171589,0.281862037,Imaging,0.7101463,TRUE,9.857142857,0.147875564,,,0,0.403234768,,,0.275555166
779,Favipiravir antiviral efficacy against SARS-CoV-2 in a hamster model.,Nat Commun,33741945,3/21/21,pubmed,0,14,genomes,0.789890823,0.144858615,0.058534646,0.002238603,0.002238639,0.002238673,Drug discovery,0.50830686,TRUE,84.07142857,0.821943225,147.8571429,0.881656409,0,0.403234768,,,0.702278134
780,"Substance use, mental disorders and COVID-19: a volatile mix.",Curr Opin Psychiatry,33741762,3/21/21,pubmed,0,3,digital health,0.001565429,0.001565327,0.001565424,0.54811528,0.445622968,0.001565573,Epidemiology,0.78873813,TRUE,174,0.961222092,240,0.9339711,0,0.403234768,,,0.766142653
781,Filling in the Gaps in Metformin Biodegradation: A New Enzyme and a Metabolic Pathway for Guanylurea.,Appl Environ Microbiol,33741630,3/21/21,pubmed,0,5,"bioinformatic, sequencing, metagenom, microbiom",0.469380753,0.501165567,0.001059417,0.001059413,0.001059382,0.026275468,Genomics,0.7944294,TRUE,139.6,0.933081823,171.2,0.900254215,0,0.403234768,,,0.745523602
782,Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app.,Sci Adv,33741586,3/21/21,pubmed,0,33,dataset,0.033057374,0.002238595,0.315631884,0.002238613,0.087742533,0.559091001,Clinics,0.6747757,TRUE,139.9393939,0.933452904,,,1,0.537564047,,,0.735508475
783,Artificial Intelligence: the unstoppable revolution in ophthalmology.,Surv Ophthalmol,33741420,3/21/21,pubmed,0,2,artificial intelligence,0.001565341,0.001565334,0.466493706,0.527244898,0.001565341,0.00156538,Epidemiology,0.77931875,TRUE,4.5,0.061784897,0,0.055525823,0,0.403234768,,,0.173515162
784,"Sequence Analysis of 20,453 Severe Acute Respiratory Syndrome Coronavirus 2 Genomes from the Houston Metropolitan Area Identifies the Emergence and Widespread Distribution of Multiple Isolates of All Major Variants of Concern.",Am J Pathol,33741335,3/21/21,pubmed,0,15,"sequencing, genomes",0.001272754,0.911209042,0.001272691,0.001272723,0.001272715,0.083700076,Genomics,0.20062515,FALSE,70.4,0.765724535,,,0,0.403234768,,,0.584479651
785,Does there exist an obesity paradox in COVID-19? Insights of the international HOPE-COVID-19-registry.,Obes Res Clin Pract,33741308,3/21/21,pubmed,0,25,logistic regression,0.001593647,0.001593477,0.001593501,0.001593518,0.001593557,0.9920323,Clinics,0.84714913,TRUE,57.04,0.682107737,16.04,0.437516725,0,0.403234768,,,0.507619743
786,Quantification of COVID-19 Opacities on Chest CT - Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients.,Acad Radiol,33741210,3/21/21,pubmed,0,12,artificial intelligence,0.000946111,0.00094609,0.316835263,0.000946101,0.000946085,0.67938035,Clinics,0.84633553,TRUE,35.41666667,0.490939452,1.75,0.148381054,0,0.403234768,,,0.347518425
787,"A study of ethnic, gender and educational differences in attitudes toward COVID-19 vaccines in Israel - implications for vaccination implementation policies.",Isr J Health Policy Res,33741063,3/21/21,pubmed,0,4,logistic regression,0.001126802,0.001126835,0.001126793,0.001126861,0.994365905,0.001126803,Healthcare,0.6177856,TRUE,1.25,0.013173356,,,0,0.403234768,,,0.208204062
788,"Association of Symptoms of Attention Deficit and Hyperactivity with Problematic Internet Use among University Students in Wuhan, China During the COVID-19 Pandemic.",J Affect Disord,33740639,3/20/21,pubmed,0,26,logistic regression,0.001392882,0.00139284,0.025884732,0.001392889,0.968543729,0.001392928,Healthcare,0.17958075,FALSE,65.30769231,0.735976251,,,0,0.403234768,,,0.569605509
789,Effects of the COVID-19 pandemic on out-of-hospital cardiac arrest care in Detroit.,Am J Emerg Med,33740572,3/20/21,pubmed,0,9,logistic regression,0.002080626,0.00208067,0.078261058,0.133933218,0.422712573,0.360931855,Healthcare,0.9784303,TRUE,42.77777778,0.564908158,40.33333333,0.635469628,0,0.403234768,,,0.534537518
790,Which species does the virus like most: Binding modes study between SARS-CoV-2 S protein and ACE2 receptor.,J Mol Graph Model,33740562,3/20/21,pubmed,0,7,molecular dynamics simulation,0.825389032,0.143129193,0.001565309,0.001565366,0.026785786,0.001565315,Drug discovery,0.9065914,TRUE,59.71428571,0.701032841,88.57142857,0.79810008,0,0.403234768,,,0.634122563
791,Active learning-based STEM education for in-person and online learning.,Cell,33740446,3/20/21,pubmed,0,4,active learning,0.005047827,0.005047596,0.338980953,0.005047958,0.640828203,0.005047463,Healthcare,0.57932603,TRUE,77.66666667,0.797266374,237.3333333,0.932967621,0,0.403234768,,,0.711156254
792,"SARS-CoV-2 infection and transmission in primary schools in England in June-December, 2020 (sKIDs): an active, prospective surveillance study.",Lancet Child Adolesc Health,33740430,3/20/21,pubmed,0,25,logistic regression,0.000830631,0.095899895,0.00083063,0.000830694,0.824759172,0.076848978,Healthcare,0.59406567,TRUE,99.56,0.867895355,,,1,0.537564047,,,0.702729701
793,Lung Epithelial Cell Transcriptional Regulation as a Factor in COVID-19 Associated Coagulopathies.,Am J Respir Cell Mol Biol,33740387,3/20/21,pubmed,0,4,"sequencing, transcriptom, dataset",0.824535837,0.000830712,0.000830665,0.000830681,0.000830673,0.172141432,Drug discovery,0.7219959,TRUE,127,0.917125363,,,0,0.403234768,,,0.660180065
794,A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients.,Eur Radiol,33740092,3/20/21,pubmed,0,10,artificial intelligence,0.001046809,0.001046823,0.603674049,0.001046866,0.001046849,0.392138605,Imaging,0.90440947,TRUE,5.1,0.071061909,,,0,0.403234768,,,0.237148338
795,Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19.,PLoS One,33740030,3/20/21,pubmed,0,5,"predictive model, logistic regression, prediction model, dataset",0.001272688,0.001272657,0.466002674,0.001272705,0.001272677,0.528906599,Clinics,0.4356684,FALSE,21.4,0.316469788,8.2,0.322785657,0,0.403234768,,,0.347496737
796,SARS-CoV-2 outbreak in a tri-national urban area is dominated by a B.1 lineage variant linked to a mass gathering event.,PLoS Pathog,33740028,3/20/21,pubmed,0,22,genomes,0.000846523,0.829380817,0.00084653,0.077900429,0.038512485,0.052513216,Genomics,0.19753963,FALSE,110.9090909,0.891582658,94.22727273,0.811145304,0,0.403234768,,,0.701987576
797,"Understanding the clinical and demographic characteristics of second coronavirus spike in 192 patients in Tehran, Iran: A retrospective study.",PLoS One,33739987,3/20/21,pubmed,0,12,logistic regression,0.051196195,0.001717307,0.001717206,0.510753747,0.18418782,0.250427725,Epidemiology,0.8526603,TRUE,4.25,0.056527924,1,0.122023013,0,0.403234768,,,0.193928568
798,Lung Lesion Localization of COVID-19 from Chest CT Image: A Novel Weakly Supervised Learning Method.,IEEE J Biomed Health Inform,33739926,3/20/21,pubmed,0,4,"supervised learning, artificial intelligence, classifier, adversarial network, dataset",0.000830632,0.000830632,0.921752453,0.000830644,0.000830643,0.074924996,Imaging,0.020240963,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
799,Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.,Korean J Radiol,33739635,3/20/21,pubmed,0,22,"machine learning, radiom, prediction model",0.000822916,0.000822914,0.427458696,0.000822917,0.000822913,0.569249646,Clinics,0.7885831,TRUE,21.54545455,0.31832519,,,0,0.403234768,,,0.360779979
800,The impact of the COVID-19 pandemic on the mental health and daily life of adults with behavioral health disorders.,Transl Behav Med,33739399,3/20/21,pubmed,0,7,logistic regression,0.001438148,0.00143812,0.001438282,0.001438213,0.97307487,0.021172366,Healthcare,0.8279811,TRUE,28,0.408312202,,,0,0.403234768,,,0.405773485
801,"Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study.",J Med Internet Res,33739287,3/20/21,pubmed,0,3,dataset,0.001085379,0.129499281,0.130979582,0.736264878,0.00108545,0.00108543,Epidemiology,0.037852615,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
802,National operating volume for primary hip and knee arthroplasty in the COVID-19 era: a study utilizing the Scottish arthroplasty project dataset.,Bone Jt Open,33739125,3/20/21,pubmed,0,5,dataset,0.001622701,0.001622702,0.001622842,0.741205786,0.001622786,0.252303183,Epidemiology,0.25521186,FALSE,27,0.3960047,22.2,0.504816698,0,0.403234768,,,0.434685389
803,Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data.,Med Biol Eng Comput,33738639,3/20/21,pubmed,0,6,"deep learning, neural network, classifier, transfer learning, dataset",0.001203418,0.001203416,0.993982675,0.001203452,0.001203613,0.001203426,Imaging,0.69600856,TRUE,207.6666667,0.974642835,,,4,0.707574542,,,0.841108688
804,Clinical characteristics of SARS-CoV-2 by re-infection vs. reactivation: a case series from Iran.,Eur J Clin Microbiol Infect Dis,33738620,3/20/21,pubmed,0,9,sequencing,0.00153813,0.53715375,0.104312272,0.167508855,0.001538221,0.18794877,Genomics,0.32175595,FALSE,61.11111111,0.709505845,20.66666667,0.487958255,2,0.618927094,,,0.605463731
805,AI detection of mild COVID-19 pneumonia from chest CT scans.,Eur Radiol,33738595,3/20/21,pubmed,0,15,"deep learning, artificial intelligence, dataset",0.001112609,0.001112634,0.785206882,0.001112635,0.001112636,0.210342605,Imaging,0.945791,TRUE,1.466666667,0.013977364,,,0,0.403234768,,,0.208606066
806,Assessment of Quarantine Understanding and Adherence to Lockdown Measures During the COVID-19 Pandemic in Palestine: Community Experience and Evidence for Action.,Front Public Health,33738274,3/20/21,pubmed,0,4,logistic regression,0.000966768,0.000966768,0.000966762,0.420946935,0.575185995,0.000966773,Healthcare,0.9833137,TRUE,6.5,0.093512277,1.5,0.138747659,0,0.403234768,,,0.211831568
807,SARS-CoV-2 genomic analyses in cancer patients reveal elevated intrahost genetic diversity.,Virus Evol,33738124,3/20/21,pubmed,0,9,"sequencing, genomes, deep sequencing",0.000793453,0.634574467,0.035931329,0.000793408,0.093940113,0.23396723,Genomics,0.30957356,FALSE,28,0.408312202,27.66666667,0.550909821,1,0.537564047,,,0.49892869
808,Investigation of One Familial Cluster of COVID-19 in Taiwan: Differentiation of Genetic Variation Among Isolates and Implications for Epidemiological Investigation and Surveillance by Genomic Assay.,Infect Drug Resist,33737819,3/20/21,pubmed,0,14,"sequencing, genomes",0.002183208,0.825033668,0.002183294,0.00218337,0.002183432,0.166233028,Genomics,0.55691266,TRUE,38.71428571,0.52650133,,,0,0.403234768,,,0.464868049
809,Perceived Risk of COVID-19 and Related Factors Among University Students in Ethiopia During School Reopening.,Infect Drug Resist,33737817,3/20/21,pubmed,0,4,logistic regression,0.01796818,0.000966766,0.00096679,0.048035266,0.915499608,0.01656339,Healthcare,0.5236475,TRUE,3.5,0.044344115,0.5,0.087101953,0,0.403234768,,,0.178226945
810,0,Drug Des Devel Ther,33737804,3/20/21,pubmed,0,7,"computational, in silico",0.995269443,0.0009461,0.000946145,0.000946088,0.0009461,0.000946124,Drug discovery,0.889904,TRUE,29.14285714,0.420310471,,,2,0.618927094,,,0.519618782
811,Metabolomics analysis reveals a modified amino acid metabolism that correlates with altered oxygen homeostasis in COVID-19 patients.,Sci Rep,33737694,3/20/21,pubmed,0,14,metabolom,0.003214312,0.423326424,0.003214137,0.003214233,0.003214216,0.563816679,Clinics,0.9442687,TRUE,49,0.624281032,22,0.503746321,0,0.403234768,,,0.510420707
812,"Proteomic blood profiling in mild, severe and critical COVID-19 patients.",Sci Rep,33737684,3/20/21,pubmed,0,8,proteom,0.396167291,0.001823408,0.001823406,0.001823428,0.001823529,0.596538938,Clinics,0.4152834,FALSE,539.875,0.998082751,643.625,0.985817501,0,0.403234768,,,0.795711673
813,Computational evidence for nitro derivatives of quinoline and quinoline N-oxide as low-cost alternative for the treatment of SARS-CoV-2 infection.,Sci Rep,33737545,3/20/21,pubmed,0,7,computational,0.711046892,0.085102817,0.002296648,0.164103804,0.035153127,0.002296713,Drug discovery,0.9109676,TRUE,204.2857143,0.973776981,43.57142857,0.651792882,0,0.403234768,,,0.67626821
814,Prioritizing antiviral drugs against SARS-CoV-2 by integrating viral complete genome sequences and drug chemical structures.,Sci Rep,33737523,3/20/21,pubmed,0,9,"genome sequences, prediction model, dataset",0.796060792,0.001593536,0.197564991,0.001593557,0.001593547,0.001593577,Drug discovery,0.48747614,FALSE,5.444444444,0.0750201,,,0,0.403234768,,,0.239127434
815,SARS-CoV-2 genomics: An Indian perspective on sequencing viral variants.,J Biosci,33737495,3/20/21,pubmed,0,5,sequencing,0.001861808,0.903538472,0.001861743,0.089014476,0.001861723,0.001861778,Genomics,0.3518143,FALSE,20.4,0.302863504,3.6,0.21507894,2,0.618927094,,,0.378956513
816,Perfect Match Genomic Landscape strategy: Refinement and customization of reference genomes.,Proc Natl Acad Sci U S A,33737447,3/20/21,pubmed,0,9,genomes,0.001684511,0.65452255,0.338739238,0.001684565,0.001684569,0.001684566,Genomics,0.4985499,FALSE,29.66666667,0.42773208,44.11111111,0.654602622,0,0.403234768,,,0.495189823
817,Evaluating access to health and care services during lockdown by the COVID-19 survey in five UK national longitudinal studies.,BMJ Open,33737441,3/20/21,pubmed,0,9,logistic regression,0.00125459,0.001254609,0.001254586,0.001254638,0.860930948,0.13405063,Healthcare,0.86603034,TRUE,206.3333333,0.974209908,,,1,0.537564047,,,0.755886978
818,Characteristics and outcomes of Hispanic/Latinx patients with coronavirus disease 19 (COVID-19) requiring hospitalization in Rhode Island: a retrospective cohort study.,Ann Epidemiol,33737227,3/20/21,pubmed,0,8,logistic regression,0.001684476,0.001684497,0.001684477,0.001684544,0.315625622,0.677636385,Clinics,0.29328293,FALSE,22,0.326056033,,,0,0.403234768,,,0.3646454
819,Global and local mutations in Bangladeshi SARS-CoV-2 genomes.,Virus Res,33737154,3/20/21,pubmed,0,10,"whole genome, genomes",0.001330045,0.97557566,0.001330019,0.001330079,0.001330086,0.01910411,Genomics,0.406265,FALSE,10.1,0.152390377,2.5,0.180826866,0,0.403234768,,,0.245484004
820,"Serial population-based sero-surveys for COVID-19 in two neighborhoods of Karachi, Pakistan.",Int J Infect Dis,33737137,3/20/21,pubmed,0,19,bayes,0.002562583,0.05964697,0.002562682,0.511735007,0.352343351,0.071149407,Epidemiology,0.5661336,TRUE,23.68421053,0.349928876,,,0,0.403234768,,,0.376581822
821,Genomic surveillance of SARS-CoV-2 in the Republic of Congo.,Int J Infect Dis,33737129,3/20/21,pubmed,0,11,"sequencing, whole-genome, genomes",0.001943478,0.956760846,0.035465219,0.001943516,0.001943503,0.001943438,Genomics,0.45843312,FALSE,102,0.873523409,124.4545455,0.857104629,0,0.403234768,,,0.711287602
822,Epidemiological transcriptomic data supports BCG protection in viral diseases including COVID-19.,Gene,33737124,3/20/21,pubmed,0,1,transcriptom,0.675919568,0.002080662,0.09694246,0.220896096,0.002080668,0.002080546,Drug discovery,0.3331735,FALSE,74,0.782113922,33,0.593256623,0,0.403234768,,,0.592868437
823,Phytochemicals against SARS-CoV as potential drug leads.,Biomed J,33736953,3/20/21,pubmed,0,3,computational,0.882251491,0.11113618,0.00165309,0.001653133,0.00165308,0.001653027,Drug discovery,0.9329935,TRUE,36,0.498299215,8.333333333,0.325662296,2,0.618927094,,,0.480962868
824,Serum ferritin at admission in hospitalized COVID-19 patients as a predictor of mortality.,Braz J Infect Dis,33736948,3/20/21,pubmed,0,10,logistic regression,0.00171735,0.001717312,0.001717251,0.001717313,0.001717218,0.991413556,Clinics,0.5951555,TRUE,5.5,0.077246583,0.9,0.104361788,0,0.403234768,,,0.194947713
825,Importance of COVID-19 vaccine efficacy in older age groups.,Vaccine,33736921,3/20/21,pubmed,0,8,mathematical model,0.051604626,0.000966762,0.000966764,0.496081487,0.373280533,0.077099827,Epidemiology,0.087085396,FALSE,51.125,0.639000557,40.25,0.634934439,0,0.403234768,,,0.559056588
826,Investigating the effect of COVID-19 dissemination on symptoms of anxiety and depression among university students.,BJPsych Open,33736744,3/20/21,pubmed,0,19,probabilistic,0.001392842,0.001392917,0.001392829,0.001392979,0.993035513,0.00139292,Healthcare,0.8904824,TRUE,67.94736842,0.753169646,,,0,0.403234768,,,0.578202207
827,Clinical illness with viable SARS-CoV-2 virus presenting 72 days after infection in an immunocompromised patient.,Infect Control Hosp Epidemiol,33736721,3/20/21,pubmed,0,17,sequencing,0.002996458,0.437203426,0.002996735,0.002996691,0.313861188,0.239945502,Genomics,0.57885176,TRUE,47.52941176,0.610674748,,,0,0.403234768,,,0.506954758
828,"Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria.",Sci Total Environ,33736334,3/20/21,pubmed,0,5,"neural network, radiom",0.00123707,0.001237075,0.085765377,0.909286257,0.001237126,0.001237096,Epidemiology,0.6474452,TRUE,106.6,0.882181953,185.8,0.909686915,0,0.403234768,,,0.731701212
829,OverCOVID: an integrative web portal for SARS-CoV-2 bioinformatics resources.,J Integr Bioinform,33735949,3/19/21,pubmed,0,5,"bioinformatic, proteom",0.271966496,0.173176087,0.209634598,0.341157294,0.002032781,0.002032743,Epidemiology,0.42171454,FALSE,44,0.578390748,,,0,0.403234768,,,0.490812758
830,SARS-CoV-2 Lineages and Sub-Lineages Circulating Worldwide: A Dynamic Overview.,Chemotherapy,33735881,3/19/21,pubmed,0,11,"sequencing, whole-genome, genomes",0.002296603,0.681616821,0.002296708,0.309196723,0.00229656,0.002296585,Genomics,0.48942885,FALSE,112,0.894180221,76.90909091,0.770671662,0,0.403234768,,,0.689362217
831,Analysis of Three Mutations in Italian Strains of SARS-CoV-2: Implications for Pathogenesis.,Chemotherapy,33735872,3/19/21,pubmed,0,5,"whole genome, genomes",0.065634811,0.727507875,0.002720149,0.085905947,0.002720171,0.115511047,Genomics,0.5158798,TRUE,115,0.899066114,79.6,0.778097404,0,0.403234768,,,0.693466095
832,Life expectancy inequalities in Wales before COVID-19: an exploration of current contributions by age and cause of death and changes between 2002 and 2018.,Public Health,33735693,3/19/21,pubmed,0,13,dataset,0.038934924,0.001220034,0.022980835,0.40105263,0.35959842,0.176213157,Epidemiology,0.70441806,TRUE,23.23076923,0.343991589,39.38461538,0.629917046,0,0.403234768,,,0.459047801
833,B cell-specific XIST complex enforces X-inactivation and restrains atypical B cells.,Cell,33735607,3/19/21,pubmed,0,6,"transcriptom, proteom",0.813919323,0.001684583,0.049115631,0.001684505,0.051872516,0.081723441,Drug discovery,0.6870369,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
834,Early Detection of SARS-CoV-2 and other Infections in Solid Organ Transplant Recipients and Household Members using Wearable Devices.,Transpl Int,33735480,3/19/21,pubmed,0,45,dataset,0.001461903,0.001461934,0.373885475,0.001461971,0.318110939,0.303617778,Healthcare,0.7547095,TRUE,71.06666667,0.769126106,,,0,0.403234768,,,0.586180437
835,Impact of mobility restriction in COVID-19 superspreading events using agent-based model.,PLoS One,33735290,3/19/21,pubmed,0,2,probabilistic,0.12536692,0.001371299,0.001371268,0.869147976,0.00137129,0.001371247,Epidemiology,0.085963786,FALSE,5,0.070752675,1,0.122023013,0,0.403234768,,,0.198670152
836,A comprehensive SARS-CoV-2 genomic analysis identifies potential targets for drug repurposing.,PLoS One,33735271,3/19/21,pubmed,0,7,molecular dynamics simulation,0.418748193,0.544345336,0.001823427,0.001823414,0.031436157,0.001823473,Genomics,0.5575693,TRUE,6.714285714,0.095182139,0.428571429,0.076665775,0,0.403234768,,,0.191694227
837,The association of COVID-19 occurrence and severity with the use of angiotensin converting enzyme inhibitors or angiotensin-II receptor blockers in patients with hypertension.,PLoS One,33735262,3/19/21,pubmed,0,9,logistic regression,0.058660928,0.01830762,0.001072202,0.001072212,0.053244675,0.867642362,Clinics,0.853036,TRUE,96.11111111,0.859051271,,,0,0.403234768,,,0.631143019
838,Haematopoietic ageing through the lens of single-cell technologies.,Dis Model Mech,33735102,3/19/21,pubmed,0,2,"transcriptom, metabolom",0.723785122,0.13416854,0.001350396,0.001350422,0.137995092,0.001350428,Drug discovery,0.6824533,TRUE,52.5,0.650875131,105,0.831482473,0,0.403234768,,,0.628530791
839,Gender disparity in the authorship of biomedical research publications during the COVID-19 pandemic.,J Med Internet Res,33735097,3/19/21,pubmed,0,3,dataset,0.000830637,0.000830648,0.000830656,0.6602803,0.336397096,0.000830663,Epidemiology,0.04368618,FALSE,163,0.953491249,339.6666667,0.963941664,0,0.403234768,,,0.773555894
840,Machine learning models for image-based diagnosis and prognosis of COVID-19: A systematic review.,JMIR Med Inform,33735095,3/19/21,pubmed,0,5,"machine learning, deep learning, neural network, image processing, prediction model",0.001022617,0.001022615,0.787723321,0.001022674,0.001022735,0.208186038,Imaging,0.20952487,FALSE,9.8,0.147071557,1,0.122023013,0,0.403234768,,,0.224109779
841,"Integration of CNN, CBMIR, and Visualization Techniques for Diagnosis and Quantification of Covid-19 Disease.",IEEE J Biomed Health Inform,33735088,3/19/21,pubmed,0,5,"neural network, transfer learning",0.00208058,0.002080615,0.989597069,0.002080557,0.002080565,0.002080614,Imaging,0.41240096,FALSE,29,0.41993939,,,0,0.403234768,,,0.411587079
842,Artificial intelligence for COVID-19: saviour or saboteur?,Lancet Digit Health,33735062,3/19/21,pubmed,0,1,artificial intelligence,0.034961874,0.034961874,0.825190423,0.034962082,0.034961874,0.034961874,Epidemiology,0.62470114,TRUE,25,0.369286907,0,0.055525823,2,0.618927094,,,0.347913275
843,Proposal for a Model of Suicidal Ideation in Medical Students in Colombia: A Simulation Study.,Rev Colomb Psiquiatr,33735048,3/19/21,pubmed,0,3,model fit,0.001943527,0.001943496,0.075025783,0.491385503,0.427758194,0.001943497,Epidemiology,0.44180447,FALSE,2.333333333,0.024800544,0,0.055525823,0,0.403234768,,,0.161187045
844,Comparative effects of viral-transport-medium heat inactivation upon downstream SARS-CoV-2 detection in patient samples.,J Med Microbiol,33734960,3/19/21,pubmed,0,17,genomes,0.000863099,0.406727007,0.436431644,0.15425211,0.000863088,0.000863052,Genomics,0.57175696,TRUE,22.76470588,0.336631826,,,0,0.403234768,,,0.369933297
845,Remote Assessment of Verbal Memory in Youth With Cochlear Implants During the COVID-19 Pandemic.,Am J Speech Lang Pathol,33734823,3/19/21,pubmed,0,7,sequencing,0.001786561,0.096702179,0.162208663,0.250608896,0.486907073,0.001786629,Healthcare,0.5295843,TRUE,102.5714286,0.874512957,,,0,0.403234768,,,0.638873862
846,Macrolides May Prevent Severe Acute Respiratory Syndrome Coronavirus 2 Entry into Cells: A Quantitative Structure Activity Relationship Study and Experimental Validation.,J Chem Inf Model,33734704,3/19/21,pubmed,0,4,in silico,0.992031996,0.001593532,0.001593736,0.001593548,0.001593517,0.001593672,Drug discovery,0.9037544,TRUE,113.25,0.896468551,30.5,0.573187048,0,0.403234768,,,0.624296789
847,Clinical Characteristics and Factors Associated with Poor Outcomes in Hospitalized Patients with Novel Coronavirus Infection COVID-19.,Kardiologiia,33734042,3/19/21,pubmed,0,11,logistic regression,0.0008006,0.000800578,0.120297681,0.019200145,0.000800604,0.858100391,Clinics,0.9912149,TRUE,56.63636364,0.679510174,10.09090909,0.356101151,0,0.403234768,,,0.479615364
848,Antiretroviral drug activity and potential for pre-exposure prophylaxis against COVID-19 and HIV infection.,J Biomol Struct Dyn,33734021,3/19/21,pubmed,0,9,in silico,0.940746665,0.001622773,0.001622729,0.001622774,0.001622803,0.052762256,Drug discovery,0.9471947,TRUE,104.8888889,0.878965922,141,0.875702435,0,0.403234768,,,0.719301042
849,"Concerns, Perceived Impact, Preparedness in Coronavirus Disease (COVID-19) Pandemic and Health Outcomes among Italian Physicians: A Cross-Sectional Study.",J Prim Care Community Health,33733901,3/19/21,pubmed,0,7,logistic regression,0.001415113,0.001415147,0.001415206,0.001415186,0.992924197,0.001415152,Healthcare,0.9895865,TRUE,153.2857143,0.946749954,177.8571429,0.904803318,0,0.403234768,,,0.751596013
850,Computational Mutagenesis at the SARS-CoV-2 Spike Protein/Angiotensin-Converting Enzyme 2 Binding Interface: Comparison with Experimental Evidence.,ACS Nano,33733740,3/19/21,pubmed,0,5,"computational, in silico",0.906881149,0.067213599,0.001392852,0.001392858,0.021726581,0.001392961,Drug discovery,0.48671833,FALSE,145.4,0.939637578,28.8,0.560543216,0,0.403234768,,,0.634471854
851,A self-applied valid scale for rapid tracking of household food insecurity among pregnant women in Sri Lanka.,Matern Child Nutr,33733618,3/19/21,pubmed,0,8,model fit,0.001438158,0.001438233,0.00143822,0.169840891,0.747694599,0.078149899,Healthcare,0.7551974,TRUE,91.75,0.848042551,49.125,0.677682633,0,0.403234768,,,0.64298665
852,Case Report: Utilizing AI and NLP to Assist with Healthcare and Rehabilitation During the COVID-19 Pandemic.,Front Artif Intell,33733232,3/19/21,pubmed,0,7,"machine learning, artificial intelligence",0.027042481,0.001717188,0.286044226,0.319058856,0.302054106,0.064083143,Epidemiology,0.8594668,TRUE,15.71428571,0.236873029,1.571428571,0.139416644,0,0.403234768,,,0.25984148
853,Artificial Intelligence and Telehealth may Provide Early Warning of Epidemics.,Front Artif Intell,33733230,3/19/21,pubmed,0,2,artificial intelligence,0.002183226,0.002183267,0.095786321,0.671182894,0.226481054,0.002183238,Epidemiology,0.06434503,FALSE,33,0.466757375,32,0.585763982,0,0.403234768,,,0.485252042
854,Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development.,Front Artif Intell,33733182,3/19/21,pubmed,0,11,"machine learning, deep learning, computational, artificial intelligence, in silico, dataset",0.656646593,0.001085346,0.339012017,0.001085364,0.001085326,0.001085353,Drug discovery,0.52872926,TRUE,22.72727273,0.336075206,8.545454545,0.329341718,8,0.799987654,,,0.488468192
855,Forecasting and Evaluating Multiple Interventions for COVID-19 Worldwide.,Front Artif Intell,33733158,3/19/21,pubmed,0,6,artificial intelligence,0.001593473,0.001593578,0.001593564,0.992032288,0.001593588,0.001593509,Epidemiology,0.5555243,TRUE,284.1666667,0.9895479,894.5,0.992306663,2,0.618927094,,,0.866927219
856,"Transforming University of California, Irvine medical physiology instruction into the pandemic era.",FASEB Bioadv,33733055,3/19/21,pubmed,0,4,active learning,0.020700121,0.001072291,0.19104437,0.206693673,0.536098364,0.044391182,Healthcare,0.37256694,FALSE,7.25,0.104149917,2.5,0.180826866,0,0.403234768,,,0.22940385
857,Outbreak of SARS-CoV-2: challenge for diagnosis and medical management in patients with left ventricular assist device: a case series.,Eur Heart J Case Rep,33733044,3/19/21,pubmed,0,7,probabilistic,0.001046806,0.001046842,0.092450832,0.0010469,0.041774141,0.86263448,Clinics,0.9932884,TRUE,10.71428571,0.159688292,2.571428571,0.181495852,0,0.403234768,,,0.248139637
858,Retail customers' satisfaction with banks in Greece: A multicriteria analysis of a dataset.,Data Brief,33732828,3/19/21,pubmed,0,6,dataset,0.002639198,0.002639075,0.002639137,0.59076086,0.210734867,0.190586864,Epidemiology,0.66029227,TRUE,13.66666667,0.207310285,1.333333333,0.13252609,0,0.403234768,,,0.247690381
859,Acceptability of a COVID-19 Vaccine Among Healthcare Workers in the Kingdom of Saudi Arabia.,Front Med (Lausanne),33732723,3/19/21,pubmed,0,6,logistic regression,0.001861718,0.001861716,0.001861693,0.001861825,0.990691347,0.001861701,Healthcare,0.8572556,TRUE,17.16666667,0.258890469,5.5,0.267259834,0,0.403234768,,,0.309795024
860,Follow-Up Study of the Chest CT Characteristics of COVID-19 Survivors Seven Months After Recovery.,Front Med (Lausanne),33732719,3/19/21,pubmed,0,4,logistic regression,0.000999615,0.000999539,0.258737264,0.015351569,0.000999564,0.722912448,Clinics,0.9698986,TRUE,21.25,0.314614386,14.75,0.421327268,0,0.403234768,,,0.379725474
861,The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective.,Front Med (Lausanne),33732718,3/19/21,pubmed,0,14,"deep learning, image processing, dataset",0.001237064,0.019869188,0.975182494,0.00123714,0.001237068,0.001237046,Imaging,0.50240946,TRUE,340.9285714,0.993629785,371.3571429,0.968223174,1,0.537564047,,,0.833139002
862,0,Front Chem,33732682,3/19/21,pubmed,0,15,in silico,0.99157734,0.00168454,0.001684538,0.001684551,0.001684518,0.001684514,Drug discovery,0.65054286,TRUE,50.93333333,0.637454388,15.8,0.434104897,0,0.403234768,,,0.491598018
863,Exogenous Coronavirus Interacts With Endogenous Retrotransposon in Human Cells.,Front Cell Infect Microbiol,33732659,3/19/21,pubmed,0,4,transcriptom,0.399930679,0.408789345,0.001486477,0.001486458,0.00148651,0.186820531,Genomics,0.8675354,TRUE,12.75,0.191786752,30.75,0.57499331,2,0.618927094,,,0.461902386
864,Association of HLA Class I Genotypes With Severity of Coronavirus Disease-19.,Front Immunol,33732261,3/19/21,pubmed,0,7,sequencing,0.336226937,0.109213209,0.001203461,0.001203431,0.00120344,0.550949521,Clinics,0.5302236,TRUE,36.28571429,0.500463851,32.57142857,0.589443404,0,0.403234768,,,0.497714007
865,Cognitive Predictors of Precautionary Behavior During the COVID-19 Pandemic.,Front Psychol,33732179,3/19/21,pubmed,0,4,predictive model,0.001237111,0.001237066,0.051309417,0.264539506,0.68043981,0.00123709,Healthcare,0.13035092,FALSE,45.5,0.591440411,38.25,0.622892695,0,0.403234768,,,0.539189291
866,The Epidemiological and Mechanistic Understanding of the Neurological Manifestations of COVID-19: A Comprehensive Meta-Analysis and a Network Medicine Observation.,Front Neurosci,33732102,3/19/21,pubmed,0,6,"sequencing, interactom, dataset",0.382473376,0.080652938,0.001187325,0.001187322,0.001187329,0.53331171,Clinics,0.21142194,FALSE,81.66666667,0.813346527,95,0.813085363,1,0.537564047,,,0.721331979
867,"Integrated Bioinformatic Analysis of SARS-CoV-2 Infection Related Genes ACE2, BSG and TMPRSS2 in Aerodigestive Cancers.",J Inflamm Res,33732005,3/19/21,pubmed,0,6,"bioinformatic, dataset",0.662244655,0.001141373,0.0462201,0.001141411,0.001141374,0.288111087,Drug discovery,0.8362042,TRUE,7.666666667,0.111633373,,,0,0.403234768,,,0.25743407
868,Flipped learning: A shift in graduate nursing education.,J Am Assoc Nurse Pract,33731552,3/19/21,pubmed,0,3,active learning,0.00133009,0.00133003,0.193496925,0.001330066,0.801182829,0.001330059,Healthcare,0.20769042,FALSE,7.333333333,0.105572392,,,0,0.403234768,,,0.25440358
869,Deep Learning Analysis Improves Specificity of SARS-CoV-2 Real Time PCR.,J Clin Microbiol,33731417,3/19/21,pubmed,0,5,deep learning,0.001751194,0.25674156,0.635047896,0.102956902,0.001751207,0.001751242,Imaging,0.2761246,FALSE,27,0.3960047,65.4,0.740232807,0,0.403234768,,,0.513157425
870,"Clustering and longitudinal change in SARS-CoV-2 seroprevalence in school children in the canton of Zurich, Switzerland: prospective cohort study of 55 schools.",BMJ,33731327,3/19/21,pubmed,0,12,logistic regression,0.00087157,0.143193338,0.000871583,0.000871614,0.853320325,0.00087157,Healthcare,0.34669673,FALSE,120.0833333,0.906982497,134.9166667,0.869480867,0,0.403234768,,,0.726566044
871,Virological and immunological features of SARS-CoV-2-infected children who develop neutralizing antibodies.,Cell Rep,33730580,3/18/21,pubmed,0,45,proteom,0.441329729,0.219596994,0.001786534,0.001786689,0.26775262,0.067747435,Drug discovery,0.3189947,FALSE,59.13043478,0.696950956,,,0,0.403234768,,,0.550092862
872,Translational adaptation of human viruses to the tissues they infect.,Cell Rep,33730572,3/18/21,pubmed,0,4,in silico,0.44962041,0.541425587,0.002238495,0.002238507,0.002238444,0.002238557,Genomics,0.35757294,FALSE,41.5,0.553219123,172,0.901190795,0,0.403234768,,,0.619214895
873,Effect of the COVID-19 pandemic on behavioral and psychosocial factors related to oral health in adolescents: a cohort study.,Int J Paediatr Dent,33730371,3/18/21,pubmed,0,8,logistic regression,0.002638991,0.002639167,0.002639044,0.002639231,0.986804486,0.00263908,Healthcare,0.9518266,TRUE,16.5,0.249366071,1.125,0.122959593,0,0.403234768,,,0.258520144
874,CHA2DS2-VASc score and modified CHA2DS2-VASc score can predict mortality and intensive care unit hospitalization in COVID-19 patients.,J Thromb Thrombolysis,33730303,3/18/21,pubmed,0,8,logistic regression,0.001141381,0.029785712,0.038221451,0.001141364,0.001141377,0.928568715,Clinics,0.84026015,TRUE,15.875,0.238481044,6,0.280037463,0,0.403234768,,,0.307251092
875,Medical students' perceptions and motivations during the COVID-19 pandemic.,PLoS One,33730091,3/18/21,pubmed,0,10,logistic regression,0.00087156,0.017553124,0.108422729,0.00087158,0.871409351,0.000871655,Healthcare,0.208267,FALSE,33.4,0.47046818,,,0,0.403234768,,,0.436851474
876,Invasive pulmonary aspergillosis in critically ill patients with severe COVID-19 pneumonia: Results from the prospective AspCOVID-19 study.,PLoS One,33730058,3/18/21,pubmed,0,15,logistic regression,0.001486491,0.035946555,0.048673128,0.00148642,0.001486428,0.910920977,Clinics,0.82248926,TRUE,123.1333333,0.911930237,,,0,0.403234768,,,0.657582502
877,SARS-CoV-2 viral dynamics in non-human primates.,PLoS Comput Biol,33730053,3/18/21,pubmed,0,27,mathematical model,0.224506121,0.305548921,0.001717191,0.299941345,0.001717215,0.166569208,Genomics,0.18584731,FALSE,105.3703704,0.87976993,141.037037,0.875769334,0,0.403234768,,,0.719591344
878,Impacts of anxiety and socioeconomic factors on mental health in the early phases of the COVID-19 pandemic in the general population in Japan: A web-based survey.,PLoS One,33730044,3/18/21,pubmed,0,3,logistic regression,0.001330011,0.00133002,0.001330014,0.001330028,0.993349872,0.001330055,Healthcare,0.902091,TRUE,23.33333333,0.345723298,7.666666667,0.310877709,0,0.403234768,,,0.353278592
879,Space-time covid-19 Bayesian SIR modeling in South Carolina.,PLoS One,33730035,3/18/21,pubmed,0,2,bayes,0.002996424,0.002996488,0.002996469,0.985017592,0.002996517,0.002996509,Epidemiology,0.52471524,TRUE,182,0.965118437,,,0,0.403234768,,,0.684176602
880,"Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changes.",PLoS Biol,33730024,3/18/21,pubmed,0,27,sequencing,0.90501293,0.08941545,0.001392816,0.001392902,0.00139291,0.001392991,Drug discovery,0.46354318,FALSE,58.22222222,0.690766281,,,0,0.403234768,,,0.547000524
881,Immuno-informatics design of a multimeric epitope peptide based vaccine targeting SARS-CoV-2 spike glycoprotein.,PLoS One,33730022,3/18/21,pubmed,0,8,"bioinformatic, in silico",0.956483891,0.037570296,0.001486451,0.00148648,0.001486449,0.001486434,Drug discovery,0.1865823,FALSE,23.5,0.348506401,23.5,0.516791544,0,0.403234768,,,0.422844238
882,Structural analysis of viral ExoN domains reveals polyphyletic hijacking events.,PLoS One,33730017,3/18/21,pubmed,0,9,genomes,0.707200863,0.282837349,0.002490451,0.002490517,0.002490417,0.002490402,Drug discovery,0.43456686,FALSE,26.66666667,0.390995114,17.66666667,0.457586299,0,0.403234768,,,0.41727206
883,Gene variants of coagulation related proteins that interact with SARS-CoV-2.,PLoS Comput Biol,33730015,3/18/21,pubmed,0,10,computational,0.501795069,0.352063434,0.001861784,0.001861744,0.001861763,0.140556206,Drug discovery,0.3717243,FALSE,37.5,0.513946441,89.8,0.801110516,0,0.403234768,,,0.572763908
884,Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification.,Phys Med Biol,33729998,3/18/21,pubmed,0,12,"classifier, radiom",0.001622719,0.001622731,0.949054383,0.001622758,0.001622737,0.044454673,Imaging,0.359052,FALSE,127.25,0.917496444,,,0,0.403234768,,,0.660365606
885,"Perceived threat, negative emotions and self-efficacy in relation to mental health and personal protective behavior among 4,087 Chinese pregnant women during the COVID-19 period: Results from an online survey.",J Med Internet Res,33729983,3/18/21,pubmed,0,6,logistic regression,0.001171543,0.001171545,0.001171533,0.001171586,0.994142216,0.001171577,Healthcare,0.92876697,TRUE,29.83333333,0.428907168,,,0,0.403234768,,,0.416070968
886,SODA: Detecting COVID-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation.,IEEE/ACM Trans Comput Biol Bioinform,33729944,3/18/21,pubmed,0,5,"deep learning, neural network, adversarial network, dataset",0.00104686,0.001046841,0.99476582,0.001046833,0.001046842,0.001046804,Imaging,0.20932537,FALSE,6.333333333,0.089863319,3.666666667,0.217621086,3,0.667819001,,,0.325101135
887,The pharmacist informatician: providing an innovative model of care during the COVID-19 crisis.,Int J Pharm Pract,33729527,3/18/21,pubmed,0,3,digital health,0.001187268,0.00118728,0.23954665,0.369524978,0.25893981,0.129614014,Epidemiology,0.9436864,TRUE,32.66666667,0.462922877,9,0.337904736,0,0.403234768,,,0.401354127
888,Estimating Brazilian states' demands for intensive care unit and clinical hospital beds during the COVID-19 pandemic: development of a predictive model.,Sao Paulo Med J,33729421,3/18/21,pubmed,0,6,predictive model,0.002238493,0.002238433,0.00223857,0.815593268,0.002238554,0.175452683,Epidemiology,0.6484681,TRUE,14,0.213494959,3.333333333,0.206515922,0,0.403234768,,,0.274415216
889,"Immunity, virus evolution, and effectiveness of SARS-CoV-2 vaccines.",Braz J Med Biol Res,33729394,3/18/21,pubmed,0,1,genome sequences,0.577719534,0.369293822,0.001022625,0.001022629,0.001022685,0.049918706,Drug discovery,0.50734526,TRUE,41,0.549013544,26,0.53819909,0,0.403234768,,,0.496815801
890,An analysis of the development of digital health technologies to fight COVID-19 in Brazil and the world.,Cad Saude Publica,33729283,3/18/21,pubmed,0,5,"artificial intelligence, digital health",0.001538146,0.001538156,0.184007059,0.630925982,0.151110394,0.030880262,Epidemiology,0.6384316,TRUE,29.6,0.426495145,4.6,0.244246722,0,0.403234768,,,0.357992212
891,How Many SARS-CoV-2-Infected People Require Hospitalization? Using Random Sample Testing to Better Inform Preparedness Efforts.,J Public Health Manag Pract,33729203,3/18/21,pubmed,0,5,forecasting model,0.001486408,0.001486458,0.00148644,0.388044349,0.001486546,0.606009799,Clinics,0.15732408,FALSE,187.6,0.967654153,222.4,0.927147444,0,0.403234768,,,0.766012122
892,Influence of countries adopted social distancing policy for COVID-19 reduction under the view of the airborne transmission framework.,JMIR Public Health Surveill,33729168,3/18/21,pubmed,0,8,dataset,0.000583959,0.000584024,0.00058397,0.997080051,0.000584017,0.000583979,Epidemiology,0.5044694,TRUE,19,0.285793803,0,0.055525823,1,0.537564047,,,0.292961224
893,Genomic and healthcare dynamics of nosocomial SARS-CoV-2 transmission.,Elife,33729154,3/18/21,pubmed,0,10,"sequencing, genomes",0.00168472,0.486728324,0.001684522,0.255246415,0.104408034,0.150247984,Genomics,0.3319338,FALSE,71.7,0.771538129,,,0,0.403234768,,,0.587386448
894,[Correlation between early inflammation indicators and the severity of coronavirus disease 2019].,Zhonghua Wei Zhong Bing Ji Jiu Yi Xue,33729130,3/18/21,pubmed,0,5,logistic regression,0.000786346,0.000786335,0.066572099,0.000786345,0.000786348,0.930282527,Clinics,0.9804207,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
895,Predicting pulmonary embolism in patients infected with COVID-19 based on D-dimer levels and days between diagnosis of the infection and D-dimer determination.,Monaldi Arch Chest Dis,33728881,3/18/21,pubmed,0,8,predictive model,0.001987175,0.001987153,0.001987236,0.217934762,0.001987188,0.774116486,Clinics,0.75031924,TRUE,62.125,0.715999753,77,0.771139952,0,0.403234768,,,0.630124824
896,An Individualized Model for Predicting COVID-19 Deterioration in Patients with Cancer: A Multicenter Retrospective Study.,Cancer Sci,33728806,3/18/21,pubmed,0,20,prediction model,0.001126824,0.001126802,0.255511112,0.001126829,0.001126808,0.739981625,Clinics,0.8376011,TRUE,8.9,0.130620323,,,0,0.403234768,,,0.266927545
897,Diabetes management by either telemedicine or clinic visit improved glycemic control during COVID-19 pandemic state of emergency in Japan.,J Diabetes Investig,33728760,3/18/21,pubmed,0,10,logistic regression,0.002296513,0.002296516,0.002296561,0.002296616,0.603060245,0.387753549,Healthcare,0.9097103,TRUE,14.2,0.214793741,,,0,0.403234768,,,0.309014254
898,Nutrition care practice patterns for patients with COVID-19 - A preliminary report.,JPEN J Parenter Enteral Nutr,33728687,3/18/21,pubmed,0,8,dataset,0.04064072,0.002422308,0.002422424,0.178437722,0.284759317,0.491317508,Clinics,0.9692241,TRUE,44.5,0.581977859,28.625,0.559339042,0,0.403234768,,,0.514850556
899,Enhanced binding of the N501Y-mutated SARS-CoV-2 spike protein to the human ACE2 receptor: insights from molecular dynamics simulations.,FEBS Lett,33728680,3/18/21,pubmed,0,3,molecular dynamics simulation,0.811660308,0.177783773,0.002638986,0.002638991,0.002638982,0.00263896,Drug discovery,0.32060558,FALSE,60,0.703444864,,,0,0.403234768,,,0.553339816
900,VERSO: A comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples.,Patterns (N Y),33728416,3/18/21,pubmed,0,7,"sequencing, phylogenom, genomes",0.001461914,0.801102471,0.001462024,0.193049863,0.001461872,0.001461856,Genomics,0.24641392,FALSE,111.2857143,0.892201126,90.71428571,0.802314691,0,0.403234768,,,0.699250195
901,Immunoinformatics and Molecular Docking Studies Predicted Potential Multiepitope-Based Peptide Vaccine and Novel Compounds against Novel SARS-CoV-2 through Virtual Screening.,Biomed Res Int,33728324,3/18/21,pubmed,0,12,"virtual screening, genomes, sequence alignment",0.935234116,0.060721991,0.001010923,0.001010997,0.001010996,0.001010977,Drug discovery,0.9368801,TRUE,23.33333333,0.345723298,5.666666667,0.270203372,0,0.403234768,,,0.339720479
902,A new extended rayleigh distribution with applications of COVID-19 data.,Results Phys,33728260,3/18/21,pubmed,0,5,bayes,0.002080654,0.002080623,0.002080639,0.825091063,0.002080733,0.166586288,Epidemiology,0.7037466,TRUE,7.4,0.106190859,0,0.055525823,0,0.403234768,,,0.18831715
903,Stay-At-Home Orders Are Associated With Emergence of Novel SARS-CoV-2 Variants.,Cureus,33728228,3/18/21,pubmed,0,5,genomes,0.000752904,0.468650133,0.000752903,0.387347112,0.141744015,0.000752934,Genomics,0.44516027,FALSE,38.6,0.525078855,6.2,0.282512711,0,0.403234768,,,0.403608778
904,Sociodemographic Predictors of Outcomes in COVID-19: Examining the Impact of Ethnic Disparities in Northern Nevada.,Cureus,33728145,3/18/21,pubmed,0,10,logistic regression,0.002130773,0.024222608,0.002130694,0.002130776,0.37105699,0.598328158,Clinics,0.30456844,FALSE,19.1,0.28604119,1.2,0.126103827,0,0.403234768,,,0.271793261
905,Single Cell Transcriptomic Re-analysis of Immune Cells in Bronchoalveolar Lavage Fluids Reveals the Correlation of B Cell Characteristics and Disease Severity of Patients with SARS-CoV-2 Infection.,Immune Netw,33728103,3/18/21,pubmed,0,3,transcriptom,0.568834326,0.001901742,0.001901717,0.001901711,0.001901773,0.423558731,Drug discovery,0.53549886,TRUE,76.66666667,0.792689715,197.6666667,0.916778164,0,0.403234768,,,0.704234216
906,SARS-CoV-2 Infection of Airway Epithelial Cells.,Immune Netw,33728096,3/18/21,pubmed,0,2,sequencing,0.792723253,0.183396999,0.001141329,0.001141348,0.001141394,0.020455676,Drug discovery,0.4641323,FALSE,46,0.596882924,13,0.400521809,0,0.403234768,,,0.466879834
907,Lopsided Blood-Thinning Drug Increases the Risk of Internal Flow Choking Leading to Shock Wave Generation Causing Asymptomatic Cardiovascular Disease.,Glob Chall,33728053,3/18/21,pubmed,0,8,in silico,0.109123478,0.143529654,0.001622814,0.273560217,0.001622724,0.470541113,Clinics,0.8877893,TRUE,51,0.63875317,9.875,0.351819641,0,0.403234768,,,0.464602526
908,IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification.,Int J Mach Learn Cybern,33727984,3/18/21,pubmed,0,6,"neural network, image processing, dataset",0.018688285,0.022378857,0.955636395,0.001098835,0.001098827,0.001098801,Imaging,0.6833052,TRUE,122.1666667,0.910012988,9.666666667,0.348274017,0,0.403234768,,,0.553840591
909,"Factors associated with myocardial SARS-CoV-2 infection, myocarditis, and cardiac inflammation in patients with COVID-19.",Mod Pathol,33727695,3/18/21,pubmed,0,18,transcriptom,0.06273653,0.001220016,0.001220011,0.001220025,0.001220015,0.932383404,Clinics,0.22738525,FALSE,36.66666667,0.505102356,45.72222222,0.661827669,0,0.403234768,,,0.523388264
910,Genomic investigation of the coronavirus disease-2019 outbreak in the Republic of Korea.,Sci Rep,33727674,3/18/21,pubmed,0,13,"sequencing, whole genome, genome sequences",0.001751155,0.731798998,0.001751618,0.261195759,0.001751235,0.001751234,Genomics,0.62594366,TRUE,10.76923077,0.160368607,,,1,0.537564047,,,0.348966327
911,A national survey assessing public readiness for digital health strategies against COVID-19 within the United Kingdom.,Sci Rep,33727655,3/18/21,pubmed,0,7,digital health,0.001823307,0.001823326,0.314790221,0.328317813,0.351421897,0.001823437,Healthcare,0.73678505,TRUE,370.1428571,0.994990414,147.2857143,0.88112122,1,0.537564047,,,0.80455856
912,Metabolic dysfunction and immunometabolism in COVID-19 pathophysiology and therapeutics.,Int J Obes (Lond),33727631,3/18/21,pubmed,0,6,immunome,0.659371958,0.001751233,0.001751203,0.001751328,0.001751365,0.333622914,Drug discovery,0.91824853,TRUE,37.33333333,0.511596264,14.5,0.418450629,0,0.403234768,,,0.44442722
913,Phylo-geo-network and haplogroup analysis of 611 novel coronavirus (SARS-CoV-2) genomes from India.,Life Sci Alliance,33727249,3/18/21,pubmed,0,2,genomes,0.001901747,0.764991838,0.001901804,0.227401191,0.001901695,0.001901725,Genomics,0.51331776,TRUE,3.5,0.044344115,,,0,0.403234768,,,0.223789441
914,Development of multi-epitope peptide-based vaccines against SARS-CoV-2.,Biomed J,33727051,3/18/21,pubmed,0,5,bioinformatic,0.942395154,0.001350431,0.001350388,0.001350441,0.001350338,0.052203248,Drug discovery,0.5337919,TRUE,120.2,0.907229884,117,0.847002944,4,0.707574542,,,0.820602457
915,Outcomes of renal replacement therapy in the critically ill with COVID-19.,Med Intensiva,33726959,3/18/21,pubmed,0,4,logistic regression,0.001291248,0.001291224,0.019243552,0.001291247,0.001291226,0.975591504,Clinics,0.6300132,TRUE,11,0.167171748,0.5,0.087101953,0,0.403234768,,,0.21916949
916,Data quality methods through remote source data verification auditing: results from the Congenital Cardiac Research Collaborative.,Cardiol Young,33726868,3/18/21,pubmed,0,13,dataset,0.001220105,0.00122005,0.447179578,0.191423764,0.225760334,0.133196169,Healthcare,0.46341434,FALSE,85,0.825035562,25.23076923,0.530505753,0,0.403234768,,,0.586258694
917,Exploration and validation of related hub gene expression during SARS-CoV-2 infection of human bronchial organoids.,Hum Genomics,33726831,3/18/21,pubmed,0,9,genomes,0.8202318,0.093638931,0.001203469,0.033273929,0.001203465,0.050448405,Drug discovery,0.86828333,TRUE,1.888888889,0.017255242,,,0,0.403234768,,,0.210245005
918,A versatile web app for identifying the drivers of COVID-19 epidemics.,J Transl Med,33726787,3/18/21,pubmed,0,4,model fit,0.000677935,0.000677935,0.000677947,0.996610316,0.000677928,0.000677939,Epidemiology,0.2037912,FALSE,99.25,0.867462428,246.5,0.937048435,0,0.403234768,,,0.73591521
919,The prevalence and risk factors for depressive symptoms in frontline nurses under COVID-19 pandemic based on a large cross-sectional study using the propensity score-matched method.,BMC Psychiatry,33726704,3/18/21,pubmed,0,7,logistic regression,0.060564652,0.001565397,0.001565351,0.001565376,0.933173844,0.001565379,Healthcare,0.98878014,TRUE,37.57142857,0.514317521,,,0,0.403234768,,,0.458776144
920,"COVID-19 hospitalization rates rise exponentially with age, inversely proportional to thymic T-cell production.",J R Soc Interface,33726544,3/18/21,pubmed,0,3,"bayes, model fit",0.114090577,0.001085427,0.001085338,0.447556027,0.001085404,0.435097226,Epidemiology,0.083167344,FALSE,31,0.445111015,13.66666667,0.407412363,0,0.403234768,,,0.418586048
921,The Comparison of Lopinavir/Ritonavir Combination and Favipiravir In COVID-19 Treatment.,Turk J Med Sci,33726482,3/18/21,pubmed,0,12,logistic regression,0.096326249,0.0014619,0.001461929,0.001461884,0.001461909,0.897826129,Clinics,0.73643655,TRUE,38.58333333,0.524769621,8.833333333,0.333154937,0,0.403234768,,,0.420386442
922,The prognostic value of IL-8 for the death of severe or critical patients with COVID-19.,Medicine (Baltimore),33725924,3/18/21,pubmed,0,11,correlation analysis,0.001291239,0.001291241,0.001291276,0.001291252,0.001291283,0.99354371,Clinics,0.77089334,TRUE,4.909090909,0.065743089,,,0,0.403234768,,,0.234488928
923,Time on previous renal replacement therapy is associated with worse outcomes of COVID-19 in a regional cohort of kidney transplant and dialysis patients.,Medicine (Baltimore),33725847,3/18/21,pubmed,0,14,logistic regression,0.032449286,0.001901882,0.001901731,0.069946308,0.001901782,0.891899011,Clinics,0.40297616,FALSE,90.35714286,0.843589585,71,0.756556061,0,0.403234768,,,0.667793471
924,An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study.,Medicine (Baltimore),33725830,3/18/21,pubmed,0,5,predictive model,0.001220048,0.001220026,0.001220101,0.917705538,0.00122015,0.077414138,Epidemiology,0.68214786,TRUE,36.2,0.499412456,,,1,0.537564047,,,0.518488252
925,Clinical utility of a computed tomography-based receiver operating characteristic curve model for the diagnosis of COVID-19.,Ann Palliat Med,33725765,3/18/21,pubmed,0,14,logistic regression,0.001187287,0.168203789,0.545270374,0.001187328,0.001187287,0.282963936,Imaging,0.72513044,TRUE,2.928571429,0.031789226,,,0,0.403234768,,,0.217511997
926,Circular RNA-MicroRNA-MRNA interaction predictions in SARS-CoV-2 infection.,J Integr Bioinform,33725751,3/17/21,pubmed,0,2,machine learning,0.868091676,0.002183403,0.123175187,0.002183285,0.002183213,0.002183235,Drug discovery,0.91775733,TRUE,6.5,0.093512277,3,0.199424672,0,0.403234768,,,0.232057239
927,Healthcare indicators associated with COVID-19 death rates in the European Union.,Public Health,33725494,3/17/21,pubmed,0,3,correlation analysis,0.001622707,0.001622755,0.001622754,0.413863095,0.178499577,0.402769112,Epidemiology,0.4186955,FALSE,793,0.998824912,415.3333333,0.972237089,0,0.403234768,,,0.791432256
928,Whole genome analysis of more than 10 000 SARS-CoV-2 virus unveils global genetic diversity and target region of NSP6.,Brief Bioinform,33725111,3/17/21,pubmed,0,6,"whole genome, genomes, sequence alignment",0.000988372,0.995058062,0.000988408,0.000988412,0.000988389,0.000988358,Genomics,0.7024965,TRUE,31.16666667,0.445976869,,,0,0.403234768,,,0.424605818
929,Genetic and structural analyses of ssRNA viruses pave the way for the discovery of novel antiviral pharmacological targets.,Mol Omics,33725065,3/17/21,pubmed,0,1,artificial intelligence,0.366927461,0.202671794,0.146969258,0.279169936,0.002130927,0.002130625,Drug discovery,0.43737105,FALSE,28,0.408312202,1,0.122023013,0,0.403234768,,,0.311189994
930,Variability in digestive and respiratory tract Ace2 expression is associated with the microbiome.,PLoS One,33725024,3/17/21,pubmed,0,5,microbiom,0.586748137,0.234913265,0.001823357,0.001823424,0.001823378,0.172868439,Drug discovery,0.90984136,TRUE,9.4,0.140021028,,,0,0.403234768,,,0.271627898
931,"Knowledge of medical professionals, their practices, and their attitudes toward traditional Chinese medicine for the prevention and treatment of coronavirus disease 2019: A survey in Sichuan, China.",PLoS One,33725021,3/17/21,pubmed,0,9,logistic regression,0.059877965,0.001511877,0.001511896,0.001511879,0.906114906,0.029471478,Healthcare,0.99046624,TRUE,9.888888889,0.148122951,,,0,0.403234768,,,0.275678859
932,Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations.,J Med Internet Res,33724919,3/17/21,pubmed,0,8,"deep learning, artificial intelligence",0.001098835,0.001098823,0.097068479,0.58888989,0.310745155,0.001098818,Epidemiology,0.16226885,FALSE,26.5,0.389325252,,,0,0.403234768,,,0.39628001
933,Systemic Perturbations in Amine and Kynurenine Metabolism Associated with Acute SARS-CoV-2 Infection and Inflammatory Cytokine Responses.,J Proteome Res,33724837,3/17/21,pubmed,0,19,immunome,0.378617857,0.1460457,0.002238449,0.002238523,0.002238729,0.468620742,Clinics,0.8673886,TRUE,96.94736842,0.86096852,,,1,0.537564047,,,0.699266284
934,Adverse Perinatal Outcomes Predicted by Prenatal Maternal Stress Among U.S. Women at the COVID-19 Pandemic Onset.,Ann Behav Med,33724334,3/17/21,pubmed,0,5,logistic regression,0.001415142,0.001415158,0.001415129,0.001415181,0.992924185,0.001415207,Healthcare,0.95850545,TRUE,33.8,0.474117138,15.6,0.431428954,0,0.403234768,,,0.436260286
935,Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data.,JMIR Med Inform,33724200,3/17/21,pubmed,0,6,"logistic regression, lstm",0.000807893,0.000807901,0.000807889,0.835653158,0.161115266,0.000807892,Epidemiology,0.8425756,TRUE,0.333333333,0.007174222,,,0,0.403234768,,,0.205204495
936,CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19.,Radiology,33724065,3/17/21,pubmed,0,28,logistic regression,0.001141349,0.001141326,0.215066544,0.00114134,0.001141322,0.78036812,Clinics,0.680215,TRUE,82.96428571,0.817613953,29.07142857,0.563018464,0,0.403234768,,,0.594622395
937,Author Gender Inequality in Medical Imaging Journals and the COVID-19 Pandemic.,Radiology,33724061,3/17/21,pubmed,0,6,logistic regression,0.001371294,0.001371286,0.060831389,0.330611501,0.60444325,0.00137128,Healthcare,0.92103314,TRUE,19.5,0.29117447,7.166666667,0.301311212,1,0.537564047,,,0.376683243
938,Transmission of SARS-CoV-2 in northern Ghana: insights from whole-genome sequencing.,Arch Virol,33723631,3/17/21,pubmed,0,22,"sequencing, whole-genome, genome sequences",0.001220007,0.944251282,0.001220019,0.001220081,0.001220032,0.050868579,Genomics,0.35224688,FALSE,65.31818182,0.736038098,,,0,0.403234768,,,0.569636433
939,Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study.,Eur J Public Health,33723606,3/17/21,pubmed,0,5,logistic regression,0.00218322,0.002183259,0.032108638,0.535810708,0.425530851,0.002183324,Epidemiology,0.80815256,TRUE,43.8,0.575916878,5.4,0.263914905,0,0.403234768,,,0.414355517
940,Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis.,J Healthc Inform Res,33723525,3/17/21,pubmed,0,5,"machine learning, dataset",0.001171587,0.001171576,0.651104232,0.001171606,0.22821946,0.117161539,Healthcare,0.7746204,TRUE,59,0.696579875,75.2,0.766791544,0,0.403234768,,,0.622202062
941,Simulation and prediction of spread of COVID-19 in The Republic of Serbia by SEIRDS model of disease transmission.,Microb Risk Anal,33723516,3/17/21,pubmed,0,5,mathematical model,0.001291288,0.001291227,0.001291221,0.993543752,0.001291261,0.001291252,Epidemiology,0.12616417,FALSE,49.2,0.625579813,15.6,0.431428954,0,0.403234768,,,0.486747845
942,"Computational approach to decipher cellular interactors and drug targets during co-infection of SARS-CoV-2, Dengue, and Chikungunya virus.",Virusdisease,33723515,3/17/21,pubmed,0,2,"computational, in silico",0.992440836,0.001511831,0.001511816,0.001511817,0.001511867,0.001511833,Drug discovery,0.950243,TRUE,40,0.539860226,13,0.400521809,0,0.403234768,,,0.447872268
943,Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM).,J Big Data,33723498,3/17/21,pubmed,0,1,lstm,0.001622734,0.001622727,0.195737497,0.797771222,0.001622918,0.001622903,Epidemiology,0.09968534,FALSE,115,0.899066114,15,0.42594327,0,0.403234768,,,0.576081384
944,A computational tool for trend analysis and forecast of the COVID-19 pandemic.,Appl Soft Comput,33723487,3/17/21,pubmed,0,4,computational,0.001126817,0.00112681,0.001126817,0.994365762,0.001126918,0.001126876,Epidemiology,0.14697069,FALSE,21.75,0.321603068,5,0.257024351,0,0.403234768,,,0.327287395
945,Mediative fuzzy logic mathematical model: A contradictory management prediction in COVID-19 pandemic.,Appl Soft Comput,33723486,3/17/21,pubmed,0,4,mathematical model,0.002639022,0.002639062,0.241470452,0.703276237,0.002639035,0.047336193,Epidemiology,0.566665,TRUE,68.75,0.757808151,,,0,0.403234768,,,0.580521459
946,"Impact of COVID-19 lockdown on the fine particulate matter concentration levels: Results from Bengaluru megacity, India.",Adv Space Res,33723470,3/17/21,pubmed,0,6,dataset,0.001330082,0.001330079,0.001330081,0.916115625,0.078564074,0.001330058,Epidemiology,0.7591884,TRUE,11,0.167171748,2.666666667,0.185442869,0,0.403234768,,,0.251949795
947,Nowcasting epidemics of novel pathogens: lessons from COVID-19.,Nat Med,33723452,3/17/21,pubmed,0,7,computational,0.002032896,0.148766211,0.002032837,0.843102315,0.002032839,0.002032902,Epidemiology,0.26208797,FALSE,107.2857143,0.883851815,,,0,0.403234768,,,0.643543291
948,Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7.,Nature,33723411,3/17/21,pubmed,0,54,dataset,0.001684569,0.419734551,0.001684508,0.187158534,0.001684616,0.388053222,Genomics,0.2402482,FALSE,158.8333333,0.950398911,211.8333333,0.92313353,17,0.887338725,,,0.920290389
949,Effects of cancer screening restart strategies after COVID-19 disruption.,Br J Cancer,33723386,3/17/21,pubmed,0,9,simulation model,0.297503799,0.001717197,0.001717237,0.478670068,0.001717252,0.218674448,Epidemiology,0.79824054,TRUE,49.77777778,0.630156472,45.44444444,0.6603559,0,0.403234768,,,0.56458238
950,A modeling study to inform screening and testing interventions for the control of SARS-CoV-2 on university campuses.,Sci Rep,33723312,3/17/21,pubmed,0,7,probabilistic,0.001112631,0.001112673,0.018026437,0.560083237,0.418552381,0.001112641,Epidemiology,0.23045203,FALSE,94.28571429,0.855031233,,,0,0.403234768,,,0.629133
951,SARS-CoV-2 hijacks folate and one-carbon metabolism for viral replication.,Nat Commun,33723254,3/17/21,pubmed,0,13,"metabolom, genomes",0.576438764,0.411967688,0.002898313,0.002898438,0.002898332,0.002898466,Drug discovery,0.61852163,TRUE,43.38461538,0.571463912,,,0,0.403234768,,,0.48734934
952,Physiological parameters of mental health predict the emergence of post-traumatic stress symptoms in physicians treating COVID-19 patients.,Transl Psychiatry,33723233,3/17/21,pubmed,0,7,machine learning,0.001220034,0.001220017,0.119637718,0.001220061,0.612327875,0.264374296,Healthcare,0.99744546,TRUE,8.428571429,0.124621189,6.285714286,0.283649987,0,0.403234768,,,0.270501981
953,COVID-19 patient transcriptomic and genomic profiling reveals comorbidity interactions with psychiatric disorders.,Transl Psychiatry,33723208,3/17/21,pubmed,0,4,"transcriptom, genome-wide, whole-genome, dataset",0.390640502,0.115748995,0.001141371,0.001141367,0.224559383,0.266768382,Drug discovery,0.90979147,TRUE,97.75,0.864060857,64.5,0.736887878,0,0.403234768,,,0.668061168
954,SARS-CoV-2 infection of human iPSC-derived cardiac cells reflects cytopathic features in hearts of patients with COVID-19.,Sci Transl Med,33723017,3/17/21,pubmed,0,18,transcriptom,0.572384817,0.115726069,0.002032864,0.002032903,0.002032882,0.305790466,Drug discovery,0.63248736,TRUE,53.77777778,0.659595522,,,0,0.403234768,,,0.531415145
955,Subgenomic RNA identification in SARS-CoV-2 genomic sequencing data.,Genome Res,33722935,3/17/21,pubmed,0,33,"sequencing, genomes",0.098503636,0.895005436,0.001622748,0.00162278,0.001622701,0.0016227,Genomics,0.17466748,FALSE,51.3030303,0.640546725,,,0,0.403234768,,,0.521890746
956,How US law will evaluate artificial intelligence for covid-19.,BMJ,33722811,3/17/21,pubmed,0,5,artificial intelligence,0.034961874,0.034961874,0.825190423,0.034962082,0.034961874,0.034961874,Epidemiology,0.39977106,FALSE,97.2,0.862143608,197.6,0.916711266,0,0.403234768,,,0.727363214
957,Covid-19 driven advances in automation and artificial intelligence risk exacerbating economic inequality.,BMJ,33722806,3/17/21,pubmed,0,2,artificial intelligence,0.01952975,0.019529469,0.636196475,0.285684511,0.01953044,0.019529355,Epidemiology,0.52527374,TRUE,617.5,0.998577525,1069,0.994046026,0,0.403234768,,,0.79861944
958,Causal discovery using compression-complexity measures.,J Biomed Inform,33722730,3/17/21,pubmed,0,2,genome sequences,0.001461902,0.481736351,0.238793642,0.27508435,0.001461919,0.001461835,Genomics,0.07059276,FALSE,50,0.632073721,22.5,0.507492641,1,0.537564047,,,0.55904347
959,Peculiar clinical presentation of COVID-19 and predictors of mortality in the elderly: A multicentre retrospective cohort study.,Int J Infect Dis,33722685,3/17/21,pubmed,0,17,logistic regression,0.001565305,0.00156533,0.001565297,0.001565339,0.047817401,0.945921329,Clinics,0.68867207,TRUE,43.88235294,0.576535345,17.47058824,0.454977254,0,0.403234768,,,0.478249122
960,"Regional Survey in Lombardy, Northern Italy, on Vascular Surgery Intervention Outcomes During The COVID-19 Pandemic.",Eur J Vasc Endovasc Surg,33722483,3/17/21,pubmed,0,71,"logistic regression, dataset",0.001751196,0.001751213,0.001751316,0.001751344,0.115365524,0.877629408,Clinics,0.92993844,TRUE,186.3,0.966973839,75.9,0.768731603,0,0.403234768,,,0.71298007
961,"How does the COVID-19 pandemic impact on population mental health? A network analysis of COVID influences on depression, anxiety and traumatic stress in the UK population.",Psychol Med,33722329,3/17/21,pubmed,0,15,"network analysis, network model",0.033672438,0.119043853,0.027792319,0.001156308,0.817178833,0.001156249,Healthcare,0.25513244,FALSE,59.66666667,0.70066176,,,0,0.403234768,,,0.551948264
962,Evolutionary and genomic analysis of four SARS-CoV-2 isolates circulating in March 2020 in Sri Lanka; Additional evidence on multiple introduction and further transmission.,Epidemiol Infect,33722321,3/17/21,pubmed,0,6,"whole-genome, genome sequences",0.001751175,0.936084636,0.001751178,0.001751265,0.001751211,0.056910535,Genomics,0.325822,FALSE,12.16666667,0.184117756,5.333333333,0.262911426,0,0.403234768,,,0.283421317
963,SARS-CoV-2 vaccine ChAdOx1 nCoV-19 infection of human cell lines reveals low levels of viral backbone gene transcription alongside very high levels of SARS-CoV-2 S glycoprotein gene transcription.,Genome Med,33722288,3/17/21,pubmed,0,8,"sequencing, transcriptom, proteom, phosphoproteom",0.791594117,0.145225082,0.05946952,0.00123712,0.001237115,0.001237046,Drug discovery,0.21033603,FALSE,97.125,0.861834374,102.5,0.826933369,0,0.403234768,,,0.69733417
964,Pregnancy-related anxiety and its associated factors during COVID-19 pandemic in Iranian pregnant women: a web-based cross-sectional study.,BMC Pregnancy Childbirth,33722198,3/17/21,pubmed,0,4,logistic regression,0.001653,0.001652999,0.001653013,0.001653027,0.991734892,0.001653069,Healthcare,0.9887645,TRUE,37.75,0.516296617,16.25,0.438988493,0,0.403234768,,,0.452839959
965,A risk prediction score to identify patients at low risk for COVID-19 infection.,Singapore Med J,33721978,3/17/21,pubmed,0,11,"logistic regression, prediction model",0.00120347,0.109462792,0.144708631,0.050793531,0.001203465,0.69262811,Clinics,0.23932737,FALSE,5.818181818,0.081328468,,,0,0.403234768,,,0.242281618
966,Novel perspectives for SARS-CoV-2 genome browsing.,J Integr Bioinform,33721918,3/16/21,pubmed,0,2,genomes,0.002032791,0.862416216,0.00203277,0.12945267,0.002032802,0.002032752,Genomics,0.36379638,FALSE,39,0.530521368,13.5,0.405539203,0,0.403234768,,,0.446431779
967,Mental health issues among health care workers during the COVID-19 pandemic - A study from India.,Asian J Psychiatr,33721830,3/16/21,pubmed,0,4,logistic regression,0.001538123,0.001538132,0.001538114,0.001538164,0.992309361,0.001538106,Healthcare,0.98909855,TRUE,62,0.715814212,23.75,0.518731603,0,0.403234768,,,0.545926861
968,Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases.,Comput Biol Med,33721736,3/16/21,pubmed,0,2,"bayes, virtual screening, machine learning, deep learning, neural network",0.469372037,0.001126799,0.526120748,0.0011268,0.001126796,0.00112682,Drug discovery,0.92907584,TRUE,52,0.647349867,22.5,0.507492641,0,0.403234768,,,0.519359092
969,"Impact of COVID-19 pandemic on vaccination coverage among children aged 15 to 23 months at Dessie town, Northeast Ethiopia, 2020.",Hum Vaccin Immunother,33721546,3/16/21,pubmed,0,3,logistic regression,0.001717182,0.001717258,0.001717167,0.084833306,0.908297879,0.001717208,Healthcare,0.7797065,TRUE,4.666666667,0.063392912,0,0.055525823,0,0.403234768,,,0.174051168
970,Household- and employment-related risk factors for depressive symptoms during the COVID-19 pandemic.,Can J Public Health,33721268,3/16/21,pubmed,0,6,logistic regression,0.015576796,0.000977452,0.000977441,0.000977476,0.980513355,0.00097748,Healthcare,0.9933306,TRUE,75.66666667,0.789349991,30.33333333,0.572116671,0,0.403234768,,,0.58823381
971,Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients.,Health Care Manag Sci,33721153,3/16/21,pubmed,0,12,machine learning,0.232421603,0.001717203,0.284101378,0.06987018,0.001717264,0.410172373,Clinics,0.8585906,TRUE,115.3333333,0.899375348,272.6666667,0.946414236,0,0.403234768,,,0.749674784
972,Longitudinal multi-omics transition associated with fatality in critically ill COVID-19 patients.,Intensive Care Med Exp,33721144,3/16/21,pubmed,0,21,"multi-omics, network analysis",0.201790488,0.079537465,0.00115626,0.001156308,0.001156305,0.715203174,Clinics,0.92172325,TRUE,35.33333333,0.490259138,,,0,0.403234768,,,0.446746953
973,Strong correlation between prevalence of severe vitamin D deficiency and population mortality rate from COVID-19 in Europe.,Wien Klin Wochenschr,33721102,3/16/21,pubmed,0,2,correlation analysis,0.001684692,0.001684566,0.001684528,0.336044479,0.260125858,0.398775877,Clinics,0.10165304,FALSE,8,0.118683901,1,0.122023013,0,0.403234768,,,0.214647227
974,Modelling the impact of interventions on the progress of the COVID-19 outbreak including age segregation.,PLoS One,33720988,3/16/21,pubmed,0,4,"model simulation, mathematical model",0.00162275,0.001622769,0.001622781,0.991886059,0.001622876,0.001622764,Epidemiology,0.06547096,FALSE,16.75,0.252458408,,,0,0.403234768,,,0.327846588
975,"SARS-CoV-2 (COVID-19 pandemic) in Nigeria: Multi-institutional survey of knowledge, practices and perception amongst undergraduate veterinary medical students.",PLoS One,33720966,3/16/21,pubmed,0,8,logistic regression,0.001272655,0.001272632,0.001272644,0.048970617,0.945938812,0.001272641,Healthcare,0.9126179,TRUE,13,0.197352959,9,0.337904736,0,0.403234768,,,0.312830821
976,Laboratory biomarkers of COVID-19 disease severity and outcome: Findings from a developing country.,PLoS One,33720944,3/16/21,pubmed,0,12,logistic regression,0.001461882,0.001461897,0.001461883,0.001461883,0.001461944,0.992690511,Clinics,0.9256333,TRUE,5.333333333,0.074339786,0.583333333,0.088172331,0,0.403234768,,,0.188582295
977,Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data.,JMIR Public Health Surveill,33720841,3/16/21,pubmed,0,5,machine learning,0.000999508,0.000999529,0.000999579,0.841372994,0.154628862,0.000999529,Epidemiology,0.08017656,FALSE,44,0.578390748,24.2,0.522946214,0,0.403234768,,,0.50152391
978,The perceptions of anatomy teachers for different majors during the COVID-19 pandemic: a national Chinese survey.,Med Educ Online,33720807,3/16/21,pubmed,0,5,active learning,0.001461944,0.001461868,0.225126539,0.001461967,0.769025811,0.00146187,Healthcare,0.9391333,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
979,[Risk factors for the early development of septic shock in patients with severe COVID-19].,Ter Arkh,33720599,3/16/21,pubmed,0,42,logistic regression,0.001272683,0.001272777,0.11973238,0.001272747,0.00127272,0.875176692,Clinics,0.9978645,TRUE,36.83333333,0.506462985,2.595238095,0.181696548,0,0.403234768,,,0.3637981
980,"HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning.",J Med Internet Res,33720028,3/16/21,pubmed,0,7,machine learning,0.002357824,0.002357833,0.167180891,0.6755615,0.150184193,0.002357759,Epidemiology,0.38794157,FALSE,17.14285714,0.258519389,,,0,0.403234768,,,0.330877078
981,Impact of Headache Frequency and Sleep during the COVID-19 Pandemic among Migraine Patients.,Curr Neurovasc Res,33719972,3/16/21,pubmed,0,9,logistic regression,0.001538265,0.001538178,0.00153813,0.00153823,0.714655923,0.279191275,Healthcare,0.85080415,TRUE,50.22222222,0.632568495,,,0,0.403234768,,,0.517901631
982,"Hesitant or Not? The Association of Age, Gender, and Education with Potential Acceptance of a COVID-19 Vaccine: A Country-level Analysis.",J Health Commun,33719881,3/16/21,pubmed,0,8,logistic regression,0.002130918,0.00213084,0.002130772,0.335414189,0.656062566,0.002130716,Healthcare,0.4586618,FALSE,228,0.980580122,121.625,0.853559005,0,0.403234768,,,0.745791298
983,0,J Biomol Struct Dyn,33719855,3/16/21,pubmed,0,4,"virtual screening, molecular dynamics simulation",0.992032453,0.001593508,0.001593503,0.001593538,0.001593539,0.001593459,Drug discovery,0.8442,TRUE,41.25,0.5508071,5.25,0.260971367,0,0.403234768,,,0.405004412
984,Telerehabilitation for pelvic girdle dysfunction in pregnancy during COVID-19 pandemic crisis: A case report.,Physiother Theory Pract,33719853,3/16/21,pubmed,0,1,digital health,0.056233592,0.001823343,0.001823404,0.310925185,0.43876611,0.190428367,Healthcare,0.8957256,TRUE,9,0.135320675,3,0.199424672,0,0.403234768,,,0.245993372
985,COVID-19 and human reproduction: A pandemic that packs a serious punch.,Syst Biol Reprod Med,33719829,3/16/21,pubmed,0,12,sequencing,0.259367165,0.183507777,0.118410939,0.306518759,0.090145068,0.042050292,Epidemiology,0.84068877,TRUE,120.1666667,0.907168038,146.1666667,0.880318437,0,0.403234768,,,0.730240414
986,Disparities in Telemedicine Use for Subspecialty Diabetes Care During COVID-19 Shelter-In-Place Orders.,J Diabetes Sci Technol,33719622,3/16/21,pubmed,0,5,logistic regression,0.001511803,0.001511858,0.001511893,0.0015119,0.548609385,0.44534316,Healthcare,0.83194804,TRUE,20.4,0.302863504,3,0.199424672,0,0.403234768,,,0.301840981
987,Global Open Health Data Cooperatives Cloud in an Era of COVID-19 and Planetary Health.,OMICS,33719569,3/16/21,pubmed,0,6,"sequencing,  omics, phenomics, dataset",0.001943525,0.383740018,0.276799878,0.333629431,0.001943587,0.001943561,Genomics,0.029327303,FALSE,38,0.519327107,235.1666667,0.932231737,0,0.403234768,,,0.618264537
988,Membrane remodeling by SARS-CoV-2 - double-enveloped viral replication.,Fac Rev,33718934,3/16/21,pubmed,0,2,genomes,0.731605263,0.263304099,0.001272642,0.001272697,0.001272655,0.001272644,Drug discovery,0.12825227,FALSE,15.5,0.234028078,11,0.371287129,0,0.403234768,,,0.336183325
989,Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks.,SN Comput Sci,33718884,3/16/21,pubmed,0,5,"deep learning, neural network, dataset",0.001034577,0.001034584,0.994827019,0.00103461,0.001034613,0.001034598,Imaging,0.3162192,FALSE,24.4,0.360442823,113.6,0.843457319,1,0.537564047,,,0.580488063
990,Genetic Characteristics and Phylogeny of 969-bp S Gene Sequence of SARS-CoV-2 from Hawai'i Reveals the Worldwide Emerging P681H Mutation.,Hawaii J Health Soc Welf,33718878,3/16/21,pubmed,0,4,"sequencing, genomes",0.16164457,0.763082651,0.001438156,0.035046206,0.014104689,0.024683728,Genomics,0.7442871,TRUE,95.5,0.85793803,116,0.846133262,0,0.403234768,,,0.702435353
991,Changes to the sebum lipidome upon COVID-19 infection observed via rapid sampling from the skin.,EClinicalMedicine,33718846,3/16/21,pubmed,0,14,lipidom,0.001653074,0.165828068,0.169574477,0.001653155,0.376006464,0.285284763,Healthcare,0.49651694,FALSE,25.21428571,0.370276455,10.28571429,0.358643297,0,0.403234768,,,0.37738484
992,The evolution of chest CT findings from admission to follow-up in 30 moderate to severe adult patients with COVID-19 pneumonia.,Chin J Acad Radiol,33718774,3/16/21,pubmed,0,7,correlation analysis,0.000863037,0.066741783,0.286092717,0.000863089,0.000863095,0.644576279,Clinics,0.7276614,TRUE,107.5714286,0.884779516,28.57142857,0.558603158,0,0.403234768,,,0.615539147
993,Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia.,BJR Open,33718766,3/16/21,pubmed,0,10,"machine learning, deep-learning",0.186258318,0.001046873,0.520789075,0.06941055,0.001046819,0.221448365,Imaging,0.53301936,TRUE,151.8,0.945636712,117.5,0.847872625,0,0.403234768,,,0.732248035
994,Run length encoding based wavelet features for COVID-19 detection in X-rays.,BJR Open,33718765,3/16/21,pubmed,0,1,classifier,0.036500062,0.000966787,0.919579678,0.000966784,0.041019867,0.000966822,Imaging,0.04477772,FALSE,24,0.35574247,7,0.299973241,0,0.403234768,,,0.352983493
995,Dank or not? Analyzing and predicting the popularity of memes on Reddit.,Appl Netw Sci,33718590,3/16/21,pubmed,0,6,machine learning,0.00263914,0.0026393,0.440174109,0.549269286,0.00263915,0.002639014,Epidemiology,0.42128947,FALSE,6.833333333,0.096852001,0,0.055525823,0,0.403234768,,,0.185204197
996,Characterizing network dynamics of online hate communities around the COVID-19 pandemic.,Appl Netw Sci,33718589,3/16/21,pubmed,0,2,computational,0.132090387,0.001392937,0.053618895,0.81011194,0.001392949,0.001392892,Epidemiology,0.2924201,FALSE,343,0.993815326,587,0.983208456,0,0.403234768,,,0.793419516
997,Exploring Changes in Caregiver Burden and Caregiving Intensity due to COVID-19.,Gerontol Geriatr Med,33718523,3/16/21,pubmed,0,4,logistic regression,0.032991109,0.002422322,0.002422279,0.002422373,0.957319572,0.002422346,Healthcare,0.9221917,TRUE,76.25,0.791205393,83.25,0.786259031,0,0.403234768,,,0.660233064
998,Chest computed tomography and alveolar-arterial oxygen gradient as rapid tools to diagnose and triage mildly symptomatic COVID-19 pneumonia patients.,ERJ Open Res,33718488,3/16/21,pubmed,0,8,logistic regression,0.001330029,0.001330023,0.392187311,0.001330033,0.001330056,0.602492547,Clinics,0.7152747,TRUE,19.75,0.293215412,12.75,0.395972705,0,0.403234768,,,0.364140962
999,Single-Cell Transcriptome Analysis Decipher New Potential Regulation Mechanism of ACE2 and NPs Signaling Among Heart Failure Patients Infected With SARS-CoV-2.,Front Cardiovasc Med,33718452,3/16/21,pubmed,0,12,"sequencing, transcriptom",0.477073339,0.01878935,0.001187257,0.001187265,0.001187271,0.500575517,Clinics,0.7425337,TRUE,45.16666667,0.588162533,18.41666667,0.464744447,0,0.403234768,,,0.485380583
1000,Drug Repurposing Strategy (DRS): Emerging Approach to Identify Potential Therapeutics for Treatment of Novel Coronavirus Infection.,Front Mol Biosci,33718434,3/16/21,pubmed,0,6,"virtual screening, computational",0.953507319,0.000793419,0.000793407,0.043318958,0.000793437,0.000793461,Drug discovery,0.85327804,TRUE,84,0.821881378,8.666666667,0.331415574,0,0.403234768,,,0.518843907
1001,A mathematical model of COVID-19 transmission between frontliners and the general public.,Netw Model Anal Health Inform Bioinform,33717797,3/16/21,pubmed,0,9,mathematical model,0.001823331,0.001823335,0.001823316,0.767685714,0.22502097,0.001823333,Epidemiology,0.2141057,FALSE,11,0.167171748,0.666666667,0.096200161,0,0.403234768,,,0.222202226
1002,Wearable devices as a valid support for diagnostic excellence: lessons from a pandemic going forward.,Health Technol (Berl),33717796,3/16/21,pubmed,0,4,artificial intelligence,0.001622737,0.063724809,0.437331659,0.494075091,0.001622795,0.001622908,Epidemiology,0.6449175,TRUE,107.25,0.883728122,19,0.471367407,0,0.403234768,,,0.586110099
1003,Obesity and Disease Severity Among Patients With COVID-19.,Cureus,33717716,3/16/21,pubmed,0,6,logistic regression,0.001350305,0.001350305,0.001350306,0.001350321,0.001350327,0.993248436,Clinics,0.9100902,TRUE,44.5,0.581977859,0.5,0.087101953,0,0.403234768,,,0.357438193
1004,Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia.,J Thorac Dis,33717594,3/16/21,pubmed,0,18,"bayes, machine learning, artificial intelligence, neural network, classifier, logistic regression",0.000956313,0.00095631,0.472101216,0.000956329,0.000956324,0.524073509,Clinics,0.72003293,TRUE,25,0.369286907,,,0,0.403234768,,,0.386260837
1005,Community detection using unsupervised machine learning techniques on COVID-19 dataset.,Soc Netw Anal Min,33717366,3/16/21,pubmed,0,2,"machine learning, dataset",0.002296602,0.002296606,0.002296666,0.988516898,0.002296624,0.002296605,Epidemiology,0.505905,TRUE,24,0.35574247,5,0.257024351,0,0.403234768,,,0.338667196
1006,Value of clinical laboratory test for early prediction of mortality in patients with COVID-19: the BGM score.,J Circ Biomark,33717357,3/16/21,pubmed,0,6,"computational, neural network, logistic regression",0.047428411,0.001141342,0.460932915,0.001141412,0.001141377,0.488214543,Clinics,0.7170431,TRUE,67.66666667,0.75137609,73,0.761974846,0,0.403234768,,,0.638861901
1007,Epigenetic Landscapes of Single-Cell Chromatin Accessibility and Transcriptomic Immune Profiles of T Cells in COVID-19 Patients.,Front Immunol,33717140,3/16/21,pubmed,0,25,"sequencing, transcriptom",0.783705402,0.035939635,0.000846538,0.000846553,0.000846533,0.17781534,Drug discovery,0.60162246,TRUE,12.16,0.183870369,,,0,0.403234768,,,0.293552568
1008,A Proteome-Wide Immunoinformatics Tool to Accelerate T-Cell Epitope Discovery and Vaccine Design in the Context of Emerging Infectious Diseases: An Ethnicity-Oriented Approach.,Front Immunol,33717077,3/16/21,pubmed,0,9,"in silico, proteom",0.587337508,0.257327726,0.058624339,0.095229756,0.000740326,0.000740346,Drug discovery,0.6076673,TRUE,72.88888889,0.777104335,134.6666667,0.869079476,0,0.403234768,,,0.683139526
1009,Psychological State Among the General Chinese Population Before and During the COVID-19 Epidemic: A Network Analysis.,Front Psychiatry,33716811,3/16/21,pubmed,0,5,network analysis,0.001538173,0.001538105,0.001538162,0.351851457,0.587376703,0.0561574,Healthcare,0.9661118,TRUE,13.4,0.202548086,5.4,0.263914905,0,0.403234768,,,0.289899253
1010,Discovering Potential RNA Dependent RNA Polymerase Inhibitors as Prospective Drugs Against COVID-19: An in silico Approach.,Front Pharmacol,33716752,3/16/21,pubmed,0,5,"molecular dynamics simulation, in silico",0.995269504,0.000946104,0.0009461,0.000946114,0.000946092,0.000946085,Drug discovery,0.9601174,TRUE,21.4,0.316469788,1.4,0.133864062,0,0.403234768,,,0.284522873
1011,0,Front Pharmacol,33716737,3/16/21,pubmed,0,26,"neural network, sequencing, in silico, interactom",0.718420782,0.02408954,0.252337946,0.001717168,0.001717155,0.00171741,Drug discovery,0.7922304,TRUE,31.19230769,0.446100563,6.807692308,0.292948889,1,0.537564047,,,0.425537833
1012,The prediction of the lifetime of the new coronavirus in the USA using mathematical models.,Soft comput,33716562,3/16/21,pubmed,0,2,mathematical model,0.001059374,0.043718042,0.001059432,0.809446542,0.052791869,0.091924742,Epidemiology,0.3382751,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
1013,Optimization model design of cross-border e-commerce transportation path under the background of prevention and control of COVID-19 pneumonia.,Soft comput,33716560,3/16/21,pubmed,0,2,optimization model,0.001861762,0.001861768,0.388815759,0.603737231,0.001861746,0.001861734,Epidemiology,0.5384481,TRUE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
1014,"Knowledge, Attitude and Practice Toward COVID-19 Pandemic Among Population Visiting Dessie Health Center for COVID-19 Screening, Northeast Ethiopia.",Infect Drug Resist,33716509,3/16/21,pubmed,0,3,logistic regression,0.001085319,0.001085337,0.001085341,0.086959162,0.908699451,0.001085391,Healthcare,0.7254191,TRUE,8.333333333,0.123693488,1,0.122023013,0,0.403234768,,,0.216317089
1015,Visualizing the knowledge outburst in global research on COVID-19.,Scientometrics,33716352,3/16/21,pubmed,0,1,dataset,0.001126846,0.220714334,0.001126858,0.719846259,0.001126866,0.056058837,Epidemiology,0.7397643,TRUE,23,0.34225988,7,0.299973241,0,0.403234768,,,0.348489296
1016,"Biological perspective of thiazolide derivatives against Mpro and MTase of SARS-CoV-2: Molecular docking, DFT and MD simulation investigations.",Chem Phys Lett,33716307,3/16/21,pubmed,0,6,in silico,0.978449786,0.00431,0.004310087,0.004310097,0.004309998,0.004310033,Drug discovery,0.83859473,TRUE,25.33333333,0.37188447,3.833333333,0.222103291,0,0.403234768,,,0.33240751
1017,Accounting for health inequities in the design of contact tracing interventions: a rapid review.,Int J Infect Dis,33716194,3/16/21,pubmed,0,5,classifier,0.001593516,0.001593511,0.100388415,0.893237517,0.001593522,0.001593519,Epidemiology,0.20448533,FALSE,78,0.798812543,45.4,0.659954509,0,0.403234768,,,0.620667273
1018,Clinical and public health utility of Mycobacterium tuberculosis whole genome sequencing.,Int J Infect Dis,33716192,3/16/21,pubmed,0,4,"sequencing, whole genome",0.074574499,0.445254531,0.002490717,0.472698965,0.00249074,0.002490547,Epidemiology,0.10203856,FALSE,181.75,0.96487105,184.75,0.909285523,0,0.403234768,,,0.759130447
1019,Psychological distress assessment among patients with suspected and confirmed COVID-19: A cohort study.,J Formos Med Assoc,33715927,3/16/21,pubmed,0,10,logistic regression,0.001126789,0.001126811,0.001126797,0.001126812,0.604043861,0.391448929,Healthcare,0.90621316,TRUE,30.3,0.435153689,16.8,0.446548033,0,0.403234768,,,0.428312163
1020,Influenza vaccination in the time of COVID-19: A national U.S. survey of adults.,Vaccine,33715898,3/16/21,pubmed,0,5,logistic regression,0.00094608,0.000946126,0.000946116,0.000946144,0.995269422,0.000946112,Healthcare,0.68613446,TRUE,114,0.897767333,278.2,0.948287396,0,0.403234768,,,0.749763165
1021,A rational design of a multi-epitope vaccine against SARS-CoV-2 which accounts for the glycan shield of the spike glycoprotein.,J Biomol Struct Dyn,33715598,3/16/21,pubmed,0,2,"molecular dynamics simulation, computational",0.917249243,0.001187292,0.00118728,0.001187361,0.001187295,0.078001528,Drug discovery,0.4273677,FALSE,88,0.837095677,106,0.833154937,0,0.403234768,,,0.691161794
1022,"Black tea bioactives as inhibitors of multiple targets of SARS-CoV-2 (3CLpro, PLpro and RdRp): a virtual screening and molecular dynamic simulation study.",J Biomol Struct Dyn,33715595,3/16/21,pubmed,0,6,"virtual screening, in silico",0.993982887,0.001203403,0.001203414,0.001203417,0.001203467,0.001203412,Drug discovery,0.9566635,TRUE,13,0.197352959,2.166666667,0.166845063,0,0.403234768,,,0.25581093
1023,0,J Biomol Struct Dyn,33715592,3/16/21,pubmed,0,3,"virtual screening, molecular dynamics simulation, computational",0.836254882,0.001392868,0.069946891,0.089619674,0.001392834,0.001392851,Drug discovery,0.5918107,TRUE,45.66666667,0.592677346,3.333333333,0.206515922,0,0.403234768,,,0.400809345
1024,COVID-19 - exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study.,Wellcome Open Res,33709037,3/16/21,pubmed,0,11,"bayes, bayesian model",0.001310362,0.001310373,0.001310353,0.508062737,0.077927935,0.41007824,Epidemiology,0.26098952,FALSE,107.3636364,0.884222896,128.8181818,0.862389617,42,0.949503056,,,0.89870519
1025,Accurate Severe vs Non-severe COVID-19 Clinical Type Classification: a Multimodality Machine Learning Study.,J Med Internet Res,33714935,3/15/21,pubmed,0,12,machine learning,0.000916745,0.000916715,0.671901246,0.000916712,0.000916728,0.324431855,Clinics,0.14308259,FALSE,176.5833333,0.962891954,,,0,0.403234768,,,0.683063361
1026,"The presence of SARS-CoV-2 RNA in human sewage in Santa Catarina, Brazil, November 2019.",Sci Total Environ,33714813,3/15/21,pubmed,0,14,"sequencing, metagenom",0.001653038,0.991734681,0.001653062,0.00165305,0.00165304,0.001653129,Genomics,0.3875959,FALSE,43.14285714,0.568124188,27.28571429,0.547832486,3,0.667819001,,,0.594591892
1027,Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study.,Indian Heart J,33714394,3/15/21,pubmed,0,22,"artificial intelligence, neural network",0.001511827,0.001511888,0.374962186,0.071942567,0.548559628,0.001511905,Healthcare,0.890488,TRUE,57.31818182,0.684086833,14.81818182,0.421594862,0,0.403234768,,,0.502972154
1028,Delay to elective colorectal cancer surgery and implications for survival: a systematic review and meta-analysis.,Colorectal Dis,33714235,3/14/21,pubmed,0,7,dataset,0.001438118,0.001438113,0.043721073,0.438751293,0.001438144,0.513213259,Clinics,0.71369034,TRUE,9,0.135320675,0.428571429,0.076665775,0,0.403234768,,,0.205073739
1029,Socio-economic impact on epilepsy outside of the nation-wide COVID-19 pandemic area.,Epilepsy Behav,33714184,3/14/21,pubmed,0,6,logistic regression,0.00175129,0.001751357,0.001751321,0.001751286,0.673480239,0.319514506,Healthcare,0.9074061,TRUE,98.66666667,0.866163646,87.33333333,0.795491036,0,0.403234768,,,0.688296483
1030,Analyzing the attitude of Indian citizens towards COVID-19 vaccine - A text analytics study.,Diabetes Metab Syndr,33714134,3/14/21,pubmed,0,3,machine learning,0.001254644,0.001254616,0.001254725,0.48992539,0.505056041,0.001254584,Healthcare,0.17672017,FALSE,17.66666667,0.266373925,0.666666667,0.096200161,0,0.403234768,,,0.255269618
1031,Detecting SARS-CoV-2 RNA prone clusters in a municipal wastewater network using fuzzy-Bayesian optimization model to facilitate wastewater-based epidemiology.,Sci Total Environ,33714094,3/14/21,pubmed,0,3,"bayes, bayesian model, optimization model",0.001220067,0.215545075,0.127824222,0.561387677,0.034347214,0.059675745,Epidemiology,0.4957136,FALSE,30.66666667,0.440225122,4.333333333,0.237958255,0,0.403234768,,,0.360472715
1032,Shared genetic etiology between idiopathic pulmonary fibrosis and COVID-19 severity.,EBioMedicine,33714028,3/14/21,pubmed,0,9,genome-wide,0.001072232,0.476963225,0.001072193,0.001072252,0.072296034,0.447524064,Genomics,0.16961387,FALSE,86,0.829117447,164.7777778,0.89577201,0,0.403234768,,,0.709374741
1033,Predictors of COVID-19 vaccine hesitancy in the UK household longitudinal study.,Brain Behav Immun,33713824,3/14/21,pubmed,0,8,logistic regression,0.00299649,0.00299646,0.00299643,0.0029965,0.985017571,0.002996548,Healthcare,0.5775936,TRUE,77,0.794668811,64.75,0.737824458,1,0.537564047,,,0.690019105
1034,Impaired regulatory T cell control of astroglial overdrive and microglial pruning in schizophrenia.,Neurosci Biobehav Rev,33713699,3/14/21,pubmed,0,2,"transcriptom,  omics",0.862501065,0.002080606,0.00208055,0.00208066,0.049227958,0.082029161,Drug discovery,0.62188876,TRUE,36.5,0.503123261,39,0.62784319,0,0.403234768,,,0.511400406
1035,Soluble ACE2-mediated cell entry of SARS-CoV-2 via interaction with proteins related to the renin-angiotensin system.,Cell,33713620,3/14/21,pubmed,0,22,genome-wide,0.935493216,0.054544774,0.002490451,0.002490483,0.002490434,0.002490642,Drug discovery,0.50269,TRUE,186.5909091,0.967221226,266.2272727,0.944474177,0,0.403234768,,,0.77164339
1036,Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19.,Cell,33713619,3/14/21,pubmed,0,40,"transcriptom, network analysis",0.530997803,0.151783858,0.001438112,0.001438196,0.001438214,0.312903817,Drug discovery,0.32894152,FALSE,85.33333333,0.825901416,,,0,0.403234768,,,0.614568092
1037,Predicting endoscopic activity recovery in England after COVID-19: a national analysis.,Lancet Gastroenterol Hepatol,33713606,3/14/21,pubmed,0,5,"logistic regression, dataset",0.00111262,0.032431366,0.001112698,0.911641917,0.001112681,0.052588718,Epidemiology,0.7883011,TRUE,203.4,0.973467747,132.6,0.866470431,0,0.403234768,,,0.747724315
1038,The SARS-CoV-2 subgenome landscape and its novel regulatory features.,Mol Cell,33713597,3/14/21,pubmed,0,22,"sequencing, genomes",0.324784437,0.667608426,0.001901763,0.001901764,0.001901845,0.001901765,Genomics,0.37797877,FALSE,27.31818182,0.39866411,41.22727273,0.640687717,0,0.403234768,,,0.480862198
1039,Construction and validation of a machine learning-based nomogram: A tool to predict the risk of getting severe coronavirus disease 2019 (COVID-19).,Immun Inflamm Dis,33713584,3/14/21,pubmed,0,5,"machine learning, logistic regression",0.001291254,0.001291224,0.4521046,0.001291255,0.001291224,0.542730442,Clinics,0.5782641,TRUE,18,0.271569052,29.6,0.566630987,0,0.403234768,,,0.413811602
1040,"Estimating the impact of public health strategies on the spread of SARS-CoV-2: Epidemiological modelling for Toulouse, France.",Rev Med Virol,33713504,3/14/21,pubmed,0,5,prediction model,0.003760495,0.00376076,0.003760503,0.890114876,0.094842439,0.003760927,Epidemiology,0.47562295,FALSE,166.6,0.955965118,213,0.923534921,1,0.537564047,,,0.805688029
1041,Drug repositioning to target NSP15 protein on SARS-CoV-2 as possible COVID-19 treatment.,J Comput Chem,33713492,3/14/21,pubmed,0,2,"virtual screening, molecular dynamics simulation",0.991577493,0.001684523,0.001684516,0.001684515,0.001684487,0.001684466,Drug discovery,0.79944766,TRUE,21.5,0.318263343,2,0.164302917,0,0.403234768,,,0.295267009
1042,Factors influencing likelihood of COVID-19 vaccination: A survey of Tennessee adults.,Am J Health Syst Pharm,33713405,3/14/21,pubmed,0,5,logistic regression,0.001156286,0.001156253,0.001156275,0.13610281,0.859272125,0.001156252,Healthcare,0.07245159,FALSE,43.2,0.569051889,24.6,0.525689055,0,0.403234768,,,0.499325237
1043,A COVID-19 risk score combining chest CT radiomics and clinical characteristics to differentiate COVID-19 pneumonia from other viral pneumonias.,Aging (Albany NY),33713401,3/14/21,pubmed,0,23,radiom,0.062376273,0.063086079,0.657216331,0.002130819,0.002130737,0.213059762,Imaging,0.40523693,FALSE,95.39130435,0.857814336,,,0,0.403234768,,,0.630524552
1044,Transfer Learning for Mobile Real-Time Face Mask Detection and Localisation.,J Am Med Inform Assoc,33713140,3/14/21,pubmed,0,2,"computational, transfer learning, dataset",0.001350321,0.001350329,0.43628618,0.558312472,0.001350396,0.001350302,Epidemiology,0.3288616,FALSE,175.5,0.96221164,40,0.633395772,0,0.403234768,,,0.666280726
1045,Integrated cytokine and metabolite analysis reveals immunometabolic reprogramming in COVID-19 patients with therapeutic implications.,Nat Commun,33712622,3/14/21,pubmed,0,15,"metabolom, immunome",0.580466749,0.002358083,0.002357734,0.002357796,0.002357781,0.410101856,Drug discovery,0.82014465,TRUE,57.06666667,0.682231431,41.86666667,0.643631255,0,0.403234768,,,0.576365818
1046,Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic.,Nat Commun,33712603,3/14/21,pubmed,0,7,model fit,0.002562594,0.002562619,0.002562546,0.659282944,0.2774168,0.055612497,Epidemiology,0.23242953,FALSE,163.4285714,0.953738636,132.5714286,0.866403532,0,0.403234768,,,0.741125645
1047,Evaluating the impact of curfews and other measures on SARS-CoV-2 transmission in French Guiana.,Nat Commun,33712596,3/14/21,pubmed,0,13,mathematical model,0.002720353,0.002720144,0.002720145,0.910417784,0.078701114,0.002720459,Epidemiology,0.3100055,FALSE,65.84615385,0.739377822,99.84615385,0.82218357,0,0.403234768,,,0.654932053
1048,"Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions.",Nat Commun,33712587,3/14/21,pubmed,0,79,"transcriptom,  omics, metatranscriptom",0.300375562,0.393376944,0.001751227,0.146076592,0.001751228,0.156668447,Genomics,0.31492743,FALSE,53.27848101,0.65545179,110.5949367,0.839376505,1,0.537564047,,,0.677464114
1049,Inpatient Omission of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers Is Associated With Morbidity and Mortality in Coronavirus Disease 2019.,Clin Ther,33712270,3/14/21,pubmed,0,10,logistic regression,0.330424344,0.001034586,0.001034581,0.00103461,0.001034661,0.665437218,Clinics,0.77696556,TRUE,10.9,0.161914775,2.6,0.18256623,0,0.403234768,,,0.249238591
1050,Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis.,BMC Biotechnol,33711981,3/14/21,pubmed,0,5,"computational, network analysis",0.959756885,0.000907302,0.03661396,0.000907308,0.000907271,0.000907274,Drug discovery,0.9699882,TRUE,125,0.914280413,39.6,0.631255017,0,0.403234768,,,0.649590066
1051,Clinical characteristics and peripheral immunocyte subsets alteration of 85 COVID-19 deaths.,Aging (Albany NY),33711813,3/13/21,pubmed,0,8,logistic regression,0.181908396,0.00156531,0.001565303,0.00156535,0.001565296,0.811830344,Clinics,0.6201105,TRUE,10.75,0.160244913,3,0.199424672,0,0.403234768,,,0.254301451
1052,Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis.,JMIR Public Health Surveill,33711799,3/13/21,pubmed,0,5,network analysis,0.000854743,0.000854758,0.000854744,0.995726318,0.000854726,0.000854712,Epidemiology,0.4286537,FALSE,14.2,0.214793741,3.4,0.208589778,0,0.403234768,,,0.275539429
1053,Evaluation of potential anti-RNA-dependent RNA polymerase (RdRP) drugs against the newly emerged model of COVID-19 RdRP using computational methods.,Biophys Chem,33711743,3/13/21,pubmed,0,6,"computational, in silico",0.993899728,0.001220099,0.001220054,0.001220038,0.001220078,0.001220003,Drug discovery,0.9962902,TRUE,41.83333333,0.555878533,11.5,0.378378378,0,0.403234768,,,0.44583056
1054,Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.,Med Image Anal,33711739,3/13/21,pubmed,0,20,"deep learning, image analysis, dataset",0.000898083,0.000898079,0.99550954,0.000898092,0.000898078,0.000898128,Imaging,0.4630666,FALSE,86.25,0.829921455,51.55,0.687650522,1,0.537564047,,,0.685045341
1055,Reservoir Hosts Prediction for COVID-19 by Hybrid Transfer Learning Model.,J Biomed Inform,33711547,3/13/21,pubmed,0,8,"machine learning, ensemble learning, transfer learning, dataset",0.15797598,0.29706933,0.540776003,0.001392953,0.001392895,0.001392839,Genomics,0.8675554,TRUE,24.75,0.364710248,,,0,0.403234768,,,0.383972508
1056,Explainable automated coding of clinical notes using hierarchical label-wise attention networks and label embedding initialisation.,J Biomed Inform,33711543,3/13/21,pubmed,0,4,"deep learning, neural network",0.000728136,0.017592239,0.91557383,0.000728162,0.000728159,0.064649473,Clinics,0.37218183,FALSE,9.75,0.146267549,0.5,0.087101953,1,0.537564047,,,0.25697785
1057,SARS-CoV-2 infection in mortuary and cemetery workers.,Int J Infect Dis,33711522,3/13/21,pubmed,0,15,logistic regression,0.001861716,0.313027473,0.030820049,0.001861776,0.548527359,0.103901627,Healthcare,0.2604468,FALSE,41,0.549013544,26,0.53819909,0,0.403234768,,,0.496815801
1058,Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors.,Int J Infect Dis,33711521,3/13/21,pubmed,0,7,correlation analysis,0.019399881,0.001156266,0.016021052,0.961110243,0.001156269,0.00115629,Epidemiology,0.890923,TRUE,72,0.77302245,20,0.481000803,0,0.403234768,,,0.55241934
1059,Soluble interleukin-2 receptor levels on admission associated with mortality in coronavirus disease 2019.,Int J Infect Dis,33711520,3/13/21,pubmed,0,3,logistic regression,0.037865915,0.079004135,0.001861821,0.00186184,0.001861795,0.877544494,Clinics,0.5000291,TRUE,49,0.624281032,17,0.451097137,0,0.403234768,,,0.492870979
1060,Emetine suppresses SARS-CoV-2 replication by inhibiting interaction of viral mRNA with eIF4E.,Antiviral Res,33711336,3/13/21,pubmed,0,12,molecular dynamics simulation,0.991244109,0.001751229,0.001751162,0.001751183,0.001751167,0.00175115,Drug discovery,0.927171,TRUE,64.75,0.733440534,31.33333333,0.580010704,0,0.403234768,,,0.572228669
1061,Long-term survival of critically ill patients stratified by pandemic triage categories: a retrospective cohort study.,Chest,33711333,3/13/21,pubmed,0,5,probabilistic,0.001022613,0.00102264,0.001022638,0.092494552,0.001022672,0.903414886,Clinics,0.40613413,FALSE,533.6,0.998020904,766.4,0.989496923,0,0.403234768,,,0.796917531
1062,Factors linked to severe outcomes in multisystem inflammatory syndrome in children (MIS-C) in the USA: a retrospective surveillance study.,Lancet Child Adolesc Health,33711293,3/13/21,pubmed,0,12,logistic regression,0.000830717,0.00083071,0.000830703,0.000830696,0.038429342,0.958247833,Clinics,0.15809578,FALSE,51.91666667,0.645556311,41.58333333,0.642360182,1,0.537564047,,,0.608493514
1063,Predicting Health Disparities in Regions at Risk of Severe Illness to Inform Health Care Resource Allocation During Pandemics: Observational Study.,JMIRx Med,33711085,3/13/21,pubmed,0,1,predictive model,0.001653058,0.00165308,0.001653109,0.688521031,0.132528324,0.173991398,Epidemiology,0.40175676,FALSE,3,0.037293586,0,0.055525823,3,0.667819001,,,0.253546137
1064,"A Machine Learning Explanation of the Pathogen-Immune Relationship of SARS-CoV-2 (COVID-19), and a Model to Predict Immunity and Therapeutic Opportunity: A Comparative Effectiveness Research Study.",JMIRx Med,33711083,3/13/21,pubmed,0,1,machine learning,0.146501703,0.000657796,0.300364564,0.108089569,0.000657792,0.443728576,Clinics,0.6388422,TRUE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
1065,"Facemask wearing to prevent COVID-19 transmission and associated factors among taxi drivers in Dessie City and Kombolcha Town, Ethiopia.",PLoS One,33711038,3/13/21,pubmed,0,8,logistic regression,0.00101094,0.001010964,0.001010983,0.264790281,0.731165869,0.001010964,Healthcare,0.92104363,TRUE,6.75,0.095862453,0.75,0.099411292,0,0.403234768,,,0.199502838
1066,Evidence for CAT gene being functionally involved in the susceptibility of COVID-19.,FASEB J,33710662,3/13/21,pubmed,0,7,"bioinformatic, genome-wide",0.64753159,0.253265432,0.001717262,0.001717262,0.001717253,0.094051201,Drug discovery,0.62772447,TRUE,21,0.312016822,8,0.320511105,0,0.403234768,,,0.345254232
1067,Impact of gender on patients hospitalized for SARS-COV-2 infection: A prospective observational study.,J Med Virol,33710652,3/13/21,pubmed,0,10,logistic regression,0.00137127,0.001371238,0.001371227,0.001371262,0.001371291,0.993143711,Clinics,0.8841691,TRUE,85.1,0.825282949,66.8,0.745116404,0,0.403234768,,,0.65787804
1068,Can a modified-simplified pulmonary embolism severity index (m-sPESI) be used to predict the need for intensive care in hospitalized COVID-19 patients?,J Thromb Thrombolysis,33710508,3/13/21,pubmed,0,9,logistic regression,0.001126825,0.001126834,0.118810182,0.00112685,0.001126851,0.876682459,Clinics,0.61501306,TRUE,24.44444444,0.360752056,4.555555556,0.243109446,0,0.403234768,,,0.335698757
1069,Impact of the COVID-19 pandemic on urgent dental care delivery in a Swiss university center for dental medicine.,Clin Oral Investig,33710460,3/13/21,pubmed,0,9,logistic regression,0.001291239,0.001291217,0.001291252,0.128143289,0.627323246,0.240659757,Healthcare,0.98086613,TRUE,97.22222222,0.862329148,89.55555556,0.800508429,0,0.403234768,,,0.688690782
1070,The Doctor Will "See" You Now - Unmet Expectations of Telemedicine in Plastic Surgery.,J Craniofac Surg,33710049,3/13/21,pubmed,0,4,digital health,0.00143817,0.001438135,0.001438202,0.612050912,0.260791346,0.122843235,Epidemiology,0.9621243,TRUE,88.25,0.837466757,20,0.481000803,0,0.403234768,,,0.573900776
1071,Investigation on the psychological status of college students during the coronavirus disease-2019 epidemic.,J Gen Psychol,33709883,3/13/21,pubmed,0,8,correlation analysis,0.001751143,0.001751168,0.001751182,0.0017512,0.991244147,0.001751161,Healthcare,0.9906765,TRUE,14.125,0.213804193,4.5,0.242708055,0,0.403234768,,,0.286582338
1072,Internship preparedness among students in healthcare-related fields in the COVID-19 era: Exploring the attitude and knowledge in Saudi Arabia.,J Public Health Res,33709640,3/13/21,pubmed,0,8,logistic regression,0.001310397,0.001310338,0.026788153,0.001310437,0.967970245,0.00131043,Healthcare,0.68682915,TRUE,1.25,0.013173356,0,0.055525823,0,0.403234768,,,0.157311316
1073,Household SARS-CoV-2 transmission and children: a network prospective study.,Clin Infect Dis,33709135,3/13/21,pubmed,0,117,logistic regression,0.001438132,0.096220802,0.001438152,0.256996509,0.490331914,0.153574491,Healthcare,0.18333033,FALSE,16.16101695,0.244356485,,,1,0.537564047,,,0.390960266
1074,PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.,Brief Bioinform,33709119,3/13/21,pubmed,0,7,"machine learning, classifier, dataset",0.411543379,0.063915895,0.521071881,0.001156279,0.001156296,0.001156271,Drug discovery,0.118362606,FALSE,81.71428571,0.813532068,21.28571429,0.494380519,0,0.403234768,,,0.570382451
1075,Impact of age-selective vs non-selective physical-distancing measures against coronavirus disease 2019: a mathematical modelling study.,Int J Epidemiol,33709095,3/13/21,pubmed,0,2,mathematical model,0.001203503,0.001203462,0.001203472,0.937465883,0.001203483,0.057720197,Epidemiology,0.22284362,FALSE,29.5,0.426000371,4.5,0.242708055,0,0.403234768,,,0.357314398
1076,Evaluating the utility of synthetic COVID-19 case data.,JAMIA Open,33709065,3/13/21,pubmed,0,4,dataset,0.001622838,0.001622811,0.479379644,0.001622808,0.293265446,0.222486452,Healthcare,0.44613388,FALSE,14.75,0.222586431,9.25,0.340379984,0,0.403234768,,,0.322067061
1077,An Interpretable Model-Based Prediction of Severity and Crucial Factors in Patients with COVID-19.,Biomed Res Int,33708997,3/13/21,pubmed,0,8,machine learning,0.001156253,0.001156239,0.534716948,0.001156286,0.001156253,0.460658021,Imaging,0.85190463,TRUE,108.375,0.886634919,32.875,0.591584158,0,0.403234768,,,0.627151282
1078,Risk factors for mortality due to COVID-19 in intensive care units: a single-center study.,Ann Transl Med,33708903,3/13/21,pubmed,0,15,"classifier, predictive model",0.001254589,0.001254601,0.157839455,0.001254626,0.001254636,0.837142094,Clinics,0.7662014,TRUE,65.73333333,0.738697508,59.2,0.717955579,0,0.403234768,,,0.619962618
1079,A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.,Ann Transl Med,33708843,3/13/21,pubmed,0,12,"deep learning, logistic regression",0.001034624,0.001034627,0.332230842,0.00103459,0.001034576,0.663630741,Clinics,0.8695718,TRUE,48.91666667,0.621374235,33.75,0.597337436,0,0.403234768,,,0.540648813
1080,The interval between onset and admission predicts disease progression in COVID-19 patients.,Ann Transl Med,33708840,3/13/21,pubmed,0,14,logistic regression,0.00122002,0.001220002,0.001220017,0.001220051,0.03020691,0.964913,Clinics,0.80990183,TRUE,70.92857143,0.76770363,,,0,0.403234768,,,0.585469199
1081,Risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections: a nationwide population-based study.,Ann Transl Med,33708838,3/13/21,pubmed,0,7,"logistic regression, dataset",0.001717224,0.001717284,0.17687241,0.141168666,0.550406524,0.128117892,Healthcare,0.5057761,TRUE,18.85714286,0.281773765,2.714285714,0.186111854,0,0.403234768,,,0.290373462
1082,A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics.,Ann Transl Med,33708828,3/13/21,pubmed,0,23,artificial intelligence,0.000830673,0.000830644,0.424745152,0.000830646,0.000830686,0.5719322,Clinics,0.6199752,TRUE,50.17391304,0.632197415,,,0,0.403234768,,,0.517716091
1083,Effect of Spironolactone on COVID-19 in Patients With Underlying Liver Cirrhosis: A Nationwide Case-Control Study in South Korea.,Front Med (Lausanne),33708781,3/13/21,pubmed,0,3,logistic regression,0.214680609,0.001901768,0.001901689,0.001901848,0.001901815,0.777712272,Clinics,0.47331464,FALSE,26.33333333,0.386047375,5,0.257024351,0,0.403234768,,,0.348768831
1084,Omics-Driven Biotechnology for Industrial Applications.,Front Bioeng Biotechnol,33708762,3/13/21,pubmed,0,2,multi-omics,0.509443974,0.00203284,0.225067092,0.259390388,0.002032942,0.002032764,Drug discovery,0.7174712,TRUE,47.5,0.610551054,54.5,0.699892962,0,0.403234768,,,0.571226261
1085,Correlation of Coagulation Parameters With Clinical Outcomes During the Coronavirus-19 Surge in New York: Observational Cohort.,Front Physiol,33708136,3/13/21,pubmed,0,8,"logistic regression, dataset",0.00097745,0.00097744,0.000977482,0.000977478,0.044321133,0.951769017,Clinics,0.7105348,TRUE,44.5,0.581977859,16.375,0.440660958,0,0.403234768,,,0.475291195
1086,Smart homes for the older population: particularly important during the COVID-19 outbreak.,Reumatologia,33707795,3/13/21,pubmed,0,2,artificial intelligence,0.002357796,0.002357751,0.28572251,0.396035149,0.311168957,0.002357837,Epidemiology,0.5378312,TRUE,25,0.369286907,2,0.164302917,0,0.403234768,,,0.312274864
1087,Determinants of SARS-CoV-2 infection in Italian healthcare workers: a multicenter study.,Sci Rep,33707646,3/13/21,pubmed,0,48,logistic regression,0.00263895,0.002639335,0.002639207,0.002639149,0.787769766,0.201673594,Healthcare,0.79206944,TRUE,75.6,0.788917064,72.13333333,0.759566497,0,0.403234768,,,0.650572776
1088,Determining the level of social distancing necessary to avoid future COVID-19 epidemic waves: a modelling study for North East London.,Sci Rep,33707546,3/13/21,pubmed,0,6,mathematical model,0.000625224,0.000625217,0.000625213,0.965038394,0.000625235,0.032460717,Epidemiology,0.06879318,FALSE,45.5,0.591440411,20.5,0.486218892,0,0.403234768,,,0.493631357
1089,Pan-cancer analysis of RNA expression of ANGIOTENSIN-I-CONVERTING ENZYME 2 reveals high variability and possible impact on COVID-19 clinical outcomes.,Sci Rep,33707526,3/13/21,pubmed,0,13,transcriptom,0.67551721,0.04297862,0.00208056,0.002080568,0.002080571,0.275262472,Drug discovery,0.68557054,TRUE,206.0769231,0.974148061,144,0.878244581,0,0.403234768,,,0.751875803
1090,Metabolomic analyses of COVID-19 patients unravel stage-dependent and prognostic biomarkers.,Cell Death Dis,33707411,3/13/21,pubmed,0,40,metabolom,0.300390749,0.0962205,0.001987111,0.001987183,0.001987188,0.597427269,Clinics,0.69726646,TRUE,171.55,0.95955223,383.05,0.969159754,0,0.403234768,,,0.777315584
1091,Coding-Complete Genome Sequences and Mutation Profiles of Nine SARS-CoV-2 Strains Detected from COVID-19 Patients in Bangladesh.,Microbiol Resour Announc,33707329,3/13/21,pubmed,0,20,"genome sequences, genomes",0.007061467,0.964692641,0.007061536,0.007061454,0.007061496,0.007061406,Genomics,0.30697823,FALSE,21.95,0.323211083,6.9,0.294219963,0,0.403234768,,,0.340221938
1092,Interrogation of the cellular immunome of cancer patients with regard to the COVID-19 pandemic.,J Immunother Cancer,33707314,3/13/21,pubmed,0,7,immunome,0.582852017,0.039030992,0.001622775,0.162784626,0.001622751,0.212086839,Drug discovery,0.32052886,FALSE,244.1428571,0.983363226,175.7142857,0.903465347,0,0.403234768,,,0.763354447
1093,"Initial chest radiograph scores inform COVID-19 status, intensive care unit admission and need for mechanical ventilation.",Clin Radiol,33706997,3/13/21,pubmed,0,10,logistic regression,0.001022596,0.00102264,0.287719579,0.001022641,0.001022656,0.708189888,Clinics,0.9048977,TRUE,65.9,0.739872596,50.8,0.684372491,0,0.403234768,,,0.609159952
1094,Towards a sensitive and accurate interpretation of molecular testing for SARS-CoV-2: a rapid review of 264 studies.,Euro Surveill,33706863,3/13/21,pubmed,0,6,dataset,0.178083455,0.290809375,0.188347521,0.186022552,0.155275033,0.001462064,Genomics,0.6934827,TRUE,213.6666667,0.976683778,301,0.954508964,0,0.403234768,,,0.778142503
1095,Impact of previous exposure to systemic corticosteroids on unfavorable outcome in patients hospitalized for COVID-19.,BMC Pharmacol Toxicol,33706794,3/13/21,pubmed,0,56,logistic regression,0.001291205,0.001291226,0.001291221,0.001291265,0.048156406,0.946678678,Clinics,0.905859,TRUE,47,0.606407323,17.85714286,0.459325662,0,0.403234768,,,0.489655918
1096,"Diagnostic accuracy of symptoms as a diagnostic tool for SARS-CoV 2 infection: a cross-sectional study in a cohort of 2,173 patients.",BMC Infect Dis,33706707,3/13/21,pubmed,0,12,logistic regression,0.001072183,0.0010722,0.263575856,0.001072219,0.350230846,0.382976696,Clinics,0.69227123,TRUE,16.83333333,0.253509803,1.75,0.148381054,0,0.403234768,,,0.268375208
1097,0,J Biomol Struct Dyn,33706683,3/13/21,pubmed,0,4,"computational, in silico",0.872151254,0.001486473,0.001486506,0.121902903,0.001486428,0.001486437,Drug discovery,0.76292527,TRUE,30,0.432432432,3.75,0.21982874,0,0.403234768,,,0.35183198
1098,Baseline use of hydroxychloroquine or immunosuppressive drugs and the risk of coronavirus disease 2019.,Korean J Intern Med,33706472,3/13/21,pubmed,0,6,logistic regression,0.109778605,0.001943608,0.001943524,0.07016327,0.001943691,0.814227302,Clinics,0.81740963,TRUE,100.8333333,0.870492919,34.16666667,0.599879583,0,0.403234768,,,0.624535756
1099,"Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients.",J Am Med Inform Assoc,33706377,3/12/21,pubmed,0,15,"predictive model, prediction model",0.045120462,0.001237089,0.326208354,0.001237112,0.001237088,0.624959895,Clinics,0.3423096,FALSE,73.2,0.778774198,55.93333333,0.705913835,0,0.403234768,,,0.6293076
1100,Sub-Health Status Survey and Influential Factor Analysis in Chinese during Coronavirus Disease 2019 Pandemic.,J Korean Acad Nurs,33706327,3/12/21,pubmed,0,4,logistic regression,0.001486542,0.001486686,0.025031963,0.001486526,0.96902183,0.001486454,Healthcare,0.9763112,TRUE,16.75,0.252458408,3.25,0.203572384,0,0.403234768,,,0.286421853
1101,Evaluation of work resumption strategies after COVID-19 reopening in the Chinese city of Shenzhen: a mathematical modeling study.,Public Health,33706208,3/12/21,pubmed,0,13,mathematical model,0.002183203,0.002183228,0.002183212,0.989083879,0.002183258,0.00218322,Epidemiology,0.470782,FALSE,39.23076923,0.532067537,4.615384615,0.244313621,1,0.537564047,,,0.437981735
1102,Automatic deep learning-based pleural effusion classification in lung ultrasound images for respiratory pathology diagnosis.,Phys Med,33706149,3/12/21,pubmed,0,13,"deep learning, dataset",0.001203434,0.044580537,0.950605591,0.001203488,0.001203482,0.001203467,Imaging,0.16188687,FALSE,79.07692308,0.802647041,25.92307692,0.536392828,0,0.403234768,,,0.580758212
1103,Modelling suggests ABO histo-incompatibility may substantially reduce SARS-CoV-2 transmission.,Epidemics,33706041,3/12/21,pubmed,0,1,dataset,0.001392925,0.221279012,0.001392907,0.326980826,0.093273433,0.355680897,Clinics,0.21927443,FALSE,16,0.243552477,0,0.055525823,0,0.403234768,,,0.234104356
1104,A hijack mechanism of Indian SARS-CoV-2 isolates for relapsing contemporary antiviral therapeutics.,Comput Biol Med,33705994,3/12/21,pubmed,0,4,"computational, genome sequences",0.423916502,0.556328478,0.001237086,0.001237081,0.001237052,0.016043801,Genomics,0.7724415,TRUE,32.75,0.463788732,15,0.42594327,0,0.403234768,,,0.430988923
1105,Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach.,J Biomed Inform,33705856,3/12/21,pubmed,0,4,neural network,0.001371272,0.001371272,0.075504881,0.421577334,0.498803961,0.00137128,Healthcare,0.39357755,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
1106,Exploring dynamics and network analysis of spike glycoprotein of SARS-COV-2.,Biophys J,33705760,3/12/21,pubmed,0,3,"molecular dynamics simulation, network analysis",0.992567613,0.001486536,0.001486468,0.001486505,0.001486478,0.001486401,Drug discovery,0.71093607,TRUE,182,0.965118437,269.3333333,0.945611453,0,0.403234768,,,0.771321552
1107,Human cardiosphere-derived stromal cells exposed to SARS-CoV-2 evolve into hyper-inflammatory/pro-fibrotic phenotype and produce infective viral particles depending on the levels of ACE2 receptor expression.,Cardiovasc Res,33705542,3/12/21,pubmed,0,17,"transcriptom, proteom",0.585209039,0.089272594,0.017739443,0.000838542,0.000838514,0.306101868,Drug discovery,0.3860056,FALSE,106.2352941,0.881501639,122.0588235,0.853826599,0,0.403234768,,,0.712854335
1108,"Knowledge, practice and associated factors towards the prevention of COVID-19 among high-risk groups: A cross-sectional study in Addis Ababa, Ethiopia.",PLoS One,33705480,3/12/21,pubmed,0,25,logistic regression,0.000846575,0.000846553,0.000846541,0.105283468,0.891330327,0.000846536,Healthcare,0.49198192,FALSE,10.24,0.153874698,4.12,0.23207118,0,0.403234768,,,0.263060215
1109,"Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon.",PLoS One,33705453,3/12/21,pubmed,0,15,"computational, neural network, forecasting model",0.001593495,0.001593478,0.113552484,0.880073312,0.001593531,0.001593699,Epidemiology,0.24642536,FALSE,16.26666667,0.246088193,3.666666667,0.217621086,0,0.403234768,,,0.288981349
1110,Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study.,PLoS One,33705439,3/12/21,pubmed,0,10,machine learning,0.001046854,0.028203342,0.059330063,0.267396607,0.642976243,0.001046891,Healthcare,0.8796859,TRUE,10.8,0.161110768,5.6,0.267995718,0,0.403234768,,,0.277447085
1111,Clinical decision support tool for diagnosis of COVID-19 in hospitals.,PLoS One,33705435,3/12/21,pubmed,0,10,logistic regression,0.001010974,0.001010978,0.363759632,0.088042437,0.034787654,0.511388325,Clinics,0.90779054,TRUE,119.6,0.906611417,85.1,0.790540541,0,0.403234768,,,0.700128908
1112,An epidemic model for non-first-order transmission kinetics.,PLoS One,33705424,3/12/21,pubmed,0,2,mathematical model,0.037612226,0.051156053,0.001187298,0.907669799,0.001187303,0.001187322,Epidemiology,0.3889086,FALSE,12.5,0.189436576,0.5,0.087101953,0,0.403234768,,,0.226591099
1113,"Travel from the United Kingdom to the United States by a Symptomatic Patient Infected with the SARS-CoV-2 B.1.1.7 Variant - Texas, January 2021.",MMWR Morb Mortal Wkly Rep,33705368,3/12/21,pubmed,0,16,sequencing,0.004109743,0.800842634,0.00410975,0.004109936,0.123969349,0.062858589,Genomics,0.18914792,FALSE,6.125,0.086461748,3.5625,0.213941664,0,0.403234768,,,0.23454606
1114,"First Identified Cases of SARS-CoV-2 Variant P.1 in the United States - Minnesota, January 2021.",MMWR Morb Mortal Wkly Rep,33705367,3/12/21,pubmed,0,13,"sequencing, whole genome",0.150732687,0.721751656,0.039014037,0.002422336,0.002422438,0.083656846,Genomics,0.62162244,TRUE,61.84615385,0.71414435,156.3846154,0.888948354,1,0.537564047,,,0.713552251
1115,HLA class I-associated expansion of TRBV11-2 T cells in Multisystem Inflammatory Syndrome in Children.,J Clin Invest,33705359,3/12/21,pubmed,0,16,in silico,0.628724753,0.131538825,0.001653154,0.001653251,0.041750419,0.194679597,Drug discovery,0.5833136,TRUE,136.1875,0.92782485,201,0.917982339,0,0.403234768,,,0.749680652
1116,Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.,IEEE/ACM Trans Comput Biol Bioinform,33705321,3/12/21,pubmed,0,13,deep learning,0.001486431,0.001486448,0.895140665,0.00148654,0.001486506,0.09891341,Imaging,0.26801097,FALSE,19.84615385,0.294328654,5.923076923,0.273682098,0,0.403234768,,,0.323748506
1117,The hidden curve behind COVID-19 outbreak: the impact of delay in treatment initiation in cancer patients and how to mitigate the additional risk of dying-the head and neck cancer model.,Cancer Causes Control,33704627,3/12/21,pubmed,0,30,mathematical model,0.001717225,0.001717174,0.001717226,0.659875769,0.001717331,0.333255275,Epidemiology,0.7661929,TRUE,130.0333333,0.921825716,143.5666667,0.877508697,0,0.403234768,,,0.734189727
1118,Transmission of SARS-CoV-2 from asymptomatic and presymptomatic individuals in healthcare settings despite medical masks and eye protection.,Clin Infect Dis,33704451,3/12/21,pubmed,0,11,"sequencing, whole genome",0.004309912,0.265739582,0.004310091,0.717020231,0.004310247,0.004309938,Epidemiology,0.116874576,FALSE,63.90909091,0.727688787,110.3636364,0.838908215,0,0.403234768,,,0.65661059
1119,"The Global Landscape of SARS-CoV-2 Genomes, Variants, and Haplotypes in 2019nCoVR.",Genomics Proteomics Bioinformatics,33704069,3/12/21,pubmed,0,30,"genome sequences, genomes",0.001187288,0.538725596,0.090680231,0.367032234,0.001187344,0.001187307,Genomics,0.18309283,FALSE,19.36666667,0.2887006,37.73333333,0.620149853,0,0.403234768,,,0.43736174
1120,Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death.,Elife,33704068,3/12/21,pubmed,0,25,"machine learning, proteom",0.417665317,0.001786707,0.082652726,0.001786659,0.001786552,0.494322039,Clinics,0.64781517,TRUE,40.2,0.541159008,39.12,0.62831148,1,0.537564047,,,0.569011512
1121,Cardiovascular thrombotic complications in acute ischemic stroke assessed by chest spectral computed tomography during COVID-19.,Minerva Cardiol Angiol,33703860,3/12/21,pubmed,0,9,dataset,0.052827856,0.001220063,0.702012378,0.00122006,0.001220057,0.241499585,Imaging,0.041652918,FALSE,28.33333333,0.410724225,14.55555556,0.418785122,0,0.403234768,,,0.410914705
1122,The flipped classroom: a novel approach to physical examination skills for osteopathic medical students.,J Osteopath Med,33694343,3/12/21,pubmed,0,2,active learning,0.001085371,0.001085352,0.278323656,0.101970902,0.616449319,0.0010854,Healthcare,0.24633047,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,,,0.165351392
1123,A forecasting model to estimate the drop in blood supplies during the SARS-CoV-2 pandemic in Italy.,Transfus Med,33694217,3/12/21,pubmed,0,11,forecasting model,0.001987171,0.035954643,0.001987109,0.605631652,0.001987189,0.352452236,Epidemiology,0.54019326,TRUE,138.9090909,0.931844888,83.27272727,0.786392828,0,0.403234768,,,0.707157495
1124,The Impact of COVID-19 on US Radiation Oncology Residents.,J Cancer Educ,33694133,3/12/21,pubmed,0,6,logistic regression,0.001565296,0.001565328,0.050350793,0.001565345,0.909823089,0.035130149,Healthcare,0.9599565,TRUE,157.3333333,0.94947121,58,0.714476853,0,0.403234768,,,0.689060944
1125,Differences in COVID-19-Related Testing and Healthcare Utilization by Race and Ethnicity in the Veterans Health Administration.,J Racial Ethn Health Disparities,33694124,3/12/21,pubmed,0,7,logistic regression,0.001254596,0.001254621,0.001254601,0.001254627,0.414839719,0.580141835,Clinics,0.25867605,FALSE,230.1428571,0.981074896,197.5714286,0.916577469,0,0.403234768,,,0.766962378
1126,SARS-CoV-2 Variant of Concern 202 012/01 (B.1.1.7) in a Traveler from the United Kingdom to China.,J Travel Med,33693926,3/12/21,pubmed,0,10,sequencing,0.007061506,0.964689955,0.007061576,0.007061847,0.0070616,0.007063517,Genomics,0.2228902,FALSE,41.2,0.550126786,10.4,0.360917849,0,0.403234768,,,0.438093134
1127,De novo 3D models of SARS-CoV-2 RNA elements from consensus experimental secondary structures.,Nucleic Acids Res,33693814,3/12/21,pubmed,0,10,"computational, dataset",0.688095512,0.166060811,0.141665167,0.001392877,0.001392816,0.001392817,Drug discovery,0.09152794,FALSE,40.5,0.544746119,41.1,0.639884934,0,0.403234768,,,0.529288607
1128,"Risk of Malnutrition Is Common in Patients with Coronavirus Disease 2019 (COVID-19) in Wuhan, China: A Cross-sectional Study.",J Nutr,33693698,3/12/21,pubmed,0,10,logistic regression,0.001486469,0.001486403,0.001486472,0.001486618,0.059965111,0.934088928,Clinics,0.6457565,TRUE,32.2,0.45754221,24,0.521808938,0,0.403234768,,,0.460861972
1129,COVID-19 Associated Pulmonary Aspergillosis in Mechanically Ventilated Patients.,Clin Infect Dis,33693551,3/12/21,pubmed,0,9,logistic regression,0.001987137,0.001987118,0.059422602,0.001987137,0.001987195,0.932628811,Clinics,0.7379281,TRUE,115.6666667,0.900241202,273.2222222,0.946548033,0,0.403234768,,,0.750008001
1130,Bioinformatics helping to mitigate the impact of COVID-19 - Editorial.,Brief Bioinform,33693516,3/12/21,pubmed,0,2,bioinformatic,0.825156235,0.034965372,0.034964394,0.034981068,0.034971036,0.034961895,Drug discovery,0.50172013,TRUE,201.5,0.972849279,96,0.81509232,0,0.403234768,,,0.730392122
1131,COVID-19: disease pathways and gene expression changes predict methylprednisolone can improve outcome in severe cases.,Bioinformatics,33693506,3/12/21,pubmed,0,11,"bioinformatic, in silico",0.570242861,0.001438191,0.001438152,0.001438153,0.001438168,0.424004475,Drug discovery,0.81619465,TRUE,65.18181818,0.73523409,98.09090909,0.819173134,0,0.403234768,,,0.65254733
1132,Structural analogues of existing anti-viral drugs inhibit SARS-CoV-2 RNA dependent RNA polymerase: A computational hierarchical investigation.,Heliyon,33693066,3/12/21,pubmed,0,9,computational,0.992173293,0.001565349,0.001565301,0.001565357,0.001565311,0.001565388,Drug discovery,0.81604856,TRUE,58.22222222,0.690766281,4.333333333,0.237958255,0,0.403234768,,,0.443986435
1133,Circulating Exosomes Are Strongly Involved in SARS-CoV-2 Infection.,Front Mol Biosci,33693030,3/12/21,pubmed,0,20,proteom,0.612972911,0.117014569,0.001486498,0.001486443,0.001486426,0.265553154,Drug discovery,0.8309847,TRUE,68.65,0.757127837,155.15,0.888011774,0,0.403234768,,,0.68279146
1134,Case Report: Identification of SARS-CoV-2 in Cerebrospinal Fluid by Ultrahigh-Depth Sequencing in a Patient With Coronavirus Disease 2019 and Neurological Dysfunction.,Front Med (Lausanne),33693018,3/12/21,pubmed,0,30,sequencing,0.004309968,0.735549863,0.004310037,0.004310076,0.004310235,0.247209821,Genomics,0.71036255,TRUE,32.7,0.463046571,,,0,0.403234768,,,0.433140669
1135,"Implications of Laboratory Tests in Disease Grading and Death Risk Stratification of COVID-19: A Retrospective Study in Wuhan, China.",Front Med (Lausanne),33693017,3/12/21,pubmed,0,12,logistic regression,0.001112671,0.00111265,0.124012995,0.001112668,0.001112678,0.871536337,Clinics,0.9512771,TRUE,55.58333333,0.671779331,21.16666667,0.493310142,0,0.403234768,,,0.522774747
1136,Unification and extensive diversification of M/Orf3-related ion channel proteins in coronaviruses and other nidoviruses.,Virus Evol,33692906,3/12/21,pubmed,0,6,computational,0.532065113,0.462916448,0.001254598,0.001254618,0.001254624,0.001254599,Drug discovery,0.6741752,TRUE,104,0.877605294,729.5,0.988158951,0,0.403234768,,,0.756333004
1137,Learning Burnout: Evaluating the Role of Social Support in Medical Students.,Front Psychol,33692725,3/12/21,pubmed,0,4,logistic regression,0.001350361,0.001350322,0.197821724,0.001350361,0.796776873,0.001350358,Healthcare,0.99541473,TRUE,22.25,0.328344363,6.5,0.288132192,0,0.403234768,,,0.339903774
1138,Comparative Effectiveness of Multiple Psychological Interventions for Psychological Crisis in People Affected by Coronavirus Disease 2019: A Bayesian Network Meta-Analysis.,Front Psychol,33692715,3/12/21,pubmed,0,6,bayes,0.000838568,0.000838521,0.000838572,0.392938315,0.451234163,0.153311861,Healthcare,0.8217115,TRUE,18.16666667,0.272682293,1.5,0.138747659,0,0.403234768,,,0.271554906
1139,Drug Repurposing and Polypharmacology to Fight SARS-CoV-2 Through Inhibition of the Main Protease.,Front Pharmacol,33692695,3/12/21,pubmed,0,4,"computational, in silico",0.992440724,0.001511888,0.001511868,0.001511868,0.001511824,0.001511829,Drug discovery,0.8159076,TRUE,59.5,0.699857752,26.5,0.542012309,0,0.403234768,,,0.548368276
1140,Towards better understanding of the heparin role in NETosis: feasibility of using native mass spectrometry to monitor interactions of neutrophil elastase with heparin oligomers.,Int J Mass Spectrom,33692650,3/12/21,pubmed,0,3,molecular dynamics simulation,0.774940696,0.000871612,0.059406224,0.000871604,0.000871549,0.163038315,Drug discovery,0.12821478,FALSE,58,0.689714887,25.66666667,0.534854161,0,0.403234768,,,0.542601272
1141,Predictors of Hospitalization Among Older Adults with COVID-19 in Saudi Arabia: A Cross-Sectional Study of a Nationally Representative Sample.,Risk Manag Healthc Policy,33692640,3/12/21,pubmed,0,9,logistic regression,0.001291222,0.001291226,0.019522409,0.001291221,0.076112801,0.900491121,Clinics,0.80197537,TRUE,14.11111111,0.213618653,2,0.164302917,0,0.403234768,,,0.260385446
1142,Drug design and repurposing with DockThor-VS web server focusing on SARS-CoV-2 therapeutic targets and their non-synonym variants.,Sci Rep,33692377,3/12/21,pubmed,0,13,virtual screening,0.880796765,0.111756357,0.001861746,0.001861733,0.001861736,0.001861663,Drug discovery,0.67948973,TRUE,29.38461538,0.423402808,24.30769231,0.523682098,0,0.403234768,,,0.450106558
1143,SARS-CoV-2 rapidly adapts in aged BALB/c mice and induces typical pneumonia.,J Virol,33692211,3/12/21,pubmed,0,19,"sequencing, deep sequencing",0.272131688,0.276010875,0.035865113,0.000999558,0.103656268,0.311336498,Clinics,0.5112628,TRUE,92.52631579,0.85051642,73.05263158,0.762041745,0,0.403234768,,,0.671930978
1144,Epidemiological dynamics of enterovirus D68 in the United States and implications for acute flaccid myelitis.,Sci Transl Med,33692131,3/12/21,pubmed,0,7,mathematical model,0.002490606,0.121507316,0.00249049,0.868530289,0.002490645,0.002490655,Epidemiology,0.6539885,TRUE,102.7142857,0.874884037,246,0.93678084,0,0.403234768,,,0.738299882
1145,The pharmacological mechanism of Huashi Baidu Formula for the treatment of COVID-19 by combined network pharmacology and molecular docking.,Ann Palliat Med,33691446,3/12/21,pubmed,0,3," omics, genomes",0.946586162,0.014667442,0.000815345,0.000815332,0.036300301,0.000815418,Drug discovery,0.9940527,TRUE,17.66666667,0.266373925,4.666666667,0.246721969,0,0.403234768,,,0.305443554
1146,Physical Exercise as a Resilience Factor to Mitigate COVID-Related Allostatic Overload.,Psychother Psychosom,33691321,3/11/21,pubmed,0,4,logistic regression,0.001187255,0.001187295,0.001187313,0.001187313,0.994063504,0.00118732,Healthcare,0.9946239,TRUE,36.75,0.505968211,34.5,0.602221033,0,0.403234768,,,0.503808004
1147,Fluoxetine as an anti-inflammatory therapy in SARS-CoV-2 infection.,Biomed Pharmacother,33691249,3/11/21,pubmed,0,11,transcriptom,0.76479487,0.002080612,0.002080588,0.002080606,0.002080557,0.226882766,Drug discovery,0.6666549,TRUE,32,0.455810502,32.36363636,0.588439925,2,0.618927094,,,0.554392507
1148,Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients.,Comput Biol Med,33691201,3/11/21,pubmed,0,16,"machine learning, radiom, dataset",0.00086305,0.000863034,0.632228717,0.000863049,0.000863032,0.364319119,Imaging,0.9119407,TRUE,88.3125,0.837652298,67.6875,0.747257158,0,0.403234768,,,0.662714741
1149,Single-cell RNA sequencing reveals the sustained immune cell dysfunction in the pathogenesis of sepsis secondary to bacterial pneumonia.,Genomics,33691144,3/11/21,pubmed,0,10,sequencing,0.616210476,0.026217417,0.001538129,0.001538112,0.001538103,0.352957762,Drug discovery,0.2523118,FALSE,91.2,0.846620075,55.3,0.703170993,0,0.403234768,,,0.651008612
1150,How Much Does the (Social) Environment Matter? Using Artificial Intelligence to Predict COVID-19 Outcomes with Socio-demographic Data.,Pac Symp Biocomput,33691029,3/11/21,pubmed,0,3,"machine learning, artificial intelligence",0.00333526,0.003335287,0.371146637,0.003335516,0.399009347,0.219837952,Healthcare,0.484098,FALSE,48.66666667,0.619333292,14,0.412898047,0,0.403234768,,,0.478488702
1151,TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos.,Pac Symp Biocomput,33691019,3/11/21,pubmed,0,6,machine learning,0.001622711,0.001622725,0.916006154,0.001622778,0.077502768,0.001622864,Imaging,0.3417071,FALSE,33.5,0.471890655,64.5,0.736887878,1,0.537564047,,,0.582114193
1152,Protein sequence models for prediction and comparative analysis of the SARS-CoV-2 -human interactome.,Pac Symp Biocomput,33691013,3/11/21,pubmed,0,8,"machine learning, interactom, dataset",0.459179297,0.378160711,0.158728861,0.001310377,0.001310357,0.001310397,Drug discovery,0.17432189,FALSE,58.625,0.693611231,83.25,0.786259031,0,0.403234768,,,0.627701677
1153,Characterization of Anonymous Physician Perspectives on COVID-19 Using Social Media Data.,Pac Symp Biocomput,33691008,3/11/21,pubmed,0,5,dataset,0.001943545,0.001943534,0.124637918,0.524702398,0.344828842,0.001943763,Epidemiology,0.88017654,TRUE,37.2,0.509926402,16.2,0.438252609,1,0.537564047,,,0.495247686
1154,Timing of surgery following SARS-CoV-2 infection: an international prospective cohort study.,Anaesthesia,33690889,3/11/21,pubmed,0,1,logistic regression,0.001415091,0.001415132,0.001415132,0.064181762,0.001415167,0.930157716,Clinics,0.88397,TRUE,20.5,0.304966294,3.75,0.21982874,2,0.618927094,,,0.381240709
1155,Why do people oppose mask wearing? A comprehensive analysis of US tweets during the COVID-19 pandemic.,J Am Med Inform Assoc,33690794,3/11/21,pubmed,0,8,"machine learning, classifier",0.001330061,0.001330036,0.106420429,0.640608116,0.248981312,0.001330046,Epidemiology,0.71162546,TRUE,68.75,0.757808151,38,0.622223709,1,0.537564047,,,0.639198636
1156,A CT radiomics analysis of COVID-19-related ground-glass opacities and consolidation: Is it valuable in a differential diagnosis with other atypical pneumonias?,PLoS One,33690730,3/11/21,pubmed,0,5,"bayes, radiom",0.001684476,0.001684521,0.694452566,0.001684641,0.001684551,0.298809245,Imaging,0.96707684,TRUE,32,0.455810502,15.2,0.427214343,0,0.403234768,,,0.428753204
1157,Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021.,PLoS One,33690722,3/11/21,pubmed,0,34,logistic regression,0.000871649,0.083451251,0.248523777,0.077909545,0.075430781,0.513812997,Clinics,0.5524335,TRUE,107.9411765,0.885459831,74.17647059,0.764450094,0,0.403234768,,,0.684381564
1158,"Health professionals practice and associated factors towards precautionary measures for COVID-19 pandemic in public health facilities of Gamo zone, southern Ethiopia: A cross-sectional study.",PLoS One,33690704,3/11/21,pubmed,0,8,logistic regression,0.043772765,0.001461879,0.001462024,0.001461971,0.950379469,0.001461891,Healthcare,0.89464855,TRUE,18,0.271569052,4.5,0.242708055,1,0.537564047,,,0.350613718
1159,"Citizenship, Migration and Mobility in a Pandemic (CMMP): A global dataset of COVID-19 restrictions on human movement.",PLoS One,33690701,3/11/21,pubmed,0,3,dataset,0.002996457,0.00299653,0.072248789,0.915765276,0.002996477,0.002996471,Epidemiology,0.56243473,TRUE,67.66666667,0.75137609,17.33333333,0.45424137,0,0.403234768,,,0.536284076
1160,Infection prevention and control compliance among exposed healthcare workers in COVID-19 treatment centers in Ghana: A descriptive cross-sectional study.,PLoS One,33690699,3/11/21,pubmed,0,9,logistic regression,0.000988404,0.000988392,0.000988372,0.000988408,0.995058011,0.000988414,Healthcare,0.834237,TRUE,6,0.086028821,1,0.122023013,0,0.403234768,,,0.2037622
1161,Misinformation on COVID-19 origin and its relationship with perception and knowledge about social distancing: A cross-sectional study.,PLoS One,33690685,3/11/21,pubmed,0,6,sequencing,0.001098833,0.13417159,0.001098861,0.092541555,0.745973065,0.025116096,Healthcare,0.3225962,FALSE,2.833333333,0.031356299,0,0.055525823,0,0.403234768,,,0.163372297
1162,Ethnic disparities in COVID-19 mortality in Mexico: A cross-sectional study based on national data.,PLoS One,33690607,3/11/21,pubmed,0,7,logistic regression,0.001901692,0.001901813,0.001901754,0.001901893,0.211784114,0.780608733,Clinics,0.70516324,TRUE,4.428571429,0.05850702,0,0.055525823,0,0.403234768,,,0.172422537
1163,A straightforward molecular strategy to retrospectively investigate the spread of SARS-CoV-2 VOC202012/01 B.1.1.7 variant.,J Infect Dev Ctries,33690207,3/11/21,pubmed,0,6,"sequencing, genomes, dataset",0.001371309,0.814482564,0.085796468,0.095607046,0.001371272,0.001371342,Genomics,0.71840584,TRUE,65.5,0.737646113,66,0.742574257,0,0.403234768,,,0.627818379
1164,"Evaluation of COVID-19 Disease Awareness and Its Relation to Mental Health, Dietary Habits, and Physical Activity: A Cross-Sectional Study from Pakistan.",Am J Trop Med Hyg,33690156,3/11/21,pubmed,0,6,logistic regression,0.001486451,0.001486471,0.001486484,0.001486472,0.965739408,0.028314714,Healthcare,0.88829887,TRUE,69,0.759416167,25.16666667,0.53017126,0,0.403234768,,,0.564274065
1165,Echo Chamber Effect in Rumor Rebuttal Discussions About COVID-19 in China: Social Media Content and Network Analysis Study.,J Med Internet Res,33690145,3/11/21,pubmed,0,2,network analysis,0.221791798,0.000988376,0.000988379,0.627897549,0.147345544,0.000988353,Epidemiology,0.08026853,FALSE,112,0.894180221,90,0.801445009,0,0.403234768,,,0.699619999
1166,The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis.,JMIR Public Health Surveill,33690143,3/11/21,pubmed,0,3,data mining,0.00122001,0.001220005,0.001220108,0.976459872,0.001220073,0.018659932,Epidemiology,0.017014027,FALSE,332.3333333,0.992887624,258,0.940794755,0,0.403234768,,,0.778972382
1167,"Understanding On-Campus Interactions With a Semiautomated, Barcode-Based Platform to Augment COVID-19 Contact Tracing: App Development and Usage.",JMIR Mhealth Uhealth,33690142,3/11/21,pubmed,0,5,digital health,0.000746564,0.000746567,0.023801077,0.630755877,0.343203349,0.000746566,Epidemiology,0.99344194,TRUE,8.2,0.120477457,0.6,0.09011239,1,0.537564047,,,0.249384631
1168,Analysis of microparticle deposition in the human lung by taguchi method and response surface methodology.,Environ Res,33689824,3/11/21,pubmed,0,5,computational,0.423016156,0.00203281,0.002032787,0.299388629,0.002032783,0.271496835,Drug discovery,0.35398176,FALSE,93.8,0.853979838,55,0.702167514,0,0.403234768,,,0.653127373
1169,Ribavirin shows antiviral activity against SARS-CoV-2 and downregulates the activity of TMPRSS2 and the expression of ACE2 In Vitro.,Can J Physiol Pharmacol,33689451,3/11/21,pubmed,0,22,in silico,0.991244027,0.001751267,0.001751195,0.001751164,0.001751168,0.001751179,Drug discovery,0.8767672,TRUE,36.90909091,0.507266992,24.54545455,0.525220765,0,0.403234768,,,0.478574175
1170,"Structure, Dynamics, Receptor Binding, and Antibody Binding of the Fully Glycosylated Full-Length SARS-CoV-2 Spike Protein in a Viral Membrane.",J Chem Theory Comput,33689337,3/11/21,pubmed,0,9,molecular dynamics simulation,0.890754269,0.103585093,0.001415136,0.001415193,0.001415163,0.001415147,Drug discovery,0.58567894,TRUE,74.55555556,0.784835178,98,0.819106235,0,0.403234768,,,0.669058727
1171,Prostate Cancer Screening and Young Black Men: Can Early Communication Avoid Later Health Disparities?,J Cancer Educ,33689157,3/11/21,pubmed,0,4,logistic regression,0.001310387,0.001310428,0.00131041,0.058807364,0.913451694,0.023809716,Healthcare,0.72928846,TRUE,43.25,0.569855897,40,0.633395772,0,0.403234768,,,0.535495479
1172,Risk Factors Associated With SARS-CoV-2 Seropositivity Among US Health Care Personnel.,JAMA Netw Open,33688967,3/11/21,pubmed,0,21,logistic regression,0.000966824,0.000966819,0.00096677,0.290645346,0.583795096,0.122659145,Healthcare,0.5324791,TRUE,121.8571429,0.909456367,187.6666667,0.910556596,0,0.403234768,,,0.741082577
1173,COVID-19-related absence among surgeons: development of an international surgical workforce prediction model.,BJS Open,33688956,3/11/21,pubmed,0,146,prediction model,0.001786507,0.001786528,0.001786581,0.761076739,0.231776925,0.00178672,Epidemiology,0.9505884,TRUE,56.544,0.678953553,,,1,0.537564047,,,0.6082588
1174,"Excess deaths reveal the true spatial, temporal, and demographic impact of COVID-19 on mortality in Ecuador.",medRxiv,33688690,3/11/21,pubmed,0,8,dataset,0.001486426,0.00148646,0.001486457,0.49053742,0.001486513,0.503516724,Clinics,0.29240286,FALSE,24.25,0.358092646,9.375,0.342721434,0,0.403234768,,,0.368016283
1175,Estimation of secondary household attack rates for emergent SARS-CoV-2 variants detected by genomic surveillance at a community-based testing site in San Francisco.,medRxiv,33688689,3/11/21,pubmed,0,30,"sequencing, genomes",0.000946082,0.636121743,0.000946084,0.060639878,0.274188957,0.027157256,Genomics,0.18904653,FALSE,47.83333333,0.613148618,122.5666667,0.85469628,3,0.667819001,,,0.711887966
1176,Consistency of performance of adverse outcome prediction models for hospitalized COVID-19 patients.,medRxiv,33688684,3/11/21,pubmed,0,3,prediction model,0.001486439,0.001486454,0.13486072,0.0014865,0.001486514,0.859193372,Clinics,0.17241922,FALSE,179.6666667,0.964128889,384,0.96936045,0,0.403234768,,,0.778908035
1177,Muscle Strength Explains the Protective Effect of Physical Activity against COVID-19 Hospitalization among Adults aged 50 Years and Older.,medRxiv,33688683,3/11/21,pubmed,0,8,logistic regression,0.001392929,0.001392883,0.001393089,0.030855913,0.735281955,0.229683231,Healthcare,0.9741075,TRUE,48.75,0.62032284,39.5,0.630920524,0,0.403234768,,,0.551492711
1178,The emergence and ongoing convergent evolution of the N501Y lineages coincides with a major global shift in the SARS-CoV-2 selective landscape.,medRxiv,33688681,3/11/21,pubmed,0,21,genomes,0.040357609,0.790844392,0.001392849,0.164619412,0.001392914,0.001392823,Genomics,0.14321354,FALSE,68.91304348,0.758488466,275.7391304,0.947350816,8,0.799987654,,,0.835275645
1179,COVID-19 Related Chemosensory Changes in Individuals with Self-Reported Obesity.,medRxiv,33688677,3/11/21,pubmed,0,5,dataset,0.001141329,0.001141342,0.057047531,0.001141348,0.498550145,0.440978305,Healthcare,0.45498252,FALSE,48.8,0.620755767,23.4,0.515721167,0,0.403234768,,,0.513237234
1180,Persistent SARS-CoV-2 infection and increasing viral variants in children and young adults with impaired humoral immunity.,medRxiv,33688673,3/11/21,pubmed,0,27,"sequencing, whole-genome",0.001330083,0.738673823,0.001330059,0.001330099,0.058814114,0.198521821,Genomics,0.6331614,TRUE,64.59259259,0.732203599,117.7407407,0.848407814,3,0.667819001,,,0.749476805
1181,Mathematical modeling to inform vaccination strategies and testing approaches for COVID-19 in nursing homes.,medRxiv,33688668,3/11/21,pubmed,0,6,mathematical model,0.000946107,0.000946098,0.000946076,0.202794044,0.793421585,0.00094609,Healthcare,0.13218322,FALSE,33,0.466757375,13.66666667,0.407412363,0,0.403234768,,,0.425801502
1182,SARS-CoV-2 transmission in intercollegiate athletics not fully mitigated with daily antigen testing.,medRxiv,33688665,3/11/21,pubmed,0,12,sequencing,0.001046814,0.425646201,0.001046869,0.236019733,0.310116943,0.026123439,Genomics,0.29697627,FALSE,56.58333333,0.67920094,66.83333333,0.745250201,1,0.537564047,,,0.654005063
1183,"Genomics and epidemiology of a novel SARS-CoV-2 lineage in Manaus, Brazil.",medRxiv,33688664,3/11/21,pubmed,0,70,sequencing,0.099799187,0.860775117,0.001622732,0.00162283,0.00162284,0.034557295,Genomics,0.41570976,FALSE,19.41666667,0.289628301,21.41666667,0.496320578,25,0.918019631,,,0.567989503
1184,0,medRxiv,33688661,3/11/21,pubmed,0,6,deep learning,0.001987319,0.036909775,0.10549664,0.300925227,0.001987185,0.552693854,Clinics,0.13931987,FALSE,92.16666667,0.849341332,126.3333333,0.85991437,0,0.403234768,,,0.70416349
1185,Inhibition of amyloid formation of the Nucleoprotein of SARS-CoV-2.,bioRxiv,33688654,3/11/21,pubmed,0,24,computational,0.691915761,0.231060743,0.071061994,0.001987192,0.001987156,0.001987154,Drug discovery,0.36354965,FALSE,107.5833333,0.88490321,193.375,0.914369815,0,0.403234768,,,0.734169264
1186,Blockade of SARS-CoV-2 infection in vitro by highly potent PI3K-α/mTOR/BRD4 inhibitor.,bioRxiv,33688653,3/11/21,pubmed,0,9,"in silico, proteom",0.97112837,0.024582787,0.001072217,0.001072236,0.001072192,0.001072198,Drug discovery,0.21278459,FALSE,35.44444444,0.491124992,57.66666667,0.713473374,0,0.403234768,,,0.535944378
1187,Neutralizing IFNL3 Autoantibodies in Severe COVID-19 Identified Using Molecular Indexing of Proteins by Self-Assembly.,bioRxiv,33688651,3/11/21,pubmed,0,23,sequencing,0.396559691,0.419015642,0.00213077,0.002130678,0.099002292,0.081160926,Genomics,0.58573127,TRUE,101.3913043,0.871729853,134.2608696,0.86841049,0,0.403234768,,,0.71445837
1188,Prediction model for COVID-19 patient visits in the ambulatory setting.,Res Sq,33688638,3/11/21,pubmed,0,8,prediction model,0.001438124,0.001438117,0.045091356,0.521027439,0.092194401,0.338810563,Epidemiology,0.53387547,TRUE,30,0.432432432,16,0.437316029,0,0.403234768,,,0.424327743
1189,Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019.,J Acad Consult Liaison Psychiatry,33688635,3/11/21,pubmed,0,4,"machine learning, predictive model, prediction model",0.001330039,0.001330042,0.300792546,0.001330129,0.101982213,0.593235032,Clinics,0.71530676,TRUE,154.25,0.947306574,316.75,0.958790474,0,0.403234768,,,0.769777272
1190,Urine proteome of COVID-19 patients.,Urine (Amst),33688631,3/11/21,pubmed,0,14,proteom,0.280787916,0.003466098,0.40891317,0.003465934,0.003466032,0.29990085,Clinics,0.64346826,TRUE,16.07142857,0.243799864,,,0,0.403234768,,,0.323517316
1191,0,Heliyon,33688586,3/11/21,pubmed,0,7,in silico,0.991886047,0.00162287,0.001622724,0.001622752,0.001622745,0.001622863,Drug discovery,0.7415315,TRUE,36.14285714,0.498793988,18,0.46180091,0,0.403234768,,,0.454609889
1192,The study of antiviral drugs targeting SARS-CoV-2 nucleocapsid and spike proteins through large-scale compound repurposing.,Heliyon,33688584,3/11/21,pubmed,0,9,virtual screening,0.991577536,0.001684518,0.001684472,0.001684474,0.001684497,0.001684503,Drug discovery,0.9666809,TRUE,59.11111111,0.696827262,10.77777778,0.366537329,0,0.403234768,,,0.488866453
1193,Neurological complications of COVID19 during March 2020 at LCMC health university medical center: Dataset.,Data Brief,33688573,3/11/21,pubmed,0,7,"logistic regression, dataset",0.000667721,0.000667707,0.000667714,0.210536006,0.074264058,0.713196795,Clinics,0.36807597,FALSE,9.285714286,0.138165626,12.57142857,0.393497458,0,0.403234768,,,0.311632617
1194,Teaching a large-scale crystallography school with Zoom Webinar.,Struct Dyn,33688553,3/11/21,pubmed,0,13,dataset,0.073838429,0.001538121,0.0655678,0.342980079,0.514537414,0.001538156,Healthcare,0.030484289,FALSE,23.69230769,0.350114416,48.15384615,0.674337704,0,0.403234768,,,0.475895629
1195,"Mothers' Knowledge, Attitudes, and Fears About Dental Visits During the COVID-19 Pandemic: A Cross-sectional Study.",J Int Soc Prev Community Dent,33688477,3/11/21,pubmed,0,2,logistic regression,0.001486458,0.001486495,0.001486456,0.00148653,0.992567629,0.001486433,Healthcare,0.7672448,TRUE,20,0.298163152,8.5,0.329141022,0,0.403234768,,,0.34351298
1196,Convolution Neural Network Based Infection Transmission Analysis on Covid -19 Using GIS and Covid Data Materials.,Mater Today Proc,33688465,3/11/21,pubmed,0,3,"deep learning, neural network",0.001751168,0.001751259,0.330397562,0.662597613,0.001751226,0.001751171,Epidemiology,0.18707767,FALSE,10.66666667,0.159131672,0.666666667,0.096200161,0,0.403234768,,,0.2195222
1197,0,Arab J Sci Eng,33688457,3/11/21,pubmed,0,3,dataset,0.026652595,0.028149711,0.784268267,0.157742447,0.001593508,0.001593472,Epidemiology,0.060148537,FALSE,2.333333333,0.024800544,0,0.055525823,3,0.667819001,,,0.249381789
1198,Potential therapeutic and pharmacological strategies for SARS-CoV2.,J Pharm Investig,33688448,3/11/21,pubmed,0,17,in silico,0.808583363,0.001438267,0.001438211,0.083374228,0.10372766,0.00143827,Drug discovery,0.8113798,TRUE,9.352941176,0.139340714,2.588235294,0.181629649,0,0.403234768,,,0.24140171
1199,COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier.,Cognit Comput,33688379,3/11/21,pubmed,0,5,"deep learning, classifier",0.001350319,0.024371263,0.894959127,0.076618549,0.001350364,0.001350378,Imaging,0.76776934,TRUE,53.2,0.65508071,9.6,0.346735349,0,0.403234768,,,0.468350276
1200,Determinants of Dietary Adherence Among Type 2 Diabetes Patients Aimed COVID-19 at the University of Gondar Comprehensive Specialized Hospital.,Diabetes Metab Syndr Obes,33688226,3/11/21,pubmed,0,3,logistic regression,0.001203436,0.001203418,0.001203441,0.00120346,0.673551351,0.321634893,Healthcare,0.9764938,TRUE,5.333333333,0.074339786,0,0.055525823,0,0.403234768,,,0.177700125
1201,"The Magnitude of Hematological Abnormalities Among COVID-19 Patients in Addis Ababa, Ethiopia.",J Multidiscip Healthc,33688198,3/11/21,pubmed,0,13,logistic regression,0.001310368,0.001310379,0.030175626,0.001310403,0.146708708,0.819184515,Clinics,0.98318005,TRUE,2.923076923,0.03172738,0.769230769,0.099678887,0,0.403234768,,,0.178213678
1202,SARS-CoV-2 within-host diversity and transmission.,Science,33688063,3/11/21,pubmed,0,36,sequencing,0.002238445,0.956128403,0.034917782,0.002238487,0.002238466,0.002238416,Genomics,0.17375138,FALSE,86,0.829117447,184.5277778,0.909218625,1,0.537564047,,,0.758633373
1203,AI support for ethical decision-making around resuscitation: proceed with care.,J Med Ethics,33687916,3/11/21,pubmed,0,8,artificial intelligence,0.00141516,0.001415132,0.580037654,0.160114499,0.213921555,0.043095999,Healthcare,0.93411106,TRUE,69.75,0.762446657,35,0.605298368,0,0.403234768,,,0.590326597
1204,CoronaPep: An Anti-coronavirus Peptide Generation Tool.,IEEE/ACM Trans Comput Biol Bioinform,33687847,3/10/21,pubmed,0,6,"bioinformatic, whole genome",0.58841443,0.128168627,0.275729174,0.002562666,0.002562565,0.002562538,Drug discovery,0.36408335,FALSE,76.83333333,0.793308182,7.833333333,0.314088841,0,0.403234768,,,0.50354393
1205,Flexibilities of wavelets as a computational basis set for large-scale electronic structure calculations.,J Chem Phys,33687268,3/10/21,pubmed,0,14,computational,0.227409311,0.002183274,0.354990477,0.411050385,0.002183303,0.00218325,Epidemiology,0.022281349,FALSE,61.42857143,0.711361247,44.92857143,0.658415842,4,0.707574542,,,0.692450543
1206,Calcium sensing receptor hyperactivation through viral envelop protein E of SARS CoV2: A novel target for cardio-renal damage in COVID-19 infection.,Drug Dev Res,33687087,3/10/21,pubmed,0,13,sequencing,0.36769794,0.384732226,0.001622811,0.001622799,0.001622866,0.242701357,Genomics,0.43302235,FALSE,92.92307692,0.851258581,27.15384615,0.54669521,0,0.403234768,,,0.600396186
1207,Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings.,Korean J Radiol,33686818,3/10/21,pubmed,0,14,"deep learning, image analysis, classifier, logistic regression, dataset",0.000740438,0.000740287,0.493658333,0.000740304,0.0007403,0.503380338,Clinics,0.5771792,TRUE,79.42857143,0.804007669,191.1428571,0.912697351,0,0.403234768,,,0.706646596
1208,Estimating Baseline Incidence of Conditions Potentially Associated with Vaccine Adverse Events: a Call for Surveillance System Using the Korean National Health Insurance Claims Data.,J Korean Med Sci,33686812,3/10/21,pubmed,0,7,prediction model,0.001438145,0.001438157,0.001438117,0.854714069,0.001438201,0.139533311,Epidemiology,0.07773933,FALSE,36.57142857,0.503432494,28,0.554321648,0,0.403234768,,,0.486996303
1209,Quantifying compliance with COVID-19 mitigation policies in the US: A mathematical modeling study.,Infect Dis Model,33686377,3/10/21,pubmed,0,3,mathematical model,0.002183226,0.002183239,0.00218327,0.989083819,0.002183266,0.002183179,Epidemiology,0.26860392,FALSE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
1210,Green Chemistry and Coronavirus.,Sustain Chem Pharm,33686371,3/10/21,pubmed,0,13,"sequencing, whole genome",0.002720262,0.314977142,0.674142008,0.002720363,0.00272011,0.002720115,Imaging,0.72784835,TRUE,254,0.984847548,140.0769231,0.874230666,0,0.403234768,,,0.754104327
1211,COVID-19 in Europe: Dataset at a sub-national level.,Data Brief,33686370,3/10/21,pubmed,0,9,dataset,0.002490541,0.086558275,0.002490598,0.903479291,0.002490511,0.002490784,Epidemiology,0.13010603,FALSE,12.55555556,0.189683963,8.777777778,0.332285256,0,0.403234768,,,0.308401329
1212,Developmental Status of the Potential Vaccines for the Mitigation of the COVID-19 Pandemic and a Focus on the Effectiveness of the Pfizer-BioNTech and Moderna mRNA Vaccines.,Curr Clin Microbiol Rep,33686365,3/10/21,pubmed,0,1,in silico,0.832585286,0.160676397,0.001684516,0.001684624,0.001684636,0.00168454,Drug discovery,0.4157737,FALSE,138,0.930546107,85,0.790406743,0,0.403234768,,,0.708062539
1213,Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review.,Multimed Tools Appl,33686333,3/10/21,pubmed,0,2,"machine learning, deep learning, artificial intelligence, image processing",0.001786582,0.001786626,0.850477362,0.142376365,0.001786555,0.00178651,Imaging,0.89633065,TRUE,51.5,0.643267982,3,0.199424672,0,0.403234768,,,0.415309141
1214,Genome-wide CRISPR screening identifies TMEM106B as a proviral host factor for SARS-CoV-2.,Nat Genet,33686287,3/10/21,pubmed,0,19,"sequencing, genome-wide",0.779167651,0.181099944,0.002080592,0.002080599,0.002080582,0.033490632,Drug discovery,0.72310734,TRUE,144.0526316,0.938586183,134.7368421,0.869213273,2,0.618927094,,,0.80890885
1215,A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).,Nat Hum Behav,33686204,3/10/21,pubmed,0,11,dataset,0.001861716,0.001861718,0.001861725,0.990691458,0.001861709,0.001861675,Epidemiology,0.5495364,TRUE,17.27272727,0.260312945,3.454545455,0.209325662,5,0.739490092,,,0.4030429
1216,The in-vitro effect of famotidine on sars-cov-2 proteases and virus replication.,Sci Rep,33686143,3/10/21,pubmed,0,12,in silico,0.774134817,0.001371348,0.001371323,0.001371293,0.001371335,0.220379884,Drug discovery,0.6822921,TRUE,16.5,0.249366071,8.583333333,0.329943805,0,0.403234768,,,0.327514881
1217,"Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England.",Sci Rep,33686125,3/10/21,pubmed,0,3,bayes,0.000746548,0.000746573,0.000746548,0.996267195,0.000746559,0.000746577,Epidemiology,0.27156132,FALSE,263,0.986641103,277.6666667,0.947952903,0,0.403234768,,,0.779276258
1218,COVID-19 in early 2021: current status and looking forward.,Signal Transduct Target Ther,33686059,3/10/21,pubmed,0,6,artificial intelligence,0.238484309,0.438256794,0.251944918,0.002183372,0.002183218,0.066947389,Genomics,0.352867,FALSE,35,0.488032655,58.5,0.715814825,1,0.537564047,,,0.580470509
1219,Clinical outcome of COVID-19 in patients with adult congenital heart disease.,Heart,33685931,3/10/21,pubmed,0,32,logistic regression,0.001987082,0.001987109,0.001987454,0.07906257,0.001987329,0.912988457,Clinics,0.85486776,TRUE,111.5652174,0.89306698,113.3043478,0.84292213,0,0.403234768,,,0.713074626
1220,Impact of a Serious Game (Escape COVID-19) on the Intention to Change COVID-19 Control Practices Among Employees of Long-term Care Facilities: Web-Based Randomized Controlled Trial.,J Med Internet Res,33685854,3/10/21,pubmed,0,8,logistic regression,0.000752952,0.016028392,0.00075292,0.20265094,0.77906185,0.000752946,Healthcare,0.95559293,TRUE,29.875,0.429154555,21.125,0.492574257,0,0.403234768,,,0.441654527
1221,First confirmation of importation and transmission in Spain of the newly identified SARS-CoV-2 B.1.1.7 variant.,Enferm Infecc Microbiol Clin,33685741,3/10/21,pubmed,0,29,"sequencing, whole genome",0.001987266,0.9288459,0.001987142,0.001987175,0.033689045,0.031503473,Genomics,0.21078268,FALSE,42.31034483,0.560455192,37.62068966,0.619480867,0,0.403234768,,,0.527723609
1222,Violence and discrimination among Ugandan residents during the COVID-19 lockdown.,BMC Public Health,33685420,3/10/21,pubmed,0,7,logistic regression,0.001219998,0.001220004,0.001220012,0.227098929,0.768020993,0.001220064,Healthcare,0.8358876,TRUE,59.14285714,0.697136496,39.85714286,0.632258496,0,0.403234768,,,0.577543253
1223,0,J Biomol Struct Dyn,33685364,3/10/21,pubmed,0,9,"virtual screening, molecular dynamics simulation, computational, in silico",0.843434815,0.057607172,0.063984042,0.032097725,0.001438126,0.00143812,Drug discovery,0.66114473,TRUE,106.4444444,0.881934566,,,0,0.403234768,,,0.642584667
1224,SARS-CoV-2 infection in nonhuman primates alters the composition and functional activity of the gut microbiota.,Gut Microbes,33685349,3/10/21,pubmed,0,25,metabolom,0.126019183,0.467142987,0.001438152,0.210573746,0.001438214,0.193387718,Genomics,0.74224,TRUE,48.16,0.615622487,60.8,0.723641959,0,0.403234768,,,0.580833071
1225,"In silico identification of novel SARS-COV-2 2'-O-methyltransferase (nsp16) inhibitors: structure-based virtual screening, molecular dynamics simulation and MM-PBSA approaches.",J Enzyme Inhib Med Chem,33685335,3/10/21,pubmed,0,6,"virtual screening, molecular dynamics simulation, simulation experiment, in silico",0.921530766,0.001461869,0.001462021,0.072621456,0.001461964,0.001461924,Drug discovery,0.5358326,TRUE,30.33333333,0.436266931,2,0.164302917,0,0.403234768,,,0.334601538
1226,QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications.,Clin Imaging,33684789,3/9/21,pubmed,0,17,"artificial intelligence, dataset",0.028506426,0.000898114,0.860164466,0.108634747,0.000898122,0.000898125,Imaging,0.89018106,TRUE,265.5294118,0.98688849,333,0.96273749,0,0.403234768,,,0.784286916
1227,An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound.,Comput Biol Med,33684688,3/9/21,pubmed,0,3,"neural network, lstm",0.001203457,0.001203436,0.993982717,0.001203498,0.001203435,0.001203458,Imaging,0.6889434,TRUE,77.33333333,0.795967592,10,0.355632861,0,0.403234768,,,0.518278407
1228,Ensemble-based screening of natural products and FDA-approved drugs identified potent inhibitors of SARS-CoV-2 that work with two distinct mechanisms.,J Mol Graph Model,33684603,3/9/21,pubmed,0,6,"molecular dynamics simulation, computational",0.992924354,0.001415122,0.00141512,0.001415143,0.001415088,0.001415172,Drug discovery,0.9251705,TRUE,26.33333333,0.386047375,11.16666667,0.372357506,0,0.403234768,,,0.387213216
1229,Insilico drug repurposing using FDA approved drugs against Membrane protein of SARS-CoV-2.,J Pharm Sci,33684397,3/9/21,pubmed,0,6,"virtual screening, molecular dynamics simulation",0.994505981,0.001098805,0.001098792,0.001098811,0.001098819,0.001098792,Drug discovery,0.8745038,TRUE,47.16666667,0.607087637,25,0.529435376,0,0.403234768,,,0.513252593
1230,Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants.,J Gerontol A Biol Sci Med Sci,33684206,3/9/21,pubmed,0,10,logistic regression,0.001653048,0.001653081,0.001653055,0.001653175,0.00165316,0.991734481,Clinics,0.7555878,TRUE,51,0.63875317,79.5,0.77782981,0,0.403234768,,,0.606605916
1231,Global epidemiology and socio-economic development correlates of the reproductive ratio of COVID-19.,Int Health,33684196,3/9/21,pubmed,0,1,dataset,0.001310358,0.001310385,0.001310367,0.830134775,0.078339224,0.087594892,Epidemiology,0.32311636,FALSE,95,0.856824788,135,0.869614664,0,0.403234768,,,0.709891407
1232,A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes' interactions points to Neutrophil extracellular traps as mediators of thrombosis in COVID-19.,PLoS Comput Biol,33684134,3/9/21,pubmed,0,13,computational,0.35646371,0.053388074,0.170268615,0.089333276,0.077045358,0.253500967,Drug discovery,0.20235029,FALSE,36.53846154,0.503185107,35.84615385,0.609312283,0,0.403234768,,,0.505244052
1233,The effect of human mobility and control measures on traffic safety during COVID-19 pandemic.,PLoS One,33684104,3/9/21,pubmed,0,6,dataset,0.079266472,0.001486506,0.001486443,0.765616136,0.150657995,0.001486448,Epidemiology,0.8051548,TRUE,27.16666667,0.397303482,10.5,0.363459995,0,0.403234768,,,0.387999415
1234,"Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach.",JMIR Med Inform,33684052,3/9/21,pubmed,0,9,text mining,0.049541465,0.000988367,0.074101024,0.46301828,0.411362487,0.000988378,Epidemiology,0.643109,TRUE,48.11111111,0.615375101,62.55555556,0.730933904,0,0.403234768,,,0.583181257
1235,How to spot COVID-19 patients: Speech & sound audio analysis for preliminary diagnosis of SARS-COV-2 corona patients.,Int J Clin Pract,33683774,3/9/21,pubmed,0,3,"machine learning, artificial intelligence",0.001461857,0.143748026,0.290030452,0.561835732,0.001461938,0.001461996,Epidemiology,0.095523,FALSE,53.66666667,0.658544128,14.66666667,0.420323789,0,0.403234768,,,0.494034228
1236,Adverse cardiovascular magnetic resonance phenotypes are associated with greater likelihood of incident coronavirus disease 2019: findings from the UK Biobank.,Aging Clin Exp Res,33683678,3/9/21,pubmed,0,14,logistic regression,0.001126817,0.001126868,0.035061722,0.001126858,0.042848746,0.918708989,Clinics,0.76334095,TRUE,239.1428571,0.982559218,299.8571429,0.954308269,0,0.403234768,,,0.780034085
1237,"Emergency room comprehensive assessment of demographic, radiological, laboratory and clinical data of patients with COVID-19: determination of its prognostic value for in-hospital mortality.",Intern Emerg Med,33683539,3/9/21,pubmed,0,20,logistic regression,0.001330045,0.001330028,0.209927171,0.001330027,0.001330035,0.784752693,Clinics,0.9476694,TRUE,62.6,0.718535469,21.75,0.50046829,0,0.403234768,,,0.540746176
1238,Characterization of the bioaerosol in a natural thermal cave and assessment of the risk of transmission of SARS-CoV-2 virus.,Environ Geochem Health,33683533,3/9/21,pubmed,0,5,mathematical model,0.038508454,0.223862875,0.001786522,0.672527579,0.00178671,0.061527861,Epidemiology,0.53140116,TRUE,45.2,0.588719154,26.6,0.542413701,0,0.403234768,,,0.511455874
1239,"A novel multi-omics-based highly accurate prediction of symptoms, comorbid conditions, and possible long-term complications of COVID-19.",Mol Omics,33683246,3/9/21,pubmed,0,9,"bioinformatic, multi-omics",0.178528551,0.034438695,0.068731165,0.152255711,0.105880239,0.460165638,Clinics,0.82212937,TRUE,223.2222222,0.979405034,105.4444444,0.831950763,0,0.403234768,,,0.738196855
1240,"A SARS-CoV-2 Peptide Spectral Library Enables Rapid, Sensitive Identification of Virus Peptides in Complex Biological Samples.",J Proteome Res,33683136,3/9/21,pubmed,0,3,proteom,0.719064378,0.18432129,0.08819495,0.002806527,0.002806458,0.002806397,Drug discovery,0.31279474,FALSE,64,0.729173109,300.3333333,0.954375167,0,0.403234768,,,0.695594348
1241,Air pollutants and SARS-CoV-2 in 33 European countries.,Acta Biomed,33682802,3/9/21,pubmed,0,4,correlation analysis,0.049110313,0.00229662,0.002296523,0.605793874,0.002296556,0.338206113,Epidemiology,0.24494743,FALSE,224,0.979652421,110.75,0.839577201,0,0.403234768,,,0.740821463
1242,"Seroprevalence of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection in an Italian cohort in Marche Region, Italy.",Acta Biomed,33682801,3/9/21,pubmed,0,3,logistic regression,0.002357845,0.166046795,0.002357898,0.002357902,0.657970223,0.168909337,Healthcare,0.52451444,TRUE,73,0.778464964,61.66666667,0.727187584,0,0.403234768,,,0.636295772
1243,"Temporal changes of quantitative CT findings from 102 patients with COVID-19 in Wuhan, China: A longitudinal study.",Technol Health Care,33682766,3/9/21,pubmed,0,6,artificial intelligence,0.001461865,0.001461933,0.367954076,0.001461965,0.001461906,0.626198254,Clinics,0.5982102,TRUE,23.16666667,0.343249428,10.16666667,0.357171528,0,0.403234768,,,0.367885241
1244,Prediction and follow-up of risk factors for severe SARS-CoV-2 pneumonia and application of CT visual scoring.,Technol Health Care,33682755,3/9/21,pubmed,0,12,logistic regression,0.001156254,0.001156276,0.175730309,0.001156266,0.001156252,0.819644643,Clinics,0.8666569,TRUE,50.33333333,0.63380543,124.1666667,0.856703238,0,0.403234768,,,0.631247812
1245,Video-Based Analyses of Parkinson's Disease Severity: A Brief Review.,J Parkinsons Dis,33682727,3/9/21,pubmed,0,4,"machine learning, artificial intelligence",0.002357873,0.002357894,0.917488773,0.073079588,0.002357894,0.002357978,Epidemiology,0.78843004,TRUE,12.5,0.189436576,2.25,0.170925876,1,0.537564047,,,0.299308833
1246,"Finding potent inhibitors against SARS-CoV-2 main protease through virtual screening, ADMET, and molecular dynamics simulation studies.",J Biomol Struct Dyn,33682642,3/9/21,pubmed,0,5,"virtual screening, molecular dynamics simulation",0.993636744,0.001272634,0.001272648,0.001272655,0.001272671,0.001272648,Drug discovery,0.66079026,TRUE,13.8,0.208980147,1,0.122023013,0,0.403234768,,,0.244745976
1247,0,J Biomol Struct Dyn,33682639,3/9/21,pubmed,0,1,in silico,0.977259958,0.001237069,0.001237117,0.001237124,0.001237163,0.017791569,Drug discovery,0.88524765,TRUE,35,0.488032655,1,0.122023013,0,0.403234768,,,0.337763479
1248,"Structure-based virtual screening, molecular dynamics and binding affinity calculations of some potential phytocompounds against SARS-CoV-2.",J Biomol Struct Dyn,33682632,3/9/21,pubmed,0,8,"virtual screening, in-silico",0.90233984,0.001237108,0.02482637,0.026213626,0.001237122,0.044145935,Drug discovery,0.88651514,TRUE,58.25,0.691384749,22.5,0.507492641,0,0.403234768,,,0.534037386
1249,Flavonoids as potential therapeutics against novel coronavirus disease-2019 (nCOVID-19).,J Biomol Struct Dyn,33682606,3/9/21,pubmed,0,6,"molecular dynamics simulation, in-silico",0.991885985,0.001622784,0.00162284,0.001622865,0.001622754,0.001622772,Drug discovery,0.91144925,TRUE,40.5,0.544746119,10.5,0.363459995,0,0.403234768,,,0.43714696
1250,Investigating Popular Mental Health Mobile Application Downloads and Activity During the COVID-19 Pandemic.,Hum Factors,33682467,3/9/21,pubmed,0,3,correlation analysis,0.001486445,0.001486472,0.062646678,0.488630418,0.444263288,0.001486699,Epidemiology,0.9818784,TRUE,77.66666667,0.797266374,33.33333333,0.594995986,0,0.403234768,,,0.598499043
1251,Mathematical computations on epidemiology: a case study of the novel coronavirus (SARS-CoV-2).,Theory Biosci,33682078,3/9/21,pubmed,0,2,mathematical model,0.001593526,0.001593544,0.001593605,0.964685975,0.001593636,0.028939715,Epidemiology,0.6660081,TRUE,4,0.054734368,0.5,0.087101953,0,0.403234768,,,0.181690363
1252,Should We Embrace the Incorporation of Genetically Guided "Dopamine Homeostasis" in the Treatment of Reward Deficiency Syndrome (RSD) as a Frontline Therapeutic Modality?,Acta Sci Neurol,33681869,3/9/21,pubmed,0,14,nutrigenom,0.312314476,0.140305847,0.06779175,0.205976713,0.193333063,0.080278151,Drug discovery,0.70169866,TRUE,106.1428571,0.881316099,64.07142857,0.735616805,0,0.403234768,,,0.673389224
1253,"Clinical characteristics and mortality associated with COVID-19 in Jakarta, Indonesia: A hospital-based retrospective cohort study.",Lancet Reg Health West Pac,33681830,3/9/21,pubmed,0,17,logistic regression,0.001371227,0.001371273,0.001371271,0.001371359,0.081881401,0.912633468,Clinics,0.6227852,TRUE,63.29411765,0.723173975,100.7058824,0.82358844,0,0.403234768,,,0.649999061
1254,The new SARS-CoV-2 strain shows a stronger binding affinity to ACE2 due to N501Y mutant.,Med Drug Discov,33681755,3/9/21,pubmed,0,3,molecular dynamics simulation,0.654831562,0.33520659,0.002490502,0.002490484,0.002490438,0.002490423,Drug discovery,0.40301013,FALSE,15.33333333,0.230997588,4,0.231469093,5,0.739490092,,,0.400652258
1255,Understanding Demographic Risk Factors for Adverse Outcomes in COVID-19 Patients: Explanation of a Deep Learning Model.,J Healthc Inform Res,33681695,3/9/21,pubmed,0,6,"deep learning, neural network",0.081736226,0.001438135,0.133880094,0.001438168,0.234434088,0.547073288,Clinics,0.745924,TRUE,141.1666667,0.935308306,90.5,0.801913299,0,0.403234768,,,0.713485458
1256,"Food Insecurity Is Associated with Depression, Anxiety, and Stress: Evidence from the Early Days of the COVID-19 Pandemic in the United States.",Health Equity,33681691,3/9/21,pubmed,0,3,logistic regression,0.001141332,0.001141315,0.001141352,0.001141378,0.994293285,0.001141337,Healthcare,0.7331248,TRUE,56,0.675304595,49.33333333,0.678953706,0,0.403234768,,,0.585831023
1257,Analysis and comparison of genetic variants and mutations of the novel coronavirus SARS-CoV-2.,Gene Rep,33681535,3/9/21,pubmed,0,2,"genome sequences, genomes",0.087451835,0.740366863,0.001511867,0.167645652,0.001511871,0.001511912,Genomics,0.17246014,FALSE,47.5,0.610551054,23,0.513513514,0,0.403234768,,,0.509099779
1258,In silico investigation of potential inhibitors to main protease and spike protein of SARS-CoV-2 in propolis.,Biochem Biophys Rep,33681482,3/9/21,pubmed,0,11,in silico,0.987187329,0.002562536,0.002562536,0.002562536,0.002562543,0.00256252,Drug discovery,0.8576902,TRUE,9.454545455,0.140515802,0.272727273,0.065828204,0,0.403234768,,,0.203192925
1259,"Impact of the 2019 Novel Coronavirus Disease Pandemic on the Performance of a Cardiovascular Department in a Non-epidemic Center in Beijing, China.",Front Cardiovasc Med,33681305,3/9/21,pubmed,0,8,correlation analysis,0.000854697,0.000854708,0.062643908,0.000854778,0.110715559,0.82407635,Clinics,0.9802476,TRUE,61.25,0.710248005,24,0.521808938,0,0.403234768,,,0.545097237
1260,Case Report: Placental Maternal Vascular Malperfusion Affecting Late Fetal Development and Multiorgan Infection Caused by SARS-CoV-2 in Patient With PAI-1 4G/5G Polymorphism.,Front Med (Lausanne),33681253,3/9/21,pubmed,0,11,sequencing,0.097074786,0.354856483,0.003927409,0.003927764,0.114373083,0.425840474,Clinics,0.8754294,TRUE,26.18181818,0.383264271,7.363636364,0.304789938,0,0.403234768,,,0.363762992
1261,CANPT Score: A Tool to Predict Severe COVID-19 on Admission.,Front Med (Lausanne),33681245,3/9/21,pubmed,0,8,"logistic regression, prediction model",0.001392821,0.001392823,0.093656787,0.001392885,0.001392827,0.900771858,Clinics,0.9749172,TRUE,49.125,0.625023193,13.875,0.409486219,0,0.403234768,,,0.47924806
1262,Impact of temperature and sunshine duration on daily new cases and death due to COVID-19.,J Family Med Prim Care,33681046,3/9/21,pubmed,0,5,correlation analysis,0.001310322,0.001310337,0.001310337,0.44294256,0.00131038,0.551816064,Clinics,0.38862884,FALSE,32.4,0.45983054,16.8,0.446548033,0,0.403234768,,,0.43653778
1263,Continuum of care for non-communicable diseases during COVID-19 pandemic in rural India: A mixed methods study.,J Family Med Prim Care,33681035,3/9/21,pubmed,0,3,digital health,0.00107221,0.00107222,0.001072203,0.150692816,0.827704043,0.018386509,Healthcare,0.9777213,TRUE,70,0.764178366,31,0.578204442,0,0.403234768,,,0.581872525
1264,Classification of covid related articles using machine learning.,Mater Today Proc,33680869,3/9/21,pubmed,0,2,"machine learning, classifier, dataset",0.00190174,0.001901703,0.526471625,0.46592152,0.001901731,0.001901681,Epidemiology,0.22785568,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
1265,COVID-19 Outbreak in Malaysia: Decoding D614G Mutation of SARS-CoV-2 Virus Isolated from an Asymptomatic Case in Pahang.,Mater Today Proc,33680867,3/9/21,pubmed,0,6,"bioinformatic, sequencing, whole genome",0.001593603,0.895673278,0.078569694,0.001593554,0.020976115,0.001593758,Genomics,0.1463049,FALSE,8.333333333,0.123693488,1.666666667,0.145036125,0,0.403234768,,,0.223988127
1266,An Intelligent and Energy-Efficient Wireless Body Area Network to Control Coronavirus Outbreak.,Arab J Sci Eng,33680703,3/9/21,pubmed,0,3,"bayes, machine learning, classifier, logistic regression",0.026720935,0.055298611,0.697053353,0.15485347,0.001171626,0.064902004,Epidemiology,0.76373595,TRUE,89.66666667,0.841857876,28,0.554321648,0,0.403234768,,,0.599804764
1267,DisCoVering potential candidates of RNAi-based therapy for COVID-19 using computational methods.,PeerJ,33680575,3/9/21,pubmed,0,3,"computational, bioinformatic",0.623722905,0.369785922,0.001622836,0.001622821,0.001622749,0.001622767,Drug discovery,0.8669338,TRUE,5.333333333,0.074339786,0,0.055525823,0,0.403234768,,,0.177700125
1268,0,New Microbes New Infect,33680474,3/9/21,pubmed,0,14,bioinformatic,0.824411193,0.171138277,0.001112627,0.00111263,0.001112631,0.001112641,Drug discovery,0.9195143,TRUE,103,0.875378811,61.5,0.726585496,0,0.403234768,,,0.668399692
1269,Routine use of immunosuppressants is associated with mortality in hospitalised patients with COVID-19.,Ther Adv Drug Saf,33680426,3/9/21,pubmed,0,23,logistic regression,0.097553635,0.000793456,0.014201383,0.140329125,0.000793449,0.746328952,Clinics,0.7655488,TRUE,75.7826087,0.789535531,47.60869565,0.670858978,0,0.403234768,,,0.621209759
1270,Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer.,Comput Struct Biotechnol J,33680351,3/9/21,pubmed,0,4,"deep learning, artificial intelligence, neural network, image analysis, dataset",0.001254593,0.001254579,0.961756848,0.001254591,0.001254588,0.033224802,Imaging,0.5111416,TRUE,23.25,0.34454821,1,0.122023013,0,0.403234768,,,0.28993533
1271,A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria.,Build Simul,33680337,3/9/21,pubmed,0,3,"simulation model, probabilistic",0.001220032,0.001220037,0.001220056,0.813880637,0.181239194,0.001220045,Epidemiology,0.8438316,TRUE,100.3333333,0.869441524,28.66666667,0.559673535,0,0.403234768,,,0.610783275
1272,COVID-19 classification using deep feature concatenation technique.,J Ambient Intell Humaniz Comput,33680212,3/9/21,pubmed,0,6,"deep learning, neural network",0.000907335,0.016306102,0.980064704,0.000907306,0.000907273,0.00090728,Imaging,0.57559526,TRUE,119.5,0.90654957,12.66666667,0.39530372,0,0.403234768,,,0.568362686
1273,One-shot Cluster-Based Approach for the Detection of COVID-19 from Chest X-ray Images.,Cognit Comput,33680210,3/9/21,pubmed,0,5,"machine learning, deep learning, neural network, classifier, probabilistic, dataset",0.001022614,0.054766906,0.941142483,0.001022707,0.001022659,0.001022632,Imaging,0.58320653,TRUE,109.8,0.889789103,37.4,0.618678084,0,0.403234768,,,0.637233985
1274,Deep Learning-Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis.,Cognit Comput,33680209,3/9/21,pubmed,0,6,"deep learning, artificial intelligence, classifier, deep model, dataset",0.001187322,0.001187389,0.837846004,0.123132547,0.035459465,0.001187272,Imaging,0.60923713,TRUE,11.5,0.17416043,0.666666667,0.096200161,0,0.403234768,,,0.224531786
1275,COVID-19 in Iran: Forecasting Pandemic Using Deep Learning.,Comput Math Methods Med,33680071,3/9/21,pubmed,0,10,"deep learning, lstm",0.001461872,0.001461884,0.316413875,0.677738627,0.001461879,0.001461863,Epidemiology,0.32478756,FALSE,138.8,0.931721195,60.1,0.721032914,0,0.403234768,,,0.685329626
1276,Is COVID-19 pushing us to the Fifth Industrial Revolution (Society 5.0)?,Pak J Med Sci,33679956,3/9/21,pubmed,0,4,artificial intelligence,0.00299649,0.00299651,0.287698658,0.700315317,0.002996609,0.002996415,Epidemiology,0.73552936,TRUE,7.25,0.104149917,0,0.055525823,0,0.403234768,,,0.187636836
1277,SARS-CoV-2 Infection-Induced Promoter Hypomethylation as an Epigenetic Modulator of Heat Shock Protein A1L (HSPA1L) Gene.,Front Genet,33679887,3/9/21,pubmed,0,5,"in silico, transcriptom",0.734850105,0.21550703,0.001350342,0.001350339,0.00135033,0.045591854,Drug discovery,0.8725853,TRUE,39.6,0.53503618,12.2,0.387476585,0,0.403234768,,,0.441915844
1278,Inflammation and Antiviral Immune Response Associated With Severe Progression of COVID-19.,Front Immunol,33679778,3/9/21,pubmed,0,9,"transcriptom, whole genome",0.71624511,0.038182869,0.001392853,0.001392836,0.001392852,0.24139348,Drug discovery,0.696895,TRUE,48,0.614942173,56.88888889,0.710329141,0,0.403234768,,,0.576168694
1279,Cyclic-di-GMP Induces STING-Dependent ILC2 to ILC1 Shift During Innate Type 2 Lung Inflammation.,Front Immunol,33679760,3/9/21,pubmed,0,10,transcriptom,0.788483505,0.001565341,0.001565311,0.001565321,0.001565345,0.205255177,Drug discovery,0.19265914,FALSE,18.4,0.27558909,16.9,0.447551512,0,0.403234768,,,0.375458457
1280,Interleukin-6 Is a Biomarker for the Development of Fatal Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia.,Front Immunol,33679753,3/9/21,pubmed,0,10,logistic regression,0.07601581,0.001371246,0.001371471,0.001371323,0.001371265,0.918498885,Clinics,0.89536417,TRUE,33.3,0.469231245,28.1,0.55472304,0,0.403234768,,,0.475729684
1281,Serum Amyloid A in Inflammatory Rheumatic Diseases: A Compendious Review of a Renowned Biomarker.,Front Immunol,33679725,3/9/21,pubmed,0,3,proteom,0.371217223,0.001330104,0.001330109,0.274887624,0.001330091,0.349904849,Drug discovery,0.8173562,TRUE,27,0.3960047,3,0.199424672,0,0.403234768,,,0.332888047
1282,Coronavirus Disease 2019: Exploring Media Portrayals of Public Sentiment on Funerals Using Linguistic Dimensions.,Front Psychol,33679546,3/9/21,pubmed,0,3,"bayes, correlation analysis",0.053660162,0.001072198,0.001072213,0.55167487,0.391448361,0.001072196,Epidemiology,0.9169765,TRUE,16.33333333,0.247015895,1.666666667,0.145036125,1,0.537564047,,,0.309872022
1283,0,Front Physiol,33679437,3/9/21,pubmed,0,4,in silico,0.404398237,0.001786623,0.001786633,0.315297245,0.001786587,0.274944675,Drug discovery,0.75178486,TRUE,54.25,0.663120787,59,0.717554188,0,0.403234768,,,0.594636581
1284,Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients.,Pers Ubiquitous Comput,33679282,3/9/21,pubmed,0,5,"machine learning, deep learning, neural network, classifier, dataset",0.001203424,0.00120342,0.968913682,0.001203424,0.026272619,0.001203431,Healthcare,0.99102914,TRUE,1.8,0.017069701,0,0.055525823,0,0.403234768,,,0.158610097
1285,Impact of social media advertisements on the transmission dynamics of COVID-19 pandemic in India.,J Appl Math Comput,33679275,3/9/21,pubmed,0,5,mathematical model,0.001141324,0.018320345,0.001141346,0.902134191,0.076121417,0.001141378,Epidemiology,0.62354195,TRUE,98,0.864741171,28.6,0.559071448,0,0.403234768,,,0.609015796
1286,An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion.,Inf Fusion,33679271,3/9/21,pubmed,0,5,machine learning,0.078679415,0.001438128,0.720782384,0.001438197,0.139463591,0.058198285,Healthcare,0.9174446,TRUE,83.8,0.820582596,18.8,0.468156275,0,0.403234768,,,0.563991213
1287,Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment.,Multimed Tools Appl,33679209,3/9/21,pubmed,0,5,"deep learning, dataset",0.00159346,0.001593475,0.389979875,0.603646175,0.00159356,0.001593454,Epidemiology,0.5016546,TRUE,151,0.945203785,52.2,0.689724378,0,0.403234768,,,0.679387643
1288,Hematological and Inflammatory Parameters to Predict the Prognosis in COVID-19.,Indian J Hematol Blood Transfus,33679013,3/9/21,pubmed,0,1,logistic regression,0.030639193,0.000863044,0.015098798,0.000863043,0.000863041,0.951672881,Clinics,0.9537467,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
1289,Inhibitory efficacy of RNA virus drugs against SARS-CoV-2 proteins: An extensive study.,J Mol Struct,33678903,3/9/21,pubmed,0,7,molecular dynamics simulation,0.989083657,0.002183276,0.00218324,0.002183328,0.002183247,0.002183252,Drug discovery,0.76131606,TRUE,60.42857143,0.705300266,22.57142857,0.507827134,0,0.403234768,,,0.538787389
1290,"Hypertension in Patients Hospitalized with COVID-19 in Wuhan, China.",Int Heart J,33678794,3/9/21,pubmed,0,10,logistic regression,0.033838511,0.001350403,0.020667729,0.001350336,0.001350352,0.941442669,Clinics,0.82054865,TRUE,21.8,0.322283382,7.4,0.305392026,1,0.537564047,,,0.388413152
1291,0,Aging (Albany NY),33678621,3/9/21,pubmed,0,6,"virtual screening, molecular dynamics simulation",0.991413983,0.001717229,0.001717192,0.001717215,0.001717208,0.001717172,Drug discovery,0.9190168,TRUE,20.66666667,0.306512462,4.5,0.242708055,0,0.403234768,,,0.317485095
1292,Dysmagnesemia in Covid-19 cohort patients: prevalence and associated factors.,Magnes Res,33678604,3/9/21,pubmed,0,4,logistic regression,0.00143817,0.001438138,0.001438132,0.001438139,0.001438182,0.99280924,Clinics,0.90969014,TRUE,84.25,0.822747232,21.75,0.50046829,1,0.537564047,,,0.620259857
1293,Predictors of Pneumothorax/Pneumomediastinum in Mechanically Ventilated COVID-19 Patients.,J Cardiothorac Vasc Anesth,33678544,3/9/21,pubmed,0,20,logistic regression,0.001112628,0.001112669,0.073912142,0.00111271,0.001112689,0.921637162,Clinics,0.9554981,TRUE,113,0.89609747,73.75,0.763312818,1,0.537564047,,,0.732324778
1294,Accelerating action on cervical screening in lower- and middle-income countries (LMICs) post COVID-19 era.,Prev Med,33678225,3/9/21,pubmed,0,5,digital health,0.167911639,0.001684552,0.001684546,0.4584088,0.201926246,0.168384218,Epidemiology,0.29262796,FALSE,101,0.871296926,135.4,0.869882258,0,0.403234768,,,0.714804651
1295,Integrated gut virome and bacteriome dynamics in COVID-19 patients.,Gut Microbes,33678150,3/9/21,pubmed,0,20,"microbiom, virom",0.304722787,0.280050486,0.001751186,0.001751212,0.001751217,0.409973111,Clinics,0.6817981,TRUE,64.15,0.729358649,88.7,0.798635269,0,0.403234768,,,0.643742895
1296,Dynamic blood single-cell immune responses in patients with COVID-19.,Signal Transduct Target Ther,33677468,3/8/21,pubmed,0,21,"sequencing, transcriptom",0.623245441,0.049844389,0.000956302,0.000956319,0.000956308,0.324041242,Drug discovery,0.47620234,FALSE,13.04761905,0.197414806,,,0,0.403234768,,,0.300324787
1297,0,Comput Biol Chem,33677227,3/8/21,pubmed,0,3,virtual screening,0.92901228,0.001622741,0.064496639,0.001622731,0.001622778,0.001622831,Drug discovery,0.35220385,FALSE,25.33333333,0.37188447,9,0.337904736,0,0.403234768,,,0.371007991
1298,The extent of molecular variation in novel SARS-CoV-2 after the six-month global spread.,Infect Genet Evol,33677109,3/8/21,pubmed,0,4,genome sequences,0.001330037,0.993349707,0.001330046,0.001330123,0.001330052,0.001330034,Genomics,0.8238431,TRUE,40.5,0.544746119,28.5,0.558402462,0,0.403234768,,,0.502127783
1299,Intestinal host response to SARS-CoV-2 infection and COVID-19 outcomes in patients with gastrointestinal symptoms.,Gastroenterology,33676971,3/8/21,pubmed,0,53,"sequencing, logistic regression",0.175608516,0.180755844,0.001330055,0.001330038,0.001330063,0.639645483,Clinics,0.69113696,TRUE,136.9245283,0.929123632,209.2830189,0.921661761,1,0.537564047,,,0.79611648
1300,Antiviral peptides against Coronaviridae family: A review.,Peptides,33676968,3/8/21,pubmed,0,6,computational,0.879626439,0.115748338,0.001156263,0.001156328,0.001156308,0.001156325,Drug discovery,0.5299262,TRUE,51.66666667,0.644195683,11.16666667,0.372357506,0,0.403234768,,,0.473262652
1301,"Azithromycin for community treatment of suspected COVID-19 in people at increased risk of an adverse clinical course in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial.",Lancet,33676597,3/8/21,pubmed,0,18,"bayes, logistic regression",0.000807964,0.000807888,0.00080788,0.15130939,0.282279401,0.563987477,Clinics,0.71130645,TRUE,62.15789474,0.716061599,,,3,0.667819001,,,0.6919403
1302,One year of SARS-CoV-2 evolution.,Cell Host Microbe,33676588,3/8/21,pubmed,0,11,genome sequences,0.007061693,0.964692323,0.007061514,0.007061623,0.007061439,0.007061408,Genomics,0.32642162,FALSE,66.81818182,0.74587173,117.7272727,0.848274017,2,0.618927094,,,0.737690947
1303,"Evaluating the effectiveness of measures to control the novel coronavirus disease 2019 in Jilin Province, China.",BMC Infect Dis,33676420,3/8/21,pubmed,0,14,model fit,0.001392829,0.00139286,0.001392927,0.934603424,0.001392892,0.059825068,Epidemiology,0.5325376,TRUE,48.64285714,0.618776671,,,0,0.403234768,,,0.511005719
1304,SARS-CoV-2 variants combining spike mutations and the absence of ORF8 may be more transmissible and require close monitoring.,Biochem Biophys Res Commun,33676232,3/7/21,pubmed,0,1,genomes,0.001593553,0.992032394,0.001593503,0.001593522,0.001593518,0.00159351,Genomics,0.22404757,FALSE,7,0.10179974,0,0.055525823,1,0.537564047,,,0.23162987
1305,"Molecular designing, crystal structure determination and in silico screening of copper(II) complexes bearing 8-hydroxyquinoline derivatives as anti-COVID-19.",Bioorg Chem,33676041,3/7/21,pubmed,0,7,in silico,0.789652539,0.002238482,0.086229962,0.002238729,0.117401799,0.002238489,Drug discovery,0.77757376,TRUE,43.42857143,0.572144227,,,0,0.403234768,,,0.487689497
1306,Risk and predictive factors of prolonged viral RNA shedding in upper respiratory specimens in a large cohort of COVID-19 patients admitted to an Italian reference hospital.,Int J Infect Dis,33676001,3/7/21,pubmed,0,390,logistic regression,0.001486449,0.04097869,0.001486413,0.001486461,0.001486446,0.953075541,Clinics,0.86918,TRUE,37.54787234,0.514008287,,,0,0.403234768,,,0.458621528
1307,Implementation of a fully digital histology course in the anatomical teaching curriculum during COVID-19 pandemic.,Ann Anat,33675948,3/7/21,pubmed,0,4,active learning,0.00090731,0.038596133,0.156232717,0.237176138,0.566180332,0.00090737,Healthcare,0.27818978,FALSE,33.5,0.471890655,91.25,0.80425475,0,0.403234768,,,0.559793391
1308,Binding mode of SARS-CoV-2 fusion peptide to human cellular membrane.,Biophys J,33675757,3/7/21,pubmed,0,4,molecular dynamics simulation,0.896157938,0.100057529,0.00094613,0.000946164,0.000946112,0.000946127,Drug discovery,0.1587477,FALSE,128.75,0.919784773,323.5,0.96039604,0,0.403234768,,,0.761138527
1309,Diet and physical activity during the coronavirus disease 2019 (COVID-19) lockdown (March-May 2020): results from the French NutriNet-Santé cohort study.,Am J Clin Nutr,33675635,3/7/21,pubmed,0,20,logistic regression,0.00101097,0.001010975,0.01603786,0.309022017,0.671907193,0.001010985,Healthcare,0.85074294,TRUE,188.35,0.968210774,214.95,0.924872893,1,0.537564047,,,0.810215905
1310,Non-pharmaceutical Interventions and the Infodemic on Twitter: Lessons Learned from Italy during the Covid-19 Pandemic.,J Med Syst,33675427,3/7/21,pubmed,0,7,network analysis,0.001126786,0.001126787,0.001126792,0.994366016,0.001126843,0.001126775,Epidemiology,0.8092746,TRUE,61.85714286,0.714268044,26.85714286,0.544487557,0,0.403234768,,,0.553996789
1311,The miRNA: a small but powerful RNA for COVID-19.,Brief Bioinform,33675361,3/7/21,pubmed,0,9,bioinformatic,0.750362315,0.001653114,0.001653053,0.103590777,0.141087646,0.001653094,Drug discovery,0.511816,TRUE,57.55555556,0.685818542,8,0.320511105,0,0.403234768,,,0.469854805
1312,Clinical presentation of COVID-19 - a model derived by a machine learning algorithm.,J Integr Bioinform,33675198,3/7/21,pubmed,0,3,machine learning,0.001593507,0.138347771,0.389782104,0.095841369,0.106994546,0.267440704,Clinics,0.21840534,FALSE,85.33333333,0.825901416,153,0.88587102,0,0.403234768,,,0.705002401
1313,Can we predict which COVID-19 patients will need transfer to intensive care within 24 hours of floor admission?,Acad Emerg Med,33675164,3/7/21,pubmed,0,12,logistic regression,0.00096677,0.000966785,0.090774132,0.000966808,0.000966767,0.905358739,Clinics,0.8949792,TRUE,31,0.445111015,4.454545455,0.23963072,0,0.403234768,,,0.362658834
1314,Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism.,J Digit Imaging,33674979,3/7/21,pubmed,0,4,neural network,0.001203464,0.001203531,0.993982678,0.001203448,0.001203405,0.001203474,Imaging,0.4745011,FALSE,51.5,0.643267982,32.25,0.587570244,0,0.403234768,,,0.544690998
1315,COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model.,J Am Med Inform Assoc,33674830,3/7/21,pubmed,0,11,deep learning,0.052414344,0.001415158,0.854663213,0.001415217,0.001415196,0.088676872,Clinics,0.17937824,FALSE,31.90909091,0.453955099,11.27272727,0.374498261,0,0.403234768,,,0.410562709
1316,IFN signaling and neutrophil degranulation transcriptional signatures are induced during SARS-CoV-2 infection.,Commun Biol,33674719,3/7/21,pubmed,0,16,transcriptom,0.618335841,0.002562586,0.002562582,0.002562596,0.002562593,0.371413803,Drug discovery,0.7640133,TRUE,78.3125,0.799492857,115.125,0.844661493,0,0.403234768,,,0.682463039
1317,Individualized prediction of COVID-19 adverse outcomes with MLHO.,Sci Rep,33674708,3/7/21,pubmed,0,3,"machine learning, prediction model",0.001046869,0.001046849,0.472362187,0.186919426,0.001046887,0.337577782,Clinics,0.048144013,FALSE,111.3333333,0.892386666,105.6666667,0.832419053,0,0.403234768,,,0.709346829
1318,Stay-at-home policy is a case of exception fallacy: an internet-based ecological study.,Sci Rep,33674661,3/7/21,pubmed,0,4,mathematical model,0.002032757,0.002032855,0.002032789,0.79883916,0.103584922,0.091477516,Epidemiology,0.34344172,FALSE,61.25,0.710248005,9,0.337904736,0,0.403234768,,,0.483795836
1319,Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.,Proc Natl Acad Sci U S A,33674422,3/7/21,pubmed,0,10,"machine learning, artificial intelligence, classifier",0.002238426,0.002238588,0.695459565,0.153884569,0.143940292,0.002238559,Imaging,0.44187406,FALSE,74.6,0.785020719,156.8,0.889483543,0,0.403234768,,,0.692579676
1320,Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study.,BMJ Open,33674378,3/7/21,pubmed,0,8,"deep learning, neural network, dataset",0.001291271,0.0626275,0.859031321,0.001291238,0.074467346,0.001291323,Imaging,0.74081004,TRUE,15,0.227596017,12.375,0.389751137,0,0.403234768,,,0.340193974
1321,Pandemic Spread of COVID-19 Mutant Variants Will Facilitate Next-generation Sequencing Capacities for Personalised Medicine in Urologic Oncology.,Eur Urol,33674179,3/7/21,pubmed,0,7,sequencing,0.151644679,0.801354075,0.011750126,0.011750803,0.01175035,0.011749968,Genomics,0.5688158,TRUE,140.2857143,0.934195065,41.42857143,0.641624298,0,0.403234768,,,0.65968471
1322,Predictors of failure with high-flow nasal oxygen therapy in COVID-19 patients with acute respiratory failure: a multicenter observational study.,J Intensive Care,33673863,3/7/21,pubmed,0,520,"predictive model, logistic regression",0.001371278,0.001371307,0.021823154,0.00137129,0.00137129,0.972691681,Clinics,0.60663307,TRUE,18.98848369,0.282392232,,,0,0.403234768,,,0.3428135
1323,Vulnerability of Syrian refugees in Lebanon to COVID-19: quantitative insights.,Confl Health,33673855,3/7/21,pubmed,0,5,mathematical model,0.073893877,0.001126856,0.0011268,0.603950076,0.318775533,0.001126859,Epidemiology,0.6333497,TRUE,89.6,0.841734183,166.8,0.896909286,0,0.403234768,,,0.713959412
1324,Spatiotemporal heterogeneity and its determinants of COVID-19 transmission in typical labor export provinces of China.,BMC Infect Dis,33673819,3/7/21,pubmed,0,6,bayes,0.001156257,0.001156298,0.001156285,0.994218553,0.001156335,0.001156271,Epidemiology,0.6818212,TRUE,6.166666667,0.087080215,1.166666667,0.124565159,0,0.403234768,,,0.204960047
1325,Cross-sectional survey of depressive symptoms and suicide-related ideation at a Japanese national university during the COVID-19 stay-home order.,Environ Health Prev Med,33673802,3/7/21,pubmed,0,11,logistic regression,0.001823502,0.001823373,0.001823593,0.00182352,0.921930878,0.070775135,Healthcare,0.9107858,TRUE,43.27272727,0.569917744,42.45454545,0.646976184,0,0.403234768,,,0.540042899
1326,"Economic Stressors, COVID-19 Attitudes, Worry, and Behaviors among U.S. Working Adults: A Mixture Analysis.",Int J Environ Res Public Health,33673637,3/7/21,pubmed,0,3,dataset,0.001786533,0.001786521,0.001786594,0.316220763,0.676633034,0.001786554,Healthcare,0.74288136,TRUE,69.33333333,0.760467561,95.33333333,0.813620551,0,0.403234768,,,0.659107627
1327,On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis.,Diagnostics (Basel),33673394,3/7/21,pubmed,0,5,radiom,0.002130654,0.002130692,0.989346537,0.002130721,0.002130671,0.002130726,Imaging,0.4182971,FALSE,128.8,0.91984662,31.8,0.583288734,0,0.403234768,,,0.635456707
1328,An Optimal Nucleic Acid Testing Strategy for COVID-19 during the Spring Festival Travel Rush in Mainland China: A Modelling Study.,Int J Environ Res Public Health,33673163,3/7/21,pubmed,0,7,simulation model,0.001593576,0.060522175,0.001593543,0.877843601,0.056853581,0.001593524,Epidemiology,0.42757282,FALSE,39,0.530521368,46.85714286,0.66791544,0,0.403234768,,,0.533890525
1329,Establishing an Analogue Based In Silico Pipeline in the Pursuit of Novel Inhibitory Scaffolds against the SARS Coronavirus 2 Papain-Like Protease.,Molecules,33672721,3/7/21,pubmed,0,3,"virtual screening, in silico",0.959021757,0.000889074,0.02135854,0.000889113,0.016952457,0.00088906,Drug discovery,0.6118466,TRUE,51.66666667,0.644195683,28,0.554321648,0,0.403234768,,,0.533917366
1330,COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.,Sensors (Basel),33672585,3/7/21,pubmed,0,5,"deep learning, neural network",0.001022653,0.001022672,0.994886566,0.001022709,0.001022662,0.001022737,Imaging,0.08595914,FALSE,33.8,0.474117138,2.8,0.188787798,0,0.403234768,,,0.355379901
1331,The Pandemic within a Pandemic: Testing a Sequential Mediation Model to Better Understand Racial/Ethnic Disparities in COVID-19 Preventive Behavior.,Healthcare (Basel),33672538,3/7/21,pubmed,0,2,predictive model,0.001538129,0.001538153,0.001538158,0.189276547,0.707800391,0.098308623,Healthcare,0.12054524,FALSE,62.5,0.718164389,310,0.957385603,0,0.403234768,,,0.692928253
1332,Human-to-Cat SARS-CoV-2 Transmission: Case Report and Full-Genome Sequencing from an Infected Pet and Its Owner in Northern Italy.,Pathogens,33672421,3/7/21,pubmed,0,15,"sequencing, whole genome",0.005047655,0.671226256,0.005047538,0.005047931,0.308583195,0.005047425,Genomics,0.4560176,FALSE,166.8666667,0.956150659,124.8666667,0.857506021,1,0.537564047,,,0.783740242
1333,Mental Health Status of Healthcare Professionals and Students of Health Sciences Faculties in Kuwait during the COVID-19 Pandemic.,Int J Environ Res Public Health,33672372,3/7/21,pubmed,0,5,logistic regression,0.001254588,0.001254606,0.001254612,0.001254597,0.953306565,0.041675033,Healthcare,0.97514915,TRUE,30.6,0.439421114,24.4,0.524284185,0,0.403234768,,,0.455646689
1334,Investigation of Nasal/Oropharyngeal Microbial Community of COVID-19 Patients by 16S rDNA Sequencing.,Int J Environ Res Public Health,33672177,3/7/21,pubmed,0,15,sequencing,0.00151188,0.311680585,0.001511877,0.001511944,0.001511865,0.68227185,Clinics,0.78251135,TRUE,140.4666667,0.934380605,110.1333333,0.838640621,0,0.403234768,,,0.725418664
1335,Therapeutic Intervention of COVID-19 by Natural Products: A Population-Specific Survey Directed Approach.,Molecules,33672163,3/7/21,pubmed,0,7,in-silico,0.736138456,0.091780878,0.001653068,0.001653136,0.065188346,0.103586116,Drug discovery,0.6886208,TRUE,100,0.868884903,56.42857143,0.708656676,0,0.403234768,,,0.660258782
1336,Mental Health Outreach via Supportive Text Messages during the COVID-19 Pandemic: Improved Mental Health and Reduced Suicidal Ideation after Six Weeks in Subscribers of Text4Hope Compared to a Control Population.,Int J Environ Res Public Health,33672120,3/7/21,pubmed,0,14,logistic regression,0.001565324,0.0015653,0.001565329,0.045777813,0.947960888,0.001565346,Healthcare,0.9649093,TRUE,35.5,0.492547467,10.35714286,0.360449558,0,0.403234768,,,0.418743931
1337,0,J Fungi (Basel),33672021,3/7/21,pubmed,0,12,"sequencing, whole-genome",0.001653118,0.80743374,0.001653074,0.001653136,0.001653271,0.185953662,Genomics,0.8179978,TRUE,121.5,0.9088379,73.58333333,0.762777629,0,0.403234768,,,0.691616765
1338,COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer.,Diagnostics (Basel),33671992,3/7/21,pubmed,0,5,"computational, dataset",0.027432854,0.001593524,0.966193119,0.001593546,0.00159347,0.001593487,Imaging,0.21242139,FALSE,95.6,0.85812357,96,0.81509232,0,0.403234768,,,0.692150219
1339,Statistical Estimation of Effects of Implemented Government Policies on COVID-19 Situation in South Korea.,Int J Environ Res Public Health,33671746,3/7/21,pubmed,0,11,dataset,0.001717191,0.001717195,0.001717254,0.991413908,0.001717234,0.001717218,Epidemiology,0.24869967,FALSE,32.45454545,0.460510854,62.90909091,0.732004282,0,0.403234768,,,0.531916634
1340,"Genomic Epidemiology of SARS-CoV-2 in Madrid, Spain, during the First Wave of the Pandemic: Fast Spread and Early Dominance by D614G Variants.",Microorganisms,33671631,3/7/21,pubmed,0,15,"genomic epidemiology, genomes",0.00165308,0.838095078,0.001653046,0.136284074,0.001653074,0.020661648,Genomics,0.47005123,FALSE,84.13333333,0.822252458,122,0.8537597,2,0.618927094,,,0.764979751
1341,Targeting the Complement Serine Protease MASP-2 as a Therapeutic Strategy for Coronavirus Infections.,Viruses,33671334,3/7/21,pubmed,0,9,in silico,0.904835998,0.001126838,0.001126795,0.001126802,0.001126787,0.090656781,Drug discovery,0.9710629,TRUE,40,0.539860226,10.77777778,0.366537329,0,0.403234768,,,0.436544108
1342,Delivering Personalized Care at a Distance: How Telemedicine Can Foster Getting to Know the Patient as a Person.,J Pers Med,33671324,3/7/21,pubmed,0,5,"proteom, metabolom",0.001538155,0.001538193,0.304923425,0.21801937,0.340072268,0.133908589,Healthcare,0.81315637,TRUE,147.8,0.941925908,183.8,0.908683436,0,0.403234768,,,0.75128137
1343,"Multivalent Display of SARS-CoV-2 Spike (RBD Domain) of COVID-19 to Nanomaterial, Protein Ferritin Nanocages.",Biomolecules,33671255,3/7/21,pubmed,0,5,"molecular dynamics simulation, in silico",0.946338052,0.049571328,0.001022639,0.001022704,0.001022663,0.001022613,Drug discovery,0.52093995,TRUE,72.8,0.776547715,183.2,0.908415842,0,0.403234768,,,0.696066108
1344,Changes in Smoking Behaviour and Home-Smoking Rules during the Initial COVID-19 Lockdown Period in Israel.,Int J Environ Res Public Health,33671203,3/7/21,pubmed,0,4,logistic regression,0.001823356,0.001823335,0.001823339,0.001823445,0.990883014,0.001823511,Healthcare,0.8984852,TRUE,26.5,0.389325252,38,0.622223709,0,0.403234768,,,0.471594576
1345,Willingness to Receive SARS-CoV-2 Vaccination and Associated Factors among Chinese Adults: A Cross Sectional Survey.,Int J Environ Res Public Health,33670821,3/7/21,pubmed,0,8,logistic regression,0.001943486,0.001943487,0.001943456,0.001943533,0.990282575,0.001943463,Healthcare,0.40091354,FALSE,31.375,0.448512586,6.25,0.283315494,0,0.403234768,,,0.378354282
1346,MicroRNAs and Long Non-Coding RNAs as Potential Candidates to Target Specific Motifs of SARS-CoV-2.,Noncoding RNA,33670580,3/7/21,pubmed,0,6,computational,0.618311026,0.34534195,0.001461907,0.001461891,0.001461855,0.031961372,Drug discovery,0.91415095,TRUE,158.8333333,0.950398911,261,0.941865132,0,0.403234768,,,0.765166271
1347,The SADDEN DEATH Study: Results from a Pilot Study in Non-ICU COVID-19 Spanish Patients.,J Clin Med,33670462,3/7/21,pubmed,0,29,logistic regression,0.001072291,0.001072239,0.001072182,0.038969277,0.00107221,0.956741801,Clinics,0.9286643,TRUE,67.13793103,0.74853114,25.17241379,0.530238159,0,0.403234768,,,0.560668022
1348,Examining Public Concerns and Attitudes toward Unfair Events Involving Elderly Travelers during the COVID-19 Pandemic Using Weibo Data.,Int J Environ Res Public Health,33670271,3/7/21,pubmed,0,5,digital health,0.001415114,0.001415168,0.069590702,0.46952701,0.456636878,0.001415129,Epidemiology,0.566124,TRUE,39,0.530521368,7.8,0.313286058,0,0.403234768,,,0.415680731
1349,A Blockchain-Based Authentication Protocol for Cooperative Vehicular Ad Hoc Network.,Sensors (Basel),33670097,3/7/21,pubmed,0,6,computational,0.001461945,0.00146202,0.25478303,0.739369178,0.001461955,0.001461872,Epidemiology,0.6717777,TRUE,26.83333333,0.392726823,8.166666667,0.321915975,0,0.403234768,,,0.372625855
1350,A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients.,Sensors (Basel),33670066,3/7/21,pubmed,0,10,"deep learning, computational, dataset",0.001072188,0.001072223,0.75346043,0.077640971,0.001072286,0.165681901,Imaging,0.78397477,TRUE,78.2,0.799121776,25.7,0.534987958,0,0.403234768,,,0.579114834
1351,Association of Health Literacy with the Implementation of Exercise during the Declaration of COVID-19 State of Emergency among Japanese Community-Dwelling Old-Old Adults.,Int J Environ Res Public Health,33670041,3/7/21,pubmed,0,8,logistic regression,0.001511797,0.001511809,0.001511826,0.001511912,0.992440764,0.001511892,Healthcare,0.79902416,TRUE,54.625,0.665842043,34.75,0.603559005,0,0.403234768,,,0.557545272
1352,Awareness and Performance towards Proper Use of Disinfectants to Prevent COVID-19: The Case of Iran.,Int J Environ Res Public Health,33670033,3/7/21,pubmed,0,9,correlation analysis,0.001901707,0.001901719,0.275312512,0.19731306,0.521669211,0.001901791,Healthcare,0.6088192,TRUE,59.88888889,0.701960542,25.88888889,0.536125234,0,0.403234768,,,0.547106848
1353,Sources of COVID-19-Related Information in People with Various Levels of Risk Perception and Preventive Behaviors in Taiwan: A Latent Profile Analysis.,Int J Environ Res Public Health,33669977,3/7/21,pubmed,0,6,logistic regression,0.001653165,0.001653496,0.001653081,0.00165313,0.991734054,0.001653074,Healthcare,0.6878804,TRUE,119.1666667,0.905745562,82.33333333,0.783649987,0,0.403234768,,,0.697543439
1354,Are There Any Parameters Missing in the Mathematical Models Applied in the Process of Spreading COVID-19?,Biology (Basel),33669895,3/7/21,pubmed,0,3,mathematical model,0.002080639,0.002080637,0.0784303,0.861648267,0.00208058,0.053679576,Epidemiology,0.374377,FALSE,61.33333333,0.710680933,34.66666667,0.603157613,0,0.403234768,,,0.572357771
1355,Computational Selectivity Assessment of Protease Inhibitors against SARS-CoV-2.,Int J Mol Sci,33669738,3/7/21,pubmed,0,7,computational,0.99230933,0.00153817,0.001538134,0.001538132,0.001538125,0.001538109,Drug discovery,0.9131749,TRUE,41.14285714,0.549570165,26.28571429,0.540272946,0,0.403234768,,,0.497692626
1356,In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2.,Molecules,33669720,3/7/21,pubmed,0,3,"in silico, dataset",0.990282202,0.001943522,0.001943605,0.001943617,0.001943471,0.001943583,Drug discovery,0.81510365,TRUE,44.66666667,0.583462181,0.333333333,0.073187048,0,0.403234768,,,0.353294666
1357,"The Interplay between Public Health, Well-Being and Population Aging in Europe: An Advanced Structural Equation Modelling and Gaussian Network Approach.",Int J Environ Res Public Health,33669708,3/7/21,pubmed,0,6,network analysis,0.001717237,0.001717202,0.00171728,0.40317611,0.589954918,0.001717253,Healthcare,0.81581295,TRUE,18.5,0.278001113,1.5,0.138747659,0,0.403234768,,,0.273327846
1358,Validity of Clinical Symptoms Score to Discriminate Patients with COVID-19 from Common Cold Out-Patients in General Practitioner Clinics in Japan.,J Clin Med,33669685,3/7/21,pubmed,0,5,logistic regression,0.001486441,0.001486512,0.062020915,0.095800085,0.287597091,0.551608956,Clinics,0.66422164,TRUE,115.8,0.900303049,50,0.681763447,0,0.403234768,,,0.661767088
1359,A Typology of Poles' Attitudes toward COVID-19 during the First Wave of the Pandemic.,Int J Environ Res Public Health,33669545,3/7/21,pubmed,0,3,logistic regression,0.002032876,0.002032918,0.002033023,0.002032952,0.989835422,0.002032809,Healthcare,0.7263053,TRUE,18.33333333,0.274908776,0.333333333,0.073187048,0,0.403234768,,,0.250443531
1360,An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics.,Bioengineering (Basel),33669235,3/7/21,pubmed,0,8,"image analysis, radiom, dataset",0.001861754,0.001861748,0.990691022,0.001861832,0.001861761,0.001861883,Imaging,0.252254,FALSE,54.25,0.663120787,20.25,0.483074659,0,0.403234768,,,0.516476738
1361,0,Viruses,33669132,3/7/21,pubmed,0,9,"computational, in silico",0.787816375,0.184231076,0.024953853,0.000999606,0.000999543,0.000999546,Drug discovery,0.66093874,TRUE,29.22222222,0.420990785,24.55555556,0.525421461,0,0.403234768,,,0.449882338
1362,"Propolis, Bee Honey, and Their Components Protect against Coronavirus Disease 2019 (COVID-19): A Review of In Silico, In Vitro, and Clinical Studies.",Molecules,33669054,3/7/21,pubmed,0,2,in silico,0.828272999,0.001291237,0.001291253,0.001291262,0.001291276,0.166561973,Drug discovery,0.6613596,TRUE,260,0.986022636,329,0.961934707,1,0.537564047,,,0.82850713
1363,The Changing Acceptance of COVID-19 Vaccination in Different Epidemic Phases in China: A Longitudinal Study.,Vaccines (Basel),33668923,3/7/21,pubmed,0,10,logistic regression,0.001085383,0.001085351,0.001085334,0.187350332,0.808308252,0.001085348,Healthcare,0.911615,TRUE,22.5,0.333539489,6.3,0.283716885,1,0.537564047,,,0.384940141
1364,0,Int J Mol Sci,33668523,3/7/21,pubmed,0,3,in silico,0.904337013,0.08836946,0.001823372,0.001823393,0.001823371,0.001823391,Drug discovery,0.9308971,TRUE,58,0.689714887,14,0.412898047,0,0.403234768,,,0.501949234
1365,"EGCG, a Green Tea Catechin, as a Potential Therapeutic Agent for Symptomatic and Asymptomatic SARS-CoV-2 Infection.",Molecules,33668085,3/7/21,pubmed,0,5,in silico,0.840450131,0.001438169,0.001438113,0.153797206,0.001438229,0.001438151,Drug discovery,0.59264195,TRUE,29.6,0.426495145,14.8,0.421527964,0,0.403234768,,,0.417085959
1366,Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach.,Int J Med Inform,33667929,3/6/21,pubmed,0,5,"machine learning, text mining",0.001046828,0.001046831,0.001046881,0.697791166,0.230187863,0.068880431,Epidemiology,0.72399837,TRUE,50.6,0.635598986,22.6,0.508161627,0,0.403234768,,,0.515665127
1367,Discovery of novel inhibitors against main protease (Mpro) of SARS-CoV-2 via virtual screening and biochemical evaluation.,Bioorg Chem,33667900,3/6/21,pubmed,0,7,virtual screening,0.951020631,0.002720091,0.002720104,0.038099032,0.002720079,0.002720063,Drug discovery,0.79297537,TRUE,30,0.432432432,15.71428571,0.432833824,0,0.403234768,,,0.422833675
1368,"The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning.",J Environ Manage,33667818,3/6/21,pubmed,0,3,machine learning,0.002032842,0.002032795,0.002032952,0.989835871,0.002032755,0.002032784,Epidemiology,0.20565954,FALSE,30.33333333,0.436266931,30.66666667,0.574257426,1,0.537564047,,,0.516029468
1369,Different selection dynamics of S and RdRp between SARS-CoV-2 genomes with and without the dominant mutations.,Infect Genet Evol,33667722,3/6/21,pubmed,0,5,genomes,0.199641977,0.795116661,0.001310329,0.001310359,0.001310327,0.001310345,Genomics,0.54033226,TRUE,16.8,0.253138722,0.8,0.101351351,0,0.403234768,,,0.252574947
1370,Association of serum HDL-cholesterol and apolipoprotein A1 levels with risk of severe SARS-CoV-2 infection.,J Lipid Res,33667465,3/6/21,pubmed,0,11,logistic regression,0.002183239,0.258299554,0.002183287,0.002183356,0.002183298,0.732967266,Clinics,0.38977957,FALSE,111.8181818,0.893623601,,,0,0.403234768,,,0.648429184
1371,Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study.,JMIR Med Inform,33667172,3/6/21,pubmed,0,4,"computational, neural network",0.00129125,0.001291251,0.031875317,0.695735184,0.268515772,0.001291226,Epidemiology,0.7206776,TRUE,51.75,0.644566764,18.25,0.463272679,0,0.403234768,,,0.503691403
1372,Abstinence Among Alcohol Use Disorder Patients During the COVID-19 Pandemic: Insights From Spain.,Alcohol Clin Exp Res,33667019,3/6/21,pubmed,0,7,logistic regression,0.123918309,0.001684528,0.001684548,0.001684594,0.527976146,0.343051875,Healthcare,0.98001385,TRUE,191.5714286,0.969447709,231.2857143,0.929823388,0,0.403234768,,,0.767501955
1373,Using Real-Time Google Search Interest as a Predictive Tool for COVID-19 Cases in the United States.,Med J (Ft Sam Houst Tex),33666925,3/6/21,pubmed,0,5,predictive model,0.001330036,0.001330028,0.001330076,0.912552628,0.082127138,0.001330094,Epidemiology,0.2633201,FALSE,5,0.070752675,0.2,0.061145304,0,0.403234768,,,0.178377582
1374,Science and Technology Solutions for Scalable SARS-CoV-2 Testing to Inform Return to Full Capacity Strategy in United States Air Force Workforce Personnel.,Med J (Ft Sam Houst Tex),33666911,3/6/21,pubmed,0,10,sequencing,0.07583506,0.434396825,0.001371351,0.341692034,0.108086122,0.038618609,Genomics,0.60059494,TRUE,128.5,0.919599233,77.6,0.772411025,0,0.403234768,,,0.698415009
1375,Ubiquitin-Modified Proteome of SARS-CoV-2-Infected Host Cells Reveals Insights into Virus-Host Interaction and Pathogenesis.,J Proteome Res,33666082,3/6/21,pubmed,0,13,"bioinformatic, proteom",0.989083344,0.002183405,0.002183342,0.002183397,0.002183256,0.002183256,Drug discovery,0.7998389,TRUE,47.07692308,0.606531016,30.07692308,0.570310409,0,0.403234768,,,0.526692064
1376,A computational framework of host-based drug repositioning for broad-spectrum antivirals against RNA viruses.,iScience,33665567,3/6/21,pubmed,0,10,"computational, artificial intelligence, literature mining",0.722927344,0.107041749,0.165917122,0.001371293,0.00137126,0.001371232,Drug discovery,0.3563412,FALSE,31,0.445111015,17.3,0.453572384,0,0.403234768,,,0.433972722
1377,Goals in Nutrition Science 2020-2025.,Front Nutr,33665201,3/6/21,pubmed,0,15,"microbiom, dataset",0.034701061,0.048454991,0.001717339,0.823004154,0.090405274,0.001717181,Epidemiology,0.8096024,TRUE,225.7333333,0.980147195,369.0666667,0.968156275,0,0.403234768,,,0.783846079
1378,Rapid Screening of Physiological Changes Associated With COVID-19 Using Soft-Wearables and Structured Activities: A Pilot Study.,IEEE J Transl Eng Health Med,33665044,3/6/21,pubmed,0,12,"classifier, logistic regression",0.001565403,0.069090009,0.465841716,0.123800292,0.19038686,0.14931572,Healthcare,0.21334586,FALSE,43.16666667,0.568433422,18.75,0.467621086,0,0.403234768,,,0.479763092
1379,"Low compositions of human toll-like receptor 7/8-stimulating RNA motifs in the MERS-CoV, SARS-CoV and SARS-CoV-2 genomes imply a substantial ability to evade human innate immunity.",PeerJ,33665043,3/6/21,pubmed,0,2,"computational, genomes, logistic regression",0.280546733,0.714051567,0.00135032,0.001350583,0.001350417,0.00135038,Genomics,0.53008014,TRUE,77,0.794668811,102,0.82592989,0,0.403234768,,,0.674611156
1380,"Sociodemographic, Cognitive, and Emotional Determinants of Two Health Behaviors during SARS-CoV-2 Outbreak: An Online Study among French-Speaking Belgian Responders during the Spring Lockdown.",Psychol Belg,33664975,3/6/21,pubmed,0,4,logistic regression,0.001538126,0.001538137,0.00153812,0.331805306,0.662042186,0.001538124,Healthcare,0.92210186,TRUE,77.5,0.79658606,103.5,0.828137543,0,0.403234768,,,0.675986124
1381,MERS-CoV and SARS-CoV-2 replication can be inhibited by targeting the interaction between the viral spike protein and the nucleocapsid protein.,Theranostics,33664866,3/6/21,pubmed,0,11,proteom,0.913472741,0.082782973,0.000936063,0.000936079,0.00093608,0.000936065,Drug discovery,0.46697107,FALSE,46.90909091,0.604428227,26.81818182,0.544153064,0,0.403234768,,,0.517272019
1382,Risk Factors for Mortality of COVID-19 Patient Based on Clinical Course: A Single Center Retrospective Case-Control Study.,Front Immunol,33664741,3/6/21,pubmed,0,6,"logistic regression, correlation analysis",0.271918484,0.000846526,0.000846522,0.000846525,0.000846527,0.724695416,Clinics,0.9642739,TRUE,40,0.539860226,25.16666667,0.53017126,0,0.403234768,,,0.491088751
1383,"The Unprecedented Movement Control Order (Lockdown) and Factors Associated With the Negative Emotional Symptoms, Happiness, and Work-Life Balance of Malaysian University Students During the Coronavirus Disease (COVID-19) Pandemic.",Front Psychiatry,33664679,3/6/21,pubmed,0,4,logistic regression,0.00077262,0.000772621,0.014527986,0.000772651,0.982381503,0.00077262,Healthcare,0.98920846,TRUE,46,0.596882924,13.75,0.408348943,0,0.403234768,,,0.469488878
1384,Sex Differences in the Incidence and Risk Factors of Myocardial Injury in COVID-19 Patients: A Retrospective Cohort Study.,Front Physiol,33664674,3/6/21,pubmed,0,15,logistic regression,0.001272667,0.001272674,0.001272669,0.001272692,0.001272817,0.993636482,Clinics,0.7899994,TRUE,23.06666667,0.342507267,7.333333333,0.304656141,0,0.403234768,,,0.350132725
1385,"COVID-19 engages clinical markers for the management of cancer and cancer-relevant regulators of cell proliferation, death, migration, and immune response.",Sci Rep,33664395,3/6/21,pubmed,0,3,network analysis,0.459904165,0.048397662,0.063940812,0.001511905,0.001511857,0.424733599,Drug discovery,0.56292814,TRUE,47.66666667,0.611787989,92.66666667,0.80753278,0,0.403234768,,,0.607518512
1386,Potential neutralizing antibodies discovered for novel corona virus using machine learning.,Sci Rep,33664393,3/6/21,pubmed,0,3,"machine learning, bioinformatic, logistic regression",0.444478703,0.316128695,0.235006929,0.00146187,0.001461879,0.001461924,Drug discovery,0.27613312,FALSE,5.333333333,0.074339786,0.666666667,0.096200161,0,0.403234768,,,0.191258238
1387,Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions.,Sci Rep,33664380,3/6/21,pubmed,0,2,predictive model,0.00242227,0.002422274,0.002422318,0.948576533,0.041734107,0.002422498,Epidemiology,0.10917589,FALSE,67.5,0.750201002,77,0.771139952,0,0.403234768,,,0.64152524
1388,On topological properties of COVID-19: predicting and assessing pandemic risk with network statistics.,Sci Rep,33664280,3/6/21,pubmed,0,4,network analysis,0.001511913,0.001511935,0.00151193,0.812097607,0.001511905,0.18185471,Epidemiology,0.76185185,TRUE,17,0.257467994,4.25,0.235750602,1,0.537564047,,,0.343594215
1389,Dataset on SARS-CoV-2 non-pharmaceutical interventions in Brazilian municipalities.,Sci Data,33664243,3/6/21,pubmed,0,9,dataset,0.00299643,0.002996471,0.002996495,0.942564452,0.045449766,0.002996387,Epidemiology,0.66519386,TRUE,54.33333333,0.663677407,40.88888889,0.63868076,1,0.537564047,,,0.613307405
1390,"Role of neutrophil-to-lymphocyte, lymphocyte-to-monocyte and platelet-to-lymphocyte ratios as predictors of disease severity in COVID-19 patients.",Infez Med,33664172,3/6/21,pubmed,0,4,logistic regression,0.001310432,0.001310506,0.001310356,0.00131039,0.063863466,0.93089485,Clinics,0.8757436,TRUE,29.25,0.421671099,6.25,0.283315494,0,0.403234768,,,0.36940712
1391,Alopecia and severity of COVID-19: a cross-sectional study in Peru.,Infez Med,33664171,3/6/21,pubmed,0,4,logistic regression,0.003760401,0.048296824,0.00376049,0.003760673,0.003760909,0.936660702,Clinics,0.95762694,TRUE,166.5,0.955841425,65.25,0.73963072,0,0.403234768,,,0.699568971
1392,0,Sci Immunol,33664060,3/6/21,pubmed,0,43,"sequencing, deep sequencing",0.669749265,0.257699058,0.026611978,0.001861717,0.001861679,0.042216301,Drug discovery,0.32768297,FALSE,96.20930233,0.859546045,175.0465116,0.903130854,1,0.537564047,,,0.766746982
1393,Suicide and the agent-host-environment triad: leveraging surveillance sources to inform prevention.,Psychol Med,33663629,3/6/21,pubmed,0,7,mathematical model,0.050544119,0.002130817,0.002130691,0.940932895,0.002130813,0.002130665,Epidemiology,0.86025685,TRUE,213.8571429,0.976745624,658.5714286,0.986151994,0,0.403234768,,,0.788710795
1394,Excess mortality for care home residents during the first 23 weeks of the COVID-19 pandemic in England: a national cohort study.,BMC Med,33663498,3/6/21,pubmed,0,5,logistic regression,0.00131042,0.001310393,0.001310356,0.381431099,0.46664959,0.147988142,Healthcare,0.60051423,TRUE,77.6,0.79695714,62.6,0.7311346,0,0.403234768,,,0.643775503
1395,OmicLoupe: facilitating biological discovery by interactive exploration of multiple omic datasets and statistical comparisons.,BMC Bioinformatics,33663372,3/6/21,pubmed,0,3,"proteom,  omics, dataset",0.276860936,0.001943586,0.332896503,0.384411962,0.001943527,0.001943486,Epidemiology,0.55594796,TRUE,39,0.530521368,38.66666667,0.625501739,0,0.403234768,,,0.519752625
1396,Metadynamics-based enhanced sampling protocol for virtual screening: case study for 3CLpro protein for SARS-CoV-2.,J Biomol Struct Dyn,33663346,3/6/21,pubmed,0,5,"virtual screening, computational",0.99334958,0.001330126,0.001330107,0.001330041,0.001330117,0.00133003,Drug discovery,0.5402671,TRUE,38.2,0.520564042,3.4,0.208589778,0,0.403234768,,,0.377462863
1397,Distinguishing features in the assessment of mHealth apps.,Expert Rev Pharmacoecon Outcomes Res,33663324,3/6/21,pubmed,0,4,digital health,0.001511952,0.001511907,0.207272929,0.786679328,0.001511945,0.00151194,Epidemiology,0.8461145,TRUE,68.25,0.755086895,25.75,0.535255553,0,0.403234768,,,0.564525738
1398,"LDH, CRP and ALB predict nucleic acid turn negative within 14 days in symptomatic patients with COVID-19.",Scott Med J,33663273,3/6/21,pubmed,0,5,logistic regression,0.001098827,0.108396494,0.001098813,0.001098858,0.00109881,0.887208198,Clinics,0.8166703,TRUE,38.8,0.527119797,16.4,0.441196147,0,0.403234768,,,0.45718357
1399,Telehealth utilization among multi-ethnic patients with obesity during the COVID-19 pandemic.,J Telemed Telecare,33663260,3/6/21,pubmed,0,9,logistic regression,0.001203436,0.001203448,0.00120348,0.001203504,0.622460249,0.372725884,Healthcare,0.9335614,TRUE,41.55555556,0.553528357,27.66666667,0.550909821,0,0.403234768,,,0.502557648
1400,Early prediction model for progression and prognosis of severe patients with coronavirus disease 2019.,Medicine (Baltimore),33663123,3/6/21,pubmed,0,7,"predictive model, logistic regression, prediction model",0.001203433,0.001203417,0.122058435,0.001203444,0.001203419,0.873127851,Clinics,0.6984905,TRUE,74.28571429,0.782979776,9.714285714,0.348608509,0,0.403234768,,,0.511607684
1401,Prognostic value of CT integrated with clinical and laboratory data during the first peak of the COVID-19 pandemic in Northern Italy: A nomogram to predict unfavorable outcome.,Eur J Radiol,33662842,3/5/21,pubmed,0,13,logistic regression,0.001141323,0.001141312,0.344694133,0.001141344,0.001141329,0.650740559,Clinics,0.874148,TRUE,72.15384615,0.773640918,56.07692308,0.707051111,0,0.403234768,,,0.627975599
1402,Time series analysis and mechanistic modelling of heterogeneity and sero-reversion in antibody responses to mild SARS‑CoV-2 infection.,EBioMedicine,33662833,3/5/21,pubmed,0,28,mathematical model,0.000966817,0.3299665,0.000966771,0.336399563,0.18239307,0.149307278,Epidemiology,0.30368516,FALSE,91.5,0.847547777,65.14285714,0.739363126,2,0.618927094,,,0.735279332
1403,Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation.,Comput Methods Programs Biomed,33662804,3/5/21,pubmed,0,8,"deep learning, transfer learning, dataset",0.000652906,0.000652898,0.996735469,0.00065291,0.000652905,0.000652912,Imaging,0.37302944,FALSE,41.25,0.5508071,,,3,0.667819001,,,0.609313051
1404,Targeting multiple conformations of SARS-CoV2 Papain-Like Protease for drug repositioning: An in-silico study.,Comput Biol Med,33662683,3/5/21,pubmed,0,4,"virtual screening, in-silico",0.845351929,0.001187356,0.14989888,0.001187303,0.001187285,0.001187246,Drug discovery,0.7428939,TRUE,51.25,0.640113798,13.5,0.405539203,0,0.403234768,,,0.482962589
1405,Network pharmacology and RNA-sequencing reveal the molecular mechanism of Xuebijing injection on COVID-19-induced cardiac dysfunction.,Comput Biol Med,33662681,3/5/21,pubmed,0,6,"bioinformatic, sequencing",0.795107532,0.013653676,0.001010919,0.001010938,0.001010974,0.188205961,Drug discovery,0.8421781,TRUE,28.83333333,0.415857505,11,0.371287129,0,0.403234768,,,0.396793134
1406,Flavonoids against the SARS-CoV-2 induced inflammatory storm.,Biomed Pharmacother,33662680,3/5/21,pubmed,0,23,bioinformatic,0.946671248,0.000999534,0.000999531,0.000999572,0.000999542,0.049330573,Drug discovery,0.9868617,TRUE,78.47826087,0.800111324,45.08695652,0.658884132,0,0.403234768,,,0.620743408
1407,IL-6-based mortality prediction model for COVID-19: Validation and update in multicenter and second wave cohorts.,J Allergy Clin Immunol,33662370,3/5/21,pubmed,0,17,prediction model,0.001010929,0.001010958,0.114432959,0.181534255,0.001010988,0.700999911,Clinics,0.593727,TRUE,68.23529412,0.754839508,46.82352941,0.667781643,0,0.403234768,,,0.608618639
1408,The serum metabolome of COVID-19 patients is distinctive and predictive.,Metabolism,33662365,3/5/21,pubmed,0,16,metabolom,0.001272733,0.308138516,0.001272666,0.001272645,0.00127268,0.686770759,Clinics,0.888708,TRUE,42.125,0.55878533,76.75,0.770604763,0,0.403234768,,,0.57754162
1409,International citizen project to assess early stage adherence to public health measures for COVID-19 in South Africa.,PLoS One,33662020,3/5/21,pubmed,0,5,logistic regression,0.001010931,0.001010968,0.001010943,0.220797219,0.775158979,0.00101096,Healthcare,0.94219846,TRUE,24.4,0.360442823,10,0.355632861,0,0.403234768,,,0.373103484
1410,Genomic characterization and evolution of SARS-CoV-2 of a Canadian population.,PLoS One,33662015,3/5/21,pubmed,0,4,genomes,0.359804738,0.631462171,0.002183221,0.002183285,0.002183356,0.002183229,Genomics,0.6918733,TRUE,116,0.900735976,102.25,0.826264383,0,0.403234768,,,0.710078376
1411,Development and validation of a predictive model of in-hospital mortality in COVID-19 patients.,PLoS One,33661939,3/5/21,pubmed,0,21,"predictive model, logistic regression",0.001330011,0.001330043,0.001330062,0.001330038,0.001330037,0.993349809,Clinics,0.7887088,TRUE,13.0952381,0.197785887,1.761904762,0.148447953,0,0.403234768,,,0.249822869
1412,Hypoxia-induced inflammation: Profiling the first 24-hour posthypoxic plasma and central nervous system changes.,PLoS One,33661927,3/5/21,pubmed,0,9,proteom,0.317874788,0.001438169,0.077657965,0.136974053,0.001438173,0.464616852,Clinics,0.959769,TRUE,53.33333333,0.656255798,39.66666667,0.631790206,0,0.403234768,,,0.563760257
1413,Cardiometabolic risk factors for COVID-19 susceptibility and severity: A Mendelian randomization analysis.,PLoS Med,33661905,3/5/21,pubmed,0,6,genome-wide,0.000999546,0.290934667,0.000999532,0.000999576,0.063100409,0.64296627,Clinics,0.30874345,FALSE,207,0.974519142,698.5,0.987222371,0,0.403234768,,,0.788325427
1414,Group testing as a strategy for COVID-19 epidemiological monitoring and community surveillance.,PLoS Comput Biol,33661887,3/5/21,pubmed,0,3,dataset,0.003101501,0.236104851,0.366896391,0.217872921,0.172922712,0.003101624,Genomics,0.3015535,FALSE,40,0.539860226,16.66666667,0.444674873,0,0.403234768,,,0.462589956
1415,Molecular biology of coronaviruses: an overview of virus-host interactions and pathogenesis.,Bol Med Hosp Infant Mex,33661875,3/5/21,pubmed,0,3,genomes,0.250928704,0.288578622,0.002130723,0.002130877,0.102695566,0.353535509,Clinics,0.80601704,TRUE,16.33333333,0.247015895,3.666666667,0.217621086,0,0.403234768,,,0.289290583
1416,Temporal Dynamics of Public Emotions During the COVID-19 Pandemic at the Epicenter of the Outbreak: Sentiment Analysis of Weibo Posts From Wuhan.,J Med Internet Res,33661755,3/5/21,pubmed,0,3,dataset,0.001072212,0.106317322,0.001072214,0.461404344,0.4290617,0.001072208,Epidemiology,0.9539816,TRUE,50.33333333,0.63380543,45.33333333,0.659820712,0,0.403234768,,,0.565620303
1417,Can we trust the prediction model? Illustrating the importance of external validation by implementing the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets.,JMIR Med Inform,33661754,3/5/21,pubmed,0,36,"prediction model, dataset",0.001010992,0.001011024,0.362858429,0.046780845,0.085770386,0.502568324,Clinics,0.37015253,FALSE,137.1944444,0.929556559,386.4722222,0.969494247,0,0.403234768,,,0.767428524
1418,An Emergent Network for the Diffusion of Innovations Among Local Health Departments at the Onset of the COVID-19 Pandemic.,Prev Chronic Dis,33661727,3/5/21,pubmed,0,4,network analysis,0.03628718,0.001415129,0.001415221,0.549502825,0.409964489,0.001415155,Epidemiology,0.9383572,TRUE,39.25,0.532253077,55.25,0.7028365,0,0.403234768,,,0.546108115
1419,Exploring depressive symptoms and its associates among Bangladeshi older adults amid COVID-19 pandemic: findings from a cross-sectional study.,Soc Psychiatry Psychiatr Epidemiol,33661353,3/5/21,pubmed,0,8,logistic regression,0.001371257,0.001371261,0.001371248,0.001371281,0.993143683,0.00137127,Healthcare,0.9312535,TRUE,26.25,0.384748593,4.75,0.248260637,0,0.403234768,,,0.345414666
1420,Molecular and Cellular Biomarkers of COVID-19 Prognosis: Protocol for the Prospective Cohort TARGET Study.,JMIR Res Protoc,33661132,3/5/21,pubmed,0,19,proteom,0.257192244,0.223722206,0.001022652,0.201233786,0.00102266,0.315806452,Clinics,0.8291822,TRUE,75.63157895,0.789040757,41.89473684,0.643765052,0,0.403234768,,,0.612013526
1421,"Open science, COVID-19, and the news: Exploring controversies in the circulation of early SARS-CoV-2 genomic epidemiology research.",Glob Public Health,33661076,3/5/21,pubmed,0,3,genomic epidemiology,0.001823392,0.394266568,0.001823363,0.531269166,0.06899418,0.00182333,Epidemiology,0.34702328,FALSE,10.33333333,0.155049787,1.333333333,0.13252609,0,0.403234768,,,0.230270215
1422,Acute cardiac injury and acute kidney injury associated with severity and mortality in patients with COVID-19.,Eur Rev Med Pharmacol Sci,33660831,3/5/21,pubmed,0,7,logistic regression,0.001684455,0.001684463,0.001684457,0.001684468,0.001684475,0.991577682,Clinics,0.9355931,TRUE,7.571428571,0.108973963,2.714285714,0.186111854,0,0.403234768,,,0.232773528
1423,The highly flexible disordered regions of the SARS-CoV-2 nucleocapsid N protein within the 1-248 residue construct: sequence-specific resonance assignments through NMR.,Biomol NMR Assign,33660218,3/5/21,pubmed,0,5,bioinformatic,0.621984926,0.367764347,0.002562581,0.002562622,0.002562926,0.002562598,Drug discovery,0.4637147,FALSE,283,0.989238667,458.6,0.976786192,0,0.403234768,,,0.789753209
1424,Factors associated with emotional exhaustion in healthcare professionals involved in the COVID-19 pandemic: an application of the job demands-resources model.,Int Arch Occup Environ Health,33660030,3/5/21,pubmed,0,9,logistic regression,0.001684504,0.001684506,0.001684569,0.448300835,0.482047348,0.064598238,Healthcare,0.87326133,TRUE,55.55555556,0.67140825,16.66666667,0.444674873,0,0.403234768,,,0.506439297
1425,Digital health communication and health literacy in times of COVID-19. Planning and implementation of a special course of study in health promotion and prevention.,GMS J Med Educ,33659636,3/5/21,pubmed,0,2,digital health,0.039336251,0.025837807,0.103569057,0.472615098,0.280751132,0.077890655,Epidemiology,0.96189356,TRUE,37,0.508936854,21.5,0.498260637,0,0.403234768,,,0.470144086
1426,The use of the online Inverted Classroom Model for digital teaching with gamification in medical studies.,GMS J Med Educ,33659608,3/5/21,pubmed,0,5,active learning,0.000830686,0.000830637,0.041896725,0.359939887,0.345804892,0.250697174,Epidemiology,0.8305571,TRUE,75,0.787061661,42.6,0.647778967,0,0.403234768,,,0.612691799
1427,Data-set of academic difficulties among students in western Uganda during COVID-19 induced lockdown.,Data Brief,33659598,3/5/21,pubmed,0,3,dataset,0.002296634,0.002296699,0.167133467,0.002296702,0.823679931,0.002296566,Healthcare,0.32839966,FALSE,5.333333333,0.074339786,0,0.055525823,0,0.403234768,,,0.177700125
1428,Evaluation of the Sequence Variability within the PCR Primer/Probe Target Regions of the SARS-CoV-2 Genome.,Bio Protoc,33659508,3/5/21,pubmed,0,2,"bioinformatic, sequence alignment",0.001823405,0.844800982,0.147905305,0.001823435,0.001823346,0.001823527,Genomics,0.4387586,FALSE,36.5,0.503123261,19.5,0.475983409,0,0.403234768,,,0.460780479
1429,0,Front Cell Infect Microbiol,33659220,3/5/21,pubmed,0,11,"sequencing, microbiom",0.248341886,0.534098571,0.001538198,0.001538198,0.001538151,0.212944995,Genomics,0.8781134,TRUE,65,0.734801163,21.63636364,0.498996521,0,0.403234768,,,0.545677484
1430,Genomic Surveillance of SARS-CoV-2: Distribution of Clades in the Republic of Korea in 2020.,Osong Public Health Res Perspect,33659153,3/5/21,pubmed,0,20,"sequencing, whole genome",0.002422247,0.987888416,0.002422231,0.002422489,0.002422287,0.00242233,Genomics,0.19037402,FALSE,11.35,0.17075886,,,1,0.537564047,,,0.354161453
1431,Prevalence of Poor Sleep Quality Among Physicians During the COVID-19 Pandemic.,Cureus,33659109,3/5/21,pubmed,0,3,logistic regression,0.001438083,0.001438111,0.084532405,0.001438131,0.73623393,0.174919339,Healthcare,0.9685009,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
1432,"COVID 19: Evaluating the Knowledge, Attitude and Preventive Practices of Healthcare Workers in Northern Nigeria.",Int J MCH AIDS,33659097,3/5/21,pubmed,0,7,logistic regression,0.00114136,0.00114138,0.001141346,0.001141357,0.994293233,0.001141325,Healthcare,0.69653624,TRUE,47,0.606407323,24.14285714,0.522411025,0,0.403234768,,,0.510684372
1433,0,Front Genet,33659022,3/5/21,pubmed,0,5,in silico,0.756695583,0.236011098,0.001823313,0.001823383,0.001823326,0.001823297,Drug discovery,0.66204655,TRUE,46.2,0.598181706,102.2,0.826130586,0,0.403234768,,,0.609182353
1434,DetectaWeb-Distress Scale: A Global and Multidimensional Web-Based Screener for Emotional Disorder Symptoms in Children and Adolescents.,Front Psychol,33658965,3/5/21,pubmed,0,5,structural model,0.001098837,0.001098828,0.04718582,0.001098846,0.948418842,0.001098827,Healthcare,0.7325348,TRUE,24.6,0.362483765,25.4,0.532044421,0,0.403234768,,,0.432587651
1435,Depressive and Anxiety Symptoms Among People Under Quarantine During the COVID-19 Epidemic in China: A Cross-Sectional Study.,Front Psychiatry,33658949,3/5/21,pubmed,0,7,logistic regression,0.001059341,0.001059344,0.001059347,0.001059384,0.980398441,0.015364142,Healthcare,0.92669785,TRUE,56,0.675304595,0.571428571,0.088038534,0,0.403234768,,,0.388859299
1436,Effects of Bicultural Identity Integration and National Identity on COVID-19-Related Anxiety Among Ethnic Minority College Students: The Mediation Role of Power Values.,Psychol Res Behav Manag,33658871,3/5/21,pubmed,0,3,correlation analysis,0.024876704,0.001653043,0.001653061,0.169600662,0.800563467,0.001653064,Healthcare,0.81591964,TRUE,159.3333333,0.951079226,116.3333333,0.846400856,0,0.403234768,,,0.733571617
1437,Healthcare Worker's Mental Health and Their Associated Predictors During the Epidemic Peak of COVID-19.,Psychol Res Behav Manag,33658870,3/5/21,pubmed,0,9,logistic regression,0.00108532,0.001085332,0.001085323,0.040124103,0.955534559,0.001085363,Healthcare,0.90563405,TRUE,22.44444444,0.331560393,2,0.164302917,1,0.537564047,,,0.344475786
1438,Artificial Intelligence Clinicians Can Use Chest Computed Tomography Technology to Automatically Diagnose Coronavirus Disease 2019 (COVID-19) Pneumonia and Enhance Low-Quality Images.,Infect Drug Resist,33658806,3/5/21,pubmed,0,7,artificial intelligence,0.000800585,0.059922623,0.93687491,0.000800619,0.00080064,0.000800623,Imaging,0.080346674,FALSE,51.28571429,0.640299338,9.285714286,0.340714477,0,0.403234768,,,0.461416194
1439,A predictive internet-based model for COVID-19 hospitalization census.,Sci Rep,33658529,3/5/21,pubmed,0,4,predictive model,0.032857448,0.001684843,0.098101483,0.726841112,0.001684644,0.13883047,Epidemiology,0.43227473,FALSE,25.75,0.377698064,12.75,0.395972705,0,0.403234768,,,0.392301846
1440,"A multiplexed, next generation sequencing platform for high-throughput detection of SARS-CoV-2.",Nat Commun,33658502,3/5/21,pubmed,0,23,sequencing,0.001684539,0.390930748,0.375241854,0.136467227,0.001684549,0.093991083,Genomics,0.28242034,FALSE,76.2173913,0.791019853,316.0869565,0.958522879,0,0.403234768,,,0.7175925
1441,Circulating Vitamin D levels status and clinical prognostic indices in COVID-19 patients.,Respir Res,33658032,3/5/21,pubmed,0,15,image analysis,0.256964273,0.001622728,0.122782924,0.001622751,0.001622741,0.615384582,Clinics,0.7907033,TRUE,69.8,0.762694044,48,0.673735617,2,0.618927094,,,0.685118918
1442,"Public perceptions, individual characteristics, and preventive behaviors for COVID-19 in six countries: a cross-sectional study.",Environ Health Prev Med,33657995,3/5/21,pubmed,0,3,logistic regression,0.000977421,0.000977441,0.00097742,0.311639189,0.684451097,0.000977434,Healthcare,0.6746555,TRUE,76,0.790586926,23,0.513513514,0,0.403234768,,,0.569111736
1443,Epidemiological Surveillance of the Impact of the COVID-19 Pandemic on Stroke Care Using Artificial Intelligence.,Stroke,33657851,3/5/21,pubmed,0,21,artificial intelligence,0.000907284,0.000907315,0.510898069,0.072115236,0.000907326,0.41426477,Imaging,0.46318576,FALSE,1607.571429,0.999876307,83.57142857,0.787195611,0,0.403234768,,,0.730102229
1444,Factors Associated with COVID-19 and Asymptomatic Carriage in Healthcare Workers of a COVID-19 Hospital.,Rev Invest Clin,33657620,3/4/21,pubmed,0,15,logistic regression,0.00198711,0.001987214,0.001987164,0.001987182,0.508186337,0.483864994,Healthcare,0.8474333,TRUE,23.06666667,0.342507267,9.866666667,0.351552047,0,0.403234768,,,0.365764694
1445,Incidence of COVID-19 in 902 Patients With Immunomediated Inflammatory Diseases Treated With Biologics and Targeted Synthetic Disease-Modifying Antirheumatic Drugs-Findings From the BIOCOVID Study.,J Clin Rheumatol,33657593,3/4/21,pubmed,0,19,immunome,0.043030558,0.00123719,0.001237101,0.067500715,0.001237111,0.885757325,Clinics,0.67320836,TRUE,31.21052632,0.446595337,9.157894737,0.338306128,0,0.403234768,,,0.396045411
1446,The electrocardiographic manifestations and derangements of 2019 novel coronavirus disease (COVID-19).,Indian Pacing Electrophysiol J,33657456,3/4/21,pubmed,0,8,predictive model,0.001565299,0.001565329,0.211143147,0.001565343,0.034187393,0.74997349,Clinics,0.4133487,FALSE,4.5,0.061784897,0,0.055525823,0,0.403234768,,,0.173515162
1447,COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas.,Cell,33657410,3/4/21,pubmed,0,90,"sequencing, transcriptom, dataset",0.580615167,0.148391268,0.001751259,0.001751218,0.001751221,0.265739867,Drug discovery,0.22851285,FALSE,13.97777778,0.209846002,,,6,0.764429903,,,0.487137952
1448,An Active Site Inhibitor Induces Conformational Penalties for ACE2 Recognition by the Spike Protein of SARS-CoV-2.,J Phys Chem B,33657325,3/4/21,pubmed,0,5,molecular dynamics simulation,0.994218592,0.001156264,0.001156269,0.001156313,0.001156264,0.001156299,Drug discovery,0.20967543,FALSE,108.8,0.887500773,44.6,0.657144769,0,0.403234768,,,0.649293436
1449,Heterogeneity of Glycan Processing on Trimeric SARS-CoV-2 Spike Protein Revealed by Charge Detection Mass Spectrometry.,J Am Chem Soc,33657316,3/4/21,pubmed,0,9,"proteom, glycoproteom",0.375882964,0.002032926,0.002032931,0.615985556,0.00203278,0.002032844,Epidemiology,0.32086182,FALSE,151.1111111,0.945265632,102.1111111,0.826063687,0,0.403234768,,,0.724854696
1450,Assessing the likelihood of contracting COVID-19 disease based on a predictive tree model: A retrospective cohort study.,PLoS One,33657164,3/4/21,pubmed,0,8,"data mining, logistic regression",0.001371258,0.00137134,0.466767328,0.193987462,0.226293208,0.110209404,Healthcare,0.382528,FALSE,15.5,0.234028078,1.875,0.151792882,0,0.403234768,,,0.263018576
1451,Time series analysis of the demand for COVID-19 related chest imaging during the first wave of the SARS-CoV-2 pandemic: An explorative study.,PLoS One,33657140,3/4/21,pubmed,0,8,logistic regression,0.001684506,0.001684544,0.264969894,0.254224772,0.00168453,0.475751755,Clinics,0.7894402,TRUE,105.875,0.880635785,24.25,0.523414504,0,0.403234768,,,0.602428352
1452,Forecasting the spread of SARS-CoV-2 is inherently ambiguous given the current state of virus research.,PLoS One,33657128,3/4/21,pubmed,0,6,simulation model,0.026328768,0.002130702,0.002130724,0.884814604,0.002130635,0.082464567,Epidemiology,0.27919954,FALSE,18.16666667,0.272682293,11,0.371287129,0,0.403234768,,,0.349068063
1453,Assessing the potential impact of disruptions due to COVID-19 on HIV among key and lower-risk populations in the largest cities of Cameroon and Benin.,J Acquir Immune Defic Syndr,33657058,3/4/21,pubmed,0,21,mathematical model,0.001786494,0.001786498,0.001786578,0.729526528,0.263327292,0.00178661,Epidemiology,0.888832,TRUE,97.66666667,0.863751623,98.28571429,0.819507626,0,0.403234768,,,0.695498006
1454,Rapid Detection of COVID-19 Using MALDI-TOF-Based Serum Peptidome Profiling.,Anal Chem,33656857,3/4/21,pubmed,0,15,"machine learning, logistic regression",0.001717223,0.153671706,0.693248958,0.001717248,0.001717229,0.147927636,Genomics,0.46095416,FALSE,53.86666667,0.66002845,31,0.578204442,0,0.403234768,,,0.547155886
1455,Gender Susceptibility to COVID-19 Mortality: Androgens as the Usual Suspects?,Adv Exp Med Biol,33656731,3/4/21,pubmed,0,5,logistic regression,0.060949679,0.0024224,0.002422291,0.002422361,0.002422387,0.929360882,Clinics,0.5847085,TRUE,100.4,0.869688911,28.6,0.559071448,0,0.403234768,,,0.610665042
1456,The Primary Outcomes and Epidemiological and Clinical Features of Coronavirus Disease 2019 (COVID-19) in Iran.,Adv Exp Med Biol,33656725,3/4/21,pubmed,0,20,logistic regression,0.001565271,0.001565285,0.001565289,0.001565309,0.001565326,0.992173519,Clinics,0.93205667,TRUE,60.1,0.703754097,21.4,0.496052984,0,0.403234768,,,0.534347283
1457,Correction to: Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.,Eur J Nucl Med Mol Imaging,33656580,3/4/21,pubmed,0,10,"deep learning, radiom",0.013549631,0.013549691,0.932254016,0.013548872,0.013548786,0.013549003,Imaging,0.4818228,FALSE,39.9,0.538066671,18.9,0.469092855,0,0.403234768,,,0.470131431
1458,Psychological and occupational impact on healthcare workers and its associated factors during the COVID-19 outbreak in China.,Int Arch Occup Environ Health,33656572,3/4/21,pubmed,0,10,logistic regression,0.001511846,0.00151185,0.001511826,0.001511906,0.992440731,0.001511842,Healthcare,0.7217256,TRUE,38.3,0.521058816,11.4,0.375970029,0,0.403234768,,,0.433421204
1459,Cost-effectiveness of a Telemonitoring Program for Patients With Heart Failure During the COVID-19 Pandemic in Hong Kong: Model Development and Data Analysis.,J Med Internet Res,33656440,3/4/21,pubmed,0,3,probabilistic,0.00165304,0.001653039,0.00165313,0.571257414,0.001653175,0.422130202,Epidemiology,0.9240267,TRUE,7.666666667,0.111633373,0.666666667,0.096200161,0,0.403234768,,,0.203689434
1460,Health-related quality of life of COVID-19 patients after discharge: A multicenter follow-up study.,J Clin Nurs,33656210,3/4/21,pubmed,0,29,logistic regression,0.000966984,0.000966947,0.000966792,0.023400927,0.52983374,0.443864609,Healthcare,0.998183,TRUE,58.62068966,0.693487538,,,0,0.403234768,,,0.548361153
1461,"COVID-19: Role of Robotics, Artificial Intelligence, and Machine learning during pandemic.",Curr Med Imaging,33655845,3/4/21,pubmed,0,6,"machine learning, artificial intelligence",0.105931817,0.002639017,0.393508952,0.492642024,0.002639062,0.002639128,Epidemiology,0.7757459,TRUE,27.16666667,0.397303482,2,0.164302917,0,0.403234768,,,0.321613722
1462,Biostructural Models for the Binding of Nucleoside Analogs to SARS-CoV-2 RNA-Dependent RNA Polymerase.,J Chem Inf Model,33655751,3/4/21,pubmed,0,2,structural model,0.773424418,0.220201595,0.00159352,0.001593513,0.001593479,0.001593476,Drug discovery,0.47812325,FALSE,12.5,0.189436576,5,0.257024351,0,0.403234768,,,0.283231898
1463,Targeting the Main Protease of SARS-CoV-2: From the Establishment of High Throughput Screening to the Design of Tailored Inhibitors.,Angew Chem Int Ed Engl,33655614,3/4/21,pubmed,0,20,virtual screening,0.989083877,0.002183277,0.002183238,0.002183204,0.002183195,0.002183209,Drug discovery,0.7091869,TRUE,26.65,0.3903148,14.6,0.419253412,0,0.403234768,,,0.40426766
1464,SARS-CoV-2 genomic surveillance identifies naturally occurring truncations of ORF7a that limit immune suppression.,medRxiv,33655280,3/4/21,pubmed,0,12,"phylogenom, whole genome, genome sequences, genomes",0.167427764,0.79329993,0.002130685,0.002130734,0.002130666,0.032880222,Genomics,0.18430358,FALSE,38.66666667,0.525882862,81.91666667,0.782981001,0,0.403234768,,,0.570699544
1465,A catalog of associations between rare coding variants and COVID-19 outcomes.,medRxiv,33655273,3/4/21,pubmed,0,80,exom,0.091130152,0.603334706,0.002996466,0.002996597,0.002996627,0.296545453,Genomics,0.3820111,FALSE,88.85,0.838703692,387.1625,0.969895638,0,0.403234768,,,0.737278033
1466,Racial and ethnic differences in COVID-19 vaccine hesitancy and uptake.,medRxiv,33655271,3/4/21,pubmed,0,25,logistic regression,0.00129121,0.001291253,0.001291197,0.001291234,0.993543862,0.001291243,Healthcare,0.38211462,FALSE,149.64,0.943843157,172.16,0.901324592,0,0.403234768,,,0.749467505
1467,Transmission of SARS-CoV-2 Considering Shared Chairs in Outpatient Dialysis: A Real-World Case-Control Study.,medRxiv,33655270,3/4/21,pubmed,0,13,logistic regression,0.001034591,0.001034624,0.001034597,0.332074217,0.104601996,0.560219975,Clinics,0.2410708,FALSE,124.3076923,0.913414559,,,0,0.403234768,,,0.658324663
1468,COVID-19 Vaccine Hesitancy in Underserved Communities of North Carolina.,medRxiv,33655265,3/4/21,pubmed,0,12,logistic regression,0.024884298,0.000838532,0.000838537,0.098659239,0.87394087,0.000838524,Healthcare,0.3273086,FALSE,9.083333333,0.135568062,10.83333333,0.367273214,0,0.403234768,,,0.302025348
1469,0,medRxiv,33655264,3/4/21,pubmed,0,18,"machine learning, lipidom",0.240308509,0.002357835,0.166711285,0.002357852,0.002357777,0.585906742,Clinics,0.46521497,FALSE,51.16666667,0.639557177,55,0.702167514,0,0.403234768,,,0.581653153
1470,Informing patients that they are at high risk for serious complications of viral infection increases vaccination rates.,medRxiv,33655258,3/4/21,pubmed,0,8,machine learning,0.001786587,0.001786556,0.268554288,0.001786617,0.334640429,0.391445522,Clinics,0.3386684,FALSE,34,0.477766096,132.5,0.866336634,0,0.403234768,,,0.582445832
1471,SARS-CoV-2 Viremia is Associated with Distinct Proteomic Pathways and Predicts COVID-19 Outcomes.,medRxiv,33655257,3/4/21,pubmed,0,30,proteom,0.299046106,0.001622877,0.001622726,0.001622728,0.060644758,0.635440804,Clinics,0.39465326,FALSE,27.33333333,0.399529965,134.4333333,0.868744983,0,0.403234768,,,0.557169905
1472,"High-Throughput, Single-Copy Sequencing Reveals SARS-CoV-2 Spike Variants Coincident with Mounting Humoral Immunity during Acute COVID-19.",bioRxiv,33655255,3/4/21,pubmed,0,16,"sequencing, genomes",0.20331041,0.757058622,0.000638766,0.000638805,0.037714603,0.000638794,Genomics,0.22827455,FALSE,75.1875,0.787618282,233,0.931094461,1,0.537564047,,,0.752092263
1473,A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2.,bioRxiv,33655247,3/4/21,pubmed,0,102,"computational, transcriptom, dataset",0.741943059,0.106717589,0.037052024,0.000710644,0.000710597,0.112866086,Drug discovery,0.5410163,TRUE,55.81372549,0.673325499,226.6764706,0.928351619,2,0.618927094,,,0.740201404
1474,The proximal proteome of 17 SARS-CoV-2 proteins links to disrupted antiviral signaling and host translation.,bioRxiv,33655243,3/4/21,pubmed,0,14,proteom,0.87812364,0.118091949,0.000946092,0.000946104,0.000946109,0.000946106,Drug discovery,0.4145095,FALSE,33.78571429,0.473807904,,,0,0.403234768,,,0.438521336
1475,Adaptive Human Behavior in Epidemics: the Impact of Risk Misperception on the Spread of Epidemics.,Res Sq,33655240,3/4/21,pubmed,0,4,mathematical model,0.002639023,0.00263915,0.002639052,0.783592923,0.205850829,0.002639024,Epidemiology,0.33279824,FALSE,148,0.942606222,174.5,0.902662564,0,0.403234768,,,0.749501184
1476,Factors associated with SARS-CoV-2 infection and outbreaks in long-term care facilities in England: a national cross-sectional survey.,Lancet Healthy Longev,33655236,3/4/21,pubmed,0,11,"logistic regression, dataset",0.000657744,0.000657768,0.000657777,0.000657788,0.945563411,0.051805513,Healthcare,0.2764398,FALSE,36.45454545,0.502010019,19.18181818,0.472237089,3,0.667819001,,,0.54735537
1477,"Association between Clinical Frailty Scale score and hospital mortality in adult patients with COVID-19 (COMET): an international, multicentre, retrospective, observational cohort study.",Lancet Healthy Longev,33655235,3/4/21,pubmed,0,164,logistic regression,0.027500479,0.000662692,0.000662733,0.086936974,0.00066271,0.883574412,Clinics,0.3797351,FALSE,10.69090909,0.159317212,,,1,0.537564047,,,0.34844063
1478,Estimating risk of mechanical ventilation and in-hospital mortality among adult COVID-19 patients admitted to Mass General Brigham: The VICE and DICE scores.,EClinicalMedicine,33655204,3/4/21,pubmed,0,8,logistic regression,0.000977465,0.000977449,0.055121899,0.000977517,0.000977511,0.940968158,Clinics,0.41903633,FALSE,40.25,0.541715629,13.625,0.406408884,0,0.403234768,,,0.450453093
1479,Diffusion Earth Mover's Distance and Distribution Embeddings.,ArXiv,33655017,3/4/21,pubmed,0,8,"neural network, dataset",0.165339925,0.001823439,0.664827045,0.070925236,0.001823362,0.095260992,Drug discovery,0.14891681,FALSE,65.625,0.73807904,135.25,0.86981536,0,0.403234768,,,0.670376389
1480,In-silico nucleotide and protein analyses of S-gene region in selected zoonotic coronaviruses reveal conserved domains and evolutionary emergence with trajectory course of viral entry from SARS-CoV-2 genomic data.,Pan Afr Med J,33654512,3/4/21,pubmed,0,4,"in-silico, sequence alignment",0.545880073,0.449239761,0.001220006,0.001220023,0.001220093,0.001220044,Drug discovery,0.6366013,TRUE,15.25,0.229884347,0.75,0.099411292,0,0.403234768,,,0.244176802
1481,Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.,Pers Ubiquitous Comput,33654480,3/4/21,pubmed,0,6,"deep learning, artificial intelligence",0.01754947,0.00117155,0.807356889,0.095131972,0.001171618,0.077618502,Imaging,0.49825844,FALSE,10.33333333,0.155049787,0.333333333,0.073187048,0,0.403234768,,,0.210490534
1482,Needleless electrospun phytochemicals encapsulated nanofibre based 3-ply biodegradable mask for combating COVID-19 pandemic.,Chem Eng J,33654455,3/4/21,pubmed,0,10,computational,0.301381353,0.001034631,0.262037334,0.309551499,0.074883924,0.051111259,Epidemiology,0.92902964,TRUE,24.9,0.36638011,2.1,0.164771207,0,0.403234768,,,0.311462028
1483,Screening of inhibitors against SARS-CoV-2 spike protein and their capability to block the viral entry mechanism: A Viroinformatics study.,Saudi J Biol Sci,33654454,3/4/21,pubmed,0,6,"virtual screening, in silico",0.954895985,0.04101336,0.001022622,0.001022721,0.00102268,0.001022632,Drug discovery,0.9081334,TRUE,23.66666667,0.34986703,3.833333333,0.222103291,0,0.403234768,,,0.325068363
1484,0,J Ayurveda Integr Med,33654345,3/4/21,pubmed,0,17,"molecular dynamics simulation, in silico",0.968185821,0.001010954,0.001010956,0.001011033,0.027770247,0.001010989,Drug discovery,0.9598095,TRUE,29.17647059,0.420681551,8.058823529,0.320578004,0,0.403234768,,,0.381498107
1485,Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics.,Nat Med,33654293,3/4/21,pubmed,0,234,sequencing,0.867996597,0.082510743,0.001254595,0.001254629,0.001254654,0.045728782,Drug discovery,0.847873,TRUE,29.84210526,0.428969015,39.42982456,0.63018464,1,0.537564047,,,0.532239234
1486,COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections.,Sci Rep,33654146,3/4/21,pubmed,0,14,deep learning,0.002130745,0.002130847,0.697555704,0.087140141,0.002130753,0.20891181,Clinics,0.6018205,TRUE,24.21428571,0.357412332,13.07142857,0.400588708,0,0.403234768,,,0.387078602
1487,"Depression, anxiety and associated factors among Chinese adolescents during the COVID-19 outbreak: a comparison of two cross-sectional studies.",Transl Psychiatry,33654058,3/4/21,pubmed,0,10,logistic regression,0.001085347,0.001085361,0.001085367,0.060913177,0.934745381,0.001085367,Healthcare,0.92702425,TRUE,187.9,0.96790154,123.1,0.855365266,0,0.403234768,,,0.742167191
1488,"Digital health literacy intervention to support maternal, child and family health in primary healthcare settings of Pakistan during the age of coronavirus: study protocol for a randomised controlled trial.",BMJ Open,33653760,3/4/21,pubmed,0,5,digital health,0.00117158,0.001171618,0.001171558,0.212531865,0.782781801,0.001171577,Healthcare,0.97586095,TRUE,46.6,0.60170697,82.4,0.783783784,0,0.403234768,,,0.59624184
1489,Rapid High Throughput Whole Genome Sequencing of SARS-CoV-2 by using One-step RT-PCR Amplification with Integrated Microfluidic System and Next-Gen Sequencing.,J Clin Microbiol,33653700,3/4/21,pubmed,0,11,"sequencing, whole genome",0.002562624,0.843379124,0.14637043,0.002562732,0.002562558,0.002562533,Genomics,0.5343784,TRUE,27,0.3960047,20.45454545,0.485014718,0,0.403234768,,,0.428084729
1490,Understanding different trajectories of mental health across the general population during the COVID-19 pandemic.,Psychol Med,33653426,3/4/21,pubmed,0,4,logistic regression,0.001085338,0.001085332,0.001085357,0.213968337,0.781690239,0.001085398,Healthcare,0.73542553,TRUE,365.75,0.994743027,658.75,0.986218892,0,0.403234768,,,0.794732229
1491,Combination anti-coronavirus therapies based on nonlinear mathematical models.,Chaos,33653052,3/4/21,pubmed,0,8,mathematical model,0.457261202,0.015998982,0.015999463,0.23326521,0.015998411,0.261476731,Drug discovery,0.73938006,TRUE,14.375,0.217205764,3.75,0.21982874,0,0.403234768,,,0.280089757
1492,Use of angiotensin converting enzyme inhibitors and angiotensin receptor blockers associated with lower risk of COVID-19 in household contacts.,PLoS One,33651840,3/3/21,pubmed,0,5,logistic regression,0.096284036,0.001237107,0.001237072,0.070705241,0.399145232,0.431391312,Clinics,0.70478904,TRUE,104.8,0.878842229,153,0.88587102,0,0.403234768,,,0.722649339
1493,"Cell-free DNA maps COVID-19 tissue injury and risk of death, and can cause tissue injury.",JCI Insight,33651717,3/3/21,pubmed,0,15,sequencing,0.326567287,0.158534155,0.00104684,0.001046853,0.001046825,0.51175804,Clinics,0.78381515,TRUE,94.66666667,0.856020781,113.1333333,0.842721434,0,0.403234768,,,0.700658994
1494,The Impact of the Covid-19 Pandemic on Uptake of Influenza Vaccine: A UK-Wide Observational Study.,JMIR Public Health Surveill,33651708,3/3/21,pubmed,0,6,logistic regression,0.002032842,0.002032779,0.002033027,0.002032988,0.989835591,0.002032773,Healthcare,0.06358048,FALSE,64,0.729173109,20.16666667,0.481803586,0,0.403234768,,,0.538070487
1495,Deregulated miRNA expression is associated with endothelial dysfunction in post-mortem lung biopsies of COVID-19 patients.,Am J Physiol Lung Cell Mol Physiol,33651636,3/3/21,pubmed,0,11,correlation analysis,0.436254049,0.001237086,0.001237078,0.001237071,0.001237059,0.558797657,Clinics,0.9754107,TRUE,29.27272727,0.421732946,9.454545455,0.343925609,1,0.537564047,,,0.434407534
1496,Discrimination and Stress Among Asian Refugee Populations During the COVID-19 Pandemic: Evidence from Bhutanese and Burmese Refugees in the USA.,J Racial Ethn Health Disparities,33651371,3/3/21,pubmed,0,5,logistic regression,0.00186169,0.001861818,0.001861725,0.001861818,0.990691183,0.001861766,Healthcare,0.8928568,TRUE,17.6,0.264456676,14.8,0.421527964,0,0.403234768,,,0.363073136
1497,Structural Flexibility of Peripheral Loops and Extended C-terminal Domain of Short Length Substrate Binding Protein from Rhodothermus marinus.,Protein J,33651244,3/3/21,pubmed,0,4,computational,0.79474291,0.139458275,0.061808613,0.0013301,0.00133005,0.001330052,Drug discovery,0.2723207,FALSE,78.75,0.801533799,47,0.668718223,0,0.403234768,,,0.624495597
1498,Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males: findings from a nested case-control study.,Elife,33650967,3/3/21,pubmed,0,148,logistic regression,0.201311779,0.227182863,0.022726661,0.001538155,0.198146529,0.349094013,Clinics,0.6428415,TRUE,89,0.839384006,74.2,0.764851485,1,0.537564047,,,0.71393318
1499,Early Impact of the COVID-19 Outbreak on Sleep in a Large Spanish Sample.,Behav Sleep Med,33650896,3/3/21,pubmed,0,10,logistic regression,0.000966755,0.000966768,0.000966764,0.000966776,0.995166124,0.000966813,Healthcare,0.9581197,TRUE,107.6,0.885026903,87,0.795223441,0,0.403234768,,,0.694495037
1500,Linking Data on Constituent Health with Elected Officials' Opinions: Associations Between Urban Health Disparities and Mayoral Officials' Beliefs About Health Disparities in Their Cities.,Milbank Q,33650741,3/3/21,pubmed,0,5,logistic regression,0.000779435,0.000779446,0.000779408,0.327774821,0.635069287,0.034817602,Healthcare,0.32667178,FALSE,81,0.811552972,256.4,0.940259566,0,0.403234768,,,0.718349102
1501,Anti-A and SARS-CoV-2: an intriguing association.,Vox Sang,33650690,3/3/21,pubmed,0,21,logistic regression,0.001786562,0.203310966,0.001786538,0.001786568,0.001786675,0.789542691,Clinics,0.6056862,TRUE,53.14285714,0.654833323,17.14285714,0.451565427,0,0.403234768,,,0.503211172
1502,Genomic epidemiology of COVID-19 in care homes in the east of England.,Elife,33650490,3/3/21,pubmed,0,37,"genomic epidemiology, genomes, dataset",0.002183288,0.430401744,0.00218342,0.002183399,0.412413603,0.150634545,Genomics,0.33799934,FALSE,77.51351351,0.796647906,249.4594595,0.938051913,0,0.403234768,,,0.712644862
1503,Assessing the Impact of Individual Characteristics and Neighborhood Socioeconomic Status During the COVID-19 Pandemic in the Provinces of Milan and Lodi.,Int J Health Serv,33650453,3/3/21,pubmed,0,6,logistic regression,0.002639038,0.002639224,0.002638994,0.661938054,0.221810704,0.108333986,Epidemiology,0.4238907,FALSE,28,0.408312202,21,0.492239765,0,0.403234768,,,0.434595578
1504,Prognostic Implications of CT Feature Analysis in Patients with COVID-19: a Nationwide Cohort Study.,J Korean Med Sci,33650333,3/3/21,pubmed,0,8,"artificial intelligence, logistic regression",0.000889059,0.000889058,0.389480469,0.000889053,0.000889058,0.606963302,Clinics,0.99448854,TRUE,124.25,0.913229018,136,0.870818839,0,0.403234768,,,0.729094208
1505,Doppler assessment of the fetus in pregnant women recovered from COVID-19.,J Obstet Gynaecol Res,33650296,3/3/21,pubmed,0,8,logistic regression,0.00178653,0.071348596,0.099152012,0.00178666,0.291277069,0.534649134,Clinics,0.9708843,TRUE,53.875,0.660090296,12,0.386740701,0,0.403234768,,,0.483355255
1506,Sociodemographic characteristics of pregnant women tested positive for COVID-19 admitted to a referral center in Northern Italy during lockdown period.,J Obstet Gynaecol Res,33650278,3/3/21,pubmed,0,10,logistic regression,0.00159351,0.030357327,0.001593471,0.001593581,0.830900397,0.133961715,Healthcare,0.2700751,FALSE,63.4,0.723977983,21.9,0.501605566,0,0.403234768,,,0.542939439
1507,Risk of symptomatic COVID-19 due to aircraft transmission: a retrospective cohort study of contact-traced flights during England's containment phase.,Influenza Other Respir Viruses,33650201,3/3/21,pubmed,0,8,dataset,0.001272628,0.001272669,0.001272657,0.796291034,0.05763932,0.142251693,Epidemiology,0.1292043,FALSE,34.5,0.482404601,16,0.437316029,0,0.403234768,,,0.440985133
1508,Preexisting Executive Function Deficits and Change in Health Behaviors During the COVID-19 Pandemic.,Int J Behav Med,33649889,3/3/21,pubmed,0,5,logistic regression,0.001486474,0.001486455,0.001486446,0.001486537,0.946115699,0.047938388,Healthcare,0.95765996,TRUE,73.8,0.781124374,224.2,0.927615735,0,0.403234768,,,0.703991625
1509,0,Inform Med Unlocked,33649734,3/3/21,pubmed,0,4,"virtual screening, computational, in silico",0.950722983,0.001291221,0.044112123,0.001291219,0.001291239,0.001291215,Drug discovery,0.9865845,TRUE,72.25,0.774135692,32.75,0.590647578,0,0.403234768,,,0.589339346
1510,The pulmonary route as a way to drug repositioning in COVID-19 therapy.,J Drug Deliv Sci Technol,33649708,3/3/21,pubmed,0,8,in silico,0.609748014,0.001310381,0.001310402,0.193546092,0.001310442,0.192774668,Drug discovery,0.88460207,TRUE,1.75,0.017007855,0,0.055525823,1,0.537564047,,,0.203365908
1511,Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture.,Appl Soft Comput,33649705,3/3/21,pubmed,0,3,"artificial intelligence, neural network, image analysis, dataset",0.000988392,0.02683583,0.969210514,0.000988432,0.000988414,0.000988418,Imaging,0.5995582,TRUE,46.33333333,0.599418641,9.666666667,0.348274017,0,0.403234768,,,0.450309142
1512,COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images.,Inf Fusion,33649704,3/3/21,pubmed,0,2,"neural network, dataset",0.001538199,0.001538153,0.992309148,0.001538169,0.001538123,0.001538208,Imaging,0.84430814,TRUE,56,0.675304595,24.5,0.525086968,1,0.537564047,,,0.579318537
1513,Screening of drug databank against WT and mutant main protease of SARS-CoV-2: Towards finding potential compound for repurposing against COVID-19.,Saudi J Biol Sci,33649700,3/3/21,pubmed,0,7,computational,0.851893266,0.142865254,0.001310416,0.001310385,0.001310332,0.001310347,Drug discovery,0.82174623,TRUE,16.57142857,0.249737151,2.142857143,0.165640888,0,0.403234768,,,0.272870936
1514,Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks.,Neural Comput Appl,33649695,3/3/21,pubmed,0,5,"machine learning, artificial intelligence",0.001156233,0.001156324,0.994218708,0.001156261,0.001156236,0.001156238,Imaging,0.25727743,FALSE,26.5,0.389325252,16,0.437316029,5,0.739490092,,,0.522043791
1515,A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19.,J Manuf Syst,33649693,3/3/21,pubmed,0,5,optimization model,0.002032909,0.049300015,0.238428567,0.69078793,0.002032837,0.017417743,Epidemiology,0.63772404,TRUE,23.8,0.351227658,3.8,0.221501204,0,0.403234768,,,0.32532121
1516,Adaptive mesh refinement and coarsening for diffusion-reaction epidemiological models.,Comput Mech,33649692,3/3/21,pubmed,0,2,mathematical model,0.001565339,0.001565397,0.001565421,0.992173235,0.001565299,0.001565309,Epidemiology,0.056627363,FALSE,127.5,0.917867524,28,0.554321648,1,0.537564047,,,0.66991774
1517,Lung expression of genes putatively involved in SARS-CoV-2 infection is modulated in cis by germline variants.,Eur J Hum Genet,33649539,3/3/21,pubmed,0,4,whole-genome,0.696277262,0.244898893,0.001565324,0.001565494,0.001565433,0.054127595,Drug discovery,0.7197504,TRUE,101,0.871296926,106,0.833154937,0,0.403234768,,,0.70256221
1518,Want to track pandemic variants faster? Fix the bioinformatics bottleneck.,Nature,33649511,3/3/21,pubmed,0,9,bioinformatic,0.025061072,0.874697103,0.025060344,0.025060878,0.025060337,0.025060265,Genomics,0.39696246,FALSE,89.11111111,0.839816934,2051.888889,0.998394434,0,0.403234768,,,0.747148712
1519,Ferritin is associated with the severity of lung involvement but not with worse prognosis in patients with COVID-19: data from two Italian COVID-19 units.,Sci Rep,33649408,3/3/21,pubmed,0,12,logistic regression,0.001141404,0.001141367,0.303412962,0.001141371,0.001141337,0.692021559,Clinics,0.8790103,TRUE,85.5,0.82658173,43.58333333,0.651859781,0,0.403234768,,,0.627225426
1520,Analysis of dynamic contact network of patients with COVID-19 in Shaanxi Province of China.,Sci Rep,33649407,3/3/21,pubmed,0,1,network analysis,0.001461917,0.001461892,0.001461939,0.779798876,0.084981141,0.130834235,Epidemiology,0.7824042,TRUE,3,0.037293586,0,0.055525823,0,0.403234768,,,0.165351392
1521,Evaluation of myocardial injury patterns and ST changes among critical and non-critical patients with coronavirus-19 disease.,Sci Rep,33649391,3/3/21,pubmed,0,4,logistic regression,0.001653053,0.001653039,0.001653053,0.001653112,0.053372788,0.940014955,Clinics,0.7621025,TRUE,6.25,0.088564537,1,0.122023013,0,0.403234768,,,0.204607439
1522,Contact tracing evaluation for COVID-19 transmission in the different movement levels of a rural college town in the USA.,Sci Rep,33649364,3/3/21,pubmed,0,2,network model,0.00111263,0.001112634,0.001112676,0.979248358,0.016301091,0.00111261,Epidemiology,0.004874438,FALSE,130.5,0.922196796,87.5,0.795959326,0,0.403234768,,,0.707130297
1523,A living WHO guideline on drugs to prevent covid-19.,BMJ,33649077,3/3/21,pubmed,0,53,network analysis,0.158428779,0.000854721,0.000854729,0.548592763,0.186499292,0.104769716,Epidemiology,0.197499,FALSE,136.1320755,0.927763003,201.4150943,0.918116136,2,0.618927094,,,0.821602078
1524,The faecal metabolome in COVID-19 patients is altered and associated with clinical features and gut microbes.,Anal Chim Acta,33648648,3/3/21,pubmed,0,9,metabolom,0.282870726,0.001861872,0.001861808,0.001861746,0.001861704,0.709682144,Clinics,0.9743669,TRUE,26.33333333,0.386047375,24.77777778,0.526558737,0,0.403234768,,,0.438613626
1525,0,Aging (Albany NY),33647885,3/2/21,pubmed,0,5,"correlation analysis, prediction model, dataset",0.572418095,0.055486481,0.057892268,0.001653092,0.001653055,0.310897008,Drug discovery,0.73132014,TRUE,27,0.3960047,8.4,0.326598876,0,0.403234768,,,0.375279448
1526,UMAP-assisted K-means clustering of large-scale SARS-CoV-2 mutation datasets.,Comput Biol Med,33647832,3/2/21,pubmed,0,4,"genome sequences, dataset",0.001943586,0.380523377,0.392311822,0.221334294,0.00194347,0.001943452,Genomics,0.30628565,FALSE,50.75,0.636341147,12.5,0.392761573,0,0.403234768,,,0.477445829
1527,Machine learning-based cytokine microarray digital immunoassay analysis.,Biosens Bioelectron,33647790,3/2/21,pubmed,0,12,"machine learning, neural network, image analysis",0.1899068,0.139020538,0.419249934,0.051417887,0.001112659,0.199292183,Imaging,0.6543518,TRUE,54,0.661574618,139.75,0.873762376,0,0.403234768,,,0.646190587
1528,The impact of the initial Covid-19 lockdown upon regular sports bettors in Britain: Findings from a cross-sectional online study.,Addict Behav,33647707,3/2/21,pubmed,0,12,logistic regression,0.001350333,0.001350354,0.001350322,0.117558597,0.877040047,0.001350348,Healthcare,0.9636005,TRUE,90.66666667,0.844764673,86.08333333,0.793082687,0,0.403234768,,,0.680360709
1529,WHO digital health packages for disseminating data standards and data use practices.,Int J Med Inform,33647601,3/2/21,pubmed,0,3,digital health,0.002080587,0.002080592,0.002080657,0.989596878,0.002080727,0.002080559,Epidemiology,0.57973206,TRUE,38.66666667,0.525882862,79.33333333,0.777027027,0,0.403234768,,,0.568714886
1530,Epidemiology and clinical course of severe acute respiratory syndrome coronavirus 2 infection in cancer patients in the Veneto Oncology Network: The Rete Oncologica Veneta covID19 study.,Eur J Cancer,33647547,3/2/21,pubmed,0,29,logistic regression,0.001187275,0.001187342,0.043481438,0.066338766,0.001187326,0.886617854,Clinics,0.8807221,TRUE,127,0.917125363,72.72413793,0.760904469,1,0.537564047,,,0.738531293
1531,"Latest updates on SARS-CoV-2 genomic characterization, drug, and vaccine development; a comprehensive bioinformatics review.",Microb Pathog,33647446,3/2/21,pubmed,0,3,"bioinformatic, genomes",0.560380593,0.337006433,0.001823394,0.097142854,0.001823414,0.001823312,Drug discovery,0.5086218,TRUE,8,0.118683901,5.333333333,0.262911426,0,0.403234768,,,0.261610032
1532,Investigation of intra-hospital SARS-CoV-2 transmission using nanopore whole-genome sequencing.,J Hosp Infect,33647375,3/2/21,pubmed,0,5,"sequencing, whole-genome, whole genome",0.00146186,0.486763782,0.001461923,0.382836212,0.081485298,0.045990925,Genomics,0.44828522,FALSE,13.6,0.206011503,9.2,0.339577201,0,0.403234768,,,0.316274491
1533,"Spillover effects of the United States economic slowdown induced by COVID-19 pandemic on energy, economy, and environment in other countries.",Environ Res,33647303,3/2/21,pubmed,0,2,network analysis,0.170399022,0.001684522,0.001684569,0.822862786,0.001684546,0.001684554,Epidemiology,0.104816794,FALSE,10.5,0.157338116,3,0.199424672,0,0.403234768,,,0.253332519
1534,Rationale and Design of the Awake Prone Position for Early Hypoxemia in COVID-19 (APPEX-19) Study Protocol.,Ann Am Thorac Soc,33647225,3/2/21,pubmed,0,16,bayes,0.001593593,0.001593604,0.001593536,0.428556424,0.382857715,0.183805128,Epidemiology,0.7319448,TRUE,60.8125,0.707217515,44.9375,0.65848274,0,0.403234768,,,0.589645008
1535,Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases.,PLoS One,33647062,3/2/21,pubmed,0,4,"neural network, network model, dataset",0.001254618,0.001254594,0.993726946,0.001254632,0.001254589,0.001254622,Imaging,0.40461928,FALSE,29.25,0.421671099,13.75,0.408348943,0,0.403234768,,,0.411084937
1536,Behavioral Intention to Receive a COVID-19 Vaccination Among Chinese Factory Workers: Cross-sectional Online Survey.,J Med Internet Res,33646966,3/2/21,pubmed,0,8,logistic regression,0.000772646,0.000772623,0.000772635,0.075727106,0.921182375,0.000772615,Healthcare,0.41672352,FALSE,32.5,0.461685942,7.625,0.309071448,0,0.403234768,,,0.391330719
1537,COVID-19 anosmia and gustatory symptoms as a prognosis factor: a subanalysis of the HOPE COVID-19 (Health Outcome Predictive Evaluation for COVID-19) registry.,Infection,33646505,3/2/21,pubmed,0,25,logistic regression,0.001171652,0.001171587,0.001171624,0.109754764,0.001171606,0.885558765,Clinics,0.92435634,TRUE,96.92,0.860906673,26.88,0.544621354,0,0.403234768,,,0.602920932
1538,COVID-19: Association Between Increase of Behavioral and Psychological Symptoms of Dementia During Lockdown and Caregivers' Poor Mental Health.,J Alzheimers Dis,33646163,3/2/21,pubmed,0,9,logistic regression,0.001943455,0.001943805,0.001943442,0.051247785,0.940978035,0.001943479,Healthcare,0.7487035,TRUE,58.33333333,0.691941369,16.66666667,0.444674873,0,0.403234768,,,0.51328367
1539,Predicted SARS-CoV-2 miRNAs associated with epigenetic viral pathogenesis and the detection of new possible drugs for Covid-19.,Curr Drug Deliv,33645482,3/2/21,pubmed,0,5,genomes,0.638747286,0.291073786,0.043215035,0.001272667,0.001272666,0.024418559,Drug discovery,0.6023327,TRUE,173.8,0.961098398,122.4,0.854294889,0,0.403234768,,,0.739542685
1540,Clinical features and outcomes of hospitalized COVID-19 patients in a low burden region.,Pathog Glob Health,33645468,3/2/21,pubmed,0,8,logistic regression,0.001486401,0.001486456,0.099178019,0.095451481,0.00148645,0.800911194,Clinics,0.8312036,TRUE,1.25,0.013173356,0,0.055525823,0,0.403234768,,,0.157311316
1541,A potential peptide inhibitor of SARS-CoV-2 S and human ACE2 complex.,J Biomol Struct Dyn,33645443,3/2/21,pubmed,0,3,in-silico,0.991577452,0.001684506,0.001684553,0.001684529,0.001684483,0.001684478,Drug discovery,0.3793609,FALSE,9,0.135320675,7.666666667,0.310877709,0,0.403234768,,,0.283144384
1542,"Perinatology clinic in the coronavirus disease-2019 pandemic: what harms, often teaches.",J Matern Fetal Neonatal Med,33645392,3/2/21,pubmed,0,11,mathematical model,0.000704923,0.000704973,0.000704953,0.252093383,0.379191924,0.366599844,Healthcare,0.94370127,TRUE,17.90909091,0.268847795,1,0.122023013,0,0.403234768,,,0.264701859
1543,Initial CT features of COVID-19 predicting clinical category.,Chin J Acad Radiol,33644690,3/2/21,pubmed,0,8,logistic regression,0.001156309,0.001156282,0.309011119,0.001156355,0.117992809,0.569527126,Clinics,0.9015733,TRUE,39.125,0.531263529,,,0,0.403234768,,,0.467249148
1544,Novel Development of Predictive Feature Fingerprints to Identify Chemistry-Based Features for the Effective Drug Design of SARS-CoV-2 Target Antagonists and Inhibitors Using Machine Learning.,ACS Omega,33644594,3/2/21,pubmed,0,9,"virtual screening, machine learning",0.765999088,0.002032819,0.225869606,0.002032875,0.002032774,0.002032837,Drug discovery,0.61167574,TRUE,27.66666667,0.403488156,10.44444444,0.36131924,0,0.403234768,,,0.389347388
1545,"Long, thin transmission chains of Severe Acute Respiratory Syndrome Coronavirus 2 may go undetected for several weeks at low to moderate reproduction numbers: Implications for containment and elimination strategy.",Infect Dis Model,33644500,3/2/21,pubmed,0,4,mathematical model,0.000638761,0.283173749,0.000638795,0.598672447,0.10098556,0.015890689,Epidemiology,0.20087582,FALSE,226,0.980394582,408.5,0.971902596,0,0.403234768,,,0.785177315
1546,"A review of the characteristics and outcomes of 900 COVID-19 patients hospitalized at a Tertiary Care Medical Center in New Jersey, USA.",Arch Med Sci Atheroscler Dis,33644490,3/2/21,pubmed,0,5,logistic regression,0.000999529,0.037536426,0.000999529,0.019633723,0.000999527,0.939831267,Clinics,0.8971565,TRUE,502.8,0.997587977,238.6,0.93350281,0,0.403234768,,,0.778108518
1547,Anxiety and depression due to 2019 SARS-CoV-2 among frontier healthcare workers in Kenya.,Heliyon,33644428,3/2/21,pubmed,0,6,logistic regression,0.000898077,0.000898094,0.000898108,0.000898127,0.974778549,0.021629045,Healthcare,0.9803437,TRUE,3.5,0.044344115,0,0.055525823,1,0.537564047,,,0.212477995
1548,COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology.,Inform Med Unlocked,33644298,3/2/21,pubmed,0,12,dataset,0.001141419,0.001141368,0.940981819,0.054452533,0.001141423,0.001141438,Imaging,0.17655703,FALSE,10.16666667,0.152946997,1.833333333,0.151056998,0,0.403234768,,,0.235746254
1549,COVID-19 Vaccination Acceptance and Its Associated Factors Among a Middle Eastern Population.,Front Public Health,33643995,3/2/21,pubmed,0,2,logistic regression,0.001237136,0.001237127,0.001237189,0.001237134,0.993814226,0.001237189,Healthcare,0.84660906,TRUE,10.5,0.157338116,7.5,0.307867273,0,0.403234768,,,0.289480052
1550,"Facilitating Understanding, Modeling and Simulation of Infectious Disease Epidemics in the Age of COVID-19.",Front Public Health,33643988,3/2/21,pubmed,0,8,mathematical model,0.00194352,0.034452146,0.001943582,0.923295973,0.036421315,0.001943462,Epidemiology,0.41683397,FALSE,10.25,0.154245779,0.625,0.090446883,0,0.403234768,,,0.21597581
1551,Can ACE2 Receptor Polymorphism Predict Species Susceptibility to SARS-CoV-2?,Front Public Health,33643982,3/2/21,pubmed,0,4,"in silico, sequence alignment",0.33374179,0.605491833,0.001272676,0.056948377,0.001272645,0.001272679,Genomics,0.24937734,FALSE,855.25,0.999010452,1270,0.995584694,4,0.707574542,,,0.900723229
1552,"Viral Infections, the Microbiome, and Probiotics.",Front Cell Infect Microbiol,33643929,3/2/21,pubmed,0,9,microbiom,0.443993855,0.467310058,0.001310395,0.050272449,0.001310395,0.035802847,Genomics,0.984084,TRUE,44.55555556,0.582225246,67.33333333,0.746320578,0,0.403234768,,,0.577260197
1553,Comparison of Different Optimizers Implemented on the Deep Learning Architectures for COVID-19 Classification.,Mater Today Proc,33643854,3/2/21,pubmed,0,3,"deep learning, neural network, transfer learning",0.001786527,0.001786553,0.708770006,0.154814281,0.00178661,0.131056024,Imaging,0.5665424,TRUE,31.33333333,0.448388892,45.66666667,0.661760771,0,0.403234768,,,0.504461477
1554,Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic?,Int J Disaster Risk Reduct,33643835,3/2/21,pubmed,0,4,machine learning,0.001141425,0.001141355,0.001141421,0.879043875,0.116390588,0.001141335,Epidemiology,0.4093331,FALSE,6.25,0.088564537,,,1,0.537564047,,,0.313064292
1555,Design ensemble deep learning model for pneumonia disease classification.,Int J Multimed Inf Retr,33643764,3/2/21,pubmed,0,1,"deep learning, ensemble learning, dataset",0.002357739,0.002357782,0.95504344,0.03552519,0.002357934,0.002357917,Imaging,0.58752966,TRUE,2,0.022141134,1,0.122023013,0,0.403234768,,,0.182466305
1556,0,3 Biotech,33643762,3/2/21,pubmed,0,13,in silico,0.869751449,0.046014499,0.053354842,0.002080587,0.026717994,0.002080629,Drug discovery,0.9163891,TRUE,18.84615385,0.281650071,19.61538462,0.476919989,0,0.403234768,,,0.387268276
1557,Impact of control interventions on COVID-19 population dynamics in Malaysia: a mathematical study.,Eur Phys J Plus,33643757,3/2/21,pubmed,0,3,mathematical model,0.033907312,0.001684546,0.001684496,0.926376656,0.034662453,0.001684537,Epidemiology,0.6218253,TRUE,25.33333333,0.37188447,4.333333333,0.237958255,0,0.403234768,,,0.337692498
1558,Estimation of parameters for a humidity-dependent compartmental model of the COVID-19 outbreak.,PeerJ,33643707,3/2/21,pubmed,0,4,model fit,0.001486471,0.001486484,0.001486557,0.992567584,0.001486437,0.001486468,Epidemiology,0.1500051,FALSE,20.5,0.304966294,3.75,0.21982874,0,0.403234768,,,0.309343267
1559,Remote administration of the symbol digit modalities test to individuals with multiple sclerosis is reliable: A short report.,Mult Scler J Exp Transl Clin,33643663,3/2/21,pubmed,0,5,correlation analysis,0.197007803,0.002639046,0.002639158,0.002639103,0.792435703,0.002639187,Healthcare,0.8942864,TRUE,85.4,0.826086957,233,0.931094461,0,0.403234768,,,0.720138728
1560,Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19.,J Glob Health,33643638,3/2/21,pubmed,0,20,mathematical model,0.001059356,0.001059345,0.00105934,0.783452768,0.049632315,0.163736875,Epidemiology,0.19108486,FALSE,47.45,0.609375966,34.25,0.600280974,0,0.403234768,,,0.537630569
1561,Machine learning in a time of COVID-19 - Can machine learning support Community Health Workers (CHWs) in low and middle income countries (LMICs) in the new normal?,J Glob Health,33643627,3/2/21,pubmed,0,5,machine learning,0.008402495,0.008402938,0.545924053,0.008403675,0.420464265,0.008402574,Healthcare,0.4219208,FALSE,11.4,0.17131548,5.6,0.267995718,0,0.403234768,,,0.280848655
1562,Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network.,Health Inf Sci Syst,33643612,3/2/21,pubmed,0,9,"deep learning, artificial intelligence, neural network, dataset",0.001022608,0.001022661,0.974049254,0.021860216,0.001022633,0.001022628,Imaging,0.7740849,TRUE,30.33333333,0.436266931,3.222222222,0.20236821,0,0.403234768,,,0.347289969
1563,The challenge of privacy and security when using technology to track people in times of COVID-19 pandemic.,Procedia Comput Sci,33643498,3/2/21,pubmed,0,2,artificial intelligence,0.001237067,0.001237083,0.085182845,0.909868834,0.00123711,0.001237061,Epidemiology,0.28522235,FALSE,20,0.298163152,2,0.164302917,0,0.403234768,,,0.288566945
1564,A Comparison: Prediction of Death and Infected COVID-19 Cases in Indonesia Using Time Series Smoothing and LSTM Neural Network.,Procedia Comput Sci,33643496,3/2/21,pubmed,0,3,"neural network, network model, lstm, dataset",0.002806406,0.002806469,0.158835658,0.829938652,0.002806358,0.002806457,Epidemiology,0.4327529,FALSE,11.66666667,0.176510607,2.333333333,0.173401124,0,0.403234768,,,0.251048833
1565,Predict Mortality in Patients Infected with COVID-19 Virus Based on Observed Characteristics of the Patient using Logistic Regression.,Procedia Comput Sci,33643495,3/2/21,pubmed,0,5,"machine learning, logistic regression",0.002898263,0.002898243,0.420195361,0.002898338,0.002898319,0.568211477,Clinics,0.9269294,TRUE,12.2,0.18461253,0.4,0.075796093,0,0.403234768,,,0.221214464
1566,Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation.,Soc Netw Anal Min,33643491,3/2/21,pubmed,0,2,"deep learning, dataset",0.001392873,0.001392852,0.972145899,0.00139289,0.022282655,0.001392831,Imaging,0.29143846,FALSE,75,0.787061661,61,0.724578539,1,0.537564047,,,0.683068082
1567,Recent updates in COVID-19 with emphasis on inhalation therapeutics: Nanostructured and targeting systems.,J Drug Deliv Sci Technol,33643448,3/2/21,pubmed,0,5,genomic structure,0.798834407,0.031503216,0.00153817,0.16504787,0.001538194,0.001538143,Drug discovery,0.8835572,TRUE,47.8,0.612777537,6.2,0.282512711,0,0.403234768,,,0.432841672
1568,Automatic detection of COVID-19 disease using U-Net architecture based fully convolutional network.,Biomed Signal Process Control,33643425,3/2/21,pubmed,0,4,"deep learning, artificial intelligence, image analysis",0.001203402,0.001203462,0.907161614,0.08802461,0.001203474,0.001203438,Imaging,0.3670433,FALSE,16.75,0.252458408,2.25,0.170925876,0,0.403234768,,,0.275539684
1569,"[Diagnostic properties of case definitions of suspected COVID-19 in Chile, 2020Características diagnósticas das definições de caso suspeito de COVID-19 no Chile, 2020].",Rev Panam Salud Publica,33643397,3/2/21,pubmed,0,10,logistic regression,0.001272729,0.134071596,0.195350327,0.144005107,0.001272735,0.524027506,Clinics,0.96949995,TRUE,14.3,0.215968829,1.6,0.140687717,0,0.403234768,,,0.253297105
1570,COVID-DeepPredictor: Recurrent Neural Network to Predict SARS-CoV-2 and Other Pathogenic Viruses.,Front Genet,33643375,3/2/21,pubmed,0,5,"deep learning, neural network, dataset",0.001751213,0.286241802,0.440570903,0.267933403,0.001751317,0.001751362,Genomics,0.58340704,TRUE,64,0.729173109,14.2,0.414102221,0,0.403234768,,,0.515503366
1571,Case Report: Severe Complement-Mediated Thrombotic Microangiopathy in IgG4-Related Disease Secondary to Anti-Factor H IgG4 Autoantibodies.,Front Immunol,33643292,3/2/21,pubmed,0,6,"sequencing, exom",0.498993829,0.287266001,0.001684667,0.001684582,0.001684588,0.208686333,Drug discovery,0.5772862,TRUE,69.66666667,0.762075577,31.5,0.581883864,0,0.403234768,,,0.582398069
1572,Early Diffusion of SARS-CoV-2 Infection in the Inner Area of the Italian Sardinia Island.,Front Microbiol,33643227,3/2/21,pubmed,0,11,"sequencing, genomes",0.001310346,0.873586718,0.001310337,0.0013104,0.001310398,0.121171801,Genomics,0.15894559,FALSE,39.72727273,0.536396809,14.54545455,0.418584426,0,0.403234768,,,0.452738667
1573,The Impacts of SARS-CoV-2 Pandemic on Suicide: A Lexical Analysis.,Front Psychiatry,33643089,3/2/21,pubmed,0,4,probabilistic,0.001291303,0.001291306,0.001291318,0.320334317,0.674500451,0.001291305,Healthcare,0.9758315,TRUE,7.75,0.112808461,1,0.122023013,0,0.403234768,,,0.212688747
1574,Expeditious COVID-19 similarity measure tool based on consolidated SCA algorithm with mutation and opposition operators.,Appl Soft Comput,33642960,3/2/21,pubmed,0,1,bioinformatic,0.047000443,0.463296845,0.485588821,0.001371363,0.00137126,0.001371268,Genomics,0.586996,TRUE,40,0.539860226,15,0.42594327,0,0.403234768,,,0.456346088
1575,Why Are COVID-19 Mortality Rates by Country or Region So Different?: An Ecologic Study of Factors Associated with Mortality from Novel Coronavirus Infections by Country.,Yonago Acta Med,33642906,3/2/21,pubmed,0,7,correlation analysis,0.001371244,0.001371333,0.090979906,0.525818402,0.001371339,0.379087776,Epidemiology,0.64319843,TRUE,35.85714286,0.495330571,20.85714286,0.489496923,0,0.403234768,,,0.46268742
1576,In Silico Analysis of Quranic and Prophetic Medicinals Plants for the Treatment of Infectious Viral Diseases Including Corona Virus.,Saudi J Biol Sci,33642896,3/2/21,pubmed,0,12,in silico,0.828090173,0.001350416,0.001350391,0.070177102,0.097681538,0.00135038,Drug discovery,0.9508796,TRUE,18.30769231,0.273919228,,,0,0.403234768,,,0.338576998
1577,Forcing the Digital Economy: How will the Structure of Digital Markets Change as a Result of the COVID-19 Pandemic.,Stud Russ Econ Dev,33642845,3/2/21,pubmed,0,2,artificial intelligence,0.035909435,0.001415129,0.001415237,0.958429758,0.00141515,0.001415293,Epidemiology,0.9030479,TRUE,19,0.285793803,0,0.055525823,0,0.403234768,,,0.248184798
1578,Modeling airborne pathogen transport and transmission risks of SARS-CoV-2.,Appl Math Model,33642664,3/2/21,pubmed,0,1,"computational, probabilistic",0.001786671,0.144486284,0.001786632,0.783034411,0.001786545,0.067119457,Epidemiology,0.3674547,FALSE,48,0.614942173,15,0.42594327,0,0.403234768,,,0.481373404
1579,XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks.,New Gener Comput,33642663,3/2/21,pubmed,0,7,"neural network, network model, dataset",0.001350358,0.001350376,0.790098666,0.082253922,0.001350421,0.123596257,Imaging,0.58709687,TRUE,42.42857143,0.561692127,6.857142857,0.293684774,0,0.403234768,,,0.419537223
1580,Exploring the association between compliance with measures to prevent the spread of COVID-19 and big five traits with Bayesian generalized linear model.,Pers Individ Dif,33642661,3/2/21,pubmed,0,1,"bayes, dataset",0.002422308,0.00242232,0.200349735,0.424872397,0.367510931,0.002422309,Epidemiology,0.7479325,TRUE,61,0.709196611,21,0.492239765,0,0.403234768,,,0.534890381
1581,"Predictors of Intensive Care Unit Admission or Death in Patients with Coronavirus Disease 2019 Pneumonia in Istanbul, Turkey.",Jpn J Infect Dis,33642427,3/2/21,pubmed,0,13,logistic regression,0.001461861,0.00146187,0.100020797,0.001461877,0.001461877,0.894131717,Clinics,0.96106195,TRUE,11,0.167171748,2.615384615,0.182633128,0,0.403234768,,,0.251013215
1582,The effect of hepatic steatosis on COVID-19 severity: Chest computed tomography findings.,Saudi J Gastroenterol,33642355,3/2/21,pubmed,0,4,logistic regression,0.001085351,0.00108537,0.243124272,0.001085347,0.001085358,0.752534302,Clinics,0.8681834,TRUE,20.25,0.301317336,0.5,0.087101953,0,0.403234768,,,0.263884686
1583,"Volunteering during the COVID-19 pandemic: Attitudes and perceptions of clinical medical and dental students in Lagos, Nigeria.",Niger Postgrad Med J,33642318,3/2/21,pubmed,0,6,logistic regression,0.001291246,0.001291215,0.026226678,0.001291258,0.968608249,0.001291355,Healthcare,0.85050964,TRUE,32.33333333,0.459273919,7.5,0.307867273,0,0.403234768,,,0.39012532
1584,Systematic review and patient-level meta-analysis of SARS-CoV-2 viral dynamics to model response to antiviral therapies.,Clin Pharmacol Ther,33641159,3/1/21,pubmed,0,10,dataset,0.223307021,0.275029953,0.001126804,0.212852291,0.00112682,0.286557111,Clinics,0.42339817,FALSE,70,0.764178366,56.7,0.709793952,0,0.403234768,,,0.625735695
1585,Serum calprotectin as a novel biomarker for severity of COVID-19 disease.,Ir J Med Sci,33641087,3/1/21,pubmed,0,9,logistic regression,0.001392869,0.001392817,0.001392832,0.001392825,0.001392824,0.993035833,Clinics,0.89751244,TRUE,34.55555556,0.482590142,6.666666667,0.290607439,0,0.403234768,,,0.392144116
1586,Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.,Interdiscip Sci,33641077,3/1/21,pubmed,0,15,"deep learning, image analysis",0.001330056,0.131246347,0.830328268,0.001330072,0.034435123,0.001330134,Imaging,0.5086569,TRUE,78.93333333,0.802152267,29.73333333,0.567567568,0,0.403234768,,,0.590984867
1587,0,J Mol Model,33641023,3/1/21,pubmed,0,4,"molecular dynamics simulation, machine learning, neural network, in silico",0.909522055,0.001272646,0.085387253,0.001272671,0.001272662,0.001272714,Drug discovery,0.5804002,TRUE,2.75,0.030304904,0,0.055525823,0,0.403234768,,,0.163021832
1588,Phenotypes and Subphenotypes of Patients With Coronavirus Disease 2019: A Latent Class Modeling Analysis.,Chest,33640378,3/1/21,pubmed,0,4,prediction model,0.000793434,0.000793445,0.027651843,0.053737389,0.063989038,0.85303485,Clinics,0.31244755,FALSE,93,0.851567815,53.25,0.694674873,0,0.403234768,,,0.649825818
1589,Age and multimorbidities as poor prognostic factors for COVID-19 in hemodialysis: a Lebanese national study.,BMC Nephrol,33639881,3/1/21,pubmed,0,52,logistic regression,0.001415077,0.001415138,0.00141512,0.038404182,0.00141516,0.955935324,Clinics,0.8661189,TRUE,4.480769231,0.05869256,0.403846154,0.075862992,0,0.403234768,,,0.17926344
1590,The role of smart monitoring digital health care system based on smartphone application and personal health record platform for patients diagnosed with coronavirus disease 2019.,BMC Infect Dis,33639861,3/1/21,pubmed,0,6,digital health,0.001220014,0.001220024,0.0012201,0.518394198,0.001220084,0.47672558,Epidemiology,0.8352355,TRUE,147.1666667,0.94149298,92.83333333,0.807934172,0,0.403234768,,,0.717553973
1591,Artificial intelligence enabled preliminary diagnosis for COVID-19 from voice cues and questionnaires.,J Acoust Soc Am,33639822,3/1/21,pubmed,0,4,"machine learning, artificial intelligence",0.00194351,0.001943523,0.642117638,0.350108122,0.001943556,0.00194365,Epidemiology,0.29084063,FALSE,1.5,0.015523533,0,0.055525823,0,0.403234768,,,0.158094708
1592,"Cancer, Mortality, and Acute Kidney Injury among Hospitalized Patients with SARS-CoV-2 Infection.",Asian Pac J Cancer Prev,33639668,3/1/21,pubmed,0,7,logistic regression,0.001141314,0.00114132,0.001141318,0.00114134,0.00114134,0.994293368,Clinics,0.7515309,TRUE,103,0.875378811,35.85714286,0.60944608,0,0.403234768,,,0.62935322
1593,"Changes in SARS CoV-2 Seroprevalence Over Time in Ten Sites in the United States, March - August, 2020.",Clin Infect Dis,33639620,2/28/21,pubmed,0,17,"bayes, bayesian model",0.001438147,0.333255518,0.026495511,0.469439376,0.167933244,0.001438205,Epidemiology,0.19449776,FALSE,66.88235294,0.746304657,107.1176471,0.834760503,0,0.403234768,,,0.661433309
1594,"Multi-targeting approach for nsp3, nsp9, nsp12 and nsp15 proteins of SARS-CoV-2 by Diosmin as illustrated by molecular docking and molecular dynamics simulation methodologies.",Methods,33639316,2/28/21,pubmed,0,6,"virtual screening, molecular dynamics simulation, in-silico",0.994573276,0.001085347,0.001085357,0.00108535,0.001085344,0.001085325,Drug discovery,0.94257027,TRUE,45.5,0.591440411,12.33333333,0.389550442,0,0.403234768,,,0.46140854
1595,The role of A-to-I RNA editing in infections by RNA viruses: Possible implications for SARS-CoV-2 infection.,Clin Immunol,33639276,2/28/21,pubmed,0,5,transcriptom,0.298982506,0.675343927,0.001593506,0.001593493,0.001593508,0.020893059,Genomics,0.3823091,FALSE,126,0.915331808,76.6,0.770203372,0,0.403234768,,,0.696256649
1596,Brief review on repurposed drugs and vaccines for possible treatment of COVID-19.,Eur J Pharmacol,33639193,2/28/21,pubmed,0,4,in silico,0.960896383,0.002080695,0.030781191,0.002080565,0.002080563,0.002080602,Drug discovery,0.8261374,TRUE,24.5,0.361988991,1.5,0.138747659,0,0.403234768,,,0.301323806
1597,Lipid metabolism changes in patients with severe COVID-19.,Clin Chim Acta,33639119,2/28/21,pubmed,0,18,logistic regression,0.001461883,0.001461862,0.00146185,0.001461923,0.001461855,0.992690627,Clinics,0.8499772,TRUE,28.72222222,0.414682417,6.666666667,0.290607439,0,0.403234768,,,0.369508208
1598,"A study of the possible factors affecting COVID-19 spread, severity and mortality and the effect of social distancing on these factors: Machine learning forecasting model.",Int J Clin Pract,33639032,2/28/21,pubmed,0,10,"machine learning, forecasting model, dataset",0.000863096,0.000863149,0.110896948,0.701735819,0.000863098,0.18477789,Epidemiology,0.38980657,FALSE,26.5,0.389325252,11.6,0.379515654,0,0.403234768,,,0.390691891
1599,MicroRNA Mimics or Inhibitors as Antiviral Therapeutic Approaches Against COVID-19.,Drugs,33638807,2/28/21,pubmed,0,6,genomes,0.796190865,0.198488871,0.001330052,0.001330096,0.001330059,0.001330057,Drug discovery,0.818712,TRUE,31.5,0.450058754,24,0.521808938,0,0.403234768,,,0.458367487
1600,A Modelling Framework for Embedding-based Predictions for Compound-Viral Protein Activity.,Bioinformatics,33638345,2/28/21,pubmed,0,7,"machine learning, deep learning, bioinformatic, prediction model",0.683147395,0.001254643,0.311834091,0.001254641,0.001254607,0.001254622,Drug discovery,0.131628,FALSE,68.71428571,0.757684458,67,0.745718491,0,0.403234768,,,0.635545905
1601,Adherence to COVID-19 preventive measures and associated factors among pregnant women in Ghana.,Trop Med Int Health,33638230,2/28/21,pubmed,0,2,logistic regression,0.039363501,0.001438178,0.001438083,0.001438215,0.954883921,0.001438102,Healthcare,0.66343784,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
1602,"Mostly worse, occasionally better: impact of COVID-19 pandemic on the mental health of Canadian children and adolescents.",Eur Child Adolesc Psychiatry,33638005,2/28/21,pubmed,0,13,logistic regression,0.000838551,0.000838522,0.069153375,0.000838532,0.927492498,0.000838522,Healthcare,0.96331906,TRUE,112.2307692,0.894303915,214.4615385,0.924337704,0,0.403234768,,,0.740625462
1603,"Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies.",NPJ Digit Med,33637833,2/28/21,pubmed,0,4,"artificial intelligence, digital health",0.00112693,0.001126862,0.330229432,0.436754981,0.229634915,0.00112688,Epidemiology,0.9481316,TRUE,395.5,0.995361494,714.5,0.987556864,0,0.403234768,,,0.795384375
1604,Hospitalization and mortality associated with SARS-CoV-2 viral clades in COVID-19.,Sci Rep,33637820,2/28/21,pubmed,0,13,"machine learning, whole genome",0.001010939,0.495060049,0.092175308,0.001010988,0.001010949,0.409731767,Genomics,0.35438633,FALSE,68.07692308,0.754097347,63.30769231,0.733074659,1,0.537564047,,,0.674912018
1605,A SARS-CoV-2 cytopathicity dataset generated by high-content screening of a large drug repurposing collection.,Sci Data,33637768,2/28/21,pubmed,0,13,dataset,0.942664367,0.001823432,0.050041981,0.00182345,0.001823389,0.001823381,Drug discovery,0.7386395,TRUE,106.3846154,0.881810873,105.5384615,0.832218357,0,0.403234768,,,0.705754666
1606,Association between antecedent statin use and decreased mortality in hospitalized patients with COVID-19.,Nat Commun,33637713,2/28/21,pubmed,0,29,logistic regression,0.042188081,0.002080746,0.002080599,0.089554462,0.002080749,0.862015362,Clinics,0.9648136,TRUE,326.862069,0.992269157,378.6896552,0.968959058,1,0.537564047,,,0.832930754
1607,"Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance.",BMJ Open,33637550,2/28/21,pubmed,0,5,"machine learning, neural network, prediction model, dataset",0.001156283,0.00115626,0.31852213,0.001156288,0.001156286,0.676852752,Clinics,0.8928083,TRUE,13,0.197352959,2,0.164302917,0,0.403234768,,,0.254963548
1608,Active Learning: From Flipped Lectures to the Covid-19 Era.,Chimia (Aarau),33637141,2/28/21,pubmed,0,1,active learning,0.143051154,0.003465889,0.458322555,0.003466223,0.388228283,0.003465895,Healthcare,0.44850588,FALSE,7,0.10179974,0,0.055525823,0,0.403234768,,,0.186853444
1609,What is associated with the increased frequency of heavy episodic drinking during the COVID-19 pandemic? Data from the PAHO regional web-based survey.,Drug Alcohol Depend,33636598,2/27/21,pubmed,0,5,logistic regression,0.001823298,0.00182336,0.001823296,0.001823427,0.990883282,0.001823338,Healthcare,0.8941809,TRUE,51.4,0.642092894,69.2,0.751003479,0,0.403234768,,,0.598777047
1610,Targeting SARS-CoV-2 Nsp3 macrodomain structure with insights from human poly(ADP-ribose) glycohydrolase (PARG) structures with inhibitors.,Prog Biophys Mol Biol,33636189,2/27/21,pubmed,0,14,"computational, proteom",0.870398246,0.080522058,0.045939166,0.001046909,0.001046811,0.00104681,Drug discovery,0.2790233,FALSE,100.5714286,0.870245532,243.3571429,0.935509767,0,0.403234768,,,0.736330022
1611,Identification and validation of clinical phenotypes with prognostic implications in patients admitted to hospital with COVID-19: a multicentre cohort study.,Lancet Infect Dis,33636145,2/27/21,pubmed,0,940,"logistic regression, probabilistic",0.000504135,0.000504147,0.000504124,0.000504126,0.000504113,0.997479355,Clinics,0.4140229,FALSE,14.7893617,0.222710124,8.532978723,0.329207921,2,0.618927094,,,0.390281713
1612,In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs.,Cell,33636127,2/27/21,pubmed,0,15,"in silico, deep-learning",0.920721284,0.001653134,0.051918582,0.0016531,0.001653029,0.022400871,Drug discovery,0.41586855,FALSE,36.66666667,0.505102356,41.2,0.640620819,2,0.618927094,,,0.588216756
1613,Network study of responses to unusualness and psychological stress during the COVID-19 outbreak in Korea.,PLoS One,33635935,2/27/21,pubmed,0,11,network analysis,0.001022647,0.001022627,0.057246703,0.291768967,0.647916438,0.001022616,Healthcare,0.8870115,TRUE,90.09090909,0.843156658,53.27272727,0.694741771,0,0.403234768,,,0.647044399
1614,Severely low testosterone in males with COVID-19: A case-control study.,Andrology,33635589,2/27/21,pubmed,0,25,logistic regression,0.00137123,0.001371276,0.001371247,0.001371245,0.001371282,0.99314372,Clinics,0.46106255,FALSE,310.32,0.991155916,196.44,0.915841584,0,0.403234768,,,0.770077422
1615,A neutrophil activation signature predicts critical illness and mortality in COVID-19.,Blood Adv,33635335,2/27/21,pubmed,0,30,"machine learning, proteom",0.24939321,0.00186172,0.086362092,0.001861835,0.001861676,0.658659468,Clinics,0.5967619,TRUE,52.5,0.650875131,45.8,0.662697351,0,0.403234768,,,0.572269083
1616,Lipidomic alteration of plasma in cured COVID-19 patients using ultra high-performance liquid chromatography with high-resolution mass spectrometry.,Biosci Rep,33635316,2/27/21,pubmed,0,9,lipidom,0.328588797,0.001237166,0.035850793,0.001237129,0.001237159,0.631848956,Clinics,0.99803615,TRUE,17.11111111,0.257839075,5.666666667,0.270203372,0,0.403234768,,,0.310425738
1617,"WHO, COVID-19, and Taiwan as the Ghost Island.",Glob Public Health,33635180,2/27/21,pubmed,0,1,computational,0.001943488,0.001943522,0.001943504,0.99028242,0.001943544,0.001943522,Epidemiology,0.007825077,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
1618,"Transfusion profile, clinical characteristics, comorbidities and outcomes of 3014 hospitalized patients diagnosed with COVID-19 in Brazil.",Vox Sang,33634890,2/27/21,pubmed,0,17,logistic regression,0.001511901,0.001511827,0.001511844,0.097767101,0.001512012,0.896185315,Clinics,0.90353346,TRUE,3.529411765,0.044405962,1.058823529,0.122223709,0,0.403234768,,,0.189954813
1619,Suitability of databases in the Asia-Pacific for collaborative monitoring of vaccine safety.,Pharmacoepidemiol Drug Saf,33634545,2/27/21,pubmed,0,15,dataset,0.001059433,0.001059407,0.001059392,0.733111978,0.262650425,0.001059365,Epidemiology,0.26419145,FALSE,77.8,0.797699301,56.06666667,0.706984212,0,0.403234768,,,0.63597276
1620,The relationship between anxiety levels and anger expression styles of nurses during COVID-19 pandemic.,Perspect Psychiatr Care,33634484,2/27/21,pubmed,0,3,correlation analysis,0.10189456,0.001237088,0.001237129,0.001237108,0.893157004,0.00123711,Healthcare,0.94864446,TRUE,9.333333333,0.139278867,0,0.055525823,0,0.403234768,,,0.199346486
1621,Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.,J Digit Imaging,33634416,2/27/21,pubmed,0,15,logistic regression,0.00080793,0.000807917,0.417231511,0.000807897,0.000807897,0.579536847,Clinics,0.5878975,TRUE,51,0.63875317,55.93333333,0.705913835,1,0.537564047,,,0.62741035
1622,Auxiliary Diagnosis for COVID-19 with Deep Transfer Learning.,J Digit Imaging,33634413,2/27/21,pubmed,0,8,"transfer learning, dataset",0.000838512,0.000838518,0.959924787,0.016529731,0.000838581,0.02102987,Imaging,0.5923179,TRUE,16,0.243552477,6.625,0.289336366,0,0.403234768,,,0.312041204
1623,Adverse Outcomes Associated With Corticosteroid Use in Critical COVID-19: A Retrospective Multicenter Cohort Study.,Front Med (Lausanne),33634148,2/27/21,pubmed,0,13,logistic regression,0.038212579,0.001415228,0.001415091,0.001415107,0.001415097,0.956126899,Clinics,0.83050287,TRUE,52.69230769,0.651369905,16.38461538,0.440727857,0,0.403234768,,,0.498444177
1624,Mental Health of Physicians During COVID-19 Outbreak in Bangladesh: A Web-Based Cross-Sectional Survey.,Front Public Health,33634065,2/27/21,pubmed,0,9,logistic regression,0.001823307,0.00182332,0.054674436,0.001823419,0.885840041,0.054015477,Healthcare,0.98498976,TRUE,65.22222222,0.735667017,53.77777778,0.696280439,0,0.403234768,,,0.611727408
1625,Prolonged Oxygen Therapy Post COVID-19 Infection: Factors Leading to the Risk of Poor Outcome.,Cureus,33633916,2/27/21,pubmed,0,7,logistic regression,0.000772638,0.034717826,0.044857685,0.00077268,0.000772663,0.918106508,Clinics,0.98045754,TRUE,13.71428571,0.207619519,0.571428571,0.088038534,0,0.403234768,,,0.232964273
1626,Accessing behavioral health care during COVID: rapid transition from in-person to teleconferencing medical group visits.,Ther Adv Chronic Dis,33633823,2/27/21,pubmed,0,4,dataset,0.001046944,0.001046864,0.001046898,0.212092425,0.61279255,0.17197432,Healthcare,0.98799145,TRUE,15.5,0.234028078,3.25,0.203572384,0,0.403234768,,,0.28027841
1627,Health Information Privacy Laws in the Digital Age: HIPAA Doesn't Apply.,Perspect Health Inf Manag,33633522,2/27/21,pubmed,0,2,digital health,0.001486454,0.027770242,0.001486499,0.940541144,0.001486512,0.02722915,Epidemiology,0.6637568,TRUE,4.5,0.061784897,0,0.055525823,0,0.403234768,,,0.173515162
1628,An agent-based model of the interrelation between the COVID-19 outbreak and economic activities.,Proc Math Phys Eng Sci,33633491,2/27/21,pubmed,0,5,computational,0.217139587,0.002898304,0.002898345,0.771267057,0.002898405,0.002898302,Epidemiology,0.5153023,TRUE,111.6,0.893190674,22.8,0.509967889,4,0.707574542,,,0.703577701
1629,Identification of superspreading environment under COVID-19 through human mobility data.,Sci Rep,33633273,2/27/21,pubmed,0,4,dataset,0.036933338,0.001511915,0.001511967,0.957019104,0.001511864,0.001511812,Epidemiology,0.81225884,TRUE,75.25,0.787865669,36.75,0.615466952,1,0.537564047,,,0.646965556
1630,A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States.,Sci Rep,33633250,2/27/21,pubmed,0,11,"correlation analysis, dataset",0.002238737,0.00223862,0.002238665,0.827352071,0.163693309,0.002238598,Epidemiology,0.28139877,FALSE,43.63636364,0.574308863,123,0.855097672,0,0.403234768,,,0.610880434
1631,Immunoinformatic based identification of cytotoxic T lymphocyte epitopes from the Indian isolate of SARS-CoV-2.,Sci Rep,33633155,2/27/21,pubmed,0,2,molecular dynamics simulation,0.804795139,0.186681941,0.002130633,0.002130808,0.00213071,0.002130771,Drug discovery,0.64665854,TRUE,58,0.689714887,2.5,0.180826866,0,0.403234768,,,0.424592174
1632,Machine learning based predictors for COVID-19 disease severity.,Sci Rep,33633145,2/27/21,pubmed,0,10,"machine learning, classifier",0.001593502,0.001593533,0.45359572,0.001593608,0.001593529,0.540030109,Clinics,0.096015275,FALSE,83.7,0.819778589,42.7,0.648180359,0,0.403234768,,,0.623731238
1633,Elevation in viral entry genes and innate immunity compromise underlying increased infectivity and severity of COVID-19 in cancer patients.,Sci Rep,33633121,2/27/21,pubmed,0,7,genome-wide,0.562919257,0.193769065,0.00182334,0.001823413,0.052436088,0.187228837,Drug discovery,0.44290498,FALSE,57.85714286,0.687797637,151.2857143,0.884064758,0,0.403234768,,,0.658365721
1634,0,Microbiol Resour Announc,33632868,2/27/21,pubmed,0,6,"sequencing, deep sequencing",0.006539538,0.8587237,0.115117907,0.006539605,0.00653963,0.00653962,Genomics,0.4786571,FALSE,145.8333333,0.940317892,165.5,0.8962403,0,0.403234768,,,0.746597653
1635,Development and validation of a prediction model for 30-day mortality in hospitalised patients with COVID-19: the COVID-19 SEIMC score.,Thorax,33632764,2/27/21,pubmed,0,11,"logistic regression, prediction model",0.001310317,0.001310334,0.023808408,0.027055047,0.001310378,0.945205516,Clinics,0.68321836,TRUE,176.5454545,0.962830107,156.6363636,0.889215949,0,0.403234768,,,0.751760274
1636,The Benefits of Vaccinating With the First Available COVID-19 Coronavirus Vaccine.,Am J Prev Med,33632650,2/27/21,pubmed,0,11,computational,0.190726589,0.001861749,0.001861721,0.801826287,0.00186183,0.001861824,Epidemiology,0.10078439,FALSE,121.4545455,0.908776053,183.5454545,0.908549639,0,0.403234768,,,0.74018682
1637,Perspectives on the receipt of a COVID-19 vaccine: A survey of employees in two large hospitals in Philadelphia.,Vaccine,33632563,2/27/21,pubmed,0,8,logistic regression,0.001237083,0.001237064,0.001237093,0.001237098,0.788421949,0.206629714,Healthcare,0.5017036,TRUE,70,0.764178366,49.875,0.681161359,1,0.537564047,,,0.660967924
1638,A Proposed Epidemiologic Risk Threshold for SARS-CoV-2 for Assisting Healthcare Decision-Making.,Arch Bronconeumol,33632517,2/27/21,pubmed,0,4,predictive model,0.001511838,0.001511869,0.001511886,0.992440616,0.001511875,0.001511916,Epidemiology,0.1962896,FALSE,79.5,0.80493537,38.5,0.624765855,0,0.403234768,,,0.610978664
1639,Interplay between severities of COVID-19 and the gut microbiome: implications of bacterial co-infections?,Gut Pathog,33632296,2/27/21,pubmed,0,3,microbiom,0.217550664,0.332072765,0.078010285,0.002898506,0.002898506,0.366569273,Clinics,0.70995367,TRUE,19.66666667,0.292596945,13,0.400521809,1,0.537564047,,,0.4102276
1640,Long-term outcome after intensive care for COVID-19: differences between men and women-a nationwide cohort study.,Crit Care,33632273,2/27/21,pubmed,0,7,logistic regression,0.001237078,0.001237074,0.001237068,0.089964154,0.001237134,0.905087492,Clinics,0.5643704,TRUE,42.14285714,0.558847177,51.71428571,0.68818571,0,0.403234768,,,0.550089218
1641,Impact of COVID-19 on the Knowledge and Attitudes of Dentists toward Teledentistry.,JDR Clin Trans Res,33632011,2/27/21,pubmed,0,3,logistic regression,0.001098838,0.001098828,0.001098917,0.144442292,0.851162304,0.00109882,Healthcare,0.7042673,TRUE,36.66666667,0.505102356,12.66666667,0.39530372,0,0.403234768,,,0.434546948
1642,The Avon Longitudinal Study of Parents and Children - A resource for COVID-19 research: Questionnaire data capture April-May 2020.,Wellcome Open Res,33628949,2/27/21,pubmed,0,6,dataset,0.0014382,0.001438202,0.001438212,0.337956406,0.656290817,0.001438162,Healthcare,0.47502568,FALSE,740.5,0.998763065,1125.833333,0.995049505,4,0.707574542,,,0.900462371
1643,Spotlight on Cardiovascular Scoring Systems in Covid-19: Severity Correlations in Real-world Setting.,Curr Probl Cardiol,33631706,2/26/21,pubmed,0,8,prediction model,0.052045992,0.001371286,0.286670862,0.001371324,0.001371348,0.657169188,Clinics,0.920915,TRUE,150.625,0.944832705,115.5,0.845129783,0,0.403234768,,,0.731065752
1644,Association between coronavirus disease 2019 (COVID-19) and long-term exposure to air pollution: Evidence from the first epidemic wave in China.,Environ Pollut,33631687,2/26/21,pubmed,0,12,dataset,0.001310354,0.001310353,0.001310337,0.651791983,0.122977833,0.221299141,Epidemiology,0.19314155,FALSE,74.66666667,0.785268106,115.5,0.845129783,0,0.403234768,,,0.677877552
1645,Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling.,Comput Methods Programs Biomed,33631640,2/26/21,pubmed,0,3,"machine learning, deep learning, artificial intelligence, classifier, proteom, dataset",0.070556952,0.042423223,0.532574134,0.001085381,0.001085361,0.352274949,Clinics,0.5512775,TRUE,122.3333333,0.910322222,73.66666667,0.762911426,0,0.403234768,,,0.692156138
1646,Associations between vaping and Covid-19: Cross-sectional findings from the HEBECO study.,Drug Alcohol Depend,33631546,2/26/21,pubmed,0,6,bayes,0.001622697,0.001622718,0.0016228,0.001622706,0.991886279,0.0016228,Healthcare,0.7639878,TRUE,85.83333333,0.828127899,50.33333333,0.682967621,0,0.403234768,,,0.638110096
1647,"Impact of COVID-19 on quality of life in people with epilepsy, and a multinational comparison of clinical and psychological impacts.",Epilepsy Behav,33631434,2/26/21,pubmed,0,5,logistic regression,0.001187249,0.001187249,0.001187249,0.001187266,0.85289989,0.142351097,Healthcare,0.9872613,TRUE,17.2,0.259323397,7,0.299973241,0,0.403234768,,,0.320843802
1648,Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning.,J Ethnopharmacol,33631276,2/26/21,pubmed,0,6,"deep learning, neural network",0.261999812,0.001112632,0.3179698,0.416692364,0.001112696,0.001112695,Epidemiology,0.863644,TRUE,4,0.054734368,0,0.055525823,1,0.537564047,,,0.215941413
1649,0,Biophys J,33631204,2/26/21,pubmed,0,4,molecular dynamics simulation,0.99476572,0.001046902,0.001046851,0.001046845,0.001046868,0.001046814,Drug discovery,0.4850597,FALSE,152,0.945822252,143.75,0.87784319,0,0.403234768,,,0.74230007
1650,Impact of body composition on COVID-19 susceptibility and severity: A two-sample multivariable Mendelian randomization study.,Metabolism,33631142,2/26/21,pubmed,0,3,genome-wide,0.00133012,0.270511176,0.001330246,0.001330152,0.001330137,0.724168167,Clinics,0.30252212,FALSE,242.3333333,0.982930299,387,0.969761841,1,0.537564047,,,0.830085396
1651,"A modified vaccinia Ankara vector-based vaccine protects macaques from SARS-CoV-2 infection, immune pathology, and dysfunction in the lungs.",Immunity,33631118,2/26/21,pubmed,0,32,sequencing,0.810860837,0.052613231,0.001786544,0.001786643,0.086599042,0.046353702,Drug discovery,0.17086437,FALSE,85.03125,0.825097409,133.15625,0.866871822,0,0.403234768,,,0.698401333
1652,"Suicide Presentations to an Emergency Department Pre and During the COVID Lockdown, March-May 2020, in Spain.",Arch Suicide Res,33631086,2/26/21,pubmed,0,7,logistic regression,0.027517911,0.00178658,0.001786551,0.484298783,0.237598038,0.247012137,Epidemiology,0.3055687,FALSE,6.571428571,0.093883357,0.571428571,0.088038534,0,0.403234768,,,0.195052219
1653,Robust Multichannel Encoding for Highly Multiplexed Quantitative PCR.,Anal Chem,33631072,2/26/21,pubmed,0,8,sequencing,0.001272677,0.644004338,0.350904988,0.00127271,0.001272644,0.001272644,Genomics,0.115744084,FALSE,7.375,0.105757932,1.625,0.141222906,0,0.403234768,,,0.216738535
1654,Interleukin-6 Receptor Antagonists in Critically Ill Patients with Covid-19.,N Engl J Med,33631065,2/26/21,pubmed,0,62,bayes,0.166401919,0.001072202,0.001072196,0.001072262,0.001072225,0.829309197,Clinics,0.51372933,TRUE,224.0806452,0.979714268,212.3870968,0.923267327,11,0.840175319,,,0.914385638
1655,Forecasting national and regional level intensive care unit bed demand during COVID-19: The case of Italy.,PLoS One,33630972,2/26/21,pubmed,0,4,model fit,0.001653065,0.001653043,0.001653049,0.833106764,0.001653047,0.160281033,Epidemiology,0.2665304,FALSE,32.25,0.458098831,14.5,0.418450629,0,0.403234768,,,0.426594743
1656,"CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region.",PLoS One,33630966,2/26/21,pubmed,0,14,correlation analysis,0.001237077,0.045882438,0.001237086,0.774869161,0.175537126,0.001237113,Epidemiology,0.19070914,FALSE,76.92857143,0.793617416,25.64285714,0.534118277,0,0.403234768,,,0.576990153
1657,Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera.,PLoS One,33630951,2/26/21,pubmed,0,3,deep learning,0.00104684,0.001046838,0.170956495,0.824856114,0.001046895,0.001046817,Epidemiology,0.8693859,TRUE,19,0.285793803,1.333333333,0.13252609,0,0.403234768,,,0.273851554
1658,Transcriptomic profiling and genomic mutational analysis of Human coronavirus (HCoV)-229E -infected human cells.,PLoS One,33630927,2/26/21,pubmed,0,9,"sequencing, transcriptom",0.610976986,0.354347351,0.001438089,0.001438134,0.001438143,0.030361298,Drug discovery,0.42122927,FALSE,127.3333333,0.917620137,196,0.91557399,0,0.403234768,,,0.745476298
1659,A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis.,PLoS One,33630869,2/26/21,pubmed,0,6,"machine learning, deep learning, classifier, lstm, dataset",0.001156297,0.001156352,0.759589,0.235785819,0.001156295,0.001156237,Epidemiology,0.007248163,FALSE,23.33333333,0.345723298,2.833333333,0.189523682,0,0.403234768,,,0.312827249
1660,Historical Perspective: Metchnikoff and the intestinal microbiome.,J Leukoc Biol,33630385,2/26/21,pubmed,0,2,microbiom,0.006089758,0.006089861,0.164386458,0.811254358,0.006089666,0.006089899,Epidemiology,0.7669075,TRUE,154,0.947121034,509,0.979529034,0,0.403234768,,,0.776628279
1661,Using Machine Learning to Collect and Facilitate Remote Access to Biomedical Databases: Development of the Biomedical Database Inventory.,JMIR Med Inform,33629960,2/26/21,pubmed,0,5,"machine learning, deep learning, transfer learning",0.00114141,0.001141387,0.802210191,0.193224303,0.001141371,0.001141338,Epidemiology,0.61859995,TRUE,49.8,0.630465706,17.2,0.452568906,0,0.403234768,,,0.495423126
1662,Spatial dynamics of the COVID-19 pandemic in Brazil.,Epidemiol Infect,33629938,2/26/21,pubmed,0,7,bayes,0.001072194,0.001072209,0.00107217,0.777813141,0.001072225,0.21789806,Epidemiology,0.55253613,TRUE,7.142857143,0.102541901,0.285714286,0.066296495,0,0.403234768,,,0.190691054
1663,Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study.,Ann Med,33629918,2/26/21,pubmed,0,8,prediction model,0.0014381,0.001438113,0.164849705,0.001438165,0.001438143,0.829397774,Clinics,0.7932098,TRUE,31.125,0.445482095,7.875,0.314557131,0,0.403234768,,,0.387757998
1664,Coronavirus Disease 2019 Hospitalizations Attributable to Cardiometabolic Conditions in the United States: A Comparative Risk Assessment Analysis.,J Am Heart Assoc,33629868,2/26/21,pubmed,0,5,probabilistic,0.001072185,0.001072201,0.001072216,0.294437296,0.074178739,0.628167363,Clinics,0.42557868,FALSE,182.4,0.96524213,1117.2,0.994781911,1,0.537564047,,,0.832529363
1665,Differential expression and immune correlation analysis of COVID-19 receptor ACE2 and TMPRSS2 genes in all normal and tumor tissues.,Eur Rev Med Pharmacol Sci,33629341,2/26/21,pubmed,0,4,correlation analysis,0.608729854,0.002080558,0.002080611,0.002080731,0.002080718,0.38294753,Drug discovery,0.89899635,TRUE,11,0.167171748,24.25,0.523414504,1,0.537564047,,,0.409383433
1666,"Evaluation of molecular interaction, physicochemical parameters and conserved pattern of SARS-CoV-2 Spike RBD and hACE2: in silico and molecular dynamics approach.",Eur Rev Med Pharmacol Sci,33629340,2/26/21,pubmed,0,6,"molecular dynamics simulation, in silico, sequence alignment",0.917766244,0.0582572,0.001310368,0.00131038,0.020045464,0.001310343,Drug discovery,0.47375545,FALSE,44,0.578390748,12.66666667,0.39530372,0,0.403234768,,,0.458976412
1667,Clinical Outcomes of Patients Hospitalized with Coronavirus Disease 2019 (COVID-19) in Boston.,J Gen Intern Med,33629266,2/26/21,pubmed,0,12,logistic regression,0.0013104,0.001310345,0.00131044,0.00131043,0.069634699,0.925123687,Clinics,0.91315424,TRUE,49.33333333,0.626507514,49.33333333,0.678953706,0,0.403234768,,,0.569565329
1668,A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19).,Eur Radiol,33629156,2/26/21,pubmed,0,11,"deep learning, artificial intelligence, dataset",0.001010969,0.098429395,0.897526787,0.001010955,0.001010945,0.001010949,Imaging,0.6640505,TRUE,26.18181818,0.383264271,9.454545455,0.343925609,0,0.403234768,,,0.376808216
1669,COVID-19 in Patients with CKD in New York City.,Kidney360,33629075,2/26/21,pubmed,0,8,logistic regression,0.000926283,0.000926283,0.000926287,0.000926288,0.000926286,0.995368574,Clinics,0.9749824,TRUE,43.25,0.569855897,97.125,0.816831683,0,0.403234768,,,0.596640783
1670,Workforce challenges in digital health implementation: How are clinical psychology training programmes developing digital competences?,Digit Health,33628457,2/26/21,pubmed,0,4,digital health,0.001220002,0.001220013,0.181735422,0.247538353,0.567066218,0.001219992,Healthcare,0.9039998,TRUE,14.25,0.215535902,3.5,0.213607172,0,0.403234768,,,0.27745928
1671,Mid-Epidemic Forecasts of COVID-19 Cases and Deaths: A Bivariate Model Applied to the UK.,Interdiscip Perspect Infect Dis,33628235,2/26/21,pubmed,0,1,bayes,0.000956324,0.021413576,0.042150213,0.855200514,0.079323025,0.000956348,Epidemiology,0.1755203,FALSE,240,0.982682912,274,0.946614932,0,0.403234768,,,0.77751087
1672,Identification of candidate repurposable drugs to combat COVID-19 using a signature-based approach.,Sci Rep,33627767,2/26/21,pubmed,0,15,"bioinformatic, transcriptom",0.891248139,0.045319839,0.031749161,0.001461885,0.00146186,0.028759115,Drug discovery,0.40369505,FALSE,20.33333333,0.302059497,9.866666667,0.351552047,2,0.618927094,,,0.424179546
1673,To burn-out or not to burn-out: a cross-sectional study in healthcare professionals in Spain during COVID-19 pandemic.,BMJ Open,33627353,2/26/21,pubmed,0,9,logistic regression,0.001653006,0.001653034,0.024194685,0.001653069,0.946737476,0.024108731,Healthcare,0.894269,TRUE,82.77777778,0.816995485,55.44444444,0.703906877,0,0.403234768,,,0.641379043
1674,Gastrointestinal mucosal damage in patients with COVID-19 undergoing endoscopy: an international multicentre study.,BMJ Open Gastroenterol,33627313,2/26/21,pubmed,0,47,logistic regression,0.001565516,0.032643384,0.001565385,0.001565384,0.001565423,0.961094908,Clinics,0.9982462,TRUE,139.0638298,0.932154122,64.53191489,0.736954777,0,0.403234768,,,0.690781222
1675,Implementation evaluation of staff support and wellbeing programmes at an academic health science centre during COVID-19: study protocol.,Implement Sci Commun,33627195,2/26/21,pubmed,0,7,dataset,0.001171566,0.00117158,0.001171591,0.603231355,0.392082331,0.001171577,Epidemiology,0.4018752,FALSE,80.57142857,0.809264642,91.42857143,0.804789938,0,0.403234768,,,0.672429783
1676,Impact of COVID-19 on health services utilization in Province-2 of Nepal: a qualitative study among community members and stakeholders.,BMC Health Serv Res,33627115,2/26/21,pubmed,0,7,network analysis,0.000977473,0.000977458,0.000977477,0.14942215,0.846667908,0.000977535,Healthcare,0.9525187,TRUE,32.14285714,0.456861896,13.28571429,0.403130854,0,0.403234768,,,0.421075839
1677,The impact of COVID-19 pandemic outbreak on education and mental health of Chinese children aged 7-15 years: an online survey.,BMC Pediatr,33627089,2/26/21,pubmed,0,8,logistic regression,0.000988374,0.000988457,0.048569498,0.000988424,0.947476891,0.000988355,Healthcare,0.8344507,TRUE,42.375,0.5612592,23.875,0.519735082,0,0.403234768,,,0.494743016
1678,Behavioral changes and hygiene practices of older adults in Japan during the first wave of COVID-19 emergency.,BMC Geriatr,33627073,2/26/21,pubmed,0,7,logistic regression,0.001022633,0.001022617,0.001022624,0.001022671,0.994886824,0.001022632,Healthcare,0.9680271,TRUE,93.85714286,0.854103531,59.71428571,0.719494247,0,0.403234768,,,0.658944182
1679,"Sex Differences in the Association Between Stress, Loneliness, and COVID-19 Burden Among People with HIV in the United States.",AIDS Res Hum Retroviruses,33626967,2/26/21,pubmed,0,11,logistic regression,0.001187309,0.001187252,0.001187239,0.001187269,0.994063622,0.001187309,Healthcare,0.92205936,TRUE,23.90909091,0.352464593,12.45454545,0.390487022,0,0.403234768,,,0.382062127
1680,Preventive Behaviors and Mental Health-Related Symptoms Among Immunocompromised Adults During the COVID-19 Pandemic: An Analysis of the COVID Impact Survey.,AIDS Res Hum Retroviruses,33626959,2/26/21,pubmed,0,4,logistic regression,0.00114131,0.001141321,0.001141366,0.001141365,0.994293281,0.001141358,Healthcare,0.9933742,TRUE,41.25,0.5508071,9.75,0.349678887,0,0.403234768,,,0.434573585
1681,"Evaluation of epidemiology, clinical features, prognosis, diagnosis and treatment outcomes of patients with COVID-19 in West Azerbaijan Province.",Int J Clin Pract,33626210,2/25/21,pubmed,0,7,"sequencing, logistic regression",0.000936132,0.058089382,0.000936091,0.00093614,0.057188783,0.881913472,Clinics,0.9141115,TRUE,10.42857143,0.155853794,1.285714286,0.128512176,0,0.403234768,,,0.229200246
1682,Machine learning-based analysis of alveolar and vascular injury in SARS-CoV-2 acute respiratory failure.,J Pathol,33626204,2/25/21,pubmed,0,19,machine learning,0.048211215,0.029654223,0.188155124,0.026418668,0.000889116,0.706671654,Clinics,0.72709674,TRUE,224.2105263,0.979776115,149.8947368,0.883395772,1,0.537564047,,,0.800245311
1683,SOM-LWL method for identification of COVID-19 on chest X-rays.,PLoS One,33626053,2/25/21,pubmed,0,4,"machine learning, artificial intelligence, dataset",0.001059376,0.041173474,0.894163507,0.061484841,0.001059394,0.001059408,Imaging,0.66665107,TRUE,15.75,0.237862577,2.25,0.170925876,0,0.403234768,,,0.270674407
1684,"Prevalence of SARS-CoV-2 in an area of unrestricted viral circulation: Mass seroepidemiological screening in Castiglione d'Adda, Italy.",PLoS One,33626045,2/25/21,pubmed,0,19,logistic regression,0.002238522,0.359239738,0.002238482,0.221658796,0.310997728,0.103626734,Genomics,0.6302985,TRUE,88.05263158,0.837157524,40.15789474,0.633930961,0,0.403234768,,,0.624774417
1685,Detecting SARS-CoV-2 variants with SNP genotyping.,PLoS One,33626040,2/25/21,pubmed,0,11,sequencing,0.001371261,0.970957045,0.001371293,0.001371286,0.02355785,0.001371265,Genomics,0.16447532,FALSE,62.72727273,0.719339477,93.63636364,0.809740434,0,0.403234768,,,0.644104893
1686,SARS-CoV-2 innate effector associations and viral load in early nasopharyngeal infection.,Physiol Rep,33625796,2/25/21,pubmed,0,26,proteom,0.330775335,0.340542263,0.001112642,0.001112643,0.001112689,0.325344429,Genomics,0.2503867,FALSE,46.42307692,0.599913415,41.69230769,0.643162965,0,0.403234768,,,0.548770382
1687,Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8.,Protein Sci,33625752,2/25/21,pubmed,0,2,in silico,0.367997003,0.002357817,0.411340541,0.213589083,0.002357822,0.002357734,Drug discovery,0.33834085,FALSE,194.5,0.97093203,716,0.987690661,0,0.403234768,,,0.78728582
1688,Bioinformatics Analysis of SARS-CoV-2 to Approach an Effective Vaccine Candidate Against COVID-19.,Mol Biotechnol,33625681,2/25/21,pubmed,0,6,"bioinformatic, in silico",0.798178261,0.195330527,0.001622832,0.00162281,0.001622859,0.001622712,Drug discovery,0.6457829,TRUE,21,0.312016822,3,0.199424672,2,0.618927094,,,0.37678953
1689,An integrated emergency department/hospital at home model in mild COVID-19 pneumonia: feasibility and outcomes after discharge from the emergency department.,Intern Emerg Med,33625661,2/25/21,pubmed,0,17,logistic regression,0.001291232,0.001291231,0.001291303,0.112063352,0.001291289,0.882771594,Clinics,0.8201492,TRUE,46.41176471,0.599789721,11.23529412,0.373294086,0,0.403234768,,,0.458772858
1690,The Activin/Follistatin-axis is severely deregulated in COVID-19 and independently associated with in-hospital mortality.,J Infect Dis,33625513,2/25/21,pubmed,0,20,prediction model,0.142221791,0.00137135,0.110217142,0.001371346,0.00137139,0.743446981,Clinics,0.32054543,FALSE,62.55,0.718226235,64.9,0.738359647,0,0.403234768,,,0.619940217
1691,Factors associated with virtual care access in older adults: a cross-sectional study.,Age Ageing,33625475,2/25/21,pubmed,0,6,logistic regression,0.001622779,0.001622697,0.001622878,0.00162275,0.543817597,0.4496913,Healthcare,0.882784,TRUE,140.1666667,0.934009524,198.1666667,0.916911961,0,0.403234768,,,0.751385418
1692,Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates.,Biosci Rep,33624754,2/25/21,pubmed,0,6,"machine learning, computational",0.333550803,0.002238489,0.26196901,0.397764755,0.002238483,0.002238459,Epidemiology,0.7979415,TRUE,37.5,0.513946441,5.5,0.267259834,0,0.403234768,,,0.394813681
1693,Cancer survivor worries about treatment disruption and detrimental health outcomes due to the COVID-19 pandemic.,J Psychosoc Oncol,33624572,2/25/21,pubmed,0,7,logistic regression,0.002032821,0.002032817,0.00203284,0.002032941,0.781221629,0.210646952,Healthcare,0.9698318,TRUE,45.28571429,0.589399468,113.2857143,0.842855231,0,0.403234768,,,0.611829822
1694,A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: an in silico investigation.,Brief Bioinform,33623995,2/25/21,pubmed,0,13,"molecular dynamics simulation, in silico",0.960553045,0.001371247,0.001371258,0.033961912,0.001371258,0.001371279,Drug discovery,0.9545641,TRUE,45.69230769,0.592924733,20.07692308,0.481067701,0,0.403234768,,,0.492409067
1695,An ensemble approach for multi-stage transfer learning models for COVID-19 detection from chest CT scans.,Intell Based Med,33623929,2/25/21,pubmed,0,1,"image analysis, transfer learning",0.051355892,0.001438148,0.890815473,0.053514035,0.0014382,0.001438253,Imaging,0.4124407,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
1696,Effects of population co-location reduction on cross-county transmission risk of COVID-19 in the United States.,Appl Netw Sci,33623817,2/25/21,pubmed,0,6,network analysis,0.000889064,0.000889064,0.000889055,0.995554659,0.000889089,0.000889069,Epidemiology,0.3029359,FALSE,68,0.753973653,10.5,0.363459995,6,0.764429903,,,0.62728785
1697,Artificial Intelligence in COVID-19 Ultrastructure.,J Microsc Ultrastruct,33623737,2/25/21,pubmed,0,2,artificial intelligence,0.236575612,0.002996583,0.698314999,0.002996633,0.002996527,0.056119646,Imaging,0.37710664,FALSE,22.5,0.333539489,6,0.280037463,0,0.403234768,,,0.33893724
1698,Investigation of the dynamics of COVID-19 with a fractional mathematical model: A comparative study with actual data.,Results Phys,33623732,2/25/21,pubmed,0,5,mathematical model,0.003214179,0.003214142,0.003214288,0.983929096,0.003214169,0.003214127,Epidemiology,0.53785574,TRUE,19.6,0.291854784,1.2,0.126103827,0,0.403234768,,,0.273731126
1699,Impact of pangolin bootleg market on the dynamics of COVID-19 model.,Results Phys,33623730,2/25/21,pubmed,0,5,mathematical model,0.002562773,0.002562608,0.002562663,0.987186733,0.002562611,0.002562612,Epidemiology,0.17172867,FALSE,31.2,0.446471643,4,0.231469093,0,0.403234768,,,0.360391835
1700,0,Comput Struct Biotechnol J,33623641,2/25/21,pubmed,0,4,computational,0.815500576,0.031487939,0.001156257,0.14954264,0.001156274,0.001156314,Drug discovery,0.46094456,FALSE,80,0.807532933,25.5,0.533382392,0,0.403234768,,,0.581383364
1701,Assessment of burnout among primary teachers in confinement during the COVID-19 period in Morocco: case of the Kenitra.,Pan Afr Med J,33623616,2/25/21,pubmed,0,6,logistic regression,0.002183291,0.002183329,0.00218324,0.154575611,0.836691265,0.002183264,Healthcare,0.9097767,TRUE,13.66666667,0.207310285,0.5,0.087101953,0,0.403234768,,,0.232549002
1702,Setting up and relaxation of public health social and physical distancing measures for COVID-19: a rapid review.,Pan Afr Med J,33623600,2/25/21,pubmed,0,4,mathematical model,0.001254654,0.023774653,0.001254612,0.764190434,0.208271013,0.001254633,Epidemiology,0.48596647,FALSE,140.75,0.934875379,130.25,0.864062082,0,0.403234768,,,0.734057409
1703,Continuity of health service delivery during the COVID-19 pandemic: the role of digital health technologies in Uganda.,Pan Afr Med J,33623568,2/25/21,pubmed,0,4,digital health,0.002490445,0.002490467,0.002490524,0.658839131,0.331198867,0.002490567,Epidemiology,0.4587537,FALSE,11.25,0.169274538,6.25,0.283315494,2,0.618927094,,,0.357172375
1704,Cochrane corner: effectiveness of quarantine in reducing the spread of COVID-19.,Pan Afr Med J,33623543,2/25/21,pubmed,0,3,mathematical model,0.001350404,0.073169399,0.001350361,0.753797091,0.168982324,0.001350422,Epidemiology,0.38649243,FALSE,118.6666667,0.905127095,64.66666667,0.737556864,0,0.403234768,,,0.681972909
1705,"Machine Learning-Based Decision Model to Distinguish Between COVID-19 and Influenza: A Retrospective, Two-Centered, Diagnostic Study.",Risk Manag Healthc Policy,33623450,2/25/21,pubmed,0,7,"machine learning, dataset",0.001237085,0.175284834,0.448888522,0.001237187,0.001237134,0.372115239,Clinics,0.1858277,FALSE,43.85714286,0.576411652,20.42857143,0.484814022,0,0.403234768,,,0.48815348
1706,Alcohol-Based Handrub Utilization Practice for COVID-19 Prevention Among Pharmacy Professionals in Ethiopian Public Hospitals: A Cross-Sectional Study.,Drug Healthc Patient Saf,33623439,2/25/21,pubmed,0,7,logistic regression,0.001220012,0.001219996,0.001220111,0.023295651,0.939182824,0.033861406,Healthcare,0.94827825,TRUE,2.857142857,0.031541839,0.285714286,0.066296495,0,0.403234768,,,0.167024367
1707,Projecting potential impact of COVID-19 on major cereal crops in Senegal and Burkina Faso using crop simulation models.,Agric Syst,33623181,2/25/21,pubmed,0,7,simulation model,0.029612008,0.001085372,0.001085364,0.790740107,0.176391791,0.001085359,Epidemiology,0.8889291,TRUE,50.85714286,0.636774074,44.28571429,0.655338507,1,0.537564047,,,0.609892209
1708,Identification of high affinity and low molecular alternatives of boceprevir against SARS-CoV-2 main protease: A virtual screening approach.,Chem Phys Lett,33623170,2/25/21,pubmed,0,4,"virtual screening, molecular dynamics simulation",0.941282355,0.002806401,0.002806577,0.002806487,0.047491784,0.002806395,Drug discovery,0.6792989,TRUE,64.75,0.733440534,26.5,0.542012309,0,0.403234768,,,0.559562537
1709,Biological activity-based modeling identifies antiviral leads against SARS-CoV-2.,Nat Biotechnol,33623157,2/25/21,pubmed,0,20,computational,0.992309348,0.001538206,0.001538082,0.001538118,0.001538168,0.001538077,Drug discovery,0.3684373,FALSE,147.8,0.941925908,182.8,0.908215146,0,0.403234768,,,0.751125274
1710,Altitude conditions seem to determine the evolution of COVID-19 in Brazil.,Sci Rep,33623105,2/25/21,pubmed,0,6,correlation analysis,0.00137127,0.001371326,0.001371279,0.566970689,0.001371352,0.427544083,Epidemiology,0.4107147,FALSE,39.5,0.534232173,19,0.471367407,0,0.403234768,,,0.469611449
1711,Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients.,Sci Rep,33623096,2/25/21,pubmed,0,20,machine learning,0.00131037,0.001310458,0.277874946,0.04994493,0.00131036,0.668248936,Clinics,0.87921286,TRUE,47.35,0.608695652,65.35,0.74009901,0,0.403234768,,,0.58400981
1712,Local computational methods to improve the interpretability and analysis of cryo-EM maps.,Nat Commun,33623015,2/25/21,pubmed,0,9,computational,0.002639144,0.449264284,0.322637126,0.220181488,0.002638972,0.002638987,Genomics,0.06511748,FALSE,59.22222222,0.697693116,129.4444444,0.863259299,0,0.403234768,,,0.654729061
1713,Clonal expansion and activation of tissue-resident memory-like Th17 cells expressing GM-CSF in the lungs of severe COVID-19 patients.,Sci Immunol,33622974,2/25/21,pubmed,0,38,interactom,0.609384412,0.001565443,0.001565339,0.001565384,0.026788308,0.359131114,Drug discovery,0.69708973,TRUE,74.78947368,0.785639186,97.13157895,0.816898582,2,0.618927094,,,0.740488287
1714,Covidogram as a simple tool for predicting severe course of COVID-19: population-based study.,BMJ Open,33622955,2/25/21,pubmed,0,13,prediction model,0.077696722,0.085587791,0.001220086,0.001220073,0.001220113,0.833055216,Clinics,0.942997,TRUE,126.7692308,0.916383202,47.15384615,0.669052716,0,0.403234768,,,0.662890229
1715,Relationships between changes due to COVID-19 pandemic and the depressive and anxiety symptoms among mothers of infants and/or preschoolers: a prospective follow-up study from pre-COVID-19 Japan.,BMJ Open,33622953,2/25/21,pubmed,0,3,logistic regression,0.001272622,0.001272631,0.001272636,0.001272657,0.993636784,0.001272669,Healthcare,0.9453721,TRUE,83.66666667,0.819716742,37.33333333,0.618209794,0,0.403234768,,,0.613720434
1716,Identifying Potentially Beneficial Genetic Mutations Associated with Monophyletic Selective Sweep and a Proof-of-Concept Study with Viral Genetic Data.,mSystems,33622855,2/25/21,pubmed,0,1,genomes,0.001291285,0.967117098,0.001291527,0.001291311,0.00129124,0.027717539,Genomics,0.12474513,FALSE,65,0.734801163,55,0.702167514,0,0.403234768,,,0.613401148
1717,"Comparative Genomics and Integrated Network Approach Unveiled Undirected Phylogeny Patterns, Co-mutational Hot Spots, Functional Cross Talk, and Regulatory Interactions in SARS-CoV-2.",mSystems,33622851,2/25/21,pubmed,0,13,"phylogenom, genomes, network analysis",0.421729819,0.546203826,0.000752904,0.000752936,0.000752919,0.029807596,Genomics,0.7378834,TRUE,49.38461538,0.626693055,25.53846154,0.533449291,0,0.403234768,,,0.521125704
1718,Influenza Vaccination and Hospitalizations Among COVID-19 Infected Adults.,J Am Board Fam Med,33622834,2/25/21,pubmed,0,7,logistic regression,0.052692042,0.001156268,0.001156257,0.001156287,0.295893624,0.647945521,Clinics,0.17630428,FALSE,66.14285714,0.741604305,84.57142857,0.789470163,1,0.537564047,,,0.689546172
1719,A Stepwise Transition to Telemedicine in Response to COVID-19.,J Am Board Fam Med,33622831,2/25/21,pubmed,0,3,logistic regression,0.001156303,0.00115633,0.001156342,0.433888782,0.49020194,0.072440304,Healthcare,0.97073746,TRUE,38.66666667,0.525882862,8.333333333,0.325662296,0,0.403234768,,,0.418259975
1720,Development and Validation of the COVID-NoLab and COVID-SimpleLab Risk Scores for Prognosis in 6 US Health Systems.,J Am Board Fam Med,33622827,2/25/21,pubmed,0,11,"predictive model, logistic regression",0.001392902,0.001392873,0.028777762,0.001392867,0.001392896,0.965650701,Clinics,0.30964473,FALSE,145.2727273,0.939452038,153.5454545,0.88633931,0,0.403234768,,,0.743008705
1721,Primary Care Relevant Risk Factors for Adverse Outcomes in Patients With COVID-19 Infection: A Systematic Review.,J Am Board Fam Med,33622826,2/25/21,pubmed,0,3,prediction model,0.000926358,0.00092631,0.047669767,0.068944921,0.000926344,0.8806063,Clinics,0.84457946,TRUE,188.3333333,0.968148927,117.3333333,0.847337436,0,0.403234768,,,0.73957371
1722,On the Front (Phone) Lines: Results of a COVID-19 Hotline.,J Am Board Fam Med,33622824,2/25/21,pubmed,0,9,logistic regression,0.000846544,0.033915722,0.000846564,0.000846568,0.713791449,0.249753152,Healthcare,0.9978365,TRUE,47.44444444,0.60931412,32.88888889,0.591651057,0,0.403234768,,,0.534733315
1723,Key Factors Promoting Rapid Implementation of Virtual Screening Modalities for the COVID-19 Pandemic Response.,J Am Board Fam Med,33622819,2/25/21,pubmed,0,6,virtual screening,0.001438203,0.001438188,0.33104404,0.087946524,0.576694792,0.001438252,Healthcare,0.47509506,FALSE,28.16666667,0.409178057,5,0.257024351,0,0.403234768,,,0.356479058
1724,Digital rheumatology in the era of COVID-19: results of a national patient and physician survey.,RMD Open,33622673,2/25/21,pubmed,0,13,digital health,0.001371291,0.001371299,0.00137132,0.198459195,0.55986774,0.237559155,Healthcare,0.99113834,TRUE,30.23076923,0.434102295,3.846153846,0.222303987,1,0.537564047,,,0.39799011
1725,"Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States.",Emerg Infect Dis,33622460,2/25/21,pubmed,0,10,"bayes, mathematical model",0.002562562,0.002562646,0.002562732,0.987186487,0.002562865,0.002562708,Epidemiology,0.12681302,FALSE,69,0.759416167,42.4,0.646641691,0,0.403234768,,,0.603097542
1726,Maternal and perinatal outcomes in high vs low risk-pregnancies affected by SARS-COV-2 infection (Phase-2): The WAPM (World Association of Perinatal Medicine) working group on COVID-19.,Am J Obstet Gynecol MFM,33621713,2/24/21,pubmed,0,130,logistic regression,0.001653007,0.001653164,0.001653049,0.001653256,0.001653145,0.991734379,Clinics,0.8007265,TRUE,64.33076923,0.73010081,27.39230769,0.548769066,0,0.403234768,,,0.560701548
1727,Development and validation of a prediction model for tocilizumab failure in hospitalized patients with SARS-CoV-2 infection.,PLoS One,33621264,2/24/21,pubmed,0,25,"logistic regression, prediction model",0.001254619,0.001254595,0.06781575,0.00125464,0.001254605,0.927165791,Clinics,0.6919979,TRUE,201.28,0.972663739,167.76,0.89764517,0,0.403234768,,,0.757847892
1728,Improving the Understanding of the Immunopathogenesis of Lymphopenia as a Correlate of SARS-CoV-2 Infection Risk and Disease Progression in African Patients: Protocol for a Cross-sectional Study.,JMIR Res Protoc,33621190,2/24/21,pubmed,0,10,sequencing,0.184368741,0.302392295,0.000568968,0.000568975,0.108651614,0.403449407,Clinics,0.7764094,TRUE,49.1,0.624837652,76.4,0.769868879,1,0.537564047,,,0.644090193
1729,Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study.,JMIR Hum Factors,33621187,2/24/21,pubmed,0,10,network analysis,0.114733318,0.000793411,0.138337628,0.000793432,0.214536306,0.530805904,Clinics,0.93635106,TRUE,115.4,0.899560888,81.2,0.781375435,0,0.403234768,,,0.694723697
1730,"Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study.",J Med Internet Res,33621186,2/24/21,pubmed,0,2,forecasting model,0.000591766,0.00059178,0.012915391,0.984717471,0.000591789,0.000591803,Epidemiology,0.009833038,FALSE,49,0.624281032,12,0.386740701,0,0.403234768,,,0.471418833
1731,Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for COVID-19: Model Development and Validation Study.,J Med Internet Res,33621185,2/24/21,pubmed,0,6,"classifier, predictive model",0.001272664,0.001272662,0.201445186,0.222853467,0.204623897,0.368532125,Clinics,0.6273216,TRUE,160.5,0.95194508,402.1666667,0.971367407,0,0.403234768,,,0.775515752
1732,A safe protocol to identify low-risk patients with COVID-19 pneumonia for outpatient management.,Intern Emerg Med,33620681,2/24/21,pubmed,0,10,logistic regression,0.001511824,0.001511844,0.038262795,0.001511898,0.001511865,0.955689774,Clinics,0.93818533,TRUE,11.3,0.169583771,8.8,0.332753546,0,0.403234768,,,0.301857362
1733,"Mass testing after a single suspected or confirmed case of COVID-19 in London care homes, April-May 2020: implications for policy and practice.",Age Ageing,33620453,2/24/21,pubmed,0,20,"sequencing, whole genome",0.00133004,0.310085945,0.001330053,0.001330127,0.684593739,0.001330097,Healthcare,0.25713634,FALSE,71.8,0.772156596,68.8,0.749799304,0,0.403234768,,,0.641730223
1734,The peak levels of highly sensitive troponin I predicts in-hospital mortality in COVID-19 patients with cardiac injury: a retrospective study.,Eur Heart J Acute Cardiovasc Care,33620438,2/24/21,pubmed,0,16,logistic regression,0.001415153,0.001415115,0.001415187,0.018513756,0.001415123,0.975825666,Clinics,0.6678021,TRUE,163.875,0.953924176,,,0,0.403234768,,,0.678579472
1735,Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events.,Elife,33620317,2/24/21,pubmed,0,5,mathematical model,0.001943496,0.361155675,0.001943594,0.534494431,0.098519268,0.001943537,Epidemiology,0.17897195,FALSE,47.4,0.609004886,19.6,0.476786192,2,0.618927094,,,0.568239391
1736,Applying the CiPA Approach to Evaluate Cardiac Proarrhythmia Risk of some Antimalarials Used Off-label in the First Wave of COVID-19.,Clin Transl Sci,33620150,2/24/21,pubmed,0,20,"in silico, dataset",0.851739909,0.001823566,0.001823382,0.001823653,0.001823417,0.140966073,Drug discovery,0.3802421,FALSE,45.2,0.588719154,17.9,0.459727054,0,0.403234768,,,0.483893658
1737,Lysosome activation in peripheral blood mononuclear cells and prognostic significance of circulating LC3B in COVID-19.,Brief Bioinform,33620066,2/24/21,pubmed,0,26,"transcriptom, logistic regression",0.139334795,0.106872044,0.001461898,0.00146191,0.001461867,0.749407486,Clinics,0.51766115,TRUE,38.34615385,0.522295751,,,0,0.403234768,,,0.462765259
1738,COVID-19 CG enables SARS-CoV-2 mutation and lineage tracking by locations and dates of interest.,Elife,33620031,2/24/21,pubmed,0,5,genomes,0.107009262,0.629767085,0.000746606,0.220636266,0.041094234,0.000746545,Genomics,0.18945456,FALSE,22.2,0.327787742,49.4,0.679154402,2,0.618927094,,,0.541956413
1739,The Association of Social Determinants of Health With COVID-19 Mortality in Rural and Urban Counties.,J Rural Health,33619746,2/24/21,pubmed,0,4,bayes,0.001622687,0.001622709,0.001622698,0.647802971,0.001622801,0.345706135,Epidemiology,0.98235893,TRUE,26.25,0.384748593,16.5,0.44293551,0,0.403234768,,,0.41030629
1740,Prediction of Mortality in hospitalized COVID-19 patients in a statewide health network.,medRxiv,33619510,2/24/21,pubmed,0,9,"predictive model, prediction model",0.000565322,0.000565316,0.030316508,0.030960818,0.017516017,0.920076018,Clinics,0.17103645,FALSE,62.66666667,0.719030243,122.2222222,0.853960396,0,0.403234768,,,0.658741802
1741,Early pandemic molecular diversity of SARS-CoV-2 in children.,medRxiv,33619507,2/24/21,pubmed,0,14,"sequencing, whole genome, genome sequences, genomes",0.00118727,0.637739829,0.001187294,0.001187325,0.206098911,0.152599371,Genomics,0.4176052,FALSE,38.85714286,0.527552724,90.92857143,0.803184372,0,0.403234768,,,0.577990621
1742,Predictive Modeling of COVID-19 Case Growth Highlights Evolving Demographic Risk Factors in Tennessee and Georgia.,medRxiv,33619499,2/24/21,pubmed,0,4,"machine learning, predictive model",0.001220092,0.022460562,0.089977347,0.845840533,0.001220078,0.039281388,Epidemiology,0.4276986,FALSE,13.5,0.205393036,5.25,0.260971367,0,0.403234768,,,0.28986639
1743,0,bioRxiv,33619490,2/24/21,pubmed,0,6,"deep learning, transcriptom, genome-wide, dataset",0.592803594,0.125586348,0.050774681,0.000916701,0.038078603,0.191840074,Drug discovery,0.17267111,FALSE,65.83333333,0.739192282,54.66666667,0.700361252,0,0.403234768,,,0.614262767
1744,Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19.,bioRxiv,33619488,2/24/21,pubmed,0,25,"computational, sequencing",0.604915383,0.248397,0.000710579,0.00071059,0.032114645,0.113151804,Drug discovery,0.45822284,FALSE,39.4,0.532995238,75.48,0.767594327,2,0.618927094,,,0.639838886
1745,Probing the SAM Binding Site of SARS-CoV-2 nsp14 in vitro Using SAM Competitive Inhibitors Guides Developing Selective bi-substrate Inhibitors.,bioRxiv,33619486,2/24/21,pubmed,0,20,radiom,0.928958059,0.001684576,0.001684512,0.001684552,0.064303781,0.00168452,Drug discovery,0.849207,TRUE,78.35,0.799740244,79.15,0.776157345,0,0.403234768,,,0.659710786
1746,Rotavirus as an Expression Platform of the SARS-CoV-2 Spike Protein.,bioRxiv,33619485,2/24/21,pubmed,0,2,genomes,0.536756015,0.385626023,0.000838553,0.000838566,0.075102328,0.000838516,Drug discovery,0.17605403,FALSE,159.5,0.951202919,279,0.948554991,0,0.403234768,,,0.767664226
1747,Host-virus chimeric events in SARS-CoV2 infected cells are infrequent and artifactual.,bioRxiv,33619483,2/24/21,pubmed,0,12,"sequencing, transcriptom, genomes, sequence alignment",0.222179869,0.75261814,0.000746563,0.000746581,0.000746545,0.022962301,Genomics,0.45969924,FALSE,60.66666667,0.706351661,86.33333333,0.793751672,1,0.537564047,,,0.67922246
1748,Predicting the zoonotic capacity of mammal species for SARS-CoV-2.,bioRxiv,33619481,2/24/21,pubmed,0,5,machine learning,0.219563436,0.642964051,0.131641919,0.001943673,0.001943458,0.001943462,Genomics,0.4407499,FALSE,90.4,0.843775125,185.4,0.909620016,1,0.537564047,,,0.763653063
1749,The Host Interactome of Spike Expands the Tropism of SARS-CoV-2.,bioRxiv,33619478,2/24/21,pubmed,0,6,"proteom, interactom",0.911622603,0.027826437,0.001141357,0.001141406,0.035448619,0.022819578,Drug discovery,0.6474009,TRUE,221,0.978848414,1094.166667,0.994581215,0,0.403234768,,,0.792221465
1750,"Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts.",Res Sq,33619475,2/24/21,pubmed,0,10,dataset,0.001126824,0.001126843,0.001126794,0.405835936,0.589656742,0.001126861,Healthcare,0.76464725,TRUE,47.9,0.613643392,50,0.681763447,0,0.403234768,,,0.566213869
1751,Dysregulation of the Leukocyte Signaling Landscape during Acute COVID-19.,Res Sq,33619472,2/24/21,pubmed,0,13,"proteom, phosphoproteom",0.737421855,0.035661248,0.001653033,0.001653143,0.001653045,0.221957675,Drug discovery,0.91884893,TRUE,97.38461538,0.862885769,356.3846154,0.966818303,0,0.403234768,,,0.744312947
1752,Artificial Intelligence-Enabled POCUS in the COVID-19 ICU: A New Spin on Cardiac Ultrasound.,JACC Case Rep,33619470,2/24/21,pubmed,0,4,"deep learning, artificial intelligence",0.004109808,0.004109866,0.457159554,0.256043339,0.004109973,0.274467461,Imaging,0.7659971,TRUE,37.75,0.516296617,17.5,0.45611453,1,0.537564047,,,0.503325065
1753,A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes.,JAMIA Open,33619468,2/24/21,pubmed,0,2,bayes,0.003465888,0.00346597,0.003466156,0.720538714,0.265597288,0.003465983,Epidemiology,0.47027344,FALSE,34,0.477766096,54.5,0.699892962,0,0.403234768,,,0.526964608
1754,Bayesian estimation of the seroprevalence of antibodies to SARS-CoV-2.,JAMIA Open,33619465,2/24/21,pubmed,0,2,"bayes, dataset",0.002720123,0.282028333,0.255837686,0.45397319,0.002720339,0.00272033,Epidemiology,0.10841715,FALSE,34,0.477766096,54.5,0.699892962,0,0.403234768,,,0.526964608
1755,Mathematical modeling of COVID-19 epidemic with effect of awareness programs.,Infect Dis Model,33619461,2/24/21,pubmed,0,6,mathematical model,0.00162272,0.001622721,0.001622726,0.991886162,0.001622835,0.001622836,Epidemiology,0.51248884,TRUE,138.6666667,0.931597501,45.33333333,0.659820712,3,0.667819001,,,0.753079071
1756,0,Comput Toxicol,33619460,2/24/21,pubmed,0,4,bioinformatic,0.990064027,0.001987381,0.001987107,0.001987155,0.001987218,0.001987112,Drug discovery,0.970623,TRUE,11.25,0.169274538,0.25,0.065493712,0,0.403234768,,,0.212667672
1757,Psychological distress during pandemic Covid-19 among adult general population: Result across 13 countries.,Clin Epidemiol Glob Health,33619459,2/24/21,pubmed,0,24,logistic regression,0.001461854,0.001461907,0.001461858,0.001461977,0.971263106,0.022889298,Healthcare,0.94234645,TRUE,28.375,0.410786072,7.458333333,0.30565962,0,0.403234768,,,0.37322682
1758,A numerical approach to maximize the number of testing of COVID-19 using conditional cluster sampling method.,Inform Med Unlocked,33619454,2/24/21,pubmed,0,4,classifier,0.001943569,0.001943661,0.619227861,0.268286546,0.106654699,0.001943664,Epidemiology,0.7687623,TRUE,8.5,0.126662131,0.25,0.065493712,0,0.403234768,,,0.198463537
1759,"Rare mutations in the accessory proteins ORF6, ORF7b, and ORF10 of the SARS-CoV-2 genomes.",Meta Gene,33619452,2/24/21,pubmed,0,3,genomes,0.127205322,0.857751851,0.003760533,0.003761145,0.003760642,0.003760507,Genomics,0.4219367,FALSE,66,0.741356918,54,0.697952903,1,0.537564047,,,0.658957956
1760,0,Results Chem,33619449,2/24/21,pubmed,0,4,virtual screening,0.961212473,0.001371289,0.001371285,0.033302313,0.00137129,0.00137135,Drug discovery,0.60901254,TRUE,15.5,0.234028078,0.25,0.065493712,0,0.403234768,,,0.234252186
1761,Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic.,Sustain Cities Soc,33619448,2/24/21,pubmed,0,3,"deep learning, transfer learning",0.001126871,0.00112687,0.552376538,0.443116115,0.001126828,0.001126778,Imaging,0.092693895,FALSE,180.3333333,0.964438122,56.33333333,0.708255285,0,0.403234768,,,0.691976058
1762,Supervised machine learning approach to molecular dynamics forecast of SARS-CoV-2 spike glycoproteins at varying temperatures.,MRS Adv,33619443,2/24/21,pubmed,0,6,"molecular dynamics simulation, machine learning, neural network, lstm",0.369608788,0.001034579,0.372591244,0.254696152,0.001034606,0.001034631,Drug discovery,0.32593596,FALSE,144,0.938524337,34.83333333,0.60422799,0,0.403234768,,,0.648662365
1763,Opportunities in the cloud or pie in the sky? Current status and future perspectives of telemedicine in nephrology.,Clin Kidney J,33619442,2/24/21,pubmed,0,5,artificial intelligence,0.001653086,0.00165306,0.056415111,0.663611179,0.176235917,0.100431647,Epidemiology,0.9459909,TRUE,41,0.549013544,62.2,0.72926144,2,0.618927094,,,0.632400693
1764,4P Model for Dynamic Prediction of COVID-19: a Statistical and Machine Learning Approach.,Cognit Comput,33619436,2/24/21,pubmed,0,5,"machine learning, neural network, probabilistic",0.001220035,0.001220132,0.095974465,0.619592542,0.077365354,0.204627472,Epidemiology,0.28619123,FALSE,10.2,0.153627312,0.6,0.09011239,0,0.403234768,,,0.215658156
1765,An integrated sustainable medical supply chain network during COVID-19.,Eng Appl Artif Intell,33619424,2/24/21,pubmed,0,4,"mathematical model, network model",0.001538157,0.001538109,0.450374699,0.543472683,0.001538178,0.001538173,Epidemiology,0.7144372,TRUE,399.75,0.995547034,325.5,0.96086433,0,0.403234768,,,0.786548711
1766,Fractional model for the spread of COVID-19 subject to government intervention and public perception.,Appl Math Model,33619419,2/24/21,pubmed,0,3,"model simulation, mathematical model",0.002130666,0.002130656,0.002130766,0.989346408,0.002130888,0.002130615,Epidemiology,0.37775624,FALSE,65.66666667,0.738450121,14.66666667,0.420323789,0,0.403234768,,,0.520669559
1767,Genome-scale deconvolution of RNA structure ensembles.,Nat Methods,33619392,2/24/21,pubmed,0,7,sequencing,0.321071175,0.546725646,0.121381475,0.00360741,0.003607153,0.003607141,Genomics,0.181416,FALSE,38,0.519327107,56.85714286,0.710128445,0,0.403234768,,,0.544230107
1768,"Correlation between early features and prognosis of symptomatic COVID-19 discharged patients in Hunan, China.",Sci Rep,33619362,2/24/21,pubmed,0,12,logistic regression,0.001098831,0.001098815,0.08167552,0.001098825,0.001098823,0.913929187,Clinics,0.93864524,TRUE,41.41666667,0.552044035,38.83333333,0.626103827,0,0.403234768,,,0.527127543
1769,In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19.,Sci Rep,33619344,2/24/21,pubmed,0,25,"computational, in silico",0.469707423,0.238681224,0.258449458,0.001593555,0.029974722,0.001593618,Drug discovery,0.40307134,FALSE,106.56,0.882120106,142.8,0.877107305,0,0.403234768,,,0.720820726
1770,Use of the first National Early Warning Score recorded within 24 hours of admission to estimate the risk of in-hospital mortality in unplanned COVID-19 patients: a retrospective cohort study.,BMJ Open,33619194,2/24/21,pubmed,0,5,dataset,0.001072169,0.001072174,0.054140486,0.226025111,0.001072237,0.716617822,Clinics,0.10592121,FALSE,37.6,0.514626755,14.6,0.419253412,0,0.403234768,,,0.445704978
1771,Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings.,Sci Transl Med,33619080,2/24/21,pubmed,0,13,mathematical model,0.000977435,0.457534332,0.138156801,0.339279951,0.063074015,0.000977465,Genomics,0.028425872,FALSE,62.84615385,0.720267178,615.0769231,0.984546428,1,0.537564047,,,0.747459218
1772,Short-term impact of the COVID-19 confinement measures on health behaviours and weight gain among adults in Belgium.,Arch Public Health,33618770,2/24/21,pubmed,0,8,logistic regression,0.001350377,0.001350376,0.00135042,0.113377568,0.806547541,0.076023719,Healthcare,0.41637015,FALSE,63.875,0.727317707,43.5,0.651725983,0,0.403234768,,,0.594092819
1773,Intra-host variation and evolutionary dynamics of SARS-CoV-2 populations in COVID-19 patients.,Genome Med,33618765,2/24/21,pubmed,0,34,"sequencing, transcriptom, genomes, metatranscriptom",0.070709605,0.839795744,0.001098814,0.001098847,0.001098805,0.086198184,Genomics,0.47473824,FALSE,56.67647059,0.679819408,,,1,0.537564047,,,0.608691727
1774,Proposal of selective wedge instillation of pulmonary surfactant for COVID-19 pneumonia based on computational fluid dynamics simulation.,BMC Pulm Med,33618696,2/24/21,pubmed,0,4,computational,0.172362137,0.001330082,0.192432778,0.482549631,0.001330096,0.149995277,Epidemiology,0.9579419,TRUE,71.25,0.76980642,29.5,0.566095799,0,0.403234768,,,0.579712329
1775,Investigation of the inhibitory activity of some dietary bioactive flavonoids against SARS-CoV-2 using molecular dynamics simulations and MM-PBSA calculations.,J Biomol Struct Dyn,33618628,2/24/21,pubmed,0,5,"virtual screening, molecular dynamics simulation",0.897381247,0.001220059,0.001220022,0.00122011,0.039752619,0.059205943,Drug discovery,0.9759419,TRUE,9.4,0.140021028,0,0.055525823,1,0.537564047,,,0.244370299
1776,Structural stability predictions and molecular dynamics simulations of RBD and HR1 mutations associated with SARS-CoV-2 spike glycoprotein.,J Biomol Struct Dyn,33618621,2/24/21,pubmed,0,3,"molecular dynamics simulation, computational",0.857616489,0.099122834,0.022056647,0.001438177,0.018327767,0.001438086,Drug discovery,0.6760048,TRUE,43.66666667,0.57492733,41.66666667,0.643029168,0,0.403234768,,,0.540397088
1777,The Fear of COVID-19 Scale: A Reliability Generalization Meta-Analysis.,Assessment,33618549,2/24/21,pubmed,0,3,predictive model,0.002422309,0.047216455,0.002422462,0.765449116,0.18006716,0.002422498,Epidemiology,0.2619362,FALSE,35,0.488032655,34.33333333,0.600682366,0,0.403234768,,,0.497316596
1778,"COVID-19 International Collaborative Research by the Health Insurance Review and Assessment Service Using Its Nationwide Real-world Data: Database, Outcomes, and Implications.",J Prev Med Public Health,33618494,2/24/21,pubmed,0,9,dataset,0.030302004,0.001392922,0.001392921,0.794239463,0.00139296,0.171279731,Epidemiology,0.16385427,FALSE,129.7777778,0.921454635,59.44444444,0.718557667,0,0.403234768,,,0.681082357
1779,Correlation of SARS-CoV-2 RNA in wastewater with COVID-19 disease burden in sewersheds.,Sci Total Environ,33618308,2/23/21,pubmed,0,13,correlation analysis,0.002898409,0.647367163,0.00289843,0.341039142,0.002898478,0.002898378,Genomics,0.71221215,TRUE,23.38461538,0.346094378,14.69230769,0.420390688,11,0.840175319,,,0.535553462
1780,Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19.,Eur J Radiol,33618207,2/23/21,pubmed,0,17,"machine learning, classifier",0.001565319,0.149320609,0.794147065,0.001565378,0.0015654,0.051836228,Imaging,0.6403674,TRUE,56.35294118,0.677469231,13.52941176,0.405606101,0,0.403234768,,,0.4954367
1781,Examining the determinants of eHealth usage among elderly people with disability: The moderating role of behavioural aspects.,Int J Med Inform,33618191,2/23/21,pubmed,0,4,logistic regression,0.001538133,0.001538139,0.001538199,0.001538214,0.992309164,0.001538151,Healthcare,0.013228476,FALSE,73,0.778464964,38.5,0.624765855,0,0.403234768,,,0.602155195
1782,"Clinical, virologic and immunologic features of a mild case of SARS-CoV-2 reinfection.",Clin Microbiol Infect,33618012,2/23/21,pubmed,0,17,"sequencing, whole-genome, genomes",0.237688502,0.494662482,0.001126827,0.001126855,0.083286392,0.182108942,Genomics,0.54372895,TRUE,66.29411765,0.742532006,49.94117647,0.681228258,2,0.618927094,,,0.680895786
1783,Characterizing SARS-CoV-2 genome diversity circulating in South American countries: Signatures of potentially emergent lineages?,Int J Infect Dis,33618008,2/23/21,pubmed,0,5,"genomes, dataset",0.001392824,0.993035678,0.001392865,0.001392896,0.001392889,0.001392848,Genomics,0.5788497,TRUE,69.8,0.762694044,66.2,0.742841852,1,0.537564047,,,0.681033314
1784,In silico identification of available drugs targeting cell surface BiP to disrupt SARS-CoV-2 binding and replication: Drug repurposing approach.,Eur J Pharm Sci,33617948,2/23/21,pubmed,0,5,in silico,0.995368395,0.00092635,0.000926328,0.000926331,0.00092631,0.000926286,Drug discovery,0.9890603,TRUE,0.6,0.00779269,,,0,0.403234768,,,0.205513729
1785,Dietary intake of university students during COVID-19 social distancing in the Northeast of Brazil and associated factors.,Appetite,33617933,2/23/21,pubmed,0,6,logistic regression,0.001098962,0.032273151,0.001098835,0.130127683,0.805360064,0.030041305,Healthcare,0.29150707,FALSE,128.3333333,0.91947554,57.66666667,0.713473374,0,0.403234768,,,0.678727894
1786,Mathematical modeling of COVID-19 transmission dynamics in Uganda: Implications of complacency and early easing of lockdown.,PLoS One,33617579,2/23/21,pubmed,0,5,mathematical model,0.001272643,0.001272692,0.001272688,0.993636558,0.001272695,0.001272724,Epidemiology,0.70484126,TRUE,24.4,0.360442823,7,0.299973241,1,0.537564047,,,0.399326703
1787,"Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study.",J Med Internet Res,33617460,2/23/21,pubmed,0,4,data mining,0.00091672,0.000916721,0.000916717,0.346756428,0.649576699,0.000916716,Healthcare,0.38892514,FALSE,63.25,0.722926588,22.25,0.505284988,1,0.537564047,,,0.588591874
1788,Adoption of COVID-19 Contact Tracing Apps: A Balance Between Privacy and Effectiveness.,J Med Internet Res,33617459,2/23/21,pubmed,0,3,digital health,0.002080586,0.002080594,0.002080579,0.925780049,0.065897637,0.002080556,Epidemiology,0.074178666,FALSE,32.33333333,0.459273919,28.33333333,0.556729997,0,0.403234768,,,0.473079561
1789,Applications of Protein Secondary Structure Algorithms in SARS-CoV-2 Research.,J Proteome Res,33617253,2/23/21,pubmed,0,4,"computational, genomes",0.432487393,0.336712819,0.001371394,0.226685856,0.001371263,0.001371275,Drug discovery,0.25481075,FALSE,49.5,0.628362917,338,0.96367407,0,0.403234768,,,0.665090585
1790,Neutrophil-to-Lymphocyte Ratio as a Poor Prognostic Factor in Iranian COVID-19 Patients.,Clin Lab,33616323,2/23/21,pubmed,0,9,logistic regression,0.002490385,0.00249039,0.002490408,0.002490402,0.002490451,0.987547964,Clinics,0.82063913,TRUE,44.66666667,0.583462181,16,0.437316029,0,0.403234768,,,0.474670992
1791,A matched cohort study of convalescent plasma therapy for COVID-19.,J Clin Apher,33616257,2/23/21,pubmed,0,6,logistic regression,0.001220145,0.001220186,0.001220058,0.084760985,0.001220053,0.910358572,Clinics,0.5248407,TRUE,39.66666667,0.536211268,26.5,0.542012309,0,0.403234768,,,0.493819448
1792,0,J Biomol Struct Dyn,33615998,2/23/21,pubmed,0,8,"molecular dynamics simulation, in silico",0.961042296,0.001254604,0.001254717,0.033939119,0.001254631,0.001254633,Drug discovery,0.98859215,TRUE,8.875,0.130249242,0.875,0.103826599,1,0.537564047,,,0.257213296
1793,GESTACOVID project: psychological and perinatal effects in Spanish pregnant women subjected to confinement due to the COVID-19 pandemic.,J Matern Fetal Neonatal Med,33615968,2/23/21,pubmed,0,8,logistic regression,0.001486452,0.001486451,0.001486473,0.107477939,0.793361859,0.094700826,Healthcare,0.9137215,TRUE,9.75,0.146267549,1.875,0.151792882,0,0.403234768,,,0.233765066
1794,Predicting mask-wearing behavior intention among international students during COVID-19 based on the theory of planned behavior.,Ann Palliat Med,33615807,2/23/21,pubmed,0,7,structural model,0.001653106,0.001653044,0.001653041,0.177281742,0.81610604,0.001653028,Healthcare,0.9716282,TRUE,26.28571429,0.385119673,7,0.299973241,0,0.403234768,,,0.362775894
1795,Application of spectral library prediction for parallel reaction monitoring of viral peptides.,Proteomics,33615696,2/23/21,pubmed,0,4,"neural network, in silico",0.635120926,0.256088926,0.103921986,0.00162278,0.001622682,0.001622701,Drug discovery,0.2653884,FALSE,36.75,0.505968211,112.5,0.841784854,0,0.403234768,,,0.583662611
1796,Sub-second heat inactivation of coronavirus using a betacoronavirus model.,Biotechnol Bioeng,33615450,2/23/21,pubmed,0,8,genomes,0.064819871,0.284468078,0.001987431,0.644750146,0.001987186,0.00198729,Epidemiology,0.2660818,FALSE,59.5,0.699857752,46.25,0.665038801,0,0.403234768,,,0.589377107
1797,0,ACS Pharmacol Transl Sci,33615189,2/23/21,pubmed,0,3,in silico,0.492211971,0.005697506,0.005697737,0.005697673,0.005697519,0.484997594,Drug discovery,0.8399718,TRUE,48.33333333,0.616921269,31.33333333,0.580010704,0,0.403234768,,,0.533388913
1798,Innate Immunity Plays a Key Role in Controlling Viral Load in COVID-19: Mechanistic Insights from a Whole-Body Infection Dynamics Model.,ACS Pharmacol Transl Sci,33615177,2/23/21,pubmed,0,12,"mathematical model, in silico",0.469619176,0.194150149,0.001112643,0.201860297,0.001112845,0.13214489,Drug discovery,0.571247,TRUE,40.66666667,0.546292288,31.5,0.581883864,0,0.403234768,,,0.510470306
1799,Treatment with an Anti-CK2 Synthetic Peptide Improves Clinical Response in COVID-19 Patients with Pneumonia. A Randomized and Controlled Clinical Trial.,ACS Pharmacol Transl Sci,33615173,2/23/21,pubmed,0,25,bayes,0.220317076,0.001350407,0.0013504,0.00135044,0.001350361,0.774281316,Clinics,0.9072374,TRUE,16.08,0.243923557,3.52,0.21367407,0,0.403234768,,,0.286944132
1800,"The perspectives of biomarker-based electrochemical immunosensors, artificial intelligence and the Internet of Medical Things toward COVID-19 diagnosis and management.",Mater Today Chem,33615086,2/23/21,pubmed,0,5,"artificial intelligence, bioinformatic",0.001237121,0.219221343,0.739529252,0.001237144,0.001237104,0.037538036,Genomics,0.91917837,TRUE,55.8,0.673201806,22,0.503746321,0,0.403234768,,,0.526727631
1801,SARS-CoV-2 spillover transmission due to recombination event.,Gene Rep,33615041,2/23/21,pubmed,0,4,bioinformatic,0.179636478,0.778557009,0.002080539,0.002080631,0.035564783,0.002080559,Genomics,0.52132267,TRUE,119,0.905683716,169.5,0.89905004,0,0.403234768,,,0.735989508
1802,Suicidal behaviors and suicide risk among Bangladeshi people during the COVID-19 pandemic: An online cross-sectional survey.,Heliyon,33615003,2/23/21,pubmed,0,5,logistic regression,0.001786502,0.001786511,0.001786495,0.001786578,0.876595774,0.11625814,Healthcare,0.9035911,TRUE,4.8,0.065248315,2.2,0.16838373,0,0.403234768,,,0.212288938
1803,Cardiovascular complications in COVID-19 patients with or without diabetes mellitus.,Endocrinol Diabetes Metab,33614986,2/23/21,pubmed,0,7,logistic regression,0.001486434,0.001486444,0.001486481,0.063820837,0.001486525,0.93023328,Clinics,0.8643623,TRUE,21.57142857,0.318696271,1.857142857,0.151525288,0,0.403234768,,,0.291152109
1804,Social mobilization and polarization can create volatility in COVID-19 pandemic control.,Appl Netw Sci,33614902,2/23/21,pubmed,0,3,dataset,0.001943659,0.001943512,0.001943505,0.990282346,0.001943528,0.001943449,Epidemiology,0.8084049,TRUE,53.33333333,0.656255798,71.66666667,0.758094728,0,0.403234768,,,0.605861765
1805,Spatiotemporal evolution of COVID-19 infection and detection within night light networks: comparative analysis of USA and China.,Appl Netw Sci,33614901,2/23/21,pubmed,0,2,radiom,0.000652917,0.000652929,0.048922661,0.948465694,0.000652902,0.000652898,Epidemiology,0.727494,TRUE,80.5,0.809202795,303.5,0.955244849,0,0.403234768,,,0.722560804
1806,The Clinical Course of COVID-19 in the Outpatient Setting: A Prospective Cohort Study.,Open Forum Infect Dis,33614816,2/23/21,pubmed,0,46,logistic regression,0.001272672,0.001272683,0.021615212,0.001272686,0.13787965,0.836687097,Clinics,0.98705447,TRUE,96.33333333,0.859917125,186.0833333,0.910021408,2,0.618927094,,,0.796288542
1807,0,Front Chem,33614597,2/23/21,pubmed,0,3,"molecular dynamics simulation, in silico",0.990064394,0.001987129,0.001987131,0.001987131,0.001987135,0.00198708,Drug discovery,0.48995972,FALSE,18.33333333,0.274908776,50,0.681763447,0,0.403234768,,,0.45330233
1808,Perceptions of the Importance of Advance Care Planning During the COVID-19 Pandemic Among Older Adults Living With HIV.,Front Public Health,33614590,2/23/21,pubmed,0,5,logistic regression,0.001823355,0.056062125,0.001823515,0.0018234,0.936644216,0.001823389,Healthcare,0.85864365,TRUE,30.4,0.436823551,13.6,0.406208188,0,0.403234768,,,0.415422169
1809,Landscape Profiling Analysis of DPP4 in Malignancies: Therapeutic Implication for Tumor Patients With Coronavirus Disease 2019.,Front Oncol,33614513,2/23/21,pubmed,0,6,"immunome, dataset",0.569742373,0.001565328,0.001565349,0.001565414,0.001565323,0.423996212,Drug discovery,0.5150425,TRUE,82.5,0.816129631,30.83333333,0.576264383,1,0.537564047,,,0.643319354
1810,Evaluation of Performance of an LR and SVR models to predict COVID-19 Pandemic.,Mater Today Proc,33614417,2/23/21,pubmed,0,3,machine learning,0.001751211,0.001751182,0.376341879,0.616653283,0.001751226,0.001751219,Epidemiology,0.3862642,FALSE,7.666666667,0.111633373,0,0.055525823,0,0.403234768,,,0.190131321
1811,Predicting the Effect of Covid-19 by using Artificial Intelligence: A Case Study.,Mater Today Proc,33614416,2/23/21,pubmed,0,2,"machine learning, artificial intelligence",0.001943524,0.001943671,0.638801452,0.353424272,0.001943524,0.001943557,Epidemiology,0.5930296,TRUE,20,0.298163152,1,0.122023013,0,0.403234768,,,0.274473644
1812,Remote diagnostic and detection of coronavirus disease (COVID-19) system based on intelligent healthcare and internet of things.,Results Phys,33614401,2/23/21,pubmed,0,6,"artificial intelligence, mathematical model",0.001098884,0.001098909,0.437193165,0.386014312,0.00109887,0.17349586,Epidemiology,0.2948373,FALSE,21.16666667,0.31319191,11.16666667,0.372357506,0,0.403234768,,,0.362928061
1813,Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platformm.,J Big Data,33614394,2/23/21,pubmed,0,7,"computational, mathematical model",0.000487474,0.00945714,0.000487484,0.824569219,0.000487489,0.164511194,Epidemiology,0.26155424,FALSE,167.8571429,0.956769126,190.2857143,0.912229061,0,0.403234768,,,0.757410985
1814,Fuzzy based expert system for diagnosis of coronary artery disease in nigeria.,Health Technol (Berl),33614390,2/23/21,pubmed,0,2,"artificial intelligence, data mining",0.002080637,0.002080614,0.810950301,0.002080631,0.035599086,0.147208732,Clinics,0.9342498,TRUE,17.5,0.263776362,1,0.122023013,1,0.537564047,,,0.307787807
1815,COVID-19 incidence and mortality in Nigeria: gender based analysis.,PeerJ,33614262,2/23/21,pubmed,0,2,"bayes, neural network, predictive model",0.000793388,0.000793389,0.017283425,0.526022078,0.000793444,0.454314276,Epidemiology,0.21496713,FALSE,19,0.285793803,1.5,0.138747659,0,0.403234768,,,0.27592541
1816,High levels of soluble CD25 in COVID-19 severity suggest a divergence between anti-viral and pro-inflammatory T-cell responses.,Clin Transl Immunology,33614032,2/23/21,pubmed,0,24,"sequencing, logistic regression",0.552681881,0.060652428,0.001622768,0.001622746,0.00162276,0.381797418,Drug discovery,0.29241785,FALSE,88.5,0.837961531,90.375,0.801846401,0,0.403234768,,,0.681014233
1817,Evaluation of serum ferritin for prediction of severity and mortality in COVID-19- A cross sectional study.,Ann Med Surg (Lond),33614024,2/23/21,pubmed,0,6,logistic regression,0.046691689,0.001203424,0.001203472,0.001203473,0.079189171,0.870508772,Clinics,0.49158767,FALSE,25.83333333,0.378625765,5.166666667,0.25849612,1,0.537564047,,,0.391561977
1818,Outcomes in Patients With COVID-19 Disease and High Oxygen Requirements.,J Clin Med Res,33613798,2/23/21,pubmed,0,16,logistic regression,0.001350299,0.001350303,0.001350305,0.001350316,0.001350327,0.99324845,Clinics,0.70345783,TRUE,27.1875,0.397365329,16.625,0.443738293,1,0.537564047,,,0.45955589
1819,A dynamic optimal control model for COVID-19 and cholera co-infection in Yemen.,Adv Differ Equ,33613669,2/23/21,pubmed,0,3,mathematical model,0.002638965,0.002638965,0.00263904,0.986805028,0.002639023,0.002638979,Epidemiology,0.476276,FALSE,16.33333333,0.247015895,4.666666667,0.246721969,0,0.403234768,,,0.298990877
1820,"A fractional order mathematical model for COVID-19 dynamics with quarantine, isolation, and environmental viral load.",Adv Differ Equ,33613668,2/23/21,pubmed,0,6,mathematical model,0.001823386,0.001823346,0.001823333,0.990883255,0.00182334,0.00182334,Epidemiology,0.76614547,TRUE,47.83333333,0.613148618,5.166666667,0.25849612,4,0.707574542,,,0.526406426
1821,"SARS-CoV-2 Genomes From Oklahoma, United States.",Front Genet,33613621,2/23/21,pubmed,0,10,"sequencing, genomes",0.001684546,0.964827205,0.001684476,0.001684687,0.02843441,0.001684676,Genomics,0.66105217,TRUE,45.8,0.593852434,55.9,0.705713139,0,0.403234768,,,0.567600113
1822,0,Front Microbiol,33613483,2/23/21,pubmed,0,3,"sequencing, transcriptom, genome-wide, whole-genome",0.397098628,0.441389326,0.00141512,0.157266569,0.001415236,0.001415122,Genomics,0.4544422,FALSE,45.66666667,0.592677346,60.66666667,0.723307466,0,0.403234768,,,0.573073193
1823,Hardships in Italian Prisons During the COVID-19 Emergency: The Experience of Healthcare Personnel.,Front Psychol,33613396,2/23/21,pubmed,0,5,dataset,0.026098097,0.000946108,0.000946127,0.186195778,0.784867791,0.0009461,Healthcare,0.9876981,TRUE,22,0.326056033,12.8,0.396574793,1,0.537564047,,,0.420064958
1824,CoVNet-19: A Deep Learning model for the detection and analysis of COVID-19 patients.,Appl Soft Comput,33613140,2/23/21,pubmed,0,3,"deep learning, neural network, network model",0.001046833,0.00104683,0.821161539,0.134441163,0.04125677,0.001046865,Imaging,0.71569115,TRUE,42.33333333,0.560888119,8.333333333,0.325662296,0,0.403234768,,,0.429928394
1825,A rapid screening classifier for diagnosing COVID-19.,Int J Biol Sci,33613111,2/23/21,pubmed,0,19,"artificial intelligence, neural network, classifier",0.02432176,0.001203454,0.970864388,0.001203436,0.001203479,0.001203484,Imaging,0.85098577,TRUE,41.26315789,0.550868947,104.0526316,0.828873428,0,0.403234768,,,0.594325714
1826,Coronavirus disease 2019 (COVID-19): survival analysis using deep learning and Cox regression model.,Pattern Anal Appl,33613099,2/23/21,pubmed,0,4,"machine learning, deep learning, artificial intelligence, neural network, dataset",0.001310339,0.001310328,0.64674779,0.001310415,0.001310385,0.348010743,Clinics,0.61063665,TRUE,12.75,0.191786752,3.5,0.213607172,0,0.403234768,,,0.269542897
1827,Learning Automata-based Misinformation Mitigation via Hawkes Processes.,Inf Syst Front,33613087,2/23/21,pubmed,0,4,dataset,0.001717302,0.001717227,0.225557286,0.728091814,0.041199017,0.001717354,Epidemiology,0.8904335,TRUE,68.25,0.755086895,26.5,0.542012309,0,0.403234768,,,0.566777991
1828,Worker-firm relational contracts in the time of shutdowns: experimental evidence.,Exp Econ,33613086,2/23/21,pubmed,0,2,bayes,0.002130717,0.002130708,0.00213075,0.576115176,0.415361989,0.002130661,Epidemiology,0.36177152,FALSE,232.5,0.981445977,1825,0.997792347,0,0.403234768,,,0.794157697
1829,A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment.,Multimed Tools Appl,33613083,2/23/21,pubmed,0,3,"machine learning, artificial intelligence",0.000999528,0.000999526,0.623367905,0.249194979,0.124438516,0.000999545,Imaging,0.8556366,TRUE,14.33333333,0.216772837,6,0.280037463,0,0.403234768,,,0.300015023
1830,Aggregation hot spots in the SARS-CoV-2 proteome may constitute potential therapeutic targets for the suppression of the viral replication and multiplication.,J Proteins Proteom,33613009,2/23/21,pubmed,0,2,proteom,0.788967252,0.205547663,0.001371248,0.001371287,0.001371273,0.001371278,Drug discovery,0.51823086,TRUE,23.5,0.348506401,15.5,0.430157881,0,0.403234768,,,0.39396635
1831,Dynamic governance decisions on multi-modal inter-city travel during a large-scale epidemic spreading.,Transp Policy (Oxf),33613001,2/23/21,pubmed,0,4,optimization model,0.001786532,0.001786541,0.001786644,0.99106718,0.001786603,0.0017865,Epidemiology,0.73964626,TRUE,20.25,0.301317336,6.75,0.292079208,0,0.403234768,,,0.332210437
1832,Optimal Control of Mathematical modeling of the spread of the COVID-19 pandemic with highlighting the negative impact of quarantine on diabetics people with Cost-effectiveness.,Chaos Solitons Fractals,33613000,2/23/21,pubmed,0,5,mathematical model,0.003335313,0.003335317,0.003335337,0.884753303,0.003335374,0.101905357,Epidemiology,0.77877057,TRUE,19.2,0.287339972,0,0.055525823,0,0.403234768,,,0.248700187
1833,Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19.,Expert Syst Appl,33612998,2/23/21,pubmed,0,4,transfer learning,0.001350396,0.001350393,0.931842166,0.001350364,0.062756315,0.001350366,Imaging,0.2459585,FALSE,86.5,0.831653163,23.5,0.516791544,0,0.403234768,,,0.583893158
1834,0,Biospektrum (Heidelb),33612988,2/23/21,pubmed,0,2,proteom,0.982670164,0.003466077,0.003465909,0.003466007,0.003465958,0.003465886,Drug discovery,0.43555784,FALSE,32,0.455810502,29,0.562884667,0,0.403234768,,,0.473976645
1835,A new approximation of mean-time trends for the second wave of COVID-19 pandemic evolving in key six countries.,Nonlinear Dyn,33612969,2/23/21,pubmed,0,2,mathematical model,0.001219996,0.001220054,0.001220007,0.993899853,0.00122004,0.00122005,Epidemiology,0.072710454,FALSE,44.5,0.581977859,2.5,0.180826866,0,0.403234768,,,0.388679831
1836,Non-pharmaceutical interventions during the COVID-19 pandemic: A review.,Phys Rep,33612922,2/23/21,pubmed,0,1,dataset,0.001171645,0.036579835,0.001171626,0.791501079,0.168404236,0.00117158,Epidemiology,0.02170813,FALSE,95,0.856824788,98,0.819106235,8,0.799987654,,,0.825306226
1837,A literature survey of the robotic technologies during the COVID-19 pandemic.,J Manuf Syst,33612914,2/23/21,pubmed,0,2,artificial intelligence,0.001751224,0.055001062,0.285273024,0.540372211,0.115851305,0.001751174,Epidemiology,0.5309985,TRUE,57.5,0.685447461,28,0.554321648,1,0.537564047,,,0.592444386
1838,The effect of ego-resiliency and COVID-19-related stress on mental health among the Japanese population.,Pers Individ Dif,33612903,2/23/21,pubmed,0,3,dataset,0.002238585,0.002238731,0.002238622,0.112490398,0.878555185,0.002238478,Healthcare,0.79715747,TRUE,11,0.167171748,3.666666667,0.217621086,0,0.403234768,,,0.262675867
1839,Rational design of potent anti-COVID-19 main protease drugs: An extensive multi-spectrum in silico approach.,J Mol Liq,33612899,2/23/21,pubmed,0,5,"virtual screening, in silico",0.994638994,0.001072183,0.001072235,0.001072235,0.001072172,0.001072181,Drug discovery,0.9401009,TRUE,38,0.519327107,27.6,0.550441531,0,0.403234768,,,0.491001135
1840,Assessing the Influence of COVID-19 on the Shortwave Radiative Fluxes Over the East Asian Marginal Seas.,Geophys Res Lett,33612880,2/23/21,pubmed,0,13,model simulation,0.002898508,0.137582501,0.002898364,0.850823841,0.002898329,0.002898457,Epidemiology,0.66613233,TRUE,90,0.843094811,356.9230769,0.966952101,0,0.403234768,,,0.73776056
1841,Chemistry of Atmospheric Fine Particles During the COVID-19 Pandemic in a Megacity of Eastern China.,Geophys Res Lett,33612876,2/23/21,pubmed,0,17,model simulation,0.126538572,0.002806522,0.00280653,0.86223543,0.002806469,0.002806477,Epidemiology,0.9533665,TRUE,24.17647059,0.356732018,13,0.400521809,1,0.537564047,,,0.431605958
1842,The Impact of COVID-19 on Students' Marks: A Bayesian Hierarchical Modeling Approach.,Metron,33612860,2/23/21,pubmed,0,5,"bayes, model fit",0.001823331,0.001823369,0.055096473,0.55861134,0.380822059,0.001823428,Epidemiology,0.5017879,TRUE,5.2,0.071927763,0.2,0.061145304,0,0.403234768,,,0.178769278
1843,"Perceived Stress, Emotion Regulation and Quality of life During the Covid-19 outbreak: A Multi-Cultural Online Survey.",Ann Med Psychol (Paris),33612843,2/23/21,pubmed,0,1,prediction model,0.001237091,0.001237107,0.001237151,0.043065071,0.951986509,0.001237071,Healthcare,0.985021,TRUE,14.2,0.214793741,9.8,0.350682366,0,0.403234768,,,0.322903625
1844,"Effects of COVID-19 pandemic and lockdown on lifestyle and mental health of students: A retrospective study from Karachi,Pakistan.",Ann Med Psychol (Paris),33612842,2/23/21,pubmed,0,5,logistic regression,0.000629686,0.000629685,0.000629673,0.234229747,0.74379346,0.020087749,Healthcare,0.99108195,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
1845,Screening of COVID-19 based on the extracted radiomics features from chest CT images.,J Xray Sci Technol,33612539,2/23/21,pubmed,0,4,"bayes, classifier, radiom, logistic regression",0.000854702,0.000854725,0.995726373,0.000854715,0.000854727,0.000854758,Imaging,0.177392,FALSE,5.25,0.072855464,0,0.055525823,1,0.537564047,,,0.221981778
1846,Identification of the binding interactions of some novel antiviral compounds against Nsp1 protein from SARS-CoV-2 (COVID-19) through high throughput screening.,J Biomol Struct Dyn,33612076,2/23/21,pubmed,0,2,"virtual screening, molecular dynamics simulation, in-silico",0.992567827,0.001486439,0.001486441,0.001486469,0.001486436,0.001486388,Drug discovery,0.68341094,TRUE,75,0.787061661,17,0.451097137,0,0.403234768,,,0.547131189
1847,Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform.,Healthc Inform Res,33611880,2/22/21,pubmed,0,6,"deep learning, neural network, dataset",0.00122002,0.001220017,0.993899883,0.001220042,0.00122003,0.001220008,Imaging,0.5224323,TRUE,29.83333333,0.428907168,11.5,0.378378378,0,0.403234768,,,0.403506771
1848,Predicting the impact of COVID-19 interruptions on transmission of gambiense human African trypanosomiasis in two health zones of the Democratic Republic of Congo.,Trans R Soc Trop Med Hyg,33611586,2/22/21,pubmed,0,9,mathematical model,0.141151679,0.002490628,0.002490568,0.720785042,0.130591491,0.002490591,Epidemiology,0.3776188,FALSE,57.22222222,0.683159132,138.7777778,0.873160289,0,0.403234768,,,0.653184729
1849,Comparison of the binding characteristics of SARS-CoV and SARS-CoV-2 RBDs to ACE2 at different temperatures by MD simulations.,Brief Bioinform,33611368,2/22/21,pubmed,0,2,molecular dynamics simulation,0.909214948,0.001350347,0.001350311,0.085383735,0.001350315,0.001350344,Drug discovery,0.75526947,TRUE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
1850,Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients.,Brief Bioinform,33611340,2/22/21,pubmed,0,6,transcriptom,0.704564951,0.179294224,0.029185798,0.001141449,0.001141492,0.084672086,Drug discovery,0.7567154,TRUE,79.16666667,0.803141815,12.66666667,0.39530372,1,0.537564047,,,0.57866986
1851,Molecular modeling-guided optimization of acetylcholinesterase reactivators: A proof for reactivation of covalently inhibited targets.,Eur J Med Chem,33611189,2/22/21,pubmed,0,9,computational,0.987888239,0.002422306,0.00242255,0.002422388,0.002422251,0.002422266,Drug discovery,0.8992611,TRUE,9.555555556,0.143298905,,,0,0.403234768,,,0.273266836
1852,Non-standard bioinformatics characterization of SARS-CoV-2.,Comput Biol Med,33611129,2/22/21,pubmed,0,2,"bioinformatic, classifier, genome sequences",0.151379419,0.648967094,0.195339068,0.001438202,0.001438094,0.001438122,Genomics,0.7964848,TRUE,62.5,0.718164389,11.5,0.378378378,0,0.403234768,,,0.499925845
1853,Prediction of the spread of Corona-virus carrying droplets in a bus - A computational based artificial intelligence approach.,J Hazard Mater,33611042,2/22/21,pubmed,0,7,"deep learning, computational, artificial intelligence",0.001751256,0.001751233,0.267596956,0.698065421,0.001751187,0.029083947,Epidemiology,0.041091233,FALSE,112.5714286,0.895602697,75,0.766590848,0,0.403234768,,,0.688476104
1854,SARS-CoV-2 and immune-microbiome interactions: Lessons from respiratory viral infections.,Int J Infect Dis,33610778,2/22/21,pubmed,0,7,microbiom,0.171331218,0.294576301,0.001684528,0.001684589,0.001684641,0.529038723,Clinics,0.84317094,TRUE,16.14285714,0.244232791,6,0.280037463,0,0.403234768,,,0.309168341
1855,Generalized SIR (GSIR) epidemic model: An improved framework for the predictive monitoring of COVID-19 pandemic.,ISA Trans,33610314,2/22/21,pubmed,0,2,predictive model,0.001046872,0.001046822,0.001046882,0.94833768,0.04747489,0.001046854,Epidemiology,0.37925825,FALSE,66.5,0.743830787,19.5,0.475983409,0,0.403234768,,,0.541016321
1856,The 2021 COVID-19 Artificial Intelligence Issue.,Clin Genitourin Cancer,33610279,2/22/21,pubmed,0,1,artificial intelligence,0.025060389,0.025060295,0.874696651,0.025061364,0.025061055,0.025060245,Epidemiology,0.51232034,TRUE,260,0.986022636,384,0.96936045,0,0.403234768,,,0.786205951
1857,Young Adults' COVID-19 Testing Intentions: The Role of Health Beliefs and Anticipated Regret.,J Adolesc Health,33610234,2/22/21,pubmed,0,3,logistic regression,0.002296514,0.002296665,0.002296555,0.002296698,0.988516966,0.002296603,Healthcare,0.80470645,TRUE,162.6666667,0.953120168,467.3333333,0.977388279,0,0.403234768,,,0.777914405
1858,Next-generation diagnostics: virus capture facilitates a sensitive viral diagnosis for epizootic and zoonotic pathogens including SARS-CoV-2.,Microbiome,33610182,2/22/21,pubmed,0,7,"sequencing, genomes",0.000926325,0.857439858,0.138854825,0.000926352,0.000926296,0.000926344,Genomics,0.8792227,TRUE,191.8571429,0.969509555,173.2857143,0.901725983,0,0.403234768,,,0.758156769
1859,A stochastic numerical analysis based on hybrid NAR-RBFs networks nonlinear SITR model for novel COVID-19 dynamics.,Comput Methods Programs Biomed,33610034,2/21/21,pubmed,0,8,"computational, neural network, mathematical model",0.000880237,0.000880218,0.284674587,0.711804556,0.000880201,0.0008802,Epidemiology,0.666887,TRUE,270.125,0.987692498,66.25,0.743042547,0,0.403234768,,,0.711323271
1860,Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph.,Comput Biol Med,33610001,2/21/21,pubmed,0,4,"deep learning, artificial intelligence, classifier",0.000926358,0.000926343,0.977552496,0.018742071,0.000926348,0.000926383,Imaging,0.7119385,TRUE,6.75,0.095862453,0,0.055525823,0,0.403234768,,,0.184874348
1861,Thermodynamic evaluation of the impact of DNA mismatches in PCR-type SARS-CoV-2 primers and probes.,Mol Cell Probes,33609730,2/21/21,pubmed,0,2,genomes,0.002422402,0.743712925,0.002422459,0.246597357,0.002422358,0.0024225,Genomics,0.5316848,TRUE,21.5,0.318263343,10,0.355632861,0,0.403234768,,,0.359043657
1862,Determination of SARS-CoV-2 RNA in different particulate matter size fractions of outdoor air samples in Madrid during the lockdown.,Environ Res,33609549,2/21/21,pubmed,0,6,genomes,0.073392079,0.546547241,0.001310397,0.376129557,0.001310352,0.001310374,Genomics,0.8926071,TRUE,13,0.197352959,1.333333333,0.13252609,0,0.403234768,,,0.244371272
1863,RadTranslate: An Artificial Intelligence-Powered Intervention for Urgent Imaging to Enhance Care Equity for Patients With Limited English Proficiency During the COVID-19 Pandemic.,J Am Coll Radiol,33609456,2/21/21,pubmed,0,10,artificial intelligence,0.001371268,0.050494866,0.631012114,0.126767454,0.122677751,0.067676546,Imaging,0.8263916,TRUE,34.2,0.479188571,13.5,0.405539203,0,0.403234768,,,0.429320847
1864,Repurposing of thalidomide and its derivatives for the treatment of SARS-coV-2 infections: Hints on molecular action.,Br J Clin Pharmacol,33609410,2/21/21,pubmed,0,4,transcriptom,0.941983929,0.002032783,0.002032915,0.002032856,0.00203279,0.049884727,Drug discovery,0.34734043,FALSE,4.5,0.061784897,0,0.055525823,0,0.403234768,,,0.173515162
1865,Whole-genome Sequencing of SARS-CoV-2: Using Phylogeny and Structural Modeling to Contextualize Local Viral Evolution.,Mil Med,33609027,2/21/21,pubmed,0,3,"bioinformatic, sequencing, whole-genome, structural model",0.000956362,0.925365837,0.000956336,0.070808794,0.000956326,0.000956344,Genomics,0.6120256,TRUE,9,0.135320675,12.66666667,0.39530372,0,0.403234768,,,0.311286388
1866,Sleep quality deterioration in middle-aged and older adults living in a rural Ecuadorian village severely struck by the SARS-CoV-2 pandemic. A population-based longitudinal prospective study.,Sleep,33608714,2/21/21,pubmed,0,5,logistic regression,0.001823564,0.040821966,0.001823365,0.001823518,0.662368343,0.291339244,Healthcare,0.60818,TRUE,123.4,0.912239471,79.8,0.778565694,0,0.403234768,,,0.698013311
1867,"Characteristics and Factors Associated with COVID-19 Infection, Hospitalization, and Mortality Across Race and Ethnicity.",Clin Infect Dis,33608710,2/21/21,pubmed,0,11,logistic regression,0.001565401,0.001565358,0.001565312,0.00156539,0.26908727,0.724651269,Clinics,0.15041834,FALSE,59.90909091,0.702146082,62.27272727,0.72972973,0,0.403234768,,,0.611703526
1868,Positive effects of COVID-19 lockdown on air quality of industrial cities (Ankleshwar and Vapi) of Western India.,Sci Rep,33608603,2/21/21,pubmed,0,5,correlation analysis,0.00141512,0.001415128,0.001415135,0.992924248,0.001415176,0.001415193,Epidemiology,0.9470054,TRUE,32.2,0.45754221,19.2,0.472303987,0,0.403234768,,,0.444360322
1869,Immune transcriptomes of highly exposed SARS-CoV-2 asymptomatic seropositive versus seronegative individuals from the Ischgl community.,Sci Rep,33608566,2/21/21,pubmed,0,16,transcriptom,0.26205269,0.267202088,0.001751189,0.272511745,0.001751262,0.194731026,Epidemiology,0.108329564,FALSE,74.0625,0.782175768,133.8125,0.867407011,0,0.403234768,,,0.684272516
1870,Flexibility and mobility of SARS-CoV-2-related protein structures.,Sci Rep,33608565,2/21/21,pubmed,0,3,in silico,0.726826828,0.00194361,0.001943561,0.265398865,0.001943653,0.001943483,Drug discovery,0.50682795,TRUE,68.66666667,0.757313377,17.33333333,0.45424137,1,0.537564047,,,0.583039598
1871,Examining the utility of extended laboratory panel testing in the emergency department for risk stratification of patients with COVID-19: a single-centre retrospective service evaluation.,J Clin Pathol,33608408,2/21/21,pubmed,0,14,logistic regression,0.033910784,0.001085384,0.038554369,0.210017931,0.001085381,0.715346151,Clinics,0.2974825,FALSE,36.35714286,0.500896778,33.28571429,0.594527696,0,0.403234768,,,0.49955308
1872,People with blood disorders can be more vulnerable during COVID-19 pandemic: A hypothesis paper.,Transfus Apher Sci,33608217,2/21/21,pubmed,0,2,in silico,0.257842638,0.001861722,0.001861764,0.001861803,0.240543175,0.496028898,Clinics,0.9767026,TRUE,44,0.578390748,3,0.199424672,0,0.403234768,,,0.393683396
1873,"Clinical features and prognostic factors of critically ill patients with COVID-19 in Daegu, South Korea: A multi-center retrospective study.",Medicine (Baltimore),33607776,2/21/21,pubmed,0,11,logistic regression,0.001393024,0.001392816,0.001392812,0.001392835,0.001392843,0.99303567,Clinics,0.9923345,TRUE,41.45454545,0.552353269,11.18181818,0.372424405,0,0.403234768,,,0.442670814
1874,Novel signaling pathways regulate SARS-CoV and SARS-CoV-2 infectious disease.,Medicine (Baltimore),33607766,2/21/21,pubmed,0,11,"bioinformatic, genomes, dataset",0.932035447,0.062018779,0.001486467,0.00148644,0.001486417,0.00148645,Drug discovery,0.43480682,FALSE,34.18181818,0.47881749,15.63636364,0.431696548,1,0.537564047,,,0.482692695
1875,Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification.,Phys Med Biol,33607630,2/20/21,pubmed,0,12,"classifier, radiom",0.001653029,0.001653041,0.991734731,0.001653072,0.001653021,0.001653107,Imaging,0.3579653,FALSE,175.7,0.962335333,213,0.923534921,97,0.977467745,,,0.954446
1876,Sociodemographic factors associated with COVID-19 in-hospital mortality in Brazil.,Public Health,33607516,2/20/21,pubmed,0,10,logistic regression,0.000907282,0.000907347,0.000907306,0.000907315,0.246171992,0.750198758,Clinics,0.5583146,TRUE,38.7,0.526068402,26.8,0.544019267,1,0.537564047,,,0.535883905
1877,A survey of E-learning methods in nursing and medical education during COVID-19 pandemic in India.,Nurse Educ Today,33607513,2/20/21,pubmed,0,8,logistic regression,0.000898113,0.029558815,0.064340686,0.110401749,0.749206217,0.045594421,Healthcare,0.9275601,TRUE,12.625,0.190426124,2.5,0.180826866,0,0.403234768,,,0.258162586
1878,COVID-19 (SARS-CoV-2) outbreak monitoring using wastewater-based epidemiology in Qatar.,Sci Total Environ,33607430,2/20/21,pubmed,0,19,mathematical model,0.001461881,0.392460124,0.088231526,0.514922665,0.001461905,0.0014619,Epidemiology,0.37937608,FALSE,26.52631579,0.389387099,35.10526316,0.605632861,1,0.537564047,,,0.510861336
1879,What challenges do UK adults face when adhering to COVID-19-related instructions? Cross-sectional survey in a representative sample.,Prev Med,33607123,2/20/21,pubmed,0,5,logistic regression,0.001392822,0.001392874,0.001392877,0.109406142,0.885022443,0.001392843,Healthcare,0.90588784,TRUE,71,0.769064259,310,0.957385603,1,0.537564047,,,0.754671303
1880,Array-based Dynamic Allele Specific Hybridization (Array-DASH): optimization-free microarray processing for multiple simultaneous genomic assays.,Anal Biochem,33607059,2/20/21,pubmed,0,11,"sequencing, genomes",0.176920953,0.22368364,0.410614821,0.184619434,0.00208057,0.002080582,Genomics,0.5076133,TRUE,64.81818182,0.733687921,96.45454545,0.815962002,0,0.403234768,,,0.650961564
1881,Virtual Screening FDA Approved Drugs against Multiple Targets of SARS-CoV-2.,Clin Transl Sci,33606912,2/20/21,pubmed,0,5,"virtual screening, in-silico",0.993899765,0.00122006,0.001220055,0.001220047,0.001220025,0.001220047,Drug discovery,0.78312814,TRUE,62.8,0.719957944,30.2,0.571113192,0,0.403234768,,,0.564768635
1882,SARS-CoV-2 genetic diversity in Venezuela: Predominance of D614G variants and analysis of one outbreak.,PLoS One,33606828,2/20/21,pubmed,0,11,"sequencing, whole genome, genome sequences, genomes",0.001415152,0.97614862,0.001415119,0.00141515,0.001415102,0.018190857,Genomics,0.6937892,TRUE,30.72727273,0.440719896,29.18181818,0.563620551,0,0.403234768,,,0.469191738
1883,Exploring novel and potent cell penetrating peptides in the proteome of SARS-COV-2 using bioinformatics approaches.,PLoS One,33606823,2/20/21,pubmed,0,2,"bioinformatic, in silico, proteom",0.64918644,0.053730634,0.037778014,0.001085407,0.257134084,0.001085421,Drug discovery,0.8640835,TRUE,101.5,0.872100934,32.5,0.589376505,1,0.537564047,,,0.666347162
1884,Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry.,PLoS One,33606820,2/20/21,pubmed,0,25,logistic regression,0.001786493,0.001786505,0.001786497,0.001786513,0.169970035,0.822883957,Clinics,0.7444943,TRUE,9.76,0.146329396,0.68,0.096267059,0,0.403234768,,,0.215277074
1885,Non-Adherence Tree Analysis (NATA)-An adherence improvement framework: A COVID-19 case study.,PLoS One,33606789,2/20/21,pubmed,0,4,"predictive model, dataset",0.121269386,0.000988393,0.204508066,0.476265022,0.000988422,0.195980711,Epidemiology,0.94828296,TRUE,97.25,0.862452842,54.25,0.698354295,0,0.403234768,,,0.654680635
1886,Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units.,PLoS One,33606773,2/20/21,pubmed,0,5,"probabilistic, dataset",0.001187316,0.001187366,0.05533122,0.270515113,0.02475442,0.647024566,Clinics,0.45784438,FALSE,29,0.41993939,31.2,0.579274819,0,0.403234768,,,0.467482992
1887,"INFEKTA-An agent-based model for transmission of infectious diseases: The COVID-19 case in Bogotá, Colombia.",PLoS One,33606714,2/20/21,pubmed,0,4,computational,0.001254707,0.001254604,0.001254623,0.993726823,0.001254644,0.001254598,Epidemiology,0.14280847,FALSE,23.75,0.350547344,9,0.337904736,1,0.537564047,,,0.408672042
1888,A Bayesian reanalysis of the effects of hydroxychloroquine and azithromycin on viral carriage in patients with COVID-19.,PLoS One,33606702,2/20/21,pubmed,0,9,bayes,0.001538105,0.151261401,0.001538158,0.471196027,0.001538117,0.372928192,Epidemiology,0.16847146,FALSE,194.8888889,0.971179417,262.1111111,0.94246722,0,0.403234768,,,0.772293802
1889,"Knowledge, attitude, practice towards COVID-19 pandemic and its prevalence among hospital visitors at Ataye district hospital, Northeast Ethiopia.",PLoS One,33606678,2/20/21,pubmed,0,6,logistic regression,0.018614142,0.152919802,0.077540285,0.000880242,0.74916527,0.00088026,Healthcare,0.8592876,TRUE,8.166666667,0.119735296,2.166666667,0.166845063,0,0.403234768,,,0.229938375
1890,Clinical Characteristics and Mortality Profile of COVID-19 Patients Aged less than 20 years Old in Pernambuco - Brazil.,Am J Trop Med Hyg,33606669,2/20/21,pubmed,0,12,logistic regression,0.001565277,0.00156535,0.001565323,0.119710424,0.130165518,0.745428108,Clinics,0.93543637,TRUE,19.83333333,0.294266807,3.75,0.21982874,0,0.403234768,,,0.305776771
1891,A Bayesian Mixture Model for Predicting the COVID-19 Pandemic in the United States.,Am J Trop Med Hyg,33606666,2/20/21,pubmed,0,4,bayes,0.001653014,0.001653055,0.001653033,0.99173473,0.001653016,0.001653151,Epidemiology,0.2583713,FALSE,119.5,0.90654957,244.25,0.935911159,0,0.403234768,,,0.748565165
1892,Challenges for non-technical implementation of digital proximity tracing: a media analysis.,JMIR Mhealth Uhealth,33606658,2/20/21,pubmed,0,1,digital health,0.00064814,0.01226344,0.00064814,0.90171923,0.08407293,0.00064812,Epidemiology,0.3020835,FALSE,111,0.892015585,173,0.901659085,0,0.403234768,,,0.732303146
1893,COVID Coach: Exploring Usage of a Public Mental Health App Designed for the COVID-19 Pandemic.,J Med Internet Res,33606656,2/20/21,pubmed,0,8,digital health,0.000683169,0.000683164,0.000683166,0.363205736,0.634061591,0.000683175,Healthcare,0.81061286,TRUE,21.875,0.323025543,37.625,0.619547766,0,0.403234768,,,0.448602692
1894,A study on willingness and influencing factors to receive COVID-19 vaccination among Qingdao residents.,Hum Vaccin Immunother,33606610,2/20/21,pubmed,0,11,logistic regression,0.002130764,0.002130655,0.002130755,0.002130766,0.989346424,0.002130636,Healthcare,0.68104684,TRUE,33.18181818,0.467870617,,,0,0.403234768,,,0.435552692
1895,Post-exposure Liponucleotide Prophylaxis and Treatment Attenuates ARDS in Influenza-infected Mice.,Am J Respir Cell Mol Biol,33606602,2/20/21,pubmed,0,8,lipidom,0.646575835,0.001438164,0.001438148,0.088070874,0.001438144,0.261038835,Drug discovery,0.383994,FALSE,29.5,0.426000371,19.75,0.477990367,0,0.403234768,,,0.435741835
1896,"COVID-19 pandemic: demographic and clinical correlates of disturbed sleep among 6,041 Canadians.",Int J Psychiatry Clin Pract,33606597,2/20/21,pubmed,0,9,logistic regression,0.001010925,0.001010932,0.001010934,0.00101098,0.967662431,0.028293798,Healthcare,0.97387516,TRUE,37.88888889,0.517162471,11.22222222,0.373227188,0,0.403234768,,,0.431208142
1897,Phylodynamic analysis in the understanding of the current COVID-19 pandemic and its utility in vaccine and antiviral design and assessment.,Hum Vaccin Immunother,33606594,2/20/21,pubmed,0,5,computational,0.26124868,0.264530641,0.002562703,0.466532787,0.002562584,0.002562605,Epidemiology,0.49031028,FALSE,154.2,0.947244728,61.8,0.727856569,1,0.537564047,,,0.737555115
1898,Misinformation About COVID-19 in Sub-Saharan Africa: Evidence from a Cross-Sectional Survey.,Health Secur,33606572,2/20/21,pubmed,0,15,logistic regression,0.001511845,0.001511859,0.144262141,0.001511892,0.849690449,0.001511813,Healthcare,0.7259273,TRUE,22.4,0.331065619,29.6,0.566630987,0,0.403234768,,,0.433643791
1899,Perception of health risk and compliance with preventive measures related to COVID-19 in the Czech population: preliminary results of a rapid questionnaire survey.,Int J Occup Med Environ Health,33605937,2/20/21,pubmed,0,4,logistic regression,0.001565306,0.001565347,0.001565322,0.001565409,0.992173266,0.00156535,Healthcare,0.7807125,TRUE,42.25,0.559898571,11.75,0.381857105,0,0.403234768,,,0.448330148
1900,Advances of Inorganic Materials in the Detection and Therapeutic Uses Against Coronaviruses.,Curr Med Chem,33605848,2/20/21,pubmed,0,5,genomes,0.571528003,0.423230493,0.001310462,0.001310382,0.001310325,0.001310335,Drug discovery,0.6638488,TRUE,6,0.086028821,1,0.122023013,0,0.403234768,,,0.2037622
1901,"Effectiveness of facemasks for opening a university campus in Mississippi, United States - a modelling study.",J Am Coll Health,33605837,2/20/21,pubmed,0,4,mathematical model,0.002422255,0.002422264,0.002422503,0.987888265,0.002422431,0.002422282,Epidemiology,0.3076316,FALSE,156.25,0.948729049,127.5,0.86085095,0,0.403234768,,,0.737604922
1902,Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2.,J Proteome Res,33605735,2/20/21,pubmed,0,5,"computational, proteom, interactom",0.680715498,0.311510286,0.001943575,0.001943631,0.001943526,0.001943484,Drug discovery,0.26318413,FALSE,141.6,0.936112314,262.4,0.94293551,0,0.403234768,,,0.760760864
1903,Comparative genomics reveals early emergence and biased spatio-temporal distribution of SARS-CoV-2.,Mol Biol Evol,33605421,2/20/21,pubmed,0,4,"genome sequences, genomes",0.000786389,0.962919703,0.033934809,0.000786387,0.000786364,0.000786347,Genomics,0.3789196,FALSE,68,0.753973653,188,0.910891089,0,0.403234768,,,0.689366503
1904,Current state of trauma and violence in São Paulo - Brazil during the COVID-19 pandemic.,Rev Col Bras Cir,33605394,2/20/21,pubmed,0,6,correlation analysis,0.001684519,0.00168454,0.00168452,0.786732247,0.062751315,0.145462859,Epidemiology,0.40870446,FALSE,2.5,0.027459954,0,0.055525823,0,0.403234768,,,0.162073515
1905,Feasibility of very short-term forecast models for COVID-19 hospital-based surveillance.,Rev Soc Bras Med Trop,33605383,2/20/21,pubmed,0,8,bayes,0.003465892,0.003465943,0.208036035,0.778099754,0.003466,0.003466376,Epidemiology,0.49181268,FALSE,42.375,0.5612592,22.5,0.507492641,0,0.403234768,,,0.490662203
1906,Students Anxiety Experiences during COVID-19 in Nepal.,Kathmandu Univ Med J (KUMJ),33605239,2/20/21,pubmed,0,2,correlation analysis,0.001415101,0.001415119,0.00141514,0.001415168,0.96378491,0.030554562,Healthcare,0.7482142,TRUE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
1907,COVID-19: Nothing is Normal in this Pandemic.,J Epidemiol Glob Health,33605119,2/20/21,pubmed,0,5,mathematical model,0.001751187,0.001751164,0.001751261,0.991244019,0.001751171,0.001751198,Epidemiology,0.03170988,FALSE,14.2,0.214793741,10.4,0.360917849,0,0.403234768,,,0.326315452
1908,The Recombination Potential between SARS-CoV-2 and MERS-CoV from Cross-Species Spill-over Infections.,J Epidemiol Glob Health,33605109,2/20/21,pubmed,0,3,whole genome,0.344603019,0.648905882,0.001622783,0.001622782,0.00162276,0.001622774,Genomics,0.21712703,FALSE,7.333333333,0.105572392,3,0.199424672,0,0.403234768,,,0.236077277
1909,Nursing professionals' mental well-being and workplace impairment during the COVID-19 crisis: A Network analysis.,J Nurs Manag,33604981,2/20/21,pubmed,0,2,network analysis,0.001751161,0.001751176,0.00175115,0.001751239,0.991244081,0.001751193,Healthcare,0.9516811,TRUE,15.5,0.234028078,0.5,0.087101953,0,0.403234768,,,0.241454933
1910,A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID-19.,Dermatol Ther,33604961,2/20/21,pubmed,0,5,"machine learning, neural network, classifier",0.001371258,0.001371261,0.96450325,0.03001153,0.00137135,0.001371352,Imaging,0.9630747,TRUE,42,0.558537943,10.6,0.364262778,0,0.403234768,,,0.442011829
1911,Novel Chest Radiographic Biomarkers for COVID-19 Using Radiomic Features Associated with Diagnostics and Outcomes.,J Digit Imaging,33604807,2/20/21,pubmed,0,6,radiom,0.001187352,0.001187303,0.637435862,0.001187351,0.001187275,0.357814857,Imaging,0.91031015,TRUE,122.6666667,0.911002536,67.66666667,0.74719026,0,0.403234768,,,0.687142521
1912,Multi-omics highlights ABO plasma protein as a causal risk factor for COVID-19.,Hum Genet,33604698,2/20/21,pubmed,0,11,"bayes, genome-wide, multi-omics",0.569761446,0.155010354,0.001653057,0.001653103,0.001653063,0.270268977,Drug discovery,0.3747279,FALSE,218.8181818,0.978229946,287.1818182,0.950695745,1,0.537564047,,,0.822163246
1913,COVID-19 Prevalence and Mortality Among Schizophrenia Patients: A Large-Scale Retrospective Cohort Study.,Schizophr Bull,33604657,2/20/21,pubmed,0,9,logistic regression,0.001786506,0.001786522,0.001786507,0.001786546,0.27927709,0.713576829,Clinics,0.8142989,TRUE,33.44444444,0.470777414,10.55555556,0.363727589,0,0.403234768,,,0.412579923
1914,Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework.,Gigascience,33604654,2/20/21,pubmed,0,5,"bayes, predictive model",0.001823319,0.040634303,0.087034958,0.866860784,0.001823314,0.001823322,Epidemiology,0.17424625,FALSE,141.4,0.935741233,87.6,0.796093123,0,0.403234768,,,0.711689708
1915,Survey data on the impact of COVID-19 on parental engagement across 23 countries.,Data Brief,33604430,2/20/21,pubmed,0,40,dataset,0.001653028,0.001653066,0.11293776,0.511210467,0.370892647,0.001653032,Epidemiology,0.81125426,TRUE,22.825,0.337373987,8,0.320511105,0,0.403234768,,,0.35370662
1916,Measuring e-learning systems success: Data from students of higher education institutions in Morocco.,Data Brief,33604428,2/20/21,pubmed,0,3,dataset,0.002357857,0.00235783,0.002357862,0.294501805,0.696066904,0.002357743,Healthcare,0.446723,FALSE,10,0.15214299,0,0.055525823,0,0.403234768,,,0.203634527
1917,Pretest Symptom Duration and Cycle Threshold Values for Severe Acute Respiratory Syndrome Coronavirus 2 Reverse-Transcription Polymerase Chain Reaction Predict Coronavirus Disease 2019 Mortality.,Open Forum Infect Dis,33604401,2/20/21,pubmed,0,13,logistic regression,0.001098831,0.056030444,0.118967143,0.001098828,0.001098848,0.821705908,Clinics,0.5917976,TRUE,89.53846154,0.841363102,79.46153846,0.777495317,1,0.537564047,,,0.718807489
1918,Self-Rated Smell Ability Enables Highly Specific Predictors of COVID-19 Status: A Case-Control Study in Israel.,Open Forum Infect Dis,33604398,2/20/21,pubmed,0,11,"classifier, logistic regression",0.001392866,0.001392945,0.419276623,0.001392879,0.344936782,0.231607904,Healthcare,0.94467556,TRUE,40.27272727,0.541839322,28.45454545,0.557465882,0,0.403234768,,,0.500846657
1919,Dataset: percent of population covered by local government mask orders in the US.,F1000Res,33604027,2/20/21,pubmed,0,2,dataset,0.002490416,0.002490443,0.002490488,0.98754769,0.002490504,0.002490461,Epidemiology,0.26821026,FALSE,131.5,0.923495578,95,0.813085363,0,0.403234768,,,0.713271903
1920,"The Psychological Impact of COVID-19 Pandemic on Graduating Class Students at the University of Gondar, Northwest Ethiopia.",Psychol Res Behav Manag,33603512,2/20/21,pubmed,0,4,logistic regression,0.000956317,0.00095631,0.000956355,0.000956338,0.995218372,0.000956308,Healthcare,0.9075428,TRUE,4.75,0.064506154,1.25,0.127776291,0,0.403234768,,,0.198505737
1921,"COVID-19-Induced Anxiety and Associated Factors Among Urban Residents in West Shewa Zone, Central Ethiopia, 2020.",Psychol Res Behav Manag,33603511,2/20/21,pubmed,0,6,logistic regression,0.028174318,0.000977421,0.000977422,0.000977451,0.967915948,0.00097744,Healthcare,0.8349103,TRUE,9,0.135320675,0.333333333,0.073187048,0,0.403234768,,,0.203914164
1922,Synchrotron Radiation as a Tool for Macromolecular X-Ray Crystallography: a XXI Century Perspective.,Nucl Instrum Methods Phys Res B,33603257,2/20/21,pubmed,0,8,computational,0.399637123,0.001310381,0.320379647,0.276051971,0.001310404,0.001310473,Drug discovery,0.29653335,FALSE,124.25,0.913229018,294,0.952435108,0,0.403234768,,,0.756299631
1923,CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images.,NPJ Digit Med,33603193,2/20/21,pubmed,0,22,deep learning,0.001156261,0.001156253,0.960839858,0.001156279,0.001156259,0.03453509,Imaging,0.29896635,FALSE,109.4545455,0.889170635,114.4090909,0.844059406,0,0.403234768,,,0.712154936
1924,Early risk assessment for COVID-19 patients from emergency department data using machine learning.,Sci Rep,33603086,2/20/21,pubmed,0,19,"machine learning, logistic regression",0.00090731,0.000907396,0.18138166,0.00090734,0.000907312,0.814988983,Clinics,0.73489,TRUE,39.10526316,0.530706908,47.42105263,0.670123093,0,0.403234768,,,0.534688256
1925,Temporal association between human upper respiratory and gut bacterial microbiomes during the course of COVID-19 in adults.,Commun Biol,33603076,2/20/21,pubmed,0,10,"sequencing, microbiom",0.081290752,0.44252945,0.001901748,0.001901804,0.001901823,0.470474423,Clinics,0.49203402,FALSE,20.9,0.308491558,16.4,0.441196147,1,0.537564047,,,0.429083917
1926,Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.,Sci Rep,33603047,2/20/21,pubmed,0,45,deep learning,0.001622715,0.033707246,0.835236358,0.001622808,0.00162277,0.126188102,Imaging,0.5159839,TRUE,55.17777778,0.668996227,28.06666667,0.554455446,0,0.403234768,,,0.542228813
1927,"Molecular features similarities between SARS-CoV-2, SARS, MERS and key human genes could favour the viral infections and trigger collateral effects.",Sci Rep,33602998,2/20/21,pubmed,0,3,computational,0.464612046,0.492761099,0.000880273,0.000880237,0.000880208,0.039986137,Genomics,0.55570734,TRUE,33.33333333,0.469911559,44.66666667,0.65754616,0,0.403234768,,,0.510230829
1928,Key factors leading to fatal outcomes in COVID-19 patients with cardiac injury.,Sci Rep,33602949,2/20/21,pubmed,0,4,logistic regression,0.001415079,0.001415089,0.00141508,0.001415094,0.001415115,0.992924542,Clinics,0.95779455,TRUE,42.5,0.562743522,18,0.46180091,0,0.403234768,,,0.4759264
1929,Pseudo-safety in a cohort of patients with COVID-19 discharged home from the emergency department.,Emerg Med J,33602725,2/20/21,pubmed,0,8,logistic regression,0.001059356,0.001059387,0.02881102,0.086691492,0.05487318,0.827505565,Clinics,0.6715846,TRUE,27.625,0.402498608,5.75,0.271742039,0,0.403234768,,,0.359158472
1930,SARS-CoV-2 genomic diversity and the implications for qRT-PCR diagnostics and transmission.,Genome Res,33602693,2/20/21,pubmed,0,20,genomes,0.002032823,0.989836055,0.002032777,0.002032803,0.002032753,0.002032789,Genomics,0.3003971,FALSE,73.25,0.778836044,1076.4,0.994313621,0,0.403234768,,,0.725461477
1931,"SARS-CoV-2, the other face to SARS-CoV and MERS-CoV: Future predictions.",Biomed J,33602634,2/20/21,pubmed,0,6,genomes,0.255394764,0.364300906,0.00148649,0.321178138,0.001486483,0.056153218,Genomics,0.3332528,FALSE,12.16666667,0.184117756,1.166666667,0.124565159,1,0.537564047,,,0.282082321
1932,Characterization of the #Radiology Twitter Conversation During the Global COVID-19 Pandemic.,Curr Probl Diagn Radiol,33602536,2/20/21,pubmed,0,3,network analysis,0.001461892,0.00146192,0.001461905,0.830371396,0.163780912,0.001461974,Epidemiology,0.13414228,FALSE,45,0.587049292,18.33333333,0.464343056,0,0.403234768,,,0.484875705
1933,"COVID-19 symptoms: a case-control study, Portugal, March-April 2020.",Epidemiol Infect,33602375,2/20/21,pubmed,0,7,"logistic regression, prediction model",0.002357818,0.002357871,0.002357914,0.312009576,0.232985767,0.447931055,Clinics,0.56659997,TRUE,4.857142857,0.065557548,0,0.055525823,0,0.403234768,,,0.174772713
1934,Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters.,J Intensive Care,33602326,2/20/21,pubmed,0,17,"machine learning, neural network, logistic regression, prediction model, dataset",0.045244676,0.000863044,0.423536537,0.000863071,0.000863056,0.528629617,Clinics,0.41491082,FALSE,22.41176471,0.331127466,,,0,0.403234768,,,0.367181117
1935,SARS-CoV-2 early infection signature identified potential key infection mechanisms and drug targets.,BMC Genomics,33602138,2/20/21,pubmed,0,8,"sequencing, dataset",0.752227283,0.00095637,0.00095638,0.000956357,0.000956331,0.243947278,Drug discovery,0.4047396,FALSE,192.375,0.969818789,47.375,0.670056195,0,0.403234768,,,0.681036584
1936,Correlation between lung infection severity and clinical laboratory indicators in patients with COVID-19: a cross-sectional study based on machine learning.,BMC Infect Dis,33602128,2/20/21,pubmed,0,10,"machine learning, deep learning",0.000988409,0.000988409,0.233752349,0.019384891,0.000988375,0.743897567,Clinics,0.865623,TRUE,7.9,0.114354629,2.2,0.16838373,0,0.403234768,,,0.228657709
1937,"Phyto-compounds from a rather poisonous plant, Strychnos nux-vomica, show high potency against SARS-CoV-2 RNA-dependent RNA polymerase.",Curr Mol Med,33602083,2/20/21,pubmed,0,3,computational,0.992440652,0.001511952,0.00151185,0.001511927,0.00151182,0.001511799,Drug discovery,0.87679803,TRUE,64.33333333,0.730781124,18.66666667,0.466818303,0,0.403234768,,,0.533611398
1938,Analysis of Factors Causing False-Negative Real-Time Polymerase Chain Reaction Results in Oropharyngeal and Nasopharyngeal Swabs of Patients With COVID-19.,Ear Nose Throat J,33601901,2/20/21,pubmed,0,2,logistic regression,0.001034671,0.348244776,0.176582009,0.001034587,0.001034627,0.472069331,Clinics,0.9861635,TRUE,15.5,0.234028078,0.5,0.087101953,0,0.403234768,,,0.241454933
1939,Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19).,Acta Radiol,33601893,2/20/21,pubmed,0,7,"radiom, dataset",0.001392908,0.001392889,0.552267646,0.001392869,0.00139286,0.442160827,Imaging,0.48687747,FALSE,103.7142857,0.87692498,38.71428571,0.625702435,0,0.403234768,,,0.635287394
1940,"Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan.",Med Image Anal,33601166,2/19/21,pubmed,0,20,"supervised learning, dataset",0.000999586,0.000999558,0.744384372,0.251617435,0.000999534,0.000999515,Imaging,0.2875673,FALSE,98.8,0.866596574,63.95,0.734747123,4,0.707574542,,,0.769639413
1941,Coronary artery calcification in COVID-19 patients: an imaging biomarker for adverse clinical outcomes.,Clin Imaging,33601125,2/19/21,pubmed,0,17,logistic regression,0.001943761,0.001943497,0.116653372,0.001943503,0.00194363,0.875572238,Clinics,0.9521303,TRUE,71.35294118,0.770053807,69.35294118,0.751471769,0,0.403234768,,,0.641586781
1942,Clinical features and outcomes of critically ill patients with coronavirus disease 2019 (COVID-19): A multicenter cohort study.,Int J Infect Dis,33601030,2/19/21,pubmed,0,13,logistic regression,0.03205205,0.00118733,0.001187329,0.001187295,0.00118729,0.963198707,Clinics,0.9074459,TRUE,9,0.135320675,1.769230769,0.14858175,0,0.403234768,,,0.229045731
1943,High expression of SARS-CoV-2 entry factors in human conjunctival goblet cells.,Exp Eye Res,33600811,2/19/21,pubmed,0,7,sequencing,0.939427198,0.048587088,0.002996438,0.002996447,0.002996411,0.002996417,Drug discovery,0.6524669,TRUE,38.71428571,0.52650133,16.71428571,0.445143163,0,0.403234768,,,0.458293087
1944,DNA and RNA Oxidative Damage and Mortality of Patients With COVID-19.,Am J Med Sci,33600784,2/19/21,pubmed,0,11,logistic regression,0.001330073,0.229593335,0.001330042,0.001330037,0.001330176,0.765086337,Clinics,0.92511445,TRUE,79.18181818,0.803203661,41.27272727,0.641156007,0,0.403234768,,,0.615864812
1945,Patient factors and temporal trends associated with COVID-19 in-hospital mortality in England: an observational study using administrative data.,Lancet Respir Med,33600777,2/19/21,pubmed,0,5,"logistic regression, dataset",0.00076594,0.00076595,0.000765949,0.048677689,0.126003455,0.823021016,Clinics,0.6360113,TRUE,148.4,0.942729915,145,0.879114263,6,0.764429903,,,0.86209136
1946,Classification aware neural topic model for COVID-19 disinformation categorisation.,PLoS One,33600477,2/19/21,pubmed,0,6,computational,0.00203287,0.00203292,0.319808601,0.672060055,0.00203281,0.002032744,Epidemiology,0.57485056,TRUE,73.16666667,0.778588657,220.3333333,0.926612256,0,0.403234768,,,0.702811894
1947,A Comprehensive Overview of the COVID-19 Literature: Machine Learning-Based Bibliometric Analysis.,J Med Internet Res,33600346,2/19/21,pubmed,0,7,"machine learning, dataset",0.000854738,0.029925223,0.146197264,0.785997488,0.023074423,0.013950864,Epidemiology,0.30357158,FALSE,52,0.647349867,27.71428571,0.551110516,0,0.403234768,,,0.533898384
1948,Digital Health Solutions to Control the COVID-19 Pandemic in Countries With High Disease Prevalence: Literature Review.,J Med Internet Res,33600344,2/19/21,pubmed,0,6,digital health,0.001141329,0.001141334,0.001141424,0.908961421,0.047878196,0.039736295,Epidemiology,0.90475154,TRUE,10.83333333,0.161481848,0.833333333,0.102488627,0,0.403234768,,,0.222401748
1949,Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19.,IEEE J Biomed Health Inform,33600327,2/19/21,pubmed,0,5,dataset,0.001098813,0.001098851,0.901946095,0.001098858,0.093658535,0.001098849,Imaging,0.002805024,FALSE,71,0.769064259,30.6,0.573655339,0,0.403234768,,,0.581984788
1950,JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation.,IEEE Trans Image Process,33600316,2/19/21,pubmed,0,7,dataset,0.001203413,0.00120344,0.99398269,0.001203477,0.001203417,0.001203563,Imaging,0.5840503,TRUE,42.71428571,0.564165997,296.7142857,0.953237891,72,0.970121612,,,0.829175167
1951,Low Vitamin D Status at Admission as a Risk Factor for Poor Survival in Hospitalized Patients With COVID-19: An Italian Retrospective Study.,J Am Coll Nutr,33600292,2/19/21,pubmed,0,11,logistic regression,0.000916701,0.000916677,0.000916685,0.000916676,0.000916684,0.995416577,Clinics,0.8393978,TRUE,105.6363636,0.880326551,48.09090909,0.673869414,1,0.537564047,,,0.697253337
1952,A Discrete Event Simulation-Based Model to Optimally Design and Dimension Mobile COVID-19 Saliva-Based Testing Stations.,Simul Healthc,33600140,2/19/21,pubmed,0,2,simulation model,0.002996594,0.096494258,0.277321639,0.617194339,0.002996639,0.002996531,Epidemiology,0.17917326,FALSE,61.5,0.712350795,25,0.529435376,0,0.403234768,,,0.548340313
1953,Generalized Anxiety Disorder During the COVID-19 Outbreak in Iran: The Role of Social Dysfunction.,J Nerv Ment Dis,33600121,2/19/21,pubmed,0,5,logistic regression,0.002080521,0.002080524,0.002080518,0.002080643,0.900976018,0.090701775,Healthcare,0.9576626,TRUE,20.6,0.305832148,3.8,0.221501204,0,0.403234768,,,0.310189373
1954,Two novel nomograms for predicting the risk of hospitalization or mortality due to COVID-19 by the naïve Bayesian classifier method.,J Med Virol,33599308,2/19/21,pubmed,0,2,"bayes, classifier, logistic regression",0.001438098,0.001438114,0.265051153,0.064646859,0.001438124,0.665987651,Clinics,0.51460564,TRUE,19,0.285793803,3,0.199424672,0,0.403234768,,,0.296151081
1955,In silico prediction of SARS-CoV-2 main protease and polymerase inhibitors: 3D-Pharmacophore modelling.,J Biomol Struct Dyn,33599180,2/19/21,pubmed,0,4,in silico,0.832216756,0.001310374,0.00131037,0.162541797,0.001310389,0.001310314,Drug discovery,0.94948214,TRUE,12,0.183190055,1.5,0.138747659,0,0.403234768,,,0.24172416
1956,Lifestyle changes as a result of COVID-19 containment measures: Bodyweight and glycemic control in patients with diabetes in the Japanese declaration of a state of emergency.,J Diabetes Investig,33599073,2/19/21,pubmed,0,15,logistic regression,0.002032813,0.002032835,0.002032783,0.112934928,0.559093864,0.321872776,Healthcare,0.97084934,TRUE,94.8,0.856268167,76.06666667,0.769199893,0,0.403234768,,,0.676234276
1957,"Clinical and Laboratory Findings in Patients with Potential SARS-CoV-2 Reinfection, May-July 2020.",Clin Infect Dis,33598716,2/19/21,pubmed,0,24,"sequencing, whole genome",0.001330044,0.341500009,0.052633426,0.001330068,0.001330076,0.601876378,Clinics,0.1181733,FALSE,40.29166667,0.542086709,92.91666667,0.808268665,2,0.618927094,,,0.656427489
1958,Psychological Impact of the COVID-19 Pandemic on Frontline Health Care Workers During the Pandemic Surge in New York City.,Chronic Stress (Thousand Oaks),33598592,2/19/21,pubmed,0,14,logistic regression,0.00165303,0.001653034,0.00165301,0.001653127,0.991734653,0.001653146,Healthcare,0.9255947,TRUE,110.8571429,0.891335271,460.5,0.976919989,1,0.537564047,,,0.801939769
1959,"Managing Multimorbidity (Multiple Chronic Diseases) Amid COVID-19 Pandemic: A Community Based Study From Odisha, India.",Front Public Health,33598442,2/19/21,pubmed,0,5,logistic regression,0.001565467,0.001565427,0.08441548,0.232160282,0.645375098,0.034918246,Healthcare,0.9162238,TRUE,51.4,0.642092894,17,0.451097137,0,0.403234768,,,0.498808266
1960,Cytokine Storm as a Cellular Response to dsDNA Breaks: A New Proposal.,Front Immunol,33597956,2/19/21,pubmed,0,2,genomes,0.820585469,0.035990687,0.001538142,0.001538165,0.001538076,0.138809461,Drug discovery,0.10308713,FALSE,71,0.769064259,39,0.62784319,0,0.403234768,,,0.600047405
1961,Current mutatome of SARS-CoV-2 in Turkey reveals mutations of interest.,Turk J Biol,33597826,2/19/21,pubmed,0,5,genomes,0.001786599,0.920856944,0.001786588,0.044152132,0.029631225,0.001786512,Genomics,0.45950285,FALSE,7.8,0.113550622,0.4,0.075796093,0,0.403234768,,,0.197527161
1962,Dysregulated transcriptional responses to SARS-CoV-2 in the periphery.,Nat Commun,33597532,2/19/21,pubmed,0,20,"sequencing, classifier, transcriptom",0.686011055,0.112736645,0.048286284,0.001415142,0.001415123,0.150135751,Drug discovery,0.5169888,TRUE,246.75,0.984167234,163.85,0.89530372,0,0.403234768,,,0.760901907
1963,Bepridil is potent against SARS-CoV-2 in vitro.,Proc Natl Acad Sci U S A,33597253,2/19/21,pubmed,0,11,computational,0.94019949,0.00203291,0.002032747,0.051669175,0.002032885,0.002032792,Drug discovery,0.83580554,TRUE,35.09090909,0.488280042,48.54545455,0.675742574,0,0.403234768,,,0.522419128
1964,Obesity and COVID-19 in Adult Patients With Diabetes.,Diabetes,33597204,2/19/21,pubmed,0,12,logistic regression,0.001653198,0.001653163,0.001653024,0.001653163,0.001653129,0.991734323,Clinics,0.8770794,TRUE,87.41666667,0.834807347,66.33333333,0.743510838,0,0.403234768,,,0.660517651
1965,Association between cardiometabolic disease and severe COVID-19: a nationwide case-control study of patients requiring invasive mechanical ventilation.,BMJ Open,33597145,2/19/21,pubmed,0,5,logistic regression,0.001593489,0.047755407,0.001593485,0.001593576,0.221462955,0.726001088,Clinics,0.7712759,TRUE,108.2,0.886263838,76.4,0.769868879,1,0.537564047,,,0.731232255
1966,"Estimating seroprevalence of SARS-CoV-2 antibodies using three self-reported symptoms: development of a prediction model based on data from Ischgl, Austria.",Epidemiol Infect,33597049,2/19/21,pubmed,0,11,"logistic regression, prediction model",0.002562605,0.138223592,0.002562828,0.002562741,0.522819871,0.331268362,Healthcare,0.588971,TRUE,78.54545455,0.800729792,61.81818182,0.727923468,0,0.403234768,,,0.643962676
1967,Surge effects and survival to hospital discharge in critical care patients with COVID-19 during the early pandemic: a cohort study.,Crit Care,33596975,2/19/21,pubmed,0,9,logistic regression,0.000815322,0.000815339,0.000815327,0.124004256,0.00081537,0.872734386,Clinics,0.94687176,TRUE,20.22222222,0.300389634,26.22222222,0.539068772,0,0.403234768,,,0.414231058
1968,"Exit strategies: optimising feasible surveillance for detection, elimination, and ongoing prevention of COVID-19 community transmission.",BMC Med,33596902,2/19/21,pubmed,0,8,mathematical model,0.000638769,0.000638796,0.194278993,0.689408835,0.08218972,0.032844887,Epidemiology,0.1708354,FALSE,73.375,0.779083431,64.5,0.736887878,0,0.403234768,,,0.639735359
1969,Impact of the Covid-19 pandemic on perinatal mental health (Riseup-PPD-COVID-19): protocol for an international prospective cohort study.,BMC Public Health,33596889,2/19/21,pubmed,0,29,"machine learning, predictive model, dataset",0.001098828,0.001098856,0.044403605,0.313682488,0.638617367,0.001098856,Healthcare,0.9550586,TRUE,32.48275862,0.460696394,20.31034483,0.483609847,0,0.403234768,,,0.449180336
1970,CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia.,BMC Med Imaging,33596844,2/19/21,pubmed,0,10,"radiom, dataset",0.001156246,0.001156296,0.994218724,0.001156226,0.001156227,0.001156282,Imaging,0.61267316,TRUE,19,0.285793803,5.2,0.259700294,0,0.403234768,,,0.316242955
1971,Plasma metabolomic profiling of patients recovered from COVID-19 with pulmonary sequelae 3 months after discharge.,Clin Infect Dis,33596592,2/18/21,pubmed,0,14,metabolom,0.344187503,0.00159356,0.107896319,0.001593542,0.0015935,0.543135576,Clinics,0.9282034,TRUE,90.35714286,0.843589585,44.14285714,0.654870217,0,0.403234768,,,0.63389819
1972,Internet of medical things (IoMT)-integrated biosensors for point-of-care testing of infectious diseases.,Biosens Bioelectron,33596516,2/18/21,pubmed,0,8,bioinformatic,0.081625416,0.001112707,0.50959671,0.405439809,0.001112654,0.001112704,Epidemiology,0.7420194,TRUE,51.375,0.641721813,13.25,0.402997056,1,0.537564047,,,0.527427639
1973,Randomized placebo-controlled pilot clinical trial on the efficacy of ayurvedic treatment regime on COVID-19 positive patients.,Phytomedicine,33596494,2/18/21,pubmed,0,8,immunome,0.034117375,0.154124762,0.000956351,0.126992105,0.000956361,0.682853046,Clinics,0.8054408,TRUE,37.375,0.512029192,10,0.355632861,0,0.403234768,,,0.423632273
1974,Modelling trachoma post-2020: opportunities for mitigating the impact of COVID-19 and accelerating progress towards elimination.,Trans R Soc Trop Med Hyg,33596317,2/18/21,pubmed,0,10,mathematical model,0.174168217,0.001987169,0.001987211,0.817882983,0.001987187,0.001987233,Epidemiology,0.42671937,FALSE,132.7,0.92479436,104.8,0.830813487,0,0.403234768,,,0.719614205
1975,The analysis on the human protein domain targets and host-like interacting motifs for the MERS-CoV and SARS-CoV/CoV-2 infers the molecular mimicry of coronavirus.,PLoS One,33596252,2/18/21,pubmed,0,8,"data mining, proteom",0.99173456,0.001653173,0.001653134,0.001653077,0.001653027,0.001653029,Drug discovery,0.7539609,TRUE,43.75,0.575545798,11.5,0.378378378,0,0.403234768,,,0.452386314
1976,16S rRNA gene sequencing of rectal swab in patients affected by COVID-19.,PLoS One,33596245,2/18/21,pubmed,0,14,sequencing,0.002238661,0.244904061,0.0599242,0.002238555,0.00223849,0.688456033,Clinics,0.5382251,TRUE,143.5714286,0.937844023,96.64285714,0.816363393,0,0.403234768,,,0.719147394
1977,High throughput detection and genetic epidemiology of SARS-CoV-2 using COVIDSeq next-generation sequencing.,PLoS One,33596239,2/18/21,pubmed,0,57,sequencing,0.021058177,0.973923286,0.001254716,0.001254622,0.001254597,0.001254602,Genomics,0.6016442,TRUE,28.85964912,0.416104892,10.77192982,0.366336634,0,0.403234768,,,0.395225431
1978,Mapping major SARS-CoV-2 drug targets and assessment of druggability using computational fragment screening: Identification of an allosteric small-molecule binding site on the Nsp13 helicase.,PLoS One,33596235,2/18/21,pubmed,0,2,computational,0.995598919,0.000880231,0.000880217,0.000880237,0.000880204,0.000880192,Drug discovery,0.7361462,TRUE,21,0.312016822,35.5,0.608241905,0,0.403234768,,,0.441164498
1979,Detection of the SARS-CoV-2 D614G mutation using engineered Cas12a guide RNA.,Biotechnol J,33595922,2/18/21,pubmed,0,13,sequencing,0.002183305,0.853293891,0.137973165,0.002183253,0.002183198,0.002183187,Genomics,0.12026918,FALSE,113.6153846,0.896901478,,,0,0.403234768,,,0.650068123
1980,Genetic variation analyses indicate conserved SARS-CoV-2-host interaction and varied genetic adaptation in immune response factors in modern human evolution.,Dev Growth Differ,33595856,2/18/21,pubmed,0,5,genome-wide,0.457135533,0.48137534,0.001538117,0.001538124,0.056874726,0.001538161,Genomics,0.17478156,FALSE,83.6,0.819469355,105.2,0.83161627,0,0.403234768,,,0.684773464
1981,Mutant strains of SARS-CoV-2 are more prone to infect obese patient: a review.,Wien Klin Wochenschr,33595720,2/18/21,pubmed,0,3,in silico,0.546482498,0.096200474,0.001901697,0.08533878,0.001901905,0.268174646,Drug discovery,0.28328797,FALSE,47.66666667,0.611787989,3.333333333,0.206515922,0,0.403234768,,,0.40717956
1982,Molecular diagnostic assays for COVID-19: an overview.,Crit Rev Clin Lab Sci,33595397,2/18/21,pubmed,0,5,sequencing,0.04225701,0.644259226,0.241399675,0.068581641,0.001751176,0.001751273,Genomics,0.89146876,TRUE,45,0.587049292,70,0.753344929,0,0.403234768,,,0.581209663
1983,Risk factors for mortality in COVID-19 patients in a community teaching hospital.,J Med Virol,33595120,2/18/21,pubmed,0,3,logistic regression,0.001310329,0.001310349,0.001310352,0.001310452,0.001310447,0.99344807,Clinics,0.7174743,TRUE,8,0.118683901,1.666666667,0.145036125,0,0.403234768,,,0.222318265
1984,"Synthesis, structural, spectral, antioxidant, bioactivity and molecular docking investigations of a novel triazole derivative.",J Biomol Struct Dyn,33594957,2/18/21,pubmed,0,6,in silico,0.737574144,0.001486442,0.131274668,0.001486493,0.001486457,0.126691796,Drug discovery,0.93246835,TRUE,40.33333333,0.542890717,4,0.231469093,0,0.403234768,,,0.392531526
1985,Potential Efficacy of Nutrient Supplements for Treatment or Prevention of COVID-19.,J Diet Suppl,33594938,2/18/21,pubmed,0,5,sequencing,0.503044531,0.186325349,0.002080623,0.228131912,0.002080732,0.078336853,Drug discovery,0.8894936,TRUE,13.4,0.202548086,20.4,0.484613326,0,0.403234768,,,0.363465393
1986,Low diaphragm muscle mass predicts adverse outcome in patients hospitalized for Covid-19 pneumonia: an exploratory pilot study.,Minerva Anestesiol,33594871,2/18/21,pubmed,0,13,logistic regression,0.001823412,0.001823355,0.001823351,0.001823431,0.001823354,0.990883098,Clinics,0.71117485,TRUE,149.0769231,0.943286536,58.07692308,0.714543752,0,0.403234768,,,0.687021685
1987,In silico approach: docking study of oxindole derivatives against the main protease of COVID-19 and its comparison with existing therapeutic agents.,J Basic Clin Physiol Pharmacol,33594850,2/18/21,pubmed,0,3,in silico,0.854871751,0.001371283,0.001371335,0.102433511,0.001371327,0.038580793,Drug discovery,0.7260605,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
1988,Demonstrating the sustainability of capacity strengthening amidst COVID-19.,Int Health,33594422,2/18/21,pubmed,0,4,lstm,0.003335872,0.003335602,0.00333562,0.983321942,0.003335595,0.003335368,Epidemiology,0.29891086,FALSE,10,0.15214299,0.25,0.065493712,0,0.403234768,,,0.206957156
1989,Emergence in late 2020 of multiple lineages of SARS-CoV-2 Spike protein variants affecting amino acid position 677.,medRxiv,33594385,2/18/21,pubmed,0,19,genomes,0.186731061,0.775864209,0.000988353,0.000988365,0.000988383,0.034439629,Genomics,0.21269146,FALSE,53.36842105,0.656317645,74.89473684,0.766189457,14,0.866658436,,,0.763055179
1990,Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.,medRxiv,33594382,2/18/21,pubmed,0,12,"deep learning, dataset",0.001415401,0.001415173,0.99292376,0.001415268,0.001415258,0.001415141,Imaging,0.1012567,FALSE,25.91666667,0.379182386,56.41666667,0.708589778,0,0.403234768,,,0.497002311
1991,"A genetically-informed study disentangling the relationships between tobacco smoking, cannabis use, alcohol consumption, substance use disorders and respiratory infections, including COVID-19.",medRxiv,33594380,2/18/21,pubmed,0,3,genome-wide,0.003760581,0.195455484,0.003760719,0.003760895,0.230414993,0.562847328,Clinics,0.45665756,FALSE,52,0.647349867,71.66666667,0.758094728,0,0.403234768,,,0.602893121
1992,"Transformations, Comparisons, and Analysis of Down to Up Protomer States of Variants of the SARS-CoV-2 Prefusion Spike Protein Including the UK Variant B.1.1.7.",bioRxiv,33594365,2/18/21,pubmed,0,4,"computational, sequence alignment",0.48413827,0.512824233,0.000759363,0.000759404,0.000759374,0.000759357,Genomics,0.55461633,TRUE,16.75,0.252458408,11.25,0.374364463,0,0.403234768,,,0.343352546
1993,Bifurcated monocyte states are predictive of mortality in severe COVID-19.,bioRxiv,33594364,2/18/21,pubmed,0,29,machine learning,0.395257924,0.131628468,0.114669072,0.001022655,0.001022638,0.356399243,Drug discovery,0.29281765,FALSE,70.10344828,0.76467314,112.862069,0.842386941,0,0.403234768,,,0.670098283
1994,"Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.",bioRxiv,33594362,2/18/21,pubmed,0,38,"computational, proteom",0.580332351,0.001565352,0.001565397,0.001565315,0.001565331,0.413406253,Drug discovery,0.2558173,FALSE,57.73684211,0.686869936,82.5,0.78445277,0,0.403234768,,,0.624852491
1995,Identification of potential antivirals against SARS-CoV-2 using virtual screening method.,Inform Med Unlocked,33594342,2/18/21,pubmed,0,5,virtual screening,0.962267188,0.0014382,0.001438125,0.031980223,0.001438147,0.001438116,Drug discovery,0.9700655,TRUE,8.8,0.129630775,0.4,0.075796093,0,0.403234768,,,0.202887212
1996,"Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through analysis of Viral Genomics and Structure.",ArXiv,33594340,2/18/21,pubmed,0,32,proteom,0.564091927,0.429646519,0.001565324,0.001565413,0.00156546,0.001565356,Drug discovery,0.4284691,FALSE,32.25,0.458098831,30.78125,0.575260904,3,0.667819001,,,0.567059579
1997,Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods.,Biomed Signal Process Control,33594301,2/18/21,pubmed,0,4,"network model, forecasting model",0.001126801,0.001126813,0.119194496,0.781461532,0.067043402,0.030046956,Epidemiology,0.67128336,TRUE,8,0.118683901,2,0.164302917,1,0.537564047,,,0.273516955
1998,Respiratory viral co-infections among SARS-CoV-2 cases confirmed by virome capture sequencing.,Sci Rep,33594223,2/18/21,pubmed,0,18,"sequencing, whole-genome, virom",0.00213067,0.76699923,0.092373527,0.002130684,0.002130697,0.134235192,Genomics,0.28973845,FALSE,80,0.807532933,104.3333333,0.829676211,0,0.403234768,,,0.680147971
1999,A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.,Sci Rep,33594159,2/18/21,pubmed,0,16,"machine learning, deep learning, classifier, radiom, logistic regression, dataset",0.00156529,0.001565303,0.992173465,0.001565316,0.001565324,0.001565302,Imaging,0.51691437,TRUE,25.6875,0.376522976,12.9375,0.397578271,0,0.403234768,,,0.392445338
2000,A genomic region associated with protection against severe COVID-19 is inherited from Neandertals.,Proc Natl Acad Sci U S A,33593941,2/18/21,pubmed,0,2,genomes,0.111834823,0.612840445,0.002720102,0.002720204,0.002720316,0.267164109,Genomics,0.22884905,FALSE,245.5,0.98367246,1909.5,0.998193738,3,0.667819001,,,0.8832284
2001,A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study.,Life Sci Alliance,33593923,2/18/21,pubmed,0,9,in silico,0.798498706,0.001171661,0.146587802,0.001171592,0.051398599,0.001171641,Drug discovery,0.3230372,FALSE,11.44444444,0.171748407,18.88888889,0.468892159,0,0.403234768,,,0.347958445
2002,Mathematical modelling and projecting the second wave of COVID-19 pandemic in Europe.,J Epidemiol Community Health,33593851,2/18/21,pubmed,0,5,mathematical model,0.01354879,0.013549541,0.013548823,0.932255155,0.013548864,0.013548826,Epidemiology,0.3236717,FALSE,56,0.675304595,26.2,0.539001873,0,0.403234768,,,0.539180412
2003,How can the uptake of preventive behaviour during the COVID-19 outbreak be improved? An online survey of 4827 Chinese residents.,BMJ Open,33593779,2/18/21,pubmed,0,10,logistic regression,0.001565375,0.001565318,0.055740632,0.001565382,0.937998001,0.001565293,Healthcare,0.9268426,TRUE,37.4,0.512585812,,,0,0.403234768,,,0.45791029
2004,The Gut Microbiome: A Missing Link in Understanding the Gastrointestinal manifestations of COVID-19?,Cold Spring Harb Mol Case Stud,33593727,2/18/21,pubmed,0,2,microbiom,0.002032918,0.632690111,0.002033046,0.002032936,0.269907941,0.091303049,Genomics,0.86148,TRUE,87,0.833694106,56.5,0.709124967,0,0.403234768,,,0.648684613
2005,Predictive value of CAR for In-Hospital Mortality in Patients with COVID-19 Pneumonia: A Retrospective Cohort Study.,Arch Med Res,33593616,2/18/21,pubmed,0,11,logistic regression,0.001943665,0.001943768,0.082288702,0.001943549,0.00194366,0.909936655,Clinics,0.89417017,TRUE,49.81818182,0.630527553,5.636363636,0.268464009,0,0.403234768,,,0.434075443
2006,Evidence of protein binding by a nucleopeptide based on a thymine-decorated L-diaminopropanoic acid through CD and in silico studies.,Curr Med Chem,33593247,2/18/21,pubmed,0,4,in silico,0.76737806,0.116707382,0.034898883,0.0793697,0.000822955,0.00082302,Drug discovery,0.85859466,TRUE,88.75,0.838579999,17,0.451097137,0,0.403234768,,,0.564303968
2007,"Infection, inflammation and intervention: mechanistic modelling of epithelial cells in COVID-19.",J R Soc Interface,33593209,2/18/21,pubmed,0,6,mathematical model,0.343359176,0.127649191,0.05101562,0.227073516,0.002032872,0.248869624,Drug discovery,0.119508564,FALSE,236.8333333,0.982126291,262.3333333,0.942801713,1,0.537564047,,,0.820830684
2008,Epidemiological surveillance of SARS-CoV-2 by genome quantification in wastewater applied to a city in the northeast of France: Comparison of ultrafiltration- and protein precipitation-based methods.,Int J Hyg Environ Health,33592569,2/17/21,pubmed,0,9,genomes,0.018364219,0.623593019,0.001272731,0.252375939,0.038776319,0.065617773,Genomics,0.48830056,FALSE,33.33333333,0.469911559,42.44444444,0.646842387,0,0.403234768,,,0.506662905
2009,[Clinical and epidemiological characteristics of SARS-CoV-2 infections in family physicians: A case-control study].,Aten Primaria,33592533,2/17/21,pubmed,0,6,logistic regression,0.001371319,0.001371367,0.059571847,0.001371328,0.511233368,0.42508077,Healthcare,0.9686606,TRUE,24.5,0.361988991,2.833333333,0.189523682,0,0.403234768,,,0.318249147
2010,Associations between feelings/behaviors during COVID-19 pandemic lockdown and depression/anxiety after lockdown in a sample of Chinese children and adolescents.,J Affect Disord,33592433,2/17/21,pubmed,0,7,logistic regression,0.001291186,0.001291227,0.02850639,0.001291297,0.966328357,0.001291543,Healthcare,0.7121659,TRUE,76.57142857,0.792318634,23.28571429,0.514851485,0,0.403234768,,,0.570134962
2011,The proportion and associated factors of anxiety in Chinese adolescents with depression during the COVID-19 outbreak.,J Affect Disord,33592429,2/17/21,pubmed,0,11,logistic regression,0.001653031,0.001653117,0.022118817,0.001653225,0.971268719,0.001653091,Healthcare,0.98577523,TRUE,43.90909091,0.576782732,6.636363636,0.289470163,0,0.403234768,,,0.423162554
2012,Distinguishing SARS-CoV-2 bonafide re-infection from pre-existing minor variant reactivation.,Infect Genet Evol,33592317,2/17/21,pubmed,0,9,sequencing,0.001622759,0.774157748,0.001622781,0.092092927,0.001622717,0.128881068,Genomics,0.08687633,FALSE,37.11111111,0.509122395,45.11111111,0.65895103,0,0.403234768,,,0.523769398
2013,Glossary of phytoconstituents: Can these be repurposed against SARS CoV-2? A quick in silico screening of various phytoconstituents from plant Glycyrrhiza glabra with SARS CoV-2 main protease.,Food Chem Toxicol,33592201,2/17/21,pubmed,0,5,in silico,0.973362042,0.001511843,0.020590598,0.001511819,0.001511819,0.001511879,Drug discovery,0.85133183,TRUE,45.2,0.588719154,7.8,0.313286058,0,0.403234768,,,0.435079993
2014,Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing.,Brief Bioinform,33592108,2/17/21,pubmed,0,5,"in silico, interactom, multiom, predictive model, dataset",0.274987831,0.070683149,0.209064976,0.423792478,0.020198895,0.001272671,Epidemiology,0.41537887,FALSE,23.4,0.346527305,4.8,0.249331014,0,0.403234768,,,0.333031029
2015,Prospective observational study and serosurvey of SARS-CoV-2 infection in asymptomatic healthcare workers at a Canadian tertiary care center.,PLoS One,33592074,2/17/21,pubmed,0,27,sequencing,0.001943526,0.590945149,0.001943519,0.001943541,0.343323186,0.05990108,Genomics,0.18904817,FALSE,80.22222222,0.80778032,181.4444444,0.907144769,2,0.618927094,,,0.777950728
2016,An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case-control study.,PLoS One,33592063,2/17/21,pubmed,0,3,logistic regression,0.00186169,0.051256204,0.361356805,0.00186179,0.182045065,0.401618447,Clinics,0.3478385,FALSE,13,0.197352959,8,0.320511105,2,0.618927094,,,0.378930386
2017,Angiotensin-converting enzyme 2 (ACE2) expression increases with age in patients requiring mechanical ventilation.,PLoS One,33592054,2/17/21,pubmed,0,4,sequencing,0.333204122,0.120635855,0.000977449,0.000977447,0.000977455,0.543227671,Clinics,0.7426094,TRUE,246.5,0.983981693,598,0.983877442,1,0.537564047,,,0.835141061
2018,Role of ivermectin in the prevention of SARS-CoV-2 infection among healthcare workers in India: A matched case-control study.,PLoS One,33592050,2/17/21,pubmed,0,13,logistic regression,0.087457259,0.001593582,0.001593591,0.001593572,0.745047382,0.162714614,Healthcare,0.9769362,TRUE,36.15384615,0.498855835,7.538461538,0.308067969,1,0.537564047,,,0.448162617
2019,The association between body mass index and severity of Coronavirus Disease 2019 (COVID-19): A cohort study.,PLoS One,33592042,2/17/21,pubmed,0,10,logistic regression,0.001371289,0.001371299,0.001371273,0.001371285,0.00137127,0.993143584,Clinics,0.94342625,TRUE,33.3,0.469231245,28.1,0.55472304,0,0.403234768,,,0.475729684
2020,"Mask usage, social distancing, racial, and gender correlates of COVID-19 vaccine intentions among adults in the US.",PLoS One,33592035,2/17/21,pubmed,0,5,logistic regression,0.001461881,0.001461876,0.001461841,0.001461893,0.992690641,0.001461869,Healthcare,0.92800325,TRUE,170.2,0.958315295,146.8,0.88065293,2,0.618927094,,,0.81929844
2021,Trust and transparency in times of crisis: Results from an online survey during the first wave (April 2020) of the COVID-19 epidemic in the UK.,PLoS One,33591985,2/17/21,pubmed,0,8,logistic regression,0.000916704,0.000916699,0.018142353,0.371126246,0.607981314,0.000916685,Healthcare,0.77814007,TRUE,62,0.715814212,55,0.702167514,0,0.403234768,,,0.607072165
2022,"Model Prediction for In-Hospital Mortality in Patients with COVID-19: A Case-Control Study in Isfahan, Iran.",Am J Trop Med Hyg,33591938,2/17/21,pubmed,0,9,"predictive model, prediction model",0.00135032,0.00135034,0.30936516,0.001350424,0.001350386,0.685233371,Clinics,0.54871583,TRUE,30.77777778,0.44121467,11.55555556,0.378645973,0,0.403234768,,,0.40769847
2023,Generation and validation of in-hospital mortality prediction score in COVID-19 patients: Alba-score.,Curr Med Res Opin,33591851,2/17/21,pubmed,0,13,logistic regression,0.0009884,0.000988368,0.000988391,0.15057011,0.000988373,0.845476357,Clinics,0.69564897,TRUE,5.307692308,0.073102851,0.692307692,0.096333958,0,0.403234768,,,0.190890525
2024,How Public Health Agencies Break through COVID-19 Conversations: A Strategic Network Approach to Public Engagement.,Health Commun,33591839,2/17/21,pubmed,0,7,dataset,0.001786613,0.001786615,0.048119979,0.944733502,0.00178669,0.001786601,Epidemiology,0.70216185,TRUE,22.14285714,0.326736347,17.85714286,0.459325662,0,0.403234768,,,0.396432259
2025,Digital Health Technology and Telemedicine-Based Hospital and Home Programs in Pulmonary Medicine During the COVID-19 Pandemic.,Am J Ther,33590991,2/17/21,pubmed,0,3,digital health,0.00098842,0.000988475,0.00098843,0.485540474,0.231457799,0.280036401,Epidemiology,0.96383345,TRUE,40,0.539860226,30.66666667,0.574257426,1,0.537564047,,,0.550560566
2026,SARS-CoV-2 infection remodels the host protein thermal stability landscape.,Mol Syst Biol,33590968,2/17/21,pubmed,0,20,proteom,0.79793935,0.001823482,0.00182337,0.194767076,0.001823391,0.001823332,Drug discovery,0.23999873,FALSE,59.45,0.698868205,147.75,0.88158951,0,0.403234768,,,0.661230827
2027,"Factors driving availability of COVID-19 convalescent plasma: Insights from a demand, production, and supply model.",Transfusion,33590906,2/17/21,pubmed,0,3,simulation model,0.001254659,0.001254605,0.001254691,0.935400962,0.001254623,0.059580459,Epidemiology,0.31090325,FALSE,104.3333333,0.878223762,78,0.773548301,0,0.403234768,,,0.685002277
2028,0,Future Med Chem,33590764,2/17/21,pubmed,0,4,"virtual screening, neural network, predictive model, transfer learning",0.594996849,0.002183275,0.396269964,0.002183454,0.002183233,0.002183225,Drug discovery,0.53073144,TRUE,16.25,0.246026347,1,0.122023013,0,0.403234768,,,0.257094709
2029,From predictions to prescriptions: A data-driven response to COVID-19.,Health Care Manag Sci,33590417,2/17/21,pubmed,0,21,optimization model,0.001220021,0.001220137,0.001220057,0.741047731,0.001220069,0.254071984,Epidemiology,0.27223042,FALSE,218,0.978106253,227.6190476,0.928953706,3,0.667819001,,,0.858292987
2030,Spatio-temporal dataset of COVID-19 outbreak in Mexico.,Data Brief,33589875,2/17/21,pubmed,0,1,dataset,0.003101501,0.003101554,0.003101568,0.670023473,0.003101751,0.317570153,Epidemiology,0.14272198,FALSE,113,0.89609747,50,0.681763447,0,0.403234768,,,0.660365228
2031,The source and transport of bioaerosols in the air: A review.,Front Environ Sci Eng,33589868,2/17/21,pubmed,0,8,mathematical model,0.085406289,0.197638235,0.001565376,0.712259425,0.00156536,0.001565314,Epidemiology,0.7969331,TRUE,66.875,0.74624281,33.125,0.593457319,0,0.403234768,,,0.580978299
2032,On COVID-19-safety ranking of seats in intercontinental commercial aircrafts: A preliminary multiphysics computational perspective.,Build Simul,33589867,2/17/21,pubmed,0,3,computational,0.001901785,0.001901819,0.001901996,0.927857015,0.001901862,0.064535523,Epidemiology,0.052389026,FALSE,10.66666667,0.159131672,0.666666667,0.096200161,0,0.403234768,,,0.2195222
2033,Coronavirus (COVID-19) detection from chest radiology images using convolutional neural networks.,Biomed Signal Process Control,33589862,2/17/21,pubmed,0,9,"neural network, dataset",0.001593467,0.001593509,0.959204452,0.001593574,0.034421338,0.00159366,Imaging,0.67655057,TRUE,9.111111111,0.135939143,1.666666667,0.145036125,0,0.403234768,,,0.228070012
2034,A hybrid stochastic fractional order Coronavirus (2019-nCov) mathematical model.,Chaos Solitons Fractals,33589855,2/17/21,pubmed,0,3,mathematical model,0.003466131,0.05080714,0.003466237,0.935328678,0.003465912,0.003465902,Epidemiology,0.7287989,TRUE,577.6666667,0.998391985,161,0.892895371,0,0.403234768,,,0.764840708
2035,Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images.,Chaos Solitons Fractals,33589854,2/17/21,pubmed,0,5,"deep learning, neural network, transfer learning, dataset",0.000752911,0.056973366,0.907010083,0.000752913,0.033757787,0.000752939,Imaging,0.6569911,TRUE,17.2,0.259323397,0.8,0.101351351,0,0.403234768,,,0.254636505
2036,0,Inorganica Chim Acta,33589845,2/17/21,pubmed,0,5,"virtual screening, in silico",0.921045499,0.001350416,0.073553022,0.001350403,0.001350324,0.001350337,Drug discovery,0.59599686,TRUE,117,0.902900612,24.8,0.526826331,0,0.403234768,,,0.610987237
2037,Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing.,Nat Commun,33589624,2/17/21,pubmed,0,7,"transcriptom, proteom, network model",0.99069089,0.001861752,0.001861864,0.001861825,0.001861826,0.001861843,Drug discovery,0.4983234,FALSE,25.28571429,0.371018616,16.57142857,0.443336901,2,0.618927094,,,0.47776087
2038,Assessing the regional impact of Japan's COVID-19 state of emergency declaration: a population-level observational study using social networking services.,BMJ Open,33589454,2/17/21,pubmed,0,16,bayes,0.001371233,0.001371274,0.001371243,0.467236454,0.52727849,0.001371306,Healthcare,0.8168905,TRUE,72.375,0.774816006,49.8125,0.680626171,0,0.403234768,,,0.619558981
2039,Recurrent COVID-19 including evidence of reinfection and enhanced severity in thirty Brazilian healthcare workers.,J Infect,33589297,2/17/21,pubmed,0,27,sequencing,0.001392882,0.329410552,0.001392867,0.190150255,0.001392924,0.47626052,Clinics,0.3567164,FALSE,27.25925926,0.398231183,20.66666667,0.487958255,4,0.707574542,,,0.53125466
2040,Bioinformatics Analysis for Screening of Therapeutic Drugs in COVID-19.,Arch Med Res,33589286,2/17/21,pubmed,0,2,bioinformatic,0.902352215,0.019529866,0.01952984,0.019529285,0.019529473,0.019529321,Drug discovery,0.8087895,TRUE,2018.5,1,158,0.890487022,0,0.403234768,,,0.76457393
2041,Informatics for COVID-19 in New York and California.,Disaster Med Public Health Prep,33588982,2/17/21,pubmed,0,1,network analysis,0.002490508,0.002490541,0.06628846,0.923749223,0.002490616,0.002490652,Epidemiology,0.7344216,TRUE,17,0.257467994,0,0.055525823,0,0.403234768,,,0.238742862
2042,Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain.,Crit Care,33588914,2/17/21,pubmed,0,271,machine learning,0.00089813,0.03754653,0.156059951,0.000898109,0.000898111,0.80369917,Clinics,0.63831353,TRUE,19.74906367,0.292844332,,,0,0.403234768,,,0.34803955
2043,Clinical characteristics of COVID-19 complicated with pleural effusion.,BMC Infect Dis,33588779,2/17/21,pubmed,0,10,predictive model,0.001415211,0.001415131,0.115594104,0.001415171,0.023383124,0.85677726,Clinics,0.797287,TRUE,23,0.34225988,15.6,0.431428954,0,0.403234768,,,0.392307867
2044,COVID-19: Challenges and its Technological Solutions using IoT.,Curr Med Imaging,33588738,2/17/21,pubmed,0,5,machine learning,0.00175126,0.001751198,0.278948953,0.669813384,0.001751226,0.045983979,Epidemiology,0.51842904,TRUE,5,0.070752675,0.4,0.075796093,0,0.403234768,,,0.183261179
2045,A digital approach in the rapid response to COVID-19 - Experience of a paediatric institution.,Int J Med Inform,33588302,2/16/21,pubmed,0,3,digital health,0.00159357,0.001593524,0.001593597,0.610670313,0.207200161,0.177348834,Epidemiology,0.8095885,TRUE,15,0.227596017,5.666666667,0.270203372,0,0.403234768,,,0.300344719
2046,A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning.,Med Image Anal,33588121,2/16/21,pubmed,0,12,supervised learning,0.039286787,0.002032773,0.952581881,0.002032815,0.002032928,0.002032815,Imaging,0.50965536,TRUE,148,0.942606222,174.8333333,0.902930158,1,0.537564047,,,0.794366809
2047,Transmission dynamics of SARS-CoV-2 on the Diamond Princess uncovered using viral genome sequence analysis.,Gene,33588037,2/16/21,pubmed,0,7,"bayes, whole-genome, genome sequences",0.002806378,0.452527201,0.002806355,0.536247239,0.002806402,0.002806424,Epidemiology,0.4805281,FALSE,259,0.985713402,514,0.97972973,0,0.403234768,,,0.7895593
2048,Variant analysis of SARS-CoV-2 genomes in the Middle East.,Microb Pathog,33588026,2/16/21,pubmed,0,2,"bioinformatic, genomes, sequence alignment",0.000966776,0.995166016,0.000966766,0.000966853,0.000966776,0.000966812,Genomics,0.10776329,FALSE,20.5,0.304966294,12.5,0.392761573,0,0.403234768,,,0.366987545
2049,Positive no-touch surfaces and undetectable SARS-CoV-2 aerosols in long-term care facilities: An attempt to understand the contributing factors and the importance of timing in air sampling campaigns.,Am J Infect Control,33587983,2/16/21,pubmed,0,11,genomes,0.001392882,0.625714557,0.00139285,0.288020681,0.08208617,0.001392859,Genomics,0.7964692,TRUE,72.54545455,0.775558167,66.72727273,0.744915708,0,0.403234768,,,0.641236214
2050,0,J Theor Biol,33587929,2/16/21,pubmed,0,4,mathematical model,0.001438132,0.001438113,0.001438084,0.992809413,0.001438129,0.001438129,Epidemiology,0.1216239,FALSE,48,0.614942173,32.25,0.587570244,1,0.537564047,,,0.580025488
2051,The impact of the COVID-19 pandemic on economic security and pregnancy intentions among people at risk of pregnancy.,Contraception,33587906,2/16/21,pubmed,0,4,logistic regression,0.001593499,0.001593496,0.001593614,0.001593557,0.992032304,0.00159353,Healthcare,0.8724118,TRUE,48.25,0.616364648,39.75,0.631924003,0,0.403234768,,,0.550507806
2052,Genomic-informed pathogen surveillance in Africa: opportunities and challenges.,Lancet Infect Dis,33587898,2/16/21,pubmed,0,5,sequencing,0.001717243,0.26734864,0.088639861,0.638859763,0.001717305,0.001717187,Epidemiology,0.587513,TRUE,74,0.782113922,89.8,0.801110516,0,0.403234768,,,0.662153069
2053,DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images.,Interdiscip Sci,33587262,2/16/21,pubmed,0,4,"deep learning, deep-learning",0.019741952,0.001330137,0.974937617,0.001330103,0.001330017,0.001330175,Imaging,0.75200236,TRUE,17.25,0.260189251,25.5,0.533382392,0,0.403234768,,,0.39893547
2054,"The COVID-19 epidemic in Madagascar: clinical description and laboratory results of the first wave, march-september 2020.",Influenza Other Respir Viruses,33586912,2/16/21,pubmed,0,30,genomes,0.001593482,0.536620601,0.00159351,0.224718443,0.001593584,0.23388038,Genomics,0.7817615,TRUE,32.66666667,0.462922877,75.36666667,0.767393631,0,0.403234768,,,0.544517092
2055,The mediating role of regulatory emotional self-efficacy on negative emotions during the COVID-19 pandemic: A cross-sectional study.,Int J Ment Health Nurs,33586868,2/16/21,pubmed,0,3,correlation analysis,0.00107226,0.001072219,0.001072196,0.001072254,0.907757649,0.087953422,Healthcare,0.99551034,TRUE,13.33333333,0.201558538,6,0.280037463,0,0.403234768,,,0.29494359
2056,"Whole proteome screening and identification of potential epitopes of SARS-CoV-2 for vaccine design-an immunoinformatic, molecular docking and molecular dynamics simulation accelerated robust strategy.",J Biomol Struct Dyn,33586620,2/16/21,pubmed,0,10,"molecular dynamics simulation, proteom",0.970199023,0.024140469,0.001415129,0.001415147,0.001415089,0.001415142,Drug discovery,0.5397014,TRUE,24,0.35574247,3.3,0.204107573,0,0.403234768,,,0.32102827
2057,Concomitant use of dexamethasone and tetracyclines: a potential therapeutic option for the management of severe COVID-19 infection?,Expert Rev Clin Pharmacol,33586566,2/16/21,pubmed,0,6,in silico,0.243006176,0.002032861,0.002032905,0.51195098,0.002032826,0.238944252,Epidemiology,0.926137,TRUE,34.16666667,0.47863195,19.16666667,0.47217019,0,0.403234768,,,0.451345636
2058,"Robotics, microfluidics, nanotechnology and AI in the synthesis and evaluation of liposomes and polymeric drug delivery systems.",Drug Deliv Transl Res,33585972,2/16/21,pubmed,0,5,artificial intelligence,0.281375875,0.002562718,0.620465211,0.090470756,0.002562627,0.002562812,Drug discovery,0.6694441,TRUE,18.8,0.281278991,17.6,0.456984212,0,0.403234768,,,0.380499323
2059,Covid-19 presentation among symptomatic healthcare workers in Ireland.,Occup Med (Lond),33585881,2/16/21,pubmed,0,4,logistic regression,0.001751201,0.094922336,0.093001203,0.00175124,0.572966803,0.235607218,Healthcare,0.9439492,TRUE,17.5,0.263776362,12.5,0.392761573,0,0.403234768,,,0.353257568
2060,Mucosal immune responses in COVID19 - a living review.,Oxf Open Immunol,33585820,2/16/21,pubmed,0,3,microbiom,0.735702585,0.192796811,0.001823389,0.001823439,0.066030374,0.001823402,Drug discovery,0.865238,TRUE,16.66666667,0.251159626,33.33333333,0.594995986,0,0.403234768,,,0.41646346
2061,Transcriptomic profiling of SARS-CoV-2 infected human cell lines identifies HSP90 as target for COVID-19 therapy.,iScience,33585804,2/16/21,pubmed,0,28,"sequencing, transcriptom",0.927695718,0.066151736,0.001538096,0.001538221,0.001538101,0.001538128,Drug discovery,0.4328697,FALSE,63.35714286,0.723606902,309.1428571,0.957118009,1,0.537564047,,,0.739429653
2062,Specific and Rapid SARS-CoV-2 Identification Based on LC-MS/MS Analysis.,ACS Omega,33585737,2/16/21,pubmed,0,9,in silico,0.180356901,0.555717041,0.260669947,0.001085396,0.001085337,0.001085378,Genomics,0.37997803,FALSE,29.44444444,0.423959429,37.33333333,0.618209794,0,0.403234768,,,0.48180133
2063,""Factors influencing the outcome of COVID-19 patients admitted in a tertiary care hospital, Madurai.- a cross-sectional study".",Clin Epidemiol Glob Health,33585725,2/16/21,pubmed,0,6,logistic regression,0.001310374,0.001310378,0.001310412,0.056530072,0.00131043,0.938228334,Clinics,0.6908101,TRUE,4.166666667,0.055662069,0.5,0.087101953,0,0.403234768,,,0.181999597
2064,Increased incidence of chalazion associated with face mask wear during the COVID-19 pandemic.,Am J Ophthalmol Case Rep,33585724,2/16/21,pubmed,0,3,microbiom,0.001622736,0.033413665,0.001622799,0.528968749,0.362176513,0.072195538,Epidemiology,0.6635378,TRUE,40.66666667,0.546292288,7,0.299973241,0,0.403234768,,,0.416500099
2065,Effectiveness of glucocorticoids in patients hospitalized for severe SARS-CoV-2 pneumonia.,Med Clin (Engl Ed),33585689,2/16/21,pubmed,0,20,logistic regression,0.001786539,0.001786704,0.0017866,0.131211765,0.00178657,0.861641822,Clinics,0.8499881,TRUE,5.9,0.081823242,0.25,0.065493712,0,0.403234768,,,0.18351724
2066,SARS-CoV-2 Proteins Exploit Host's Genetic and Epigenetic Mediators for the Annexation of Key Host Signaling Pathways.,Front Mol Biosci,33585554,2/16/21,pubmed,0,2,"computational, interactom",0.939502333,0.000966773,0.00096675,0.000966769,0.000966746,0.056630628,Drug discovery,0.7310041,TRUE,21,0.312016822,12,0.386740701,0,0.403234768,,,0.367330764
2067,Would the Use of Artificial Intelligence in COVID-19 Patient Management Add Value to the Healthcare System?,Front Med (Lausanne),33585525,2/16/21,pubmed,0,2,artificial intelligence,0.013548804,0.013548952,0.932251541,0.013549533,0.013549992,0.013551179,Clinics,0.6398293,TRUE,9,0.135320675,1,0.122023013,0,0.403234768,,,0.220192819
2068,Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network.,Front Med (Lausanne),33585511,2/16/21,pubmed,0,6,"deep learning, neural network, transfer learning, dataset",0.001010956,0.001011004,0.99494518,0.001010968,0.001010951,0.001010941,Imaging,0.61165035,TRUE,20.16666667,0.299585627,1.166666667,0.124565159,0,0.403234768,,,0.275795185
2069,Structural Comparison of the SARS CoV 2 Spike Protein Relative to Other Human-Infecting Coronaviruses.,Front Med (Lausanne),33585502,2/16/21,pubmed,0,2,computational,0.268150086,0.700484624,0.028526995,0.000946108,0.000946099,0.000946088,Genomics,0.5071872,TRUE,73.5,0.780011132,24,0.521808938,1,0.537564047,,,0.613128039
2070,Microsecond MD Simulation and Multiple-Conformation Virtual Screening to Identify Potential Anti-COVID-19 Inhibitors Against SARS-CoV-2 Main Protease.,Front Chem,33585398,2/16/21,pubmed,0,7,virtual screening,0.99363617,0.001272682,0.001272879,0.001272719,0.001272817,0.001272733,Drug discovery,0.91392434,TRUE,82.57142857,0.816500711,33.71428571,0.597002944,2,0.618927094,,,0.677476916
2071,COVID-19 Dynamics: A Heterogeneous Model.,Front Public Health,33585377,2/16/21,pubmed,0,4,mathematical model,0.00218324,0.002183272,0.002183227,0.989083693,0.002183287,0.00218328,Epidemiology,0.29469916,FALSE,43.25,0.569855897,1.5,0.138747659,0,0.403234768,,,0.370612774
2072,"Unraveling the Interconnection Patterns Across Lung Microbiome, Respiratory Diseases, and COVID-19.",Front Cell Infect Microbiol,33585285,2/16/21,pubmed,0,7,"sequencing, microbiom",0.366815827,0.398370835,0.225823603,0.002996645,0.002996545,0.002996545,Genomics,0.6601807,TRUE,52.42857143,0.65013297,18.71428571,0.467152796,1,0.537564047,,,0.551616605
2073,Impact of COVID-19 pandemic on oral health procedures provided by the Brazilian public health system: COVID-19 and oral health in Brazil.,Health Policy Technol,33585171,2/16/21,pubmed,0,5,dataset,0.002032779,0.002032808,0.002032827,0.376464882,0.417418115,0.200018589,Healthcare,0.9842693,TRUE,11.6,0.175211825,2.6,0.18256623,0,0.403234768,,,0.253670941
2074,A novel social distancing analysis in urban public space: A new online spatio-temporal trajectory approach.,Sustain Cities Soc,33585169,2/16/21,pubmed,0,6,dataset,0.001272642,0.001272675,0.183120314,0.811788972,0.001272726,0.001272673,Epidemiology,0.08507064,FALSE,46.83333333,0.604057146,11,0.371287129,0,0.403234768,,,0.459526348
2075,Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging.,Health Technol (Berl),33585153,2/16/21,pubmed,0,5,artificial intelligence,0.001823386,0.001823464,0.819186327,0.173520102,0.001823359,0.001823362,Imaging,0.5334565,TRUE,9.4,0.140021028,0,0.055525823,2,0.618927094,,,0.271491315
2076,Evolutionary analysis and lineage designation of SARS-CoV-2 genomes.,Sci Bull (Beijing),33585048,2/16/21,pubmed,0,23,"genome sequences, genomes",0.001622757,0.888047371,0.001622856,0.105461541,0.001622725,0.00162275,Genomics,0.8369817,TRUE,48.95652174,0.621436081,60.13043478,0.721166711,0,0.403234768,,,0.581945853
2077,"Early estimation of the epidemiological parameters of novel coronavirus disease (COVID-2019) outbreak in Iran: 19 Feb-15 March, 2020.",Gastroenterol Hepatol Bed Bench,33585015,2/16/21,pubmed,0,8,mathematical model,0.001717168,0.00171722,0.001717544,0.708692799,0.284437916,0.001717353,Epidemiology,0.40472853,FALSE,60.75,0.706784588,31.125,0.578538935,0,0.403234768,,,0.562852763
2078,3044 Cases reveal important prognosis signatures of COVID-19 patients.,Comput Struct Biotechnol J,33584997,2/16/21,pubmed,0,24,transcriptom,0.080442563,0.001310379,0.109405757,0.001310395,0.001310386,0.806220519,Clinics,0.61307,TRUE,31.125,0.445482095,21.45833333,0.496454375,0,0.403234768,,,0.448390413
2079,Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography.,Front Plant Sci,33584752,2/16/21,pubmed,0,6,metabolom,0.185918539,0.001350446,0.3007759,0.390619816,0.119984954,0.001350345,Epidemiology,0.9734286,TRUE,67.83333333,0.752674872,35.5,0.608241905,0,0.403234768,,,0.588050515
2080,"Disease Severity, Fever, Age, and Sex Correlate With SARS-CoV-2 Neutralizing Antibody Responses.",Front Immunol,33584731,2/16/21,pubmed,0,18,logistic regression,0.001593542,0.309403782,0.001593556,0.001593622,0.001593604,0.684221894,Clinics,0.6886196,TRUE,163.9444444,0.953986023,254.1111111,0.939657479,0,0.403234768,,,0.76562609
2081,Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19.,Front Immunol,33584672,2/16/21,pubmed,0,6,dataset,0.518684627,0.001987223,0.33734189,0.001987215,0.001987184,0.138011861,Drug discovery,0.4294176,FALSE,22.5,0.333539489,10.16666667,0.357171528,0,0.403234768,,,0.364648595
2082,Mental Distress and Its Contributing Factors Among Young People During the First Wave of COVID-19: A Belgian Survey Study.,Front Psychiatry,33584379,2/16/21,pubmed,0,5,logistic regression,0.001046865,0.001046831,0.001046826,0.001046866,0.994765762,0.001046849,Healthcare,0.76754415,TRUE,103.6,0.876677593,74.6,0.765453572,0,0.403234768,,,0.681788644
2083,"Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA).",Appl Soft Comput,33584158,2/16/21,pubmed,0,6,"bayes, machine learning",0.000863048,0.000863044,0.000863061,0.995684761,0.000863039,0.000863047,Epidemiology,0.26180154,FALSE,30.66666667,0.440225122,11.16666667,0.372357506,0,0.403234768,,,0.405272465
2084,DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images.,Appl Soft Comput,33584157,2/16/21,pubmed,0,1,"deep learning, transfer learning, lstm, dataset",0.001112609,0.00111268,0.936746588,0.029044086,0.001112619,0.030871417,Imaging,0.40767264,FALSE,15,0.227596017,1,0.122023013,1,0.537564047,,,0.295727692
2085,"The CLAIRE COVID-19 initiative: approach, experiences and recommendations.",Ethics Inf Technol,33584129,2/16/21,pubmed,0,14,"artificial intelligence, dataset",0.093314756,0.002183255,0.516057339,0.384078048,0.002183413,0.00218319,Imaging,0.94014275,TRUE,119.2142857,0.905807409,251.7857143,0.938587102,0,0.403234768,,,0.74920976
2086,Transfer learning for establishment of recognition of COVID-19 on CT imaging using small-sized training datasets.,Knowl Based Syst,33584016,2/16/21,pubmed,0,4,"deep learning, neural network, transfer learning, dataset",0.000838561,0.000838535,0.964132316,0.032513489,0.00083853,0.000838569,Imaging,0.1861625,FALSE,74,0.782113922,27.75,0.551645705,0,0.403234768,,,0.578998131
2087,"Computational search for drug repurposing to identify potential inhibitors against SARS-COV-2 using Molecular Docking, QTAIM and IQA methods in viral Spike protein - Human ACE2 interface.",J Mol Struct,33583954,2/16/21,pubmed,0,2,computational,0.931828368,0.002357837,0.002357728,0.00235778,0.002357734,0.058740553,Drug discovery,0.56663597,TRUE,7,0.10179974,0,0.055525823,0,0.403234768,,,0.186853444
2088,"Ventilator-associated bacterial pneumonia in coronavirus 2019 disease, a retrospective monocentric cohort study.",J Infect Chemother,33583739,2/16/21,pubmed,0,5,logistic regression,0.001371301,0.001371296,0.001371348,0.161658491,0.001371351,0.832856213,Clinics,0.75113904,TRUE,131.8,0.923742965,91,0.803987155,0,0.403234768,,,0.710321629
2089,Development of severe psychological distress among low-income individuals during the COVID-19 pandemic: longitudinal study.,BJPsych Open,33583484,2/16/21,pubmed,0,8,logistic regression,0.001684464,0.001684479,0.001684471,0.001684514,0.99157754,0.001684532,Healthcare,0.4282868,FALSE,111.125,0.892077432,62.25,0.729595933,0,0.403234768,,,0.674969377
2090,Family medicine practitioners' stress during the COVID-19 pandemic: a cross-sectional survey.,BMC Fam Pract,33583410,2/16/21,pubmed,0,9,logistic regression,0.00115626,0.06706341,0.001156286,0.091081537,0.838386233,0.001156275,Healthcare,0.98531973,TRUE,1.888888889,0.017255242,0,0.055525823,0,0.403234768,,,0.158671944
2091,Ensemble bootstrap methodology for forecasting dynamic growth processes using differential equations: application to epidemic outbreaks.,BMC Med Res Methodol,33583405,2/16/21,pubmed,0,2,"probabilistic, dataset",0.001112629,0.040839201,0.352344265,0.603478659,0.001112622,0.001112623,Epidemiology,0.23028776,FALSE,185.5,0.966664605,286,0.950428151,0,0.403234768,,,0.773442508
2092,0,J Biomol Struct Dyn,33583328,2/16/21,pubmed,0,5,"virtual screening, molecular dynamics simulation, computational, in silico",0.976988644,0.018131186,0.001220026,0.00122007,0.001220034,0.00122004,Drug discovery,0.90387106,TRUE,64.6,0.73238914,23,0.513513514,0,0.403234768,,,0.549712474
2093,Clinical outcomes of initially asymptomatic patients with COVID-19: a Korean nationwide cohort study.,Ann Med,33583290,2/16/21,pubmed,0,7,prediction model,0.043264227,0.001187273,0.001187249,0.001187295,0.001187321,0.951986635,Clinics,0.78768575,TRUE,52.71428571,0.651679139,11.42857143,0.37643832,0,0.403234768,,,0.477117409
2094,Acute-onset delirium in intensive care COVID patients: association of imperfect brain repair with foodborne micro-pollutants.,Eur J Neurol,33583103,2/15/21,pubmed,0,12,metabolom,0.216745368,0.001653057,0.082117313,0.001653109,0.1354975,0.562333654,Clinics,0.2624823,FALSE,43.08333333,0.567691261,39,0.62784319,0,0.403234768,,,0.532923073
2095,Network Meta-Analysis on the Mechanisms Underlying Alcohol Augmentation of COVID-19 Pathologies.,Alcohol Clin Exp Res,33583045,2/15/21,pubmed,0,6,"sequencing, dataset",0.705345445,0.001141419,0.001141376,0.001141357,0.001141393,0.29008901,Drug discovery,0.81428695,TRUE,105.3333333,0.879708083,294.6666667,0.952568906,0,0.403234768,,,0.745170585
2096,Higher vs Lower Doses of Dexamethasone in Patients with COVID-19 and Severe Hypoxia (COVID STEROID 2) trial: Protocol for a secondary Bayesian analysis.,Acta Anaesthesiol Scand,33583027,2/15/21,pubmed,0,45,"bayes, logistic regression",0.001461876,0.001461883,0.001461906,0.303383536,0.001461935,0.690768865,Clinics,0.17410743,FALSE,86.04444444,0.829179294,61.2,0.725180626,0,0.403234768,,,0.652531562
2097,"Molecular docking, binding mode analysis, molecular dynamics, and prediction of ADMET/toxicity properties of selective potential antiviral agents against SARS-CoV-2 main protease: an effort toward drug repurposing to combat COVID-19.",Mol Divers,33582935,2/15/21,pubmed,0,9,in silico,0.954169592,0.000977446,0.000977462,0.041920648,0.00097743,0.000977422,Drug discovery,0.9812145,TRUE,38.77777778,0.526934257,15.55555556,0.430759968,0,0.403234768,,,0.453642997
2098,Grocery food taxes and U.S. county obesity and diabetes rates.,Health Econ Rev,33582928,2/15/21,pubmed,0,5,dataset,0.001415157,0.001415171,0.001415111,0.616494067,0.022743857,0.356516638,Epidemiology,0.079766214,FALSE,75.6,0.788917064,45.4,0.659954509,0,0.403234768,,,0.61736878
2099,"Extracorporeal Membrane Oxygenation Therapy for Critically Ill Coronavirus Disease 2019 Patients in Wuhan, China: A Retrospective Multicenter Cohort Study.",Curr Med Sci,33582899,2/15/21,pubmed,0,21,logistic regression,0.001371282,0.001371317,0.00137132,0.001371282,0.001371246,0.993143554,Clinics,0.8507221,TRUE,99.66666667,0.868142742,91.76190476,0.805392026,0,0.403234768,,,0.692256512
2100,Metformin use and risk of COVID-19 among patients with type II diabetes mellitus: an NHIS-COVID-19 database cohort study.,Acta Diabetol,33582839,2/15/21,pubmed,0,2,logistic regression,0.001415099,0.001415125,0.001415107,0.066134664,0.001415181,0.928204825,Clinics,0.8901706,TRUE,124,0.912981632,13,0.400521809,0,0.403234768,,,0.572246069
2101,"Loneliness in the COVID-19 pandemic: Associations with age, gender and their interaction.",J Psychiatr Res,33582608,2/15/21,pubmed,0,7,logistic regression,0.001220107,0.00122001,0.001220003,0.049892244,0.94522758,0.001220057,Healthcare,0.87173593,TRUE,65.71428571,0.738635661,26.28571429,0.540272946,0,0.403234768,,,0.560714458
2102,Psychological distress associated with the COVID-19 lockdown: A two-wave network analysis.,J Affect Disord,33582428,2/15/21,pubmed,0,12,network analysis,0.058758802,0.001187279,0.001187329,0.147599712,0.790079616,0.001187261,Healthcare,0.46489447,FALSE,45.16666667,0.588162533,21,0.492239765,1,0.537564047,,,0.539322115
2103,Hyper-Inflammatory Response Involves in Cardiac Injury among Patients with Coronavirus Disease 2019.,Am J Med Sci,33582156,2/15/21,pubmed,0,7,logistic regression,0.054220227,0.001171556,0.001171541,0.001171549,0.00117155,0.941093577,Clinics,0.7920487,TRUE,23.42857143,0.346836539,4.857142857,0.249933101,0,0.403234768,,,0.333334803
2104,A novel computational method for assigning weights of importance to symptoms of COVID-19 patients.,Artif Intell Med,33581830,2/15/21,pubmed,0,5,"computational, dataset",0.001010952,0.001010954,0.684856953,0.001010976,0.190929382,0.121180784,Healthcare,0.26518407,FALSE,17,0.257467994,1.2,0.126103827,1,0.537564047,,,0.307045289
2105,Factors associated with non-adherence to social distancing rules during the COVID-19 pandemic: a logistic regression analysis.,BMC Public Health,33581734,2/15/21,pubmed,0,2,logistic regression,0.001486409,0.001486432,0.001486423,0.001486517,0.99256778,0.001486438,Healthcare,0.95655024,TRUE,15.5,0.234028078,3,0.199424672,1,0.537564047,,,0.323672266
2106,Chemical composition and pharmacological mechanism of ephedra-glycyrrhiza drug pair against coronavirus disease 2019 (COVID-19).,Aging (Albany NY),33581688,2/14/21,pubmed,0,12,molecular dynamics simulation,0.991413859,0.001717185,0.001717184,0.001717258,0.00171727,0.001717244,Drug discovery,0.9267415,TRUE,17.08333333,0.257653535,2.666666667,0.185442869,0,0.403234768,,,0.28211039
2107,Disposable face masks and reusable face coverings as non-pharmaceutical interventions (NPIs) to prevent transmission of SARS-CoV-2 variants that cause coronavirus disease (COVID-19): Role of new sustainable NPI design innovations and predictive mathematical modelling.,Sci Total Environ,33581526,2/14/21,pubmed,0,2,mathematical model,0.001486476,0.025842289,0.001486484,0.863660922,0.106037241,0.001486587,Epidemiology,0.23722535,FALSE,63.5,0.72496753,74,0.764182499,0,0.403234768,,,0.630794932
2108,Compositional Variability and Mutation Spectra of Monophyletic SARS-CoV-2 Clades.,Genomics Proteomics Bioinformatics,33581339,2/14/21,pubmed,0,9,"transcriptom, proteom",0.172379584,0.745273111,0.00104686,0.001046905,0.001046871,0.07920667,Genomics,0.33862504,FALSE,43.88888889,0.576659039,68.88888889,0.75,0,0.403234768,,,0.576631269
2109,Functional alterations caused by mutations reflect evolutionary trends of SARS-CoV-2.,Brief Bioinform,33580783,2/14/21,pubmed,0,6,whole-genome,0.10574103,0.889445236,0.001203416,0.001203485,0.001203409,0.001203423,Genomics,0.4209764,FALSE,188.6666667,0.968396314,33,0.593256623,0,0.403234768,,,0.654962568
2110,"Clinically important drug-drug interactions in patients admitted to hospital with COVID-19: drug pairs, risk factors, and management.",Drug Metab Pers Ther,33580642,2/14/21,pubmed,0,2,logistic regression,0.43239364,0.002032856,0.002032785,0.002032783,0.002032829,0.559475106,Clinics,0.94683903,TRUE,30.5,0.438493413,19,0.471367407,0,0.403234768,,,0.437698529
2111,The opportunity costs of birth in Australia: Hospital resource savings for a post-COVID-19 era.,Birth,33580537,2/14/21,pubmed,0,4,simulation model,0.001371266,0.001371345,0.001371276,0.16392347,0.492779571,0.339183072,Healthcare,0.7480283,TRUE,49.5,0.628362917,30.75,0.57499331,0,0.403234768,,,0.535530331
2112,Optimal Investment in Prevention and Recovery for Mitigating Epidemic Risks.,Risk Anal,33580512,2/14/21,pubmed,0,4,mathematical model,0.00186172,0.001861735,0.001861749,0.990691314,0.001861764,0.001861718,Epidemiology,0.6535176,TRUE,26,0.382398417,20.75,0.488827937,0,0.403234768,,,0.424820374
2113,High amounts of SARS-CoV-2 precede sickness among asymptomatic healthcare workers.,J Infect Dis,33580261,2/14/21,pubmed,0,27,logistic regression,0.002032787,0.252374844,0.270680094,0.00203287,0.470846445,0.002032959,Healthcare,0.20126602,FALSE,106.7777778,0.88242934,200.2592593,0.917714744,0,0.403234768,,,0.734459617
2114,Impact of COPD on COVID-19 prognosis: A nationwide population-based study in South Korea.,Sci Rep,33580190,2/14/21,pubmed,0,5,logistic regression,0.001329999,0.001330003,0.001330017,0.00133003,0.001330074,0.993349877,Clinics,0.91456705,TRUE,13.6,0.206011503,0.6,0.09011239,0,0.403234768,,,0.233119554
2115,"Phylogenomics reveals viral sources, transmission, and potential superinfection in early-stage COVID-19 patients in Ontario, Canada.",Sci Rep,33580132,2/14/21,pubmed,0,13,"bioinformatic, sequencing, phylogenom, genomes",0.001203448,0.89555162,0.001203455,0.088300596,0.001203435,0.012537446,Genomics,0.7693721,TRUE,27.46153846,0.400828746,52.53846154,0.690995451,0,0.403234768,,,0.498352988
2116,Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China.,Sci Rep,33580113,2/14/21,pubmed,0,5,correlation analysis,0.021443812,0.001220029,0.001220022,0.934455028,0.040441085,0.001220024,Epidemiology,0.60900486,TRUE,22.2,0.327787742,9.6,0.346735349,0,0.403234768,,,0.35925262
2117,Development of a novel computational method using computed tomography images for the early detection and severity classification of COVID-19 cases.,J Xray Sci Technol,33579889,2/14/21,pubmed,0,7,computational,0.001141407,0.001141381,0.851557601,0.001141417,0.143876805,0.001141389,Imaging,0.045250267,FALSE,10.71428571,0.159688292,0.857142857,0.103224512,1,0.537564047,,,0.266825617
2118,Seroprevalence of antibodies to SARS-CoV-2 in healthcare workers: a cross-sectional study.,BMJ Open,33579769,2/14/21,pubmed,0,39,bayes,0.001112747,0.109735381,0.001112676,0.052929704,0.756409603,0.078699889,Healthcare,0.41317385,FALSE,50.12820513,0.632135568,77.71794872,0.772611721,2,0.618927094,,,0.674558128
2119,Contraceptive dynamics during COVID-19 in sub-Saharan Africa: longitudinal evidence from Burkina Faso and Kenya.,BMJ Sex Reprod Health,33579717,2/14/21,pubmed,0,9,logistic regression,0.001861755,0.001861721,0.001861684,0.244390296,0.74816283,0.001861714,Healthcare,0.8558972,TRUE,44.11111111,0.578638135,31,0.578204442,0,0.403234768,,,0.520025781
2120,INSIGHT: A population-scale COVID-19 testing strategy combining point-of-care diagnosis with centralized high-throughput sequencing.,Sci Adv,33579697,2/14/21,pubmed,0,6,sequencing,0.001786545,0.707174307,0.199208005,0.088257735,0.001786719,0.00178669,Genomics,0.19364211,FALSE,87.5,0.835363968,277,0.947819106,0,0.403234768,,,0.728805947
2121,Age-Related Changes in Clinical Presentation of Covid-19: the EPICOVID19 Web-Based Survey.,Eur J Intern Med,33579579,2/14/21,pubmed,0,13,logistic regression,0.001350318,0.130700709,0.034583019,0.001350359,0.428289525,0.403726069,Healthcare,0.7481382,TRUE,180.3076923,0.964376276,98.76923077,0.820042815,0,0.403234768,,,0.729217953
2122,Detection of Coronaviruses Using RNA Toehold Switch Sensors.,Int J Mol Sci,33578973,2/14/21,pubmed,0,2,microbiom,0.00203281,0.827268052,0.164600745,0.002032873,0.002032773,0.002032747,Genomics,0.3181445,FALSE,56.5,0.678891706,99,0.820444207,0,0.403234768,,,0.634190227
2123,Statin Therapy and the Risk of COVID-19: A Cohort Study of the National Health Insurance Service in South Korea.,J Pers Med,33578937,2/14/21,pubmed,0,3,logistic regression,0.001901695,0.001901761,0.024792845,0.001901803,0.001901854,0.967600043,Clinics,0.8461647,TRUE,148,0.942606222,29.66666667,0.567099277,0,0.403234768,,,0.637646756
2124,"The Use of Antiviral Agents against SARS-CoV-2: Ineffective or Time and Age Dependent Result? A Retrospective, Observational Study among COVID-19 Older Adults.",J Clin Med,33578922,2/14/21,pubmed,0,10,logistic regression,0.162382728,0.001237061,0.001237069,0.001237114,0.001237126,0.832668902,Clinics,0.53118217,TRUE,13,0.197352959,15.7,0.43256623,0,0.403234768,,,0.344384652
2125,A Novel Method for a COVID-19 Classification of Countries Based on an Intelligent Fuzzy Fractal Approach.,Healthcare (Basel),33578902,2/14/21,pubmed,0,2,dataset,0.001565362,0.0015653,0.475641758,0.518096989,0.001565304,0.001565288,Epidemiology,0.36256257,FALSE,897.5,0.999257839,226.5,0.928217822,0,0.403234768,,,0.776903476
2126,"Field-Template, QSAR, Ensemble Molecular Docking, and 3D-RISM Solvation Studies Expose Potential of FDA-Approved Marine Drugs as SARS-CoVID-2 Main Protease Inhibitors.",Molecules,33578831,2/14/21,pubmed,0,4,computational,0.974793994,0.001272686,0.001272691,0.020115359,0.001272634,0.001272636,Drug discovery,0.85945606,TRUE,33.25,0.468860165,14,0.412898047,0,0.403234768,,,0.428330993
2127,Evaluation of the mental health status of community healthcare workers during the COVID-19 outbreak.,Medicine (Baltimore),33578622,2/14/21,pubmed,0,5,logistic regression,0.000999566,0.000999519,0.000999592,0.000999568,0.995002211,0.000999543,Healthcare,0.9832493,TRUE,6.4,0.090667326,1.8,0.150120417,0,0.403234768,,,0.21467417
2128,ABO blood group and COVID-19: a review on behalf of the ISBT COVID-19 working group.,Vox Sang,33578447,2/13/21,pubmed,0,24,genome-wide,0.525773069,0.086809961,0.001254618,0.045713984,0.001254664,0.339193705,Drug discovery,0.22404289,FALSE,77.04166667,0.794792504,39.875,0.632459192,1,0.537564047,,,0.654938581
2129,Computational evaluation of rebreathing and effective dead space on a helmet-like interface during the COVID-19 pandemic.,J Biomech,33578054,2/13/21,pubmed,0,4,computational,0.002238677,0.00223852,0.155270323,0.719587098,0.002238747,0.118426635,Epidemiology,0.9642341,TRUE,3.25,0.039148989,0.75,0.099411292,0,0.403234768,,,0.18059835
2130,Duration of SARS-CoV-2 positive in quarantine room environments: A perspective analysis.,Int J Infect Dis,33578005,2/13/21,pubmed,0,13,sequencing,0.001751367,0.56744769,0.0017512,0.158671221,0.001751297,0.268627227,Genomics,0.94975615,TRUE,49.23076923,0.625703507,7.461538462,0.305793417,0,0.403234768,,,0.444910564
2131,SARS-CoV-2 Infection and Viral Load are Associated with the Upper Respiratory Tract Microbiome.,J Allergy Clin Immunol,33577896,2/13/21,pubmed,0,11,"sequencing, microbiom",0.03046832,0.634934016,0.001511816,0.001511844,0.001511933,0.330062072,Genomics,0.8295586,TRUE,34.45454545,0.481353207,34.27272727,0.600347873,1,0.537564047,,,0.539755042
2132,Strengthening Digital Health Technology Capacity in Navajo Communities to Help Counter the COVID-19 Pandemic.,Ann Am Thorac Soc,33577743,2/13/21,pubmed,0,4,digital health,0.019529393,0.019529916,0.019529965,0.902351322,0.019530018,0.019529385,Epidemiology,0.66530794,TRUE,128,0.919104459,61.75,0.727588975,0,0.403234768,,,0.683309401
2133,Integration of genetically regulated gene expression and pharmacological library provides therapeutic drug candidates.,Hum Mol Genet,33577681,2/13/21,pubmed,0,3,"in silico, transcriptom, genome-wide",0.656797832,0.160504725,0.001861895,0.001861845,0.001861813,0.177111889,Drug discovery,0.24992883,FALSE,107,0.883357041,270.3333333,0.945879047,0,0.403234768,,,0.744156952
2134,"Running behaviors, motivations, and injury risk during the COVID-19 pandemic: A survey of 1147 runners.",PLoS One,33577584,2/13/21,pubmed,0,3,logistic regression,0.075410083,0.000926287,0.000926319,0.110237397,0.673679103,0.138820813,Healthcare,0.9084101,TRUE,28,0.408312202,17,0.451097137,0,0.403234768,,,0.420881369
2135,Evaluating epidemic forecasts in an interval format.,PLoS Comput Biol,33577550,2/13/21,pubmed,0,4,probabilistic,0.002080575,0.002080555,0.002080694,0.781337307,0.002080608,0.210340261,Epidemiology,0.38545448,FALSE,44,0.578390748,453.5,0.976117206,12,0.850299401,,,0.801602452
2136,Phylogenetic pattern of SARS-CoV-2 from COVID-19 patients from Bosnia and Herzegovina: lessons learned to optimize future molecular and epidemiological approaches.,Bosn J Basic Med Sci,33577445,2/13/21,pubmed,0,16,whole genome,0.003927505,0.980362476,0.003927445,0.003927595,0.003927433,0.003927547,Genomics,0.65910256,TRUE,24.6875,0.363473313,9.9375,0.352287931,0,0.403234768,,,0.372998671
2137,Identifying vulnerable populations at a university during the COVID-19 pandemic.,J Am Coll Health,33577412,2/13/21,pubmed,0,4,dataset,0.001901833,0.001901837,0.226433861,0.001901812,0.765958807,0.001901851,Healthcare,0.4193093,FALSE,50.25,0.633001422,28,0.554321648,0,0.403234768,,,0.530185946
2138,Montelukast in hospitalized patients diagnosed with COVID-19.,J Asthma,33577360,2/13/21,pubmed,0,16,logistic regression,0.142832876,0.001330068,0.001330056,0.001330064,0.001330147,0.851846789,Clinics,0.7727443,TRUE,75.125,0.787185355,63.0625,0.732673267,0,0.403234768,,,0.64103113
2139,"Kobophenol A Inhibits Binding of Host ACE2 Receptor with Spike RBD Domain of SARS-CoV-2, a Lead Compound for Blocking COVID-19.",J Phys Chem Lett,33577324,2/13/21,pubmed,0,12,"virtual screening, molecular dynamics simulation",0.991735021,0.00165301,0.001652993,0.001652998,0.00165299,0.001652987,Drug discovery,0.6772635,TRUE,119.4166667,0.90617849,106.8333333,0.834292213,0,0.403234768,,,0.71456849
2140,Trends in US Pediatric Hospital Admissions in 2020 Compared With the Decade Before the COVID-19 Pandemic.,JAMA Netw Open,33576819,2/13/21,pubmed,0,6,forecasting model,0.001098833,0.00109884,0.043136956,0.199940989,0.040132236,0.714592147,Clinics,0.64961684,TRUE,41.83333333,0.555878533,123.5,0.855833556,1,0.537564047,,,0.649758712
2141,Differential diagnosis of coronavirus disease 2019 pneumonia or influenza A pneumonia by clinical characteristics and laboratory findings.,J Clin Lab Anal,33576536,2/13/21,pubmed,0,11,logistic regression,0.001237177,0.001237081,0.283913035,0.00123709,0.001237063,0.711138554,Clinics,0.33357677,FALSE,25.09090909,0.369410601,9.090909091,0.338038534,0,0.403234768,,,0.370227967
2142,The "Maskne" microbiome - pathophysiology and therapeutics.,Int J Dermatol,33576511,2/13/21,pubmed,0,1,microbiom,0.071536292,0.09118452,0.001717219,0.660173458,0.001717265,0.173671246,Epidemiology,0.9664643,TRUE,18,0.271569052,1,0.122023013,0,0.403234768,,,0.265608944
2143,Perceived declining physical and cognitive fitness during the COVID-19 state of emergency among community-dwelling Japanese old-old adults.,Geriatr Gerontol Int,33576180,2/13/21,pubmed,0,7,logistic regression,0.001156227,0.001156225,0.001156233,0.001156303,0.994218759,0.001156254,Healthcare,0.9850631,TRUE,61.42857143,0.711361247,37.71428571,0.620016056,0,0.403234768,,,0.578204023
2144,"Racial, Ethnic, and Geographic Disparities in Novel Coronavirus (Severe Acute Respiratory Syndrome Coronavirus 2) Test Positivity in North Carolina.",Open Forum Infect Dis,33575416,2/13/21,pubmed,0,7,dataset,0.001371306,0.001371294,0.001371281,0.17536389,0.819150918,0.00137131,Healthcare,0.3905192,FALSE,66,0.741356918,86.85714286,0.794888948,1,0.537564047,,,0.691269971
2145,Decision Model for Allocation of Intensive Care Unit Beds for Suspected COVID-19 Patients under Scarce Resources.,Comput Math Methods Med,33574887,2/13/21,pubmed,0,5,optimization model,0.001786794,0.00178653,0.35327184,0.475500455,0.001786566,0.165867815,Epidemiology,0.24594685,FALSE,19.2,0.287339972,5.4,0.263914905,0,0.403234768,,,0.318163215
2146,"COVID-19 Knowledge, Attitude, Practices and Their Associated Factors Among Dessie City Residents, Northeast Ethiopia: A Cross-Sectional Study.",Risk Manag Healthc Policy,33574719,2/13/21,pubmed,0,6,logistic regression,0.000977424,0.000977442,0.000977429,0.00097747,0.995112794,0.00097744,Healthcare,0.6655212,TRUE,3.333333333,0.04044777,0,0.055525823,0,0.403234768,,,0.166402787
2147,Identification of ebselen and its analogues as potent covalent inhibitors of papain-like protease from SARS-CoV-2.,Sci Rep,33574416,2/13/21,pubmed,0,6,bayes,0.940259654,0.002490491,0.002490466,0.049778444,0.002490429,0.002490516,Drug discovery,0.9265016,TRUE,66.66666667,0.745191416,95.83333333,0.814289537,1,0.537564047,,,0.699015
2148,"The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research.",Sci Data,33574342,2/13/21,pubmed,0,5,probabilistic,0.002183227,0.002183256,0.002183307,0.989083748,0.002183258,0.002183204,Epidemiology,0.1345346,FALSE,31.4,0.44882182,53.2,0.694407279,0,0.403234768,,,0.515487955
2149,A genome-wide CRISPR screen identifies host factors that regulate SARS-CoV-2 entry.,Nat Commun,33574281,2/13/21,pubmed,0,20,genome-wide,0.835164042,0.156513673,0.002080604,0.002080601,0.00208052,0.002080559,Drug discovery,0.4687938,FALSE,53.7,0.658667821,,,3,0.667819001,,,0.663243411
2150,"Coding-Complete Genome Sequences of NITMA1086 and NITMA1139, Two SARS-CoV-2 Isolates from Belagavi District, Karnataka State, India, Harboring the D614G Mutation.",Microbiol Resour Announc,33574104,2/13/21,pubmed,0,4,genome sequences,0.006539588,0.967301928,0.00653959,0.006539713,0.006539628,0.006539554,Genomics,0.53950274,TRUE,17.5,0.263776362,3,0.199424672,0,0.403234768,,,0.288811934
2151,Point-of-care lung ultrasound assessment for risk stratification and therapy guiding in COVID-19 patients. A prospective non-interventional study.,Eur Respir J,33574074,2/13/21,pubmed,0,15,logistic regression,0.001237086,0.001237154,0.001237161,0.053503987,0.001237104,0.941547507,Clinics,0.951686,TRUE,23.46666667,0.347145773,3.466666667,0.209526358,0,0.403234768,,,0.319968966
2152,SARS-CoV-2 transmission during rugby league matches: do players become infected after participating with SARS-CoV-2 positive players?,Br J Sports Med,33574043,2/13/21,pubmed,0,7,dataset,0.056730868,0.156679388,0.001486481,0.588211088,0.089240749,0.107651427,Epidemiology,0.6993712,TRUE,32.71428571,0.463479498,61.14285714,0.724913032,0,0.403234768,,,0.530542432
2153,A simple nomogram for predicting failure of non-invasive respiratory strategies in adults with COVID-19: a retrospective multicentre study.,Lancet Digit Health,33573999,2/13/21,pubmed,0,17,"logistic regression, dataset",0.000807876,0.000807877,0.101004122,0.000807919,0.023333641,0.873238564,Clinics,0.7077234,TRUE,142.5882353,0.936916321,322.2352941,0.960128445,1,0.537564047,,,0.811536271
2154,Differences in mortality of cancer patients with COVID-19 in a Brazilian cancer center.,Semin Oncol,33573780,2/13/21,pubmed,0,8,logistic regression,0.001538103,0.001538125,0.001538127,0.10228897,0.001538112,0.891558564,Clinics,0.93060446,TRUE,31.125,0.445482095,49.25,0.678552315,0,0.403234768,,,0.509089726
2155,"Socioeconomic Inequalities in COVID-19 in a European Urban Area: Two Waves, Two Patterns.",Int J Environ Res Public Health,33573323,2/13/21,pubmed,0,8,"bayes, bayesian model",0.001901693,0.001901807,0.001901716,0.377629648,0.290926351,0.325738785,Epidemiology,0.70739484,TRUE,84.5,0.823489393,49,0.677481937,2,0.618927094,,,0.706632808
2156,"Pseudo-Dipeptide Bearing α,α-Difluoromethyl Ketone Moiety as Electrophilic Warhead with Activity against Coronaviruses.",Int J Mol Sci,33573283,2/13/21,pubmed,0,10,"molecular dynamics simulation, in silico",0.990883015,0.001823438,0.001823423,0.001823414,0.001823379,0.001823331,Drug discovery,0.82614005,TRUE,142.1,0.936421547,24.2,0.522946214,0,0.403234768,,,0.62086751
2157,Phytonutrient and Nutraceutical Action against COVID-19: Current Review of Characteristics and Benefits.,Nutrients,33573173,2/13/21,pubmed,0,3,in silico,0.725930831,0.003760732,0.003760493,0.203550775,0.003760777,0.059236393,Drug discovery,0.92974365,TRUE,161.3333333,0.952254314,238.3333333,0.933435911,0,0.403234768,,,0.762974998
2158,In Silico Study of Polyunsaturated Fatty Acids as Potential SARS-CoV-2 Spike Protein Closed Conformation Stabilizers: Epidemiological and Computational Approaches.,Molecules,33573088,2/13/21,pubmed,0,8,"computational, in silico",0.745358406,0.001901847,0.001901681,0.247034615,0.001901692,0.00190176,Drug discovery,0.7376156,TRUE,9.625,0.143917373,4,0.231469093,0,0.403234768,,,0.259540411
2159,Alcohol Consumption during a Pandemic Lockdown Period and Change in Alcohol Consumption Related to Worries and Pandemic Measures.,Int J Environ Res Public Health,33572994,2/13/21,pubmed,0,7,logistic regression,0.002422234,0.002422259,0.002422233,0.002422387,0.987888528,0.002422359,Healthcare,0.97755337,TRUE,94.28571429,0.855031233,156,0.888747659,0,0.403234768,,,0.71567122
2160,Perceived Benefits and Harms of the COVID-19 Pandemic on Family Well-Being and Their Sociodemographic Disparities in Hong Kong: A Cross-Sectional Study.,Int J Environ Res Public Health,33572977,2/13/21,pubmed,0,5,logistic regression,0.001415205,0.00141514,0.001415087,0.029079876,0.965259568,0.001415124,Healthcare,0.7285431,TRUE,415.2,0.996041808,201.6,0.918183035,0,0.403234768,,,0.772486537
2161,Nicotinic Acetylcholine Receptor Involvement in Inflammatory Bowel Disease and Interactions with Gut Microbiota.,Int J Environ Res Public Health,33572734,2/13/21,pubmed,0,3,microbiom,0.803040077,0.188004896,0.002238524,0.002238621,0.002239096,0.002238786,Drug discovery,0.71760595,TRUE,11,0.167171748,5,0.257024351,0,0.403234768,,,0.275810289
2162,Effect of An 84-bp Deletion of the Receptor-Binding Domain on the ACE2 Binding Affinity of the SARS-CoV-2 Spike Protein: An In Silico Analysis.,Genes (Basel),33572725,2/13/21,pubmed,0,9,"sequencing, in silico",0.41912491,0.558931596,0.001371309,0.001371281,0.001371271,0.017829634,Genomics,0.2851327,FALSE,77.11111111,0.795039891,35.33333333,0.607439122,0,0.403234768,,,0.601904594
2163,Dietary Intake and Mental Health among Saudi Adults during COVID-19 Lockdown.,Int J Environ Res Public Health,33572328,2/13/21,pubmed,0,8,logistic regression,0.001652997,0.001653033,0.001653012,0.001653045,0.924594235,0.068793678,Healthcare,0.9166448,TRUE,73.125,0.778526811,45.5,0.660957988,0,0.403234768,,,0.614239855
2164,Molecular Epidemiology Surveillance of SARS-CoV-2: Mutations and Genetic Diversity One Year after Emerging.,Pathogens,33572190,2/13/21,pubmed,0,8,genomes,0.00190177,0.970628847,0.001901682,0.001901753,0.021764107,0.001901841,Genomics,0.70747614,TRUE,28.5,0.412579628,21.875,0.501471769,2,0.618927094,,,0.51099283
2165,Quantitative Evaluation of COVID-19 Pneumonia Lung Extension by Specific Software and Correlation with Patient Clinical Outcome.,Diagnostics (Basel),33572122,2/13/21,pubmed,0,14,logistic regression,0.000793411,0.000793401,0.435513624,0.000793425,0.000793397,0.561312741,Clinics,0.7197468,TRUE,200.5714286,0.972540046,74.78571429,0.765721167,0,0.403234768,,,0.713831993
2166,COVID-19 pandemic gestational diabetes screening guidelines: A retrospective study in Australian women.,Diabetes Metab Syndr,33571889,2/12/21,pubmed,0,4,logistic regression,0.001717201,0.001717285,0.052150782,0.001717275,0.403546537,0.539150921,Clinics,0.9980403,TRUE,10.5,0.157338116,17.75,0.458322184,0,0.403234768,,,0.339631689
2167,Drug repurposing for COVID-19 via knowledge graph completion.,J Biomed Inform,33571675,2/12/21,pubmed,0,6,"neural network, classifier, knowledge graph",0.543558594,0.023800973,0.429259964,0.001126843,0.00112684,0.001126786,Drug discovery,0.7103776,TRUE,60,0.703444864,70.33333333,0.754415306,0,0.403234768,,,0.620364979
2168,Transmission of SARS-CoV-2 among healthcare workers and patients in a teaching hospital in the Netherlands confirmed by whole-genome sequencing.,J Hosp Infect,33571558,2/12/21,pubmed,0,6,"sequencing, whole-genome, genome sequences",0.002032735,0.424320119,0.002032773,0.002032967,0.172067338,0.397514068,Genomics,0.3100218,FALSE,122.8333333,0.911311769,145.5,0.879783249,1,0.537564047,,,0.776219688
2169,Dissecting lipid metabolism alterations in SARS-CoV-2.,Prog Lipid Res,33571544,2/12/21,pubmed,0,4,"metabolom, lipidom",0.989835649,0.002032968,0.002032793,0.002032841,0.002032796,0.002032953,Drug discovery,0.66394395,TRUE,46.5,0.601026656,11.25,0.374364463,0,0.403234768,,,0.459541962
2170,A certain proportion of docosahexaenoic acid tends to revert structural and dynamical effects of cholesterol on lipid membranes.,Biochim Biophys Acta Biomembr,33571481,2/12/21,pubmed,0,6,molecular dynamics simulation,0.770484143,0.004110011,0.004109958,0.213076056,0.004109995,0.004109838,Drug discovery,0.5743802,TRUE,30,0.432432432,1.666666667,0.145036125,0,0.403234768,,,0.326901108
2171,"SARS-CoV-2 transmission among children and staff in daycare centres during a nationwide lockdown in France: a cross-sectional, multicentre, seroprevalence study.",Lancet Child Adolesc Health,33571450,2/12/21,pubmed,0,30,bayes,0.000956301,0.16712442,0.000956381,0.000956383,0.603988472,0.226018043,Healthcare,0.86926043,TRUE,72.84210526,0.776795102,46.84210526,0.667848542,3,0.667819001,,,0.704154215
2172,Multi-clonal SARS-CoV-2 neutralization by antibodies isolated from severe COVID-19 convalescent donors.,PLoS Pathog,33571304,2/12/21,pubmed,0,28,sequencing,0.751697676,0.18146323,0.001593483,0.001593491,0.001593517,0.062058603,Drug discovery,0.5298171,TRUE,27.28571429,0.398540417,31.60714286,0.582285256,1,0.537564047,,,0.506129907
2173,Clinical presentation and outcomes of the first patients with COVID-19 in Argentina: Results of 207079 cases from a national database.,PLoS One,33571300,2/12/21,pubmed,0,6,"sequencing, dataset",0.001098811,0.141034458,0.001098875,0.135926605,0.108301003,0.612540247,Clinics,0.9002143,TRUE,196.5,0.971736038,293.3333333,0.952234413,0,0.403234768,,,0.775735073
2174,Spatial pattern of COVID-19 deaths and infections in small areas of Brazil.,PLoS One,33571268,2/12/21,pubmed,0,4,"bayes, bayesian model",0.00198712,0.001987267,0.001987108,0.726444076,0.001987292,0.265607137,Epidemiology,0.57915604,TRUE,50.25,0.633001422,8.5,0.329141022,0,0.403234768,,,0.455125737
2175,Prevalence and risk indicators of first-wave COVID-19 among oral health-care workers: A French epidemiological survey.,PLoS One,33571264,2/12/21,pubmed,0,7,logistic regression,0.00131038,0.143175922,0.023533196,0.001310407,0.568119565,0.26255053,Healthcare,0.920281,TRUE,21,0.312016822,4.428571429,0.239296227,1,0.537564047,,,0.362959032
2176,In silico analysis suggests less effective MHC-II presentation of SARS-CoV-2 RBM peptides: Implication for neutralizing antibody responses.,PLoS One,33571241,2/12/21,pubmed,0,4,in silico,0.725499122,0.183096455,0.001593493,0.001593612,0.086623789,0.00159353,Drug discovery,0.1720416,FALSE,194.25,0.970808337,140.25,0.874431362,1,0.537564047,,,0.794267915
2177,Prediction and control of COVID-19 spreading based on a hybrid intelligent model.,PLoS One,33571234,2/12/21,pubmed,0,2,"predictive model, lstm",0.002080585,0.030629069,0.271920102,0.691209118,0.002080562,0.002080563,Epidemiology,0.62317413,TRUE,5,0.070752675,0.5,0.087101953,1,0.537564047,,,0.231806225
2178,Antibody responses to endemic coronaviruses modulate COVID-19 convalescent plasma functionality.,J Clin Invest,33571169,2/12/21,pubmed,0,29,genomes,0.662844445,0.251782515,0.001538272,0.001538117,0.001538129,0.080758521,Drug discovery,0.6332463,TRUE,110.5172414,0.890654957,174.3793103,0.902394969,1,0.537564047,,,0.776871324
2179,Lifestyle and mental health disruptions during COVID-19.,Proc Natl Acad Sci U S A,33571107,2/12/21,pubmed,0,4,dataset,0.002080578,0.002080577,0.002080613,0.219604984,0.772072572,0.002080676,Healthcare,0.5797794,TRUE,16,0.243552477,4.75,0.248260637,0,0.403234768,,,0.298349294
2180,The origin and early spread of SARS-CoV-2 in Europe.,Proc Natl Acad Sci U S A,33571105,2/12/21,pubmed,0,5,"genome sequences, genomes",0.001392831,0.480482896,0.001392837,0.513945782,0.001392832,0.001392821,Epidemiology,0.07874033,FALSE,58.2,0.690518894,337.8,0.963607172,0,0.403234768,,,0.685786944
2181,Influence of health beliefs on adherence to COVID-19 preventative practices: an online international study via social media.,J Med Internet Res,33571103,2/12/21,pubmed,0,8,logistic regression,0.000988363,0.000988375,0.000988351,0.167601705,0.828444826,0.00098838,Healthcare,0.7232627,TRUE,7.125,0.102232667,4.5,0.242708055,0,0.403234768,,,0.24939183
2182,An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis.,IEEE Trans Neural Netw Learn Syst,33571095,2/12/21,pubmed,0,8,"machine learning, neural network, network model, transfer learning",0.001126812,0.001126797,0.920434436,0.075058368,0.001126787,0.0011268,Imaging,0.4179561,FALSE,169.5,0.957944214,102.5,0.826933369,5,0.739490092,,,0.841455892
2183,Evaluation of COVID-19 phobia and the feeling of loneliness in the geriatric age group.,Int J Clin Pract,33570809,2/12/21,pubmed,0,2,correlation analysis,0.00143815,0.001438133,0.001438156,0.001438167,0.992809217,0.001438177,Healthcare,0.64777184,TRUE,6,0.086028821,1,0.122023013,0,0.403234768,,,0.2037622
2184,"Intentions to Seek Mental Health Services During the COVID-19 Pandemic Among Chinese Pregnant Women With Probable Depression or Anxiety: Cross-sectional, Web-Based Survey Study.",JMIR Ment Health,33570500,2/12/21,pubmed,0,9,logistic regression,0.001171523,0.001171542,0.001171548,0.001171567,0.982209581,0.01310424,Healthcare,0.93977916,TRUE,99.22222222,0.867338735,55.66666667,0.705244849,0,0.403234768,,,0.658606117
2185,Nurses' willingness to work with COVID-19 patients: The role of knowledge and attitude.,Nurs Open,33570275,2/12/21,pubmed,0,5,logistic regression,0.002422255,0.00242258,0.00242228,0.00242227,0.836097977,0.154212638,Healthcare,0.9393976,TRUE,2.2,0.022759602,0,0.055525823,1,0.537564047,,,0.205283157
2186,Predictors of poor mental health among nurses during COVID-19 pandemic.,Nurs Open,33570266,2/12/21,pubmed,0,4,logistic regression,0.001901786,0.001901712,0.001901693,0.001901733,0.951538816,0.04085426,Healthcare,0.9347297,TRUE,15.75,0.237862577,25.25,0.530773348,0,0.403234768,,,0.390623564
2187,Efficacy of copeptin in distinguishing COVID-19 pneumonia from community-acquired pneumonia.,J Med Virol,33570194,2/12/21,pubmed,0,5,logistic regression,0.001310354,0.001310348,0.216177393,0.001310372,0.001310361,0.778581171,Clinics,0.8167054,TRUE,18.8,0.281278991,2,0.164302917,0,0.403234768,,,0.282938892
2188,Frequency of Routine Testing for Coronavirus Disease 2019 (COVID-19) in High-risk Healthcare Environments to Reduce Outbreaks.,Clin Infect Dis,33570097,2/12/21,pubmed,0,6,simulation model,0.005697711,0.175700911,0.005698,0.403869805,0.403335978,0.005697595,Epidemiology,0.34153038,FALSE,24.33333333,0.359700662,10.33333333,0.36038266,9,0.814309525,,,0.511464282
2189,"Acute kidney injury in patients with Covid-19 in a Brazilian ICU: incidence, predictors and in-hospital mortality.",J Bras Nefrol,33570081,2/12/21,pubmed,0,10,logistic regression,0.001461845,0.001461861,0.001461875,0.001461932,0.00146195,0.992690537,Clinics,0.9557713,TRUE,2.6,0.028263962,0.1,0.056328606,0,0.403234768,,,0.162609112
2190,Challenges in estimating virus divergence times in short epidemic timescales with special reference to the evolution of SARS-CoV-2 pandemic.,Genet Mol Biol,33570080,2/12/21,pubmed,0,2,dataset,0.001653046,0.186653504,0.001653246,0.806734194,0.001653011,0.001652999,Epidemiology,0.049194098,FALSE,5.5,0.077246583,19,0.471367407,0,0.403234768,,,0.317282919
2191,Psychosocial impact of the COVID-19 outbreak and lockdown on Spanish oncological patients: a quantitative approach.,J Psychosoc Oncol,33570014,2/12/21,pubmed,0,5,logistic regression,0.00133004,0.001330075,0.034048222,0.001330102,0.665078096,0.296883465,Healthcare,0.9627819,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
2192,CRISPR/Cas technology as a promising weapon to combat viral infections.,Bioessays,33569817,2/12/21,pubmed,0,3,genomes,0.199535447,0.519810938,0.274692131,0.001987228,0.001987116,0.001987139,Genomics,0.581943,TRUE,4.333333333,0.057950399,0.333333333,0.073187048,0,0.403234768,,,0.178124072
2193,Pharmacotherapy Management for COVID-19 and Cardiac Safety: A Data Mining Approach for Pharmacovigilance Evidence from the FDA Adverse Event Reporting System (FAERS).,Drugs Real World Outcomes,33569736,2/12/21,pubmed,0,8,data mining,0.317651504,0.083355019,0.001538184,0.001538161,0.001538237,0.594378896,Clinics,0.506808,TRUE,25.5,0.37435834,6.125,0.280907145,0,0.403234768,,,0.352833417
2194,Integrated network analysis reveals new genes suggesting COVID-19 chronic effects and treatment.,Brief Bioinform,33569598,2/12/21,pubmed,0,7,network analysis,0.864113169,0.002357824,0.002357801,0.126455621,0.002357833,0.002357752,Drug discovery,0.5211201,TRUE,36.42857143,0.501762632,43.42857143,0.651123896,0,0.403234768,,,0.518707099
2195,Elucidating the Interactions Between Heparin/Heparan Sulfate and SARS-CoV-2-Related Proteins-An Important Strategy for Developing Novel Therapeutics for the COVID-19 Pandemic.,Front Mol Biosci,33569392,2/12/21,pubmed,0,6,computational,0.920663971,0.001538162,0.00153813,0.07318336,0.001538129,0.001538248,Drug discovery,0.740235,TRUE,39.5,0.534232173,28.16666667,0.555458924,0,0.403234768,,,0.497641955
2196,"A Revisit to the Research Updates of Drugs, Vaccines, and Bioinformatics Approaches in Combating COVID-19 Pandemic.",Front Mol Biosci,33569389,2/12/21,pubmed,0,11,"molecular dynamics simulation, bioinformatic, in silico",0.596160377,0.067143794,0.001141341,0.311719617,0.022693507,0.001141365,Drug discovery,0.7320519,TRUE,15.27272727,0.229946193,5.181818182,0.258830613,0,0.403234768,,,0.297337191
2197,Healthcare Professionals' Willingness and Preparedness to Work During COVID-19 in Selected Hospitals of Southwest Ethiopia.,Risk Manag Healthc Policy,33568957,2/12/21,pubmed,0,8,logistic regression,0.022461942,0.001046867,0.001046847,0.001046896,0.888567673,0.085829775,Healthcare,0.9548247,TRUE,6.75,0.095862453,0,0.055525823,0,0.403234768,,,0.184874348
2198,Ventilation and air cleaning to limit aerosol particle concentrations in a gym during the COVID-19 pandemic.,Build Environ,33568882,2/12/21,pubmed,0,14,mathematical model,0.029975426,0.001593532,0.001593511,0.735596372,0.00159352,0.22964764,Epidemiology,0.28979087,FALSE,13.57142857,0.205578576,10,0.355632861,1,0.537564047,,,0.366258495
2199,Genomic mutations and changes in protein secondary structure and solvent accessibility of SARS-CoV-2 (COVID-19 virus).,Sci Rep,33568759,2/12/21,pubmed,0,10,"deep learning, genome sequences, dataset",0.194512701,0.598794288,0.138758166,0.064432462,0.001751218,0.001751165,Genomics,0.24194786,FALSE,34.2,0.479188571,15.5,0.430157881,0,0.403234768,,,0.437527073
2200,Optimal control of the COVID-19 pandemic: controlled sanitary deconfinement in Portugal.,Sci Rep,33568716,2/12/21,pubmed,0,12,mathematical model,0.001291236,0.00129124,0.001291232,0.993543759,0.001291267,0.001291265,Epidemiology,0.363164,FALSE,63.16666667,0.722431814,20.5,0.486218892,3,0.667819001,,,0.625489902
2201,Molecular Evolution of Human Coronavirus 229E in Hong Kong and a Fatal COVID-19 Case Involving Coinfection with a Novel Human Coronavirus 229E Genogroup.,mSphere,33568452,2/12/21,pubmed,0,17,sequencing,0.073681295,0.813051458,0.000898093,0.000898076,0.000898064,0.110573013,Genomics,0.7516284,TRUE,122.8235294,0.911249923,199.2941176,0.917313353,0,0.403234768,,,0.743932681
2202,High intake of vegetables is linked to lower white blood cell profile and the effect is mediated by the gut microbiome.,BMC Med,33568158,2/12/21,pubmed,0,15,"sequencing, microbiom",0.219522082,0.127371602,0.001220033,0.00122004,0.001220129,0.649446113,Clinics,0.6897695,TRUE,161.8,0.952439854,231.2,0.929756489,1,0.537564047,,,0.806586797
2203,Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19.,BMC Med Res Methodol,33568100,2/12/21,pubmed,0,10,dataset,0.001371247,0.001371304,0.026864144,0.967650685,0.001371295,0.001371326,Epidemiology,0.1233691,FALSE,93.7,0.853856144,32.7,0.590179288,0,0.403234768,,,0.615756733
2204,In Silico Investigation of the New UK (B.1.1.7) and South African (501Y.V2) SARS-CoV-2 Variants with a Focus at the ACE2-Spike RBD Interface.,Int J Mol Sci,33567580,2/12/21,pubmed,0,4,in silico,0.34560739,0.650302122,0.001022612,0.00102264,0.001022624,0.001022612,Genomics,0.72133934,TRUE,272,0.988063578,263.5,0.943671394,0,0.403234768,,,0.778323247
2205,COVID-19 on the Nile: Review on the Management and Outcomes of the COVID-19 Pandemic in the Arab Republic of Egypt from February to August 2020.,Int J Environ Res Public Health,33567519,2/12/21,pubmed,0,3,"mathematical model, prediction model",0.001511856,0.001511852,0.001511844,0.992440562,0.001511903,0.001511984,Epidemiology,0.3043051,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
2206,"Study Protocol: Understanding SARS-Cov-2 infection, immunity and its duration in care home residents and staff in England (VIVALDI).",Wellcome Open Res,33564722,2/12/21,pubmed,0,12,"sequencing, whole genome, dataset",0.02846564,0.24661588,0.000946099,0.099404019,0.532979147,0.091589216,Healthcare,0.46209866,FALSE,107.25,0.883728122,124.6666667,0.857305325,2,0.618927094,,,0.786653514
2207,Frontotemporal EEG to guide sedation in COVID-19 related acute respiratory distress syndrome.,Clin Neurophysiol,33567379,2/11/21,pubmed,0,14,machine learning,0.002238496,0.002238528,0.225984345,0.349464614,0.00223859,0.417835428,Clinics,0.98731244,TRUE,38.21428571,0.520625889,34.07142857,0.599478191,0,0.403234768,,,0.507779616
2208,A patient-specific approach for quantitative and automatic analysis of computed tomography images in lung disease: Application to COVID-19 patients.,Phys Med,33567361,2/11/21,pubmed,0,10,radiom,0.001141346,0.00114136,0.562468058,0.242537523,0.001141338,0.191570375,Imaging,0.27103105,FALSE,61.6,0.713031109,62.3,0.729930425,0,0.403234768,,,0.615398767
2209,Artificial intelligence and cardiac surgery during COVID-19 era.,J Card Surg,33567126,2/11/21,pubmed,0,5,"machine learning, artificial intelligence",0.001622731,0.001622776,0.517065991,0.297752706,0.02768702,0.154248776,Epidemiology,0.91399443,TRUE,71.4,0.7701775,4.2,0.234211935,0,0.403234768,,,0.469208067
2210,"Community health worker knowledge, attitudes and practices towards COVID-19: Learnings from an online cross-sectional survey using a digital health platform, UpSCALE, in Mozambique.",PLoS One,33566850,2/11/21,pubmed,0,12,digital health,0.001156315,0.001156283,0.001156336,0.273230204,0.706718649,0.016582213,Healthcare,0.93272513,TRUE,9.583333333,0.143484446,5.833333333,0.273213808,0,0.403234768,,,0.273311007
2211,Factors Contributing to Parents' Psychological and Medical Help Seeking During the COVID-19 Global Pandemic.,Fam Community Health,33565782,2/11/21,pubmed,0,5,logistic regression,0.001943535,0.001943514,0.001943586,0.054541376,0.937684486,0.001943502,Healthcare,0.8776448,TRUE,23.6,0.349124869,6.2,0.282512711,0,0.403234768,,,0.344957449
2212,[Intenational breakthroughs in critical care medicine 2020].,Zhonghua Wei Zhong Bing Ji Jiu Yi Xue,33565392,2/11/21,pubmed,0,3,artificial intelligence,0.001901871,0.001901867,0.113748542,0.135209456,0.060367937,0.686870327,Clinics,0.84695476,TRUE,53.66666667,0.658544128,10,0.355632861,0,0.403234768,,,0.472470585
2213,Impacts of COVID-19 on caregivers of childhood cancer survivors.,Pediatr Blood Cancer,33565259,2/11/21,pubmed,0,5,logistic regression,0.026736233,0.001112663,0.018819184,0.001112698,0.865279899,0.086939323,Healthcare,0.9827149,TRUE,10.6,0.158142124,1.6,0.140687717,0,0.403234768,,,0.234021536
2214,Visualizing COVID-19 Mortality Rates and African-American Populations in the USA and Pennsylvania.,J Racial Ethn Health Disparities,33565050,2/11/21,pubmed,0,3,logistic regression,0.001565301,0.001565372,0.00156531,0.390268422,0.363554087,0.241481508,Epidemiology,0.7024812,TRUE,8,0.118683901,0,0.055525823,0,0.403234768,,,0.192481497
2215,Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort.,Interdiscip Sci,33565027,2/11/21,pubmed,0,13,"deep learning, artificial intelligence",0.001187259,0.143394561,0.851856215,0.001187314,0.00118731,0.001187342,Imaging,0.5064992,TRUE,16,0.243552477,,,0,0.403234768,,,0.323393622
2216,Novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) and Other Coronaviruses: A Genome-wide Comparative Annotation and Analysis.,Mol Cell Biochem,33564990,2/11/21,pubmed,0,11,"genome-wide, whole genome, genomes",0.101425516,0.811409282,0.001237105,0.00123713,0.001237144,0.083453823,Genomics,0.44902667,FALSE,28.09090909,0.408621436,11,0.371287129,0,0.403234768,,,0.394381111
2217,Single-cell transcriptomes of peripheral blood cells indicate and elucidate severity of COVID-19.,Sci China Life Sci,33564978,2/11/21,pubmed,0,11,transcriptom,0.703375767,0.001310427,0.001310356,0.001310402,0.00131035,0.291382698,Drug discovery,0.688241,TRUE,49.54545455,0.628424763,15.90909091,0.434640086,0,0.403234768,,,0.488766539
2218,"Modeling effectiveness of testing strategies to prevent COVID-19 in nursing homes -United States, 2020.",Clin Infect Dis,33564862,2/11/21,pubmed,0,10,mathematical model,0.001350308,0.001350386,0.001350385,0.584306657,0.410291926,0.001350338,Epidemiology,0.35817307,FALSE,84.9,0.824231554,120.1,0.851485149,0,0.403234768,,,0.692983823
2219,COVID-19 Diagnostic Clinical Decision Support: a Pre-Post Implementation Study of CORAL (COvid Risk cALculator).,Clin Infect Dis,33564833,2/11/21,pubmed,0,28,logistic regression,0.001272673,0.001272728,0.247356116,0.169426594,0.093332784,0.487339106,Clinics,0.9735637,TRUE,79.5,0.80493537,126.3928571,0.859981268,0,0.403234768,,,0.689383802
2220,A Molecular network approach reveals shared cellular and molecular signatures between chronic fatigue syndrome and other fatiguing illnesses.,medRxiv,33564792,2/11/21,pubmed,0,22,"sequencing, probabilistic",0.510413636,0.227966755,0.001593558,0.082265691,0.00159359,0.17616677,Drug discovery,0.11374095,FALSE,72.31818182,0.774444925,251.2272727,0.938453305,0,0.403234768,,,0.705377666
2221,DrugWAS: Leveraging drug-wide association studies to facilitate drug repurposing for COVID-19.,medRxiv,33564788,2/11/21,pubmed,0,6,logistic regression,0.348142284,0.000999525,0.054031232,0.000999563,0.000999601,0.594827794,Clinics,0.101973355,FALSE,96.83333333,0.86078298,83.5,0.787128713,0,0.403234768,,,0.683715487
2222,International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study.,medRxiv,33564777,2/11/21,pubmed,0,85,prediction model,0.000898116,0.000898158,0.084074961,0.000898129,0.000898089,0.912332547,Clinics,0.14549947,FALSE,62.65853659,0.718659163,43.87804878,0.653264651,3,0.667819001,,,0.679914272
2223,Extensive recombination-driven coronavirus diversification expands the pool of potential pandemic pathogens.,bioRxiv,33564759,2/11/21,pubmed,0,5,"computational, genomes",0.001046856,0.893992834,0.001046829,0.101819735,0.001046864,0.001046882,Genomics,0.5555935,TRUE,60.4,0.705114726,467.8,0.977522077,1,0.537564047,,,0.74006695
2224,The Spread of COVID-19 Increases With Individual Mobility and Depends on Political Leaning.,Res Sq,33564757,2/11/21,pubmed,0,3,dataset,0.002238478,0.002238514,0.002238443,0.896741994,0.094303972,0.0022386,Epidemiology,0.4068095,FALSE,57,0.68204589,45.66666667,0.661760771,0,0.403234768,,,0.582347143
2225,miR-24 targets SARS-CoV-2 co-factor Neuropilin-1 in human brain microvascular endothelial cells: Insights for COVID-19 neurological manifestations.,Res Sq,33564755,2/11/21,pubmed,0,6,bioinformatic,0.98036288,0.003927445,0.003927349,0.003927396,0.003927431,0.003927499,Drug discovery,0.70978594,TRUE,62.33333333,0.717236687,28.83333333,0.560944608,0,0.403234768,,,0.560472021
2226,Global Evidence on the Determinants of Public Trust in Governments during the COVID-19.,Appl Res Qual Life,33564341,2/11/21,pubmed,0,1,dataset,0.002898298,0.002898292,0.002898428,0.462977224,0.525429444,0.002898313,Healthcare,0.6693954,TRUE,125,0.914280413,74,0.764182499,2,0.618927094,,,0.765796669
2227,Shallow Convolutional Neural Network for COVID-19 Outbreak Screening Using Chest X-rays.,Cognit Comput,33564340,2/11/21,pubmed,0,6,"deep learning, computational, neural network, dataset",0.001291223,0.001291272,0.993543718,0.001291316,0.001291232,0.00129124,Imaging,0.52329314,TRUE,79.5,0.80493537,19.66666667,0.477321381,0,0.403234768,,,0.561830506
2228,Multivariate Analysis of COVID-19 for Countries with Limited and Scarce Data: Examples from Nepal.,J Environ Public Health,33564315,2/11/21,pubmed,0,1,logistic regression,0.001751175,0.00175122,0.001751191,0.809603738,0.001751274,0.183391402,Epidemiology,0.7734213,TRUE,17,0.257467994,1,0.122023013,0,0.403234768,,,0.260908592
2229,ACE2 Is a Prognostic Biomarker and Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma: Implication for COVID-19.,J Oncol,33564310,2/11/21,pubmed,0,4,bioinformatic,0.575810506,0.00151183,0.001511812,0.001511807,0.00151186,0.418142186,Drug discovery,0.9642875,TRUE,8.75,0.129383388,1.75,0.148381054,0,0.403234768,,,0.226999737
2230,The value of artificial intelligence and imaging diagnosis in the fight against COVID-19.,Pers Ubiquitous Comput,33564287,2/11/21,pubmed,0,6,artificial intelligence,0.001219997,0.001220001,0.993899909,0.001220051,0.001220037,0.001220005,Imaging,0.8797327,TRUE,81.33333333,0.812480673,31.83333333,0.58348943,0,0.403234768,,,0.599734957
2231,Coagulopathy is associated with multiple organ damage and prognosis of COVID-19.,EXCLI J,33564286,2/11/21,pubmed,0,9,logistic regression,0.001684621,0.001684466,0.001684453,0.001684461,0.001684532,0.991577467,Clinics,0.7928504,TRUE,63.44444444,0.724287216,2.222222222,0.168651325,0,0.403234768,,,0.432057769
2232,"Knowledge, Attitudes, and Practices Toward Covid-19 and Associated Factors Among University Students in Mizan Tepi University, 2020.",Infect Drug Resist,33564243,2/11/21,pubmed,0,3,logistic regression,0.063026575,0.000999547,0.000999532,0.000999589,0.932975223,0.000999534,Healthcare,0.7595498,TRUE,8.333333333,0.123693488,25.33333333,0.531576131,0,0.403234768,,,0.352834795
2233,"Healthcare Workers' Knowledge, Attitude, and Practice Regarding Personal Protective Equipment for the Prevention of COVID-19.",J Multidiscip Healthc,33564239,2/11/21,pubmed,0,6,logistic regression,0.001085317,0.001085316,0.001085321,0.001085333,0.99457338,0.001085332,Healthcare,0.53584284,TRUE,11.83333333,0.178304162,1.333333333,0.13252609,0,0.403234768,,,0.238021673
2234,"Practice of COVID-19 Prevention Measures and Associated Factors Among Residents of Dire Dawa City, Eastern Ethiopia: Community-Based Study.",J Multidiscip Healthc,33564238,2/11/21,pubmed,0,10,logistic regression,0.00114132,0.001141365,0.001141385,0.029186086,0.966248485,0.001141359,Healthcare,0.82416564,TRUE,5.1,0.071061909,0.1,0.056328606,0,0.403234768,,,0.176875094
2235,Structural modeling and analysis of the SARS-CoV-2 cell entry inhibitor camostat bound to the trypsin-like protease TMPRSS2.,Med Chem Res,33564221,2/11/21,pubmed,0,2,structural model,0.936827625,0.001350368,0.057770721,0.001350491,0.001350424,0.001350369,Drug discovery,0.13857591,FALSE,51.5,0.643267982,264.5,0.943738293,0,0.403234768,,,0.663413681
2236,A review on COVID-19 forecasting models.,Neural Comput Appl,33564213,2/11/21,pubmed,0,3,"machine learning, forecasting model",0.00299648,0.00299647,0.464672488,0.523341605,0.002996486,0.002996471,Epidemiology,0.70799416,TRUE,135.6666667,0.927330076,159.6666667,0.891557399,1,0.537564047,,,0.785483841
2237,Network analysis of population flow among major cities and its influence on COVID-19 transmission in China.,Cities,33564205,2/11/21,pubmed,0,4,network analysis,0.001565361,0.001565366,0.031663509,0.962075099,0.001565324,0.001565341,Epidemiology,0.6252876,TRUE,3,0.037293586,0,0.055525823,1,0.537564047,,,0.210127819
2238,[Usefulness and acceptance of telephone monitoring by a virtual assistant for patients with COVID-19 following discharge].,Rev Clin Esp,33564196,2/11/21,pubmed,0,6,artificial intelligence,0.002080632,0.002080603,0.139519715,0.164512054,0.311008454,0.380798542,Clinics,0.81992525,TRUE,10.83333333,0.161481848,0.666666667,0.096200161,0,0.403234768,,,0.220305592
2239,Evidence for SARS-CoV-2 related coronaviruses circulating in bats and pangolins in Southeast Asia.,Nat Commun,33563978,2/11/21,pubmed,0,24,"whole genome, genome sequences",0.16221172,0.830642125,0.001786539,0.001786581,0.001786516,0.001786519,Genomics,0.5180813,TRUE,43.95833333,0.576844579,78.08333333,0.773682098,12,0.850299401,,,0.733608693
2240,"Losing ground at the wrong time: trends in self-reported influenza vaccination uptake in Switzerland, Swiss Health Survey 2007-2017.",BMJ Open,33563620,2/11/21,pubmed,0,5,logistic regression,0.001438098,0.001438124,0.001438087,0.0014382,0.992809295,0.001438198,Healthcare,0.89344335,TRUE,219.2,0.97835364,830,0.990901793,0,0.403234768,,,0.790830067
2241,"Coronavirus disease 2019, food security and maternal mental health in Ceará, Brazil: a repeated cross-sectional survey.",Public Health Nutr,33563355,2/11/21,pubmed,0,8,logistic regression,0.00198712,0.001987288,0.001987155,0.001987246,0.99006403,0.001987161,Healthcare,0.82612765,TRUE,39.75,0.536829736,13,0.400521809,0,0.403234768,,,0.446862104
2242,A simple transmission dynamics model for predicting the evolution of COVID-19 under control measures in China.,Epidemiol Infect,33563354,2/11/21,pubmed,0,4,"mathematical model, prediction model",0.00165304,0.001653046,0.001653092,0.949721666,0.001653052,0.043666103,Epidemiology,0.25054455,FALSE,36.5,0.503123261,8.25,0.323789136,1,0.537564047,,,0.454825481
2243,CoronaHiT: high-throughput sequencing of SARS-CoV-2 genomes.,Genome Med,33563320,2/11/21,pubmed,0,29,"sequencing, genomes",0.003214169,0.929572703,0.057570147,0.003214295,0.00321442,0.003214266,Genomics,0.61525404,TRUE,31.89655172,0.453831406,49.37931034,0.679020605,0,0.403234768,,,0.512028926
2244,Coumarins and Quinolones as Effective Multiple Targeted Agents Versus Covid-19: An in Silico Study.,Med Chem,33563156,2/11/21,pubmed,0,3,in silico,0.993726811,0.001254647,0.001254622,0.001254661,0.001254644,0.001254615,Drug discovery,0.9460833,TRUE,88.66666667,0.838394459,19,0.471367407,1,0.537564047,,,0.615775304
2245,Population-Level Death Rates From Novel Coronavirus (COVID-19) in South Korea.,Asia Pac J Public Health,33562998,2/11/21,pubmed,0,4,logistic regression,0.002296497,0.00229655,0.00229651,0.49042398,0.131290664,0.371395798,Epidemiology,0.30131614,FALSE,26.5,0.389325252,159,0.891156007,0,0.403234768,,,0.561238676
2246,The Role of Societal Aspects in the Formation of Official COVID-19 Reports: A Data-Driven Analysis.,Int J Environ Res Public Health,33562501,2/11/21,pubmed,0,4,dataset,0.002238563,0.002238506,0.002238525,0.988807273,0.002238557,0.002238576,Epidemiology,0.24738926,FALSE,27.25,0.398169336,2.5,0.180826866,0,0.403234768,,,0.327410323
2247,"Tryptophan Metabolites and Aryl Hydrocarbon Receptor in Severe Acute Respiratory Syndrome, Coronavirus-2 (SARS-CoV-2) Pathophysiology.",Int J Mol Sci,33562472,2/11/21,pubmed,0,3,microbiom,0.855350668,0.001751255,0.001751154,0.001751224,0.001751213,0.137644487,Drug discovery,0.916296,TRUE,127.6666667,0.918114911,68.33333333,0.748728927,0,0.403234768,,,0.690026202
2248,Passive Microwave Radiometry for the Diagnosis of Coronavirus Disease 2019 Lung Complications in Kyrgyzstan.,Diagnostics (Basel),33562419,2/11/21,pubmed,0,13,radiom,0.001486438,0.001486464,0.711150724,0.001486535,0.03925708,0.245132759,Imaging,0.5140642,TRUE,21.15384615,0.31282083,14.15384615,0.413500134,0,0.403234768,,,0.376518577
2249,Population Genomics Insights into the First Wave of COVID-19.,Life (Basel),33562321,2/11/21,pubmed,0,5,"computational, genomes",0.001486479,0.772489776,0.001486409,0.205578153,0.001486439,0.017472744,Genomics,0.31678405,FALSE,60.4,0.705114726,108,0.836232272,0,0.403234768,,,0.648193922
2250,An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis.,Entropy (Basel),33562309,2/11/21,pubmed,0,3,"deep learning, neural network, deep model",0.000999513,0.000999535,0.963115542,0.00099968,0.000999505,0.032886225,Imaging,0.42969036,FALSE,21.66666667,0.320737213,4.333333333,0.237958255,0,0.403234768,,,0.320643412
2251,Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis.,Cancers (Basel),33562011,2/11/21,pubmed,0,14,"deep learning, artificial intelligence, neural network",0.01882402,0.001046819,0.685354943,0.001046877,0.001046957,0.292680384,Imaging,0.88573295,TRUE,130.7857143,0.922567877,85,0.790406743,1,0.537564047,,,0.750179556
2252,Inherently High Uncertainty in Predicting the Time Evolution of Epidemics.,Epidemiol Health,33561915,2/10/21,pubmed,0,3,mathematical model,0.001593542,0.00159358,0.001593521,0.932093649,0.06153214,0.001593568,Epidemiology,0.040830344,FALSE,36,0.498299215,10.33333333,0.36038266,0,0.403234768,,,0.420638881
2253,Discovering symptom patterns of COVID-19 patients using association rule mining.,Comput Biol Med,33561673,2/10/21,pubmed,0,4,machine learning,0.082365054,0.000677924,0.106536643,0.000677946,0.223388778,0.586353655,Clinics,0.9707153,TRUE,16.75,0.252458408,0.25,0.065493712,0,0.403234768,,,0.240395629
2254,"Repurposing potential of Ayurvedic medicinal plants derived active principles against SARS-CoV-2 associated target proteins revealed by molecular docking, molecular dynamics and MM-PBSA studies.",Biomed Pharmacother,33561649,2/10/21,pubmed,0,8,"bayes, machine learning",0.935329218,0.000786386,0.03464617,0.027665519,0.000786346,0.00078636,Drug discovery,0.97583807,TRUE,45.75,0.593419506,21.5,0.498260637,0,0.403234768,,,0.49830497
2255,Temporal association of contamination obsession on the prehospital delay of STEMI during COVID-19 pandemic.,Am J Emerg Med,33561622,2/10/21,pubmed,0,12,logistic regression,0.021630312,0.001622732,0.001622714,0.001622852,0.282570688,0.690930702,Clinics,0.99407965,TRUE,48.66666667,0.619333292,5.5,0.267259834,0,0.403234768,,,0.429942631
2256,"Spatiotemporal characteristics and factor analysis of SARS-CoV-2 infections among healthcare workers in Wuhan, China.",J Hosp Infect,33561504,2/10/21,pubmed,0,6,dataset,0.001593524,0.001593534,0.001593607,0.773139997,0.054182313,0.167897025,Epidemiology,0.7931857,TRUE,10.83333333,0.161481848,0.833333333,0.102488627,0,0.403234768,,,0.222401748
2257,Current and prospective computational approaches and challenges for developing COVID-19 vaccines.,Adv Drug Deliv Rev,33561453,2/10/21,pubmed,0,6,"computational, bioinformatic, in silico",0.531189983,0.272416673,0.188921771,0.002490646,0.002490487,0.00249044,Drug discovery,0.8474064,TRUE,21.5,0.318263343,15.33333333,0.428351619,0,0.403234768,,,0.383283243
2258,Willingness to work during initial lockdown due to COVID-19 pandemic: Study based on an online survey among physicians of Bangladesh.,PLoS One,33561180,2/10/21,pubmed,0,9,logistic regression,0.001622739,0.001622723,0.001622736,0.001622787,0.927692314,0.065816701,Healthcare,0.9826315,TRUE,21.55555556,0.318448884,4.666666667,0.246721969,0,0.403234768,,,0.322801874
2259,Artificial Intelligence-Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development.,J Med Internet Res,33560998,2/10/21,pubmed,0,9,"artificial intelligence, pharmacogenom",0.091840084,0.003927435,0.642115616,0.254261348,0.003927497,0.00392802,Epidemiology,0.40210107,FALSE,57.44444444,0.684952687,45.66666667,0.661760771,0,0.403234768,,,0.583316075
2260,Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images.,IEEE J Biomed Health Inform,33560995,2/10/21,pubmed,0,7,"deep learning, dataset",0.001272637,0.025121918,0.969787517,0.001272676,0.001272635,0.001272618,Imaging,0.011615753,FALSE,73.42857143,0.779392665,,,0,0.403234768,,,0.591313716
2261,Comparative Effectiveness of Famotidine in Hospitalized COVID-19 Patients.,Am J Gastroenterol,33560648,2/10/21,pubmed,0,5,logistic regression,0.060058197,0.001350334,0.001350326,0.318106562,0.001350393,0.617784188,Clinics,0.3534095,FALSE,94.2,0.854845692,310.8,0.957452502,0,0.403234768,,,0.738510987
2262,Differentially conserved amino acid positions may reflect differences in SARS-CoV-2 and SARS-CoV behaviour.,Bioinformatics,33560365,2/10/21,pubmed,0,11,"bioinformatic, in-silico",0.656459346,0.334108768,0.002357836,0.002357906,0.002358068,0.002358077,Drug discovery,0.5082788,TRUE,82.09090909,0.814830849,139.9090909,0.873829275,0,0.403234768,,,0.697298297
2263,Association of metformin with susceptibility to COVID-19 in people with Type 2 diabetes.,J Clin Endocrinol Metab,33560344,2/10/21,pubmed,0,26,dataset,0.101018865,0.001943569,0.036349347,0.001943639,0.001943584,0.856800996,Clinics,0.87279344,TRUE,85.53846154,0.826643577,59.84615385,0.720029435,0,0.403234768,,,0.64996926
2264,The prevalence and impact of pre-existing sleep disorder diagnoses and objective sleep parameters in patients hospitalized for COVID-19.,J Clin Sleep Med,33560208,2/10/21,pubmed,0,10,logistic regression,0.001310328,0.001310397,0.001310436,0.001310364,0.321989291,0.672769185,Clinics,0.96851295,TRUE,74.2,0.782608696,77.4,0.771808938,0,0.403234768,,,0.6525508
2265,COVID-19 Imaging: What We Know Now and What Remains Unknown.,Radiology,33560192,2/10/21,pubmed,0,9,artificial intelligence,0.001717186,0.001717262,0.647570438,0.001717222,0.001717184,0.345560707,Imaging,0.735681,TRUE,237.1111111,0.982373678,258.8888889,0.941196147,1,0.537564047,,,0.820377957
2266,The Value of Triage during Periods of Intense COVID-19 Demand: Simulation Modeling Study.,Med Decis Making,33560181,2/10/21,pubmed,0,8,simulation model,0.001237182,0.001237343,0.001237186,0.468955265,0.001237085,0.526095938,Clinics,0.49566606,FALSE,23.5,0.348506401,7.875,0.314557131,0,0.403234768,,,0.355432767
2267,COVID-19 Vaccine Acceptability among U.S. Firefighters and Emergency Medical Services Workers: A Cross-Sectional Study.,J Occup Environ Med,33560073,2/10/21,pubmed,0,9,probabilistic,0.002806401,0.002806406,0.002806374,0.002806463,0.985967899,0.002806457,Healthcare,0.08770183,FALSE,74.11111111,0.782361309,42.11111111,0.645169922,0,0.403234768,,,0.610255333
2268,The ICON Trauma Study: the impact of the COVID-19 lockdown on major trauma workload in the UK.,Eur J Trauma Emerg Surg,33559697,2/10/21,pubmed,0,45,logistic regression,0.001593579,0.001593514,0.001593476,0.001593557,0.145432329,0.848193545,Clinics,0.54419976,TRUE,22.56521739,0.334157957,,,0,0.403234768,,,0.368696362
2269,Soybean-associated endophytic fungi as potential source for anti-COVID-19 metabolites supported by docking analysis.,J Appl Microbiol,33559270,2/10/21,pubmed,0,9,"in-silico, metabolom",0.912409061,0.001438183,0.001438147,0.047895268,0.001438217,0.035381124,Drug discovery,0.99069244,TRUE,37.55555556,0.514193828,8.666666667,0.331415574,0,0.403234768,,,0.41628139
2270,Genetic diversity and genomic epidemiology of SARS-CoV-2 in Morocco.,Biosaf Health,33558859,2/10/21,pubmed,0,5,"genomic epidemiology, genome sequences, genomes",0.001823336,0.963622265,0.001823352,0.001823421,0.001823347,0.029084279,Genomics,0.41249773,FALSE,15.4,0.231616055,15.8,0.434104897,0,0.403234768,,,0.356318573
2271,Proteomic investigation reveals dominant alterations of neutrophil degranulation and mRNA translation pathways in patients with COVID-19.,iScience,33558857,2/10/21,pubmed,0,19,"in silico, proteom",0.635410133,0.180018041,0.04569345,0.001461927,0.001461871,0.135954579,Drug discovery,0.34959778,FALSE,208.9473684,0.975013916,90.26315789,0.801645705,0,0.403234768,,,0.726631463
2272,A metapopulation network model for the spreading of SARS-CoV-2: Case study for Ireland.,Infect Dis Model,33558856,2/10/21,pubmed,0,6,network model,0.001511839,0.001511843,0.001511891,0.992440716,0.001511852,0.001511859,Epidemiology,0.15838379,FALSE,32.83333333,0.464592739,20,0.481000803,1,0.537564047,,,0.494385863
2273,Decreasing Incidence of Acute Kidney Injury in Patients with COVID-19 Critical Illness in New York City.,Kidney Int Rep,33558853,2/10/21,pubmed,0,7,logistic regression,0.001538118,0.001538174,0.001538083,0.001538174,0.001538128,0.992309324,Clinics,0.28981587,FALSE,72.85714286,0.777042489,97.28571429,0.817166176,1,0.537564047,,,0.710590904
2274,"SUMO pathway, blood coagulation and oxidative stress in SARS-CoV-2 infection.",Biochem Biophys Rep,33558851,2/10/21,pubmed,0,2,"transcriptom, network analysis, dataset",0.827509141,0.001310378,0.001310377,0.00131041,0.089327249,0.079232445,Drug discovery,0.93561625,TRUE,9,0.135320675,0,0.055525823,0,0.403234768,,,0.198027089
2275,COVID-19 early-alert signals using human behavior alternative data.,Soc Netw Anal Min,33558823,2/10/21,pubmed,0,5,predictive model,0.001141349,0.00114134,0.001141369,0.994293155,0.001141374,0.001141413,Epidemiology,0.13706753,FALSE,10.2,0.153627312,4.4,0.238961734,0,0.403234768,,,0.265274604
2276,Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia.,Int J Environ Sci Technol (Tehran),33558809,2/10/21,pubmed,0,8,machine learning,0.001943566,0.001943577,0.263100583,0.481492096,0.249576609,0.001943569,Epidemiology,0.82868433,TRUE,55.5,0.671346404,18.25,0.463272679,0,0.403234768,,,0.51261795
2277,Mathematical modeling of coronavirus disease COVID-19 dynamics using CF and ABC non-singular fractional derivatives.,Chaos Solitons Fractals,33558794,2/10/21,pubmed,0,3,mathematical model,0.071518801,0.003607356,0.003607223,0.914052169,0.003607271,0.003607179,Epidemiology,0.65057635,TRUE,83,0.818170573,7,0.299973241,0,0.403234768,,,0.507126194
2278,Tracking COVID-19 using online search.,NPJ Digit Med,33558607,2/10/21,pubmed,0,9,transfer learning,0.001291265,0.00129125,0.04429053,0.745108912,0.206726805,0.001291238,Epidemiology,0.09942117,FALSE,81.88888889,0.814212382,171.3333333,0.900521809,36,0.941169208,,,0.885301133
2279,"A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.",Sci Rep,33558602,2/10/21,pubmed,0,6,"machine learning, neural network",0.001156314,0.001156287,0.601800518,0.001156306,0.001156257,0.393574318,Clinics,0.5222237,TRUE,4.5,0.061784897,2,0.164302917,0,0.403234768,,,0.209774194
2280,Is area deprivation associated with greater impacts of COVID-19 in care homes across England? A preliminary analysis of COVID-19 outbreaks and deaths.,J Epidemiol Community Health,33558430,2/10/21,pubmed,0,2,dataset,0.002130649,0.00213078,0.0021307,0.403220962,0.588256108,0.0021308,Healthcare,0.79459345,TRUE,11,0.167171748,2,0.164302917,0,0.403234768,,,0.244903144
2281,"Self-Reported Symptoms of COVID-19, Including Symptoms Most Predictive of SARS-CoV-2 Infection, Are Heritable.",Twin Res Hum Genet,33558003,2/10/21,pubmed,0,15,genome-wide,0.002806565,0.526077957,0.002806899,0.197287696,0.268214291,0.002806593,Genomics,0.39389616,FALSE,241.6,0.982806605,299.6666667,0.954174472,0,0.403234768,,,0.780071948
2282,Gastrointestinal disturbance and effect of fecal microbiota transplantation in discharged COVID-19 patients.,J Med Case Rep,33557941,2/10/21,pubmed,0,12,microbiom,0.178195311,0.086361295,0.001593507,0.001593585,0.001593621,0.730662682,Clinics,0.9657197,TRUE,34.16666667,0.47863195,14.91666667,0.422263848,0,0.403234768,,,0.434710189
2283,Static compliance of the respiratory system in COVID-19 related ARDS: an international multicenter study.,Crit Care,33557868,2/10/21,pubmed,0,64,logistic regression,0.001438114,0.001438245,0.001438121,0.059863087,0.001438197,0.934384236,Clinics,0.89186764,TRUE,37.70769231,0.515554456,,,0,0.403234768,,,0.459394612
2284,Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning.,BMC Med Inform Decis Mak,33557818,2/10/21,pubmed,0,5,"deep learning, neural network, lstm, dataset",0.000863118,0.000863073,0.32134284,0.149836778,0.00086306,0.526231131,Clinics,0.8191991,TRUE,31,0.445111015,3.2,0.202100615,0,0.403234768,,,0.350148799
2285,Renal dysfunction and prognosis of COVID-19 patients: a hospital-based retrospective cohort study.,BMC Infect Dis,33557785,2/10/21,pubmed,0,8,logistic regression,0.001392844,0.001392835,0.001392821,0.001392863,0.001392848,0.993035789,Clinics,0.951435,TRUE,21.75,0.321603068,13.125,0.400990099,0,0.403234768,,,0.375275978
2286,"Prevalence and predictors of posttraumatic stress disorder, depression and anxiety among hospitalized patients with coronavirus disease 2019 in China.",BMC Psychiatry,33557776,2/10/21,pubmed,0,7,logistic regression,0.00156529,0.001565278,0.0015653,0.001565322,0.777092435,0.216646376,Healthcare,0.9328872,TRUE,40.28571429,0.542024862,5.857142857,0.273414504,0,0.403234768,,,0.406224711
2287,COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet.,BMC Med Imaging,33557772,2/10/21,pubmed,0,2,deep learning,0.075500324,0.001072193,0.920210855,0.001072192,0.001072181,0.001072256,Imaging,0.39523637,FALSE,2,0.022141134,0,0.055525823,1,0.537564047,,,0.205077001
2288,A validation of the Postpartum Specific Anxiety Scale 12-item research short-form for use during global crises with five translations.,BMC Pregnancy Childbirth,33557764,2/10/21,pubmed,0,16,model fit,0.001461947,0.001461951,0.213581802,0.140444953,0.641587405,0.001461942,Healthcare,0.9561651,TRUE,86.5,0.831653163,60.3125,0.722036393,0,0.403234768,,,0.652308108
2289,"Associations between Socioeconomic Status, Social Participation, and Physical Activity in Older People during the COVID-19 Pandemic: A Cross-Sectional Study in a Northern Japanese City.",Int J Environ Res Public Health,33557257,2/10/21,pubmed,0,10,logistic regression,0.001622689,0.00162268,0.001622673,0.001622751,0.991886472,0.001622736,Healthcare,0.9818436,TRUE,7.9,0.114354629,2.1,0.164771207,0,0.403234768,,,0.227453535
2290,"Predicting Potential SARS-COV-2 Drugs-In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking.",Int J Mol Sci,33557253,2/10/21,pubmed,0,6,"virtual screening, machine learning, neural network",0.665010524,0.001291237,0.32982451,0.001291271,0.001291266,0.001291192,Drug discovery,0.7823092,TRUE,64.66666667,0.732822067,23,0.513513514,0,0.403234768,,,0.549856783
2291,COVID-19 Management in a UK NHS Foundation Trust with a High Consequence Infectious Diseases Centre: A Retrospective Analysis.,Med Sci (Basel),33557238,2/10/21,pubmed,0,27,logistic regression,0.001511825,0.001511838,0.001511896,0.159437603,0.037440104,0.798586735,Clinics,0.908445,TRUE,33.62962963,0.472509122,49.14814815,0.677749532,2,0.618927094,,,0.589728583
2292,"Evolution of SARS-CoV-2 Envelope, Membrane, Nucleocapsid, and Spike Structural Proteins from the Beginning of the Pandemic to September 2020: A Global and Regional Approach by Epidemiological Week.",Viruses,33557213,2/10/21,pubmed,0,3,bioinformatic,0.165183666,0.813245638,0.017806811,0.001254652,0.00125463,0.001254603,Genomics,0.84105384,TRUE,49,0.624281032,31,0.578204442,0,0.403234768,,,0.53524008
2293,Depressive Symptoms in Swiss University Students during the COVID-19 Pandemic and Its Correlates.,Int J Environ Res Public Health,33557193,2/10/21,pubmed,0,7,logistic regression,0.001254608,0.001254629,0.00125462,0.001254662,0.993726806,0.001254675,Healthcare,0.66404754,TRUE,37,0.508936854,18,0.46180091,0,0.403234768,,,0.457990844
2294,"Impact of Employment, Essential Work, and Risk Factors on Food Access during the COVID-19 Pandemic in New York State.",Int J Environ Res Public Health,33557142,2/10/21,pubmed,0,2,logistic regression,0.001415104,0.001415099,0.001415105,0.001415254,0.992924301,0.001415137,Healthcare,0.9117509,TRUE,17,0.257467994,4,0.231469093,0,0.403234768,,,0.297390618
2295,Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease.,Molecules,33557115,2/10/21,pubmed,0,11,"virtual screening, computational",0.644705561,0.001330068,0.349974201,0.001330098,0.001330048,0.001330024,Drug discovery,0.64298046,TRUE,60.72727273,0.706599048,35,0.605298368,0,0.403234768,,,0.571710728
2296,In Silico Screening of Natural Products Isolated from Mexican Herbal Medicines against COVID-19.,Biomolecules,33557097,2/10/21,pubmed,0,2,in silico,0.724420776,0.002422426,0.002422326,0.265889915,0.002422302,0.002422255,Drug discovery,0.66961825,TRUE,25,0.369286907,7.5,0.307867273,0,0.403234768,,,0.360129649
2297,Wireless and battery-free platforms for collection of biosignals.,Biosens Bioelectron,33556807,2/9/21,pubmed,0,4,"machine learning, artificial intelligence",0.142677905,0.001392916,0.480219035,0.286570439,0.001392935,0.087746769,Epidemiology,0.74791646,TRUE,27.25,0.398169336,13.5,0.405539203,0,0.403234768,,,0.402314436
2298,Resilience and mental health during the COVID-19 pandemic.,J Affect Disord,33556749,2/9/21,pubmed,0,16,logistic regression,0.001415124,0.001415156,0.001415244,0.238291607,0.725527923,0.031934946,Healthcare,0.9925832,TRUE,166.625,0.956026965,246.5625,0.937115333,1,0.537564047,,,0.810235448
2299,New onset atrial fibrilation and risk faktors in COVID-19.,J Electrocardiol,33556739,2/9/21,pubmed,0,10,logistic regression,0.001203472,0.001203475,0.130477584,0.001203533,0.001203511,0.864708426,Clinics,0.41764435,FALSE,32.4,0.45983054,4.9,0.250334493,0,0.403234768,,,0.371133267
2300,Interaction of small molecules with the SARS-CoV-2 papain-like protease: In silico studies and in vitro validation of protease activity inhibition using an enzymatic inhibition assay.,J Mol Graph Model,33556646,2/9/21,pubmed,0,5,"molecular dynamics simulation, in silico",0.967928988,0.001291254,0.001291234,0.026906041,0.001291279,0.001291205,Drug discovery,0.82600856,TRUE,63.6,0.725462304,40.4,0.636138614,1,0.537564047,,,0.633054988
2301,SARS-CoV-2 mutation 614G creates an elastase cleavage site enhancing its spread in high AAT-deficient regions.,Infect Genet Evol,33556558,2/9/21,pubmed,0,8,"computational, bioinformatic",0.435053475,0.443170168,0.001371259,0.001371276,0.001371247,0.117662575,Genomics,0.17424846,FALSE,136.5,0.928690704,95,0.813085363,0,0.403234768,,,0.715003612
2302,"The Challenges of Caring for People Dying From COVID-19: A Multinational, Observational Study (CovPall).",J Pain Symptom Manage,33556496,2/9/21,pubmed,0,13,logistic regression,0.001220018,0.001220039,0.001220022,0.067366561,0.774740067,0.154233293,Healthcare,0.8068731,TRUE,136.7692308,0.928938091,129.3846154,0.8631924,1,0.537564047,,,0.776564846
2303,"Peginterferon lambda for the treatment of outpatients with COVID-19: a phase 2, placebo-controlled randomised trial.",Lancet Respir Med,33556319,2/9/21,pubmed,0,35,logistic regression,0.111526374,0.17606144,0.000786379,0.000786372,0.084150888,0.626688547,Clinics,0.32068592,FALSE,100.3142857,0.86931783,103.6857143,0.828405138,7,0.785110192,,,0.827611053
2304,A SARS-CoV-2 Cluster in an Acute Care Hospital.,Ann Intern Med,33556277,2/9/21,pubmed,0,22,"sequencing, whole-genome",0.000704946,0.303535781,0.00070492,0.000704961,0.455429247,0.238920145,Healthcare,0.48287636,FALSE,50.22727273,0.632630342,67.36363636,0.746454375,1,0.537564047,,,0.638882921
2305,Estimation of the fraction of COVID-19 infected people in U.S. states and countries worldwide.,PLoS One,33556142,2/9/21,pubmed,0,2,machine learning,0.001461847,0.001461861,0.00146196,0.992690574,0.001461867,0.001461892,Epidemiology,0.10141918,FALSE,138,0.930546107,508.5,0.979395237,0,0.403234768,,,0.771058704
2306,Effectiveness of mid-regional pro-adrenomedullin (MR-proADM) as prognostic marker in COVID-19 critically ill patients: An observational prospective study.,PLoS One,33556140,2/9/21,pubmed,0,9,logistic regression,0.001112698,0.001112661,0.00111266,0.00111268,0.001112635,0.994436668,Clinics,0.82118964,TRUE,71.11111111,0.769249799,49.66666667,0.680157881,0,0.403234768,,,0.617547482
2307,A robust pooled testing approach to expand COVID-19 screening capacity.,PLoS One,33556129,2/9/21,pubmed,0,4,mathematical model,0.001237093,0.229513411,0.254541751,0.311768127,0.201702475,0.001237142,Epidemiology,0.06946108,FALSE,25.75,0.377698064,32,0.585763982,1,0.537564047,,,0.500342031
2308,Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study.,J Med Internet Res,33556034,2/9/21,pubmed,0,18,artificial intelligence,0.000807903,0.000807911,0.641656521,0.000807904,0.355111829,0.000807932,Healthcare,0.44359064,FALSE,12.05555556,0.183251902,1.222222222,0.126304522,0,0.403234768,,,0.237597064
2309,Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of Patient Voice.,J Med Internet Res,33556031,2/9/21,pubmed,0,3,"machine learning, prediction model",0.001272689,0.001272696,0.437329928,0.001272748,0.286774726,0.272077212,Healthcare,0.3967635,FALSE,77,0.794668811,64.33333333,0.736218892,0,0.403234768,,,0.64470749
2310,"Proteo-Genomic Analysis of SARS-CoV-2: A Clinical Landscape of Single-Nucleotide Polymorphisms, COVID-19 Proteome, and Host Responses.",J Proteome Res,33555895,2/9/21,pubmed,0,7,"sequencing, proteom",0.306729295,0.64717682,0.001486424,0.001486442,0.001486408,0.041634611,Genomics,0.24132293,FALSE,15.71428571,0.236873029,21.28571429,0.494380519,0,0.403234768,,,0.378162772
2311,Clinical characteristics of COVID-19 infection in a dialysis center during a nosocomial outbreak.,Clin Exp Nephrol,33555454,2/9/21,pubmed,0,4,logistic regression,0.001565318,0.001565396,0.00156536,0.094714263,0.001565339,0.899024322,Clinics,0.62408465,TRUE,125.25,0.9145278,222.75,0.92734814,0,0.403234768,,,0.748370236
2312,Trajectories of Mental Distress Among U.S. Adults During the COVID-19 Pandemic.,Ann Behav Med,33555336,2/9/21,pubmed,0,13,logistic regression,0.001291273,0.00129127,0.001291242,0.001291298,0.965433425,0.029401492,Healthcare,0.9230491,TRUE,120.9230769,0.908343126,209.6153846,0.922063152,0,0.403234768,,,0.744547015
2313,COVID-19 reminds us: community vitamin D deficiency.,Ann Ital Chir,33554941,2/9/21,pubmed,0,2,logistic regression,0.001593588,0.001593565,0.001593597,0.001593636,0.001593635,0.992031979,Clinics,0.7595015,TRUE,36,0.498299215,7.5,0.307867273,0,0.403234768,,,0.403133752
2314,SARS-CoV-2 S protein:ACE2 interaction reveals novel allosteric targets.,Elife,33554856,2/9/21,pubmed,0,12,molecular dynamics simulation,0.993448267,0.001310382,0.001310341,0.001310371,0.001310322,0.001310318,Drug discovery,0.68364096,TRUE,34.41666667,0.48110582,26.33333333,0.540607439,0,0.403234768,,,0.474982675
2315,Targeting the N-terminal domain of the RNA-binding protein of the SARS-CoV-2 with high affinity natural compounds to abrogate the protein-RNA interaction: a molecular dynamics study.,J Biomol Struct Dyn,33554747,2/9/21,pubmed,0,5,"molecular dynamics simulation, computational",0.950527803,0.044453633,0.001254632,0.001254695,0.001254638,0.001254599,Drug discovery,0.7905299,TRUE,54.2,0.662749706,7.8,0.313286058,1,0.537564047,,,0.504533271
2316,The vasoactive peptide MR-pro-adrenomedullin in COVID-19 patients: an observational study.,Clin Chem Lab Med,33554516,2/9/21,pubmed,0,10,logistic regression,0.03135216,0.001511817,0.08578339,0.001511853,0.001511831,0.878328949,Clinics,0.8852706,TRUE,113.6,0.896839631,93.3,0.809071448,0,0.403234768,,,0.703048616
2317,Mental health difficulties of adults with COVID-19-like symptoms in Bangladesh: A cross-sectional correlational study.,J Affect Disord Rep,33554191,2/9/21,pubmed,0,10,logistic regression,0.001415149,0.001415111,0.001415125,0.001415184,0.946506992,0.047832439,Healthcare,0.46207795,FALSE,23.8,0.351227658,2.2,0.16838373,1,0.537564047,,,0.352391812
2318,Interpretable detection of novel human viruses from genome sequencing data.,NAR Genom Bioinform,33554119,2/9/21,pubmed,0,3,"machine learning, deep learning, bioinformatic, sequencing, genomes, dataset",0.001291269,0.436290659,0.54501942,0.001291276,0.014816059,0.001291317,Genomics,0.25954345,FALSE,44,0.578390748,37,0.616671127,0,0.403234768,,,0.532765547
2319,SARSCOVIDB-A New Platform for the Analysis of the Molecular Impact of SARS-CoV-2 Viral Infection.,ACS Omega,33553941,2/9/21,pubmed,0,13,"transcriptom, proteom",0.58353248,0.084080781,0.00168464,0.261291772,0.067725838,0.001684488,Drug discovery,0.5586042,TRUE,28.07692308,0.408435896,17.15384615,0.451766123,0,0.403234768,,,0.421145595
2320,Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2.,ACS Omega,33553890,2/9/21,pubmed,0,16,machine learning,0.00153812,0.354090004,0.639757546,0.00153814,0.001538086,0.001538104,Imaging,0.20312503,FALSE,20.8125,0.307749397,28.1875,0.55565962,0,0.403234768,,,0.422214595
2321,Screening possible drug molecules for Covid-19. The example of vanadium (III/IV/V) complex molecules with computational chemistry and molecular docking.,Comput Toxicol,33553857,2/9/21,pubmed,0,2,computational,0.797514704,0.00190181,0.001901838,0.165196398,0.001901743,0.031583506,Drug discovery,0.9205035,TRUE,3.5,0.044344115,0,0.055525823,1,0.537564047,,,0.212477995
2322,"Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19.",Gene Rep,33553808,2/9/21,pubmed,0,3,"bioinformatic, sequencing",0.772646342,0.057864505,0.166821874,0.000889067,0.000889071,0.000889141,Drug discovery,0.85691667,TRUE,31.33333333,0.448388892,2.333333333,0.173401124,0,0.403234768,,,0.341674928
2323,IL-6 and IL-10 as predictors of disease severity in COVID-19 patients: results from meta-analysis and regression.,Heliyon,33553782,2/9/21,pubmed,0,5,"classifier, logistic regression",0.102420257,0.001010948,0.134229574,0.001011001,0.00101094,0.760317279,Clinics,0.50444376,TRUE,26.4,0.386913229,13.4,0.404602622,0,0.403234768,,,0.398250206
2324,A data analytics approach for COVID-19 spread and end prediction (with a case study in Iran).,Model Earth Syst Environ,33553577,2/9/21,pubmed,0,2,dataset,0.002422303,0.002422265,0.21286874,0.777441889,0.00242237,0.002422434,Epidemiology,0.2494452,FALSE,1.5,0.015523533,0,0.055525823,0,0.403234768,,,0.158094708
2325,Spatio-temporal analysis of air quality and its relationship with major COVID-19 hotspot places in India.,Remote Sens Appl,33553572,2/9/21,pubmed,0,4,dataset,0.022774422,0.001538127,0.001538155,0.907897873,0.001538232,0.064713192,Epidemiology,0.8166738,TRUE,18,0.271569052,5.5,0.267259834,0,0.403234768,,,0.314021218
2326,The screening and evaluation of potential clinically significant HIV drug combinations against the SARS-CoV-2 virus.,Inform Med Unlocked,33553571,2/9/21,pubmed,0,14,in silico,0.857306799,0.002422295,0.002422312,0.039887483,0.002422282,0.09553883,Drug discovery,0.893613,TRUE,25.89361702,0.378996846,31.95744681,0.584024619,0,0.403234768,,,0.455418744
2327,Risk Factors in Patients with Diabetes Hospitalized for COVID-19: Findings from a Multicenter Retrospective Study.,J Diabetes Res,33553435,2/9/21,pubmed,0,24,logistic regression,0.04829334,0.001684498,0.001684544,0.001684539,0.001684611,0.944968467,Clinics,0.9829244,TRUE,72.375,0.774816006,21.625,0.498929623,0,0.403234768,,,0.558993465
2328,Deep Learning-Based Haptic Guidance for Surgical Skills Transfer.,Front Robot AI,33553246,2/9/21,pubmed,0,3,deep learning,0.001098883,0.001098833,0.596712818,0.238699421,0.161291186,0.00109886,Epidemiology,0.525562,TRUE,78,0.798812543,25,0.529435376,0,0.403234768,,,0.577160895
2329,Short-Term Variations in Neutrophil-to-Lymphocyte and Urea-to-Creatinine Ratios Anticipate Intensive Care Unit Admission of COVID-19 Patients in the Emergency Department.,Front Med (Lausanne),33553217,2/9/21,pubmed,0,21,logistic regression,0.001046843,0.001046846,0.001046869,0.001046882,0.001046849,0.994765711,Clinics,0.9199679,TRUE,55.19047619,0.669119921,34.04761905,0.599143698,0,0.403234768,,,0.557166129
2330,Computational Characterizations of the Interactions Between the Pontacyl Violet 6R and Exoribonuclease as a Potential Drug Target Against SARS-CoV-2.,Front Chem,33553109,2/9/21,pubmed,0,2,"virtual screening, molecular dynamics simulation, computational",0.989346768,0.002130661,0.002130647,0.002130672,0.002130644,0.002130607,Drug discovery,0.58433455,TRUE,21,0.312016822,6,0.280037463,0,0.403234768,,,0.331763018
2331,"The Combined Impact of Gender and Age on Post-traumatic Stress Symptoms, Depression, and Insomnia During COVID-19 Outbreak in China.",Front Public Health,33553099,2/9/21,pubmed,0,10,logistic regression,0.001392813,0.001392858,0.001392815,0.001392909,0.889011302,0.105417302,Healthcare,0.92788535,TRUE,9.5,0.143051518,,,0,0.403234768,,,0.273143143
2332,Adaptive Time-Dependent Priors and Bayesian Inference to Evaluate SARS-CoV-2 Public Health Measures Validated on 31 Countries.,Front Public Health,33553088,2/9/21,pubmed,0,6,bayes,0.001622736,0.001622776,0.001622726,0.991886248,0.001622738,0.001622776,Epidemiology,0.1846767,FALSE,68.5,0.756571217,25.33333333,0.531576131,0,0.403234768,,,0.563794038
2333,Stability analysis and optimal control of Covid-19 pandemic SEIQR fractional mathematical model with harmonic mean type incidence rate and treatment.,Results Phys,33552882,2/9/21,pubmed,0,5,mathematical model,0.015998844,0.01599879,0.015998462,0.920007014,0.015998498,0.015998394,Epidemiology,0.6926776,TRUE,48.8,0.620755767,2.6,0.18256623,0,0.403234768,,,0.402185588
2334,Dynamics of fractional order COVID-19 model with a case study of Saudi Arabia.,Results Phys,33552881,2/9/21,pubmed,0,5,mathematical model,0.002422318,0.002422355,0.002422305,0.987888492,0.002422287,0.002422242,Epidemiology,0.6415415,TRUE,39.2,0.53182015,5.2,0.259700294,3,0.667819001,,,0.486446482
2335,The rhythms of the night: increase in online night activity and emotional resilience during the spring 2020 Covid-19 lockdown.,EPJ Data Sci,33552837,2/9/21,pubmed,0,4,dataset,0.037255296,0.001415165,0.001415154,0.586287867,0.372211375,0.001415143,Epidemiology,0.5895203,TRUE,47.25,0.608015338,56,0.706850415,0,0.403234768,,,0.572700173
2336,A robust study on 2019-nCOV outbreaks through non-singular derivative.,Eur Phys J Plus,33552828,2/9/21,pubmed,0,3,mathematical model,0.002562662,0.002562575,0.002562608,0.987187039,0.002562564,0.002562552,Epidemiology,0.6380908,TRUE,51.66666667,0.644195683,9.666666667,0.348274017,2,0.618927094,,,0.537132265
2337,0,Biodes Manuf,33552630,2/9/21,pubmed,0,3,molecular dynamics simulation,0.973851049,0.001272705,0.001272663,0.001272651,0.001272689,0.021058243,Drug discovery,0.23839664,FALSE,23.33333333,0.345723298,3.666666667,0.217621086,0,0.403234768,,,0.322193051
2338,Anticoagulation in COVID-19: a single-center retrospective study.,J Community Hosp Intern Med Perspect,33552407,2/9/21,pubmed,0,7,logistic regression,0.085446304,0.001291223,0.001291237,0.001291275,0.001291249,0.909388712,Clinics,0.6650702,TRUE,59.14285714,0.697136496,8.285714286,0.324324324,0,0.403234768,,,0.474898529
2339,Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug-Target Interaction Model.,Cognit Comput,33552306,2/9/21,pubmed,0,10,"deep learning, dataset",0.763853774,0.001901704,0.202940957,0.001901941,0.001901699,0.027499925,Drug discovery,0.42797425,FALSE,47.1,0.606592863,13.3,0.403331549,0,0.403234768,,,0.47105306
2340,The Spatial and Cell-Type Distribution of SARS-CoV-2 Receptor ACE2 in the Human and Mouse Brains.,Front Neurol,33551947,2/9/21,pubmed,0,8,"transcriptom, dataset",0.67900477,0.001901911,0.001901813,0.282775098,0.032514617,0.00190179,Drug discovery,0.5853739,TRUE,39.75,0.536829736,24,0.521808938,3,0.667819001,,,0.575485892
2341,Psychological Resilience as a Protective Factor for Depression and Anxiety Among the Public During the Outbreak of COVID-19.,Front Psychol,33551929,2/9/21,pubmed,0,14,logistic regression,0.001141312,0.001141302,0.001141314,0.001141309,0.994293384,0.001141378,Healthcare,0.9811443,TRUE,16.78571429,0.252582102,7.5,0.307867273,0,0.403234768,,,0.321228047
2342,CSF3 Is a Potential Drug Target for the Treatment of COVID-19.,Front Physiol,33551833,2/9/21,pubmed,0,8,dataset,0.945967032,0.023409292,0.001371308,0.026509759,0.001371334,0.001371276,Drug discovery,0.86966425,TRUE,64.5,0.731832519,48.375,0.675341183,0,0.403234768,,,0.60346949
2343,Novel Targets of SARS-CoV-2 Spike Protein in Human Fetal Brain Development Suggest Early Pregnancy Vulnerability.,Front Neurosci,33551727,2/9/21,pubmed,0,4,"sequencing, dataset",0.709393282,0.145576233,0.031462753,0.001538286,0.060937854,0.051091592,Drug discovery,0.33550757,FALSE,21.5,0.318263343,73.75,0.763312818,1,0.537564047,,,0.539713403
2344,Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics.,Neural Process Lett,33551665,2/9/21,pubmed,0,10,"machine learning, deep learning, dataset",0.000977443,0.000977535,0.75260057,0.056660459,0.039129417,0.149654576,Clinics,0.86005676,TRUE,22.3,0.328715443,7.5,0.307867273,0,0.403234768,,,0.346605828
2345,The Ockham's razor applied to COVID-19 model fitting French data.,Annu Rev Control,33551664,2/9/21,pubmed,0,2,model fit,0.003214172,0.003214251,0.003214164,0.983928805,0.003214215,0.003214392,Epidemiology,0.3416466,FALSE,169.5,0.957944214,57.5,0.712938186,2,0.618927094,,,0.763269831
2346,Online learning: How do brick and mortar schools stack up to virtual schools?,Educ Inf Technol (Dordr),33551662,2/9/21,pubmed,0,1,active learning,0.001112643,0.001112629,0.169537996,0.05214643,0.774977658,0.001112644,Healthcare,0.7547727,TRUE,1,0.012307502,0,0.055525823,2,0.618927094,,,0.22892014
2347,Computational drug screening against the SARS-CoV-2 Saudi Arabia isolates through a multiple-sequence alignment approach.,Saudi J Biol Sci,33551661,2/9/21,pubmed,0,6,"computational, data mining, genomic structure, sequence alignment",0.477505438,0.4960735,0.022952265,0.001156314,0.001156262,0.001156222,Genomics,0.81039363,TRUE,18.16666667,0.272682293,2,0.164302917,0,0.403234768,,,0.280073326
2348,Temperature and Latitude Correlate with SARS-CoV-2 Epidemiological Variables but not with Genomic Change Worldwide.,Evol Bioinform Online,33551640,2/9/21,pubmed,0,6,genome sequences,0.001330203,0.180595471,0.001330017,0.600063267,0.001330052,0.21535099,Epidemiology,0.33319175,FALSE,44.33333333,0.580679077,20,0.481000803,1,0.537564047,,,0.533081309
2349,Optimal control of a fractional order model for the COVID - 19 pandemic.,Chaos Solitons Fractals,33551581,2/9/21,pubmed,0,2,mathematical model,0.007061661,0.007061559,0.007061843,0.964691911,0.007061609,0.007061417,Epidemiology,0.8577728,TRUE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
2350,A classification of countries and regions by degree of the spread of coronavirus based on statistical criteria.,Expert Syst Appl,33551577,2/9/21,pubmed,0,2,classifier,0.001593489,0.001593533,0.067685557,0.925940437,0.001593492,0.001593491,Epidemiology,0.36534923,FALSE,16.5,0.249366071,0.5,0.087101953,0,0.403234768,,,0.246567597
2351,Determinants of the community mobility during the COVID-19 epidemic: The role of government regulations and information.,J Econ Behav Organ,33551525,2/9/21,pubmed,0,3,dataset,0.00141513,0.001415139,0.001415111,0.992924387,0.001415125,0.001415109,Epidemiology,0.15498164,FALSE,28,0.408312202,17.66666667,0.457586299,1,0.537564047,,,0.46782085
2352,"Government trust, perceptions of COVID-19 and behaviour change: cohort surveys, Singapore.",Bull World Health Organ,33551503,2/9/21,pubmed,0,13,logistic regression,0.000734175,0.000734173,0.000734164,0.131861699,0.865201598,0.000734192,Healthcare,0.98852086,TRUE,41.07692308,0.549199085,36.84615385,0.615734546,1,0.537564047,,,0.567499226
2353,COVIDetection-Net: A tailored COVID-19 detection from chest radiography images using deep learning.,Optik (Stuttg),33551492,2/9/21,pubmed,0,2,"deep learning, dataset",0.001622728,0.001622751,0.9918864,0.001622722,0.001622696,0.001622702,Imaging,0.7682834,TRUE,30,0.432432432,0.5,0.087101953,0,0.403234768,,,0.307589718
2354,Liver fibrosis in patients with metabolic associated fatty liver disease is a risk factor for adverse outcomes in COVID-19.,Dig Liver Dis,33551355,2/9/21,pubmed,0,18,logistic regression,0.001565289,0.001565302,0.109569329,0.001565299,0.025637857,0.860096924,Clinics,0.82431054,TRUE,43.55555556,0.573690395,23.66666667,0.518062617,1,0.537564047,,,0.543105686
2355,Psychological Adjustment in Spain during the COVID-19 Pandemic: Positive and Negative Mental Health Outcomes in the General Population.,Span J Psychol,33551011,2/9/21,pubmed,0,6,logistic regression,0.002238503,0.002238561,0.002238447,0.100728658,0.86281526,0.029740571,Healthcare,0.6189964,TRUE,42.16666667,0.55915641,32,0.585763982,1,0.537564047,,,0.560828146
2356,The computational intervention of macrolide antibiotics in the treatment of COVID-19.,Curr Pharm Des,33550966,2/9/21,pubmed,0,5,computational,0.981143729,0.000977453,0.014946529,0.000977435,0.000977427,0.000977428,Drug discovery,0.80548674,TRUE,166.6,0.955965118,46.2,0.664704308,0,0.403234768,,,0.674634731
2357,Natural Products as Potential Agents Against SARS-CoV and SARS-CoV-2.,Curr Med Chem,33550959,2/9/21,pubmed,0,12,"computational, in silico",0.993448049,0.001310416,0.001310425,0.001310459,0.001310338,0.001310313,Drug discovery,0.94128406,TRUE,77.41666667,0.796091286,17.91666667,0.459860851,0,0.403234768,,,0.553062301
2358,[Hypoxia and inflammation are risk factors for acute myocardial injury in patients with coronavirus disease 2019].,Beijing Da Xue Xue Bao Yi Xue Ban,33550351,2/8/21,pubmed,0,10,logistic regression,0.036609938,0.001371262,0.001371255,0.001371299,0.001371256,0.95790499,Clinics,0.96497506,TRUE,14.8,0.222957511,3.9,0.223374364,0,0.403234768,,,0.283188881
2359,[Homologous modeling and binding ability analysis of Spike protein after point mutation of severe acute respiratory syndrome coronavirus 2 to receptor proteins and potential antiviral drugs].,Beijing Da Xue Xue Bao Yi Xue Ban,33550350,2/8/21,pubmed,0,3,"computational, bioinformatic, sequencing, structural model",0.753283909,0.243424434,0.000822908,0.000822933,0.000822913,0.000822904,Drug discovery,0.7247183,TRUE,18,0.271569052,2.333333333,0.173401124,0,0.403234768,,,0.282734981
2360,Knowledge-attitude-practice and psychological status of college students during the early stage of COVID-19 outbreak in China: a cross-sectional study.,BMJ Open,33550266,2/8/21,pubmed,0,6,logistic regression,0.001220011,0.001220031,0.001220001,0.001220028,0.993899899,0.00122003,Healthcare,0.98495543,TRUE,5,0.070752675,3.833333333,0.222103291,0,0.403234768,,,0.232030245
2361,Novel insights into impacts of the COVID-19 pandemic on aquatic environment of Beijing-Hangzhou Grand Canal in southern Jiangsu region.,Water Res,33550167,2/8/21,pubmed,0,9,correlation analysis,0.095609587,0.002080627,0.063332116,0.789689354,0.002080631,0.047207686,Epidemiology,0.99243355,TRUE,87.77777778,0.836291669,61.44444444,0.725916511,0,0.403234768,,,0.655147649
2362,Long-term exposure to fine particulate matter and hospitalization in COVID-19 patients.,Respir Med,33550152,2/8/21,pubmed,0,8,logistic regression,0.001593466,0.001593479,0.001593522,0.001593624,0.128630762,0.864995146,Clinics,0.67898554,TRUE,68,0.753973653,37.375,0.618477388,0,0.403234768,,,0.59189527
2363,Non-invasive positive pressure ventilation versus endotracheal intubation in treatment of COVID-19 patients requiring ventilatory support.,Am J Emerg Med,33550100,2/8/21,pubmed,0,11,logistic regression,0.001126775,0.001126782,0.001126778,0.001126821,0.001126848,0.994365996,Clinics,0.7449284,TRUE,18.09090909,0.271940132,6.727272727,0.291209526,1,0.537564047,,,0.366904569
2364,A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.,Comput Biol Med,33550068,2/8/21,pubmed,0,46,artificial intelligence,0.000988415,0.000988396,0.700567626,0.141978825,0.000988427,0.154488311,Imaging,0.7643628,TRUE,162.8695652,0.953243862,70.10869565,0.753679422,0,0.403234768,,,0.703386017
2365,Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information.,Med Image Anal,33550007,2/8/21,pubmed,0,25,artificial intelligence,0.001187259,0.001187292,0.894640083,0.001187281,0.037651732,0.064146352,Imaging,0.56126356,TRUE,57.08,0.682355124,30.72,0.57452502,0,0.403234768,,,0.553371637
2366,Comparison of droplet spread in standard and laminar flow operating theatres: SPRAY study group.,J Hosp Infect,33549768,2/8/21,pubmed,0,10,image analysis,0.001987291,0.001987233,0.156942644,0.790011913,0.047083751,0.001987169,Epidemiology,0.7546285,TRUE,2.4,0.025419012,0.5,0.087101953,0,0.403234768,,,0.171918578
2367,Mutational analysis and assessment of its impact on proteins of SARS-CoV-2 genomes from India.,Gene,33549714,2/8/21,pubmed,0,2,genomes,0.002238511,0.791343462,0.002238459,0.002238548,0.002238595,0.199702424,Genomics,0.5442982,TRUE,8.5,0.126662131,2,0.164302917,0,0.403234768,,,0.231399939
2368,Health disparities: Intracellular consequences of social determinants of health.,Toxicol Appl Pharmacol,33549591,2/8/21,pubmed,0,3,immunome,0.476237922,0.001072253,0.001072185,0.166426707,0.263474313,0.09171662,Drug discovery,0.6568363,TRUE,261,0.986208176,317,0.958857372,1,0.537564047,,,0.827543199
2369,Obesity as a risk factor for hospitalization in COronaVirus Disease-19 (COVID-19) patients: Analysis of the Tuscany regional database.,Nutr Metab Cardiovasc Dis,33549434,2/8/21,pubmed,0,10,logistic regression,0.002183187,0.002183179,0.00218319,0.002183223,0.002183253,0.989083968,Clinics,0.5265884,TRUE,102.7,0.874822191,79.4,0.777160824,0,0.403234768,,,0.685072594
2370,COVID-19: Understanding Inter-Individual Variability and Implications for Precision Medicine.,Mayo Clin Proc,33549263,2/8/21,pubmed,0,8,artificial intelligence,0.330499568,0.07133167,0.068489399,0.218002096,0.001072218,0.310605048,Drug discovery,0.8428453,TRUE,99.875,0.868513823,698.5,0.987222371,1,0.537564047,,,0.797766747
2371,"Clinical characteristics and risk factors for mortality in patients with coronavirus disease 2019 in intensive care unit: a single- center, retrospective, observational study in China.",Ann Palliat Med,33548994,2/8/21,pubmed,0,10,logistic regression,0.001272707,0.001272623,0.001272701,0.001272664,0.001272672,0.993636633,Clinics,0.8128814,TRUE,65.8,0.739068588,18.1,0.462135403,0,0.403234768,,,0.534812919
2372,Prognostic bioindicators in severe COVID-19 patients.,Cytokine,33548798,2/7/21,pubmed,0,15,logistic regression,0.00123721,0.017611048,0.081028972,0.001237059,0.001237052,0.897648658,Clinics,0.82317555,TRUE,139.6666667,0.933205517,69.2,0.751003479,1,0.537564047,,,0.740591014
2373,Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models.,J Biomed Inform,33548541,2/7/21,pubmed,0,13,dataset,0.000793466,0.000793427,0.265751455,0.542150203,0.000793429,0.189718019,Epidemiology,0.38873783,FALSE,7.769230769,0.112932154,1.307692308,0.128846668,0,0.403234768,,,0.21500453
2374,Genomic monitoring of SARS-CoV-2 uncovers an Nsp1 deletion variant that modulates type I interferon response.,Cell Host Microbe,33548198,2/7/21,pubmed,0,74,sequencing,0.126823619,0.786942918,0.001901799,0.001901733,0.001901676,0.080528254,Genomics,0.3766784,FALSE,21.22972973,0.313748531,,,1,0.537564047,,,0.425656289
2375,"Impact of the COVID-19 pandemic on faecal immunochemical test-based colorectal cancer screening programmes in Australia, Canada, and the Netherlands: a comparative modelling study.",Lancet Gastroenterol Hepatol,33548185,2/7/21,pubmed,0,13,simulation model,0.111610566,0.000554687,0.000554709,0.540969557,0.000554707,0.345755773,Epidemiology,0.9120989,TRUE,60.07692308,0.703568557,36.92307692,0.616002141,0,0.403234768,,,0.574268488
2376,"COVID-19 in chronic kidney disease: a retrospective, propensity score-matched cohort study.",Int Urol Nephrol,33548044,2/7/21,pubmed,0,15,logistic regression,0.001392823,0.001392828,0.001392856,0.001392864,0.001392828,0.9930358,Clinics,0.81515193,TRUE,36.93333333,0.507390686,9.933333333,0.352154134,1,0.537564047,,,0.465702956
2377,Implications of liver injury in risk-stratification and management of patients with COVID-19.,Hepatol Int,33548030,2/7/21,pubmed,0,17,transcriptom,0.126611549,0.001272718,0.001272695,0.001272646,0.001272629,0.868297763,Clinics,0.8596226,TRUE,50.41176471,0.634671285,10.52941176,0.363526893,1,0.537564047,,,0.511920742
2378,Perceptions of Seasonal Influenza Vaccine Among U.S. Army Civilians and Dependents in the Kaiserslautern Military Community: A Mixed-Methods Survey.,Mil Med,33547793,2/7/21,pubmed,0,6,logistic regression,0.00112686,0.031602641,0.001126809,0.077968205,0.887048549,0.001126936,Healthcare,0.59842974,TRUE,6.5,0.093512277,8.5,0.329141022,0,0.403234768,,,0.275296022
2379,Pandemic nightmares: Effects on dream activity of the COVID-19 lockdown in Italy.,J Sleep Res,33547703,2/7/21,pubmed,0,16,logistic regression,0.001310354,0.001310401,0.020064897,0.001310437,0.944977917,0.031025994,Healthcare,0.97026145,TRUE,94.0625,0.854721999,54.875,0.700695745,0,0.403234768,,,0.652884171
2380,"Influence of age and gender on the epidemic of COVID-19 : Evidence from 177 countries and territories-an exploratory, ecological study.",Wien Klin Wochenschr,33547492,2/7/21,pubmed,0,7,correlation analysis,0.001330077,0.001330105,0.00133005,0.354948536,0.183284015,0.457777216,Clinics,0.8534349,TRUE,64.71428571,0.733069454,19.28571429,0.473374364,0,0.403234768,,,0.536559529
2381,PACIFIC: a lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses.,Sci Rep,33547380,2/7/21,pubmed,0,5,"classifier, in silico, deep-learning, dataset",0.052505808,0.49475967,0.224673546,0.002422359,0.00242225,0.223216366,Genomics,0.41431296,FALSE,54.2,0.662749706,107,0.834559807,0,0.403234768,,,0.63351476
2382,Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.,Sci Rep,33547335,2/7/21,pubmed,0,35,"machine learning, prediction model",0.001392851,0.001392855,0.265955101,0.001392881,0.001392965,0.728473347,Clinics,0.82272565,TRUE,82,0.814769002,100.8571429,0.823789136,0,0.403234768,,,0.680597635
2383,An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.,Sci Rep,33547334,2/7/21,pubmed,0,3,"deep learning, computational, artificial intelligence, bioinformatic, neural network, in silico",0.649267776,0.083840072,0.215826509,0.049020322,0.001022654,0.001022666,Drug discovery,0.26624668,FALSE,83,0.818170573,46,0.66416912,5,0.739490092,,,0.740609928
2384,"Mobility, exposure, and epidemiological timelines of COVID-19 infections in China outside Hubei province.",Sci Data,33547315,2/7/21,pubmed,0,3,dataset,0.001987202,0.157970518,0.001987401,0.759573217,0.076494378,0.001987285,Epidemiology,0.20605972,FALSE,27.66666667,0.403488156,30.66666667,0.574257426,0,0.403234768,,,0.460326783
2385,SARS-CoV-2 infection in acute pancreatitis increases disease severity and 30-day mortality: COVID PAN collaborative study.,Gut,33547182,2/7/21,pubmed,0,167,logistic regression,0.018003339,0.001461952,0.001461848,0.001461962,0.001461896,0.976149004,Clinics,0.94989336,TRUE,38.14285714,0.520069268,25.80357143,0.535523147,0,0.403234768,,,0.486275728
2386,Management of Renin-Angiotensin-Aldosterone System blockade in patients admitted to hospital with confirmed coronavirus disease (COVID-19) infection (The McGill RAAS-COVID- 19): A structured summary of a study protocol for a randomized controlled trial.,Trials,33546734,2/7/21,pubmed,0,17,mathematical model,0.089321193,0.00025245,0.000252451,0.11385794,0.112516533,0.683799433,Clinics,0.7938324,TRUE,217.7647059,0.977982559,295.3529412,0.952702703,0,0.403234768,,,0.777973343
2387,Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital.,J Transl Med,33546711,2/7/21,pubmed,0,7,machine learning,0.001046827,0.001046844,0.30895221,0.001046877,0.001046838,0.686860404,Clinics,0.75504017,TRUE,43.14285714,0.568124188,13.14285714,0.401525288,0,0.403234768,,,0.457628081
2388,An easy-to-use nomogram for predicting in-hospital mortality risk in COVID-19: a retrospective cohort study in a university hospital.,BMC Infect Dis,33546639,2/7/21,pubmed,0,10,logistic regression,0.001538176,0.001538099,0.096248195,0.001538229,0.001538094,0.897599207,Clinics,0.85847527,TRUE,65.9,0.739872596,28.8,0.560543216,0,0.403234768,,,0.567883527
2389,"Relationships among Healthcare Digitalization, Social Capital, and Supply Chain Performance in the Healthcare Manufacturing Industry.",Int J Environ Res Public Health,33546393,2/7/21,pubmed,0,2,artificial intelligence,0.001011004,0.001010972,0.379623304,0.475885435,0.141458348,0.001010937,Epidemiology,0.97634536,TRUE,27,0.3960047,2.5,0.180826866,0,0.403234768,,,0.326688778
2390,Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach.,J Clin Med,33546319,2/7/21,pubmed,0,14,"machine learning, dataset",0.001717166,0.001717228,0.714944849,0.00171723,0.141550355,0.138353172,Healthcare,0.61016244,TRUE,9,0.135320675,2.285714286,0.171193471,1,0.537564047,,,0.281359398
2391,Shedding Light on the Main Characteristics and Perspectives of Romanian Medicinal Oxygen Market.,Healthcare (Basel),33546111,2/7/21,pubmed,0,3,"logistic regression, probabilistic",0.000657808,0.000657793,0.108766996,0.582580361,0.046843568,0.260493475,Epidemiology,0.9171133,TRUE,3.333333333,0.04044777,0,0.055525823,0,0.403234768,,,0.166402787
2392,Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic.,Healthcare (Basel),33546110,2/7/21,pubmed,0,9,"machine learning, neural network",0.001538114,0.001538166,0.666708555,0.327138788,0.001538239,0.001538138,Epidemiology,0.22563145,FALSE,16.22222222,0.245160492,2.333333333,0.173401124,0,0.403234768,,,0.273932128
2393,SARS-CoV-2 evolution during treatment of chronic infection.,Nature,33545711,2/6/21,pubmed,0,35,"sequencing, whole-genome",0.121927893,0.678593923,0.02325201,0.001022646,0.00102263,0.174180899,Genomics,0.35282338,FALSE,128.2285714,0.919228153,165.0285714,0.895905807,35,0.939317242,,,0.9181504
2394,RNA sequence and ligand binding alter conformational profile of SARS-CoV-2 stem loop II motif.,Biochem Biophys Res Commun,33545635,2/6/21,pubmed,0,7,molecular dynamics simulation,0.836785172,0.157367416,0.001461851,0.001461865,0.001461841,0.001461855,Drug discovery,0.77378845,TRUE,15.28571429,0.230131734,3.285714286,0.203973776,2,0.618927094,,,0.351010868
2395,Potential APOBEC-mediated RNA editing of the genomes of SARS-CoV-2 and other coronaviruses and its impact on their longer term evolution.,Virology,33545556,2/6/21,pubmed,0,2,"genome sequences, genomes",0.209026373,0.782651187,0.002080562,0.002080671,0.002080656,0.00208055,Genomics,0.5056606,TRUE,337.5,0.993258705,838,0.990968691,1,0.537564047,,,0.840597148
2396,SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review.,Clin Imaging,33545517,2/6/21,pubmed,0,6,"deep learning, artificial intelligence",0.001291236,0.024761285,0.970073688,0.001291303,0.001291225,0.001291263,Imaging,0.91912556,TRUE,31,0.445111015,2.333333333,0.173401124,2,0.618927094,,,0.412479744
2397,Mediastinal lymphadenopathy may predict 30-day mortality in patients with COVID-19.,Clin Imaging,33545439,2/6/21,pubmed,0,6,logistic regression,0.001538066,0.001538094,0.12383636,0.001538084,0.001538104,0.870011292,Clinics,0.8365515,TRUE,20.5,0.304966294,1.666666667,0.145036125,0,0.403234768,,,0.284412395
2398,Prospective prediction of PTSD and depressive symptoms during social unrest and COVID-19 using a brief online tool.,Psychiatry Res,33545423,2/6/21,pubmed,0,8,logistic regression,0.001717179,0.001717197,0.264615925,0.001717319,0.623967228,0.106265152,Healthcare,0.85587734,TRUE,45.66666667,0.592677346,27,0.546026224,0,0.403234768,,,0.513979446
2399,"An open-sourced, web-based application to analyze weekly excess mortality based on the Short-term Mortality Fluctuations data series.",PLoS One,33544767,2/6/21,pubmed,0,3,dataset,0.001310375,0.001310367,0.001310403,0.827380019,0.001310367,0.167378469,Epidemiology,0.057028055,FALSE,93.66666667,0.853794298,156.3333333,0.888881456,0,0.403234768,,,0.715303507
2400,Socio-ecological predictors of mental health outcomes among healthcare workers during the COVID-19 pandemic in the United States.,PLoS One,33544761,2/6/21,pubmed,0,3,logistic regression,0.080523273,0.001098817,0.001098832,0.001098873,0.915081379,0.001098827,Healthcare,0.98030555,TRUE,48,0.614942173,42.33333333,0.6462403,1,0.537564047,,,0.599582173
2401,"Molecular characterization and the mutation pattern of SARS-CoV-2 during first and second wave outbreaks in Hiroshima, Japan.",PLoS One,33544733,2/6/21,pubmed,0,16,sequencing,0.001171544,0.978598561,0.001171535,0.001171562,0.001171594,0.016715204,Genomics,0.91818833,TRUE,136.375,0.928505164,97.3125,0.817233075,0,0.403234768,,,0.716324335
2402,SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19.,PLoS Comput Biol,33544720,2/6/21,pubmed,0,4,"in-silico, interactom",0.886776757,0.001141388,0.053415161,0.001141377,0.013540451,0.043984866,Drug discovery,0.5192479,TRUE,16.41666667,0.247758056,6.833333333,0.293484078,2,0.618927094,,,0.386723076
2403,Effects of COVID-19 Emergency Alert Text Messages on Practicing Preventive Behaviors: Cross-sectional Web-Based Survey in South Korea.,J Med Internet Res,33544691,2/6/21,pubmed,0,2,logistic regression,0.000846558,0.000846565,0.000846592,0.246813869,0.749799892,0.000846524,Healthcare,0.94491106,TRUE,52,0.647349867,2,0.164302917,0,0.403234768,,,0.404962517
2404,Public Opinions and Concerns Regarding the Canadian Prime Minister's Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques.,J Med Internet Res,33544690,2/6/21,pubmed,0,4,machine learning,0.001653074,0.00165308,0.121405705,0.688646392,0.184988733,0.001653017,Epidemiology,0.5303829,TRUE,26.25,0.384748593,8,0.320511105,0,0.403234768,,,0.369498155
2405,Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation.,JMIR Med Inform,33544689,2/6/21,pubmed,0,6,"computational, prediction model",0.000846545,0.000846562,0.205057491,0.442073769,0.150820485,0.200355149,Epidemiology,0.32533044,FALSE,30.33333333,0.436266931,16,0.437316029,0,0.403234768,,,0.425605909
2406,Short Communication:Evidence of Novel SARS-CoV-2 Variants Circulation in Romania.,AIDS Res Hum Retroviruses,33544010,2/6/21,pubmed,0,12,sequencing,0.001861729,0.990691262,0.001861726,0.001861792,0.001861819,0.00186167,Genomics,0.5477882,TRUE,30.75,0.441090977,12.91666667,0.397444474,1,0.537564047,,,0.458699833
2407,"Artificial intelligence, machine learning, and drug repurposing in cancer.",Expert Opin Drug Discov,33543671,2/6/21,pubmed,0,3,"machine learning, computational, artificial intelligence",0.592128106,0.018100022,0.386161416,0.001203496,0.001203444,0.001203515,Drug discovery,0.42432338,FALSE,138.6666667,0.931597501,122,0.8537597,0,0.403234768,,,0.729530656
2408,Cardiac arrest in COVID-19: characteristics and outcomes of in- and out-of-hospital cardiac arrest. A report from the Swedish Registry for Cardiopulmonary Resuscitation.,Eur Heart J,33543259,2/6/21,pubmed,0,20,logistic regression,0.001943501,0.00194353,0.001943476,0.001943614,0.001943523,0.990282357,Clinics,0.9845513,TRUE,96.05,0.858989424,111.3,0.840246187,0,0.403234768,,,0.70082346
2409,Age and frailty are independently associated with increased COVID-19 mortality and increased care needs in survivors: results of an international multi-centre study.,Age Ageing,33543243,2/6/21,pubmed,0,51,logistic regression,0.002130659,0.002130755,0.002130641,0.002130897,0.070587509,0.920889539,Clinics,0.9563728,TRUE,1,0.012307502,0,0.055525823,2,0.618927094,,,0.22892014
2410,[A cross-sectional survey of attention and knowledge level among dental students in Jiangxi province during the epidemic period of COVID-19].,Shanghai Kou Qiang Yi Xue,33543225,2/6/21,pubmed,0,6,logistic regression,0.001059353,0.001059351,0.001059441,0.078342315,0.871081962,0.047397579,Healthcare,0.13614503,FALSE,79.5,0.80493537,27.16666667,0.546829007,0,0.403234768,,,0.584999715
2411,Impact of Obesity and Its Associated Comorbid Conditions on COVID-19 Presentation.,Diabetes Metab Syndr Obes,33542640,2/6/21,pubmed,0,6,logistic regression,0.001461858,0.001461888,0.001461885,0.001461893,0.001461885,0.992690592,Clinics,0.9395988,TRUE,10.83333333,0.161481848,3.166666667,0.201097137,0,0.403234768,,,0.255271251
2412,Predicting COVID-19 mortality with electronic medical records.,NPJ Digit Med,33542473,2/6/21,pubmed,0,6,computational,0.001486489,0.001486541,0.212566348,0.001486523,0.077100999,0.705873101,Clinics,0.24669898,FALSE,71.5,0.770734121,59.16666667,0.717821782,2,0.618927094,,,0.702494332
2413,Pharmacogenomics and COVID-19: clinical implications of human genome interactions with repurposed drugs.,Pharmacogenomics J,33542445,2/6/21,pubmed,0,1,pharmacogenom,0.653930904,0.188945574,0.001415151,0.001415201,0.026371619,0.127921552,Drug discovery,0.7885953,TRUE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
2414,Saliva is more sensitive than nasopharyngeal or nasal swabs for diagnosis of asymptomatic and mild COVID-19 infection.,Sci Rep,33542443,2/6/21,pubmed,0,11,sequencing,0.001786516,0.641115494,0.111933694,0.001786574,0.241591039,0.001786683,Genomics,0.27034283,FALSE,18,0.271569052,14.54545455,0.418584426,2,0.618927094,,,0.436360191
2415,Predictors for the severe coronavirus disease 2019 (COVID-19) infection in patients with underlying liver disease: a retrospective analytical study in Iran.,Sci Rep,33542426,2/6/21,pubmed,0,10,logistic regression,0.001392811,0.00139284,0.001392828,0.001392855,0.001392887,0.993035778,Clinics,0.9079999,TRUE,12,0.183190055,1.3,0.12877977,0,0.403234768,,,0.238401531
2416,Dynamics of binding ability prediction between spike protein and human ACE2 reveals the adaptive strategy of SARS-CoV-2 in humans.,Sci Rep,33542420,2/6/21,pubmed,0,18,genome sequences,0.480421325,0.513087412,0.001622717,0.001622884,0.001622765,0.001622897,Genomics,0.39938748,FALSE,12.33333333,0.186467932,,,1,0.537564047,,,0.36201599
2417,COVID-19 in patients undergoing long-term dialysis in Ontario.,CMAJ,33542093,2/6/21,pubmed,0,10,logistic regression,0.001823322,0.001823336,0.001823357,0.001823426,0.283275501,0.709431059,Clinics,0.58682907,TRUE,63.1,0.72212258,63.8,0.734345732,0,0.403234768,,,0.619901027
2418,Severity of Chest Imaging is Correlated with Risk of Acute Neuroimaging Findings among Patients with COVID-19.,AJNR Am J Neuroradiol,33541897,2/6/21,pubmed,0,16,"neural network, logistic regression",0.001034563,0.001034562,0.391366437,0.001034558,0.001034596,0.604495284,Clinics,0.99023426,TRUE,16.625,0.250231925,7,0.299973241,0,0.403234768,,,0.317813311
2419,Relationships between the changes in sleep patterns and sleep quality among Chinese people during the 2019 coronavirus disease outbreak.,Sleep Med,33541805,2/6/21,pubmed,0,5,logistic regression,0.001461858,0.0014619,0.001461864,0.001461978,0.823145049,0.17100735,Healthcare,0.91882825,TRUE,25.2,0.370214608,8.2,0.322785657,0,0.403234768,,,0.365411678
2420,"Antibodies to SARS-CoV-2 protect against re-infection during outbreaks in care homes, September and October 2020.",Euro Surveill,33541486,2/6/21,pubmed,0,18,logistic regression,0.005697528,0.005697684,0.00569762,0.005697793,0.971511713,0.005697663,Healthcare,0.2990235,FALSE,84.22222222,0.822561692,117.2777778,0.847203639,2,0.618927094,,,0.762897475
2421,"COVID-19 Lockdown and Self-Perceived Changes of Food Choice, Waste, Impulse Buying and Their Determinants in Italy: QuarantEat, a Cross-Sectional Study.",Foods,33540825,2/6/21,pubmed,0,5,logistic regression,0.001415164,0.001415128,0.001415097,0.001415189,0.992924247,0.001415175,Healthcare,0.92215943,TRUE,99.6,0.868019049,45.6,0.661292481,0,0.403234768,,,0.644182099
2422,"The Impact of COVID-19 on the Interrelation of Physical Activity, Screen Time and Health-Related Quality of Life in Children and Adolescents in Germany: Results of the Motorik-Modul Study.",Children (Basel),33540824,2/6/21,pubmed,0,11,prediction model,0.002080587,0.002080787,0.002080849,0.002080693,0.862942875,0.128734209,Healthcare,0.89696777,TRUE,61.27272727,0.710371699,13.90909091,0.410021408,0,0.403234768,,,0.507875958
2423,miR-24 Targets the Transmembrane Glycoprotein Neuropilin-1 in Human Brain Microvascular Endothelial Cells.,Noncoding RNA,33540664,2/6/21,pubmed,0,6,bioinformatic,0.981197258,0.003760532,0.003760445,0.003760488,0.00376052,0.003760757,Drug discovery,0.734923,TRUE,45.83333333,0.594223514,28.83333333,0.560944608,0,0.403234768,,,0.51946763
2424,Young Adults View Smartphone Tracking Technologies for COVID-19 as Acceptable: The Case of Taiwan.,Int J Environ Res Public Health,33540628,2/6/21,pubmed,0,9,bayes,0.001943483,0.001943599,0.001943501,0.631580061,0.360645833,0.001943524,Epidemiology,0.5787707,TRUE,76.88888889,0.793493723,,,2,0.618927094,,,0.706210408
2425,Dynamics of a Dual SARS-CoV-2 Lineage Co-Infection on a Prolonged Viral Shedding COVID-19 Case: Insights into Clinical Severity and Disease Duration.,Microorganisms,33540596,2/6/21,pubmed,0,20,genome-wide,0.001310394,0.599265904,0.001310381,0.001310417,0.001310469,0.395492435,Genomics,0.3107344,FALSE,38.05,0.519388954,23.2,0.514383195,1,0.537564047,,,0.523778732
2426,Mechanisms of Coronavirus Nsp1-Mediated Control of Host and Viral Gene Expression.,Cells,33540583,2/6/21,pubmed,0,2,genomes,0.917333673,0.077265027,0.001350304,0.001350336,0.001350346,0.001350314,Drug discovery,0.9472836,TRUE,170.5,0.958562682,218,0.926077067,0,0.403234768,,,0.762624839
2427,Genomic Signatures of SARS-CoV-2 Associated with Patient Mortality.,Viruses,33540576,2/6/21,pubmed,0,4,genome sequences,0.001786614,0.449820396,0.001786603,0.001786609,0.001786662,0.543033117,Clinics,0.3513207,FALSE,54.5,0.665161729,60,0.720899117,0,0.403234768,,,0.596431871
2428,"Profiling of the immune repertoire in COVID-19 patients with mild, severe, convalescent, or retesting-positive status.",J Autoimmun,33540371,2/5/21,pubmed,0,17,"sequencing, transcriptom",0.460742343,0.20030426,0.05990463,0.001861694,0.001861695,0.275325378,Drug discovery,0.5367573,TRUE,36,0.498299215,29.82352941,0.568035858,0,0.403234768,,,0.489856613
2429,"Attitude, anxiety and perceived mental health care needs among parents of children with Autism Spectrum Disorder (ASD) in Saudi Arabia during COVID-19 pandemic.",Res Dev Disabil,33540358,2/5/21,pubmed,0,1,correlation analysis,0.000946119,0.000946105,0.000946118,0.000946154,0.995269424,0.000946081,Healthcare,0.88696325,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
2430,Stress and associated factors among French university students under the COVID-19 lockdown: The results of the PIMS-CoV 19 study.,J Affect Disord,33540333,2/5/21,pubmed,0,9,logistic regression,0.001392811,0.001392854,0.040443359,0.001392876,0.93279048,0.022587621,Healthcare,0.9747167,TRUE,27.77777778,0.404601398,9.666666667,0.348274017,1,0.537564047,,,0.430146487
2431,Remdesivir and systemic corticosteroids for the treatment of COVID-19: A Bayesian re-analysis.,Int J Infect Dis,33540128,2/5/21,pubmed,0,6,"bayes, probabilistic",0.001098894,0.00109879,0.001098892,0.146253111,0.001098825,0.849351487,Clinics,0.18377328,FALSE,109,0.888304781,63.66666667,0.734145036,1,0.537564047,,,0.720004621
2432,Importance of glutamine 189 flexibility in SARS-CoV-2 main protease: Lesson learned from in silico virtual screening of ChEMBL database and molecular dynamics.,Eur J Pharm Sci,33540040,2/5/21,pubmed,0,4,"virtual screening, in silico",0.808715842,0.001371303,0.024089431,0.123822864,0.00137131,0.04062925,Drug discovery,0.9176965,TRUE,15.75,0.237862577,2.75,0.187583623,0,0.403234768,,,0.276226989
2433,"FunCoup 5: Functional Association Networks in All Domains of Life, Supporting Directed Links and Tissue-Specificity.",J Mol Biol,33539890,2/5/21,pubmed,0,5,"bayes, proteom, interactom, genomes",0.443056016,0.15438917,0.077232249,0.322777133,0.001272732,0.0012727,Drug discovery,0.14220592,FALSE,34,0.477766096,873.2,0.991704576,0,0.403234768,,,0.624235146
2434,Relation of Cardiovascular Risk Factors to Mortality and Cardiovascular Events in Hospitalized Patients With Coronavirus Disease 2019 (from the Yale COVID-19 Cardiovascular Registry).,Am J Cardiol,33539857,2/5/21,pubmed,0,19,logistic regression,0.001254694,0.001254583,0.001254574,0.00125458,0.068637704,0.926343865,Clinics,0.95093423,TRUE,77.10526316,0.794978044,100.6315789,0.823521541,0,0.403234768,,,0.673911451
2435,SARS-CoV-2 entry inhibitors by dual targeting TMPRSS2 and ACE2: An in silico drug repurposing study.,Eur J Pharmacol,33539819,2/5/21,pubmed,0,6,"molecular dynamics simulation, in silico",0.992032279,0.001593522,0.001593571,0.00159361,0.001593544,0.001593475,Drug discovery,0.8364496,TRUE,29.66666667,0.42773208,5.833333333,0.273213808,1,0.537564047,,,0.412836645
2436,"A multi-regional, hierarchical-tier mathematical model of the spread and control of COVID-19 epidemics from epicentre to adjacent regions.",Transbound Emerg Dis,33539678,2/5/21,pubmed,0,7,mathematical model,0.001861713,0.001861751,0.001861709,0.990691435,0.001861717,0.001861675,Epidemiology,0.36015373,FALSE,560.7142857,0.998268291,385.1428571,0.969427348,0,0.403234768,,,0.790310136
2437,Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases.,Brief Bioinform,33539530,2/5/21,pubmed,0,8,"bioinformatic, transcriptom, dataset",0.7466742,0.001461896,0.001461894,0.001461876,0.001461856,0.247478277,Drug discovery,0.6748986,TRUE,21.875,0.323025543,3.5,0.213607172,0,0.403234768,,,0.313289161
2438,"Delirium in older patients with COVID-19: prevalence, risk factors and clinical relevance.",J Gerontol A Biol Sci Med Sci,33539505,2/5/21,pubmed,0,6,logistic regression,0.002080647,0.002080579,0.002080725,0.002080557,0.002080654,0.989596839,Clinics,0.8856012,TRUE,62,0.715814212,74.83333333,0.765854964,0,0.403234768,,,0.628301315
2439,Comparing Public Perceptions and Preventive Behaviors During the Early Phase of the COVID-19 Pandemic in Hong Kong and the United Kingdom: Cross-sectional Survey Study.,J Med Internet Res,33539309,2/5/21,pubmed,0,8,logistic regression,0.000759359,0.000759361,0.000759352,0.244021648,0.752940903,0.000759377,Healthcare,0.95964634,TRUE,30.125,0.433174593,16.875,0.44715012,0,0.403234768,,,0.42785316
2440,Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study.,J Med Internet Res,33539308,2/5/21,pubmed,0,10,"machine learning, deep learning, prediction model",0.0008229,0.000822899,0.579260279,0.000822927,0.000822915,0.41744808,Clinics,0.68563473,TRUE,28.4,0.411157153,12.4,0.390286326,0,0.403234768,,,0.401559415
2441,SARS-CoV-2 seroprevalence trends in healthy blood donors during the COVID-19 outbreak in Milan.,Blood Transfus,33539289,2/5/21,pubmed,0,17,"bayes, logistic regression",0.001392902,0.310458105,0.001392904,0.176232862,0.194861183,0.315662043,Clinics,0.50686616,TRUE,68.05882353,0.7540355,57.17647059,0.711734011,3,0.667819001,,,0.711196171
2442,"SARS-CoV-2 Testing, Positivity Rates, and Healthcare Outcomes in a Cohort of 22,481 Breast Cancer Survivors.",JCO Clin Cancer Inform,33539175,2/5/21,pubmed,0,2,logistic regression,0.001220012,0.001220006,0.00122004,0.001220042,0.554004389,0.441115511,Healthcare,0.6131418,TRUE,73,0.778464964,100.5,0.823454643,0,0.403234768,,,0.668384791
2443,The value of federated learning during and post-COVID-19.,Int J Qual Health Care,33538778,2/5/21,pubmed,0,2,machine learning,0.003761048,0.0037609,0.872453083,0.003760825,0.112503649,0.003760495,Healthcare,0.34831387,FALSE,1.5,0.015523533,0,0.055525823,0,0.403234768,,,0.158094708
2444,SARS-CoV-2 and self-medication in Cameroon: a mathematical model.,J Biol Dyn,33538240,2/5/21,pubmed,0,7,mathematical model,0.002238594,0.002238507,0.002238516,0.657296121,0.333749705,0.002238557,Epidemiology,0.7417942,TRUE,12,0.183190055,2.142857143,0.165640888,0,0.403234768,,,0.25068857
2445,COVID-19's impact on China's economy: a prediction model based on railway transportation statistics.,Disasters,33538045,2/5/21,pubmed,0,3,prediction model,0.003927369,0.003927407,0.00392754,0.980362772,0.003927469,0.003927443,Epidemiology,0.526395,TRUE,47.33333333,0.608633805,,,0,0.403234768,,,0.505934287
2446,Systematic Network and Meta-analysis on the Antiviral Mechanisms of Probiotics: A Preventive and Treatment Strategy to Mitigate SARS-CoV-2 Infection.,Probiotics Antimicrob Proteins,33537958,2/5/21,pubmed,0,5,microbiom,0.653898676,0.028380671,0.11004788,0.076425447,0.00129129,0.129956036,Drug discovery,0.8667645,TRUE,6.6,0.094007051,0.6,0.09011239,0,0.403234768,,,0.195784736
2447,Impact of COVID-19 on longitudinal ophthalmology authorship gender trends.,Graefes Arch Clin Exp Ophthalmol,33537883,2/5/21,pubmed,0,4,dataset,0.001187277,0.0011873,0.001187289,0.493458378,0.501792426,0.001187329,Healthcare,0.4894746,FALSE,14.75,0.222586431,1.75,0.148381054,0,0.403234768,,,0.258067418
2448,Data on media use and mental health during the outbreak of COVID-19 in China.,Data Brief,33537381,2/5/21,pubmed,0,5,dataset,0.001220066,0.001220001,0.001220068,0.267724199,0.727395654,0.001220012,Healthcare,0.9342073,TRUE,13,0.197352959,1,0.122023013,0,0.403234768,,,0.240870247
2449,Prognostic Value of a Clinical Biochemistry-Based Nomogram for Coronavirus Disease 2019.,Front Med (Lausanne),33537326,2/5/21,pubmed,0,8,logistic regression,0.001350407,0.001350418,0.063269946,0.001350336,0.001350312,0.931328583,Clinics,0.91231364,TRUE,56,0.675304595,28.625,0.559339042,0,0.403234768,,,0.545959468
2450,"The Dynamic Expression of Potential Mediators of Severe Acute Respiratory Syndrome Coronavirus 2 Cellular Entry in Fetal, Neonatal, and Adult Rhesus Monkeys.",Front Genet,33537060,2/5/21,pubmed,0,5,transcriptom,0.733381246,0.001684689,0.001684577,0.001684608,0.065313241,0.19625164,Drug discovery,0.8585111,TRUE,26.4,0.386913229,15.6,0.431428954,0,0.403234768,,,0.407192317
2451,"Which Factors, Smoking, Drinking Alcohol, Betel Quid Chewing, or Underlying Diseases, Are More Likely to Influence the Severity of COVID-19?",Front Physiol,33536941,2/5/21,pubmed,0,8,logistic regression,0.001330031,0.001330056,0.029309155,0.08636559,0.282590915,0.599074253,Clinics,0.8148949,TRUE,10.375,0.15535902,3.25,0.203572384,1,0.537564047,,,0.298831817
2452,The Impact of COVID-19 Quarantine on Patients With Dementia and Family Caregivers: A Nation-Wide Survey.,Front Aging Neurosci,33536898,2/5/21,pubmed,0,132,logistic regression,0.001438115,0.001438124,0.001438137,0.001438152,0.576756535,0.417490936,Healthcare,0.99135244,TRUE,67.24,0.74871668,,,0,0.403234768,,,0.575975724
2453,Public attention about COVID-19 on social media: An investigation based on data mining and text analysis.,Pers Individ Dif,33536695,2/5/21,pubmed,0,3,"data mining, correlation analysis",0.002806432,0.002806424,0.171048821,0.817725417,0.002806487,0.002806418,Epidemiology,0.50310785,TRUE,9,0.135320675,1.666666667,0.145036125,0,0.403234768,,,0.227863856
2454,Highly multiplexed oligonucleotide probe-ligation testing enables efficient extraction-free SARS-CoV-2 detection and viral genotyping.,Mod Pathol,33536572,2/5/21,pubmed,0,16,sequencing,0.001392903,0.730109433,0.26431898,0.001392919,0.001392882,0.001392884,Genomics,0.20463064,FALSE,50.6875,0.635970066,83.875,0.788132192,0,0.403234768,,,0.609112342
2455,Dysregulation of lipid metabolism and pathological inflammation in patients with COVID-19.,Sci Rep,33536486,2/5/21,pubmed,0,23,lipidom,0.638316681,0.001622749,0.026897346,0.001622782,0.001622823,0.329917618,Drug discovery,0.9330336,TRUE,77.13043478,0.795225431,34.7826087,0.603692802,0,0.403234768,,,0.600717667
2456,Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.,Sci Rep,33536460,2/5/21,pubmed,0,7,"machine learning, deep learning, artificial intelligence, prediction model",0.001272701,0.001272694,0.549432325,0.001272746,0.00127271,0.445476823,Clinics,0.8773229,TRUE,46.57142857,0.60152143,26,0.53819909,0,0.403234768,,,0.514318429
2457,Vaccine optimization for COVID-19: Who to vaccinate first?,Sci Adv,33536223,2/5/21,pubmed,0,4,mathematical model,0.002130734,0.002130693,0.002130747,0.82687757,0.002130828,0.164599428,Epidemiology,0.12179944,FALSE,9.75,0.146267549,14.25,0.415038801,0,0.403234768,,,0.321513706
2458,A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood.,Sci Adv,33536218,2/5/21,pubmed,0,32,"machine learning, sequencing, classifier",0.300016967,0.29381752,0.186918967,0.001861697,0.001861712,0.215523136,Drug discovery,0.5237466,TRUE,39.46875,0.533428165,79.53125,0.778030506,0,0.403234768,,,0.571564479
2459,Leveraging systems biology for predicting modulators of inflammation in patients with COVID-19.,Sci Adv,33536217,2/5/21,pubmed,0,4,"computational, dataset",0.82500323,0.00182335,0.001823479,0.001823406,0.001823356,0.167703178,Drug discovery,0.67408216,TRUE,18.75,0.28072237,21,0.492239765,0,0.403234768,,,0.392065634
2460,Genetic variants are identified to increase risk of COVID-19 related mortality from UK Biobank data.,Hum Genomics,33536081,2/5/21,pubmed,0,5,"genome-wide, dataset",0.222617239,0.4183666,0.001310408,0.09107571,0.001310363,0.26531968,Genomics,0.31873462,FALSE,66.8,0.745809883,13.4,0.404602622,1,0.537564047,,,0.562658851
2461,Modifiable lifestyle factors and severe COVID-19 risk: a Mendelian randomisation study.,BMC Med Genomics,33536004,2/5/21,pubmed,0,2,genome-wide,0.00168455,0.183850332,0.001684521,0.001684545,0.395512621,0.415583432,Clinics,0.57556677,TRUE,36.5,0.503123261,4,0.231469093,2,0.618927094,,,0.451173149
2462,The Use and Misuse of Mathematical Modeling for Infectious Disease Policymaking: Lessons for the COVID-19 Pandemic.,Med Decis Making,33535889,2/5/21,pubmed,0,4,mathematical model,0.003465897,0.003465932,0.003465965,0.982670305,0.00346598,0.00346592,Epidemiology,0.14513135,FALSE,70.5,0.766033768,72,0.7594327,0,0.403234768,,,0.642900412
2463,Risk factors for severe and critical Covid-19 in pregnant women in a single center in Brazil.,J Matern Fetal Neonatal Med,33535850,2/5/21,pubmed,0,9,logistic regression,0.00137126,0.001371255,0.001371312,0.001371285,0.163004319,0.83151057,Clinics,0.8751162,TRUE,34.33333333,0.480425506,27.88888889,0.552515387,1,0.537564047,,,0.523501646
2464,Acute Ischemic Stroke and COVID-19: An Analysis of 27 676 Patients.,Stroke,33535779,2/5/21,pubmed,0,13,dataset,0.001653014,0.001653038,0.001653247,0.001653128,0.049286927,0.944100646,Clinics,0.892949,TRUE,134.2307692,0.926278681,83.46153846,0.78679422,2,0.618927094,,,0.777333332
2465,Soil Lead (Pb) in New Orleans: A Spatiotemporal and Racial Analysis.,Int J Environ Res Public Health,33535687,2/5/21,pubmed,0,5,dataset,0.00213069,0.002130806,0.002130689,0.312350585,0.38819466,0.293062569,Healthcare,0.53220946,TRUE,48.8,0.620755767,78.4,0.774351084,0,0.403234768,,,0.599447206
2466,jSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data.,Brief Bioinform,33535230,2/4/21,pubmed,0,3,"sequencing, transcriptom, dataset",0.322096071,0.026019523,0.51400742,0.135706298,0.001085345,0.001085343,Drug discovery,0.5510748,TRUE,67.66666667,0.75137609,40,0.633395772,0,0.403234768,,,0.59600221
2467,Analogue discovery of safer alternatives to HCQ and CQ drugs for SAR-CoV-2 by computational design.,Comput Biol Med,33535144,2/4/21,pubmed,0,7,"virtual screening, computational",0.987186468,0.002562689,0.002562674,0.002562693,0.002562804,0.002562671,Drug discovery,0.70769787,TRUE,5,0.070752675,0.857142857,0.103224512,0,0.403234768,,,0.192403985
2468,Introduction into the Marseille geographical area of a mild SARS-CoV-2 variant originating from sub-Saharan Africa: An investigational study.,Travel Med Infect Dis,33535105,2/4/21,pubmed,0,21,"genome sequences, genomes",0.001901715,0.809824667,0.001901756,0.001901779,0.001901724,0.182568359,Genomics,0.45604125,FALSE,109.047619,0.888366627,150.4761905,0.883730265,1,0.537564047,,,0.76988698
2469,"Anxiety, sleep disorders and self-efficacy among nurses during COVID-19 pandemic: A large cross-sectional study.",J Clin Nurs,33534934,2/4/21,pubmed,0,10,logistic regression,0.001112623,0.001112613,0.001112623,0.0011127,0.994436776,0.001112665,Healthcare,0.98334974,TRUE,80.9,0.810625271,31.4,0.5802114,0,0.403234768,,,0.598023813
2470,ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways.,PLoS Comput Biol,33534793,2/4/21,pubmed,0,12,"transcriptom, dataset",0.603885056,0.247352671,0.001371346,0.144648354,0.001371284,0.001371289,Drug discovery,0.23390457,FALSE,23,0.34225988,18.33333333,0.464343056,1,0.537564047,,,0.448055661
2471,SARS-CoV-2 RNA in Swabbed Samples from Latrines and Flushing Toilets: A Case-Control Study in a Rural Latin American Setting.,Am J Trop Med Hyg,33534773,2/4/21,pubmed,0,7,logistic regression,0.002898365,0.458607579,0.002898401,0.002898396,0.263639895,0.269057365,Genomics,0.6765003,TRUE,130.5714286,0.922258643,117.7142857,0.848207118,0,0.403234768,,,0.724566843
2472,Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study.,J Med Internet Res,33534724,2/4/21,pubmed,0,6,"machine learning, artificial intelligence",0.000699385,0.000699373,0.40349684,0.00069936,0.000699363,0.593705679,Clinics,0.6927855,TRUE,22.33333333,0.330261612,10.33333333,0.36038266,0,0.403234768,,,0.364626346
2473,Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study.,JMIR Med Inform,33534723,2/4/21,pubmed,0,14,"machine learning, logistic regression",0.001010931,0.001010935,0.849779421,0.001010974,0.001010999,0.146176741,Imaging,0.80761266,TRUE,76.07142857,0.790648772,67.07142857,0.745785389,0,0.403234768,,,0.64655631
2474,Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study.,J Med Internet Res,33534722,2/4/21,pubmed,0,8,"machine learning, classifier, logistic regression",0.000988468,0.000988426,0.564480828,0.000988386,0.000988385,0.431565507,Imaging,0.9209828,TRUE,26.75,0.391799122,,,0,0.403234768,,,0.397516945
2475,Toolkit for Quickly Generating and Characterizing Molecular Probes Specific for SARS-CoV-2 Nucleocapsid as a Primer for Future Coronavirus Pandemic Preparedness.,ACS Synth Biol,33534552,2/4/21,pubmed,0,2,in silico,0.43282679,0.341961549,0.121144797,0.065841605,0.00143822,0.03678704,Drug discovery,0.20519441,FALSE,17.5,0.263776362,16.5,0.44293551,0,0.403234768,,,0.369982213
2476,A peek into the future - How a pandemic resulted in the creation of models to predict the impact on sexually transmitted infection(s) in New York State (excluding New York City).,Sex Transm Dis,33534404,2/4/21,pubmed,0,6,prediction model,0.005047536,0.005047645,0.005047666,0.701339015,0.005047964,0.278470174,Epidemiology,0.7361758,TRUE,7.666666667,0.111633373,2.5,0.180826866,0,0.403234768,,,0.231898336
2477,Risk factors associated with intensive care unit (ICU) admission and in-hospital death among adults hospitalized with COVID-19: a two-center retrospective observational study in tertiary care hospitals.,Emerg Radiol,33534017,2/4/21,pubmed,0,9,logistic regression,0.001046805,0.001046854,0.327067804,0.001046827,0.001046832,0.668744877,Clinics,0.5878837,TRUE,10.44444444,0.155977488,0.888888889,0.10422799,0,0.403234768,,,0.221146749
2478,[Nationwide exposure model for COVID-19 intensive care unit admission].,Med Klin Intensivmed Notfmed,33533980,2/4/21,pubmed,0,5,forecasting model,0.000946102,0.00094612,0.000946167,0.711692626,0.000946108,0.284522876,Epidemiology,0.5597854,TRUE,7.6,0.109283196,0.8,0.101351351,0,0.403234768,,,0.204623105
2479,Prediction of Single Point Mutations in Human Coronavirus and Their Effects on Binding to 9-O-Acetylated Sialic Acid and Hidroxychloroquine.,Acta Chim Slov,33533428,2/4/21,pubmed,0,1,in silico,0.883714651,0.041548604,0.001438163,0.070422266,0.00143818,0.001438136,Drug discovery,0.41399857,FALSE,46,0.596882924,5,0.257024351,0,0.403234768,,,0.419047348
2480,Age-associated Ligand-receptor Interactions Imputed from Nasopharyngeal Transcriptomes of COVID-19 Patients.,Immunol Invest,33533266,2/4/21,pubmed,0,2,transcriptom,0.728637964,0.056005752,0.000999529,0.000999608,0.000999558,0.212357589,Drug discovery,0.43272394,FALSE,9.5,0.143051518,0.5,0.087101953,0,0.403234768,,,0.211129413
2481,Isoelectric point determination by imaged CIEF of commercially available SARS-CoV-2 proteins and the hACE2 receptor.,Electrophoresis,33533060,2/4/21,pubmed,0,5,bioinformatic,0.623019891,0.057747514,0.062397768,0.253188028,0.001823392,0.001823407,Drug discovery,0.19268498,FALSE,37.2,0.509926402,5.8,0.272678619,1,0.537564047,,,0.440056356
2482,Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs.,Int J Comput Assist Radiol Surg,33532975,2/4/21,pubmed,0,23,"machine learning, deep learning, artificial intelligence, transfer learning",0.027590895,0.001112664,0.857913483,0.001112668,0.001112701,0.111157589,Imaging,0.6907564,TRUE,179.7826087,0.964190735,79.86956522,0.778632593,0,0.403234768,,,0.715352699
2483,Computational and Experimental Studies Reveal That Thymoquinone Blocks the Entry of Coronaviruses Into In Vitro Cells.,Infect Dis Ther,33532909,2/4/21,pubmed,0,14,computational,0.991066997,0.00178671,0.00178652,0.001786605,0.001786563,0.001786606,Drug discovery,0.8570339,TRUE,80.78571429,0.80988311,67.42857143,0.746655071,0,0.403234768,,,0.653257649
2484,Passing the Test: A model-based analysis of safe school-reopening strategies.,medRxiv,33532804,2/4/21,pubmed,0,5,network model,0.000688489,0.032907578,0.010543493,0.446308206,0.500128754,0.009423481,Healthcare,0.024270952,FALSE,13.4,0.202548086,20.8,0.489229328,0,0.403234768,,,0.365004061
2485,SARS-CoV-2 infection in pregnancy is associated with robust inflammatory response at the maternal-fetal interface.,medRxiv,33532791,2/4/21,pubmed,0,31,transcriptom,0.628280207,0.145383914,0.001291236,0.001291261,0.165807183,0.0579462,Drug discovery,0.5496087,TRUE,1.03125,0.012431195,,,1,0.537564047,,,0.274997621
2486,The World Mortality Dataset: Tracking excess mortality across countries during the COVID-19 pandemic.,medRxiv,33532789,2/4/21,pubmed,0,2,dataset,0.001786501,0.001786519,0.001786612,0.761502528,0.001786512,0.231351327,Epidemiology,0.079096705,FALSE,5,0.070752675,0,0.055525823,3,0.667819001,,,0.264699166
2487,0,bioRxiv,33532769,2/4/21,pubmed,0,10,"metagenom, genomes",0.003927358,0.909012852,0.07527756,0.003927459,0.003927403,0.003927367,Genomics,0.5278325,TRUE,25.4,0.372626631,40.8,0.638346267,0,0.403234768,,,0.471402555
2488,Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2.,bioRxiv,33532766,2/4/21,pubmed,0,2,"deep learning, neural network",0.618164885,0.163553498,0.215983744,0.000765989,0.000765926,0.000765958,Drug discovery,0.25360757,FALSE,179,0.963695961,419.5,0.972504683,1,0.537564047,,,0.82458823
2489,Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity.,iScience,33532709,2/4/21,pubmed,0,5,phylogenom,0.099754963,0.843097524,0.001786598,0.001786579,0.001786534,0.051787803,Genomics,0.41032207,FALSE,76.8,0.793184489,35.8,0.609178485,1,0.537564047,,,0.64664234
2490,From genomes to molecular dynamics - A bottom up approach in extrication of SARS CoV-2 main protease inhibitors.,Comput Toxicol,33532671,2/4/21,pubmed,0,4,"molecular dynamics simulation, genome sequences, genomes",0.741501695,0.253811947,0.001171593,0.001171625,0.001171584,0.001171556,Drug discovery,0.9310982,TRUE,21,0.312016822,0.75,0.099411292,0,0.403234768,,,0.271554294
2491,0,Heliyon,33532652,2/4/21,pubmed,0,13,exom,0.336212651,0.588223891,0.001943451,0.001943521,0.001943694,0.069732792,Genomics,0.69688725,TRUE,37.76923077,0.51642031,33.23076923,0.593992507,1,0.537564047,,,0.549325622
2492,IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) with digital drug development.,Bioeng Transl Med,33532594,2/4/21,pubmed,0,16,artificial intelligence,0.597059575,0.001684657,0.169835778,0.228050624,0.001684636,0.001684729,Drug discovery,0.40654963,FALSE,42.8125,0.565341085,28.25,0.556061012,0,0.403234768,,,0.508212288
2493,In silico virtual screening-based study of nutraceuticals predicts the therapeutic potentials of folic acid and its derivatives against COVID-19.,Virusdisease,33532517,2/4/21,pubmed,0,3,"virtual screening, in silico",0.934489843,0.00194358,0.001943492,0.05773596,0.001943612,0.001943513,Drug discovery,0.96793056,TRUE,25.66666667,0.376461129,6.666666667,0.290607439,6,0.764429903,,,0.477166157
2494,The Impact of Various Policy Factors Implemented for Controlling the Spread of COVID-19.,Mater Today Proc,33532246,2/4/21,pubmed,0,5,dataset,0.00175117,0.001751178,0.00175141,0.991243849,0.001751201,0.001751191,Epidemiology,0.7372674,TRUE,11.4,0.17131548,5.8,0.272678619,0,0.403234768,,,0.282409622
2495,Mathematical modelling and analysis of COVID-19 epidemic and predicting its future situation in Ethiopia.,Results Phys,33532177,2/4/21,pubmed,0,3,mathematical model,0.001371302,0.001371283,0.001371316,0.970423777,0.024090994,0.001371328,Epidemiology,0.6178892,TRUE,6.333333333,0.089863319,1,0.122023013,0,0.403234768,,,0.205040366
2496,Spatial statistical analysis of pre-existing mortalities of 20 diseases with COVID-19 mortalities in the continental United States.,Sustain Cities Soc,33532175,2/4/21,pubmed,0,3,logistic regression,0.027108459,0.001538209,0.001538135,0.368442719,0.193530384,0.407842094,Clinics,0.55488086,TRUE,15,0.227596017,8,0.320511105,0,0.403234768,,,0.317113963
2497,"Metabolic Healthy Obesity, Vitamin D Status, and Risk of COVID-19.",Aging Dis,33532128,2/4/21,pubmed,0,6,logistic regression,0.001593501,0.049254896,0.001593484,0.001593585,0.099597338,0.846367196,Clinics,0.68236786,TRUE,106.1666667,0.881377945,,,0,0.403234768,,,0.642306357
2498,Aurintricarboxylic acid and its metal ion complexes in comparative virtual screening versus Lopinavir and Hydroxychloroquine in fighting COVID-19 pandemic: Synthesis and characterization.,Inorg Chem Commun,33531865,2/4/21,pubmed,0,10,virtual screening,0.648899197,0.001237086,0.001237174,0.346152326,0.001237144,0.001237073,Drug discovery,0.572083,TRUE,51,0.63875317,2.8,0.188787798,0,0.403234768,,,0.410258578
2499,0,J King Saud Univ Sci,33531790,2/4/21,pubmed,0,4,bioinformatic,0.858577738,0.056194541,0.001310387,0.081296604,0.001310341,0.001310389,Drug discovery,0.9579283,TRUE,11,0.167171748,3.25,0.203572384,0,0.403234768,,,0.257992967
2500,Projections and fractional dynamics of COVID-19 with optimal control strategies.,Chaos Solitons Fractals,33531738,2/4/21,pubmed,0,3,mathematical model,0.057080685,0.003466209,0.003466122,0.929055185,0.003465954,0.003465845,Epidemiology,0.5061254,TRUE,63,0.721998887,31.66666667,0.582686647,0,0.403234768,,,0.569306767
2501,A novel Covid-19 and pneumonia classification method based on F-transform.,Chemometr Intell Lab Syst,33531722,2/4/21,pubmed,0,4,"machine learning, classifier, dataset",0.00153809,0.001538098,0.992309289,0.001538213,0.001538144,0.001538167,Imaging,0.59973204,TRUE,20.75,0.307254623,1.25,0.127776291,0,0.403234768,,,0.279421894
2502,Rethinking SME default prediction: a systematic literature review and future perspectives.,Scientometrics,33531720,2/4/21,pubmed,0,4,"machine learning, artificial intelligence, prediction model",0.001901742,0.001901712,0.372847113,0.61954583,0.001901794,0.001901809,Epidemiology,0.56662184,TRUE,81,0.811552972,808.5,0.990165908,0,0.403234768,,,0.734984549
2503,Sixteen novel lineages of SARS-CoV-2 in South Africa.,Nat Med,33531709,2/4/21,pubmed,0,31,"whole genome, genomes",0.001684494,0.950104243,0.001684474,0.043157749,0.001684562,0.001684477,Genomics,0.16515836,FALSE,64.61290323,0.732450986,112.8064516,0.842253144,18,0.891474782,,,0.822059638
2504,Implications of monsoon season and UVB radiation for COVID-19 in India.,Sci Rep,33531606,2/4/21,pubmed,0,2,dataset,0.001203447,0.001203517,0.001203406,0.788313236,0.001203493,0.2068729,Epidemiology,0.35458177,FALSE,4,0.054734368,1,0.122023013,0,0.403234768,,,0.193330716
2505,Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States.,Proc Natl Acad Sci U S A,33531345,2/4/21,pubmed,0,1,"machine learning, dataset",0.001861775,0.001861722,0.059065873,0.542633822,0.074988488,0.31958832,Epidemiology,0.4385035,FALSE,14,0.213494959,5,0.257024351,0,0.403234768,,,0.291251359
2506,"COVID-19 case fatality risk by age and gender in a high testing setting in Latin America: Chile, March-August 2020.",Infect Dis Poverty,33531085,2/4/21,pubmed,0,3,bayes,0.001072168,0.001072193,0.001072182,0.673804495,0.001072267,0.321906695,Epidemiology,0.6306598,TRUE,72.33333333,0.774630466,166.6666667,0.896842387,1,0.537564047,,,0.736345633
2507,De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning.,BMC Chem,33531083,2/4/21,pubmed,0,2,"deep learning, artificial intelligence, neural network, classifier, transfer learning",0.635853926,0.000966757,0.360278997,0.000966803,0.000966758,0.000966759,Drug discovery,0.33418888,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
2508,"Cluster analysis of coronavirus sequences using computational sequence descriptors: With applications to SARS, MERS and SARS-CoV-2 (CoVID-19).",Curr Comput Aided Drug Des,33530913,2/4/21,pubmed,0,4,"computational, genome sequences",0.003760554,0.981197316,0.003760581,0.003760584,0.003760514,0.003760452,Genomics,0.47570518,FALSE,103.5,0.876306512,45.5,0.660957988,0,0.403234768,,,0.646833089
2509,"Peripheral blood microRNAs and the COVID-19 patient: methodological considerations, technical challenges and practice points.",RNA Biol,33530819,2/4/21,pubmed,0,6,transcriptom,0.185087939,0.109246951,0.002238592,0.511242976,0.088977858,0.103205683,Epidemiology,0.70134556,TRUE,323.5,0.991959923,190.3333333,0.912295959,0,0.403234768,,,0.76916355
2510,Current Updates on Naturally Occurring Compounds Recognizing SARS-CoV-2 Druggable Targets.,Molecules,33530467,2/4/21,pubmed,0,4,"computational, in-silico",0.660551743,0.002130739,0.002130784,0.291901574,0.041154455,0.002130704,Drug discovery,0.890749,TRUE,84.5,0.823489393,28,0.554321648,0,0.403234768,,,0.593681936
2511,Human iPSC-Based Modeling of Central Nerve System Disorders for Drug Discovery.,Int J Mol Sci,33530458,2/4/21,pubmed,0,2, omics,0.808384367,0.001272708,0.168048535,0.001272693,0.019748896,0.001272801,Drug discovery,0.74868524,TRUE,96.5,0.860226359,19,0.471367407,0,0.403234768,,,0.578276178
2512,One Year of SARS-CoV-2: How Much Has the Virus Changed?,Biology (Basel),33530355,2/4/21,pubmed,0,2,proteom,0.150815066,0.844236565,0.00123707,0.001237132,0.00123709,0.001237076,Genomics,0.3022881,FALSE,12.5,0.189436576,2,0.164302917,0,0.403234768,,,0.252324754
2513,Lactic dehydrogenase-lymphocyte ratio for predicting prognosis of severe COVID-19.,Medicine (Baltimore),33530248,2/4/21,pubmed,0,9,logistic regression,0.001237084,0.001237046,0.116398723,0.001237053,0.001237066,0.878653028,Clinics,0.8132818,TRUE,32.66666667,0.462922877,7.333333333,0.304656141,0,0.403234768,,,0.390271262
2514,"Relationship between physical and mental health comorbidities and COVID-19 positivity, hospitalization, and mortality.",J Affect Disord,33530015,2/3/21,pubmed,0,6,logistic regression,0.00131035,0.001310337,0.001310423,0.00131033,0.504000847,0.490757712,Healthcare,0.8359029,TRUE,120.5,0.907848352,90.33333333,0.801779502,0,0.403234768,,,0.704287541
2515,Personalized Monitoring Model for Electrocardiogram Signals: Diagnostic Accuracy Study.,JMIR Biomed Eng,33529270,2/3/21,pubmed,0,3,"machine learning, computational",0.000916703,0.000916697,0.839127285,0.000916735,0.157205823,0.000916756,Healthcare,0.8183393,TRUE,10,0.15214299,5.333333333,0.262911426,0,0.403234768,,,0.272763061
2516,Clinical and whole genome characterization of SARS-CoV-2 in India.,PLoS One,33529260,2/3/21,pubmed,0,15,"whole genome, genome sequences",0.099554071,0.357158476,0.001565279,0.001565365,0.001565336,0.538591472,Clinics,0.49352515,FALSE,24.06666667,0.356051704,19.26666667,0.473173669,1,0.537564047,,,0.455596473
2517,Dromedary camels as a natural source of neutralizing nanobodies against SARS-CoV-2.,JCI Insight,33529170,2/3/21,pubmed,0,17,"sequencing, proteom",0.441446524,0.551259816,0.001823414,0.001823392,0.001823435,0.001823419,Genomics,0.5626088,TRUE,30.35294118,0.436328777,33.52941176,0.596133262,0,0.403234768,,,0.478565602
2518,Using Tweets to Understand How COVID-19-Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study.,J Med Internet Res,33529155,2/3/21,pubmed,0,5,"machine learning, classifier",0.000772632,0.000772641,0.171291645,0.684087939,0.142302511,0.000772632,Epidemiology,0.87398577,TRUE,164,0.95423341,108,0.836232272,0,0.403234768,,,0.731233483
2519,Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study.,J Med Internet Res,33529154,2/3/21,pubmed,0,2,"deep learning, neural network, classifier, transfer learning, dataset",0.000620833,0.000620837,0.996895783,0.000620852,0.000620838,0.000620858,Imaging,0.003103912,FALSE,13.5,0.205393036,1,0.122023013,0,0.403234768,,,0.243550272
2520,Use of dipeptidyl peptidase-4 inhibitors and prognosis of COVID-19 in hospitalized patients with type 2 diabetes: A propensity score analysis from the CORONADO study.,Diabetes Obes Metab,33528920,2/3/21,pubmed,0,39,logistic regression,0.1463518,0.001717169,0.001717215,0.063221365,0.001717274,0.785275177,Clinics,0.6511226,TRUE,46.87179487,0.604242687,39.64102564,0.631522612,1,0.537564047,,,0.591109782
2521,Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective.,J Korean Med Sci,33527788,2/3/21,pubmed,0,9,"deep learning, classifier, radiom",0.000772612,0.052101923,0.794940863,0.000772638,0.00077261,0.150639354,Imaging,0.50894445,TRUE,35.55555556,0.492733008,19.11111111,0.4717019,0,0.403234768,,,0.455889892
2522,Predictive value of the preliminary findings in the severity of COVID-19 disease and the effect on therapeutic approaches.,Dermatol Ther,33527692,2/3/21,pubmed,0,4,neural network,0.001751237,0.001751189,0.164404101,0.001751282,0.001751168,0.828591023,Clinics,0.9857492,TRUE,2.5,0.027459954,0,0.055525823,0,0.403234768,,,0.162073515
2523,T cell counts and IL-6 concentration in blood of North African COVID-19 patients are two independent prognostic factors for severe disease and death.,J Leukoc Biol,33527487,2/3/21,pubmed,0,23,logistic regression,0.15823335,0.001022621,0.001022646,0.001022651,0.001022612,0.83767612,Clinics,0.9415231,TRUE,7.173913043,0.102974828,0.652173913,0.090714477,0,0.403234768,,,0.198974691
2524,Development and validation of clinical prediction model to estimate the probability of death in hospitalized patients with COVID-19: Insights from a nationwide database.,J Med Virol,33527474,2/3/21,pubmed,0,11,"logistic regression, prediction model",0.001538108,0.001538202,0.133647944,0.050937201,0.001538076,0.81080047,Clinics,0.5589562,TRUE,174.0909091,0.961345785,29.09090909,0.56321916,0,0.403234768,,,0.642599904
2525,SARS-CoV-2 seroprevalence among blood donors after the first COVID-19 wave in Canada.,Transfusion,33527398,2/3/21,pubmed,0,6,logistic regression,0.001330019,0.177763322,0.001330048,0.272971255,0.284950111,0.261655245,Healthcare,0.5400709,TRUE,37.5,0.513946441,13,0.400521809,1,0.537564047,,,0.484010766
2526,Association between periodontitis and severity of COVID-19 infection: A case-control study.,J Clin Periodontol,33527378,2/3/21,pubmed,0,10,logistic regression,0.00162285,0.001622743,0.001622776,0.001622817,0.036572441,0.956936373,Clinics,0.99599785,TRUE,59.9,0.702022389,69.8,0.752675943,1,0.537564047,,,0.66408746
2527,"Factors associated with psychological distress among patients with breast cancer during the COVID-19 pandemic: a cross-sectional study in Wuhan, China.",Support Care Cancer,33527226,2/3/21,pubmed,0,7,logistic regression,0.001272625,0.001272656,0.001272634,0.001272672,0.647092339,0.347817074,Healthcare,0.9270268,TRUE,70.57142857,0.766343002,19.42857143,0.474310945,0,0.403234768,,,0.547962905
2528,"Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy.",Eur Radiol Exp,33527198,2/3/21,pubmed,0,10,"machine learning, deep learning, neural network, classifier, dataset",0.001098791,0.001098807,0.840714988,0.00109881,0.001098833,0.15488977,Imaging,0.7240503,TRUE,70.3,0.765106067,46.2,0.664704308,1,0.537564047,,,0.655791474
2529,SARS-COV-2 outbreak and control in Kenya - Mathematical model analysis.,Infect Dis Model,33527092,2/3/21,pubmed,0,2,mathematical model,0.001438124,0.001438143,0.001438117,0.992809264,0.001438246,0.001438107,Epidemiology,0.3539077,FALSE,18.5,0.278001113,2.5,0.180826866,0,0.403234768,,,0.287354249
2530,Variations in Orf3a protein of SARS-CoV-2 alter its structure and function.,Biochem Biophys Rep,33527091,2/3/21,pubmed,0,2,in-silico,0.43980081,0.499026001,0.001987167,0.001987259,0.001987261,0.055211503,Genomics,0.5046669,TRUE,36,0.498299215,14,0.412898047,0,0.403234768,,,0.43814401
2531,Virtual screening of phytochemical compounds as potential inhibitors against SARS-CoV-2 infection.,Beni Suef Univ J Basic Appl Sci,33527080,2/3/21,pubmed,0,5,"virtual screening, in silico",0.820947249,0.001220059,0.053635476,0.036197519,0.086779611,0.001220086,Drug discovery,0.77361953,TRUE,1.2,0.012740429,0,0.055525823,0,0.403234768,,,0.157167007
2532,Clinical immunity and medical cost of COVID-19 patients under grey relational mathematical model.,Results Phys,33527070,2/3/21,pubmed,0,4,mathematical model,0.02484866,0.000779444,0.066569628,0.065797608,0.000779441,0.841225219,Clinics,0.93088365,TRUE,14,0.213494959,0,0.055525823,0,0.403234768,,,0.224085183
2533,Analysis of the outbreak of the novel coronavirus COVID-19 dynamic model with control mechanisms.,Results Phys,33527069,2/3/21,pubmed,0,3,mathematical model,0.002130831,0.002130676,0.002130732,0.989346229,0.002130821,0.00213071,Epidemiology,0.80385506,TRUE,115.6666667,0.900241202,59,0.717554188,0,0.403234768,,,0.673676719
2534,COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network.,Ing Rech Biomed,33527035,2/3/21,pubmed,0,1,"neural network, image processing, classifier, network model, transfer learning, dataset",0.00099955,0.000999558,0.968208171,0.027793482,0.000999609,0.000999629,Imaging,0.6486677,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
2535,0,Biocatal Agric Biotechnol,33527003,2/3/21,pubmed,0,9,"molecular dynamics simulation, in-silico",0.935963946,0.001901824,0.001901734,0.001901746,0.028818337,0.029512411,Drug discovery,0.97848785,TRUE,54.33333333,0.663677407,13.88888889,0.409686915,0,0.403234768,,,0.492199697
2536,COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images.,J Ambient Intell Humaniz Comput,33527000,2/3/21,pubmed,0,5,"neural network, dataset",0.001112664,0.001112625,0.957284509,0.038264759,0.001112748,0.001112695,Imaging,0.39476413,FALSE,54.4,0.664048488,9.6,0.346735349,0,0.403234768,,,0.471339535
2537,The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19.,Pers Ubiquitous Comput,33526997,2/3/21,pubmed,0,4,machine learning,0.002720274,0.002720223,0.570770242,0.32568247,0.00272028,0.095386509,Epidemiology,0.70554996,TRUE,5.5,0.077246583,1,0.122023013,0,0.403234768,,,0.200834788
2538,0,Int J Med Sci,33526974,2/3/21,pubmed,0,7,"bioinformatic, sequencing",0.781455414,0.104828375,0.001254651,0.02629971,0.001254637,0.084907212,Drug discovery,0.8102386,TRUE,49,0.624281032,26.42857143,0.540875033,0,0.403234768,,,0.522796944
2539,Identification of natural compounds as potent inhibitors of SARS-CoV-2 main protease using combined docking and molecular dynamics simulations.,Saudi J Biol Sci,33526965,2/3/21,pubmed,0,9,"virtual screening, molecular dynamics simulation",0.959366285,0.036292313,0.001085354,0.001085409,0.001085325,0.001085314,Drug discovery,0.96672595,TRUE,45,0.587049292,5.222222222,0.259834092,0,0.403234768,,,0.41670605
2540,TLCoV- An automated Covid-19 screening model using Transfer Learning from chest X-ray images.,Chaos Solitons Fractals,33526961,2/3/21,pubmed,0,4,"deep learning, transfer learning, dataset",0.001565308,0.0015653,0.946709244,0.047029407,0.001565325,0.001565415,Imaging,0.5352656,TRUE,58.5,0.693240151,13,0.400521809,0,0.403234768,,,0.498998909
2541,How did socio-demographic status and personal attributes influence compliance to COVID-19 preventive behaviours during the early outbreak in Japan? Lessons for pandemic management.,Pers Individ Dif,33526954,2/3/21,pubmed,0,5,"data mining, dataset",0.001511827,0.001511889,0.082490694,0.187301392,0.725672387,0.00151181,Healthcare,0.4846848,FALSE,97.8,0.864184551,50.6,0.683435911,0,0.403234768,,,0.650285076
2542,Addendum: Early triage of critically ill COVID-19 patients using deep learning.,Nat Commun,33526778,2/3/21,pubmed,0,33,deep learning,0.015998345,0.015998346,0.516895731,0.015998345,0.015998391,0.419110842,Clinics,0.55464494,TRUE,89.48484848,0.841053869,177.0909091,0.904401927,0,0.403234768,,,0.716230188
2543,"The nurse COVID and historical epidemics literature repository: Development, description, and summary.",Nurs Outlook,33526252,2/3/21,pubmed,0,9,"text mining, dataset",0.001823366,0.001823388,0.00182348,0.868819981,0.123886452,0.001823333,Epidemiology,0.758986,TRUE,9.666666667,0.144968767,1.666666667,0.145036125,0,0.403234768,,,0.231079887
2544,"Flavonol morin targets host ACE2, IMP-α, PARP-1 and viral proteins of SARS-CoV-2, SARS-CoV and MERS-CoV critical for infection and survival: a computational analysis.",J Biomol Struct Dyn,33526003,2/3/21,pubmed,0,10,computational,0.993543788,0.001291275,0.001291237,0.001291265,0.001291221,0.001291214,Drug discovery,0.8816085,TRUE,26.8,0.392417589,7,0.299973241,0,0.403234768,,,0.365208532
2545,"Isolation of SARS-CoV-2 strains carrying a nucleotide mutation, leading to a stop codon in the ORF 6 protein.",Emerg Microbes Infect,33525998,2/3/21,pubmed,0,11,genome sequences,0.29626743,0.567014985,0.001823389,0.001823355,0.001823482,0.131247359,Genomics,0.37409192,FALSE,79.09090909,0.802708887,47.27272727,0.669521006,0,0.403234768,,,0.625154887
2546,Impact of dimerization and N3 binding on molecular dynamics of SARS-CoV and SARS-CoV-2 main proteases.,J Biomol Struct Dyn,33525993,2/3/21,pubmed,0,2,molecular dynamics simulation,0.871391339,0.001392862,0.001392842,0.123037212,0.001392891,0.001392853,Drug discovery,0.87681854,TRUE,150.5,0.944523471,28,0.554321648,0,0.403234768,,,0.634026629
2547,Comparing telehealth to traditional office visits for patient management in the COVID-19 pandemic: A cross-sectional study in a respiratory assessment clinic.,J Telemed Telecare,33525950,2/3/21,pubmed,0,5,logistic regression,0.001022634,0.001022623,0.001022624,0.001022645,0.554581875,0.4413276,Healthcare,0.9625642,TRUE,38,0.519327107,27.2,0.547230399,0,0.403234768,,,0.489930758
2548,Impact of the COVID-19 Pandemic on Psychiatric Admissions to a Large Swiss Emergency Department: An Observational Study.,Int J Environ Res Public Health,33525740,2/3/21,pubmed,0,12,logistic regression,0.002183213,0.002183274,0.002183236,0.002183404,0.559560084,0.431706789,Healthcare,0.9460332,TRUE,66.91666667,0.746490197,43.66666667,0.652261172,0,0.403234768,,,0.600662046
2549,Summary of the Available Molecular Methods for Detection of SARS-CoV-2 during the Ongoing Pandemic.,Int J Mol Sci,33525651,2/3/21,pubmed,0,4,in silico,0.00178664,0.957942372,0.001786657,0.001786675,0.001786517,0.034911138,Genomics,0.86826444,TRUE,290.5,0.990042674,416.25,0.972303987,0,0.403234768,,,0.788527143
2550,"The SARS-Coronavirus Infection Cycle: A Survey of Viral Membrane Proteins, Their Functional Interactions and Pathogenesis.",Int J Mol Sci,33525632,2/3/21,pubmed,0,2,"bioinformatic, genomes",0.714184489,0.210720222,0.000926277,0.072316387,0.000926302,0.000926323,Drug discovery,0.4497022,FALSE,398.5,0.995485188,1065,0.993979128,0,0.403234768,,,0.797566361
2551,Harnessing SmartPhones to Personalize Nutrition in a Time of Global Pandemic.,Nutrients,33525593,2/3/21,pubmed,0,2,machine learning,0.001717278,0.001717194,0.284040955,0.486077058,0.224730222,0.001717293,Epidemiology,0.9448526,TRUE,112,0.894180221,536.5,0.981402194,0,0.403234768,,,0.759605728
2552,Chloroquine and Hydroxychloroquine Interact Differently with ACE2 Domains Reported to Bind with the Coronavirus Spike Protein: Mediation by ACE2 Polymorphism.,Molecules,33525415,2/3/21,pubmed,0,4,computational,0.778168247,0.212400446,0.002357719,0.002357932,0.002357796,0.00235786,Drug discovery,0.6731267,TRUE,22.5,0.333539489,3,0.199424672,0,0.403234768,,,0.31206631
2553,Computational Determination of Potential Multiprotein Targeting Natural Compounds for Rational Drug Design Against SARS-COV-2.,Molecules,33525411,2/3/21,pubmed,0,5,"molecular dynamics simulation, computational",0.979342961,0.000800565,0.000800591,0.017454703,0.000800585,0.000800596,Drug discovery,0.944116,TRUE,13.2,0.199270208,14.8,0.421527964,0,0.403234768,,,0.341344313
2554,Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control.,Healthcare (Basel),33525393,2/3/21,pubmed,0,4,computational,0.002080547,0.002080535,0.432728797,0.558948904,0.002080668,0.002080548,Epidemiology,0.9409461,TRUE,15.5,0.234028078,13.75,0.408348943,0,0.403234768,,,0.348537263
2555,Controlling the spread of COVID-19 on college campuses.,Math Biosci Eng,33525107,2/3/21,pubmed,0,6,mathematical model,0.004775269,0.004775392,0.004775308,0.749807092,0.231091744,0.004775195,Epidemiology,0.523634,TRUE,12.33333333,0.186467932,93.66666667,0.810008028,4,0.707574542,,,0.568016834
2556,"A mathematical model for the impacts of face mask, hospitalization and quarantine on the dynamics of COVID-19 in India: deterministic vs. stochastic.",Math Biosci Eng,33525087,2/3/21,pubmed,0,5,mathematical model,0.001486411,0.001486447,0.00148646,0.992567742,0.001486475,0.001486466,Epidemiology,0.32794178,FALSE,212,0.976189004,56.2,0.707586299,1,0.537564047,,,0.74044645
2557,COVID-19 convalescent plasma composition and immunological effects in severe patients.,J Autoimmun,33524876,2/2/21,pubmed,0,15,"metabolom, lipidom",0.269974108,0.148305956,0.000999558,0.014094872,0.000999552,0.565625954,Clinics,0.4863603,FALSE,20.6,0.305832148,7.133333333,0.300307733,1,0.537564047,,,0.381234643
2558,RNA-dependent RNA polymerase (RdRp) inhibitors: The current landscape and repurposing for the COVID-19 pandemic.,Eur J Med Chem,33524687,2/2/21,pubmed,0,12,genomes,0.795182307,0.197524207,0.001823344,0.001823414,0.001823385,0.001823343,Drug discovery,0.78520656,TRUE,45.66666667,0.592677346,22.33333333,0.505753278,1,0.537564047,,,0.545331557
2559,"Air quality and health impact of 2019-20 Black Summer megafires and COVID-19 lockdown in Melbourne and Sydney, Australia.",Environ Pollut,33524649,2/2/21,pubmed,0,3,"machine learning, dataset",0.001187314,0.001187318,0.115079581,0.742075512,0.139282969,0.001187306,Epidemiology,0.74759746,TRUE,30.33333333,0.436266931,25.33333333,0.531576131,0,0.403234768,,,0.457025943
2560,A global analysis of replacement of genetic variants of SARS-CoV-2 in association with containment capacity and changes in disease severity.,Clin Microbiol Infect,33524589,2/2/21,pubmed,0,9,"genomes, logistic regression",0.00129123,0.369834975,0.001291198,0.346947883,0.00129127,0.279343444,Genomics,0.1860703,FALSE,31.88888889,0.453769559,24.55555556,0.525421461,0,0.403234768,,,0.460808596
2561,Inhibitory efficiency of potential drugs against SARS-CoV-2 by blocking human angiotensin converting enzyme-2: Virtual screening and molecular dynamics study.,Microb Pathog,33524563,2/2/21,pubmed,0,4,virtual screening,0.989836183,0.002032754,0.002032769,0.002032817,0.002032728,0.002032749,Drug discovery,0.7517377,TRUE,29.25,0.421671099,8,0.320511105,1,0.537564047,,,0.426582084
2562,Smartwatch Electrocardiogram and Artificial Intelligence for Assessing Cardiac-Rhythm Safety of Drug Therapy in the COVID-19 Pandemic. The QT-logs study.,Int J Cardiol,33524462,2/2/21,pubmed,0,14,artificial intelligence,0.1125663,0.001593552,0.167684921,0.25837661,0.001593533,0.458185084,Clinics,0.9822583,TRUE,38.57142857,0.524645927,21.64285714,0.49906342,0,0.403234768,,,0.475648038
2563,Dysregulated innate and adaptive immune responses discriminate disease severity in COVID-19.,J Infect Dis,33524124,2/2/21,pubmed,0,38,proteom,0.565051326,0.113409044,0.00182333,0.001823427,0.001823316,0.316069556,Drug discovery,0.85942763,TRUE,98.94736842,0.866720267,131,0.864597271,0,0.403234768,,,0.711517435
2564,Simulation-based what-if analysis for controlling the spread of Covid-19 in universities.,PLoS One,33524045,2/2/21,pubmed,0,1,simulation model,0.001371276,0.001371271,0.001371277,0.665345052,0.32916987,0.001371253,Epidemiology,0.35709944,FALSE,12,0.183190055,1,0.122023013,0,0.403234768,,,0.236149279
2565,Metabolomic/lipidomic profiling of COVID-19 and individual response to tocilizumab.,PLoS Pathog,33524041,2/2/21,pubmed,0,18,"metabolom, lipidom",0.248142422,0.269659809,0.045531527,0.002639142,0.002639106,0.431387994,Clinics,0.8675147,TRUE,131.6666667,0.923681118,173.4444444,0.901859781,0,0.403234768,,,0.742925222
2566,Machine learning model for predicting severity prognosis in patients infected with COVID-19: Study protocol from COVID-AI Brasil.,PLoS One,33524039,2/2/21,pubmed,0,5,machine learning,0.001085358,0.043915227,0.345762439,0.001085423,0.001085378,0.607066175,Clinics,0.8125859,TRUE,19.2,0.287339972,2.4,0.174872893,0,0.403234768,,,0.288482544
2567,"Exposure to COVID-19-Related Information and its Association With Mental Health Problems in Thailand: Nationwide, Cross-sectional Survey Study.",J Med Internet Res,33523828,2/2/21,pubmed,0,8,logistic regression,0.000779409,0.000779412,0.000779414,0.000779436,0.904555313,0.092327015,Healthcare,0.9198525,TRUE,99.75,0.868328283,62.25,0.729595933,0,0.403234768,,,0.667052994
2568,Retrained Generic Antibodies Can Recognize SARS-CoV-2.,J Phys Chem Lett,33523655,2/2/21,pubmed,0,3,"molecular dynamics simulation, computational",0.694970056,0.154543271,0.002130696,0.144094509,0.002130839,0.002130629,Drug discovery,0.6552545,TRUE,123,0.911806543,21.66666667,0.499732406,1,0.537564047,,,0.649700999
2569,Diagnosis of COVID-19 using CT scan images and deep learning techniques.,Emerg Radiol,33523309,2/2/21,pubmed,0,6,"deep learning, neural network",0.002033072,0.002032813,0.959685276,0.002032832,0.032183287,0.00203272,Imaging,0.6773376,TRUE,3.333333333,0.04044777,0,0.055525823,1,0.537564047,,,0.211179213
2570,"Body-Worn Sensors for Remote Monitoring of Parkinson's Disease Motor Symptoms: Vision, State of the Art, and Challenges Ahead.",J Parkinsons Dis,33523020,2/2/21,pubmed,0,5,digital health,0.001171578,0.001171559,0.087568506,0.655731092,0.147969495,0.106387769,Epidemiology,0.9950737,TRUE,54.8,0.666769745,51.4,0.686914637,0,0.403234768,,,0.585639717
2571,Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types.,Microsc Res Tech,33522669,2/2/21,pubmed,0,4,"machine learning, forecasting model",0.001237079,0.001237143,0.149296373,0.845755174,0.001237109,0.001237121,Epidemiology,0.59973025,TRUE,32,0.455810502,9.75,0.349678887,1,0.537564047,,,0.447684479
2572,An online survey of the attitude and willingness of Chinese adults to receive COVID-19 vaccination.,Hum Vaccin Immunother,33522405,2/2/21,pubmed,0,10,logistic regression,0.001786625,0.001786529,0.001786494,0.001786547,0.991067265,0.00178654,Healthcare,0.823443,TRUE,36.6,0.503865421,30.9,0.576465079,1,0.537564047,,,0.539298183
2573,Harnessing machine learning for development of microbiome therapeutics.,Gut Microbes,33522391,2/2/21,pubmed,0,5,"machine learning, artificial intelligence, in silico, microbiom",0.218048343,0.232051596,0.545203969,0.001565397,0.001565364,0.001565331,Genomics,0.49281004,FALSE,74.4,0.78366009,86.6,0.79435376,0,0.403234768,,,0.660416206
2574,COVID-19 pandemic: demographic and clinical correlates of passive death wish and thoughts of self-harm among Canadians.,J Ment Health,33522340,2/2/21,pubmed,0,10,logistic regression,0.053012245,0.001717246,0.001717241,0.001717324,0.876199241,0.065636704,Healthcare,0.9621327,TRUE,35.8,0.49495949,15.1,0.426210864,1,0.537564047,,,0.486244801
2575,The value of infectious disease modeling and trend assessment: a public health perspective.,Expert Rev Anti Infect Ther,33522327,2/2/21,pubmed,0,3,mathematical model,0.0016531,0.001653112,0.001653133,0.922656022,0.070731516,0.001653117,Epidemiology,0.56171405,TRUE,73,0.778464964,56.66666667,0.709660155,0,0.403234768,,,0.630453296
2576,Metformin and risk of mortality in patients hospitalised with COVID-19: a retrospective cohort analysis.,Lancet Healthy Longev,33521772,2/2/21,pubmed,0,17,logistic regression,0.026913454,0.000759368,0.03013423,0.0007594,0.085884617,0.855548931,Clinics,0.89990354,TRUE,37.64705882,0.514874142,23.52941176,0.517059138,11,0.840175319,,,0.6240362
2577,Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysis.,Lancet Healthy Longev,33521769,2/2/21,pubmed,0,13,"logistic regression, dataset",0.00104687,0.001046873,0.0010469,0.001046889,0.540691623,0.455120844,Healthcare,0.49189663,FALSE,59.15384615,0.697198343,41.61538462,0.642694675,11,0.840175319,,,0.726689446
2578,Patient perspective: Wearable and digital health tools to support managing our health during the COVID-19 pandemic and beyond.,Cardiovasc Digit Health J,33521766,2/2/21,pubmed,0,1,digital health,0.019529497,0.019529424,0.019530147,0.66183016,0.019529809,0.260050963,Epidemiology,0.6889471,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
2579,"Predictive Model and Risk Factors for Case Fatality of COVID-19: A Cohort of 21,392 Cases in Hubei, China.",Innovation (N Y),33521759,2/2/21,pubmed,0,12,predictive model,0.001310373,0.001310335,0.001310337,0.042470207,0.001310384,0.952288363,Clinics,0.79476917,TRUE,84.75,0.824046014,84,0.788399786,6,0.764429903,,,0.792291901
2580,Racial and geographic disparities in influenza vaccination in the U.S. among individuals with atherosclerotic cardiovascular disease: Renewed importance in the setting of COVID-19.,Am J Prev Cardiol,33521756,2/2/21,pubmed,0,10,logistic regression,0.000779423,0.000779459,0.000779427,0.000779446,0.937415957,0.059466289,Healthcare,0.60008156,TRUE,211.1,0.97587977,293.9,0.95236821,0,0.403234768,,,0.777160916
2581,"Cell-Free DNA Tissues-of-Origin by Methylation Profiling Reveals Significant Cell, Tissue and Organ-Specific injury related to COVID-19 Severity.",Med (N Y),33521749,2/2/21,pubmed,0,13,genome-wide,0.309314363,0.16316831,0.001538147,0.001538112,0.001538129,0.522902939,Clinics,0.66351,TRUE,108.6923077,0.887191539,153.8461538,0.886606904,1,0.537564047,,,0.770454163
2582,Power of universal health coverage in the era of COVID-19: A nationwide observational study.,Lancet Reg Health West Pac,33521744,2/2/21,pubmed,0,4,logistic regression,0.001272652,0.001272661,0.001272704,0.257805045,0.419003845,0.319373093,Healthcare,0.4066704,FALSE,46,0.596882924,15,0.42594327,2,0.618927094,,,0.547251096
2583,Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study.,Lancet Microbe,33521709,2/2/21,pubmed,0,19,"bayes, machine learning, mathematical model, classifier",0.000722198,0.435672418,0.263812007,0.076745232,0.147559511,0.075488634,Genomics,0.2595742,FALSE,65.05263158,0.734863009,57.52631579,0.713005084,2,0.618927094,,,0.688931729
2584,A distinct innate immune signature marks progression from mild to severe COVID-19.,Cell Rep Med,33521697,2/2/21,pubmed,0,16,proteom,0.462888236,0.002130811,0.00213068,0.061057694,0.002130733,0.469661846,Clinics,0.3655697,FALSE,39.4375,0.533057085,64.8125,0.737958255,4,0.707574542,,,0.65952996
2585,Comprehensive analysis of T cell immunodominance and immunoprevalence of SARS-CoV-2 epitopes in COVID-19 cases.,Cell Rep Med,33521695,2/2/21,pubmed,0,22,proteom,0.769284299,0.223846877,0.001717223,0.001717213,0.001717199,0.001717189,Drug discovery,0.1525993,FALSE,99.59090909,0.867957202,282.0909091,0.949357774,0,0.403234768,,,0.740183248
2586,COVID-19 impacts the expression of molecular markers associated with neuropsychiatric disorders.,Brain Behav Immun Health,33521688,2/2/21,pubmed,0,8,dataset,0.437962195,0.044735839,0.002490667,0.00249052,0.262434522,0.249886257,Drug discovery,0.9504185,TRUE,41.375,0.551858495,13.25,0.402997056,0,0.403234768,,,0.452696773
2587,BIN1 rs744373 SNP and COVID-19 mortality.,World Acad Sci J,33521641,2/2/21,pubmed,0,2,logistic regression,0.23413186,0.223733881,0.001538084,0.001538114,0.001538158,0.537519902,Clinics,0.41484997,FALSE,105.5,0.880017317,24.5,0.525086968,0,0.403234768,,,0.602779684
2588,Planning for monitoring the introduction and effectiveness of new vaccines using real-word data and geospatial visualization: An example using rotavirus vaccines with potential application to SARS-CoV-2.,Vaccine X,33521625,2/2/21,pubmed,0,6,dataset,0.001046877,0.032302083,0.001046861,0.752173438,0.212383893,0.001046848,Epidemiology,0.08450249,FALSE,84.33333333,0.823056466,150.8333333,0.883930961,0,0.403234768,,,0.703407398
2589,Risk factors of developing critical conditions in Iranian patients with COVID-19.,Glob Epidemiol,33521624,2/2/21,pubmed,0,8,logistic regression,0.002238457,0.002238456,0.002238544,0.098394126,0.002238609,0.892651809,Clinics,0.9660946,TRUE,55.375,0.670295009,24.125,0.522076532,0,0.403234768,,,0.53186877
2590,AI drug discovery screening for COVID-19 reveals zafirlukast as a repurposing candidate.,Med Drug Discov,33521623,2/2/21,pubmed,0,2,machine learning,0.744835522,0.001392842,0.249592939,0.001392895,0.001392914,0.001392888,Drug discovery,0.7630207,TRUE,12,0.183190055,1.5,0.138747659,0,0.403234768,,,0.24172416
2591,Local measures enable COVID-19 containment with fewer restrictions due to cooperative effects.,EClinicalMedicine,33521609,2/2/21,pubmed,0,4,mathematical model,0.000474393,0.000474397,0.021562051,0.90970891,0.067305828,0.000474421,Epidemiology,0.16869137,FALSE,71.5,0.770734121,49.75,0.680291678,0,0.403234768,,,0.618086855
2592,"Descriptive epidemiology of SARS-CoV-2 infection in Karnataka state, South India: Transmission dynamics of symptomatic vs. asymptomatic infections.",EClinicalMedicine,33521608,2/2/21,pubmed,0,9,network analysis,0.000956315,0.000956355,0.000956319,0.573397153,0.26021699,0.163516868,Epidemiology,0.43319234,FALSE,31.22222222,0.44671903,23.11111111,0.513714209,2,0.618927094,,,0.526453445
2593,Digital Health Surveillance Strategies for Management of Coronavirus Disease 2019.,Mayo Clin Proc Innov Qual Outcomes,33521582,2/2/21,pubmed,0,7,digital health,0.00137126,0.001371294,0.088466681,0.264212793,0.26440359,0.380174382,Clinics,0.7134392,TRUE,112.4285714,0.894922382,93.71428571,0.810141825,0,0.403234768,,,0.702766325
2594,Micronutrient deficiencies in patients with COVID-19: how metabolomics can contribute to their prevention and replenishment.,BMJ Nutr Prev Health,33521556,2/2/21,pubmed,0,2,metabolom,0.025062474,0.025062952,0.025061268,0.02506279,0.472679972,0.427070544,Healthcare,0.49723226,FALSE,16,0.243552477,1.5,0.138747659,2,0.618927094,,,0.33374241
2595,CoVerifi: A COVID-19 news verification system.,Online Soc Netw Media,33521412,2/2/21,pubmed,0,2,machine learning,0.001511932,0.001511848,0.081807422,0.85876113,0.054895831,0.001511836,Epidemiology,0.35346097,FALSE,28,0.408312202,15.5,0.430157881,0,0.403234768,,,0.413901617
2596,Dynamic model of COVID-19 disease with exploratory data analysis.,Sci Afr,33521409,2/2/21,pubmed,0,7,mathematical model,0.047274387,0.002080575,0.002080631,0.812497758,0.133985921,0.00208073,Epidemiology,0.7980817,TRUE,4.571428571,0.062032284,0.142857143,0.057398983,2,0.618927094,,,0.246119454
2597,Assessing the future progression of COVID-19 in Iran and its neighbors using Bayesian models.,Infect Dis Model,33521407,2/2/21,pubmed,0,1,"bayes, bayesian model",0.000999547,0.000999525,0.000999609,0.995002154,0.000999601,0.000999563,Epidemiology,0.67070854,TRUE,48,0.614942173,12,0.386740701,0,0.403234768,,,0.468305881
2598,Clarifying predictions for COVID-19 from testing data: The example of New York State.,Infect Dis Model,33521405,2/2/21,pubmed,0,2,model simulation,0.002720229,0.002720184,0.002720335,0.986398888,0.002720192,0.002720173,Epidemiology,0.07285085,FALSE,25.5,0.37435834,30.5,0.573187048,0,0.403234768,,,0.450260052
2599,Mathematical modeling of COVID-19 infection dynamics in Ghana: Impact evaluation of integrated government and individual level interventions.,Infect Dis Model,33521403,2/2/21,pubmed,0,7,mathematical model,0.038753713,0.001085333,0.001085325,0.874324579,0.001085408,0.083665641,Epidemiology,0.15436536,FALSE,29.28571429,0.421918486,16,0.437316029,0,0.403234768,,,0.420823094
2600,Public perception and preparedness for the pandemic COVID 19: A Health Belief Model approach.,Clin Epidemiol Glob Health,33521389,2/2/21,pubmed,0,6,logistic regression,0.001415188,0.001415195,0.001415141,0.178845905,0.815493444,0.001415127,Healthcare,0.62596583,TRUE,13.16666667,0.198528047,0.833333333,0.102488627,7,0.785110192,,,0.362042289
2601,"Factors associated with preventive behaviors, anxiety among healthcare workers and response preparedness against COVID-19 outbreak: A one health approach.",Clin Epidemiol Glob Health,33521387,2/2/21,pubmed,0,5,logistic regression,0.001717315,0.141850213,0.00171718,0.001717256,0.851280813,0.001717222,Healthcare,0.92766297,TRUE,7.4,0.106190859,13,0.400521809,1,0.537564047,,,0.348092238
2602,Genetic variations among SARS-CoV-2 strains isolated in China.,Gene Rep,33521384,2/2/21,pubmed,0,2,genome sequences,0.04606516,0.935873771,0.00093608,0.000936105,0.015252753,0.000936131,Genomics,0.8186625,TRUE,11,0.167171748,2,0.164302917,0,0.403234768,,,0.244903144
2603,0,Gene Rep,33521382,2/2/21,pubmed,0,2,genomes,0.001751235,0.991243982,0.001751164,0.001751251,0.001751171,0.001751197,Genomics,0.8699672,TRUE,21.5,0.318263343,16.5,0.44293551,0,0.403234768,,,0.38814454
2604,"The polybasic insert, the RBD of the SARS-CoV-2 spike protein, and the feline coronavirus - evolved or yet to evolve.",Biochem Biophys Rep,33521335,2/2/21,pubmed,0,5,"molecular dynamics simulation, whole genome, structural model",0.829476419,0.165575183,0.001237155,0.001237102,0.001237057,0.001237084,Drug discovery,0.5683063,TRUE,16.8,0.253138722,17.6,0.456984212,0,0.403234768,,,0.371119234
2605,Financial hardship and health risk behavior during COVID-19 in a large US national sample of women.,SSM Popul Health,33521228,2/2/21,pubmed,0,7,logistic regression,0.001291251,0.001291252,0.001291233,0.001291317,0.950238306,0.044596642,Healthcare,0.5186566,TRUE,187.7142857,0.967777846,351.5714286,0.965814825,1,0.537564047,,,0.823718906
2606,Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making.,Smart Health (Amst),33521226,2/2/21,pubmed,0,2,"machine learning, artificial intelligence, neural network, classifier, predictive model, logistic regression, dataset",0.001652995,0.001653068,0.756480682,0.001653104,0.001653055,0.236907097,Clinics,0.7112454,TRUE,36,0.498299215,8,0.320511105,1,0.537564047,,,0.452124789
2607,MaskedFace-Net - A dataset of correctly/incorrectly masked face images in the context of COVID-19.,Smart Health (Amst),33521223,2/2/21,pubmed,0,4,"deep learning, dataset",0.000926266,0.000926278,0.387970556,0.348011059,0.261239581,0.00092626,Imaging,0.23732358,FALSE,37,0.508936854,9.75,0.349678887,3,0.667819001,,,0.508811581
2608,"Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods.",Data Brief,33521186,2/2/21,pubmed,0,2,dataset,0.000722166,0.000722189,0.000722194,0.996389101,0.000722167,0.000722184,Epidemiology,0.055582225,FALSE,20.5,0.304966294,15.5,0.430157881,1,0.537564047,,,0.424229407
2609,ECG Images dataset of Cardiac and COVID-19 Patients.,Data Brief,33521183,2/2/21,pubmed,0,3,"deep learning, dataset",0.001653041,0.001653128,0.668855003,0.118787721,0.001653161,0.207397947,Imaging,0.84052,TRUE,20,0.298163152,1,0.122023013,0,0.403234768,,,0.274473644
2610,"Risk factors associated with acute respiratory distress syndrome in COVID-19 patients outside Wuhan: A double-center retrospective cohort study of 197 cases in Hunan, China.",World J Clin Cases,33521102,2/2/21,pubmed,0,5,logistic regression,0.001310387,0.001310359,0.001310541,0.001310399,0.001310358,0.993447956,Clinics,0.80978066,TRUE,20.8,0.30768755,10.8,0.366871822,0,0.403234768,,,0.359264713
2611,"Gene Expression Profiling Reveals the Shared and Distinct Transcriptional Signatures in Human Lung Epithelial Cells Infected With SARS-CoV-2, MERS-CoV, or SARS-CoV: Potential Implications in Cardiovascular Complications of COVID-19.",Front Cardiovasc Med,33521069,2/2/21,pubmed,0,5,"transcriptom, dataset",0.638653328,0.286328613,0.0012371,0.001237085,0.001237072,0.071306801,Drug discovery,0.5625351,TRUE,32.2,0.45754221,30.8,0.575729195,0,0.403234768,,,0.478835391
2612,Predictive Value of Prognostic Nutritional Index on COVID-19 Severity.,Front Nutr,33521032,2/2/21,pubmed,0,10,logistic regression,0.001622692,0.00162273,0.001622721,0.001622702,0.0016227,0.991886454,Clinics,0.9032803,TRUE,16.2,0.244974952,3,0.199424672,0,0.403234768,,,0.282544797
2613,Susceptibility Factors of Stomach for SARS-CoV-2 and Treatment Implication of Mucosal Protective Agent in COVID-19.,Front Med (Lausanne),33521016,2/2/21,pubmed,0,9,sequencing,0.181126833,0.158503525,0.001415124,0.001415168,0.056517881,0.601021469,Clinics,0.8520415,TRUE,59.22222222,0.697693116,14.55555556,0.418785122,0,0.403234768,,,0.506571002
2614,ACE2 Expression in Kidney and Testis May Cause Kidney and Testis Infection in COVID-19 Patients.,Front Med (Lausanne),33521006,2/2/21,pubmed,0,5,dataset,0.61933029,0.001751262,0.05970731,0.027795596,0.027118071,0.264297471,Drug discovery,0.7518457,TRUE,30.6,0.439421114,26.2,0.539001873,0,0.403234768,,,0.460552585
2615,Evolutionarily Conserved Long Non-coding RNA Regulates Gene Expression in Cytokine Storm During COVID-19.,Front Bioeng Biotechnol,33520952,2/2/21,pubmed,0,5,computational,0.846830658,0.146431254,0.001684471,0.00168453,0.001684546,0.001684541,Drug discovery,0.55456036,TRUE,16.6,0.250046385,6,0.280037463,0,0.403234768,,,0.311106205
2616,Mathematical Modeling Predicts That Strict Social Distancing Measures Would Be Needed to Shorten the Duration of Waves of COVID-19 Infections in Vietnam.,Front Public Health,33520905,2/2/21,pubmed,0,3,mathematical model,0.002080608,0.002080953,0.002080634,0.989596657,0.002080615,0.002080533,Epidemiology,0.26162487,FALSE,17,0.257467994,1.333333333,0.13252609,0,0.403234768,,,0.264409617
2617,SARS-CoV-2 Infections in Italian Schools: Preliminary Findings After 1 Month of School Opening During the Second Wave of the Pandemic.,Front Pediatr,33520898,2/2/21,pubmed,0,4,dataset,0.001371252,0.001371341,0.001371388,0.365114632,0.629400111,0.001371276,Healthcare,0.11015612,FALSE,49.75,0.630032779,10.75,0.366269735,6,0.764429903,,,0.586910806
2618,0,J Tradit Complement Med,33520685,2/2/21,pubmed,0,6,in silico,0.97032719,0.001330104,0.02435248,0.001330049,0.001330107,0.00133007,Drug discovery,0.94781137,TRUE,34.16666667,0.47863195,14.66666667,0.420323789,0,0.403234768,,,0.434063502
2619,Anti-COVID-19 drug candidates: A review on potential biological activities of natural products in the management of new coronavirus infection.,J Tradit Complement Med,33520683,2/2/21,pubmed,0,6,in silico,0.881139304,0.0009361,0.000936086,0.115116236,0.000936116,0.000936158,Drug discovery,0.77685046,TRUE,23.66666667,0.34986703,13.83333333,0.409084827,2,0.618927094,,,0.459292984
2620,Molecular docking analysis of rutin reveals possible inhibition of SARS-CoV-2 vital proteins.,J Tradit Complement Med,33520682,2/2/21,pubmed,0,6,in silico,0.993143609,0.001371275,0.001371284,0.001371288,0.001371273,0.001371272,Drug discovery,0.9874875,TRUE,17.5,0.263776362,4.166666667,0.233074659,1,0.537564047,,,0.344805023
2621,Convolutional neural network use chest radiography images for identification of COVID-19.,Mater Today Proc,33520674,2/2/21,pubmed,0,4,"neural network, dataset",0.00153819,0.058202865,0.64926049,0.287922063,0.001538159,0.001538233,Epidemiology,0.5519795,TRUE,7.75,0.112808461,0,0.055525823,0,0.403234768,,,0.190523017
2622,FDA Recommended Potent Drugs against Covid-19: Insight through Molecular Docking.,Mater Today Proc,33520672,2/2/21,pubmed,0,2,in silico,0.559553179,0.002238597,0.002238661,0.431492204,0.002238627,0.002238733,Drug discovery,0.537156,TRUE,9,0.135320675,0,0.055525823,0,0.403234768,,,0.198027089
2623,A new model for epidemic prediction: COVID-19 in kingdom saudi arabia case study.,Mater Today Proc,33520671,2/2/21,pubmed,0,5,"artificial intelligence, prediction model",0.002996598,0.039866135,0.125405159,0.825739189,0.002996497,0.002996422,Epidemiology,0.37548923,FALSE,10.8,0.161110768,5,0.257024351,0,0.403234768,,,0.273789962
2624,"In-silico drug repurposing study: Amprenavir, enalaprilat, and plerixafor, potential drugs for destabilizing the SARS-CoV-2 S-protein-angiotensin-converting enzyme 2 complex.",Results Chem,33520633,2/2/21,pubmed,0,3,"molecular dynamics simulation, in-silico",0.978397069,0.016654615,0.001237084,0.001237073,0.001237093,0.001237066,Drug discovery,0.89279747,TRUE,6.666666667,0.094996598,2.333333333,0.173401124,0,0.403234768,,,0.223877497
2625,Mathematical analysis of SIRD model of COVID-19 with Caputo fractional derivative based on real data.,Results Phys,33520629,2/2/21,pubmed,0,6,mathematical model,0.002562606,0.002562622,0.002562658,0.987187013,0.002562534,0.002562567,Epidemiology,0.904125,TRUE,108,0.886078298,4.666666667,0.246721969,1,0.537564047,,,0.556788105
2626,On the analysis of number of deaths due to Covid -19 outbreak data using a new class of distributions.,Results Phys,33520628,2/2/21,pubmed,0,3,bayes,0.002562743,0.002562605,0.002562907,0.898484466,0.002562611,0.091264667,Epidemiology,0.5741843,TRUE,77.33333333,0.795967592,13,0.400521809,1,0.537564047,,,0.578017816
2627,Numerical simulation and stability analysis for the fractional-order dynamics of COVID-19.,Results Phys,33520625,2/2/21,pubmed,0,4,computational,0.171337596,0.003607203,0.152240234,0.665600617,0.003607227,0.003607124,Epidemiology,0.80280054,TRUE,33.25,0.468860165,6,0.280037463,1,0.537564047,,,0.428820558
2628,Mathematical model to assess the imposition of lockdown during COVID-19 pandemic.,Results Phys,33520624,2/2/21,pubmed,0,5,mathematical model,0.003214225,0.00321414,0.003214171,0.983928754,0.003214432,0.003214279,Epidemiology,0.69741005,TRUE,127.8,0.918424145,4.4,0.238961734,6,0.764429903,,,0.640605261
2629,Fractal-fractional mathematical modeling and forecasting of new cases and deaths of COVID-19 epidemic outbreaks in India.,Results Phys,33520622,2/2/21,pubmed,0,7,mathematical model,0.003101473,0.003101497,0.003101599,0.984492402,0.003101595,0.003101434,Epidemiology,0.64687943,TRUE,43.71428571,0.575174717,2.714285714,0.186111854,6,0.764429903,,,0.508572158
2630,Modeling and analysis of the dynamics of novel coronavirus (COVID-19) with Caputo fractional derivative.,Results Phys,33520621,2/2/21,pubmed,0,5,mathematical model,0.103754314,0.001684584,0.001684526,0.889507554,0.001684493,0.001684529,Epidemiology,0.69803363,TRUE,21.6,0.319438432,3,0.199424672,6,0.764429903,,,0.427764336
2631,Optimal control and sensitivity analysis for transmission dynamics of Coronavirus.,Results Phys,33520619,2/2/21,pubmed,0,3,mathematical model,0.002422331,0.002422282,0.002422355,0.905547313,0.042583799,0.044601921,Epidemiology,0.57297134,TRUE,11.66666667,0.176510607,1,0.122023013,1,0.537564047,,,0.278699222
2632,Mathematical modeling for the outbreak of the coronavirus (COVID-19) under fractional nonlocal operator.,Results Phys,33520618,2/2/21,pubmed,0,7,mathematical model,0.003465999,0.003465956,0.003465886,0.982670271,0.003465969,0.00346592,Epidemiology,0.56250244,TRUE,79.57142857,0.80512091,27.57142857,0.550173936,5,0.739490092,,,0.698261646
2633,A study on the spread of COVID 19 outbreak by using mathematical modeling.,Results Phys,33520617,2/2/21,pubmed,0,1,mathematical model,0.001901815,0.001901757,0.001901746,0.990491233,0.001901745,0.001901704,Epidemiology,0.3851718,FALSE,58,0.689714887,2,0.164302917,0,0.403234768,,,0.41908419
2634,On a new conceptual mathematical model dealing the current novel coronavirus-19 infectious disease.,Results Phys,33520616,2/2/21,pubmed,0,5,mathematical model,0.200817537,0.006090186,0.006089846,0.774823187,0.00608965,0.006089595,Epidemiology,0.83052266,TRUE,130.2,0.921949409,17.8,0.458790474,2,0.618927094,,,0.666555659
2635,Assessing and controlling infection risk with Wells-Riley model and spatial flow impact factor (SFIF).,Sustain Cities Soc,33520610,2/2/21,pubmed,0,10,mathematical model,0.002898339,0.00289832,0.002898381,0.985508183,0.002898376,0.002898402,Epidemiology,0.83917594,TRUE,34.2,0.479188571,21.7,0.499799304,1,0.537564047,,,0.505517307
2636,COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach.,Sustain Cities Soc,33520607,2/2/21,pubmed,0,7,"machine learning, prediction model, dataset",0.001511863,0.001511824,0.51424469,0.479708017,0.001511815,0.001511791,Epidemiology,0.57707536,TRUE,48.28571429,0.616550189,9.571428571,0.346133262,2,0.618927094,,,0.527203515
2637,Training Feedforward Neural Network Using Enhanced Black Hole Algorithm: A Case Study on COVID-19 Related ACE2 Gene Expression Classification.,Arab J Sci Eng,33520590,2/2/21,pubmed,0,2,"neural network, classifier, dataset",0.179686312,0.01619016,0.756326575,0.00115633,0.001156352,0.045484272,Drug discovery,0.4550761,FALSE,18.5,0.278001113,5.5,0.267259834,0,0.403234768,,,0.316165238
2638,Screening of potent drug inhibitors against SARS-CoV-2 RNA polymerase: an in silico approach.,3 Biotech,33520579,2/2/21,pubmed,0,3,"virtual screening, in silico",0.980111443,0.000999519,0.000999536,0.015890507,0.000999502,0.000999493,Drug discovery,0.9042425,TRUE,142.6666667,0.937101862,34.66666667,0.603157613,0,0.403234768,,,0.647831414
2639,Clinical Significance of the Correlation between Changes in the Major Intestinal Bacteria Species and COVID-19 Severity.,Engineering (Beijing),33520333,2/2/21,pubmed,0,12,microbiom,0.001486463,0.135803836,0.001486506,0.001486503,0.001486516,0.858250176,Clinics,0.71310335,TRUE,35.83333333,0.495206877,30,0.570176612,5,0.739490092,,,0.601624527
2640,In silico prediction of structure and function for a large family of transmembrane proteins that includes human Tmem41b.,F1000Res,33520197,2/2/21,pubmed,0,5,"computational, bioinformatic, in silico",0.529270733,0.341320476,0.125023096,0.001461938,0.001461877,0.00146188,Drug discovery,0.6935996,TRUE,12.8,0.192343373,11.2,0.373026492,0,0.403234768,,,0.322868211
2641,Meta-analysis of transcriptome datasets: An alternative method to study IL-6 regulation in coronavirus disease 2019.,Comput Struct Biotechnol J,33520118,2/2/21,pubmed,0,12,"transcriptom, dataset",0.531323252,0.001823426,0.056992192,0.001823466,0.001823395,0.406214269,Drug discovery,0.6903173,TRUE,40.25,0.541715629,10.16666667,0.357171528,0,0.403234768,,,0.434040641
2642,COVID-19 dynamic model: balanced identification of general biological and country specific features.,Procedia Comput Sci,33520019,2/2/21,pubmed,0,2,mathematical model,0.172337344,0.002183252,0.002183371,0.818929572,0.002183255,0.002183206,Epidemiology,0.4766294,FALSE,10,0.15214299,0,0.055525823,0,0.403234768,,,0.203634527
2643,COVID-19 in Bangladesh: A Deeper Outlook into The Forecast with Prediction of Upcoming Per Day Cases Using Time Series.,Procedia Comput Sci,33520018,2/2/21,pubmed,0,5,"machine learning, deep learning, neural network, lstm, dataset",0.001272642,0.001272643,0.255133332,0.739775872,0.001272691,0.001272819,Epidemiology,0.67848516,TRUE,21.2,0.313686684,1.4,0.133864062,0,0.403234768,,,0.283595171
2644,Analysis of the mechanism of Shufeng Jiedu capsule prevention and treatment for COVID-19 by network pharmacology tools.,Eur J Integr Med,33520015,2/2/21,pubmed,0,6,"bioinformatic, toxicogenom",0.890795725,0.000898115,0.038282139,0.034031536,0.03509438,0.000898105,Drug discovery,0.96550417,TRUE,15.5,0.234028078,4.666666667,0.246721969,1,0.537564047,,,0.339438032
2645,"Two pronged approach for prevention and therapy of COVID-19 (Sars-CoV-2) by a multi-targeted herbal drug, a component of ayurvedic decoction.",Eur J Integr Med,33520014,2/2/21,pubmed,0,4,in silico,0.992690398,0.001461985,0.00146186,0.001461885,0.001461964,0.001461906,Drug discovery,0.9552537,TRUE,49,0.624281032,14.5,0.418450629,0,0.403234768,,,0.481988809
2646,Assessing and predicting air quality in northern Jordan during the lockdown due to the COVID-19 virus pandemic using artificial neural network.,Air Qual Atmos Health,33520010,2/2/21,pubmed,0,2,neural network,0.002032792,0.002032755,0.20241698,0.789451888,0.002032802,0.002032783,Epidemiology,0.7855624,TRUE,9,0.135320675,3,0.199424672,0,0.403234768,,,0.245993372
2647,Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network.,Cognit Comput,33520007,2/2/21,pubmed,0,4,"deep learning, adversarial network, dataset",0.001371251,0.001371248,0.993143486,0.001371329,0.001371328,0.001371358,Imaging,0.6613397,TRUE,29,0.41993939,48,0.673735617,0,0.403234768,,,0.498969925
2648,Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 Periods.,Cognit Comput,33520006,2/2/21,pubmed,0,4,correlation analysis,0.001171557,0.00117158,0.018714327,0.976599334,0.001171595,0.001171606,Epidemiology,0.6526696,TRUE,3.25,0.039148989,0,0.055525823,0,0.403234768,,,0.16596986
2649,Forecasting of medical equipment demand and outbreak spreading based on deep long short-term memory network: the COVID-19 pandemic in Turkey.,Signal Image Video Process,33520001,2/2/21,pubmed,0,2,"deep learning, artificial intelligence, deep model, network model, forecasting model, lstm",0.001203412,0.001203424,0.535577016,0.45960928,0.001203451,0.001203418,Epidemiology,0.3515079,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
2650,Detecting the Multiomics Signatures of Factor-Specific Inflammatory Effects on Airway Smooth Muscles.,Front Genet,33519902,2/2/21,pubmed,0,7,"machine learning, computational,  omics, multiom",0.756246834,0.001593586,0.140861482,0.098110952,0.001593518,0.001593627,Drug discovery,0.8580034,TRUE,90.57142857,0.844517286,123.7142857,0.856168049,1,0.537564047,,,0.746083128
2651,Metformin Use Is Associated With Reduced Mortality in a Diverse Population With COVID-19 and Diabetes.,Front Endocrinol (Lausanne),33519709,2/2/21,pubmed,0,6,logistic regression,0.001751246,0.001751262,0.00175123,0.001751281,0.001751261,0.99124372,Clinics,0.5995352,TRUE,50,0.632073721,52.83333333,0.69246722,0,0.403234768,,,0.575925236
2652,Continuity of Care During COVID-19 Lockdown: A Survey on Stakeholders' Experience With Telerehabilitation.,Front Neurol,33519697,2/2/21,pubmed,0,7,logistic regression,0.001310356,0.001310336,0.001310411,0.001310418,0.827887522,0.166870957,Healthcare,0.9891859,TRUE,13.85714286,0.209351228,7,0.299973241,0,0.403234768,,,0.304186412
2653,Mental Health Outcomes Amongst Health Care Workers During COVID 19 Pandemic in Saudi Arabia.,Front Psychiatry,33519559,2/2/21,pubmed,0,5,logistic regression,0.00112678,0.001126825,0.001126813,0.001126809,0.969518199,0.025974573,Healthcare,0.94178987,TRUE,5.2,0.071927763,2.2,0.16838373,0,0.403234768,,,0.21451542
2654,A Review on Plant Bioactive Compounds and Their Modes of Action Against Coronavirus Infection.,Front Pharmacol,33519449,2/2/21,pubmed,0,2,in silico,0.969746439,0.001203449,0.001203557,0.00120346,0.02543953,0.001203564,Drug discovery,0.89992607,TRUE,27,0.3960047,3.5,0.213607172,0,0.403234768,,,0.337615546
2655,COVID-19 Incidence in Patients With Immunomediated Inflammatory Diseases: Influence of Immunosuppressant Treatments.,Front Pharmacol,33519443,2/2/21,pubmed,0,9,immunome,0.159047038,0.001622744,0.001622742,0.001622831,0.117964519,0.718120125,Clinics,0.9445828,TRUE,63.77777778,0.726575546,105.8888889,0.832887343,0,0.403234768,,,0.654232552
2656,Understanding Obesity: The Role of Adipose Tissue Microenvironment and the Gut Microbiome.,Saudi J Med Med Sci,33519338,2/2/21,pubmed,0,3,microbiom,0.18461927,0.08348036,0.002806473,0.259997803,0.059186629,0.409909464,Clinics,0.70071983,TRUE,11.33333333,0.170635166,10,0.355632861,0,0.403234768,,,0.309834265
2657,Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network.,Appl Soft Comput,33519327,2/2/21,pubmed,0,3,neural network,0.001310318,0.001310356,0.84173053,0.153028043,0.001310374,0.001310379,Imaging,0.3632795,FALSE,42.66666667,0.563856763,21.66666667,0.499732406,16,0.881782826,,,0.648457332
2658,Real-time measurement of the uncertain epidemiological appearances of COVID-19 infections.,Appl Soft Comput,33519324,2/2/21,pubmed,0,6,"deep learning, prediction model, lstm",0.001203452,0.047893606,0.278124585,0.670371471,0.001203431,0.001203456,Epidemiology,0.39236915,FALSE,42,0.558537943,7.833333333,0.314088841,0,0.403234768,,,0.425287184
2659,Mitigating the risk of infection spread in manual order picking operations: A multi-objective approach.,Appl Soft Comput,33519323,2/2/21,pubmed,0,5,optimization model,0.003214259,0.00321418,0.003214397,0.89197458,0.095168467,0.003214117,Epidemiology,0.9544257,TRUE,19.8,0.293957573,5.6,0.267995718,0,0.403234768,,,0.321729353
2660,Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study.,Appl Soft Comput,33519322,2/2/21,pubmed,0,4,"probabilistic, dataset",0.687840358,0.035152067,0.208516153,0.066178959,0.001156246,0.001156217,Drug discovery,0.04152298,FALSE,8.75,0.129383388,1.5,0.138747659,0,0.403234768,,,0.223788605
2661,COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis.,Inf Fusion,33519321,2/2/21,pubmed,0,5,"correlation analysis, transfer learning, dataset",0.001156259,0.02768886,0.821924388,0.001156291,0.001156292,0.146917909,Imaging,0.75337297,TRUE,79.4,0.803945822,26.2,0.539001873,6,0.764429903,,,0.702459199
2662,The Bayesian Susceptible-Exposed-Infected-Recovered model for the outbreak of COVID-19 on the Diamond Princess Cruise Ship.,Stoch Environ Res Risk Assess,33519302,2/2/21,pubmed,0,6,bayes,0.001203448,0.001203441,0.001203424,0.954613874,0.001203514,0.0405723,Epidemiology,0.43042496,FALSE,86,0.829117447,70.33333333,0.754415306,0,0.403234768,,,0.66225584
2663,Genome composition and genetic characterization of SARS-CoV-2.,Saudi J Biol Sci,33519278,2/2/21,pubmed,0,6,whole-genome,0.001593576,0.969402913,0.001593597,0.001593543,0.001593489,0.024222882,Genomics,0.6280959,TRUE,13.16666667,0.198528047,4.666666667,0.246721969,0,0.403234768,,,0.282828262
2664,0,Saudi J Biol Sci,33519272,2/2/21,pubmed,0,14,in-silico,0.930083161,0.000916763,0.020055131,0.000916735,0.047111493,0.000916717,Drug discovery,0.7071416,TRUE,60.42857143,0.705300266,39.35714286,0.62971635,0,0.403234768,,,0.579417128
2665,Self-medication practices during the COVID-19 pandemic among the adult population in Peru: A cross-sectional survey.,Saudi Pharm J,33519270,2/2/21,pubmed,0,12,logistic regression,0.07039698,0.001203453,0.00120341,0.001203441,0.924789271,0.001203445,Healthcare,0.97496045,TRUE,17.16666667,0.258890469,8.333333333,0.325662296,0,0.403234768,,,0.329262511
2666,Presence of SARS-CoV-2 in the air of public places and transportation.,Atmos Pollut Res,33519256,2/2/21,pubmed,0,20,logistic regression,0.001717192,0.527984212,0.051596154,0.384775173,0.032209972,0.001717297,Genomics,0.79339296,TRUE,91.95,0.848537325,95.85,0.814356436,1,0.537564047,,,0.733485936
2667,"Magnitude and Associated Factors for Attitude and Practice Toward COVID-19 and Its Prevention Among the Residents of Gedeo Zone, Southern Ethiopia: A Community-Based Cross-Sectional Study.",Risk Manag Healthc Policy,33519250,2/2/21,pubmed,0,8,logistic regression,0.001237054,0.001237077,0.001237065,0.001237112,0.993814594,0.001237099,Healthcare,0.13533297,FALSE,7,0.10179974,,,0,0.403234768,,,0.252517254
2668,Modeling epidemic spreading through public transit using time-varying encounter network.,Transp Res Part C Emerg Technol,33519128,2/2/21,pubmed,0,7,computational,0.001901755,0.001901789,0.001901783,0.990491268,0.001901723,0.001901683,Epidemiology,0.6315671,TRUE,128,0.919104459,79.28571429,0.776960128,7,0.785110192,,,0.82705826
2669,Assessing the role of quarantine and isolation as control strategies for COVID-19 outbreak: A case study.,Chaos Solitons Fractals,33519123,2/2/21,pubmed,0,3,mathematical model,0.003607168,0.003607167,0.003607171,0.981963984,0.003607378,0.003607131,Epidemiology,0.85870713,TRUE,22.33333333,0.330261612,1.666666667,0.145036125,5,0.739490092,,,0.404929276
2670,"Fractional model of COVID-19 applied to Galicia, Spain and Portugal.",Chaos Solitons Fractals,33519122,2/2/21,pubmed,0,5,mathematical model,0.00653969,0.006539849,0.006539797,0.967301291,0.006539705,0.006539668,Epidemiology,0.78919965,TRUE,175.4,0.962149793,118.4,0.849611988,2,0.618927094,,,0.810229625
2671,A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19.,Chaos Solitons Fractals,33519121,2/2/21,pubmed,0,4,mathematical model,0.001438157,0.00143819,0.060262906,0.933984498,0.001438116,0.001438133,Epidemiology,0.23321187,FALSE,57,0.68204589,32.5,0.589376505,1,0.537564047,,,0.602995481
2672,Stability analysis of fractional order model on corona transmission dynamics.,Chaos Solitons Fractals,33519120,2/2/21,pubmed,0,2,mathematical model,0.003927588,0.003927425,0.003927505,0.980362661,0.003927476,0.003927345,Epidemiology,0.5525861,TRUE,26,0.382398417,2.5,0.180826866,0,0.403234768,,,0.32215335
2673,A novel grey model based on traditional Richards model and its application in COVID-19.,Chaos Solitons Fractals,33519114,2/2/21,pubmed,0,3,"simulation experiment, prediction model",0.002357805,0.086630356,0.300028789,0.606267268,0.0023578,0.002357982,Epidemiology,0.47129455,FALSE,31.33333333,0.448388892,16,0.437316029,0,0.403234768,,,0.429646563
2674,Optimal control approach of a mathematical modeling with multiple delays of the negative impact of delays in applying preventive precautions against the spread of the COVID-19 pandemic with a case study of Brazil and cost-effectiveness.,Chaos Solitons Fractals,33519112,2/2/21,pubmed,0,5,mathematical model,0.002720151,0.002720139,0.00272017,0.986399147,0.002720232,0.002720161,Epidemiology,0.59651667,TRUE,17.6,0.264456676,0,0.055525823,4,0.707574542,,,0.342519014
2675,Mathematical modeling of the spread of COVID-19 among different age groups in Morocco: Optimal control approach for intervention strategies.,Chaos Solitons Fractals,33519111,2/2/21,pubmed,0,5,mathematical model,0.002422342,0.002422332,0.002422479,0.889791224,0.002422344,0.10051928,Epidemiology,0.7654291,TRUE,20.8,0.30768755,0.2,0.061145304,4,0.707574542,,,0.358802465
2676,Comparison of deep learning approaches to predict COVID-19 infection.,Chaos Solitons Fractals,33519109,2/2/21,pubmed,0,2,"deep learning, predictive model",0.001538096,0.001538289,0.621211675,0.001538174,0.001538136,0.37263563,Clinics,0.432508,FALSE,9.5,0.143051518,0.5,0.087101953,12,0.850299401,,,0.360150958
2677,A mathematical model for COVID-19 transmission by using the Caputo fractional derivative.,Chaos Solitons Fractals,33519107,2/2/21,pubmed,0,3,mathematical model,0.004310043,0.004310058,0.004310049,0.978449957,0.004309971,0.004309922,Epidemiology,0.57460153,TRUE,93.33333333,0.852433669,9,0.337904736,41,0.948144947,,,0.712827784
2678,Short-term forecasts of the COVID-19 pandemic: a study case of Cameroon.,Chaos Solitons Fractals,33519106,2/2/21,pubmed,0,4,mathematical model,0.004110176,0.06253522,0.004110306,0.921024694,0.004109799,0.004109804,Epidemiology,0.48686832,FALSE,41,0.549013544,15,0.42594327,6,0.764429903,,,0.579795573
2679,FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection.,Knowl Based Syst,33519100,2/2/21,pubmed,0,5,"deep learning, computational, deep-learning",0.033710371,0.000907344,0.962660378,0.000907307,0.000907292,0.000907308,Imaging,0.17102462,FALSE,57.6,0.686127775,24.8,0.526826331,4,0.707574542,,,0.640176216
2680,"Review article: Probiotics, prebiotics and dietary approaches during COVID-19 pandemic.",Trends Food Sci Technol,33519087,2/2/21,pubmed,0,6,microbiom,0.376588087,0.216465921,0.00141514,0.001415212,0.169788783,0.234326856,Drug discovery,0.9705005,TRUE,26,0.382398417,30.16666667,0.570711801,2,0.618927094,,,0.524012437
2681,Supply chain game theory network modeling under labor constraints: Applications to the Covid-19 pandemic.,Eur J Oper Res,33519049,2/2/21,pubmed,0,1,network model,0.001943529,0.001943517,0.001943606,0.88327403,0.108951765,0.001943553,Epidemiology,0.46544883,FALSE,465,0.996907663,595,0.983743645,2,0.618927094,,,0.866526134
2682,How to go viral: A COVID-19 model with endogenously time-varying parameters.,J Econom,33519026,2/2/21,pubmed,0,3,bayes,0.002806604,0.112036995,0.002806949,0.87673653,0.002806399,0.002806523,Epidemiology,0.400034,FALSE,48,0.614942173,170.3333333,0.899785925,0,0.403234768,,,0.639320955
2683,Molecular modeling evaluation of the binding effect of five protease inhibitors to COVID-19 main protease.,Chem Phys,33519023,2/2/21,pubmed,0,6,molecular dynamics simulation,0.985017472,0.002996575,0.002996483,0.002996498,0.002996424,0.002996549,Drug discovery,0.96360564,TRUE,21.83333333,0.322468922,5.5,0.267259834,0,0.403234768,,,0.330987841
2684,Family tree of a deadly virus.,New Sci,33518937,2/2/21,pubmed,0,1,sequencing,0.013549609,0.53118896,0.013548946,0.013551126,0.414612455,0.013548905,Genomics,0.23273972,FALSE,204,0.973715134,26,0.53819909,0,0.403234768,,,0.638382997
2685,A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images.,Biocybern Biomed Eng,33518878,2/2/21,pubmed,0,17,"deep learning, probabilistic",0.001141333,0.001141391,0.918170501,0.077264078,0.001141325,0.001141372,Imaging,0.6364792,TRUE,89.94117647,0.84235265,36.29411765,0.612924806,0,0.403234768,,,0.619504075
2686,Mental health outcomes during the COVID-19 and perceptions towards the pandemic: Findings from a cross sectional study among Bangladeshi students.,Child Youth Serv Rev,33518861,2/2/21,pubmed,0,6,logistic regression,0.001511798,0.001511811,0.001511795,0.00151182,0.992440952,0.001511824,Healthcare,0.7102543,TRUE,15.66666667,0.236563795,0.333333333,0.073187048,1,0.537564047,,,0.282438297
2687,A computational approach for the screening of potential antiviral compounds against SARS-CoV-2 protease: Ionic liquid vs herbal and natural compounds.,J Mol Liq,33518856,2/2/21,pubmed,0,3,computational,0.901083442,0.001653036,0.001653074,0.092304131,0.001653157,0.001653159,Drug discovery,0.54919946,TRUE,21.33333333,0.31548024,6,0.280037463,0,0.403234768,,,0.33291749
2688,Computational study on peptidomimetic inhibitors against SARS-CoV-2 main protease.,J Mol Liq,33518853,2/2/21,pubmed,0,7,computational,0.954623499,0.001237112,0.001237097,0.001237093,0.001237146,0.040428053,Drug discovery,0.98861897,TRUE,56.28571429,0.676974457,8.857142857,0.333422531,0,0.403234768,,,0.471210585
2689,"The good, the bad and the ugly on COVID-19 tourism recovery.",Ann Tour Res,33518847,2/2/21,pubmed,0,3,neural network,0.002562594,0.002562675,0.313723909,0.67602568,0.002562587,0.002562555,Epidemiology,0.23970097,FALSE,22,0.326056033,5.666666667,0.270203372,2,0.618927094,,,0.405062166
2690,Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine.,Comput Electr Eng,33518824,2/2/21,pubmed,0,7,"deep learning, neural network, classifier",0.001622702,0.001622699,0.991886436,0.001622742,0.001622716,0.001622705,Imaging,0.8060304,TRUE,92.14285714,0.849217639,35.85714286,0.60944608,4,0.707574542,,,0.72207942
2691,Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19.,Comput Methods Appl Mech Eng,33518823,2/2/21,pubmed,0,7,bayes,0.00089807,0.037147759,0.000898079,0.75642772,0.203730254,0.000898118,Epidemiology,0.3312967,FALSE,20.57142857,0.305399221,3,0.199424672,2,0.618927094,,,0.374583662
2692,Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images.,Pattern Recognit,33518813,2/2/21,pubmed,0,5,"deep learning, dataset",0.001461864,0.001461964,0.992690393,0.001461931,0.00146198,0.001461868,Imaging,0.61763906,TRUE,137.6,0.929989486,80.2,0.779502275,14,0.866658436,,,0.858716732
2693,Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19.,Pattern Recognit,33518812,2/2/21,pubmed,0,7,"deep learning, neural network, dataset",0.001254602,0.001254626,0.993726939,0.001254632,0.001254592,0.001254608,Imaging,0.42394152,FALSE,28.85714286,0.416043045,13.85714286,0.40941932,1,0.537564047,,,0.454342138
2694,0,J Mol Struct,33518802,2/2/21,pubmed,0,7,in silico,0.987887886,0.002422428,0.002422505,0.002422456,0.00242238,0.002422344,Drug discovery,0.77929974,TRUE,82.85714286,0.817366566,6,0.280037463,0,0.403234768,,,0.500212932
2695,Molecular docking identification for the efficacy of some zinc complexes with chloroquine and hydroxychloroquine against main protease of COVID-19.,J Mol Struct,33518801,2/2/21,pubmed,0,2,molecular dynamics simulation,0.962345145,0.00129123,0.001291316,0.001291303,0.032489711,0.001291296,Drug discovery,0.76651824,TRUE,3.5,0.044344115,0,0.055525823,0,0.403234768,,,0.167701569
2696,Computed optical spectra of SARS-CoV-2 proteins.,Chem Phys Lett,33518776,2/2/21,pubmed,0,2,computational,0.626911257,0.007061827,0.344841758,0.007061808,0.007061813,0.007061537,Drug discovery,0.52922744,TRUE,146,0.940688973,17.5,0.45611453,0,0.403234768,,,0.600012757
2697,Nasopharyngeal SARS-CoV-2 Viral Load Response among COVID-19 Patients Receiving Favipiravir.,Jpn J Infect Dis,33518623,2/2/21,pubmed,0,12,logistic regression,0.001486455,0.372181415,0.020618566,0.001486488,0.001486435,0.602740641,Clinics,0.9242188,TRUE,25.66666667,0.376461129,17.66666667,0.457586299,0,0.403234768,,,0.412427399
2698,What social media told us in the time of COVID-19: a scoping review.,Lancet Digit Health,33518503,2/2/21,pubmed,0,6,machine learning,0.001943492,0.00194358,0.001943723,0.616004714,0.376221036,0.001943455,Epidemiology,0.6327791,TRUE,47,0.606407323,71,0.756556061,1,0.537564047,,,0.633509144
2699,High Prevalence of Elder Abuse During the COVID-19 Pandemic: Risk and Resilience Factors.,Am J Geriatr Psychiatry,33518464,2/2/21,pubmed,0,2,logistic regression,0.001653019,0.026665665,0.00165303,0.239954321,0.728420866,0.001653099,Healthcare,0.34757578,FALSE,80,0.807532933,246,0.93678084,0,0.403234768,,,0.715849514
2700,"Mental health impact of the Covid-19 pandemic on parents in high-risk, low income communities.",Int J Soc Psychiatry,33517821,2/2/21,pubmed,0,3,logistic regression,0.001330052,0.001330062,0.001330115,0.139105407,0.855574327,0.001330037,Healthcare,0.9127171,TRUE,17.33333333,0.261178799,12,0.386740701,0,0.403234768,,,0.350384756
2701,Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.,Circulation,33517677,2/2/21,pubmed,0,14,"artificial intelligence, neural network, dataset",0.042841912,0.120747708,0.522620168,0.085790156,0.000977442,0.227022613,Clinics,0.5331953,TRUE,196.5,0.971736038,234.5,0.931964142,0,0.403234768,,,0.768978316
2702,Virtual screening and molecular dynamics simulation study of plant-derived compounds to identify potential inhibitors of main protease from SARS-CoV-2.,Brief Bioinform,33517367,2/1/21,pubmed,0,11,"virtual screening, molecular dynamics simulation, computational, dataset",0.895957176,0.001461882,0.001462007,0.098195024,0.00146197,0.001461942,Drug discovery,0.544326,TRUE,18.09090909,0.271940132,8.454545455,0.327200963,0,0.403234768,,,0.334125288
2703,Human coronaviruses in idiopathic Parkinson's disease: Implications of SARS-CoV-2's modulation of the host's transcriptome.,Infect Genet Evol,33516970,2/1/21,pubmed,0,1,"transcriptom, interactom, dataset",0.761170444,0.183445098,0.00171728,0.001717251,0.001717183,0.050232744,Drug discovery,0.59676176,TRUE,49,0.624281032,3,0.199424672,0,0.403234768,,,0.408980157
2704,Codon usage analysis of zoonotic coronaviruses reveals lower adaptation to humans by SARS-CoV-2.,Infect Genet Evol,33516969,2/1/21,pubmed,0,5,genomes,0.001823393,0.990882865,0.001823332,0.001823435,0.001823448,0.001823527,Genomics,0.6335231,TRUE,71.2,0.769744573,190,0.912095264,0,0.403234768,,,0.695024868
2705,Edible plant-derived exosomal microRNAs: Exploiting a cross-kingdom regulatory mechanism for targeting SARS-CoV-2.,Toxicol Appl Pharmacol,33516820,2/1/21,pubmed,0,2,"sequencing, in silico, dataset",0.738674028,0.223216606,0.001538196,0.001538132,0.001538193,0.033494845,Drug discovery,0.5097608,TRUE,12,0.183190055,9.5,0.345531175,0,0.403234768,,,0.310651999
2706,Role of Famotidine and Other Acid Reflux Medications for SARS-CoV-2: A Pilot Study.,J Voice,33516648,2/1/21,pubmed,0,5,logistic regression,0.107195164,0.00115627,0.001156252,0.00115626,0.269268464,0.620067589,Clinics,0.9430622,TRUE,31.6,0.451048302,6.8,0.292881991,1,0.537564047,,,0.42716478
2707,Detection of SARS-CoV-2 from patient fecal samples by whole genome sequencing.,Gut Pathog,33516247,2/1/21,pubmed,0,7,"sequencing, whole genome",0.00186177,0.886233376,0.001861727,0.001861737,0.030138946,0.078042444,Genomics,0.8819047,TRUE,37.28571429,0.510606717,32.42857143,0.588774418,0,0.403234768,,,0.500871967
2708,Workplace violence and its association with quality of life among mental health professionals in China during the COVID-19 pandemic.,J Psychiatr Res,33516081,1/31/21,pubmed,0,9,logistic regression,0.001823302,0.001823312,0.001823315,0.001823321,0.78468282,0.20802393,Healthcare,0.9566263,TRUE,102.2222222,0.873894489,61.88888889,0.728057265,0,0.403234768,,,0.668395507
2709,"Efficacy of ribavirin and interferon-α therapy for hospitalized patients with COVID-19: A multicenter, retrospective cohort study.",Int J Infect Dis,33515771,1/31/21,pubmed,0,12,logistic regression,0.001622798,0.001622716,0.001622698,0.001622772,0.001622813,0.991886203,Clinics,0.92824465,TRUE,56.08333333,0.675428289,174.4166667,0.902461868,1,0.537564047,,,0.705151401
2710,Machine learning predictive model for severe COVID-19.,Infect Genet Evol,33515712,1/31/21,pubmed,0,6,"machine learning, neural network, predictive model, network model",0.001751205,0.001751216,0.525364162,0.158652419,0.001751221,0.310729776,Clinics,0.8201151,TRUE,14.66666667,0.221163956,2.833333333,0.189523682,0,0.403234768,,,0.271307468
2711,"Anticipated mental health consequences of COVID-19 in a nationally-representative sample: Context, coverage, and economic consequences.",Prev Med,33515588,1/31/21,pubmed,0,3,logistic regression,0.001310353,0.001310331,0.001310341,0.293650047,0.701108587,0.001310339,Healthcare,0.7013964,TRUE,16.33333333,0.247015895,4,0.231469093,0,0.403234768,,,0.293906585
2712,"Delays in lymphatic filariasis elimination programmes due to COVID-19, and possible mitigation strategies.",Trans R Soc Trop Med Hyg,33515454,1/31/21,pubmed,0,11,mathematical model,0.034316545,0.002032763,0.002032816,0.957552294,0.002032802,0.00203278,Epidemiology,0.81879306,TRUE,55.63636364,0.671964871,84.09090909,0.788466685,1,0.537564047,,,0.665998534
2713,Identifying prognostic risk factors for poor outcome following COVID-19 disease among in-centre haemodialysis patients: role of inflammation and frailty.,J Nephrol,33515380,1/31/21,pubmed,0,9,logistic regression,0.001291287,0.043576071,0.001291227,0.001291257,0.033282939,0.919267219,Clinics,0.72772455,TRUE,43.88888889,0.576659039,36.88888889,0.615935242,0,0.403234768,,,0.531943016
2714,Integration of heparin-binding protein and interleukin-6 in the early prediction of respiratory failure and mortality in pneumonia by SARS-CoV-2 (COVID-19).,Eur J Clin Microbiol Infect Dis,33515095,1/31/21,pubmed,0,10,logistic regression,0.031503124,0.001751167,0.258623628,0.001751324,0.001751159,0.704619598,Clinics,0.93193483,TRUE,55.4,0.670542396,47.6,0.670792079,0,0.403234768,,,0.581523081
2715,What does the COVID-19 pandemic mean for the next decade of onchocerciasis control and elimination?,Trans R Soc Trop Med Hyg,33515042,1/31/21,pubmed,0,10,mathematical model,0.04286224,0.002130877,0.002130641,0.676613522,0.177633119,0.098629601,Epidemiology,0.9118872,TRUE,75.3,0.788051209,69.2,0.751003479,0,0.403234768,,,0.647429818
2716,Disruptions to schistosomiasis programmes due to COVID-19: an analysis of potential impact and mitigation strategies.,Trans R Soc Trop Med Hyg,33515038,1/31/21,pubmed,0,5,mathematical model,0.03611668,0.001987119,0.001987172,0.524627001,0.433294831,0.001987196,Epidemiology,0.9521574,TRUE,43.4,0.571958686,70,0.753344929,0,0.403234768,,,0.576179461
2717,Addendum: A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology.,Nat Microbiol,33514928,1/31/21,pubmed,0,8,genomic epidemiology,0.034962549,0.03496251,0.034964526,0.825186316,0.034961903,0.034962197,Epidemiology,0.640971,TRUE,157.375,0.949533057,829.25,0.990834894,1,0.537564047,,,0.825977333
2718,Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT.,NPJ Digit Med,33514852,1/31/21,pubmed,0,29,"deep learning, neural network",0.00194371,0.001943709,0.873716535,0.001943632,0.001943516,0.118508898,Imaging,0.43474263,FALSE,32.03448276,0.455872348,17.75862069,0.458389082,0,0.403234768,,,0.439165399
2719,Transmission dynamics and control measures of COVID-19 outbreak in China: a modelling study.,Sci Rep,33514781,1/31/21,pubmed,0,6,"mathematical model, dataset",0.00229657,0.091865512,0.002296681,0.858821676,0.002296618,0.042422942,Epidemiology,0.38329268,FALSE,103.6666667,0.876863133,118,0.848876104,0,0.403234768,,,0.709658002
2720,Prediction of patients requiring intensive care for COVID-19: development and validation of an integer-based score using data from Centers for Disease Control and Prevention of South Korea.,J Intensive Care,33514443,1/31/21,pubmed,0,10,"logistic regression, prediction model, dataset",0.000956317,0.000956299,0.131776999,0.055081036,0.000956339,0.81027301,Clinics,0.26706898,FALSE,14,0.213494959,1.9,0.151993578,0,0.403234768,,,0.256241102
2721,Social distancing during the COVID-19 pandemic: quantifying the practice in Michigan - a "hotspot state" early in the pandemic - using a volunteer-based online survey.,BMC Public Health,33514350,1/31/21,pubmed,0,11,logistic regression,0.00133001,0.001330017,0.001330004,0.288281789,0.70639815,0.00133003,Healthcare,0.70989287,TRUE,117.7272727,0.903828313,131.8181818,0.865734546,0,0.403234768,,,0.724265876
2722,Prevalence and its associated factors of depressive symptoms among Chinese college students during the COVID-19 pandemic.,BMC Psychiatry,33514336,1/31/21,pubmed,0,4,logistic regression,0.001392857,0.001392832,0.023757387,0.00139287,0.970671228,0.001392826,Healthcare,0.9810246,TRUE,73,0.778464964,22.75,0.509232004,0,0.403234768,,,0.563643912
2723,Chest Imaging of Patients with Sarcoidosis and SARS-CoV-2 Infection. Current Evidence and Clinical Perspectives.,Diagnostics (Basel),33514012,1/31/21,pubmed,0,4,deep learning,0.148031074,0.002720423,0.403927134,0.063340056,0.002720375,0.379260938,Imaging,0.7739395,TRUE,66.75,0.745438803,58.75,0.716283115,0,0.403234768,,,0.621652228
2724,COVID-19 Impact on Diagnostic Innovations: Emerging Trends and Implications.,Diagnostics (Basel),33513988,1/31/21,pubmed,0,9,dataset,0.001237168,0.107128598,0.380004347,0.509155714,0.001237094,0.001237079,Epidemiology,0.79455304,TRUE,3.777777778,0.047003525,2.555555556,0.181161359,0,0.403234768,,,0.210466551
2725,"Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions.",Int J Environ Res Public Health,33513984,1/31/21,pubmed,0,5,"machine learning, deep learning, artificial intelligence, dataset",0.001943535,0.001943483,0.660960362,0.255614735,0.077594378,0.001943507,Epidemiology,0.54699224,TRUE,21.6,0.319438432,9.6,0.346735349,1,0.537564047,,,0.401245943
2726,COVID-19-Related Changes in Perceived Household Food Waste in the United States: A Cross-Sectional Descriptive Study.,Int J Environ Res Public Health,33513709,1/31/21,pubmed,0,3,logistic regression,0.001392836,0.00139286,0.001392851,0.001392904,0.993035653,0.001392897,Healthcare,0.9555441,TRUE,30.33333333,0.436266931,26.66666667,0.543216484,0,0.403234768,,,0.460906061
2727,COVID-19 Infection and Previous BCG Vaccination Coverage in the Ecuadorian Population.,Vaccines (Basel),33513693,1/31/21,pubmed,0,7,mathematical model,0.001943514,0.214997272,0.001943461,0.383376172,0.395795996,0.001943585,Healthcare,0.15493938,FALSE,23.85714286,0.352031666,4.428571429,0.239296227,0,0.403234768,,,0.331520887
2728,Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods.,Comput Methods Programs Biomed,33513487,1/30/21,pubmed,0,3,"machine learning, deep learning, data mining, classifier, dataset",0.158793774,0.000822915,0.453489167,0.055218337,0.00082298,0.330852826,Clinics,0.73946846,TRUE,59.33333333,0.698497124,23.33333333,0.515386674,0,0.403234768,,,0.539039522
2729,Different SARS-CoV-2 haplotypes associate with geographic origin and case fatality rates of COVID-19 patients.,Infect Genet Evol,33513449,1/30/21,pubmed,0,4,"bioinformatic, genome sequences",0.001786559,0.805145414,0.001786531,0.001786562,0.001786562,0.187708374,Genomics,0.12759218,FALSE,180.75,0.964623663,518,0.980064223,0,0.403234768,,,0.782640884
2730,"Knowledge, attitudes, and practices related to COVID-19 pandemic among adult population in Sidama Regional State, Southern Ethiopia: A community based cross-sectional study.",PLoS One,33513211,1/30/21,pubmed,0,3,logistic regression,0.000916703,0.000916687,0.000916693,0.102388953,0.893944266,0.000916699,Healthcare,0.8635475,TRUE,8.666666667,0.12839384,4,0.231469093,0,0.403234768,,,0.2543659
2731,Estimating the time-varying reproduction number of COVID-19 with a state-space method.,PLoS Comput Biol,33513137,1/30/21,pubmed,0,3,"mathematical model, dataset",0.00156533,0.051740095,0.001565358,0.941998555,0.001565335,0.001565327,Epidemiology,0.015124053,FALSE,52,0.647349867,78,0.773548301,0,0.403234768,,,0.608044312
2732,How IvIg Can Mitigate Covid-19 Disease: A Symmetrical Immune Network Model.,Monoclon Antib Immunodiagn Immunother,33513050,1/30/21,pubmed,0,2,network model,0.79914482,0.079081323,0.002296668,0.067880333,0.002296556,0.0493003,Drug discovery,0.29430136,FALSE,224,0.979652421,170,0.899719026,0,0.403234768,,,0.760868738
2733,Quantification of Occupational and Community Risk Factors for SARS-CoV-2 Seropositivity Among Health Care Workers in a Large U.S. Health Care System.,Ann Intern Med,33513035,1/30/21,pubmed,0,10,logistic regression,0.001022628,0.001022672,0.001022674,0.00102265,0.928767497,0.067141878,Healthcare,0.78543174,TRUE,116.4,0.901292597,254.9,0.939791276,2,0.618927094,,,0.820003656
2734,Digital Technology for AMD Management in the Post-COVID-19 New Normal.,Asia Pac J Ophthalmol (Phila),33512827,1/30/21,pubmed,0,10,artificial intelligence,0.00131038,0.019198371,0.36836268,0.422787923,0.18703017,0.001310477,Epidemiology,0.95148224,TRUE,216.3,0.977302245,357.2,0.967018999,0,0.403234768,,,0.782518671
2735,Strong Binding of Leupeptin with TMPRSS2 Protease May Be an Alternative to Camostat and Nafamostat for SARS-CoV-2 Repurposed Drug: Evaluation from Molecular Docking and Molecular Dynamics Simulations.,Appl Biochem Biotechnol,33512650,1/30/21,pubmed,0,6,molecular dynamics simulation,0.99344835,0.00131034,0.001310313,0.001310341,0.001310345,0.00131031,Drug discovery,0.9163228,TRUE,8.333333333,0.123693488,0.166666667,0.058736955,0,0.403234768,,,0.195221737
2736,The microbiome of bat guano: for what is this knowledge important?,Appl Microbiol Biotechnol,33512572,1/30/21,pubmed,0,8,"sequencing, microbiom",0.104061276,0.743418793,0.001203466,0.130180871,0.019932186,0.001203408,Genomics,0.8774617,TRUE,20.25,0.301317336,5.375,0.263446615,0,0.403234768,,,0.322666239
2737,Rapid Spread of Mutant Alleles in Worldwide SARS-CoV-2 Strains Revealed by Genome-Wide Single Nucleotide Polymorphism and Variation Analysis.,Genome Biol Evol,33512495,1/30/21,pubmed,0,6,genome-wide,0.002183263,0.950500959,0.002183229,0.002183288,0.040765889,0.002183371,Genomics,0.2927125,FALSE,20.16666667,0.299585627,12.66666667,0.39530372,0,0.403234768,,,0.366041371
2738,"Honduras: two hurricanes, COVID-19, dengue and the need for a new digital health surveillance system.",J Public Health (Oxf),33512491,1/30/21,pubmed,0,15,digital health,0.003214184,0.00321424,0.003214195,0.983929092,0.003214194,0.003214095,Epidemiology,0.46620488,FALSE,13.06666667,0.197662193,2.933333333,0.191196147,0,0.403234768,,,0.264031036
2739,A comparison of DNA/RNA extraction protocols for high-throughput sequencing of microbial communities.,Biotechniques,33512248,1/30/21,pubmed,0,18,sequencing,0.001861728,0.682848513,0.309704499,0.001861822,0.00186172,0.001861718,Genomics,0.79499984,TRUE,27.55555556,0.401880141,112.0555556,0.84151726,0,0.403234768,,,0.548877389
2740,Study of Specific Receptor Binding Mode Suggests a Possible Enzymatic Disinfectant for SARS-CoV-2.,Langmuir,33512172,1/30/21,pubmed,0,2,molecular dynamics simulation,0.989083936,0.002183212,0.002183242,0.002183245,0.002183195,0.002183169,Drug discovery,0.59793717,TRUE,15,0.227596017,5,0.257024351,0,0.403234768,,,0.295951712
2741,"COVID-19 encephalopathy, Bayes rule, and a plea for case-control studies.",Ann Clin Transl Neurol,33512092,1/30/21,pubmed,0,4,bayes,0.057803515,0.057800712,0.71098561,0.057802356,0.057804832,0.057802974,Healthcare,0.15837589,FALSE,20.25,0.301317336,26,0.53819909,1,0.537564047,,,0.459026824
2742,"Reply to COVID-19 encephalopathy, Bayes rule, and a plea for case-control studies.",Ann Clin Transl Neurol,33512080,1/30/21,pubmed,0,3,bayes,0.057803515,0.057800712,0.71098561,0.057802356,0.057804832,0.057802974,Healthcare,0.17479098,FALSE,103.3333333,0.875935432,116,0.846133262,0,0.403234768,,,0.708434487
2743,Mesenchymal Stem Cell-Derived Extracellular Vesicles Carrying miRNA as a Potential Multi Target Therapy to COVID-19: an In Silico Analysis.,Stem Cell Rev Rep,33511519,1/30/21,pubmed,0,3,"bioinformatic, in silico, dataset",0.992440546,0.001511883,0.001511917,0.001511948,0.001511799,0.001511906,Drug discovery,0.6864499,TRUE,48.66666667,0.619333292,21.33333333,0.495450896,1,0.537564047,,,0.550782745
2744,0,Patterns (N Y),33511363,1/30/21,pubmed,0,5,artificial intelligence,0.078125787,0.001861768,0.547540504,0.368748421,0.001861799,0.001861722,Epidemiology,0.34513655,FALSE,79.8,0.806234152,11.4,0.375970029,0,0.403234768,,,0.52847965
2745,Adhesive contact between cylindrical (Ebola) and spherical (SARS-CoV-2) viral particles and a cell membrane.,Mech Soft Mater,33511329,1/30/21,pubmed,0,4,mathematical model,0.629234992,0.001593558,0.001593559,0.364390792,0.001593531,0.001593569,Drug discovery,0.15882131,FALSE,25.75,0.377698064,17,0.451097137,0,0.403234768,,,0.410676656
2746,Face Mask Use in the Community for Reducing the Spread of COVID-19: A Systematic Review.,Front Med (Lausanne),33511141,1/30/21,pubmed,0,13,"mathematical model, predictive model",0.001141387,0.023825846,0.001141359,0.861751043,0.001141361,0.110999003,Epidemiology,0.4490645,FALSE,62.76923077,0.719710557,15.15384615,0.426746053,0,0.403234768,,,0.516563793
2747,Impact of Coronavirus Disease (COVID-19) Pandemic on Psychological Well-Being of the Pakistani General Population.,Front Psychiatry,33510654,1/30/21,pubmed,0,8,logistic regression,0.000946128,0.000946093,0.000946131,0.00094618,0.995269372,0.000946096,Healthcare,0.9132749,TRUE,21,0.312016822,3.625,0.215346535,0,0.403234768,,,0.310199375
2748,Scalable Analysis of Authentic Viral Envelopes on FRONTERA.,Comput Sci Eng,33510584,1/30/21,pubmed,0,5,computational,0.527357324,0.144590373,0.085228006,0.23925118,0.001786603,0.001786514,Drug discovery,0.229355,FALSE,45.2,0.588719154,112.8,0.842186246,0,0.403234768,,,0.611380056
2749,"Stress, Anxiety, Depression and Their Associated Factors among Health Care Workers During COVID -19 Pandemic in Nepal.",J Nepal Health Res Counc,33510505,1/30/21,pubmed,0,7,logistic regression,0.001098852,0.001098855,0.001098938,0.001098842,0.994505543,0.001098971,Healthcare,0.95575035,TRUE,11,0.167171748,1.285714286,0.128512176,1,0.537564047,,,0.277749324
2750,0,Cell Res,33510385,1/30/21,pubmed,0,17,transcriptom,0.535579823,0.455897044,0.002130662,0.002130788,0.002130688,0.002130995,Drug discovery,0.77642953,TRUE,51.76470588,0.64462861,51.35294118,0.686580145,1,0.537564047,,,0.622924267
2751,Meta-analysis of host transcriptional responses to SARS-CoV-2 infection reveals their manifestation in human tumors.,Sci Rep,33510359,1/30/21,pubmed,0,7,transcriptom,0.863533914,0.046798715,0.001220024,0.001220051,0.001220011,0.086007286,Drug discovery,0.38777304,FALSE,157.8571429,0.949965984,331.4285714,0.962336098,1,0.537564047,,,0.816622043
2752,Network theoretic analysis of JAK/STAT pathway and extrapolation to drugs and viruses including COVID-19.,Sci Rep,33510353,1/30/21,pubmed,0,3,genomes,0.445387322,0.123854767,0.001861824,0.336982469,0.001861783,0.090051836,Drug discovery,0.7335835,TRUE,4.333333333,0.057950399,0,0.055525823,0,0.403234768,,,0.172236996
2753,Genomic epidemiology of the early stages of the SARS-CoV-2 outbreak in Russia.,Nat Commun,33510171,1/30/21,pubmed,0,14,genomic epidemiology,0.002720084,0.677884173,0.002720142,0.3112345,0.002720213,0.002720888,Genomics,0.3456431,FALSE,26.64285714,0.390252953,9.071428571,0.337971635,0,0.403234768,,,0.377153119
2754,Genome Sequences of 10 SARS-CoV-2 Viral Strains Obtained by Nanopore Sequencing of Nasopharyngeal Swabs in Malta.,Microbiol Resour Announc,33509993,1/30/21,pubmed,0,8,"sequencing, genome sequences, genomes",0.00706214,0.964689,0.007063628,0.007062232,0.007061506,0.007061494,Genomics,0.63760835,TRUE,7.375,0.105757932,3.875,0.222972973,0,0.403234768,,,0.243988557
2755,Genome Sequencing of a Novel Coronavirus SARS-CoV-2 Isolate from Iraq.,Microbiol Resour Announc,33509990,1/30/21,pubmed,0,14,sequencing,0.006089752,0.871637495,0.006089651,0.006089606,0.006089532,0.104003964,Genomics,0.6743833,TRUE,17.14285714,0.258519389,12.5,0.392761573,0,0.403234768,,,0.351505243
2756,Mental Health Multimorbidity among Caregivers of Older Adults During the COVID-19 Epidemic.,Am J Geriatr Psychiatry,33509676,1/30/21,pubmed,0,11,logistic regression,0.001112602,0.001112605,0.001112661,0.001112645,0.981823245,0.013726242,Healthcare,0.8937035,TRUE,184.2727273,0.966231678,180.3636364,0.906408884,0,0.403234768,,,0.75862511
2757,"A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA.",Spat Spatiotemporal Epidemiol,33509436,1/30/21,pubmed,0,4,bayes,0.002806361,0.002806557,0.071619268,0.671714982,0.24824623,0.002806602,Epidemiology,0.3872368,FALSE,191.5,0.969385862,232.5,0.930826866,0,0.403234768,,,0.767815832
2758,Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.,Lancet Digit Health,33509388,1/30/21,pubmed,0,10,"machine learning, artificial intelligence, classifier",0.000501264,0.013498507,0.240122055,0.032592227,0.076979503,0.636306445,Clinics,0.3600367,FALSE,58.7,0.694291546,82.7,0.785054857,3,0.667819001,,,0.715721801
2759,Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology.,Lancet Digit Health,33509383,1/30/21,pubmed,0,5,"artificial intelligence, digital health",0.001943487,0.001943485,0.351416041,0.640809707,0.001943555,0.001943724,Epidemiology,0.9195336,TRUE,332.8,0.992949471,593,0.983542949,4,0.707574542,,,0.894688987
2760,"Epidemiology of COVID-19 in Northern Ireland, 26 February 2020-26 April 2020.",Epidemiol Infect,33509318,1/30/21,pubmed,0,7,logistic regression,0.001861665,0.001861755,0.001861705,0.607037855,0.259131646,0.128245374,Epidemiology,0.6265616,TRUE,12.71428571,0.191106438,12.85714286,0.39717688,0,0.403234768,,,0.330506029
2761,Examining Australian public perceptions and behaviors towards a future COVID-19 vaccine.,BMC Infect Dis,33509104,1/30/21,pubmed,0,7,logistic regression,0.001593536,0.001593531,0.001593642,0.001593691,0.992032025,0.001593576,Healthcare,0.748764,TRUE,103.4285714,0.876059125,85.28571429,0.790875033,0,0.403234768,,,0.690056309
2762,"Applying high throughput and comprehensive immunoinformatics approaches to design a trivalent subunit vaccine for induction of immune response against emerging human coronaviruses SARS-CoV, MERS-CoV and SARS-CoV-2.",J Biomol Struct Dyn,33509045,1/30/21,pubmed,0,5,"molecular dynamics simulation, in silico",0.948034874,0.047457872,0.001126799,0.001126814,0.001126823,0.001126817,Drug discovery,0.7757809,TRUE,16.4,0.247572515,4,0.231469093,0,0.403234768,,,0.294092125
2763,Association of clade-G SARS-CoV-2 viruses and age with increased mortality rates across 57 countries and India.,Infect Genet Evol,33508515,1/29/21,pubmed,0,3,"genomes, network analysis",0.000999609,0.67082529,0.097306946,0.000999551,0.000999565,0.22886904,Genomics,0.7092436,TRUE,22,0.326056033,5.666666667,0.270203372,1,0.537564047,,,0.377941151
2764,Variations in health behaviors among pregnant women during the COVID-19 pandemic.,Midwifery,33508485,1/29/21,pubmed,0,5,logistic regression,0.001653086,0.001653027,0.001653071,0.001653095,0.991734458,0.001653264,Healthcare,0.9336306,TRUE,89.4,0.840806482,117.8,0.848474712,0,0.403234768,,,0.69750532
2765,Latent periodicity-2 in coronavirus SARS-CoV-2 genome: Evolutionary implications.,J Theor Biol,33508323,1/29/21,pubmed,0,1,whole genome,0.001987235,0.75932091,0.001987172,0.232730457,0.001987116,0.001987109,Genomics,0.6593214,TRUE,41,0.549013544,29,0.562884667,0,0.403234768,,,0.505044326
2766,Inflammatory Leptomeningeal Cytokines Mediate COVID-19 Neurologic Symptoms in Cancer Patients.,Cancer Cell,33508216,1/29/21,pubmed,0,12,proteom,0.376416413,0.00218332,0.002183251,0.002183381,0.002183285,0.61485035,Clinics,0.92765814,TRUE,79.83333333,0.806295999,242.0833333,0.934506288,1,0.537564047,,,0.759455445
2767,On Drug-Membrane Permeability of Antivirals for SARS-CoV-2.,J Phys Chem Lett,33508197,1/29/21,pubmed,0,3,"molecular dynamics simulation, computational",0.863610201,0.001371279,0.001371311,0.13090444,0.001371397,0.001371372,Drug discovery,0.38871926,FALSE,56,0.675304595,20.66666667,0.487958255,0,0.403234768,,,0.522165873
2768,An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States.,PLoS One,33508030,1/29/21,pubmed,0,4,bayes,0.001565292,0.061347197,0.001565383,0.851641265,0.001565434,0.082315431,Epidemiology,0.032292694,FALSE,55.75,0.672768879,59.75,0.719761841,1,0.537564047,,,0.643364922
2769,SARS-CoV-2 infection in asymptomatic healthcare workers at a clinic in Chile.,PLoS One,33507981,1/29/21,pubmed,0,16,"sequencing, genomes",0.002183195,0.575498961,0.002183253,0.098726684,0.231675946,0.089731962,Genomics,0.24726233,FALSE,15.5625,0.234646546,15.0625,0.426010169,1,0.537564047,,,0.399406921
2770,The high volume of patients admitted during the SARS-CoV-2 pandemic has an independent harmful impact on in-hospital mortality from COVID-19.,PLoS One,33507954,1/29/21,pubmed,0,7,dataset,0.001022614,0.00102261,0.001022635,0.174220132,0.019689596,0.803022414,Clinics,0.8986292,TRUE,80,0.807532933,98.28571429,0.819507626,0,0.403234768,,,0.676758442
2771,"Single cell resolution of SARS-CoV-2 tropism, antiviral responses, and susceptibility to therapies in primary human airway epithelium.",PLoS Pathog,33507952,1/29/21,pubmed,0,14,sequencing,0.940417218,0.001717306,0.001717177,0.052713916,0.001717177,0.001717207,Drug discovery,0.6714581,TRUE,34.35714286,0.480487352,59.71428571,0.719494247,0,0.403234768,,,0.534405456
2772,No association between use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers prior to hospital admission and clinical course of COVID-19 in the COvid MEdicaTion (COMET) study.,Br J Clin Pharmacol,33507556,1/29/21,pubmed,0,8,logistic regression,0.067014718,0.001461859,0.001461836,0.001461852,0.001461884,0.927137851,Clinics,0.8895322,TRUE,48.375,0.617106809,35.125,0.605833556,0,0.403234768,,,0.542058378
2773,[Higher risk of COVID-19 hospitalization for unemployed: an analysis of health insurance data from 1.28 million insured individuals in Germany].,Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz,33507323,1/29/21,pubmed,0,5,logistic regression,0.001565333,0.001565328,0.001565339,0.170852369,0.277614289,0.546837342,Clinics,0.85117185,TRUE,30.8,0.441523904,29.8,0.567968959,1,0.537564047,,,0.515685637
2774,Outcomes of COVID-19 Among Hospitalized Health Care Workers in North America.,JAMA Netw Open,33507259,1/29/21,pubmed,0,8,logistic regression,0.000889049,0.000889059,0.000889064,0.000889074,0.08882096,0.907622794,Clinics,0.9031001,TRUE,57.125,0.682540664,29.5,0.566095799,0,0.403234768,,,0.550623744
2775,Intractable COVID-19 and Prolonged SARS-CoV-2 Replication in a CAR-T-cell Therapy Recipient: A Case Study.,Clin Infect Dis,33507235,1/29/21,pubmed,0,33,"sequencing, deep sequencing",0.23778809,0.626407911,0.064981081,0.003607344,0.003607265,0.063608309,Genomics,0.36680797,FALSE,61.24242424,0.710000618,160.8181818,0.892828472,2,0.618927094,,,0.740585395
2776,Will the emergent SARS-CoV2 B.1.1.7 lineage affect molecular diagnosis of COVID-19?,J Med Virol,33506970,1/29/21,pubmed,0,5,genomes,0.002639093,0.753743782,0.00263921,0.23569983,0.002639106,0.00263898,Genomics,0.4338025,FALSE,46.4,0.599727874,32,0.585763982,1,0.537564047,,,0.574351968
2777,A parsimonious approach for recognizing SARS-CoV-2 and host interactions.,J Med Virol,33506962,1/29/21,pubmed,0,1,genomes,0.714185416,0.278945595,0.001717221,0.001717285,0.001717199,0.001717284,Drug discovery,0.51232296,TRUE,34,0.477766096,8,0.320511105,0,0.403234768,,,0.400503989
2778,High expression of ACE2 in the human lung leads to the release of IL6 by suppressing cellular immunity: IL6 plays a key role in COVID-19.,Eur Rev Med Pharmacol Sci,33506945,1/29/21,pubmed,0,8,sequencing,0.792703393,0.066375149,0.002032795,0.002032848,0.002032926,0.134822889,Drug discovery,0.9357219,TRUE,8,0.118683901,1.75,0.148381054,0,0.403234768,,,0.223433241
2779,Structural similarity-based prediction of host factors associated with SARS-CoV-2 infection and pathogenesis.,J Biomol Struct Dyn,33506741,1/29/21,pubmed,0,4,"computational, interactom",0.848805021,0.065408329,0.001237119,0.082075279,0.001237154,0.001237098,Drug discovery,0.63219833,TRUE,15.75,0.237862577,11.75,0.381857105,0,0.403234768,,,0.340984816
2780,Genotype-phenotype correlation identified a novel SARS-CoV-2 variant possibly linked to severe disease.,Transbound Emerg Dis,33506644,1/29/21,pubmed,0,18,"sequencing, genome sequences, genomes",0.000838506,0.57706961,0.000838505,0.000838521,0.000838579,0.419576279,Genomics,0.32600784,FALSE,36.22222222,0.499597996,23.5,0.516791544,2,0.618927094,,,0.545105545
2781,RAAS Blockade and COVID-19: Mechanistic Modeling of Mas and AT1 Receptor Occupancy as Indicators of Pro-Inflammatory and Anti-Inflammatory Balance.,Clin Pharmacol Ther,33506503,1/29/21,pubmed,0,2,mathematical model,0.44003909,0.001786571,0.001786551,0.05792137,0.001786612,0.496679806,Clinics,0.3412692,FALSE,13.5,0.205393036,1.5,0.138747659,0,0.403234768,,,0.249125154
2782,Impact of Race and Socioeconomic Status on Outcomes in Patients Hospitalized with COVID-19.,J Gen Intern Med,33506402,1/29/21,pubmed,0,12,logistic regression,0.000988336,0.000988354,0.000988349,0.000988372,0.348607353,0.647439235,Clinics,0.514215,TRUE,37.25,0.51035933,44.75,0.657947552,1,0.537564047,,,0.568623643
2783,Modeling the transmission of COVID-19 in the US - A case study.,Infect Dis Model,33506152,1/29/21,pubmed,0,2,mathematical model,0.062333479,0.002720205,0.002720117,0.926786048,0.002720086,0.002720065,Epidemiology,0.3781047,FALSE,20,0.298163152,7,0.299973241,0,0.403234768,,,0.333790387
2784,Pre-existing Health Conditions and Epicardial Adipose Tissue Volume: Potential Risk Factors for Myocardial Injury in COVID-19 Patients.,Front Cardiovasc Med,33505992,1/29/21,pubmed,0,23,logistic regression,0.000880237,0.000880207,0.158842123,0.000880222,0.000880226,0.837636985,Clinics,0.95369726,TRUE,50.34782609,0.633867277,,,0,0.403234768,,,0.518551022
2785,Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection.,Front Cell Dev Biol,33505977,1/29/21,pubmed,0,7,"machine learning, classifier, transcriptom",0.506977767,0.044511455,0.443896279,0.001538152,0.001538114,0.001538232,Drug discovery,0.5289442,TRUE,45.85714286,0.594656441,81.85714286,0.782646508,1,0.537564047,,,0.638288999
2786,Spontaneous binding of potential COVID-19 drugs (Camostat and Nafamostat) to human serine protease TMPRSS2.,Comput Struct Biotechnol J,33505639,1/29/21,pubmed,0,6,molecular dynamics simulation,0.987888016,0.002422369,0.002422343,0.002422355,0.002422365,0.002422553,Drug discovery,0.84837705,TRUE,18.83333333,0.281588224,25.66666667,0.534854161,0,0.403234768,,,0.406559051
2787,"Deciphering the Subtype Differentiation History of SARS-CoV-2 Based on a New Breadth-First Searching Optimized Alignment Method Over a Global Data Set of 24,768 Sequences.",Front Genet,33505425,1/29/21,pubmed,0,5,"sequence alignment, network analysis",0.001022658,0.882512806,0.054576954,0.059842321,0.001022636,0.001022625,Genomics,0.65441036,TRUE,21.2,0.313686684,3.8,0.221501204,0,0.403234768,,,0.312807552
2788,"Assessing Knowledge, Attitudes and Practices Towards COVID-19 Public Health Preventive Measures Among Patients at Mulago National Referral Hospital.",Risk Manag Healthc Policy,33505175,1/29/21,pubmed,0,6,logistic regression,0.001022616,0.001022623,0.001022641,0.001022651,0.818828595,0.177080875,Healthcare,0.93341887,TRUE,3.5,0.044344115,1.833333333,0.151056998,0,0.403234768,,,0.199545293
2789,Lipidome is lipids regulator in gastrointestinal tract and it is a life collar in COVID-19: A review.,World J Gastroenterol,33505149,1/29/21,pubmed,0,1,lipidom,0.635391995,0.17519188,0.001684655,0.184362313,0.001684528,0.001684629,Drug discovery,0.52065456,TRUE,36,0.498299215,14,0.412898047,0,0.403234768,,,0.43814401
2790,Author Correction: Genomic epidemiology reveals multiple introductions of SARS-CoV-2 from mainland Europe into Scotland.,Nat Microbiol,33504980,1/29/21,pubmed,0,52,genomic epidemiology,0.015998527,0.920006058,0.015998657,0.015999544,0.015998715,0.015998499,Genomics,0.6278038,TRUE,52.71153846,0.651431752,66.55769231,0.744313621,0,0.403234768,,,0.599660047
2791,Altered high-density lipoprotein composition and functions during severe COVID-19.,Sci Rep,33504824,1/29/21,pubmed,0,14,proteom,0.305409612,0.069424927,0.002080619,0.002080681,0.00208068,0.618923482,Clinics,0.7113528,TRUE,41.71428571,0.555012679,35.5,0.608241905,0,0.403234768,,,0.522163117
2792,Cryo-EM structures of the SARS-CoV-2 endoribonuclease Nsp15 reveal insight into nuclease specificity and dynamics.,Nat Commun,33504779,1/29/21,pubmed,0,16,dataset,0.915121967,0.07710378,0.001943712,0.001943616,0.001943461,0.001943465,Drug discovery,0.26274064,FALSE,42,0.558537943,44.875,0.658282044,0,0.403234768,,,0.540018252
2793,"Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.",Nat Commun,33504775,1/29/21,pubmed,0,48,"deep learning, neural network, network analysis",0.001901776,0.001901785,0.442895595,0.001901745,0.001901725,0.549497374,Clinics,0.15418372,FALSE,34.14583333,0.478199023,30.14583333,0.570578004,0,0.403234768,,,0.484003931
2794,SARS-CoV-2 Transmission Dynamics in a Sleep-Away Camp.,Pediatrics,33504612,1/29/21,pubmed,0,26,genomes,0.001511822,0.211354103,0.001511823,0.273123997,0.32697853,0.185519724,Healthcare,0.858509,TRUE,72.26923077,0.774259385,105.6923077,0.83255285,1,0.537564047,,,0.714792094
2795,Factors associated with COVID-19-related death in people with rheumatic diseases: results from the COVID-19 Global Rheumatology Alliance physician-reported registry.,Ann Rheum Dis,33504483,1/29/21,pubmed,0,278,logistic regression,0.085893719,0.001653013,0.001653063,0.00165306,0.146586693,0.762560451,Clinics,0.8955966,TRUE,128.9189189,0.92003216,112.9189189,0.84245384,20,0.900117291,,,0.88753443
2796,"When a pandemic and an epidemic collide: COVID-19, gut microbiota, and the double burden of malnutrition.",BMC Med,33504332,1/29/21,pubmed,0,2,microbiom,0.001046892,0.298150899,0.00104689,0.23292455,0.399046254,0.067784515,Healthcare,0.872264,TRUE,14,0.213494959,4.5,0.242708055,2,0.618927094,,,0.358376703
2797,Phenylbenzopyrone of flavonoids as a potential scaffold to prevent SARS-CoV-2 replication by inhibiting its MPRO main protease.,Curr Pharm Biotechnol,33504301,1/29/21,pubmed,0,4,"virtual screening, molecular dynamics simulation",0.969087104,0.001254634,0.001254622,0.025894338,0.001254647,0.001254655,Drug discovery,0.8790169,TRUE,17.75,0.267672707,0,0.055525823,0,0.403234768,,,0.242144432
2798,Intensive care digital health response to emerging infectious disease outbreaks such as COVID-19.,Anaesth Intensive Care,33504171,1/29/21,pubmed,0,8,digital health,0.002639047,0.002639005,0.049590548,0.789205555,0.002639207,0.153286638,Epidemiology,0.51764154,TRUE,86.875,0.833075639,123.125,0.855432165,0,0.403234768,,,0.697247524
2799,Identification of 37 Heterogeneous Drug Candidates for Treatment of COVID-19 via a Rational Transcriptomics-Based Drug Repurposing Approach.,Pharmaceuticals (Basel),33504008,1/29/21,pubmed,0,4,"computational, transcriptom, dataset",0.992309059,0.001538186,0.001538223,0.001538226,0.001538186,0.001538119,Drug discovery,0.33957237,FALSE,19.75,0.293215412,3.5,0.213607172,0,0.403234768,,,0.30335245
2800,The Impact of Poor Nutrient Intakes and Food Insecurity on the Psychological Distress among Community-Dwelling Middle-Aged and Older Adults during the COVID-19 Pandemic.,Nutrients,33503860,1/29/21,pubmed,0,10,logistic regression,0.0546103,0.001751167,0.001751293,0.001751229,0.938384732,0.001751279,Healthcare,0.9126405,TRUE,30.3,0.435153689,15.4,0.42875301,0,0.403234768,,,0.422380489
2801,"Reproducibly sampling SARS-CoV-2 genomes across time, geography, and viral diversity.",F1000Res,33500774,1/29/21,pubmed,0,19,"computational, genomic epidemiology, genomes",0.001622734,0.890535667,0.102973365,0.001622789,0.001622758,0.001622687,Genomics,0.45779854,FALSE,84.88888889,0.824169707,680.5555556,0.986820979,1,0.537564047,,,0.782851578
2802,The impact of COVID-19 infection on the cytokine profile of pregnant women: A prospective case-control study.,Cytokine,33503581,1/28/21,pubmed,0,10,correlation analysis,0.001059394,0.001059341,0.00105935,0.001059357,0.044208894,0.951553663,Clinics,0.93682796,TRUE,34.4,0.480982126,3.2,0.202100615,0,0.403234768,,,0.362105836
2803,Nicotinic cholinergic system and COVID-19: In silico identification of interactions between α7 nicotinic acetylcholine receptor and the cryptic epitopes of SARS-Co-V and SARS-CoV-2 Spike glycoproteins.,Food Chem Toxicol,33503469,1/28/21,pubmed,0,8,in silico,0.68889671,0.19006257,0.001901704,0.001901698,0.001901773,0.115335546,Drug discovery,0.5174725,TRUE,96.875,0.860844827,73.875,0.763513514,3,0.667819001,,,0.764059114
2804,Multi-organ proteomic landscape of COVID-19 autopsies.,Cell,33503446,1/28/21,pubmed,0,51,proteom,0.421169562,0.15973793,0.002898374,0.045614013,0.002898287,0.367681833,Drug discovery,0.3963007,FALSE,74.45098039,0.783907477,40.8627451,0.638613861,0,0.403234768,,,0.608585369
2805,Adverse drug reactions in patients with COVID-19 in Brazil: analysis of spontaneous notifications of the Brazilian pharmacovigilance system.,Cad Saude Publica,33503163,1/28/21,pubmed,0,6,logistic regression,0.314016135,0.001565417,0.001565408,0.179862142,0.183048489,0.319942408,Clinics,0.872887,TRUE,3,0.037293586,0.166666667,0.058736955,0,0.403234768,,,0.16642177
2806,Correction: Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.,PLoS One,33503039,1/28/21,pubmed,0,1,machine learning,0.057799812,0.057799601,0.057801681,0.711000481,0.057799162,0.057799264,Epidemiology,0.5380592,TRUE,503,0.997649824,2,0.164302917,0,0.403234768,,,0.521729169
2807,Factors associated with admission to intensive care units in COVID-19 patients in Lyon-France.,PLoS One,33503018,1/28/21,pubmed,0,16,logistic regression,0.001219996,0.001220033,0.001220004,0.001220066,0.001220062,0.993899839,Clinics,0.90390587,TRUE,39.75,0.536829736,29.4375,0.565493712,0,0.403234768,,,0.501852738
2808,Digital Pathology During the COVID-19 Outbreak in Italy: Survey Study.,J Med Internet Res,33503002,1/28/21,pubmed,0,22,"bayes, probabilistic",0.001156259,0.001156254,0.321524099,0.464212395,0.210794741,0.001156252,Epidemiology,0.16857013,FALSE,28.09090909,0.408621436,21.36363636,0.495517795,0,0.403234768,,,0.435791333
2809,Collaborating in the Time of COVID-19: The Scope and Scale of Innovative Responses to a Global Pandemic.,JMIR Public Health Surveill,33503001,1/28/21,pubmed,0,7,artificial intelligence,0.001085371,0.001085361,0.160629153,0.835029325,0.001085427,0.001085363,Epidemiology,0.7348053,TRUE,12.14285714,0.183808522,1.285714286,0.128512176,0,0.403234768,,,0.238518489
2810,Analyzing Digital Evidence From a Telemental Health Platform to Assess Complex Psychological Responses to the COVID-19 Pandemic: Content Analysis of Text Messages.,JMIR Form Res,33502999,1/28/21,pubmed,0,5,machine learning,0.001371294,0.00137132,0.188685659,0.001371331,0.666402603,0.140797793,Healthcare,0.68079966,TRUE,33.4,0.47046818,11.4,0.375970029,0,0.403234768,,,0.416557659
2811,"Are clarithromycin, azithromycin and their analogues effective in the treatment of COVID19?",Bratisl Lek Listy,33502877,1/28/21,pubmed,0,5,in silico,0.899182856,0.001943642,0.001943521,0.093042956,0.001943503,0.001943521,Drug discovery,0.9724777,TRUE,7.2,0.103716989,0,0.055525823,0,0.403234768,,,0.187492527
2812,Detection of Bacterial Coinfection in COVID-19 Patients Is a Missing Piece of the Puzzle in the COVID-19 Management in Indonesia.,ACS Infect Dis,33502840,1/28/21,pubmed,0,1,sequencing,0.003214217,0.34484531,0.003214293,0.252733783,0.003214357,0.39277804,Clinics,0.74919486,TRUE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
2813,Pulmonary vascular enlargement and lesion extent on computed tomography are correlated with COVID-19 disease severity.,Jpn J Radiol,33502657,1/28/21,pubmed,0,6,"logistic regression, correlation analysis",0.001254583,0.001254607,0.484163296,0.001254617,0.00125463,0.510818267,Clinics,0.7867496,TRUE,169.6666667,0.958129754,80.5,0.780572652,0,0.403234768,,,0.713979058
2814,Development of a Model to Estimate the Association Between Delay in Cancer Treatment and Local Tumor Control and Risk of Metastases.,JAMA Netw Open,33502482,1/28/21,pubmed,0,5,mathematical model,0.00127277,0.001272659,0.001272702,0.736726205,0.00127277,0.258182896,Epidemiology,0.300527,FALSE,138.2,0.930793494,134.6,0.868945678,0,0.403234768,,,0.734324647
2815,"Evidence of Severe Acute Respiratory Syndrome Coronavirus 2 Replication and Tropism in the Lungs, Airways, and Vascular Endothelium of Patients With Fatal Coronavirus Disease 2019: An Autopsy Case Series.",J Infect Dis,33502471,1/28/21,pubmed,0,19,"sequencing, whole-genome",0.372960482,0.396525843,0.001511895,0.001511816,0.001511813,0.22597815,Genomics,0.8599071,TRUE,69.73684211,0.76219927,181.5789474,0.907345464,0,0.403234768,,,0.690926501
2816,Universal screening for SARS-CoV-2 infection: a rapid review.,Cochrane Database Syst Rev,33502003,1/28/21,pubmed,0,11,mathematical model,0.000210936,0.000210934,0.265082304,0.403581067,0.144702491,0.186212269,Epidemiology,0.93760324,TRUE,80,0.807532933,66.90909091,0.745383998,6,0.764429903,,,0.772448945
2817,The benefits of peer transparency in safe workplace operation post pandemic lockdown.,J R Soc Interface,33501885,1/28/21,pubmed,0,5,mathematical model,0.001684669,0.069405043,0.001684515,0.848331238,0.077210022,0.001684513,Epidemiology,0.24373537,FALSE,84,0.821881378,129.2,0.862791009,0,0.403234768,,,0.695969051
2818,A deletion in SARS-CoV-2 ORF7 identified in COVID-19 outbreak in Uruguay.,Transbound Emerg Dis,33501730,1/28/21,pubmed,0,18,sequencing,0.001717247,0.944517182,0.001717213,0.001717579,0.001717352,0.048613427,Genomics,0.47032622,FALSE,19.44444444,0.289813841,21.94444444,0.501806262,0,0.403234768,,,0.398284957
2819,BAYESIAN GROUP TESTING WITH DILUTION EFFECTS.,medRxiv,33501464,1/28/21,pubmed,0,2,bayes,0.002238526,0.002238573,0.24452452,0.746521081,0.002238652,0.002238648,Epidemiology,0.11275315,FALSE,13,0.197352959,2.5,0.180826866,0,0.403234768,,,0.260471531
2820,Severity Prediction for COVID-19 Patients via Recurrent Neural Networks.,medRxiv,33501460,1/28/21,pubmed,0,5,neural network,0.002080556,0.002080583,0.462968952,0.002080701,0.002080617,0.528708591,Clinics,0.25720894,FALSE,2.6,0.028263962,0,0.055525823,0,0.403234768,,,0.162341517
2821,Development and validation of multivariable prediction models for adverse COVID-19 outcomes in IBD patients.,medRxiv,33501455,1/28/21,pubmed,0,10,"logistic regression, prediction model",0.000620842,0.000620868,0.053972689,0.075175874,0.068505197,0.80110453,Clinics,0.064751,FALSE,90,0.843094811,126.7,0.860181964,0,0.403234768,,,0.702170514
2822,"The impacts of COVID-19 vaccine timing, number of doses, and risk prioritization on mortality in the US.",medRxiv,33501453,1/28/21,pubmed,0,7,mathematical model,0.006539713,0.006539694,0.006539653,0.967300489,0.006540027,0.006540425,Epidemiology,0.17186862,FALSE,5.714285714,0.079782299,55.14285714,0.702568906,2,0.618927094,,,0.467092766
2823,High-throughput sequencing of SARS-CoV-2 in wastewater provides insights into circulating variants.,medRxiv,33501452,1/28/21,pubmed,0,28,"sequencing, genomic epidemiology, genomes, correlation analysis",0.000759358,0.845645159,0.000759368,0.141313529,0.000759385,0.010763201,Genomics,0.12712881,FALSE,32.89285714,0.464901973,54.42857143,0.699223976,3,0.667819001,,,0.610648317
2824,DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins.,bioRxiv,33501448,1/28/21,pubmed,0,6,computational,0.852199775,0.001717248,0.063322638,0.079325758,0.001717319,0.001717262,Drug discovery,0.52778006,TRUE,64.33333333,0.730781124,148.1666667,0.882258496,0,0.403234768,,,0.672091463
2825,Rapid protection from COVID-19 in nonhuman primates vaccinated intramuscularly but not intranasally with a single dose of a recombinant vaccine.,bioRxiv,33501447,1/28/21,pubmed,0,15,sequencing,0.566900835,0.11924946,0.001438218,0.125536408,0.185436824,0.001438254,Drug discovery,0.06927371,FALSE,4.266666667,0.056651617,,,0,0.403234768,,,0.229943192
2826,Interferon-regulated genetic programs and JAK/STAT pathway activate the intronic promoter of the short ACE2 isoform in renal proximal tubules.,bioRxiv,33501441,1/28/21,pubmed,0,4,genome-wide,0.932999551,0.001901834,0.001901698,0.001901788,0.00190177,0.059393359,Drug discovery,0.7743039,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
2827,A comparative survey of Betacoronavirus binding dynamics relevant to the functional evolution of the highly transmissible SARS-CoV-2 variant N501Y.,bioRxiv,33501438,1/28/21,pubmed,0,5,molecular dynamics simulation,0.638906175,0.356075447,0.0012546,0.001254597,0.0012546,0.001254581,Drug discovery,0.20973715,FALSE,37,0.508936854,15.8,0.434104897,1,0.537564047,,,0.493535266
2828,Predicting thromboembolic complications in COVID-19 ICU patients using machine learning.,J Clin Transl Res,33501388,1/28/21,pubmed,0,10,"machine learning, classifier, dataset",0.001085364,0.001085364,0.253441078,0.001085365,0.001085362,0.742217467,Clinics,0.94927,TRUE,41.7,0.554888985,39.5,0.630920524,1,0.537564047,,,0.574457852
2829,Fostering global data sharing: highlighting the recommendations of the Research Data Alliance COVID-19 working group.,Wellcome Open Res,33501381,1/28/21,pubmed,0,36, omics,0.001717264,0.001717225,0.11626855,0.800189671,0.078390022,0.001717268,Epidemiology,0.7995674,TRUE,47.5,0.610551054,41.44444444,0.641691196,0,0.403234768,,,0.551825673
2830,Digital Health: Opportunities and Challenges to Develop the Next-Generation Technology-Enabled Models of Cardiovascular Care.,Methodist Debakey Cardiovasc J,33500758,1/28/21,pubmed,0,1,digital health,0.0010854,0.033622071,0.001085389,0.810737266,0.076867166,0.076602709,Epidemiology,0.2590393,FALSE,61,0.709196611,22,0.503746321,0,0.403234768,,,0.5387259
2831,"Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19.",J Real Time Image Process,33500738,1/28/21,pubmed,0,3,"deep learning, artificial intelligence, dataset",0.001219996,0.001220034,0.437684625,0.557435291,0.001220046,0.001220008,Epidemiology,0.16704425,FALSE,174,0.961222092,25.66666667,0.534854161,1,0.537564047,,,0.6778801
2832,Clinical analysis and pluripotent stem cells-based model reveal possible impacts of ACE2 and lung progenitor cells on infants vulnerable to COVID-19.,Theranostics,33500718,1/28/21,pubmed,0,34,sequencing,0.475169771,0.047193088,0.001126844,0.001126832,0.251637426,0.223746039,Drug discovery,0.71599597,TRUE,80.70588235,0.809697569,72.52941176,0.760569976,0,0.403234768,,,0.657834104
2833,A novel framework for rapid diagnosis of COVID-19 on computed tomography scans.,Pattern Anal Appl,33500681,1/28/21,pubmed,0,8,"bayes, classifier",0.001310428,0.027351321,0.928719214,0.039998302,0.001310334,0.001310401,Imaging,0.6096462,TRUE,80.875,0.810563424,13.25,0.402997056,4,0.707574542,,,0.640378341
2834,Probing CAS database as prospective antiviral agents against SARS-CoV-2 main protease.,J Mol Struct,33500591,1/28/21,pubmed,0,6,virtual screening,0.96903843,0.001254669,0.001254607,0.0259431,0.001254618,0.001254577,Drug discovery,0.9034623,TRUE,35.66666667,0.493908096,15.66666667,0.432365534,0,0.403234768,,,0.443169466
2835,0,Sci Rep,33500507,1/28/21,pubmed,0,10,"logistic regression, prediction model",0.001653209,0.001653054,0.119164266,0.001653179,0.001653063,0.87422323,Clinics,0.9244155,TRUE,209.6,0.975323149,214.1,0.92407011,0,0.403234768,,,0.767542676
2836,The prevalence of depressive and anxiety symptoms and their associations with quality of life among clinically stable older patients with psychiatric disorders during the COVID-19 pandemic.,Transl Psychiatry,33500389,1/28/21,pubmed,0,14,logistic regression,0.001330064,0.001330004,0.00133004,0.001330011,0.721520127,0.273159753,Healthcare,0.9964698,TRUE,86.78571429,0.832828252,59.35714286,0.718356971,0,0.403234768,,,0.65147333
2837,Effectiveness of lock down to curtail the spread of corona virus: A mathematical model.,ISA Trans,33500125,1/28/21,pubmed,0,3,mathematical model,0.002032799,0.002032797,0.002032824,0.989836061,0.002032784,0.002032735,Epidemiology,0.6898041,TRUE,33.33333333,0.469911559,3,0.199424672,0,0.403234768,,,0.357523666
2838,"Molecular Docking of Azithromycin, Ritonavir, Lopinavir, Oseltamivir, Ivermectin and Heparin Interacting with Coronavirus Disease 2019 Main and Severe Acute Respiratory Syndrome Coronavirus-2 3C-Like Proteases.",J Nanosci Nanotechnol,33500022,1/28/21,pubmed,0,7,in silico,0.9962673,0.000746562,0.000746538,0.000746543,0.000746529,0.000746528,Drug discovery,0.77440786,TRUE,53.71428571,0.658853361,6,0.280037463,0,0.403234768,,,0.447375197
2839,Predictors of households at risk for food insecurity in the United States during the COVID-19 pandemic.,Public Health Nutr,33500018,1/28/21,pubmed,0,7,logistic regression,0.001350327,0.001350324,0.001350313,0.00135033,0.968382375,0.02621633,Healthcare,0.67637765,TRUE,17.14285714,0.258519389,3.142857143,0.200561948,0,0.403234768,,,0.287438702
2840,A Comprehensive Review of Detection Methods for SARS-CoV-2.,Microorganisms,33499379,1/28/21,pubmed,0,12,"sequencing, metagenom",0.002562697,0.265904654,0.449900156,0.276507255,0.002562628,0.00256261,Epidemiology,0.85724926,TRUE,54,0.661574618,21.33333333,0.495450896,0,0.403234768,,,0.520086761
2841,Diabetic Foot Disease during the COVID-19 Pandemic.,Medicina (Kaunas),33499251,1/28/21,pubmed,0,1,digital health,0.003466015,0.003465936,0.003466196,0.840385921,0.003466207,0.145749725,Epidemiology,0.9537562,TRUE,105,0.879213309,263,0.943203104,0,0.403234768,,,0.741883727
2842,Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm.,Int J Environ Res Public Health,33499219,1/28/21,pubmed,0,10,"computational, artificial intelligence, dataset",0.116005275,0.000854814,0.232299137,0.535082846,0.000854704,0.114903224,Epidemiology,0.13571033,FALSE,22.2,0.327787742,7.5,0.307867273,0,0.403234768,,,0.346296594
2843,Evaluating the Impact of Intervention Strategies on the First Wave and Predicting the Second Wave of COVID-19 in Thailand: A Mathematical Modeling Study.,Biology (Basel),33499138,1/28/21,pubmed,0,4,"bayes, mathematical model",0.002032747,0.002032739,0.002032823,0.989836111,0.002032801,0.002032779,Epidemiology,0.60411876,TRUE,17.25,0.260189251,6.5,0.288132192,0,0.403234768,,,0.317185403
2844,A Bibliometric Network Analysis of Coronavirus during the First Eight Months of COVID-19 in 2020.,Int J Environ Res Public Health,33499127,1/28/21,pubmed,0,10,network analysis,0.002238612,0.002238598,0.002238515,0.920609097,0.037961139,0.03471404,Epidemiology,0.9486048,TRUE,19.5,0.29117447,2.3,0.171394166,4,0.707574542,,,0.390047726
2845,LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework.,Diagnostics (Basel),33498999,1/28/21,pubmed,0,4,deep learning,0.000966802,0.000966782,0.944070851,0.000966785,0.052061993,0.000966786,Imaging,0.512415,TRUE,69.25,0.760034634,31.75,0.583088039,0,0.403234768,,,0.582119147
2846,The COVID-19 pandemic face mask waste: A blooming threat to the marine environment.,Chemosphere,33497928,1/27/21,pubmed,0,7,genomic structure,0.035912008,0.063669955,0.00148646,0.895958658,0.001486495,0.001486424,Epidemiology,0.8431196,TRUE,31,0.445111015,7,0.299973241,1,0.537564047,,,0.427549434
2847,Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features.,Eur J Radiol,33497881,1/27/21,pubmed,0,14,"classifier, radiom, dataset",0.000988364,0.00098838,0.882973516,0.000988379,0.000988402,0.113072959,Imaging,0.9260181,TRUE,74.71428571,0.785391799,28.28571429,0.556261707,0,0.403234768,,,0.581629425
2848,Eosinopenia is a reliable marker of severe disease and unfavourable outcome in patients with COVID-19 pneumonia.,Int J Clin Pract,33497517,1/27/21,pubmed,0,14,logistic regression,0.001438179,0.001438337,0.001438167,0.001438204,0.001438125,0.992808989,Clinics,0.67865753,TRUE,4.857142857,0.065557548,3.214285714,0.202167514,0,0.403234768,,,0.223653277
2849,Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies.,Brief Bioinform,33497434,1/27/21,pubmed,0,4,"computational, classifier, dataset",0.490086761,0.001392881,0.504341744,0.001392885,0.001392846,0.001392883,Drug discovery,0.08667037,FALSE,38.25,0.520811429,83.25,0.786259031,1,0.537564047,,,0.614878169
2850,Isolating SARS-CoV-2 Strains From Countries in the Same Meridian: Genome Evolutionary Analysis.,JMIR Bioinform Biotech,33497425,1/27/21,pubmed,0,3,genomes,0.265666362,0.708117647,0.024166433,0.000683179,0.000683196,0.000683182,Genomics,0.830024,TRUE,14,0.213494959,1,0.122023013,1,0.537564047,,,0.29102734
2851,Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study.,J Med Internet Res,33497351,1/27/21,pubmed,0,2,text mining,0.001291216,0.001291223,0.019407095,0.975427916,0.00129134,0.001291209,Epidemiology,0.25055727,FALSE,18,0.271569052,6,0.280037463,0,0.403234768,,,0.318280428
2852,Interdisciplinary Online Hackathons as an Approach to Combat the COVID-19 Pandemic: Case Study.,J Med Internet Res,33497350,1/27/21,pubmed,0,10,digital health,0.000854736,0.022933799,0.000854742,0.62558135,0.348920661,0.000854712,Epidemiology,0.9504676,TRUE,18,0.271569052,3.8,0.221501204,0,0.403234768,,,0.298768341
2853,Six-month Follow-up Chest CT Findings after Severe COVID-19 Pneumonia.,Radiology,33497317,1/27/21,pubmed,0,11,logistic regression,0.000683187,0.000683158,0.226709017,0.000683168,0.215642032,0.555599438,Clinics,0.9769889,TRUE,57.36363636,0.684457913,44.18181818,0.654937115,6,0.764429903,,,0.701274977
2854,"A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort.",J Med Syst,33496876,1/27/21,pubmed,0,10,"machine learning, deep learning, artificial intelligence, neural network, classifier, transfer learning",0.00114134,0.001141353,0.848080621,0.001141336,0.001141332,0.147354018,Imaging,0.43315387,FALSE,208.8,0.974952069,82.5,0.78445277,0,0.403234768,,,0.720879869
2855,Prevalence and Risk Factors Associated With Self-reported Psychological Distress Among Children and Adolescents During the COVID-19 Pandemic in China.,JAMA Netw Open,33496797,1/27/21,pubmed,0,15,logistic regression,0.000716311,0.00071633,0.021935582,0.000716345,0.975199111,0.000716321,Healthcare,0.62109506,TRUE,54.13333333,0.662069392,,,0,0.403234768,,,0.53265208
2856,The Novel Coronavirus Enigma: Phylogeny and Analyses of Coevolving Mutations Among the SARS-CoV-2 Viruses Circulating in India.,JMIR Bioinform Biotech,33496683,1/27/21,pubmed,0,6,genome sequences,0.000716364,0.952396866,0.000716323,0.000716345,0.044737748,0.000716353,Genomics,0.8879498,TRUE,38.33333333,0.522233904,29.66666667,0.567099277,2,0.618927094,,,0.569420092
2857,Association Between Care Utilization and Anxiety Outcomes in an On-Demand Mental Health System: Retrospective Observational Study.,JMIR Form Res,33496679,1/27/21,pubmed,0,8,logistic regression,0.000926299,0.000926284,0.039309459,0.090216579,0.867695061,0.000926317,Healthcare,0.72215724,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
2858,A streamlined whole blood CyTOF workflow defines a circulating immune cell signature of COVID-19.,Cytometry A,33496367,1/27/21,pubmed,0,16,proteom,0.099050829,0.564395198,0.197946374,0.001072257,0.00107225,0.136463092,Genomics,0.39423367,FALSE,8,0.118683901,,,0,0.403234768,,,0.260959334
2859,"Local Transmission of SARS-CoV-2 Lineage B.1.1.7, Brazil, December 2020.",Emerg Infect Dis,33496249,1/27/21,pubmed,0,14,sequencing,0.005352762,0.907751283,0.00535285,0.005352998,0.005352811,0.070837297,Genomics,0.35713908,FALSE,1.571428571,0.015647226,,,6,0.764429903,,,0.390038565
2860,"Early Transmission Dynamics, Spread, and Genomic Characterization of SARS-CoV-2 in Panama.",Emerg Infect Dis,33496228,1/27/21,pubmed,0,27,genomes,0.01354893,0.932252307,0.013549092,0.013550642,0.013549852,0.013549176,Genomics,0.4102042,FALSE,4.888888889,0.065681242,,,0,0.403234768,,,0.234458005
2861,"Modeling anxiety and fear of COVID-19 using machine learning in a sample of Chinese adults: associations with psychopathology, sociodemographic, and exposure variables.",Anxiety Stress Coping,33496211,1/27/21,pubmed,0,5,machine learning,0.048056509,0.001350338,0.164463133,0.168128864,0.616650739,0.001350416,Healthcare,0.41954514,FALSE,8.4,0.124435648,,,0,0.403234768,,,0.263835208
2862,Epidemiologic Linkage of COVID-19 Outbreaks at Two University-affiliated Hospitals in the Seoul Metropolitan Area in March 2020.,J Korean Med Sci,33496089,1/27/21,pubmed,0,13,"sequencing, genome-wide, whole genome",0.001330025,0.410440733,0.001330093,0.001330068,0.026136589,0.559432492,Clinics,0.5386471,TRUE,16.92307692,0.254004577,,,0,0.403234768,,,0.328619672
2863,Epitope-resolved profiling of the SARS-CoV-2 antibody response identifies cross-reactivity with endemic human coronaviruses.,Cell Rep Med,33495758,1/27/21,pubmed,0,22,proteom,0.488910187,0.502135944,0.002238491,0.002238475,0.002238468,0.002238434,Genomics,0.204725,FALSE,71,0.769064259,239.4090909,0.933770404,3,0.667819001,,,0.790217888
2864,Use of machine learning to identify a T cell response to SARS-CoV-2.,Cell Rep Med,33495756,1/27/21,pubmed,0,6,machine learning,0.346801193,0.143655507,0.362931436,0.001653084,0.001653051,0.143305728,Drug discovery,0.08169618,FALSE,3.166666667,0.037973901,0.333333333,0.073187048,1,0.537564047,,,0.216241665
2865,Temporal increase in D614G mutation of SARS-CoV-2 in the Middle East and North Africa.,Heliyon,33495741,1/27/21,pubmed,0,5,bayes,0.001350351,0.887190595,0.001350325,0.001350391,0.001350382,0.107407956,Genomics,0.7336913,TRUE,26.6,0.390191106,9.4,0.343256623,3,0.667819001,,,0.46708891
2866,Prediction modelling of COVID using machine learning methods from B-cell dataset.,Results Phys,33495725,1/27/21,pubmed,0,8,"bayes, machine learning, neural network, ensemble learning, prediction model, dataset",0.001684525,0.001684587,0.841907397,0.087947112,0.001684647,0.065091732,Epidemiology,0.58830327,TRUE,36.25,0.500154617,9,0.337904736,1,0.537564047,,,0.458541133
2867,A contemporary insight of metabolomics approach for COVID-19: Potential for novel therapeutic and diagnostic targets.,Nepal J Epidemiol,33495710,1/27/21,pubmed,0,4,metabolom,0.511440055,0.002130769,0.127568708,0.002130826,0.002130772,0.35459887,Drug discovery,0.9373593,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
2868,COVID-19 and Artificial Intelligence: the pandemic pacifier.,Nepal J Epidemiol,33495709,1/27/21,pubmed,0,5,artificial intelligence,0.034961874,0.034961874,0.825190423,0.034962082,0.034961874,0.034961874,Epidemiology,0.6316596,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
2869,Modeling and forecasting the spread of COVID-19 with stochastic and deterministic approaches: Africa and Europe.,Adv Differ Equ,33495699,1/27/21,pubmed,0,2,mathematical model,0.042394995,0.002357771,0.002357772,0.948173912,0.002357754,0.002357796,Epidemiology,0.5619107,TRUE,41.5,0.553219123,8.5,0.329141022,2,0.618927094,,,0.50042908
2870,Luteolin and abyssinone II as potential inhibitors of SARS-CoV-2: an in silico molecular modeling approach in battling the COVID-19 outbreak.,Bull Natl Res Cent,33495684,1/27/21,pubmed,0,3,"molecular dynamics simulation, computational, bioinformatic, in silico",0.944040009,0.000838509,0.052605791,0.00083857,0.000838595,0.000838527,Drug discovery,0.91910297,TRUE,5.666666667,0.079473066,1.666666667,0.145036125,1,0.537564047,,,0.254024413
2871,The Impact of the COVID Crisis on the Innovative Potential of China's Internet Platforms.,Her Russ Acad Sci,33495683,1/27/21,pubmed,0,1,digital health,0.001203476,0.027542039,0.001203465,0.967644069,0.001203498,0.001203454,Epidemiology,0.9975527,TRUE,13,0.197352959,1,0.122023013,0,0.403234768,,,0.240870247
2872,Comments on potential re-purposing of medicines against high-altitude illnesses towards SARS-CoV2: possibilities and pitfalls.,J Proteins Proteom,33495677,1/27/21,pubmed,0,4,proteom,0.638012,0.00190173,0.001901801,0.001901851,0.215551243,0.140731375,Drug discovery,0.9686507,TRUE,26.25,0.384748593,0.25,0.065493712,0,0.403234768,,,0.284492357
2873,COVID-19 and Gut Microbiota: A Potential Connection.,Indian J Clin Biochem,33495676,1/27/21,pubmed,0,6,microbiom,0.497012922,0.173777467,0.00272025,0.321048656,0.002720375,0.002720329,Drug discovery,0.87283814,TRUE,31.33333333,0.448388892,16.66666667,0.444674873,0,0.403234768,,,0.432099511
2874,Modelling uncertainty in the relative risk of exposure to the SARS-CoV-2 virus by airborne aerosol transmission in well mixed indoor air.,Build Environ,33495667,1/27/21,pubmed,0,6,mathematical model,0.009284469,0.233517628,0.009284349,0.729344778,0.009284335,0.009284441,Epidemiology,0.40514603,FALSE,13.83333333,0.209227534,15.16666667,0.426812952,1,0.537564047,,,0.391201511
2875,Visual SLAM for robot navigation in healthcare facility.,Pattern Recognit,33495660,1/27/21,pubmed,0,6,"computational, knowledge graph, dataset",0.001187297,0.001187281,0.548406442,0.446844297,0.001187338,0.001187345,Imaging,0.89183867,TRUE,24.33333333,0.359700662,2,0.164302917,0,0.403234768,,,0.309079449
2876,"Knowledge, attitudes, practices, and influencing factors of anxiety among pregnant women in Wuhan during the outbreak of COVID-19: a cross-sectional study.",BMC Pregnancy Childbirth,33494723,1/27/21,pubmed,0,6,logistic regression,0.001350329,0.001350338,0.022245433,0.001350391,0.972353159,0.00135035,Healthcare,0.9225844,TRUE,64.66666667,0.732822067,88.83333333,0.798835965,0,0.403234768,,,0.644964266
2877,The Overlap between Genetic Susceptibility to COVID-19 and Skin Diseases.,Immunol Invest,33494631,1/27/21,pubmed,0,4,genome-wide,0.272450039,0.406548587,0.123239573,0.002898415,0.002898376,0.19196501,Genomics,0.69144595,TRUE,0,0.006432061,,,2,0.618927094,,,0.312679578
2878,Prevalence of SARS-CoV-2 IgG/IgM Antibodies among Danish and Swedish Falck Emergency and Non-Emergency Healthcare Workers.,Int J Environ Res Public Health,33494409,1/27/21,pubmed,0,5,logistic regression,0.001901796,0.083299642,0.001901708,0.155274246,0.691220309,0.066402299,Healthcare,0.85179204,TRUE,64.4,0.731028511,,,0,0.403234768,,,0.567131639
2879,Prevalence and Factors Associated with Mental and Emotional Health Outcomes among Africans during the COVID-19 Lockdown Period-A Web-based Cross-Sectional Study.,Int J Environ Res Public Health,33494209,1/27/21,pubmed,0,14,logistic regression,0.001171584,0.001171592,0.001171842,0.001171614,0.959900473,0.035412895,Healthcare,0.9524446,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
2880,Psychological burden of the COVID-19 pandemic and its associated factors among frontline doctors of Bangladesh: a cross-sectional study.,F1000Res,33447383,1/27/21,pubmed,0,4,logistic regression,0.001254587,0.001254619,0.001254616,0.001254653,0.947377832,0.047603692,Healthcare,0.98950624,TRUE,16.25,0.246026347,1,0.122023013,1,0.537564047,,,0.301871136
2881,Identification of potential SARS-CoV-2 entry inhibitors by targeting the interface region between the spike RBD and human ACE2.,J Infect Public Health,33493919,1/26/21,pubmed,0,5,in silico,0.995463583,0.000907285,0.000907284,0.000907299,0.000907273,0.000907275,Drug discovery,0.90231586,TRUE,38,0.519327107,12.6,0.394300241,0,0.403234768,,,0.438954039
2882,Pregnancy as a risk factor for severe coronavirus disease 2019 using standardized clinical criteria.,Am J Obstet Gynecol MFM,33493707,1/26/21,pubmed,0,7,logistic regression,0.01975526,0.001438195,0.001438161,0.080827969,0.394794059,0.501746356,Clinics,0.34483743,FALSE,40.42857143,0.543385491,3.285714286,0.203973776,1,0.537564047,,,0.428307771
2883,"Host-cell recognition through GRP78 is enhanced in the new UK variant of SARS-CoV-2, in silico.",J Infect,33493496,1/26/21,pubmed,0,2,in silico,0.735220619,0.210583606,0.013549328,0.01354889,0.013548802,0.013548756,Drug discovery,0.33949065,FALSE,30,0.432432432,36.5,0.614329676,0,0.403234768,,,0.483332292
2884,Effect of anakinra versus usual care in adults in hospital with COVID-19 and mild-to-moderate pneumonia (CORIMUNO-ANA-1): a randomised controlled trial.,Lancet Respir Med,33493450,1/26/21,pubmed,0,601,bayes,0.038670399,0.000652916,0.000652919,0.057737909,0.000652939,0.901632918,Clinics,0.78238094,TRUE,40.78077572,0.546848908,37.6391231,0.619614664,9,0.814309525,,,0.660257699
2885,"The impact of the COVID-19 pandemic on radiotherapy services in England, UK: a population-based study.",Lancet Oncol,33493433,1/26/21,pubmed,0,13,dataset,0.001272663,0.001272662,0.001272688,0.419766087,0.177966478,0.398449423,Epidemiology,0.8091541,TRUE,40.15384615,0.540416847,43.15384615,0.650053519,0,0.403234768,,,0.531235044
2886,Model for Mitigation of Workplace Transmission of COVID-19 Through Population-Based Testing and Surveillance.,Popul Health Manag,33493409,1/26/21,pubmed,0,6,bioinformatic,0.001272717,0.121318295,0.001272672,0.727451021,0.147412541,0.001272755,Epidemiology,0.49938732,FALSE,3.833333333,0.047745686,,,1,0.537564047,,,0.292654867
2887,Kidney injury molecule-1 is a potential receptor for SARS-CoV-2.,J Mol Cell Biol,33493263,1/26/21,pubmed,0,12,in silico,0.697242908,0.001565367,0.001565353,0.001565312,0.001565288,0.296495772,Drug discovery,0.27488172,FALSE,32.83333333,0.464592739,,,0,0.403234768,,,0.433913753
2888,Knowledge of COVID-19 and preventive behaviors among waiters working in food and drinking establishments in Southwest Ethiopia.,PLoS One,33493226,1/26/21,pubmed,0,5,logistic regression,0.001291293,0.001291245,0.001291238,0.001291273,0.993543726,0.001291225,Healthcare,0.7682991,TRUE,3.2,0.038468675,,,0,0.403234768,,,0.220851721
2889,Patients' Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment.,J Med Internet Res,33493130,1/26/21,pubmed,0,10,artificial intelligence,0.000846531,0.00084653,0.798658835,0.056113071,0.142688477,0.000846557,Healthcare,0.297209,FALSE,9.5,0.143051518,,,0,0.403234768,,,0.273143143
2890,Dissemination and Refutation of Rumors During the COVID-19 Outbreak in China: Infodemiology Study.,J Med Internet Res,33493124,1/26/21,pubmed,0,10,correlation analysis,0.001126869,0.001126854,0.01951099,0.975981635,0.001126839,0.001126814,Epidemiology,0.9557022,TRUE,63.6,0.725462304,,,0,0.403234768,,,0.564348536
2891,Evaluating Mental Health-Related Symptoms Among Cancer Survivors During the COVID-19 Pandemic: An Analysis of the COVID Impact Survey.,JCO Oncol Pract,33492984,1/26/21,pubmed,0,3,logistic regression,0.000999521,0.000999501,0.000999507,0.000999533,0.740253507,0.255748431,Healthcare,0.94580233,TRUE,0,0.006432061,,,2,0.618927094,,,0.312679578
2892,"Emergency physician stressors, concerns, and behavioral changes during COVID-19: A longitudinal study.",Acad Emerg Med,33492755,1/26/21,pubmed,0,9,logistic regression,0.000898068,0.00089807,0.015005743,0.000898093,0.840010566,0.14228946,Healthcare,0.97099847,TRUE,8.111111111,0.119240522,,,0,0.403234768,,,0.261237645
2893,"Computational assessment of saikosaponins as adjuvant treatment for COVID-19: molecular docking, dynamics, and network pharmacology analysis.",Mol Divers,33492566,1/26/21,pubmed,0,10,"molecular dynamics simulation, computational",0.973964776,0.001371296,0.001371268,0.001371261,0.001371246,0.020550153,Drug discovery,0.8033881,TRUE,33.5,0.471890655,,,1,0.537564047,,,0.504727351
2894,Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison.,J Xray Sci Technol,33492267,1/26/21,pubmed,0,5,"bayes, machine learning, classifier, transfer learning",0.001220007,0.00122,0.993899951,0.001219993,0.00122001,0.001220039,Imaging,0.8685388,TRUE,9.4,0.140021028,,,0,0.403234768,,,0.271627898
2895,Assessment of knowledge and attitude of allied healthcare professionals about COVID-19 across Saudi Arabia.,Work,33492261,1/26/21,pubmed,0,7,logistic regression,0.001593566,0.001593551,0.001593548,0.048475141,0.945150631,0.001593564,Healthcare,0.952639,TRUE,7.428571429,0.106376399,,,0,0.403234768,,,0.254805583
2896,One Digital Health: A Unified Framework for Future Health Ecosystems.,J Med Internet Res,33492240,1/26/21,pubmed,0,4,digital health,0.141161064,0.001350475,0.001350409,0.750772619,0.104015041,0.001350392,Epidemiology,0.61639005,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
2897,A rapid and cost-effective multiplex ARMS-PCR method for the simultaneous genotyping of the circulating SARS-CoV-2 phylogenetic clades.,J Med Virol,33491822,1/26/21,pubmed,0,10,sequencing,0.001486441,0.992567577,0.001486572,0.001486493,0.001486402,0.001486516,Genomics,0.65153974,TRUE,4.5,0.061784897,,,0,0.403234768,,,0.232509832
2898,Immunomodulatory and anti-cytokine therapeutic potential of curcumin and its derivatives for treating COVID-19 - a computational modeling.,J Biomol Struct Dyn,33491580,1/26/21,pubmed,0,6,"molecular dynamics simulation, computational",0.973406756,0.001022624,0.001022643,0.001022692,0.001022639,0.022502646,Drug discovery,0.9652326,TRUE,4.166666667,0.055662069,,,0,0.403234768,,,0.229448418
2899,Molecular basis for the repurposing of histamine H2-receptor antagonist to treat COVID-19.,J Biomol Struct Dyn,33491579,1/26/21,pubmed,0,6,molecular dynamics simulation,0.991243885,0.001751214,0.001751218,0.00175133,0.00175118,0.001751173,Drug discovery,0.929525,TRUE,7,0.10179974,,,0,0.403234768,,,0.252517254
2900,Exploring potential inhibitor of SARS-CoV2 replicase from FDA approved drugs using insilico drug discovery methods.,J Biomol Struct Dyn,33491573,1/26/21,pubmed,0,6,"molecular dynamics simulation, in silico",0.897202647,0.027221526,0.001310367,0.071644714,0.001310363,0.001310383,Drug discovery,0.6996937,TRUE,53.66666667,0.658544128,,,0,0.403234768,,,0.530889448
2901,Gender Differences in Concerns About Participating in Cancer Research During the COVID-19 Pandemic.,Cancer Control,33491475,1/26/21,pubmed,0,9,dataset,0.000946102,0.000946091,0.000946115,0.000946107,0.712952729,0.283262856,Healthcare,0.6767052,TRUE,0.444444444,0.007483456,,,0,0.403234768,,,0.205359112
2902,Noncoding RNAs: modulators and modulatable players during infection-induced stress response.,Brief Funct Genomics,33491070,1/26/21,pubmed,0,3,transcriptom,0.387809667,0.483033788,0.00175118,0.001751261,0.123902883,0.001751222,Genomics,0.76346076,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
2903,Impact of Serum 25(OH) Vitamin D Level on Mortality in Patients with COVID-19 in Turkey.,J Nutr Health Aging,33491033,1/26/21,pubmed,0,2,logistic regression,0.001237075,0.00123707,0.02749495,0.001237093,0.024615288,0.944178525,Clinics,0.95180327,TRUE,1.5,0.015523533,0.5,0.087101953,13,0.858880178,,,0.320501888
2904,What Are People Concerned About During the Pandemic? Detecting Evolving Topics about COVID-19 from Twitter.,J Healthc Inform Res,33490856,1/26/21,pubmed,0,3,dataset,0.000898082,0.000898119,0.105259149,0.89114845,0.000898104,0.000898097,Epidemiology,0.024315834,FALSE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
2905,"Can limonene be a possible candidate for evaluation as an agent or adjuvant against infection, immunity, and inflammation in COVID-19?",Heliyon,33490659,1/26/21,pubmed,0,8,in-silico,0.941796368,0.001330093,0.001330066,0.052883287,0.001330086,0.0013301,Drug discovery,0.9675191,TRUE,53.375,0.656441338,26.75,0.543617875,0,0.403234768,,,0.534431327
2906,Mathematical modeling and a month ahead forecast of the coronavirus disease 2019 (COVID-19) pandemic: an Indian scenario.,Model Earth Syst Environ,33490366,1/26/21,pubmed,0,2,mathematical model,0.002296539,0.002296549,0.002296569,0.988517225,0.002296546,0.002296572,Epidemiology,0.35058662,FALSE,3.5,0.044344115,0,0.055525823,0,0.403234768,,,0.167701569
2907,Media use pattern as an indicator of mental health in the COVID-19 pandemic: Dataset from India.,Data Brief,33490336,1/26/21,pubmed,0,3,dataset,0.001511832,0.001511856,0.001511867,0.123003363,0.870949274,0.001511808,Healthcare,0.45978996,FALSE,1.333333333,0.01366813,0,0.055525823,0,0.403234768,,,0.15747624
2908,"A narrative review on the basic and clinical aspects of the novel SARS-CoV-2, the etiologic agent of COVID-19.",Ann Transl Med,33490198,1/26/21,pubmed,0,4,genomic structure,0.204091458,0.074973971,0.062739318,0.655720918,0.001237146,0.001237188,Epidemiology,0.5332244,TRUE,4,0.054734368,,,1,0.537564047,,,0.296149208
2909,The CB index predicts prognosis of critically ill COVID-19 patients.,Ann Transl Med,33490166,1/26/21,pubmed,0,10,"logistic regression, prediction model",0.001072231,0.001072205,0.094093835,0.00107222,0.001072205,0.901617305,Clinics,0.7054212,TRUE,77.5,0.79658606,,,0,0.403234768,,,0.599910414
2910,Comorbidities might be a risk factor for the incidence of COVID-19: Evidence from a web-based survey.,Prev Med Rep,33489728,1/26/21,pubmed,0,3,logistic regression,0.001415194,0.026391333,0.001415109,0.00141517,0.243666722,0.725696472,Clinics,0.7685056,TRUE,12,0.183190055,0,0.055525823,1,0.537564047,,,0.258759975
2911,Fast track triage for COVID-19 based on a population study: The soda score.,Prev Med Rep,33489725,1/26/21,pubmed,0,17,logistic regression,0.001010932,0.001010987,0.135459667,0.001010991,0.069739916,0.791767508,Clinics,0.8498988,TRUE,67.05882353,0.748221906,64.47058824,0.736553385,0,0.403234768,,,0.629336686
2912,"Knowledge, attitudes, and practices related to the COVID-19 pandemic among Bangladeshi youth: a web-based cross-sectional analysis.",Z Gesundh Wiss,33489718,1/26/21,pubmed,0,5,logistic regression,0.001254602,0.001254581,0.001254593,0.001254617,0.993727007,0.0012546,Healthcare,0.5722556,TRUE,21.4,0.316469788,2.4,0.174872893,1,0.537564047,,,0.342968909
2913,Current Available Computer-Aided Detection Catches Cancer but Requires a Human Operator.,Cureus,33489588,1/26/21,pubmed,0,4,artificial intelligence,0.001823358,0.00182346,0.610943981,0.001823475,0.128433171,0.255152555,Imaging,0.6558624,TRUE,3.5,0.044344115,0,0.055525823,0,0.403234768,,,0.167701569
2914,COVID-19 and Obstetrical Care: Coping With New Stress.,Cureus,33489531,1/26/21,pubmed,0,6,logistic regression,0.00148641,0.001486429,0.001486438,0.001486495,0.930764377,0.063289853,Healthcare,0.97651905,TRUE,16.66666667,0.251159626,3,0.199424672,0,0.403234768,,,0.284606355
2915,Immunological alternation in COVID-19 patients with cancer and its implications on mortality.,Oncoimmunology,33489469,1/26/21,pubmed,0,11,logistic regression,0.120159865,0.001098803,0.001098798,0.001098816,0.001098824,0.875444894,Clinics,0.78555363,TRUE,33.81818182,0.474240831,,,0,0.403234768,,,0.438737799
2916,A parsimonious approach for spatial transmission and heterogeneity in the COVID-19 propagation.,R Soc Open Sci,33489282,1/26/21,pubmed,0,5,probabilistic,0.031467965,0.001622732,0.001622713,0.962041086,0.001622743,0.001622762,Epidemiology,0.33458257,FALSE,43.6,0.574061476,157.2,0.889951833,1,0.537564047,,,0.667192452
2917,Elevated Serum Pentraxin-3 Levels is Positively Correlated to Disease Severity and Coagulopathy in COVID-19 Patients.,Mediterr J Hematol Infect Dis,33489054,1/26/21,pubmed,0,11,correlation analysis,0.081766929,0.001203541,0.133827355,0.001203412,0.001203478,0.780795285,Clinics,0.89430296,TRUE,174.2727273,0.961531325,109.8181818,0.838373026,0,0.403234768,,,0.734379706
2918,COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting.,Cognit Comput,33488837,1/26/21,pubmed,0,4,transfer learning,0.001987167,0.001987315,0.990063504,0.001987215,0.001987574,0.001987224,Imaging,0.8375839,TRUE,106,0.881192405,31.5,0.581883864,1,0.537564047,,,0.666880105
2919,Application of a prediction model with laboratory indexes in the risk stratification of patients with COVID-19.,Exp Ther Med,33488791,1/26/21,pubmed,0,4,"logistic regression, prediction model",0.001415241,0.001415121,0.223698562,0.001415145,0.001415116,0.770640816,Clinics,0.427706,FALSE,21,0.312016822,,,0,0.403234768,,,0.357625795
2920,Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images.,Comput Intell Neurosci,33488691,1/26/21,pubmed,0,3,"deep learning, deep model, ensemble learning, dataset",0.001141339,0.001141348,0.8608935,0.134540957,0.001141395,0.001141461,Imaging,0.38130274,FALSE,42,0.558537943,2,0.164302917,0,0.403234768,,,0.375358542
2921,0,Front Immunol,33488611,1/26/21,pubmed,0,12,"bayes, transcriptom, network analysis",0.585171688,0.280085459,0.000898138,0.000898147,0.00089813,0.132048438,Drug discovery,0.4581319,FALSE,154,0.947121034,342.4166667,0.964476853,0,0.403234768,,,0.771610885
2922,Sex Differences in Immunity: Implications for the Development of Novel Vaccines Against Emerging Pathogens.,Front Immunol,33488596,1/26/21,pubmed,0,3,microbiom,0.462097607,0.038725993,0.001653058,0.272166345,0.223703868,0.001653129,Drug discovery,0.55056584,TRUE,62,0.715814212,191,0.912630452,1,0.537564047,,,0.722002904
2923,"Stroke Admissions, Stroke Severity, and Treatment Rates in Urban and Rural Areas During the COVID-19 Pandemic.",Front Neurol,33488501,1/26/21,pubmed,0,14,dataset,0.001203409,0.001203454,0.001203436,0.430941407,0.001203476,0.564244818,Clinics,0.782894,TRUE,92.78571429,0.850825654,85.71428571,0.792146107,0,0.403234768,,,0.682068843
2924,"Prevalence and Risk Factors of Anxiety, Depression, and Sleep Problems Among Caregivers of People Living With Neurocognitive Disorders During the COVID-19 Pandemic.",Front Psychiatry,33488423,1/26/21,pubmed,0,11,logistic regression,0.000977412,0.000977416,0.00097746,0.000977437,0.981678991,0.014411284,Healthcare,0.917333,TRUE,38.72727273,0.526563176,37.27272727,0.617340112,0,0.403234768,,,0.515712685
2925,Could Dermaseptin Analogue be a Competitive Inhibitor for ACE2 Towards Binding with Viral Spike Protein Causing COVID19?: Computational Investigation.,Int J Pept Res Ther,33488318,1/26/21,pubmed,0,2,"molecular dynamics simulation, computational",0.960695952,0.001171563,0.001171546,0.034617812,0.00117158,0.001171547,Drug discovery,0.6453308,TRUE,27,0.3960047,6,0.280037463,1,0.537564047,,,0.404535404
2926,"Antimicrobial Activity, DFT Calculations, and Molecular Docking of Dialdehyde Cellulose/Graphene Oxide Film Against Covid-19.",J Polym Environ,33488314,1/26/21,pubmed,0,1,computational,0.800900092,0.002080609,0.150743446,0.00208065,0.002080699,0.042114504,Drug discovery,0.96126235,TRUE,3,0.037293586,0,0.055525823,0,0.403234768,,,0.165351392
2927,"Knowledge, Attitudes, Practices and Information Needs During the COVID-19 Pandemic in Indonesia.",Risk Manag Healthc Policy,33488129,1/26/21,pubmed,0,7,logistic regression,0.030250126,0.001156284,0.001156289,0.139993956,0.826287099,0.001156245,Healthcare,0.789078,TRUE,36.57142857,0.503432494,1.857142857,0.151525288,1,0.537564047,,,0.397507276
2928,Efficient deep learning approach for augmented detection of Coronavirus disease.,Neural Comput Appl,33487885,1/26/21,pubmed,0,5,"deep learning, neural network, lstm, dataset",0.001220089,0.001219994,0.936810655,0.058309259,0.001220003,0.001220001,Imaging,0.81444895,TRUE,34.6,0.483023069,8.8,0.332753546,3,0.667819001,,,0.494531872
2929,[Right ventricular systolic dysfunction as a predictor of adverse outcome in patients with COVID-19].,Kardiologiia,33487146,1/26/21,pubmed,0,8,predictive model,0.000854704,0.000854745,0.151302134,0.000854754,0.000854749,0.845278914,Clinics,0.9183549,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
2930,"Psychosocial, Lifestyle, and Body Weight Impact of COVID-19-Related Lockdown in a Sample of Participants with Current or Past History of Obesity in Spain.",Obes Surg,33486709,1/25/21,pubmed,0,15,logistic regression,0.001538173,0.001538266,0.00153809,0.001538127,0.80263116,0.191216184,Healthcare,0.91747475,TRUE,47,0.606407323,31.93333333,0.583890822,1,0.537564047,,,0.575954064
2931,Hantavirus: The Next Pandemic We Are Waiting For?,Interdiscip Sci,33486690,1/25/21,pubmed,0,4,"artificial intelligence, proteom",0.306430341,0.149134191,0.00162294,0.495225968,0.045963799,0.001622761,Epidemiology,0.85131776,TRUE,7,0.10179974,,,0,0.403234768,,,0.252517254
2932,The mortality rate of COVID-19 was high in cancer patients: a retrospective single-center study.,Int J Clin Oncol,33486624,1/25/21,pubmed,0,9,logistic regression,0.00137127,0.001371276,0.021447839,0.001371352,0.001371312,0.973066951,Clinics,0.879691,TRUE,39.44444444,0.533180778,9,0.337904736,1,0.537564047,,,0.469549854
2933,STAN: spatio-temporal attention network for pandemic prediction using real-world evidence.,J Am Med Inform Assoc,33486527,1/25/21,pubmed,0,8,"deep learning, prediction model",0.001187265,0.001187271,0.333741799,0.661508976,0.001187319,0.00118737,Epidemiology,0.35300636,FALSE,2,0.022141134,,,0,0.403234768,,,0.212687951
2934,A meta-analysis of comorbidities in COVID-19: Which diseases increase the susceptibility of SARS-CoV-2 infection?,Comput Biol Med,33486379,1/25/21,pubmed,0,5,dataset,0.689687224,0.00143818,0.001438161,0.001438137,0.001438133,0.304560166,Drug discovery,0.9289108,TRUE,12.6,0.190302431,2.2,0.16838373,0,0.403234768,,,0.253973643
2935,Bioinformatics prediction of B and T cell epitopes within the spike and nucleocapsid proteins of SARS-CoV2.,J Infect Public Health,33486372,1/25/21,pubmed,0,6,"bioinformatic, genomes",0.671870039,0.322282254,0.001461959,0.001461955,0.001461875,0.001461918,Drug discovery,0.4826714,FALSE,22.16666667,0.327107428,3.666666667,0.217621086,0,0.403234768,,,0.315987761
2936,"Why Do Some People Develop Serious COVID-19 Disease After Infection, While Others Only Exhibit Mild Symptoms?",J Allergy Clin Immunol Pract,33486141,1/25/21,pubmed,0,2,genome-wide,0.394631029,0.252547874,0.001350326,0.001350379,0.001350426,0.348769966,Drug discovery,0.7614046,TRUE,24,0.35574247,32,0.585763982,0,0.403234768,,,0.448247073
2937,Associations between COVID-19 and skin conditions identified through epidemiology and genomic studies.,J Allergy Clin Immunol,33485957,1/25/21,pubmed,0,9,"genome-wide, multiom",0.495798991,0.126839898,0.001415113,0.001415151,0.001415183,0.373115664,Drug discovery,0.88007975,TRUE,144.8888889,0.939266498,215,0.925073588,0,0.403234768,,,0.755858285
2938,Pharmacoinformatics approach based identification of potential Nsp15 endoribonuclease modulators for SARS-CoV-2 inhibition.,Arch Biochem Biophys,33485847,1/25/21,pubmed,0,8,virtual screening,0.914075183,0.001310322,0.001310343,0.080683357,0.001310448,0.001310346,Drug discovery,0.93246627,TRUE,24.5,0.361988991,1,0.122023013,0,0.403234768,,,0.295748924
2939,Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs.,Acad Radiol,33485773,1/25/21,pubmed,0,8,"artificial intelligence, dataset",0.001392823,0.00139285,0.683474465,0.051374138,0.001392861,0.260972864,Imaging,0.8994088,TRUE,86.5,0.831653163,70.375,0.754549104,0,0.403234768,,,0.663145678
2940,Hospital-Wide SARS-CoV-2 seroprevalence in health care workers in a Spanish teaching hospital.,Enferm Infecc Microbiol Clin,33485676,1/25/21,pubmed,0,27,logistic regression,0.002238478,0.061246856,0.00223857,0.002238559,0.443688335,0.488349202,Clinics,0.5277838,TRUE,0.333333333,0.007174222,,,2,0.618927094,,,0.313050658
2941,People with dyssomnia showed increased vulnerability to CoVID-19 pandemic: a questionnaire-based study exploring the patterns and predictors of sleep quality using the latent class analysis technique in Indian population.,Sleep Med,33485259,1/24/21,pubmed,0,6,logistic regression,0.001684609,0.001684601,0.108947717,0.235774999,0.650223515,0.00168456,Healthcare,0.944056,TRUE,51.83333333,0.645185231,14.83333333,0.421862457,1,0.537564047,,,0.534870578
2942,Environmental management strategy in response to COVID-19 in China: Based on text mining of government open information.,Sci Total Environ,33485207,1/24/21,pubmed,0,7,text mining,0.001272658,0.001272652,0.001272678,0.993636599,0.001272726,0.001272687,Epidemiology,0.84061337,TRUE,9.714285714,0.145401695,5.428571429,0.264249398,0,0.403234768,,,0.270961953
2943,Utility of visual coronary artery calcification on non-cardiac gated thoracic CT in predicting clinical severity and outcome in COVID-19.,Clin Imaging,33485116,1/24/21,pubmed,0,6,logistic regression,0.001034557,0.001034575,0.409044197,0.001034577,0.001034573,0.586817521,Clinics,0.23911774,FALSE,38.83333333,0.527367184,13.5,0.405539203,0,0.403234768,,,0.445380385
2944,Elucidation of interactions regulating conformational stability and dynamics of SARS-CoV-2 S-protein.,Biophys J,33484712,1/24/21,pubmed,0,6,molecular dynamics simulation,0.97766258,0.017771971,0.001141398,0.001141367,0.001141351,0.001141333,Drug discovery,0.6599583,TRUE,72.16666667,0.773764611,64,0.735549906,1,0.537564047,,,0.682292855
2945,Green giant-a tiny chloroplast genome with mighty power to produce high-value proteins: history and phylogeny.,Plant Biotechnol J,33484606,1/24/21,pubmed,0,6,"genome sequences, genomes",0.267671765,0.483712193,0.001622816,0.110572409,0.054552638,0.081868179,Genomics,0.9870991,TRUE,259.8333333,0.985898942,1239.5,0.995450896,1,0.537564047,,,0.839637962
2946,Electrolyte imbalance in COVID-19 patients admitted to the Emergency Department: a case-control study.,Intern Emerg Med,33484453,1/24/21,pubmed,0,11,logistic regression,0.00203281,0.002032829,0.002032782,0.0020328,0.177969985,0.813898794,Clinics,0.8693385,TRUE,9.090909091,0.135629909,1.727272727,0.145838908,1,0.537564047,,,0.273010955
2947,Association of AI quantified COVID-19 chest CT and patient outcome.,Int J Comput Assist Radiol Surg,33484428,1/24/21,pubmed,0,9,"machine learning, artificial intelligence, neural network, dataset",0.00096681,0.000966805,0.699710515,0.000966781,0.000966754,0.296422335,Imaging,0.77798325,TRUE,99.55555556,0.867833509,119.5555556,0.850682366,0,0.403234768,,,0.707250214
2948,The impact of quarantine on mental health status among general population in China during the COVID-19 pandemic.,Mol Psychiatry,33483692,1/24/21,pubmed,0,15,logistic regression,0.001310318,0.001310328,0.00131035,0.001310376,0.971504429,0.023254199,Healthcare,0.9679567,TRUE,40.53333333,0.544869813,,,1,0.537564047,,,0.54121693
2949,Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study.,Lancet Digit Health,33483277,1/24/21,pubmed,0,13,logistic regression,0.000793382,0.000793398,0.000793398,0.561637556,0.435188862,0.000793404,Epidemiology,0.91252434,TRUE,89.07692308,0.839569547,163.3076923,0.894835429,6,0.764429903,,,0.83294496
2950,Privacy-preserving chi-squared test of independence for small samples.,BioData Min,33482874,1/24/21,pubmed,0,2,"genome-wide, dataset",0.001653079,0.311340181,0.495029514,0.125978788,0.001653193,0.064345245,Genomics,0.38094926,FALSE,185,0.966479065,17,0.451097137,0,0.403234768,,,0.60693699
2951,A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses.,BMC Biol,33482803,1/24/21,pubmed,0,10,"proteom, genomes, knowledge graph",0.180569718,0.267920599,0.001717349,0.44411894,0.103956198,0.001717197,Epidemiology,0.47827637,FALSE,103.2,0.875626198,183.9,0.908750334,0,0.403234768,,,0.729203767
2952,COVID-19 in ocrelizumab-treated people with multiple sclerosis.,Mult Scler Relat Disord,33482590,1/23/21,pubmed,0,12,dataset,0.000759381,0.000759389,0.000759395,0.081808925,0.000759391,0.91515352,Clinics,0.27089515,FALSE,50.75,0.636341147,146.75,0.880586032,2,0.618927094,,,0.711951424
2953,Could targeting immunometabolism be a way to control the burden of COVID-19 infection?,Microbes Infect,33482357,1/23/21,pubmed,0,3,immunome,0.005047749,0.005047696,0.005047718,0.766188798,0.005047753,0.213620286,Epidemiology,0.91585016,TRUE,192.3333333,0.969756942,196.6666667,0.91604228,0,0.403234768,,,0.76301133
2954,Transcriptome network analyses in human coronavirus infections suggest a rational use of immunomodulatory drugs for COVID-19 therapy.,Genomics,33482326,1/23/21,pubmed,0,6,transcriptom,0.630649346,0.002996533,0.050133223,0.086711732,0.002996605,0.22651256,Drug discovery,0.4098949,FALSE,78.83333333,0.80190488,149.3333333,0.883061279,0,0.403234768,,,0.696066975
2955,A review of potential suggested drugs for coronavirus disease (COVID-19) treatment.,Eur J Pharmacol,33482181,1/23/21,pubmed,0,6,"computational, in silico",0.676235994,0.118236441,0.001987298,0.129120241,0.072432778,0.001987248,Drug discovery,0.88634485,TRUE,7.333333333,0.105572392,1,0.122023013,0,0.403234768,,,0.210276724
2956,Biochemical features and mutations of key proteins in SARS-CoV-2 and their impacts on RNA therapeutics.,Biochem Pharmacol,33482149,1/23/21,pubmed,0,5,"machine learning, computational",0.697221002,0.265184249,0.034725749,0.000956346,0.000956351,0.000956304,Drug discovery,0.8577924,TRUE,28.4,0.411157153,51,0.685509767,0,0.403234768,,,0.499967229
2957,Exacerbation of Inequities in Use of Diagnostic Radiology During the Early Stages of Reopening After COVID-19.,J Am Coll Radiol,33482115,1/23/21,pubmed,0,6,logistic regression,0.001171577,0.001171562,0.162533853,0.001171585,0.242273699,0.591677723,Clinics,0.8066493,TRUE,115.1666667,0.899127961,92.66666667,0.80753278,0,0.403234768,,,0.703298503
2958,"Predictors of adherence to COVID-19 prevention measure among communities in North Shoa Zone, Ethiopia based on health belief model: A cross-sectional study.",PLoS One,33481962,1/23/21,pubmed,0,13,logistic regression,0.001415152,0.001415141,0.001415211,0.001415221,0.992924096,0.001415179,Healthcare,0.91473794,TRUE,6.615384615,0.094068897,0.846153846,0.102622424,1,0.537564047,,,0.24475179
2959,"Social media exposure, risk perception, preventive behaviors and attitudes during the COVID-19 epidemic in La Paz, Bolivia: A cross sectional study.",PLoS One,33481945,1/23/21,pubmed,0,6,logistic regression,0.016541089,0.001112618,0.00111261,0.001112666,0.979008397,0.001112619,Healthcare,0.49142355,FALSE,1.166666667,0.012493042,0,0.055525823,0,0.403234768,,,0.157084544
2960,Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic and estimates of lockdown-induced 2nd waves.,PLoS One,33481925,1/23/21,pubmed,0,3,"bayes, forecasting model, probabilistic",0.002806486,0.002806435,0.044615199,0.738817289,0.002806404,0.208148187,Epidemiology,0.40574804,FALSE,80.33333333,0.808213248,59,0.717554188,0,0.403234768,,,0.643000734
2961,Risk factors for neuropsychiatric symptoms in patients with Parkinson's disease during COVID-19 pandemic in Japan.,PLoS One,33481879,1/23/21,pubmed,0,8,logistic regression,0.041253259,0.001392844,0.001393118,0.00139314,0.632758985,0.321808654,Healthcare,0.9859798,TRUE,87.625,0.835487662,64.375,0.736285791,0,0.403234768,,,0.658336073
2962,The acceptability and uptake of smartphone tracking for COVID-19 in Australia.,PLoS One,33481841,1/23/21,pubmed,0,10,bayes,0.001786504,0.065552592,0.00178653,0.59040688,0.338680986,0.001786508,Epidemiology,0.5993344,TRUE,95.7,0.858370957,125,0.857706717,3,0.667819001,,,0.794632225
2963,Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies.,PLoS Comput Biol,33481773,1/23/21,pubmed,0,11,mathematical model,0.000822916,0.000822921,0.000822961,0.934953546,0.000822943,0.061754712,Epidemiology,0.23951066,FALSE,39.18181818,0.531634609,97.09090909,0.816764785,1,0.537564047,,,0.62865448
2964,The Use of Digital Platforms for Adults' and Adolescents' Physical Activity During the COVID-19 Pandemic (Our Life at Home): Survey Study.,J Med Internet Res,33481759,1/23/21,pubmed,0,12,logistic regression,0.000779457,0.000779415,0.000779457,0.266513726,0.730368524,0.000779421,Healthcare,0.9857348,TRUE,116.0833333,0.90085967,316.25,0.958656676,0,0.403234768,,,0.754250371
2965,The US Public's Perception of the Threat of COVID-19 During the Rapid Spread of the COVID-19 Outbreak: Cross-Sectional Survey Study.,J Med Internet Res,33481753,1/23/21,pubmed,0,4,dataset,0.034355093,0.000898083,0.000898113,0.000898143,0.962052479,0.000898089,Healthcare,0.22901976,FALSE,14.75,0.222586431,3.5,0.213607172,0,0.403234768,,,0.279809457
2966,[Association between mandatory lockdown due to COVID-19 and severe arterial hypertension].,Medicina (B Aires),33481729,1/23/21,pubmed,0,4,logistic regression,0.001415099,0.001415122,0.001415112,0.288810801,0.279498732,0.427445135,Clinics,0.9990548,TRUE,7.5,0.108355495,1.5,0.138747659,0,0.403234768,,,0.216779307
2967,Expression of SARS-CoV-2 Host Cell Entry Factors in Immune System Components of Healthy Individuals and Its Relevance for COVID-19 Immunopathology.,Viral Immunol,33481684,1/23/21,pubmed,0,6,"in silico, transcriptom, proteom",0.837365834,0.097143638,0.059905259,0.001861813,0.001861664,0.001861793,Drug discovery,0.6363828,TRUE,101,0.871296926,17.5,0.45611453,0,0.403234768,,,0.576882075
2968,Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground Truth.,Invest Radiol,33481459,1/23/21,pubmed,0,8,"neural network, dataset",0.000999516,0.000999518,0.933516931,0.000999552,0.000999524,0.062484958,Imaging,0.26155502,FALSE,47.625,0.611293215,,,1,0.537564047,,,0.574428631
2969,"Absence of association between 2019-20 influenza vaccination and COVID-19: Results of the European I-MOVE-COVID-19 primary care project, March-August 2020.",Influenza Other Respir Viruses,33481344,1/23/21,pubmed,0,21,logistic regression,0.001203425,0.206081615,0.018823389,0.001203517,0.666083456,0.106604598,Healthcare,0.25206172,FALSE,46.19047619,0.597748779,51.85714286,0.688854696,0,0.403234768,,,0.563279414
2970,Association between chronic kidney disease and COVID-19-related mortality in New York.,World J Urol,33481113,1/23/21,pubmed,0,13,logistic regression,0.001511833,0.001511865,0.001511863,0.110084021,0.0015119,0.883868518,Clinics,0.8085336,TRUE,39.23076923,0.532067537,26.61538462,0.542547498,2,0.618927094,,,0.564514043
2971,COVID-19 Pandemic 101: An Epidemiology and Public Health Primer for Oncology Nurses.,Clin J Oncol Nurs,33480886,1/23/21,pubmed,0,2,predictive model,0.002183223,0.002183253,0.048498409,0.844657269,0.100294584,0.002183262,Epidemiology,0.41367128,FALSE,101,0.871296926,30.5,0.573187048,0,0.403234768,,,0.615906247
2972,Effective inactivation of porcine epidemic diarrhea virus on contaminated surgery masks by low-concentrated sodium hypochlorite dispersion.,Pol J Vet Sci,33480506,1/23/21,pubmed,0,3,genomic structure,0.285106735,0.168283206,0.001861723,0.541024789,0.001861849,0.001861698,Epidemiology,0.585995,TRUE,11.66666667,0.176510607,0.666666667,0.096200161,0,0.403234768,,,0.225315178
2973,Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients-a nationwide study.,J Am Med Inform Assoc,33479727,1/23/21,pubmed,0,12,machine learning,0.001171548,0.001171625,0.001171643,0.262522738,0.001171571,0.732790876,Clinics,0.71508676,TRUE,16.75,0.252458408,39.5,0.630920524,0,0.403234768,,,0.428871233
2974,"An integrated approach to determine the abundance, mutation rate and phylogeny of the SARS-CoV-2 genome.",Brief Bioinform,33479725,1/23/21,pubmed,0,6,"computational, sequencing, dataset",0.001072232,0.633992483,0.106136305,0.256654574,0.001072221,0.001072184,Genomics,0.3885545,FALSE,8.666666667,0.12839384,0.666666667,0.096200161,1,0.537564047,,,0.254052683
2975,Network analysis of Down syndrome and SARS-CoV-2 identifies risk and protective factors for COVID-19.,Sci Rep,33479353,1/23/21,pubmed,0,2,"transcriptom, network analysis, dataset",0.685199526,0.134462852,0.00125466,0.001254672,0.001254682,0.176573608,Drug discovery,0.6052237,TRUE,80,0.807532933,111.5,0.84058068,4,0.707574542,,,0.785229385
2976,Model-informed COVID-19 vaccine prioritization strategies by age and serostatus.,Science,33479118,1/23/21,pubmed,0,7,mathematical model,0.002562634,0.002562637,0.002562594,0.745200758,0.244548588,0.00256279,Epidemiology,0.11250383,FALSE,123.7142857,0.912425011,271.2857143,0.946079743,0,0.403234768,,,0.753913174
2977,"Older age, comorbidity, glucocorticoid use and disease activity are risk factors for COVID-19 hospitalisation in patients with inflammatory rheumatic and musculoskeletal diseases.",RMD Open,33479021,1/23/21,pubmed,0,14,logistic regression,0.002296626,0.0022966,0.002296546,0.002296629,0.081937962,0.908875637,Clinics,0.9332728,TRUE,108.0714286,0.886140145,86.14285714,0.793216484,3,0.667819001,,,0.782391877
2978,0,Sci Immunol,33478949,1/23/21,pubmed,0,11,transcriptom,0.637314253,0.075222802,0.001203465,0.001203418,0.001203434,0.283852627,Drug discovery,0.4398897,FALSE,31.81818182,0.453089245,63.81818182,0.734546428,7,0.785110192,,,0.657581955
2979,0,Vaccine,33478794,1/23/21,pubmed,0,6,genomes,0.858631257,0.118981972,0.001220086,0.001220072,0.001220082,0.01872653,Drug discovery,0.513395,TRUE,8.833333333,0.129816315,6.166666667,0.281977522,1,0.537564047,,,0.316452628
2980,Immuno-informatics approach for B-cell and T-cell epitope based peptide vaccine design against novel COVID-19 virus.,Vaccine,33478787,1/23/21,pubmed,0,3,"computational, bioinformatic",0.60261961,0.18784396,0.001085352,0.206280395,0.001085353,0.001085329,Drug discovery,0.5058859,TRUE,6.333333333,0.089863319,1.666666667,0.145036125,0,0.403234768,,,0.212711404
2981,"Two-step strategy for the identification of SARS-CoV-2 variant of concern 202012/01 and other variants with spike deletion H69-V70, France, August to December 2020.",Euro Surveill,33478625,1/23/21,pubmed,0,30,whole genome,0.004775453,0.976123808,0.004775189,0.004775086,0.004775188,0.004775278,Genomics,0.51913583,TRUE,40.52631579,0.544807966,35.15789474,0.606034252,10,0.828199272,,,0.659680496
2982,Catastrophic cognitions about coronavirus: the Oxford psychological investigation of coronavirus questionnaire [TOPIC-Q].,Psychol Med,33478604,1/23/21,pubmed,0,9,model fit,0.001156314,0.001156316,0.001156335,0.203770259,0.732513518,0.060247258,Healthcare,0.77769,TRUE,116.2222222,0.90104521,549.1111111,0.981736687,1,0.537564047,,,0.806781981
2983,COVID-19 and healthcare system in China: challenges and progression for a sustainable future.,Global Health,33478558,1/23/21,pubmed,0,6,artificial intelligence,0.00186173,0.035531487,0.139002183,0.556737567,0.265005091,0.001861942,Epidemiology,0.71332526,TRUE,9.166666667,0.136619457,0.333333333,0.073187048,1,0.537564047,,,0.249123518
2984,Allergen fragrance molecules: a potential relief for COVID-19.,BMC Complement Med Ther,33478471,1/23/21,pubmed,0,4,virtual screening,0.995463408,0.000907305,0.000907344,0.000907313,0.000907319,0.000907311,Drug discovery,0.95790535,TRUE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
2985,Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: - evidence from Baidu index.,BMC Infect Dis,33478425,1/23/21,pubmed,0,4,correlation analysis,0.001272682,0.023144066,0.00127266,0.593275969,0.001272717,0.379761906,Epidemiology,0.45628983,FALSE,7.75,0.112808461,0,0.055525823,0,0.403234768,,,0.190523017
2986,In silico evaluation of lapachol derivatives binding to the Nsp9 of SARS-CoV-2.,J Biomol Struct Dyn,33478342,1/23/21,pubmed,0,8,"virtual screening, in silico",0.91177592,0.081850131,0.001593466,0.001593501,0.001593479,0.001593503,Drug discovery,0.90506554,TRUE,10.5,0.157338116,7,0.299973241,0,0.403234768,,,0.286848708
2987,[Current Status and Progress of Early Lung Cancer Screening under the
 Normal State of COVID-19 Epidemic Prevention and Control].,Zhongguo Fei Ai Za Zhi,33478188,1/23/21,pubmed,0,4,proteom,0.001486481,0.001486445,0.500600683,0.238588264,0.001486526,0.2563516,Clinics,0.82276034,TRUE,42.25,0.559898571,,,0,0.403234768,,,0.481566669
2988,"Structural Mapping of Mutations in Spike, RdRp and Orf3a Genes of SARS-CoV-2 in Influenza Like Illness (ILI) Patients.",Viruses,33477951,1/23/21,pubmed,0,8,sequencing,0.033526167,0.750258841,0.001203439,0.107145548,0.001203443,0.106662562,Genomics,0.6773733,TRUE,8.375,0.123940875,2.25,0.170925876,0,0.403234768,,,0.232700506
2989,"Intra-Host Diversity of SARS-Cov-2 Should Not Be Neglected: Case of the State of Victoria, Australia.",Viruses,33477885,1/23/21,pubmed,0,3,"sequencing, dataset",0.001310363,0.993448178,0.001310399,0.001310383,0.001310349,0.001310328,Genomics,0.48392084,FALSE,25,0.369286907,26.66666667,0.543216484,1,0.537564047,,,0.483355813
2990,"As the Pandemic Progresses, How Does Willingness to Vaccinate against COVID-19 Evolve?",Int J Environ Res Public Health,33477825,1/23/21,pubmed,0,9,logistic regression,0.002080588,0.002080573,0.002080557,0.002080649,0.989597032,0.002080601,Healthcare,0.8153646,TRUE,72.44444444,0.775187086,,,4,0.707574542,,,0.741380814
2991,Smartphone and Tablet Usage during COVID-19 Pandemic Confinement in Children under 48 Months in Barcelona (Spain).,Healthcare (Basel),33477770,1/23/21,pubmed,0,7,probabilistic,0.001751165,0.001751205,0.001751156,0.120906091,0.872089164,0.001751219,Healthcare,0.7993382,TRUE,62.28571429,0.71680376,18,0.46180091,0,0.403234768,,,0.527279813
2992,Burnout in ICU doctors and nurses in mainland China-A national cross-sectional study.,J Crit Care,33477093,1/22/21,pubmed,0,9,logistic regression,0.002357694,0.002357779,0.002357755,0.002357777,0.846518837,0.144050158,Healthcare,0.9694369,TRUE,86.11111111,0.829426681,33.22222222,0.593925609,0,0.403234768,,,0.608862352
2993,Duration of SARS-CoV-2 viral shedding in faeces as a parameter for wastewater-based epidemiology: Re-analysis of patient data using a shedding dynamics model.,Sci Total Environ,33477053,1/22/21,pubmed,0,3,bayes,0.001415186,0.125636435,0.052012978,0.708047247,0.00141517,0.111472984,Epidemiology,0.42121413,FALSE,56,0.675304595,91,0.803987155,1,0.537564047,,,0.672285266
2994,Clade GR and clade GH isolates of SARS-CoV-2 in Asia show highest amount of SNPs.,Infect Genet Evol,33476804,1/22/21,pubmed,0,3,"genome sequences, genomes",0.001786629,0.860133086,0.001786548,0.132720571,0.00178662,0.001786546,Genomics,0.5193774,TRUE,47.33333333,0.608633805,7,0.299973241,0,0.403234768,,,0.437280605
2995,Predictors for development of critical illness amongst older adults with COVID-19: Beyond age to age-associated factors.,Arch Gerontol Geriatr,33476755,1/22/21,pubmed,0,11,logistic regression,0.001219999,0.00122002,0.001220015,0.001220034,0.001220064,0.993899869,Clinics,0.7234216,TRUE,46.27272727,0.598923867,49.18181818,0.678084025,0,0.403234768,,,0.560080886
2996,Psychological Risk Factors of Functional Impairment After COVID-19 Deaths.,J Pain Symptom Manage,33476753,1/22/21,pubmed,0,3,logistic regression,0.18303887,0.001392887,0.00139287,0.001392872,0.811389631,0.00139287,Healthcare,0.9454451,TRUE,218,0.978106253,518.6666667,0.980131121,0,0.403234768,,,0.787157381
2997,Self-Report Assessment of Nurses' Risk for Infection After Exposure to Patients With Coronavirus Disease (COVID-19) in the United Arab Emirates.,J Nurs Scholarsh,33476482,1/22/21,pubmed,0,9,logistic regression,0.060315017,0.001126866,0.001126866,0.001126834,0.872437811,0.063866606,Healthcare,0.8878065,TRUE,4.777777778,0.064753541,1.444444444,0.134399251,0,0.403234768,,,0.200795853
2998,A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.,J Med Internet Res,33476281,1/22/21,pubmed,0,17,"machine learning, predictive model, logistic regression, prediction model",0.001141321,0.00114131,0.234703889,0.001141343,0.001141367,0.760730769,Clinics,0.9713744,TRUE,58.85714286,0.6951574,104.2857143,0.829609312,0,0.403234768,,,0.64266716
2999,How to Best Protect People With Diabetes From the Impact of SARS-CoV-2: Report of the International COVID-19 and Diabetes Summit.,J Diabetes Sci Technol,33476193,1/22/21,pubmed,0,13,digital health,0.070463742,0.000898155,0.030399648,0.530867849,0.111564037,0.25580657,Epidemiology,0.6220076,TRUE,42.30769231,0.560393345,,,0,0.403234768,,,0.481814056
3000,How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.,Rev Med Virol,33476063,1/22/21,pubmed,0,13,"machine learning, artificial intelligence",0.044225664,0.001622759,0.558001626,0.350009581,0.044517537,0.001622833,Epidemiology,0.7881526,TRUE,89.61538462,0.841796029,47.76923077,0.671461065,0,0.403234768,,,0.638830621
3001,SARS-CoV infection crosstalk with human host cell noncoding-RNA machinery: An in-silico approach.,Biomed Pharmacother,33475497,1/22/21,pubmed,0,7,"computational, bioinformatic, in-silico",0.885662944,0.068919577,0.001371256,0.041303615,0.001371278,0.00137133,Drug discovery,0.4625131,FALSE,57.85714286,0.687797637,28.28571429,0.556261707,0,0.403234768,,,0.549098037
3002,"Insight into molecular characteristics of SARS-CoV-2 spike protein following D614G point mutation, a molecular dynamics study.",J Biomol Struct Dyn,33475020,1/22/21,pubmed,0,4,bioinformatic,0.639548055,0.351928614,0.00213068,0.002130763,0.002130844,0.002131045,Drug discovery,0.17313391,FALSE,16.75,0.252458408,8.25,0.323789136,1,0.537564047,,,0.37127053
3003,ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images.,J Biomol Struct Dyn,33475019,1/22/21,pubmed,0,4,"deep learning, neural network",0.048826316,0.001171537,0.946487456,0.001171573,0.00117153,0.001171588,Imaging,0.6544578,TRUE,62.5,0.718164389,42.75,0.648381054,1,0.537564047,,,0.634703163
3004,Evaluation of UV-C Decontamination of Clinical Tissue Sections for Spatially Resolved Analysis by Mass Spectrometry Imaging (MSI).,Anal Chem,33474935,1/22/21,pubmed,0,13,metabolom,0.554491555,0.372779708,0.064309299,0.002806525,0.002806452,0.002806461,Drug discovery,0.82847816,TRUE,52.23076923,0.648463108,80.46153846,0.78017126,0,0.403234768,,,0.610623045
3005,Impact of the coronavirus disease 2019 pandemic on periodontal practice: A questionnaire survey.,J Clin Periodontol,33474762,1/22/21,pubmed,0,7,logistic regression,0.002296561,0.002296611,0.002296562,0.002296651,0.988517043,0.002296573,Healthcare,0.953088,TRUE,43.28571429,0.570165131,33.28571429,0.594527696,0,0.403234768,,,0.522642531
3006,Social influence matters: We follow pandemic guidelines most when our close circle does.,Br J Psychol,33474747,1/22/21,pubmed,0,7,bayes,0.001943496,0.00194352,0.001943514,0.607091205,0.3851348,0.001943465,Epidemiology,0.37545183,FALSE,46.42857143,0.600098955,42.14285714,0.64530372,0,0.403234768,,,0.549545814
3007,"A primer on using mathematics to understand COVID-19 dynamics: Modeling, analysis and simulations.",Infect Dis Model,33474518,1/22/21,pubmed,0,4,mathematical model,0.002639068,0.002639011,0.002639017,0.98680495,0.002638988,0.002638965,Epidemiology,0.4918205,FALSE,44,0.578390748,63.5,0.733676746,6,0.764429903,,,0.692165799
3008,Identification of 3-chymotrypsin like protease (3CLPro) inhibitors as potential anti-SARS-CoV-2 agents.,Commun Biol,33473151,1/22/21,pubmed,0,9,"molecular dynamics simulation, computational",0.988807855,0.002238436,0.002238421,0.002238442,0.002238419,0.002238426,Drug discovery,0.90916806,TRUE,15.11111111,0.227967098,12.66666667,0.39530372,2,0.618927094,,,0.41406597
3009,[Role of the information systems and e-health in the COVID-19 pandemic. A call to action.],Rev Esp Salud Publica,33473100,1/22/21,pubmed,0,1,digital health,0.002996604,0.002996475,0.002996754,0.985017089,0.002996608,0.002996471,Epidemiology,0.7495379,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
3010,"The hygiene hypothesis, the COVID pandemic, and consequences for the human microbiome.",Proc Natl Acad Sci U S A,33472859,1/22/21,pubmed,0,23,microbiom,0.001511928,0.413649119,0.001511866,0.401925325,0.124556534,0.056845227,Genomics,0.93022954,TRUE,154.3043478,0.947368421,457.5652174,0.976652395,3,0.667819001,,,0.863946606
3011,Association between regional distributions of SARS-CoV-2 seroconversion and out-of-hospital sudden death during the first epidemic outbreak in New York.,Heart Rhythm,33472765,1/22/21,pubmed,0,3,correlation analysis,0.001901714,0.157546707,0.00190171,0.374341172,0.001901775,0.462406921,Clinics,0.63938355,TRUE,13.66666667,0.207310285,2,0.164302917,1,0.537564047,,,0.303059083
3012,Association of Overlapped and Un-overlapped Comorbidities with COVID-19 Severity and Treatment Outcomes: A Retrospective Cohort Study from Nine Provinces in China.,Biomed Environ Sci,33472729,1/22/21,pubmed,0,64,logistic regression,0.001371264,0.001371256,0.001371264,0.001371298,0.001371346,0.993143573,Clinics,0.92522407,TRUE,45.8125,0.59391428,,,0,0.403234768,,,0.498574524
3013,Prediction of adverse drug reactions using drug convolutional neural networks.,J Bioinform Comput Biol,33472571,1/22/21,pubmed,0,6,"neural network, in silico",0.460472229,0.001034609,0.503718464,0.001034608,0.03270545,0.00103464,Drug discovery,0.17326128,FALSE,4,0.054734368,0.833333333,0.102488627,0,0.403234768,,,0.186819254
3014,Use of Artificial Intelligence to understand adults' thoughts and behaviours relating to COVID-19.,Perspect Public Health,33472547,1/22/21,pubmed,0,3,artificial intelligence,0.001156269,0.001156284,0.115201841,0.273992873,0.607336456,0.001156277,Healthcare,0.63160485,TRUE,12.33333333,0.186467932,3.666666667,0.217621086,0,0.403234768,,,0.269107929
3015,ACE-2-Derived Biomimetic Peptides for the Inhibition of Spike Protein of SARS-CoV-2.,J Proteome Res,33472369,1/22/21,pubmed,0,5,virtual screening,0.97398945,0.020690371,0.001330063,0.001330068,0.001330026,0.001330023,Drug discovery,0.05423802,FALSE,22.8,0.337064754,2.8,0.188787798,0,0.403234768,,,0.309695773
3016,In silico comparative genomics of SARS-CoV-2 to determine the source and diversity of the pathogen in Bangladesh.,PLoS One,33471859,1/21/21,pubmed,0,3,"in silico, whole-genome, genome sequences",0.002130786,0.989346666,0.002130613,0.002130688,0.002130636,0.002130611,Genomics,0.66248244,TRUE,53.33333333,0.656255798,171,0.900120417,0,0.403234768,,,0.653203661
3017,Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: A population-based cohort study from Italy.,PLoS One,33471809,1/21/21,pubmed,0,10,logistic regression,0.025471122,0.001438135,0.03767148,0.229323105,0.364678071,0.341418087,Healthcare,0.6083901,TRUE,46.1,0.597315851,22.8,0.509967889,0,0.403234768,,,0.503506169
3018,"Physical Activity, Mental Health, and Technology Preferences to Support Cancer Survivors During the COVID-19 Pandemic: Cross-sectional Study.",JMIR Cancer,33471776,1/21/21,pubmed,0,7,digital health,0.000648144,0.000648136,0.000648148,0.000648162,0.971148654,0.026258756,Healthcare,0.999166,TRUE,78.71428571,0.801286412,106.5714286,0.833890822,0,0.403234768,,,0.679470667
3019,Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning.,Anal Chem,33471512,1/21/21,pubmed,0,30,"machine learning, metabolom",0.030663452,0.001511861,0.650222412,0.160559726,0.001511895,0.155530653,Epidemiology,0.53575146,TRUE,73.83333333,0.781186221,46.63333333,0.666644367,0,0.403234768,,,0.617021785
3020,TeleConnect: digitally connecting physicians across the health care system.,Am J Manag Care,33471462,1/21/21,pubmed,0,4,digital health,0.001786534,0.001786603,0.104169793,0.32963758,0.458592206,0.104027284,Healthcare,0.96930945,TRUE,82.5,0.816129631,48,0.673735617,0,0.403234768,,,0.631033338
3021,Differences in the use of telephone and video telemedicine visits during the COVID-19 pandemic.,Am J Manag Care,33471458,1/21/21,pubmed,0,4,logistic regression,0.050708821,0.001291303,0.001291283,0.001291294,0.760110314,0.185306985,Healthcare,0.8884139,TRUE,78.25,0.799369163,128.75,0.862322719,0,0.403234768,,,0.688308883
3022,The Expanding Digital Divide: Digital Health Access Inequities during the COVID-19 Pandemic in New York City.,J Urban Health,33471281,1/21/21,pubmed,0,8,digital health,0.011749866,0.011750092,0.011750008,0.941249403,0.011750417,0.011750214,Epidemiology,0.8413336,TRUE,22.75,0.336508133,3.375,0.207051111,1,0.537564047,,,0.36037443
3023,Artificial Intelligence-assisted chest X-ray assessment scheme for COVID-19.,Eur Radiol,33471219,1/21/21,pubmed,0,29,"artificial intelligence, neural network",0.000793409,0.000793425,0.908509091,0.000793412,0.00079342,0.088317243,Imaging,0.55789655,TRUE,91.82758621,0.848289938,23.51724138,0.516858443,0,0.403234768,,,0.589461049
3024,Cellinker: a platform of ligand-receptor interactions for intercellular communication analysis.,Bioinformatics,33471060,1/21/21,pubmed,0,11,"bioinformatic, sequencing",0.703621501,0.025914836,0.001046887,0.26732307,0.001046855,0.001046852,Drug discovery,0.8683647,TRUE,46.45454545,0.600284495,13.18181818,0.401859781,0,0.403234768,,,0.468459681
3025,Next-generation sequencing and RT-PCR to identify a 32-day SARS-CoV-2 carrier.,Clin Chem Lab Med,33470954,1/21/21,pubmed,0,12,sequencing,0.01599839,0.744645074,0.015999031,0.015999924,0.015998728,0.191358853,Genomics,0.60233206,TRUE,131.1666667,0.922877111,73,0.761974846,0,0.403234768,,,0.696028908
3026,Association between maternity harassment and depression during pregnancy amid the COVID-19 state of emergency.,J Occup Health,33470006,1/21/21,pubmed,0,7,logistic regression,0.002183197,0.002183231,0.002183206,0.002183284,0.989083793,0.002183289,Healthcare,0.6785312,TRUE,152.2857143,0.945884099,162.7142857,0.894434038,0,0.403234768,,,0.747850968
3027,"Molecular Simulations suggest Vitamins, Retinoids and Steroids as Ligands of the Free Fatty Acid Pocket of the SARS-CoV-2 Spike Protein*.",Angew Chem Int Ed Engl,33469977,1/21/21,pubmed,0,10,molecular dynamics simulation,0.991067455,0.001786546,0.001786499,0.001786529,0.001786495,0.001786477,Drug discovery,0.71798754,TRUE,75.4,0.788174903,92.1,0.805860316,1,0.537564047,,,0.710533089
3028,Assessment of COVID-19 Information Overload Among the General Public.,J Racial Ethn Health Disparities,33469869,1/21/21,pubmed,0,9,logistic regression,0.001461887,0.001461925,0.001461963,0.001461991,0.951819071,0.042333163,Healthcare,0.8601488,TRUE,18.77777778,0.280784217,3.111111111,0.199959861,0,0.403234768,,,0.294659615
3029,"Understanding How Race, Ethnicity, and Gender Shape Mask-Wearing Adherence During the COVID-19 Pandemic: Evidence from the COVID Impact Survey.",J Racial Ethn Health Disparities,33469866,1/21/21,pubmed,0,2,logistic regression,0.001291232,0.001291257,0.001291231,0.231992703,0.762842341,0.001291237,Healthcare,0.8975935,TRUE,18.5,0.278001113,4.5,0.242708055,1,0.537564047,,,0.352757738
3030,Deep vein thrombosis in COVID-19 patients in general wards: prevalence and association with clinical and laboratory variables.,Radiol Med,33469817,1/21/21,pubmed,0,11,logistic regression,0.001310414,0.001310387,0.027192541,0.001310353,0.063417214,0.905459091,Clinics,0.40303034,FALSE,117.0909091,0.902962459,73.36363636,0.762576933,0,0.403234768,,,0.689591387
3031,SARS-CoV-2/human interactome reveals ACE2 locus crosstalk with the immune regulatory network in the host.,Pathog Dis,33469663,1/21/21,pubmed,0,4,"proteom, interactom",0.946760814,0.04988508,0.000838514,0.000838526,0.000838518,0.000838548,Drug discovery,0.6879316,TRUE,8,0.118683901,2.75,0.187583623,0,0.403234768,,,0.236500764
3032,Deep RNA Sequencing of Intensive Care Unit Patients with COVID-19.,medRxiv,33469603,1/21/21,pubmed,0,13,"computational, sequencing",0.116331249,0.381581756,0.070851543,0.017770713,0.000846568,0.412618171,Clinics,0.23456702,FALSE,35.30769231,0.489578824,,,0,0.403234768,,,0.446406796
3033,Enisamium is an inhibitor of the SARS-CoV-2 RNA polymerase and shows improvement of recovery in COVID-19 patients in an interim analysis of a clinical trial.,medRxiv,33469600,1/21/21,pubmed,0,19,molecular dynamics simulation,0.563951593,0.000977497,0.000977467,0.024006475,0.000977493,0.409109475,Drug discovery,0.4410517,FALSE,22.36842105,0.330447152,23.52631579,0.51699224,1,0.537564047,,,0.461667813
3034,Optimal SARS-CoV-2 vaccine allocation using real-time seroprevalence estimates in Rhode Island and Massachusetts.,medRxiv,33469599,1/21/21,pubmed,0,14,mathematical model,0.001085519,0.001085357,0.001085335,0.696621014,0.135095692,0.165027082,Epidemiology,0.061754644,FALSE,37.5,0.513946441,83.64285714,0.787530104,1,0.537564047,,,0.613013531
3035,The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction.,medRxiv,33469592,1/21/21,pubmed,0,45,"machine learning, logistic regression, dataset",0.000662706,0.000662723,0.106456631,0.224973157,0.000662716,0.666582067,Clinics,0.29871118,FALSE,31.30232558,0.447523038,32.18604651,0.586700562,1,0.537564047,,,0.523929216
3036,Mutation rates and selection on synonymous mutations in SARS-CoV-2.,bioRxiv,33469589,1/21/21,pubmed,0,6,"sequencing, genomes",0.001593572,0.847112693,0.001593527,0.124864991,0.001593564,0.023241653,Genomics,0.10845697,FALSE,0.833333333,0.008287464,,,3,0.667819001,,,0.338053232
3037,Paranoia and belief updating during a crisis.,Res Sq,33469574,1/21/21,pubmed,0,12,computational,0.001622765,0.001622769,0.001622896,0.548327181,0.445181683,0.001622705,Epidemiology,0.92399704,TRUE,36,0.498299215,65.83333333,0.741905272,0,0.403234768,,,0.547813085
3038,Small Molecules to Destabilize the ACE2-RBD Complex: A Molecular Dynamics Study for Potential COVID-19 Therapeutics.,ChemRxiv,33469570,1/21/21,pubmed,0,3,molecular dynamics simulation,0.993448193,0.001310366,0.001310348,0.001310375,0.001310376,0.001310343,Drug discovery,0.75338006,TRUE,16.66666667,0.251159626,4.666666667,0.246721969,0,0.403234768,,,0.300372121
3039,COVID-19 Deterioration Prediction via Self-Supervised Representation Learning and Multi-Image Prediction.,ArXiv,33469559,1/21/21,pubmed,0,10,"machine learning, supervised learning",0.000999548,0.033273936,0.576083451,0.000999536,0.000999554,0.387643975,Imaging,0.07039818,FALSE,63.1,0.72212258,,,1,0.537564047,,,0.629843314
3040,CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network.,ArXiv,33469558,1/21/21,pubmed,0,12,neural network,0.00123718,0.033323256,0.893585832,0.001237125,0.001237052,0.069379555,Imaging,0.051734537,FALSE,142.9166667,0.937349249,125.75,0.858643297,0,0.403234768,,,0.733075771
3041,Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations.,Front Chem,33469527,1/21/21,pubmed,0,9,"molecular dynamics simulation, in silico",0.995218199,0.000956383,0.000956303,0.0009564,0.000956366,0.000956349,Drug discovery,0.75037277,TRUE,49,0.624281032,60.11111111,0.721099813,0,0.403234768,,,0.582871871
3042,Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy.,J Ambient Intell Humaniz Comput,33469467,1/21/21,pubmed,0,4,bayes,0.001220064,0.055972107,0.852481284,0.001220091,0.001220015,0.087886438,Clinics,0.3853697,FALSE,17.5,0.263776362,2.25,0.170925876,0,0.403234768,,,0.279312335
3043,0,Front Genet,33469465,1/21/21,pubmed,0,8,sequencing,0.945668822,0.045598148,0.002183241,0.00218324,0.002183217,0.002183332,Drug discovery,0.6058745,TRUE,21.125,0.312511596,16.875,0.44715012,0,0.403234768,,,0.387632161
3044,Diagnostic Performance of a Blood Urea Nitrogen to Creatinine Ratio-based Nomogram for Predicting In-hospital Mortality in COVID-19 Patients.,Risk Manag Healthc Policy,33469395,1/21/21,pubmed,0,8,logistic regression,0.00127264,0.001272628,0.130319721,0.00127266,0.001272659,0.864589691,Clinics,0.6406135,TRUE,43.375,0.571340219,,,0,0.403234768,,,0.487287493
3045,Partial N Gene Sequencing for SARS-CoV-2 Verification and Pathway Tracing.,Int Med Case Rep J,33469388,1/21/21,pubmed,0,4,sequencing,0.01295629,0.824884996,0.001034633,0.001034619,0.063675674,0.096413788,Genomics,0.59475404,TRUE,48.25,0.616364648,26.75,0.543617875,0,0.403234768,,,0.52107243
3046,"The Effects of Fear and Knowledge of COVID-19 on Preventive Practice Among Pregnant Women Who Attend Antenatal Care in Northwest Ethiopia, 2020: Institution-Based Cross-Sectional Study.",Int J Womens Health,33469387,1/21/21,pubmed,0,4,logistic regression,0.001220036,0.001220009,0.022198859,0.001220118,0.972920849,0.001220129,Healthcare,0.9405757,TRUE,4.75,0.064506154,0.25,0.065493712,0,0.403234768,,,0.177744878
3047,"Combining serology with case-detection, to allow the easing of restrictions against SARS-CoV-2: a modelling-based study in India.",Sci Rep,33469083,1/21/21,pubmed,0,4,mathematical model,0.001622694,0.095576845,0.001622774,0.863708086,0.035846771,0.00162283,Epidemiology,0.24282512,FALSE,18,0.271569052,4.25,0.235750602,0,0.403234768,,,0.303518141
3048,"Genomic epidemiology of SARS-CoV-2 reveals multiple lineages and early spread of SARS-CoV-2 infections in Lombardy, Italy.",Nat Commun,33469026,1/21/21,pubmed,0,18,"genomic epidemiology, genomes",0.003214116,0.983929267,0.003214096,0.003214195,0.003214183,0.003214143,Genomics,0.5833881,TRUE,83.16666667,0.818294267,57.83333333,0.713807867,3,0.667819001,,,0.733307045
3049,SARS-CoV-2 Genomic Variation in Space and Time in Hospitalized Patients in Philadelphia.,mBio,33468702,1/21/21,pubmed,0,18,"sequencing, genome sequences, genomes",0.001085352,0.77746193,0.00108533,0.104923249,0.001085365,0.114358774,Genomics,0.23476678,FALSE,39.77777778,0.537015276,88.38888889,0.797497993,2,0.618927094,,,0.651146788
3050,Atypical Divergence of SARS-CoV-2 Orf8 from Orf7a within the Coronavirus Lineage Suggests Potential Stealthy Viral Strategies in Immune Evasion.,mBio,33468697,1/21/21,pubmed,0,3,genomes,0.325535494,0.619191429,0.023887961,0.001010978,0.001010969,0.02936317,Genomics,0.3083061,FALSE,346,0.994124559,1123,0.994915708,1,0.537564047,,,0.842201438
3051,At Least Seven Distinct Rotavirus Genotype Constellations in Bats with Evidence of Reassortment and Zoonotic Transmissions.,mBio,33468689,1/21/21,pubmed,0,24,"metagenom, genomes",0.001126821,0.994365882,0.001126852,0.001126822,0.001126827,0.001126796,Genomics,0.78040624,TRUE,55.95833333,0.674005814,169.7083333,0.899317634,0,0.403234768,,,0.658852739
3052,Genome Sequencing of Sewage Detects Regionally Prevalent SARS-CoV-2 Variants.,mBio,33468686,1/21/21,pubmed,0,19,"sequencing, transcriptom, metagenom, genomes, metatranscriptom",0.001187262,0.949239749,0.001187284,0.001187293,0.001187259,0.046011152,Genomics,0.41247666,FALSE,20.15789474,0.299090853,91.10526316,0.804054054,4,0.707574542,,,0.60357315
3053,Impact on health and provision of healthcare services during the COVID-19 lockdown in India: a multicentre cross-sectional study.,BMJ Open,33468529,1/21/21,pubmed,0,9,logistic regression,0.001593553,0.001593529,0.001593497,0.293707288,0.665419202,0.036092931,Healthcare,0.9661937,TRUE,124.3333333,0.913476405,100,0.822584961,2,0.618927094,,,0.784996154
3054,"SARS-CoV-2 Pneumonia Affects Male Reproductive Hormone Levels: A Prospective, Cohort Study.",J Sex Med,33468445,1/21/21,pubmed,0,5,correlation analysis,0.082156679,0.000815384,0.009716004,0.037603984,0.000815346,0.868892604,Clinics,0.9768779,TRUE,34.2,0.479188571,9.8,0.350682366,1,0.537564047,,,0.455811661
3055,Spread of COVID-19 in urban neighbourhoods and slums of the developing world.,J R Soc Interface,33468021,1/21/21,pubmed,0,2,network model,0.001622738,0.001622751,0.001622721,0.991886234,0.00162282,0.001622736,Epidemiology,0.21456397,FALSE,177.5,0.963263034,120.5,0.852421729,3,0.667819001,,,0.827834588
3056,Inter-proteomic posttranslational modifications of the SARS-CoV-2 and the host proteins ‒ A new frontier.,Exp Biol Med (Maywood),33467896,1/21/21,pubmed,0,3,proteom,0.938094126,0.001254679,0.001254649,0.05688726,0.001254689,0.001254597,Drug discovery,0.6066517,TRUE,46,0.596882924,60,0.720899117,0,0.403234768,,,0.57367227
3057,Controlling COVID-19 Outbreaks with Financial Incentives.,Int J Environ Res Public Health,33467714,1/21/21,pubmed,0,3,mathematical model,0.001987114,0.001987239,0.001987167,0.915078352,0.001987119,0.076973009,Epidemiology,0.39017895,FALSE,57.33333333,0.68433422,27.33333333,0.54856837,0,0.403234768,,,0.545379119
3058,Mitochondrial Functionality in Inflammatory Pathology-Modulatory Role of Physical Activity.,Life (Basel),33467642,1/21/21,pubmed,0,2,immunome,0.563667025,0.002033002,0.002032871,0.23341575,0.030950541,0.167900811,Drug discovery,0.819873,TRUE,50.5,0.635351599,19,0.471367407,0,0.403234768,,,0.503317924
3059,Obstetric Outcomes of SARS-CoV-2 Infection in Asymptomatic Pregnant Women.,Viruses,33467629,1/21/21,pubmed,0,54,logistic regression,0.001622856,0.001622786,0.001622757,0.001622788,0.594548561,0.398960253,Healthcare,0.66448236,TRUE,3.425925926,0.041004391,0.740740741,0.097002944,1,0.537564047,,,0.225190461
3060,A Global Review on Short Peptides: Frontiers and Perspectives.,Molecules,33467522,1/21/21,pubmed,0,25,in silico,0.775720327,0.001085353,0.219938178,0.001085401,0.001085374,0.001085368,Drug discovery,0.7860689,TRUE,96.2,0.859484198,38.96,0.626705914,0,0.403234768,,,0.629808293
3061,Triple-Negative Breast Cancer and the COVID-19 Pandemic: Clinical Management Perspectives and Potential Consequences of Infection.,Cancers (Basel),33467411,1/21/21,pubmed,0,3,dataset,0.302256922,0.001653084,0.00165306,0.44726569,0.001653117,0.245518126,Epidemiology,0.7605281,TRUE,36.66666667,0.505102356,41.33333333,0.641356703,0,0.403234768,,,0.516564609
3062,"MicroRNAs for Virus Pathogenicity and Host Responses, Identified in SARS-CoV-2 Genomes, May Play Roles in Viral-Host Co-Evolution in Putative Zoonotic Host Species.",Viruses,33467206,1/21/21,pubmed,0,4,"in silico, genomes",0.308157485,0.613428973,0.001415097,0.001415106,0.001415141,0.074168199,Genomics,0.98581266,TRUE,75.75,0.789473684,27.75,0.551645705,0,0.403234768,,,0.581451386
3063,Primary and Secondary Health Impacts of COVID-19 among Minority Individuals in New York State.,Int J Environ Res Public Health,33466938,1/21/21,pubmed,0,2,logistic regression,0.001717182,0.001717208,0.00171719,0.158357528,0.834773681,0.00171721,Healthcare,0.8538965,TRUE,16,0.243552477,4,0.231469093,0,0.403234768,,,0.292752112
3064,Cheminformatics-Based Identification of Potential Novel Anti-SARS-CoV-2 Natural Compounds of African Origin.,Molecules,33466743,1/21/21,pubmed,0,8,"bayes, molecular dynamics simulation, machine learning",0.929683629,0.000977469,0.02716434,0.018596659,0.02260045,0.000977454,Drug discovery,0.6611115,TRUE,23,0.34225988,17.375,0.454375167,1,0.537564047,,,0.444733031
3065,Effectiveness of In-Hospital Cholecalciferol Use on Clinical Outcomes in Comorbid COVID-19 Patients: A Hypothesis-Generating Study.,Nutrients,33466642,1/21/21,pubmed,0,21,logistic regression,0.001156264,0.001156265,0.001156336,0.001156308,0.001156302,0.994218524,Clinics,0.864789,TRUE,136.2857143,0.928257777,92.33333333,0.806529302,2,0.618927094,,,0.784571391
3066,Postponed Dental Visits during the COVID-19 Pandemic and their Correlates. Evidence from the Nationally Representative COVID-19 Snapshot Monitoring in Germany (COSMO).,Healthcare (Basel),33466552,1/21/21,pubmed,0,5,logistic regression,0.001717246,0.001717207,0.001717225,0.001717259,0.991413719,0.001717343,Healthcare,0.92775285,TRUE,138.2,0.930793494,99.2,0.820912497,0,0.403234768,,,0.718313586
3067,Eastern Cape Healthcare Workers Acquisition of SARS-CoV-2 (ECHAS): Cross-Sectional (Nested Cohort) Study Protocol.,Int J Environ Res Public Health,33466227,1/21/21,pubmed,0,8,logistic regression,0.001272677,0.33273749,0.001272684,0.158197167,0.505247257,0.001272726,Healthcare,0.6752976,TRUE,13.5,0.205393036,4,0.231469093,0,0.403234768,,,0.280032299
3068,In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives.,Adv Drug Deliv Rev,33465451,1/20/21,pubmed,0,4,"machine learning, in silico",0.729089329,0.001751191,0.263905819,0.001751233,0.001751162,0.001751266,Drug discovery,0.566175,TRUE,59.75,0.701156534,96,0.81509232,0,0.403234768,,,0.639827874
3069,COVID-19 Among US Dialysis Patients: Risk Factors and Outcomes From a National Dialysis Provider.,Am J Kidney Dis,33465417,1/20/21,pubmed,0,11,logistic regression,0.001310328,0.001310382,0.001310375,0.001310405,0.294391264,0.700367246,Clinics,0.92501974,TRUE,55.72727273,0.672583338,60,0.720899117,1,0.537564047,,,0.643682168
3070,Olfactory function and viral recovery in COVID-19.,Brain Behav,33465295,1/20/21,pubmed,0,8,logistic regression,0.001511887,0.160261246,0.032663828,0.001511842,0.001511884,0.802539313,Clinics,0.3888227,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
3071,"A study of community knowledge, attitudes, practices, and health in Pakistan during the COVID-19 pandemic.",J Community Psychol,33465247,1/20/21,pubmed,0,2,logistic regression,0.001653072,0.001653096,0.001653046,0.165880359,0.827507386,0.001653041,Healthcare,0.9013031,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
3072,Model-based forecasting for Canadian COVID-19 data.,PLoS One,33465142,1/20/21,pubmed,0,4,"neural network, predictive model",0.001237061,0.00123707,0.063700557,0.931351154,0.001237071,0.001237087,Epidemiology,0.4325928,FALSE,7.5,0.108355495,,,1,0.537564047,,,0.322959771
3073,A behavioral economic risk aversion experiment in the context of the COVID-19 pandemic.,PLoS One,33465121,1/20/21,pubmed,0,7,predictive model,0.001538101,0.001538168,0.051064384,0.147306968,0.797014178,0.001538201,Healthcare,0.20040047,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
3074,Modeling the structure of the frameshift-stimulatory pseudoknot in SARS-CoV-2 reveals multiple possible conformers.,PLoS Comput Biol,33465066,1/20/21,pubmed,0,6,"molecular dynamics simulation, computational, structural model",0.887009749,0.002422416,0.00242243,0.103300658,0.002422398,0.00242235,Drug discovery,0.71120846,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
3075,Proteomic Approaches to Study SARS-CoV-2 Biology and COVID-19 Pathology.,J Proteome Res,33464917,1/20/21,pubmed,0,5,proteom,0.678660862,0.001330115,0.03545309,0.28189578,0.001330116,0.001330037,Drug discovery,0.83092165,TRUE,82.8,0.817242872,,,0,0.403234768,,,0.61023882
3076,Genome-wide DNA methylation profiling of peripheral blood reveals an epigenetic signature associated with severe COVID-19.,J Leukoc Biol,33464637,1/20/21,pubmed,0,19,genome-wide,0.432263262,0.102062387,0.001310349,0.001310385,0.001310369,0.461743248,Clinics,0.07712245,FALSE,8.157894737,0.119487909,,,2,0.618927094,,,0.369207502
3077,Customer Centricity in Medical Affairs Needs Human-centric Artificial Intelligence.,Pharmaceut Med,33464482,1/20/21,pubmed,0,7,artificial intelligence,0.002422461,0.157819435,0.410618899,0.002422462,0.424294381,0.002422363,Healthcare,0.7348485,TRUE,67.85714286,0.752798565,75.71428571,0.768263313,0,0.403234768,,,0.641432215
3078,Correction to: How Resiliency and Hope Can Predict Stress of Covid-19 by Mediating Role of Spiritual Well-being Based on Machine Learning.,J Relig Health,33464433,1/20/21,pubmed,0,9,machine learning,0.015998973,0.0159985,0.493827019,0.015999194,0.442177946,0.015998368,Healthcare,0.7519144,TRUE,2.666666667,0.029377203,,,0,0.403234768,,,0.216305985
3079,Comprehensive analysis of genomic diversity of SARS-CoV-2 in different geographic regions of India: an endeavour to classify Indian SARS-CoV-2 strains on the basis of co-existing mutations.,Arch Virol,33464421,1/20/21,pubmed,0,7,"genome-wide, whole genome, genome sequences",0.068880578,0.926981034,0.001034583,0.001034604,0.001034596,0.001034604,Genomics,0.82927585,TRUE,31.85714286,0.453336632,,,0,0.403234768,,,0.4282857
3080,Digital Health Tools for Managing Noncommunicable Diseases During and After the COVID-19 Pandemic: Perspectives of Patients and Caregivers.,J Med Internet Res,33464206,1/20/21,pubmed,0,13,digital health,0.001272715,0.00127267,0.001272688,0.541268479,0.193110261,0.261803187,Epidemiology,0.9740379,TRUE,2.5,0.027459954,,,0,0.403234768,,,0.215347361
3081,COVID-19 Pandemic Planning: Simulation Models to Predict Biochemistry Test Capacity for Patient Surges.,J Appl Lab Med,33463684,1/20/21,pubmed,0,6,simulation model,0.001653126,0.170655885,0.001653123,0.400192198,0.001653012,0.424192657,Clinics,0.5955097,TRUE,65.5,0.737646113,21.16666667,0.493310142,0,0.403234768,,,0.544730341
3082,Exclusion of bacterial co-infection in COVID-19 using baseline inflammatory markers and their response to antibiotics.,J Antimicrob Chemother,33463683,1/20/21,pubmed,0,11,logistic regression,0.104129212,0.001141367,0.101631463,0.001141369,0.001141345,0.790815244,Clinics,0.4002943,FALSE,2.363636364,0.024862391,,,0,0.403234768,,,0.214048579
3083,The Role of Justicia Adhatoda as prophylaxis for COVID-19 - Analysis based on docking study.,Infect Disord Drug Targets,33463480,1/20/21,pubmed,0,6,in silico,0.691886081,0.001565361,0.001565351,0.001565385,0.301852417,0.001565403,Drug discovery,0.88151467,TRUE,9.833333333,0.147628177,,,0,0.403234768,,,0.275431472
3084,Variable Clinical Manifestations of COVID-19: Viral and Human Genomes Talk.,Iran J Allergy Asthma Immunol,33463113,1/20/21,pubmed,0,5,genomes,0.721170783,0.164064775,0.032962055,0.001098895,0.001098891,0.079604602,Drug discovery,0.7439018,TRUE,19.8,0.293957573,6,0.280037463,0,0.403234768,,,0.325743268
3085,Using COVID-19 as a teaching tool in a time of remote learning: A workflow for bioinformatic approaches to identifying candidates for therapeutic and vaccine development.,Biochem Mol Biol Educ,33463080,1/20/21,pubmed,0,5,"computational, bioinformatic",0.138597092,0.462717843,0.001593587,0.223728816,0.17176915,0.001593513,Genomics,0.89352584,TRUE,8.6,0.127280599,7.8,0.313286058,0,0.403234768,,,0.281267142
3086,Analyzing biological models and data sets using Jupyter notebooks as an alternate to laboratory-based exercises during COVID-19.,Biochem Mol Biol Educ,33462960,1/20/21,pubmed,0,1,"computational, transcriptom",0.175208532,0.082405996,0.157750334,0.316824952,0.265319555,0.00249063,Epidemiology,0.35777605,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
3087,"Age-associated difference in circulating ACE2, the gateway for SARS-COV-2, in humans: results from the InCHIANTI study.",Geroscience,33462706,1/20/21,pubmed,0,6,proteom,0.138316238,0.095735587,0.001751189,0.001751271,0.138727506,0.62371821,Clinics,0.7576922,TRUE,318.6666667,0.991774383,,,0,0.403234768,,,0.697504575
3088,Evaluation of SARS-CoV-2 transmission mitigation strategies on a university campus using an agent-based network model.,Clin Infect Dis,33462589,1/20/21,pubmed,0,5,network model,0.002296665,0.080803783,0.075588995,0.836717379,0.002296637,0.002296542,Epidemiology,0.27026504,FALSE,67.4,0.749706228,72.8,0.761038266,3,0.667819001,,,0.726187832
3089,A survey of psychological distress among Bangladeshi people during the COVID-19 pandemic.,Clin Epidemiol Glob Health,33462563,1/20/21,pubmed,0,3,logistic regression,0.001126804,0.001126865,0.001126788,0.001126921,0.99436578,0.001126842,Healthcare,0.8487606,TRUE,14.33333333,0.216772837,0.333333333,0.073187048,1,0.537564047,,,0.275841311
3090,Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?,Results Phys,33462560,1/20/21,pubmed,0,8,"deep learning, artificial intelligence, correlation analysis, lstm, dataset",0.025273814,0.000977452,0.353032798,0.603521317,0.00097748,0.01621714,Epidemiology,0.83436596,TRUE,10.625,0.158265817,2.125,0.165306396,0,0.403234768,,,0.242268993
3091,Advancing automatic guidance in virtual science inquiry: from ease of use to personalization.,Educ Technol Res Dev,33462532,1/20/21,pubmed,0,1,machine learning,0.001786591,0.001786537,0.127382337,0.501035123,0.366222882,0.001786531,Epidemiology,0.5560083,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
3092,Publisher Correction: Genomic epidemiology reveals multiple introductions of SARS-CoV-2 from mainland Europe into Scotland.,Nat Microbiol,33462438,1/20/21,pubmed,0,52,genomic epidemiology,0.01952929,0.902352696,0.019529286,0.019529802,0.019529448,0.019529478,Genomics,0.6666157,TRUE,40.21153846,0.541220855,,,0,0.403234768,,,0.472227811
3093,Mathematical model of COVID-19 intervention scenarios for São Paulo-Brazil.,Nat Commun,33462211,1/20/21,pubmed,0,12,mathematical model,0.001717188,0.027447119,0.001717214,0.965684019,0.001717266,0.001717194,Epidemiology,0.54236287,TRUE,0.5,0.007668996,,,0,0.403234768,,,0.205451882
3094,Factors associated with adherence to COVID-19 prevention measures in the Democratic Republic of the Congo (DRC): results of an online survey.,BMJ Open,33462101,1/20/21,pubmed,0,10,logistic regression,0.001291207,0.001291226,0.001291247,0.001291278,0.99354381,0.001291231,Healthcare,0.7595737,TRUE,45.9,0.595151215,,,0,0.403234768,,,0.499192991
3095,0,BMJ Case Rep,33462030,1/20/21,pubmed,0,4,"sequencing, whole genome",0.002720246,0.320698519,0.074803569,0.002720333,0.208041889,0.391015443,Clinics,0.82012403,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
3096,Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy-TOPSIS methods.,Artif Intell Med,33461683,1/20/21,pubmed,0,4,dataset,0.000846544,0.000846543,0.267007089,0.421226292,0.000846549,0.309226983,Epidemiology,0.93882376,TRUE,13.5,0.205393036,2.5,0.180826866,1,0.537564047,,,0.307927983
3097,Freshwater monitoring by nanopore sequencing.,Elife,33461660,1/20/21,pubmed,0,12,"bioinformatic, sequencing, metagenom, microbiom",0.0006039,0.462706169,0.180707918,0.318298601,0.03707951,0.000603902,Genomics,0.4224964,FALSE,3.916666667,0.04805492,,,0,0.403234768,,,0.225644844
3098,Ligand-based approach for predicting drug targets and for virtual screening against COVID-19.,Brief Bioinform,33461215,1/19/21,pubmed,0,9,virtual screening,0.939556207,0.001203476,0.055629962,0.001203484,0.00120345,0.00120342,Drug discovery,0.4916537,FALSE,36.55555556,0.503246954,,,0,0.403234768,,,0.453240861
3099,"Multifunctional angiotensin converting enzyme 2, the SARS-CoV-2 entry receptor, and critical appraisal of its role in acute lung injury.",Biomed Pharmacother,33461019,1/19/21,pubmed,0,2,microbiom,0.670097642,0.050477599,0.001438132,0.001438154,0.001438149,0.275110324,Drug discovery,0.9572368,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
3100,TSP-based PCR for rapid identification of L and S type strains of SARS-CoV-2.,Indian J Med Microbiol,33460733,1/19/21,pubmed,0,2,"sequencing, whole genome, genome sequences",0.001059348,0.973357264,0.022405259,0.001059355,0.001059384,0.001059391,Genomics,0.48417595,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
3101,"Interpreting SARS-CoV-2 seroprevalence, deaths, and fatality rate - Making a case for standardized reporting to improve communication.",Math Biosci,33460673,1/19/21,pubmed,0,2,mathematical model,0.001717216,0.001717209,0.001717236,0.991413886,0.001717243,0.00171721,Epidemiology,0.1920698,FALSE,55.5,0.671346404,34.5,0.602221033,1,0.537564047,,,0.603710495
3102,"SARS-CoV-2, the pandemic coronavirus: Molecular and structural insights.",J Basic Microbiol,33460172,1/19/21,pubmed,0,5,bioinformatic,0.346144633,0.648764736,0.001272685,0.001272667,0.001272642,0.001272637,Genomics,0.6089092,TRUE,4.8,0.065248315,,,0,0.403234768,,,0.234241541
3103,Automated processing of social media content for radiologists: applied deep learning to radiological content on twitter during COVID-19 pandemic.,Emerg Radiol,33459907,1/19/21,pubmed,0,3,"deep learning, neural network, classifier, dataset",0.00108533,0.001085344,0.747675146,0.247983515,0.001085338,0.001085327,Imaging,0.76162237,TRUE,51.66666667,0.644195683,18.66666667,0.466818303,0,0.403234768,,,0.504749585
3104,Addressing the potential role of curcumin in the prevention of COVID-19 by targeting the Nsp9 replicase protein through molecular docking.,Arch Microbiol,33459817,1/19/21,pubmed,0,3,in silico,0.738265246,0.003607404,0.003607413,0.247305068,0.003607638,0.003607231,Drug discovery,0.8833921,TRUE,808,0.998886759,401,0.971300508,0,0.403234768,,,0.791140678
3105,COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader.,J Xray Sci Technol,33459685,1/19/21,pubmed,0,6,"neural network, network model, transfer learning, dataset",0.000956355,0.000956362,0.995218348,0.000956314,0.000956305,0.000956316,Imaging,0.58169097,TRUE,10.5,0.157338116,0,0.055525823,0,0.403234768,,,0.205366236
3106,Natural agents modulating ACE-2: A review of compounds with potential against SARS-CoV-2 infections.,Curr Pharm Des,33459225,1/19/21,pubmed,0,6,in silico,0.937869972,0.001220082,0.001220007,0.001220081,0.001220026,0.057249832,Drug discovery,0.93621564,TRUE,18.83333333,0.281588224,2.166666667,0.166845063,0,0.403234768,,,0.283889352
3107,0,J Biomol Struct Dyn,33459192,1/19/21,pubmed,0,5,in silico,0.993143597,0.001371259,0.001371263,0.001371338,0.001371274,0.001371269,Drug discovery,0.77935624,TRUE,9.2,0.136990537,0.6,0.09011239,0,0.403234768,,,0.210112565
3108,"Identification of 1,2,3-triazole-phthalimide derivatives as potential drugs against COVID-19: a virtual screening, docking and molecular dynamic study.",J Biomol Struct Dyn,33459182,1/19/21,pubmed,0,8,"virtual screening, in silico",0.957885268,0.001272629,0.001272688,0.001272662,0.001272657,0.037024096,Drug discovery,0.9806013,TRUE,21,0.312016822,8.125,0.321046294,0,0.403234768,,,0.345432628
3109,0,J Biomol Struct Dyn,33459174,1/19/21,pubmed,0,9,molecular dynamics simulation,0.994945226,0.001010935,0.001010951,0.00101094,0.001010973,0.001010975,Drug discovery,0.980271,TRUE,24.44444444,0.360752056,,,1,0.537564047,,,0.449158052
3110,Estimation of COVID-19 epidemic curves using genetic programming algorithm.,Health Informatics J,33459107,1/19/21,pubmed,0,5,mathematical model,0.002080561,0.042322232,0.144947098,0.806488839,0.002080608,0.002080662,Epidemiology,0.7284835,TRUE,31.4,0.44882182,0.4,0.075796093,1,0.537564047,,,0.354060653
3111,Predictors of clinical deterioration in non-severe patients with COVID-19: a retrospective cohort study.,Curr Med Res Opin,33459077,1/19/21,pubmed,0,12,"logistic regression, prediction model",0.092089103,0.001291229,0.001291279,0.001291236,0.001291234,0.902745919,Clinics,0.9518305,TRUE,34.75,0.484507391,,,0,0.403234768,,,0.443871079
3112,"World-wide tracking of major SARS-CoV-2 genome haplotypes in sequences of June 1 to November 15, 2020 and discovery of rapid expansion of a new haplotype.",J Med Virol,33458832,1/19/21,pubmed,0,3,genome haplotypes,0.001987142,0.99006406,0.001987144,0.001987202,0.001987176,0.001987276,Genomics,0.2865824,FALSE,43.33333333,0.571154679,43,0.649785925,0,0.403234768,,,0.54139179
3113,Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities.,Brief Bioinform,33458761,1/19/21,pubmed,0,5,bioinformatic,0.658800239,0.000815402,0.000815322,0.000815327,0.000815342,0.337938368,Drug discovery,0.97199774,TRUE,18.8,0.281278991,4.8,0.249331014,1,0.537564047,,,0.356058017
3114,Cross-regional variations of Covid-19 mortality in Italy: an ecological study.,J Public Health (Oxf),33458758,1/19/21,pubmed,0,4,predictive model,0.001415114,0.001415153,0.00141512,0.60596764,0.149517117,0.240269857,Epidemiology,0.6788025,TRUE,73,0.778464964,26.75,0.543617875,0,0.403234768,,,0.575105869
3115,Serial co-expression analysis of host factors from SARS-CoV viruses highly converges with former high-throughput screenings and proposes key regulators.,Brief Bioinform,33458747,1/19/21,pubmed,0,7,transcriptom,0.77750599,0.001392967,0.03741851,0.142992869,0.001392886,0.039296777,Drug discovery,0.37035245,FALSE,24.14285714,0.356608325,9.285714286,0.340714477,0,0.403234768,,,0.366852523
3116,Robust Detection of COVID-19 in Cough Sounds: Using Recurrence Dynamics and Variable Markov Model.,SN Comput Sci,33458700,1/19/21,pubmed,0,3,dataset,0.001987229,0.001987124,0.821508951,0.170542144,0.001987288,0.001987265,Epidemiology,0.6299397,TRUE,3,0.037293586,0,0.055525823,2,0.618927094,,,0.237248835
3117,On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques.,Infect Dis Model,33458453,1/19/21,pubmed,0,4,"bayes, artificial intelligence, bayesian model, network model, dataset",0.00093608,0.000936098,0.099542515,0.815242297,0.000936111,0.0824069,Epidemiology,0.08680403,FALSE,2.25,0.023439916,,,0,0.403234768,,,0.213337342
3118,0,Heliyon,33458435,1/19/21,pubmed,0,4,"molecular dynamics simulation, in silico",0.865695137,0.101166297,0.001622757,0.001622856,0.001622818,0.028270135,Drug discovery,0.85374737,TRUE,7.75,0.112808461,0.75,0.099411292,0,0.403234768,,,0.205151507
3119,Assessing the interplay between travel patterns and SARS-CoV-2 outbreak in realistic urban setting.,Appl Netw Sci,33457497,1/19/21,pubmed,0,6,dataset,0.00115626,0.001156263,0.001156309,0.946800414,0.048574488,0.001156265,Epidemiology,0.21276069,FALSE,22.5,0.333539489,9.666666667,0.348274017,0,0.403234768,,,0.361682758
3120,0,Inform Med Unlocked,33457495,1/19/21,pubmed,0,3,"virtual screening, molecular dynamics simulation, in-silico",0.975189948,0.001272636,0.001272744,0.019719266,0.001272734,0.001272674,Drug discovery,0.5332429,TRUE,102.3333333,0.874203723,56.33333333,0.708255285,0,0.403234768,,,0.661897925
3121,0,Futur J Pharm Sci,33457429,1/19/21,pubmed,0,6,in silico,0.916501319,0.001461956,0.001461887,0.077650885,0.001461955,0.001461998,Drug discovery,0.85479856,TRUE,4.333333333,0.057950399,0.333333333,0.073187048,0,0.403234768,,,0.178124072
3122,Deep Learning applications for COVID-19.,J Big Data,33457181,1/19/21,pubmed,0,3,"deep learning, image analysis, information retrieval",0.07139974,0.00120347,0.743577604,0.12467744,0.057938355,0.001203392,Imaging,0.06572232,FALSE,315,0.991341456,409,0.972103291,2,0.618927094,,,0.860790614
3123,Factual insights of the allosteric inhibition mechanism of SARS-CoV-2 main protease by quercetin: an in silico analysis.,3 Biotech,33457176,1/19/21,pubmed,0,2,in silico,0.995058206,0.000988375,0.000988362,0.000988371,0.000988344,0.000988343,Drug discovery,0.9285157,TRUE,20,0.298163152,8.5,0.329141022,0,0.403234768,,,0.34351298
3124,Smart technologies driven approaches to tackle COVID-19 pandemic: a review.,3 Biotech,33457174,1/19/21,pubmed,0,6,artificial intelligence,0.001987185,0.001987149,0.259021775,0.733029654,0.001987153,0.001987084,Epidemiology,0.85296863,TRUE,53,0.654400396,6.666666667,0.290607439,2,0.618927094,,,0.521311643
3125,Designing a next generation multi-epitope based peptide vaccine candidate against SARS-CoV-2 using computational approaches.,3 Biotech,33457172,1/19/21,pubmed,0,3,"computational, in silico",0.798051017,0.000880272,0.000880237,0.129833142,0.040026365,0.030328967,Drug discovery,0.42184767,FALSE,11,0.167171748,2.333333333,0.173401124,1,0.537564047,,,0.292712306
3126,0,3 Biotech,33457171,1/19/21,pubmed,0,8,in silico,0.920823713,0.001203542,0.001203543,0.001203553,0.07436212,0.00120353,Drug discovery,0.90118945,TRUE,18,0.271569052,5.625,0.26839711,1,0.537564047,,,0.359176736
3127,Recent biotechnological advances as potential intervention strategies against COVID-19.,3 Biotech,33457170,1/19/21,pubmed,0,5,in silico,0.520103604,0.001823402,0.001823479,0.318996391,0.001823405,0.155429718,Drug discovery,0.7901656,TRUE,14,0.213494959,4.2,0.234211935,1,0.537564047,,,0.328423647
3128,A mathematical model for COVID-19 pandemic-SIIR model: Effects of asymptomatic individuals.,J Gen Fam Med,33457150,1/19/21,pubmed,0,2,mathematical model,0.002898603,0.213602972,0.002898746,0.774802985,0.002898312,0.002898382,Epidemiology,0.2085712,FALSE,58,0.689714887,2.5,0.180826866,2,0.618927094,,,0.496489616
3129,Decoding Covid-19 with the SARS-CoV-2 Genome.,Curr Genet Med Rep,33457109,1/19/21,pubmed,0,5,genome sequences,0.230215008,0.764836547,0.001237134,0.001237082,0.001237115,0.001237113,Genomics,0.39643544,FALSE,38.8,0.527119797,40.4,0.636138614,1,0.537564047,,,0.566940819
3130,A large-scale transcriptional study reveals inhibition of COVID-19 related cytokine storm by traditional chinese medicines.,Sci Bull (Beijing),33457042,1/19/21,pubmed,0,16,bioinformatic,0.839757887,0.00333534,0.003335496,0.146900679,0.00333536,0.003335238,Drug discovery,0.90159744,TRUE,18.3125,0.273981075,17.25,0.453104094,0,0.403234768,,,0.376773312
3131,"SARS-CoV-2 meta-interactome suggests disease-specific, autoimmune pathophysiologies and therapeutic targets.",F1000Res,33456761,1/19/21,pubmed,0,12,interactom,0.788384372,0.052091707,0.001272653,0.001272671,0.001272676,0.15570592,Drug discovery,0.35408175,FALSE,40.33333333,0.542890717,85.58333333,0.791544019,0,0.403234768,,,0.579223168
3132,Unraveling the molecular basis of host cell receptor usage in SARS-CoV-2 and other human pathogenic β-CoVs.,Comput Struct Biotechnol J,33456724,1/19/21,pubmed,0,4,in silico,0.507262943,0.487923313,0.001203443,0.001203465,0.00120344,0.001203396,Drug discovery,0.69812083,TRUE,17.25,0.260189251,55.5,0.704575863,0,0.403234768,,,0.455999961
3133,The Global Impact of COVID-19 on Craniomaxillofacial Surgeons.,Craniomaxillofac Trauma Reconstr,33456681,1/19/21,pubmed,0,11,logistic regression,0.001291209,0.001291305,0.001291265,0.151885191,0.842949758,0.001291272,Healthcare,0.59552586,TRUE,59.45454545,0.698991898,63.45454545,0.73347605,4,0.707574542,,,0.713347497
3134,Principal Component Analysis Applications in COVID-19 Genome Sequence Studies.,Cognit Comput,33456620,1/19/21,pubmed,0,2,"genome sequences, genomes",0.001861819,0.751070062,0.162015971,0.081328614,0.001861734,0.0018618,Genomics,0.45319262,FALSE,114,0.897767333,19.5,0.475983409,0,0.403234768,,,0.592328503
3135,Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks.,Pers Ubiquitous Comput,33456433,1/19/21,pubmed,0,7,"deep learning, neural network, forecasting model, lstm",0.001861717,0.001861727,0.416422312,0.576130788,0.001861713,0.001861741,Epidemiology,0.587499,TRUE,25.57142857,0.374853114,0.714285714,0.096936045,0,0.403234768,,,0.291674642
3136,A quantitative exploration of symptoms in COVID-19 patients: an observational cohort study.,Int J Med Sci,33456367,1/19/21,pubmed,0,9,logistic regression,0.00088023,0.000880274,0.048929475,0.101935027,0.076222022,0.771152971,Clinics,0.9951021,TRUE,61.77777778,0.71377327,24.22222222,0.523146909,0,0.403234768,,,0.546718316
3137,Sleep quality and mental health of medical workers during the coronavirus disease 2019 pandemic.,Sleep Biol Rhythms,33456342,1/19/21,pubmed,0,6,logistic regression,0.001461941,0.001461853,0.001461858,0.001461874,0.992690519,0.001461955,Healthcare,0.95608807,TRUE,15.16666667,0.228709258,1.666666667,0.145036125,1,0.537564047,,,0.30376981
3138,Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction.,Soft comput,33456340,1/19/21,pubmed,0,4,neural network,0.001593517,0.001593497,0.692613653,0.301012321,0.001593499,0.001593512,Epidemiology,0.41176882,FALSE,408,0.995918115,115.75,0.845397378,0,0.403234768,,,0.74818342
3139,Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research.,Eur J Hum Genet,33456056,1/19/21,pubmed,0,137,"sequencing, exom, genomes",0.044372691,0.334870824,0.001415253,0.001415208,0.001415126,0.616510898,Clinics,0.39711592,FALSE,46.77777778,0.603438679,59.66666667,0.71936045,5,0.739490092,,,0.68742974
3140,Levels of burn-out among healthcare workers during the COVID-19 pandemic and their associated factors: a cross-sectional study in a tertiary hospital of a highly burdened area of north-east Italy.,BMJ Open,33455940,1/19/21,pubmed,0,8,logistic regression,0.001310342,0.001310448,0.001310363,0.001310342,0.84059392,0.154164585,Healthcare,0.98796886,TRUE,162.25,0.952934628,140.625,0.874966551,1,0.537564047,,,0.788488409
3141,Biological agents for rheumatic diseases in the outbreak of COVID-19: friend or foe?,RMD Open,33455920,1/19/21,pubmed,0,7,logistic regression,0.17393467,0.000544446,0.008200322,0.000544457,0.000544469,0.816231636,Clinics,0.8136945,TRUE,5.428571429,0.074958253,,,2,0.618927094,,,0.346942674
3142,Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study.,JMIR Med Inform,33455900,1/19/21,pubmed,0,11,"deep learning, neural network",0.001330074,0.001330053,0.688358141,0.001330155,0.001330068,0.306321508,Imaging,0.38540184,FALSE,55.27272727,0.669676542,22.72727273,0.509098207,0,0.403234768,,,0.527336505
3143,Palliative Care and COVID-19 Pandemic: Retrospective Study of Factors Associated With Infection and Death at an Oncological Palliative Care Reference Center.,Am J Hosp Palliat Care,33455418,1/19/21,pubmed,0,10,logistic regression,0.001291243,0.001291238,0.061560132,0.001291234,0.001291255,0.933274898,Clinics,0.8405035,TRUE,3.9,0.047931226,0.1,0.056328606,0,0.403234768,,,0.169164867
3144,Computational screening for potential drug candidates against the SARS-CoV-2 main protease.,F1000Res,33447372,1/19/21,pubmed,0,14,"computational, dataset",0.991734872,0.001653064,0.001653046,0.001653015,0.001653012,0.00165299,Drug discovery,0.9216696,TRUE,35.57142857,0.492918548,15.14285714,0.426545357,1,0.537564047,,,0.485675984
3145,[The clinical application of blood routine and liver and kidney function test in COVID-19 disease prediction].,Zhonghua Yu Fang Yi Xue Za Zhi,33455138,1/18/21,pubmed,0,8,correlation analysis,0.000662695,0.063877351,0.27981631,0.000662697,0.000662701,0.654318246,Clinics,0.9519905,TRUE,9,0.135320675,4.375,0.238426545,0,0.403234768,,,0.258993996
3146,Risk factors for COVID-19 and rheumatic disease flare in a US cohort of Latino patients.,Arthritis Rheumatol,33455077,1/18/21,pubmed,0,10,logistic regression,0.002638999,0.002639126,0.09271196,0.002639062,0.002639109,0.896731744,Clinics,0.41667032,FALSE,26.3,0.385305214,22.4,0.506087771,0,0.403234768,,,0.431542584
3147,Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images.,Comput Methods Programs Biomed,33454574,1/18/21,pubmed,0,4,"neural network, optimization model, dataset",0.001310351,0.001310462,0.993448061,0.001310388,0.001310385,0.001310353,Imaging,0.61848295,TRUE,35,0.488032655,,,0,0.403234768,,,0.445633711
3148,Factors associated with drinking behaviour during COVID-19 social distancing and lockdown among adults in the UK.,Drug Alcohol Depend,33454159,1/18/21,pubmed,0,6,logistic regression,0.001461863,0.00146189,0.001461837,0.001461879,0.992690557,0.001461974,Healthcare,0.9881855,TRUE,114.1666667,0.897952873,93.5,0.809673535,3,0.667819001,,,0.791815136
3149,Black-white disparities in 2009 H1N1 vaccination among adults in the United States: A cautionary tale for the COVID-19 pandemic.,Vaccine,33454136,1/18/21,pubmed,0,4,logistic regression,0.000786363,0.00078635,0.000786376,0.036139872,0.960714662,0.000786378,Healthcare,0.6894196,TRUE,18,0.271569052,30.25,0.57171528,2,0.618927094,,,0.487403809
3150,Clinically applicable approach for predicting mechanical ventilation in patients with COVID-19.,Br J Anaesth,33454051,1/18/21,pubmed,0,7,machine learning,0.034320865,0.001085363,0.241730932,0.052072192,0.001085361,0.669705288,Clinics,0.6301701,TRUE,96.14285714,0.859298658,80.42857143,0.780037463,0,0.403234768,,,0.680856963
3151,Impact of the COVID-19 pandemic on the detection and management of colorectal cancer in England: a population-based study.,Lancet Gastroenterol Hepatol,33453763,1/18/21,pubmed,0,21,dataset,0.000898164,0.000898131,0.00089816,0.560547963,0.000898131,0.435859453,Epidemiology,0.86164373,TRUE,56.28571429,0.676974457,163.0952381,0.894634734,4,0.707574542,,,0.759727911
3152,Physical Activity and Prevention of Depressive Symptoms in the Spanish Population during Confinement due to COVID-19.,Psicothema,33453743,1/18/21,pubmed,0,4,logistic regression,0.00165304,0.001653047,0.001653079,0.366462271,0.626925475,0.001653088,Healthcare,0.977561,TRUE,97,0.861586987,34.75,0.603559005,0,0.403234768,,,0.622793587
3153,Enhanced surveillance of COVID-19 in Scotland: population-based seroprevalence surveillance for SARS-CoV-2 during the first wave of the epidemic.,Public Health,33453689,1/17/21,pubmed,0,13,bayes,0.001330026,0.371594018,0.001330064,0.405741614,0.001330109,0.218674168,Epidemiology,0.20806491,FALSE,100.4615385,0.869750758,56,0.706850415,0,0.403234768,,,0.659945313
3154,Bisindolylmaleimide IX: A novel anti-SARS-CoV2 agent targeting viral main protease 3CLpro demonstrated by virtual screening pipeline and in-vitro validation assays.,Methods,33453392,1/17/21,pubmed,0,18,"virtual screening, molecular dynamics simulation, data mining, proteom",0.994505883,0.001098864,0.001098825,0.001098818,0.001098799,0.001098812,Drug discovery,0.8945632,TRUE,30.33333333,0.436266931,,,0,0.403234768,,,0.419750849
3155,COVID-19 outbreaks in care homes during the first wave: are Care Quality Commission ratings a good predictor of at-risk homes?,J Hosp Infect,33453350,1/17/21,pubmed,0,6,logistic regression,0.002080639,0.002080708,0.002080637,0.002080675,0.989596515,0.002080826,Healthcare,0.15455738,FALSE,10,0.15214299,2.166666667,0.166845063,0,0.403234768,,,0.24074094
3156,The proteomic characteristics of airway mucus from critical ill COVID-19 patients.,Life Sci,33453245,1/17/21,pubmed,0,13,proteom,0.873508865,0.002490569,0.002490564,0.002490559,0.002490513,0.116528929,Drug discovery,0.82053006,TRUE,69.30769231,0.760220174,50.30769231,0.682432432,0,0.403234768,,,0.615295791
3157,"Assessment of thirty-day readmission rate, timing, causes and predictors after hospitalization with COVID-19.",J Intern Med,33452824,1/17/21,pubmed,0,10,logistic regression,0.001786502,0.001786508,0.001786518,0.001786561,0.001786529,0.991067382,Clinics,0.98469174,TRUE,15.3,0.23019358,7.4,0.305392026,2,0.618927094,,,0.384837567
3158,Admission avoidance in acute epistaxis: A prospective national audit during the initial peak of the COVID-19 pandemic.,Clin Otolaryngol,33452756,1/17/21,pubmed,0,177,logistic regression,0.001511837,0.001511861,0.001511818,0.001511955,0.286192609,0.70775992,Clinics,0.7600964,TRUE,14,0.213494959,2,0.164302917,1,0.537564047,,,0.305120641
3159,Exploring the treatment of COVID-19 with Yinqiao powder based on network pharmacology.,Phytother Res,33452734,1/17/21,pubmed,0,10,genomes,0.937326573,0.030760404,0.001684526,0.001684535,0.001684493,0.026859468,Drug discovery,0.98412347,TRUE,27,0.3960047,30,0.570176612,0,0.403234768,,,0.456472027
3160,Increased vulnerability to COVID-19 in chronic kidney disease.,J Intern Med,33452733,1/17/21,pubmed,0,8,logistic regression,0.001861684,0.001861746,0.001861795,0.054611874,0.001861745,0.937941155,Clinics,0.97136045,TRUE,244,0.983301379,250.25,0.938118812,2,0.618927094,,,0.846782428
3161,Association of ABO blood group type with cardiovascular events in COVID-19.,J Thromb Thrombolysis,33452583,1/17/21,pubmed,0,4,logistic regression,0.001291203,0.001291225,0.001291208,0.001291235,0.001291236,0.993543894,Clinics,0.6702367,TRUE,267,0.987197724,529.25,0.981067701,1,0.537564047,,,0.835276491
3162,SARS-CoV-2 infection susceptibility influenced by ACE2 genetic polymorphisms: insights from Tehran Cardio-Metabolic Genetic Study.,Sci Rep,33452303,1/17/21,pubmed,0,8,"bioinformatic, sequencing, whole-genome",0.537098965,0.437987019,0.001461861,0.001461874,0.020528385,0.001461896,Drug discovery,0.83002406,TRUE,195.75,0.971488651,165.375,0.896173401,0,0.403234768,,,0.756965607
3163,"On the prediction of isolation, release, and decease states for COVID-19 patients: A case study in South Korea.",ISA Trans,33451801,1/17/21,pubmed,0,4,"machine learning, logistic regression, dataset",0.001861719,0.001861913,0.584176313,0.408376444,0.001861743,0.001861867,Epidemiology,0.38423568,FALSE,6.5,0.093512277,1,0.122023013,0,0.403234768,,,0.206256686
3164,Should all patients with hypertension be worried about developing severe coronavirus disease 2019 (COVID-19)?,Clin Hypertens,33451360,1/17/21,pubmed,0,9,logistic regression,0.001203405,0.001203417,0.001203427,0.001203444,0.001203438,0.99398287,Clinics,0.95630956,TRUE,15.44444444,0.231987136,3,0.199424672,0,0.403234768,,,0.278215525
3165,Socioeconomic disparity and the risk of contracting COVID-19 in South Korea: an NHIS-COVID-19 database cohort study.,BMC Public Health,33451306,1/17/21,pubmed,0,3,logistic regression,0.001717204,0.001717208,0.001717236,0.001717281,0.484745573,0.508385498,Clinics,0.70841485,TRUE,104.3333333,0.878223762,20,0.481000803,0,0.403234768,,,0.587486444
3166,Age is not the only risk factor in COVID-19: the role of comorbidities and of long staying in residential care homes.,BMC Geriatr,33451296,1/17/21,pubmed,0,13,logistic regression,0.00102267,0.001022696,0.057500495,0.001022715,0.001022683,0.938408741,Clinics,0.81615376,TRUE,23.69230769,0.350114416,9.769230769,0.349812684,1,0.537564047,,,0.412497049
3167,Pregnant women's well-being and worry during the COVID-19 pandemic: a cross-sectional study.,BMC Pregnancy Childbirth,33451292,1/17/21,pubmed,0,3,logistic regression,0.001219988,0.001219998,0.001219997,0.001220036,0.993899905,0.001220076,Healthcare,0.9033197,TRUE,21.66666667,0.320737213,5,0.257024351,0,0.403234768,,,0.326998777
3168,Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil.,Sensors (Basel),33451092,1/17/21,pubmed,0,4,"machine learning, computational, mathematical model",0.001203473,0.025968314,0.086801086,0.883620164,0.001203479,0.001203484,Epidemiology,0.40304804,FALSE,51.5,0.643267982,19.25,0.47310677,0,0.403234768,,,0.506536507
3169,Host genomics of COVID-19: Evidence point towards Alpha 1 antitrypsin deficiency as a putative risk factor for higher mortality rate.,Med Hypotheses,33450625,1/16/21,pubmed,0,2,"in silico, dataset",0.736528745,0.002720478,0.002720273,0.002720319,0.002720326,0.252589859,Drug discovery,0.32782423,FALSE,13.5,0.205393036,4,0.231469093,0,0.403234768,,,0.280032299
3170,Sequence analysis of Indian SARS-CoV-2 isolates shows a stronger interaction of mutant receptor-binding domain with ACE2.,Int J Infect Dis,33450373,1/16/21,pubmed,0,17,"sequencing, whole-genome",0.447240079,0.521796392,0.001098817,0.001098823,0.001098792,0.027667096,Genomics,0.6578482,TRUE,22.76470588,0.336631826,15.35294118,0.428485416,0,0.403234768,,,0.38945067
3171,Post-acute COVID-19 syndrome. Incidence and risk factors: A Mediterranean cohort study.,J Infect,33450302,1/16/21,pubmed,0,15,logistic regression,0.001943486,0.001943581,0.087658745,0.00194354,0.001943561,0.904567087,Clinics,0.92925507,TRUE,53.66666667,0.658544128,18.2,0.462938186,6,0.764429903,,,0.628637405
3172,rfaRm: An R client-side interface to facilitate the analysis of the Rfam database of RNA families.,PLoS One,33449976,1/16/21,pubmed,0,4,sequence alignment,0.002898527,0.985507473,0.002898515,0.002898548,0.002898653,0.002898285,Genomics,0.19791391,FALSE,33.5,0.471890655,20.5,0.486218892,0,0.403234768,,,0.453781438
3173,Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing.,JMIR Med Inform,33449908,1/16/21,pubmed,0,13,"supervised learning, information retrieval, logistic regression, text mining",0.000838513,0.000838514,0.460882394,0.000838538,0.044711981,0.491890061,Clinics,0.51743686,TRUE,22.46153846,0.331684087,39.30769231,0.629314959,0,0.403234768,,,0.454744604
3174,Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set.,J Med Internet Res,33449904,1/16/21,pubmed,0,6,"neural network, classifier",0.043364272,0.001085365,0.443256893,0.510122733,0.001085408,0.00108533,Epidemiology,0.008763343,FALSE,16.83333333,0.253509803,19.5,0.475983409,0,0.403234768,,,0.377575993
3175,Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.,IEEE J Biomed Health Inform,33449891,1/16/21,pubmed,0,2,"machine learning, computational",0.001943588,0.001943551,0.428077593,0.564148306,0.001943495,0.001943467,Epidemiology,0.8432496,TRUE,9.5,0.143051518,0,0.055525823,0,0.403234768,,,0.200604036
3176,Distant Domain Transfer Learning for Medical Imaging.,IEEE J Biomed Health Inform,33449887,1/16/21,pubmed,0,5,"deep learning, image processing, transfer learning",0.001072211,0.00107218,0.977289392,0.018421848,0.001072208,0.001072162,Imaging,0.39212632,FALSE,36.4,0.501329705,3.4,0.208589778,1,0.537564047,,,0.415827843
3177,Prioritizing Delivery of Cancer Treatment During a COVID-19 Lockdown: The Experience of a Clinical Oncology Service in India.,JCO Glob Oncol,33449800,1/16/21,pubmed,0,21,logistic regression,0.001565336,0.001565326,0.001565313,0.361594426,0.331583551,0.302126047,Epidemiology,0.92379427,TRUE,19.80952381,0.29401942,4.285714286,0.236018196,0,0.403234768,,,0.311090795
3178,Dynamics and electrostatics define an allosteric druggable site within the receptor-binding domain of SARS-CoV-2 spike protein.,FEBS Lett,33449359,1/16/21,pubmed,0,3,molecular dynamics simulation,0.988517198,0.002296577,0.002296525,0.002296609,0.002296581,0.00229651,Drug discovery,0.7138908,TRUE,29.33333333,0.423093574,68.66666667,0.749397913,0,0.403234768,,,0.525242085
3179,"An investigation of risk factors of in-hospital death due to COVID-19: a case-control study in Rasht, Iran.",Ir J Med Sci,33449333,1/16/21,pubmed,0,8,logistic regression,0.001486431,0.001486501,0.001486555,0.036758339,0.001486461,0.957295712,Clinics,0.89999866,TRUE,15.25,0.229884347,2,0.164302917,0,0.403234768,,,0.265807344
3180,Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system.,Eur Radiol,33449185,1/16/21,pubmed,0,9,predictive model,0.000880197,0.000880198,0.442201718,0.000880225,0.000880217,0.554277446,Clinics,0.92737895,TRUE,36.11111111,0.498546602,12.22222222,0.387811078,0,0.403234768,,,0.429864149
3181,Comparison of Saliva and Nasopharyngeal Swab Nucleic Acid Amplification Testing for Detection of SARS-CoV-2: A Systematic Review and Meta-analysis.,JAMA Intern Med,33449069,1/16/21,pubmed,0,7,bayes,0.000786354,0.265600674,0.461768242,0.000786406,0.039272232,0.231786092,Genomics,0.63895273,TRUE,61.57142857,0.712907415,92.14285714,0.805927214,13,0.858880178,,,0.792571603
3182,Dynamic Network Modeling of Allosteric Interactions and Communication Pathways in the SARS-CoV-2 Spike Trimer Mutants: Differential Modulation of Conformational Landscapes and Signal Transmission via Cascades of Regulatory Switches.,J Phys Chem B,33448856,1/16/21,pubmed,0,2,network model,0.806117642,0.088382082,0.000956391,0.102631169,0.000956321,0.000956396,Drug discovery,0.88268983,TRUE,74.5,0.784464098,56.5,0.709124967,2,0.618927094,,,0.704172053
3183,The impetus to interactive learning: Whiteboarding for online dental education in COVID-19.,J Dent Educ,33448378,1/16/21,pubmed,0,4,active learning,0.02506022,0.02506022,0.320648149,0.025060539,0.579110649,0.025060222,Healthcare,0.40245536,FALSE,67.5,0.750201002,36.25,0.61272411,0,0.403234768,,,0.58871996
3184,Corrigendum to: Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19.,Brief Bioinform,33448287,1/16/21,pubmed,0,4,sequencing,0.71706996,0.241439413,0.010373057,0.010372722,0.010372411,0.010372436,Drug discovery,0.3305961,FALSE,63.25,0.722926588,14.75,0.421327268,0,0.403234768,,,0.515829541
3185,Metagenomics: preventing future pandemics.,Biotechniques,33448236,1/16/21,pubmed,0,3,metagenom,0.010372656,0.010372678,0.010372906,0.948135689,0.010373494,0.010372578,Epidemiology,0.44259503,FALSE,10.66666667,0.159131672,1.333333333,0.13252609,0,0.403234768,,,0.231630843
3186,In silico and in vitro evaluation of kaempferol as a potential inhibitor of the SARS-CoV-2 main protease (3CLpro).,Phytother Res,33448101,1/16/21,pubmed,0,10,in silico,0.920006505,0.015998462,0.015999421,0.015998571,0.015998466,0.015998575,Drug discovery,0.71950066,TRUE,32,0.455810502,,,1,0.537564047,,,0.496687274
3187,Cardiovascular medications and regulation of COVID-19 receptors expression.,Int J Cardiol Hypertens,33447763,1/16/21,pubmed,0,7,"in silico, transcriptom",0.489489287,0.001461916,0.001461855,0.001461889,0.001461895,0.504663158,Clinics,0.76447237,TRUE,39,0.530521368,32.85714286,0.591450361,4,0.707574542,,,0.609848757
3188,Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials.,J Med Imaging (Bellingham),33447646,1/16/21,pubmed,0,9,computational,0.170667184,0.003101426,0.774913232,0.045115045,0.003101498,0.003101615,Imaging,0.62064445,TRUE,102.8888889,0.875069578,60.44444444,0.72217019,0,0.403234768,,,0.666824845
3189,Seroprevalence and Correlates of SARS-CoV-2 Antibodies in Health Care Workers in Chicago.,Open Forum Infect Dis,33447642,1/16/21,pubmed,0,14,logistic regression,0.001371244,0.023258333,0.001371306,0.001371273,0.755398604,0.217229241,Healthcare,0.6956909,TRUE,108.5,0.886882306,241.9285714,0.93443939,1,0.537564047,,,0.786295248
3190,Mental wellbeing of healthcare workers working in quarantine centers during the COVID-19 pandemic in Qatar.,Qatar Med J,33447538,1/16/21,pubmed,0,8,logistic regression,0.0012546,0.001254608,0.001254624,0.001254669,0.950019445,0.044962053,Healthcare,0.9577911,TRUE,9.5,0.143051518,0.875,0.103826599,0,0.403234768,,,0.216704295
3191,Nexus Between the COVID-19 Dynamics and Environmental Pollution Indicators in South America.,Risk Manag Healthc Policy,33447110,1/16/21,pubmed,0,6,correlation analysis,0.002080579,0.002080618,0.002080571,0.758195892,0.233481699,0.002080641,Epidemiology,0.7783414,TRUE,16,0.243552477,12,0.386740701,2,0.618927094,,,0.416406757
3192,Disinfection of Stethoscope and Non-Infrared Thermometer: Practices of Physicians in Ethiopia in the Era of COVID-19.,Risk Manag Healthc Policy,33447105,1/16/21,pubmed,0,5,logistic regression,0.001310378,0.001310523,0.001310444,0.001310429,0.879871313,0.114886912,Healthcare,0.9660446,TRUE,10.8,0.161110768,1,0.122023013,0,0.403234768,,,0.228789516
3193,"The Psychological Impact of COVID-19 Outbreak on Nurses Working in the Northwest of Amhara Regional State Referral Hospitals, Northwest Ethiopia.",Psychol Res Behav Manag,33447101,1/16/21,pubmed,0,3,logistic regression,0.00077944,0.000779431,0.000779464,0.000779436,0.996102778,0.000779451,Healthcare,0.9612273,TRUE,4,0.054734368,1.666666667,0.145036125,0,0.403234768,,,0.201001754
3194,Knowledge and Attitude Towards the Current Pandemic Corona Virus Disease and Associated Factors Among Pregnant Women Attending Antenatal Care in Debre Tabor General Hospital Northwest Ethiopia: An Institutional-Based Cross-Sectional Study.,Int J Womens Health,33447093,1/16/21,pubmed,0,5,logistic regression,0.001272641,0.001272648,0.001272817,0.001272653,0.906247039,0.088662202,Healthcare,0.93659437,TRUE,2.4,0.025419012,0.2,0.061145304,0,0.403234768,,,0.163266361
3195,A Retrospective Observational Study to Determine the Early Predictors of In-hospital Mortality at Admission with COVID-19.,Indian J Crit Care Med,33446968,1/16/21,pubmed,0,6,logistic regression,0.000916682,0.000916688,0.018378021,0.000916729,0.000916701,0.977955179,Clinics,0.6219358,TRUE,20.5,0.304966294,7.666666667,0.310877709,1,0.537564047,,,0.38446935
3196,Learning the language of viral evolution and escape.,Science,33446556,1/16/21,pubmed,0,4,machine learning,0.410200924,0.39243824,0.190811076,0.002183314,0.00218326,0.002183187,Drug discovery,0.33740473,FALSE,33,0.466757375,60.5,0.722772277,0,0.403234768,,,0.530921473
3197,"Coronavirus COVID-19 outbreak and control: Effect of temperature, relative humidity, and lockdown implementation.",Arch Pediatr,33446429,1/16/21,pubmed,0,4,predictive model,0.001371241,0.001371278,0.001371255,0.965579038,0.001371291,0.028935897,Epidemiology,0.73649937,TRUE,0.5,0.007668996,,,1,0.537564047,,,0.272616522
3198,"Mask use among pedestrians during the Covid-19 pandemic in Southwest Iran: an observational study on 10,440 people.",BMC Public Health,33446172,1/16/21,pubmed,0,5,logistic regression,0.001171544,0.001171557,0.001171552,0.230084073,0.765229687,0.001171588,Healthcare,0.92226803,TRUE,59.6,0.70029068,14,0.412898047,0,0.403234768,,,0.505474498
3199,Hydroxychloroquine in the treatment of outpatients with mildly symptomatic COVID-19: a multi-center observational study.,BMC Infect Dis,33446136,1/16/21,pubmed,0,25,logistic regression,0.001461894,0.001461877,0.020918252,0.001461943,0.001461929,0.973234105,Clinics,0.29055429,FALSE,24.12,0.356299091,20.48,0.485081616,0,0.403234768,,,0.414871825
3200,"Incidence, characteristics and clinical relevance of acute stroke in old patients hospitalized with COVID-19.",BMC Geriatr,33446113,1/16/21,pubmed,0,10,logistic regression,0.001717212,0.001717205,0.001717196,0.001717285,0.055704342,0.93742676,Clinics,0.9380003,TRUE,73.3,0.779021585,69.6,0.752274552,1,0.537564047,,,0.689620061
3201,0,J Biomol Struct Dyn,33446058,1/16/21,pubmed,0,6,"computational, in silico",0.991886283,0.001622851,0.001622752,0.00162272,0.001622698,0.001622695,Drug discovery,0.9753623,TRUE,17.66666667,0.266373925,7.833333333,0.314088841,0,0.403234768,,,0.327899178
3202,Validity and reliability testing of the Indonesian version of the eHealth Literacy Scale during the COVID-19 pandemic.,Health Informatics J,33446030,1/16/21,pubmed,0,2,correlation analysis,0.002639258,0.002639166,0.002639181,0.459971316,0.529471839,0.002639239,Healthcare,0.44348866,FALSE,8.5,0.126662131,1,0.122023013,0,0.403234768,,,0.217306637
3203,"Life during COVID-19 lockdown in Italy: the influence of cognitive state on psychosocial, behavioral and lifestyle profiles of older adults.",Aging Ment Health,33445968,1/16/21,pubmed,0,12,logistic regression,0.00109887,0.001098828,0.001098865,0.102481355,0.893123204,0.001098878,Healthcare,0.9833536,TRUE,23.08333333,0.342569114,9.166666667,0.338774418,0,0.403234768,,,0.3615261
3204,Socioeconomic disparities in Korea by health insurance type during the COVID-19 pandemic: a nationwide study.,Epidemiol Health,33445821,1/16/21,pubmed,0,4,logistic regression,0.001901734,0.001901811,0.001901845,0.07085764,0.250344627,0.673092343,Clinics,0.6676209,TRUE,61.25,0.710248005,18.5,0.46554723,0,0.403234768,,,0.526343334
3205,The Long-Term Evolutionary History of Gradual Reduction of CpG Dinucleotides in the SARS-CoV-2 Lineage.,Biology (Basel),33445785,1/16/21,pubmed,0,1,genomes,0.087984475,0.818296066,0.001786512,0.088359822,0.001786567,0.001786558,Genomics,0.5854818,TRUE,57,0.68204589,304,0.955512443,0,0.403234768,,,0.680264367
3206,CIoTVID: Towards an Open IoT-Platform for Infective Pandemic Diseases such as COVID-19.,Sensors (Basel),33445499,1/16/21,pubmed,0,3,"classifier, machine intelligence",0.001823448,0.001823394,0.41518369,0.577522725,0.001823388,0.001823356,Epidemiology,0.1430276,FALSE,60,0.703444864,14.66666667,0.420323789,0,0.403234768,,,0.50900114
3207,A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections.,F1000Res,33299552,1/16/21,pubmed,0,3,genomes,0.580212388,0.38179725,0.000916686,0.000916739,0.000916748,0.035240189,Drug discovery,0.30771446,FALSE,79.33333333,0.803822129,73,0.761974846,0,0.403234768,,,0.656343914
3208,"Use of the informational spectrum methodology for rapid biological analysis of the novel coronavirus 2019-nCoV: prediction of potential receptor, natural reservoir, tropism and therapeutic/vaccine target.",F1000Res,32419926,1/16/21,pubmed,0,4,in silico,0.838616901,0.153935951,0.001861686,0.001861968,0.001861815,0.001861679,Drug discovery,0.62888587,TRUE,140.5,0.934504298,317.5,0.958991169,12,0.850299401,,,0.91459829
3209,Probable anxiety and components of psychological resilience amid COVID-19: A population-based study.,J Affect Disord,33445081,1/15/21,pubmed,0,8,logistic regression,0.001461903,0.001461896,0.0014619,0.118377723,0.875774673,0.001461904,Healthcare,0.9011852,TRUE,46.625,0.601830664,24.875,0.527160824,1,0.537564047,,,0.555518512
3210,COVID-19: Performance study of microplastic inhalation risk posed by wearing masks.,J Hazard Mater,33445045,1/15/21,pubmed,0,4,simulation experiment,0.186191113,0.002639041,0.002639105,0.803252416,0.002639163,0.002639161,Epidemiology,0.46781528,FALSE,32.25,0.458098831,6.75,0.292079208,0,0.403234768,,,0.384470936
3211,Impact of reproduction number on the multiwave spreading dynamics of COVID-19 with temporary immunity: A mathematical model.,Int J Infect Dis,33444746,1/15/21,pubmed,0,4,"computational, mathematical model",0.175701322,0.001538234,0.001538173,0.818145843,0.001538191,0.001538237,Epidemiology,0.09784764,FALSE,26.25,0.384748593,3,0.199424672,0,0.403234768,,,0.329136011
3212,Association analysis framework of genetic and exposure risks for COVID-19 in middle-aged and elderly adults.,Mech Ageing Dev,33444631,1/15/21,pubmed,0,6,genome-wide,0.05864267,0.259095392,0.001622793,0.001622782,0.268663108,0.410353255,Clinics,0.7167407,TRUE,93.5,0.85317583,,,0,0.403234768,,,0.628205299
3213,Comprehensive in vivo secondary structure of the SARS-CoV-2 genome reveals novel regulatory motifs and mechanisms.,Mol Cell,33444546,1/15/21,pubmed,0,6,genomic structure,0.295659102,0.69772867,0.001653147,0.00165305,0.001653019,0.001653012,Genomics,0.5385727,TRUE,59.83333333,0.701651308,103.3333333,0.827869949,0,0.403234768,,,0.644252008
3214,Improving clinical management of COVID-19: the role of prediction models.,Lancet Respir Med,33444541,1/15/21,pubmed,0,2,prediction model,0.034962796,0.034962288,0.825180844,0.034965306,0.034963265,0.0349655,Clinics,0.53716755,TRUE,290.5,0.990042674,191.5,0.913031844,0,0.403234768,,,0.768769762
3215,Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study.,Lancet Respir Med,33444539,1/15/21,pubmed,0,375,logistic regression,0.014238704,0.001059391,0.057602876,0.218997419,0.113174082,0.594927529,Clinics,0.51411754,TRUE,76.975,0.793679263,102.05,0.825996789,11,0.840175319,,,0.819950457
3216,"Pandemic-related attitudes, stressors and work outcomes among medical assistants during the SARS-CoV-2 ("Coronavirus") pandemic in Germany: A cross-sectional Study.",PLoS One,33444419,1/15/21,pubmed,0,3,logistic regression,0.001203421,0.001203427,0.00120345,0.137252906,0.818655318,0.040481478,Healthcare,0.33880562,FALSE,118,0.904632321,93,0.808670056,0,0.403234768,,,0.705512382
3217,High-risk chest radiographic features associated with COVID-19 disease severity.,PLoS One,33444415,1/15/21,pubmed,0,7,logistic regression,0.001237057,0.001237078,0.508907774,0.001237074,0.001237111,0.486143906,Imaging,0.86582875,TRUE,61.42857143,0.711361247,73.71428571,0.763045223,0,0.403234768,,,0.625880413
3218,"Knowledge, attitude and practice survey of COVID-19 pandemic in Northern Nigeria.",PLoS One,33444360,1/15/21,pubmed,0,4,logistic regression,0.00082291,0.013967811,0.000822938,0.037222412,0.909041656,0.038122274,Healthcare,0.24583277,FALSE,21.5,0.318263343,8.5,0.329141022,1,0.537564047,,,0.394989471
3219,Seroprevalence of SARS-CoV-2 antibodies in children of United Kingdom healthcare workers: a prospective multicentre cohort study protocol.,BMJ Open,33444212,1/15/21,pubmed,0,19,logistic regression,0.001330057,0.190503889,0.00133007,0.175292054,0.526639298,0.104904631,Healthcare,0.6902427,TRUE,44.94736842,0.585008349,35.10526316,0.605632861,0,0.403234768,,,0.531291992
3220,Patient Journey Map to Improve the Home Isolation Experience of Persons with Mild COVID-19 Symptoms: Design Research for Service Touchpoints of Artificial Intelligence in eHealth.,JMIR Med Inform,33444156,1/15/21,pubmed,0,3,artificial intelligence,0.00081536,0.000815343,0.136564827,0.166397605,0.566267054,0.129139811,Healthcare,0.31290543,FALSE,3.666666667,0.045952131,1.666666667,0.145036125,0,0.403234768,,,0.198074341
3221,Using Machine Learning Algorithms to Predict People's Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach.,JMIR Med Educ,33444154,1/15/21,pubmed,0,5,"machine learning, classifier",0.001072206,0.0010722,0.312843878,0.164966483,0.518973057,0.001072176,Healthcare,0.020754635,FALSE,2.6,0.028263962,1.8,0.150120417,0,0.403234768,,,0.193873049
3222,Functional pathophysiology of SARS-CoV-2-induced acute lung injury and clinical implications.,J Appl Physiol (1985),33444117,1/15/21,pubmed,0,4,computational,0.241221337,0.001254644,0.047025153,0.001254697,0.001254692,0.707989478,Clinics,0.4083516,FALSE,142.5,0.936854475,181.5,0.907278566,1,0.537564047,,,0.793899029
3223,[Psychiatry].,Rev Med Suisse,33443837,1/15/21,pubmed,0,10,computational,0.145099772,0.002720242,0.139692999,0.549016875,0.160749998,0.002720113,Epidemiology,0.95864606,TRUE,59.4,0.698682664,48.9,0.676812952,0,0.403234768,,,0.592910128
3224,An early experience on the effect of solid organ transplant status on hospitalized COVID-19 patients.,Am J Transplant,33443778,1/15/21,pubmed,0,62,logistic regression,0.001684458,0.001684458,0.001684453,0.001684492,0.001684526,0.991577613,Clinics,0.71087027,TRUE,50.48571429,0.635042365,45.45714286,0.660422799,3,0.667819001,,,0.654428055
3225,Predictors of self-perceived health worsening over COVID-19 emergency in ALS.,Neurol Sci,33443667,1/15/21,pubmed,0,8,logistic regression,0.001237052,0.001237095,0.001237097,0.001237097,0.718904215,0.276147444,Healthcare,0.9629067,TRUE,70.125,0.764734987,46.875,0.668049237,2,0.618927094,,,0.683903773
3226,Socioeconomic Disparities in Patient Use of Telehealth During the Coronavirus Disease 2019 Surge.,JAMA Otolaryngol Head Neck Surg,33443539,1/15/21,pubmed,0,4,logistic regression,0.000871623,0.000871572,0.000871557,0.000871562,0.71563605,0.280877636,Healthcare,0.7981093,TRUE,14.5,0.219617787,7.25,0.302983677,1,0.537564047,,,0.353388504
3227,Identification of SARS-CoV-2 Nucleocapsid and Spike T-Cell Epitopes for Assessing T-Cell Immunity.,J Virol,33443088,1/15/21,pubmed,0,10,in silico,0.729208868,0.228960874,0.000889064,0.039162993,0.000889075,0.000889126,Drug discovery,0.3290301,FALSE,93.2,0.851938895,154.3,0.887208991,0,0.403234768,,,0.714127551
3228,The impact of lockdown during SARS-CoV-2 outbreak on behavioral and psychological symptoms of dementia.,Neurol Sci,33442845,1/15/21,pubmed,0,5,logistic regression,0.002080609,0.00208063,0.002080661,0.05100703,0.865049435,0.077701635,Healthcare,0.9863851,TRUE,102.4,0.874327417,197.6,0.916711266,0,0.403234768,,,0.731424483
3229,"The Joint Impact of COVID-19 Vaccination and Non-Pharmaceutical Interventions on Infections, Hospitalizations, and Mortality: An Agent-Based Simulation.",medRxiv,33442712,1/15/21,pubmed,0,10,simulation model,0.001085364,0.001085348,0.00108533,0.994573183,0.001085376,0.0010854,Epidemiology,0.13400257,FALSE,29.5,0.426000371,20.1,0.481268397,2,0.618927094,,,0.508731954
3230,Pharmacokinetic modelling to estimate intracellular favipiravir ribofuranosyl-5'-triphosphate exposure to support posology for SARS-CoV-2.,medRxiv,33442711,1/15/21,pubmed,0,4,"model fit, mathematical model",0.33951741,0.001059371,0.00105935,0.480229135,0.0010594,0.177075333,Epidemiology,0.4632718,FALSE,11.5,0.17416043,2.75,0.187583623,1,0.537564047,,,0.299769367
3231,Twitter Discourse on Nicotine as Potential Prophylactic or Therapeutic for COVID-19.,medRxiv,33442710,1/15/21,pubmed,0,3,classifier,0.109987592,0.001156273,0.064774938,0.561726169,0.17677256,0.085582468,Epidemiology,0.17283425,FALSE,32,0.455810502,32.66666667,0.59011239,0,0.403234768,,,0.483052553
3232,"Increased Risk of Autopsy-Proven Pneumonia with Sex, Season and Neurodegenerative Disease.",medRxiv,33442709,1/15/21,pubmed,0,44,logistic regression,0.001511853,0.098780359,0.001511918,0.001511906,0.001511925,0.895172038,Clinics,0.8050499,TRUE,33.52272727,0.471952502,74.25,0.764985282,2,0.618927094,,,0.618621626
3233,Center-Based Experiences Implementing Strategies to Reduce Risk of Horizontal Transmission of SARS-Cov-2: Potential for Compromise of Neonatal Microbiome Assemblage.,medRxiv,33442705,1/15/21,pubmed,0,13,microbiom,0.002422371,0.109823642,0.00242229,0.294040117,0.178742786,0.412548795,Clinics,0.78138816,TRUE,5.230769231,0.072113303,1.461538462,0.134533048,0,0.403234768,,,0.203293706
3234,Competing Health Risks Associated with the COVID-19 Pandemic and Response: A Scoping Review.,medRxiv,33442703,1/15/21,pubmed,0,13,mathematical model,0.030111276,0.001254675,0.03893999,0.618890253,0.309549176,0.001254629,Epidemiology,0.35194302,FALSE,48.30769231,0.616735729,,,1,0.537564047,,,0.577149888
3235,Molecular dynamics simulations and functional studies reveal that hBD-2 binds SARS-CoV-2 spike RBD and blocks viral entry into ACE2 expressing cells.,bioRxiv,33442698,1/15/21,pubmed,0,9,"molecular dynamics simulation, in-silico",0.968243474,0.001987188,0.023807483,0.001987258,0.001987324,0.001987273,Drug discovery,0.083066046,FALSE,43.11111111,0.567814955,39.33333333,0.629649451,0,0.403234768,,,0.533566391
3236,Self-organized stem cell-derived human lung buds with proximo-distal patterning and novel targets of SARS-CoV-2.,bioRxiv,33442697,1/15/21,pubmed,0,6,transcriptom,0.789376087,0.152830305,0.001072221,0.001072223,0.037914889,0.017734276,Drug discovery,0.20066866,FALSE,127.8333333,0.918547838,868.8333333,0.991637677,0,0.403234768,,,0.771140094
3237,Role of spatial patterning of N-protein interactions in SARS-CoV-2 genome packaging.,bioRxiv,33442696,1/15/21,pubmed,0,3,"in silico, genomes",0.238778246,0.6935356,0.000629698,0.065797078,0.000629701,0.000629677,Genomics,0.49982682,FALSE,42.66666667,0.563856763,101.6666667,0.825260904,0,0.403234768,,,0.597450812
3238,COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes.,bioRxiv,33442689,1/15/21,pubmed,0,9,"computational, mathematical model, in silico",0.534187245,0.060370343,0.001272743,0.123703047,0.001272701,0.27919392,Drug discovery,0.14090407,FALSE,30.44444444,0.437132785,65.66666667,0.741236286,0,0.403234768,,,0.527201279
3239,Fibrinolysis influences SARS-CoV-2 infection in ciliated cells.,bioRxiv,33442688,1/15/21,pubmed,0,4,dataset,0.831262989,0.001684669,0.001684549,0.13835312,0.001684509,0.025330164,Drug discovery,0.40201217,FALSE,25,0.369286907,9.75,0.349678887,0,0.403234768,,,0.374066854
3240,Lipidomic Signatures Align with Inflammatory Patterns and Outcomes in Critical Illness.,Res Sq,33442677,1/15/21,pubmed,0,22,lipidom,0.002422468,0.170999831,0.057521427,0.002422452,0.002422368,0.764211455,Clinics,0.85217,TRUE,181,0.964747356,217.3181818,0.925809473,0,0.403234768,,,0.764597199
3241,Federated Learning used for predicting outcomes in SARS-COV-2 patients.,Res Sq,33442676,1/15/21,pubmed,0,98,"artificial intelligence, dataset",0.002080569,0.002080587,0.72041169,0.21722866,0.002080706,0.056117787,Imaging,0.2649566,FALSE,55.24489796,0.669305461,36.2244898,0.612590313,1,0.537564047,,,0.606486607
3242,Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science.,Digit Biomark,33442579,1/15/21,pubmed,0,42,digital health,0.256547015,0.001112671,0.271891622,0.397146624,0.072189338,0.001112731,Epidemiology,0.96926296,TRUE,54.21428571,0.662811553,124.3095238,0.856837035,0,0.403234768,,,0.640961119
3243,"A Roadmap to Inform Development, Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach.",Digit Biomark,33442578,1/15/21,pubmed,0,18,digital health,0.001098938,0.001098875,0.130971534,0.718908047,0.031747402,0.116175204,Epidemiology,0.887387,TRUE,88.94444444,0.838951079,134.8888889,0.869413968,4,0.707574542,,,0.805313196
3244,"NODE. Health Meeting Report and Panel Discussion - The FDA's Changing Regulatory Landscape for Digital Health Technologies and Digital Health Innovation during COVID-19: A Discussion with Eric Topol and Bakul Patel, Moderated by Aenor Sawyer.",Digit Biomark,33442575,1/15/21,pubmed,0,4,digital health,0.229197702,0.007674451,0.007673887,0.74010408,0.007674932,0.007674948,Epidemiology,0.49774095,FALSE,11,0.167171748,2.75,0.187583623,0,0.403234768,,,0.25266338
3245,Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients.,ArXiv,33442560,1/15/21,pubmed,0,16,machine learning,0.020655729,0.001291286,0.735559383,0.001291272,0.086303146,0.154899183,Clinics,0.036622465,FALSE,57.8125,0.687488404,,,1,0.537564047,,,0.612526225
3246,The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of U.S. COVID-19 Cases.,ArXiv,33442559,1/15/21,pubmed,0,4,lstm,0.001943659,0.001943531,0.114742081,0.877483499,0.001943606,0.001943624,Epidemiology,0.08440456,FALSE,53.5,0.657245346,1012.5,0.993778432,0,0.403234768,,,0.684752849
3247,Hyperosmolarity Deserves More Attention in Critically Ill COVID-19 Patients with Diabetes: A Cohort-Based Study.,Diabetes Metab Syndr Obes,33442281,1/15/21,pubmed,0,15,logistic regression,0.001371286,0.001371327,0.001371249,0.00137126,0.001371288,0.993143589,Clinics,0.5662698,TRUE,54.86666667,0.667326365,30.8,0.575729195,0,0.403234768,,,0.548763442
3248,Clinical Features for Severely and Critically Ill Patients with COVID-19 in Shandong: A Retrospective Cohort Study.,Ther Clin Risk Manag,33442256,1/15/21,pubmed,0,30,logistic regression,0.000956353,0.000956352,0.030718012,0.000956325,0.000956307,0.965456651,Clinics,0.5711562,TRUE,107.4,0.884346589,92.16666667,0.806061012,1,0.537564047,,,0.742657216
3249,Delayed discharge is associated with higher complement C3 levels and a longer nucleic acid-negative conversion time in patients with COVID-19.,Sci Rep,33441900,1/15/21,pubmed,0,10,logistic regression,0.001371371,0.073823505,0.001371243,0.001371296,0.00137126,0.920691325,Clinics,0.7511178,TRUE,11.6,0.175211825,4.2,0.234211935,0,0.403234768,,,0.270886176
3250,Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning.,Sci Rep,33441822,1/15/21,pubmed,0,9,"deep learning, artificial intelligence, neural network, classifier",0.00120343,0.379624014,0.615562102,0.001203484,0.001203523,0.001203447,Genomics,0.07020399,FALSE,12.44444444,0.18745748,23.77777778,0.518932299,0,0.403234768,,,0.369874849
3251,Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.,Sci Rep,33441578,1/15/21,pubmed,0,13,"artificial intelligence, logistic regression",0.000846602,0.000846551,0.21574214,0.000846523,0.000846533,0.78087165,Clinics,0.59031373,TRUE,36.07692308,0.498361061,36,0.611118544,0,0.403234768,,,0.504238124
3252,Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing.,Proc Natl Acad Sci U S A,33441450,1/15/21,pubmed,0,3,bayes,0.002296587,0.071511402,0.190463681,0.246072404,0.487359275,0.00229665,Healthcare,0.28114313,FALSE,187,0.967344919,278,0.948153599,0,0.403234768,,,0.772911095
3253,"An Observational Laboratory-Based Assessment of SARS-CoV-2 Molecular Diagnostics in Benin, Western Africa.",mSphere,33441410,1/15/21,pubmed,0,18,"sequencing, genomes",0.021621439,0.810377543,0.000815377,0.165554901,0.000815352,0.000815387,Genomics,0.24229822,FALSE,31.27777778,0.447337498,86.38888889,0.793818571,0,0.403234768,,,0.548130279
3254,Study protocol for COVID-RV: a multicentre prospective observational cohort study of right ventricular dysfunction in ventilated patients with COVID-19.,BMJ Open,33441361,1/15/21,pubmed,0,6,logistic regression,0.037294719,0.001112676,0.00111271,0.100266105,0.001112704,0.859101086,Clinics,0.98123705,TRUE,73,0.778464964,63.5,0.733676746,1,0.537564047,,,0.683235252
3255,"Fighting viruses with materials science: Prospects for antivirus surfaces, drug delivery systems and artificial intelligence.",Dent Mater,33441249,1/15/21,pubmed,0,5,artificial intelligence,0.51344105,0.103155978,0.145268834,0.001187365,0.235759369,0.001187404,Drug discovery,0.9845169,TRUE,86.8,0.833013792,71.2,0.756756757,0,0.403234768,,,0.664335105
3256,The spatial transmission of SARS-CoV-2 in China under the prevention and control measures at the early outbreak.,Arch Public Health,33441168,1/15/21,pubmed,0,6,mathematical model,0.000898067,0.000898086,0.000898121,0.995509513,0.000898103,0.000898109,Epidemiology,0.32747287,FALSE,49,0.624281032,4.833333333,0.249598609,0,0.403234768,,,0.425704803
3257,Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients.,Genome Med,33441124,1/15/21,pubmed,0,136,transcriptom,0.403157385,0.225580454,0.000956328,0.000956337,0.000956321,0.368393173,Drug discovery,0.21314657,FALSE,52.29090909,0.649019729,82.61818182,0.784720364,0,0.403234768,,,0.612324954
3258,Deep learning in the quest for compound nomination for fighting COVID-19.,Curr Med Chem,33441063,1/15/21,pubmed,0,4,"machine learning, deep learning, computational",0.373325024,0.001987192,0.409146654,0.211566573,0.001987334,0.001987223,Drug discovery,0.35031122,FALSE,19,0.285793803,9.5,0.345531175,0,0.403234768,,,0.344853248
3259,Viroinformatics-Based Analysis of SARS-CoV-2 Core Proteins for Potential Therapeutic Targets.,Antibodies (Basel),33440681,1/15/21,pubmed,0,7,"computational, bioinformatic, in silico, sequence alignment",0.882952415,0.093968084,0.001392831,0.00139283,0.018900878,0.001392962,Drug discovery,0.5835032,TRUE,0.285714286,0.006864989,,,0,0.403234768,,,0.205049878
3260,Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning.,Sensors (Basel),33440674,1/15/21,pubmed,0,4,"deep learning, transfer learning, dataset",0.001392817,0.001392856,0.993035856,0.001392844,0.001392813,0.001392813,Imaging,0.23085612,FALSE,82.5,0.816129631,142.5,0.87690661,4,0.707574542,,,0.800203594
3261,SARS-CoV-2 Epitope Mapping on Microarrays Highlights Strong Immune-Response to N Protein Region.,Vaccines (Basel),33440622,1/15/21,pubmed,0,12,proteom,0.36386409,0.383012527,0.145807195,0.001538162,0.001538178,0.104239848,Genomics,0.50807124,TRUE,45,0.587049292,17.25,0.453104094,0,0.403234768,,,0.481129385
3262,COVID-19: Short-term forecast of ICU beds in times of crisis.,PLoS One,33439917,1/14/21,pubmed,0,4,"machine learning, forecasting model",0.002183259,0.002183201,0.09530718,0.875059394,0.002183234,0.023083731,Epidemiology,0.09794897,FALSE,19.75,0.293215412,25.25,0.530773348,0,0.403234768,,,0.409074509
3263,"Diet, Nutrition, Obesity, and Their Implications for COVID-19 Mortality: Development of a Marginalized Two-Part Model for Semicontinuous Data.",JMIR Public Health Surveill,33439850,1/14/21,pubmed,0,4,immunome,0.001141419,0.098796898,0.001141378,0.489417268,0.039034455,0.370468581,Epidemiology,0.8872453,TRUE,13.25,0.199950523,1.25,0.127776291,2,0.618927094,,,0.315551303
3264,"A Near-Peer Educational Model for Online, Interactive Learning in Emergency Medicine.",West J Emerg Med,33439819,1/14/21,pubmed,0,13,active learning,0.002032793,0.002032786,0.22501621,0.002032874,0.766852543,0.002032793,Healthcare,0.55970037,TRUE,12.53846154,0.189498423,1.846153846,0.151123896,0,0.403234768,,,0.247952362
3265,The impact of ventilation-perfusion inequality in COVID-19: a computational model.,J Appl Physiol (1985),33439790,1/14/21,pubmed,0,16,computational,0.059250633,0.001330092,0.258668997,0.317832377,0.001330052,0.36158785,Clinics,0.7107683,TRUE,81.6875,0.813408374,126,0.859245384,0,0.403234768,,,0.691962842
3266,Are college campuses superspreaders? A data-driven modeling study.,Comput Methods Biomech Biomed Engin,33439055,1/14/21,pubmed,0,6,bayes,0.00153825,0.028891511,0.001538205,0.964955759,0.001538177,0.001538098,Epidemiology,0.15136486,FALSE,6.833333333,0.096852001,0.333333333,0.073187048,0,0.403234768,,,0.191091272
3267,Texture Analysis in the Evaluation of Covid-19 Pneumonia in Chest X-Ray Images: a Proof of Concept Study.,Curr Med Imaging,33438548,1/14/21,pubmed,0,10,machine learning,0.001059337,0.00105935,0.93433685,0.061425692,0.001059356,0.001059415,Imaging,0.9017036,TRUE,47,0.606407323,12.4,0.390286326,2,0.618927094,,,0.538540248
3268,An Efficient Method for Coronavirus Detection Through X-rays using deep Neural Network.,Curr Med Imaging,33438544,1/14/21,pubmed,0,4,"deep learning, neural network, network model, dataset",0.00111267,0.040187114,0.764867662,0.001112651,0.001112631,0.191607272,Imaging,0.8151183,TRUE,4.25,0.056527924,0.5,0.087101953,1,0.537564047,,,0.227064641
3269,0,J Biomol Struct Dyn,33438525,1/14/21,pubmed,0,5,in silico,0.961435347,0.001072222,0.034275752,0.001072258,0.00107221,0.001072211,Drug discovery,0.48408246,FALSE,34.4,0.480982126,4.8,0.249331014,0,0.403234768,,,0.377849303
3270,Sense of coherence mediates the relationship between digital health literacy and anxiety about the future in aging population during the COVID-19 pandemic: a path analysis.,Aging Ment Health,33438448,1/14/21,pubmed,0,7,digital health,0.00148644,0.001486439,0.001486475,0.09784599,0.896208184,0.001486473,Healthcare,0.9838811,TRUE,22.14285714,0.326736347,5.428571429,0.264249398,0,0.403234768,,,0.331406838
3271,The swedish covid-19 intensive care cohort: Risk factors of ICU admission and ICU mortality.,Acta Anaesthesiol Scand,33438198,1/14/21,pubmed,0,6,logistic regression,0.001310421,0.001310365,0.001310383,0.001310396,0.001310514,0.993447922,Clinics,0.5942196,TRUE,36.66666667,0.505102356,11.83333333,0.382927482,1,0.537564047,,,0.475197962
3272,A Systematic Review and Meta-Analysis of Clinical Characteristics and Outcomes in Patients With Lung Cancer with Coronavirus Disease 2019.,JTO Clin Res Rep,33437971,1/14/21,pubmed,0,4,logistic regression,0.001141382,0.001141344,0.001141431,0.001141445,0.001141346,0.994293053,Clinics,0.97695374,TRUE,6.5,0.093512277,5.75,0.271742039,0,0.403234768,,,0.256163028
3273,Algorithms meet sequencing technologies - 10th edition of the RECOMB-Seq workshop.,iScience,33437938,1/14/21,pubmed,0,2,"computational, sequencing",0.002806536,0.491811789,0.323379912,0.176388833,0.002806558,0.002806372,Genomics,0.68328446,TRUE,54.5,0.665161729,283.5,0.949826064,0,0.403234768,,,0.672740854
3274,Transcriptomic similarities and differences in host response between SARS-CoV-2 and other viral infections.,iScience,33437935,1/14/21,pubmed,0,19,transcriptom,0.508678951,0.21898688,0.001861776,0.001861745,0.001861744,0.266748905,Drug discovery,0.1884354,FALSE,52.89473684,0.652916074,90.84210526,0.802916778,1,0.537564047,,,0.664465633
3275,Clinical Course of Cancer Patients With COVID-19: A Retrospective Cohort Study.,JNCI Cancer Spectr,33437923,1/14/21,pubmed,0,6,logistic regression,0.001511886,0.001511798,0.00151178,0.001511799,0.001511858,0.99244088,Clinics,0.9243895,TRUE,165.1666667,0.954975571,190.8333333,0.912429756,1,0.537564047,,,0.801656458
3276,Pandemic Equation for Describing and Predicting COVID19 Evolution.,J Healthc Inform Res,33437912,1/14/21,pubmed,0,1,artificial intelligence,0.001823432,0.040347753,0.077047136,0.877134992,0.001823344,0.001823343,Epidemiology,0.074383765,FALSE,1126,0.999690766,609,0.984278833,1,0.537564047,,,0.840511216
3277,COVID-19 epidemic prediction and the impact of public health interventions: A review of COVID-19 epidemic models.,Infect Dis Model,33437897,1/14/21,pubmed,0,6,"mathematical model, prediction model",0.001187273,0.001187279,0.00118729,0.994063529,0.001187286,0.001187344,Epidemiology,0.49559012,FALSE,28.83333333,0.415857505,3.166666667,0.201097137,0,0.403234768,,,0.340063136
3278,A SIQ mathematical model on COVID-19 investigating the lockdown effect.,Infect Dis Model,33437896,1/14/21,pubmed,0,3,mathematical model,0.001901724,0.001901744,0.001901715,0.946536446,0.045856703,0.001901669,Epidemiology,0.3274827,FALSE,17.66666667,0.266373925,3.333333333,0.206515922,0,0.403234768,,,0.292041538
3279,Clinically distinct COVID-19 cases share notably similar immune response progression: A follow-up analysis.,Heliyon,33437888,1/14/21,pubmed,0,4,"proteom, dataset",0.402879204,0.001751271,0.001751246,0.001751274,0.186031537,0.405835467,Clinics,0.6460469,TRUE,84.75,0.824046014,45.75,0.662229061,0,0.403234768,,,0.629836614
3280,A predictive model for respiratory distress in patients with COVID-19: a retrospective study.,Ann Transl Med,33437784,1/14/21,pubmed,0,15,predictive model,0.001593517,0.001593485,0.087573144,0.001593602,0.001593628,0.906052624,Clinics,0.80488276,TRUE,70.93333333,0.767827324,21,0.492239765,0,0.403234768,,,0.554433952
3281,Adherence to Self-isolation measures by older adults during coronavirus disease 2019 (COVID-19) epidemic: A phone survey in Iran.,Med J Islam Repub Iran,33437748,1/14/21,pubmed,0,6,logistic regression,0.000907287,0.000907332,0.021259905,0.063979578,0.912038575,0.000907324,Healthcare,0.758278,TRUE,19,0.285793803,5.5,0.267259834,0,0.403234768,,,0.318762802
3282,"Awareness, anxiety, and depression in healthcare professionals, medical students, and general population of Pakistan during COVID-19 Pandemic: A cross sectional online survey.",Med J Islam Repub Iran,33437727,1/14/21,pubmed,0,6,logistic regression,0.001291195,0.001291202,0.00129121,0.001291207,0.993543974,0.001291212,Healthcare,0.9327887,TRUE,3,0.037293586,0,0.055525823,1,0.537564047,,,0.210127819
3283,Impact of COVID-19 on TB active case finding in Nigeria.,Public Health Action,33437681,1/14/21,pubmed,0,12,digital health,0.000926301,0.000926331,0.038588679,0.497680643,0.333214643,0.128663403,Epidemiology,0.96834075,TRUE,6.25,0.088564537,1.75,0.148381054,0,0.403234768,,,0.213393453
3284,Ångström- and Nano-scale Pore-Based Nucleic Acid Sequencing of Current and Emergent Pathogens.,MRS Adv,33437534,1/14/21,pubmed,0,6,"sequencing, genomes",0.071852815,0.591892865,0.228546657,0.10516235,0.00127265,0.001272664,Genomics,0.42181107,FALSE,13.5,0.205393036,5.666666667,0.270203372,1,0.537564047,,,0.337720152
3285,A disease progression prediction model and nervous system symptoms in coronavirus disease 2019 patients.,Am J Transl Res,33437392,1/14/21,pubmed,0,5,prediction model,0.001350327,0.001350314,0.001350453,0.001350305,0.001350331,0.99324827,Clinics,0.99336886,TRUE,111,0.892015585,41.6,0.642493979,0,0.403234768,,,0.645914777
3286,"ABO / Rh-D Blood types and susceptibility to Corona Virus Disease-19 in Peshawar, Pakistan.",Pak J Med Sci,33437242,1/14/21,pubmed,0,6,logistic regression,0.001593517,0.061197712,0.001593498,0.001593504,0.001593578,0.932428192,Clinics,0.67831993,TRUE,14.16666667,0.214175274,5.666666667,0.270203372,1,0.537564047,,,0.340647564
3287,In-silico analysis of the inhibition of the SARS-CoV-2 main protease by some active compounds from selected African plants.,J Taibah Univ Med Sci,33437230,1/14/21,pubmed,0,6,"in silico, in-silico",0.993899853,0.001220005,0.001220026,0.001220005,0.001220061,0.00122005,Drug discovery,0.99953175,TRUE,8.166666667,0.119735296,1,0.122023013,0,0.403234768,,,0.214997692
3288,Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms.,Multimed Tools Appl,33437173,1/14/21,pubmed,0,5,"bayes, machine learning, deep learning, classifier, predictive model",0.001943446,0.001943575,0.990282016,0.001943711,0.001943597,0.001943655,Imaging,0.47051796,FALSE,22.2,0.327787742,12.6,0.394300241,0,0.403234768,,,0.375107583
3289,COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays.,Neural Comput Appl,33437132,1/14/21,pubmed,0,3,"bayes, deep learning, dataset",0.001010977,0.001010977,0.888598259,0.107357854,0.001010975,0.001010958,Imaging,0.26813257,FALSE,2,0.022141134,0.333333333,0.073187048,0,0.403234768,,,0.16618765
3290,[Is it possible to prevent diabetic ketoacidosis at diagnosis of pediatric type 1 diabetes? Lessons from the COVID-19 pandemic].,Monatsschr Kinderheilkd,33437098,1/14/21,pubmed,0,10,logistic regression,0.001538082,0.001538146,0.068057985,0.001538205,0.40598567,0.521341912,Clinics,0.9767504,TRUE,95.9,0.85868019,61,0.724578539,0,0.403234768,,,0.662164499
3291,The association of Coronavirus Disease-19 mortality and prior bacille Calmette-Guerin vaccination: a robust ecological analysis using unsupervised machine learning.,Sci Rep,33436946,1/14/21,pubmed,0,10,machine learning,0.002130728,0.002130711,0.06159122,0.412180464,0.404115757,0.11785112,Epidemiology,0.2939108,FALSE,69.5,0.761271569,47.8,0.671862457,2,0.618927094,,,0.684020373
3292,"Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil.",Nat Commun,33436608,1/14/21,pubmed,0,17,mathematical model,0.002080548,0.002080666,0.002080641,0.870632543,0.00208072,0.121044882,Epidemiology,0.5124072,TRUE,18.05882353,0.271692745,7.235294118,0.302448488,7,0.785110192,,,0.453083808
3293,Sleep apnoea is a risk factor for severe COVID-19.,BMJ Open Respir Res,33436406,1/14/21,pubmed,0,346,logistic regression,0.002357815,0.002357785,0.002357775,0.002357827,0.002357991,0.988210808,Clinics,0.304732,FALSE,46.45454545,0.600284495,,,0,0.403234768,,,0.501759631
3294,"A Low-Cost, Ear-Contactless Electronic Stethoscope Powered by Raspberry Pi for Auscultation of Patients With COVID-19: Prototype Development and Feasibility Study.",JMIR Med Inform,33436354,1/14/21,pubmed,0,10,correlation analysis,0.001156313,0.001156283,0.36993003,0.092861231,0.193642287,0.341253856,Clinics,0.9319804,TRUE,121.8,0.90920898,104.8,0.830813487,0,0.403234768,,,0.714419078
3295,"Ruling out COVID-19 by chest CT at emergency admission when prevalence is low: the prospective, observational SCOUT study.",Respir Res,33435973,1/14/21,pubmed,0,11,bayes,0.000999528,0.000999626,0.459095899,0.000999527,0.291682142,0.246223277,Imaging,0.5136912,TRUE,80.36363636,0.808336941,44.54545455,0.656810276,0,0.403234768,,,0.622793995
3296,"Depression, risk factors, and coping strategies in the context of social dislocations resulting from the second wave of COVID-19 in Japan.",BMC Psychiatry,33435930,1/14/21,pubmed,0,4,logistic regression,0.001511827,0.001511832,0.001511894,0.10401674,0.861619114,0.029828591,Healthcare,0.98495084,TRUE,24.75,0.364710248,4.25,0.235750602,0,0.403234768,,,0.334565206
3297,The spread of COVID-19 outbreak in the first 120 days: a comparison between Nigeria and seven other countries.,BMC Public Health,33435922,1/14/21,pubmed,0,8,model fit,0.001751295,0.001751201,0.001751177,0.893913372,0.001751306,0.099081648,Epidemiology,0.81208706,TRUE,18.5,0.278001113,8.25,0.323789136,0,0.403234768,,,0.335008339
3298,Immunoinformatics-guided designing and in silico analysis of epitope-based polyvalent vaccines against multiple strains of human coronavirus (HCoV).,Expert Rev Vaccines,33435759,1/14/21,pubmed,0,5,in silico,0.76550008,0.227494939,0.001751298,0.001751245,0.0017512,0.001751238,Drug discovery,0.41628176,FALSE,17.8,0.268229328,0.6,0.09011239,0,0.403234768,,,0.253858828
3299,Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera.,J Diabetes Sci Technol,33435711,1/14/21,pubmed,0,11,"deep learning, artificial intelligence, neural network, dataset",0.001203445,0.001203457,0.726088558,0.001203455,0.135674889,0.134626196,Imaging,0.66842896,TRUE,22,0.326056033,2.909090909,0.190928552,0,0.403234768,,,0.306739784
3300,Transcriptomic Analysis of Respiratory Tissue and Cell Line Models to Examine Glycosylation Machinery during SARS-CoV-2 Infection.,Viruses,33435561,1/14/21,pubmed,0,3,"in silico, transcriptom",0.99173437,0.001653164,0.001653106,0.001653076,0.001653068,0.001653217,Drug discovery,0.4457787,FALSE,16,0.243552477,7,0.299973241,0,0.403234768,,,0.315586828
3301,Physical Activity during COVID-19 in German Adults: Analyses in the COVID-19 Snapshot Monitoring Study (COSMO).,Int J Environ Res Public Health,33435497,1/14/21,pubmed,0,7,logistic regression,0.001622717,0.001622728,0.00162271,0.001622797,0.991886288,0.001622761,Healthcare,0.98320025,TRUE,17.14285714,0.258519389,8.142857143,0.321447685,0,0.403234768,,,0.327733947
3302,Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets.,Metabolites,33435351,1/14/21,pubmed,0,4,"computational, data mining,  omics, metabolom, dataset",0.307521884,0.324838162,0.121496677,0.24317033,0.001486433,0.001486514,Genomics,0.43040168,FALSE,31.25,0.447213804,341.5,0.964209259,1,0.537564047,,,0.64966237
3303,"Modeling the transmission of the new coronavirus in São Paulo State, Brazil-assessing the epidemiological impacts of isolating young and elder persons.",Math Med Biol,33434925,1/13/21,pubmed,0,3,mathematical model,0.002490486,0.002490453,0.002490431,0.908554925,0.081483078,0.002490628,Epidemiology,0.29841262,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
3304,Utilization of greenhouse effect for the treatment of COVID-19 contaminated disposable waste - A simple technology for developing countries.,Int J Hyg Environ Health,33434878,1/13/21,pubmed,0,4,mathematical model,0.024185578,0.169332562,0.001203471,0.783037693,0.021037242,0.001203454,Epidemiology,0.25116062,FALSE,55.5,0.671346404,14.25,0.415038801,0,0.403234768,,,0.496539991
3305,Biological characteristics and biomarkers of novel SARS-CoV-2 facilitated rapid development and implementation of diagnostic tools and surveillance measures.,Biosens Bioelectron,33434780,1/13/21,pubmed,0,9,sequencing,0.345645975,0.177428654,0.29823432,0.176493391,0.001098849,0.001098811,Drug discovery,0.5526496,TRUE,74.77777778,0.785577339,36.33333333,0.613393096,1,0.537564047,,,0.645511494
3306,Wastewater-based epidemiology as a useful tool to track SARS-CoV-2 and support public health policies at municipal level in Brazil.,Water Res,33434709,1/13/21,pubmed,0,11,"sequencing, whole-genome, genomes",0.001291228,0.604846288,0.001291248,0.389988762,0.001291237,0.001291236,Genomics,0.38971195,FALSE,24.81818182,0.365390562,,,4,0.707574542,,,0.536482552
3307,Undetected infectives in the Covid-19 pandemic.,Int J Infect Dis,33434673,1/13/21,pubmed,0,2,mathematical model,0.002080628,0.040256499,0.00208062,0.951420946,0.002080644,0.002080661,Epidemiology,0.284418,FALSE,107,0.883357041,54.5,0.699892962,0,0.403234768,,,0.66216159
3308,Connecting the dots: A network approach to post-traumatic stress symptoms in Chinese healthcare workers during the peak of the Coronavirus Disease 2019 outbreak.,Stress Health,33434296,1/13/21,pubmed,0,4,network model,0.001622857,0.001622812,0.088090328,0.185765668,0.721275543,0.001622791,Healthcare,0.80224824,TRUE,79.5,0.80493537,156.75,0.889416644,0,0.403234768,,,0.699195594
3309,Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients.,Nucleic Acids Res,33434272,1/13/21,pubmed,0,6,"computational, dataset",0.6726953,0.001415166,0.214144395,0.001415146,0.001415116,0.108914878,Drug discovery,0.13143995,FALSE,117.8333333,0.9040757,65,0.739028633,0,0.403234768,,,0.682113034
3310,Unique inflammatory profile is associated with higher SARS-CoV-2 acute respiratory distress syndrome (ARDS) mortality.,Am J Physiol Regul Integr Comp Physiol,33434104,1/13/21,pubmed,0,14,transcriptom,0.317744279,0.001786664,0.001786502,0.001786516,0.00178652,0.675109519,Clinics,0.45263398,FALSE,73.5,0.780011132,66.42857143,0.743644635,0,0.403234768,,,0.642296845
3311,Determinants of COVID-19 Mortality in Patients With Cancer From a Community Oncology Practice in Brazil.,JCO Glob Oncol,33434066,1/13/21,pubmed,0,12,logistic regression,0.001126815,0.001126835,0.001126826,0.085265242,0.118020579,0.793333704,Clinics,0.9066326,TRUE,38.33333333,0.522233904,56,0.706850415,0,0.403234768,,,0.544106362
3312,A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning.,Interdiscip Sci,33433784,1/13/21,pubmed,0,2,"deep learning, artificial intelligence, neural network, deep-learning",0.658982278,0.000800641,0.337815269,0.000800658,0.000800578,0.000800576,Drug discovery,0.6985211,TRUE,13.5,0.205393036,2.5,0.180826866,0,0.403234768,,,0.263151557
3313,The Association Between Chronic Disease and Serious COVID-19 Outcomes and Its Influence on Risk Perception: Survey Study and Database Analysis.,JMIR Public Health Surveill,33433397,1/13/21,pubmed,0,8,logistic regression,0.000988392,0.043306547,0.000988375,0.000988426,0.474610068,0.479118192,Clinics,0.7906221,TRUE,39,0.530521368,9.875,0.351819641,0,0.403234768,,,0.428525259
3314,Prognostic roles of KL-6 in disease severity and lung injury in COVID-19 patients: A longitudinal retrospective analysis.,J Med Virol,33433006,1/13/21,pubmed,0,23,correlation analysis,0.001392875,0.001392826,0.001392921,0.001392848,0.001392835,0.993035695,Clinics,0.9435177,TRUE,34.2173913,0.479250417,,,1,0.537564047,,,0.508407232
3315,Mutations in SARS-CoV-2 nsp7 and nsp8 proteins and their predicted impact on replication/transcription complex structure.,J Med Virol,33433004,1/13/21,pubmed,0,5,"in silico, genomes",0.43751377,0.553300221,0.002296509,0.00229651,0.002296495,0.002296496,Genomics,0.6305234,TRUE,27.2,0.397674562,5.4,0.263914905,1,0.537564047,,,0.399717838
3316,Proteolytic Activation of SARS-CoV-2 Spike at the S1/S2 Boundary: Potential Role of Proteases beyond Furin.,ACS Infect Dis,33432808,1/13/21,pubmed,0,6,bioinformatic,0.939936313,0.054118017,0.001486407,0.001486444,0.001486404,0.001486415,Drug discovery,0.5260392,TRUE,39.5,0.534232173,99.16666667,0.820845598,7,0.785110192,,,0.713395988
3317,Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2.,Brief Bioinform,33432321,1/13/21,pubmed,0,7,"computational, bioinformatic",0.585738157,0.097729709,0.311996289,0.001511939,0.001511969,0.001511938,Drug discovery,0.6867311,TRUE,123,0.911806543,29.28571429,0.564824726,0,0.403234768,,,0.626622012
3318,Risk of in-hospital death associated with Covid-19 lung consolidations on chest computed tomography - A novel translational approach using a radiation oncology contour software.,Eur J Radiol Open,33432297,1/13/21,pubmed,0,6,logistic regression,0.000977422,0.000977421,0.119864583,0.000977428,0.000977437,0.87622571,Clinics,0.9824215,TRUE,20.5,0.304966294,5.5,0.267259834,0,0.403234768,,,0.325153632
3319,A mathematical model of Coronavirus Disease (COVID-19) containing asymptomatic and symptomatic classes.,Results Phys,33432294,1/13/21,pubmed,0,5,mathematical model,0.00310156,0.003101501,0.003101663,0.98449231,0.003101495,0.00310147,Epidemiology,0.5203102,TRUE,218.4,0.978168099,44,0.654401927,2,0.618927094,,,0.75049904
3320,Exploring the effect of ritonavir and TMC-310911 on SARS-CoV-2 and SARS-CoV main proteases: potential from a molecular perspective.,Future Sci OA,33432269,1/13/21,pubmed,0,4,computational,0.921920973,0.002898292,0.066485503,0.002898297,0.002898485,0.002898451,Drug discovery,0.9566598,TRUE,30.5,0.438493413,3.25,0.203572384,0,0.403234768,,,0.348433522
3321,Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images.,Signal Image Video Process,33432267,1/13/21,pubmed,0,4,"deep learning, computational, neural network, transfer learning, dataset",0.001350346,0.001350401,0.993248201,0.001350383,0.001350306,0.001350364,Imaging,0.635196,TRUE,12.5,0.189436576,9,0.337904736,1,0.537564047,,,0.354968453
3322,Fear of COVID-19 in Romania: Validation of the Romanian Version of the Fear of COVID-19 Scale Using Graded Response Model Analysis.,Int J Ment Health Addict,33432266,1/13/21,pubmed,0,1,model fit,0.001565418,0.001565338,0.001565358,0.279963285,0.713775225,0.001565376,Healthcare,0.9119761,TRUE,19,0.285793803,35,0.605298368,0,0.403234768,,,0.431442313
3323,Assessment of Fear of COVID-19 in Older Adults: Validation of the Fear of COVID-19 Scale.,Int J Ment Health Addict,33432265,1/13/21,pubmed,0,7,structural model,0.001310434,0.001310355,0.001310347,0.001310396,0.97553356,0.019224908,Healthcare,0.6348943,TRUE,60.71428571,0.706537201,17.42857143,0.454776559,0,0.403234768,,,0.521516176
3324,Comparison of the SARS-CoV-2 (2019-nCoV) M protein with its counterparts of SARS-CoV and MERS-CoV species.,J King Saud Univ Sci,33432259,1/13/21,pubmed,0,2,bioinformatic,0.99106715,0.001786672,0.001786523,0.001786517,0.001786538,0.001786599,Drug discovery,0.7051482,TRUE,21,0.312016822,6,0.280037463,0,0.403234768,,,0.331763018
3325,When "Shelter-in-Place" Isn't Shelter That's Safe: a Rapid Analysis of Domestic Violence Case Differences during the COVID-19 Pandemic and Stay-at-Home Orders.,J Fam Violence,33432255,1/13/21,pubmed,0,1,logistic regression,0.001486406,0.001486476,0.001486412,0.580619419,0.41343481,0.001486478,Epidemiology,0.12952164,FALSE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
3326,A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19).,Sci Rep,33432072,1/13/21,pubmed,0,11,deep learning,0.001156249,0.001156233,0.645092855,0.001156335,0.001156258,0.35028207,Imaging,0.6494739,TRUE,40.90909091,0.547405529,30.90909091,0.576531978,1,0.537564047,,,0.553833851
3327,ALLY in fighting COVID-19: magnitude of albumin decline and lymphopenia (ALLY) predict progression to critical disease.,J Investig Med,33431604,1/13/21,pubmed,0,11,logistic regression,0.001330021,0.00133013,0.001330106,0.067047766,0.001330074,0.927631903,Clinics,0.6823869,TRUE,59.45454545,0.698991898,21.09090909,0.492373562,0,0.403234768,,,0.531533409
3328,Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19.,Gut,33431578,1/13/21,pubmed,0,24,"sequencing, microbiom",0.00131042,0.307591254,0.001310347,0.001310328,0.001310344,0.687167306,Clinics,0.9748393,TRUE,105.5,0.880017317,135.875,0.870551244,14,0.866658436,,,0.872408999
3329,Single-cell RNA sequencing reveals SARS-CoV-2 infection dynamics in lungs of African green monkeys.,Sci Transl Med,33431511,1/13/21,pubmed,0,15,"sequencing, dataset",0.62458367,0.267691259,0.001203453,0.001203458,0.041255828,0.064062333,Drug discovery,0.3908519,FALSE,80.06666667,0.80759478,207.2,0.920858978,2,0.618927094,,,0.782460284
3330,Pediatric Faculty and Trainee Attitudes Toward the COVID-19 Pandemic.,Hosp Pediatr,33431427,1/13/21,pubmed,0,9,logistic regression,0.001717211,0.001717215,0.001717302,0.001717304,0.927602079,0.065528889,Healthcare,0.9228196,TRUE,27.66666667,0.403488156,22.44444444,0.506288467,0,0.403234768,,,0.437670464
3331,Ventilator-associated pneumonia in critically ill patients with COVID-19.,Crit Care,33430915,1/13/21,pubmed,0,22,microbiom,0.001291304,0.134820582,0.001291251,0.001291236,0.001291223,0.860014405,Clinics,0.5528845,TRUE,71.5,0.770734121,126.4090909,0.860048167,1,0.537564047,,,0.722782112
3332,Potential impact of individual exposure histories to endemic human coronaviruses on age-dependent severity of COVID-19.,BMC Med,33430856,1/13/21,pubmed,0,9,mathematical model,0.001823507,0.132159656,0.001823398,0.76630618,0.00182346,0.0960638,Epidemiology,0.2546482,FALSE,13.44444444,0.203104707,6.444444444,0.286392828,0,0.403234768,,,0.297577434
3333,In silico Prediction and Designing of Potential siRNAs to be Used as Antivirals Against SARS-CoV-2.,Curr Pharm Des,33430748,1/13/21,pubmed,0,5,in silico,0.294971535,0.69802349,0.001751307,0.001751245,0.001751207,0.001751216,Genomics,0.7441263,TRUE,55.8,0.673201806,34.6,0.602488627,0,0.403234768,,,0.559641734
3334,The COVID-19 Preventive Behaviors Index: Development and Validation in Two Samples From the United Kingdom.,Eval Health Prof,33430623,1/13/21,pubmed,0,3,model fit,0.001987219,0.001987139,0.001987321,0.33900756,0.653043494,0.001987268,Healthcare,0.44507262,FALSE,131.6666667,0.923681118,131.3333333,0.864864865,4,0.707574542,,,0.832040175
3335,Pan-Enterovirus Amplicon-Based High-Throughput Sequencing Detects the Complete Capsid of a EVA71 Genotype C1 Variant via Wastewater-Based Epidemiology in Arizona.,Viruses,33430521,1/13/21,pubmed,0,8,sequencing,0.004109904,0.842086197,0.004109781,0.004110105,0.141474096,0.004109917,Genomics,0.4718651,FALSE,83.5,0.819221968,186,0.90988761,0,0.403234768,,,0.710781449
3336,Mapping the SARS-CoV-2-Host Protein-Protein Interactome by Affinity Purification Mass Spectrometry and Proximity-Dependent Biotin Labeling: A Rational and Straightforward Route to Discover Host-Directed Anti-SARS-CoV-2 Therapeutics.,Int J Mol Sci,33430309,1/13/21,pubmed,0,6,"proteom, interactom, multi-omics",0.994293209,0.001141383,0.001141373,0.001141385,0.001141326,0.001141324,Drug discovery,0.09645563,FALSE,51.5,0.643267982,22,0.503746321,0,0.403234768,,,0.51674969
3337,On Pilot Massive COVID-19 Testing by Antigen Tests in Europe. Case Study: Slovakia.,Infect Dis Rep,33430283,1/13/21,pubmed,0,2,mathematical model,0.002562616,0.002562726,0.161879901,0.827869487,0.002562648,0.002562622,Epidemiology,0.31850058,FALSE,18.5,0.278001113,0.5,0.087101953,1,0.537564047,,,0.300889038
3338,A Predictive Model and Risk Factors for Case Fatality of COVID-19.,J Pers Med,33430129,1/13/21,pubmed,0,15,predictive model,0.001438109,0.001438173,0.001438206,0.194406916,0.00143814,0.799840455,Clinics,0.54650384,TRUE,14.86666667,0.223204898,6.133333333,0.280974043,0,0.403234768,,,0.302471236
3339,Drug Repurposing: Dipeptidyl Peptidase IV (DPP4) Inhibitors as Potential Agents to Treat SARS-CoV-2 (2019-nCoV) Infection.,Pharmaceuticals (Basel),33430081,1/13/21,pubmed,0,7,computational,0.87931194,0.001717215,0.001717211,0.001717248,0.066768172,0.048768215,Drug discovery,0.6011229,TRUE,8.571428571,0.126785825,1.857142857,0.151525288,1,0.537564047,,,0.271958387
3340,The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic.,Viruses,33430050,1/13/21,pubmed,0,2,dataset,0.001565335,0.473253756,0.00156553,0.520484766,0.001565304,0.001565309,Epidemiology,0.12736237,FALSE,59,0.696579875,115,0.844594595,0,0.403234768,,,0.648136412
3341,The Growing Skyline of Advanced Hepatocellular Carcinoma Treatment: A Review.,Pharmaceuticals (Basel),33429973,1/13/21,pubmed,0,7,radiom,0.215015605,0.002490552,0.002490577,0.319581131,0.002490593,0.457931542,Clinics,0.74191725,TRUE,71.57142857,0.770981508,67.71428571,0.747324057,0,0.403234768,,,0.640513444
3342,"Differential cytology profiles in bronchoalveolar lavage (BAL) in COVID-19 patients: A descriptive observation and comparison with other corona viruses, Influenza virus, Haemophilus influenzae, and Pneumocystis jirovecii.",Medicine (Baltimore),33429831,1/13/21,pubmed,0,9,dataset,0.171193632,0.389768621,0.062513599,0.002490605,0.002490571,0.371542972,Genomics,0.49448106,FALSE,47.33333333,0.608633805,24.22222222,0.523146909,0,0.403234768,,,0.511671827
3343,Symptoms and Characteristics Which Require Attention During COVID-19 Screening at a Port of Entry.,J Korean Med Sci,33429473,1/12/21,pubmed,0,4,logistic regression,0.085622241,0.105493019,0.001310398,0.297742473,0.362032131,0.147799739,Healthcare,0.7138189,TRUE,5.75,0.080957388,0,0.055525823,0,0.403234768,,,0.179905993
3344,Attendance at London workplaces after symptom onset: a retrospective cohort study of staff members with confirmed COVID-19.,J Public Health (Oxf),33429439,1/12/21,pubmed,0,6,logistic regression,0.001371262,0.001371311,0.001371289,0.001371373,0.9931434,0.001371365,Healthcare,0.67973125,TRUE,16.83333333,0.253509803,14.83333333,0.421862457,0,0.403234768,,,0.359535676
3345,Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia.,Nature,33429418,1/12/21,pubmed,0,157,"sequencing, transcriptom",0.629576814,0.112545373,0.001415163,0.001415132,0.00141515,0.253632368,Drug discovery,0.34121186,FALSE,41.72222222,0.555074525,50.27777778,0.682298635,16,0.881782826,,,0.706385329
3346,Lung adenocarcinoma patients have higher risk of SARS-CoV-2 infection.,Aging (Albany NY),33429366,1/12/21,pubmed,0,7,bioinformatic,0.71895456,0.026419021,0.001593508,0.001593511,0.001593511,0.249845889,Drug discovery,0.9478781,TRUE,48.85714286,0.621126848,12.57142857,0.393497458,6,0.764429903,,,0.59301807
3347,CRISPR systems: Novel approaches for detection and combating COVID-19.,Virus Res,33428981,1/12/21,pubmed,0,6,genomes,0.382149672,0.350221292,0.263450405,0.001392916,0.00139286,0.001392856,Drug discovery,0.514996,TRUE,22.83333333,0.337868761,9.5,0.345531175,0,0.403234768,,,0.362211568
3348,"Waste not and stay at home" evidence of decreased food waste during the COVID-19 pandemic from the U.S. and Italy.,Appetite,33428972,1/12/21,pubmed,0,9,logistic regression,0.001310351,0.001310405,0.00131034,0.0013104,0.99344815,0.001310355,Healthcare,0.863085,TRUE,96,0.858927577,90.66666667,0.802247792,0,0.403234768,,,0.688136712
3349,6-month consequences of COVID-19 in patients discharged from hospital: a cohort study.,Lancet,33428867,1/12/21,pubmed,0,32,logistic regression,0.009966434,0.000474429,0.057381352,0.037598887,0.424447877,0.470131021,Clinics,0.96376365,TRUE,25.66666667,0.376461129,637.3333333,0.985483008,81,0.973331687,,,0.778425275
3350,COVID-19 Social Distancing Measures and Loneliness Among Older Adults.,J Gerontol B Psychol Sci Soc Sci,33428753,1/12/21,pubmed,0,4,logistic regression,0.001272671,0.001272648,0.001272629,0.001272729,0.993636665,0.001272658,Healthcare,0.82043624,TRUE,46,0.596882924,30.75,0.57499331,0,0.403234768,,,0.525037001
3351,The psychological effects of COVID-19 on hospital workers at the beginning of the outbreak with a large disease cluster on the Diamond Princess cruise ship.,PLoS One,33428676,1/12/21,pubmed,0,24,logistic regression,0.001486392,0.001486498,0.001486394,0.001486436,0.893333446,0.100720833,Healthcare,0.9751731,TRUE,41.45833333,0.552415115,16.20833333,0.438319508,0,0.403234768,,,0.464656464
3352,Full length genomic sanger sequencing and phylogenetic analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Nigeria.,PLoS One,33428634,1/12/21,pubmed,0,19,sequencing,0.043557796,0.899980379,0.052697948,0.001254648,0.001254622,0.001254608,Genomics,0.7756492,TRUE,17.36842105,0.261364339,15,0.42594327,1,0.537564047,,,0.408290552
3353,Social determinants of mortality from COVID-19: A simulation study using NHANES.,PLoS Med,33428624,1/12/21,pubmed,0,3,logistic regression,0.001059328,0.001059355,0.001059336,0.059961516,0.557690418,0.379170047,Healthcare,0.52142227,TRUE,164.3333333,0.954357103,469.3333333,0.977588975,2,0.618927094,,,0.850291057
3354,Empiric evidence of ethnic disparities in coronavirus positivity in Washington State.,Ethn Health,33428455,1/12/21,pubmed,0,2,logistic regression,0.001350384,0.001350361,0.001350351,0.001350445,0.611589461,0.383008999,Healthcare,0.48848253,FALSE,21,0.312016822,3.5,0.213607172,1,0.537564047,,,0.354396014
3355,Screening druggable targets and predicting therapeutic drugs for COVID-19 via integrated bioinformatics analysis.,Genes Genomics,33428154,1/12/21,pubmed,0,6,"bioinformatic, data mining",0.928687028,0.001330061,0.001330083,0.065992681,0.00133009,0.001330056,Drug discovery,0.9834727,TRUE,9.5,0.143051518,0.5,0.087101953,0,0.403234768,,,0.211129413
3356,SARS-CoV-2-specific CD8+ T cell responses in convalescent COVID-19 individuals.,J Clin Invest,33427749,1/12/21,pubmed,0,22,proteom,0.766670617,0.22831079,0.001254675,0.001254641,0.001254608,0.00125467,Drug discovery,0.21539512,FALSE,115.3636364,0.899437195,161.2727273,0.893229864,3,0.667819001,,,0.82016202
3357,0,J Biomol Struct Dyn,33427588,1/12/21,pubmed,0,5,in silico,0.902509801,0.001203418,0.001203429,0.001203413,0.001203443,0.092676496,Drug discovery,0.95701146,TRUE,3.2,0.038468675,0,0.055525823,0,0.403234768,,,0.165743088
3358,Spontaneous pregnancies among infertile couples during assisted reproduction lockdown for COVID-19 pandemic.,Andrology,33427417,1/12/21,pubmed,0,6,neural network,0.001022649,0.001022661,0.218723292,0.393161456,0.385047258,0.001022684,Epidemiology,0.97025555,TRUE,55.66666667,0.672335952,19.83333333,0.478458657,0,0.403234768,,,0.518009792
3359,A systematic review of experimental evidence for antiviral effects of ivermectin and an in silico analysis of ivermectin's possible mode of action against SARS-CoV-2.,Fundam Clin Pharmacol,33427370,1/12/21,pubmed,0,2,in silico,0.822369794,0.080637087,0.001415159,0.057337006,0.001415175,0.036825779,Drug discovery,0.82533896,TRUE,75,0.787061661,69.5,0.752006957,0,0.403234768,,,0.647434462
3360,Routine saliva testing for the identification of silent coronavirus disease 2019 (COVID-19) in healthcare workers.,Infect Control Hosp Epidemiol,33427141,1/12/21,pubmed,0,6,simulation model,0.001156251,0.001156372,0.150010729,0.356006887,0.43265279,0.05901697,Healthcare,0.2968989,FALSE,116.6666667,0.901787371,123.5,0.855833556,0,0.403234768,,,0.720285232
3361,"ACE2-derived peptides interact with the RBD domain of SARS-CoV-2 spike glycoprotein, disrupting the interaction with the human ACE2 receptor.",J Biomol Struct Dyn,33427102,1/12/21,pubmed,0,7,"bioinformatic, in silico",0.991413811,0.001717241,0.001717205,0.001717299,0.001717249,0.001717195,Drug discovery,0.47086236,FALSE,13,0.197352959,1.571428571,0.139416644,1,0.537564047,,,0.29144455
3362,Adapting the role of handheld echocardiography during the COVID-19 pandemic: A practical guide.,Perfusion,33427055,1/12/21,pubmed,0,28,dataset,0.002422374,0.058541732,0.325110916,0.31148628,0.133033077,0.169405623,Epidemiology,0.9118537,TRUE,94.71428571,0.856082627,111.3571429,0.840379984,0,0.403234768,,,0.699899126
3363,Chest CT features associated with the clinical characteristics of patients with COVID-19 pneumonia.,Ann Med,33426973,1/12/21,pubmed,0,9,sequencing,0.001565285,0.052050856,0.584734251,0.001565335,0.001565361,0.358518914,Imaging,0.96574396,TRUE,64.44444444,0.731214052,10.66666667,0.365333155,0,0.403234768,,,0.499927325
3364,Application of Plastic Sheet Barrier and Video Intubating Stylet to Protect Tracheal Intubators During Coronavirus Disease 2019 Pandemic: A Taiwan Experience.,Cell Transplant,33426911,1/12/21,pubmed,0,5,simulation model,0.134797887,0.001593502,0.349127308,0.194399895,0.217309064,0.102772344,Healthcare,0.92706394,TRUE,15.6,0.235450554,3.6,0.21507894,0,0.403234768,,,0.284588087
3365,NMR Spectroscopic Windows on the Systemic Effects of SARS-CoV-2 Infection on Plasma Lipoproteins and Metabolites in Relation to Circulating Cytokines.,J Proteome Res,33426894,1/12/21,pubmed,0,15,"proteom, immunome",0.172588768,0.222931283,0.001046848,0.001046855,0.001046841,0.601339405,Clinics,0.708385,TRUE,141.2,0.935370153,222.6,0.927214343,0,0.403234768,,,0.755273088
3366,Role of Multiomics Data to Understand Host-Pathogen Interactions in COVID-19 Pathogenesis.,J Proteome Res,33426872,1/12/21,pubmed,0,5,"bioinformatic, proteom, metabolom, multiom",0.485832837,0.4269856,0.002032782,0.002032825,0.002032881,0.081083075,Drug discovery,0.80752623,TRUE,47.4,0.609004886,21.8,0.501204174,0,0.403234768,,,0.504481276
3367,Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond.,Int J Nurs Stud Adv,33426534,1/12/21,pubmed,0,2,artificial intelligence,0.029637442,0.001987213,0.482526699,0.254384681,0.111410356,0.120053609,Epidemiology,0.95235646,TRUE,42,0.558537943,20,0.481000803,0,0.403234768,,,0.480924504
3368,A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening.,iScience,33426509,1/12/21,pubmed,0,37,"virtual screening, in silico",0.989346449,0.002130746,0.0021307,0.00213073,0.002130734,0.002130641,Drug discovery,0.6571083,TRUE,22.83783784,0.337930608,46.40540541,0.666109178,1,0.537564047,,,0.513867945
3369,Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic.,Int J Inf Technol,33426425,1/12/21,pubmed,0,5,"deep learning, artificial intelligence, lstm",0.001461879,0.001461893,0.689838032,0.304314321,0.001461947,0.001461929,Epidemiology,0.6576731,TRUE,34.8,0.484940318,2.4,0.174872893,0,0.403234768,,,0.354349326
3370,ALeRT-COVID: Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries.,J Healthc Inform Res,33426422,1/12/21,pubmed,0,8,"neural network, transfer learning",0.001371337,0.001371261,0.330723202,0.616328034,0.048834848,0.001371318,Epidemiology,0.32258523,FALSE,27.625,0.402498608,22.875,0.510971367,0,0.403234768,,,0.438901581
3371,Matched cohort study on the efficacy of tocilizumab in patients with COVID-19.,One Health,33426262,1/12/21,pubmed,0,36,logistic regression,0.001461883,0.001461859,0.001461963,0.072589339,0.001461872,0.921563083,Clinics,0.59793735,TRUE,4.459459459,0.058568866,,,1,0.537564047,,,0.298066457
3372,0,Front Mol Biosci,33425994,1/12/21,pubmed,0,4,bioinformatic,0.910296722,0.001684481,0.001684521,0.001684492,0.001684606,0.082965178,Drug discovery,0.95490354,TRUE,41,0.549013544,28.25,0.556061012,1,0.537564047,,,0.547546201
3373,"Association of Cigarette Smoking, COPD, and Lung Cancer With Expression of SARS-CoV-2 Entry Genes in Human Airway Epithelial Cells.",Front Med (Lausanne),33425965,1/12/21,pubmed,0,4,"transcriptom, dataset",0.706157807,0.001622902,0.00162277,0.001622773,0.001622774,0.287350975,Drug discovery,0.6088145,TRUE,26,0.382398417,23.25,0.514583891,0,0.403234768,,,0.433405692
3374,Case Report: Famotidine for Neuropsychiatric Symptoms in COVID-19.,Front Med (Lausanne),33425958,1/12/21,pubmed,0,1,in silico,0.116083267,0.001565391,0.001565456,0.001565383,0.30555553,0.573664972,Clinics,0.9213476,TRUE,39,0.530521368,142,0.87643832,0,0.403234768,,,0.603398152
3375,COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images.,Front Med (Lausanne),33425953,1/12/21,pubmed,0,3,"neural network, dataset",0.000854709,0.000854706,0.995726287,0.000854756,0.00085477,0.000854772,Imaging,0.41781145,FALSE,26,0.382398417,52,0.689523682,13,0.858880178,,,0.643600759
3376,Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis.,Front Med (Lausanne),33425939,1/12/21,pubmed,0,14,prediction model,0.001171541,0.001171546,0.377129052,0.001171569,0.001171552,0.61818474,Clinics,0.7445032,TRUE,30,0.432432432,10.57142857,0.363794488,0,0.403234768,,,0.399820563
3377,Computational Prediction of Potential Inhibitors of the Main Protease of SARS-CoV-2.,Front Chem,33425850,1/12/21,pubmed,0,12,"virtual screening, molecular dynamics simulation, computational, dataset",0.889469705,0.001565342,0.001565367,0.027020803,0.001565345,0.078813438,Drug discovery,0.82303697,TRUE,102,0.873523409,446.3333333,0.975046829,1,0.537564047,,,0.795378095
3378,"Knowledge, Attitude, and Self-Reported Practice Toward Measures for Prevention of the Spread of COVID-19 Among Ugandans: A Nationwide Online Cross-Sectional Survey.",Front Public Health,33425842,1/12/21,pubmed,0,7,logistic regression,0.00159349,0.001593492,0.001593494,0.001593582,0.992032455,0.001593487,Healthcare,0.21140078,FALSE,16.71428571,0.251407013,10.14285714,0.356636339,1,0.537564047,,,0.381869133
3379,0,J Adv Pharm Technol Res,33425697,1/12/21,pubmed,0,5,in silico,0.949862572,0.041814955,0.002080615,0.002080667,0.002080577,0.002080613,Drug discovery,0.6901833,TRUE,14.6,0.220298101,,,1,0.537564047,,,0.378931074
3380,Predicting the European stock market during COVID-19: A machine learning approach.,MethodsX,33425689,1/12/21,pubmed,0,3,machine learning,0.001786553,0.126490921,0.086434369,0.55152884,0.001786592,0.231972725,Epidemiology,0.671229,TRUE,28,0.408312202,10.66666667,0.365333155,0,0.403234768,,,0.392293375
3381,SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2.,Sustain Cities Soc,33425664,1/12/21,pubmed,0,6,"deep learning, image processing, classifier, dataset",0.001717215,0.001717189,0.867848828,0.125282278,0.001717304,0.001717186,Imaging,0.5455619,TRUE,40.66666667,0.546292288,3.833333333,0.222103291,4,0.707574542,,,0.49199004
3382,Designing a conserved peptide-based subunit vaccine against SARS-CoV-2 using immunoinformatics approach.,In Silico Pharmacol,33425647,1/12/21,pubmed,0,16,bioinformatic,0.590349131,0.192335388,0.042229061,0.002422425,0.170241702,0.002422293,Drug discovery,0.5333677,TRUE,7.1875,0.103098522,0.625,0.090446883,0,0.403234768,,,0.198926724
3383,An epidemic model integrating direct and fomite transmission as well as household structure applied to COVID-19.,J Math Ind,33425640,1/12/21,pubmed,0,6,dataset,0.000926337,0.000926309,0.000926299,0.774841011,0.221453769,0.000926275,Epidemiology,0.23068473,FALSE,16.83333333,0.253509803,8.333333333,0.325662296,0,0.403234768,,,0.327468955
3384,ENJ algorithm can construct triple phylogenetic trees.,Mol Ther Nucleic Acids,33425487,1/12/21,pubmed,0,3,dataset,0.001350353,0.578981139,0.344763752,0.072204118,0.001350318,0.001350321,Genomics,0.41980278,FALSE,160.6666667,0.952068774,111.6666667,0.840915173,0,0.403234768,,,0.732072905
3385,Computational Biology Analysis of COVID-19 Receptor-Binding Domains: A Target Site for Indocyanine Green Through Antimicrobial Photodynamic Therapy.,J Lasers Med Sci,33425294,1/12/21,pubmed,0,2,"computational, bioinformatic, in silico",0.993636377,0.001272761,0.001272703,0.00127272,0.001272692,0.001272746,Drug discovery,0.9628526,TRUE,144.5,0.939080957,42,0.644902328,0,0.403234768,,,0.662406018
3386,Predisposing risk factors for COVID-19 infection: A case-control study.,Caspian J Intern Med,33425266,1/12/21,pubmed,0,3,logistic regression,0.002238459,0.002238466,0.002238538,0.372705718,0.206160832,0.414417987,Clinics,0.75123465,TRUE,109.3333333,0.888923248,19.66666667,0.477321381,0,0.403234768,,,0.589826466
3387,"Host transcriptomic profiling of COVID-19 patients with mild, moderate, and severe clinical outcomes.",Comput Struct Biotechnol J,33425248,1/12/21,pubmed,0,13,"sequencing, transcriptom",0.578806866,0.165679039,0.001593507,0.001593516,0.00159351,0.250733561,Drug discovery,0.8501837,TRUE,39.46153846,0.533366318,27.76923077,0.551712604,0,0.403234768,,,0.496104563
3388,"Stigma, Discrimination, and Hate Crimes in Chinese-Speaking World amid Covid-19 Pandemic.",Asian J Criminol,33425062,1/12/21,pubmed,0,7,data mining,0.002357888,0.00235788,0.002357754,0.549978609,0.4405901,0.002357768,Epidemiology,0.88598835,TRUE,22.28571429,0.328653596,8.142857143,0.321447685,0,0.403234768,,,0.351112016
3389,A novel periocular biometrics solution for authentication during Covid-19 pandemic situation.,J Ambient Intell Humaniz Comput,33425055,1/12/21,pubmed,0,2,classifier,0.001511845,0.001511868,0.497470061,0.410624912,0.087369439,0.001511876,Epidemiology,0.9174423,TRUE,42.5,0.562743522,10.5,0.363459995,0,0.403234768,,,0.443146095
3390,E-DiCoNet: Extreme learning machine based classifier for diagnosis of COVID-19 using deep convolutional network.,J Ambient Intell Humaniz Comput,33425051,1/12/21,pubmed,0,2,"computational, neural network, classifier, dataset",0.001220001,0.001220016,0.896703948,0.098415926,0.001220067,0.001220041,Imaging,0.79492116,TRUE,13,0.197352959,1,0.122023013,0,0.403234768,,,0.240870247
3391,COV19-CNNet and COV19-ResNet: Diagnostic Inference Engines for Early Detection of COVID-19.,Cognit Comput,33425046,1/12/21,pubmed,0,3,"deep learning, neural network",0.001085357,0.00108542,0.971591153,0.001085386,0.001085316,0.024067367,Imaging,0.3180092,FALSE,1,0.012307502,0,0.055525823,1,0.537564047,,,0.201799124
3392,Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19.,Cognit Comput,33425044,1/12/21,pubmed,0,5,deep learning,0.00060805,0.00060808,0.996959742,0.000608052,0.000608032,0.000608043,Imaging,0.2883754,FALSE,20.6,0.305832148,10.2,0.357706717,2,0.618927094,,,0.427488653
3393,A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset.,Cognit Comput,33425043,1/12/21,pubmed,0,4,"deep learning, transfer learning, dataset",0.075894043,0.000898093,0.885415172,0.035996517,0.000898075,0.0008981,Imaging,0.4616733,FALSE,45,0.587049292,20.66666667,0.487958255,4,0.707574542,,,0.59419403
3394,A fractional complex network model for novel corona virus in China.,Adv Differ Equ,33424956,1/12/21,pubmed,0,3,network model,0.005697772,0.005697526,0.005697969,0.97151172,0.005697486,0.005697527,Epidemiology,0.800696,TRUE,21,0.312016822,39,0.62784319,0,0.403234768,,,0.44769826
3395,Mathematical model of SIR epidemic system (COVID-19) with fractional derivative: stability and numerical analysis.,Adv Differ Equ,33424955,1/12/21,pubmed,0,1,mathematical model,0.003927632,0.003927363,0.003927448,0.98036221,0.003927451,0.003927895,Epidemiology,0.5830014,TRUE,49,0.624281032,6,0.280037463,0,0.403234768,,,0.435851087
3396,Collection of SARS-CoV-2 Virus from the Air of a Clinic Within a University Student Health Care Center and Analyses of the Viral Genomic Sequence.,Aerosol Air Qual Res,33424954,1/12/21,pubmed,0,12,"sequencing, genomes",0.076376207,0.698685062,0.001220025,0.076110143,0.026705025,0.120903539,Genomics,0.17179847,FALSE,49.83333333,0.630651246,66.41666667,0.743577736,3,0.667819001,,,0.680682661
3397,0,Front Genet,33424918,1/12/21,pubmed,0,9,"bioinformatic, genomes",0.257042962,0.73891328,0.001010929,0.001010975,0.001010933,0.001010921,Genomics,0.8442239,TRUE,12.55555556,0.189683963,6.888888889,0.294153064,1,0.537564047,,,0.340467025
3398,Comparative Genomic Analyses Reveal a Specific Mutation Pattern Between Human Coronavirus SARS-CoV-2 and Bat-CoV RaTG13.,Front Microbiol,33424784,1/12/21,pubmed,0,5,genomes,0.001684557,0.991577222,0.001684482,0.001684529,0.001684618,0.001684592,Genomics,0.29646152,FALSE,21.8,0.322283382,7.8,0.313286058,1,0.537564047,,,0.391044496
3399,Prevalence and Associated Factors for Depressive Symptomatology in Chinese Adults During COVID-19 Epidemic.,Front Psychol,33424729,1/12/21,pubmed,0,6,logistic regression,0.0015381,0.001538107,0.020981909,0.001538142,0.972865614,0.001538129,Healthcare,0.5577471,TRUE,71.16666667,0.769559033,28.66666667,0.559673535,0,0.403234768,,,0.577489112
3400,Reduction of Physical Activity Levels During the COVID-19 Pandemic Might Negatively Disturb Sleep Pattern.,Front Psychol,33424702,1/12/21,pubmed,0,9,logistic regression,0.001310355,0.001310336,0.00131036,0.058785118,0.917408097,0.019875734,Healthcare,0.700342,TRUE,63.11111111,0.722184427,21.33333333,0.495450896,0,0.403234768,,,0.54029003
3401,Assessment of Depression Severity During Coronavirus Disease 2019 Pandemic Among the Palestinian Population: A Growing Concern and an Immediate Consideration.,Front Psychiatry,33424656,1/12/21,pubmed,0,4,logistic regression,0.000854712,0.000854731,0.000854743,0.000854773,0.995726279,0.000854762,Healthcare,0.9480108,TRUE,5.5,0.077246583,1.5,0.138747659,0,0.403234768,,,0.20640967
3402,Mental Health Outcomes and Associations During the COVID-19 Pandemic: A Cross-Sectional Population-Based Study in the United States.,Front Psychiatry,33424655,1/12/21,pubmed,0,2,logistic regression,0.000926272,0.000926285,0.000926295,0.000926286,0.894579016,0.101715846,Healthcare,0.8222232,TRUE,4.5,0.061784897,0,0.055525823,1,0.537564047,,,0.218291589
3403,Occupational Stress and Mental Health: A Comparison Between Frontline Medical Staff and Non-frontline Medical Staff During the 2019 Novel Coronavirus Disease Outbreak.,Front Psychiatry,33424651,1/12/21,pubmed,0,12,logistic regression,0.000988361,0.000988354,0.000988355,0.000988369,0.965549019,0.030497541,Healthcare,0.9554145,TRUE,79.16666667,0.803141815,22.41666667,0.50615467,0,0.403234768,,,0.570843751
3404,The impact of modelling choices on modelling outcomes: a spatio-temporal study of the association between COVID-19 spread and environmental conditions in Catalonia (Spain).,Stoch Environ Res Risk Assess,33424434,1/12/21,pubmed,0,1,dataset,0.001653059,0.001653098,0.001653045,0.931444792,0.06194285,0.001653156,Epidemiology,0.1654281,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
3405,A rotation based regularization method for semi-supervised learning.,Pattern Anal Appl,33424433,1/12/21,pubmed,0,4,"supervised learning, dataset",0.120700953,0.002183258,0.644222399,0.228526973,0.00218319,0.002183227,Epidemiology,0.1560947,FALSE,112.5,0.895479003,58,0.714476853,0,0.403234768,,,0.671063541
3406,Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML.,Soft comput,33424432,1/12/21,pubmed,0,5,"machine learning, dataset",0.001046822,0.001046812,0.485154453,0.450853614,0.001046835,0.060851464,Epidemiology,0.9335311,TRUE,54.6,0.66553281,51,0.685509767,0,0.403234768,,,0.584759115
3407,0,Saudi J Biol Sci,33424398,1/12/21,pubmed,0,10,"molecular dynamics simulation, in silico",0.937027959,0.001237101,0.001237084,0.02537182,0.001237091,0.033888945,Drug discovery,0.9693994,TRUE,21,0.312016822,2.7,0.185576666,1,0.537564047,,,0.345052512
3408,Behavioral and psychosocial factors associated with COVID-19 skepticism in the United States.,Curr Psychol,33424206,1/12/21,pubmed,0,5,logistic regression,0.001751131,0.001751134,0.001751123,0.001751202,0.957606117,0.035389293,Healthcare,0.9326813,TRUE,58.6,0.693425691,14.6,0.419253412,1,0.537564047,,,0.55008105
3409,Mathematical prediction of the spreading rate of COVID-19 using entropy-based thermodynamic model.,Indian J Phys Proc Indian Assoc Cultiv Sci (2004),33424191,1/12/21,pubmed,0,3,"model fit, mathematical prediction, mathematical model",0.002032802,0.002032783,0.002032806,0.989836027,0.00203275,0.002032833,Epidemiology,0.65436965,TRUE,61,0.709196611,4,0.231469093,0,0.403234768,,,0.447966824
3410,Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method.,Neural Comput Appl,33424133,1/12/21,pubmed,0,13,"machine learning, logistic regression",0.001684508,0.001684517,0.444328942,0.00168455,0.001684596,0.548932887,Clinics,0.5915014,TRUE,22.53846154,0.333725029,,,1,0.537564047,,,0.435644538
3411,Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning.,J Supercomput,33424118,1/12/21,pubmed,0,2,"machine learning, data mining, classifier, information retrieval, dataset",0.001046868,0.213746482,0.721710273,0.001046912,0.001046851,0.061402614,Genomics,0.8469411,TRUE,9,0.135320675,2.5,0.180826866,0,0.403234768,,,0.239794103
3412,"Age Distribution of Clinical Symptoms, Isolation, Co-morbidities and Case Fatality Rate of COVID-19 Cases in Najaf City, Iraq.",Med Arch,33424091,1/12/21,pubmed,0,5,sequencing,0.001438091,0.194369863,0.001438118,0.001438229,0.134903933,0.666411766,Clinics,0.66407335,TRUE,33.4,0.47046818,5.8,0.272678619,0,0.403234768,,,0.382127189
3413,Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec.,Ann Oper Res,33424076,1/12/21,pubmed,0,2,mathematical model,0.001653091,0.001653048,0.278240225,0.715147408,0.001653099,0.001653128,Epidemiology,0.69709516,TRUE,13.5,0.205393036,7,0.299973241,0,0.403234768,,,0.302867015
3414,Convolutional Neural Networks for Semantic Segmentation as a Tool for Multiclass Face Analysis in Thermal Infrared.,J Nondestr Eval,33424071,1/12/21,pubmed,0,3,neural network,0.001237091,0.001237105,0.761695505,0.233356105,0.001237126,0.001237068,Imaging,0.7588123,TRUE,3.666666667,0.045952131,0.666666667,0.096200161,2,0.618927094,,,0.253693128
3415,Big data augmentated business trend identification: the case of mobile commerce.,Scientometrics,33424052,1/12/21,pubmed,0,4,"machine learning, text-mining",0.001272697,0.001272717,0.153165125,0.841744174,0.001272657,0.001272629,Epidemiology,0.9092226,TRUE,11.75,0.177562001,0,0.055525823,0,0.403234768,,,0.212107531
3416,Using altmetrics for detecting impactful research in quasi-zero-day time-windows: the case of COVID-19.,Scientometrics,33424050,1/12/21,pubmed,0,8,knowledge graph,0.001254632,0.058774327,0.09908377,0.838377889,0.001254686,0.001254696,Epidemiology,0.14559168,FALSE,114.375,0.898076566,130,0.863928285,1,0.537564047,,,0.766522966
3417,Restoring Good Health in Elderly with Diverse Gut Microbiome and Food Intake Restriction to Combat COVID-19.,Indian J Microbiol,33424043,1/12/21,pubmed,0,1,microbiom,0.425816394,0.098609627,0.001254602,0.00125469,0.239936593,0.233128093,Drug discovery,0.59848756,TRUE,7,0.10179974,1,0.122023013,2,0.618927094,,,0.280916616
3418,0,J Mol Struct,33424034,1/12/21,pubmed,0,8,in silico,0.988517113,0.002296555,0.002296659,0.002296592,0.00229655,0.00229653,Drug discovery,0.6339048,TRUE,75.25,0.787865669,5.625,0.26839711,1,0.537564047,,,0.531275609
3419,Recent advances in the diagnosis of COVID-19: a bird's eye view.,Expert Rev Mol Diagn,33423567,1/12/21,pubmed,0,4,proteom,0.001653058,0.001653125,0.578307499,0.298808275,0.11792494,0.001653104,Epidemiology,0.80122817,TRUE,21.25,0.314614386,4.5,0.242708055,0,0.403234768,,,0.320185736
3420,Glucagon-like peptide-1 receptor agonists in the era of COVID-19: Friend or foe?,Clin Obes,33423388,1/11/21,pubmed,0,3,microbiom,0.662164657,0.065657853,0.003335312,0.003335354,0.003335288,0.262171536,Drug discovery,0.8669821,TRUE,25.66666667,0.376461129,0.333333333,0.073187048,0,0.403234768,,,0.284294315
3421,Selecting pharmacies for COVID-19 testing to ensure access.,Health Care Manag Sci,33423180,1/11/21,pubmed,0,4,optimization model,0.001901722,0.084773945,0.001901846,0.720751478,0.188769275,0.001901735,Epidemiology,0.23854575,FALSE,59,0.696579875,95,0.813085363,0,0.403234768,,,0.637633335
3422,Transcriptome analysis of cepharanthine against a SARS-CoV-2-related coronavirus.,Brief Bioinform,33423067,1/11/21,pubmed,0,10,"sequencing, transcriptom",0.958403733,0.034727328,0.001717167,0.001717165,0.001717406,0.001717201,Drug discovery,0.8447143,TRUE,43.4,0.571958686,34.8,0.604094193,0,0.403234768,,,0.526429216
3423,Identification of SARS-CoV-2 CTL epitopes for development of a multivalent subunit vaccine for COVID-19.,Infect Genet Evol,33422682,1/11/21,pubmed,0,5,"computational, proteom",0.944963064,0.048298604,0.001684645,0.001684558,0.001684589,0.001684541,Drug discovery,0.74842227,TRUE,22.6,0.334652731,5.8,0.272678619,0,0.403234768,,,0.336855372
3424,Expression of SARS-COV-2 cell receptor gene ACE2 is associated with immunosuppression and metabolic reprogramming in lung adenocarcinoma based on bioinformatics analyses of gene expression profiles.,Chem Biol Interact,33422520,1/11/21,pubmed,0,4,"bioinformatic, network analysis",0.778128205,0.001415116,0.001415111,0.0014151,0.001415113,0.216211354,Drug discovery,0.7010675,TRUE,16.75,0.252458408,5.75,0.271742039,0,0.403234768,,,0.309145072
3425,Transfer Learning for COVID-19 cases and deaths forecast using LSTM network.,ISA Trans,33422330,1/11/21,pubmed,0,1,"transfer learning, lstm",0.004109909,0.004109916,0.272278698,0.711281876,0.004109805,0.004109796,Epidemiology,0.58443123,TRUE,3,0.037293586,0,0.055525823,0,0.403234768,,,0.165351392
3426,[Increased levels of ferritin on admission predicts intensive care unit mortality in patients with COVID-19].,Med Clin (Barc),33422296,1/11/21,pubmed,0,10,logistic regression,0.001350389,0.001350367,0.027848734,0.001350384,0.001350309,0.966749817,Clinics,0.87522125,TRUE,23.9,0.352279053,5,0.257024351,0,0.403234768,,,0.337512724
3427,"Relationship of socio-demographics, comorbidities, symptoms and healthcare access with early COVID-19 presentation and disease severity.",BMC Infect Dis,33421991,1/11/21,pubmed,0,6,in silico,0.001943465,0.001943517,0.001943586,0.001943618,0.123751784,0.86847403,Clinics,0.7238716,TRUE,31.66666667,0.451976003,23,0.513513514,0,0.403234768,,,0.456241428
3428,Evaluation of Systemic Involvement of Coronavirus Disease 2019 through Spleen; Size and Texture Analysis.,Turk J Med Sci,33421971,1/11/21,pubmed,0,5,radiom,0.001371393,0.001371307,0.431594025,0.13557199,0.001371315,0.428719971,Imaging,0.78317153,TRUE,27,0.3960047,2.4,0.174872893,0,0.403234768,,,0.32470412
3429,Association between psychological resilience and changes in mental distress during the COVID-19 pandemic.,J Affect Disord,33421866,1/10/21,pubmed,0,11,logistic regression,0.001291246,0.001291255,0.00129127,0.050534946,0.944300005,0.001291278,Healthcare,0.8582964,TRUE,97.54545455,0.863566083,180.2727273,0.906275087,2,0.618927094,,,0.796256088
3430,Network analysis and molecular mapping for SARS-CoV-2 to reveal drug targets and repurposing of clinically developed drugs.,Virology,33421743,1/10/21,pubmed,0,4,network analysis,0.912928721,0.001330077,0.001330031,0.001330042,0.001330039,0.081751091,Drug discovery,0.7497934,TRUE,11.25,0.169274538,0.25,0.065493712,0,0.403234768,,,0.212667672
3431,Impairment in selenocysteine synthesis as a candidate mechanism of inducible coagulopathy in COVID-19 patients.,Med Hypotheses,33421689,1/10/21,pubmed,0,3,transcriptom,0.492704244,0.004109909,0.004110043,0.12107961,0.004110277,0.373885917,Drug discovery,0.841214,TRUE,20,0.298163152,1.666666667,0.145036125,0,0.403234768,,,0.282144682
3432,Topological Analysis for Sequence Variability: Case Study on more than 2K SARS-CoV-2 sequences of COVID-19 infected 54 countries in comparison with SARS-CoV-1 and MERS-CoV.,Infect Genet Evol,33421654,1/10/21,pubmed,0,5,"computational, bioinformatic",0.001291272,0.53690629,0.001291312,0.45792861,0.001291281,0.001291235,Genomics,0.27465945,FALSE,100.4,0.869688911,123.4,0.855632861,0,0.403234768,,,0.709518846
3433,"Denovo designing, retro-combinatorial synthesis, and molecular dynamics analysis identify novel antiviral VTRM1.1 against RNA-dependent RNA polymerase of SARS CoV2 virus.",Int J Biol Macromol,33421473,1/10/21,pubmed,0,1,"molecular dynamics simulation, in-silico",0.994218755,0.001156267,0.001156267,0.001156245,0.001156229,0.001156236,Drug discovery,0.5970963,TRUE,43,0.567629414,11,0.371287129,0,0.403234768,,,0.44738377
3434,First genome sequencing of SARS-CoV-2 recovered from an infected cat and its owner in Latin America.,Transbound Emerg Dis,33421326,1/10/21,pubmed,0,12,"sequencing, genome sequences",0.00321411,0.490237371,0.003214217,0.003214259,0.289915058,0.210204985,Genomics,0.3636239,FALSE,22.83333333,0.337868761,9.833333333,0.351284453,0,0.403234768,,,0.364129327
3435,Prevalence of asymptomatic SARS-CoV-2 infection in elective surgical patients in Australia: a prospective surveillance study.,ANZ J Surg,33421257,1/10/21,pubmed,0,9,bayes,0.001461911,0.330439462,0.001462103,0.182804943,0.205330217,0.278501365,Genomics,0.5014233,TRUE,194.1111111,0.97074649,244.4444444,0.936044956,1,0.537564047,,,0.814785164
3436,Pre-diagnostic circulating concentrations of insulin-like growth factor-1 and risk of COVID-19 mortality: results from UK Biobank.,Eur J Epidemiol,33420872,1/10/21,pubmed,0,10,logistic regression,0.08665949,0.001987184,0.00198715,0.164379987,0.001987137,0.742999053,Clinics,0.40160552,FALSE,90.7,0.844888367,60.7,0.723374364,0,0.403234768,,,0.657165833
3437,Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network.,Int J Comput Assist Radiol Surg,33420641,1/10/21,pubmed,0,5,"computational, neural network, dataset",0.00076593,0.00076594,0.996170328,0.000765951,0.00076592,0.000765931,Imaging,0.23743054,FALSE,64.75,0.733440534,34,0.5990768,0,0.403234768,,,0.578584034
3438,Glycemic status affects the severity of coronavirus disease 2019 in patients with diabetes mellitus: an observational study of CT radiological manifestations using an artificial intelligence algorithm.,Acta Diabetol,33420614,1/10/21,pubmed,0,8,"artificial intelligence, logistic regression",0.0006165,0.000616502,0.347733015,0.000616509,0.000616512,0.649800961,Clinics,0.8868437,TRUE,54.625,0.665842043,32.75,0.590647578,0,0.403234768,,,0.553241463
3439,"COVID-19 Hospitalization by Race and Ethnicity: Association with Chronic Conditions Among Medicare Beneficiaries, January 1-September 30, 2020.",J Racial Ethn Health Disparities,33420609,1/10/21,pubmed,0,3,logistic regression,0.001187283,0.001187277,0.001187304,0.001187343,0.282804009,0.712446784,Clinics,0.75736445,TRUE,44,0.578390748,64,0.735549906,1,0.537564047,,,0.617168234
3440,ABBV-744 as a potential inhibitor of SARS-CoV-2 main protease enzyme against COVID-19.,Sci Rep,33420186,1/10/21,pubmed,0,5,"virtual screening, molecular dynamics simulation, computational",0.993814621,0.001237081,0.00123712,0.001237069,0.001237046,0.001237063,Drug discovery,0.8301264,TRUE,14,0.213494959,1.8,0.150120417,0,0.403234768,,,0.255616715
3441,Host mitochondrial transcriptome response to SARS-CoV-2 in multiple cell models and clinical samples.,Sci Rep,33420163,1/10/21,pubmed,0,9,transcriptom,0.90669717,0.082077312,0.002806382,0.002806384,0.002806365,0.002806386,Drug discovery,0.35732275,FALSE,46.11111111,0.597377698,78.88888889,0.775822853,2,0.618927094,,,0.664042548
3442,Pathological features of COVID-19-associated liver injury-a preliminary proteomics report based on clinical samples.,Signal Transduct Target Ther,33419962,1/10/21,pubmed,0,10,proteom,0.015998879,0.266590685,0.01600146,0.015998881,0.01599846,0.669411636,Clinics,0.44506663,FALSE,80.5,0.809202795,88.5,0.797966283,0,0.403234768,,,0.670134615
3443,Without a trace: Why did corona apps fail?,J Med Ethics,33419939,1/10/21,pubmed,0,2,mathematical model,0.002032758,0.002032833,0.002032956,0.989835838,0.002032855,0.002032761,Epidemiology,0.27448273,FALSE,16,0.243552477,4.5,0.242708055,0,0.403234768,,,0.296498433
3444,Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK.,Science,33419936,1/10/21,pubmed,0,26,genomes,0.001751164,0.615132635,0.00175118,0.377862639,0.001751209,0.001751173,Genomics,0.40880752,FALSE,69.26923077,0.760096481,637.8076923,0.985616805,24,0.914439163,,,0.886717483
3445,What are SARS-CoV-2 genomes from the WHO Africa region member states telling us?,BMJ Glob Health,33419930,1/10/21,pubmed,0,5,genomes,0.025060795,0.874694222,0.025060474,0.025060871,0.025063415,0.025060224,Genomics,0.35842574,FALSE,84,0.821881378,190.2,0.912162162,0,0.403234768,,,0.712426103
3446,Influenza virus infection increases ACE2 expression and shedding in human small airway epithelial cells.,Eur Respir J,33419885,1/10/21,pubmed,0,14,sequencing,0.764917215,0.081914294,0.001565338,0.051586113,0.001565322,0.098451718,Drug discovery,0.30820593,FALSE,121.8571429,0.909456367,120.8571429,0.85282312,0,0.403234768,,,0.721838085
3447,Network graph representation of COVID-19 scientific publications to aid knowledge discovery.,BMJ Health Care Inform,33419870,1/10/21,pubmed,0,7,"text mining, dataset",0.001901777,0.001901757,0.227742844,0.764649984,0.00190185,0.001901787,Epidemiology,0.7415082,TRUE,59.71428571,0.701032841,6.571428571,0.288734279,0,0.403234768,,,0.464333962
3448,"Lymphocytes, Interleukin 6 and D-dimer Cannot Predict Clinical Outcome in Coronavirus Cancer Patients: LyNC1.20 Study.",Anticancer Res,33419825,1/10/21,pubmed,0,18,logistic regression,0.001861787,0.001861697,0.001861698,0.001861717,0.070656906,0.921896195,Clinics,0.9345681,TRUE,39.27777778,0.532314924,15.66666667,0.432365534,0,0.403234768,,,0.455971742
3449,Nutritional and Behavioral Approaches to Body Composition and Low-Grade Chronic Inflammation Management for Older Adults in the Ordinary and COVID-19 Times.,Nutrients,33419325,1/10/21,pubmed,0,1,microbiom,0.120252831,0.041189594,0.001622762,0.338718512,0.406124945,0.092091356,Healthcare,0.96282876,TRUE,160,0.951697693,189,0.911560075,0,0.403234768,,,0.755497512
3450,Can Coronaviruses Steal Genes from the Host as Evidenced in Western European Hedgehogs by EriCoV Genetic Characterization?,Viruses,33419245,1/10/21,pubmed,0,8,"sequencing, genomes",0.064856279,0.928405768,0.001684486,0.001684498,0.001684479,0.001684489,Genomics,0.8062445,TRUE,50.375,0.633929124,64.25,0.736018196,0,0.403234768,,,0.591060696
3451,"Rise of 'Lonely' Consumers in the Post-COVID-19 Era: A Synthesised Review on Psychological, Commercial and Social Implications.",Int J Environ Res Public Health,33419194,1/10/21,pubmed,0,3,network analysis,0.001943603,0.001943577,0.001943546,0.811770132,0.180455657,0.001943484,Epidemiology,0.9448211,TRUE,28,0.408312202,9,0.337904736,0,0.403234768,,,0.383150569
3452,0,Viruses,33418950,1/10/21,pubmed,0,5,molecular dynamics simulation,0.830662872,0.163289708,0.001511823,0.00151189,0.001511809,0.001511897,Drug discovery,0.5816862,TRUE,15.2,0.229142186,13.4,0.404602622,1,0.537564047,,,0.390436285
3453,An in-silico evaluation of different bioactive molecules of tea for their inhibition potency against non structural protein-15 of SARS-CoV-2.,Food Chem,33418408,1/9/21,pubmed,0,6,"molecular dynamics simulation, in-silico",0.991577255,0.001684542,0.001684515,0.001684606,0.001684589,0.001684494,Drug discovery,0.9438568,TRUE,487,0.997216897,258.8333333,0.940995451,0,0.403234768,,,0.780482372
3454,"Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19, a study in Hong Kong.",Sci Total Environ,33418356,1/9/21,pubmed,0,13,logistic regression,0.002130674,0.002130881,0.002130728,0.801641883,0.143431039,0.048534795,Epidemiology,0.6440918,TRUE,26.92307692,0.393283444,6,0.280037463,0,0.403234768,,,0.358851891
3455,Increased expression of antibiotic-resistance genes in biofilm communities upon exposure to cetyltrimethylammonium bromide (CTAB) and other stress conditions.,Sci Total Environ,33418325,1/9/21,pubmed,0,8,transcriptom,0.497405505,0.002238612,0.098824921,0.002238576,0.397053756,0.00223863,Drug discovery,0.88062286,TRUE,121.25,0.908466819,282.125,0.949424672,0,0.403234768,,,0.753708753
3456,Could cilostazol be beneficial in COVID-19 treatment? Thinking about phosphodiesterase-3 as a therapeutic target.,Int Immunopharmacol,33418248,1/9/21,pubmed,0,7,virtual screening,0.84448589,0.001415158,0.001415177,0.052780071,0.001415165,0.09848854,Drug discovery,0.955606,TRUE,7.428571429,0.106376399,0,0.055525823,1,0.537564047,,,0.233155423
3457,Increased risk for COVID-19 in patients with vitamin D deficiency.,Nutrition,33418230,1/9/21,pubmed,0,3,logistic regression,0.00137134,0.001371303,0.08359053,0.001371317,0.110912582,0.801382927,Clinics,0.7633829,TRUE,50.33333333,0.63380543,30,0.570176612,5,0.739490092,,,0.647824045
3458,Reciprocating-flowing on-a-chip enables ultra-fast immunobinding for multiplexed rapid ELISA detection of SARS-CoV-2 antibody.,Biosens Bioelectron,33418184,1/9/21,pubmed,0,9,proteom,0.002422339,0.384964687,0.380851167,0.226916868,0.002422416,0.002422523,Genomics,0.8864198,TRUE,10.77777778,0.1604923,12.11111111,0.3868076,0,0.403234768,,,0.316844889
3459,Recent trends in analytical and digital techniques for the detection of the SARS-Cov-2.,Biophys Chem,33418105,1/9/21,pubmed,0,4,sequencing,0.001823419,0.377285784,0.615420478,0.001823516,0.001823431,0.001823372,Genomics,0.8989829,TRUE,8.75,0.129383388,3,0.199424672,2,0.618927094,,,0.315911718
3460,"MicroPhenoDB Associates Metagenomic Data with Pathogenic Microbes, Microbial Core Genes, and Human Disease Phenotypes.",Genomics Proteomics Bioinformatics,33418085,1/9/21,pubmed,0,9,"computational, metagenom",0.636360121,0.135489104,0.122899124,0.001392946,0.102465799,0.001392907,Drug discovery,0.65196085,TRUE,44.77777778,0.583833261,73.66666667,0.762911426,0,0.403234768,,,0.583326485
3461,0,Environ Res,33417906,1/9/21,pubmed,0,4,neural network,0.00242262,0.002422395,0.257459113,0.732851264,0.002422279,0.002422328,Epidemiology,0.66160434,TRUE,18,0.271569052,1,0.122023013,0,0.403234768,,,0.265608944
3462,"Association between exposure to airborne pollutants and COVID-19 in Los Angeles, United States with ensemble-based dynamic emission model.",Environ Res,33417905,1/9/21,pubmed,0,2,"machine learning, ensemble learning, dataset",0.001565368,0.027596358,0.251012866,0.492712259,0.001565415,0.225547734,Epidemiology,0.6123355,TRUE,29,0.41993939,3.5,0.213607172,0,0.403234768,,,0.345593776
3463,"Clinical Characteristics, Frailty, and Mortality of Residents With COVID-19 in Nursing Homes of a Region of Madrid.",J Am Med Dir Assoc,33417840,1/9/21,pubmed,0,24,logistic regression,0.001371384,0.001371283,0.001371404,0.001371366,0.285185178,0.709329385,Clinics,0.8129452,TRUE,23.25,0.34454821,2.166666667,0.166845063,4,0.707574542,,,0.406322605
3464,Artificial Intelligence Empowers Radiologists to Differentiate Pneumonia Induced by COVID-19 versus Influenza Viruses.,Acta Inform Med,33417642,1/9/21,pubmed,0,6,"artificial intelligence, neural network, network model, dataset",0.001072262,0.132687647,0.863023363,0.001072186,0.001072264,0.001072279,Imaging,0.5579317,TRUE,9.666666667,0.144968767,1,0.122023013,2,0.618927094,,,0.295306292
3465,Automatic clustering method to segment COVID-19 CT images.,PLoS One,33417610,1/9/21,pubmed,0,6,image analysis,0.00182337,0.001823399,0.990883083,0.001823458,0.001823349,0.001823341,Imaging,0.63897145,TRUE,8.333333333,0.123693488,0.333333333,0.073187048,0,0.403234768,,,0.200038435
3466,Computational drug repurposing strategy predicted peptide-based drugs that can potentially inhibit the interaction of SARS-CoV-2 spike protein with its target (humanACE2).,PLoS One,33417604,1/9/21,pubmed,0,5,computational,0.977997408,0.001237068,0.001237107,0.00123706,0.001237118,0.017054238,Drug discovery,0.593062,TRUE,15.2,0.229142186,26,0.53819909,2,0.618927094,,,0.462089457
3467,Early Returns on Small Molecule Therapeutics for SARS-CoV-2.,ACS Infect Dis,33417425,1/9/21,pubmed,0,1,computational,0.577380881,0.00392752,0.003927547,0.406909101,0.003927503,0.003927449,Drug discovery,0.61077625,TRUE,9,0.135320675,20,0.481000803,0,0.403234768,,,0.339852082
3468,The meaning of Freedom after Covid-19.,Hist Philos Life Sci,33417016,1/9/21,pubmed,0,2,digital health,0.002639213,0.002639066,0.002639098,0.986804567,0.002639089,0.002638967,Epidemiology,0.66207874,TRUE,52,0.647349867,5.5,0.267259834,0,0.403234768,,,0.43928149
3469,Impact of oral anticoagulation on clinical outcomes of COVID-19: a nationwide cohort study of hospitalized patients in Germany.,Clin Res Cardiol,33416918,1/9/21,pubmed,0,11,logistic regression,0.130292943,0.001943541,0.001943519,0.001943637,0.001943548,0.861932813,Clinics,0.8393475,TRUE,150.5454545,0.944647164,216.4545455,0.925608777,2,0.618927094,,,0.829727679
3470,Bioinformatics resources for SARS-CoV-2 discovery and surveillance.,Brief Bioinform,33416890,1/9/21,pubmed,0,6,"bioinformatic, sequencing",0.002296699,0.691408004,0.002296772,0.299405339,0.002296594,0.002296592,Genomics,0.7466332,TRUE,160.1666667,0.95175954,821.5,0.9905673,0,0.403234768,,,0.781853869
3471,SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets.,Brief Bioinform,33416848,1/9/21,pubmed,0,10,"computational, proteom",0.803833998,0.105434754,0.001786682,0.085371328,0.001786556,0.001786682,Drug discovery,0.2171947,FALSE,80,0.807532933,215.3,0.925140487,0,0.403234768,,,0.711969396
3472,Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning.,J Womens Health (Larchmt),33416429,1/9/21,pubmed,0,3,machine learning,0.008403002,0.008402592,0.646845951,0.008402805,0.008403124,0.319542525,Clinics,0.5967559,TRUE,126.3333333,0.915888428,166.3333333,0.89670859,0,0.403234768,,,0.738610595
3473,Searching for potential drugs against SARS-CoV-2 through virtual screening on several molecular targets.,J Biomol Struct Dyn,33416020,1/9/21,pubmed,0,20,virtual screening,0.990282309,0.001943534,0.001943511,0.001943597,0.001943566,0.001943484,Drug discovery,0.7839871,TRUE,64.1,0.729296803,17.95,0.459994648,0,0.403234768,,,0.530842073
3474,Can natural products stop the SARS-CoV-2 virus? A docking and molecular dynamics study of a natural product database.,Future Med Chem,33415989,1/9/21,pubmed,0,5,"virtual screening, molecular dynamics simulation",0.986399359,0.002720102,0.002720136,0.002720182,0.002720113,0.002720108,Drug discovery,0.88326585,TRUE,42.8,0.565279238,4,0.231469093,0,0.403234768,,,0.399994366
3475,Unsupervised cluster analysis of SARS-CoV-2 genomes reflects its geographic progression and identifies distinct genetic subgroups of SARS-CoV-2 virus.,Genet Epidemiol,33415739,1/9/21,pubmed,0,4,"sequencing, genome-wide, genome sequences, genomes",0.001171615,0.938374966,0.001171648,0.001171616,0.00117159,0.056938566,Genomics,0.20277864,FALSE,59,0.696579875,16.75,0.446012845,3,0.667819001,,,0.603470574
3476,Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence.,Environ Sci Pollut Res Int,33415612,1/9/21,pubmed,0,2,"artificial intelligence, neural network, prediction model, dataset",0.001330033,0.001330029,0.226073934,0.768605911,0.001330032,0.00133006,Epidemiology,0.42436916,FALSE,1.5,0.015523533,0,0.055525823,0,0.403234768,,,0.158094708
3477,Cascaded deep transfer learning on thoracic CT in COVID-19 patients treated with steroids.,J Med Imaging (Bellingham),33415179,1/9/21,pubmed,0,6,transfer learning,0.001751185,0.001751175,0.845927029,0.052218807,0.032601503,0.065750301,Imaging,0.7600935,TRUE,100.1666667,0.86913229,88.66666667,0.798501472,0,0.403234768,,,0.69028951
3478,Public Database-Driven Insights Into Aging Stress-Associated Defective Gut Barrier With Low SARS-CoV-2 Receptors.,Front Med (Lausanne),33415119,1/9/21,pubmed,0,1,transcriptom,0.525500994,0.002183248,0.002183239,0.002183272,0.281531884,0.186417363,Drug discovery,0.8652543,TRUE,11,0.167171748,0,0.055525823,0,0.403234768,,,0.208644113
3479,"Prioritized Research for the Prevention, Treatment, and Reversal of Chronic Disease: Recommendations From the Lifestyle Medicine Research Summit.",Front Med (Lausanne),33415115,1/9/21,pubmed,0,29,computational,0.132753764,0.000838528,0.044053863,0.478522604,0.173864044,0.169967198,Epidemiology,0.82961977,TRUE,204.7931034,0.973900674,679.2758621,0.986754081,0,0.403234768,,,0.787963174
3480,Telepsychiatry and the Role of Artificial Intelligence in Mental Health in Post-COVID-19 India: A Scoping Review on Opportunities.,Indian J Psychol Med,33414589,1/9/21,pubmed,0,2,artificial intelligence,0.001461891,0.001461974,0.239210977,0.49541091,0.260992351,0.001461896,Epidemiology,0.50112134,TRUE,1.5,0.015523533,0,0.055525823,0,0.403234768,,,0.158094708
3481,Evaluating splatter and settled aerosol during orthodontic debonding: implications for the COVID-19 pandemic.,Br Dent J,33414542,1/9/21,pubmed,0,9,image analysis,0.002806408,0.184547855,0.270393309,0.376884849,0.162560919,0.00280666,Epidemiology,0.7924622,TRUE,31.88888889,0.453769559,44.11111111,0.654602622,1,0.537564047,,,0.54864541
3482,Optimal COVID-19 quarantine and testing strategies.,Nat Commun,33414470,1/9/21,pubmed,0,9,mathematical model,0.102003284,0.002490506,0.002490536,0.888034606,0.002490555,0.002490512,Epidemiology,0.19542804,FALSE,108.6666667,0.887129693,145.6666667,0.879983944,3,0.667819001,,,0.811644213
3483,Interleukin-38 ameliorates poly(I:C) induced lung inflammation: therapeutic implications in respiratory viral infections.,Cell Death Dis,33414457,1/9/21,pubmed,0,10,transcriptom,0.726688653,0.001310374,0.001310352,0.001310345,0.001310332,0.268069944,Drug discovery,0.74539137,TRUE,58.3,0.691570289,69.4,0.751605566,0,0.403234768,,,0.615470207
3484,Mutation Profile of SARS-CoV-2 Genome Sequences Originating from Eight Israeli Patient Isolates.,Microbiol Resour Announc,33414330,1/9/21,pubmed,0,10,genome sequences,0.004109772,0.832144084,0.004109804,0.004109901,0.151416559,0.004109881,Genomics,0.26852006,FALSE,28.6,0.413321789,25,0.529435376,0,0.403234768,,,0.448663977
3485,Genome Sequences of Two GH Clade SARS-CoV-2 Strains Isolated from Patients with COVID-19 in South Korea.,Microbiol Resour Announc,33414329,1/9/21,pubmed,0,7,genome sequences,0.005047429,0.911970259,0.005047533,0.005047622,0.005047416,0.067839741,Genomics,0.357647,FALSE,43.28571429,0.570165131,21.57142857,0.498528231,0,0.403234768,,,0.49064271
3486,Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework.,BMJ Open,33414147,1/9/21,pubmed,0,15,mathematical model,0.001371246,0.02422177,0.001371259,0.857105934,0.030491652,0.085438139,Epidemiology,0.42126068,FALSE,26,0.382398417,34.66666667,0.603157613,0,0.403234768,,,0.462930266
3487,"Bacterial/fungal infection in hospitalized patients with COVID-19 in a tertiary hospital in the Community of Castilla y León, Spain.",Enferm Infecc Microbiol Clin,33413990,1/9/21,pubmed,0,8,logistic regression,0.001684529,0.087569596,0.001684513,0.001684489,0.001684497,0.905692375,Clinics,0.8665048,TRUE,6.125,0.086461748,1.75,0.148381054,0,0.403234768,,,0.212692523
3488,A nidovirus perspective on SARS-CoV-2.,Biochem Biophys Res Commun,33413979,1/9/21,pubmed,0,2,proteom,0.188272631,0.667797471,0.001861799,0.10730249,0.032903843,0.001861766,Genomics,0.8653571,TRUE,116,0.900735976,731.5,0.988426545,0,0.403234768,,,0.76413243
3489,Inverse Relationship of Maximal Exercise Capacity to Hospitalization Secondary to Coronavirus Disease 2019.,Mayo Clin Proc,33413833,1/9/21,pubmed,0,10,logistic regression,0.001943486,0.043406133,0.001943793,0.164989366,0.056038174,0.731679048,Clinics,0.62203115,TRUE,75.5,0.78860783,56.6,0.709392561,3,0.667819001,,,0.721939797
3490,"Clinical Characteristics, Treatment, and Outcomes of Critically Ill Patients With COVID-19: A Scoping Review.",Mayo Clin Proc,33413817,1/9/21,pubmed,0,8,predictive model,0.074455113,0.084868386,0.087371851,0.221156761,0.001272682,0.530875207,Clinics,0.44742694,FALSE,35.5,0.492547467,9.75,0.349678887,1,0.537564047,,,0.459930134
3491,Association of Timing and Balance of Physical Activity and Rest/Sleep With Risk of COVID-19: A UK Biobank Study.,Mayo Clin Proc,33413813,1/9/21,pubmed,0,12,logistic regression,0.001717218,0.001717383,0.001717259,0.0017173,0.897234037,0.095896803,Healthcare,0.5345392,TRUE,108.25,0.886387532,127.8333333,0.861118544,2,0.618927094,,,0.788811057
3492,Impact of mass testing during an epidemic rebound of SARS-CoV-2: a modelling study using the example of France.,Euro Surveill,33413741,1/9/21,pubmed,0,7,mathematical model,0.004530662,0.004530938,0.004530775,0.977345821,0.004530907,0.004530898,Epidemiology,0.17986548,FALSE,63.85714286,0.727194013,170.5714286,0.899852823,0,0.403234768,,,0.676760535
3493,Development of a Predictive Model for Mortality in Hospitalized Patients With COVID-19.,Disaster Med Public Health Prep,33413721,1/9/21,pubmed,0,7,"predictive model, logistic regression",0.001901751,0.001901749,0.0593184,0.001901741,0.001901744,0.933074615,Clinics,0.5467915,TRUE,28.42857143,0.411280846,7.142857143,0.300709125,0,0.403234768,,,0.37174158
3494,"Applications of Robotics, Artificial Intelligence, and Digital Technologies During COVID-19: A Review.",Disaster Med Public Health Prep,33413717,1/9/21,pubmed,0,9,artificial intelligence,0.02880808,0.001901733,0.256770766,0.708715853,0.001901771,0.001901797,Epidemiology,0.7727368,TRUE,63.77777778,0.726575546,13.88888889,0.409686915,0,0.403234768,,,0.513165743
3495,An integrated in silico immuno-genetic analytical platform provides insights into COVID-19 serological and vaccine targets.,Genome Med,33413610,1/9/21,pubmed,0,6,in silico,0.333371151,0.528809295,0.002357799,0.002357791,0.130746067,0.002357897,Genomics,0.6158839,TRUE,147.6666667,0.941802214,310.8333333,0.957519401,0,0.403234768,,,0.767518794
3496,HiDeF: identifying persistent structures in multiscale 'omics data.,Genome Biol,33413539,1/9/21,pubmed,0,6,transcriptom,0.689340461,0.002996638,0.002996576,0.298673293,0.002996586,0.002996447,Drug discovery,0.19872096,FALSE,67,0.748160059,776.1666667,0.98963072,0,0.403234768,,,0.713675182
3497,CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS.,J Transl Med,33413480,1/9/21,pubmed,0,14,"machine learning, radiom, dataset",0.001046832,0.001046872,0.814572548,0.001046807,0.001046824,0.181240117,Imaging,0.51973855,TRUE,19.64285714,0.291916631,5.142857143,0.25802783,0,0.403234768,,,0.317726409
3498,Income-related health inequalities associated with the coronavirus pandemic in South Africa: A decomposition analysis.,Int J Equity Health,33413442,1/9/21,pubmed,0,2,dataset,0.001156243,0.001156257,0.001156279,0.258132925,0.737242021,0.001156276,Healthcare,0.77471215,TRUE,14.5,0.219617787,2,0.164302917,2,0.618927094,,,0.334282599
3499,Transcriptome of nasopharyngeal samples from COVID-19 patients and a comparative analysis with other SARS-CoV-2 infection models reveal disparate host responses against SARS-CoV-2.,J Transl Med,33413422,1/9/21,pubmed,0,7,transcriptom,0.642545956,0.303374201,0.001156279,0.001156265,0.001156246,0.050611053,Drug discovery,0.7939671,TRUE,18.28571429,0.273671841,11,0.371287129,0,0.403234768,,,0.349397912
3500,"Sputum ACE2, TMPRSS2 and FURIN gene expression in severe neutrophilic asthma.",Respir Res,33413387,1/9/21,pubmed,0,43,transcriptom,0.418161081,0.001415215,0.001415093,0.00141512,0.001415137,0.576178353,Clinics,0.87727785,TRUE,114.1136364,0.897891026,,,0,0.403234768,,,0.650562897
3501,Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2.,BMC Med Inform Decis Mak,33413329,1/9/21,pubmed,0,5,computational,0.946694805,0.001085333,0.001085343,0.001085334,0.001085339,0.048963846,Drug discovery,0.88102186,TRUE,42.8,0.565279238,147.2,0.880987423,0,0.403234768,,,0.616500476
3502,Sex differences and psychological stress: responses to the COVID-19 pandemic in China.,BMC Public Health,33413224,1/9/21,pubmed,0,10,probabilistic,0.000966764,0.000966792,0.000966775,0.000966813,0.981425653,0.014707202,Healthcare,0.9325595,TRUE,22.8,0.337064754,14.4,0.416644367,0,0.403234768,,,0.385647963
3503,Misinformation about COVID-19: evidence for differential latent profiles and a strong association with trust in science.,BMC Public Health,33413219,1/9/21,pubmed,0,2,logistic regression,0.099065053,0.210362496,0.001237113,0.278190095,0.409908158,0.001237085,Healthcare,0.876783,TRUE,47.5,0.610551054,9.5,0.345531175,0,0.403234768,,,0.453105666
3504,"H2V: a database of human genes and proteins that respond to SARS-CoV-2, SARS-CoV, and MERS-CoV infection.",BMC Bioinformatics,33413085,1/9/21,pubmed,0,3,"transcriptom, proteom",0.75450088,0.056053446,0.001330041,0.155199067,0.031586529,0.001330037,Drug discovery,0.51796085,TRUE,51,0.63875317,11.66666667,0.38065293,1,0.537564047,,,0.518990049
3505,Machine Intelligence Techniques for the Identification and Diagnosis of COVID-19.,Curr Med Chem,33413059,1/9/21,pubmed,0,4,"machine learning, deep learning, artificial intelligence, machine intelligence",0.001538112,0.001538163,0.605588115,0.336902045,0.052895407,0.001538159,Imaging,0.7051298,TRUE,27.75,0.404292164,4,0.231469093,0,0.403234768,,,0.346332008
3506,0,J Biomol Struct Dyn,33413032,1/9/21,pubmed,0,5,in silico,0.910811225,0.001203452,0.084374983,0.001203458,0.001203474,0.001203408,Drug discovery,0.10800406,FALSE,41.6,0.553961284,8.2,0.322785657,0,0.403234768,,,0.426660569
3507,"The Future of Substance Abuse Now: Relationships among Adolescent Use of Vaping Devices, Marijuana, and Synthetic Cannabinoids.",Subst Use Misuse,33412950,1/9/21,pubmed,0,4,logistic regression,0.129578277,0.002183282,0.333476059,0.33955208,0.193026998,0.002183304,Epidemiology,0.03509417,FALSE,140.75,0.934875379,299.75,0.95424137,0,0.403234768,,,0.764117172
3508,"Stress, coping, and preventing contagion during the SARS-COV2 epidemic in a sample of Mexican adults.",J Health Psychol,33412947,1/9/21,pubmed,0,2,logistic regression,0.003214225,0.003214147,0.003214285,0.113961483,0.87318162,0.00321424,Healthcare,0.8367025,TRUE,1.5,0.015523533,0,0.055525823,0,0.403234768,,,0.158094708
3509,Computational Insight Into the Mechanism of SARS-CoV-2 Membrane Fusion.,J Chem Inf Model,33412850,1/9/21,pubmed,0,3,"molecular dynamics simulation, computational",0.868565387,0.0441476,0.00129123,0.001291252,0.001291211,0.083413319,Drug discovery,0.7000318,TRUE,15.33333333,0.230997588,3.666666667,0.217621086,0,0.403234768,,,0.283951147
3510,Factors associated with self-perceived knowledge of COVID-19: a study among women from the NINFEA birth cohort.,Epidemiol Prev,33412830,1/9/21,pubmed,0,9,logistic regression,0.001392808,0.001392854,0.001392966,0.00139288,0.993035585,0.001392907,Healthcare,0.8137156,TRUE,120.2222222,0.907291731,134.3333333,0.868611185,0,0.403234768,,,0.726379228
3511,[Did social isolation during the SARS-CoV-2 epidemic have an impact on the lifestyles of citizens?],Epidemiol Prev,33412829,1/9/21,pubmed,0,8,probabilistic,0.000746555,0.000746566,0.000746572,0.222428775,0.774584976,0.000746556,Healthcare,0.8230952,TRUE,30.25,0.434782609,11.875,0.383596468,1,0.537564047,,,0.451981041
3512,"[Chronic diseases and risk of symptomatic COVID-19: results of a case-population study on a sample of patients in the Local Health Unit 'Toscana Centro' (Tuscany Region, Central Italy)].",Epidemiol Prev,33412823,1/9/21,pubmed,0,8,logistic regression,0.001022637,0.001022677,0.001022644,0.104344804,0.287277803,0.605309434,Clinics,0.5713887,TRUE,47.5,0.610551054,22.875,0.510971367,0,0.403234768,,,0.508252396
3513,A municipality-level analysis of excess mortality in Italy in the period January-April 2020.,Epidemiol Prev,33412822,1/9/21,pubmed,0,5,"bayes, bayesian model",0.001254674,0.001254709,0.001254776,0.815807666,0.001254689,0.179173487,Epidemiology,0.39915645,FALSE,43,0.567629414,15,0.42594327,1,0.537564047,,,0.51037891
3514,"COVID-19 pandemic: a mobility-dependent SEIR model with undetected cases in Italy, Europe, and US.",Epidemiol Prev,33412804,1/9/21,pubmed,0,4,mathematical model,0.001415096,0.001415108,0.001415195,0.933908383,0.060431106,0.001415113,Epidemiology,0.57943463,TRUE,13.75,0.208299833,0,0.055525823,9,0.814309525,,,0.359378394
3515,Updated confidence intervals for the COVID-19 antibody retention rate in the Korean population.,Genomics Inform,33412761,1/9/21,pubmed,0,3,bayes,0.044349049,0.065233486,0.002080589,0.674506809,0.211749347,0.00208072,Epidemiology,0.17122307,FALSE,107,0.883357041,,,1,0.537564047,,,0.710460544
3516,Druggability for COVID-19: in silico discovery of potential drug compounds against nucleocapsid (N) protein of SARS-CoV-2.,Genomics Inform,33412759,1/9/21,pubmed,0,3,in silico,0.928813356,0.066999291,0.00104683,0.001046834,0.001046829,0.001046859,Drug discovery,0.95664537,TRUE,15.66666667,0.236563795,2.333333333,0.173401124,3,0.667819001,,,0.359261307
3517,Doubled mortality rate during the COVID-19 pandemic in Italy: quantifying what is not captured by surveillance.,Public Health,33412438,1/8/21,pubmed,0,4,mathematical model,0.00125458,0.001254634,0.001254674,0.739448924,0.001254682,0.255532506,Epidemiology,0.06573099,FALSE,91,0.846310842,19.25,0.47310677,0,0.403234768,,,0.57421746
3518,Promising phytochemicals of traditional Indian herbal steam inhalation therapy to combat COVID-19 - An in silico study.,Food Chem Toxicol,33412235,1/8/21,pubmed,0,8,in silico,0.992809418,0.00143812,0.001438121,0.001438125,0.001438112,0.001438104,Drug discovery,0.98546183,TRUE,33.875,0.474921145,22.875,0.510971367,0,0.403234768,,,0.463042427
3519,A scientometric overview of CORD-19.,PLoS One,33411846,1/8/21,pubmed,0,6,dataset,0.069366498,0.437291312,0.001901749,0.487636982,0.00190177,0.00190169,Epidemiology,0.6975342,TRUE,78.83333333,0.80190488,180.8333333,0.906810276,2,0.618927094,,,0.77588075
3520,"Anxiety and depression symptoms, the recovery from symptoms, and loneliness before and after the COVID-19 outbreak among the general population: Findings from a Dutch population-based longitudinal study.",PLoS One,33411843,1/8/21,pubmed,0,6,logistic regression,0.001371234,0.00137128,0.001371291,0.098953685,0.895561167,0.001371342,Healthcare,0.72106516,TRUE,30.5,0.438493413,20.5,0.486218892,1,0.537564047,,,0.487425451
3521,Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States.,PLoS One,33411812,1/8/21,pubmed,0,15,logistic regression,0.000907315,0.0009073,0.000907296,0.000907335,0.995463463,0.000907292,Healthcare,0.64597386,TRUE,17.93333333,0.268909642,9.6,0.346735349,4,0.707574542,,,0.441073178
3522,A multi-mechanism approach reduces length of stay in the ICU for severe COVID-19 patients.,PLoS One,33411780,1/8/21,pubmed,0,19,logistic regression,0.022253451,0.000966826,0.000966776,0.069546871,0.00096681,0.905299265,Clinics,0.8559431,TRUE,9.315789474,0.138413013,2.842105263,0.189590581,0,0.403234768,,,0.24374612
3523,"Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy.",PLoS One,33411744,1/8/21,pubmed,0,3,prediction model,0.001392842,0.001392864,0.093538429,0.900890183,0.001392829,0.001392853,Epidemiology,0.788834,TRUE,49,0.624281032,25.66666667,0.534854161,1,0.537564047,,,0.565566413
3524,The value of decreasing the duration of the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.,PLoS Comput Biol,33411742,1/8/21,pubmed,0,8,"simulation experiment, computational",0.00156535,0.001565299,0.001565296,0.845084276,0.00156534,0.148654438,Epidemiology,0.64618117,TRUE,36.875,0.506895912,17.625,0.457051111,0,0.403234768,,,0.455727263
3525,"Participation in Fraternity and Sorority Activities and the Spread of COVID-19 Among Residential University Communities - Arkansas, August 21-September 5, 2020.",MMWR Morb Mortal Wkly Rep,33411698,1/8/21,pubmed,0,8,network analysis,0.001350409,0.040880218,0.001350352,0.361086149,0.513631474,0.081701397,Healthcare,0.7907634,TRUE,20.25,0.301317336,11.875,0.383596468,3,0.667819001,,,0.450910935
3526,Mathematical models as public troubles in COVID-19 infection control: following the numbers.,Health Sociol Rev,33411652,1/8/21,pubmed,0,2,mathematical model,0.002898499,0.002898294,0.002898305,0.985507986,0.002898532,0.002898385,Epidemiology,0.20265952,FALSE,138,0.930546107,290,0.95143163,12,0.850299401,,,0.910759046
3527,Chronic comorbidities and clinical outcomes in patients with and without COVID-19: a large population-based study using national administrative healthcare open data of Mexico.,Intern Emerg Med,33411264,1/8/21,pubmed,0,3,logistic regression,0.001272666,0.071928749,0.001272703,0.001272743,0.07879241,0.845460729,Clinics,0.3060162,FALSE,9,0.135320675,0.333333333,0.073187048,1,0.537564047,,,0.24869059
3528,A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions.,Interdiscip Sci,33411162,1/8/21,pubmed,0,8,"radiom, logistic regression",0.001072209,0.001072216,0.691619357,0.001072206,0.001072199,0.304091813,Imaging,0.55502725,TRUE,35.875,0.495516111,6.875,0.29388547,0,0.403234768,,,0.397545449
3529,Inequities in Diabetic Ketoacidosis Among Patients With Type 1 Diabetes and COVID-19: Data From 52 US Clinical Centers.,J Clin Endocrinol Metab,33410917,1/8/21,pubmed,0,27,logistic regression,0.001461887,0.001461896,0.001461849,0.001461877,0.001461954,0.992690537,Clinics,0.92576957,TRUE,40.18518519,0.540664234,48.22222222,0.674739096,0,0.403234768,,,0.539546032
3530,Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study.,Ann Med,33410720,1/8/21,pubmed,0,9,"machine learning, prediction model",0.001254629,0.001254604,0.40813666,0.001254618,0.001254587,0.586844901,Clinics,0.83583736,TRUE,91.66666667,0.84785701,43.88888889,0.653465347,2,0.618927094,,,0.706749817
3531,"COVID-19: Advances in diagnostic tools, treatment strategies, and vaccine development.",J Biosci,33410425,1/8/21,pubmed,0,3,"machine learning, artificial intelligence",0.195514791,0.001751255,0.415009519,0.384222023,0.001751229,0.001751183,Epidemiology,0.58827376,TRUE,12,0.183190055,6,0.280037463,2,0.618927094,,,0.360718204
3532,Pitfalls of barcodes in the study of worldwide SARS-CoV-2 variation and phylodynamics.,Zool Res,33410308,1/8/21,pubmed,0,5,genomes,0.001987224,0.722554811,0.001987234,0.269495993,0.001987406,0.001987332,Genomics,0.28145725,FALSE,75.8,0.789659224,74.2,0.764851485,0,0.403234768,,,0.652581826
3533,The role of mathematical models in developing policies for controlling COVID-19 transmission.,Med J Aust,33410179,1/8/21,pubmed,0,1,mathematical model,0.019529294,0.019529664,0.019529428,0.902352859,0.019529404,0.01952935,Epidemiology,0.1209428,FALSE,437,0.996227349,632,0.985349211,0,0.403234768,,,0.794937109
3534,Mechanistic Modeling of SARS-CoV-2 and Other Infectious Diseases and the Effects of Therapeutics.,Clin Pharmacol Ther,33410134,1/8/21,pubmed,0,2,mathematical model,0.190882064,0.110627134,0.001901822,0.662063894,0.001901831,0.032623256,Epidemiology,0.40767503,FALSE,29.5,0.426000371,16.5,0.44293551,2,0.618927094,,,0.495954325
3535,"Unpacking the health and social consequences of COVID-19 through a race, migration and gender lens.",Can J Public Health,33410121,1/8/21,pubmed,0,2,dataset,0.001593524,0.001593541,0.055283715,0.255199714,0.684735876,0.001593631,Healthcare,0.86666876,TRUE,43.5,0.573381161,71,0.756556061,0,0.403234768,,,0.577723997
3536,Brain Disease Network Analysis to Elucidate the Neurological Manifestations of COVID-19.,Mol Neurobiol,33409839,1/8/21,pubmed,0,5,network analysis,0.648035269,0.029579954,0.00146195,0.00146198,0.001461948,0.317998898,Drug discovery,0.6130893,TRUE,36.6,0.503865421,8,0.320511105,1,0.537564047,,,0.453980191
3537,The Epidemiology and Predictors of Outcomes Among Confirmed COVID-19 Cases in a Large Community Healthcare System in South Florida.,J Community Health,33409769,1/8/21,pubmed,0,4,logistic regression,0.001861735,0.00186196,0.001861764,0.001861868,0.13157608,0.860976593,Clinics,0.42648256,FALSE,22.5,0.333539489,20.25,0.483074659,0,0.403234768,,,0.406616305
3538,Novel NGS pipeline for virus discovery from a wide spectrum of hosts and sample types.,Virus Evol,33408878,1/8/21,pubmed,0,7,"bioinformatic, sequencing, metagenom, microbiom",0.00229666,0.867199625,0.002296704,0.123613921,0.00229657,0.00229652,Genomics,0.6708468,TRUE,64.71428571,0.733069454,440.1428571,0.974444742,0,0.403234768,,,0.703582988
3539,Prevalence of Depression and Anxiety Symptoms of High School Students in Shandong Province During the COVID-19 Epidemic.,Front Psychiatry,33408653,1/8/21,pubmed,0,8,correlation analysis,0.000956309,0.000956334,0.00095632,0.000956337,0.960262684,0.035912016,Healthcare,0.64978325,TRUE,30.125,0.433174593,12,0.386740701,1,0.537564047,,,0.452493114
3540,A Policy Category Analysis Model for Tourism Promotion in China During the COVID-19 Pandemic Based on Data Mining and Binary Regression.,Risk Manag Healthc Policy,33408543,1/8/21,pubmed,0,7,"data mining, logistic regression",0.001072237,0.00107219,0.001072215,0.818414438,0.177296715,0.001072205,Epidemiology,0.9614136,TRUE,28.85714286,0.416043045,4,0.231469093,3,0.667819001,,,0.438443713
3541,Isolation Compliance and Associated Factors Among COVID-19 Patients in North-West Iran: A Cross-Sectional Study.,Int J Gen Med,33408507,1/8/21,pubmed,0,6,logistic regression,0.001593476,0.001593542,0.001593525,0.075760481,0.526458664,0.393000313,Healthcare,0.8986168,TRUE,25.83333333,0.378625765,2.833333333,0.189523682,0,0.403234768,,,0.323794738
3542,Monitoring COVID-19 Transmission Risks by Quantitative Real-Time PCR Tracing of Droplets in Hospital and Living Environments.,mSphere,33408231,1/8/21,pubmed,0,19,sequencing,0.000838554,0.741540846,0.000838526,0.143397525,0.000838528,0.112546021,Genomics,0.6976079,TRUE,69.42105263,0.760591255,36.31578947,0.612991705,1,0.537564047,,,0.637049002
3543,Psychological impact of COVID-19 outbreak among Jimma University Medical Centere visitors in Southwestern Ethiopia: a cross-sectional study.,BMJ Open,33408210,1/8/21,pubmed,0,5,logistic regression,0.000936087,0.053522868,0.000936124,0.000936105,0.942732672,0.000936145,Healthcare,0.99042207,TRUE,27.4,0.400395819,16,0.437316029,0,0.403234768,,,0.413648872
3544,Demographic and social correlates and indicators for behavioural compliance with personal protection among Chinese community-dwellers during COVID-19: a cross-sectional study.,BMJ Open,33408203,1/8/21,pubmed,0,6,logistic regression,0.001046803,0.001046819,0.001046804,0.178481665,0.817331056,0.001046853,Healthcare,0.9489483,TRUE,15.83333333,0.238233657,2.833333333,0.189523682,0,0.403234768,,,0.276997369
3545,Adverse outcomes in COVID-19 and diabetes: a retrospective cohort study from three London teaching hospitals.,BMJ Open Diabetes Res Care,33408084,1/8/21,pubmed,0,26,logistic regression,0.001291224,0.001291254,0.001291219,0.001291237,0.001291321,0.993543744,Clinics,0.86725986,TRUE,16.69230769,0.251221473,12.57692308,0.393564356,1,0.537564047,,,0.394116626
3546,Association of mental disorders with SARS-CoV-2 infection and severe health outcomes: nationwide cohort study.,Br J Psychiatry,33407954,1/8/21,pubmed,0,4,logistic regression,0.001511802,0.00151182,0.001511796,0.001511833,0.528043125,0.465909625,Healthcare,0.6827376,TRUE,55,0.668686994,12.5,0.392761573,0,0.403234768,,,0.488227778
3547,Nosocomial transmission and outbreaks of coronavirus disease 2019: the need to protect both patients and healthcare workers.,Antimicrob Resist Infect Control,33407833,1/8/21,pubmed,0,7,sequencing,0.001943513,0.167965489,0.001943556,0.350239267,0.293177484,0.184730691,Epidemiology,0.78500426,TRUE,48.14285714,0.615560641,27.71428571,0.551110516,2,0.618927094,,,0.595199417
3548,Clinical characteristics and predictors of mortality in young adults with severe COVID-19: a retrospective observational study.,Ann Clin Microbiol Antimicrob,33407543,1/8/21,pubmed,0,9,logistic regression,0.001786613,0.001786534,0.001786498,0.001786489,0.001786601,0.991067264,Clinics,0.9064019,TRUE,20.88888889,0.308367864,129,0.86272411,2,0.618927094,,,0.596673023
3549,Impact of coronavirus disease-2019 on chronic respiratory disease in South Korea: an NHIS COVID-19 database cohort study.,BMC Pulm Med,33407347,1/8/21,pubmed,0,2,logistic regression,0.001330148,0.001330077,0.026237742,0.001330134,0.001330042,0.968441857,Clinics,0.8485012,TRUE,103.5,0.876306512,13,0.400521809,0,0.403234768,,,0.56002103
3550,Rapid single cell evaluation of human disease and disorder targets using REVEAL: SingleCell™.,BMC Genomics,33407110,1/8/21,pubmed,0,8,"sequencing, dataset",0.708283268,0.069025537,0.220891721,0.00059983,0.000599829,0.000599815,Drug discovery,0.43744707,FALSE,12.5,0.189436576,14.375,0.416109178,0,0.403234768,,,0.336260174
3551,COVID-19 epidemic in Libya.,Libyan J Med,33407008,1/8/21,pubmed,0,2,mathematical model,0.002898318,0.002898336,0.002898306,0.709923018,0.278483419,0.002898603,Epidemiology,0.45837298,FALSE,47.5,0.610551054,25,0.529435376,0,0.403234768,,,0.514407066
3552,The COVID-19 International Student Well-being Study.,Scand J Public Health,33406995,1/8/21,pubmed,0,7,dataset,0.001438113,0.001438137,0.00143821,0.436765259,0.557482182,0.0014381,Healthcare,0.8598815,TRUE,51.85714286,0.645370771,38.85714286,0.626371421,4,0.707574542,,,0.659772245
3553,Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks.,J Pers Med,33406788,1/8/21,pubmed,0,10,"neural network, dataset",0.001622706,0.001622766,0.958997772,0.001622753,0.001622757,0.034511246,Imaging,0.80529165,TRUE,23.4,0.346527305,5.7,0.270404067,1,0.537564047,,,0.384831807
3554,Social Patterning and Stability of Intention to Accept a COVID-19 Vaccine in Scotland: Will Those Most at Risk Accept a Vaccine?,Vaccines (Basel),33406762,1/8/21,pubmed,0,6,logistic regression,0.001653138,0.001653179,0.001653042,0.001653169,0.991734422,0.001653052,Healthcare,0.58953935,TRUE,114.5,0.8983858,329,0.961934707,3,0.667819001,,,0.842713169
3555,Estimating the Effect of Social Distancing Interventions on COVID-19 in the United States.,Am J Epidemiol,33406533,1/7/21,pubmed,0,5,bayes,0.001565302,0.00156538,0.001565343,0.992173311,0.001565351,0.001565312,Epidemiology,0.14634311,FALSE,65.6,0.7378935,72.2,0.759700294,0,0.403234768,,,0.633609521
3556,Outcomes of COVID-19 in hospitalized solid organ transplant recipients compared to a matched cohort of non-transplant patients at a national healthcare system in the United States.,Clin Transplant,33406279,1/7/21,pubmed,0,11,logistic regression,0.001823301,0.001823324,0.001823333,0.001823363,0.001823462,0.990883217,Clinics,0.6880495,TRUE,14.72727273,0.221596883,12.54545455,0.393029168,1,0.537564047,,,0.384063366
3557,HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid-protein interface with a case study.,Brief Bioinform,33406224,1/7/21,pubmed,0,6,"molecular dynamics simulation, computational, bioinformatic, in silico",0.873894281,0.122195785,0.000977475,0.000977555,0.000977469,0.000977435,Drug discovery,0.44874898,FALSE,66.33333333,0.742903086,,,0,0.403234768,,,0.573068927
3558,Exploration of natural compounds with anti-SARS-CoV-2 activity via inhibition of SARS-CoV-2 Mpro.,Brief Bioinform,33406222,1/7/21,pubmed,0,6,"virtual screening, in silico",0.994638508,0.001072469,0.001072376,0.001072253,0.001072201,0.001072193,Drug discovery,0.9655325,TRUE,31.16666667,0.445976869,8.833333333,0.333154937,1,0.537564047,,,0.438898618
3559,A comprehensive drug repurposing study for COVID19 treatment: novel putative dihydroorotate dehydrogenase inhibitors show association to serotonin-dopamine receptors.,Brief Bioinform,33406218,1/7/21,pubmed,0,2,in silico,0.942909751,0.001156248,0.00115626,0.001156267,0.019583705,0.034037769,Drug discovery,0.72334707,TRUE,12,0.183190055,1,0.122023013,0,0.403234768,,,0.236149279
3560,"Relaxation of social distancing restrictions: Model estimated impact on COVID-19 epidemic in Manitoba, Canada.",PLoS One,33406102,1/7/21,pubmed,0,3,mathematical model,0.001392831,0.001392839,0.001392822,0.886504198,0.107924391,0.00139292,Epidemiology,0.51332086,TRUE,68,0.753973653,37.66666667,0.619949157,0,0.403234768,,,0.592385859
3561,Quantitative UV-C dose validation with photochromic indicators for informed N95 emergency decontamination.,PLoS One,33406084,1/7/21,pubmed,0,5,radiom,0.00151191,0.001511869,0.268069955,0.465142498,0.001511864,0.262251906,Epidemiology,0.26951033,FALSE,43.8,0.575916878,22.2,0.504816698,2,0.618927094,,,0.566553557
3562,Nowcasting for Real-Time COVID-19 Tracking in New York City: An Evaluation Using Reportable Disease Data From Early in the Pandemic.,JMIR Public Health Surveill,33406053,1/7/21,pubmed,0,7,bayes,0.000863028,0.0098736,0.106950058,0.723539003,0.025450263,0.133324049,Epidemiology,0.2839619,FALSE,108.4285714,0.886696765,226.2857143,0.928084025,1,0.537564047,,,0.784114946
3563,Willingness to Adopt mHealth Among Chinese Parents During the COVID-19 Outbreak: Cross-sectional Questionnaire Study.,JMIR Mhealth Uhealth,33406052,1/7/21,pubmed,0,5,logistic regression,0.00089808,0.000898085,0.000898093,0.000898123,0.910894151,0.085513468,Healthcare,0.99712193,TRUE,6.8,0.09629538,1.6,0.140687717,0,0.403234768,,,0.213405955
3564,It Is Time to REACT: Opportunities for Digital Mental Health Apps to Reduce Mental Health Disparities in Racially and Ethnically Minoritized Groups.,JMIR Ment Health,33406050,1/7/21,pubmed,0,3,digital health,0.001861811,0.001861738,0.001861757,0.829036106,0.163516715,0.001861872,Epidemiology,0.7092203,TRUE,81.66666667,0.813346527,160.3333333,0.892226385,0,0.403234768,,,0.702935893
3565,"2019nCoVAS: Developing the Web Service for Epidemic Transmission Prediction, Genome Analysis, and Psychological Stress Assessment for 2019-nCoV.",IEEE/ACM Trans Comput Biol Bioinform,33406042,1/7/21,pubmed,0,8,"whole genome, prediction model",0.001786542,0.296168382,0.00178662,0.451628981,0.246842971,0.001786505,Epidemiology,0.42299157,FALSE,24.75,0.364710248,6.125,0.280907145,0,0.403234768,,,0.349617387
3566,0,Mol Inform,33405340,1/7/21,pubmed,0,8,"virtual screening, computational",0.991243956,0.001751278,0.001751237,0.001751198,0.001751175,0.001751155,Drug discovery,0.9846036,TRUE,46.75,0.603314985,87.625,0.796160021,10,0.828199272,,,0.742558093
3567,"Symptoms and recovery among adult outpatients with and without COVID-19 at 11 healthcare facilities-July 2020, United States.",Influenza Other Respir Viruses,33405338,1/7/21,pubmed,0,26,logistic regression,0.001254578,0.001254616,0.001254639,0.001254613,0.587814438,0.407167115,Healthcare,0.977213,TRUE,122.5,0.910755149,120.5,0.852421729,0,0.403234768,,,0.722137215
3568,"Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.",J Med Internet Res,33404516,1/7/21,pubmed,0,17,"machine learning, deep learning",0.000838523,0.000838564,0.923754946,0.000838514,0.029674373,0.04405508,Imaging,0.42066514,FALSE,24.23529412,0.357597872,5.823529412,0.272879315,0,0.403234768,,,0.344570652
3569,In silico analyses on the comparative sensing of SARS-CoV-2 mRNA by the intracellular TLRs of humans.,J Med Virol,33404091,1/7/21,pubmed,0,4,in silico,0.91527953,0.001461902,0.001461878,0.078872793,0.001461934,0.001461963,Drug discovery,0.95815873,TRUE,17,0.257467994,4.25,0.235750602,1,0.537564047,,,0.343594215
3570,BMI and Risk for Severe COVID-19 Among Veterans Health Administration Patients.,Obesity (Silver Spring),33403755,1/7/21,pubmed,0,6,logistic regression,0.021823153,0.001901695,0.001901707,0.001901798,0.065893159,0.906578488,Clinics,0.63107276,TRUE,36.16666667,0.499165069,28.83333333,0.560944608,2,0.618927094,,,0.559678924
3571,Human Oocytes Express Both ACE2 and BSG Genes and Corresponding Proteins: Is SARS-CoV-2 Infection Possible?,Stem Cell Rev Rep,33403489,1/7/21,pubmed,0,2,"transcriptom, proteom",0.664867206,0.002490678,0.002490572,0.002490628,0.32517045,0.002490465,Drug discovery,0.74493086,TRUE,135.5,0.927206383,276,0.947484613,2,0.618927094,,,0.83120603
3572,"Risk Factors for Severe Acute Respiratory Syndrome Coronavirus 2 Infection in Hospital Workers: Results From a Screening Study in New Jersey, United States in Spring 2020.",Open Forum Infect Dis,33403219,1/7/21,pubmed,0,17,logistic regression,0.001310328,0.035880949,0.001310338,0.001310352,0.662539278,0.297648755,Healthcare,0.7619697,TRUE,78.05882353,0.798874389,83.41176471,0.786660423,0,0.403234768,,,0.662923193
3573,A statistical theory of the strength of epidemics: an application to the Italian COVID-19 case.,Proc Math Phys Eng Sci,33402873,1/7/21,pubmed,0,2,probabilistic,0.001593627,0.001593535,0.001593591,0.812898086,0.049152229,0.133168932,Epidemiology,0.25120214,FALSE,81.5,0.8129136,34,0.5990768,0,0.403234768,,,0.605075056
3574,"COVID-19: Adaptation of a model to predicting healthcare resources needs in Valle del Cauca, Colombia.",Colomb Med (Cali),33402754,1/7/21,pubmed,0,15,predictive model,0.000871551,0.000871606,0.024261278,0.749228019,0.000871572,0.223895975,Epidemiology,0.064512104,FALSE,52.26666667,0.648710495,21.2,0.493644635,0,0.403234768,,,0.515196633
3575,Sexuality during COVID lockdown: a cross-sectional Italian study among hospital workers and their relatives.,Int J Impot Res,33402720,1/7/21,pubmed,0,7,logistic regression,0.002130713,0.002130686,0.002130716,0.002130774,0.989346319,0.002130792,Healthcare,0.9243742,TRUE,211,0.975817923,81.42857143,0.781776826,0,0.403234768,,,0.720276506
3576,Self-protection strategies and health behaviour in patients with inflammatory rheumatic diseases during the COVID-19 pandemic: results and predictors in more than 12 000 patients with inflammatory rheumatic diseases followed in the Danish DANBIO registry.,RMD Open,33402443,1/7/21,pubmed,0,21,logistic regression,0.001059381,0.001059411,0.001059349,0.00105941,0.687963062,0.307799386,Healthcare,0.94910383,TRUE,57.42857143,0.68489084,38.76190476,0.625836232,1,0.537564047,,,0.61609704
3577,COVID-19 related health inequality exists even in a city where disease incidence is relatively low: a telephone survey in Hong Kong.,J Epidemiol Community Health,33402396,1/7/21,pubmed,0,11,logistic regression,0.00127269,0.001272656,0.001272638,0.001272711,0.993636642,0.001272662,Healthcare,0.9120437,TRUE,76.18181818,0.790896159,134,0.867808402,1,0.537564047,,,0.732089536
3578,"Knowledge, attitude and perception of Pakistanis towards COVID-19; a large cross-sectional survey.",BMC Public Health,33402145,1/7/21,pubmed,0,7,logistic regression,0.000916697,0.000916702,0.000916694,0.000916725,0.995416477,0.000916704,Healthcare,0.94560826,TRUE,17.85714286,0.268600408,5.285714286,0.26130586,0,0.403234768,,,0.311047012
3579,Transmission rates and environmental reservoirs for COVID-19 - a modeling study.,J Biol Dyn,33402047,1/7/21,pubmed,0,2,mathematical model,0.101939257,0.003101638,0.003101501,0.846035227,0.042720878,0.003101499,Epidemiology,0.26281554,FALSE,45,0.587049292,15,0.42594327,0,0.403234768,,,0.472075776
3580,Computational analysis of dynamic allostery and control in the SARS-CoV-2 main protease.,J R Soc Interface,33402024,1/7/21,pubmed,0,2,"computational, network model",0.934890397,0.030225161,0.030498817,0.001461905,0.001461863,0.001461858,Drug discovery,0.603913,TRUE,7.5,0.108355495,0,0.055525823,0,0.403234768,,,0.189038695
3581,Using in silico modelling and FRET-based assays in the discovery of novel FDA-approved drugs as inhibitors of MERS-CoV helicase.,SAR QSAR Environ Res,33401979,1/7/21,pubmed,0,7,in silico,0.992173271,0.001565465,0.001565347,0.001565308,0.001565295,0.001565315,Drug discovery,0.80817276,TRUE,23.85714286,0.352031666,19.14285714,0.472036393,0,0.403234768,,,0.409100942
3582,The scRNA-seq Expression Profiling of the Receptor ACE2 and the Cellular Protease TMPRSS2 Reveals Human Organs Susceptible to SARS-CoV-2 Infection.,Int J Environ Res Public Health,33401657,1/7/21,pubmed,0,8,sequencing,0.69381622,0.10100039,0.002183273,0.002183347,0.002183265,0.198633505,Drug discovery,0.85101455,TRUE,55.75,0.672768879,69.5,0.752006957,3,0.667819001,,,0.697531612
3583,COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning.,Int J Environ Res Public Health,33401512,1/7/21,pubmed,0,4,"machine learning, dataset",0.001291252,0.001291265,0.148418788,0.82660109,0.00129127,0.021106335,Epidemiology,0.48134595,FALSE,20.75,0.307254623,6.75,0.292079208,1,0.537564047,,,0.378965959
3584,Nucleic Acid-Based Diagnostic Tests for the Detection SARS-CoV-2: An Update.,Diagnostics (Basel),33401392,1/7/21,pubmed,0,4,sequencing,0.033812084,0.2423656,0.213587467,0.484642407,0.023805881,0.001786561,Epidemiology,0.71507347,TRUE,43.5,0.573381161,26,0.53819909,1,0.537564047,,,0.549714766
3585,Factors Associated with Worsening Oxygenation in Patients with Non-severe COVID-19 Pneumonia.,Tuberc Respir Dis (Seoul),33401345,1/6/21,pubmed,0,9,"artificial intelligence, logistic regression",0.001237072,0.001237127,0.255924899,0.001237089,0.001237072,0.739126741,Clinics,0.93174076,TRUE,24.33333333,0.359700662,11.44444444,0.376705914,0,0.403234768,,,0.379880448
3586,Unveiling Long COVID-19 Disease.,AIDS Rev,33401287,1/6/21,pubmed,0,4,genomes,0.4045896,0.424419933,0.00840301,0.008403561,0.00840301,0.145780887,Genomics,0.5648375,TRUE,201.5,0.972849279,79.25,0.776759433,0,0.403234768,,,0.717614493
3587,A novel screening strategy of anti-SARS-CoV-2 drugs via blocking interaction between Spike RBD and ACE2.,Environ Int,33401173,1/6/21,pubmed,0,13,computational,0.944375038,0.001622775,0.001622863,0.04913375,0.001622788,0.001622785,Drug discovery,0.98063993,TRUE,87.69230769,0.835673202,,,1,0.537564047,,,0.686618624
3588,Using bioinformatic protein sequence similarity to investigate if SARS CoV-2 infection could cause an ocular autoimmune inflammatory reactions?,Exp Eye Res,33400927,1/6/21,pubmed,0,6,"bioinformatic, sequence alignment",0.833464744,0.163904137,0.000657777,0.000657786,0.000657781,0.000657775,Drug discovery,0.54981244,TRUE,56.66666667,0.679757561,18.83333333,0.468624565,1,0.537564047,,,0.561982058
3589,"The duration, dynamics and determinants of SARS-CoV-2 antibody responses in individual healthcare workers.",Clin Infect Dis,33400782,1/6/21,pubmed,0,27,bayes,0.001220004,0.283638305,0.001219995,0.303431683,0.326493718,0.083996295,Healthcare,0.45600533,FALSE,74.51851852,0.784587791,128.8518519,0.862456516,0,0.403234768,,,0.683426358
3590,Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach.,JMIR Med Inform,33400679,1/6/21,pubmed,0,23,"machine learning, predictive model, logistic regression, dataset",0.00190187,0.001901853,0.562806161,0.001901783,0.001901716,0.429586619,Clinics,0.5044993,TRUE,87.60869565,0.835425815,70.08695652,0.753612523,0,0.403234768,,,0.664091035
3591,Mobile App-Based Remote Patient Monitoring in Acute Medical Conditions: Prospective Feasibility Study Exploring Digital Health Solutions on Clinical Workload During the COVID Crisis.,JMIR Form Res,33400675,1/6/21,pubmed,0,4,digital health,0.050334713,0.00070492,0.00070494,0.649687568,0.00070496,0.297862899,Epidemiology,0.34490585,FALSE,46.75,0.603314985,33.25,0.594193203,1,0.537564047,,,0.578357412
3592,Genomic Evidence of In-Flight Transmission of SARS-CoV-2 Despite Predeparture Testing.,Emerg Infect Dis,33400642,1/6/21,pubmed,0,26,genomes,0.002996416,0.386533463,0.002996492,0.386783835,0.217693166,0.002996629,Epidemiology,0.1963991,FALSE,75.84615385,0.789782918,687.2692308,0.987021675,3,0.667819001,,,0.814874531
3593,"The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning.",Environ Health Perspect,33400596,1/6/21,pubmed,0,10,machine learning,0.009284205,0.009284322,0.187653343,0.775209199,0.009284493,0.009284439,Epidemiology,0.518994,TRUE,71.6,0.771105201,130.2,0.863995183,0,0.403234768,,,0.679445051
3594,European context of the diversity and phylogenetic position of SARS-CoV-2 sequences from Polish COVID-19 patients.,J Appl Genet,33400131,1/6/21,pubmed,0,7,"whole-genome, genomes, sequence alignment, dataset",0.000977434,0.995112707,0.000977493,0.000977483,0.000977445,0.000977439,Genomics,0.33853173,FALSE,29.42857143,0.423835735,14.42857143,0.41697886,0,0.403234768,,,0.414683121
3595,A supply chain disruption risk mitigation model to manage COVID-19 pandemic risk.,Environ Sci Pollut Res Int,33400113,1/6/21,pubmed,0,4,"mathematical model, optimization model",0.001717213,0.032252592,0.167706537,0.794889265,0.001717208,0.001717184,Epidemiology,0.886395,TRUE,33.5,0.471890655,10.75,0.366269735,2,0.618927094,,,0.485695828
3596,Overall management of emergency general surgery patients during the surge of the COVID-19 pandemic: an analysis of procedures and outcomes from a teaching hospital at the worst hit area in Spain.,Eur J Trauma Emerg Surg,33399877,1/6/21,pubmed,0,6,logistic regression,0.001392826,0.001392856,0.027400088,0.001392896,0.001392884,0.96702845,Clinics,0.56700057,TRUE,1,0.012307502,0,0.055525823,1,0.537564047,,,0.201799124
3597,"[Distribution of COVID-19 and tuberculosis in the Metropolitan Region of Chile: differents diseases, similar inequalities].",Rev Med Chil,33399681,1/6/21,pubmed,0,2,correlation analysis,0.002032845,0.002032974,0.002032804,0.676926848,0.314941708,0.00203282,Epidemiology,0.7570078,TRUE,11,0.167171748,0.5,0.087101953,0,0.403234768,,,0.21916949
3598,Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T-cell memory formation after mild COVID-19 infection.,Elife,33399535,1/6/21,pubmed,0,13,"sequencing, dataset",0.593405395,0.294236939,0.00186176,0.001861776,0.001861739,0.10677239,Drug discovery,0.33042365,FALSE,66.53846154,0.744016328,84.69230769,0.789804656,0,0.403234768,,,0.64568525
3599,The RSNA International COVID-19 Open Radiology Database (RICORD).,Radiology,33399506,1/6/21,pubmed,0,23,"machine learning, prediction model",0.001272675,0.159916341,0.834992734,0.001272705,0.001272755,0.001272789,Imaging,0.41719064,FALSE,55.47826087,0.670789783,58.91304348,0.716617608,2,0.618927094,,,0.668778162
3600,Longitudinal characterisation of haematological and biochemical parameters in cancer patients prior to and during COVID-19 reveals features associated with outcome.,ESMO Open,33399072,1/6/21,pubmed,0,40,logistic regression,0.000966763,0.000966795,0.000966765,0.000966806,0.000966787,0.995166084,Clinics,0.9739094,TRUE,12.35,0.186529779,3.75,0.21982874,1,0.537564047,,,0.314640855
3601,Clinical Characteristics and Outcomes of COVID-19 Cohort Patients in Daegu Metropolitan City Outbreak in 2020.,J Korean Med Sci,33398946,1/6/21,pubmed,0,8,logistic regression,0.000977451,0.05224873,0.000977474,0.000977503,0.000977482,0.943841361,Clinics,0.97274566,TRUE,66.625,0.744634795,88.875,0.798902863,2,0.618927094,,,0.720821584
3602,How Resiliency and Hope Can Predict Stress of Covid-19 by Mediating Role of Spiritual Well-being Based on Machine Learning.,J Relig Health,33398655,1/6/21,pubmed,0,9,"machine learning, artificial intelligence",0.001272681,0.001272694,0.407775052,0.001272704,0.587134223,0.001272645,Healthcare,0.9471899,TRUE,41.11111111,0.549260931,15.22222222,0.42734814,0,0.403234768,,,0.459947946
3603,Identification of novel inhibitors of angiotensin-converting enzyme 2 (ACE-2) receptor from Urtica dioica to combat coronavirus disease 2019 (COVID-19).,Mol Divers,33398633,1/6/21,pubmed,0,5,bioinformatic,0.993349859,0.001330037,0.001330028,0.001330031,0.001330036,0.001330009,Drug discovery,0.9216924,TRUE,26.6,0.390191106,6.6,0.28913567,0,0.403234768,,,0.360853848
3604,Using Test Positivity and Reported Case Rates to Estimate State-Level COVID-19 Prevalence and Seroprevalence in the United States.,medRxiv,33398306,1/6/21,pubmed,0,2,"bayes, computational, bayesian model",0.001486462,0.001486463,0.001486482,0.699092193,0.29496195,0.00148645,Epidemiology,0.065778166,FALSE,27,0.3960047,27.5,0.549906342,1,0.537564047,,,0.494491697
3605,REAL-TIME MECHANISTIC BAYESIAN FORECASTS OF COVID-19 MORTALITY.,medRxiv,33398305,1/6/21,pubmed,0,3,"bayes, bayesian model, forecasting model, probabilistic",0.001220002,0.001220034,0.045369526,0.949750399,0.001220015,0.001220024,Epidemiology,0.15261629,FALSE,37.33333333,0.511596264,82.33333333,0.783649987,3,0.667819001,,,0.654355084
3606,An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility.,medRxiv,33398303,1/6/21,pubmed,0,16,"transcriptom, genome-wide",0.447523729,0.292125414,0.138821098,0.001085397,0.001085373,0.119358989,Drug discovery,0.23251984,FALSE,8.3125,0.122085472,13,0.400521809,1,0.537564047,,,0.353390443
3607,Neutralising antibodies in Spike mediated SARS-CoV-2 adaptation.,medRxiv,33398302,1/6/21,pubmed,0,33,whole genome,0.279011489,0.587711582,0.000854711,0.000854735,0.000854712,0.13071277,Genomics,0.31758016,FALSE,14.73333333,0.221720576,12.6,0.394300241,39,0.945737391,,,0.520586069
3608,How do Covid-19 policy options depend on end-of-year holiday contacts in Mexico City Metropolitan Area? A Modeling Study.,medRxiv,33398301,1/6/21,pubmed,0,10,"bayes, artificial intelligence, bayesian model",0.000576352,0.000576353,0.000576373,0.848094095,0.000576405,0.149600422,Epidemiology,0.08130559,FALSE,61.09090909,0.709443998,104.0909091,0.829007225,0,0.403234768,,,0.647228663
3609,Substantial Impact of Post Vaccination Contacts on Cumulative Infections during Viral Epidemics.,medRxiv,33398300,1/6/21,pubmed,0,3,"computational, mathematical model",0.001272695,0.128298964,0.001272645,0.755670013,0.112212932,0.001272751,Epidemiology,0.14011487,FALSE,496.6666667,0.997402437,4261.333333,0.999732406,1,0.537564047,,,0.84489963
3610,"SARS-CoV-2 Testing in Florida, Illinois, and Maryland: Access and Barriers.",medRxiv,33398298,1/6/21,pubmed,0,10,logistic regression,0.001751181,0.001751242,0.001751197,0.06425721,0.928737972,0.001751197,Healthcare,0.5512822,TRUE,71.8,0.772156596,128.2,0.861519936,0,0.403234768,,,0.678970433
3611,"A method for prioritizing risk groups for early SARS-CoV-2 Vaccination, By the Numbers.",medRxiv,33398296,1/6/21,pubmed,0,4,"predictive model, logistic regression",0.001220052,0.001220023,0.001220032,0.152376414,0.449234752,0.394728728,Healthcare,0.009524047,FALSE,2.5,0.027459954,0.5,0.087101953,1,0.537564047,,,0.217375318
3612,Patterns of SARS-CoV-2 testing preferences in a national cohort in the United States.,medRxiv,33398293,1/6/21,pubmed,0,15,simulation model,0.000800608,0.346465035,0.069090103,0.387962649,0.194880975,0.00080063,Epidemiology,0.19483146,FALSE,31,0.445111015,,,0,0.403234768,,,0.424172891
3613,"TCR meta-clonotypes for biomarker discovery with tcrdist3: quantification of public, HLA-restricted TCR biomarkers of SARS-CoV-2 infection.",bioRxiv,33398288,1/6/21,pubmed,0,10,sequencing,0.304570801,0.200983097,0.269332271,0.060792102,0.001538142,0.162783587,Drug discovery,0.07737306,FALSE,56.2,0.676294143,111.3,0.840246187,2,0.618927094,,,0.711822475
3614,0,bioRxiv,33398287,1/6/21,pubmed,0,18,transcriptom,0.442485364,0.00171723,0.001717257,0.550645659,0.001717254,0.001717236,Epidemiology,0.2782315,FALSE,15.88888889,0.238790278,18.94444444,0.46936045,0,0.403234768,,,0.370461832
3615,DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity.,bioRxiv,33398286,1/6/21,pubmed,0,5,"machine learning, deep learning, computational, adversarial network, prediction model, dataset",0.53882613,0.000936158,0.457429358,0.000936137,0.000936093,0.000936124,Drug discovery,0.26592112,FALSE,50.8,0.636588534,85.4,0.791142628,0,0.403234768,,,0.610321976
3616,Analyzing the vast coronavirus literature with CoronaCentral.,bioRxiv,33398279,1/6/21,pubmed,0,2,machine learning,0.083015678,0.002032875,0.002032983,0.908852928,0.002032776,0.00203276,Epidemiology,0.13146234,FALSE,283,0.989238667,,,1,0.537564047,,,0.763401357
3617,0,bioRxiv,33398278,1/6/21,pubmed,0,18,computational,0.383633392,0.441627285,0.002080554,0.002080635,0.002080574,0.16849756,Genomics,0.27078256,FALSE,86.27777778,0.830045148,157.3333333,0.890152529,55,0.960800049,,,0.893665909
3618,Non-covalent TMPRSS2 inhibitors identified from virtual screening.,bioRxiv,33398276,1/6/21,pubmed,0,9,"virtual screening, in silico, structural model",0.945182506,0.002238509,0.002238507,0.002238529,0.045863373,0.002238575,Drug discovery,0.5956102,TRUE,129.8888889,0.921578329,188.4444444,0.911158683,0,0.403234768,,,0.745323927
3619,"Prevalent, protective, and convergent IgG recognition of SARS-CoV-2 non-RBD spike epitopes in COVID-19 convalescent plasma.",bioRxiv,33398269,1/6/21,pubmed,0,35,proteom,0.498870366,0.496315881,0.001203424,0.001203428,0.001203429,0.001203473,Drug discovery,0.17960247,FALSE,66.88571429,0.746428351,138.9428571,0.873294086,15,0.874313229,,,0.831345222
3620,Cross-reactive coronavirus antibodies with diverse epitope specificities and extra-neutralization functions.,bioRxiv,33398266,1/6/21,pubmed,0,31,sequencing,0.600680962,0.39383388,0.001371283,0.001371283,0.001371297,0.001371294,Drug discovery,0.19459566,FALSE,126.6774194,0.916321356,349.3870968,0.96554723,1,0.537564047,,,0.806477544
3621,LinearSampling: Linear-Time Stochastic Sampling of RNA Secondary Structure with Applications to SARS-CoV-2.,bioRxiv,33398265,1/6/21,pubmed,0,5,"genomes, dataset",0.027801982,0.433559942,0.5350761,0.001187368,0.001187324,0.001187285,Genomics,0.14483386,FALSE,53.6,0.657987507,261.8,0.942333423,0,0.403234768,,,0.667851899
3622,Predictors for County Level Variations in Initial 4-week COVID-19 Incidence and Case Fatality Risk in the United States.,Res Sq,33398262,1/6/21,pubmed,0,4,logistic regression,0.001486403,0.001486441,0.001486405,0.408772493,0.220986856,0.365781403,Epidemiology,0.83424723,TRUE,100.5,0.869936298,78,0.773548301,0,0.403234768,,,0.682239789
3623,Broad Auto-Reactive IgM Responses Are Common In Critically Ill COVID-19 Patients.,Res Sq,33398261,1/6/21,pubmed,0,6,proteom,0.723206178,0.001717422,0.001717245,0.001717303,0.001717209,0.269924643,Drug discovery,0.1907655,FALSE,21.5,0.318263343,31.16666667,0.579074124,0,0.403234768,,,0.433524078
3624,Structure-Based Virtual Screening and Biochemical Validation to Discover a Potential Inhibitor of the SARS-CoV-2 Main Protease.,ACS Omega,33398250,1/6/21,pubmed,0,9,"virtual screening, computational, in silico",0.992440258,0.001511805,0.001512089,0.001511889,0.001512017,0.001511942,Drug discovery,0.98574114,TRUE,144,0.938524337,59.66666667,0.71936045,1,0.537564047,,,0.731816278
3625,"Computationally predicted SARS-COV-2 encoded microRNAs target NFKB, JAK/STAT and TGFB signaling pathways.",Gene Rep,33398248,1/6/21,pubmed,0,6,"computational, network analysis",0.896133664,0.097818967,0.001511826,0.001511845,0.001511827,0.001511871,Drug discovery,0.88568336,TRUE,11.66666667,0.176510607,6.666666667,0.290607439,1,0.537564047,,,0.334894031
3626,Interactive SARS-CoV-2 mutation timemaps.,ArXiv,33398246,1/6/21,pubmed,0,2,genomes,0.002806448,0.710120273,0.123598253,0.157862232,0.002806453,0.002806341,Genomics,0.23531848,FALSE,160,0.951697693,1337.5,0.996387477,1,0.537564047,,,0.828549739
3627,Potential therapeutic approaches of microRNAs for COVID-19: Challenges and opportunities.,J Oral Biol Craniofac Res,33398242,1/6/21,pubmed,0,5,"transcriptom, proteom",0.818566371,0.138626876,0.001371324,0.001371323,0.03869277,0.001371335,Drug discovery,0.93099785,TRUE,36.4,0.501329705,23.8,0.519199893,0,0.403234768,,,0.474588122
3628,Computational design of SARS-CoV-2 spike glycoproteins to increase immunogenicity by T cell epitope engineering.,Comput Struct Biotechnol J,33398234,1/6/21,pubmed,0,5,computational,0.843872297,0.132834338,0.019912871,0.001126872,0.001126795,0.001126827,Drug discovery,0.18943265,FALSE,23,0.34225988,27.4,0.548969762,1,0.537564047,,,0.476264563
3629,Novel coronavirus disease (COVID-19) pandemic: A recent mini review.,Comput Struct Biotechnol J,33398233,1/6/21,pubmed,0,7,genomes,0.515528881,0.416420436,0.001141375,0.00114137,0.064626568,0.001141371,Drug discovery,0.91810024,TRUE,11.42857143,0.17150102,,,5,0.739490092,,,0.455495556
3630,Psychological Health Among Armed Forces Doctors During COVID-19 Pandemic in India.,Indian J Psychol Med,33398226,1/6/21,pubmed,0,10,logistic regression,0.001141328,0.00114133,0.001141318,0.001141353,0.994293295,0.001141377,Healthcare,0.98940814,TRUE,6.2,0.087574989,0.3,0.06649719,3,0.667819001,,,0.273963727
3631,"Prevalence and Predictors of Stress, anxiety, and Depression among Healthcare Workers Managing COVID-19 Pandemic in India: A Nationwide Observational Study.",Indian J Psychol Med,33398224,1/6/21,pubmed,0,7,logistic regression,0.001187265,0.001187275,0.00118733,0.001187302,0.940723582,0.054527245,Healthcare,0.97187537,TRUE,53.28571429,0.655575484,29.71428571,0.567366872,23,0.91129082,,,0.711411059
3632,COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak.,Sci Data,33398078,1/6/21,pubmed,0,129,dataset,0.001684533,0.001684541,0.001684562,0.525239901,0.468021968,0.001684495,Epidemiology,0.5759507,TRUE,39.6,0.53503618,13.7,0.407613059,0,0.403234768,,,0.448628002
3633,Fast automated detection of COVID-19 from medical images using convolutional neural networks.,Commun Biol,33398067,1/6/21,pubmed,0,9,"deep learning, neural network",0.001717191,0.001717335,0.937764051,0.001717243,0.001717244,0.055366936,Imaging,0.58983475,TRUE,46.33333333,0.599418641,8.555555556,0.329542414,1,0.537564047,,,0.4888417
3634,Machine learning-based prediction of COVID-19 diagnosis based on symptoms.,NPJ Digit Med,33398013,1/6/21,pubmed,0,3,"machine learning, prediction model",0.001987107,0.001987127,0.582222109,0.220586052,0.191230379,0.001987226,Epidemiology,0.53747535,TRUE,7.666666667,0.111633373,12,0.386740701,1,0.537564047,,,0.345312707
3635,The aging transcriptome and cellular landscape of the human lung in relation to SARS-CoV-2.,Nat Commun,33397975,1/6/21,pubmed,0,3,"transcriptom, proteom",0.810451008,0.002490501,0.002490507,0.002490462,0.002490698,0.179586825,Drug discovery,0.4815867,FALSE,14.66666667,0.221163956,15,0.42594327,0,0.403234768,,,0.350113998
3636,Early perceptions and behavioural responses during the COVID-19 pandemic: a cross-sectional survey of UK adults.,BMJ Open,33397669,1/6/21,pubmed,0,7,mathematical model,0.000926278,0.000926343,0.000926281,0.27131297,0.72498185,0.000926278,Healthcare,0.34825242,FALSE,25.42857143,0.372997712,28.14285714,0.55519133,6,0.764429903,,,0.564206315
3637,The influence of pre-existing hypertension on coronavirus disease 2019 patients.,Epidemiol Infect,33397519,1/6/21,pubmed,0,18,logistic regression,0.001861767,0.00186185,0.001861808,0.001861952,0.00186179,0.990690834,Clinics,0.810071,TRUE,85.83333333,0.828127899,,,0,0.403234768,,,0.615681333
3638,SARS-CoV-2: An insight into detection tools.,Infect Disord Drug Targets,33397246,1/6/21,pubmed,0,4,"sequencing, whole genome",0.002996453,0.500140662,0.487873345,0.0029965,0.00299642,0.00299662,Genomics,0.38945752,FALSE,55.5,0.671346404,17.75,0.458322184,0,0.403234768,,,0.510967785
3639,Virtual screening of curcumin and its analogs against the spike surface glycoprotein of SARS-CoV-2 and SARS-CoV.,J Biomol Struct Dyn,33397223,1/6/21,pubmed,0,6,virtual screening,0.994505897,0.001098851,0.001098814,0.001098835,0.001098794,0.001098808,Drug discovery,0.8070371,TRUE,33.66666667,0.47312759,20.16666667,0.481803586,0,0.403234768,,,0.452721981
3640,0,J Biomol Struct Dyn,33397209,1/6/21,pubmed,0,2,virtual screening,0.99421872,0.001156245,0.001156282,0.001156235,0.001156257,0.001156261,Drug discovery,0.90014327,TRUE,16,0.243552477,0.5,0.087101953,0,0.403234768,,,0.244629733
3641,0,Genes (Basel),33396837,1/6/21,pubmed,0,2,genomes,0.379448916,0.27192466,0.001684504,0.154313778,0.00168456,0.190943582,Drug discovery,0.3682204,FALSE,52.5,0.650875131,21,0.492239765,2,0.618927094,,,0.58734733
3642,"Computational Analysis of SARS-CoV-2 and SARS-Like Coronavirus Diversity in Human, Bat and Pangolin Populations.",Viruses,33396801,1/6/21,pubmed,0,3,computational,0.001684616,0.937561224,0.055700592,0.001684574,0.001684499,0.001684495,Genomics,0.44636512,FALSE,8.666666667,0.12839384,0.333333333,0.073187048,4,0.707574542,,,0.30305181
3643,A Sentiment Analysis Approach to Predict an Individual's Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia.,Int J Environ Res Public Health,33396713,1/6/21,pubmed,0,8,"bayes, machine learning, classifier, predictive model, dataset",0.001823348,0.001823446,0.418499007,0.433188752,0.142842112,0.001823335,Epidemiology,0.48576778,FALSE,2,0.022141134,0.25,0.065493712,0,0.403234768,,,0.163623204
3644,Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19.,Diagnostics (Basel),33396587,1/6/21,pubmed,0,21,"radiom, dataset",0.001310328,0.001310343,0.786778377,0.10639415,0.001310396,0.102896406,Imaging,0.47135147,FALSE,55.76190476,0.672830725,35.76190476,0.609111587,1,0.537564047,,,0.60650212
3645,Unpacking Pandora From Its Box: Deciphering the Molecular Basis of the SARS-CoV-2 Coronavirus.,Int J Mol Sci,33396557,1/6/21,pubmed,0,5,genomes,0.429231323,0.411113734,0.154184428,0.001823472,0.001823679,0.001823364,Drug discovery,0.5934105,TRUE,85.6,0.826890964,71.8,0.75849612,1,0.537564047,,,0.707650377
3646,Artificial Intelligence Model of Drive-Through Vaccination Simulation.,Int J Environ Res Public Health,33396526,1/6/21,pubmed,0,5,"machine learning, artificial intelligence, dataset",0.002296613,0.002296595,0.049793773,0.761529,0.181787393,0.002296625,Epidemiology,0.46990108,FALSE,1350.2,0.99981446,153.8,0.886473107,1,0.537564047,,,0.807950538
3647,Analytical Parameter Estimation of the SIR Epidemic Model. Applications to the COVID-19 Pandemic.,Entropy (Basel),33396355,1/6/21,pubmed,0,1,mathematical model,0.001751161,0.001751181,0.001751247,0.991244074,0.001751163,0.001751175,Epidemiology,0.42887658,FALSE,34,0.477766096,4,0.231469093,0,0.403234768,,,0.370823319
3648,Singapore's Pandemic Preparedness: An Overview of the First Wave of COVID-19.,Int J Environ Res Public Health,33396275,1/6/21,pubmed,0,5,mathematical model,0.001717214,0.001717236,0.001717186,0.875309356,0.11782171,0.001717298,Epidemiology,0.6599996,TRUE,37.4,0.512585812,40.4,0.636138614,0,0.403234768,,,0.517319731
3649,Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis.,Environ Int,33395952,1/6/21,pubmed,0,6,bayes,0.001901716,0.001901745,0.001901751,0.764319718,0.00190178,0.22807329,Epidemiology,0.23288158,FALSE,87.5,0.835363968,324,0.960596735,0,0.403234768,,,0.733065157
3650,"Data Collection during the COVID-19 Pandemic: Learning from Experience, Resulting in a Bayesian Repertory.",Homeopathy,33395709,1/5/21,pubmed,0,14,bayes,0.001565522,0.0015654,0.212244219,0.276791275,0.506268229,0.001565355,Healthcare,0.36500484,FALSE,36.85714286,0.506710372,12.42857143,0.390420123,0,0.403234768,,,0.433455087
3651,Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools.,J Am Soc Mass Spectrom,33395529,1/5/21,pubmed,0,7,machine learning,0.219709913,0.165759166,0.383438869,0.133853876,0.096165973,0.001072203,Drug discovery,0.8543367,TRUE,6.571428571,0.093883357,0.142857143,0.057398983,0,0.403234768,,,0.184839036
3652,Digital Health Literacy and Web-Based Information-Seeking Behaviors of University Students in Germany During the COVID-19 Pandemic: Cross-sectional Survey Study.,J Med Internet Res,33395396,1/5/21,pubmed,0,7,digital health,0.000880204,0.000880203,0.000880216,0.316906096,0.67957307,0.000880211,Healthcare,0.7849822,TRUE,25.14285714,0.369781681,6.142857143,0.281241638,0,0.403234768,,,0.351419362
3653,A National US Survey of Pediatric Emergency Department Coronavirus Pandemic Preparedness.,Pediatr Emerg Care,33394945,1/5/21,pubmed,0,8,deep learning,0.083184479,0.001085375,0.208264385,0.001085402,0.53928454,0.16709582,Healthcare,0.96602744,TRUE,54,0.661574618,14.125,0.413232539,1,0.537564047,,,0.537457068
3654,Type 2 Immunity and Age Modify Gene Expression of COVID19 Receptors in Eosinophilic Gastrointestinal Disorders.,J Pediatr Gastroenterol Nutr,33394891,1/5/21,pubmed,0,19,"sequencing, dataset",0.77385512,0.002032894,0.002032827,0.002032762,0.218013484,0.002032913,Drug discovery,0.81455255,TRUE,158.4736842,0.950151525,250.3157895,0.93818571,1,0.537564047,,,0.808633761
3655,For better or for worse? A pre-post exploration of the impact of the COVID-19 lockdown on cannabis users.,Addiction,33394560,1/5/21,pubmed,0,4,bayes,0.00115622,0.001156246,0.001156239,0.156374932,0.742663172,0.09749319,Healthcare,0.86821264,TRUE,27.5,0.401570907,20,0.481000803,1,0.537564047,,,0.473378586
3656,Chest X-ray in the emergency department during COVID-19 pandemic descending phase in Italy: correlation with patients' outcome.,Radiol Med,33394364,1/5/21,pubmed,0,11,logistic regression,0.00120342,0.001203422,0.497022401,0.00120347,0.001203469,0.498163817,Clinics,0.8346099,TRUE,42.63636364,0.563547529,18.27272727,0.463406476,0,0.403234768,,,0.476729591
3657,Urgent and emergency surgery for secondary peritonitis during the COVID-19 outbreak: an unseen burden of a healthcare crisis.,Updates Surg,33394354,1/5/21,pubmed,0,20,logistic regression,0.001486425,0.001486482,0.001486436,0.160941031,0.07313249,0.761467135,Clinics,0.80615216,TRUE,43.6,0.574061476,17.05,0.451230934,0,0.403234768,,,0.476175726
3658,A Preparedness Model for Mother-Baby Linked Longitudinal Surveillance for Emerging Threats.,Matern Child Health J,33394275,1/5/21,pubmed,0,31,dataset,0.001046854,0.231396827,0.183201733,0.001046883,0.582260817,0.001046886,Healthcare,0.6652793,TRUE,37.5483871,0.514070134,58.51612903,0.715881723,0,0.403234768,,,0.544395542
3659,Psychological distress among Egyptian physicians during COVID-19 pandemic.,Int Arch Occup Environ Health,33394181,1/5/21,pubmed,0,4,logistic regression,0.001861663,0.001861674,0.001861661,0.00186169,0.990691554,0.001861758,Healthcare,0.98728883,TRUE,14.5,0.219617787,24.5,0.525086968,0,0.403234768,,,0.382646508
3660,Risk Factors Associated With All-Cause 30-Day Mortality in Nursing Home Residents With COVID-19.,JAMA Intern Med,33394006,1/5/21,pubmed,0,11,prediction model,0.000716327,0.00071632,0.000716344,0.00071633,0.607772609,0.38936207,Healthcare,0.7860646,TRUE,122.5454545,0.910816995,144.9090909,0.878712871,7,0.785110192,,,0.858213353
3661,Metaproteomics Analysis of SARS-CoV-2-Infected Patient Samples Reveals Presence of Potential Coinfecting Microorganisms.,J Proteome Res,33393790,1/5/21,pubmed,0,10,proteom,0.003101522,0.664361541,0.227043472,0.003101557,0.003101612,0.099290297,Genomics,0.8371383,TRUE,42.9,0.566330633,21.8,0.501204174,1,0.537564047,,,0.535032951
3662,Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia.,J Vis Exp,33393515,1/5/21,pubmed,0,5,"machine learning, radiom",0.001538195,0.001538135,0.947772277,0.04607478,0.001538258,0.001538354,Imaging,0.10219246,FALSE,72.4,0.774939699,48.2,0.674672197,0,0.403234768,,,0.617615555
3663,"Recent advancements in glycoproteomic studies: Glycopeptide enrichment and derivatization, characterization of glycosylation in SARS CoV2, and interacting glycoproteins.",Mass Spectrom Rev,33393161,1/5/21,pubmed,0,2,"proteom, glycoproteom",0.466357649,0.275019548,0.001461952,0.224002543,0.031696347,0.00146196,Drug discovery,0.23015317,FALSE,23.5,0.348506401,18,0.46180091,0,0.403234768,,,0.404514026
3664,CRISPR-based strategies in infectious disease diagnosis and therapy.,Infection,33393066,1/5/21,pubmed,0,5,genomes,0.508066089,0.178235681,0.153144711,0.158008195,0.001272643,0.00127268,Drug discovery,0.62757,TRUE,18.6,0.278990661,45.2,0.659084827,0,0.403234768,,,0.447103419
3665,Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department.,J Am Coll Emerg Physicians Open,33392549,1/5/21,pubmed,0,6,"artificial intelligence, deep-learning",0.001291295,0.001291216,0.610325212,0.001291296,0.384509696,0.001291285,Healthcare,0.95096195,TRUE,43,0.567629414,13,0.400521809,0,0.403234768,,,0.457128664
3666,Atypical presentations of COVID-19 in care home residents presenting to secondary care: A UK single centre study.,Aging Med (Milton),33392429,1/5/21,pubmed,0,3,logistic regression,0.001538075,0.0015381,0.001538072,0.001538101,0.432896917,0.560950734,Clinics,0.7522007,TRUE,24.66666667,0.36334962,10,0.355632861,1,0.537564047,,,0.418848842
3667,Dynamics of COVID-19 transmission in Dhaka and Chittagong: Two business hubs of Bangladesh.,Clin Epidemiol Glob Health,33392419,1/5/21,pubmed,0,6,forecasting model,0.00153811,0.001538097,0.001538083,0.797170198,0.196677334,0.001538179,Epidemiology,0.5144515,TRUE,3.333333333,0.04044777,0,0.055525823,0,0.403234768,,,0.166402787
3668,Network model and analysis of the spread of Covid-19 with social distancing.,Appl Netw Sci,33392389,1/5/21,pubmed,0,2,network model,0.170918637,0.001392875,0.001392857,0.823509917,0.001392886,0.001392828,Epidemiology,0.27982467,FALSE,4,0.054734368,0,0.055525823,1,0.537564047,,,0.215941413
3669,FCOD: Fast COVID-19 Detector based on deep learning techniques.,Inform Med Unlocked,33392388,1/5/21,pubmed,0,3,"deep learning, dataset",0.001684497,0.001684497,0.920242884,0.001684545,0.001684571,0.073019007,Imaging,0.7393047,TRUE,20,0.298163152,0.333333333,0.073187048,0,0.403234768,,,0.258194989
3670,Identification of Potential Inhibitors of 3CL Protease of SARS-CoV-2 From ZINC Database by Molecular Docking-Based Virtual Screening.,Front Mol Biosci,33392261,1/5/21,pubmed,0,2,virtual screening,0.992809373,0.001438112,0.00143816,0.001438159,0.001438095,0.001438102,Drug discovery,0.95286965,TRUE,69,0.759416167,16.5,0.44293551,0,0.403234768,,,0.535195481
3671,Surveying the Side-Chain Network Approach to Protein Structure and Dynamics: The SARS-CoV-2 Spike Protein as an Illustrative Case.,Front Mol Biosci,33392257,1/5/21,pubmed,0,6,network analysis,0.569093376,0.001371338,0.001371326,0.425421421,0.001371284,0.001371256,Drug discovery,0.15832591,FALSE,58.33333333,0.691941369,21,0.492239765,1,0.537564047,,,0.57391506
3672,Causal Relationship Between the Spread of the COVID-19 and Geopolitical Risks in Emerging Economies.,Front Public Health,33392146,1/5/21,pubmed,0,4,dataset,0.004310066,0.004310372,0.0043101,0.978449001,0.004310097,0.004310364,Epidemiology,0.44107834,FALSE,17.25,0.260189251,2.5,0.180826866,0,0.403234768,,,0.281416962
3673,Public Health Interventions for the COVID-19 Pandemic Reduce Respiratory Tract Infection-Related Visits at Pediatric Emergency Departments in Taiwan.,Front Public Health,33392141,1/5/21,pubmed,0,6,dataset,0.001438123,0.001438166,0.001438117,0.301159879,0.192111412,0.502414304,Clinics,0.74358183,TRUE,42.5,0.562743522,25,0.529435376,0,0.403234768,,,0.498471222
3674,Internet of Things and Artificial Intelligence in Healthcare During COVID-19 Pandemic-A South American Perspective.,Front Public Health,33392139,1/5/21,pubmed,0,4,"artificial intelligence, digital health",0.002183234,0.002183235,0.434388005,0.556878824,0.002183387,0.002183315,Epidemiology,0.5183549,TRUE,45.5,0.591440411,5.5,0.267259834,0,0.403234768,,,0.420645004
3675,"A simple, SIR-like but individual-based epidemic model: Application in comparison of COVID-19 in New York City and Wuhan.",Results Phys,33391987,1/5/21,pubmed,0,1,model fit,0.002806395,0.002806395,0.002806377,0.98596806,0.002806392,0.00280638,Epidemiology,0.6238351,TRUE,5,0.070752675,0,0.055525823,1,0.537564047,,,0.221280848
3676,A fuzzy fractional model of coronavirus (COVID-19) and its study with Legendre spectral method.,Results Phys,33391986,1/5/21,pubmed,0,4,mathematical model,0.004530836,0.108431894,0.004530794,0.873444689,0.0045311,0.004530687,Epidemiology,0.69017625,TRUE,23.25,0.34454821,3.5,0.213607172,1,0.537564047,,,0.365239809
3677,"Full-length genome characterization and phylogenetic analysis of SARS-CoV-2 virus strains from Yogyakarta and Central Java, Indonesia.",PeerJ,33391880,1/5/21,pubmed,0,18,"sequencing, whole genome, genome sequences, genomes, dataset",0.00114132,0.91484812,0.001141354,0.001141362,0.001141357,0.080586487,Genomics,0.38524067,FALSE,20.83333333,0.307996784,3.888888889,0.223240567,2,0.618927094,,,0.383388149
3678,Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2.,Comput Struct Biotechnol J,33391634,1/5/21,pubmed,0,5,"machine learning, dataset",0.705043879,0.051987031,0.208448098,0.000612273,0.000612264,0.033296454,Drug discovery,0.7458805,TRUE,20.2,0.300327788,19.8,0.478257961,0,0.403234768,,,0.393940172
3679,Computational modelling supports that dengue virus envelope antibodies can bind to SARS-CoV-2 receptor binding sites: Is pre-exposure to dengue virus protective against COVID-19 severity?,Comput Struct Biotechnol J,33391633,1/5/21,pubmed,0,5,computational,0.413489391,0.448501905,0.067219855,0.001943621,0.001943479,0.06690175,Genomics,0.6023204,TRUE,12,0.183190055,3.2,0.202100615,1,0.537564047,,,0.307618239
3680,Rapid design and development of CRISPR-Cas13a targeting SARS-CoV-2 spike protein.,Theranostics,33391497,1/5/21,pubmed,0,5,"bioinformatic, in-silico, sequence alignment",0.78389085,0.207786548,0.002080712,0.002080796,0.002080563,0.002080532,Drug discovery,0.92284024,TRUE,23.2,0.343496815,,,0,0.403234768,,,0.373365791
3681,"Mathematical model of COVID-19 spread in Turkey and South Africa: theory, methods, and applications.",Adv Differ Equ,33391372,1/5/21,pubmed,0,2,mathematical model,0.00141512,0.001415131,0.001415201,0.992924205,0.001415142,0.001415201,Epidemiology,0.6617086,TRUE,39.5,0.534232173,8.5,0.329141022,16,0.881782826,,,0.581718674
3682,Class I HLA Allele Predicted Restricted Antigenic Coverages for Spike and Nucleocapsid Proteins Are Associated With Deaths Related to COVID-19.,Front Immunol,33391255,1/5/21,pubmed,0,6,in silico,0.163439439,0.544325005,0.00143816,0.203887114,0.001438187,0.085472095,Genomics,0.3060667,FALSE,30.16666667,0.433731214,16.66666667,0.444674873,2,0.618927094,,,0.49911106
3683,The Impact of the COVID-19 Pandemic on People With Epilepsy. An Italian Survey and a Global Perspective.,Front Neurol,33391172,1/5/21,pubmed,0,14,logistic regression,0.0014619,0.00146193,0.001461929,0.126203511,0.549838837,0.319571892,Healthcare,0.99148345,TRUE,78.78571429,0.801595646,63.64285714,0.733877442,0,0.403234768,,,0.646235952
3684,0,Front Neurol,33391152,1/5/21,pubmed,0,6,machine learning,0.001486524,0.001486524,0.089962961,0.001486458,0.393418332,0.512159201,Clinics,0.99756014,TRUE,49.83333333,0.630651246,8.5,0.329141022,0,0.403234768,,,0.454342345
3685,Impact of the COVID-19 Pandemic on Patients With Alcohol Use Disorder and Associated Risk Factors for Relapse.,Front Psychiatry,33391060,1/5/21,pubmed,0,4,logistic regression,0.001141331,0.00114136,0.001141386,0.001141427,0.81187131,0.183563185,Healthcare,0.9776912,TRUE,5.75,0.080957388,0.25,0.065493712,1,0.537564047,,,0.228005049
3686,"Perinatal Depression of Exposed Maternal Women in the COVID-19 Pandemic in Wuhan, China.",Front Psychiatry,33391042,1/5/21,pubmed,0,12,logistic regression,0.001350336,0.00135041,0.00135033,0.124796934,0.752768777,0.118383214,Healthcare,0.8764828,TRUE,37.41666667,0.512647659,9.833333333,0.351284453,0,0.403234768,,,0.42238896
3687,Repurposing Approved Drugs for Guiding COVID-19 Prophylaxis: A Systematic Review.,Front Pharmacol,33390967,1/5/21,pubmed,0,10,in silico,0.719661999,0.001291282,0.001291318,0.275172805,0.001291318,0.001291277,Drug discovery,0.6979439,TRUE,51.7,0.64425753,40.8,0.638346267,0,0.403234768,,,0.561946188
3688,Multi-criterion Intelligent Decision Support system for COVID-19.,Appl Soft Comput,33390874,1/5/21,pubmed,0,3,"machine learning, dataset",0.001022628,0.018982516,0.251449735,0.726499872,0.001022615,0.001022636,Epidemiology,0.5613049,TRUE,11.33333333,0.170635166,1,0.122023013,1,0.537564047,,,0.276740742
3689,Trends beyond the new normal: from remote monitoring to digital connectivity.,Eur Heart J Suppl,33390863,1/5/21,pubmed,0,6,digital health,0.002639373,0.002639037,0.002639155,0.674585243,0.314858221,0.002638973,Epidemiology,0.8475491,TRUE,72.83333333,0.776733255,36.33333333,0.613393096,1,0.537564047,,,0.642563466
3690,SARS-CoV-2 variants evolved during the early stage of the pandemic and effects of mutations on adaptation in Wuhan populations.,Int J Biol Sci,33390836,1/5/21,pubmed,0,4,genomes,0.001622764,0.954818205,0.001622751,0.001622818,0.001622812,0.03869065,Genomics,0.37549114,FALSE,26.25,0.384748593,21.5,0.498260637,2,0.618927094,,,0.500645441
3691,ACE2 Correlated With Immune Infiltration Serves As A Novel Prognostic Biomarker In Clear Cell Renal Cell Carcinoma: Implication For COVID-19.,Int J Biol Sci,33390830,1/5/21,pubmed,0,8,dataset,0.548390932,0.001786513,0.001786644,0.001786552,0.00178654,0.444462818,Drug discovery,0.8551732,TRUE,20.375,0.302245037,4.875,0.250066899,1,0.537564047,,,0.363291994
3692,Risk factors related to the severity of COVID-19 in Wuhan.,Int J Med Sci,33390780,1/5/21,pubmed,0,8,"machine learning, classifier, logistic regression",0.001046826,0.001046828,0.335874074,0.001046824,0.001046829,0.659938619,Clinics,0.88363093,TRUE,16.25,0.246026347,0.75,0.099411292,2,0.618927094,,,0.321454911
3693,Based on Computational Communication Paradigm: Simulation of Public Opinion Communication Process of Panic Buying During the COVID-19 Pandemic.,Psychol Res Behav Manag,33390730,1/5/21,pubmed,0,4,computational,0.001059421,0.001059371,0.155009678,0.481989823,0.359822356,0.00105935,Epidemiology,0.26063985,FALSE,61,0.709196611,67.25,0.746052984,0,0.403234768,,,0.619494787
3694,Analyzing the Human Rights Impact of Increased Digital Public Health Surveillance during the COVID-19 Crisis.,Health Hum Rights,33390688,1/5/21,pubmed,0,4,digital health,0.001684568,0.274006181,0.001684619,0.719255611,0.001684545,0.001684476,Epidemiology,0.7658946,TRUE,53.75,0.659348135,18.25,0.463272679,0,0.403234768,,,0.508618527
3695,Mathematical analysis of COVID-19 via new mathematical model.,Chaos Solitons Fractals,33390671,1/5/21,pubmed,0,7,mathematical model,0.009284584,0.009284415,0.405263137,0.557599337,0.009284294,0.009284233,Epidemiology,0.72772723,TRUE,95.85714286,0.858618344,8.571428571,0.329810008,4,0.707574542,,,0.632000964
3696,ResGNet-C: A graph convolutional neural network for detection of COVID-19.,Neurocomputing,33390662,1/5/21,pubmed,0,5,"neural network, classifier, dataset",0.001187366,0.001187318,0.963451295,0.001187349,0.001187303,0.031799369,Imaging,0.30781448,FALSE,55,0.668686994,52.6,0.691263045,0,0.403234768,,,0.587728269
3697,Characterising early agricultural and food policy responses to the outbreak of COVID-19.,Food Policy,33390644,1/5/21,pubmed,0,2,dataset,0.002898431,0.049424043,0.00289836,0.741380216,0.200500603,0.002898348,Epidemiology,0.793039,TRUE,43,0.567629414,81.5,0.782044421,1,0.537564047,,,0.629079294
3698,Impact of prior statin use on clinical outcomes in COVID-19 patients: data from tertiary referral hospitals during COVID-19 pandemic in Italy.,J Clin Lipidol,33390341,1/5/21,pubmed,0,16,logistic regression,0.001486536,0.001486405,0.001486412,0.147863982,0.001486447,0.846190219,Clinics,0.89289784,TRUE,76.8125,0.793246336,33.875,0.598006422,5,0.739490092,,,0.710247617
3699,Equipping community health workers with digital tools for pandemic response in LMICs.,Arch Public Health,33390163,1/5/21,pubmed,0,3,digital health,0.001059354,0.001059369,0.175379965,0.511536842,0.309905139,0.001059332,Epidemiology,0.98144215,TRUE,22.33333333,0.330261612,15.66666667,0.432365534,0,0.403234768,,,0.388620638
3700,Application of Artificial Intelligence to Address Issues Related to the COVID-19 Virus.,SLAS Technol,33390088,1/5/21,pubmed,0,1,"machine learning, artificial intelligence, neural network",0.185161426,0.00156533,0.65157213,0.158570478,0.001565323,0.001565314,Drug discovery,0.929391,TRUE,16,0.243552477,0,0.055525823,0,0.403234768,,,0.234104356
3701,Immune Computation and COVID-19 Mortality: A Rationale for IVIg.,Crit Rev Immunol,33389884,1/4/21,pubmed,0,3,machine learning,0.395253712,0.002080688,0.303575845,0.002080593,0.08936068,0.207648484,Drug discovery,0.20593739,FALSE,254.6666667,0.985094935,260.6666667,0.941798234,0,0.403234768,,,0.776709312
3702,Clinical and laboratory characteristics of severe and non-severe patients with COVID-19: A retrospective cohort study in China.,J Clin Lab Anal,33389777,1/4/21,pubmed,0,7,logistic regression,0.001046911,0.001046827,0.00104684,0.001046841,0.001046817,0.994765763,Clinics,0.763011,TRUE,12.42857143,0.187395634,11.14285714,0.372089912,0,0.403234768,,,0.320906771
3703,Ivermectin as a potential drug for treatment of COVID-19: an in-sync review with clinical and computational attributes.,Pharmacol Rep,33389725,1/4/21,pubmed,0,6,"computational, artificial intelligence, in silico",0.812716037,0.001511905,0.112270146,0.070478229,0.001511846,0.001511838,Drug discovery,0.95920014,TRUE,84,0.821881378,32.83333333,0.591249666,4,0.707574542,,,0.706901862
3704,CoViTris2020 and ChloViD2020: a striking new hope in COVID-19 therapy.,Mol Divers,33389560,1/4/21,pubmed,0,1,computational,0.975023673,0.001291256,0.001291201,0.019811418,0.001291233,0.001291218,Drug discovery,0.9501712,TRUE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
3705,Modelling Internet of things (IoT)-driven global sustainability in multi-tier agri-food supply chain under natural epidemic outbreaks.,Environ Sci Pollut Res Int,33389466,1/4/21,pubmed,0,3,structural model,0.001717255,0.001717234,0.128837044,0.838932313,0.027078806,0.001717347,Epidemiology,0.8942411,TRUE,86,0.829117447,21,0.492239765,0,0.403234768,,,0.574863993
3706,"The anti-HCV, Sofosbuvir, versus the anti-EBOV Remdesivir against SARS-CoV-2 RNA dependent RNA polymerase in silico.",Mol Divers,33389441,1/4/21,pubmed,0,3,in silico,0.994063651,0.001187307,0.001187259,0.001187294,0.001187245,0.001187244,Drug discovery,0.92384136,TRUE,25.66666667,0.376461129,22.33333333,0.505753278,1,0.537564047,,,0.473259485
3707,Designing of peptide aptamer targeting the receptor-binding domain of spike protein of SARS-CoV-2: an in silico study.,Mol Divers,33389440,1/4/21,pubmed,0,2,in silico,0.957411567,0.027098883,0.001112638,0.001112624,0.00111261,0.012151678,Drug discovery,0.5513527,TRUE,13,0.197352959,0.5,0.087101953,1,0.537564047,,,0.27400632
3708,Incremental diagnostic role of left atrial strain analysis in thrombotic risk assessment of nonvalvular atrial fibrillation patients planned for electrical cardioversion.,Int J Cardiovasc Imaging,33389359,1/4/21,pubmed,0,5,logistic regression,0.001751181,0.081640621,0.075842612,0.208095692,0.001751188,0.630918706,Clinics,0.92917484,TRUE,85.8,0.827818665,36.4,0.613593792,0,0.403234768,,,0.614882408
3709,Predicting outcome in acute severe colitis - controversies in clinical practice in 2021.,J Crohns Colitis,33388777,1/4/21,pubmed,0,4,multi-omics,0.002562591,0.002562619,0.002562817,0.319405375,0.002562797,0.670343801,Clinics,0.9209609,TRUE,41.75,0.555383759,66.5,0.744246722,0,0.403234768,,,0.56762175
3710,"Prospective Longitudinal Serosurvey of Health Care Workers in the First Wave of the SARS-CoV-2 Pandemic in a Quaternary Care Hospital in Munich, Germany.",Clin Infect Dis,33388756,1/4/21,pubmed,0,15,"sequencing, whole genome",0.001511848,0.302683697,0.001511873,0.001511915,0.505407088,0.187373578,Healthcare,0.5946996,TRUE,51.13333333,0.639062403,69.73333333,0.752542146,1,0.537564047,,,0.643056199
3711,"Distinct disease severity between children and older adults with COVID-19: Impacts of ACE2 expression, distribution, and lung progenitor cells.",Clin Infect Dis,33388749,1/4/21,pubmed,0,37,dataset,0.357776579,0.001010972,0.001010983,0.001010953,0.13031041,0.508880103,Clinics,0.4397654,FALSE,89.97297297,0.842414497,85.59459459,0.791610918,2,0.618927094,,,0.75098417
3712,Comparing COVID-19 vaccine allocation strategies in India: A mathematical modelling study.,Int J Infect Dis,33388436,1/4/21,pubmed,0,6,mathematical model,0.04894724,0.000988373,0.000988346,0.833934097,0.000988398,0.114153546,Epidemiology,0.06492007,FALSE,35.66666667,0.493908096,32.5,0.589376505,1,0.537564047,,,0.540282883
3713,A longitudinal study of psychological distress in the United States before and during the COVID-19 pandemic.,Prev Med,33388325,1/4/21,pubmed,0,6,logistic regression,0.001141405,0.001141363,0.001141331,0.001141395,0.994293149,0.001141357,Healthcare,0.6220151,TRUE,66,0.741356918,133.5,0.867139417,1,0.537564047,,,0.71535346
3714,Induction of alarmin S100A8/A9 mediates activation of aberrant neutrophils in the pathogenesis of COVID-19.,Cell Host Microbe,33388094,1/4/21,pubmed,0,23,transcriptom,0.707349679,0.002806685,0.002806427,0.002806632,0.002806494,0.281424085,Drug discovery,0.43019018,FALSE,26.34782609,0.386109221,32.95652174,0.591851753,3,0.667819001,,,0.548593325
3715,Validity and reliability of the Self-Care Activities Screening Scale (SASS-14) during COVID-19 lockdown.,Health Qual Life Outcomes,33388064,1/4/21,pubmed,0,7,model fit,0.001350453,0.001350366,0.180487573,0.001350421,0.814110843,0.001350344,Healthcare,0.9117877,TRUE,37.28571429,0.510606717,32.28571429,0.587704041,0,0.403234768,,,0.500515175
3716,Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence.,Rev Cardiovasc Med,33387999,1/4/21,pubmed,0,43,artificial intelligence,0.00198715,0.048801461,0.582886384,0.225303148,0.001987153,0.139034704,Epidemiology,0.32558882,FALSE,164.1162791,0.954295256,120.3488372,0.851752743,0,0.403234768,,,0.736427589
3717,In silico study of the potential interactions of 4'-acetamidechalcones with protein targets in SARS-CoV-2.,Biochem Biophys Res Commun,33387885,1/3/21,pubmed,0,12,in silico,0.918984377,0.001392908,0.001392836,0.05825986,0.001392861,0.018577159,Drug discovery,0.81068814,TRUE,17.33333333,0.261178799,2.166666667,0.166845063,0,0.403234768,,,0.27708621
3718,Changing air pollution scenario during COVID-19: Redefining the hotspot regions over India.,Environ Pollut,33387785,1/3/21,pubmed,0,5,radiom,0.001751429,0.079685724,0.061598857,0.853461574,0.001751215,0.001751201,Epidemiology,0.7551912,TRUE,40.4,0.543261797,50,0.681763447,0,0.403234768,,,0.542753337
3719,Psychological stresses among Chinese university students during the COVID-19 epidemic: The effect of early life adversity on emotional distress.,J Affect Disord,33387744,1/3/21,pubmed,0,8,logistic regression,0.001415152,0.001415158,0.001415105,0.069148719,0.925190749,0.001415118,Healthcare,0.94907767,TRUE,58.875,0.695281093,23.875,0.519735082,0,0.403234768,,,0.539416981
3720,Developing a multivariable risk prediction model to predict prolonged viral clearance in patients with COVID-19.,J Infect,33387568,1/3/21,pubmed,0,17,prediction model,0.025062734,0.299588562,0.02506575,0.025062662,0.025060224,0.600160068,Clinics,0.6525985,TRUE,98.47058824,0.865730719,23.88235294,0.51980198,2,0.618927094,,,0.668153265
3721,A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images.,Interdiscip Sci,33387306,1/3/21,pubmed,0,5,"machine learning, deep learning, neural network, classifier, adversarial network, dataset",0.001291215,0.001291226,0.993543734,0.001291293,0.001291308,0.001291224,Imaging,0.83012843,TRUE,23.4,0.346527305,6,0.280037463,5,0.739490092,,,0.45535162
3722,In Silico Evaluation of Prospective Anti-COVID-19 Drug Candidates as Potential SARS-CoV-2 Main Protease Inhibitors.,Protein J,33387249,1/3/21,pubmed,0,6,in silico,0.994638848,0.001072223,0.001072206,0.001072252,0.001072251,0.001072221,Drug discovery,0.9522089,TRUE,38.83333333,0.527367184,10.16666667,0.357171528,2,0.618927094,,,0.501155269
3723,Population dynamics and structural effects at short and long range support the hypothesis of the selective advantage of the G614 SARS-Cov2 spike variant.,Mol Biol Evol,33386849,1/3/21,pubmed,0,9,molecular dynamics simulation,0.335082194,0.482859742,0.001823346,0.176588046,0.001823328,0.001823344,Genomics,0.25004828,FALSE,35.77777778,0.494650257,70.88888889,0.755820177,0,0.403234768,,,0.551235067
3724,Social restriction versus herd immunity policies in the early phase of the SARS-CoV-2 pandemic: A mathematical modelling study.,Asian Pac J Allergy Immunol,33386788,1/3/21,pubmed,0,8,mathematical model,0.001538188,0.001538092,0.001538068,0.992309236,0.001538158,0.001538258,Epidemiology,0.386124,FALSE,97.375,0.862823922,126,0.859245384,0,0.403234768,,,0.708434691
3725,High Systolic Blood Pressure at Hospital Admission Is an Important Risk Factor in Models Predicting Outcome of COVID-19 Patients.,Am J Hypertens,33386395,1/3/21,pubmed,0,7,"logistic regression, prediction model",0.001220005,0.001220004,0.001220051,0.0679148,0.001220042,0.927205098,Clinics,0.19800198,FALSE,52.71428571,0.651679139,68,0.748193738,1,0.537564047,,,0.645812308
3726,Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19.,Hum Genomics,33386081,1/3/21,pubmed,0,11,"machine learning, computational, in silico, interactom",0.585208881,0.024523683,0.290014399,0.051704329,0.035233911,0.013314797,Drug discovery,0.819814,TRUE,50,0.632073721,131.4545455,0.865132459,0,0.403234768,,,0.633480316
3727,Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach.,Brief Bioinform,33386025,1/2/21,pubmed,0,9,"virtual screening, molecular dynamics simulation, computational",0.972867551,0.022994059,0.001034586,0.001034657,0.001034583,0.001034565,Drug discovery,0.8414509,TRUE,35.77777778,0.494650257,20.44444444,0.484947819,1,0.537564047,,,0.505720708
3728,Genome-wide analysis of Indian SARS-CoV-2 genomes to identify T-cell and B-cell epitopes from conserved regions based on immunogenicity and antigenicity.,Int Immunopharmacol,33385714,1/2/21,pubmed,0,4,"genome-wide, genomes, sequence alignment",0.418452462,0.51057944,0.067544068,0.001141363,0.001141335,0.001141333,Genomics,0.8071893,TRUE,71.75,0.771909209,51.25,0.686245652,1,0.537564047,,,0.665239636
3729,Optimal strategies for social distancing and testing to control COVID-19.,J Theor Biol,33385403,1/2/21,pubmed,0,2,mathematical model,0.001156232,0.001156253,0.001156257,0.976842348,0.018532656,0.001156254,Epidemiology,0.29774722,FALSE,24,0.35574247,36,0.611118544,0,0.403234768,,,0.456698594
3730,A simple correction for COVID-19 sampling bias.,J Theor Biol,33385402,1/2/21,pubmed,0,2,dataset,0.002080571,0.002080615,0.002080681,0.784151721,0.207525818,0.002080593,Epidemiology,0.15697491,FALSE,9.5,0.143051518,1,0.122023013,0,0.403234768,,,0.222769766
3731,The functional connectome predicts feeling of stress on regular days and during the COVID-19 pandemic.,Neurobiol Stress,33385021,1/2/21,pubmed,0,7,"predictive model, prediction model, dataset",0.282788673,0.001987273,0.228966011,0.19728561,0.286985183,0.001987251,Healthcare,0.92290294,TRUE,51,0.63875317,44.42857143,0.655940594,0,0.403234768,,,0.565976177
3732,National preparedness survey of pediatric intensive care units with simulation centers during the coronavirus pandemic.,World J Crit Care Med,33384950,1/2/21,pubmed,0,9,deep learning,0.000752923,0.000752921,0.188067662,0.21517323,0.433513207,0.161740056,Healthcare,0.9907392,TRUE,63,0.721998887,20.22222222,0.482338774,0,0.403234768,,,0.535857476
3733,Mutational spectra of SARS-CoV-2 isolated from animals.,PeerJ,33384909,1/2/21,pubmed,0,4,genome sequences,0.058265401,0.937736577,0.000999503,0.000999513,0.000999506,0.0009995,Genomics,0.7168772,TRUE,41,0.549013544,16,0.437316029,2,0.618927094,,,0.535085556
3734,Diabetic patients with COVID-19 need more attention and better glycemic control.,World J Diabetes,33384770,1/2/21,pubmed,0,4,logistic regression,0.000977431,0.000977432,0.023015982,0.000977446,0.000977462,0.973074246,Clinics,0.98809314,TRUE,370.5,0.99505226,262.25,0.942734814,1,0.537564047,,,0.825117041
3735,Deep Sequencing of B Cell Receptor Repertoires From COVID-19 Patients Reveals Strong Convergent Immune Signatures.,Front Immunol,33384691,1/2/21,pubmed,0,21,"sequencing, deep sequencing",0.389613753,0.323045791,0.039845858,0.002183291,0.002183343,0.243127964,Drug discovery,0.42161715,FALSE,66.52380952,0.743892634,206.7619048,0.920658282,0,0.403234768,,,0.689261895
3736,#Everything Will Be Fine. Duration of Home Confinement and "All-or-Nothing" Cognitive Thinking Style as Predictors of Traumatic Distress in Young University Students on a Digital Platform During the COVID-19 Italian Lockdown.,Front Psychiatry,33384623,1/2/21,pubmed,0,7,logistic regression,0.016700848,0.000956355,0.000956341,0.145531486,0.834898609,0.000956361,Healthcare,0.9619661,TRUE,83.85714286,0.82070629,43.42857143,0.651123896,0,0.403234768,,,0.625021651
3737,0,Indian J Crit Care Med,33384521,1/2/21,pubmed,0,3,mathematical model,0.001823349,0.001823374,0.001823324,0.9521604,0.001823402,0.04054615,Epidemiology,0.27675253,FALSE,3.666666667,0.045952131,0.333333333,0.073187048,0,0.403234768,,,0.174124649
3738,TW-SIR: time-window based SIR for COVID-19 forecasts.,Sci Rep,33384444,1/2/21,pubmed,0,5,"machine learning, prediction model",0.002130708,0.002130713,0.002130852,0.989346396,0.002130657,0.002130674,Epidemiology,0.51123804,TRUE,62.4,0.717422228,14,0.412898047,1,0.537564047,,,0.55596144
3739,A model to rate strategies for managing disease due to COVID-19 infection.,Sci Rep,33384432,1/2/21,pubmed,0,2,"machine learning, mathematical model",0.001237111,0.064136268,0.180663066,0.751489332,0.001237142,0.001237081,Epidemiology,0.704489,TRUE,3.5,0.044344115,0,0.055525823,0,0.403234768,,,0.167701569
3740,Clinical Outcomes in Patients With Heart Failure Hospitalized With COVID-19.,JACC Heart Fail,33384064,1/2/21,pubmed,0,10,logistic regression,0.001085332,0.001085339,0.001085364,0.001085366,0.001085373,0.994573226,Clinics,0.89705944,TRUE,152.9,0.946688107,143.1,0.877241102,4,0.707574542,,,0.843834584
3741,Discovery of a Bradykinin B2 Partial Agonist Profile of Raloxifene in a Drug Repurposing Campaign.,Int J Mol Sci,33383825,1/2/21,pubmed,0,2,virtual screening,0.849343736,0.002720175,0.00272012,0.002720271,0.002720312,0.139775386,Drug discovery,0.9351041,TRUE,15.5,0.234028078,4,0.231469093,0,0.403234768,,,0.289577313
3742,Identification and computational analysis of mutations in SARS-CoV-2.,Comput Biol Med,33383528,1/1/21,pubmed,0,5,computational,0.594367197,0.391768791,0.003466069,0.00346616,0.003465944,0.003465839,Drug discovery,0.696446,TRUE,10,0.15214299,2.4,0.174872893,2,0.618927094,,,0.315314326
3743,Comprehensive analysis of TCR repertoire in COVID-19 using single cell sequencing.,Genomics,33383142,1/1/21,pubmed,0,14,sequencing,0.4997708,0.421574097,0.001823406,0.001823369,0.001823361,0.073184968,Drug discovery,0.26973373,FALSE,51.92857143,0.645618158,29.5,0.566095799,0,0.403234768,,,0.538316242
3744,Neurological Manifestations of COVID-19 Feature T Cell Exhaustion and Dedifferentiated Monocytes in Cerebrospinal Fluid.,Immunity,33382973,1/1/21,pubmed,0,18,sequencing,0.515473771,0.068980857,0.002357816,0.002357777,0.002357773,0.408472005,Drug discovery,0.69454074,TRUE,72.05555556,0.773084297,84.83333333,0.790005352,3,0.667819001,,,0.743636217
3745,Effectiveness of COVID-19 shelter-in-place orders varied by state.,PLoS One,33382849,1/1/21,pubmed,0,4,dataset,0.002296527,0.002296562,0.002296675,0.911990182,0.07882348,0.002296573,Epidemiology,0.07609776,FALSE,40.5,0.544746119,36.25,0.61272411,0,0.403234768,,,0.520234999
3746,Severity of COVID-19 at elevated exposure to perfluorinated alkylates.,PLoS One,33382826,1/1/21,pubmed,0,8,logistic regression,0.036834937,0.001717318,0.001717246,0.001717327,0.068522517,0.889490656,Clinics,0.7970129,TRUE,128.625,0.91966108,204.375,0.919253412,0,0.403234768,,,0.747383086
3747,SARS-CoV-2 PCR cycle threshold at hospital admission associated with patient mortality.,PLoS One,33382805,1/1/21,pubmed,0,9,logistic regression,0.000936103,0.063799772,0.217577189,0.00093612,0.000936104,0.715814712,Clinics,0.6123824,TRUE,22,0.326056033,9.333333333,0.342253144,0,0.403234768,,,0.357181315
3748,A novel virtual screening procedure identifies Pralatrexate as inhibitor of SARS-CoV-2 RdRp and it reduces viral replication in vitro.,PLoS Comput Biol,33382685,1/1/21,pubmed,0,17,virtual screening,0.892424225,0.001717215,0.100706981,0.001717232,0.001717184,0.001717163,Drug discovery,0.5504343,TRUE,47.82352941,0.612963077,35.11764706,0.605766658,0,0.403234768,,,0.540654834
3749,Risk factors and outcomes for acute respiratory failure in coronavirus disease 2019: An observational cohort study.,Adv Clin Exp Med,33382215,1/1/21,pubmed,0,7,logistic regression,0.00151183,0.001511874,0.00151182,0.001511847,0.001511834,0.992440796,Clinics,0.52013075,TRUE,14.14285714,0.213989733,2.428571429,0.175274284,0,0.403234768,,,0.264166262
3750,Virtual repurposing of ursodeoxycholate and chenodeoxycholate as lead candidates against SARS-Cov2-Envelope protein: A molecular dynamics investigation.,J Biomol Struct Dyn,33382021,1/1/21,pubmed,0,4,"molecular dynamics simulation, computational",0.970198368,0.00120348,0.001203472,0.001203519,0.001203444,0.024987717,Drug discovery,0.7098556,TRUE,28.5,0.412579628,11,0.371287129,0,0.403234768,,,0.395700508
3751,FBA reveals guanylate kinase as a potential target for antiviral therapies against SARS-CoV-2.,Bioinformatics,33381848,1/1/21,pubmed,0,3,computational,0.74499494,0.184927833,0.001943549,0.064246663,0.001943522,0.001943493,Drug discovery,0.4501817,FALSE,38.66666667,0.525882862,42,0.644902328,5,0.739490092,,,0.636758427
3752,Clinical Features and Risk Factors of ICU Admission for COVID-19 Patients with Diabetes.,J Diabetes Res,33381599,1/1/21,pubmed,0,13,logistic regression,0.001254599,0.001254597,0.001254608,0.001254586,0.001254628,0.993726981,Clinics,0.9156861,TRUE,115.5384615,0.899746428,83.61538462,0.787396307,0,0.403234768,,,0.696792501
3753,Potential Mechanisms for Traditional Chinese Medicine in Treating Airway Mucus Hypersecretion Associated With Coronavirus Disease 2019.,Front Mol Biosci,33381519,1/1/21,pubmed,0,6,network analysis,0.776476531,0.001187318,0.026125352,0.001187305,0.066919049,0.128104444,Drug discovery,0.99537516,TRUE,55.66666667,0.672335952,82.33333333,0.783649987,1,0.537564047,,,0.664516662
3754,0,Evid Based Complement Alternat Med,33381197,1/1/21,pubmed,0,4,structural model,0.920680085,0.000926321,0.000926328,0.075614606,0.000926323,0.000926338,Drug discovery,0.7694826,TRUE,43.75,0.575545798,11,0.371287129,0,0.403234768,,,0.450022565
3755,Modeling the Effects of Nonpharmaceutical Interventions on COVID-19 Spread in Kenya.,Interdiscip Perspect Infect Dis,33381170,1/1/21,pubmed,0,4,mathematical model,0.002238528,0.002238469,0.00223848,0.954667872,0.002238758,0.036377894,Epidemiology,0.34678185,FALSE,8.25,0.121343311,0.75,0.099411292,0,0.403234768,,,0.207996457
3756,"Changes in Motor, Cognitive, and Behavioral Symptoms in Parkinson's Disease and Mild Cognitive Impairment During the COVID-19 Lockdown.",Front Psychiatry,33381057,1/1/21,pubmed,0,11,logistic regression,0.022002511,0.001438134,0.001438118,0.00143822,0.696951453,0.276731564,Healthcare,0.9779676,TRUE,45.09090909,0.587358526,,,0,0.403234768,,,0.495296647
3757,"Hypertension, Diabetes and Obesity, Major Risk Factors for Death in Patients With COVID-19 in Mexico.",Arch Med Res,33380361,1/1/21,pubmed,0,9,logistic regression,0.001392844,0.001392854,0.001392859,0.133335928,0.001392899,0.861092615,Clinics,0.6135926,TRUE,26.55555556,0.389696332,35,0.605298368,1,0.537564047,,,0.510852916
3758,Design of a companion bioinformatic tool to detect the emergence and geographical distribution of SARS-CoV-2 Spike protein genetic variants.,J Transl Med,33380328,1/1/21,pubmed,0,8,"bioinformatic, dataset",0.301724464,0.675587189,0.001126863,0.001126863,0.019307823,0.001126799,Genomics,0.5028642,TRUE,80.875,0.810563424,57.125,0.711533315,0,0.403234768,,,0.641777169
3759,The impact of intervention strategies and prevention measurements for controlling COVID-19 outbreak in Saudi Arabia.,Math Biosci Eng,33378936,1/1/21,pubmed,0,2,mathematical model,0.001622701,0.001622696,0.001622723,0.991886387,0.001622783,0.00162271,Epidemiology,0.43710238,FALSE,48.5,0.618158204,19.5,0.475983409,0,0.403234768,,,0.49912546
3760,Modelling the transmission of infectious diseases inside hospital bays: implications for COVID-19.,Math Biosci Eng,33378934,1/1/21,pubmed,0,3,"computational, mathematical model",0.001538154,0.019944567,0.001538171,0.615703981,0.001538174,0.359736953,Epidemiology,0.67160934,TRUE,19.33333333,0.288638753,9,0.337904736,0,0.403234768,,,0.343259419
3761,"Predicting COVID-19 using past pandemics as a guide: how reliable were mathematical models then, and how reliable will they be now?",Math Biosci Eng,33378907,1/1/21,pubmed,0,3,mathematical model,0.002183332,0.090759813,0.072187342,0.830502947,0.002183274,0.002183292,Epidemiology,0.3115759,FALSE,20,0.298163152,8.333333333,0.325662296,1,0.537564047,,,0.387129832
3762,Mathematical modeling and analysis of COVID-19 pandemic in Nigeria.,Math Biosci Eng,33378893,1/1/21,pubmed,0,4,mathematical model,0.001943473,0.001943476,0.001943448,0.990282572,0.001943535,0.001943496,Epidemiology,0.30337495,FALSE,21.75,0.321603068,21.5,0.498260637,1,0.537564047,,,0.452475917
3763,A new dynamical modeling SEIR with global analysis applied to the real data of spreading COVID-19 in Saudi Arabia.,Math Biosci Eng,33378886,1/1/21,pubmed,0,5,mathematical model,0.044334036,0.001462036,0.129106726,0.822173507,0.001461845,0.001461849,Epidemiology,0.17298529,FALSE,98.8,0.866596574,61,0.724578539,0,0.403234768,,,0.664803293
3764,Mathematical analysis for COVID-19 resurgence in the contaminated environment.,Math Biosci Eng,33378881,1/1/21,pubmed,0,2,"model fit, mathematical model",0.002296582,0.002296789,0.002296684,0.988516814,0.002296558,0.002296575,Epidemiology,0.34574845,FALSE,12.5,0.189436576,26.5,0.542012309,1,0.537564047,,,0.423004311
3765,Optimal control on COVID-19 eradication program in Indonesia under the effect of community awareness.,Math Biosci Eng,33378859,1/1/21,pubmed,0,3,mathematical model,0.001751167,0.001751194,0.001751248,0.991243945,0.001751253,0.001751193,Epidemiology,0.3776292,FALSE,32.33333333,0.459273919,2.666666667,0.185442869,4,0.707574542,,,0.450763776
3766,Development of a Physiologically-Based Pharmacokinetic (PBPK) Model of Nebulized Hydroxychloroquine for Pulmonary Delivery to COVID-19 Patients.,Drug Res (Stuttg),33378773,12/31/20,pubmed,0,4,in-silico,0.499054957,0.001861698,0.001861826,0.142824533,0.001861783,0.352535202,Drug discovery,0.5596988,TRUE,28.25,0.410105758,10.5,0.363459995,0,0.403234768,,,0.39226684
3767,"Depression, anxiety symptoms, Insomnia, and coping during the COVID-19 pandemic period among individuals living with disabilities in Ethiopia, 2020.",PLoS One,33378397,12/31/20,pubmed,0,6,logistic regression,0.00109884,0.001098838,0.001098818,0.001098857,0.994505738,0.001098909,Healthcare,0.91492796,TRUE,8.666666667,0.12839384,0,0.055525823,0,0.403234768,,,0.195718143
3768,Adherence towards COVID-19 mitigation measures and its associated factors among Gondar City residents: A community-based cross-sectional study in Northwest Ethiopia.,PLoS One,33378332,12/31/20,pubmed,0,28,logistic regression,0.001072276,0.001072203,0.001072192,0.152032123,0.843679025,0.001072181,Healthcare,0.50773823,TRUE,13.78571429,0.208485373,4.035714286,0.231535991,2,0.618927094,,,0.35298282
3769,"Mental health conditions among the general population, healthcare workers and quarantined population during the coronavirus disease 2019 (COVID-19) pandemic.",Psychol Health Med,33378219,12/31/20,pubmed,0,13,logistic regression,0.0016845,0.001684476,0.0016845,0.001684533,0.991577495,0.001684497,Healthcare,0.9163359,TRUE,118.6923077,0.905188942,106.6153846,0.83395772,0,0.403234768,,,0.714127143
3770,Digital health strategies in the war against COVID-19 and beyond.,Br J Hosp Med (Lond),33377831,12/31/20,pubmed,0,4,digital health,0.010372797,0.010373246,0.698237594,0.010373388,0.260268904,0.01037407,Healthcare,0.6199272,TRUE,22.5,0.333539489,3,0.199424672,0,0.403234768,,,0.31206631
3771,"Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera: A semi-quantitative, high throughput, microscopy-based assay expands existing approaches to measure SARS-CoV-2 specific antibody levels in human sera.",Bioessays,33377226,12/31/20,pubmed,0,27,"image analysis, proteom",0.002080689,0.761567103,0.230110384,0.002080664,0.002080619,0.002080541,Genomics,0.37920582,FALSE,48.11111111,0.615375101,83.25925926,0.78632593,3,0.667819001,,,0.689840011
3772,INHALEd nebulised unfractionated HEParin for the treatment of hospitalised patients with COVID-19 (INHALE-HEP): Protocol and statistical analysis plan for an investigator-initiated international metatrial of randomised studies.,Br J Clin Pharmacol,33377218,12/31/20,pubmed,0,29,"bayes, dataset",0.199805961,0.001291255,0.001291238,0.172425085,0.001291322,0.623895139,Clinics,0.85238695,TRUE,44.93103448,0.584884656,48.44827586,0.67547498,0,0.403234768,,,0.554531468
3773,Hydroxychloroquine Inhibits the Trained Innate Immune Response to Interferons.,Cell Rep Med,33377122,12/31/20,pubmed,0,19,lipidom,0.838522214,0.002080676,0.082448952,0.002080568,0.002080567,0.072787023,Drug discovery,0.25298077,FALSE,90.94736842,0.845383141,156.6315789,0.88914905,0,0.403234768,,,0.712588986
3774,Optimized workflow for single-cell transcriptomics on infectious diseases including COVID-19.,STAR Protoc,33377120,12/31/20,pubmed,0,6,transcriptom,0.177946728,0.412573764,0.003607488,0.003607504,0.273641421,0.128623095,Genomics,0.4820475,FALSE,18.83333333,0.281588224,16.33333333,0.440460262,0,0.403234768,,,0.375094418
3775,Glycomics-informed glycoproteomic analysis of site-specific glycosylation for SARS-CoV-2 spike protein.,STAR Protoc,33377107,12/31/20,pubmed,0,5,"molecular dynamics simulation, bioinformatic, proteom, glycomics, glycoproteom",0.611457713,0.377661515,0.002720192,0.002720254,0.002720184,0.002720142,Drug discovery,0.11919284,FALSE,90.6,0.844579133,137,0.87168852,0,0.403234768,,,0.706500807
3776,Correct Use of Facemask Among Health Professionals in the Context of Coronavirus Disease (COVID-19).,Risk Manag Healthc Policy,33376425,12/31/20,pubmed,0,13,logistic regression,0.001112622,0.001112674,0.001112708,0.001112828,0.966895315,0.028653853,Healthcare,0.8961842,TRUE,7.461538462,0.10656194,0.538461538,0.087235751,0,0.403234768,,,0.199010819
3777,Factors Associated with Coronavirus Disease 2019 Prevention Practices in Three Zones of Southwest Ethiopia: Community-Based Cross-Sectional Study.,Int J Gen Med,33376385,12/31/20,pubmed,0,8,logistic regression,0.000988354,0.000988355,0.000988369,0.072658529,0.911205792,0.013170601,Healthcare,0.665262,TRUE,3.375,0.040509617,0.625,0.090446883,0,0.403234768,,,0.178063756
3778,Association of Daily Home-Based Hot Water Bathing and Glycemic Control in Ambulatory Japanese Patients with Type 2 Diabetes Mellitus During the COVID-19 Pandemic: A Multicenter Cross-Sectional Study.,Diabetes Metab Syndr Obes,33376375,12/31/20,pubmed,0,7,logistic regression,0.001653026,0.001653069,0.001653004,0.001653149,0.521126986,0.472260767,Healthcare,0.8828594,TRUE,126.2857143,0.915702888,84.14285714,0.788600482,0,0.403234768,,,0.702512712
3779,In-depth blood proteome profiling analysis revealed distinct functional characteristics of plasma proteins between severe and non-severe COVID-19 patients.,Sci Rep,33376242,12/31/20,pubmed,0,8,proteom,0.537190791,0.00198721,0.001987237,0.001987246,0.001987104,0.454860412,Drug discovery,0.5642581,TRUE,47.875,0.613272311,23,0.513513514,0,0.403234768,,,0.510006864
3780,"Cross-sectional analysis to explore the awareness, attitudes and actions of UK adults at high risk of severe illness from COVID-19.",BMJ Open,33376185,12/31/20,pubmed,0,5,artificial intelligence,0.001141319,0.001141359,0.060514034,0.001141406,0.934920392,0.001141491,Healthcare,0.8509148,TRUE,15.4,0.231616055,3.8,0.221501204,0,0.403234768,,,0.285450676
3781,Progress in understanding COVID-19: insights from the omics approach.,Crit Rev Clin Lab Sci,33375876,12/31/20,pubmed,0,4,"sequencing, proteom,  omics, metabolom",0.409464193,0.328566738,0.21396871,0.00182347,0.001823373,0.044353516,Drug discovery,0.8134582,TRUE,78,0.798812543,9.75,0.349678887,0,0.403234768,,,0.517242066
3782,The Italian COVID-19 Psychological Research Consortium (IT C19PRC): General Overview and Replication of the UK Study.,J Clin Med,33375763,12/31/20,pubmed,0,10,logistic regression,0.013719652,0.000916731,0.000916688,0.055827794,0.927702448,0.000916687,Healthcare,0.56979173,TRUE,79.5,0.80493537,47.9,0.672397645,2,0.618927094,,,0.69875337
3783,"Structural Variability, Expression Profile, and Pharmacogenetic Properties of TMPRSS2 Gene as a Potential Target for COVID-19 Therapy.",Genes (Basel),33375616,12/31/20,pubmed,0,16,"sequencing, pharmacogenom, genome-wide",0.649867799,0.345041632,0.001272636,0.001272647,0.001272627,0.001272659,Drug discovery,0.9029685,TRUE,19.875,0.294576041,44.6875,0.657613059,2,0.618927094,,,0.523705398
3784,Hand-Washing Practices among Adolescents Aged 12-15 Years from 80 Countries.,Int J Environ Res Public Health,33375506,12/31/20,pubmed,0,12,logistic regression,0.001350312,0.001350363,0.001350325,0.05100844,0.943590218,0.001350342,Healthcare,0.81121063,TRUE,111,0.892015585,76.08333333,0.769266792,0,0.403234768,,,0.688172382
3785,A Computational Approach to Explore the Interaction of Semisynthetic Nitrogenous Heterocyclic Compounds with the SARS-CoV-2 Main Protease.,Biomolecules,33375460,12/31/20,pubmed,0,5,computational,0.89262926,0.001415146,0.001415317,0.10171006,0.001415098,0.001415119,Drug discovery,0.66929066,TRUE,35.8,0.49495949,10.8,0.366871822,1,0.537564047,,,0.46646512
3786,A New Transmission Route for the Propagation of the SARS-CoV-2 Coronavirus.,Biology (Basel),33375381,12/31/20,pubmed,0,3,model fit,0.001987143,0.001987234,0.00198714,0.990064229,0.001987137,0.001987118,Epidemiology,0.15151715,FALSE,39,0.530521368,61.66666667,0.727187584,2,0.618927094,,,0.625545349
3787,Promoting Public Engagement during the COVID-19 Crisis: How Effective Is the Wuhan Local Government's Information Release?,Int J Environ Res Public Health,33375307,12/31/20,pubmed,0,4,data mining,0.001486434,0.001486685,0.033719792,0.960334051,0.001486558,0.00148648,Epidemiology,0.102502465,FALSE,134.25,0.926340528,34,0.5990768,0,0.403234768,,,0.642884032
3788,Sterols and Triterpenes: Antiviral     Potential Supported by In-Silico Analysis.,Plants (Basel),33375282,12/31/20,pubmed,0,7,"virtual screening, in-silico",0.987888317,0.002422325,0.002422389,0.00242241,0.002422301,0.002422257,Drug discovery,0.9875467,TRUE,74.42857143,0.783783784,16.85714286,0.446882526,0,0.403234768,,,0.544633692
3789,Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation.,Sensors (Basel),33374913,12/31/20,pubmed,0,3,"machine learning, artificial intelligence, ensemble learning, dataset",0.00165307,0.001653037,0.742309481,0.126872909,0.061229768,0.066281735,Epidemiology,0.93714297,TRUE,27.66666667,0.403488156,10.66666667,0.365333155,0,0.403234768,,,0.39068536
3790,Structure and Hierarchy of SARS-CoV-2 Infection Dynamics Models Revealed by Reaction Network Analysis.,Viruses,33374824,12/31/20,pubmed,0,3,network analysis,0.179917798,0.30234562,0.001861849,0.512151227,0.001861743,0.001861763,Epidemiology,0.5533484,TRUE,69,0.759416167,35.33333333,0.607439122,1,0.537564047,,,0.634806445
3791,Drug Repurposing Approach against Novel Coronavirus Disease (COVID-19) through Virtual Screening Targeting SARS-CoV-2 Main Protease.,Biology (Basel),33374717,12/31/20,pubmed,0,9,"virtual screening, molecular dynamics simulation, immunome",0.975694218,0.020167358,0.001034612,0.001034664,0.001034582,0.001034566,Drug discovery,0.9256096,TRUE,41.33333333,0.551549261,28.11111111,0.554990634,1,0.537564047,,,0.548034647
3792,Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data.,Molecules,33374492,12/31/20,pubmed,0,6,"machine learning, artificial intelligence",0.001141392,0.186484341,0.808950143,0.001141372,0.001141357,0.001141395,Genomics,0.6524635,TRUE,17.66666667,0.266373925,2,0.164302917,0,0.403234768,,,0.277970537
3793,Effect of Sex on Clinical Outcomes in Patients with Coronavirus Disease: A Population-Based Study.,J Clin Med,33374452,12/31/20,pubmed,0,5,dataset,0.001486434,0.00148641,0.054218307,0.001486435,0.001486485,0.939835928,Clinics,0.8046372,TRUE,36.8,0.506339291,7,0.299973241,0,0.403234768,,,0.403182433
3794,The Influence of COVID-19 on Community Disaster Resilience.,Int J Environ Res Public Health,33374318,12/31/20,pubmed,0,4,structural model,0.001461949,0.001461896,0.00146189,0.676332955,0.317819439,0.001461871,Epidemiology,0.9615858,TRUE,29.25,0.421671099,12.5,0.392761573,0,0.403234768,,,0.405889147
3795,It Takes More than Two to Tango with COVID-19: Analyzing Argentina's Early Pandemic Response (Jan 2020-April 2020).,Int J Environ Res Public Health,33374162,12/31/20,pubmed,0,2,mathematical model,0.001901723,0.001901744,0.001901701,0.923688647,0.00190175,0.068704435,Epidemiology,0.20438743,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
3796,Doctor at Your Fingertips: An Exploration of Digital Visits from Stakeholders' Perspectives.,Life (Basel),33374106,12/31/20,pubmed,0,3,artificial intelligence,0.00143813,0.001438133,0.404799272,0.365583259,0.225302962,0.001438245,Epidemiology,0.90900993,TRUE,2.666666667,0.029377203,0,0.055525823,0,0.403234768,,,0.162712598
3797,Correlates of psychological distress in epileptic patients during the COVID-19 outbreak.,Epilepsy Behav,33373874,12/30/20,pubmed,0,7,logistic regression,0.001538157,0.063761084,0.001538156,0.001538215,0.576878944,0.354745445,Healthcare,0.99139977,TRUE,111.4285714,0.89288144,98.85714286,0.820243511,0,0.403234768,,,0.705453239
3798,Temperature dependence of COVID-19 transmission.,Sci Total Environ,33373782,12/30/20,pubmed,0,1,dataset,0.001171596,0.001171598,0.001171589,0.901002505,0.001171599,0.094311113,Epidemiology,0.1221658,FALSE,73,0.778464964,80,0.779033984,1,0.537564047,,,0.698354332
3799,Views on the need to implement restriction policies to be able to address COVID-19 in the United States.,Prev Med,33373605,12/30/20,pubmed,0,2,logistic regression,0.001272634,0.001272657,0.001272716,0.668387551,0.326521768,0.001272674,Epidemiology,0.3333385,FALSE,12,0.183190055,1,0.122023013,1,0.537564047,,,0.280925705
3800,Which COVID policies are most effective? A Bayesian analysis of COVID-19 by jurisdiction.,PLoS One,33373384,12/30/20,pubmed,0,3,bayes,0.00137131,0.001371279,0.001371251,0.947462368,0.04705251,0.001371283,Epidemiology,0.32868275,FALSE,53,0.654400396,141.6666667,0.876036928,0,0.403234768,,,0.644557364
3801,Uncovering Flexible Active Site Conformations of SARS-CoV-2 3CL Proteases through Protease Pharmacophore Clusters and COVID-19 Drug Repurposing.,ACS Nano,33373194,12/30/20,pubmed,0,12,"virtual screening, dataset",0.994293039,0.001141438,0.001141411,0.001141397,0.001141371,0.001141344,Drug discovery,0.30636904,FALSE,52,0.647349867,53,0.693738293,0,0.403234768,,,0.581440976
3802,Using in silico viral kinetic models to guide therapeutic strategies during a pandemic: An example in SARS-CoV-2.,Br J Clin Pharmacol,33373059,12/30/20,pubmed,0,7,"mathematical model, in silico, dataset",0.471455864,0.001126875,0.001126839,0.468219055,0.001126871,0.056944496,Drug discovery,0.20511997,FALSE,92.85714286,0.851011194,104.4285714,0.830077602,0,0.403234768,,,0.694774521
3803,Assessment of Neutrophil Extracellular Traps in Coronary Thrombus of a Case Series of Patients With COVID-19 and Myocardial Infarction.,JAMA Cardiol,33372956,12/30/20,pubmed,0,13,image analysis,0.07447792,0.048518366,0.158003399,0.000999586,0.000999577,0.717001152,Clinics,0.9229511,TRUE,70.07692308,0.7644876,45.84615385,0.662898047,0,0.403234768,,,0.610206805
3804,Deep Venous Thrombosis in COVID-19 Patients: A Cohort Analysis.,Clin Appl Thromb Hemost,33372807,12/30/20,pubmed,0,12,logistic regression,0.001415142,0.001415204,0.065924366,0.001415157,0.00141514,0.928414991,Clinics,0.91344374,TRUE,40,0.539860226,16.5,0.44293551,0,0.403234768,,,0.462010168
3805,0,Antivir Chem Chemother,33372806,12/30/20,pubmed,0,7,"virtual screening, molecular dynamics simulation",0.945679039,0.001220066,0.001220042,0.001220037,0.001220046,0.049440771,Drug discovery,0.9689988,TRUE,13.42857143,0.202795473,11.14285714,0.372089912,0,0.403234768,,,0.326040051
3806,0,J Biomol Struct Dyn,33372574,12/30/20,pubmed,0,4,in silico,0.957491202,0.035062107,0.001861688,0.001861685,0.001861658,0.00186166,Drug discovery,0.98655736,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
3807,The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study.,Eur Radiol,33372243,12/30/20,pubmed,0,9,"deep learning, neural network, dataset",0.000854699,0.000854711,0.869639046,0.000854707,0.000854712,0.126942126,Imaging,0.3667978,FALSE,11,0.167171748,1.333333333,0.13252609,0,0.403234768,,,0.234310869
3808,SARS-CoV-2 Proteome Microarray for Mapping COVID-19 Antibody Interactions at Amino Acid Resolution.,ACS Cent Sci,33372199,12/30/20,pubmed,0,12,proteom,0.591888723,0.335550224,0.001751261,0.001751168,0.001751172,0.067307453,Drug discovery,0.7109518,TRUE,64.66666667,0.732822067,28.25,0.556061012,5,0.739490092,,,0.67612439
3809,Conservation analysis of SARS-CoV-2 spike suggests complicated viral adaptation history from bat to human.,Evol Med Public Health,33372198,12/30/20,pubmed,0,2,"whole-genome, sequence alignment",0.151534377,0.844640173,0.000956363,0.000956349,0.00095638,0.000956358,Genomics,0.34712362,FALSE,49.5,0.628362917,64,0.735549906,1,0.537564047,,,0.633825623
3810,Superspreading in early transmissions of COVID-19 in Indonesia.,Sci Rep,33372191,12/30/20,pubmed,0,7,bayes,0.001538106,0.001538162,0.031968158,0.961879308,0.001538118,0.001538148,Epidemiology,0.25872058,FALSE,43.28571429,0.570165131,3.857142857,0.222705379,0,0.403234768,,,0.398701759
3811,In silico discovery of antigenic proteins and epitopes of SARS-CoV-2 for the development of a vaccine or a diagnostic approach for COVID-19.,Sci Rep,33372181,12/30/20,pubmed,0,8,in silico,0.722215307,0.272383096,0.001350369,0.001350401,0.001350415,0.001350411,Drug discovery,0.6804225,TRUE,19.375,0.288947987,4.25,0.235750602,1,0.537564047,,,0.354087545
3812,Mental health symptoms in a cohort of hospital healthcare workers following the first peak of the COVID-19 pandemic in the UK.,BJPsych Open,33371927,12/30/20,pubmed,0,10,logistic regression,0.001203385,0.0012034,0.001203394,0.001203443,0.905369635,0.089816744,Healthcare,0.48929986,FALSE,32,0.455810502,25.5,0.533382392,2,0.618927094,,,0.536039996
3813,Accelerating bioinformatics research with International Conference on Intelligent Biology and Medicine 2020.,BMC Bioinformatics,33371868,12/30/20,pubmed,0,6,"machine learning, bioinformatic, network analysis",0.438146693,0.002130962,0.18338813,0.160014428,0.214188976,0.002130811,Drug discovery,0.72331876,TRUE,236.1666667,0.981817057,133,0.866804924,0,0.403234768,,,0.750618916
3814,A Review on Screening Models for Potential Therapeutic Candidates and Targets Against SARS-CoV-2.,Curr Drug Targets,33371846,12/30/20,pubmed,0,7,in-silico,0.856761113,0.081379178,0.002357765,0.002357814,0.054786235,0.002357895,Drug discovery,0.8997028,TRUE,14.71428571,0.221473189,1.285714286,0.128512176,0,0.403234768,,,0.251073378
3815,The fight against human viruses: how NMR can help.,Curr Med Chem,33371830,12/30/20,pubmed,0,2,"metabolom, genomes",0.737809155,0.130278609,0.128200779,0.001237169,0.001237109,0.001237179,Drug discovery,0.22789761,FALSE,51.5,0.643267982,36,0.611118544,0,0.403234768,,,0.552540431
3816,The RNA Architecture of the SARS-CoV-2 3'-Untranslated Region.,Viruses,33371200,12/30/20,pubmed,0,5,sequencing,0.4012024,0.590666323,0.002032976,0.002032812,0.002032752,0.002032737,Genomics,0.26934206,FALSE,38.4,0.523285299,51.8,0.688587102,0,0.403234768,,,0.538369056
3817,Stress and Anxiety among Healthcare Workers Associated with COVID-19 Pandemic in Russia.,Psychiatr Danub,33370765,12/29/20,pubmed,0,4,logistic regression,0.001203533,0.001203569,0.1852935,0.00120343,0.809892434,0.001203534,Healthcare,0.9642474,TRUE,36.25,0.500154617,22.25,0.505284988,0,0.403234768,,,0.469558124
3818,Mental Health Outcomes of Adults with Comorbidity and Chronic Diseases during the COVID-19 Pandemic: A Matched Case-Control Study.,Psychiatr Danub,33370758,12/29/20,pubmed,0,8,logistic regression,0.001272654,0.001272628,0.001272637,0.001272649,0.659340669,0.335568763,Healthcare,0.8696643,TRUE,10.75,0.160244913,1,0.122023013,3,0.667819001,,,0.316695642
3819,A Text Messaging Intervention for Coping With Social Distancing During COVID-19 (StayWell at Home): Protocol for a Randomized Controlled Trial.,JMIR Res Protoc,33370721,12/29/20,pubmed,0,11,machine learning,0.000889142,0.000889058,0.108319828,0.405471642,0.483541193,0.000889137,Healthcare,0.82925403,TRUE,22.45454545,0.33162224,13.72727273,0.407746856,0,0.403234768,,,0.380867954
3820,Repurposing of renin inhibitors as SARS-COV-2 main protease inhibitors: A computational study.,Virology,33370597,12/29/20,pubmed,0,3,"virtual screening, molecular dynamics simulation, computational",0.990282672,0.001943453,0.001943474,0.001943486,0.001943467,0.001943447,Drug discovery,0.9681458,TRUE,14.33333333,0.216772837,0.333333333,0.073187048,0,0.403234768,,,0.231064884
3821,Comparison of in-hospital mortality risk prediction models from COVID-19.,PLoS One,33370409,12/29/20,pubmed,0,5,"predictive model, prediction model",0.001717182,0.001717257,0.087963346,0.001717294,0.001717187,0.905167734,Clinics,0.69659925,TRUE,39.8,0.537200816,39.2,0.628712871,2,0.618927094,,,0.594946927
3822,Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.,PLoS One,33370397,12/29/20,pubmed,0,13,predictive model,0.022130746,0.001330082,0.316391589,0.00133008,0.001330036,0.657487468,Clinics,0.7958024,TRUE,68.15384615,0.754468427,36.61538462,0.614731068,0,0.403234768,,,0.590811421
3823,"Epidemiology, clinical characteristics, household transmission, and lethality of severe acute respiratory syndrome coronavirus-2 infection among healthcare workers in Ontario, Canada.",PLoS One,33370384,12/29/20,pubmed,0,8,logistic regression,0.001684543,0.001684562,0.001684566,0.301308693,0.590088043,0.103549592,Healthcare,0.39532888,FALSE,64.25,0.729791576,46.125,0.664236018,1,0.537564047,,,0.643863881
3824,ACE2 polymorphisms as potential players in COVID-19 outcome.,PLoS One,33370311,12/29/20,pubmed,0,14,"in silico, exom",0.313859651,0.251406976,0.002422282,0.203310184,0.002422429,0.226578478,Drug discovery,0.5549564,TRUE,20,0.298163152,8.142857143,0.321447685,0,0.403234768,,,0.340948535
3825,"The COVID-19 outbreak in Sichuan, China: Epidemiology and impact of interventions.",PLoS Comput Biol,33370263,12/29/20,pubmed,0,12,bayes,0.001717155,0.001717268,0.001717201,0.809658177,0.001717339,0.18347286,Epidemiology,0.723335,TRUE,59.33333333,0.698497124,99.58333333,0.82171528,0,0.403234768,,,0.641149057
3826,COVID-19 and Renin Angiotensin Aldosterone System: A Pharmacogenomic View.,Am J Ther,33369911,12/29/20,pubmed,0,1,pharmacogenom,0.05780351,0.057801339,0.057803871,0.710989218,0.057802617,0.057799445,Epidemiology,0.5504775,TRUE,22,0.326056033,7,0.299973241,0,0.403234768,,,0.343088014
3827,Role of the Health System in Combating Covid-19: Cross-Section Analysis and Artificial Neural Network Simulation for 124 Country Cases.,Soc Work Public Health,33369535,12/29/20,pubmed,0,6,neural network,0.002422272,0.002422266,0.16511256,0.762157647,0.00242245,0.065462805,Epidemiology,0.36628467,FALSE,20.33333333,0.302059497,0.833333333,0.102488627,0,0.403234768,,,0.269260964
3828,Digital Eye Strain Epidemic amid COVID-19 Pandemic - A Cross-sectional Survey.,Ophthalmic Epidemiol,33369521,12/29/20,pubmed,0,5,logistic regression,0.001272656,0.063793814,0.001272683,0.001272687,0.931115439,0.001272721,Healthcare,0.9894539,TRUE,35,0.488032655,4.2,0.234211935,2,0.618927094,,,0.447057228
3829,Anxiety and Depression in Health Workers and General Population During COVID-19 in IRAN: A Cross-Sectional Study.,Neuropsychopharmacol Rep,33369264,12/29/20,pubmed,0,10,logistic regression,0.001392803,0.001392809,0.001392819,0.001392821,0.993035861,0.001392887,Healthcare,0.9510331,TRUE,47.6,0.611169522,28,0.554321648,0,0.403234768,,,0.522908646
3830,Survival prediction algorithms for COVID-19 patients admitted to a UK district general hospital.,Int J Clin Pract,33368796,12/29/20,pubmed,0,6,logistic regression,0.001653009,0.001652994,0.103118399,0.001653073,0.001653017,0.890269509,Clinics,0.83646715,TRUE,36.83333333,0.506462985,11.33333333,0.375501739,0,0.403234768,,,0.428399831
3831,Socio-demographic predictors of uptake of a virtual group weight management program during the COVID-19 pandemic.,J Hum Nutr Diet,33368737,12/29/20,pubmed,0,4,logistic regression,0.001486431,0.001486408,0.001486496,0.001486437,0.679764999,0.31428923,Healthcare,0.9176674,TRUE,24.25,0.358092646,13,0.400521809,0,0.403234768,,,0.387283074
3832,Low influenza vaccination coverage in subjects with liver cirrhosis. An alert waiting for winter season 2020-2021 during the COVID-19 pandemic.,J Med Virol,33368427,12/29/20,pubmed,0,11,logistic regression,0.001593513,0.001593563,0.001593498,0.001593579,0.992032286,0.00159356,Healthcare,0.54980683,TRUE,144.9090909,0.939328344,95.27272727,0.813286058,0,0.403234768,,,0.71861639
3833,Dynamic tracking of variant frequencies depicts the evolution of mutation sites amongst SARS-CoV-2 genomes from India.,J Med Virol,33368386,12/29/20,pubmed,0,4,genomes,0.001901739,0.750273252,0.00190177,0.214343287,0.001901804,0.029678149,Genomics,0.5018424,TRUE,13.5,0.205393036,3.75,0.21982874,0,0.403234768,,,0.276152181
3834,Intimate partner violence among prenatal care attendees amidst the COVID-19 crisis: The incidence in Ethiopia.,Int J Gynaecol Obstet,33368273,12/29/20,pubmed,0,6,logistic regression,0.003335285,0.003335384,0.003335393,0.003335393,0.983323048,0.003335497,Healthcare,0.8350361,TRUE,10.83333333,0.161481848,6.333333333,0.285121755,0,0.403234768,,,0.283279457
3835,0,J Pathol,33368245,12/29/20,pubmed,0,3,dataset,0.620158051,0.002080585,0.002080653,0.273760215,0.002080765,0.09983973,Drug discovery,0.9093455,TRUE,100.3333333,0.869441524,62,0.728659352,2,0.618927094,,,0.739009324
3836,Risk-mitigating behaviours in people with inflammatory skin and joint disease during the COVID-19 pandemic differ by treatment type: a cross-sectional patient survey.,Br J Dermatol,33368145,12/29/20,pubmed,0,43,logistic regression,0.015975687,0.000988407,0.000988352,0.132564088,0.416495496,0.432987969,Clinics,0.64187264,TRUE,77.79069767,0.797513761,109.3488372,0.838105432,0,0.403234768,,,0.679617987
3837,Association of Personal Protective Equipment with De Novo Headaches in Frontline Healthcare Workers during COVID-19 Pandemic: A Cross-Sectional Study.,Eur J Dent,33368069,12/29/20,pubmed,0,5,logistic regression,0.001291283,0.00129122,0.001291304,0.001291307,0.993543619,0.001291268,Healthcare,0.9870475,TRUE,24.4,0.360442823,11.8,0.38252609,1,0.537564047,,,0.42684432
3838,Neglected tropical disease control in a world with COVID-19: an opportunity and a necessity for innovation.,Trans R Soc Trop Med Hyg,33367883,12/29/20,pubmed,0,5,mathematical model,0.005352857,0.005352855,0.005352877,0.973235369,0.00535315,0.005352892,Epidemiology,0.5699184,TRUE,59.8,0.701403921,213.6,0.923869414,1,0.537564047,,,0.720945794
3839,"Introduction to and spread of COVID-19-like illness in care homes in Norfolk, UK.",J Public Health (Oxf),33367852,12/29/20,pubmed,0,4,dataset,0.001565354,0.001565326,0.001565367,0.090622828,0.794513787,0.110167339,Healthcare,0.44201598,FALSE,124,0.912981632,259,0.941329944,0,0.403234768,,,0.752515448
3840,Association of substance use disorders and drug overdose with adverse COVID-19 outcomes in New York City: January-October 2020.,J Public Health (Oxf),33367823,12/29/20,pubmed,0,6,logistic regression,0.022205741,0.001684485,0.001684456,0.00168454,0.179263243,0.793477534,Clinics,0.6441809,TRUE,44.83333333,0.584389882,28.5,0.558402462,0,0.403234768,,,0.51534237
3841,"Intention to receive a vaccine against SARS-CoV-2 in Italy and its association with trust, worry and beliefs about the origin of the virus.",Health Educ Res,33367772,12/29/20,pubmed,0,1,logistic regression,0.00194352,0.105218367,0.001943461,0.001943551,0.887007634,0.001943469,Healthcare,0.8846303,TRUE,173,0.960603624,191,0.912630452,3,0.667819001,,,0.847017693
3842,Structure and regulation of coronavirus genomes: state-of-the-art and novel insights from SARS-CoV-2 studies.,Biochem Soc Trans,33367597,12/29/20,pubmed,0,2,genomes,0.286387043,0.703923651,0.002422304,0.002422358,0.002422358,0.002422287,Genomics,0.575447,TRUE,26.5,0.389325252,29,0.562884667,2,0.618927094,,,0.523712338
3843,Recent Smell Loss Is the Best Predictor of COVID-19 Among Individuals With Recent Respiratory Symptoms.,Chem Senses,33367502,12/29/20,pubmed,0,129,logistic regression,0.001171593,0.001171655,0.175297569,0.0011716,0.436216075,0.384971507,Healthcare,0.91746444,TRUE,58.91549296,0.695466634,59.12676056,0.717621086,11,0.840175319,,,0.75108768
3844,A Correlation Analysis between the Nutritional Status and Prognosis of COVID-19 Patients.,J Nutr Health Aging,33367467,12/29/20,pubmed,0,7,"logistic regression, correlation analysis",0.00089812,0.000898061,0.000898094,0.000898083,0.000898105,0.995509536,Clinics,0.96648407,TRUE,7,0.10179974,2,0.164302917,2,0.618927094,,,0.295009917
3845,"An adaptive, interacting, cluster-based model for predicting the transmission dynamics of COVID-19.",Heliyon,33367130,12/29/20,pubmed,0,7,"mathematical model, predictive model",0.001538144,0.001538145,0.001538207,0.99230931,0.0015381,0.001538095,Epidemiology,0.21358678,FALSE,46.28571429,0.599109407,42.14285714,0.64530372,1,0.537564047,,,0.593992391
3846,Clinical Characteristics and Immune Responses of 137 Deceased Patients With COVID-19: A Retrospective Study.,Front Cell Infect Microbiol,33365277,12/29/20,pubmed,0,4,logistic regression,0.140036875,0.000779418,0.000779406,0.000779436,0.000779417,0.856845448,Clinics,0.9596812,TRUE,6.25,0.088564537,0,0.055525823,1,0.537564047,,,0.227218136
3847,Transaminases are Potential Biomarkers of Disease Severity in COVID-19 Patients: A Single-Center Experience.,Cureus,33365223,12/29/20,pubmed,0,10,logistic regression,0.001330004,0.001330013,0.001330006,0.001330044,0.00133012,0.993349812,Clinics,0.9914274,TRUE,84.5,0.823489393,13.9,0.40988761,0,0.403234768,,,0.545537257
3848,Computational Studies of Hydroxychloroquine and Chloroquine Metabolites as Possible Candidates for Coronavirus (COVID-19) Treatment.,Front Pharmacol,33364944,12/29/20,pubmed,0,2,computational,0.894034456,0.00190174,0.001901852,0.098358262,0.001901771,0.001901919,Drug discovery,0.7369477,TRUE,1.5,0.015523533,0,0.055525823,0,0.403234768,,,0.158094708
3849,The Association between Chronic Use of Renin-Angiotensin-Aldosterone System Blockers and in-Hospital Adverse Events among COVID-19 Patients with Hypertension.,Sisli Etfal Hastan Tip Bul,33364877,12/29/20,pubmed,0,9,logistic regression,0.073714085,0.001171584,0.001171567,0.001171759,0.001171612,0.921599392,Clinics,0.99021256,TRUE,22.33333333,0.330261612,2.777777778,0.187985015,1,0.537564047,,,0.351936891
3850,"Moving More and Sitting Less as Healthy Lifestyle Behaviors are Protective Factors for Insomnia, Depression, and Anxiety Among Adolescents During the COVID-19 Pandemic.",Psychol Res Behav Manag,33364864,12/29/20,pubmed,0,13,logistic regression,0.001156245,0.001156222,0.001156238,0.001156269,0.976393943,0.018981083,Healthcare,0.9683199,TRUE,72.84615385,0.776918795,34.38461538,0.600883061,1,0.537564047,,,0.638455301
3851,Mapping of type 2 diabetes proteins to COVID-19 biomarkers: A proteomic analysis.,Metabol Open,33364597,12/29/20,pubmed,0,5,proteom,0.471069685,0.015999627,0.015999642,0.01599905,0.015998459,0.464933538,Drug discovery,0.35960633,FALSE,184,0.965922444,140.8,0.875100348,0,0.403234768,,,0.748085853
3852,0,Heliyon,33364503,12/29/20,pubmed,0,6,molecular dynamics simulation,0.993247983,0.00135041,0.001350429,0.001350402,0.001350376,0.0013504,Drug discovery,0.82389456,TRUE,43.66666667,0.57492733,6.166666667,0.281977522,0,0.403234768,,,0.42004654
3853,Bioinformatic evaluation of the potential animal models for studying SARS-Cov-2.,Heliyon,33364494,12/29/20,pubmed,0,5,bioinformatic,0.756301067,0.136859435,0.001622768,0.001622779,0.001622832,0.10197112,Drug discovery,0.6238413,TRUE,76,0.790586926,40.4,0.636138614,0,0.403234768,,,0.609986769
3854,"Analysis of non-structural proteins, NSPs of SARS-CoV-2 as targets for computational drug designing.",Biochem Biophys Rep,33364445,12/29/20,pubmed,0,1,computational,0.949188599,0.046062214,0.001187273,0.001187284,0.001187321,0.001187309,Drug discovery,0.61524695,TRUE,7,0.10179974,2,0.164302917,1,0.537564047,,,0.267888901
3855,Virtual auscultation course for medical students via video chat in times of COVID-19.,GMS J Med Educ,33364381,12/29/20,pubmed,0,7,active learning,0.070678906,0.001350414,0.336557507,0.190933991,0.100689021,0.299790161,Clinics,0.9085657,TRUE,100.1428571,0.869070443,87.42857143,0.795691731,0,0.403234768,,,0.689332314
3856,"Retrospective investigation of organization and examination results of the state examination in restorative dentistry, endodontology and periodontology under simulated conditions in times of Covid-19 compared to standard conditions when treating patients.",GMS J Med Educ,33364366,12/29/20,pubmed,0,5,correlation analysis,0.044159905,0.001112642,0.086753247,0.095913169,0.209787164,0.562273873,Clinics,0.97409225,TRUE,19,0.285793803,4.6,0.244246722,0,0.403234768,,,0.311091764
3857,"Survey data of coronavirus (COVID-19) thought concern, employees' work performance, employees background, feeling about job, work motivation, job satisfaction, psychological state of mind and family commitment in two middle east countries.",Data Brief,33364275,12/29/20,pubmed,0,2,dataset,0.001823344,0.001823397,0.107753514,0.080162036,0.806614396,0.001823313,Healthcare,0.89963675,TRUE,26,0.382398417,23,0.513513514,0,0.403234768,,,0.433048899
3858,"The lockdown may contribute to the COVID-19 cases in developing countries, different perspectives on the curfew act, a report from Jordan.",Ann Med Surg (Lond),33363726,12/29/20,pubmed,0,8,mathematical model,0.001622791,0.001622758,0.001622812,0.778545027,0.214963927,0.001622684,Epidemiology,0.9432483,TRUE,15.75,0.237862577,5.625,0.26839711,0,0.403234768,,,0.303164818
3859,4th ISCB Latin American Student Council Symposium: a virtual and inclusive experience during COVID-19 times.,F1000Res,33363714,12/29/20,pubmed,0,8,"computational, bioinformatic",0.258247577,0.00263916,0.002639136,0.390877807,0.342957341,0.00263898,Epidemiology,0.6468029,TRUE,7.625,0.109530583,1.5,0.138747659,1,0.537564047,,,0.26194743
3860,HOW GENETICISTS CONTRIBUTE TO UNDERSTANDING OF COVID-19 DISEASE PATHOGENICITY.,Acta Endocrinol (Buchar),33363658,12/29/20,pubmed,0,2,"bioinformatic, sequencing, whole genome",0.190214182,0.616523652,0.00143816,0.001438223,0.064306713,0.12607907,Genomics,0.6328299,TRUE,71.5,0.770734121,61.5,0.726585496,1,0.537564047,,,0.678294555
3861,0,Front Plant Sci,33363552,12/29/20,pubmed,0,8,"transcriptom, metabolom",0.671412253,0.092423381,0.002032867,0.230065478,0.002033074,0.002032948,Drug discovery,0.86514217,TRUE,27.375,0.399839198,19.75,0.477990367,0,0.403234768,,,0.427021444
3862,MicroRNAs Bioinformatics Analyses Identifying HDAC Pathway as a Putative Target for Existing Anti-COVID-19 Therapeutics.,Front Pharmacol,33363465,12/29/20,pubmed,0,8,"bioinformatic, in silico",0.802179236,0.001622753,0.001622711,0.123384756,0.001622784,0.06956776,Drug discovery,0.8008138,TRUE,52.25,0.648648649,36,0.611118544,0,0.403234768,,,0.554333987
3863,A critical review of emerging technologies for tackling COVID-19 pandemic.,Hum Behav Emerg Technol,33363278,12/29/20,pubmed,0,5,"computational, artificial intelligence, dataset",0.091314776,0.001717286,0.389641571,0.513891964,0.001717233,0.001717171,Epidemiology,0.8474573,TRUE,13.4,0.202548086,1.4,0.133864062,3,0.667819001,,,0.334743716
3864,Sport and exercise participation in time of Covid-19-A narrative review of medical and health perspective.,Transl Sports Med,33363268,12/29/20,pubmed,0,2,artificial intelligence,0.002296585,0.002296605,0.323359252,0.508370055,0.161380825,0.002296678,Epidemiology,0.9372091,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
3865,Liver Fibrosis Index FIB-4 Is Associated With Mortality in COVID-19.,Hepatol Commun,33363264,12/29/20,pubmed,0,16,logistic regression,0.001237192,0.073781638,0.001237045,0.001237062,0.001237174,0.921269889,Clinics,0.7516069,TRUE,61.6875,0.713340343,78.875,0.775622157,1,0.537564047,,,0.675508849
3866,Do knowledge and attitudes matter for preventive behavioral practices toward the COVID-19? A cross-sectional online survey among the adult population in Bangladesh.,Heliyon,33363262,12/29/20,pubmed,0,12,logistic regression,0.001072213,0.001072185,0.001072228,0.106204087,0.889507065,0.00107222,Healthcare,0.06577498,FALSE,16,0.243552477,5.333333333,0.262911426,1,0.537564047,,,0.348009317
3867,Natural Products Database Screening for the Discovery of Naturally Occurring SARS-Cov-2 Spike Glycoprotein Blockers.,ChemistrySelect,33363254,12/29/20,pubmed,0,7,"virtual screening, in silico",0.991413791,0.001717256,0.001717341,0.001717248,0.001717188,0.001717176,Drug discovery,0.6113367,TRUE,129.2857143,0.920898015,12.85714286,0.39717688,1,0.537564047,,,0.618546314
3868,EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers.,Inform Med Unlocked,33363252,12/29/20,pubmed,0,3,"machine learning, deep learning, neural network, classifier, dataset",0.001392816,0.00139282,0.993035843,0.001392853,0.001392839,0.001392829,Imaging,0.82551646,TRUE,13.33333333,0.201558538,2.333333333,0.173401124,1,0.537564047,,,0.30417457
3869,Computational and network pharmacology analysis of bioflavonoids as possible natural antiviral compounds in COVID-19.,Inform Med Unlocked,33363251,12/29/20,pubmed,0,10,"molecular dynamics simulation, computational",0.993636589,0.001272669,0.001272655,0.001272699,0.001272707,0.00127268,Drug discovery,0.9621818,TRUE,44.5,0.581977859,,,2,0.618927094,,,0.600452477
3870,Spike Proteins of SARS-CoV and SARS-CoV-2 Utilize Different Mechanisms to Bind With Human ACE2.,Front Mol Biosci,33363207,12/29/20,pubmed,0,9,computational,0.963618823,0.030533544,0.00146196,0.00146194,0.001461872,0.001461861,Drug discovery,0.7150342,TRUE,51.33333333,0.641288886,37.77777778,0.620551244,0,0.403234768,,,0.555024966
3871,ACE2 and Furin Expressions in Oral Epithelial Cells Possibly Facilitate COVID-19 Infection via Respiratory and Fecal-Oral Routes.,Front Med (Lausanne),33363183,12/29/20,pubmed,0,15,"bioinformatic, dataset",0.993448149,0.00131038,0.001310405,0.001310348,0.001310337,0.001310381,Drug discovery,0.884335,TRUE,31.46666667,0.449131053,23.86666667,0.519601284,1,0.537564047,,,0.502098795
3872,Have COVID-19-Related Economic Shocks Affected the Health Levels of Individuals in the United States and the United Kingdom?,Front Public Health,33363099,12/29/20,pubmed,0,6,dataset,0.003760609,0.003760547,0.003760534,0.680373086,0.304584576,0.003760648,Epidemiology,0.639227,TRUE,10.66666667,0.159131672,2,0.164302917,0,0.403234768,,,0.242223119
3873,New applications related to Covid-19.,Results Phys,33362986,12/29/20,pubmed,0,7,mathematical model,0.006089823,0.006089561,0.006089754,0.969551863,0.006089496,0.006089502,Epidemiology,0.48752484,FALSE,55,0.668686994,11.71428571,0.380987423,1,0.537564047,,,0.529079488
3874,A Network-Based Analysis Reveals the Mechanism Underlying Vitamin D in Suppressing Cytokine Storm and Virus in SARS-CoV-2 Infection.,Front Immunol,33362771,12/29/20,pubmed,0,1,"bioinformatic, transcriptom, literature mining",0.99610281,0.000779448,0.000779443,0.000779422,0.000779421,0.000779456,Drug discovery,0.24051696,FALSE,3,0.037293586,0,0.055525823,2,0.618927094,,,0.237248835
3875,SARS-CoV-2 Is Not Detected in the Cerebrospinal Fluid of Encephalopathic COVID-19 Patients.,Front Neurol,33362695,12/29/20,pubmed,0,10,"computational, dataset",0.335160009,0.285211364,0.150783909,0.001943503,0.001943618,0.224957597,Drug discovery,0.45232603,FALSE,45.7,0.593048426,58.3,0.714878245,6,0.764429903,,,0.690785525
3876,"COVID-19, Coronavirus, Wuhan Virus, or China Virus? Understanding How to "Do No Harm" When Naming an Infectious Disease.",Front Psychol,33362626,12/29/20,pubmed,0,3,bayes,0.001511868,0.001511908,0.001511854,0.668189256,0.280597721,0.046677393,Epidemiology,0.15484849,FALSE,49,0.624281032,40,0.633395772,1,0.537564047,,,0.598413617
3877,"Prognostic Genetic Markers for Thrombosis in COVID-19 Patients: A Focused Analysis on D-Dimer, Homocysteine and Thromboembolism.",Front Pharmacol,33362545,12/29/20,pubmed,0,14,genome-wide,0.476850924,0.19066862,0.001901892,0.001901795,0.00190184,0.326774928,Drug discovery,0.76171577,TRUE,27.28571429,0.398540417,33.57142857,0.596267059,1,0.537564047,,,0.510790508
3878,Customers response to online food delivery services during COVID-19 outbreak using binary logistic regression.,Int J Consum Stud,33362434,12/29/20,pubmed,0,3,logistic regression,0.002183352,0.002183305,0.002183284,0.002183389,0.939079409,0.052187261,Healthcare,0.92904985,TRUE,7,0.10179974,0,0.055525823,3,0.667819001,,,0.275048188
3879,Effects of mask-wearing on the inhalability and deposition of airborne SARS-CoV-2 aerosols in human upper airway.,Phys Fluids (1994),33362401,12/29/20,pubmed,0,3,computational,0.001486534,0.142412528,0.094095444,0.759032544,0.001486489,0.001486462,Epidemiology,0.084561944,FALSE,77.66666667,0.797266374,36,0.611118544,3,0.667819001,,,0.692067973
3880,[Digital innovation in medicine: the COVID-19 pandemic as an accelerator of "digital health"].,J Urol Urogynakologie,33362395,12/29/20,pubmed,0,5,"artificial intelligence, digital health",0.004310162,0.004310084,0.601778297,0.380981005,0.004310199,0.004310252,Epidemiology,0.7552453,TRUE,13,0.197352959,4.6,0.244246722,0,0.403234768,,,0.281611483
3881,An integrated feature frame work for automated segmentation of COVID-19 infection from lung CT images.,Int J Imaging Syst Technol,33362346,12/29/20,pubmed,0,4,"artificial intelligence, neural network, dataset",0.00135034,0.001350372,0.993248316,0.001350351,0.00135031,0.001350312,Imaging,0.53473765,TRUE,6.75,0.095862453,0.5,0.087101953,0,0.403234768,,,0.195399725
3882,Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism.,Int J Imaging Syst Technol,33362345,12/29/20,pubmed,0,3,dataset,0.001486448,0.001486422,0.992567845,0.001486444,0.001486417,0.001486424,Imaging,0.7323063,TRUE,66.66666667,0.745191416,39,0.62784319,1,0.537564047,,,0.636866218
3883,Mathematical model for spreading of COVID-19 virus with the Mittag-Leffler kernel.,Numer Methods Partial Differ Equ,33362342,12/29/20,pubmed,0,3,mathematical model,0.517588858,0.005353164,0.005353031,0.325871782,0.00535373,0.140479435,Drug discovery,0.74298114,TRUE,132.3333333,0.924299586,4.666666667,0.246721969,1,0.537564047,,,0.569528534
3884,Mathematical modeling for novel coronavirus (COVID-19) and control.,Numer Methods Partial Differ Equ,33362341,12/29/20,pubmed,0,5,mathematical model,0.003335418,0.003335311,0.003335332,0.983323426,0.003335245,0.003335268,Epidemiology,0.3580875,FALSE,73.2,0.778774198,9.4,0.343256623,6,0.764429903,,,0.628820241
3885,Online food prices during the COVID-19 pandemic.,Agribusiness (N Y N Y),33362338,12/29/20,pubmed,0,1,dataset,0.002562681,0.002562591,0.002562594,0.628506927,0.361242561,0.002562645,Epidemiology,0.0802359,FALSE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
3886,Economic and Distributional Impact of COVID-19: Evidence from Macro-Micro Modelling of the South African Economy.,S Afr J Econ,33362302,12/29/20,pubmed,0,4,simulation model,0.002296543,0.002296565,0.002296526,0.774055746,0.216758004,0.002296615,Epidemiology,0.40469283,FALSE,37,0.508936854,13.75,0.408348943,0,0.403234768,,,0.440173522
3887,Estimation of the incubation period of COVID-19 in Vietnam.,PLoS One,33362233,12/29/20,pubmed,0,10,bayes,0.001861668,0.001861674,0.024534464,0.904126752,0.00186188,0.065753562,Epidemiology,0.28109193,FALSE,19.9,0.294637887,13.9,0.40988761,1,0.537564047,,,0.414029848
3888,Calcification of the thoracic aorta on low-dose chest CT predicts severe COVID-19.,PLoS One,33362199,12/29/20,pubmed,0,9,logistic regression,0.001392813,0.001392822,0.232407873,0.00139285,0.001392833,0.76202081,Clinics,0.5417585,TRUE,77.33333333,0.795967592,30.33333333,0.572116671,0,0.403234768,,,0.590439677
3889,Hypertension delays viral clearance and exacerbates airway hyperinflammation in patients with COVID-19.,Nat Biotechnol,33361824,12/29/20,pubmed,0,38,sequencing,0.571734553,0.001538271,0.001538129,0.001538137,0.001538108,0.422112803,Drug discovery,0.8773389,TRUE,44.97368421,0.585132043,76.31578947,0.769534386,12,0.850299401,,,0.73498861
3890,Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs.,Sci Rep,33361777,12/29/20,pubmed,0,11,"artificial intelligence, proteom",0.55675921,0.258950162,0.085327861,0.097049895,0.000956397,0.000956475,Drug discovery,0.25883013,FALSE,83.54545455,0.819345661,57,0.711198823,2,0.618927094,,,0.716490526
3891,Longitudinal transcriptome analyses show robust T cell immunity during recovery from COVID-19.,Signal Transduct Target Ther,33361761,12/29/20,pubmed,0,14,"sequencing, transcriptom",0.697496023,0.181984314,0.001751223,0.001751223,0.00175119,0.115266027,Drug discovery,0.33784866,FALSE,47.78571429,0.61234461,60.14285714,0.721434306,2,0.618927094,,,0.650902003
3892,[Prognosis of patients with COVID-19 admitted to a tertiary center in Chile: A cohort study].,Medwave,33361751,12/29/20,pubmed,0,5,logistic regression,0.000822928,0.000822955,0.000822938,0.034531868,0.092381372,0.870617939,Clinics,0.98554456,TRUE,9.4,0.140021028,2,0.164302917,0,0.403234768,,,0.235852904
3893,"Immunometabolism at the cornerstone of inflammaging, immunosenescence, and autoimmunity in COVID-19.",Aging (Albany NY),33361522,12/29/20,pubmed,0,6,immunome,0.553513,0.00609,0.006090235,0.006090163,0.006089897,0.422126705,Drug discovery,0.9265312,TRUE,156.5,0.949038283,78,0.773548301,0,0.403234768,,,0.708607117
3894,The Importance and Challenges of Identifying SARS-CoV-2 Reinfections.,J Clin Microbiol,33361342,12/29/20,pubmed,0,5,sequencing,0.001310407,0.516173582,0.001310403,0.285479293,0.001310402,0.194415914,Genomics,0.6420649,TRUE,31.4,0.44882182,28.2,0.555860316,7,0.785110192,,,0.596597442
3895,Modelling the COVID-19 pandemic in context: an international participatory approach.,BMJ Glob Health,33361188,12/29/20,pubmed,0,72,mathematical model,0.001684507,0.001684515,0.001684524,0.869339742,0.123922169,0.001684544,Epidemiology,0.4154403,FALSE,32.85714286,0.464654586,19.71428571,0.477588975,2,0.618927094,,,0.520390218
3896,Profiling of immune dysfunction in COVID-19 patients allows early prediction of disease progression.,Life Sci Alliance,33361110,12/29/20,pubmed,0,22,classifier,0.485372777,0.001538158,0.02801229,0.075308379,0.001538125,0.408230271,Drug discovery,0.38262805,FALSE,25.5,0.37435834,35.04545455,0.605365266,1,0.537564047,,,0.505762551
3897,Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study.,BMJ Open,33361083,12/29/20,pubmed,0,12,prediction model,0.000999532,0.000999533,0.181209268,0.000999557,0.000999572,0.814792538,Clinics,0.7457212,TRUE,47.75,0.612220917,7.916666667,0.314891624,1,0.537564047,,,0.488225529
3898,"Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis.",BMJ Open,33361074,12/29/20,pubmed,0,8,prediction model,0.001220067,0.00122006,0.156559825,0.363169105,0.039676536,0.438154408,Clinics,0.94535816,TRUE,42.375,0.5612592,8.75,0.332218357,0,0.403234768,,,0.432237441
3899,A computational drug repurposing approach in identifying the cephalosporin antibiotic and anti-hepatitis C drug derivatives for COVID-19 treatment.,Comput Biol Med,33360831,12/29/20,pubmed,0,3,"virtual screening, computational",0.967787535,0.001126817,0.001126851,0.027705159,0.00112681,0.001126828,Drug discovery,0.9308431,TRUE,78.33333333,0.79961655,46.33333333,0.665908483,1,0.537564047,,,0.66769636
3900,Preliminary impact of the COVID-19 pandemic on smoking and vaping in college students.,Addict Behav,33360444,12/29/20,pubmed,0,6,logistic regression,0.001350392,0.001350389,0.001350324,0.084077354,0.910521123,0.001350418,Healthcare,0.536017,TRUE,72,0.77302245,179,0.905739898,0,0.403234768,,,0.693999039
3901,Comparative efficacy of respiratory personal protective equipment against viral respiratory infectious diseases in healthcare workers: a network meta-analysis.,Public Health,33360295,12/29/20,pubmed,0,4,bayes,0.001254674,0.091776419,0.001254651,0.550593204,0.179163485,0.175957567,Epidemiology,0.83332044,TRUE,4,0.054734368,0.75,0.099411292,2,0.618927094,,,0.257690918
3902,A semi-automatic methodology for analysing distributed and private biobanks.,Comput Biol Med,33360272,12/29/20,pubmed,0,3,dataset,0.002639194,0.221278977,0.455377011,0.315426738,0.002639122,0.002638958,Epidemiology,0.4636787,FALSE,27.66666667,0.403488156,15,0.42594327,0,0.403234768,,,0.410888731
3903,Lightweight deep learning models for detecting COVID-19 from chest X-ray images.,Comput Biol Med,33360271,12/29/20,pubmed,0,2,"deep learning, adversarial network",0.001220079,0.001220009,0.99389989,0.001220018,0.001220014,0.00121999,Imaging,0.712883,TRUE,1,0.012307502,0,0.055525823,5,0.739490092,,,0.269107806
3904,0,Sci Total Environ,33360135,12/29/20,pubmed,0,8,machine learning,0.001112667,0.001112661,0.001112673,0.949731329,0.045818001,0.001112669,Epidemiology,0.48919913,FALSE,17.125,0.258148308,11.5,0.378378378,0,0.403234768,,,0.346587151
3905,The Molecular Mechanism of Domain Swapping of the C-Terminal Domain of the SARS-Coronavirus Main Protease.,Biophys J,33359834,12/29/20,pubmed,0,2,molecular dynamics simulation,0.845418148,0.149768052,0.001203449,0.001203485,0.001203446,0.00120342,Drug discovery,0.9052654,TRUE,31.5,0.450058754,10.5,0.363459995,0,0.403234768,,,0.405584506
3906,SARS-Cov-2 ORF3a: Mutability and function.,Int J Biol Macromol,33359807,12/29/20,pubmed,0,4,genomes,0.221949945,0.744958221,0.028311213,0.001593573,0.001593535,0.001593512,Genomics,0.6216924,TRUE,39.75,0.536829736,35.25,0.606837035,2,0.618927094,,,0.587531288
3907,Transmission dynamics of the COVID-19 epidemic in England.,Int J Infect Dis,33359440,12/29/20,pubmed,0,3,bayes,0.001943454,0.001943506,0.001943453,0.990282632,0.001943488,0.001943468,Epidemiology,0.25315773,FALSE,59.33333333,0.698497124,250.3333333,0.938319508,0,0.403234768,,,0.680017133
3908,Prospective Latin American cohort evaluating outcomes of patients with COVID-19 and abnormal liver tests on admission.,Ann Hepatol,33359234,12/29/20,pubmed,0,41,logistic regression,0.001171532,0.00117158,0.00117156,0.001171677,0.001171579,0.994142072,Clinics,0.7486,TRUE,23.97560976,0.352650133,8.073170732,0.320644902,0,0.403234768,,,0.358843268
3909,Tmprss2 specific miRNAs as promising regulators for SARS-CoV-2 entry checkpoint.,Virus Res,33359190,12/29/20,pubmed,0,7,computational,0.755680991,0.002296648,0.059091435,0.17833757,0.002296672,0.002296683,Drug discovery,0.36517835,FALSE,42.85714286,0.565959552,53.28571429,0.69480867,1,0.537564047,,,0.59944409
3910,Molecular epidemiology of COVID-19 in Oman: A molecular and surveillance study for the early transmission of COVID-19 in the country.,Int J Infect Dis,33359061,12/29/20,pubmed,0,14,whole-genome,0.001684588,0.856580677,0.001684561,0.136681082,0.001684586,0.001684505,Genomics,0.7171733,TRUE,45.5,0.591440411,24.14285714,0.522411025,1,0.537564047,,,0.550471828
3911,Factors influencing social distancing to prevent the community spread of COVID-19 among Chinese adults.,Prev Med,33359017,12/29/20,pubmed,0,4,logistic regression,0.001350313,0.001350322,0.001350304,0.282743414,0.71185533,0.001350317,Healthcare,0.98327124,TRUE,51.5,0.643267982,22.25,0.505284988,0,0.403234768,,,0.517262579
3912,Right Heart Strain on Presenting 12-Lead Electrocardiogram Predicts Critical Illness in COVID-19.,JACC Clin Electrophysiol,33358667,12/29/20,pubmed,0,6,logistic regression,0.001565338,0.001565394,0.001565408,0.001565344,0.02702855,0.966709966,Clinics,0.394219,FALSE,17.83333333,0.268476715,0.5,0.087101953,0,0.403234768,,,0.252937812
3913,Application of the delirium risk prediction model in the TED ICU smart intensive care system during the current COVID-19 pandemic.,Intensive Crit Care Nurs,33358600,12/29/20,pubmed,0,2,prediction model,0.025060778,0.025060456,0.569819511,0.025061824,0.025060255,0.329937176,Clinics,0.6413496,TRUE,12,0.183190055,1,0.122023013,0,0.403234768,,,0.236149279
3914,Visceral adipose tissue area predicts intensive care unit admission in COVID-19 patients.,Obes Res Clin Pract,33358147,12/29/20,pubmed,0,8,"predictive model, logistic regression",0.048212117,0.00242227,0.261507058,0.002422364,0.002422295,0.683013895,Clinics,0.20229387,FALSE,25.5,0.37435834,11.25,0.374364463,0,0.403234768,,,0.383985857
3915,Traditional Chinese medicine network pharmacology study on exploring the mechanism of Xuebijing Injection in the treatment of coronavirus disease 2019.,Chin J Nat Med,33357725,12/29/20,pubmed,0,5,"genomes, literature mining",0.884110391,0.021941439,0.090692144,0.001085359,0.00108535,0.001085318,Drug discovery,0.97009385,TRUE,31.6,0.451048302,10,0.355632861,1,0.537564047,,,0.448081737
3916,Recent advances of traditional Chinese medicine on the prevention and treatment of COVID-19.,Chin J Nat Med,33357718,12/29/20,pubmed,0,6,literature mining,0.610428947,0.002238523,0.002238669,0.380616718,0.002238492,0.00223865,Drug discovery,0.90998745,TRUE,45.16666667,0.588162533,14,0.412898047,0,0.403234768,,,0.468098449
3917,Cardiovascular complications and its impact on outcomes in COVID-19.,Indian Heart J,33357651,12/29/20,pubmed,0,8,logistic regression,0.001392878,0.001392875,0.001392847,0.001392865,0.001392867,0.993035667,Clinics,0.97312075,TRUE,22.75,0.336508133,2.125,0.165306396,0,0.403234768,,,0.301683099
3918,Association of tiered restrictions and a second lockdown with COVID-19 deaths and hospital admissions in England: a modelling study.,Lancet Infect Dis,33357518,12/29/20,pubmed,0,7,mathematical model,0.000629662,0.00062967,0.000629666,0.861067055,0.000629694,0.136414253,Epidemiology,0.20417923,FALSE,67.28571429,0.749025914,181.1428571,0.907010971,9,0.814309525,,,0.823448803
3919,Host genetic effects in pneumonia.,Am J Hum Genet,33357513,12/29/20,pubmed,0,18,"genome-wide, dataset",0.001392973,0.554022929,0.001392952,0.001392914,0.001392852,0.44040538,Genomics,0.43526322,FALSE,87.44444444,0.834992888,286.9444444,0.950561948,1,0.537564047,,,0.774372961
3920,Risks of and risk factors for COVID-19 disease in people with diabetes: a cohort study of the total population of Scotland.,Lancet Diabetes Endocrinol,33357491,12/29/20,pubmed,0,68,"predictive model, logistic regression, prediction model",0.00058785,0.000587857,0.000587847,0.068095447,0.000587919,0.929553079,Clinics,0.3968052,FALSE,103.5151515,0.876368359,151.9393939,0.884733744,15,0.874313229,,,0.878471777
3921,Functional interrogation of a SARS-CoV-2 host protein interactome identifies unique and shared coronavirus host factors.,Cell Host Microbe,33357464,12/29/20,pubmed,0,19,interactom,0.526846274,0.210991645,0.002357832,0.25508867,0.002357826,0.002357753,Drug discovery,0.46762997,FALSE,91.05263158,0.846372688,514.7368421,0.979863527,0,0.403234768,,,0.743156994
3922,Cell-Type-Specific Immune Dysregulation in Severely Ill COVID-19 Patients.,Cell Rep,33357411,12/29/20,pubmed,0,21,"sequencing, transcriptom",0.515008354,0.056409106,0.001861681,0.001861729,0.001861726,0.422997404,Drug discovery,0.8051671,TRUE,22.9047619,0.338239842,31.47619048,0.58067969,0,0.403234768,,,0.4407181
3923,Lack of Association of Initial Viral Load in SARS-CoV-2 Patients with In-Hospital Mortality.,Am J Trop Med Hyg,33357280,12/29/20,pubmed,0,12,logistic regression,0.001141341,0.339730512,0.001141325,0.00114134,0.001141348,0.655704134,Clinics,0.85124385,TRUE,26.16666667,0.383078731,12.83333333,0.396775488,0,0.403234768,,,0.394362996
3924,"High prevalence of food insecurity, the adverse impact of COVID-19 in Brazilian favela.",Public Health Nutr,33357256,12/29/20,pubmed,0,6,logistic regression,0.001486437,0.001486456,0.001486597,0.001486508,0.992567547,0.001486455,Healthcare,0.5791812,TRUE,18,0.271569052,8.166666667,0.321915975,0,0.403234768,,,0.332239932
3925,Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models.,Genome Biol,33357233,12/29/20,pubmed,0,15,"artificial intelligence, genomes",0.2177463,0.570145124,0.083802993,0.125993079,0.001156259,0.001156246,Genomics,0.5962783,TRUE,19.46666667,0.290061228,40.73333333,0.637944876,5,0.739490092,,,0.555832065
3926,Application of high-flow nasal cannula in hypoxemic patients with COVID-19: a retrospective cohort study.,BMC Pulm Med,33357219,12/29/20,pubmed,0,8,logistic regression,0.001751198,0.001751172,0.001751222,0.198729053,0.001751245,0.79426611,Clinics,0.8724791,TRUE,62.75,0.719586864,39.875,0.632459192,1,0.537564047,,,0.629870034
3927,In silico studies reveal antiviral effects of traditional Indian spices on COVID-19.,Curr Pharm Des,33357192,12/29/20,pubmed,0,8,in silico,0.931720998,0.001684497,0.001684557,0.061540896,0.00168453,0.001684522,Drug discovery,0.980022,TRUE,129.5,0.921269095,39.5,0.630920524,0,0.403234768,,,0.651808129
3928,Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized COVID-19 Patients.,Ann Am Thorac Soc,33357088,12/29/20,pubmed,0,15,prediction model,0.001141335,0.001141344,0.001141393,0.001141382,0.001141415,0.994293131,Clinics,0.67913437,TRUE,60.93333333,0.707712289,75.6,0.768062617,3,0.667819001,,,0.714531302
3929,"Dual inhibition of SARS-CoV-2 spike and main protease through a repurposed drug, rutin.",J Biomol Struct Dyn,33357073,12/29/20,pubmed,0,6,"molecular dynamics simulation, in silico",0.994063554,0.001187324,0.001187274,0.001187308,0.001187264,0.001187276,Drug discovery,0.79789037,TRUE,21.66666667,0.320737213,3.833333333,0.222103291,0,0.403234768,,,0.315358424
3930,Prediction of potential inhibitors against SARS-CoV-2 endoribonuclease: RNA immunity sensing.,J Biomol Struct Dyn,33357040,12/29/20,pubmed,0,3,"virtual screening, molecular dynamics simulation",0.974994098,0.001272676,0.001272706,0.001272649,0.001272649,0.019915222,Drug discovery,0.9485607,TRUE,4.666666667,0.063392912,0,0.055525823,0,0.403234768,,,0.174051168
3931,"Evaluation of the relationship between perceived social support, coping strategies, anxiety, and depression symptoms among hospitalized COVID-19 patients.",Int J Psychiatry Med,33356704,12/29/20,pubmed,0,16,logistic regression,0.001272654,0.001272663,0.04319254,0.001272666,0.789354002,0.163635475,Healthcare,0.9393499,TRUE,20.125,0.298657926,5.3125,0.261372759,0,0.403234768,,,0.321088484
3932,Do People Actually "Listen to the Experts"? A Cautionary Note on Assuming Expert Credibility and Persuasiveness on Public Health Policy Advocacy.,Health Commun,33356584,12/29/20,pubmed,0,1,bayes,0.002720168,0.002720227,0.200261604,0.540121371,0.251456487,0.002720142,Epidemiology,0.029392242,FALSE,8,0.118683901,0,0.055525823,0,0.403234768,,,0.192481497
3933,DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation.,Chem Res Toxicol,33356151,12/29/20,pubmed,0,5,"machine learning, deep learning, neural network, prediction model",0.329770257,0.000999535,0.622650307,0.00099956,0.00099957,0.044580771,Drug discovery,0.5690445,TRUE,122.2,0.910074835,145.4,0.879515654,0,0.403234768,,,0.730941752
3934,Association of Paraspinal Muscle Measurements on Chest Computed Tomography With Clinical Outcomes in Patients With Severe Coronavirus Disease 2019.,J Gerontol A Biol Sci Med Sci,33355656,12/24/20,pubmed,0,9,logistic regression,0.001237096,0.029623891,0.102717536,0.00123706,0.001237102,0.863947314,Clinics,0.99436915,TRUE,63.44444444,0.724287216,34.22222222,0.600080278,0,0.403234768,,,0.575867421
3935,"Knowledge, attitudes and practices towards COVID-19 among Pakistani residents: information access and low literacy vulnerabilities.",East Mediterr Health J,33355383,12/24/20,pubmed,0,7,logistic regression,0.000752898,0.000752932,0.011432925,0.000752913,0.965216536,0.021091796,Healthcare,0.9989085,TRUE,27.57142857,0.401941988,22.28571429,0.505351887,0,0.403234768,,,0.436842881
3936,Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation.,Med Phys,33354790,12/24/20,pubmed,0,14,"deep learning, computational, dataset",0.001072275,0.001072193,0.994638932,0.001072216,0.001072205,0.001072179,Imaging,0.13553363,FALSE,23.23076923,0.343991589,,,7,0.785110192,,,0.56455089
3937,Characteristics and Risk Factors for Hospitalization and Mortality among Persons with COVID-19 in Atlanta Metropolitan Area.,medRxiv,33354690,12/24/20,pubmed,0,11,logistic regression,0.00151181,0.001511791,0.001511832,0.001512044,0.196241402,0.797711121,Clinics,0.5682158,TRUE,20.90909091,0.308615251,20.72727273,0.488426545,0,0.403234768,,,0.400092188
3938,"Anosmia and other SARS-CoV-2 positive test-associated symptoms, across three national, digital surveillance platforms as the COVID-19 pandemic and response unfolded: an observation study.",medRxiv,33354683,12/24/20,pubmed,0,23,logistic regression,0.000988383,0.000988407,0.000988374,0.500610171,0.495436277,0.000988388,Epidemiology,0.2690656,FALSE,31.86956522,0.453460325,52.7826087,0.692266524,1,0.537564047,,,0.561096965
3939,Real-world data suggest antibody positivity to SARS-CoV-2 is associated with a decreased risk of future infection.,medRxiv,33354682,12/24/20,pubmed,0,16,dataset,0.00083066,0.418769927,0.000830693,0.000830689,0.000830697,0.577907335,Clinics,0.3805315,FALSE,64.3125,0.730038963,178.6875,0.905338507,2,0.618927094,,,0.751434855
3940,Host-Viral Interactions Revealed among Shared Transcriptomics Signatures of ARDS and Thrombosis: A Clue into COVID-19 Pathogenesis.,TH Open,33354650,12/24/20,pubmed,0,3,"transcriptom, dataset",0.763008573,0.001141359,0.001141423,0.167834569,0.001141408,0.065732669,Drug discovery,0.3681106,FALSE,23.33333333,0.345723298,12.33333333,0.389550442,0,0.403234768,,,0.379502836
3941,Risk aversion connectedness in developed and emerging equity markets before and after the COVID-19 pandemic.,Heliyon,33354633,12/24/20,pubmed,0,1,bayes,0.005697558,0.005697488,0.005697844,0.971512094,0.005697482,0.005697535,Epidemiology,0.3289852,FALSE,82,0.814769002,20,0.481000803,0,0.403234768,,,0.566334858
3942,Telepsychiatry and Outpatient Department Services.,Indian J Psychol Med,33354060,12/24/20,pubmed,0,4,"machine learning, artificial intelligence",0.001461877,0.001461872,0.19806189,0.301998573,0.495553927,0.001461861,Healthcare,0.972126,TRUE,12.75,0.191786752,5,0.257024351,0,0.403234768,,,0.28401529
3943,The Future of Telepsychiatry in India.,Indian J Psychol Med,33354056,12/24/20,pubmed,0,6,artificial intelligence,0.001717281,0.001717225,0.001717382,0.780038334,0.213092608,0.001717169,Epidemiology,0.9052088,TRUE,88.66666667,0.838394459,32.16666667,0.586633663,0,0.403234768,,,0.609420963
3944,Use of Artificial Intelligence-based Computer Vision System to Practice Social Distancing in Hospitals to Prevent Transmission of COVID-19.,Indian J Community Med,33354024,12/24/20,pubmed,0,6,artificial intelligence,0.010372362,0.010372535,0.476789426,0.481716096,0.010374492,0.010375088,Epidemiology,0.57497394,TRUE,17.66666667,0.266373925,0,0.055525823,1,0.537564047,,,0.286487932
3945,Phylogenetic supertree reveals detailed evolution of SARS-CoV-2.,Sci Rep,33353955,12/24/20,pubmed,0,8,genome sequences,0.001622749,0.865478277,0.128030742,0.001622791,0.001622715,0.001622726,Genomics,0.70388466,TRUE,17.125,0.258148308,6.125,0.280907145,3,0.667819001,,,0.402291485
3946,Structure and dynamics of membrane protein in SARS-CoV-2.,J Biomol Struct Dyn,33353499,12/24/20,pubmed,0,6,molecular dynamics simulation,0.957301421,0.00111268,0.001112665,0.001112658,0.001112628,0.038247948,Drug discovery,0.65903115,TRUE,27.5,0.401570907,8,0.320511105,2,0.618927094,,,0.447003036
3947,Lung ultrasound education: simulation and hands-on.,Br J Radiol,33353379,12/24/20,pubmed,0,2,machine learning,0.00203283,0.002032756,0.644490232,0.142674894,0.063449224,0.145320064,Imaging,0.7865608,TRUE,62,0.715814212,19,0.471367407,0,0.403234768,,,0.530138796
3948,Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19.,Biology (Basel),33353045,12/24/20,pubmed,0,11,"machine learning, dataset",0.002639182,0.002639059,0.077254257,0.912189421,0.002639023,0.002639058,Epidemiology,0.49076068,FALSE,19.18181818,0.286659657,16.45454545,0.441865132,3,0.667819001,,,0.46544793
3949,A Transdisciplinary Analysis of COVID-19 in Italy: The Most Affected Country in Europe.,Int J Environ Res Public Health,33352883,12/24/20,pubmed,0,3,mathematical model,0.001461854,0.001461886,0.001461904,0.992690566,0.001461929,0.001461861,Epidemiology,0.29682168,FALSE,39.66666667,0.536211268,118,0.848876104,2,0.618927094,,,0.668004822
3950,A Cross-Regional Analysis of the COVID-19 Spread during the 2020 Italian Vacation Period: Results from Three Computational Models Are Compared.,Sensors (Basel),33352802,12/24/20,pubmed,0,2,computational,0.001861706,0.00186176,0.001861726,0.990691088,0.001861872,0.001861848,Epidemiology,0.5769408,TRUE,148.5,0.942853609,51.5,0.687382927,2,0.618927094,,,0.74972121
3951,Emerging Complexity in Distributed Intelligent Systems.,Entropy (Basel),33352754,12/24/20,pubmed,0,6,"artificial intelligence, mathematical model",0.28588858,0.001085372,0.430550481,0.280304805,0.001085424,0.001085338,Drug discovery,0.37925348,FALSE,57.16666667,0.682788051,4.5,0.242708055,0,0.403234768,,,0.442910291
3952,SARS-Cov-2 Interactome with Human Ghost Proteome: A Neglected World Encompassing a Wealth of Biological Data.,Microorganisms,33352703,12/24/20,pubmed,0,3,"proteom, interactom",0.936078402,0.00127271,0.001272698,0.058830791,0.001272747,0.001272651,Drug discovery,0.5756412,TRUE,203.6666667,0.97359144,129,0.86272411,0,0.403234768,,,0.746516773
3953,Appetitive Traits in a Population-Based Study of Polish Adolescents within the PLACE-19 Study: Validation of the Adult Eating Behavior Questionnaire.,Nutrients,33352678,12/24/20,pubmed,0,3,model fit,0.001237074,0.00123709,0.001237134,0.12439023,0.870661366,0.001237106,Healthcare,0.91372573,TRUE,61.33333333,0.710680933,4,0.231469093,1,0.537564047,,,0.493238024
3954,How the public used face masks in China during the coronavirus disease pandemic: A survey study.,Int J Nurs Stud,33352496,12/23/20,pubmed,0,4,logistic regression,0.000807874,0.000807886,0.00080788,0.123572495,0.873195991,0.000807874,Healthcare,0.85814065,TRUE,125.25,0.9145278,,,1,0.537564047,,,0.726045924
3955,Identification of FDA approved drugs and nucleoside analogues as potential SARS-CoV-2 A1pp domain inhibitor: An in silico study.,Comput Biol Med,33352458,12/23/20,pubmed,0,6,"molecular dynamics simulation, in silico",0.995002316,0.000999538,0.000999546,0.000999538,0.00099954,0.000999522,Drug discovery,0.8125538,TRUE,85.66666667,0.827509432,47.33333333,0.669855499,0,0.403234768,,,0.633533233
3956,Spatial distribution characteristics of the COVID-19 pandemic in Beijing and its relationship with environmental factors.,Sci Total Environ,33352341,12/23/20,pubmed,0,8,correlation analysis,0.002238441,0.002238506,0.002238474,0.988807634,0.002238471,0.002238474,Epidemiology,0.7495547,TRUE,86.375,0.830478075,85.125,0.790607439,1,0.537564047,,,0.719549854
3957,Association of prescribed medications with the risk of COVID-19 infection and severity among adults in South Korea.,Int J Infect Dis,33352326,12/23/20,pubmed,0,8,logistic regression,0.195914356,0.00186174,0.001861763,0.001861743,0.13619615,0.662304248,Clinics,0.3485591,FALSE,16.625,0.250231925,,,3,0.667819001,,,0.459025463
3958,Visualizing and assessing US county-level COVID19 vulnerability.,Am J Infect Control,33352253,12/23/20,pubmed,0,3,dataset,0.001861705,0.03722127,0.059473712,0.751565676,0.001861852,0.148015786,Epidemiology,0.6551856,TRUE,22.33333333,0.330261612,21.33333333,0.495450896,0,0.403234768,,,0.409649092
3959,Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches.,PLoS One,33351863,12/23/20,pubmed,0,9,in silico,0.99141397,0.001717209,0.00171722,0.001717233,0.001717208,0.00171716,Drug discovery,0.71608263,TRUE,35.77777778,0.494650257,19.22222222,0.472370886,2,0.618927094,,,0.528649412
3960,Coronavirus genomes carry the signatures of their habitats.,PLoS One,33351847,12/23/20,pubmed,0,4,genomes,0.25831447,0.714874696,0.001538078,0.00153812,0.022196559,0.001538078,Genomics,0.61587197,TRUE,49.75,0.630032779,337.75,0.963540273,0,0.403234768,,,0.665602606
3961,Public policy and economic dynamics of COVID-19 spread: A mathematical modeling study.,PLoS One,33351835,12/23/20,pubmed,0,3,mathematical model,0.001438116,0.001438104,0.001438138,0.992809317,0.001438139,0.001438186,Epidemiology,0.35861826,FALSE,162,0.952749088,386.6666667,0.969561145,0,0.403234768,,,0.775181667
3962,A spatial-temporal description of the SARS-CoV-2 infections in Indonesia during the first six months of outbreak.,PLoS One,33351801,12/23/20,pubmed,0,6,prediction model,0.001046818,0.001046867,0.040937378,0.626583295,0.001046868,0.329338773,Epidemiology,0.5898456,TRUE,45.83333333,0.594223514,6.833333333,0.293484078,1,0.537564047,,,0.475090546
3963,Harnessing Digital Health Technologies During and After the COVID-19 Pandemic: Context Matters.,J Med Internet Res,33351777,12/23/20,pubmed,0,4,digital health,0.002238478,0.002238516,0.002238481,0.887061044,0.103985045,0.002238437,Epidemiology,0.9084364,TRUE,42,0.558537943,10.75,0.366269735,2,0.618927094,,,0.514578257
3964,"Territorywide Study of Early Coronavirus Disease Outbreak, Hong Kong, China.",Emerg Infect Dis,33350913,12/23/20,pubmed,0,26,"sequencing, whole-genome",0.002357726,0.619335837,0.00235773,0.253064958,0.002357774,0.120525975,Genomics,0.4934572,FALSE,25.57692308,0.374914961,37.80769231,0.620885737,1,0.537564047,,,0.511121582
3965,Artificial Intelligence of COVID-19 Imaging: A Hammer in Search of a Nail.,Radiology,33350895,12/23/20,pubmed,0,1,artificial intelligence,0.019530807,0.019529689,0.902348397,0.01953235,0.019529373,0.019529384,Imaging,0.61124444,TRUE,574,0.998330138,791,0.989965213,4,0.707574542,,,0.898623297
3966,Long-term effects of appendectomy in humans: is it the optimal management of appendicitis?,Expert Rev Gastroenterol Hepatol,33350352,12/23/20,pubmed,0,6,microbiom,0.181136893,0.03394392,0.001593626,0.486420796,0.001593548,0.295311217,Epidemiology,0.9816711,TRUE,20.5,0.304966294,2.833333333,0.189523682,0,0.403234768,,,0.299241581
3967,0,J Proteome Res,33350309,12/23/20,pubmed,0,3,virtual screening,0.990282258,0.001943561,0.00194361,0.001943643,0.001943463,0.001943465,Drug discovery,0.5831027,TRUE,20.66666667,0.306512462,1.333333333,0.13252609,0,0.403234768,,,0.280757773
3968,A Ligand Selection Strategy Identifies Chemical Probes Targeting the Proteases of SARS-CoV-2.,Angew Chem Int Ed Engl,33350010,12/23/20,pubmed,0,8,proteom,0.905710359,0.002080666,0.085967199,0.00208062,0.002080638,0.002080519,Drug discovery,0.65521145,TRUE,13.75,0.208299833,5,0.257024351,0,0.403234768,,,0.289519651
3969,The effect of angiotensin-converting enzyme levels on COVID-19 susceptibility and severity: a Mendelian randomization study.,Int J Epidemiol,33349849,12/23/20,pubmed,0,9,genome-wide,0.138485583,0.336294806,0.001187297,0.018229934,0.001187306,0.504615075,Clinics,0.32712486,FALSE,25.77777778,0.377945451,36.77777778,0.615533851,0,0.403234768,,,0.465571356
3970,Development and validation of a prognostic model based on comorbidities to predict COVID-19 severity: a population-based study.,Int J Epidemiol,33349845,12/23/20,pubmed,0,8,logistic regression,0.001072178,0.001072196,0.060747475,0.022189229,0.001072219,0.913846703,Clinics,0.5574496,TRUE,19.375,0.288947987,6.125,0.280907145,2,0.618927094,,,0.396260742
3971,Essential interpretations of bioinformatics in COVID-19 pandemic.,Meta Gene,33349792,12/23/20,pubmed,0,4,"bioinformatic, sequencing, in silico, genome-wide",0.338347079,0.588816432,0.001653198,0.067877194,0.001653071,0.001653026,Genomics,0.7905127,TRUE,17,0.257467994,3.25,0.203572384,1,0.537564047,,,0.332868142
3972,Analysis and prediction of COVID-19 trajectory: A machine learning approach.,J Public Aff,33349741,12/23/20,pubmed,0,4,"machine learning, predictive model",0.003927351,0.003927546,0.61047745,0.373812742,0.003927471,0.003927441,Epidemiology,0.4391261,FALSE,6.5,0.093512277,17.5,0.45611453,1,0.537564047,,,0.362396951
3973,Framework for PESTEL dimensions of sustainable healthcare waste management: Learnings from COVID-19 outbreak.,J Clean Prod,33349739,12/23/20,pubmed,0,1,structural model,0.001751285,0.001751238,0.072912106,0.786395014,0.110347003,0.026843353,Epidemiology,0.99075574,TRUE,102,0.873523409,36,0.611118544,3,0.667819001,,,0.717486985
3974,Genomic epidemiology reveals multiple introductions of SARS-CoV-2 from mainland Europe into Scotland.,Nat Microbiol,33349681,12/23/20,pubmed,0,52,"sequencing, genomic epidemiology, genomes",0.002080526,0.393512744,0.002080575,0.411342269,0.002080652,0.188903235,Epidemiology,0.59353733,TRUE,58.36538462,0.692065063,84.55769231,0.789403265,5,0.739490092,,,0.740319473
3975,The SARS-CoV-2 RNA-protein interactome in infected human cells.,Nat Microbiol,33349665,12/23/20,pubmed,0,21,"proteom, interactom",0.824633987,0.169794655,0.001392816,0.001392885,0.001392846,0.001392811,Drug discovery,0.6633302,TRUE,77.57142857,0.796895293,1267.761905,0.995517795,10,0.828199272,,,0.873537453
3976,[Influence of the covid-19 pandemic period of alert on perceived quality of hospital emergencies].,J Healthc Qual Res,33349561,12/23/20,pubmed,0,4,logistic regression,0.002296607,0.002296566,0.002296882,0.002296648,0.502421733,0.488391564,Healthcare,0.9328439,TRUE,3.5,0.044344115,0.25,0.065493712,0,0.403234768,,,0.171024198
3977,The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond.,BMC Med Inform Decis Mak,33349259,12/23/20,pubmed,0,10,dataset,0.157808288,0.00131041,0.474225785,0.167174933,0.111684569,0.087796015,Imaging,0.37200636,FALSE,23.1,0.34263096,16.4,0.441196147,1,0.537564047,,,0.440463718
3978,Impact of simulation-based teamwork training on COVID-19 distress in healthcare professionals.,BMC Med Educ,33349248,12/23/20,pubmed,0,8,logistic regression,0.055277521,0.001072162,0.001072231,0.001072245,0.858236797,0.083269045,Healthcare,0.99151516,TRUE,36.5,0.503123261,19.5,0.475983409,0,0.403234768,,,0.460780479
3979,Correlation analysis between disease severity and inflammation-related parameters in patients with COVID-19: a retrospective study.,BMC Infect Dis,33349241,12/23/20,pubmed,0,11,correlation analysis,0.083537771,0.001171542,0.001171553,0.001171553,0.001171553,0.911776029,Clinics,0.92267215,TRUE,111.5454545,0.893005133,51.81818182,0.688720899,7,0.785110192,,,0.788945408
3980,"Building a Resilient, Sustainable, and Healthier Food Supply Through Innovation and Technology.",Annu Rev Food Sci Technol,33348992,12/23/20,pubmed,0,7,"machine learning, artificial intelligence",0.024122401,0.000988411,0.160766454,0.372745783,0.357971396,0.083405556,Epidemiology,0.9199266,TRUE,318,0.991712536,852.5714286,0.991436982,0,0.403234768,,,0.795461428
3981,The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies.,Brief Bioinform,33348379,12/22/20,pubmed,0,8,bioinformatic,0.861573216,0.134428645,0.000999548,0.00099955,0.000999509,0.000999532,Drug discovery,0.5943962,TRUE,92.5,0.850454574,347.875,0.965279636,1,0.537564047,,,0.784432752
3982,A review on viral data sources and search systems for perspective mitigation of COVID-19.,Brief Bioinform,33348368,12/22/20,pubmed,0,5,"genome sequences, dataset",0.001511895,0.409318254,0.001511915,0.584634277,0.001511856,0.001511803,Epidemiology,0.34969682,FALSE,95,0.856824788,27.4,0.548969762,0,0.403234768,,,0.603009773
3983,Diagnostic utility of C-reactive protein to albumin ratio as an early warning sign in hospitalized severe COVID-19 patients.,Int Immunopharmacol,33348293,12/22/20,pubmed,0,8,logistic regression,0.001371289,0.001371276,0.001371354,0.001371261,0.001371235,0.993143585,Clinics,0.5011544,TRUE,30.125,0.433174593,9.375,0.342721434,0,0.403234768,,,0.393043598
3984,Effects of Intestinal Fungi and Viruses on Immune Responses and Inflammatory Bowel Diseases.,Gastroenterology,33347881,12/22/20,pubmed,0,2,metagenom,0.545003227,0.407667422,0.039857725,0.002490576,0.00249056,0.002490491,Drug discovery,0.68516064,TRUE,67.5,0.750201002,234.5,0.931964142,1,0.537564047,,,0.73990973
3985,Comparative insight into the genomic landscape of SARS-CoV-2 and identification of mutations associated with the origin of infection and diversity.,J Med Virol,33347622,12/22/20,pubmed,0,4,genomes,0.149845174,0.844002139,0.001538121,0.00153824,0.001538136,0.001538191,Genomics,0.84741426,TRUE,2.75,0.030304904,0,0.055525823,3,0.667819001,,,0.251216576
3986,"Knowledge, attitudes, and practices toward COVID-19 among university students in Japan and associated factors: An online cross-sectional survey.",PLoS One,33347488,12/22/20,pubmed,0,8,logistic regression,0.0382255,0.001187322,0.001187301,0.055041257,0.903171334,0.001187286,Healthcare,0.94439936,TRUE,82.875,0.817490259,50.125,0.682031041,2,0.618927094,,,0.706149465
3987,Exercise routine change is associated with prenatal depression scores during the COVID-19 pandemic among pregnant women across the United States.,PLoS One,33347484,12/22/20,pubmed,0,3,logistic regression,0.001371263,0.001371278,0.064491846,0.001371312,0.93002283,0.001371471,Healthcare,0.8195504,TRUE,21.66666667,0.320737213,12.66666667,0.39530372,2,0.618927094,,,0.444989342
3988,"Positive association of angiotensin II receptor blockers, not angiotensin-converting enzyme inhibitors, with an increased vulnerability to SARS-CoV-2 infection in patients hospitalized for suspected COVID-19 pneumonia.",PLoS One,33347477,12/22/20,pubmed,0,16,logistic regression,0.214296784,0.001291274,0.001291233,0.001291208,0.001291263,0.780538237,Clinics,0.94148135,TRUE,14,0.213494959,3.3125,0.204174472,0,0.403234768,,,0.273634733
3989,Evaluation of pool-based testing approaches to enable population-wide screening for COVID-19.,PLoS One,33347458,12/22/20,pubmed,0,5,mathematical model,0.001511874,0.028141149,0.319400251,0.64792291,0.001511891,0.001511924,Epidemiology,0.47376215,FALSE,7.8,0.113550622,2.6,0.18256623,8,0.799987654,,,0.365368168
3990,The SARS-CoV-2 Spike protein has a broad tropism for mammalian ACE2 proteins.,PLoS Biol,33347434,12/22/20,pubmed,0,23,bioinformatic,0.487322898,0.507796993,0.001219983,0.001220013,0.001220005,0.001220107,Genomics,0.82627606,TRUE,26.69565217,0.391180654,22.65217391,0.508295424,0,0.403234768,,,0.434236949
3991,Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data.,JMIR Public Health Surveill,33347422,12/22/20,pubmed,0,3,dataset,0.00095636,0.016657867,0.000956342,0.719923087,0.26054998,0.000956364,Epidemiology,0.5804617,TRUE,36.33333333,0.500773084,0.666666667,0.096200161,0,0.403234768,,,0.333402671
3992,College Campuses and COVID-19 Mitigation: Clinical and Economic Value.,Ann Intern Med,33347322,12/22/20,pubmed,0,10,simulation model,0.000779431,0.000779441,0.000779458,0.525682816,0.380615017,0.091363836,Epidemiology,0.7605028,TRUE,87.2,0.83418888,108.1,0.83629917,0,0.403234768,,,0.691240939
3993,Shortage of Physicians: A Critical Review.,Adv Exp Med Biol,33346901,12/22/20,pubmed,0,2,artificial intelligence,0.002296617,0.002296635,0.248259648,0.323986405,0.420864128,0.002296567,Healthcare,0.8484893,TRUE,20.5,0.304966294,2,0.164302917,0,0.403234768,,,0.290834659
3994,CorGAT: a tool for the functional annotation of SARS-CoV-2 genomes.,Bioinformatics,33346830,12/22/20,pubmed,0,6,"bioinformatic, genomes",0.00346603,0.911497654,0.074638494,0.003466041,0.003465892,0.003465889,Genomics,0.74625933,TRUE,36.16666667,0.499165069,80.5,0.780572652,0,0.403234768,,,0.560990829
3995,The COVID-19 Ontology.,Bioinformatics,33346828,12/22/20,pubmed,0,13,text mining,0.526999939,0.002490563,0.091790491,0.37373795,0.002490573,0.002490485,Drug discovery,0.6222485,TRUE,22,0.326056033,8.384615385,0.325862992,0,0.403234768,,,0.351717931
3996,"Health Care Providers' Performance, Mindset, and Attitudes Toward a Neonatal Resuscitation Computer-Based Simulator: Empirical Study.",JMIR Serious Games,33346741,12/22/20,pubmed,0,9,active learning,0.000807919,0.02946952,0.289986346,0.154267232,0.524661081,0.000807902,Healthcare,0.84892344,TRUE,83,0.818170573,117.4444444,0.847404335,0,0.403234768,,,0.689603225
3997,Recognition of plausible therapeutic agents to combat COVID-19: An omics data based combined approach.,Gene,33346100,12/22/20,pubmed,0,9,"sequencing, in silico,  omics, genome sequences, dataset",0.686009993,0.283956195,0.001653158,0.001653044,0.001653023,0.025074587,Drug discovery,0.53810716,TRUE,3.666666667,0.045952131,0.333333333,0.073187048,2,0.618927094,,,0.246022091
3998,Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.,Chest,33345948,12/22/20,pubmed,0,13,deep learning,0.001330052,0.001330036,0.499012881,0.001330035,0.001330279,0.495666716,Clinics,0.86240995,TRUE,43.46153846,0.572329767,54.61538462,0.700160557,0,0.403234768,,,0.55857503
3999,Can drug repurposing strategies be the solution to the COVID-19 crisis?,Expert Opin Drug Discov,33345645,12/22/20,pubmed,0,7,in silico,0.566873644,0.001622751,0.001622771,0.426635288,0.001622821,0.001622725,Drug discovery,0.40704823,FALSE,40,0.539860226,14.28571429,0.4151057,0,0.403234768,,,0.452733565
4000,Increased expression of hypoxia-induced factor 1α mRNA and its related genes in myeloid blood cells from critically ill COVID-19 patients.,Ann Med,33345622,12/22/20,pubmed,0,25,immunome,0.612128625,0.001461901,0.001461925,0.001461866,0.001461872,0.382023811,Drug discovery,0.92230237,TRUE,33.68,0.473189437,12.12,0.386874498,0,0.403234768,,,0.421099567
4001,Ocular surface assessment in times of sanitary crisis: What lessons and solutions for the present and the future?,Eur J Ophthalmol,33345619,12/22/20,pubmed,0,4,artificial intelligence,0.101472226,0.001622759,0.53642631,0.001622814,0.357233017,0.001622873,Healthcare,0.94313765,TRUE,326.25,0.992145464,186,0.90988761,0,0.403234768,,,0.768422614
4002,Investigation on penetration of saffron components through lipid bilayer bound to spike protein of SARS-CoV-2 using steered molecular dynamics simulation.,Heliyon,33344790,12/22/20,pubmed,0,3,molecular dynamics simulation,0.989083905,0.002183224,0.002183242,0.00218323,0.00218321,0.00218319,Drug discovery,0.9621575,TRUE,16.66666667,0.251159626,5,0.257024351,0,0.403234768,,,0.303806248
4003,A population-based nationwide dataset concerning the COVID-19 pandemic and serious psychological consequences in Bangladesh.,Data Brief,33344737,12/22/20,pubmed,0,5,dataset,0.001653032,0.001653207,0.001653114,0.169270744,0.824116876,0.001653026,Healthcare,0.58169436,TRUE,50.2,0.632444802,11.8,0.38252609,0,0.403234768,,,0.47273522
4004,Lipid Profile Features and Their Associations With Disease Severity and Mortality in Patients With COVID-19.,Front Cardiovasc Med,33344516,12/22/20,pubmed,0,12,correlation analysis,0.001022686,0.001022642,0.036163839,0.001022667,0.001022627,0.959745539,Clinics,0.6694275,TRUE,64.83333333,0.733811615,9.916666667,0.352020337,0,0.403234768,,,0.496355573
4005,COVID-19 Chest Computed Tomography to Stratify Severity and Disease Extension by Artificial Neural Network Computer-Aided Diagnosis.,Front Med (Lausanne),33344471,12/22/20,pubmed,0,11,"neural network, classifier",0.000752907,0.000752895,0.567292803,0.000752901,0.000752887,0.429695606,Imaging,0.6673235,TRUE,59.18181818,0.697383883,27.45454545,0.549103559,1,0.537564047,,,0.59468383
4006,IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning.,J Healthc Eng,33343851,12/22/20,pubmed,0,5,"machine learning, deep learning, neural network, dataset",0.001237258,0.001237089,0.828918557,0.001237128,0.001237132,0.166132836,Imaging,0.8381102,TRUE,21.4,0.316469788,4.4,0.238961734,1,0.537564047,,,0.364331856
4007,Temperature dependence of the SARS-CoV-2 affinity to human ACE2 determines COVID-19 progression and clinical outcome.,Comput Struct Biotechnol J,33343834,12/22/20,pubmed,0,7,molecular dynamics simulation,0.794603693,0.001350349,0.00135031,0.001350398,0.001350316,0.199994933,Drug discovery,0.8314588,TRUE,12,0.183190055,3.428571429,0.208991169,3,0.667819001,,,0.353333409
4008,Poor knowledge of COVID-19 and unfavourable perception of the response to the pandemic by healthcare workers at the Bafoussam Regional Hospital (West Region-Cameroon).,Pan Afr Med J,33343798,12/22/20,pubmed,0,20,probabilistic,0.074876198,0.001291361,0.001291281,0.098267876,0.705749132,0.118524153,Healthcare,0.9822799,TRUE,27,0.3960047,9.15,0.338239229,0,0.403234768,,,0.379159566
4009,The Recent Progress and Applications of Digital Technologies in Healthcare: A Review.,Int J Telemed Appl,33343657,12/22/20,pubmed,0,8,"artificial intelligence, digital health",0.000838562,0.000838555,0.313567069,0.519787137,0.164130126,0.00083855,Epidemiology,0.97381,TRUE,10.375,0.15535902,1,0.122023013,1,0.537564047,,,0.271648694
4010,"Novelty Seeking and Mental Health in Chinese University Students Before, During, and After the COVID-19 Pandemic Lockdown: A Longitudinal Study.",Front Psychol,33343473,12/22/20,pubmed,0,5,correlation analysis,0.000907284,0.000907291,0.000907288,0.367167928,0.629202909,0.0009073,Healthcare,0.8902689,TRUE,12,0.183190055,1.2,0.126103827,0,0.403234768,,,0.23750955
4011,Chinese College Students Have Higher Anxiety in New Semester of Online Learning During COVID-19: A Machine Learning Approach.,Front Psychol,33343461,12/22/20,pubmed,0,3,machine learning,0.001010953,0.001010977,0.177100391,0.060968231,0.758898454,0.001010994,Healthcare,0.89455247,TRUE,63,0.721998887,52.33333333,0.690326465,1,0.537564047,,,0.649963133
4012,Gambling Despite Nationwide Self-Exclusion-A Survey in Online Gamblers in Sweden.,Front Psychiatry,33343428,12/22/20,pubmed,0,2,logistic regression,0.001187265,0.001187299,0.001187285,0.001187343,0.994063538,0.00118727,Healthcare,0.97846866,TRUE,13,0.197352959,5,0.257024351,1,0.537564047,,,0.330647119
4013,"Psychological Status Among Anesthesiologists and Operating Room Nurses During the Outbreak Period of COVID-19 in Wuhan, China.",Front Psychiatry,33343417,12/22/20,pubmed,0,10,logistic regression,0.000966767,0.010713209,0.000966777,0.00096679,0.930429332,0.055957126,Healthcare,0.9749148,TRUE,34.2,0.479188571,,,0,0.403234768,,,0.441211669
4014,0,Front Physiol,33343395,12/22/20,pubmed,0,6,computational,0.224257716,0.198356297,0.193154326,0.269612313,0.001098852,0.113520497,Epidemiology,0.45759165,FALSE,29.66666667,0.42773208,23.16666667,0.514182499,1,0.537564047,,,0.493159542
4015,Biosensor and Lab-on-a-chip Biomarker-identifying Technologies for Oral and Periodontal Diseases.,Front Pharmacol,33343358,12/22/20,pubmed,0,6,microbiom,0.246855163,0.074258417,0.124518396,0.057435457,0.001565352,0.495367216,Clinics,0.9969789,TRUE,62.16666667,0.716308986,87.66666667,0.796293819,0,0.403234768,,,0.638612524
4016,Retrospective Study of Clinical Features of COVID-19 in Inpatients and Their Association with Disease Severity.,Med Sci Monit,33342993,12/22/20,pubmed,0,9,logistic regression,0.001565307,0.001565281,0.001565309,0.001565268,0.001565305,0.99217353,Clinics,0.9933362,TRUE,160.7777778,0.95213062,66.22222222,0.74290875,0,0.403234768,,,0.699424713
4017,"Efficacy of early hydroxychloroquine treatment in preventing COVID-19 pneumonia aggravation, the experience from Shanghai, China.",Biosci Trends,33342929,12/22/20,pubmed,0,9,logistic regression,0.00153811,0.090507682,0.001538148,0.00153812,0.001538126,0.903339813,Clinics,0.9145843,TRUE,43.77777778,0.575607644,45.44444444,0.6603559,0,0.403234768,,,0.546399437
4018,An inventory-location optimization model for equitable influenza vaccine distribution in developing countries during the COVID-19 pandemic.,Vaccine,33342632,12/22/20,pubmed,0,4,optimization model,0.002357874,0.002357797,0.002357889,0.411469654,0.579098978,0.002357809,Healthcare,0.6030578,TRUE,75,0.787061661,40.75,0.638011774,1,0.537564047,,,0.654212494
4019,Prevalence of malnutrition in coronavirus disease 19: the NUTRICOV study.,Br J Nutr,33342449,12/22/20,pubmed,0,11,logistic regression,0.001565371,0.083087173,0.001565392,0.001565432,0.001565416,0.910651216,Clinics,0.9814258,TRUE,74.54545455,0.784773332,39.09090909,0.628110784,1,0.537564047,,,0.650149388
4020,"A Prospective Study of Voice, Swallow, and Airway Outcomes Following Tracheostomy for COVID-19.",Laryngoscope,33341953,12/21/20,pubmed,0,9,correlation analysis,0.001684474,0.001684606,0.001684516,0.09639986,0.085302251,0.813244293,Clinics,0.89472544,TRUE,23.55555556,0.348691941,25.77777778,0.53538935,2,0.618927094,,,0.501002795
4021,SARS-CoV-2 hot-spot mutations are significantly enriched within inverted repeats and CpG island loci.,Brief Bioinform,33341900,12/21/20,pubmed,0,11,bioinformatic,0.002357883,0.941172057,0.049396536,0.002357896,0.002357827,0.002357801,Genomics,0.8912967,TRUE,42.54545455,0.562990909,11.72727273,0.381188119,1,0.537564047,,,0.493914358
4022,Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19.,Brief Bioinform,33341869,12/21/20,pubmed,0,4,"sequencing, dataset",0.83383907,0.034298794,0.126710163,0.001717306,0.001717205,0.001717462,Drug discovery,0.5400838,TRUE,62.25,0.71661822,14.75,0.421327268,0,0.403234768,,,0.513726752
4023,Acute Kidney Injury and Renal Replacement Therapy in Critically Ill COVID-19 Patients: Risk Factors and Outcomes: A Single-Center Experience in Brazil.,Blood Purif,33341806,12/21/20,pubmed,0,18,logistic regression,0.001486426,0.001486409,0.001486396,0.001486395,0.001486419,0.992567954,Clinics,0.91848075,TRUE,106.9444444,0.882862267,182.7777778,0.908081349,1,0.537564047,,,0.776169221
4024,Does tetanus vaccination contribute to reduced severity of the COVID-19 infection?,Med Hypotheses,33341328,12/21/20,pubmed,0,3,"artificial intelligence, dataset",0.00289847,0.002898424,0.247612199,0.740793923,0.002898441,0.002898542,Epidemiology,0.33659554,FALSE,31.66666667,0.451976003,18,0.46180091,0,0.403234768,,,0.439003894
4025,In vitro display evolution of IL-6R-binding unnatural peptides ribosomally initiated and cyclized with m-(chloromethyl)benzoic acid.,Biochem Biophys Res Commun,33340765,12/20/20,pubmed,0,6,sequencing,0.834511982,0.159916328,0.001392925,0.001393036,0.001392825,0.001392905,Drug discovery,0.9047549,TRUE,46.5,0.601026656,13,0.400521809,2,0.618927094,,,0.54015852
4026,Machine Learning Prediction of SARS-CoV-2 Polymerase Chain Reaction Results with Routine Blood Tests.,Lab Med,33340312,12/20/20,pubmed,0,5,machine learning,0.002422252,0.002422354,0.72365354,0.002422253,0.002422256,0.266657346,Clinics,0.42106226,FALSE,15.2,0.229142186,7.4,0.305392026,2,0.618927094,,,0.384487102
4027,[Direct and indirect neurological manifestations of COVID-19].,Zh Nevrol Psikhiatr Im S S Korsakova,33340292,12/20/20,pubmed,0,5,virom,0.200557182,0.002490483,0.04070608,0.238236627,0.00249066,0.515518967,Clinics,0.97241855,TRUE,67.4,0.749706228,1.8,0.150120417,1,0.537564047,,,0.479130231
4028,Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19.,Sci Rep,33339864,12/20/20,pubmed,0,11,dataset,0.76106413,0.00135038,0.001350489,0.211304589,0.001350332,0.023580081,Drug discovery,0.33854562,FALSE,23.63636364,0.349186715,57.36363636,0.712336098,5,0.739490092,,,0.600337635
4029,Clinical Characteristics and Risk Factors in Coronavirus Disease 2019 Patients with Liver Injury.,Med Sci Monit,33339813,12/20/20,pubmed,0,7,logistic regression,0.138482808,0.001438102,0.001438127,0.001438176,0.001438174,0.855764614,Clinics,0.87464786,TRUE,63.14285714,0.72230812,31.57142857,0.581950763,0,0.403234768,,,0.56916455
4030,Comparison of Mortality Rate and Severity of Pulmonary Involvement in Coronavirus Disease-2019 Adults Patients With and Without Type 2 Diabetes: A Cohort Study.,Can J Diabetes,33339741,12/20/20,pubmed,0,6,logistic regression,0.001237065,0.001237067,0.001237075,0.00123717,0.001237069,0.993814555,Clinics,0.9920386,TRUE,33.83333333,0.474488218,9.833333333,0.351284453,0,0.403234768,,,0.409669146
4031,The French general population's attitudes toward lockdown against COVID-19: a fragile consensus.,BMC Public Health,33339543,12/20/20,pubmed,0,10,logistic regression,0.001392898,0.001392947,0.001392892,0.260320028,0.734108327,0.001392908,Healthcare,0.48807323,FALSE,91.54545455,0.847609623,,,0,0.403234768,,,0.625422195
4032,"Corticosteroid therapy in critically ill patients with COVID-19: a multicenter, retrospective study.",Crit Care,33339536,12/20/20,pubmed,0,18,"structural model, logistic regression",0.001350438,0.096600826,0.001350317,0.001350388,0.001350388,0.897997643,Clinics,0.9010892,TRUE,52.16666667,0.648153875,52,0.689523682,1,0.537564047,,,0.625080535
4033,Prevalence and correlates of fatigue and its association with quality of life among clinically stable older psychiatric patients during the COVID-19 outbreak: a cross-sectional study.,Global Health,33339523,12/20/20,pubmed,0,12,logistic regression,0.002238548,0.002238481,0.002238476,0.002238474,0.590252946,0.400793075,Healthcare,0.97516584,TRUE,97.08333333,0.861772528,71.66666667,0.758094728,0,0.403234768,,,0.674367341
4034,Bioactivity Potential of Marine Natural Products from Scleractinia-Associated Microbes and In Silico Anti-SARS-COV-2 Evaluation.,Mar Drugs,33339096,12/20/20,pubmed,0,8,in silico,0.943641039,0.00186179,0.001861753,0.04891191,0.001861754,0.001861754,Drug discovery,0.5692583,TRUE,26.25,0.384748593,8.75,0.332218357,1,0.537564047,,,0.418176999
4035,"Psychological impact of COVID-19 pandemic on chronic disease patients in Dessie town government and private hospitals, Northeast Ethiopia.",Diabetes Metab Syndr,33338951,12/19/20,pubmed,0,3,logistic regression,0.001717196,0.001717263,0.001717267,0.179853915,0.515434464,0.299559896,Healthcare,0.96682423,TRUE,3.333333333,0.04044777,0.666666667,0.096200161,2,0.618927094,,,0.251858342
4036,"Identification of potential lockdown areas during COVID-19 transmission in Punjab, Pakistan.",Public Health,33338902,12/19/20,pubmed,0,7,"predictive model, prediction model",0.078893849,0.00153817,0.001538115,0.822441546,0.001538165,0.094050154,Epidemiology,0.31846517,FALSE,11.14285714,0.167728369,3.285714286,0.203973776,1,0.537564047,,,0.303088731
4037,A multidisciplinary registry of patients with autoimmune and immune-mediated diseases with symptomatic COVID-19 from a single center.,J Autoimmun,33338707,12/19/20,pubmed,0,34,logistic regression,0.001187315,0.001187293,0.155153698,0.001187326,0.042476397,0.798807971,Clinics,0.83846724,TRUE,110.4411765,0.890469417,123.8235294,0.856368745,0,0.403234768,,,0.716690976
4038,A Crisp(r) New Perspective on SARS-CoV-2 Biology.,Cell,33338422,12/19/20,pubmed,0,2,genome-wide,0.690498722,0.293791167,0.003927423,0.003927479,0.003927603,0.003927605,Drug discovery,0.3502978,FALSE,252,0.984662008,1113,0.994715012,2,0.618927094,,,0.866101371
4039,RBM-MHC: A Semi-Supervised Machine-Learning Method for Sample-Specific Prediction of Antigen Presentation by HLA-I Alleles.,Cell Syst,33338400,12/19/20,pubmed,0,6,immunopeptidom,0.257743564,0.313751898,0.374004346,0.001786591,0.00178655,0.050927051,Genomics,0.143276,FALSE,81.83333333,0.814026841,80.66666667,0.780773348,2,0.618927094,,,0.737909094
4040,Response to COVID-19 in Cyprus: Policy changes and epidemic trends.,Int J Clin Pract,33338320,12/19/20,pubmed,0,9,prediction model,0.001861743,0.001861728,0.001861783,0.778181728,0.214371206,0.001861812,Epidemiology,0.0638285,FALSE,29.33333333,0.423093574,9.333333333,0.342253144,0,0.403234768,,,0.389527162
4041,Ten simple rules for creating a brand-new virtual academic meeting (even amid a pandemic).,PLoS Comput Biol,33338032,12/19/20,pubmed,0,4,computational,0.001538205,0.001538169,0.001538241,0.574248198,0.41959895,0.001538237,Epidemiology,0.8248028,TRUE,13.75,0.208299833,3.75,0.21982874,0,0.403234768,,,0.277121113
4042,Use of Telepresence Robots in Glaucoma Patient Education.,J Glaucoma,33337723,12/19/20,pubmed,0,9,logistic regression,0.001254684,0.095972517,0.001254644,0.111151567,0.580431535,0.209935052,Healthcare,0.9575144,TRUE,31.22222222,0.44671903,3.222222222,0.20236821,0,0.403234768,,,0.350774003
4043,Prevalence of depressive symptoms among Chinese pregnant and postpartum women during the COVID-19 pandemic.,Psychosom Med,33337594,12/19/20,pubmed,0,11,logistic regression,0.001593472,0.001593481,0.001593514,0.001593532,0.968870653,0.024755349,Healthcare,0.9634429,TRUE,68.54545455,0.75669491,30.36363636,0.572250468,0,0.403234768,,,0.577393382
4044,Same pandemic but different studies: an exploration of COVID-19 research at the early stage.,Eur Rev Med Pharmacol Sci,33336775,12/19/20,pubmed,0,4,mathematical model,0.001823392,0.001823412,0.171603455,0.769286639,0.053639702,0.001823401,Epidemiology,0.5405094,TRUE,5.75,0.080957388,2.25,0.170925876,0,0.403234768,,,0.218372677
4045,"Analysis of factors for disease progression in 61 patients with COVID-19 in Xiaogan, Hubei, China.",Eur Rev Med Pharmacol Sci,33336768,12/19/20,pubmed,0,9,logistic regression,0.001220039,0.001220128,0.001220044,0.001220086,0.001220015,0.993899687,Clinics,0.9808146,TRUE,4.222222222,0.05603315,0.222222222,0.061412898,0,0.403234768,,,0.173560272
4046,Determinants of physical activity among adults in the United Kingdom during the COVID-19 pandemic: The DUK-COVID study.,Br J Health Psychol,33336562,12/19/20,pubmed,0,6,logistic regression,0.000907294,0.000907308,0.000907299,0.200870943,0.795499856,0.000907301,Healthcare,0.8305756,TRUE,117.5,0.903642773,200.1666667,0.917647846,0,0.403234768,,,0.741508462
4047,Elevated α-hydroxybutyrate dehydrogenase as an independent prognostic factor for mortality in hospitalized patients with COVID-19.,ESC Heart Fail,33336560,12/19/20,pubmed,0,13,logistic regression,0.001156245,0.001156226,0.001156273,0.001156243,0.001156235,0.994218778,Clinics,0.9870604,TRUE,132.3846154,0.924361432,160.5384615,0.892761573,0,0.403234768,,,0.740119258
4048,"MicroRNA-146a and -155, upregulated by periodontitis and type 2 diabetes in oral fluids, are predicted to regulate SARS-CoV-2 oral receptors genes.",J Periodontol,33336412,12/19/20,pubmed,0,1,bioinformatic,0.808595793,0.001622736,0.001622708,0.001622709,0.001622714,0.184913341,Drug discovery,0.91910756,TRUE,46,0.596882924,8,0.320511105,0,0.403234768,,,0.440209599
4049,MIS-C in February 2020 and Implications of Genomic Sequencing for SARS-CoV-2.,J Pediatric Infect Dis Soc,33336251,12/19/20,pubmed,0,7,sequencing,0.005353383,0.707576055,0.005353019,0.005353011,0.094105534,0.182258999,Genomics,0.5804491,TRUE,4.428571429,0.05850702,0.428571429,0.076665775,0,0.403234768,,,0.179469187
4050,Ten Epidemiological Parameters of COVID-19: Use of Rapid Literature Review to Inform Predictive Models During the Pandemic.,Front Public Health,33335879,12/19/20,pubmed,0,10,predictive model,0.002238443,0.00223857,0.002238621,0.868982692,0.002238544,0.122063131,Epidemiology,0.8208822,TRUE,9.8,0.147071557,4.4,0.238961734,2,0.618927094,,,0.334986795
4051,"Repurposing FDA-approved phytomedicines, natural products, antivirals and cell protectives against SARS-CoV-2 (COVID-19) RNA-dependent RNA polymerase.",PeerJ,33335812,12/19/20,pubmed,0,3,"virtual screening, molecular dynamics simulation",0.993982719,0.001203442,0.001203474,0.00120345,0.001203473,0.001203441,Drug discovery,0.9428184,TRUE,80.66666667,0.809512029,42.33333333,0.6462403,0,0.403234768,,,0.619662365
4052,"Comparison of Psychological Stress Levels and Associated Factors Among Healthcare Workers, Frontline Workers, and the General Public During the Novel Coronavirus Pandemic.",Front Psychiatry,33335490,12/19/20,pubmed,0,5,logistic regression,0.021623181,0.001371322,0.00137125,0.00137131,0.972891628,0.001371309,Healthcare,0.7872686,TRUE,20.4,0.302863504,9.8,0.350682366,1,0.537564047,,,0.397036639
4053,Antenatal Care Service Utilization of Pregnant Women Attending Antenatal Care in Public Hospitals During the COVID-19 Pandemic Period.,Int J Womens Health,33335430,12/19/20,pubmed,0,1,logistic regression,0.001350346,0.001350335,0.001350369,0.066288815,0.928309768,0.001350367,Healthcare,0.95317745,TRUE,4,0.054734368,3,0.199424672,0,0.403234768,,,0.219131269
4054,A report on COVID-19 epidemic in Pakistan using SEIR fractional model.,Sci Rep,33335284,12/19/20,pubmed,0,5,mathematical model,0.002238491,0.002238539,0.002238487,0.988807544,0.002238466,0.002238474,Epidemiology,0.6152798,TRUE,190.2,0.968705548,34.8,0.604094193,2,0.618927094,,,0.730575612
4055,Advancing digital health: FDA innovation during COVID-19.,NPJ Digit Med,33335250,12/19/20,pubmed,0,3,digital health,0.430076253,0.034961874,0.034962554,0.430074587,0.034962859,0.034961874,Drug discovery,0.39610797,FALSE,15.66666667,0.236563795,3.666666667,0.217621086,0,0.403234768,,,0.28580655
4056,COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis.,Sci Rep,33335243,12/19/20,pubmed,0,1,bayes,0.00156541,0.001565337,0.133835371,0.859903248,0.001565335,0.001565299,Epidemiology,0.3462829,FALSE,140,0.933823984,271,0.946012845,0,0.403234768,,,0.761023865
4057,"Two SARS-CoV-2 Genome Sequences of Isolates from Rural U.S. Patients Harboring the D614G Mutation, Obtained Using Nanopore Sequencing.",Microbiol Resour Announc,33334896,12/19/20,pubmed,0,10,"sequencing, genome sequences",0.008402789,0.764939985,0.118974993,0.008402939,0.00840264,0.090876655,Genomics,0.52650476,TRUE,71.9,0.772403983,138.4,0.873026492,1,0.537564047,,,0.727664841
4058,Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports.,AJNR Am J Neuroradiol,33334851,12/19/20,pubmed,0,9,"classifier, dataset",0.00104685,0.001046872,0.701715333,0.120667412,0.001046865,0.174476668,Imaging,0.37462848,FALSE,41.22222222,0.550312326,24.66666667,0.526090447,0,0.403234768,,,0.493212514
4059,Patient characteristics associated with COVID-19 positivity and fatality in Nigeria: retrospective cohort study.,BMJ Open,33334842,12/19/20,pubmed,0,29,logistic regression,0.001717156,0.001717249,0.001717232,0.070124195,0.28729482,0.637429348,Clinics,0.96765184,TRUE,23.48275862,0.34720762,21.10344828,0.492507359,1,0.537564047,,,0.459093009
4060,The Need for Research-Grade Systems Modeling Technologies for Life Science Education.,Trends Mol Med,33334676,12/19/20,pubmed,0,1,computational,0.005353451,0.005353148,0.04983083,0.773959133,0.160150687,0.005352751,Epidemiology,0.8582983,TRUE,53,0.654400396,23,0.513513514,0,0.403234768,,,0.523716226
4061,"Evaluation of lockdown effect on SARS-CoV-2 dynamics through viral genome quantification in waste water, Greater Paris, France, 5 March to 23 April 2020.",Euro Surveill,33334397,12/19/20,pubmed,0,8,genomes,0.001786566,0.639871551,0.001786569,0.256575457,0.001786635,0.098193222,Genomics,0.613478,TRUE,49.25,0.625889047,106.5,0.833823923,12,0.850299401,,,0.770004124
4062,"Clinical characteristics, outcomes, and risk factors for mortality in hospitalized patients with COVID-19 and cancer history: a propensity score-matched study.",Infect Agent Cancer,33334375,12/19/20,pubmed,0,18,logistic regression,0.001046799,0.001046807,0.109773946,0.001046825,0.001046851,0.886038772,Clinics,0.94172823,TRUE,43.22222222,0.56923743,48.72222222,0.676344661,1,0.537564047,,,0.594382046
4063,Increased care at discharge from COVID-19: The association between pre-admission frailty and increased care needs after hospital discharge; a multicentre European observational cohort study.,BMC Med,33334341,12/19/20,pubmed,0,50,"predictive model, logistic regression",0.001330082,0.001330045,0.00133004,0.068190802,0.152637166,0.775181866,Clinics,0.98201424,TRUE,52.1372549,0.647906488,,,3,0.667819001,,,0.657862744
4064,"Novel risk scoring system for predicting acute respiratory distress syndrome among hospitalized patients with coronavirus disease 2019 in Wuhan, China.",BMC Infect Dis,33334314,12/19/20,pubmed,0,12,"logistic regression, dataset",0.002130665,0.002130622,0.242256037,0.002130688,0.002130873,0.749221116,Clinics,0.68122876,TRUE,59.33333333,0.698497124,158.0833333,0.89055392,0,0.403234768,,,0.664095271
4065,Identification of Host Cellular Protein Substrates of SARS-COV-2 Main Protease.,Int J Mol Sci,33333742,12/19/20,pubmed,0,6,in silico,0.949012195,0.001653199,0.044375522,0.001653053,0.001653023,0.001653008,Drug discovery,0.89113545,TRUE,31.33333333,0.448388892,9,0.337904736,0,0.403234768,,,0.396509465
4066,[Application of artificial intelligence in prevention and control of COVID-19 in Guangzhou city].,Zhonghua Yu Fang Yi Xue Za Zhi,33333669,12/18/20,pubmed,0,10,artificial intelligence,0.002422314,0.002422319,0.210967903,0.673917161,0.107848058,0.002422244,Epidemiology,0.7262902,TRUE,11.7,0.176572453,4.6,0.244246722,0,0.403234768,,,0.274684648
4067,Gene expression profiling of SARS-CoV-2 infections reveal distinct primary lung cell and systemic immune infection responses that identify pathways relevant in COVID-19 disease.,Brief Bioinform,33333559,12/18/20,pubmed,0,4,transcriptom,0.638472718,0.188007773,0.001861717,0.001861771,0.00186169,0.167934331,Drug discovery,0.5550821,TRUE,24.75,0.364710248,5,0.257024351,2,0.618927094,,,0.413553898
4068,"Effect of nelfinavir stereoisomers on coronavirus main protease: Molecular docking, molecular dynamics simulation and MM/GBSA study.",J Mol Graph Model,33333424,12/18/20,pubmed,0,1,molecular dynamics simulation,0.985967341,0.002806407,0.002806575,0.002806627,0.002806471,0.002806578,Drug discovery,0.9789503,TRUE,23,0.34225988,0,0.055525823,0,0.403234768,,,0.267006823
4069,Synergistic and Antagonistic Drug Combinations against SARS-CoV-2.,Mol Ther,33333292,12/18/20,pubmed,0,13,in silico,0.989596553,0.002080681,0.002080691,0.002080665,0.002080708,0.002080703,Drug discovery,0.4095509,FALSE,161.5384615,0.952316161,174.8461538,0.902997056,1,0.537564047,,,0.797625755
4070,Tocilizumab in the treatment of critical COVID-19 pneumonia: A retrospective cohort study of mechanically ventilated patients.,Int J Infect Dis,33333252,12/18/20,pubmed,0,5,logistic regression,0.00118725,0.001187278,0.001187265,0.026212142,0.001187314,0.969038751,Clinics,0.7392278,TRUE,16.8,0.253138722,5.2,0.259700294,1,0.537564047,,,0.350134355
4071,Genomic sequencing effort for SARS-CoV-2 by country during the pandemic.,Int J Infect Dis,33333251,12/18/20,pubmed,0,1,sequencing,0.002720183,0.833586612,0.064882593,0.002720359,0.093370121,0.002720133,Genomics,0.14482278,FALSE,63,0.721998887,55,0.702167514,6,0.764429903,,,0.729532101
4072,Genetic Screens Identify Host Factors for SARS-CoV-2 and Common Cold Coronaviruses.,Cell,33333024,12/18/20,pubmed,0,20,genome-wide,0.606695262,0.384571571,0.002183203,0.00218324,0.002183381,0.002183344,Drug discovery,0.6389367,TRUE,45.2,0.588719154,198.55,0.917045759,21,0.903944688,,,0.803236534
4073,Genomic epidemiology reveals multiple introductions and spread of SARS-CoV-2 in the Indian state of Karnataka.,PLoS One,33332472,12/18/20,pubmed,0,20,"genomic epidemiology, genomes",0.001392831,0.776439586,0.001392817,0.21798906,0.001392859,0.001392846,Genomics,0.27586466,FALSE,34.15,0.47826087,18.8,0.468156275,0,0.403234768,,,0.449883971
4074,Clinical features and risk factors for severe inpatients with COVID-19: A retrospective study in China.,PLoS One,33332437,12/18/20,pubmed,0,23,logistic regression,0.00101102,0.001010945,0.062391796,0.001011024,0.001010973,0.933564242,Clinics,0.99375355,TRUE,69.65217391,0.76182819,23.65217391,0.51746053,1,0.537564047,,,0.605617589
4075,Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks.,PLoS One,33332412,12/18/20,pubmed,0,3,"neural network, dataset",0.10245828,0.001141356,0.836537624,0.057579953,0.001141403,0.001141385,Imaging,0.7742743,TRUE,39,0.530521368,21.33333333,0.495450896,6,0.764429903,,,0.596800723
4076,Real-time neural network based predictor for cov19 virus spread.,PLoS One,33332363,12/18/20,pubmed,0,5,"artificial intelligence, neural network, prediction model",0.001415099,0.001415118,0.184398164,0.769556134,0.001415207,0.041800278,Epidemiology,0.41039163,FALSE,135.4,0.927082689,22.8,0.509967889,2,0.618927094,,,0.685325891
4077,Recombination events are concentrated in the spike protein region of Betacoronaviruses.,PLoS Genet,33332358,12/18/20,pubmed,0,3,genome-wide,0.085373329,0.906852653,0.001943527,0.001943517,0.00194348,0.001943495,Genomics,0.33792162,FALSE,81.33333333,0.812480673,553.6666667,0.981870484,5,0.739490092,,,0.84461375
4078,Post-acute COVID-19 associated with evidence of bystander T-cell activation and a recurring antibiotic-resistant bacterial pneumonia.,Elife,33331820,12/18/20,pubmed,0,30,metagenom,0.46436863,0.007674508,0.007674643,0.007674343,0.007674497,0.50493338,Clinics,0.77898085,TRUE,33.63333333,0.472570969,40.03333333,0.633462671,0,0.403234768,,,0.503089469
4079,Measures implemented in the school setting to contain the COVID-19 pandemic: a scoping review.,Cochrane Database Syst Rev,33331665,12/18/20,pubmed,0,17,mathematical model,0.000531382,0.106287003,0.000531399,0.688683481,0.203435354,0.000531381,Epidemiology,0.29730374,FALSE,23.82352941,0.351413198,21.94117647,0.501739363,5,0.739490092,,,0.530880884
4080,Dysregulation of the mevalonate pathway during SARS-CoV-2 infection: An in silico study.,J Med Virol,33331649,12/18/20,pubmed,0,3,"in silico, transcriptom",0.965920712,0.026472221,0.001901751,0.001901778,0.0019018,0.001901737,Drug discovery,0.31069964,FALSE,258,0.985466015,353.6666667,0.966350013,1,0.537564047,,,0.829793359
4081,"COVID-19: a cross-sectional study of suspected cases admitted to a federal hospital in Rio de Janeiro, Brazil, and factors associated with hospital death.",Epidemiol Serv Saude,33331600,12/18/20,pubmed,0,6,logistic regression,0.002806396,0.002806608,0.002806357,0.187141188,0.002806544,0.801632908,Clinics,0.995432,TRUE,14.5,0.219617787,2.833333333,0.189523682,1,0.537564047,,,0.315568505
4082,Mental health problems among healthcare workers involved with the COVID-19 outbreak.,Braz J Psychiatry,33331498,12/18/20,pubmed,0,24,probabilistic,0.001330026,0.001330031,0.001330032,0.084605037,0.91007482,0.001330054,Healthcare,0.9910061,TRUE,17.04166667,0.257529841,10.08333333,0.356034252,0,0.403234768,,,0.338932954
4083,Psychological impact of COVID-19 pandemic on healthcare workers in a highly burdened area of north-east Italy.,Epidemiol Psychiatr Sci,33331255,12/18/20,pubmed,0,8,logistic regression,0.000880195,0.000880231,0.00088021,0.000880218,0.814900978,0.18157817,Healthcare,0.96356285,TRUE,162.25,0.952934628,140.625,0.874966551,2,0.618927094,,,0.815609424
4084,Comparative molecular dynamics study of the receptor-binding domains in SARS-CoV-2 and SARS-CoV and the effects of mutations on the binding affinity.,J Biomol Struct Dyn,33331243,12/18/20,pubmed,0,3,computational,0.864711442,0.130663584,0.001156284,0.001156244,0.001156227,0.001156219,Drug discovery,0.5901192,TRUE,49,0.624281032,19,0.471367407,1,0.537564047,,,0.544404162
4085,The Risk Factors for the Exacerbation of COVID-19 Disease: A Case-control Study.,J Clin Nurs,33331072,12/18/20,pubmed,0,5,logistic regression,0.055297877,0.001171559,0.045899963,0.001171603,0.001171639,0.89528736,Clinics,0.98510915,TRUE,16.4,0.247572515,1.2,0.126103827,0,0.403234768,,,0.25897037
4086,Diverse Functional Autoantibodies in Patients with COVID-19.,medRxiv,33330894,12/18/20,pubmed,0,31,proteom,0.69475871,0.001823427,0.00182338,0.00182336,0.083716601,0.216054522,Drug discovery,0.27185053,FALSE,41.83870968,0.556002226,,,28,0.926168282,,,0.741085254
4087,Impact of Nasopharyngeal Specimen Quality on SARS-CoV-2 Test Sensitivity.,medRxiv,33330893,12/18/20,pubmed,0,9,logistic regression,0.001059391,0.596170479,0.211704347,0.00105939,0.188946964,0.00105943,Genomics,0.68490124,TRUE,21.11111111,0.312326056,8.444444444,0.327134065,1,0.537564047,,,0.392341389
4088,0,medRxiv,33330892,12/18/20,pubmed,0,7,bayes,0.001538121,0.001538202,0.587658094,0.001538289,0.40618901,0.001538284,Healthcare,0.2825634,FALSE,20.14285714,0.298967159,16.28571429,0.439256088,0,0.403234768,,,0.380486005
4089,Predicting Hospital Utilization and Inpatient Mortality of Patients Tested for COVID-19.,medRxiv,33330887,12/18/20,pubmed,0,4,predictive model,0.001254717,0.001254707,0.001254711,0.106232667,0.001254631,0.888748567,Clinics,0.16311648,FALSE,4,0.054734368,0.25,0.065493712,1,0.537564047,,,0.219264042
4090,"Multifaceted strategies for the control of COVID-19 outbreaks in long-term care facilities in Ontario, Canada.",medRxiv,33330884,12/18/20,pubmed,0,9,model simulation,0.000871558,0.000871575,0.000871595,0.269665185,0.62266076,0.105059328,Healthcare,0.06323728,FALSE,89.22222222,0.839940627,89,0.799304255,0,0.403234768,,,0.68082655
4091,Integrative analyses identify susceptibility genes underlying COVID-19 hospitalization.,medRxiv,33330876,12/18/20,pubmed,0,15,genome-wide,0.596666839,0.079320378,0.032642719,0.002130667,0.002130687,0.287108709,Drug discovery,0.25151375,FALSE,30.8,0.441523904,34.4,0.601150656,1,0.537564047,,,0.526746202
4092,SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential.,bioRxiv,33330873,12/18/20,pubmed,0,7,molecular dynamics simulation,0.883327615,0.000871575,0.068461014,0.045596579,0.000871679,0.000871537,Drug discovery,0.3625791,FALSE,34.42857143,0.481229513,33,0.593256623,0,0.403234768,,,0.492573635
4093,Profound Treg perturbations correlate with COVID-19 severity.,bioRxiv,33330871,12/18/20,pubmed,0,17,transcriptom,0.530848588,0.148536593,0.002720134,0.002720198,0.002720164,0.312454323,Drug discovery,0.48285753,FALSE,103.8235294,0.87711052,260,0.94153064,4,0.707574542,,,0.8420719
4094,"Circular RNA profiling reveals abundant and diverse circRNAs of SARS-CoV-2, SARS-CoV and MERS-CoV origin.",bioRxiv,33330860,12/18/20,pubmed,0,11,"sequencing, transcriptom, genomes",0.164219243,0.804528733,0.0261985,0.001684509,0.0016845,0.001684514,Genomics,0.48767468,FALSE,41.36363636,0.551672954,48.18181818,0.6745384,0,0.403234768,,,0.543148707
4095,"Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation.",Res Sq,33330858,12/18/20,pubmed,0,8,"neural network, knowledge graph",0.706753538,0.0013505,0.249871514,0.001350342,0.001350364,0.03932374,Drug discovery,0.4045441,FALSE,66.875,0.74624281,,,0,0.403234768,,,0.574738789
4096,Activation of Interferon-Stimulated Transcriptomes and ACE2 Isoforms in Human Airway Epithelium Is Curbed by Janus Kinase Inhibitors.,Res Sq,33330857,12/18/20,pubmed,0,3,transcriptom,0.91637263,0.075853338,0.001943464,0.001943528,0.00194353,0.00194351,Drug discovery,0.8412683,TRUE,136.6666667,0.928876245,435,0.973775756,0,0.403234768,,,0.768628923
4097,0,ACS Pharmacol Transl Sci,33330842,12/18/20,pubmed,0,13,computational,0.957551145,0.001565348,0.001565399,0.001565382,0.001565327,0.0361874,Drug discovery,0.97688085,TRUE,115.8461538,0.900426743,156.5384615,0.889082151,3,0.667819001,,,0.819109298
4098,"Ebselen, Disulfiram, Carmofur, PX-12, Tideglusib, and Shikonin Are Nonspecific Promiscuous SARS-CoV-2 Main Protease Inhibitors.",ACS Pharmacol Transl Sci,33330841,12/18/20,pubmed,0,7,molecular dynamics simulation,0.99292425,0.001415195,0.001415115,0.001415159,0.001415139,0.001415142,Drug discovery,0.9090673,TRUE,49,0.624281032,27.28571429,0.547832486,10,0.828199272,,,0.66677093
4099,Probing the Dynamic Structure-Function and Structure-Free Energy Relationships of the Coronavirus Main Protease with Biodynamics Theory.,ACS Pharmacol Transl Sci,33330838,12/18/20,pubmed,0,3,virtual screening,0.743513281,0.001203487,0.001203444,0.180753121,0.04028908,0.033037587,Drug discovery,0.69398797,TRUE,24,0.35574247,11.33333333,0.375501739,1,0.537564047,,,0.422936086
4100,0,Front Cardiovasc Med,33330645,12/18/20,pubmed,0,11,network analysis,0.572942464,0.133865904,0.001022646,0.001022641,0.001022628,0.290123717,Drug discovery,0.95743275,TRUE,28.27272727,0.410229451,17.45454545,0.454843457,0,0.403234768,,,0.422769225
4101,Computational Investigation of Structural Dynamics of SARS-CoV-2 Methyltransferase-Stimulatory Factor Heterodimer nsp16/nsp10 Bound to the Cofactor SAM.,Front Mol Biosci,33330626,12/18/20,pubmed,0,5,"molecular dynamics simulation, computational",0.993035772,0.001392867,0.001392824,0.001392857,0.001392838,0.001392842,Drug discovery,0.7537528,TRUE,19,0.285793803,3.4,0.208589778,0,0.403234768,,,0.299206116
4102,Renal Carcinoma Is Associated With Increased Risk of Coronavirus Infections.,Front Mol Biosci,33330620,12/18/20,pubmed,0,4,bioinformatic,0.640049701,0.028169274,0.000956324,0.000956338,0.000956329,0.328912034,Drug discovery,0.88689643,TRUE,139.5,0.93295813,300.5,0.954442066,0,0.403234768,,,0.763544988
4103,Fibrotic Changes Depicted by Thin-Section CT in Patients With COVID-19 at the Early Recovery Stage: Preliminary Experience.,Front Med (Lausanne),33330571,12/18/20,pubmed,0,7,logistic regression,0.001219999,0.001220033,0.291397,0.132851271,0.001220054,0.572091643,Clinics,0.89942765,TRUE,72.57142857,0.775743707,20.42857143,0.484814022,1,0.537564047,,,0.599373925
4104,The Impact of Periodontal Disease on Hospital Admission and Mortality During COVID-19 Pandemic.,Front Med (Lausanne),33330570,12/18/20,pubmed,0,4,logistic regression,0.001823324,0.001823361,0.001823355,0.001823361,0.347351523,0.645355077,Clinics,0.89691985,TRUE,7.75,0.112808461,0.5,0.087101953,2,0.618927094,,,0.272945836
4105,Screening of Natural Products Targeting SARS-CoV-2-ACE2 Receptor Interface - A MixMD Based HTVS Pipeline.,Front Chem,33330376,12/18/20,pubmed,0,4,"virtual screening, molecular dynamics simulation",0.908108521,0.001098912,0.001098883,0.087495887,0.001098989,0.001098808,Drug discovery,0.7535396,TRUE,28,0.408312202,21.75,0.50046829,1,0.537564047,,,0.482114847
4106,Effects of Pandemics-Related Uncertainty on Household Consumption: Evidence From the Cross-Country Data.,Front Public Health,33330348,12/18/20,pubmed,0,1,dataset,0.002490497,0.002490475,0.002490479,0.826786012,0.163252082,0.002490454,Epidemiology,0.6764988,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
4107,Analysis of COVID-19 Infections on a CT Image Using DeepSense Model.,Front Public Health,33330341,12/18/20,pubmed,0,6,"machine learning, deep learning, neural network, data mining, classifier, dataset",0.001461848,0.001461837,0.992690718,0.001461882,0.001461847,0.001461867,Imaging,0.7138698,TRUE,18.16666667,0.272682293,3,0.199424672,1,0.537564047,,,0.336557004
4108,Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map.,Front Public Health,33330323,12/18/20,pubmed,0,12,"machine learning, dataset",0.001098859,0.001098869,0.001098922,0.585983867,0.330976316,0.079743167,Epidemiology,0.92221,TRUE,28.25,0.410105758,21.16666667,0.493310142,1,0.537564047,,,0.480326649
4109,Prediction of COVID-19 Patients at High Risk of Progression to Severe Disease.,Front Public Health,33330318,12/18/20,pubmed,0,17,prediction model,0.001622846,0.001622793,0.001622793,0.199581955,0.001622813,0.793926799,Clinics,0.91247344,TRUE,23.11764706,0.342816501,6.705882353,0.290875033,1,0.537564047,,,0.390418527
4110,Prediction of Epidemics Trend of COVID-19 in Bangladesh.,Front Public Health,33330309,12/18/20,pubmed,0,8,mathematical model,0.001622704,0.001622729,0.001622732,0.890226577,0.069822489,0.03508277,Epidemiology,0.27643508,FALSE,12.125,0.183622982,2.25,0.170925876,0,0.403234768,,,0.252594542
4111,Modeling the pandemic trend of 2019 Coronavirus with optimal control analysis.,Results Phys,33329991,12/18/20,pubmed,0,4,mathematical model,0.119078018,0.007673995,0.007674298,0.766992704,0.09090718,0.007673805,Epidemiology,0.7299925,TRUE,102,0.873523409,52.75,0.691932031,0,0.403234768,,,0.656230069
4112,Whole Genome Identification of Potential G-Quadruplexes and Analysis of the G-Quadruplex Binding Domain for SARS-CoV-2.,Front Genet,33329730,12/18/20,pubmed,0,5,"transcriptom, whole genome",0.625150584,0.365160167,0.002422353,0.002422293,0.002422311,0.002422293,Drug discovery,0.8508761,TRUE,180.4,0.964499969,135.2,0.869681563,0,0.403234768,,,0.745805433
4113,Rapid Scanning Electron Microscopy Detection and Sequencing of Severe Acute Respiratory Syndrome Coronavirus 2 and Other Respiratory Viruses.,Front Microbiol,33329483,12/18/20,pubmed,0,8,"sequencing, metagenom",0.002080589,0.614383694,0.377293854,0.00208061,0.002080557,0.002080696,Genomics,0.70848715,TRUE,103.5,0.876306512,161.25,0.893162965,2,0.618927094,,,0.796132191
4114,Analysis of Indian SARS-CoV-2 Genomes Reveals Prevalence of D614G Mutation in Spike Protein Predicting an Increase in Interaction With TMPRSS2 and Virus Infectivity.,Front Microbiol,33329480,12/18/20,pubmed,0,96,"sequencing, genomes, network analysis",0.127534859,0.844968604,0.023506365,0.001330072,0.00133006,0.00133004,Genomics,0.69100136,TRUE,18,0.271569052,,,5,0.739490092,,,0.505529572
4115,Effects of COVID-19 Infection Control Measures on Appointment Cancelation in an Italian Outpatient Memory Clinic.,Front Psychiatry,33329152,12/18/20,pubmed,0,5,dataset,0.00133003,0.001330058,0.001330108,0.544451686,0.274893036,0.176665082,Epidemiology,0.96909004,TRUE,11,0.167171748,2.2,0.16838373,1,0.537564047,,,0.291039842
4116,Clinical Value of SARS-CoV2 IgM and IgG Antibodies in Diagnosis of COVID-19 in Suspected Cases.,J Inflamm Res,33328754,12/18/20,pubmed,0,1,sequencing,0.001653023,0.602611872,0.194978043,0.00165304,0.001653107,0.197450915,Genomics,0.5705903,TRUE,8,0.118683901,0,0.055525823,0,0.403234768,,,0.192481497
4117,Exploring optimal control of epidemic spread using reinforcement learning.,Sci Rep,33328551,12/18/20,pubmed,0,4,artificial intelligence,0.127091507,0.001187294,0.134184801,0.735161844,0.001187288,0.001187266,Epidemiology,0.23002607,FALSE,35.25,0.489331437,15,0.42594327,0,0.403234768,,,0.439503158
4118,Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification.,Sci Rep,33328512,12/18/20,pubmed,0,7,artificial intelligence,0.001156221,0.001156228,0.378406658,0.001156251,0.001156243,0.616968398,Clinics,0.9541031,TRUE,31.85714286,0.453336632,14.14285714,0.413433235,0,0.403234768,,,0.423334878
4119,Genetic architecture of host proteins involved in SARS-CoV-2 infection.,Nat Commun,33328453,12/18/20,pubmed,0,18,omicscience,0.803040791,0.189512033,0.001861964,0.001861702,0.001861715,0.001861796,Drug discovery,0.5502651,TRUE,54.72222222,0.666274971,229.8333333,0.929488895,3,0.667819001,,,0.754527622
4120,Analyses of abdominal adiposity and metabolic syndrome as risk factors for respiratory distress in COVID-19.,BMJ Open Respir Res,33328246,12/18/20,pubmed,0,6,logistic regression,0.002238531,0.002238626,0.002238467,0.002238659,0.002238583,0.988807135,Clinics,0.9397589,TRUE,42.5,0.562743522,36.5,0.614329676,1,0.537564047,,,0.571545748
4121,"'Dark matter', second waves and epidemiological modelling.",BMJ Glob Health,33328201,12/18/20,pubmed,0,3,"bayes, bayesian model",0.001085352,0.068764043,0.001085337,0.926894541,0.001085364,0.001085364,Epidemiology,0.06553358,FALSE,941.6666667,0.999567073,5097,1,0,0.403234768,,,0.800933947
4122,Persistence of COVID-19 Symptoms after Recovery in Mexican Population.,Int J Environ Res Public Health,33327641,12/18/20,pubmed,0,18,dataset,0.001943532,0.001943525,0.001943527,0.584369651,0.297530579,0.112269185,Epidemiology,0.30816883,FALSE,18.5,0.278001113,4.111111111,0.232004282,1,0.537564047,,,0.349189814
4123,Genomic Epidemiology of the First Wave of SARS-CoV-2 in Italy.,Viruses,33327566,12/18/20,pubmed,0,10,"genomic epidemiology, genomes",0.001653008,0.655122639,0.001653017,0.338265208,0.001653079,0.001653049,Genomics,0.3874309,FALSE,137.7,0.930051333,466.5,0.977254482,3,0.667819001,,,0.858374939
4124,Analysis of Mortality and Morbidity in COVID-19 Patients with Obesity Using Clinical Epidemiological Data from the Korean Center for Disease Control & Prevention.,Int J Environ Res Public Health,33327389,12/18/20,pubmed,0,6,logistic regression,0.00190168,0.001901693,0.001901665,0.001901726,0.001901768,0.990491468,Clinics,0.7774237,TRUE,47.33333333,0.608633805,8.666666667,0.331415574,0,0.403234768,,,0.447761382
4125,Investigating ion transport inside the pentameric ion channel encoded in COVID-19 E protein.,Phys Rev E,33327170,12/18/20,pubmed,0,3,mathematical model,0.702408898,0.00186181,0.001861925,0.290143679,0.001861872,0.001861817,Drug discovery,0.6359673,TRUE,48,0.614942173,11,0.371287129,0,0.403234768,,,0.46315469
4126,Optimal allocation of limited test resources for the quantification of COVID-19 infections.,Swiss Med Wkly,33327002,12/17/20,pubmed,0,8,bayes,0.001717231,0.001717232,0.001717298,0.99141387,0.001717179,0.001717189,Epidemiology,0.08359358,FALSE,48.625,0.618714825,20.25,0.483074659,1,0.537564047,,,0.546451177
4127,Clinical outcomes and inflammatory marker levels in patients with Covid-19 and obesity at an inner-city safety net hospital.,PLoS One,33326480,12/17/20,pubmed,0,9,logistic regression,0.001415119,0.001415103,0.001415109,0.001415088,0.001415112,0.992924469,Clinics,0.8868561,TRUE,57.55555556,0.685818542,96.33333333,0.815761306,1,0.537564047,,,0.679714632
4128,Glucocorticoids with low-dose anti-IL1 anakinra rescue in severe non-ICU COVID-19 infection: A cohort study.,PLoS One,33326457,12/17/20,pubmed,0,34,"bayes, logistic regression",0.001291237,0.001291255,0.001291376,0.128630762,0.001291234,0.866204136,Clinics,0.9544158,TRUE,121.4411765,0.908714206,124,0.856569441,1,0.537564047,,,0.767615898
4129,Drivers of Acceptance of COVID-19 Proximity Tracing Apps in Switzerland: Panel Survey Analysis.,JMIR Public Health Surveill,33326411,12/17/20,pubmed,0,10,logistic regression,0.001022637,0.00102266,0.001022636,0.282288422,0.713621017,0.001022628,Healthcare,0.4860609,FALSE,63.8,0.726822933,104.4,0.830010704,7,0.785110192,,,0.780647943
4130,A Novel Machine Learning Framework for Comparison of Viral COVID-19-Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis.,J Med Internet Res,33326408,12/17/20,pubmed,0,8,machine learning,0.000956338,0.063996435,0.185935104,0.668252881,0.079902926,0.000956315,Epidemiology,0.8329375,TRUE,147,0.94130744,105.25,0.831816965,0,0.403234768,,,0.725453058
4131,Parental Acceptability of COVID-19 Vaccination for Children Under the Age of 18 Years: Cross-Sectional Online Survey.,JMIR Pediatr Parent,33326406,12/17/20,pubmed,0,8,logistic regression,0.000772648,0.000772628,0.000772629,0.14944627,0.847463207,0.000772618,Healthcare,0.78635824,TRUE,33.625,0.472447276,7.625,0.309071448,4,0.707574542,,,0.496364422
4132,Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review.,JMIR Med Inform,33326405,12/17/20,pubmed,0,8,"machine learning, computational, artificial intelligence",0.02534972,0.000871559,0.362956132,0.493134228,0.000871664,0.116816697,Epidemiology,0.77456033,TRUE,32,0.455810502,49.125,0.677682633,0,0.403234768,,,0.512242634
4133,MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection.,IEEE Trans Med Imaging,33326377,12/17/20,pubmed,0,6,dataset,0.001046856,0.027077782,0.813454035,0.156327694,0.001046818,0.001046814,Epidemiology,0.23530641,FALSE,35.66666667,0.493908096,,,0,0.403234768,,,0.448571432
4134,Genomic analysis of SARS-CoV-2 reveals local viral evolution in Ghana.,Exp Biol Med (Maywood),33325750,12/17/20,pubmed,0,26,"sequencing, whole genome, genomes",0.001220005,0.993899919,0.001219996,0.001220083,0.001219996,0.001220002,Genomics,0.1958876,FALSE,18.19230769,0.272805987,16,0.437316029,1,0.537564047,,,0.415895354
4135,Peptide Correlation Analysis (PeCorA) Reveals Differential Proteoform Regulation.,J Proteome Res,33325715,12/17/20,pubmed,0,4,"proteom, correlation analysis",0.625084613,0.001593603,0.159375423,0.119716018,0.001593612,0.092636731,Drug discovery,0.121492535,FALSE,26,0.382398417,5.5,0.267259834,1,0.537564047,,,0.395740766
4136,Food insecurity and mental health during the COVID-19 pandemic.,Health Rep,33325672,12/17/20,pubmed,0,2,logistic regression,0.000946079,0.00094607,0.000946081,0.000946115,0.995269557,0.000946099,Healthcare,0.84944856,TRUE,15,0.227596017,9,0.337904736,0,0.403234768,,,0.32291184
4137,Natural History of Coronavirus Disease 2019: Risk Factors for Hospitalizations and Deaths Among >26 Million US Medicare Beneficiaries.,J Infect Dis,33325510,12/17/20,pubmed,0,12,logistic regression,0.001823334,0.00182335,0.001823335,0.001823433,0.276880876,0.715825672,Clinics,0.60980904,TRUE,160.4166667,0.951883233,87.16666667,0.795424137,8,0.799987654,,,0.849098341
4138,"During COVID-19, which is more effective in work accident prevention behavior of healthcare professionals: Safety awareness or fatalism perception?",Work,33325425,12/17/20,pubmed,0,1,correlation analysis,0.001717258,0.00171719,0.001717281,0.00171729,0.991413816,0.001717166,Healthcare,0.81246865,TRUE,20,0.298163152,1,0.122023013,0,0.403234768,,,0.274473644
4139,The impact of family engagement in opioid assisted treatment: Results from a randomised controlled trial.,Int J Soc Psychiatry,33325311,12/17/20,pubmed,0,12,logistic regression,0.001717225,0.001717225,0.001717301,0.001717341,0.80099959,0.192131318,Healthcare,0.95413136,TRUE,37.33333333,0.511596264,54.83333333,0.700628847,0,0.403234768,,,0.538486626
4140,Association Between Blood Pressure Control and Coronavirus Disease 2019 Outcomes in 45 418 Symptomatic Patients With Hypertension: An Observational Cohort Study.,Hypertension,33325240,12/17/20,pubmed,0,21,logistic regression,0.001415243,0.001415232,0.001415143,0.001415209,0.125855843,0.86848333,Clinics,0.82916886,TRUE,75.61904762,0.78897891,56.47619048,0.708790474,0,0.403234768,,,0.63366805
4141,T and B cell Epitope analysis of SARS-CoV-2 S protein based on immunoinformatics and experimental research.,J Cell Mol Med,33325143,12/17/20,pubmed,0,6,molecular dynamics simulation,0.972396661,0.001272729,0.001272637,0.022512557,0.001272681,0.001272735,Drug discovery,0.4130923,FALSE,31.33333333,0.448388892,35.83333333,0.609245384,1,0.537564047,,,0.531732774
4142,Controversy over smoking in COVID-19-A real world experience in New York city.,J Med Virol,33325049,12/17/20,pubmed,0,5,logistic regression,0.001330016,0.001330027,0.00133008,0.001330051,0.0288117,0.965868126,Clinics,0.79826695,TRUE,41.2,0.550126786,10,0.355632861,0,0.403234768,,,0.436331471
4143,A cross-sectional study to determine factors affecting dental and medical students' preference for virtual learning during the COVID-19 outbreak.,Heliyon,33324768,12/17/20,pubmed,0,3,logistic regression,0.001171574,0.001171565,0.086788588,0.069050616,0.840646105,0.001171552,Healthcare,0.54220045,TRUE,8.666666667,0.12839384,0.666666667,0.096200161,1,0.537564047,,,0.254052683
4144,"Social Determinants Predict Outcomes in Data From a Multi-Ethnic Cohort of 20,899 Patients Investigated for COVID-19.",Front Public Health,33324596,12/17/20,pubmed,0,6,logistic regression,0.001538133,0.001538161,0.00153816,0.243240943,0.27331756,0.478827044,Clinics,0.6606343,TRUE,43.83333333,0.576287958,40.16666667,0.634131656,1,0.537564047,,,0.582661221
4145,Gut Microbiota Status in COVID-19: An Unrecognized Player?,Front Cell Infect Microbiol,33324572,12/17/20,pubmed,0,5,microbiom,0.295446777,0.232247847,0.001593521,0.0015936,0.228839246,0.240279009,Drug discovery,0.8509871,TRUE,47,0.606407323,30,0.570176612,6,0.764429903,,,0.647004613
4146,Identification of 14 Known Drugs as Inhibitors of the Main Protease of SARS-CoV-2.,ACS Med Chem Lett,33324471,12/17/20,pubmed,0,9,virtual screening,0.988807492,0.002238537,0.002238496,0.002238506,0.002238513,0.002238456,Drug discovery,0.9352678,TRUE,150.4444444,0.944399777,306.5555556,0.956315226,0,0.403234768,,,0.767983257
4147,Comparative Transcriptome Analysis Reveals the Intensive Early Stage Responses of Host Cells to SARS-CoV-2 Infection.,Front Microbiol,33324374,12/17/20,pubmed,0,13,"bioinformatic, sequencing, transcriptom, genomes",0.855887848,0.128331492,0.001291223,0.001291282,0.001291219,0.011906936,Drug discovery,0.65330696,TRUE,41.38461538,0.551920341,113.5384615,0.843189724,0,0.403234768,,,0.599448278
4148,Deciphering the Pharmacological Mechanisms of Ma Xing Shi Gan Decoction against COVID-19 through Integrating Network Pharmacology and Experimental Exploration.,Front Pharmacol,33324213,12/17/20,pubmed,0,10,transcriptom,0.788567128,0.001141351,0.082393516,0.039514559,0.001141436,0.087242011,Drug discovery,0.98957604,TRUE,12.5,0.189436576,4.6,0.244246722,2,0.618927094,,,0.350870131
4149,High Mortality Rate in Adult COVID-19 Inpatients in Eastern Sudan: A Retrospective Study.,J Multidiscip Healthc,33324068,12/17/20,pubmed,0,6,logistic regression,0.000988347,0.000988356,0.00098836,0.019689669,0.065136533,0.912208734,Clinics,0.9732697,TRUE,68.66666667,0.757313377,55.33333333,0.703371689,3,0.667819001,,,0.709501356
4150,Novel Deep Learning Technique Used in Management and Discharge of Hospitalized Patients with COVID-19 in China.,Ther Clin Risk Manag,33324064,12/17/20,pubmed,0,8,deep learning,0.000880214,0.143216357,0.400394768,0.000880232,0.000880232,0.453748197,Clinics,0.8372288,TRUE,22.875,0.338116148,6.875,0.29388547,0,0.403234768,,,0.345078795
4151,A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19.,Energy (Oxf),33324028,12/17/20,pubmed,0,3,prediction model,0.045381962,0.001371275,0.432113372,0.518390883,0.00137127,0.001371237,Epidemiology,0.6394613,TRUE,38.33333333,0.522233904,4,0.231469093,0,0.403234768,,,0.385645922
4152,Gene Expression of Angiotensin-Converting Enzyme 2 Receptor in Skin and the Implications for COVID-19.,Adv Skin Wound Care,33323800,12/17/20,pubmed,0,1,transcriptom,0.991414055,0.001717184,0.001717181,0.001717183,0.001717159,0.001717238,Drug discovery,0.99470985,TRUE,64,0.729173109,23,0.513513514,0,0.403234768,,,0.548640463
4153,Cannabis Use During the COVID-19 Pandemic in Canada: A Repeated Cross-sectional Study.,J Addict Med,33323693,12/17/20,pubmed,0,8,logistic regression,0.002357807,0.00235789,0.002357731,0.365123966,0.625444794,0.002357812,Healthcare,0.44657582,FALSE,45.125,0.587605913,79.25,0.776759433,0,0.403234768,,,0.589200038
4154,Global evidence for ultraviolet radiation decreasing COVID-19 growth rates.,Proc Natl Acad Sci U S A,33323525,12/17/20,pubmed,0,5,dataset,0.001392823,0.024782094,0.001392872,0.969646359,0.001392921,0.001392931,Epidemiology,0.52820754,TRUE,41.6,0.553961284,86.8,0.79482205,5,0.739490092,,,0.696091142
4155,Inferring the effectiveness of government interventions against COVID-19.,Science,33323424,12/17/20,pubmed,0,19,bayes,0.002080555,0.002080571,0.002080622,0.989597001,0.002080622,0.002080629,Epidemiology,0.5225755,TRUE,21.68421053,0.32079906,,,48,0.955614544,,,0.638206802
4156,NDRindex: a method for the quality assessment of single-cell RNA-Seq preprocessing data.,BMC Bioinformatics,33323107,12/17/20,pubmed,0,4,"sequencing, data mining, dataset",0.252674344,0.185493937,0.257649799,0.301105659,0.001538127,0.001538135,Epidemiology,0.17611125,FALSE,23.5,0.348506401,18.75,0.467621086,0,0.403234768,,,0.406454085
4157,Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA.,J R Soc Interface,33323055,12/17/20,pubmed,0,7,dataset,0.001072208,0.022162173,0.001072215,0.973548969,0.001072255,0.00107218,Epidemiology,0.15121329,FALSE,34.71428571,0.48413631,20.57142857,0.486553385,2,0.618927094,,,0.529872263
4158,Hospitalisations for COVID-19 - a comparison of different data sources.,Tidsskr Nor Laegeforen,33322870,12/17/20,pubmed,0,11,information retrieval,0.002080566,0.002080618,0.002080605,0.595695467,0.002080651,0.395982092,Epidemiology,0.41131938,FALSE,38.27272727,0.520935123,78.72727273,0.77502007,1,0.537564047,,,0.61117308
4159,"Frontline Healthcare Workers' Knowledge and Perception of COVID-19, and Willingness to Work during the Pandemic in Nepal.",Healthcare (Basel),33322486,12/17/20,pubmed,0,18,logistic regression,0.001684489,0.001684547,0.001684502,0.00168456,0.991577214,0.001684688,Healthcare,0.7787942,TRUE,27.44444444,0.4007669,,,2,0.618927094,,,0.509846997
4160,Parental Hesitancy and Concerns around Accessing Paediatric Unscheduled Healthcare during COVID-19: A Cross-Sectional Survey.,Int J Environ Res Public Health,33322332,12/17/20,pubmed,0,7,logistic regression,0.001861665,0.001861678,0.00186167,0.001861744,0.990691522,0.001861721,Healthcare,0.9222872,TRUE,36,0.498299215,19.57142857,0.476317902,2,0.618927094,,,0.531181404
4161,High-Dose Cholecalciferol Booster Therapy is Associated with a Reduced Risk of Mortality in Patients with COVID-19: A Cross-Sectional Multi-Centre Observational Study.,Nutrients,33322317,12/17/20,pubmed,0,7,logistic regression,0.001486538,0.001486542,0.001486524,0.128023222,0.001486541,0.866030633,Clinics,0.47633493,FALSE,45.14285714,0.587791453,47.85714286,0.672196949,9,0.814309525,,,0.691432642
4162,A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition.,Mar Drugs,33322052,12/17/20,pubmed,0,2,virtual screening,0.665817086,0.001254618,0.329164391,0.001254641,0.001254643,0.00125462,Drug discovery,0.9086188,TRUE,21,0.312016822,8,0.320511105,1,0.537564047,,,0.390030658
4163,A New Geo-Propagation Model of Event Evolution Chain Based on Public Opinion and Epidemic Coupling.,Int J Environ Res Public Health,33321897,12/17/20,pubmed,0,5,network model,0.001622795,0.042484662,0.001622774,0.892356332,0.06029072,0.001622717,Epidemiology,0.55366963,TRUE,72.8,0.776547715,21.4,0.496052984,0,0.403234768,,,0.558611822
4164,Molecular Docking Study on Several Benzoic Acid Derivatives against SARS-CoV-2.,Molecules,33321862,12/17/20,pubmed,0,4,in silico,0.88732098,0.002238457,0.052290037,0.053673483,0.002238511,0.002238533,Drug discovery,0.85336405,TRUE,18.25,0.273609994,0.5,0.087101953,0,0.403234768,,,0.254648905
4165,Psychological Distress during COVID-19 Lockdown among Dental Students and Practitioners in India: A Cross-Sectional Survey.,Eur J Dent,33321545,12/16/20,pubmed,0,3,"logistic regression, correlation analysis",0.001072174,0.001072178,0.001072196,0.046445987,0.867023622,0.083313842,Healthcare,0.8788632,TRUE,3.333333333,0.04044777,0,0.055525823,0,0.403234768,,,0.166402787
4166,A computational approach to design potential siRNA molecules as a prospective tool for silencing nucleocapsid phosphoprotein and surface glycoprotein gene of SARS-CoV-2.,Genomics,33321203,12/16/20,pubmed,0,6,computational,0.799909211,0.194770376,0.001330129,0.001330148,0.001330077,0.001330059,Drug discovery,0.7534928,TRUE,17.66666667,0.266373925,2.666666667,0.185442869,0,0.403234768,,,0.285017187
4167,An aberration detection-based approach for sentinel syndromic surveillance of COVID-19 and other novel influenza-like illnesses.,J Biomed Inform,33321199,12/16/20,pubmed,0,13,deep learning,0.001126865,0.234459986,0.227459318,0.255212445,0.280614515,0.001126872,Healthcare,0.20333272,FALSE,48.07692308,0.615127714,,,0,0.403234768,,,0.509181241
4168,Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and Their Augmentation by Compact Peptide Sets.,Cell Syst,33321075,12/16/20,pubmed,0,3,machine learning,0.700111475,0.002130829,0.159321139,0.13417499,0.002130675,0.002130891,Drug discovery,0.06847519,FALSE,84.33333333,0.823056466,705.6666667,0.987423067,0,0.403234768,,,0.737904767
4169,Clinical characteristics and outcomes of critically ill patients with COVID-19 admitted to an intensive care unit in London: A prospective observational cohort study.,PLoS One,33320904,12/16/20,pubmed,0,8,logistic regression,0.001415125,0.001415196,0.001415116,0.196585297,0.001415174,0.797754092,Clinics,0.8321744,TRUE,47.375,0.608819346,29.25,0.564557131,2,0.618927094,,,0.597434524
4170,Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection.,PLoS One,33320858,12/16/20,pubmed,0,6,"machine learning, deep learning, computational, neural network, classifier, dataset",0.000889071,0.01396468,0.969266258,0.000889057,0.000889047,0.014101887,Imaging,0.620299,TRUE,19.66666667,0.292596945,3.333333333,0.206515922,0,0.403234768,,,0.300782545
4171,Global Infectious Disease Surveillance and Case Tracking System for COVID-19: Development Study.,JMIR Med Inform,33320826,12/16/20,pubmed,0,11,information retrieval,0.040199874,0.000779457,0.237281889,0.560050935,0.000779481,0.160908363,Epidemiology,0.19175711,FALSE,28.54545455,0.412765168,10.81818182,0.366938721,0,0.403234768,,,0.394312885
4172,Factors Influencing Patients' Initial Decisions Regarding Telepsychiatry Participation During the COVID-19 Pandemic: Telephone-Based Survey.,JMIR Form Res,33320823,12/16/20,pubmed,0,6,logistic regression,0.000699353,0.000699351,0.000699374,0.104618599,0.681871408,0.211411915,Healthcare,0.9787171,TRUE,5.5,0.077246583,0,0.055525823,0,0.403234768,,,0.178669058
4173,A Novel Artificial Intelligence-Powered Emotional Intelligence and Mindfulness App (Ajivar) for the College Student Population During the COVID-19 Pandemic: Quantitative Questionnaire Study.,JMIR Form Res,33320822,12/16/20,pubmed,0,4,"machine learning, artificial intelligence",0.000752909,0.000752894,0.143245269,0.000752931,0.853743049,0.000752947,Healthcare,0.96765065,TRUE,13,0.197352959,12.5,0.392761573,0,0.403234768,,,0.331116433
4174,Impact of COVID-19 infection control measures on influenza-related outcomes in Hong Kong.,Pathog Glob Health,33320773,12/16/20,pubmed,0,1,simulation model,0.001786556,0.001786579,0.001786566,0.382426474,0.415795848,0.196417978,Healthcare,0.31992954,FALSE,9,0.135320675,2,0.164302917,0,0.403234768,,,0.23428612
4175,Are e-learning Webinars the future of medical education? An exploratory study of a disruptive innovation in the COVID-19 era.,Cardiol Young,33320078,12/16/20,pubmed,0,6,deep learning,0.001538204,0.00153816,0.415927302,0.139988536,0.439469595,0.001538202,Healthcare,0.88169324,TRUE,22.5,0.333539489,25,0.529435376,0,0.403234768,,,0.422069878
4176,Integrating Proteomics for Facilitating Drug Identification and Repurposing During an Emerging Virus Pandemic.,ACS Infect Dis,33319978,12/16/20,pubmed,0,4,proteom,0.480716246,0.001987247,0.001987176,0.511335048,0.001987198,0.001987086,Epidemiology,0.42929038,FALSE,5.25,0.072855464,4.5,0.242708055,0,0.403234768,,,0.239599429
4177,"Face-Masking, an Acceptable Protective Measure against COVID-19 in Ugandan High-Risk Groups.",Am J Trop Med Hyg,33319741,12/16/20,pubmed,0,4,logistic regression,0.001565266,0.001565289,0.001565279,0.001565344,0.992173475,0.001565347,Healthcare,0.7452089,TRUE,29.25,0.421671099,12.75,0.395972705,0,0.403234768,,,0.406959524
4178,0,Curr Res Struct Biol,33319212,12/16/20,pubmed,0,3,virtual screening,0.992690391,0.001461896,0.001461998,0.001461974,0.001461893,0.001461849,Drug discovery,0.46069065,FALSE,12.66666667,0.191044592,0.666666667,0.096200161,0,0.403234768,,,0.23015984
4179,Mutational insights into the envelope protein of SARS-CoV-2.,Gene Rep,33319124,12/16/20,pubmed,0,8,genome sequences,0.315962285,0.679530547,0.001126788,0.0011268,0.001126791,0.00112679,Genomics,0.7075935,TRUE,15,0.227596017,2.875,0.190259566,3,0.667819001,,,0.361891528
4180,Machine Learning Models for covid-19 future forecasting.,Mater Today Proc,33318952,12/16/20,pubmed,0,4,"machine learning, computational",0.002296607,0.002296669,0.476727137,0.447971979,0.002296605,0.068411003,Epidemiology,0.37761196,FALSE,20.5,0.304966294,2.25,0.170925876,0,0.403234768,,,0.293042312
4181,Theoretical and numerical analysis of novel COVID-19 via fractional order mathematical model.,Results Phys,33318893,12/16/20,pubmed,0,7,mathematical model,0.002898467,0.002898336,0.002898413,0.985508199,0.002898293,0.002898291,Epidemiology,0.71355027,TRUE,25.85714286,0.378811306,3.571428571,0.21414236,0,0.403234768,,,0.332062811
4182,Mental health outcomes among patients from Fangcang shelter hospitals exposed to coronavirus disease 2019: An observational cross-sectional study.,Chronic Dis Transl Med,33318879,12/16/20,pubmed,0,6,correlation analysis,0.001187251,0.001187272,0.001187253,0.001187287,0.683100017,0.31215092,Healthcare,0.9796114,TRUE,62.16666667,0.716308986,16.16666667,0.438051913,0,0.403234768,,,0.519198556
4183,Evaluation of NGS-based approaches for SARS-CoV-2 whole genome characterisation.,Virus Evol,33318859,12/16/20,pubmed,0,13,"bioinformatic, sequencing, metagenom, whole-genome, whole genome, genomes",0.001141332,0.844612195,0.150822484,0.001141342,0.001141325,0.001141321,Genomics,0.38386348,FALSE,28.15384615,0.408992517,15.15384615,0.426746053,0,0.403234768,,,0.412991112
4184,The correlation analysis between the appearance anxiety and personality traits of the medical staff on nasal and facial pressure ulcers during the novel coronavirus disease 2019 outbreak.,Nurs Open,33318822,12/16/20,pubmed,0,4,correlation analysis,0.001943489,0.001943475,0.00194354,0.001943606,0.990282362,0.001943528,Healthcare,0.89526784,TRUE,182,0.965118437,55.5,0.704575863,0,0.403234768,,,0.690976356
4185,Identification of similar epitopes between severe acute respiratory syndrome coronavirus-2 and Bacillus Calmette-Guérin: potential for cross-reactive adaptive immunity.,Clin Transl Immunology,33318797,12/16/20,pubmed,0,5,proteom,0.988807235,0.00223868,0.002238452,0.002238484,0.002238642,0.002238507,Drug discovery,0.3696817,FALSE,44.8,0.584142495,29.2,0.563888146,1,0.537564047,,,0.561864896
4186,"Impact of aerosols on surface ozone during COVID-19 pandemic in southern India: A multi-instrumental approach from ground and satellite observations, and model simulations.",J Atmos Sol Terr Phys,33318726,12/16/20,pubmed,0,7,"model simulation, radiom",0.001141393,0.085168836,0.025861266,0.849739727,0.03694739,0.001141388,Epidemiology,0.71616155,TRUE,6,0.086028821,2.857142857,0.189925074,0,0.403234768,,,0.226396221
4187,Global and regional variations in aerosol loading during COVID-19 imposed lockdown.,Atmos Environ (1994),33318725,12/16/20,pubmed,0,1,"radiom, dataset",0.001786573,0.00178664,0.041306274,0.951547302,0.001786628,0.001786583,Epidemiology,0.9719007,TRUE,9,0.135320675,3,0.199424672,0,0.403234768,,,0.245993372
4188,How artificial intelligence and machine learning can help healthcare systems respond to COVID-19.,Mach Learn,33318723,12/16/20,pubmed,0,10,"machine learning, artificial intelligence",0.001622782,0.001622718,0.415667182,0.577841731,0.001622816,0.001622772,Epidemiology,0.6782267,TRUE,51.3,0.640484878,80.1,0.779167782,11,0.840175319,,,0.753275993
4189,The impact of COVID-19 on stock market performance in Africa: A Bayesian structural time series approach.,J Econ Bus,33318718,12/16/20,pubmed,0,2,bayes,0.002032819,0.00203297,0.148552741,0.843315785,0.002032898,0.002032787,Epidemiology,0.17834365,FALSE,7,0.10179974,2.5,0.180826866,0,0.403234768,,,0.228620458
4190,"Correlation between hospitalized patients' demographics, symptoms, comorbidities, and COVID-19 pandemic in Bahia, Brazil.",PLoS One,33318711,12/16/20,pubmed,0,15,"logistic regression, dataset",0.001415114,0.001415125,0.095051278,0.001415229,0.001415211,0.899288043,Clinics,0.09912416,FALSE,6.333333333,0.089863319,0.066666667,0.055726519,1,0.537564047,,,0.227717961
4191,Gene editing and RNAi approaches for COVID-19 diagnostics and therapeutics.,Gene Ther,33318646,12/16/20,pubmed,0,10,sequencing,0.232590153,0.491738758,0.241251578,0.00131043,0.001310448,0.031798633,Genomics,0.8981886,TRUE,11.6,0.175211825,2.2,0.16838373,1,0.537564047,,,0.293719868
4192,Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients.,Mol Psychiatry,33318619,12/16/20,pubmed,0,19,network analysis,0.001350357,0.001350334,0.001350336,0.525400263,0.469198351,0.001350358,Epidemiology,0.7921355,TRUE,14.24050633,0.214917435,,,1,0.537564047,,,0.376240741
4193,Gene expression network analysis provides potential targets against SARS-CoV-2.,Sci Rep,33318519,12/16/20,pubmed,0,11,"bioinformatic, network analysis",0.966424748,0.027629329,0.001486442,0.001486456,0.001486499,0.001486526,Drug discovery,0.57298887,TRUE,130.9090909,0.922629724,191.5454545,0.913098742,0,0.403234768,,,0.746321078
4194,"Predicting severe or critical symptoms in hospitalized patients with COVID-19 from Yichang, China.",Aging (Albany NY),33318316,12/16/20,pubmed,0,8,prediction model,0.001751247,0.001751193,0.167503215,0.001751318,0.001751216,0.825491811,Clinics,0.93698585,TRUE,71.75,0.771909209,30.5,0.573187048,0,0.403234768,,,0.582777008
4195,Impact of COVID-19 on seizure-related emergency attendances and hospital admissions - A territory-wide observational study.,Epilepsy Behav,33317939,12/16/20,pubmed,0,4,logistic regression,0.001901754,0.001901784,0.001901778,0.156544566,0.375999697,0.461750421,Clinics,0.5476994,TRUE,40.75,0.546539675,125.5,0.858442601,1,0.537564047,,,0.647515441
4196,Priority allocation of pandemic influenza vaccines in Australia - Recommendations of 3 community juries.,Vaccine,33317870,12/16/20,pubmed,0,11,simulation model,0.001220033,0.001220051,0.001220046,0.4514476,0.543672239,0.001220031,Healthcare,0.65430146,TRUE,27.63636364,0.402560455,15.54545455,0.430559272,0,0.403234768,,,0.412118165
4197,The nervous system-A new territory being explored of SARS-CoV-2.,J Clin Neurosci,33317745,12/16/20,pubmed,0,7,sequencing,0.003101733,0.521883466,0.229737425,0.003101696,0.003101708,0.239073972,Genomics,0.7882825,TRUE,50.57142857,0.635413445,,,1,0.537564047,,,0.586488746
4198,Viral RNA load in plasma is associated with critical illness and a dysregulated host response in COVID-19.,Crit Care,33317616,12/16/20,pubmed,0,66,logistic regression,0.104740093,0.422812096,0.000728122,0.01090212,0.000728128,0.460089441,Clinics,0.9031326,TRUE,35.90909091,0.495763498,22.45454545,0.506422264,12,0.850299401,,,0.617495054
4199,"COVID-19: a National Survey on perceived level of knowledge, attitude and practice among frontline healthcare Workers in Nepal.",BMC Public Health,33317486,12/16/20,pubmed,0,7,correlation analysis,0.000907268,0.000907277,0.0009073,0.000907289,0.995463542,0.000907323,Healthcare,0.9727921,TRUE,16.42857143,0.248067289,0.571428571,0.088038534,2,0.618927094,,,0.318344306
4200,A household case evidences shorter shedding of SARS-CoV-2 in naturally infected cats compared to their human owners.,Emerg Microbes Infect,33317424,12/16/20,pubmed,0,22,sequencing,0.002183299,0.59079349,0.002183236,0.002183449,0.240557872,0.162098654,Genomics,0.6592291,TRUE,80,0.807532933,327.5454545,0.96133262,1,0.537564047,,,0.768809867
4201,0,J Biomol Struct Dyn,33317409,12/16/20,pubmed,0,11,metabolom,0.919370876,0.075227802,0.001350379,0.001350338,0.001350309,0.001350297,Drug discovery,0.90025985,TRUE,30,0.432432432,7.818181818,0.313352957,0,0.403234768,,,0.383006719
4202,Advances in Microbiome Research for Animal Health.,Annu Rev Anim Biosci,33317323,12/16/20,pubmed,0,3,microbiom,0.187840888,0.476340703,0.001943546,0.140164194,0.191767182,0.001943488,Genomics,0.5871911,TRUE,140.3333333,0.934318758,1210,0.995383998,0,0.403234768,,,0.777645841
4203,Post-Traumatic Stress Disorder and Associated Factors during the Early Stage of the COVID-19 Pandemic in Norway.,Int J Environ Res Public Health,33317135,12/16/20,pubmed,0,6,logistic regression,0.001371263,0.001371342,0.001371348,0.001371323,0.993143351,0.001371373,Healthcare,0.946373,TRUE,89.33333333,0.840435401,83.66666667,0.787597003,2,0.618927094,,,0.748986499
4204,0,Molecules,33316996,12/16/20,pubmed,0,3,virtual screening,0.906241734,0.086311123,0.001861752,0.001861899,0.001861793,0.001861698,Drug discovery,0.48224992,FALSE,36,0.498299215,7.333333333,0.304656141,0,0.403234768,,,0.402063374
4205,Multiple Introductions Followed by Ongoing Community Spread of SARS-CoV-2 at One of the Largest Metropolitan Areas of Northeast Brazil.,Viruses,33316947,12/16/20,pubmed,0,30,genomes,0.001310325,0.871782328,0.001310364,0.083684767,0.001310469,0.040601747,Genomics,0.5242366,TRUE,15.73333333,0.236996722,9.733333333,0.348742307,6,0.764429903,,,0.450056311
4206,"The Relationships among Media Usage Regarding COVID-19, Knowledge about Infection, and Anxiety: Structural Model Analysis.",J Korean Med Sci,33316862,12/15/20,pubmed,0,9,structural model,0.001371278,0.00137128,0.001371256,0.001371329,0.993143602,0.001371254,Healthcare,0.7263753,TRUE,82.88888889,0.817552106,35.11111111,0.605699759,2,0.618927094,,,0.68072632
4207,Changing Patterns of Medical Visits and Factors Associated with No-show in Patients with Rheumatoid Arthritis during COVID-19 Pandemic.,J Korean Med Sci,33316859,12/15/20,pubmed,0,7,logistic regression,0.024776033,0.001565347,0.001565315,0.001565361,0.401665792,0.568862152,Clinics,0.9401969,TRUE,20.71428571,0.306759849,5.571428571,0.267527428,1,0.537564047,,,0.370617108
4208,Evaluating the impact of mobility on COVID-19 pandemic with machine learning hybrid predictions.,Sci Total Environ,33316596,12/15/20,pubmed,0,2,machine learning,0.001085339,0.00108534,0.001085397,0.994573248,0.001085354,0.001085323,Epidemiology,0.17425793,FALSE,99.5,0.867709815,96,0.81509232,0,0.403234768,,,0.695345634
4209,Effect of hydroxychloroquine pre-exposure on infection with SARS-CoV-2 in rheumatic disease patients: a population-based cohort study.,Clin Microbiol Infect,33316402,12/15/20,pubmed,0,6,logistic regression,0.135950656,0.003101476,0.003101538,0.153996212,0.152502284,0.551347834,Clinics,0.5290845,TRUE,63,0.721998887,21.16666667,0.493310142,0,0.403234768,,,0.539514599
4210,Corticosteroid Therapy Is Associated With Improved Outcome in Critically Ill Patients With COVID-19 With Hyperinflammatory Phenotype.,Chest,33316235,12/15/20,pubmed,0,14,"machine learning, structural model",0.069657401,0.001141344,0.032409275,0.001141399,0.001141364,0.894509217,Clinics,0.92019296,TRUE,79.85714286,0.806419692,50.35714286,0.68303452,0,0.403234768,,,0.630896326
4211,Are People With Chronic Diseases Satisfied With the Online Health Information Related to COVID-19 During the Pandemic?,J Nurs Scholarsh,33316121,12/15/20,pubmed,0,11,digital health,0.001171548,0.001171554,0.001171702,0.246175898,0.749137707,0.001171591,Healthcare,0.9657567,TRUE,37.36363636,0.511781805,17,0.451097137,0,0.403234768,,,0.455371236
4212,Phylogenetic analysis of SARS-CoV-2 data is difficult.,Mol Biol Evol,33316067,12/15/20,pubmed,0,13,computational,0.002032844,0.566708362,0.167235011,0.259958204,0.002032814,0.002032765,Genomics,0.5750304,TRUE,45.07692308,0.587234832,1022.923077,0.99384533,1,0.537564047,,,0.706214737
4213,A Phylodynamic Workflow to Rapidly Gain Insights into the Dispersal History and Dynamics of SARS-CoV-2 Lineages.,Mol Biol Evol,33316043,12/15/20,pubmed,0,20,"computational, genomes",0.002296589,0.413428134,0.002296658,0.577385529,0.002296553,0.002296538,Epidemiology,0.35130844,FALSE,69.75,0.762446657,122.25,0.854027295,3,0.667819001,,,0.761430984
4214,Estimation of the basic reproduction number (𝑅0) of the COVID-19 epidemic in Iran.,Med J Islam Repub Iran,33315980,12/15/20,pubmed,0,12,bayes,0.001392825,0.001392859,0.001392849,0.777339002,0.00139292,0.217089544,Epidemiology,0.59997404,TRUE,36,0.498299215,18.33333333,0.464343056,0,0.403234768,,,0.455292346
4215,Integrated Bioinformatics Analysis Reveals Key Candidate Genes and Cytokine Pathways Involved in COVID-19 After Rhinovirus Infection in Asthma Patients.,Med Sci Monit,33315853,12/15/20,pubmed,0,10,"bioinformatic, dataset",0.895109551,0.001786823,0.001786587,0.001786561,0.001786693,0.097743785,Drug discovery,0.92040396,TRUE,98.1,0.864803018,47.1,0.668918919,0,0.403234768,,,0.645652235
4216,Hospital care: improving outcomes in type 1 diabetes.,Curr Opin Endocrinol Diabetes Obes,33315629,12/15/20,pubmed,0,2,prediction model,0.120332707,0.001943518,0.001943655,0.469790224,0.001943528,0.404046369,Epidemiology,0.9502691,TRUE,11,0.167171748,2.5,0.180826866,0,0.403234768,,,0.250411127
4217,Public Health Interventions' Effect on Hospital Use in Patients With COVID-19: Comparative Study.,JMIR Public Health Surveill,33315585,12/15/20,pubmed,0,6,bayes,0.000999509,0.000999515,0.000999505,0.890970841,0.000999561,0.10503107,Epidemiology,0.7550993,TRUE,44.33333333,0.580679077,307,0.956449023,0,0.403234768,,,0.646787623
4218,Use of Asynchronous Virtual Mental Health Resources for COVID-19 Pandemic-Related Stress Among the General Population in Canada: Cross-Sectional Survey Study.,J Med Internet Res,33315583,12/15/20,pubmed,0,10,logistic regression,0.000880216,0.000880241,0.000880257,0.113878556,0.882600491,0.000880239,Healthcare,0.8178519,TRUE,40.2,0.541159008,49.7,0.680224779,1,0.537564047,,,0.586315945
4219,Factors affecting mortality in geriatric patients hospitalized with COVID-19.,Turk J Med Sci,33315348,12/15/20,pubmed,0,13,logistic regression,0.001653046,0.00165305,0.001653106,0.001653169,0.001653118,0.991734511,Clinics,0.906448,TRUE,30.84615385,0.441956831,15.53846154,0.430492374,0,0.403234768,,,0.425227991
4220,Assessment of the risk of SARS-CoV-2 reinfection in an intense re-exposure setting.,Clin Infect Dis,33315061,12/15/20,pubmed,0,22,sequencing,0.001461868,0.405921984,0.001461888,0.176602606,0.121328966,0.293222688,Genomics,0.07356441,FALSE,50.90909091,0.637268848,85.13636364,0.790674338,2,0.618927094,,,0.682290093
4221,Symptomatic SARS-CoV-2 reinfection of a health care worker in a Belgian nosocomial outbreak despite primary neutralizing antibody response.,Clin Infect Dis,33315049,12/15/20,pubmed,0,9,"sequencing, whole genome, genomes",0.00127272,0.732476202,0.001272641,0.001272695,0.13619016,0.127515582,Genomics,0.3975333,FALSE,22.66666667,0.335580432,11.77777778,0.3820578,9,0.814309525,,,0.510649252
4222,Clostridioides difficile infection in coronavirus disease 2019 (COVID-19): an underestimated problem?,Pol Arch Intern Med,33314875,12/15/20,pubmed,0,7,logistic regression,0.04268337,0.002296729,0.002296559,0.143020671,0.00229672,0.80740595,Clinics,0.98282826,TRUE,42.57142857,0.563052755,6.285714286,0.283649987,1,0.537564047,,,0.461422263
4223,Computational drug repurposing study of the RNA binding domain of SARS-CoV-2 nucleocapsid protein with antiviral agents.,Biotechnol Prog,33314794,12/15/20,pubmed,0,3,"molecular dynamics simulation, computational",0.994505929,0.00109883,0.001098803,0.001098824,0.001098803,0.001098811,Drug discovery,0.9435053,TRUE,2.666666667,0.029377203,0.333333333,0.073187048,1,0.537564047,,,0.2133761
4224,Evaluation of maternal serum afamin and vitamin E levels in pregnant women with COVID-19 and its association with composite adverse perinatal outcomes.,J Med Virol,33314206,12/15/20,pubmed,0,9,correlation analysis,0.001350387,0.001350367,0.001350463,0.001350349,0.045965971,0.948632463,Clinics,0.9527523,TRUE,39.66666667,0.536211268,2.444444444,0.175408081,1,0.537564047,,,0.416394466
4225,Research on COVID-19 and mental health: Data mining reveals blind spots.,Acta Psychiatr Scand,33314026,12/15/20,pubmed,0,2,data mining,0.034964558,0.034961875,0.034964844,0.430070245,0.430076604,0.034961874,Healthcare,0.5751726,TRUE,25,0.369286907,0.5,0.087101953,0,0.403234768,,,0.286541209
4226,Chest CT imaging features and severity scores as biomarkers for prognostic prediction in patients with COVID-19.,Ann Transl Med,33313194,12/15/20,pubmed,0,6,logistic regression,0.000716323,0.000716327,0.267662009,0.000716344,0.000716329,0.729472669,Clinics,0.97328603,TRUE,14,0.213494959,25.66666667,0.534854161,1,0.537564047,,,0.428637723
4227,A bioinformatics analysis on the potential role of ACE2 in cardiac impairment of patients with coronavirus disease 2019.,Ann Transl Med,33313148,12/15/20,pubmed,0,5,bioinformatic,0.696956013,0.000936114,0.000936064,0.033595357,0.000936086,0.266640366,Drug discovery,0.76827526,TRUE,10.6,0.158142124,1,0.122023013,0,0.403234768,,,0.227799968
4228,Single-Cell Sequencing of Glioblastoma Reveals Central Nervous System Susceptibility to SARS-CoV-2.,Front Oncol,33312949,12/15/20,pubmed,0,13,"sequencing, transcriptom",0.744255965,0.087813447,0.087187351,0.001717175,0.001717328,0.077308734,Drug discovery,0.5460638,TRUE,44.53846154,0.582101552,39.38461538,0.629917046,0,0.403234768,,,0.538417789
4229,Physical activity and COVID-19: an observational and Mendelian randomisation study.,J Glob Health,33312507,12/15/20,pubmed,0,9,logistic regression,0.002183305,0.002183242,0.002183637,0.12968021,0.445700362,0.418069245,Healthcare,0.52390873,TRUE,101.8888889,0.872966788,276.2222222,0.94761841,0,0.403234768,,,0.741273322
4230,L1000 connectivity map interrogation identifies candidate drugs for repurposing as SARS-CoV-2 antiviral therapies.,Comput Struct Biotechnol J,33312454,12/15/20,pubmed,0,1,"computational, in silico, transcriptom",0.877549376,0.113717359,0.002183334,0.002183445,0.002183239,0.002183248,Drug discovery,0.122233,FALSE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
4231,Transcriptomic signatures and repurposing drugs for COVID-19 patients: findings of bioinformatics analyses.,Comput Struct Biotechnol J,33312453,12/15/20,pubmed,0,6,"bioinformatic, transcriptom, network analysis",0.814960887,0.001310343,0.001310381,0.050008606,0.001310388,0.131099395,Drug discovery,0.6961094,TRUE,43.5,0.573381161,19.66666667,0.477321381,1,0.537564047,,,0.529422196
4232,Potential implications of SARS-CoV-2 oral infection in the host microbiota.,J Oral Microbiol,33312449,12/15/20,pubmed,0,6,microbiom,0.441061888,0.403141045,0.001987211,0.001987241,0.088124735,0.063697879,Drug discovery,0.8283896,TRUE,76,0.790586926,83,0.785590045,1,0.537564047,,,0.704580339
4233,Pharmacogenomics landscape of COVID-19 therapy response in Serbian population and comparison with worldwide populations.,J Med Biochem,33312066,12/15/20,pubmed,0,8,"pharmacogenom, virom",0.325921161,0.306911267,0.001486456,0.001486486,0.001486505,0.362708124,Clinics,0.95892084,TRUE,40.5,0.544746119,10,0.355632861,1,0.537564047,,,0.479314342
4234,Forecasting COVID-19 pandemic using optimal singular spectrum analysis.,Chaos Solitons Fractals,33311861,12/15/20,pubmed,0,1,"neural network, forecasting model, dataset",0.001392865,0.001392858,0.160411174,0.834017394,0.00139284,0.00139287,Epidemiology,0.2057811,FALSE,15,0.227596017,0,0.055525823,0,0.403234768,,,0.228785536
4235,The world is its own best model: modelling and future pandemic planning in dentistry.,Br Dent J,33311676,12/15/20,pubmed,0,1,mathematical model,0.003101463,0.003101594,0.003101541,0.984492284,0.003101632,0.003101486,Epidemiology,0.2578767,FALSE,5,0.070752675,2,0.164302917,0,0.403234768,,,0.212763453
4236,Structural and functional comparison of SARS-CoV-2-spike receptor binding domain produced in Pichia pastoris and mammalian cells.,Sci Rep,33311634,12/15/20,pubmed,0,31,computational,0.848823635,0.097938295,0.047878282,0.001786669,0.00178658,0.001786538,Drug discovery,0.64148545,TRUE,21.75,0.321603068,,,0,0.403234768,,,0.362418918
4237,Genomic epidemiology reveals transmission patterns and dynamics of SARS-CoV-2 in Aotearoa New Zealand.,Nat Commun,33311501,12/15/20,pubmed,0,21,"genomic epidemiology, genome sequences",0.002357709,0.44597732,0.002357757,0.512878353,0.002357806,0.034071055,Epidemiology,0.3022514,FALSE,127.952381,0.918671532,903.952381,0.992574257,0,0.403234768,,,0.771493519
4238,Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.,Signal Transduct Target Ther,33311438,12/15/20,pubmed,0,6,machine learning,0.511730068,0.015998578,0.424276175,0.015998479,0.015998345,0.015998355,Drug discovery,0.53494847,TRUE,30.66666667,0.440225122,47.83333333,0.672063152,3,0.667819001,,,0.593369092
4239,Acute food insecurity and short-term coping strategies of urban and rural households of Bangladesh during the lockdown period of COVID-19 pandemic of 2020: report of a cross-sectional survey.,BMJ Open,33310813,12/15/20,pubmed,0,7,logistic regression,0.001112619,0.001112624,0.001112618,0.00111266,0.994436826,0.001112654,Healthcare,0.9379538,TRUE,78.57142857,0.800853485,119.1428571,0.85048167,1,0.537564047,,,0.729633067
4240,"Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype and prognosis value.",J Clin Pathol,33310786,12/15/20,pubmed,0,14,neural network,0.133246216,0.081186607,0.321232008,0.001371314,0.001371244,0.461592611,Clinics,0.8427776,TRUE,26.28571429,0.385119673,11.42857143,0.37643832,0,0.403234768,,,0.388264254
4241,"Substance use and abuse, COVID-19-related distress, and disregard for social distancing: A network analysis.",Addict Behav,33310690,12/15/20,pubmed,0,5,network analysis,0.156211064,0.001943552,0.001943558,0.226134484,0.611823866,0.001943476,Healthcare,0.5201793,TRUE,132,0.923990352,373.4,0.968356971,4,0.707574542,,,0.866640621
4242,Pay to skip the line: The political economy of digital testing services for HIV and other sexually transmitted infections.,Soc Sci Med,33310396,12/15/20,pubmed,0,6,digital health,0.020798032,0.062715538,0.001034652,0.579424219,0.246418693,0.089608867,Epidemiology,0.27659112,FALSE,76,0.790586926,60.83333333,0.723708857,0,0.403234768,,,0.63917685
4243,Recent trends on omics and bioinformatics approaches to study SARS-CoV-2: A bibliometric analysis and mini-review.,Comput Biol Med,33310371,12/15/20,pubmed,0,4,"computational, bioinformatic, sequencing, transcriptom, metagenom,  omics",0.3345259,0.309908774,0.001565339,0.350869333,0.001565345,0.001565309,Epidemiology,0.82672805,TRUE,2.75,0.030304904,0.25,0.065493712,2,0.618927094,,,0.238241903
4244,Association of metformin with mortality or ARDS in patients with COVID-19 and type 2 diabetes: A retrospective cohort study.,Diabetes Res Clin Pract,33310173,12/15/20,pubmed,0,12,logistic regression,0.001751141,0.001751171,0.001751158,0.001751208,0.00175121,0.991244113,Clinics,0.7524936,TRUE,61.08333333,0.709320304,167.8333333,0.897845866,1,0.537564047,,,0.714910072
4245,Retrospective study identifies infection related risk factors in close contacts during COVID-19 epidemic.,Int J Infect Dis,33310026,12/15/20,pubmed,0,10,logistic regression,0.001565293,0.001565316,0.001565304,0.001565415,0.810892008,0.182846664,Healthcare,0.43056202,FALSE,33.1,0.467190302,13.4,0.404602622,0,0.403234768,,,0.425009231
4246,Metformin use is associated with a reduced risk of mortality in patients with diabetes hospitalised for COVID-19.,Diabetes Metab,33309936,12/15/20,pubmed,0,33,logistic regression,0.001652998,0.00165302,0.001653049,0.10139492,0.00165308,0.891992932,Clinics,0.65757227,TRUE,65.21212121,0.73560517,70.12121212,0.753746321,6,0.764429903,,,0.751260465
4247,Ritonavir may inhibit exoribonuclease activity of nsp14 from the SARS-CoV-2 virus and potentiate the activity of chain terminating drugs.,Int J Biol Macromol,33309661,12/15/20,pubmed,0,2,"computational, in silico",0.904465284,0.089382307,0.001538083,0.001538138,0.001538098,0.00153809,Drug discovery,0.72885156,TRUE,73.5,0.780011132,57.5,0.712938186,0,0.403234768,,,0.632061362
4248,Asynchrony Between Individual and Government Actions Accounts for Disproportionate Impact of COVID-19 on Vulnerable Communities.,Am J Prev Med,33309450,12/15/20,pubmed,0,4,machine learning,0.000608028,0.000608046,0.000608055,0.859954657,0.137613151,0.000608063,Epidemiology,0.43355408,FALSE,16.75,0.252458408,5.5,0.267259834,0,0.403234768,,,0.307651003
4249,Integrated Bioinformatics Analysis for the Screening of Associated Pathways and Therapeutic Drugs in Coronavirus Disease 2019.,Arch Med Res,33309308,12/15/20,pubmed,0,5,"bioinformatic, network analysis",0.992309458,0.001538132,0.001538124,0.001538098,0.001538086,0.001538102,Drug discovery,0.84287226,TRUE,49,0.624281032,22,0.503746321,0,0.403234768,,,0.510420707
4250,A parametrized nonlinear predictive control strategy for relaxing COVID-19 social distancing measures in Brazil.,ISA Trans,33309260,12/15/20,pubmed,0,4,model fit,0.001538093,0.001538106,0.001538158,0.992309327,0.001538123,0.001538194,Epidemiology,0.46797267,FALSE,67,0.748160059,35.5,0.608241905,6,0.764429903,,,0.706943956
4251,Using Genetics To Dissect SARS-CoV-2 Infection.,Trends Genet,33309104,12/15/20,pubmed,0,4,whole-genome,0.689798748,0.279504703,0.007674713,0.007674173,0.007673875,0.007673787,Drug discovery,0.3088082,FALSE,78.25,0.799369163,116.5,0.846467755,0,0.403234768,,,0.683023895
4252,"Timely Intervention and Control of a Novel Coronavirus (COVID-19) Outbreak at a Large Skilled Nursing Facility - San Francisco, California, 2020.",Infect Control Hosp Epidemiol,33308357,12/15/20,pubmed,0,34,"sequencing, whole genome",0.001112691,0.368023627,0.001112637,0.139316165,0.452118909,0.038315971,Healthcare,0.63314354,TRUE,30.29411765,0.435029996,48.11764706,0.67407011,0,0.403234768,,,0.504111624
4253,"How Relevant is the Basic Reproductive Number Computed During COVID-19, Especially During Lockdowns?",Infect Control Hosp Epidemiol,33308355,12/15/20,pubmed,0,8,computational,0.007674029,0.007674045,0.007674364,0.961629122,0.007674507,0.007673933,Epidemiology,0.48094895,FALSE,150.625,0.944832705,131.75,0.865667648,2,0.618927094,,,0.809809149
4254,Detection of SARS-CoV-2 pneumonia: two case reports.,J Med Case Rep,33308254,12/15/20,pubmed,0,10,radiom,0.002490587,0.415788348,0.369718001,0.002490475,0.002490527,0.207022062,Genomics,0.48203212,FALSE,42.7,0.563980456,21.6,0.498795826,0,0.403234768,,,0.48867035
4255,Identification of biological correlates associated with respiratory failure in COVID-19.,BMC Med Genomics,33308225,12/15/20,pubmed,0,3,genome-wide,0.564233107,0.110384665,0.02941576,0.001310402,0.001310452,0.293345614,Drug discovery,0.9254439,TRUE,263.3333333,0.98670295,202,0.91838373,0,0.403234768,,,0.769440483
4256,"Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: a retrospective study from Wuhan, China.",BMC Infect Dis,33308183,12/15/20,pubmed,0,10,logistic regression,0.001187263,0.00118727,0.16978793,0.001187299,0.001187317,0.825462921,Clinics,0.93094015,TRUE,112.6,0.895664543,32.9,0.591717956,1,0.537564047,,,0.674982182
4257,In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform.,BMC Bioinformatics,33308153,12/15/20,pubmed,0,7,in silico,0.801372845,0.001538188,0.001538169,0.192474431,0.001538184,0.001538183,Drug discovery,0.64516145,TRUE,63.57142857,0.725214917,12.57142857,0.393497458,0,0.403234768,,,0.507315714
4258,0,BMC Bioinformatics,33308150,12/15/20,pubmed,0,4,dataset,0.647105787,0.001653048,0.346281922,0.001653121,0.001653079,0.001653043,Drug discovery,0.15097582,FALSE,34,0.477766096,99.5,0.821581482,1,0.537564047,,,0.612303875
4259,Coronavirus stigmatization and psychological distress among Asians in the United States.,Ethn Health,33307773,12/15/20,pubmed,0,4,logistic regression,0.001310333,0.001310345,0.001310319,0.001310358,0.99344828,0.001310365,Healthcare,0.42957246,FALSE,22.25,0.328344363,16.25,0.438988493,0,0.403234768,,,0.390189208
4260,Possible consequences of Covid-19 on the nervous system.,Neuro Endocrinol Lett,33307651,12/15/20,pubmed,0,4,artificial intelligence,0.002898356,0.002898463,0.410844568,0.224860979,0.081927093,0.276570542,Clinics,0.57264775,TRUE,36.75,0.505968211,4,0.231469093,0,0.403234768,,,0.380224024
4261,Genetic mechanisms of critical illness in COVID-19.,Nature,33307546,12/12/20,pubmed,0,79,"transcriptom, genome-wide",0.539581948,0.145892941,0.001272638,0.001272734,0.045212727,0.266767012,Drug discovery,0.7161243,TRUE,91.16666667,0.846434535,350.0606061,0.965681028,1,0.537564047,,,0.783226537
4262,Total infectomes of 162 SARS-CoV-2 cases using meta-transcriptomic sequencing.,J Infect,33307139,12/12/20,pubmed,0,16,"sequencing, transcriptom",0.05779917,0.711004237,0.057799148,0.057799148,0.057799148,0.057799148,Genomics,0.59564173,TRUE,44.8125,0.584204342,200.6875,0.917848542,1,0.537564047,,,0.67987231
4263,"Trends in Intensive Care for Patients with COVID-19 in England, Wales, and Northern Ireland.",Am J Respir Crit Care Med,33306946,12/12/20,pubmed,0,8,logistic regression,0.001203407,0.001203436,0.001203479,0.387648888,0.00120345,0.607537339,Clinics,0.75186104,TRUE,94.75,0.856144474,163.75,0.895103024,22,0.908142478,,,0.886463325
4264,Behavioral preventive measures and the use of medicines and herbal products among the public in response to Covid-19 in Bangladesh: A cross-sectional study.,PLoS One,33306725,12/12/20,pubmed,0,7,logistic regression,0.092911606,0.001861691,0.00186167,0.001861749,0.89964159,0.001861694,Healthcare,0.7516655,TRUE,22.14285714,0.326736347,9.714285714,0.348608509,0,0.403234768,,,0.359526541
4265,COVID-19 and human-nature relationships: Vermonters' activities in nature and associated nonmaterial values during the pandemic.,PLoS One,33306716,12/12/20,pubmed,0,4,logistic regression,0.170763169,0.001717258,0.001717282,0.31654471,0.507540377,0.001717203,Healthcare,0.5347225,TRUE,16.75,0.252458408,37,0.616671127,2,0.618927094,,,0.496018876
4266,SARS-CoV-2 genomic characterization and clinical manifestation of the COVID-19 outbreak in Uruguay.,Emerg Microbes Infect,33306459,12/12/20,pubmed,0,25,genomes,0.001486437,0.621918197,0.001486416,0.130417268,0.001486516,0.243205165,Genomics,0.54656434,TRUE,36.8,0.506339291,83.48,0.786861119,0,0.403234768,,,0.565478392
4267,Candidate Binding Sites for Allosteric Inhibition of the SARS-CoV-2 Main Protease from the Analysis of Large-Scale Molecular Dynamics Simulations.,J Phys Chem Lett,33306377,12/12/20,pubmed,0,4,molecular dynamics simulation,0.607894031,0.002296599,0.002296642,0.355976088,0.029240079,0.00229656,Drug discovery,0.7184702,TRUE,38.5,0.524089307,44.25,0.655271608,1,0.537564047,,,0.572308321
4268,A SENSITIVE INDICATOR FOR THE SEVERITY OF COVID-19: THIOL.,Turk J Med Sci,33306332,12/12/20,pubmed,0,8,logistic regression,0.076823056,0.001538122,0.001538146,0.00153812,0.001538146,0.91702441,Clinics,0.9916041,TRUE,42.75,0.564598924,8.625,0.330144501,0,0.403234768,,,0.432659397
4269,Transcriptional profiling of leukocytes in critically ill COVID19 patients: implications for interferon response and coagulation.,Intensive Care Med Exp,33306162,12/12/20,pubmed,0,24,"sequencing, transcriptom",0.380057223,0.044183871,0.001010964,0.001010944,0.001010952,0.572726046,Clinics,0.4414323,FALSE,173.2,0.960665471,,,3,0.667819001,,,0.814242236
4270,Rethinking Clinical Trials and Personalized Medicine with Placebogenomics and Placebo Dose.,OMICS,33305994,12/12/20,pubmed,0,2," omics, pharmacogenom, nutrigenom, vaccinom",0.287916888,0.001203485,0.001203471,0.539402763,0.140023829,0.030249564,Epidemiology,0.17387226,FALSE,141.5,0.936050467,70.5,0.755351887,0,0.403234768,,,0.698212374
4271,Anisotine and amarogentin as promising inhibitory candidates against SARS-CoV-2 proteins: a computational investigation.,J Biomol Struct Dyn,33305988,12/12/20,pubmed,0,14,"molecular dynamics simulation, computational, in silico",0.994293191,0.001141351,0.001141367,0.001141419,0.001141333,0.00114134,Drug discovery,0.9793532,TRUE,85,0.825035562,118.3571429,0.849411292,1,0.537564047,,,0.737336967
4272,Access denied: the shortage of digitized fitness resources for people with disabilities.,Disabil Rehabil,33305961,12/12/20,pubmed,0,13,machine learning,0.00129128,0.001291238,0.072695882,0.568994001,0.354436374,0.001291225,Epidemiology,0.6454996,TRUE,97.46153846,0.863195003,103.4615385,0.827936848,0,0.403234768,,,0.698122206
4273,"COVID-19 vaccine hesitancy in the UK: the Oxford coronavirus explanations, attitudes, and narratives survey (Oceans) II.",Psychol Med,33305716,12/12/20,pubmed,0,17,model fit,0.035133793,0.001486461,0.001486488,0.237360685,0.723046112,0.001486461,Healthcare,0.45728377,FALSE,105.6470588,0.880388398,267.5294118,0.945076264,19,0.89561084,,,0.907025167
4274,Structure-aided ACEI-capped remdesivir-loaded novel PLGA nanoparticles: toward a computational simulation design for anti-SARS-CoV-2 therapy.,Phys Chem Chem Phys,33305304,12/12/20,pubmed,0,3,computational,0.993035752,0.001392901,0.00139282,0.001392837,0.001392821,0.001392869,Drug discovery,0.3521698,FALSE,27.33333333,0.399529965,10.66666667,0.365333155,2,0.618927094,,,0.461263405
4275,A preliminary study of coronavirus disease 2019 in China: the impact of cardiovascular disease on death risk.,Arch Med Sci Atheroscler Dis,33305058,12/12/20,pubmed,0,1,logistic regression,0.002032852,0.002032833,0.00203293,0.00203299,0.002032938,0.989835457,Clinics,0.43954876,FALSE,24,0.35574247,6,0.280037463,1,0.537564047,,,0.39111466
4276,Psychological health problems during the lockdown: A survey of Indian population in COVID-19 pandemic.,Data Brief,33304963,12/12/20,pubmed,0,2,dataset,0.001461885,0.001461886,0.001461929,0.245586923,0.748565517,0.00146186,Healthcare,0.35017872,FALSE,87,0.833694106,15.5,0.430157881,0,0.403234768,,,0.555695585
4277,"Clinical Characteristics of Patients With Progressive and Non-progressive Coronavirus Disease 2019: Evidence From 365 Hospitalised Patients in Honghu and Nanchang, China.",Front Med (Lausanne),33304910,12/12/20,pubmed,0,15,logistic regression,0.001511899,0.001511922,0.001511853,0.001511891,0.001511814,0.992440622,Clinics,0.9721211,TRUE,109.7333333,0.889603562,62.26666667,0.729662831,1,0.537564047,,,0.71894348
4278,Repurposing Known Drugs as Covalent and Non-covalent Inhibitors of the SARS-CoV-2 Papain-Like Protease.,Front Chem,33304884,12/12/20,pubmed,0,4,"virtual screening, in silico, dataset",0.992809097,0.001438165,0.001438218,0.001438198,0.001438219,0.001438102,Drug discovery,0.82322824,TRUE,98.25,0.865483332,21,0.492239765,2,0.618927094,,,0.658883397
4279,CALL Score and RAS Score as Predictive Models for Coronavirus Disease 2019.,Cureus,33304701,12/12/20,pubmed,0,10,predictive model,0.000889057,0.000889067,0.168576301,0.0008891,0.00088906,0.827867414,Clinics,0.6684512,TRUE,9.3,0.138289319,1.8,0.150120417,1,0.537564047,,,0.275324595
4280,Evolutionary dynamics and geographic dispersal of beta coronaviruses in African bats.,PeerJ,33304657,12/12/20,pubmed,0,3,"phylogenom, whole genome, genome sequences",0.001272671,0.993636758,0.001272627,0.001272675,0.001272635,0.001272634,Genomics,0.8426293,TRUE,15,0.227596017,4.666666667,0.246721969,0,0.403234768,,,0.292517585
4281,"Utilizing microbiome approaches to assist source tracking, treatment and prevention of COVID-19: Review and assessment.",Comput Struct Biotechnol J,33304459,12/12/20,pubmed,0,5,microbiom,0.002357956,0.494748086,0.002357941,0.495820522,0.002357749,0.002357746,Epidemiology,0.7969605,TRUE,55.4,0.670542396,29,0.562884667,0,0.403234768,,,0.545553943
4282,Poor Sleep Quality and Its Consequences on Mental Health During the COVID-19 Lockdown in Italy.,Front Psychol,33304294,12/12/20,pubmed,0,17,logistic regression,0.000926307,0.000926362,0.000926287,0.082241277,0.897813624,0.017166143,Healthcare,0.94943285,TRUE,75.64705882,0.789102604,37.41176471,0.618744983,7,0.785110192,,,0.730985926
4283,Antecedents of Public Mental Health During the COVID-19 Pandemic: Mediation of Pandemic-Related Knowledge and Self-Efficacy and Moderation of Risk Level.,Front Psychiatry,33304280,12/12/20,pubmed,0,6,logistic regression,0.001098803,0.001098788,0.00109881,0.001098819,0.98450941,0.01109537,Healthcare,0.9226345,TRUE,27.66666667,0.403488156,4.5,0.242708055,1,0.537564047,,,0.394586753
4284,Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model.,Sci Rep,33303925,12/12/20,pubmed,0,11,model fit,0.001254593,0.001254598,0.001254588,0.993727042,0.001254588,0.001254591,Epidemiology,0.11473891,FALSE,17,0.257467994,5.090909091,0.257358844,6,0.764429903,,,0.426418914
4285,Genomic recombination events may reveal the evolution of coronavirus and the origin of SARS-CoV-2.,Sci Rep,33303849,12/12/20,pubmed,0,3,genomes,0.174072057,0.819775278,0.001538091,0.001538166,0.001538285,0.001538123,Genomics,0.42210364,FALSE,18.66666667,0.279670975,20.66666667,0.487958255,4,0.707574542,,,0.491734591
4286,Reliable and accurate diagnostics from highly multiplexed sequencing assays.,Sci Rep,33303831,12/12/20,pubmed,0,9,"computational, sequencing",0.002562685,0.300059837,0.68968968,0.002562684,0.002562556,0.002562558,Genomics,0.50982,TRUE,44.88888889,0.584575422,800.7777778,0.990032111,1,0.537564047,,,0.704057194
4287,Studying the effect of lockdown using epidemiological modelling of COVID-19 and a quantum computational approach using the Ising spin interaction.,Sci Rep,33303815,12/12/20,pubmed,0,6,"computational, probabilistic",0.001861824,0.001861856,0.001861754,0.940222407,0.001861731,0.052330426,Epidemiology,0.699137,TRUE,53.83333333,0.659904756,9,0.337904736,0,0.403234768,,,0.467014753
4288,Public perceptions of multiple risks during the COVID-19 pandemic in Italy and Sweden.,Sci Data,33303742,12/12/20,pubmed,0,5,dataset,0.00249048,0.002490524,0.002490599,0.412550978,0.57748698,0.002490439,Healthcare,0.4011696,FALSE,33,0.466757375,19.6,0.476786192,0,0.403234768,,,0.448926112
4289,Phylogenetic analysis of SARS-CoV-2 in Boston highlights the impact of superspreading events.,Science,33303686,12/12/20,pubmed,0,53,"genomic epidemiology, genomes",0.002238461,0.537904788,0.002238445,0.453141143,0.002238563,0.002238601,Genomics,0.33107772,FALSE,49.56603774,0.628610304,225,0.927883329,21,0.903944688,,,0.820146107
4290,A data-driven approach to identify risk profiles and protective drugs in COVID-19.,Proc Natl Acad Sci U S A,33303654,12/12/20,pubmed,0,12,bayes,0.445483996,0.030688568,0.001823471,0.16126012,0.001823361,0.358920484,Drug discovery,0.83797145,TRUE,63.83333333,0.727008473,73.16666667,0.762443136,1,0.537564047,,,0.675671885
4291,Factors associated with the mental health status of medical students during the COVID-19 pandemic: a cross-sectional study in Japan.,BMJ Open,33303472,12/12/20,pubmed,0,6,logistic regression,0.00086304,0.000863031,0.000863038,0.000863043,0.995684796,0.000863053,Healthcare,0.9302039,TRUE,19.83333333,0.294266807,6.833333333,0.293484078,0,0.403234768,,,0.330328551
4292,Leveraging digital tools to support recovery from substance use disorder during the COVID-19 pandemic response.,J Subst Abuse Treat,33303253,12/12/20,pubmed,0,6,digital health,0.093736654,0.001684523,0.001684605,0.471949737,0.429259948,0.001684534,Epidemiology,0.81457806,TRUE,21.5,0.318263343,7.5,0.307867273,0,0.403234768,,,0.343121795
4293,Anti-malarial Drugs are Not Created Equal for SARS-CoV-2 Treatment: A Computational Analysis Evidence.,Curr Pharm Des,33302855,12/12/20,pubmed,0,6,"computational, in-silico",0.78798447,0.002422441,0.002422342,0.167924225,0.002422451,0.036824071,Drug discovery,0.8413464,TRUE,41.33333333,0.551549261,44,0.654401927,1,0.537564047,,,0.581171745
4294,Silybin B and Cianidanol Inhibit M pro and Spike Protein of SARS-CoV-2: Evidence from in Silico Molecular Docking Studies.,Curr Pharm Des,33302853,12/12/20,pubmed,0,8,in silico,0.993814795,0.00123705,0.001237048,0.001237039,0.001237033,0.001237035,Drug discovery,0.9435524,TRUE,69.25,0.760034634,22.25,0.505284988,0,0.403234768,,,0.556184797
4295,Factors for peripherally inserted central catheters care delay in cancer patients during the COVID-19 pandemic.,Ann Palliat Med,33302650,12/12/20,pubmed,0,14,logistic regression,0.001291219,0.001291327,0.001291284,0.081742269,0.343136536,0.571247366,Clinics,0.98934036,TRUE,21.64285714,0.319623972,10.21428571,0.357773615,0,0.403234768,,,0.360210785
4296,"Comprehensive Structural and Molecular Comparison of Spike Proteins of SARS-CoV-2, SARS-CoV and MERS-CoV, and Their Interactions with ACE2.",Cells,33302501,12/12/20,pubmed,0,10,transcriptom,0.715147209,0.279972651,0.00122002,0.001220077,0.001220026,0.001220017,Drug discovery,0.61598176,TRUE,59.6,0.70029068,36.6,0.614664169,4,0.707574542,,,0.674176463
4297,Inhibition of SARS-CoV-2 Entry into Host Cells Using Small Molecules.,Pharmaceuticals (Basel),33302344,12/12/20,pubmed,0,4,computational,0.940753552,0.053761061,0.00137135,0.001371342,0.001371295,0.001371399,Drug discovery,0.6964413,TRUE,57.25,0.683344672,21.75,0.50046829,0,0.403234768,,,0.52901591
4298,COVID-19 Misinformation Trends in Australia: Prospective Longitudinal National Survey.,J Med Internet Res,33302250,12/11/20,pubmed,0,13,digital health,0.001622769,0.031325916,0.001622714,0.148523524,0.815282353,0.001622724,Healthcare,0.89440143,TRUE,40.30769231,0.542210403,27.38461538,0.548702168,1,0.537564047,,,0.542825539
4299,A molecular docking study of EGCG and theaflavin digallate with the druggable targets of SARS-CoV-2.,Comput Biol Med,33302163,12/11/20,pubmed,0,3,in-silico,0.99280925,0.001438148,0.00143816,0.001438103,0.001438182,0.001438156,Drug discovery,0.97555685,TRUE,40.33333333,0.542890717,48.33333333,0.675073588,3,0.667819001,,,0.628594435
4300,Early extrapulmonary prognostic features in chest computed tomography in COVID-19 pneumonia: Bone mineral density is a relevant predictor for the clinical outcome - A multicenter feasibility study.,Bone,33301962,12/11/20,pubmed,0,10,logistic regression,0.000907283,0.000907322,0.396147988,0.000907334,0.000907301,0.600222772,Clinics,0.9631453,TRUE,67.1,0.7483456,26.8,0.544019267,3,0.667819001,,,0.653394623
4301,Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery.,Comput Biol Med,33301953,12/11/20,pubmed,0,7,"computational, bioinformatic, proteom, phylogenom",0.532837468,0.426698073,0.001272739,0.001272735,0.001272646,0.036646338,Drug discovery,0.61647713,TRUE,20.14285714,0.298967159,10.14285714,0.356636339,0,0.403234768,,,0.352946089
4302,Effect of internationally imported cases on internal spread of COVID-19: a mathematical modelling study.,Lancet Public Health,33301722,12/11/20,pubmed,0,6,mathematical model,0.000572619,0.000572634,0.000572627,0.987465683,0.010243802,0.000572636,Epidemiology,0.22460473,FALSE,127.5,0.917867524,250.3333333,0.938319508,10,0.828199272,,,0.894795434
4303,COVID-19: The Effect of Host Genetic Variations on Host-Virus Interactions.,J Proteome Res,33301685,12/11/20,pubmed,0,1,exom,0.515927299,0.391673901,0.001717187,0.001717266,0.052199666,0.036764682,Drug discovery,0.24867722,FALSE,14,0.213494959,3,0.199424672,0,0.403234768,,,0.272051466
4304,SARS-CoV-2 virtual biochemistry labs on bioinformatics and drug design.,Biochem Mol Biol Educ,33301634,12/11/20,pubmed,0,2,"computational, bioinformatic",0.486854091,0.003101457,0.117371061,0.003101538,0.386470389,0.003101465,Drug discovery,0.14365521,FALSE,33.5,0.471890655,33,0.593256623,0,0.403234768,,,0.489460682
4305,Caregivers' mental distress and child health during the COVID-19 outbreak in Japan.,PLoS One,33301517,12/11/20,pubmed,0,9,logistic regression,0.0013503,0.001350301,0.001350301,0.001350331,0.993248438,0.001350329,Healthcare,0.90737104,TRUE,26.11111111,0.382769497,4.888888889,0.250200696,0,0.403234768,,,0.345401654
4306,Analyzing changes in respiratory rate to predict the risk of COVID-19 infection.,PLoS One,33301493,12/11/20,pubmed,0,7,dataset,0.001350344,0.001350429,0.328941449,0.093102673,0.293431053,0.281824052,Healthcare,0.54280293,TRUE,44.42857143,0.581050158,46.28571429,0.665440193,0,0.403234768,,,0.549908373
4307,"The transcriptomic profiling of SARS-CoV-2 compared to SARS, MERS, EBOV, and H1N1.",PLoS One,33301474,12/11/20,pubmed,0,2,transcriptom,0.917056565,0.077852686,0.001272632,0.001272693,0.001272676,0.001272748,Drug discovery,0.7419413,TRUE,51,0.63875317,10,0.355632861,3,0.667819001,,,0.554068344
4308,False-negative results of initial RT-PCR assays for COVID-19: A systematic review.,PLoS One,33301459,12/11/20,pubmed,0,13,logistic regression,0.001022622,0.001022676,0.641736441,0.354172884,0.001022686,0.001022692,Epidemiology,0.38770533,FALSE,97.69230769,0.86381347,154.2307692,0.887142093,8,0.799987654,,,0.850314405
4309,Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics.,JMIR Res Protoc,33301418,12/11/20,pubmed,0,7,"artificial intelligence, predictive model",0.000800601,0.000800604,0.00080062,0.995996996,0.000800601,0.000800578,Epidemiology,0.3489133,FALSE,67.71428571,0.751685324,53.42857143,0.695210062,1,0.537564047,,,0.661486477
4310,"Computing SARS-CoV-2 Infection Risk From Symptoms, Imaging, and Test Data: Diagnostic Model Development.",J Med Internet Res,33301417,12/11/20,pubmed,0,3,"bayes, machine learning, probabilistic",0.000822974,0.043480701,0.46518286,0.124316427,0.000822955,0.365374083,Imaging,0.61376005,TRUE,25.66666667,0.376461129,16.33333333,0.440460262,0,0.403234768,,,0.40671872
4311,Attitudes Toward Using COVID-19 mHealth Tools Among Adults With Chronic Health Conditions: Secondary Data Analysis of the COVID-19 Impact Survey.,JMIR Mhealth Uhealth,33301415,12/11/20,pubmed,0,4,logistic regression,0.000699336,0.000699367,0.000699339,0.000699387,0.935767475,0.061435096,Healthcare,0.11276683,FALSE,39,0.530521368,11.25,0.374364463,0,0.403234768,,,0.4360402
4312,An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model.,J Med Internet Res,33301414,12/11/20,pubmed,0,14,"artificial intelligence, neural network, ensemble learning",0.001272639,0.001272659,0.593055781,0.001272676,0.001272717,0.401853528,Clinics,0.6710316,TRUE,33.14285714,0.46762323,10.92857143,0.3679422,0,0.403234768,,,0.412933399
4313,Hybrid-COVID: a novel hybrid 2D/3D CNN based on cross-domain adaptation approach for COVID-19 screening from chest X-ray images.,Phys Eng Sci Med,33301073,12/11/20,pubmed,0,3,"deep learning, computational, neural network, deep model, transfer learning, dataset",0.000956328,0.000956363,0.995218289,0.000956364,0.000956322,0.000956334,Imaging,0.3943333,FALSE,56.66666667,0.679757561,7,0.299973241,1,0.537564047,,,0.505764949
4314,Association between seasonal flu vaccination and COVID-19 among healthcare workers.,Occup Med (Lond),33300998,12/11/20,pubmed,0,5,logistic regression,0.001823326,0.24273286,0.001823457,0.001823379,0.642079339,0.109717639,Healthcare,0.14589,FALSE,35.8,0.49495949,3.2,0.202100615,0,0.403234768,,,0.366764958
4315,Aging Narratives over 210 years (1810-2019).,J Gerontol B Psychol Sci Soc Sci,33300996,12/11/20,pubmed,0,2,"computational, dataset",0.001653113,0.00165316,0.001653107,0.549088389,0.444299085,0.001653146,Epidemiology,0.89188445,TRUE,23,0.34225988,12.5,0.392761573,0,0.403234768,,,0.37941874
4316,Prediction of Potential Respiratory Tract Infection from SARS-CoV-2 Through Hand-to-face Contact Transmission.,Tokai J Exp Clin Med,33300586,12/11/20,pubmed,0,1,mathematical model,0.001717174,0.00171722,0.001717313,0.923349447,0.001717258,0.069781588,Epidemiology,0.28492254,FALSE,64,0.729173109,15,0.42594327,0,0.403234768,,,0.519450382
4317,Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review.,J Biomol Struct Dyn,33300456,12/11/20,pubmed,0,5,"machine learning, deep learning, artificial intelligence",0.696191271,0.001371298,0.298323653,0.0013713,0.001371242,0.001371237,Drug discovery,0.83122134,TRUE,16.6,0.250046385,3.8,0.221501204,1,0.537564047,,,0.336370545
4318,0,J Biomol Struct Dyn,33300454,12/11/20,pubmed,0,4,computational,0.995846852,0.000830635,0.000830633,0.00083064,0.000830623,0.000830618,Drug discovery,0.8795597,TRUE,18.25,0.273609994,14,0.412898047,0,0.403234768,,,0.363247603
4319,Psychiatry research in the COVID-19 era and beyond: A role for mathematical models.,Aust N Z J Psychiatry,33300362,12/11/20,pubmed,0,3,mathematical model,0.034961941,0.034961874,0.034962477,0.82518967,0.034961874,0.034962164,Epidemiology,0.5289454,TRUE,74.66666667,0.785268106,37.66666667,0.619949157,0,0.403234768,,,0.602817343
4320,Repurposing of some anti-infective drugs for COVID-19 treatment: A surveillance study supported by an in silico investigation.,Int J Clin Pract,33300221,12/11/20,pubmed,0,4,"computational, in silico",0.51865978,0.000710608,0.000710626,0.000710597,0.000710606,0.478497783,Drug discovery,0.9501395,TRUE,18.75,0.28072237,3.25,0.203572384,1,0.537564047,,,0.3406196
4321,Understanding the pathophysiological changes via untargeted metabolomics in COVID-19 patients.,J Med Virol,33300133,12/11/20,pubmed,0,9,metabolom,0.432059436,0.176250669,0.165266612,0.002996542,0.002996464,0.220430277,Drug discovery,0.78405,TRUE,27.55555556,0.401880141,6.777777778,0.292279904,0,0.403234768,,,0.365798271
4322,NIH Workshop Report: sensory nutrition and disease.,Am J Clin Nutr,33300030,12/11/20,pubmed,0,25,microbiom,0.436066557,0.04531064,0.001438157,0.406229617,0.109516768,0.001438262,Drug discovery,0.8699398,TRUE,119.36,0.905992949,213.64,0.923936313,0,0.403234768,,,0.74438801
4323,Predicting patients with false negative SARS-CoV-2 testing at hospital admission: A retrospective multi-center study.,medRxiv,33300016,12/11/20,pubmed,0,8,"logistic regression, prediction model",0.00111265,0.001112649,0.14604054,0.001112649,0.108561376,0.742060137,Clinics,0.4321082,FALSE,17.5,0.263776362,10.75,0.366269735,0,0.403234768,,,0.344426955
4324,Supervised Image Classification Algorithm Using Representative Spatial Texture Features: Application to COVID-19 Diagnosis Using CT Images.,medRxiv,33300010,12/11/20,pubmed,0,3,"bayes, classifier, predictive model, dataset",0.001085349,0.138545559,0.623119727,0.192880084,0.043283798,0.001085484,Imaging,0.3723988,FALSE,116.6666667,0.901787371,57,0.711198823,0,0.403234768,,,0.672073654
4325,Household factors and the risk of severe COVID-like illness early in the US pandemic.,medRxiv,33300008,12/11/20,pubmed,0,15,logistic regression,0.002357735,0.00235794,0.002357708,0.002357803,0.807203426,0.183365387,Healthcare,0.63903415,TRUE,55.86666667,0.673758427,126.8,0.860315761,0,0.403234768,,,0.645769652
4326,Chromatin remodeling in peripheral blood cells reflects COVID-19 symptom severity.,bioRxiv,33300002,12/11/20,pubmed,0,22,transcriptom,0.345020058,0.160477781,0.001538121,0.001538146,0.00153817,0.489887723,Clinics,0.42205486,FALSE,60.95454545,0.707835982,78.77272727,0.775220765,0,0.403234768,,,0.628763838
4327,Amilorides inhibit SARS-CoV-2 replication in vitro by targeting RNA structures.,bioRxiv,33299997,12/11/20,pubmed,0,16,genomes,0.782494072,0.213949698,0.000889051,0.000889072,0.000889057,0.000889049,Drug discovery,0.49220565,FALSE,27.0625,0.396190241,38.3125,0.623227188,1,0.537564047,,,0.518993825
4328,Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic.,bioRxiv,33299989,12/11/20,pubmed,0,64,"proteom, structural model",0.639921685,0.355682913,0.001098915,0.001098859,0.001098812,0.001098816,Drug discovery,0.34929156,FALSE,30.75,0.441090977,140.46875,0.874765855,2,0.618927094,,,0.644927975
4329,Identification of Repurposable Drugs and Adverse Drug Reactions for Various Courses of COVID-19 Based on Single-Cell RNA Sequencing Data.,ArXiv,33299905,12/11/20,pubmed,0,7,"sequencing, dataset",0.990882972,0.001823437,0.001823369,0.001823377,0.001823371,0.001823473,Drug discovery,0.39357454,FALSE,37,0.508936854,29,0.562884667,0,0.403234768,,,0.49168543
4330,Random effects meta-analysis of COVID-19/S. aureus partnership in co-infection.,GMS Hyg Infect Control,33299742,12/11/20,pubmed,0,3,computational,0.000898121,0.105962316,0.000898109,0.198199391,0.000898122,0.693143942,Clinics,0.9609826,TRUE,4.333333333,0.057950399,0,0.055525823,1,0.537564047,,,0.217013423
4331,Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients.,J Healthc Eng,33299538,12/11/20,pubmed,0,5,"transfer learning, dataset",0.062457266,0.001392924,0.93197109,0.001392903,0.001392881,0.001392935,Imaging,0.46500343,FALSE,20.4,0.302863504,1.6,0.140687717,3,0.667819001,,,0.370456741
4332,An Immunoinformatics Study to Predict Epitopes in the Envelope Protein of SARS-CoV-2.,Can J Infect Dis Med Microbiol,33299503,12/11/20,pubmed,0,2,"computational, bioinformatic",0.939784838,0.052440975,0.001943591,0.001943555,0.001943587,0.001943454,Drug discovery,0.65389323,TRUE,19.5,0.29117447,3.5,0.213607172,0,0.403234768,,,0.302672136
4333,Assessment of Three Mathematical Prediction Models for Forecasting the COVID-19 Outbreak in Iran and Turkey.,Comput Math Methods Med,33299466,12/11/20,pubmed,0,4,"mathematical prediction, prediction model",0.002296558,0.002296533,0.178198727,0.812615075,0.002296544,0.002296562,Epidemiology,0.37063605,FALSE,9,0.135320675,0.25,0.065493712,2,0.618927094,,,0.27324716
4334,"Depression is Associated with Moderate-Intensity Physical Activity Among College Students During the COVID-19 Pandemic: Differs by Activity Level, Gender and Gender Role.",Psychol Res Behav Manag,33299364,12/11/20,pubmed,0,16,correlation analysis,0.000936093,0.0009361,0.019011271,0.000936155,0.910519884,0.067660498,Healthcare,0.9796631,TRUE,106,0.881192405,39.875,0.632459192,0,0.403234768,,,0.638962122
4335,"Knowledge, Attitude, and Precautionary Measures Towards COVID-19 Among Medical Visitors at the University of Gondar Comprehensive Specialized Hospital Northwest Ethiopia.",Infect Drug Resist,33299333,12/11/20,pubmed,0,2,logistic regression,0.001291272,0.001291245,0.001291283,0.031917306,0.962917568,0.001291326,Healthcare,0.5745135,TRUE,4.5,0.061784897,0,0.055525823,0,0.403234768,,,0.173515162
4336,Intention and Practice on Personal Preventive Measures Against the COVID-19 Pandemic Among Adults with Chronic Conditions in Southern Ethiopia: A Survey Using the Theory of Planned Behavior.,J Multidiscip Healthc,33299323,12/11/20,pubmed,0,10,logistic regression,0.001112678,0.001112626,0.001112737,0.036161111,0.903400867,0.057099982,Healthcare,0.9834964,TRUE,9.3,0.138289319,3.3,0.204107573,1,0.537564047,,,0.293320313
4337,Belief of having had unconfirmed Covid-19 infection reduces willingness to participate in app-based contact tracing.,NPJ Digit Med,33299071,12/11/20,pubmed,0,4,logistic regression,0.000677905,0.000677915,0.000677934,0.453453888,0.53252278,0.011989579,Healthcare,0.45305791,FALSE,161.75,0.952378007,95.5,0.813821247,2,0.618927094,,,0.795042116
4338,Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system.,Sci Rep,33298991,12/11/20,pubmed,0,5,"machine learning, logistic regression",0.002032809,0.002032787,0.270204606,0.259251628,0.002032898,0.464445272,Clinics,0.75179183,TRUE,20.4,0.302863504,15.2,0.427214343,4,0.707574542,,,0.479217463
4339,Analytical validity of nanopore sequencing for rapid SARS-CoV-2 genome analysis.,Nat Commun,33298935,12/11/20,pubmed,0,17,"sequencing, whole-genome",0.000999524,0.703968637,0.246999711,0.000999581,0.000999542,0.046033005,Genomics,0.22722739,FALSE,39.82352941,0.53732451,44.11764706,0.65473642,0,0.403234768,,,0.531765232
4340,Antihypertensive drugs are associated with reduced fatal outcomes and improved clinical characteristics in elderly COVID-19 patients.,Cell Discov,33298897,12/11/20,pubmed,0,17,logistic regression,0.104818679,0.00203277,0.002032757,0.131083638,0.002032805,0.75799935,Clinics,0.99115986,TRUE,123.0588235,0.91186839,233.4117647,0.931362055,2,0.618927094,,,0.82071918
4341,Initial whole-genome sequencing and analysis of the host genetic contribution to COVID-19 severity and susceptibility.,Cell Discov,33298875,12/11/20,pubmed,0,27,"sequencing, genome-wide, whole-genome",0.1141454,0.534784468,0.000946113,0.000946127,0.000946109,0.348231782,Genomics,0.62039304,TRUE,80.66666667,0.809512029,188.9259259,0.911426278,21,0.903944688,,,0.874960998
4342,A clash of epidemics: Impact of the COVID-19 pandemic response on opioid overdose.,J Subst Abuse Treat,33298298,12/11/20,pubmed,0,7,simulation model,0.132418327,0.002898307,0.002898297,0.478510198,0.380376375,0.002898496,Epidemiology,0.75912225,TRUE,136.2857143,0.928257777,171.2857143,0.90045491,1,0.537564047,,,0.788758912
4343,A simultaneous exploratory and quantitative amino acid and biogenic amine metabolic profiling platform for rapid disease phenotyping via UPLC-QToF-MS.,Talanta,33298292,12/11/20,pubmed,0,9,data mining,0.000999587,0.38487534,0.380411614,0.000999559,0.000999532,0.231714369,Genomics,0.7372706,TRUE,126.3333333,0.915888428,237.7777778,0.933302114,0,0.403234768,,,0.750808437
4344,Coronavirus Disease 2019: Virology and Drug Targets.,Infect Disord Drug Targets,33297920,12/11/20,pubmed,0,1,artificial intelligence,0.894096133,0.002422344,0.071892304,0.002422304,0.026744532,0.002422383,Drug discovery,0.80228144,TRUE,43,0.567629414,10,0.355632861,0,0.403234768,,,0.442165681
4345,[Prediction of severe outcomes of patients with COVID-19].,Zhonghua Liu Xing Bing Xue Za Zhi,33297614,12/11/20,pubmed,0,10,prediction model,0.001072232,0.001072196,0.205849659,0.001072233,0.001072203,0.789861477,Clinics,0.76695275,TRUE,14.8,0.222957511,9.8,0.350682366,0,0.403234768,,,0.325624881
4346,Technology Enabled Clinical Care (TECC): Protocol for a Prospective Longitudinal Cohort Study of Smartphone-Augmented Mental Health Treatment.,JMIR Res Protoc,33296869,12/10/20,pubmed,0,2,digital health,0.000966785,0.00096684,0.000966827,0.325965768,0.615708897,0.055424883,Healthcare,0.76671565,TRUE,157.5,0.949718597,80.5,0.780572652,0,0.403234768,,,0.711175339
4347,"Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19.",Immunity,33296687,12/10/20,pubmed,0,166,"transcriptom, multi-omics",0.507175501,0.001717372,0.001717263,0.001717272,0.001717195,0.485955397,Drug discovery,0.53871244,TRUE,87.99401198,0.83666275,,,9,0.814309525,,,0.825486137
4348,COVID-MATCH65-A prospectively derived clinical decision rule for severe acute respiratory syndrome coronavirus 2.,PLoS One,33296409,12/10/20,pubmed,0,17,logistic regression,0.001310342,0.001310428,0.200990163,0.065321624,0.001310436,0.729757007,Clinics,0.89974666,TRUE,31.52941176,0.450120601,10.05882353,0.355766658,0,0.403234768,,,0.403040675
4349,COVID-19 mortality risk assessment: An international multi-center study.,PLoS One,33296405,12/10/20,pubmed,0,18,machine learning,0.001237077,0.001237087,0.217049387,0.001237089,0.001237128,0.778002234,Clinics,0.786243,TRUE,35.5,0.492547467,176.2777778,0.903799839,1,0.537564047,,,0.644637118
4350,Knowledge of prevention of COVID-19 among the general people in Bangladesh: A cross-sectional study in Rajshahi district.,PLoS One,33296403,12/10/20,pubmed,0,7,logistic regression,0.027476225,0.001392854,0.001392917,0.001392927,0.966952238,0.001392838,Healthcare,0.09588829,FALSE,25.14285714,0.369781681,6.142857143,0.281241638,0,0.403234768,,,0.351419362
4351,Development and external validation of a prognostic tool for COVID-19 critical disease.,PLoS One,33296357,12/10/20,pubmed,0,16,logistic regression,0.00077264,0.000772634,0.0887409,0.039405338,0.000772638,0.86953585,Clinics,0.49456394,FALSE,76.625,0.792504175,97.4375,0.81743377,0,0.403234768,,,0.671057571
4352,Food Worry in the Deaf and Hard-of-Hearing Population During the COVID-19 Pandemic.,Public Health Rep,33296264,12/10/20,pubmed,0,5,"model fit, logistic regression",0.001438093,0.001438109,0.001438118,0.001438133,0.992809397,0.00143815,Healthcare,0.93959,TRUE,20,0.298163152,9,0.337904736,0,0.403234768,,,0.346434219
4353,A CT-based radiomics nomogram for predicting prognosis of coronavirus disease 2019 (COVID-19) radiomics nomogram predicting COVID-19.,Br J Radiol,33296222,12/10/20,pubmed,0,7,"classifier, radiom",0.000830642,0.000830629,0.561043825,0.000830657,0.000830655,0.435633592,Imaging,0.6703267,TRUE,33.28571429,0.469107551,4.142857143,0.232472572,0,0.403234768,,,0.36827163
4354,Validation of pneumonia prognostic scores in a statewide cohort of hospitalised patients with COVID-19.,Int J Clin Pract,33296132,12/10/20,pubmed,0,8,dataset,0.001059365,0.001059438,0.110182101,0.001059387,0.00105938,0.885580329,Clinics,0.4868162,FALSE,34.625,0.483208609,27.5,0.549906342,1,0.537564047,,,0.523559666
4355,Prediction of putative epitope-based vaccine against all corona virus strains for the Chinese population: Approach toward development of vaccine.,Microbiol Immunol,33295677,12/10/20,pubmed,0,8,molecular dynamics simulation,0.500725593,0.370774254,0.001371265,0.124386251,0.001371315,0.001371321,Drug discovery,0.5400003,TRUE,9.625,0.143917373,0.25,0.065493712,0,0.403234768,,,0.204215284
4356,Saliva for diagnosis of SARS-CoV-2: First report from India.,J Med Virol,33295640,12/10/20,pubmed,0,16,sequencing,0.001350374,0.621415036,0.128646746,0.001350364,0.109622441,0.137615039,Genomics,0.49195585,FALSE,15.9375,0.239099511,3.4375,0.209124967,0,0.403234768,,,0.283819749
4357,On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic.,J Am Med Inform Assoc,33295626,12/10/20,pubmed,0,3,deep learning,0.002130726,0.002130653,0.510853594,0.480623317,0.002131044,0.002130666,Epidemiology,0.8837501,TRUE,6.333333333,0.089863319,1.666666667,0.145036125,0,0.403234768,,,0.212711404
4358,SARS-CoV-2 spike protein binds to bacterial lipopolysaccharide and boosts proinflammatory activity.,J Mol Cell Biol,33295606,12/10/20,pubmed,0,10,"molecular dynamics simulation, computational",0.831616324,0.037879021,0.001823334,0.001823514,0.001823328,0.125034479,Drug discovery,0.7066548,TRUE,49.7,0.629228771,30.7,0.574391223,0,0.403234768,,,0.535618254
4359,[Artificial intelligence as a support for non-pharmacological interventions against COVID-19].,Rev Peru Med Exp Salud Publica,33295569,12/10/20,pubmed,0,3,artificial intelligence,0.025060562,0.025060322,0.579227764,0.320528932,0.025061809,0.025060611,Epidemiology,0.6561887,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
4360,Tracing the driving forces responsible for the remarkable infectivity of 2019-nCoV: 1. Receptor binding domain in its bound and unbound states.,Phys Chem Chem Phys,33295347,12/10/20,pubmed,0,4,molecular dynamics simulation,0.694468196,0.001511877,0.043160253,0.257835991,0.001511849,0.001511833,Drug discovery,0.5891724,TRUE,85.5,0.82658173,12.5,0.392761573,0,0.403234768,,,0.540859357
4361,COVID-19 in Cuba: Assessing the National Response.,MEDICC Rev,33295317,12/10/20,pubmed,0,2,mathematical model,0.001987097,0.001987111,0.001987095,0.761822684,0.117007278,0.115208736,Epidemiology,0.21785071,FALSE,2.5,0.027459954,0,0.055525823,1,0.537564047,,,0.206849941
4362,Ethno-cultural disparities in mental health during the COVID-19 pandemic: a cross-sectional study on the impact of exposure to the virus and COVID-19-related discrimination and stigma on mental health across ethno-cultural groups in Quebec (Canada).,BJPsych Open,33295270,12/10/20,pubmed,0,7,logistic regression,0.001350331,0.001350392,0.001350349,0.001350357,0.993248223,0.001350348,Healthcare,0.92852724,TRUE,15.14285714,0.228276331,2.714285714,0.186111854,2,0.618927094,,,0.344438427
4363,Factors associated with mental health outcomes among health care workers in the Fangcang shelter hospital in China.,Int J Soc Psychiatry,33295238,12/10/20,pubmed,0,3,logistic regression,0.001219974,0.001219991,0.001219983,0.001220017,0.993899964,0.001220071,Healthcare,0.9597011,TRUE,220.6666667,0.978662873,218.6666667,0.926277763,4,0.707574542,,,0.870838393
4364,Racial disparities in patients with coronavirus disease 2019 infection and gynecologic malignancy.,Cancer,33294978,12/10/20,pubmed,0,18,logistic regression,0.001291203,0.00129124,0.00129121,0.001291419,0.183055546,0.811779382,Clinics,0.7809695,TRUE,93.38888889,0.85261921,,,0,0.403234768,,,0.627926989
4365,Communication strategies and media discourses in the age of COVID-19: an urgent need for action.,Health Promot Int,33294917,12/10/20,pubmed,0,19,network analysis,0.001461907,0.037982832,0.043742744,0.54310577,0.372244849,0.001461897,Epidemiology,0.9390881,TRUE,36,0.498299215,30.78947368,0.575327803,0,0.403234768,,,0.492287262
4366,Identification of a SARS-like bat coronavirus that shares structural features with the spike glycoprotein receptor-binding domain of SARS-CoV-2.,Access Microbiol,33294769,12/10/20,pubmed,0,2,in silico,0.808056686,0.185997501,0.001486463,0.001486501,0.00148643,0.001486419,Drug discovery,0.63350683,TRUE,201,0.972601893,110,0.838573722,1,0.537564047,,,0.782913221
4367,Correlation of subway turnstile entries and COVID-19 incidence and deaths in New York City.,Infect Dis Model,33294750,12/10/20,pubmed,0,4,neural network,0.002357941,0.002357731,0.038676242,0.951892318,0.002357784,0.002357984,Epidemiology,0.4958341,FALSE,70.75,0.767270703,37,0.616671127,0,0.403234768,,,0.595725532
4368,Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections.,Infect Dis Model,33294749,12/10/20,pubmed,0,3,"mathematical model, dataset",0.0014619,0.001461862,0.001461977,0.992690504,0.001461874,0.001461883,Epidemiology,0.49562362,FALSE,5.666666667,0.079473066,3.333333333,0.206515922,0,0.403234768,,,0.229741252
4369,"Widespread testing, case isolation and contact tracing may allow safe school reopening with continued moderate physical distancing: A modeling analysis of King County, WA data.",Infect Dis Model,33294745,12/10/20,pubmed,0,7,"model fit, mathematical model",0.015792826,0.001022618,0.001022618,0.77049186,0.168405113,0.043264966,Epidemiology,0.512499,TRUE,38.57142857,0.524645927,16.71428571,0.445143163,0,0.403234768,,,0.457674619
4370,0,Gene Rep,33294728,12/10/20,pubmed,0,4,bioinformatic,0.871788885,0.001653168,0.001653022,0.001653082,0.001653157,0.121598686,Drug discovery,0.76817966,TRUE,25.25,0.370709382,12.25,0.388546963,1,0.537564047,,,0.432273464
4371,0,Heliyon,33294721,12/10/20,pubmed,0,4,"virtual screening, computational",0.74946548,0.001220056,0.00122004,0.085186374,0.001220091,0.161687959,Drug discovery,0.9598124,TRUE,59.5,0.699857752,27.75,0.551645705,0,0.403234768,,,0.551579408
4372,"The prevalence of general anxiety disorder and its associated factors among women's attending at the perinatal service of Dilla University referral hospital, Dilla town, Ethiopia, April, 2020 in Covid pandemic.",Heliyon,33294715,12/10/20,pubmed,0,2,logistic regression,0.001072202,0.001072164,0.001072274,0.019242261,0.976468802,0.001072297,Healthcare,0.9929882,TRUE,9,0.135320675,0.5,0.087101953,0,0.403234768,,,0.208552465
4373,The use of Twitter by state leaders and its impact on the public during the COVID-19 pandemic.,Heliyon,33294685,12/10/20,pubmed,0,1,dataset,0.002422269,0.002422338,0.002422317,0.987888396,0.002422425,0.002422255,Epidemiology,0.35778362,FALSE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
4374,COVID-19 Pandemic Accelerates Need to Improve Online Patient Engagement Practices to Enhance Patient Experience.,J Patient Exp,33294595,12/10/20,pubmed,0,1,digital health,0.002357872,0.002357748,0.068523557,0.361558158,0.252901608,0.312301057,Epidemiology,0.9619328,TRUE,35,0.488032655,27,0.546026224,0,0.403234768,,,0.479097882
4375,The Use of Empathic Communication During the COVID-19 Outbreak.,J Patient Exp,33294593,12/10/20,pubmed,0,9,prediction model,0.033178932,0.002130645,0.00213079,0.344326211,0.522703259,0.095530162,Healthcare,0.67034423,TRUE,10.55555556,0.157523656,2.888888889,0.190460262,0,0.403234768,,,0.250406229
4376,A comprehensive dataset for bibliometric analysis of SARS and coronavirus impact on social sciences.,Data Brief,33294517,12/10/20,pubmed,0,7,dataset,0.002996627,0.002996518,0.339289736,0.648724196,0.00299648,0.002996442,Epidemiology,0.41403645,FALSE,62.42857143,0.717545921,24.28571429,0.523615199,0,0.403234768,,,0.548131963
4377,Are losartan and imatinib effective against SARS-CoV2 pathogenesis? A pathophysiologic-based in silico study.,In Silico Pharmacol,33294307,12/10/20,pubmed,0,2,in silico,0.953583559,0.001593541,0.001593524,0.040042318,0.00159354,0.001593518,Drug discovery,0.27489617,FALSE,12,0.183190055,5.5,0.267259834,4,0.707574542,,,0.386008144
4378,"SARS-CoV-2 receptor ACE2 is co-expressed with genes related to transmembrane serine proteases, viral entry, immunity and cellular stress.",Sci Rep,33293627,12/10/20,pubmed,0,2,"sequencing, dataset",0.874034152,0.029777453,0.001538119,0.00153818,0.054774224,0.038337872,Drug discovery,0.75763273,TRUE,145.5,0.939884965,160,0.891824993,3,0.667819001,,,0.83317632
4379,A cross-talk between epithelium and endothelium mediates human alveolar-capillary injury during SARS-CoV-2 infection.,Cell Death Dis,33293527,12/10/20,pubmed,0,13,proteom,0.959118128,0.00178659,0.001786546,0.001786552,0.001786571,0.033735613,Drug discovery,0.5539659,TRUE,18.30769231,0.273919228,,,2,0.618927094,,,0.446423161
4380,"A blood transcriptome-based analysis of disease progression, immune regulation, and symptoms in coronavirus-infected patients.",Cell Death Discov,33293514,12/10/20,pubmed,0,9,transcriptom,0.587697529,0.000880278,0.00088024,0.000880251,0.000880229,0.408781473,Drug discovery,0.45552066,FALSE,47.44444444,0.60931412,62,0.728659352,0,0.403234768,,,0.580402747
4381,"Utility of established prognostic scores in COVID-19 hospital admissions: multicentre prospective evaluation of CURB-65, NEWS2 and qSOFA.",BMJ Open Respir Res,33293361,12/10/20,pubmed,0,21,logistic regression,0.001237065,0.001237078,0.033367883,0.001237099,0.0012371,0.961683775,Clinics,0.7591493,TRUE,100,0.868884903,83.25,0.786259031,3,0.667819001,,,0.774320979
4382,Epidemiological characterisation of asymptomatic carriers of COVID-19 in Colombia: a cross-sectional study.,BMJ Open,33293326,12/10/20,pubmed,0,12,logistic regression,0.002296596,0.002296668,0.002296644,0.412783505,0.337886069,0.242440517,Epidemiology,0.3563828,FALSE,23.66666667,0.34986703,16.75,0.446012845,0,0.403234768,,,0.399704881
4383,"Study protocol for a multicentre, prospective cohort study of the association of angiotensin II type 1 receptor blockers on outcomes of coronavirus infection.",BMJ Open,33293316,12/10/20,pubmed,0,13,logistic regression,0.111517123,0.015155313,0.001010954,0.304272081,0.001011006,0.567033523,Clinics,0.9187355,TRUE,193.0769231,0.97018987,452.3846154,0.975782713,1,0.537564047,,,0.827845543
4384,High serum nitrates levels in non-survivor COVID-19 patients.,Med Intensiva,33293102,12/10/20,pubmed,0,11,logistic regression,0.001653025,0.001653013,0.001653021,0.001653052,0.001653025,0.991734862,Clinics,0.94231707,TRUE,52.27272727,0.648772342,28.45454545,0.557465882,0,0.403234768,,,0.536490997
4385,Food insecurity and its association with changes in nutritional habits among adults during the COVID-19 confinement measures in Belgium.,Public Health Nutr,33292888,12/10/20,pubmed,0,6,logistic regression,0.001511853,0.001511865,0.001511857,0.001511873,0.992440691,0.001511861,Healthcare,0.8995223,TRUE,55.66666667,0.672335952,47,0.668718223,1,0.537564047,,,0.626206074
4386,Application of artificial neural networks to predict the COVID-19 outbreak.,Glob Health Res Policy,33292780,12/10/20,pubmed,0,2,"neural network, prediction model",0.001538092,0.001538097,0.254993868,0.738853723,0.001538109,0.001538111,Epidemiology,0.2583318,FALSE,13,0.197352959,0.5,0.087101953,3,0.667819001,,,0.317424638
4387,Exploring active ingredients and function mechanisms of Ephedra-bitter almond for prevention and treatment of Corona virus disease 2019 (COVID-19) based on network pharmacology.,BioData Min,33292385,12/10/20,pubmed,0,3,data mining,0.947958243,0.001350338,0.001350346,0.017923975,0.030066747,0.001350351,Drug discovery,0.98833,TRUE,13,0.197352959,0.666666667,0.096200161,0,0.403234768,,,0.232262629
4388,SARS-CoV-2 and mitochondrial health: implications of lifestyle and ageing.,Immun Ageing,33292333,12/10/20,pubmed,0,7,immunome,0.648170751,0.105948951,0.00186174,0.181423003,0.001861913,0.060733641,Drug discovery,0.92156243,TRUE,198.1428571,0.972045272,191.8571429,0.913232539,3,0.667819001,,,0.851032271
4389,Expression and clinical significance of SARS-CoV-2 human targets in neoplastic and non-neoplastic lung tissues.,Curr Cancer Drug Targets,33292131,12/10/20,pubmed,0,4,bioinformatic,0.577258227,0.001291275,0.00129125,0.001291273,0.001291253,0.417576722,Drug discovery,0.8457998,TRUE,166.5,0.955841425,136.25,0.871153332,1,0.537564047,,,0.788186268
4390,0,J Biomol Struct Dyn,33292085,12/10/20,pubmed,0,4,computational,0.992032045,0.001593608,0.001593516,0.001593598,0.001593715,0.001593518,Drug discovery,0.9608092,TRUE,50.5,0.635351599,21.75,0.50046829,0,0.403234768,,,0.513018219
4391,0,J Biomol Struct Dyn,33292056,12/10/20,pubmed,0,7,in silico,0.993349704,0.001330071,0.001330089,0.001330077,0.001330024,0.001330036,Drug discovery,0.88361716,TRUE,548.1428571,0.998144598,246.5714286,0.937182232,0,0.403234768,,,0.779520532
4392,Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State.,Int J Environ Res Public Health,33291673,12/10/20,pubmed,0,5,bayes,0.002183241,0.002183298,0.002183281,0.924323258,0.002183317,0.066943605,Epidemiology,0.55527306,TRUE,60,0.703444864,36.8,0.615600749,0,0.403234768,,,0.57409346
4393,FDA-Approved Drugs with Potent In Vitro Antiviral Activity against Severe Acute Respiratory Syndrome Coronavirus 2.,Pharmaceuticals (Basel),33291642,12/10/20,pubmed,0,20,virtual screening,0.953009277,0.001823444,0.001823312,0.001823331,0.039697287,0.00182335,Drug discovery,0.7615365,TRUE,29.35,0.423155421,27.75,0.551645705,3,0.667819001,,,0.547540042
4394,Prognostic Factors of COVID-19 Infection in Elderly Patients: A Multicenter Study.,J Clin Med,33291617,12/10/20,pubmed,0,6,logistic regression,0.001350401,0.001350349,0.022520182,0.036570924,0.001350397,0.936857746,Clinics,0.9690027,TRUE,72.16666667,0.773764611,36.16666667,0.612122023,1,0.537564047,,,0.641150227
4395,Is It Possible to Find Something Positive in Being Confined Due to COVID-19? Implications for Well-Being.,Int J Environ Res Public Health,33291398,12/10/20,pubmed,0,4,logistic regression,0.002562632,0.002562689,0.312198613,0.002562763,0.677550606,0.002562697,Healthcare,0.8347542,TRUE,15,0.227596017,1.75,0.148381054,1,0.537564047,,,0.304513706
4396,Multiple SARS-CoV-2 Introductions Shaped the Early Outbreak in Central Eastern Europe: Comparing Hungarian Data to a Worldwide Sequence Data-Matrix.,Viruses,33291299,12/10/20,pubmed,0,22,"genomes, network analysis",0.001717247,0.857925953,0.135204833,0.001717328,0.001717236,0.001717402,Genomics,0.65653515,TRUE,55.5,0.671346404,50.72727273,0.6839711,0,0.403234768,,,0.58618409
4397,The International Virus Bioinformatics Meeting 2020.,Viruses,33291220,12/10/20,pubmed,0,19,"bioinformatic, proteom, metagenom",0.138542211,0.33349679,0.066368073,0.38543847,0.074642613,0.001511843,Epidemiology,0.8044807,TRUE,30.31578947,0.435215536,52.57894737,0.691062349,0,0.403234768,,,0.509837551
4398,In silico screening of potential anti-COVID-19 bioactive natural constituents from food sources by molecular docking.,Nutrition,33290972,12/9/20,pubmed,0,4,"virtual screening, in silico",0.848142355,0.002130675,0.002130818,0.143334772,0.002130737,0.002130643,Drug discovery,0.9810126,TRUE,34.5,0.482404601,162.5,0.894233342,0,0.403234768,,,0.593290904
4399,Genomic and evolutionary comparison between SARS-CoV-2 and other human coronaviruses.,J Virol Methods,33290786,12/9/20,pubmed,0,5,"genomes, genomic structure",0.036811065,0.957036522,0.001538079,0.001538135,0.001538107,0.001538092,Genomics,0.43519807,FALSE,33.8,0.474117138,34.8,0.604094193,1,0.537564047,,,0.538591793
4400,Assessment of antiviral potencies of cannabinoids against SARS-CoV-2 using computational and in vitro approaches.,Int J Biol Macromol,33290767,12/9/20,pubmed,0,7,"virtual screening, computational, in silico",0.990882999,0.001823371,0.001823349,0.00182358,0.001823353,0.001823349,Drug discovery,0.91272056,TRUE,48.28571429,0.616550189,33,0.593256623,4,0.707574542,,,0.639127118
4401,MINERVA: A Facile Strategy for SARS-CoV-2 Whole-Genome Deep Sequencing of Clinical Samples.,Mol Cell,33290743,12/9/20,pubmed,0,14,"sequencing, transcriptom, metagenom, whole-genome, genomes, metatranscriptom, deep sequencing",0.002130698,0.758900483,0.232576541,0.002130742,0.002130717,0.00213082,Genomics,0.6710541,TRUE,17.07142857,0.257591688,13.78571429,0.408549639,0,0.403234768,,,0.356458698
4402,Prevalence of anxiety towards COVID-19 and its associated factors among healthcare workers in a Hospital of Ethiopia.,PLoS One,33290427,12/9/20,pubmed,0,5,logistic regression,0.001438093,0.001438088,0.00143812,0.001438089,0.846513683,0.147733927,Healthcare,0.9069878,TRUE,4.8,0.065248315,0.6,0.09011239,1,0.537564047,,,0.230974917
4403,Targeting TMPRSS2 and Cathepsin B/L together may be synergistic against SARS-CoV-2 infection.,PLoS Comput Biol,33290397,12/9/20,pubmed,0,3,mathematical model,0.950749617,0.001046838,0.001046837,0.045063018,0.001046827,0.001046863,Drug discovery,0.5076488,TRUE,40.66666667,0.546292288,30.33333333,0.572116671,7,0.785110192,,,0.634506384
4404,Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis.,J Med Internet Res,33290247,12/9/20,pubmed,0,6,"machine learning, predictive model",0.001126845,0.001126803,0.00112691,0.516167582,0.479325043,0.001126817,Epidemiology,0.95809853,TRUE,27.66666667,0.403488156,,,0,0.403234768,,,0.403361462
4405,Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics.,Bull Math Biol,33289877,12/9/20,pubmed,0,4,predictive model,0.001987105,0.00198715,0.045046787,0.920807676,0.028184107,0.001987174,Epidemiology,0.23120981,FALSE,80,0.807532933,249,0.937918116,0,0.403234768,,,0.716228606
4406,Practice of COVID-19 Preventive Measures and Its Associated Factors among Students in Ghana.,Am J Trop Med Hyg,33289471,12/9/20,pubmed,0,3,logistic regression,0.001371221,0.00137126,0.001371224,0.001371331,0.97255729,0.021957675,Healthcare,0.34491217,FALSE,9.666666667,0.144968767,4.666666667,0.246721969,1,0.537564047,,,0.309751595
4407,0,J Biomol Struct Dyn,33289456,12/9/20,pubmed,0,10,"virtual screening, molecular dynamics simulation, in silico",0.993982703,0.001203451,0.001203445,0.001203417,0.001203531,0.001203452,Drug discovery,0.9859744,TRUE,28.1,0.408683283,20.5,0.486218892,1,0.537564047,,,0.477488741
4408,Prevalence and Associated Factors of Intimate Partner Violence Among Married Women During COVID-19 Pandemic Restrictions: A Community-Based Study.,J Interpers Violence,33289437,12/9/20,pubmed,0,5,logistic regression,0.001511835,0.00151184,0.001511916,0.188480863,0.805471714,0.001511832,Healthcare,0.9059813,TRUE,3.8,0.047374606,0.8,0.101351351,1,0.537564047,,,0.228763335
4409,Repurposing potential of FDA-approved and investigational drugs for COVID-19 targeting SARS-CoV-2 spike and main protease and validation by machine learning algorithm.,Chem Biol Drug Des,33289334,12/9/20,pubmed,0,2,machine learning,0.89631019,0.00104682,0.099502565,0.001046803,0.001046806,0.001046817,Drug discovery,0.96739745,TRUE,9.5,0.143051518,0,0.055525823,0,0.403234768,,,0.200604036
4410,Alimentary system is directly attacked by SARS-COV-2 and further prevents immune dysregulation caused by COVID-19.,Int J Clin Pract,33289233,12/9/20,pubmed,0,7,bioinformatic,0.144764248,0.033243144,0.00127268,0.001272758,0.001272725,0.818174445,Clinics,0.8793968,TRUE,55.42857143,0.670604243,46.28571429,0.665440193,0,0.403234768,,,0.579759734
4411,Antigenic variation of SARS-CoV-2 in response to immune pressure.,Mol Ecol,33289207,12/9/20,pubmed,0,7,genomes,0.567698627,0.427487438,0.001203416,0.001203526,0.001203494,0.001203499,Drug discovery,0.19139946,FALSE,138,0.930546107,145,0.879114263,0,0.403234768,,,0.737631712
4412,Hidden in plain sight: The effects of BCG vaccination in the COVID-19 pandemic.,J Med Virol,33289122,12/9/20,pubmed,0,8,"bioinformatic, transcriptom, genomes, dataset",0.776762628,0.042105941,0.001861715,0.001861727,0.175546156,0.001861834,Drug discovery,0.6182637,TRUE,31.25,0.447213804,9.875,0.351819641,0,0.403234768,,,0.400756071
4413,Outlier-SMOTE: A refined oversampling technique for improved detection of COVID-19.,Intell Based Med,33289013,12/9/20,pubmed,0,2,"classifier, dataset",0.001565311,0.001565379,0.950190965,0.04354756,0.001565391,0.001565394,Epidemiology,0.28219557,FALSE,6.5,0.093512277,0.5,0.087101953,0,0.403234768,,,0.194616333
4414,Asymptomatic reactivation of SARS-CoV-2 in a child with neuroblastoma characterised by whole genome sequencing.,IDCases,33288996,12/9/20,pubmed,0,16,"sequencing, whole genome",0.015998367,0.708713711,0.015998421,0.015998721,0.227292194,0.015998586,Genomics,0.4874031,FALSE,27.6875,0.403550003,4.4375,0.239363126,0,0.403234768,,,0.348715965
4415,A mathematical model to examine the effect of quarantine on the spread of coronavirus.,Chaos Solitons Fractals,33288973,12/9/20,pubmed,0,4,mathematical model,0.003607244,0.003607263,0.003607291,0.810500082,0.175070846,0.003607274,Epidemiology,0.8778064,TRUE,7.5,0.108355495,7.5,0.307867273,1,0.537564047,,,0.317928938
4416,Development of a novel risk score to predict mortality in patients admitted to hospital with COVID-19.,Sci Rep,33288840,12/9/20,pubmed,0,6,logistic regression,0.00118728,0.001187318,0.001187328,0.00118735,0.053071147,0.942179577,Clinics,0.8238355,TRUE,38.83333333,0.527367184,7.833333333,0.314088841,3,0.667819001,,,0.503091676
4417,Hyperglycemia associated with lymphopenia and disease severity of COVID-19 in type 2 diabetes mellitus.,J Diabetes Complications,33288414,12/9/20,pubmed,0,5,logistic regression,0.174079681,0.00083852,0.000838505,0.000838514,0.000838511,0.82256627,Clinics,0.92878485,TRUE,17.6,0.264456676,3,0.199424672,0,0.403234768,,,0.289038705
4418,Development of a data-driven COVID-19 prognostication tool to inform triage and step-down care for hospitalised patients in Hong Kong: a population-based cohort study.,BMC Med Inform Decis Mak,33287804,12/9/20,pubmed,0,9,dataset,0.000916693,0.000916719,0.205487921,0.134162515,0.000916742,0.657599409,Clinics,0.9634625,TRUE,11.33333333,0.170635166,13.55555556,0.405806797,0,0.403234768,,,0.32655891
4419,Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic.,BMC Public Health,33287789,12/9/20,pubmed,0,6,bayes,0.000846553,0.000846552,0.000846555,0.995767264,0.00084655,0.000846526,Epidemiology,0.058448166,FALSE,50.33333333,0.63380543,43.5,0.651725983,0,0.403234768,,,0.56292206
4420,0,Antibiotics (Basel),33287311,12/9/20,pubmed,0,4,in silico,0.932793433,0.001392843,0.001392834,0.026337163,0.00139303,0.036690696,Drug discovery,0.932724,TRUE,28.5,0.412579628,31.75,0.583088039,1,0.537564047,,,0.511077238
4421,Dental Challenges and the Needs of the Population during the Covid-19 Pandemic Period. Real-Time Surveillance Using Google Trends.,Int J Environ Res Public Health,33287130,12/9/20,pubmed,0,2,correlation analysis,0.001987108,0.001987175,0.001987119,0.990064265,0.00198719,0.001987143,Epidemiology,0.902985,TRUE,29.5,0.426000371,13.5,0.405539203,0,0.403234768,,,0.411591447
4422,Information Search and Financial Markets under COVID-19.,Entropy (Basel),33286562,12/9/20,pubmed,0,3,bayes,0.002562886,0.00256263,0.047098027,0.942650799,0.002563009,0.002562648,Epidemiology,0.30395937,FALSE,13.66666667,0.207310285,2,0.164302917,3,0.667819001,,,0.346477401
4423,"Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features.",Entropy (Basel),33286289,12/9/20,pubmed,0,6,"deep learning, neural network, classifier, lstm, dataset",0.001538111,0.032018877,0.961828567,0.001538172,0.0015381,0.001538173,Imaging,0.94695365,TRUE,13.5,0.205393036,2,0.164302917,17,0.887338725,,,0.419011559
4424,Hypergraph learning for identification of COVID-19 with CT imaging.,Med Image Anal,33285483,12/8/20,pubmed,0,16,"radiom, dataset",0.00139286,0.001392879,0.993035518,0.001392952,0.001392882,0.001392909,Imaging,0.49304557,FALSE,39.66666667,0.536211268,39.46666667,0.630318437,6,0.764429903,,,0.643653203
4425,COVID-AL: The diagnosis of COVID-19 with deep active learning.,Med Image Anal,33285482,12/8/20,pubmed,0,5,"deep learning, active learning, dataset",0.00135046,0.022459315,0.972139145,0.001350387,0.001350361,0.001350332,Imaging,0.7979152,TRUE,27.2,0.397674562,6.8,0.292881991,0,0.403234768,,,0.364597107
4426,An insight into the interaction between α-ketoamide- based inhibitor and coronavirus main protease: A detailed in silico study.,Biophys Chem,33285430,12/8/20,pubmed,0,1,in silico,0.707523115,0.001593536,0.001593635,0.197307197,0.001593554,0.090388963,Drug discovery,0.9598553,TRUE,20,0.298163152,0,0.055525823,0,0.403234768,,,0.252307914
4427,Infection of human Nasal Epithelial Cells with SARS-CoV-2 and a 382-nt deletion isolate lacking ORF8 reveals similar viral kinetics and host transcriptional profiles.,PLoS Pathog,33284849,12/8/20,pubmed,0,11,transcriptom,0.739897086,0.202340902,0.001072218,0.001072251,0.024030274,0.031587269,Drug discovery,0.57923406,TRUE,57.90909091,0.688045024,57.90909091,0.714008563,6,0.764429903,,,0.722161164
4428,In silico mutagenesis of human ACE2 with S protein and translational efficiency explain SARS-CoV-2 infectivity in different species.,PLoS Comput Biol,33284795,12/8/20,pubmed,0,4,in silico,0.615162763,0.379818769,0.001254627,0.00125463,0.001254589,0.001254622,Drug discovery,0.4325224,FALSE,12,0.183190055,16.25,0.438988493,5,0.739490092,,,0.453889547
4429,Artificial Intelligence in the Fight Against COVID-19: Scoping Review.,J Med Internet Res,33284779,12/8/20,pubmed,0,8,"artificial intelligence, neural network",0.104326996,0.001126827,0.392514883,0.3831648,0.051160848,0.067705647,Epidemiology,0.87125444,TRUE,37.375,0.512029192,16.125,0.437784319,14,0.866658436,,,0.605490649
4430,Inpatient COVID-19 outcomes in solid organ transplant recipients compared to non-solid organ transplant patients: A retrospective cohort.,Am J Transplant,33284498,12/8/20,pubmed,0,15,logistic regression,0.001486435,0.001486442,0.001486445,0.001486506,0.001486484,0.992567688,Clinics,0.8485528,TRUE,161.1333333,0.952192467,264.8666667,0.944005887,2,0.618927094,,,0.838375149
4431,Endotoxemia and circulating bacteriome in severe COVID-19 patients.,Intensive Care Med Exp,33284413,12/8/20,pubmed,0,32,sequencing,0.048406917,0.324443653,0.001565335,0.001565333,0.001565374,0.622453388,Clinics,0.9537742,TRUE,69.46875,0.760714948,90.625,0.802180894,0,0.403234768,,,0.65537687
4432,Metabolic reprogramming and epigenetic changes of vital organs in SARS-CoV-2-induced systemic toxicity.,JCI Insight,33284134,12/8/20,pubmed,0,24,metabolom,0.534640546,0.067955665,0.001438114,0.001438123,0.001438146,0.393089407,Drug discovery,0.26917905,FALSE,97.41666667,0.863009463,,,2,0.618927094,,,0.740968278
4433,Elevated serum ferritin level effectively discriminates severity illness and liver injury of coronavirus disease 2019 pneumonia.,Biomarkers,33284041,12/8/20,pubmed,0,8,correlation analysis,0.001717416,0.001717493,0.001717295,0.001717283,0.001717237,0.991413276,Clinics,0.94491017,TRUE,98,0.864741171,55.625,0.704776559,1,0.537564047,,,0.702360592
4434,Three-Dimensional Analysis of Particle Distribution on Filter Layers inside N95 Respirators by Deep Learning.,Nano Lett,33283521,12/8/20,pubmed,0,10,deep learning,0.002183333,0.002183291,0.571500789,0.221752316,0.200197041,0.00218323,Imaging,0.41392893,FALSE,80.3,0.808089554,41.3,0.641222906,4,0.707574542,,,0.718962334
4435,"Low bioavailability hinders drug discovery against COVID-19, guided by in silico docking.",Br J Pharmacol,33283265,12/8/20,pubmed,0,1,in silico,0.920006133,0.015998856,0.01599887,0.01599897,0.01599846,0.015998711,Drug discovery,0.49522552,FALSE,59,0.696579875,14,0.412898047,0,0.403234768,,,0.504237563
4436,"The Intersection between COVID-19, the Gene Family of ACE2 and Alzheimer's Disease.",Neurosci Insights,33283188,12/8/20,pubmed,0,5,genome-wide,0.507627426,0.317897547,0.002130674,0.002130797,0.168082759,0.002130797,Drug discovery,0.7018873,TRUE,52.8,0.652173913,163.8,0.895236821,0,0.403234768,,,0.650215167
4437,"The COVID-19 pandemics and the relevance of biosafety facilities for metagenomics surveillance, structured disease prevention and control.",Biosaf Health,33283181,12/8/20,pubmed,0,2,metagenom,0.216800243,0.126040292,0.002720167,0.648998319,0.002720442,0.002720538,Epidemiology,0.7243496,TRUE,109.5,0.889479869,107,0.834559807,1,0.537564047,,,0.753867908
4438,SSSCPreds: Deep Neural Network-Based Software for the Prediction of Conformational Variability and Application to SARS-CoV-2.,ACS Omega,33283104,12/8/20,pubmed,0,3,"neural network, dataset",0.423831965,0.195802526,0.377069018,0.001098838,0.001098832,0.00109882,Drug discovery,0.66102624,TRUE,170.6666667,0.958748222,28.33333333,0.556729997,0,0.403234768,,,0.639570996
4439,"Combating the Pandemic COVID-19: Clinical Trials, Therapies and Perspectives.",Front Mol Biosci,33282914,12/8/20,pubmed,0,7,in silico,0.701377464,0.001717236,0.001717221,0.29175367,0.001717207,0.001717203,Drug discovery,0.9370564,TRUE,36,0.498299215,15.57142857,0.430960664,3,0.667819001,,,0.532359626
4440,Pre-existing Liver Diseases and On-Admission Liver-Related Laboratory Tests in COVID-19: A Prognostic Accuracy Meta-Analysis With Systematic Review.,Front Med (Lausanne),33282888,12/8/20,pubmed,0,19,predictive model,0.001203524,0.001203423,0.001203438,0.001203526,0.001203455,0.993982634,Clinics,0.8315357,TRUE,26.73684211,0.391489888,2.473684211,0.175541879,2,0.618927094,,,0.39531962
4441,Dental Risks and Precautions during COVID-19 Pandemic: A Systematic Review.,J Int Soc Prev Community Dent,33282761,12/8/20,pubmed,0,2,dataset,0.03844974,0.00111268,0.001112659,0.603680895,0.269943126,0.0857009,Epidemiology,0.97220373,TRUE,6,0.086028821,2,0.164302917,1,0.537564047,,,0.262631928
4442,Comparison of clinical characteristics between patients with coronavirus disease 2019 (COVID-19) who retested RT-PCR positive versus negative: a retrospective study of data from Nanjing.,J Thorac Dis,33282346,12/8/20,pubmed,0,20,logistic regression,0.001371307,0.001371288,0.001371327,0.001371262,0.001371296,0.993143519,Clinics,0.9190353,TRUE,92.25,0.849650566,31.45,0.580545892,0,0.403234768,,,0.611143742
4443,The gut microbiome: an under-recognised contributor to the COVID-19 pandemic?,Therap Adv Gastroenterol,33281941,12/8/20,pubmed,0,6,microbiom,0.529183542,0.268196878,0.000926309,0.051352457,0.000926307,0.149414507,Drug discovery,0.86838454,TRUE,55.66666667,0.672335952,51.66666667,0.687918116,4,0.707574542,,,0.689276203
4444,Crowding effects on the dynamics of COVID-19 mathematical model.,Adv Differ Equ,33281894,12/8/20,pubmed,0,4,mathematical model,0.003335297,0.003335284,0.003335468,0.983323083,0.003335514,0.003335354,Epidemiology,0.5056204,TRUE,52,0.647349867,7.25,0.302983677,0,0.403234768,,,0.451189437
4445,Identification of Potential Key Agents for Targeting RNA-Dependent RNA Polymerase of SARS-CoV-2 by Integrated Analysis and Virtual Drug Screening.,Front Genet,33281876,12/8/20,pubmed,0,6,virtual screening,0.958096923,0.036954771,0.001237068,0.001237053,0.001237108,0.001237077,Drug discovery,0.9555803,TRUE,10.66666667,0.159131672,9.666666667,0.348274017,0,0.403234768,,,0.303546819
4446,"Perceived Impact of Covid-19 Across Different Mental Disorders: A Study on Disorder-Specific Symptoms, Psychosocial Stress and Behavior.",Front Psychol,33281685,12/8/20,pubmed,0,6,bayes,0.001098806,0.001098815,0.001098834,0.053402202,0.942202488,0.001098855,Healthcare,0.90861535,TRUE,34.33333333,0.480425506,53,0.693738293,1,0.537564047,,,0.570575949
4447,Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods.,Chaos Solitons Fractals,33281306,12/8/20,pubmed,0,1,"machine learning, prediction model, dataset",0.001823307,0.001823327,0.316772882,0.675933799,0.001823347,0.001823338,Epidemiology,0.40805748,FALSE,49,0.624281032,21,0.492239765,1,0.537564047,,,0.551361614
4448,Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization.,Chaos Solitons Fractals,33281305,12/8/20,pubmed,0,2,"bayes, deep learning, neural network, lstm",0.001272638,0.001272631,0.486233122,0.508676308,0.001272652,0.001272648,Epidemiology,0.6123857,TRUE,12,0.183190055,2,0.164302917,0,0.403234768,,,0.25024258
4449,Modeling and simulations of CoViD-19 molecular mechanism induced by cytokines storm during SARS-CoV2 infection.,J Mol Liq,33281252,12/8/20,pubmed,0,5,computational,0.753457848,0.001392934,0.001393003,0.240970464,0.001392834,0.001392916,Drug discovery,0.78032756,TRUE,12.6,0.190302431,1.2,0.126103827,0,0.403234768,,,0.239880342
4450,Decrease in hemoglobin level predicts increased risk for severe respiratory failure in COVID-19 patients with pneumonia.,Respir Investig,33281114,12/8/20,pubmed,0,18,logistic regression,0.001652999,0.001653059,0.001653091,0.001653073,0.001653043,0.991734736,Clinics,0.9665209,TRUE,29.38888889,0.423464655,7.944444444,0.31509232,1,0.537564047,,,0.425373674
4451,Reduced cardiac function is associated with cardiac injury and mortality risk in hospitalized COVID-19 Patients.,Clin Cardiol,33280140,12/7/20,pubmed,0,25,logistic regression,0.001438123,0.001438094,0.001438081,0.001438093,0.001438093,0.992809515,Clinics,0.672634,TRUE,36.88,0.506957759,37.92,0.621086433,1,0.537564047,,,0.555202746
4452,Text mining approaches for dealing with the rapidly expanding literature on COVID-19.,Brief Bioinform,33279995,12/7/20,pubmed,0,2,text mining,0.001254682,0.001254664,0.400393977,0.594587445,0.001254636,0.001254597,Epidemiology,0.04359454,FALSE,29,0.41993939,121.5,0.853425207,0,0.403234768,,,0.558866455
4453,"Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities.",Brief Bioinform,33279989,12/7/20,pubmed,0,11,"sequencing, genomes",0.001392904,0.710591771,0.001392879,0.283836731,0.001392892,0.001392822,Genomics,0.45586646,FALSE,99.72727273,0.868266436,298.4545455,0.953839979,3,0.667819001,,,0.829975139
4454,A review of COVID-19 biomarkers and drug targets: resources and tools.,Brief Bioinform,33279954,12/7/20,pubmed,0,4,"machine learning, computational, artificial intelligence, bioinformatic",0.495178116,0.001861758,0.363831419,0.135405119,0.001861753,0.001861835,Drug discovery,0.32715112,FALSE,12.25,0.185107304,27.75,0.551645705,0,0.403234768,,,0.379995926
4455,Metabolomics of exhaled breath in critically ill COVID-19 patients: A pilot study.,EBioMedicine,33279860,12/7/20,pubmed,0,13,metabolom,0.03288994,0.002296579,0.347993429,0.002296631,0.002296599,0.612226822,Clinics,0.86922306,TRUE,85.15384615,0.825468489,139.0769231,0.873360985,6,0.764429903,,,0.821086459
4456,Cardiac Injury and Outcomes of Patients with COVID-19 in New York City.,Heart Lung Circ,33279410,12/7/20,pubmed,0,4,logistic regression,0.000988376,0.000988341,0.023635984,0.000988346,0.01572111,0.957677842,Clinics,0.933803,TRUE,50,0.632073721,4.75,0.248260637,1,0.537564047,,,0.472632802
4457,"Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study.",Lancet Public Health,33278917,12/7/20,pubmed,0,21,"logistic regression, dataset",0.000815328,0.054808914,0.000815374,0.607623552,0.257924446,0.078012386,Epidemiology,0.6474714,TRUE,154.8571429,0.947801348,355.6666667,0.966751405,1,0.537564047,,,0.817372267
4458,Implementation of a cluster randomized controlled trial: Identifying student peer leaders to lead E-cigarette interventions.,Addict Behav,33278717,12/6/20,pubmed,0,7,network analysis,0.001461904,0.001461858,0.044940935,0.088768911,0.861904516,0.001461877,Healthcare,0.6679267,TRUE,88.85714286,0.838889232,711.8571429,0.987489965,0,0.403234768,,,0.743204655
4459,"Spatiotemporal ecological study of COVID-19 mortality in the city of São Paulo, Brazil: Shifting of the high mortality risk from areas with the best to those with the worst socio-economic conditions.",Travel Med Infect Dis,33278610,12/6/20,pubmed,0,6,"bayes, bayesian model",0.00178656,0.001786551,0.047157188,0.745230015,0.001786633,0.202253053,Epidemiology,0.49110553,FALSE,31,0.445111015,7.166666667,0.301311212,4,0.707574542,,,0.48466559
4460,2-Pyridone natural products as inhibitors of SARS-CoV-2 main protease.,Chem Biol Interact,33278462,12/6/20,pubmed,0,5,in silico,0.993349714,0.001330044,0.00133004,0.001330116,0.001330064,0.001330024,Drug discovery,0.9451479,TRUE,39,0.530521368,28.2,0.555860316,0,0.403234768,,,0.496538817
4461,An evaluation of MRI lumbar spine scans within a community-based diagnostic setting.,Musculoskeletal Care,33278329,12/6/20,pubmed,0,3,logistic regression,0.001350489,0.001350386,0.194277286,0.375089923,0.206401517,0.221530398,Epidemiology,0.78872204,TRUE,32.33333333,0.459273919,15,0.42594327,0,0.403234768,,,0.429483986
4462,Medication access difficulty and COVID-related distress are associated with disease flares in rheumatology patients during the COVID-19 pandemic.,Arthritis Care Res (Hoboken),33278068,12/6/20,pubmed,0,12,logistic regression,0.001254594,0.00125458,0.001254588,0.001254685,0.757491371,0.237490183,Healthcare,0.96803844,TRUE,24.66666667,0.36334962,28.33333333,0.556729997,0,0.403234768,,,0.441104795
4463,Germany's next shutdown-Possible scenarios and outcomes.,Influenza Other Respir Viruses,33277962,12/6/20,pubmed,0,2,mathematical model,0.005047526,0.005048034,0.005047636,0.974760657,0.005047834,0.005048314,Epidemiology,0.19780558,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
4464,"Host genetics and infectious disease: new tools, insights and translational opportunities.",Nat Rev Genet,33277640,12/6/20,pubmed,0,3,multi-omics,0.355565825,0.334888015,0.001786592,0.304186283,0.001786685,0.0017866,Drug discovery,0.43843406,FALSE,64.33333333,0.730781124,158,0.890487022,1,0.537564047,,,0.719610731
4465,Untuned antiviral immunity in COVID-19 revealed by temporal type I/III interferon patterns and flu comparison.,Nat Immunol,33277638,12/6/20,pubmed,0,16,transcriptom,0.390507758,0.001126875,0.001126816,0.001126892,0.001126817,0.604984843,Clinics,0.3709919,FALSE,29.8125,0.428597934,23.125,0.513848006,0,0.403234768,,,0.448560236
4466,Management strategies in a SEIR-type model of COVID 19 community spread.,Sci Rep,33277553,12/6/20,pubmed,0,3,predictive model,0.001717196,0.001717201,0.00171721,0.991413998,0.001717218,0.001717177,Epidemiology,0.13738671,FALSE,14.33333333,0.216772837,4.666666667,0.246721969,5,0.739490092,,,0.400994966
4467,Age-stratified discrete compartment model of the COVID-19 epidemic with application to Switzerland.,Sci Rep,33277545,12/6/20,pubmed,0,2,computational,0.001653038,0.001653043,0.001653056,0.852252488,0.001653071,0.141135303,Epidemiology,0.109089464,FALSE,65,0.734801163,82.5,0.78445277,1,0.537564047,,,0.685605993
4468,"Cutting Edge: Severe SARS-CoV-2 Infection in Humans Is Defined by a Shift in the Serum Lipidome, Resulting in Dysregulation of Eicosanoid Immune Mediators.",J Immunol,33277388,12/6/20,pubmed,0,12,lipidom,0.320869197,0.003466074,0.003466057,0.003465989,0.003465904,0.665266779,Clinics,0.849146,TRUE,43.33333333,0.571154679,61.58333333,0.726652395,5,0.739490092,,,0.679099055
4469,0,J Clin Microbiol,33277340,12/6/20,pubmed,0,11,logistic regression,0.001392855,0.123893658,0.001392893,0.001392846,0.001392895,0.870534853,Clinics,0.6316979,TRUE,26.36363636,0.386294762,42.18181818,0.645437517,1,0.537564047,,,0.523098775
4470,Molecular docking of potential SARS-CoV-2 papain-like protease inhibitors.,Biochem Biophys Res Commun,33276953,12/6/20,pubmed,0,3,in silico,0.989083911,0.002183227,0.002183206,0.002183245,0.002183221,0.00218319,Drug discovery,0.9521962,TRUE,17.33333333,0.261178799,25.66666667,0.534854161,0,0.403234768,,,0.399755909
4471,"A rapid, cost-effective tailed amplicon method for sequencing SARS-CoV-2.",BMC Genomics,33276717,12/6/20,pubmed,0,9,"sequencing, metagenom",0.001823375,0.802151386,0.190555158,0.001823431,0.001823345,0.001823306,Genomics,0.20424372,FALSE,26.55555556,0.389696332,39.33333333,0.629649451,0,0.403234768,,,0.474193517
4472,Associations between COVID-19-Related Digital Health Literacy and Online Information-Seeking Behavior among Portuguese University Students.,Int J Environ Res Public Health,33276647,12/6/20,pubmed,0,10,"logistic regression, digital health",0.002080567,0.002080545,0.002080829,0.108826548,0.88285092,0.002080592,Healthcare,0.7690177,TRUE,32.8,0.464221659,7.7,0.311011507,1,0.537564047,,,0.437599071
4473,DBCOVP: A database of coronavirus virulent glycoproteins.,Comput Biol Med,33276297,12/5/20,pubmed,0,9,"genomes, sequence alignment",0.540326237,0.453826053,0.001461954,0.001461965,0.001461918,0.001461873,Drug discovery,0.46632606,FALSE,30.11111111,0.432803513,13.77777778,0.40848274,0,0.403234768,,,0.41484034
4474,Techniques assisting peptide vaccine and peptidomimetic design. Sidechain exposure in the SARS-CoV-2 spike glycoprotein.,Comput Biol Med,33276271,12/5/20,pubmed,0,1,"machine learning, bioinformatic, data mining",0.621207275,0.129566469,0.0621474,0.090727472,0.094813283,0.001538101,Drug discovery,0.50267327,TRUE,6,0.086028821,6,0.280037463,0,0.403234768,,,0.256433684
4475,Drug designing against NSP15 of SARS-COV2 via high throughput computational screening and structural dynamics approach.,Eur J Pharmacol,33275961,12/5/20,pubmed,0,4,"computational, dataset",0.957982156,0.001187282,0.037268727,0.001187302,0.001187271,0.001187262,Drug discovery,0.9455067,TRUE,16,0.243552477,2.5,0.180826866,2,0.618927094,,,0.347768813
4476,Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.,Cell,33275900,12/5/20,pubmed,0,581,"whole genome, dataset",0.002238487,0.883963261,0.00223863,0.002238625,0.002238541,0.107082457,Genomics,0.13684306,FALSE,39.20446735,0.531881996,,,4,0.707574542,,,0.619728269
4477,COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network.,IEEE J Biomed Health Inform,33275588,12/5/20,pubmed,0,4,"machine learning, adversarial network, deep-learning",0.001112656,0.015526759,0.98002263,0.001112642,0.001112703,0.001112611,Imaging,0.4675283,FALSE,90.5,0.844331746,12.5,0.392761573,3,0.667819001,,,0.634970774
4478,"A Study of Potential SARS-CoV-2 Antiviral Drugs and Preliminary Research of Their Molecular Mechanism, Based on Anti-SARS-CoV Drug Screening and Molecular Dynamics Simulation.",J Comput Biol,33275483,12/5/20,pubmed,0,5,molecular dynamics simulation,0.957987027,0.008402592,0.008402653,0.00840269,0.008402501,0.008402537,Drug discovery,0.7669592,TRUE,29,0.41993939,11.2,0.373026492,0,0.403234768,,,0.39873355
4479,Application of machine intelligence technology in the detection of vaccines and medicines for SARS-CoV-2.,Eur Rev Med Pharmacol Sci,33275275,12/5/20,pubmed,0,8,"artificial intelligence, machine intelligence",0.502452562,0.002490465,0.487585402,0.002490517,0.002490548,0.002490505,Drug discovery,0.90033805,TRUE,3.75,0.046817985,0,0.055525823,1,0.537564047,,,0.213302618
4480,SARS-CoV-2 systemic infection in a kidney transplant recipient: sequence analysis in clinical specimens.,Eur Rev Med Pharmacol Sci,33275263,12/5/20,pubmed,0,12,genome sequences,0.001371352,0.746942185,0.00137126,0.001371273,0.001371279,0.247572652,Genomics,0.8505738,TRUE,19.41666667,0.289628301,7.666666667,0.310877709,0,0.403234768,,,0.334580259
4481,StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images.,Phys Eng Sci Med,33275187,12/5/20,pubmed,0,4,"deep learning, classifier, adversarial network, transfer learning, dataset",0.001237096,0.056161934,0.938889718,0.001237111,0.001237074,0.001237067,Imaging,0.15586609,FALSE,5.5,0.077246583,0.25,0.065493712,0,0.403234768,,,0.181991687
4482,"Insulin Use, Diabetes Control, and Outcomes in Patients with COVID-19.",Endocr Res,33275067,12/5/20,pubmed,0,9,logistic regression,0.001237055,0.001237055,0.00123706,0.001237113,0.00123719,0.993814527,Clinics,0.98863053,TRUE,27.22222222,0.397736409,2.333333333,0.173401124,1,0.537564047,,,0.369567193
4483,Computational Insights into the Conformational Accessibility and Binding Strength of SARS-CoV-2 Spike Protein to Human Angiotensin-Converting Enzyme 2.,J Phys Chem Lett,33274945,12/5/20,pubmed,0,9,computational,0.990883133,0.001823377,0.001823358,0.001823429,0.001823357,0.001823346,Drug discovery,0.16870975,FALSE,55.33333333,0.670171315,17.77777778,0.458589778,1,0.537564047,,,0.555441714
4484,"An Artificial Intelligence-Based, Personalized Smartphone App to Improve Childhood Immunization Coverage and Timelines Among Children in Pakistan: Protocol for a Randomized Controlled Trial.",JMIR Res Protoc,33274726,12/5/20,pubmed,0,15,artificial intelligence,0.001022648,0.001022629,0.024323934,0.271074091,0.701534035,0.001022663,Healthcare,0.9293667,TRUE,9.266666667,0.137980085,2.533333333,0.180893765,0,0.403234768,,,0.240702873
4485,Loneliness and depression in patients with cancer during COVID-19.,J Psychosoc Oncol,33274697,12/5/20,pubmed,0,3,dataset,0.002357806,0.0023578,0.084838846,0.002357893,0.573756915,0.33433074,Healthcare,0.7834342,TRUE,14,0.213494959,5.333333333,0.262911426,2,0.618927094,,,0.36511116
4486,Differentiating influenza from COVID-19 in patients presenting with suspected sepsis.,Eur J Clin Microbiol Infect Dis,33274416,12/5/20,pubmed,0,8,logistic regression,0.001987096,0.001987233,0.28839469,0.001987123,0.001987177,0.703656681,Clinics,0.2644701,FALSE,29.625,0.426680685,24.5,0.525086968,0,0.403234768,,,0.451667474
4487,"Derivation of a Clinical Risk Score to Predict 14-Day Occurrence of Hypoxia, ICU Admission, and Death Among Patients with Coronavirus Disease 2019.",J Gen Intern Med,33274414,12/5/20,pubmed,0,6,logistic regression,0.001034591,0.001034577,0.10304571,0.001034639,0.033616497,0.860233987,Clinics,0.98291,TRUE,247.3333333,0.984352774,342.8333333,0.964543752,1,0.537564047,,,0.828820191
4488,Curbing the COVID-19 pandemic with facility-based isolation of mild cases: a mathematical modeling study.,J Travel Med,33274387,12/5/20,pubmed,0,10,mathematical model,0.000936059,0.000936102,0.000936179,0.834396135,0.103012509,0.059783015,Epidemiology,0.66721725,TRUE,45.8,0.593852434,45.3,0.659285523,2,0.618927094,,,0.624021684
4489,A Novel Approach to Hematology Testing at the Point of Care.,J Appl Lab Med,33274357,12/5/20,pubmed,0,5,artificial intelligence,0.057577665,0.001330089,0.557652353,0.10961347,0.068525341,0.205301082,Imaging,0.7918143,TRUE,93.4,0.852681056,157.6,0.890286326,0,0.403234768,,,0.715400717
4490,"CovMulNet19, Integrating Proteins, Diseases, Drugs, and Symptoms: A Network Medicine Approach to COVID-19.",Netw Syst Med,33274348,12/5/20,pubmed,0,6,network analysis,0.687559075,0.001438191,0.001438217,0.066237899,0.001438199,0.24188842,Drug discovery,0.5629878,TRUE,9.5,0.143051518,4.166666667,0.233074659,2,0.618927094,,,0.331684424
4491,0,J Immunol Res,33274246,12/5/20,pubmed,0,7,"in silico, structural model",0.954513835,0.001622791,0.001622778,0.038994987,0.001622865,0.001622744,Drug discovery,0.8689443,TRUE,38.14285714,0.520069268,18.71428571,0.467152796,3,0.667819001,,,0.551680355
4492,Mental Health Impact of COVID-19 on Quarantine Hotel Employees in China.,Risk Manag Healthc Policy,33273872,12/5/20,pubmed,0,4,logistic regression,0.001010933,0.001010931,0.035841793,0.001010978,0.960114407,0.001010958,Healthcare,0.8909458,TRUE,58.25,0.691384749,34,0.5990768,0,0.403234768,,,0.564565439
4493,Forecasting the long-term trend of COVID-19 epidemic using a dynamic model.,Sci Rep,33273592,12/5/20,pubmed,0,11,machine learning,0.001126787,0.001126787,0.060816225,0.93467662,0.001126792,0.00112679,Epidemiology,0.5364978,TRUE,13.81818182,0.209041994,5.727272727,0.270604763,4,0.707574542,,,0.395740433
4494,State-level tracking of COVID-19 in the United States.,Nat Commun,33273462,12/5/20,pubmed,0,53,bayes,0.001987096,0.001987133,0.001987103,0.990064371,0.00198717,0.001987126,Epidemiology,0.25772637,FALSE,56.13207547,0.675799369,205.754717,0.920256891,5,0.739490092,,,0.778515451
4495,Establishing a mass spectrometry-based system for rapid detection of SARS-CoV-2 in large clinical sample cohorts.,Nat Commun,33273458,12/5/20,pubmed,0,11,proteom,0.10162076,0.885971696,0.003101766,0.003101971,0.003101618,0.003102189,Genomics,0.36662132,FALSE,24.90909091,0.366441957,14.18181818,0.413767728,4,0.707574542,,,0.495928076
4496,Identifying and repurposing antiviral drugs against severe acute respiratory syndrome coronavirus 2 with in silico and in vitro approaches.,Biochem Biophys Res Commun,33272566,12/5/20,pubmed,0,1,in silico,0.985967572,0.002806446,0.002806452,0.002806657,0.002806461,0.002806411,Drug discovery,0.75366986,TRUE,164,0.95423341,195,0.9151057,3,0.667819001,,,0.84571937
4497,Mental health status of Chinese residents during the COVID-19 epidemic.,BMC Psychiatry,33272247,12/5/20,pubmed,0,4,logistic regression,0.001126832,0.001126809,0.045934277,0.058073908,0.892611348,0.001126826,Healthcare,0.943265,TRUE,14.75,0.222586431,8,0.320511105,0,0.403234768,,,0.315444101
4498,What Can COVID-19 Teach Us about Using AI in Pandemics?,Healthcare (Basel),33271960,12/5/20,pubmed,0,5,artificial intelligence,0.002639151,0.145672213,0.427340279,0.331295346,0.090414048,0.002638963,Epidemiology,0.33450928,FALSE,26.8,0.392417589,28.8,0.560543216,0,0.403234768,,,0.452065191
4499,COVID-19 Down Under: Australia's Initial Pandemic Experience.,Int J Environ Res Public Health,33271867,12/5/20,pubmed,0,5,mathematical model,0.001987112,0.00198714,0.001987105,0.955720111,0.00198717,0.036331362,Epidemiology,0.3131921,FALSE,69,0.759416167,102.8,0.827200963,3,0.667819001,,,0.75147871
4500,"Social Media Activities, Emotion Regulation Strategies, and Their Interactions on People's Mental Health in COVID-19 Pandemic.",Int J Environ Res Public Health,33271779,12/5/20,pubmed,0,4,dataset,0.077747922,0.001438154,0.001438197,0.162727532,0.755210091,0.001438105,Healthcare,0.7112433,TRUE,40.5,0.544746119,63,0.732606369,2,0.618927094,,,0.632093194
4501,Identification of Persuasive Antiviral Natural Compounds for COVID-19 by Targeting Endoribonuclease NSP15: A Structural-Bioinformatics Approach.,Molecules,33271751,12/5/20,pubmed,0,4,"virtual screening, bioinformatic, genomes",0.933656751,0.060082046,0.00156531,0.001565316,0.001565291,0.001565286,Drug discovery,0.7809646,TRUE,23.25,0.34454821,3,0.199424672,1,0.537564047,,,0.36051231
4502,Making mental health more accessible in light of COVID-19: Scalable digital health with digital navigators in low and middle-income countries.,Asian J Psychiatr,33271713,12/5/20,pubmed,0,5,digital health,0.002806582,0.002806483,0.002806604,0.702660633,0.286113355,0.002806343,Epidemiology,0.5574526,TRUE,337.8,0.993320552,637.8,0.985549906,0,0.403234768,,,0.794035075
4503,Data mining can play a critical role in COVID-19 linked mental health studies.,Asian J Psychiatr,33271698,12/5/20,pubmed,0,1,data mining,0.019530926,0.019530965,0.019530299,0.566209114,0.355666844,0.019531852,Epidemiology,0.6396723,TRUE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
4504,Psychological Screening and Tracking of Athletes and Digital Mental Health Solutions in a Hybrid Model of Care: Mini Review.,JMIR Form Res,33271497,12/4/20,pubmed,0,2,"machine learning, deep learning, artificial intelligence",0.000580143,0.00058014,0.193217038,0.247461844,0.541054655,0.017106181,Healthcare,0.97406256,TRUE,157.5,0.949718597,239.5,0.933904201,0,0.403234768,,,0.762285855
4505,"Chest CT in COVID-19 at the ED: Validation of the COVID-19 Reporting and Data System (CO-RADS) and CT Severity Score: A Prospective, Multicenter, Observational Study.",Chest,33271157,12/4/20,pubmed,0,10,logistic regression,0.001254593,0.001254642,0.362548638,0.001254667,0.001254646,0.632432814,Clinics,0.9741738,TRUE,28.7,0.414373183,23,0.513513514,1,0.537564047,,,0.488483581
4506,Investigation of beta-lactoglobulin derived bioactive peptides against SARS-CoV-2 (COVID-19): In silico analysis.,Eur J Pharmacol,33271151,12/4/20,pubmed,0,4,"virtual screening, in silico",0.853420302,0.001861796,0.001861719,0.11703008,0.001861703,0.0239644,Drug discovery,0.907388,TRUE,32.5,0.461685942,17,0.451097137,0,0.403234768,,,0.438672616
4507,A snapshot of COVID-19 infection in patients with solid tumors.,Int J Cancer,33270902,12/4/20,pubmed,0,18,logistic regression,0.001254654,0.001254638,0.001254637,0.001254661,0.001254634,0.993726776,Clinics,0.50829273,TRUE,46.38888889,0.599666028,77.44444444,0.771875836,0,0.403234768,,,0.59159221
4508,The lower COVID-19 related mortality and incidence rates in Eastern European countries are associated with delayed start of community circulation.,PLoS One,33270782,12/4/20,pubmed,0,5,dataset,0.001203408,0.001203437,0.001203411,0.763925862,0.001203468,0.231260414,Epidemiology,0.44328988,FALSE,84,0.821881378,39.2,0.628712871,0,0.403234768,,,0.617943006
4509,Principal component analysis of coronaviruses reveals their diversity and seasonal and pandemic potential.,PLoS One,33270726,12/4/20,pubmed,0,1,sequencing,0.002806757,0.985967494,0.002806446,0.002806466,0.002806443,0.002806394,Genomics,0.4584595,FALSE,56,0.675304595,20,0.481000803,2,0.618927094,,,0.591744164
4510,"Lockdown, relaxation, and acme period in COVID-19: A study of disease dynamics in Hermosillo, Sonora, Mexico.",PLoS One,33270705,12/4/20,pubmed,0,7,mathematical model,0.001653016,0.001653026,0.001653032,0.872886025,0.120501815,0.001653086,Epidemiology,0.18174222,FALSE,6.142857143,0.086523595,0.714285714,0.096936045,0,0.403234768,,,0.195564802
4511,Insights on cross-species transmission of SARS-CoV-2 from structural modeling.,PLoS Comput Biol,33270653,12/4/20,pubmed,0,7,"computational, structural model",0.318441964,0.614461718,0.001901753,0.061391157,0.001901726,0.001901682,Genomics,0.5664527,TRUE,44.42857143,0.581050158,112.2857143,0.841651057,0,0.403234768,,,0.608645327
4512,Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing.,PLoS Comput Biol,33270633,12/4/20,pubmed,0,12,"bayes, bayesian model",0.001022608,0.001022621,0.001022623,0.861154674,0.134754792,0.001022682,Epidemiology,0.17839664,FALSE,47.66666667,0.611787989,55.91666667,0.705780037,3,0.667819001,,,0.661795676
4513,GENCODE 2021.,Nucleic Acids Res,33270111,12/4/20,pubmed,0,56,"bioinformatic, genomes",0.297429684,0.402208412,0.293094897,0.00242242,0.002422286,0.002422301,Genomics,0.7214693,TRUE,64.55357143,0.731956213,552.875,0.981803586,0,0.403234768,,,0.705664855
4514,Molecular Dynamics Reveals Complex Compensatory Effects of Ionic Strength on the Severe Acute Respiratory Syndrome Coronavirus 2 Spike/Human Angiotensin-Converting Enzyme 2 Interaction.,J Phys Chem Lett,33269932,12/4/20,pubmed,0,10,computational,0.966556849,0.001786604,0.026296881,0.001786588,0.001786536,0.001786542,Drug discovery,0.5457083,TRUE,37.5,0.513946441,27.9,0.552649184,0,0.403234768,,,0.489943464
4515,Admission avoidance in tonsillitis and peritonsillar abscess: a prospective national audit during the initial peak of the COVID-19 pandemic.,Clin Otolaryngol,33269538,12/4/20,pubmed,0,1,logistic regression,0.002296533,0.002296562,0.002296584,0.151755695,0.171422518,0.669932108,Clinics,0.9761841,TRUE,50,0.632073721,25,0.529435376,1,0.537564047,,,0.566357715
4516,Investigating the interaction between nifedipine- and ritonavir-containing antiviral regimens: A physiologically based pharmacokinetic/pharmacodynamic analysis.,Br J Clin Pharmacol,33269470,12/4/20,pubmed,0,8,model simulation,0.373030446,0.001901754,0.001901776,0.171141867,0.001901717,0.45012244,Clinics,0.86852443,TRUE,24.125,0.356484631,8,0.320511105,0,0.403234768,,,0.360076835
4517,Applying the electronic nose for pre-operative SARS-CoV-2 screening.,Surg Endosc,33269428,12/4/20,pubmed,0,8,"machine learning, classifier, logistic regression",0.018996133,0.166418593,0.614570237,0.001291246,0.197432486,0.001291305,Healthcare,0.91190076,TRUE,65.875,0.739625209,88.625,0.798166979,2,0.618927094,,,0.718906427
4518,Autosomal Dominant Polycystic Kidney Disease does not significantly alter major COVID-19 outcomes among veterans.,medRxiv,33269373,12/4/20,pubmed,0,4,logistic regression,0.001823373,0.050177591,0.001823402,0.001823453,0.001823404,0.942528777,Clinics,0.22105381,FALSE,3.5,0.044344115,6.5,0.288132192,0,0.403234768,,,0.245237025
4519,Systematic evaluation of transcriptomic disease risk and diagnostic biomarker overlap between COVID-19 and tuberculosis: a patient-level meta-analysis.,medRxiv,33269371,12/4/20,pubmed,0,5,"sequencing, transcriptom, dataset",0.358685343,0.256489204,0.06743001,0.000946159,0.000946144,0.315503141,Drug discovery,0.15052599,FALSE,4.8,0.065248315,0.2,0.061145304,2,0.618927094,,,0.248440238
4520,Alcohol Consumption is Associated with Poor Prognosis in Obese Patients with COVID-19: a Mendelian Randomization Study using UK Biobank.,medRxiv,33269370,12/4/20,pubmed,0,8,logistic regression,0.034788603,0.132256272,0.001538114,0.001538136,0.269266761,0.560612114,Clinics,0.394356,FALSE,36.875,0.506895912,43.75,0.652863259,0,0.403234768,,,0.52099798
4521,Emergence and spread of a SARS-CoV-2 variant through Europe in the summer of 2020.,medRxiv,33269368,12/4/20,pubmed,0,11,genome sequences,0.001112661,0.641799993,0.001112639,0.353749379,0.001112664,0.001112663,Genomics,0.14421391,FALSE,30,0.432432432,96.25,0.815694407,57,0.962281622,,,0.736802821
4522,"Identification of optimal symptom combinations to trigger diagnostic work-up of suspected COVID-19 cases: analysis from a community-based, prospective, observational cohort.",medRxiv,33269364,12/4/20,pubmed,0,23,dataset,0.00096677,0.025680007,0.499112083,0.07812663,0.205953661,0.190160849,Healthcare,0.027591884,FALSE,92.56521739,0.850578267,91.91304348,0.80565962,0,0.403234768,,,0.686490885
4523,Routine saliva testing for the identification of silent COVID-19 infections in healthcare workers.,medRxiv,33269362,12/4/20,pubmed,0,6,simulation model,0.001141337,0.001141457,0.144708922,0.360338003,0.43613317,0.056537111,Healthcare,0.6955447,TRUE,113.8333333,0.897396252,122.5,0.854562483,4,0.707574542,,,0.819844426
4524,COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19.,ChemRxiv,33269341,12/4/20,pubmed,0,11,dataset,0.612977338,0.001072221,0.126442023,0.257364009,0.001072213,0.001072196,Drug discovery,0.7788732,TRUE,61.72727273,0.713525883,53.81818182,0.696548033,0,0.403234768,,,0.604436228
4525,The Association Between BMI and Inpatient Mortality Outcomes in Older Adults With COVID-19.,Cureus,33269116,12/4/20,pubmed,0,5,logistic regression,0.001220007,0.001220007,0.001219989,0.001220003,0.028740924,0.96637907,Clinics,0.6449107,TRUE,13.2,0.199270208,2.8,0.188787798,0,0.403234768,,,0.263764258
4526,A Potential Role for Photobiomodulation Therapy in Disease Treatment and Prevention in the Era of COVID-19.,Aging Dis,33269093,12/4/20,pubmed,0,4,microbiom,0.2258121,0.059628611,0.001622766,0.431940083,0.001622814,0.279373626,Epidemiology,0.90054315,TRUE,51.75,0.644566764,55,0.702167514,1,0.537564047,,,0.628099442
4527,The spatial econometrics of the coronavirus pandemic.,Lett Spat Resour Sci,33269031,12/4/20,pubmed,0,3,bayes,0.002806569,0.002806441,0.002806462,0.985967765,0.002806388,0.002806375,Epidemiology,0.2200566,FALSE,28.33333333,0.410724225,1.666666667,0.145036125,6,0.764429903,,,0.440063418
4528,Forecasting COVID-19 daily cases using phone call data.,Appl Soft Comput,33269029,12/4/20,pubmed,0,2,probabilistic,0.001141336,0.01189159,0.119065497,0.865618917,0.00114134,0.00114132,Epidemiology,0.22859567,FALSE,4.5,0.061784897,0,0.055525823,2,0.618927094,,,0.245412605
4529,0,J Virol,33268522,12/4/20,pubmed,0,2,bioinformatic,0.875987528,0.120383252,0.000907289,0.000907303,0.00090732,0.000907308,Drug discovery,0.06035149,FALSE,41.5,0.553219123,29.5,0.566095799,1,0.537564047,,,0.55229299
4530,The global and local distribution of RNA structure throughout the SARS-CoV-2 genome.,J Virol,33268519,12/4/20,pubmed,0,5,"in-silico, genomes",0.28438165,0.712967546,0.000662702,0.000662733,0.000662691,0.000662677,Genomics,0.7905899,TRUE,57,0.68204589,122.8,0.854896976,3,0.667819001,,,0.734920623
4531,Severity of COVID-19 and survival in patients with rheumatic and inflammatory diseases: data from the French RMD COVID-19 cohort of 694 patients.,Ann Rheum Dis,33268442,12/4/20,pubmed,0,396,logistic regression,0.001237113,0.001237052,0.001237045,0.001237063,0.001237085,0.993814642,Clinics,0.23645967,FALSE,55.58333333,0.671779331,5.333333333,0.262911426,9,0.814309525,,,0.583000094
4532,Recalling the COVID-19 lockdown: Insights from patients with epilepsy.,Epilepsy Behav,33268021,12/4/20,pubmed,0,3,logistic regression,0.024153988,0.00103466,0.001034609,0.065871662,0.862111579,0.045793502,Healthcare,0.9756199,TRUE,24,0.35574247,16.33333333,0.440460262,3,0.667819001,,,0.488007245
4533,Does respiratory co-infection facilitate dispersal of SARS-CoV-2? investigation of a super-spreading event in an open-space office.,Antimicrob Resist Infect Control,33267855,12/4/20,pubmed,0,10,"sequencing, whole-genome",0.001538116,0.366260261,0.001538168,0.288217217,0.340907942,0.001538296,Genomics,0.6615275,TRUE,38.7,0.526068402,40.5,0.637342788,0,0.403234768,,,0.522215319
4534,"High prevalence of SARS-CoV-2 infection among symptomatic healthcare workers in a large university tertiary hospital in São Paulo, Brazil.",BMC Infect Dis,33267836,12/4/20,pubmed,0,14,logistic regression,0.001220039,0.040281344,0.001220072,0.082701214,0.743318932,0.131258399,Healthcare,0.91740024,TRUE,13.35714286,0.201620385,8,0.320511105,1,0.537564047,,,0.353231846
4535,Review and Analysis of Massively Registered Clinical Trials of COVID-19 using the Text Mining Approach.,Rev Recent Clin Trials,33267765,12/4/20,pubmed,0,7,"artificial intelligence, text mining, text-mining, dataset",0.001538213,0.145371915,0.085334144,0.764679375,0.001538141,0.001538212,Epidemiology,0.3071555,FALSE,33.14285714,0.46762323,8.571428571,0.329810008,0,0.403234768,,,0.400222668
4536,"COMPARE Analysis, a Bioinformatic Approach to Accelerate Drug Repurposing against Covid-19 and Other Emerging Epidemics.",SLAS Discov,33267713,12/4/20,pubmed,0,1,"bioinformatic, in silico",0.852592424,0.001438153,0.001438338,0.14165482,0.001438118,0.001438146,Drug discovery,0.5217326,TRUE,8,0.118683901,0,0.055525823,0,0.403234768,,,0.192481497
4537,On the Challenges for the Diagnosis of SARS-CoV-2 Based on a Review of Current Methodologies.,ACS Sens,33267587,12/4/20,pubmed,0,4,artificial intelligence,0.001511854,0.180465238,0.526211079,0.259590714,0.03070928,0.001511836,Epidemiology,0.48522323,FALSE,121.75,0.909147133,25.25,0.530773348,2,0.618927094,,,0.686282525
4538,"Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?",Int J Environ Res Public Health,33266276,12/4/20,pubmed,0,4,neural network,0.046174965,0.001901751,0.081384821,0.352054696,0.35755215,0.160931618,Healthcare,0.93948996,TRUE,5.25,0.072855464,0.25,0.065493712,1,0.537564047,,,0.225304408
4539,Self-Reported Impact of the COVID-19 Pandemic on Nutrition and Physical Activity Behaviour in Dutch Older Adults Living Independently.,Nutrients,33266217,12/4/20,pubmed,0,3,logistic regression,0.021170269,0.001786678,0.001786587,0.001786571,0.926015415,0.04745448,Healthcare,0.679153,TRUE,55.33333333,0.670171315,92.33333333,0.806529302,4,0.707574542,,,0.72809172
4540,Modeling the Molecular Impact of SARS-CoV-2 Infection on the Renin-Angiotensin System.,Viruses,33265982,12/4/20,pubmed,0,3,"mathematical model, in silico",0.847509321,0.002357852,0.002357926,0.102896611,0.002357802,0.042520488,Drug discovery,0.47664142,FALSE,125.3333333,0.914651494,105,0.831482473,7,0.785110192,,,0.843748053
4541,"Adaptive platform trials using multi-arm, multi-stage protocols: getting fast answers in pandemic settings.",F1000Res,33149899,12/4/20,pubmed,0,7,"sequencing, in-silico",0.251040333,0.03811044,0.001350416,0.643040988,0.001350368,0.065107455,Epidemiology,0.1977408,FALSE,18.57142857,0.278619581,9.428571429,0.343591116,0,0.403234768,,,0.341815155
4542,COVID-19 in older adults: What are the differences with younger patients?,Geriatr Gerontol Int,33264816,12/3/20,pubmed,0,7,dataset,0.001059364,0.030740217,0.001059437,0.001059396,0.061833786,0.904247801,Clinics,0.9376547,TRUE,10.14285714,0.15269961,2.857142857,0.189925074,1,0.537564047,,,0.293396244
4543,Scrutinizing the SARS-CoV-2 protein information for designing an effective vaccine encompassing both the T-cell and B-cell epitopes.,Infect Genet Evol,33264668,12/3/20,pubmed,0,4,"in silico, proteom",0.658451795,0.172814178,0.001126816,0.165353591,0.001126832,0.001126787,Drug discovery,0.36001408,FALSE,11.75,0.177562001,6,0.280037463,0,0.403234768,,,0.286944744
4544,Early predictive factors of progression from severe type to critical ill type in patients with Coronavirus Disease 2019: A retrospective cohort study.,PLoS One,33264366,12/3/20,pubmed,0,9,logistic regression,0.001046813,0.001046807,0.001046808,0.001046819,0.001046818,0.994765934,Clinics,0.98249584,TRUE,103.1111111,0.875502505,42.88888889,0.648849344,1,0.537564047,,,0.687305299
4545,In silico immune infiltration profiling combined with functional enrichment analysis reveals a potential role for naïve B cells as a trigger for severe immune responses in the lungs of COVID-19 patients.,PLoS One,33264345,12/3/20,pubmed,0,7,"sequencing, in silico, transcriptom",0.652264959,0.061009919,0.019378956,0.000898138,0.000898097,0.265549932,Drug discovery,0.46851432,FALSE,18.57142857,0.278619581,5,0.257024351,0,0.403234768,,,0.312959566
4546,Smartphone-Based Virtual Agents to Help Individuals With Sleep Concerns During COVID-19 Confinement: Feasibility Study.,J Med Internet Res,33264099,12/3/20,pubmed,0,9,artificial intelligence,0.145376208,0.000807897,0.104766987,0.323259791,0.4249812,0.000807917,Healthcare,0.9638115,TRUE,104,0.877605294,218.6666667,0.926277763,0,0.403234768,,,0.735705942
4547,In silico analysis of mismatches in RT-qPCR assays of 177 SARS-CoV-2 sequences from Brazil.,Rev Soc Bras Med Trop,33263691,12/3/20,pubmed,0,7,"in silico, genome sequences",0.002639026,0.986804675,0.002639137,0.002639066,0.002639056,0.00263904,Genomics,0.46785444,FALSE,34.28571429,0.479683345,8.857142857,0.333422531,0,0.403234768,,,0.405446881
4548,Comparative Multiplexed Interactomics of SARS-CoV-2 and Homologous Coronavirus Nonstructural Proteins Identifies Unique and Shared Host-Cell Dependencies.,ACS Infect Dis,33263384,12/3/20,pubmed,0,4,"proteom, interactom",0.723665994,0.271938712,0.001098813,0.001098864,0.001098816,0.0010988,Drug discovery,0.6725003,TRUE,36,0.498299215,23.5,0.516791544,5,0.739490092,,,0.584860284
4549,"COVID-19 infection: comparing the knowledge, attitude and practices in a sample of nursing students.",Acta Biomed,33263338,12/3/20,pubmed,0,9,logistic regression,0.00139285,0.001392975,0.00139294,0.001393011,0.993035204,0.00139302,Healthcare,0.88662934,TRUE,27.55555556,0.401880141,3.666666667,0.217621086,1,0.537564047,,,0.385688425
4550,Confirmation of COVID-19 in Out-of-Hospital Cardiac Arrest Patients and Postmortem Management in the Emergency Department during the COVID-19 Outbreak.,Infect Chemother,33263244,12/3/20,pubmed,0,13,logistic regression,0.001034583,0.001034578,0.304088486,0.001034603,0.001034622,0.691773128,Clinics,0.90107334,TRUE,39.69230769,0.536273115,7.307692308,0.303385068,0,0.403234768,,,0.41429765
4551,Which role for chest x-ray score in predicting the outcome in COVID-19 pneumonia?,Eur Radiol,33263159,12/3/20,pubmed,0,6,logistic regression,0.000907272,0.000907276,0.175441768,0.000907338,0.000907288,0.820929058,Clinics,0.6986211,TRUE,62.66666667,0.719030243,50.33333333,0.682967621,3,0.667819001,,,0.689938955
4552,Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset.,SN Comput Sci,33263111,12/3/20,pubmed,0,6,"bayes, machine learning, deep learning, artificial intelligence, data mining, logistic regression, dataset",0.100915421,0.001141418,0.766905902,0.077695594,0.052200296,0.001141369,Drug discovery,0.61545146,TRUE,13,0.197352959,1,0.122023013,3,0.667819001,,,0.329064991
4553,Gene expression profiling of corona virus microarray datasets to identify crucial targets in COVID-19 patients.,Gene Rep,33263093,12/3/20,pubmed,0,3,"network analysis, dataset",0.789624805,0.001272684,0.001272667,0.047211036,0.001272685,0.159346123,Drug discovery,0.40664184,FALSE,8,0.118683901,0.666666667,0.096200161,0,0.403234768,,,0.20603961
4554,Validation of a composed COVID-19 chest radiography score: the CARE project.,ERJ Open Res,33263058,12/3/20,pubmed,0,19,logistic regression,0.001371239,0.001371241,0.298264428,0.001371282,0.001371298,0.696250512,Clinics,0.8596006,TRUE,149.1052632,0.943348383,119.0526316,0.850347873,2,0.618927094,,,0.804207783
4555,"Prediction on the number of confirmed Covid-19 with the FUDAN-CCDC mathematical model and its epidemiology, clinical manifestations, and prevention and treatment effects.",Results Phys,33262927,12/3/20,pubmed,0,6,mathematical model,0.019447228,0.000977485,0.036171307,0.346238071,0.000977467,0.596188443,Clinics,0.47555447,FALSE,12.66666667,0.191044592,1.666666667,0.145036125,0,0.403234768,,,0.246438495
4556,Mathematical modelling and optimal cost-effective control of COVID-19 transmission dynamics.,Eur Phys J Plus,33262923,12/3/20,pubmed,0,5,mathematical model,0.001438156,0.017174687,0.001438137,0.926883946,0.001438132,0.051626942,Epidemiology,0.49475425,FALSE,7,0.10179974,3.4,0.208589778,3,0.667819001,,,0.326069506
4557,Agile Application of Video Telemedicine During the COVID-19 Pandemic.,Cureus,33262918,12/3/20,pubmed,0,3,digital health,0.001538123,0.001538093,0.001538193,0.226907856,0.303031215,0.465446521,Clinics,0.9986459,TRUE,8.333333333,0.123693488,0,0.055525823,2,0.618927094,,,0.266048802
4558,Genomic characterization of SARS-CoV-2 in Egypt.,J Adv Res,33262895,12/3/20,pubmed,0,21,"sequencing, genomes",0.001511807,0.866235566,0.001511794,0.001511805,0.001511869,0.127717159,Genomics,0.74737954,TRUE,14.57142857,0.220112561,,,1,0.537564047,,,0.378838304
4559,Barriers and facilitators to changes in adolescent physical activity during COVID-19.,BMJ Open Sport Exerc Med,33262893,12/3/20,pubmed,0,5,logistic regression,0.001438119,0.001438115,0.001438129,0.001438196,0.992809318,0.001438122,Healthcare,0.9211921,TRUE,10.2,0.153627312,3,0.199424672,1,0.537564047,,,0.29687201
4560,Evaluation of the effect of different policies in the containment of epidemic spreads for the COVID-19 case.,Biomed Signal Process Control,33262807,12/3/20,pubmed,0,2,mathematical model,0.00477558,0.00477534,0.004775467,0.929536123,0.004775224,0.051362266,Epidemiology,0.6923415,TRUE,73.5,0.780011132,12.5,0.392761573,3,0.667819001,,,0.613530569
4561,"Causal Inference for Genetic Obesity, Cardiometabolic Profile and COVID-19 Susceptibility: A Mendelian Randomization Study.",Front Genet,33262790,12/3/20,pubmed,0,4,"logistic regression, dataset",0.001291278,0.229540459,0.001291313,0.001291294,0.084392585,0.682193071,Clinics,0.5380204,TRUE,189,0.968581854,562.75,0.982204977,3,0.667819001,,,0.872868611
4562,Clinical Features and Short-Term Outcomes in COVID-19-Infected Patients with Cancer.,Cancer Manag Res,33262652,12/3/20,pubmed,0,7,correlation analysis,0.000966791,0.000966781,0.000966858,0.122739486,0.000966797,0.873393288,Clinics,0.80913854,TRUE,15.57142857,0.23495578,,,0,0.403234768,,,0.319095274
4563,"COVID-19 Knowledge, Attitudes, and Prevention Practices Among People with Hypertension and Diabetes Mellitus Attending Public Health Facilities in Ambo, Ethiopia.",Infect Drug Resist,33262615,12/3/20,pubmed,0,8,logistic regression,0.001072175,0.001072165,0.001072177,0.001072187,0.857775601,0.137935695,Healthcare,0.38376367,FALSE,5.875,0.081761395,1.375,0.132927482,2,0.618927094,,,0.27787199
4564,Determinants of COVID-19 Vaccine Acceptance in Saudi Arabia: A Web-Based National Survey.,J Multidiscip Healthc,33262600,12/3/20,pubmed,0,2,logistic regression,0.00127267,0.001272654,0.001272637,0.001272703,0.993636698,0.001272637,Healthcare,0.69666237,TRUE,14.5,0.219617787,4,0.231469093,28,0.926168282,,,0.459085054
4565,A critical analysis of CTRI registered AYUSH studies for COVID- 19.,J Ayurveda Integr Med,33262559,12/3/20,pubmed,0,3,dataset,0.060986387,0.202898547,0.001237103,0.55638771,0.001237148,0.177253106,Epidemiology,0.65245485,TRUE,6,0.086028821,0,0.055525823,3,0.667819001,,,0.269791215
4566,COVID-19: Mechanistic model calibration subject to active and varying non-pharmaceutical interventions.,Chem Eng Sci,33262543,12/3/20,pubmed,0,4,mathematical model,0.102767537,0.002183259,0.002183246,0.888499226,0.002183415,0.002183317,Epidemiology,0.17744851,FALSE,8.75,0.129383388,1.75,0.148381054,0,0.403234768,,,0.226999737
4567,Dalbavancin binds ACE2 to block its interaction with SARS-CoV-2 spike protein and is effective in inhibiting SARS-CoV-2 infection in animal models.,Cell Res,33262453,12/3/20,pubmed,0,18,virtual screening,0.938511806,0.001823357,0.001823306,0.001823371,0.00182336,0.054194799,Drug discovery,0.83008635,TRUE,53.11111111,0.65470963,52.94444444,0.692868611,5,0.739490092,,,0.695689444
4568,"Targeting the coronavirus SARS-CoV-2: computational insights into the mechanism of action of the protease inhibitors lopinavir, ritonavir and nelfinavir.",Sci Rep,33262359,12/3/20,pubmed,0,5,computational,0.87020612,0.001823445,0.001823403,0.122500344,0.001823377,0.001823311,Drug discovery,0.71546006,TRUE,36.6,0.503865421,36.6,0.614664169,7,0.785110192,,,0.634546594
4569,Coordinating and Assisting Research at the SARS-CoV-2/Microbiome Nexus.,mSystems,33262241,12/3/20,pubmed,0,15,microbiom,0.204123571,0.467783703,0.00249066,0.142387275,0.180724323,0.002490468,Genomics,0.7620709,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
4570,"Epidemiology and diagnosis, environmental resources quality and socio-economic perspectives for COVID-19 pandemic.",J Environ Manage,33261988,12/3/20,pubmed,0,7,sequencing,0.001786608,0.156862206,0.001786578,0.835991408,0.001786633,0.001786567,Epidemiology,0.8752253,TRUE,24.28571429,0.358340033,20.42857143,0.484814022,2,0.618927094,,,0.487360383
4571,Effects of mask wearing on anxiety of teachers affected by COVID-19: A large cross-sectional study in China.,J Affect Disord,33261933,12/3/20,pubmed,0,6,logistic regression,0.001022614,0.001022624,0.001022617,0.115161135,0.880748369,0.001022641,Healthcare,0.5394487,TRUE,21,0.312016822,1.166666667,0.124565159,0,0.403234768,,,0.279938916
4572,Sleep Conditions Associate with Anxiety and Depression Symptoms among Pregnant Women during the Epidemic of COVID-19 in Shenzhen.,J Affect Disord,33261931,12/3/20,pubmed,0,11,logistic regression,0.001237079,0.001237244,0.001237182,0.001237165,0.95662695,0.03842438,Healthcare,0.9710776,TRUE,72.09090909,0.773146144,,,0,0.403234768,,,0.588190456
4573,"Synthesis, molecular docking, and in silico ADME/Tox profiling studies of new 1-aryl-5-(3-azidopropyl)indol-4-ones: Potential inhibitors of SARS CoV-2 main protease.",Bioorg Chem,33261847,12/3/20,pubmed,0,3,in silico,0.986803956,0.002639078,0.00263919,0.002639248,0.002639293,0.002639235,Drug discovery,0.93120956,TRUE,16,0.243552477,1.333333333,0.13252609,1,0.537564047,,,0.304547538
4574,Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review.,Bioorg Chem,33261845,12/3/20,pubmed,0,4,computational,0.888029606,0.000907338,0.000907318,0.080051686,0.02919676,0.000907292,Drug discovery,0.9538953,TRUE,26,0.382398417,5.5,0.267259834,11,0.840175319,,,0.49661119
4575,Why psychiatry is different - challenges and difficulties in managing a nosocomial outbreak of coronavirus disease (COVID-19) in hospital care.,Antimicrob Resist Infect Control,33261660,12/3/20,pubmed,0,8,"sequencing, whole genome",0.001717228,0.171723769,0.001717259,0.260095261,0.222198371,0.342548111,Clinics,0.92428684,TRUE,16.5,0.249366071,6.125,0.280907145,0,0.403234768,,,0.311169328
4576,"Comparison of patients hospitalized with COVID-19, H7N9 and H1N1.",Infect Dis Poverty,33261654,12/3/20,pubmed,0,14,logistic regression,0.001059367,0.057570466,0.054577644,0.001059398,0.00105936,0.884673765,Clinics,0.87909806,TRUE,37.57142857,0.514317521,33.78571429,0.597471234,2,0.618927094,,,0.576905283
4577,Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data.,Scand J Trauma Resusc Emerg Med,33261629,12/3/20,pubmed,0,18,"machine learning, artificial intelligence, data mining",0.001187283,0.001187295,0.698093077,0.001187339,0.001187321,0.297157684,Clinics,0.6817293,TRUE,47.11111111,0.606778403,41.83333333,0.643497458,3,0.667819001,,,0.639364954
4578,Modeling and control of COVID-19: A short-term forecasting in the context of India.,Chaos,33261356,12/3/20,pubmed,0,4,mathematical model,0.001987148,0.001987183,0.053207403,0.938843852,0.00198724,0.001987175,Epidemiology,0.5281955,TRUE,9.75,0.146267549,0,0.055525823,1,0.537564047,,,0.246452473
4579,Mathematical modeling of COVID-19: Impact of non-pharmaceutical interventions in India.,Chaos,33261327,12/3/20,pubmed,0,3,mathematical model,0.032526843,0.001653034,0.001653021,0.960861055,0.001653038,0.001653009,Epidemiology,0.18235296,FALSE,16,0.243552477,2.333333333,0.173401124,0,0.403234768,,,0.273396123
4580,Understanding the Dynamics of the COVID-19 Pandemic: A Real-Time Analysis of Switzerland's First Wave.,Int J Environ Res Public Health,33261039,12/3/20,pubmed,0,6,mathematical model,0.001823323,0.00182335,0.001823364,0.990883169,0.001823469,0.001823324,Epidemiology,0.6858793,TRUE,56.83333333,0.680437875,86.66666667,0.794487557,1,0.537564047,,,0.670829826
4581,"Relationships between Perceived Stress, Depression and Alcohol Use Disorders in University Students during the COVID-19 Pandemic: A Socio-Economic Dimension.",Int J Environ Res Public Health,33260778,12/3/20,pubmed,0,3,correlation analysis,0.001593497,0.001593529,0.077123199,0.001593536,0.916502457,0.001593781,Healthcare,0.6102071,TRUE,67,0.748160059,6,0.280037463,2,0.618927094,,,0.549041539
4582,Risk Factors on the Progression to Clinical Outcomes of COVID-19 Patients in South Korea: Using National Data.,Int J Environ Res Public Health,33260724,12/3/20,pubmed,0,3,logistic regression,0.000977496,0.000977456,0.000977512,0.108938746,0.00097745,0.887151339,Clinics,0.94399107,TRUE,37,0.508936854,2.666666667,0.185442869,3,0.667819001,,,0.454066241
4583,Repositioning of Ligands That Target the Spike Glycoprotein as Potential Drugs for SARS-CoV-2 in an In Silico Study.,Molecules,33260370,12/3/20,pubmed,0,4,"computational, in silico",0.993035825,0.001392851,0.001392843,0.001392836,0.001392835,0.00139281,Drug discovery,0.9441319,TRUE,58.5,0.693240151,10.75,0.366269735,2,0.618927094,,,0.559478993
4584,A New Social Network Scale for Detecting Depressive Symptoms in Older Japanese Adults.,Int J Environ Res Public Health,33260326,12/3/20,pubmed,0,8,logistic regression,0.001237172,0.001237068,0.192635858,0.001237125,0.802415632,0.001237145,Healthcare,0.9903567,TRUE,66.875,0.74624281,32.75,0.590647578,1,0.537564047,,,0.624818145
4585,Virtual learning during the COVID-19 pandemic: a turning point in neurosurgical education.,Neurosurg Focus,33260124,12/2/20,pubmed,0,6,logistic regression,0.001187371,0.001187369,0.03835365,0.32681471,0.63126962,0.001187281,Healthcare,0.08836621,FALSE,24.83333333,0.365823489,10.83333333,0.367273214,0,0.403234768,,,0.378777157
4586,Recapitulating the Bayesian framework for neurosurgical outpatient care and a cost-benefit analysis of telemedicine for socioeconomically disadvantaged patients in the Philippines during the pandemic.,Neurosurg Focus,33260122,12/2/20,pubmed,0,7,bayes,0.000871551,0.000871553,0.033182784,0.659305261,0.186622618,0.119146234,Epidemiology,0.7943677,TRUE,3.142857143,0.037912054,0,0.055525823,1,0.537564047,,,0.210333975
4587,Anxiety in neurosurgical patients undergoing nonurgent surgery during the COVID-19 pandemic.,Neurosurg Focus,33260119,12/2/20,pubmed,0,28,machine learning,0.001059379,0.001059357,0.130286428,0.125974353,0.443710244,0.297910239,Healthcare,0.986894,TRUE,30.25,0.434782609,15.71428571,0.432833824,1,0.537564047,,,0.468393493
4588,The effect of lockdowns and infection rates on supermarket sales.,Econ Hum Biol,33260036,12/2/20,pubmed,0,2,dataset,0.002806465,0.002806576,0.002806728,0.774067255,0.214706444,0.002806531,Epidemiology,0.11258125,FALSE,16,0.243552477,1,0.122023013,0,0.403234768,,,0.256270086
4589,0,Comput Biol Med,33260035,12/2/20,pubmed,0,3,dataset,0.593207497,0.167231338,0.193506453,0.002357794,0.002357776,0.041339141,Drug discovery,0.80280536,TRUE,40,0.539860226,8,0.320511105,0,0.403234768,,,0.421202033
4590,"Comparative transcriptome analysis of SARS-CoV, MERS-CoV, and SARS-CoV-2 to identify potential pathways for drug repurposing.",Comput Biol Med,33260034,12/2/20,pubmed,0,5,"transcriptom, dataset",0.952271466,0.001901788,0.001901794,0.001901791,0.001901716,0.040121445,Drug discovery,0.81151605,TRUE,11.4,0.17131548,1.8,0.150120417,1,0.537564047,,,0.286333315
4591,COVID-19 in hospitalised patients in Spain: a cohort study in Madrid.,Int J Antimicrob Agents,33259918,12/2/20,pubmed,0,20,logistic regression,0.098171318,0.001751225,0.001751244,0.06441108,0.001751206,0.832163927,Clinics,0.33402923,FALSE,51.2,0.639866411,41.5,0.642092588,2,0.618927094,,,0.633628698
4592,Evolution and genetic diversity of SARS-CoV-2 in Africa using whole genome sequences.,Int J Infect Dis,33259879,12/2/20,pubmed,0,10,"bayes, sequencing, whole genome, genome sequences",0.001415146,0.970669062,0.001415125,0.001415172,0.00141515,0.023670344,Genomics,0.43705344,FALSE,11.4,0.17131548,2,0.164302917,2,0.618927094,,,0.31818183
4593,Mendelian randomization analysis identified genes pleiotropically associated with the risk and prognosis of COVID-19.,J Infect,33259846,12/2/20,pubmed,0,6,genome-wide,0.4591507,0.191472671,0.001861709,0.001861746,0.001861836,0.343791338,Drug discovery,0.91688836,TRUE,18.33333333,0.274908776,3.666666667,0.217621086,2,0.618927094,,,0.370485652
4594,Actionable Cytopathogenic Host Responses of Human Alveolar Type 2 Cells to SARS-CoV-2.,Mol Cell,33259812,12/2/20,pubmed,0,50,"proteom, phosphoproteom",0.799810605,0.002032848,0.00203281,0.090921706,0.070062439,0.035139592,Drug discovery,0.338526,FALSE,45.84,0.594285361,139.2,0.873427883,4,0.707574542,,,0.725095929
4595,The Short- and Long-Range RNA-RNA Interactome of SARS-CoV-2.,Mol Cell,33259809,12/2/20,pubmed,0,7,"interactom, genomes",0.538453528,0.456061425,0.00137127,0.001371288,0.001371265,0.001371225,Drug discovery,0.3945091,FALSE,36.71428571,0.505349743,129.7142857,0.863526893,1,0.537564047,,,0.635480228
4596,"Knowledge, attitude and practice of residents in the prevention and control of COVID-19: An online questionnaire survey.",J Adv Nurs,33259651,12/2/20,pubmed,0,9,correlation analysis,0.000926299,0.00092631,0.031061626,0.000926372,0.965233108,0.000926285,Healthcare,0.80535376,TRUE,38.88888889,0.527800111,14.33333333,0.415975381,2,0.618927094,,,0.520900862
4597,A Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs: A Multireader Validation Study.,Invest Radiol,33259441,12/2/20,pubmed,0,15,"artificial intelligence, deep-learning",0.000977425,0.000977437,0.894372378,0.000977442,0.050509844,0.052185474,Imaging,0.8874221,TRUE,52.86666667,0.65260684,51.8,0.688587102,2,0.618927094,,,0.653373679
4598,"An Epidemiological Model Considering Isolation to Predict COVID-19 Trends in Tokyo, Japan: Numerical Analysis.",JMIR Public Health Surveill,33259325,12/2/20,pubmed,0,3,mathematical model,0.000752914,0.030020149,0.000752928,0.841960204,0.000752907,0.125760898,Epidemiology,0.2680577,FALSE,50.33333333,0.63380543,8.666666667,0.331415574,0,0.403234768,,,0.456151924
4599,"Beta- and Novel Delta-Coronaviruses Are Identified from Wild Animals in the Qinghai-Tibetan Plateau, China.",Virol Sin,33259031,12/2/20,pubmed,0,9,sequencing,0.002183258,0.989083806,0.002183183,0.002183319,0.002183235,0.002183199,Genomics,0.4967414,FALSE,78.33333333,0.79961655,88.11111111,0.797029703,2,0.618927094,,,0.738524449
4600,The zoonotic potential of bat-borne coronaviruses.,Emerg Top Life Sci,33258903,12/2/20,pubmed,0,7,genomes,0.095815933,0.868944427,0.001187296,0.001187321,0.001187319,0.031677706,Genomics,0.65382254,TRUE,8,0.118683901,1.714285714,0.14577201,1,0.537564047,,,0.267339986
4601,Nowcasting the COVID-19 pandemic in Bavaria.,Biom J,33258177,12/2/20,pubmed,0,5,"bayes, bayesian model",0.002720109,0.002720141,0.002720123,0.986399184,0.002720214,0.002720228,Epidemiology,0.28130874,FALSE,59.6,0.70029068,83.2,0.785790741,5,0.739490092,,,0.741857171
4602,Identifying public concerns and reactions during the COVID-19 pandemic on Twitter: A text-mining analysis.,Public Health Nurs,33258149,12/2/20,pubmed,0,4,"text mining, text-mining",0.002238487,0.002238552,0.002238526,0.81662561,0.174420358,0.002238467,Epidemiology,0.85691094,TRUE,4.75,0.064506154,0,0.055525823,0,0.403234768,,,0.174422248
4603,"Dataset on the fear, preventive behaviour and anxiety disorder during the COVID-19 pandemic in Khyber Pakhtunkhwa, Pakistan.",Data Brief,33257917,12/2/20,pubmed,0,3,dataset,0.001291227,0.001291233,0.001291227,0.34832525,0.646509859,0.001291203,Healthcare,0.69464445,TRUE,43.33333333,0.571154679,2.666666667,0.185442869,1,0.537564047,,,0.431387198
4604,Investigation of COVID-19 comorbidities reveals genes and pathways coincident with the SARS-CoV-2 viral disease.,Sci Rep,33257774,12/2/20,pubmed,0,6,"bioinformatic, dataset",0.666393872,0.002183407,0.002183362,0.002183298,0.002183253,0.324872808,Drug discovery,0.8942584,TRUE,79.66666667,0.805924918,614.5,0.984479529,0,0.403234768,,,0.731213072
4605,Conserved interactions required for inhibition of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).,Sci Rep,33257760,12/2/20,pubmed,0,11,in silico,0.993448233,0.001310407,0.00131035,0.001310341,0.001310343,0.001310325,Drug discovery,0.74988943,TRUE,12.36363636,0.186653473,5.181818182,0.258830613,4,0.707574542,,,0.384352876
4606,Immunoinformatic design of a COVID-19 subunit vaccine using entire structural immunogenic epitopes of SARS-CoV-2.,Sci Rep,33257716,12/2/20,pubmed,0,4,"molecular dynamics simulation, computational",0.992690358,0.001461936,0.001461956,0.001461986,0.001461886,0.001461878,Drug discovery,0.49877992,FALSE,38.5,0.524089307,9,0.337904736,4,0.707574542,,,0.523189528
4607,Monocyte absolute count as a preliminary tool to distinguish between SARS-CoV-2 and influenza A/B infections in patients requiring hospitalization.,Infez Med,33257627,12/2/20,pubmed,0,8,"predictive model, logistic regression",0.001310353,0.001310389,0.001310415,0.053376733,0.001310398,0.941381711,Clinics,0.32588363,FALSE,75.25,0.787865669,28.375,0.556863794,0,0.403234768,,,0.582654744
4608,Lung transplantation for patients with severe COVID-19.,Sci Transl Med,33257409,12/2/20,pubmed,0,15,"machine learning, sequencing",0.313797332,0.147978651,0.201597528,0.001085365,0.001085338,0.334455785,Clinics,0.7757776,TRUE,57.73333333,0.686746243,65.8,0.741771474,4,0.707574542,,,0.712030753
4609,Mental health of healthcare professionals during the early stage of the COVID-19 pandemic in Ethiopia.,BJPsych Open,33256883,12/2/20,pubmed,0,5,logistic regression,0.001272648,0.032913823,0.026628246,0.001272722,0.936639855,0.001272707,Healthcare,0.6848269,TRUE,34.4,0.480982126,34.4,0.601150656,0,0.403234768,,,0.495122516
4610,COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling.,Epidemiol Infect,33256878,12/2/20,pubmed,0,10,bayes,0.001622709,0.001622766,0.039572574,0.561825137,0.001622737,0.393734078,Epidemiology,0.4980586,FALSE,7.7,0.111880759,0.4,0.075796093,0,0.403234768,,,0.19697054
4611,Pervasive generation of non-canonical subgenomic RNAs by SARS-CoV-2.,Genome Med,33256807,12/2/20,pubmed,0,3,"sequencing, transcriptom",0.135064344,0.860249392,0.001171553,0.001171595,0.001171557,0.001171558,Genomics,0.12783912,FALSE,106,0.881192405,1525,0.996989564,0,0.403234768,,,0.760472246
4612,Prediction of the incubation period for COVID-19 and future virus disease outbreaks.,BMC Biol,33256718,12/2/20,pubmed,0,4,"machine learning, genomes, predictive model",0.001371324,0.505009164,0.098981555,0.391895285,0.001371297,0.001371376,Genomics,0.6082798,TRUE,379,0.995114107,3171,0.999598609,4,0.707574542,,,0.900762419
4613,Burnout in Spanish Security Forces during the COVID-19 Pandemic.,Int J Environ Res Public Health,33256195,12/2/20,pubmed,0,4,logistic regression,0.001943477,0.00194351,0.051950259,0.296008075,0.646211103,0.001943577,Healthcare,0.97705364,TRUE,16,0.243552477,0.75,0.099411292,0,0.403234768,,,0.248732846
4614,Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine.,Int J Environ Res Public Health,33256160,12/2/20,pubmed,0,9,machine learning,0.001751211,0.001751222,0.233782099,0.511102095,0.249862035,0.001751338,Epidemiology,0.40476894,FALSE,60.77777778,0.706908281,31.88888889,0.583690126,0,0.403234768,,,0.564611058
4615,Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?-The IDENTIFY Trial.,J Clin Med,33256141,12/2/20,pubmed,0,14,machine learning,0.001622865,0.001622736,0.294929337,0.001622778,0.00162273,0.698579554,Clinics,0.7437321,TRUE,58.07142857,0.68983858,158.3571429,0.890754616,0,0.403234768,,,0.661275988
4616,An Analysis Review of Detection Coronavirus Disease 2019 (COVID-19) Based on Biosensor Application.,Sensors (Basel),33256085,12/2/20,pubmed,0,5,artificial intelligence,0.001565375,0.049572632,0.579972392,0.365758815,0.001565403,0.001565383,Imaging,0.6240137,TRUE,7.4,0.106190859,0,0.055525823,0,0.403234768,,,0.18831715
4617,Characterization of Structural and Energetic Differences between Conformations of the SARS-CoV-2 Spike Protein.,Materials (Basel),33255977,12/2/20,pubmed,0,5,computational,0.886937275,0.001126869,0.0011268,0.108555453,0.001126824,0.001126779,Drug discovery,0.74535275,TRUE,14,0.213494959,3.8,0.221501204,1,0.537564047,,,0.324186737
4618,In Silico Discovery of Antimicrobial Peptides as an Alternative to Control SARS-CoV-2.,Molecules,33255849,12/2/20,pubmed,0,3,"computational, in silico",0.99230957,0.001538108,0.00153809,0.001538092,0.001538069,0.00153807,Drug discovery,0.6219299,TRUE,9,0.135320675,0.333333333,0.073187048,0,0.403234768,,,0.203914164
4619,Novel Small-Molecule Scaffolds as Candidates against the SARS Coronavirus 2 Main Protease: A Fragment-Guided in Silico Approach.,Molecules,33255326,12/2/20,pubmed,0,4,in silico,0.945578912,0.002032763,0.00203282,0.046289668,0.002032859,0.002032978,Drug discovery,0.88433754,TRUE,44,0.578390748,21,0.492239765,1,0.537564047,,,0.536064853
4620,Alkaloids: Therapeutic Potential against Human Coronaviruses.,Molecules,33255253,12/2/20,pubmed,0,4,in silico,0.9871872,0.002562568,0.002562581,0.002562597,0.002562531,0.002562523,Drug discovery,0.862836,TRUE,48.25,0.616364648,49.25,0.678552315,1,0.537564047,,,0.610827003
4621,Host - virus - drug interactions as determinants of COVID-19's phenotypes: A data-driven hypothesis.,Med Hypotheses,33254579,12/2/20,pubmed,0,1,interactom,0.778634037,0.002357882,0.002357743,0.002357801,0.002357742,0.211934796,Drug discovery,0.93542826,TRUE,46,0.596882924,3,0.199424672,0,0.403234768,,,0.399847455
4622,Washing hands and the face may reduce COVID-19 infection.,Med Hypotheses,33254560,12/2/20,pubmed,0,2,computational,0.309740493,0.002183427,0.002183387,0.681525985,0.00218335,0.002183358,Epidemiology,0.31366894,FALSE,48.5,0.618158204,10.5,0.363459995,1,0.537564047,,,0.506394082
4623,Red blood cell exchange for SARS-CoV-2: A Gemini of therapeutic opportunities.,Med Hypotheses,33254534,12/2/20,pubmed,0,1,"proteom, metabolom, lipidom",0.426330821,0.001593577,0.001593585,0.480949677,0.001593551,0.087938788,Epidemiology,0.6510503,TRUE,19,0.285793803,1,0.122023013,0,0.403234768,,,0.270350528
4624,COVID 19 mortality: Probable role of microbiome to explain disparity.,Med Hypotheses,33254516,12/2/20,pubmed,0,2,"microbiom, predictive model",0.001415141,0.028160262,0.00141515,0.583340471,0.228330445,0.15733853,Epidemiology,0.95460075,TRUE,55.5,0.671346404,24,0.521808938,7,0.785110192,,,0.659421844
4625,Can charcoal improve outcomes in COVID-19 infections?,Med Hypotheses,33254498,12/2/20,pubmed,0,8,microbiom,0.353112856,0.113438631,0.005698279,0.005697797,0.061953425,0.460099011,Clinics,0.58108974,TRUE,137,0.929371019,140.875,0.875167246,1,0.537564047,,,0.780700771
4626,The influence of ABO blood groups on COVID-19 susceptibility and severity: A molecular hypothesis based on carbohydrate-carbohydrate interactions.,Med Hypotheses,33254482,12/2/20,pubmed,0,3,in silico,0.910955229,0.001171612,0.001171569,0.001171605,0.084358329,0.001171655,Drug discovery,0.49030304,FALSE,16,0.243552477,2.333333333,0.173401124,12,0.850299401,,,0.422417667
4627,Targeting the SARS-CoV2 nucleocapsid protein for potential therapeutics using immuno-informatics and structure-based drug discovery techniques.,Biomed Pharmacother,33254432,12/2/20,pubmed,0,4,bioinformatic,0.901205635,0.093133858,0.001415109,0.00141515,0.001415116,0.001415132,Drug discovery,0.6253791,TRUE,4.5,0.061784897,1.25,0.127776291,2,0.618927094,,,0.269496094
4628,"Key genetic elements, single and in clusters, underlying geographically dependent SARS-CoV-2 genetic adaptation and their impact on binding affinity for drugs and immune control.",J Antimicrob Chemother,33254234,12/1/20,pubmed,0,14,virtual screening,0.623861682,0.372617426,0.000880222,0.000880231,0.000880236,0.000880202,Drug discovery,0.79846627,TRUE,80.42857143,0.808460635,37,0.616671127,2,0.618927094,,,0.681352952
4629,Temporal variations in the diagnostic performance of chest CT for Covid-19 depending on disease prevalence: Experience from North-Eastern France.,Eur J Radiol,33254065,12/1/20,pubmed,0,8,bayes,0.001010923,0.001011,0.561764192,0.227517069,0.065871842,0.142824974,Imaging,0.49440116,FALSE,42.625,0.563485682,14.5,0.418450629,0,0.403234768,,,0.461723693
4630,Prevalence of SARS-CoV-2 IgG antibodies in an area of northeastern Italy with a high incidence of COVID-19 cases: a population-based study.,Clin Microbiol Infect,33253941,12/1/20,pubmed,0,22,logistic regression,0.00159353,0.271383044,0.028839605,0.001593629,0.694996598,0.001593595,Healthcare,0.37061054,FALSE,34.68181818,0.48376523,35.22727273,0.606502542,4,0.707574542,,,0.599280771
4631,"Peripheral immunological features of COVID-19 patients in Taizhou, China: A retrospective study.",Clin Immunol,33253854,12/1/20,pubmed,0,11,logistic regression,0.097410795,0.002130679,0.002130709,0.002130714,0.002130656,0.894066447,Clinics,0.70438826,TRUE,10.27272727,0.154307626,2.363636364,0.173468022,0,0.403234768,,,0.243670139
4632,Exploring the magic bullets to identify Achilles' heel in SARS-CoV-2: Delving deeper into the sea of possible therapeutic options in Covid-19 disease: An update.,Food Chem Toxicol,33253764,12/1/20,pubmed,0,8,proteom,0.779485968,0.055111384,0.001593593,0.098605718,0.063609717,0.00159362,Drug discovery,0.82028747,TRUE,28.625,0.413445482,35.75,0.609044688,2,0.618927094,,,0.547139088
4633,One year update on the COVID-19 pandemic: Where are we now?,Acta Trop,33253656,12/1/20,pubmed,0,2,genomes,0.555851444,0.108559974,0.001593553,0.302372272,0.001593585,0.030029172,Drug discovery,0.65511656,TRUE,70,0.764178366,21.5,0.498260637,6,0.764429903,,,0.675622969
4634,A multiscale coarse-grained model of the SARS-CoV-2 virion.,Biophys J,33253634,12/1/20,pubmed,0,9,computational,0.702899841,0.001861804,0.203426617,0.001861823,0.088088193,0.001861721,Drug discovery,0.2594685,FALSE,110.5555556,0.890778651,62.88888889,0.731937383,0,0.403234768,,,0.675316934
4635,A model of workflow in the hospital during a pandemic to assist management.,PLoS One,33253323,12/1/20,pubmed,0,4,computational,0.002562645,0.002562713,0.207125554,0.558751983,0.002562757,0.226434348,Epidemiology,0.17092606,FALSE,63.75,0.726390006,17.25,0.453104094,0,0.403234768,,,0.527576289
4636,Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion.,PLoS One,33253274,12/1/20,pubmed,0,13,computational,0.001901757,0.001901767,0.092467721,0.508473623,0.393353412,0.001901721,Epidemiology,0.8187235,TRUE,33.46153846,0.47083926,33.61538462,0.596333958,1,0.537564047,,,0.534912422
4637,"The role of personality in the thoughts, feelings, and behaviors of students in Germany during the first weeks of the COVID-19 pandemic.",PLoS One,33253271,12/1/20,pubmed,0,4,logistic regression,0.001310387,0.001310344,0.001310355,0.451328411,0.543430176,0.001310326,Healthcare,0.6345982,TRUE,56,0.675304595,16.5,0.44293551,2,0.618927094,,,0.579055733
4638,SARS-CoV-2 lineage B.6 was the major contributor to early pandemic transmission in Malaysia.,PLoS Negl Trop Dis,33253226,12/1/20,pubmed,0,11,"whole genome, genome sequences",0.001046852,0.946691479,0.001046905,0.001046931,0.017138931,0.033028903,Genomics,0.1827771,FALSE,51,0.63875317,54.27272727,0.698488092,3,0.667819001,,,0.668353421
4639,"A Gapless, Unambiguous RNA Metagenome-Assembled Genome Sequence of a Unique SARS-CoV-2 Variant Encoding Spike S813I and ORF1a A859V Substitutions.",OMICS,33253058,12/1/20,pubmed,0,5,"metagenom, genomes",0.055650569,0.916160321,0.001237098,0.001237133,0.02447782,0.001237059,Genomics,0.41301042,FALSE,26.4,0.386913229,189.6,0.911827669,0,0.403234768,,,0.567325222
4640,Pre-COVID-19 deterrents to practicing with videoconferencing telepsychology among psychologists who didn't.,Psychol Serv,33252923,12/1/20,pubmed,0,3,logistic regression,0.00146198,0.001461962,0.001461963,0.149256751,0.844895429,0.001461914,Healthcare,0.67558134,TRUE,35.33333333,0.490259138,24,0.521808938,0,0.403234768,,,0.471767614
4641,"SARS-CoV-2 Among Military and Civilian Patients, Metro Manila, Philippines.",Mil Med,33252670,12/1/20,pubmed,0,19,"sequencing, genomes",0.00109881,0.610920126,0.001098829,0.070452724,0.001098871,0.31533064,Genomics,0.595008,TRUE,17.73684211,0.266868699,15.68421053,0.432432432,0,0.403234768,,,0.367511966
4642,Employer-Sponsored Sports Programs Amid COVID-19: The Approach of Social Capital.,J Occup Environ Med,33252374,12/1/20,pubmed,0,2,structural model,0.002562747,0.002562597,0.00256279,0.09795392,0.891795366,0.00256258,Healthcare,0.9550733,TRUE,9.5,0.143051518,4.5,0.242708055,0,0.403234768,,,0.262998114
4643,Identification of naphthyridine and quinoline derivatives as potential Nsp16-Nsp10 inhibitors: a pharmacoinformatics study.,J Biomol Struct Dyn,33252031,12/1/20,pubmed,0,6,"virtual screening, in silico",0.994639064,0.001072183,0.001072203,0.001072192,0.00107218,0.001072178,Drug discovery,0.9713998,TRUE,19.5,0.29117447,2,0.164302917,0,0.403234768,,,0.286237385
4644,Design of a multi-epitope-based vaccine targeting M-protein of SARS-CoV2: an immunoinformatics approach.,J Biomol Struct Dyn,33252008,12/1/20,pubmed,0,4,in silico,0.994063467,0.001187324,0.001187276,0.001187336,0.001187292,0.001187305,Drug discovery,0.8178845,TRUE,6.75,0.095862453,2.25,0.170925876,1,0.537564047,,,0.268117459
4645,Synthetic flavonoids as potential antiviral agents against SARS-CoV-2 main protease.,J Biomol Struct Dyn,33251983,12/1/20,pubmed,0,8,"virtual screening, computational, in silico, in-silico",0.932388373,0.000966756,0.000966769,0.038497565,0.026213792,0.000966744,Drug discovery,0.96542716,TRUE,26.375,0.386418455,7.375,0.304923736,2,0.618927094,,,0.436756428
4646,"Combination of QSAR, molecular docking, molecular dynamic simulation and MM-PBSA: analogues of lopinavir and favipiravir as potential drug candidates against COVID-19.",J Biomol Struct Dyn,33251975,12/1/20,pubmed,0,8,molecular dynamics simulation,0.995218464,0.000956329,0.000956317,0.000956304,0.000956293,0.000956292,Drug discovery,0.96935284,TRUE,11.875,0.178737089,1.5,0.138747659,0,0.403234768,,,0.240239839
4647,Evaluation of traditional ayurvedic Kadha for prevention and management of the novel Coronavirus (SARS-CoV-2) using in silico approach.,J Biomol Struct Dyn,33251972,12/1/20,pubmed,0,2,"molecular dynamics simulation, in silico, in-silico",0.823588047,0.001486472,0.001486509,0.118483435,0.001486537,0.053469,Drug discovery,0.91554165,TRUE,59,0.696579875,26,0.53819909,0,0.403234768,,,0.546004578
4648,Implications of telehealth and digital care solutions during COVID-19 pandemic: a qualitative literature review.,Inform Health Soc Care,33251894,12/1/20,pubmed,0,1,digital health,0.001511818,0.001511815,0.00151189,0.694935688,0.278511907,0.022016882,Epidemiology,0.50907147,TRUE,5,0.070752675,0,0.055525823,2,0.618927094,,,0.248401864
4649,A Novel Angiotensin Converting Enzyme 2 (ACE2) Activating Peptide: A Reflection of 10 Years of Research on a Small Peptide Ile-Arg-Trp (IRW).,J Agric Food Chem,33251800,12/1/20,pubmed,0,1,in silico,0.788565455,0.002296623,0.002296652,0.002296575,0.002296568,0.202248128,Drug discovery,0.7508694,TRUE,126,0.915331808,144,0.878244581,0,0.403234768,,,0.732270386
4650,Higher ACE2 expression levels in epicardial cells than subcutaneous stromal cells from patients with cardiovascular disease: Diabetes and obesity as possible enhancer.,Eur J Clin Invest,33251580,12/1/20,pubmed,0,7,logistic regression,0.363668161,0.001786578,0.001786569,0.001786548,0.001786516,0.629185628,Clinics,0.8605181,TRUE,22.14285714,0.326736347,8.857142857,0.333422531,0,0.403234768,,,0.354464549
4651,SARS-CoV-2 Receptors and Entry Genes Are Expressed in the Human Olfactory Neuroepithelium and Brain.,iScience,33251489,12/1/20,pubmed,0,11,dataset,0.987547276,0.002490579,0.002490582,0.002490528,0.002490503,0.002490531,Drug discovery,0.6271433,TRUE,24.09090909,0.356175397,32,0.585763982,2,0.618927094,,,0.520288824
4652,BSR 2020 Annual Meeting: Program.,J Belg Soc Radiol,33251479,12/1/20,pubmed,0,6,artificial intelligence,0.002490774,0.00249049,0.413405198,0.402625612,0.176497366,0.002490561,Imaging,0.1369347,FALSE,36.16666667,0.499165069,22.83333333,0.510235483,0,0.403234768,,,0.47087844
4653,Biodegradable Gloves for Waste Management Post-COVID-19 Outbreak: A Shelf-Life Prediction.,ACS Omega,33251468,12/1/20,pubmed,0,4,prediction model,0.197788961,0.001786574,0.001786609,0.795064452,0.001786726,0.001786677,Epidemiology,0.810601,TRUE,17.25,0.260189251,3.75,0.21982874,1,0.537564047,,,0.339194013
4654,0,ACS Omega,33251412,12/1/20,pubmed,0,3,interactom,0.644065534,0.002183262,0.002183355,0.002183429,0.002183302,0.347201118,Drug discovery,0.5825961,TRUE,38.66666667,0.525882862,5,0.257024351,1,0.537564047,,,0.440157087
4655,Screening marine algae metabolites as high-affinity inhibitors of SARS-CoV-2 main protease (3CLpro): an in silico analysis to identify novel drug candidates to combat COVID-19 pandemic.,Appl Biol Chem,33251389,12/1/20,pubmed,0,5,"virtual screening, molecular dynamics simulation, computational, in silico",0.962593494,0.001171562,0.001171594,0.032720211,0.001171564,0.001171575,Drug discovery,0.9058603,TRUE,27.2,0.397674562,3.8,0.221501204,2,0.618927094,,,0.412700954
4656,High affinity of host human microRNAs to SARS-CoV-2 genome: An in silico analysis.,Noncoding RNA Res,33251388,12/1/20,pubmed,0,4,in silico,0.802917781,0.191421724,0.001415105,0.001415113,0.001415158,0.001415119,Drug discovery,0.9621242,TRUE,8.75,0.129383388,1.5,0.138747659,1,0.537564047,,,0.268565031
4657,A multivariate data analysis approach for investigating daily statistics of countries affected with COVID-19 pandemic.,Heliyon,33251372,12/1/20,pubmed,0,5,dataset,0.001350367,0.001350388,0.001350437,0.735339198,0.001350393,0.259259217,Epidemiology,0.5145563,TRUE,26,0.382398417,3.8,0.221501204,0,0.403234768,,,0.335711463
4658,Dataset of potential Rhizoma Polygonati compound-druggable targets and partial pharmacokinetics for treatment of COVID-19.,Data Brief,33251300,12/1/20,pubmed,0,6,dataset,0.986804818,0.002639042,0.002639028,0.002639036,0.002639085,0.002638991,Drug discovery,0.47626728,FALSE,53.5,0.657245346,65.83333333,0.741905272,0,0.403234768,,,0.600795128
4659,A Risk Prediction Model for Evaluating the Disease Progression of COVID-19 Pneumonia.,Front Med (Lausanne),33251226,12/1/20,pubmed,0,12,"logistic regression, prediction model",0.001272649,0.001272653,0.067496126,0.030241027,0.001272688,0.898444856,Clinics,0.5574142,TRUE,27.5,0.401570907,19.08333333,0.471434306,1,0.537564047,,,0.470189753
4660,0,Front Chem,33251185,12/1/20,pubmed,0,5,virtual screening,0.807175673,0.00115632,0.18819928,0.001156259,0.001156241,0.001156228,Drug discovery,0.8137957,TRUE,49,0.624281032,23.8,0.519199893,2,0.618927094,,,0.58746934
4661,COVID-19 Knowledge Test: An Assessment Tool for Health Educators During the COVID-19 Pandemic.,Front Public Health,33251174,12/1/20,pubmed,0,2,model fit,0.001823377,0.001823351,0.001823434,0.271486457,0.72122001,0.001823371,Healthcare,0.6362083,TRUE,41.5,0.553219123,45,0.658817233,0,0.403234768,,,0.538423708
4662,Presence and mobility of the population during the first wave of Covid-19 outbreak and lockdown in Italy.,Sustain Cities Soc,33251092,12/1/20,pubmed,0,2,dataset,0.002032743,0.002032791,0.002032793,0.989836079,0.002032824,0.00203277,Epidemiology,0.56885564,TRUE,48.5,0.618158204,10.5,0.363459995,5,0.739490092,,,0.573702764
4663,Identification of potential mRNA panels for severe acute respiratory syndrome coronavirus 2 (COVID-19) diagnosis and treatment using microarray dataset and bioinformatics methods.,3 Biotech,33251083,12/1/20,pubmed,0,3,"bioinformatic, dataset",0.79861668,0.001392916,0.19581181,0.001392849,0.001392838,0.001392907,Drug discovery,0.6817194,TRUE,17,0.257467994,2.333333333,0.173401124,4,0.707574542,,,0.37948122
4664,Intelligent computing with Levenberg-Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control.,Eur Phys J Plus,33251082,12/1/20,pubmed,0,6,"neural network, mathematical model, dataset",0.00178664,0.001786561,0.334709439,0.633161627,0.001786521,0.026769212,Epidemiology,0.69160444,TRUE,19.83333333,0.294266807,2.5,0.180826866,5,0.739490092,,,0.404861255
4665,Leveraging public data to offer online inquiry opportunities.,Ecol Evol,33250994,12/1/20,pubmed,0,4,dataset,0.001823405,0.001823375,0.138960283,0.514075451,0.341494142,0.001823343,Epidemiology,0.52086836,TRUE,27.25,0.398169336,42,0.644902328,0,0.403234768,,,0.482102144
4666,0,Ecol Evol,33250987,12/1/20,pubmed,0,7,active learning,0.060809196,0.001112654,0.367891879,0.215879775,0.353193785,0.001112711,Healthcare,0.25241107,FALSE,20.42857143,0.302987198,18.28571429,0.463473374,1,0.537564047,,,0.434674873
4667,Teaching quantitative ecology online: An evidence-based prescription of best practices.,Ecol Evol,33250986,12/1/20,pubmed,0,1,computational,0.0012727,0.067602016,0.396260601,0.276378394,0.257213522,0.001272766,Epidemiology,0.6818466,TRUE,9,0.135320675,23,0.513513514,1,0.537564047,,,0.395466079
4668,A ligand-based computational drug repurposing pipeline using KNIME and Programmatic Data Access: case studies for rare diseases and COVID-19.,J Cheminform,33250934,12/1/20,pubmed,0,2,"information mining, computational, data mining, in silico",0.537701325,0.001593578,0.236515044,0.221002947,0.001593596,0.001593511,Drug discovery,0.55399597,TRUE,24,0.35574247,10.5,0.363459995,0,0.403234768,,,0.374145744
4669,Potential CD8+ T Cell Cross-Reactivity Against SARS-CoV-2 Conferred by Other Coronavirus Strains.,Front Immunol,33250893,12/1/20,pubmed,0,8,"in silico, proteom",0.77923746,0.175764151,0.001415133,0.001415129,0.04075294,0.001415187,Drug discovery,0.3977578,FALSE,67,0.748160059,125.125,0.857773615,1,0.537564047,,,0.714499241
4670,A fuzzy rough hybrid decision making technique for identifying the infected population of COVID-19.,Soft comput,33250663,12/1/20,pubmed,0,3,"mathematical model, probabilistic",0.002638999,0.002638997,0.002639196,0.986804574,0.00263925,0.002638985,Epidemiology,0.1037218,FALSE,12,0.183190055,2.666666667,0.185442869,1,0.537564047,,,0.302065657
4671,COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images.,Soft comput,33250662,12/1/20,pubmed,0,8,"deep learning, network model, dataset",0.001187262,0.001187277,0.922439326,0.072811544,0.001187286,0.001187304,Imaging,0.6179988,TRUE,73.625,0.780320366,29.875,0.568169655,2,0.618927094,,,0.655805705
4672,CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images.,Chaos Solitons Fractals,33250589,12/1/20,pubmed,0,6,"machine learning, deep learning, neural network, data mining, dataset",0.014502749,0.000599825,0.885262166,0.050218057,0.048817362,0.000599842,Imaging,0.73202527,TRUE,51.5,0.643267982,36.5,0.614329676,2,0.618927094,,,0.625508251
4673,CPAS: the UK's national machine learning-based hospital capacity planning system for COVID-19.,Mach Learn,33250568,12/1/20,pubmed,0,3,machine learning,0.001254616,0.00125461,0.413619591,0.514716314,0.001254645,0.067900223,Epidemiology,0.48238954,FALSE,244,0.983301379,191.3333333,0.912831148,3,0.667819001,,,0.854650509
4674,Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019.,Socioecon Plann Sci,33250530,12/1/20,pubmed,0,4,neural network,0.001415096,0.001415135,0.433116558,0.561222928,0.00141512,0.001415163,Epidemiology,0.6503264,TRUE,20.25,0.301317336,9,0.337904736,0,0.403234768,,,0.347485613
4675,"[The COVID-19 crisis, revealing the challenges of computational psychiatry].",Ann Med Psychol (Paris),33250519,12/1/20,pubmed,0,3,computational,0.025063094,0.025060641,0.025062356,0.874692301,0.025061365,0.025060244,Epidemiology,0.62609696,TRUE,35.66666667,0.493908096,6,0.280037463,0,0.403234768,,,0.392393442
4676,Monitoring the status of selected health related sustainable development goals: methods and projections to 2030.,Glob Health Action,33250013,12/1/20,pubmed,0,17,"bayes, bayesian model",0.042553554,0.000966802,0.000966804,0.825084789,0.129461251,0.0009668,Epidemiology,0.51308817,TRUE,45.23529412,0.588904694,162.7058824,0.894367139,0,0.403234768,,,0.628835534
4677,A multifactorial score including autophagy for prognosis and care of COVID-19 patients.,Autophagy,33249989,12/1/20,pubmed,0,9,"sequencing, genomes",0.30467489,0.251769357,0.001751223,0.123939814,0.001751213,0.316113503,Clinics,0.39798197,FALSE,116.1111111,0.900921516,456.7777778,0.976518598,0,0.403234768,,,0.760224961
4678,Derivation With Internal Validation of a Multivariable Predictive Model to Predict COVID-19 Test Results in Emergency Department Patients.,Acad Emerg Med,33249683,11/30/20,pubmed,0,5,"predictive model, logistic regression",0.000830687,0.00083067,0.463583263,0.000830695,0.070725117,0.463199568,Imaging,0.81161535,TRUE,12,0.183190055,11.4,0.375970029,4,0.707574542,,,0.422244875
4679,Effect of steroids on Covid-19 mortality risk: a Bayesian interpretation.,Clin Infect Dis,33249430,11/30/20,pubmed,0,2,bayes,0.025060342,0.025061068,0.025061501,0.360931431,0.02506065,0.538825008,Clinics,0.12477368,FALSE,101,0.871296926,140,0.874096869,0,0.403234768,,,0.716209521
4680,Nationwide seroprevalence of SARS-CoV-2 and identification of risk factors in the general population of the Netherlands during the first epidemic wave.,J Epidemiol Community Health,33249407,11/30/20,pubmed,0,14,logistic regression,0.001565366,0.226314271,0.024019239,0.11052457,0.470229306,0.167347248,Healthcare,0.53860325,TRUE,49.78571429,0.630218319,53.85714286,0.696614932,11,0.840175319,,,0.72233619
4681,Coevolutionary forces shaping the fitness of SARS-CoV-2 spike glycoprotein against human receptor ACE2.,Infect Genet Evol,33249264,11/30/20,pubmed,0,2,bioinformatic,0.88061163,0.002490653,0.002490541,0.10942601,0.002490623,0.002490542,Drug discovery,0.54372317,TRUE,35,0.488032655,5,0.257024351,2,0.618927094,,,0.454661367
4682,0,Eur J Pharmacol,33249077,11/30/20,pubmed,0,5,computational,0.993543739,0.001291247,0.00129127,0.001291299,0.001291246,0.0012912,Drug discovery,0.7889262,TRUE,15.2,0.229142186,3.4,0.208589778,1,0.537564047,,,0.32509867
4683,A Shift Towards an Immature Myeloid Profile in Peripheral Blood of Critically Ill COVID-19 Patients.,Arch Med Res,33248817,11/30/20,pubmed,0,36,transcriptom,0.631947053,0.140237234,0.029237129,0.001987157,0.001987096,0.19460433,Drug discovery,0.16027081,FALSE,28.08333333,0.408497743,18.11111111,0.462202301,1,0.537564047,,,0.469421364
4684,"Levels of resilience, anxiety and depression in nurses working in respiratory clinical areas during the COVID pandemic.",Respir Med,33248362,11/29/20,pubmed,0,6,model fit,0.001415157,0.001415093,0.052508835,0.00141515,0.941830374,0.001415391,Healthcare,0.97784495,TRUE,8,0.118683901,0.166666667,0.058736955,0,0.403234768,,,0.193551875
4685,Assessment of cerebrovascular disease with computed tomography in COVID-19 patients: correlation of a novel specific visual score with increased mortality risk.,Radiol Med,33247816,11/29/20,pubmed,0,11,logistic regression,0.001511816,0.001511827,0.311260079,0.001511857,0.001511838,0.682692583,Clinics,0.8032149,TRUE,55.27272727,0.669676542,15.45454545,0.429020605,0,0.403234768,,,0.500643971
4686,Antiretroviral treatment interruption among people living with HIV during COVID-19 outbreak in China: a nationwide cross-sectional study.,J Int AIDS Soc,33247541,11/29/20,pubmed,0,16,logistic regression,0.001330048,0.001330075,0.079958629,0.101530771,0.81452038,0.001330096,Healthcare,0.9397578,TRUE,33.5,0.471890655,,,0,0.403234768,,,0.437562711
4687,Variation in microparasite free-living survival and indirect transmission can modulate the intensity of emerging outbreaks.,Sci Rep,33247174,11/29/20,pubmed,0,6,mathematical model,0.242715453,0.001751297,0.001751183,0.750279509,0.001751192,0.001751366,Epidemiology,0.4879097,FALSE,26,0.382398417,16,0.437316029,0,0.403234768,,,0.407649738
4688,Benford's law and metabolomics: A tale of numbers and blood.,Transfus Apher Sci,33246837,11/29/20,pubmed,0,1," omics, metabolom, dataset",0.035979017,0.001901746,0.001901802,0.838947648,0.001901787,0.119367999,Epidemiology,0.19427672,FALSE,199,0.972354506,108,0.836232272,0,0.403234768,,,0.737273848
4689,An inter-correlated cytokine network identified at the center of cytokine storm predicted COVID-19 prognosis.,Cytokine,33246770,11/29/20,pubmed,0,11,network analysis,0.116219308,0.001823403,0.047716067,0.001823361,0.001823349,0.830594513,Clinics,0.66264886,TRUE,26.54545455,0.389510792,5.272727273,0.261038266,2,0.618927094,,,0.423158718
4690,Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning.,Environ Pollut,33246767,11/29/20,pubmed,0,6,machine learning,0.001291358,0.001291278,0.14368847,0.851146425,0.001291243,0.001291226,Epidemiology,0.6006705,TRUE,19.66666667,0.292596945,3.333333333,0.206515922,0,0.403234768,,,0.300782545
4691,Antibody-guided structure-based vaccines.,Semin Immunol,33246736,11/29/20,pubmed,0,3,sequencing,0.588193392,0.179016176,0.001538172,0.228176034,0.001538136,0.00153809,Drug discovery,0.3186164,FALSE,217,0.977487785,736.6666667,0.988493444,3,0.667819001,,,0.87793341
4692,A modelling study highlights the power of detecting and isolating asymptomatic or very mildly affected individuals for COVID-19 epidemic management.,BMC Public Health,33246432,11/29/20,pubmed,0,7,mathematical model,0.001371259,0.001371378,0.001371347,0.964809321,0.001371334,0.029705361,Epidemiology,0.16807503,FALSE,24.42857143,0.360628363,24.42857143,0.524351084,1,0.537564047,,,0.474181165
4693,Risk factors for mortality in hospitalized patients with COVID-19 at the start of the pandemic in Belgium: a retrospective cohort study.,BMC Infect Dis,33246431,11/29/20,pubmed,0,10,logistic regression,0.001254644,0.001254624,0.001254614,0.234463618,0.001254649,0.760517852,Clinics,0.9000542,TRUE,12.5,0.189436576,6.7,0.290808135,3,0.667819001,,,0.382687904
4694,0,J Biomol Struct Dyn,33246398,11/29/20,pubmed,0,6,in silico,0.994063455,0.001187311,0.001187266,0.001187292,0.001187289,0.001187386,Drug discovery,0.9352442,TRUE,13.33333333,0.201558538,2.666666667,0.185442869,0,0.403234768,,,0.263412058
4695,Multiple epitope-based vaccine prediction against SARS-CoV-2 spike glycoprotein.,J Biomol Struct Dyn,33246394,11/29/20,pubmed,0,5,"molecular dynamics simulation, in-silico",0.973244795,0.001022695,0.001022665,0.001022666,0.022664554,0.001022626,Drug discovery,0.68678516,TRUE,36.2,0.499412456,10.8,0.366871822,1,0.537564047,,,0.467949442
4696,A bioinformatic approach to investigating cytokine genes and their receptor variants in relation to COVID-19 progression.,Int J Immunogenet,33246355,11/28/20,pubmed,0,3,"computational, bioinformatic, in silico",0.62983348,0.221100159,0.00127269,0.001272772,0.001272709,0.14524819,Drug discovery,0.8172841,TRUE,39,0.530521368,9.333333333,0.342253144,0,0.403234768,,,0.425336427
4697,Outcomes of COVID-19: Disparities by ethnicity.,Infect Genet Evol,33246086,11/28/20,pubmed,0,13,logistic regression,0.002638926,0.002638988,0.002638928,0.002638994,0.002639206,0.986804958,Clinics,0.61901885,TRUE,13.76923077,0.20836168,2.923076923,0.191129248,2,0.618927094,,,0.339472674
4698,"Revisiting the levels of Aerosol Optical Depth in south-southeast Asia, Europe and USA amid the COVID-19 pandemic using satellite observations.",Environ Res,33245884,11/28/20,pubmed,0,9,radiom,0.047435006,0.06856211,0.050720838,0.83162072,0.000830654,0.000830672,Epidemiology,0.93709266,TRUE,15.55555556,0.234522852,2.666666667,0.185442869,1,0.537564047,,,0.319176589
4699,Integrative Imaging Reveals SARS-CoV-2-Induced Reshaping of Subcellular Morphologies.,Cell Host Microbe,33245857,11/28/20,pubmed,0,29,dataset,0.820277005,0.001987292,0.171773846,0.001987308,0.001987421,0.001987129,Drug discovery,0.2206729,FALSE,27.75862069,0.404354011,75.72413793,0.768330211,14,0.866658436,,,0.679780886
4700,The European Bioinformatics Institute: empowering cooperation in response to a global health crisis.,Nucleic Acids Res,33245775,11/28/20,pubmed,0,8,bioinformatic,0.001823461,0.077872563,0.143987091,0.772670111,0.001823448,0.001823326,Epidemiology,0.153135,FALSE,132.625,0.924670666,1095.25,0.994648113,2,0.618927094,,,0.846081958
4701,Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection.,IEEE Trans Med Imaging,33245693,11/28/20,pubmed,0,11,dataset,0.000946107,0.137663603,0.814914158,0.044583927,0.000946106,0.000946099,Imaging,0.11923629,FALSE,36.72727273,0.505473437,30,0.570176612,4,0.707574542,,,0.594408197
4702,COVID-19 mortality risk factors in older people in a long-term care center.,Eur Geriatr Med,33245505,11/28/20,pubmed,0,13,logistic regression,0.001565382,0.001565306,0.001565318,0.001565374,0.167479955,0.826258665,Clinics,0.9054941,TRUE,38.38461538,0.522728678,28.61538462,0.559138346,9,0.814309525,,,0.63205885
4703,Molecular docking and simulation investigation: effect of beta-sesquiphellandrene with ionic integration on SARS-CoV2 and SFTS viruses.,J Genet Eng Biotechnol,33245459,11/28/20,pubmed,0,3,"computational, in silico",0.99203223,0.001593569,0.001593542,0.001593488,0.001593514,0.001593657,Drug discovery,0.64827,TRUE,13,0.197352959,1.333333333,0.13252609,0,0.403234768,,,0.244371272
4704,COVID-19 and breast cancer: may the microbiome be the issue?,Future Oncol,33244989,11/28/20,pubmed,0,6,microbiom,0.025061228,0.332343411,0.025063282,0.27669634,0.025063172,0.315772566,Genomics,0.7388451,TRUE,126,0.915331808,55,0.702167514,0,0.403234768,,,0.67357803
4705,"Viral Infection-Induced Gut Dysbiosis, Neuroinflammation, and α-Synuclein Aggregation: Updates and Perspectives on COVID-19 and Neurodegenerative Disorders.",ACS Chem Neurosci,33244974,11/28/20,pubmed,0,1,microbiom,0.450801576,0.106065006,0.00229659,0.336776688,0.101763553,0.002296586,Drug discovery,0.7319274,TRUE,49,0.624281032,68,0.748193738,0,0.403234768,,,0.591903179
4706,"Pandemics: past, present, future: That is like choosing between cholera and plague.",APMIS,33244837,11/28/20,pubmed,0,1,mathematical model,0.003607353,0.143301167,0.003607294,0.622244264,0.223632491,0.003607431,Epidemiology,0.36483702,FALSE,514,0.997835364,940,0.993042547,0,0.403234768,,,0.79803756
4707,Predicting cryptic ligand binding sites based on normal modes guided conformational sampling.,Proteins,33244830,11/28/20,pubmed,0,1,"molecular dynamics simulation, dataset",0.377183064,0.112456842,0.506699967,0.00122006,0.001220042,0.001220024,Drug discovery,0.74928296,TRUE,56,0.675304595,64,0.735549906,0,0.403234768,,,0.604696423
4708,The Changing Aspects of Motherhood in Face of the COVID-19 Pandemic in Low- and Middle-Income Countries.,Matern Child Health J,33244678,11/28/20,pubmed,0,4,digital health,0.001291242,0.001291239,0.001291325,0.569846342,0.424988586,0.001291267,Epidemiology,0.8638095,TRUE,42,0.558537943,57,0.711198823,0,0.403234768,,,0.557657178
4709,Novel SARS-CoV-2 encoded small RNAs in the passage to humans.,Bioinformatics,33244583,11/28/20,pubmed,0,9,"deep learning, bioinformatic",0.324599438,0.626718162,0.044631388,0.001350353,0.001350335,0.001350324,Genomics,0.27190667,FALSE,33.55555556,0.472014348,33.11111111,0.59339042,2,0.618927094,,,0.561443954
4710,Investigating the human protein-host protein interactome of SARS-CoV-2 infection in the small intestine.,Gastroenterol Hepatol Bed Bench,33244381,11/28/20,pubmed,0,5,interactom,0.973121974,0.000977469,0.000977443,0.000977438,0.022968187,0.000977489,Drug discovery,0.8883695,TRUE,21.8,0.322283382,3.2,0.202100615,0,0.403234768,,,0.309206255
4711,Introducing APOA1 as a key protein in COVID-19 infection: a bioinformatics approach.,Gastroenterol Hepatol Bed Bench,33244380,11/28/20,pubmed,0,4,"bioinformatic, proteom",0.717872134,0.001272673,0.128272802,0.0012727,0.067704464,0.083605227,Drug discovery,0.9286353,TRUE,81.5,0.8129136,12.25,0.388546963,0,0.403234768,,,0.534898443
4712,"Nutrition, the digestive system and immunity in COVID-19 infection.",Gastroenterol Hepatol Bed Bench,33244375,11/28/20,pubmed,0,4,microbiom,0.001684631,0.041091721,0.001684539,0.421333122,0.323656678,0.210549309,Epidemiology,0.88325274,TRUE,8.25,0.121343311,1,0.122023013,0,0.403234768,,,0.215533697
4713,Level of Perceived Attitude and Practice and Associated Factors Towards the Prevention of the COVID-19 Epidemic Among Residents of Dessie and Kombolcha Town Administrations: A Population-Based Survey.,Res Rep Trop Med,33244287,11/28/20,pubmed,0,3,logistic regression,0.001371299,0.001371283,0.001371253,0.001371295,0.974230612,0.020284258,Healthcare,0.8688967,TRUE,1.666666667,0.016451234,0,0.055525823,0,0.403234768,,,0.158403941
4714,Community Level of COVID-19 Information Exposure and Influencing Factors in Northwest Ethiopia.,Risk Manag Healthc Policy,33244280,11/28/20,pubmed,0,28,logistic regression,0.000898089,0.000898083,0.000898101,0.136024405,0.860383206,0.000898116,Healthcare,0.3449773,FALSE,12.82142857,0.19240522,3.25,0.203572384,0,0.403234768,,,0.266404124
4715,Assessing the Impact of COVID-19 on the Mental Health of Healthcare Workers in Three Metropolitan Cities of Pakistan.,Psychol Res Behav Manag,33244279,11/28/20,pubmed,0,15,logistic regression,0.002183194,0.002183246,0.002183201,0.002183299,0.989083821,0.002183238,Healthcare,0.9777365,TRUE,18,0.271569052,4,0.231469093,0,0.403234768,,,0.302090971
4716,Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States.,Sci Rep,33244071,11/28/20,pubmed,0,7,dataset,0.001330034,0.001330059,0.001330101,0.993349574,0.001330088,0.001330144,Epidemiology,0.21534795,FALSE,58,0.689714887,42.42857143,0.646775488,1,0.537564047,,,0.624684807
4717,Characteristics of viral specimens collected from asymptomatic and fatal cases of COVID-19.,J Biomed Res,33243941,11/28/20,pubmed,0,10,"sequencing, whole genome",0.001622762,0.801755479,0.028260607,0.001622759,0.001622757,0.165115636,Genomics,0.43313566,FALSE,23.4,0.346527305,34.1,0.59954509,5,0.739490092,,,0.561854162
4718,Effect of the COVID-19 pandemic on drug compliance and stigmatization in patients with epilepsy.,Epilepsy Behav,33243679,11/28/20,pubmed,0,2,correlation analysis,0.117816664,0.00117157,0.001171559,0.00117163,0.534729997,0.343938581,Healthcare,0.93797827,TRUE,8,0.118683901,0,0.055525823,3,0.667819001,,,0.280676242
4719,Changes in Social Media Impact of the Radiological Literature During the Covid-19 Pandemic.,Acad Radiol,33243677,11/28/20,pubmed,0,5,forecasting model,0.001461882,0.001461879,0.158786536,0.531259235,0.226339695,0.080690773,Epidemiology,0.5796591,TRUE,51,0.63875317,20.4,0.484613326,0,0.403234768,,,0.508867088
4720,"Analysis of anxiety-related factors amongst frontline dental staff during the COVID-19 pandemic in Yichang, China.",BMC Oral Health,33243197,11/28/20,pubmed,0,5,logistic regression,0.001461887,0.001461934,0.03491374,0.001461961,0.917550045,0.043150433,Healthcare,0.8104211,TRUE,27.8,0.404848785,7,0.299973241,1,0.537564047,,,0.414128691
4721,Assessing COVID-19 susceptibility through analysis of the genetic and epigenetic diversity of ACE2-mediated SARS-CoV-2 entry.,Pharmacogenomics,33243086,11/28/20,pubmed,0,2,classifier,0.302239481,0.459199946,0.09857928,0.00249059,0.002490691,0.135000011,Genomics,0.52723813,TRUE,86,0.829117447,51,0.685509767,1,0.537564047,,,0.684063754
4722,Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users' perceptions.,Int J Med Inform,33242762,11/27/20,pubmed,0,6,machine learning,0.001371272,0.001371288,0.047322825,0.880198079,0.068365271,0.001371265,Epidemiology,0.846123,TRUE,11.33333333,0.170635166,2.5,0.180826866,0,0.403234768,,,0.2515656
4723,Structural and functional insights into non-structural proteins of coronaviruses.,Microb Pathog,33242646,11/27/20,pubmed,0,4,"interactom, genomes",0.505985028,0.489328586,0.001171579,0.001171597,0.001171556,0.001171654,Drug discovery,0.42757583,FALSE,58.25,0.691384749,32.25,0.587570244,3,0.667819001,,,0.648924664
4724,COVID-19 Susceptibility in Bronchial Asthma.,J Allergy Clin Immunol Pract,33242628,11/27/20,pubmed,0,5,logistic regression,0.2082209,0.001653166,0.001653074,0.001653066,0.001653117,0.785166677,Clinics,0.8830696,TRUE,69,0.759416167,51.2,0.686178753,7,0.785110192,,,0.743568371
4725,The phylogenetic relationship within SARS-CoV-2s: An expanding basal clade.,Mol Phylogenet Evol,33242581,11/27/20,pubmed,0,3,"whole genome, genome sequences, sequence alignment, dataset",0.00127267,0.942671243,0.052238109,0.001272712,0.001272639,0.001272626,Genomics,0.23498353,FALSE,9.333333333,0.139278867,4.333333333,0.237958255,1,0.537564047,,,0.304933723
4726,Identification of key signaling pathways induced by SARS-CoV2 that underlie thrombosis and vascular injury in COVID-19 patients.,J Leukoc Biol,33242368,11/27/20,pubmed,0,9,bioinformatic,0.624763136,0.00229658,0.002296539,0.002296617,0.002296572,0.366050556,Drug discovery,0.49403134,FALSE,66.33333333,0.742903086,145.6666667,0.879983944,1,0.537564047,,,0.720150359
4727,The impact of episodic screening interruption: COVID-19 and population-based cancer screening in Canada.,J Med Screen,33241760,11/27/20,pubmed,0,10,"simulation model, mathematical model",0.127555524,0.00104682,0.001046881,0.272408786,0.001046854,0.596895134,Clinics,0.48112494,FALSE,121.9,0.909518214,182.9,0.908282044,3,0.667819001,,,0.828539753
4728,Anti-coronavirus disease 2019 (COVID-19) targets and mechanisms of puerarin.,J Cell Mol Med,33241658,11/27/20,pubmed,0,7,"bioinformatic, in silico",0.990282248,0.00194355,0.001943478,0.001943555,0.001943504,0.001943665,Drug discovery,0.9519405,TRUE,95.28571429,0.857257715,40.42857143,0.636205512,0,0.403234768,,,0.632232665
4729,Remote Patient Monitoring Program for Hospital Discharged COVID-19 Patients.,Appl Clin Inform,33241547,11/27/20,pubmed,0,10,logistic regression,0.001156292,0.001156273,0.001156332,0.052465643,0.307994212,0.636071248,Clinics,0.9963659,TRUE,6.4,0.090667326,8.3,0.324458121,1,0.537564047,,,0.317563165
4730,Medication adherence among patients with chronic obstructive pulmonary disease treated in a primary general hospital during the COVID-19 pandemic.,Ann Transl Med,33241028,11/27/20,pubmed,0,6,logistic regression,0.069701467,0.001237067,0.001237076,0.001237098,0.317501416,0.609085876,Clinics,0.99444795,TRUE,28,0.408312202,1.666666667,0.145036125,0,0.403234768,,,0.318861032
4731,0,Front Vet Sci,33240967,11/27/20,pubmed,0,9,in silico,0.879976308,0.114176247,0.001461853,0.001461894,0.001461846,0.001461852,Drug discovery,0.57916385,TRUE,20.88888889,0.308367864,13.22222222,0.402394969,0,0.403234768,,,0.371332534
4732,Molecular Basis of Cardiac and Vascular Injuries Associated With COVID-19.,Front Cardiovasc Med,33240937,11/27/20,pubmed,0,5,"in silico, transcriptom, dataset",0.589578054,0.001072267,0.001072259,0.001072201,0.001072233,0.406132986,Drug discovery,0.95509875,TRUE,109,0.888304781,80.4,0.779970565,0,0.403234768,,,0.690503371
4733,Bilirubin Levels as Potential Indicators of Disease Severity in Coronavirus Disease Patients: A Retrospective Cohort Study.,Front Med (Lausanne),33240911,11/27/20,pubmed,0,13,logistic regression,0.001538165,0.001538067,0.001538079,0.001538119,0.001538163,0.992309407,Clinics,0.93568265,TRUE,95.46153846,0.857876183,93.07692308,0.808736955,3,0.667819001,,,0.778144046
4734,The frequency of combined IFITM3 haplotype involving the reference alleles of both rs12252 and rs34481144 is in line with COVID-19 standardized mortality ratio of ethnic groups in England.,PeerJ,33240681,11/27/20,pubmed,0,3,genomes,0.220529437,0.355873033,0.001330051,0.128035084,0.001330142,0.292902253,Genomics,0.16566196,FALSE,2.333333333,0.024800544,2.333333333,0.173401124,0,0.403234768,,,0.200478812
4735,Prevalence and Associated Factors of Posttraumatic Stress Symptoms and Stigma among Health Care Workers in Contact with COVID-19 Patients.,Iran J Psychiatry,33240384,11/27/20,pubmed,0,6,logistic regression,0.001392866,0.001392891,0.001392983,0.001392875,0.952936486,0.041491899,Healthcare,0.9961989,TRUE,187.5,0.967592306,153.8333333,0.886540005,2,0.618927094,,,0.824353135
4736,Phototrophic Co-cultures From Extreme Environments: Community Structure and Potential Value for Fundamental and Applied Research.,Front Microbiol,33240229,11/27/20,pubmed,0,13,sequencing,0.001486495,0.554740989,0.001486488,0.439313081,0.001486525,0.001486422,Genomics,0.54714024,TRUE,57.30769231,0.683901293,164.9230769,0.895838908,0,0.403234768,,,0.660991656
4737,Identifying Predictors of Psychological Distress During COVID-19: A Machine Learning Approach.,Front Psychol,33240178,11/27/20,pubmed,0,8,machine learning,0.001392899,0.001392861,0.109327714,0.001392864,0.885100819,0.001392843,Healthcare,0.94358027,TRUE,10.375,0.15535902,1.125,0.122959593,6,0.764429903,,,0.347582839
4738,Is It Just About Physical Health? An Online Cross-Sectional Study Exploring the Psychological Distress Among University Students in Jordan in the Midst of COVID-19 Pandemic.,Front Psychol,33240151,11/27/20,pubmed,0,3,logistic regression,0.000846545,0.000846529,0.068594653,0.000846561,0.928019159,0.000846553,Healthcare,0.84042037,TRUE,5,0.070752675,0.333333333,0.073187048,2,0.618927094,,,0.254288939
4739,Emotion Network Analysis During COVID-19 Quarantine - A Longitudinal Study.,Front Psychol,33240149,11/27/20,pubmed,0,3,network analysis,0.001310388,0.001310408,0.001310418,0.777181471,0.217576935,0.001310379,Epidemiology,0.6223973,TRUE,15.33333333,0.230997588,1.666666667,0.145036125,1,0.537564047,,,0.304532587
4740,A Network Analysis of Post-traumatic Stress Disorder Symptoms and Correlates During the COVID-19 Pandemic.,Front Psychiatry,33240124,11/27/20,pubmed,0,5,network analysis,0.001254654,0.001254634,0.135514342,0.001254672,0.859467076,0.001254622,Healthcare,0.9394396,TRUE,11.2,0.168594224,1.6,0.140687717,0,0.403234768,,,0.23750557
4741,In silico studies evidenced the role of structurally diverse plant secondary metabolites in reducing SARS-CoV-2 pathogenesis.,Sci Rep,33239694,11/27/20,pubmed,0,12,in silico,0.995058108,0.0009884,0.000988362,0.00098836,0.000988365,0.000988405,Drug discovery,0.8988485,TRUE,3.75,0.046817985,0.416666667,0.075996789,1,0.537564047,,,0.220126274
4742,Longitudinal proteomic profiling reveals increased early inflammation and sustained apoptosis proteins in severe COVID-19.,Sci Rep,33239683,11/27/20,pubmed,0,16,proteom,0.195312517,0.044092494,0.002130727,0.002130712,0.002130664,0.754202886,Clinics,0.44484326,FALSE,49,0.624281032,138.3125,0.872959593,2,0.618927094,,,0.70538924
4743,"Response2covid19, a dataset of governments' responses to COVID-19 all around the world.",Sci Data,33239654,11/27/20,pubmed,0,1,dataset,0.002357747,0.002357768,0.002357906,0.988211031,0.002357827,0.002357721,Epidemiology,0.19400239,FALSE,10,0.15214299,0,0.055525823,2,0.618927094,,,0.275531969
4744,No evidence for increased transmissibility from recurrent mutations in SARS-CoV-2.,Nat Commun,33239633,11/27/20,pubmed,0,6,"genomes, dataset",0.042227935,0.950164942,0.001901794,0.001901804,0.001901731,0.001901795,Genomics,0.23529807,FALSE,47.16666667,0.607087637,331.1666667,0.962135403,4,0.707574542,,,0.758932527
4745,Gammacoronavirus Avian Infectious Bronchitis Virus and Alphacoronavirus Porcine Epidemic Diarrhea Virus Exploit a Cell-Survival Strategy via Upregulation of cFOS to Promote Viral Replication.,J Virol,33239458,11/27/20,pubmed,0,7,genome-wide,0.817361564,0.178640253,0.000999526,0.000999597,0.000999532,0.000999528,Drug discovery,0.9474504,TRUE,15.57142857,0.23495578,13.14285714,0.401525288,1,0.537564047,,,0.391348371
4746,The Human Adenovirus Type 2 Transcriptome: An Amazing Complexity of Alternatively Spliced mRNAs.,J Virol,33239457,11/27/20,pubmed,0,8,"sequencing, transcriptom, proteom",0.489580339,0.474011998,0.000880236,0.033766729,0.000880212,0.000880487,Drug discovery,0.11391634,FALSE,93.625,0.853423217,180.875,0.906877174,0,0.403234768,,,0.721178386
4747,Conserved Genomic Terminals of SARS-CoV-2 as Coevolving Functional Elements and Potential Therapeutic Targets.,mSphere,33239366,11/27/20,pubmed,0,3,"computational, genome-wide, genomes",0.443147121,0.529436925,0.00083854,0.000838545,0.000838528,0.02490034,Genomics,0.4156268,FALSE,212.3333333,0.976312697,839,0.991102489,4,0.707574542,,,0.891663242
4748,Development and validation of the patient history COVID-19 (PH-Covid19) scoring system: a multivariable prediction model of death in Mexican patients with COVID-19.,Epidemiol Infect,33239114,11/27/20,pubmed,0,9,"predictive model, prediction model",0.001565307,0.001565413,0.232281169,0.001565372,0.001565421,0.761457318,Clinics,0.3894224,FALSE,10.66666667,0.159131672,0.888888889,0.10422799,0,0.403234768,,,0.222198143
4749,Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection.,Biomed Eng Online,33239006,11/27/20,pubmed,0,9,"deep-learning, dataset",0.001085405,0.001085421,0.906672838,0.001085332,0.001085369,0.088985636,Imaging,0.9453391,TRUE,11.44444444,0.171748407,2.555555556,0.181161359,2,0.618927094,,,0.32394562
4750,"Prevalence and factors associated with postpartum depression during the COVID-19 pandemic among women in Guangzhou, China: a cross-sectional study.",BMC Psychiatry,33238927,11/27/20,pubmed,0,5,logistic regression,0.002296514,0.002296549,0.002296514,0.00229664,0.988517188,0.002296594,Healthcare,0.9403466,TRUE,28.2,0.409734677,,,1,0.537564047,,,0.473649362
4751,Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19.,J Crit Care,33238219,11/26/20,pubmed,0,5,machine learning,0.001684489,0.001684514,0.455353753,0.001684492,0.001684497,0.537908254,Clinics,0.8860128,TRUE,26.8,0.392417589,7.8,0.313286058,1,0.537564047,,,0.414422565
4752,Digital health interventions in children with asthma.,Clin Exp Allergy,33238032,11/26/20,pubmed,0,4,digital health,0.078164799,0.001330035,0.101173015,0.485523017,0.332478939,0.001330195,Epidemiology,0.9872223,TRUE,165,0.954851877,53.25,0.694674873,0,0.403234768,,,0.684253839
4753,"Estimated incidence of COVID-19 illness and hospitalization - United States, February-September, 2020.",Clin Infect Dis,33237993,11/26/20,pubmed,0,9,probabilistic,0.001593483,0.059182955,0.001593612,0.594794318,0.001593624,0.341242007,Epidemiology,0.26651907,FALSE,98.55555556,0.865978106,174.4444444,0.902528766,18,0.891474782,,,0.886660552
4754,Computational simulation to assess patient safety of uncompensated COVID-19 two-patient ventilator sharing using the Pulse Physiology Engine.,PLoS One,33237927,11/26/20,pubmed,0,8,computational,0.000830665,0.000830674,0.000830682,0.526309888,0.000830668,0.470367423,Epidemiology,0.5885128,TRUE,19,0.285793803,12,0.386740701,0,0.403234768,,,0.358589757
4755,"COVID-19 Outbreak Associated with a 10-Day Motorcycle Rally in a Neighboring State - Minnesota, August-September 2020.",MMWR Morb Mortal Wkly Rep,33237891,11/26/20,pubmed,0,18,sequencing,0.002032742,0.090924667,0.002032744,0.396117626,0.286900441,0.221991779,Epidemiology,0.7159142,TRUE,59.61111111,0.700352526,134.0555556,0.867875301,0,0.403234768,,,0.657154198
4756,Immunoglobulin Deficiency as an Indicator of Disease Severity in Patients with COVID-19.,Am J Physiol Lung Cell Mol Physiol,33237794,11/26/20,pubmed,0,25,logistic regression,0.001310449,0.186885066,0.001310328,0.001310325,0.001310345,0.807873487,Clinics,0.6376504,TRUE,107.92,0.885336137,136.08,0.870885737,0,0.403234768,,,0.719818881
4757,Molecular investigation of adequate sources of mesenchymal stem cells for cell therapy of COVID-19-associated organ failure.,Stem Cells Transl Med,33237619,11/26/20,pubmed,0,8,transcriptom,0.915419745,0.002720169,0.002720149,0.002720177,0.002720484,0.073699277,Drug discovery,0.657908,TRUE,58.5,0.693240151,39.625,0.631388815,0,0.403234768,,,0.575954578
4758,Perceived Care and Well-being of Patients With Cancer and Matched Norm Participants in the COVID-19 Crisis: Results of a Survey of Participants in the Dutch PROFILES Registry.,JAMA Oncol,33237294,11/26/20,pubmed,0,18,logistic regression,0.000830641,0.000830647,0.016846452,0.000830681,0.579027334,0.401634246,Healthcare,0.97566235,TRUE,110.6111111,0.890902344,91.44444444,0.804856837,2,0.618927094,,,0.771562092
4759,UniProt: the universal protein knowledgebase in 2021.,Nucleic Acids Res,33237286,11/26/20,pubmed,0,133,proteom,0.270049975,0.154657429,0.109474835,0.461930663,0.001943582,0.001943516,Epidemiology,0.64885676,TRUE,28.91791045,0.416475973,,,14,0.866658436,,,0.641567204
4760,Chemsex practice among men who have sex with men (MSM) during social isolation from COVID-19: multicentric online survey.,Cad Saude Publica,33237252,11/26/20,pubmed,0,12,logistic regression,0.019597202,0.001126816,0.001126832,0.148780349,0.828241987,0.001126814,Healthcare,0.92673266,TRUE,45.08333333,0.587296679,11.25,0.374364463,3,0.667819001,,,0.543160048
4761,Host-shift as the cause of emerging infectious diseases: Experimental approaches using Drosophila-virus interactions.,Genet Mol Biol,33237151,11/26/20,pubmed,0,3,sequencing,0.19383368,0.510855829,0.037786709,0.227960105,0.028077274,0.001486404,Genomics,0.46755466,FALSE,8.666666667,0.12839384,5,0.257024351,1,0.537564047,,,0.307660746
4762,The Potential of Artificial Intelligence to Analyze Chest Radiographs for Signs of COVID-19 Pneumonia.,Radiology,33236962,11/26/20,pubmed,0,1,artificial intelligence,0.015998362,0.015998433,0.920007495,0.015998407,0.015998372,0.015998931,Clinics,0.49991482,FALSE,194,0.970684643,231,0.929689591,1,0.537564047,,,0.812646094
4763,"Airborne Transmission of COVID-19: Aerosol Dispersion, Lung Deposition, and Virus-Receptor Interactions.",ACS Nano,33236896,11/26/20,pubmed,0,3,molecular dynamics simulation,0.600683336,0.001622829,0.001622743,0.39282551,0.00162279,0.001622792,Drug discovery,0.70152646,TRUE,116,0.900735976,44,0.654401927,7,0.785110192,,,0.780082698
4764,Effects of adjunct treatment with intravenous immunoglobulins on the course of severe COVID-19: results from a retrospective cohort study.,Curr Med Res Opin,33236646,11/26/20,pubmed,0,11,logistic regression,0.001291264,0.00129128,0.028628113,0.0012913,0.001291267,0.966206777,Clinics,0.8765625,TRUE,30.81818182,0.441585751,5.818181818,0.272812416,0,0.403234768,,,0.372544311
4765,Proteomics in the COVID-19 Battlefield: First Semester Check-Up.,Proteomics,33236484,11/26/20,pubmed,0,2,proteom,0.677539374,0.002130822,0.141539081,0.174529188,0.00213074,0.002130795,Drug discovery,0.74748963,TRUE,99,0.867029501,75.5,0.767661226,0,0.403234768,,,0.679308498
4766,SARS-CoV-2 infection and neonates: a review of evidence and unresolved questions.,Pediatr Allergy Immunol,33236433,11/26/20,pubmed,0,9,dataset,0.070403849,0.003927551,0.003927599,0.913885973,0.003927573,0.003927455,Epidemiology,0.14797893,FALSE,79.22222222,0.803327355,62.44444444,0.730398716,1,0.537564047,,,0.690430039
4767,Role of extracorporeal membrane oxygenation in critically Ill COVID-19 patients and predictors of mortality.,Artif Organs,33236373,11/26/20,pubmed,0,10,prediction model,0.001593574,0.001593485,0.001593574,0.001593499,0.001593498,0.99203237,Clinics,0.8208002,TRUE,78.6,0.800977179,24.1,0.521942735,2,0.618927094,,,0.647282336
4768,Computational guided identification of a citrus flavonoid as potential inhibitor of SARS-CoV-2 main protease.,Mol Divers,33236176,11/26/20,pubmed,0,6,"molecular dynamics simulation, computational",0.993035679,0.001392845,0.001392868,0.001392903,0.001392838,0.001392868,Drug discovery,0.9467802,TRUE,11,0.167171748,0.666666667,0.096200161,2,0.618927094,,,0.294099668
4769,Genetic Liability to Cannabis Use Disorder and COVID-19 Hospitalization.,medRxiv,33236033,11/26/20,pubmed,0,6,genome-wide,0.002490583,0.273596368,0.002490595,0.002490667,0.355089106,0.36384268,Clinics,0.44909793,FALSE,8,0.118683901,0.666666667,0.096200161,0,0.403234768,,,0.20603961
4770,Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment.,medRxiv,33236030,11/26/20,pubmed,0,35,"sequencing, classifier, microbiom, dataset",0.00109887,0.623206085,0.047257966,0.001098852,0.05503997,0.272298258,Genomics,0.56249017,TRUE,30.68571429,0.440348816,104.1714286,0.829141022,4,0.707574542,,,0.65902146
4771,High Resolution analysis of Transmission Dynamics of Sars-Cov-2 in Two Major Hospital Outbreaks in South Africa Leveraging Intrahost Diversity.,medRxiv,33236025,11/26/20,pubmed,0,15,whole-genome,0.046178908,0.86640049,0.027273832,0.057706644,0.001220105,0.00122002,Genomics,0.3578008,FALSE,20.33333333,0.302059497,11,0.371287129,2,0.618927094,,,0.430757907
4772,Predictive performance of international COVID-19 mortality forecasting models.,medRxiv,33236023,11/26/20,pubmed,0,13,forecasting model,0.001684506,0.001684506,0.0950434,0.844509755,0.001684562,0.055393272,Epidemiology,0.25614706,FALSE,101,0.871296926,530.2307692,0.981201499,20,0.900117291,,,0.917538572
4773,Mathematical modeling of ventilator-induced lung inflammation.,bioRxiv,33236015,11/26/20,pubmed,0,5,mathematical model,0.509104098,0.00137131,0.119142579,0.06525076,0.001371317,0.303759935,Drug discovery,0.4730225,FALSE,13.8,0.208980147,4.4,0.238961734,0,0.403234768,,,0.28372555
4774,Ketogenesis restrains aging-induced exacerbation of COVID in a mouse model.,bioRxiv,33236006,11/26/20,pubmed,0,17,immunome,0.570801669,0.001486553,0.001486445,0.001486452,0.001486538,0.423252343,Drug discovery,0.2611028,FALSE,83.70588235,0.820025976,236.3529412,0.932766925,1,0.537564047,,,0.763452316
4775,"Hematopoietic mosaic chromosomal alterations and risk for infection among 767,891 individuals without blood cancer.",Res Sq,33236004,11/26/20,pubmed,0,31,genome-wide,0.243014797,0.594702555,0.001717184,0.001717254,0.001717295,0.157130915,Genomics,0.4021209,FALSE,160.3548387,0.951821387,761.6129032,0.989296227,0,0.403234768,,,0.781450794
4776,Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations.,Glob Epidemiol,33235991,11/26/20,pubmed,0,7,mathematical model,0.001901761,0.214902773,0.001901672,0.738611763,0.040780199,0.001901832,Epidemiology,0.37542382,FALSE,19.57142857,0.291421857,13.14285714,0.401525288,3,0.667819001,,,0.453588715
4777,Modeling Donor Screening Strategies to Reduce the Risk of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission via Fecal Microbiota Transplantation.,Open Forum Infect Dis,33235890,11/26/20,pubmed,0,4,mathematical model,0.010372434,0.010372931,0.010372955,0.948136467,0.010372694,0.010372518,Epidemiology,0.6412191,TRUE,29.75,0.428226854,30.25,0.57171528,0,0.403234768,,,0.467725634
4778,Temporal signal and the phylodynamic threshold of SARS-CoV-2.,Virus Evol,33235813,11/26/20,pubmed,0,6,"genome sequences, genomes",0.001272649,0.729265725,0.001272676,0.265643649,0.001272661,0.001272641,Genomics,0.13348922,FALSE,147.5,0.941678521,2326.333333,0.99906342,2,0.618927094,,,0.853223012
4779,Coronavirus disease 2019 re-infection: first report from Turkey.,New Microbes New Infect,33235800,11/26/20,pubmed,0,3,genomes,0.001330011,0.367681602,0.05653614,0.001330104,0.139166007,0.433956135,Clinics,0.1458095,FALSE,79.66666667,0.805924918,41.33333333,0.641356703,0,0.403234768,,,0.616838796
4780,Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?,Health Inf Sci Syst,33235710,11/26/20,pubmed,0,1,deep learning,0.003466009,0.003465967,0.982669828,0.003466061,0.003466097,0.003466038,Healthcare,0.79950774,TRUE,259,0.985713402,144,0.878244581,6,0.764429903,,,0.876129295
4781,"Immunopathology, host-virus genome interactions, and effective vaccine development in SARS-CoV-2.",Comput Struct Biotechnol J,33235690,11/26/20,pubmed,0,4,sequencing,0.256515035,0.470946005,0.001511885,0.156463439,0.001511924,0.113051711,Genomics,0.60849786,TRUE,60.75,0.706784588,14,0.412898047,1,0.537564047,,,0.552415561
4782,Predictability of CRP and D-Dimer levels for in-hospital outcomes and mortality of COVID-19.,J Community Hosp Intern Med Perspect,33235672,11/26/20,pubmed,0,11,logistic regression,0.000977437,0.000977421,0.000977422,0.000977426,0.000977443,0.995112852,Clinics,0.85973394,TRUE,61.90909091,0.714391737,21.27272727,0.494179823,4,0.707574542,,,0.638715367
4783,Community evidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission through air.,Atmos Environ (1994),33235537,11/26/20,pubmed,0,19,simulation experiment,0.001220046,0.483092378,0.001220062,0.41069944,0.001220109,0.102547964,Genomics,0.66565806,TRUE,191,0.969138475,214.6842105,0.9245384,2,0.618927094,,,0.837534656
4784,Establishment of an African green monkey model for COVID-19 and protection against re-infection.,Nat Immunol,33235385,11/26/20,pubmed,0,15,transcriptom,0.502124919,0.346293494,0.002080683,0.002080617,0.002080659,0.145339628,Drug discovery,0.37208998,FALSE,48.73333333,0.619642526,96.46666667,0.8160289,12,0.850299401,,,0.761990276
4785,Factors associated with acute cardiac injury and their effects on mortality in patients with COVID-19.,Sci Rep,33235220,11/26/20,pubmed,0,10,logistic regression,0.001371329,0.001371237,0.001371234,0.001371271,0.001371237,0.993143691,Clinics,0.9021388,TRUE,74.1,0.782299462,46.3,0.665507091,0,0.403234768,,,0.617013774
4786,Impacts of Working From Home During COVID-19 Pandemic on Physical and Mental Well-Being of Office Workstation Users.,J Occup Environ Med,33234875,11/26/20,pubmed,0,4,logistic regression,0.002806338,0.002806396,0.002806438,0.002806489,0.985967964,0.002806375,Healthcare,0.86173034,TRUE,74.75,0.785515493,109.75,0.838306128,1,0.537564047,,,0.720461889
4787,Expression profiles revealed potential kidney injury caused by SARS-CoV-2: a systematic analysis of ACE2 and clinical lessons learned from this discovery.,Aging (Albany NY),33234728,11/26/20,pubmed,0,12,"sequencing, transcriptom, dataset",0.324170435,0.074087218,0.000854743,0.000854771,0.000854784,0.599178047,Clinics,0.4367487,FALSE,105.25,0.879522543,46.25,0.665038801,0,0.403234768,,,0.649265371
4788,Inhibiting the reproduction of SARS-CoV-2 through perturbations in human lung cell metabolic network.,Life Sci Alliance,33234678,11/26/20,pubmed,0,3,computational,0.582034363,0.224538712,0.001622829,0.188558659,0.001622704,0.001622734,Drug discovery,0.25931138,FALSE,47,0.606407323,25,0.529435376,0,0.403234768,,,0.513025822
4789,Gender-based approach on the social impact and mental health in Spain during COVID-19 lockdown: a cross-sectional study.,BMJ Open,33234664,11/26/20,pubmed,0,7,logistic regression,0.001046801,0.001046801,0.00104681,0.001046847,0.980714236,0.015098506,Healthcare,0.9771856,TRUE,25.42857143,0.372997712,11.14285714,0.372089912,5,0.739490092,,,0.494859238
4790,"Benefits of Sexual Activity on Psychological, Relational, and Sexual Health During the COVID-19 Breakout.",J Sex Med,33234430,11/26/20,pubmed,0,7,logistic regression,0.06314737,0.00076602,0.000765925,0.044319895,0.872155608,0.018845181,Healthcare,0.9905945,TRUE,68.85714286,0.758241079,56.14285714,0.707318705,3,0.667819001,,,0.711126262
4791,High-Risk Drinking in Midlife Before Versus During the COVID-19 Crisis: Longitudinal Evidence From the United Kingdom.,Am J Prev Med,33234355,11/26/20,pubmed,0,2,logistic regression,0.001565326,0.050474029,0.001565321,0.001565374,0.943264555,0.001565395,Healthcare,0.73994994,TRUE,109,0.888304781,37,0.616671127,0,0.403234768,,,0.636070225
4792,Inference of COVID-19 epidemiological distributions from Brazilian hospital data.,J R Soc Interface,33234065,11/26/20,pubmed,0,11,"bayes, mathematical model, dataset",0.00162272,0.001622774,0.00162273,0.703139908,0.00162281,0.290369059,Epidemiology,0.13181543,FALSE,60.36363636,0.704805492,,,0,0.403234768,,,0.55402013
4793,Phenotypic Characteristics and Development of a Hospitalization Prediction Risk Score for Outpatients with Diabetes and COVID-19: The DIABCOVID Study.,J Clin Med,33233575,11/26/20,pubmed,0,16,logistic regression,0.001010943,0.001010946,0.074582908,0.001010965,0.001010947,0.921373291,Clinics,0.7668537,TRUE,53.4375,0.656688725,39.5625,0.630987423,1,0.537564047,,,0.608413399
4794,Immunogenetic Association Underlying Severe COVID-19.,Vaccines (Basel),33233531,11/26/20,pubmed,0,4,"sequencing, genome-wide",0.566146053,0.217912213,0.001593566,0.047767258,0.001593554,0.164987356,Drug discovery,0.34024918,FALSE,16.25,0.246026347,51.5,0.687382927,5,0.739490092,,,0.557633122
4795,ACE2 Interaction Networks in COVID-19: A Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors.,J Clin Med,33233425,11/26/20,pubmed,0,8,"interactom, dataset",0.597893676,0.000889062,0.121420761,0.025777558,0.000889119,0.253129823,Drug discovery,0.8411246,TRUE,52.875,0.652730534,21.875,0.501471769,1,0.537564047,,,0.563922117
4796,High Probability of Long Diagnostic Delay in Coronavirus Disease 2019 Cases with Unknown Transmission Route in Japan.,Int J Environ Res Public Health,33233409,11/26/20,pubmed,0,2,logistic regression,0.001751163,0.110745834,0.001751265,0.280815985,0.128365971,0.476569782,Clinics,0.80300987,TRUE,501,0.99752613,374,0.968490768,0,0.403234768,,,0.789750555
4797,SARS-CoV-2 attachment to host cells is possibly mediated via RGD-integrin interaction in a calcium-dependent manner and suggests pulmonary EDTA chelation therapy as a novel treatment for COVID 19.,Immunobiology,33232865,11/25/20,pubmed,0,1,bioinformatic,0.987452864,0.000672776,0.00067277,0.000672765,0.000672753,0.009856072,Drug discovery,0.39986295,FALSE,25,0.369286907,26,0.53819909,1,0.537564047,,,0.481683348
4798,Modeling and interpreting the COVID-19 intervention strategy of China: A human mobility view.,PLoS One,33232385,11/25/20,pubmed,0,6,dataset,0.001786522,0.00178657,0.001786579,0.991067116,0.001786597,0.001786616,Epidemiology,0.5469082,TRUE,14.33333333,0.216772837,1,0.122023013,0,0.403234768,,,0.247343539
4799,Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study.,PLoS One,33232368,11/25/20,pubmed,0,11,deep-learning,0.000936066,0.000936081,0.902619622,0.000936117,0.000936188,0.093635926,Imaging,0.87398875,TRUE,26.18181818,0.383264271,9,0.337904736,2,0.618927094,,,0.446698701
4800,Depression and anxiety symptoms among returning workers during the COVID-19 period in East China.,Soc Psychiatry Psychiatr Epidemiol,33231710,11/25/20,pubmed,0,7,logistic regression,0.001350317,0.001350344,0.001350318,0.001350389,0.978675704,0.015922929,Healthcare,0.7925916,TRUE,43.28571429,0.570165131,16.14285714,0.437851218,0,0.403234768,,,0.470417039
4801,Mouse Genome Database (MGD): Knowledgebase for mouse-human comparative biology.,Nucleic Acids Res,33231642,11/25/20,pubmed,0,37,"genomes, dataset",0.001751293,0.744576169,0.001751267,0.248418823,0.001751191,0.001751256,Genomics,0.35562584,FALSE,115,0.899066114,881.1666667,0.991905272,1,0.537564047,,,0.809511811
4802,"Genomic, epigenomic, and immune subtype analysis of CTSL/B and SARS-CoV-2 receptor ACE2 in pan-cancer.",Aging (Albany NY),33231569,11/25/20,pubmed,0,7,bioinformatic,0.526946133,0.156361012,0.002296676,0.002296714,0.002296848,0.309802618,Drug discovery,0.6953661,TRUE,87.42857143,0.834869194,53,0.693738293,0,0.403234768,,,0.643947418
4803,DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set.,Radiology,33231531,11/25/20,pubmed,0,15,"deep learning, artificial intelligence, neural network",0.000871555,0.000871578,0.859899991,0.000871548,0.000871615,0.136613714,Imaging,0.8501738,TRUE,73,0.778464964,81.33333333,0.781709928,6,0.764429903,,,0.774868265
4804,"Association of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Blockers With Severity of COVID-19: A Multicenter, Prospective Study.",J Cardiovasc Pharmacol Ther,33231487,11/25/20,pubmed,0,12,logistic regression,0.090053944,0.000846509,0.000846506,0.000846524,0.000846531,0.906559986,Clinics,0.9017488,TRUE,10.41666667,0.155730101,4.416666667,0.239095531,2,0.618927094,,,0.337917575
4805,Prefusion spike protein stabilization through computational mutagenesis.,Proteins,33231324,11/25/20,pubmed,0,3,"molecular dynamics simulation, computational, bioinformatic",0.955900325,0.03783838,0.001565315,0.001565327,0.001565333,0.00156532,Drug discovery,0.7298292,TRUE,145.6666667,0.940194199,,,0,0.403234768,,,0.671714483
4806,COVID-19 research risks ignoring important host genes due to pre-established research patterns.,Elife,33231169,11/25/20,pubmed,0,2,genome-wide,0.003101663,0.734719365,0.003101564,0.252874314,0.003101563,0.003101531,Genomics,0.41887066,FALSE,14.5,0.219617787,62,0.728659352,1,0.537564047,,,0.495280396
4807,Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems.,Curr Med Imaging,33231160,11/25/20,pubmed,0,2,"deep learning, neural network, image analysis, transfer learning, dataset",0.001187256,0.001187257,0.99406367,0.001187281,0.001187278,0.001187259,Imaging,0.021173805,FALSE,14,0.213494959,1,0.122023013,0,0.403234768,,,0.246250913
4808,COVID-19 risk in elective surgery during a second wave: a prospective cohort study.,ANZ J Surg,33230886,11/25/20,pubmed,0,13,bayes,0.00129122,0.056384647,0.001291255,0.128033479,0.397979631,0.415019768,Clinics,0.9389326,TRUE,117.8461538,0.904137547,137.3076923,0.872089912,1,0.537564047,,,0.771263835
4809,"Application of the Farm Simulation Model approach on economic loss estimation due to Coronavirus (COVID-19) in Bangladesh dairy farms-strategies, options, and way forward.",Trop Anim Health Prod,33230604,11/25/20,pubmed,0,5,simulation model,0.001350396,0.001350341,0.050333971,0.836641344,0.1089736,0.001350347,Epidemiology,0.5633161,TRUE,33.4,0.47046818,19.8,0.478257961,0,0.403234768,,,0.450653636
4810,COVID-19 drive-through testing survey: Measuring the burden on healthcare workers.,J Am Coll Emerg Physicians Open,33230507,11/25/20,pubmed,0,5,logistic regression,0.00208053,0.002080548,0.002080532,0.002080638,0.870860107,0.120817644,Healthcare,0.21015465,FALSE,20.2,0.300327788,15.6,0.431428954,0,0.403234768,,,0.378330503
4811,The low-harm score for predicting mortality in patients diagnosed with COVID-19: A multicentric validation study.,J Am Coll Emerg Physicians Open,33230506,11/25/20,pubmed,0,19,bayes,0.00165306,0.00165301,0.040230273,0.001653046,0.001653024,0.953157587,Clinics,0.83615553,TRUE,2.368421053,0.024924238,0.052631579,0.05565962,1,0.537564047,,,0.206049302
4812,COVID-19 pneumonia accurately detected on chest radiographs with artificial intelligence.,Intell Based Med,33230503,11/25/20,pubmed,0,67,artificial intelligence,0.001141339,0.001141355,0.797801391,0.001141327,0.001141391,0.197633197,Imaging,0.8141501,TRUE,14.25,0.215535902,,,0,0.403234768,,,0.309385335
4813,Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study.,Biomed Signal Process Control,33230398,11/25/20,pubmed,0,5,"deep learning, artificial intelligence, neural network",0.001392855,0.001392872,0.993035705,0.001392854,0.001392848,0.001392866,Imaging,0.7021401,TRUE,65,0.734801163,50.4,0.683101418,7,0.785110192,,,0.734337591
4814,CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection.,Appl Soft Comput,33230395,11/25/20,pubmed,0,4,"deep learning, neural network, transfer learning, lstm",0.001203436,0.001203437,0.881630352,0.113555955,0.001203408,0.001203411,Imaging,0.33655524,FALSE,24,0.35574247,2.25,0.170925876,6,0.764429903,,,0.430366083
4815,A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network.,Soft comput,33230389,11/25/20,pubmed,0,4,"neural network, dataset",0.00168451,0.001684513,0.758193927,0.235067878,0.001684583,0.001684591,Epidemiology,0.16085806,FALSE,405,0.995856268,110.25,0.838707519,0,0.403234768,,,0.745932852
4816,Molecular targets and system biology approaches for drug repurposing against SARS-CoV-2.,Bull Natl Res Cent,33230386,11/25/20,pubmed,0,3,"virtual screening, bioinformatic, network analysis",0.95204748,0.0019017,0.001901726,0.001901741,0.040345576,0.001901778,Drug discovery,0.93464315,TRUE,891.3333333,0.999195992,524.3333333,0.98066631,1,0.537564047,,,0.839142116
4817,Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing.,Scientometrics,33230352,11/25/20,pubmed,0,6,machine learning,0.001751203,0.114184225,0.086740614,0.696951344,0.001751319,0.098621295,Epidemiology,0.17828432,FALSE,37.33333333,0.511596264,25.5,0.533382392,3,0.667819001,,,0.570932553
4818,0,Am J Agric Econ,33230345,11/25/20,pubmed,0,2,dataset,0.00249061,0.002490518,0.002490608,0.803274894,0.186762892,0.002490479,Epidemiology,0.62493396,TRUE,10,0.15214299,0,0.055525823,5,0.739490092,,,0.315719635
4819,"Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis.",Sci Rep,33230180,11/25/20,pubmed,0,7,"virtual screening, molecular dynamics simulation, machine learning, deep learning, predictive model, sequence alignment",0.775490348,0.057641777,0.162553298,0.001438154,0.001438183,0.00143824,Drug discovery,0.9567541,TRUE,46.57142857,0.60152143,17.85714286,0.459325662,0,0.403234768,,,0.488027287
4820,Targeted intracellular degradation of SARS-CoV-2 via computationally optimized peptide fusions.,Commun Biol,33230174,11/25/20,pubmed,0,6,computational,0.987546886,0.002490583,0.002490504,0.002490596,0.002490737,0.002490694,Drug discovery,0.6809473,TRUE,24.83333333,0.365823489,27.33333333,0.54856837,0,0.403234768,,,0.439208876
4821,Sexual Behavior and Its Determinants During COVID-19 Restrictions Among Men Who Have Sex With Men in Amsterdam.,J Acquir Immune Defic Syndr,33230027,11/25/20,pubmed,0,11,logistic regression,0.00165301,0.001653075,0.001653025,0.001653133,0.99173467,0.001653087,Healthcare,0.48221734,FALSE,93.72727273,0.853917991,125.7272727,0.858576398,0,0.403234768,,,0.705243052
4822,Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2.,Sci Transl Med,33229462,11/25/20,pubmed,0,38,"sequencing, whole-genome, genomic epidemiology",0.001511872,0.762214899,0.056684332,0.176565281,0.001511813,0.001511804,Genomics,0.30778566,FALSE,62.55263158,0.718288082,133.4473684,0.867005619,25,0.918019631,,,0.834437777
4823,"Digital Technology, Health and Well-Being and the Covid-19 Pandemic: It's Time to Call Forward Informal Carers from the Back of the Queue.",Semin Oncol Nurs,33229183,11/25/20,pubmed,0,1,digital health,0.002296876,0.00229657,0.002296908,0.560465076,0.430346992,0.002297578,Epidemiology,0.84372854,TRUE,27,0.3960047,26,0.53819909,0,0.403234768,,,0.445812853
4824,Patient characteristics and predictors of mortality in 470 adults admitted to a district general hospital in England with Covid-19.,Epidemiol Infect,33228824,11/25/20,pubmed,0,13,logistic regression,0.001751302,0.001751322,0.041129427,0.00175128,0.001751336,0.951865333,Clinics,0.5313811,TRUE,7.538461538,0.108479189,6.538461538,0.288332887,1,0.537564047,,,0.311458708
4825,Molecular dynamics simulation perception study of the binding affinity performance for main protease of SARS-CoV-2.,J Biomol Struct Dyn,33228481,11/25/20,pubmed,0,4,molecular dynamics simulation,0.975504761,0.001438131,0.001438185,0.001438129,0.00143815,0.018742644,Drug discovery,0.8568051,TRUE,38.5,0.524089307,7.5,0.307867273,0,0.403234768,,,0.411730449
4826,"Computational gene expression profiling in the exploration of biomarkers, non-coding functional RNAs and drug perturbagens for COVID-19.",J Biomol Struct Dyn,33228475,11/25/20,pubmed,0,3,"computational, dataset",0.858529357,0.001237151,0.001237195,0.039416298,0.001237062,0.098342937,Drug discovery,0.38614342,FALSE,11.33333333,0.170635166,1.333333333,0.13252609,1,0.537564047,,,0.280241768
4827,The spatial association of social vulnerability with COVID-19 prevalence in the contiguous United States.,Int J Environ Health Res,33228411,11/25/20,pubmed,0,6,dataset,0.001943488,0.00194352,0.001943488,0.990282193,0.001943643,0.001943667,Epidemiology,0.46605876,FALSE,6.833333333,0.096852001,3.166666667,0.201097137,2,0.618927094,,,0.305625411
4828,COVID-19 and the epistemology of epidemiological models at the dawn of AI.,Ann Hum Biol,33228409,11/25/20,pubmed,0,1,"artificial intelligence, prediction model",0.019289613,0.102624935,0.097512867,0.67693549,0.102953911,0.000683184,Epidemiology,0.6480539,TRUE,46,0.596882924,32,0.585763982,0,0.403234768,,,0.528627224
4829,Reuse of Clinical COVID-19 Patient Data: Pre-Processing for Future Classification.,Stud Health Technol Inform,33227752,11/24/20,pubmed,0,5,dataset,0.002996489,0.002996622,0.354106411,0.228569652,0.051912801,0.359418024,Clinics,0.24841252,FALSE,70.8,0.767456243,19.4,0.474110249,0,0.403234768,,,0.548267087
4830,Latent COVID-19 Clusters in Patients with Chronic Respiratory Conditions.,Stud Health Technol Inform,33227735,11/24/20,pubmed,0,3,"machine learning, dataset",0.002296663,0.21501647,0.344787378,0.002296688,0.002296732,0.433306068,Clinics,0.8459147,TRUE,51.33333333,0.641288886,17,0.451097137,0,0.403234768,,,0.498540263
4831,Exploring the Social Drivers of Health During a Pandemic: Leveraging Knowledge Graphs and Population Trends in COVID-19.,Stud Health Technol Inform,33227730,11/24/20,pubmed,0,7,knowledge graph,0.00203283,0.002032904,0.081638541,0.824731133,0.087531855,0.002032737,Epidemiology,0.2523871,FALSE,23.28571429,0.344795597,29.57142857,0.566363393,1,0.537564047,,,0.482907679
4832,Decoding the silent walk of COVID-19: Halting its spread using old bullets.,Biomed Pharmacother,33227700,11/24/20,pubmed,0,5,proteom,0.762876573,0.036721567,0.001593541,0.001593579,0.001593506,0.195621234,Drug discovery,0.7700945,TRUE,57.4,0.684828994,20.6,0.487021675,0,0.403234768,,,0.525028479
4833,Mental strain and changes in psychological health hub among intern-nursing students at pediatric and medical-surgical units amid ambience of COVID-19 pandemic: A comprehensive survey.,Nurse Educ Pract,33227694,11/24/20,pubmed,0,4,logistic regression,0.001371272,0.060549109,0.001371302,0.001371296,0.862208027,0.073128995,Healthcare,0.9001987,TRUE,4.25,0.056527924,0,0.055525823,0,0.403234768,,,0.171762838
4834,"No credible evidence for links between 2D:4D and COVID-19 outcomes: A probabilistic perspective on digit ratio, ACE variants, and national case fatalities.",Early Hum Dev,33227636,11/24/20,pubmed,0,3,"bayes, probabilistic",0.11655309,0.175002819,0.001593506,0.458464613,0.001593633,0.246792339,Epidemiology,0.5074192,TRUE,29.33333333,0.423093574,12.66666667,0.39530372,0,0.403234768,,,0.407210687
4835,"Physical inactivity and elevated TV-viewing reported changes during the COVID-19 pandemic are associated with mental health: A survey with 43,995 Brazilian adults.",J Psychosom Res,33227555,11/24/20,pubmed,0,7,logistic regression,0.001717204,0.001717185,0.001717202,0.001717243,0.991413953,0.001717214,Healthcare,0.961817,TRUE,73.85714286,0.781248067,61.28571429,0.725515119,1,0.537564047,,,0.681442411
4836,Innovative trial designs and analyses for vaccine clinical development.,Contemp Clin Trials,33227451,11/24/20,pubmed,0,5,bayes,0.412834882,0.001254636,0.05494736,0.528453848,0.001254643,0.001254631,Epidemiology,0.57553166,TRUE,66.8,0.745809883,8.8,0.332753546,0,0.403234768,,,0.493932732
4837,What Is the Preparedness and Capacity of Palliative Care Services in Middle-Eastern and North African Countries to Respond to COVID-19? A Rapid Survey.,J Pain Symptom Manage,33227380,11/24/20,pubmed,0,6,probabilistic,0.00107226,0.001072212,0.001072263,0.302047569,0.659154046,0.03558165,Healthcare,0.8997444,TRUE,63,0.721998887,120.6666667,0.852689323,0,0.403234768,,,0.659307659
4838,A review on possible mechanistic insights of Nitazoxanide for repurposing in COVID-19.,Eur J Pharmacol,33227285,11/24/20,pubmed,0,2,in silico,0.940158045,0.001350338,0.001350332,0.001350335,0.00135035,0.0544406,Drug discovery,0.8913493,TRUE,57,0.68204589,27,0.546026224,2,0.618927094,,,0.615666403
4839,Internet use during coronavirus disease of 2019 pandemic: Psychiatric history and sociodemographics as predictors.,Indian J Psychiatry,33227063,11/24/20,pubmed,0,9,logistic regression,0.001653022,0.001653089,0.001653101,0.317064858,0.676322875,0.001653055,Healthcare,0.76331973,TRUE,47.88888889,0.613457851,23,0.513513514,0,0.403234768,,,0.510068711
4840,"Digital psychiatry in low- and middle-income countries post-COVID-19: Opportunities, challenges, and solutions.",Indian J Psychiatry,33227054,11/24/20,pubmed,0,4,digital health,0.001901705,0.001901725,0.00190181,0.700442957,0.291950057,0.001901746,Epidemiology,0.7209578,TRUE,50.33333333,0.63380543,33.66666667,0.596802248,0,0.403234768,,,0.544614149
4841,Risk factors for COVID-19 patients with cardiac injury: pulmonary ventilation dysfunction and oxygen inhalation insufficiency are not the direct causes.,Aging (Albany NY),33226958,11/24/20,pubmed,0,9,logistic regression,0.0010988,0.001098817,0.014805943,0.001098819,0.00109884,0.980798781,Clinics,0.9735669,TRUE,43.33333333,0.571154679,8.555555556,0.329542414,1,0.537564047,,,0.47942038
4842,"Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study.",J Med Internet Res,33226957,11/24/20,pubmed,0,8,machine learning,0.000889042,0.000889074,0.620368591,0.000889068,0.000889097,0.376075128,Clinics,0.9747311,TRUE,13.875,0.209536768,7.25,0.302983677,1,0.537564047,,,0.350028164
4843,Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning.,ACS Nano,33226787,11/24/20,pubmed,0,15,"neural network, image processing, classifier, adversarial network, deep-learning, dataset",0.00127269,0.376149456,0.6187597,0.001272769,0.001272656,0.00127273,Imaging,0.053800106,FALSE,58.8,0.694848166,69,0.750535189,0,0.403234768,,,0.616206041
4844,Pharmacokinetics under the COVID-19 storm.,Br J Clin Pharmacol,33226664,11/24/20,pubmed,0,9,model simulation,0.560045403,0.001220007,0.001220019,0.001220072,0.147164345,0.289130154,Drug discovery,0.46489024,FALSE,23.55555556,0.348691941,37.33333333,0.618209794,0,0.403234768,,,0.456712168
4845,Probiotics-Derived Peptides and Their Immunomodulatory Molecules Can Play a Preventive Role Against Viral Diseases Including COVID-19.,Probiotics Antimicrob Proteins,33226581,11/24/20,pubmed,0,4,in silico,0.63387119,0.191723386,0.001272679,0.139766811,0.001272694,0.03209324,Drug discovery,0.8197701,TRUE,10.5,0.157338116,2.5,0.180826866,1,0.537564047,,,0.291909677
4846,"Targeting SARS-CoV-2 main protease: structure based virtual screening, in silico ADMET studies and molecular dynamics simulation for identification of potential inhibitors.",J Biomol Struct Dyn,33226303,11/24/20,pubmed,0,5,"virtual screening, molecular dynamics simulation, computational, in silico",0.947523438,0.000977437,0.048566669,0.000977477,0.000977525,0.000977454,Drug discovery,0.9443234,TRUE,16.2,0.244974952,2,0.164302917,1,0.537564047,,,0.315613972
4847,Risk factors associated with worse outcomes in COVID-19: a retrospective study in Saudi Arabia.,East Mediterr Health J,33226105,11/24/20,pubmed,0,8,logistic regression,0.001010973,0.04800891,0.001011001,0.110072806,0.001011021,0.838885289,Clinics,0.99971074,TRUE,18.125,0.272063826,5.625,0.26839711,2,0.618927094,,,0.386462677
4848,Prioritizing IVF treatment in the post-COVID 19 era: a predictive modelling study based on UK national data.,Hum Reprod,33226080,11/24/20,pubmed,0,6,"predictive model, prediction model",0.000657771,0.00065777,0.000657796,0.559204509,0.376723911,0.062098242,Epidemiology,0.82149804,TRUE,123.5,0.912301317,207.3333333,0.920925876,0,0.403234768,,,0.74548732
4849,"Evaluation on the use of Nanopore sequencing for direct characterization of coronaviruses from respiratory specimens, and a study on emerging missense mutations in partial RdRP gene of SARS-CoV-2.",Virol J,33225958,11/24/20,pubmed,0,12,"sequencing, genomes",0.138911932,0.807771364,0.050416242,0.000966816,0.000966865,0.00096678,Genomics,0.91606915,TRUE,13.75,0.208299833,61.08333333,0.724712336,2,0.618927094,,,0.517313088
4850,0,J Biomol Struct Dyn,33225826,11/24/20,pubmed,0,1,computational,0.966972215,0.027707568,0.001330041,0.001330053,0.001330058,0.001330064,Drug discovery,0.95384437,TRUE,16,0.243552477,0,0.055525823,0,0.403234768,,,0.234104356
4851,Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction.,Med Phys,33225476,11/24/20,pubmed,0,9,"deep learning, neural network, dataset",0.000807889,0.000807893,0.995960536,0.000807888,0.000807882,0.000807912,Imaging,0.51299936,TRUE,70.88888889,0.767641784,69,0.750535189,5,0.739490092,,,0.752555688
4852,The psychological symptoms of patients with mild symptoms of coronavirus disease (2019) in China: A cross-sectional study.,J Adv Nurs,33225451,11/24/20,pubmed,0,10,logistic regression,0.000946108,0.000946088,0.000946094,0.000946093,0.718755672,0.277459945,Healthcare,0.9880128,TRUE,77.4,0.796029439,28.1,0.55472304,0,0.403234768,,,0.584662415
4853,A Machine Learning-Aided Global Diagnostic and Comparative Tool to Assess Effect of Quarantine Control in COVID-19 Spread.,Patterns (N Y),33225319,11/24/20,pubmed,0,3,"machine learning, neural network",0.002639178,0.111144163,0.314835026,0.566103665,0.002638996,0.002638972,Epidemiology,0.7119365,TRUE,22.33333333,0.330261612,2.666666667,0.185442869,0,0.403234768,,,0.306313083
4854,Ultrasensitive and Selective Detection of SARS-CoV-2 Using Thermotropic Liquid Crystals and Image-Based Machine Learning.,Cell Rep Phys Sci,33225318,11/24/20,pubmed,0,11,machine learning,0.002720275,0.285123336,0.341850821,0.36486497,0.002720464,0.002720134,Epidemiology,0.52383876,TRUE,13.27272727,0.200012369,4,0.231469093,1,0.537564047,,,0.32301517
4855,Predictive models on COVID 19: What Africans should do?,Infect Dis Model,33225115,11/24/20,pubmed,0,4,predictive model,0.004775226,0.004775422,0.004775454,0.976122994,0.004775684,0.00477522,Epidemiology,0.737564,TRUE,6,0.086028821,0.5,0.087101953,1,0.537564047,,,0.236898274
4856,Designing spike protein (S-Protein) based multi-epitope peptide vaccine against SARS COVID-19 by immunoinformatics.,Heliyon,33225084,11/24/20,pubmed,0,3,"computational, bioinformatic",0.922746065,0.070992444,0.001565354,0.001565372,0.001565319,0.001565445,Drug discovery,0.7584337,TRUE,7.666666667,0.111633373,3.666666667,0.217621086,3,0.667819001,,,0.33235782
4857,Evaluating Training Need for Epidemic Control in Three Metropolitans: Implications for COVID-19 Preparedness in Vietnam.,Front Public Health,33224921,11/24/20,pubmed,0,22,logistic regression,0.001415192,0.001415142,0.186574441,0.302107672,0.507072426,0.001415127,Healthcare,0.5978322,TRUE,42.81818182,0.565402932,18.36363636,0.464409955,0,0.403234768,,,0.477682551
4858,Designing Futuristic Telemedicine Using Artificial Intelligence and Robotics in the COVID-19 Era.,Front Public Health,33224912,11/24/20,pubmed,0,9,artificial intelligence,0.00242228,0.002422294,0.646053896,0.344256904,0.002422395,0.002422231,Epidemiology,0.9363778,TRUE,33.33333333,0.469911559,12.55555556,0.393162965,7,0.785110192,,,0.549394905
4859,"Acute cardiac injury in adult hospitalized COVID-19 patients in Zhuhai, China.",Cardiovasc Diagn Ther,33224754,11/24/20,pubmed,0,10,logistic regression,0.001291192,0.001291205,0.033144202,0.001291204,0.001291244,0.961690954,Clinics,0.8346206,TRUE,17.7,0.266435772,5.4,0.263914905,0,0.403234768,,,0.311195148
4860,Recurrent neural network based prediction of number of COVID-19 cases in India.,Mater Today Proc,33224735,11/24/20,pubmed,0,3,"neural network, lstm, dataset",0.002720143,0.097339085,0.67106513,0.223435239,0.002720089,0.002720314,Epidemiology,0.3350799,FALSE,1.666666667,0.016451234,0,0.055525823,0,0.403234768,,,0.158403941
4861,Modeling and simulation of the novel coronavirus in Caputo derivative.,Results Phys,33224721,11/24/20,pubmed,0,5,mathematical model,0.005353213,0.005352974,0.005352849,0.973235382,0.005352839,0.005352743,Epidemiology,0.59683335,TRUE,19.6,0.291854784,2.8,0.188787798,9,0.814309525,,,0.431650702
4862,The need to return to the basics of predictive modelling for disease outbreak response: lessons from COVID-19.,Pan Afr Med J,33224421,11/24/20,pubmed,0,1,predictive model,0.00453089,0.004530967,0.00453078,0.977346114,0.004530642,0.004530607,Epidemiology,0.30110314,FALSE,32,0.455810502,43,0.649785925,0,0.403234768,,,0.502943731
4863,Analysis and vulnerability of the international wheat trade network.,Food Secur,33224317,11/24/20,pubmed,0,3,network analysis,0.002238514,0.002238508,0.002238542,0.863054008,0.127991848,0.002238581,Epidemiology,0.6997807,TRUE,37.33333333,0.511596264,66,0.742574257,1,0.537564047,,,0.597244856
4864,Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images.,J Ambient Intell Humaniz Comput,33224307,11/24/20,pubmed,0,5,"classifier, transfer learning, dataset",0.002238456,0.002238532,0.988807623,0.00223852,0.002238415,0.002238454,Imaging,0.7339033,TRUE,33.2,0.468056157,8.8,0.332753546,5,0.739490092,,,0.513433265
4865,"Forecasting of the SARS-CoV-2 epidemic in India using SIR model, flatten curve and herd immunity.",J Ambient Intell Humaniz Comput,33224306,11/24/20,pubmed,0,10,mathematical model,0.059201084,0.001823496,0.001823396,0.933505319,0.001823332,0.001823373,Epidemiology,0.32738543,FALSE,8.6,0.127280599,0.8,0.101351351,1,0.537564047,,,0.255398666
4866,The Use of System Dynamics Methodology in Building a COVID-19 Confirmed Case Model.,Comput Math Methods Med,33224268,11/24/20,pubmed,0,1,"bayes, bayesian model",0.002996422,0.002996419,0.002996488,0.985017714,0.00299647,0.002996487,Epidemiology,0.4077205,FALSE,2,0.022141134,1,0.122023013,0,0.403234768,,,0.182466305
4867,0,Comput Math Methods Med,33224266,11/24/20,pubmed,0,7,"computational, dataset",0.001126873,0.00112685,0.213410662,0.782081867,0.001126854,0.001126895,Epidemiology,0.025511473,FALSE,22.57142857,0.334343497,4.714285714,0.24719026,0,0.403234768,,,0.328256175
4868,Perversely expressed long noncoding RNAs can alter host response and viral proliferation in SARS-CoV-2 infection.,Future Virol,33224264,11/24/20,pubmed,0,3,dataset,0.98908355,0.002183296,0.002183237,0.002183228,0.002183327,0.002183361,Drug discovery,0.2719425,FALSE,5.333333333,0.074339786,0.666666667,0.096200161,0,0.403234768,,,0.191258238
4869,"Fear of COVID-19, Stress, and Anxiety in University Undergraduate Students: A Predictive Model for Depression.",Front Psychol,33224080,11/24/20,pubmed,0,4,predictive model,0.001392931,0.001392842,0.001392905,0.192591465,0.80183701,0.001392847,Healthcare,0.950024,TRUE,11.5,0.17416043,0.25,0.065493712,2,0.618927094,,,0.286193745
4870,A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment.,Pers Ubiquitous Comput,33223984,11/24/20,pubmed,0,4,"data mining, classifier",0.001291301,0.001291292,0.769828626,0.001291305,0.176233087,0.050064389,Healthcare,0.7862307,TRUE,109.25,0.888799555,33.5,0.595999465,0,0.403234768,,,0.629344596
4871,Data-Driven Modeling for Different Stages of Pandemic Response.,J Indian Inst Sci,33223629,11/24/20,pubmed,0,6,dataset,0.001717237,0.001717224,0.00171726,0.963048625,0.03008249,0.001717164,Epidemiology,0.18526268,FALSE,118.5,0.905003402,147.6666667,0.881455713,0,0.403234768,,,0.729897961
4872,Selection of the best healthcare waste disposal techniques during and post COVID-19 pandemic era.,J Clean Prod,33223625,11/24/20,pubmed,0,4,dataset,0.001593514,0.001593518,0.753828224,0.239797755,0.001593517,0.001593471,Epidemiology,0.8789619,TRUE,18.75,0.28072237,4.25,0.235750602,2,0.618927094,,,0.378466689
4873,Numerical Study of Three Ventilation Strategies in a prefabricated COVID-19 inpatient ward.,Build Environ,33223598,11/24/20,pubmed,0,4,computational,0.057102794,0.001861753,0.001861726,0.648454717,0.032746618,0.257972392,Epidemiology,0.7255739,TRUE,37.75,0.516296617,,,1,0.537564047,,,0.526930332
4874,0,Chem Phys Lett,33223560,11/24/20,pubmed,0,6,molecular dynamics simulation,0.946529766,0.002720135,0.002720193,0.002720228,0.042589589,0.002720089,Drug discovery,0.57017064,TRUE,14,0.213494959,12,0.386740701,0,0.403234768,,,0.334490143
4875,Impact of age on duration of viral RNA shedding in patients with COVID-19.,Aging (Albany NY),33223506,11/24/20,pubmed,0,8,logistic regression,0.001751188,0.431828641,0.001751125,0.001751179,0.001751163,0.561166705,Clinics,0.9864543,TRUE,42.375,0.5612592,45.625,0.661359379,2,0.618927094,,,0.613848558
4876,Automatic detection of COVID-19 from chest radiographs using deep learning.,Radiography (Lond),33223418,11/24/20,pubmed,0,4,"deep learning, computational, neural network, network model, transfer learning, dataset",0.000765973,0.032806091,0.744936244,0.219959752,0.00076598,0.000765961,Imaging,0.53838426,TRUE,24.25,0.358092646,2.25,0.170925876,1,0.537564047,,,0.355527523
4877,Digitally-Enabled Remote Care for Cancer Patients: Here to Stay.,Semin Oncol Nurs,33223410,11/24/20,pubmed,0,3,digital health,0.000823108,0.000822917,0.0348582,0.626108002,0.269577058,0.067810715,Epidemiology,0.9920944,TRUE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
4878,Delaying surgery for clinical T1b-T2bN0M0 renal cell carcinoma: Oncologic implications in the COVID-19 era and beyond.,Urol Oncol,33223368,11/24/20,pubmed,0,14,logistic regression,0.001330121,0.001330091,0.001330057,0.068566176,0.001330059,0.926113496,Clinics,0.9373232,TRUE,61,0.709196611,15.85714286,0.43443939,0,0.403234768,,,0.515623589
4879,Endocrine disrupting chemicals and COVID-19 relationships: A computational systems biology approach.,Environ Int,33223326,11/24/20,pubmed,0,5,computational,0.673472524,0.00153818,0.001538159,0.095139922,0.001538166,0.226773048,Drug discovery,0.7541634,TRUE,48.8,0.620755767,71.4,0.757358844,0,0.403234768,,,0.593783126
4880,SARS-CoV-2 droplet deposition path and its effects on the human upper airway in the oral inhalation.,Comput Methods Programs Biomed,33223278,11/24/20,pubmed,0,4,computational,0.208345755,0.287344138,0.377709238,0.123166428,0.001717204,0.001717237,Imaging,0.6885861,TRUE,4.75,0.064506154,,,0,0.403234768,,,0.233870461
4881,Enhanced photochemical formation of secondary organic aerosols during the COVID-19 lockdown in Northern China.,Sci Total Environ,33223177,11/24/20,pubmed,0,11,correlation analysis,0.152600836,0.001653118,0.001653021,0.840786938,0.00165305,0.001653036,Epidemiology,0.90946364,TRUE,112.7272727,0.89572639,23.72727273,0.518330211,0,0.403234768,,,0.60576379
4882,The UCSC Genome Browser database: 2021 update.,Nucleic Acids Res,33221922,11/23/20,pubmed,0,23,genomes,0.104029249,0.796401362,0.09317719,0.002130824,0.002130717,0.002130657,Genomics,0.20724654,FALSE,53.82608696,0.659781063,882.173913,0.992039069,4,0.707574542,,,0.786464891
4883,Racial Disparities in Coronavirus Disease 2019 (COVID-19) Mortality Are Driven by Unequal Infection Risks.,Clin Infect Dis,33221832,11/23/20,pubmed,0,8,bayes,0.001371264,0.001371297,0.001371268,0.274779303,0.292653557,0.428453311,Clinics,0.37860078,FALSE,23.25,0.34454821,13,0.400521809,6,0.764429903,,,0.503166641
4884,The association between body mass index class and coronavirus disease 2019 outcomes.,Int J Obes (Lond),33221825,11/23/20,pubmed,0,17,logistic regression,0.001823331,0.00182333,0.001823387,0.001823387,0.001823387,0.990883178,Clinics,0.8799574,TRUE,45.58823529,0.591873338,30.76470588,0.575060209,1,0.537564047,,,0.568165865
4885,"Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California.",Epidemics,33221671,11/23/20,pubmed,0,9,logistic regression,0.00109882,0.001098839,0.001098822,0.380270813,0.238290343,0.378142363,Epidemiology,0.11301467,FALSE,60.11111111,0.703877791,1008.333333,0.993644635,1,0.537564047,,,0.745028824
4886,Epicardial adipose tissue is associated with extent of pneumonia and adverse outcomes in patients with COVID-19.,Metabolism,33221381,11/23/20,pubmed,0,21,"deep learning, logistic regression",0.000956327,0.000956301,0.274574213,0.000956319,0.000956317,0.721600524,Clinics,0.9755674,TRUE,52.33333333,0.649452656,25.61904762,0.533783784,4,0.707574542,,,0.630270327
4887,The Human Leukocyte Antigen Class II Immunopeptidome of the SARS-CoV-2 Spike Glycoprotein.,Cell Rep,33220791,11/23/20,pubmed,0,6,immunopeptidom,0.663503142,0.330005947,0.001622763,0.001622729,0.001622712,0.001622707,Drug discovery,0.280961,FALSE,38.16666667,0.520378502,69.66666667,0.752475248,2,0.618927094,,,0.630593615
4888,"Frequency, Associated Risk Factors, and Characteristics of COVID-19 Among Healthcare Personnel in a Spanish Health Department.",Am J Prev Med,33220760,11/23/20,pubmed,0,29,logistic regression,0.00148641,0.001486542,0.001486452,0.123684468,0.789959883,0.081896245,Healthcare,0.7716471,TRUE,10.10344828,0.152452223,3.689655172,0.217754884,3,0.667819001,,,0.346008703
4889,"The relationship between post-traumatic stress and positive mental health symptoms among health workers during COVID-19 pandemic in Lombardy, Italy.",J Affect Disord,33220632,11/22/20,pubmed,0,4,logistic regression,0.001350525,0.001350333,0.151725465,0.001350491,0.842872579,0.001350607,Healthcare,0.98814446,TRUE,26.75,0.391799122,5.75,0.271742039,0,0.403234768,,,0.355591976
4890,Validation of a modified CDC assay and performance comparison with the NeuMoDx™ and DiaSorin® automated assays for rapid detection of SARS-CoV-2 in respiratory specimens.,J Clin Virol,33220549,11/22/20,pubmed,0,4,in silico,0.001461927,0.661755984,0.257432626,0.00146197,0.001462015,0.076425478,Genomics,0.50629884,TRUE,19,0.285793803,21.5,0.498260637,1,0.537564047,,,0.440539496
4891,Pneumonia in medical professionals during COVID-19 outbreak in cardiovascular hospital.,Int J Infect Dis,33220441,11/22/20,pubmed,0,6,logistic regression,0.001310362,0.001310378,0.109296175,0.001310383,0.225575188,0.661197514,Clinics,0.5193933,TRUE,44.33333333,0.580679077,8.166666667,0.321915975,0,0.403234768,,,0.435276607
4892,Evidence of Severe Acute Respiratory Syndrome Coronavirus 2 Reinfection After Recovery from Mild Coronavirus Disease 2019.,Clin Infect Dis,33219681,11/22/20,pubmed,0,21,"sequencing, whole-genome, genomes",0.001330037,0.731944018,0.001330042,0.001330041,0.001330063,0.2627358,Genomics,0.42166325,FALSE,50.38095238,0.634176511,27.23809524,0.547364196,10,0.828199272,,,0.669913326
4893,"Genomic Epidemiology of Severe Acute Respiratory Syndrome Coronavirus 2, Colombia.",Emerg Infect Dis,33219646,11/22/20,pubmed,0,17,"sequencing, genomic epidemiology, genome sequences",0.002422274,0.987888372,0.002422296,0.002422428,0.002422275,0.002422355,Genomics,0.3668523,FALSE,31.23529412,0.44690457,35.94117647,0.609646775,5,0.739490092,,,0.598680479
4894,Current methods for diagnosis of human coronaviruses: pros and cons.,Anal Bioanal Chem,33219449,11/22/20,pubmed,0,4,sequencing,0.001486508,0.461014145,0.533039713,0.001486564,0.001486462,0.001486608,Genomics,0.8766012,TRUE,40,0.539860226,11.25,0.374364463,4,0.707574542,,,0.540599744
4895,Battling COVID-19 pandemic waves in six South-East Asian countries: A real-time consensus review.,Med J Malaysia,33219168,11/22/20,pubmed,0,8,information retrieval,0.000800581,0.00080062,0.000800591,0.904097689,0.052833945,0.040666572,Epidemiology,0.82820624,TRUE,45.375,0.589894242,41.875,0.643698154,3,0.667819001,,,0.633803799
4896,Metagenomic Next-Generation Sequencing of Nasopharyngeal Specimens Collected from Confirmed and Suspect COVID-19 Patients.,mBio,33219095,11/22/20,pubmed,0,9,"bioinformatic, sequencing, transcriptom, metagenom, microbiom, metatranscriptom",0.000595766,0.769452385,0.012729904,0.000595779,0.000595771,0.216030395,Genomics,0.45927754,FALSE,174.4444444,0.961593172,354.7777778,0.966550709,6,0.764429903,,,0.897524595
4897,"Trust, risk perception, and COVID-19 infections: Evidence from multilevel analyses of combined original dataset in China.",Soc Sci Med,33218890,11/22/20,pubmed,0,2,dataset,0.001565341,0.001565332,0.001565341,0.601793544,0.39194511,0.00156533,Epidemiology,0.57870865,TRUE,7.5,0.108355495,0,0.055525823,2,0.618927094,,,0.260936137
4898,"Transparency, trust, and community welfare: towards a precision public health ethics framework for the genomics era.",Genome Med,33218363,11/22/20,pubmed,0,2,sequencing,0.003335407,0.121885375,0.373751197,0.316449316,0.181243409,0.003335297,Epidemiology,0.5321533,TRUE,163,0.953491249,267,0.944741771,0,0.403234768,,,0.767155929
4899,Psychological Distress in Patients with Autoimmune Arthritis during the COVID-19 Induced Lockdown in Italy.,Microorganisms,33218124,11/22/20,pubmed,0,12,logistic regression,0.001254607,0.001254601,0.001254602,0.001254661,0.768276838,0.226704691,Healthcare,0.9530386,TRUE,32.66666667,0.462922877,27.83333333,0.552247792,0,0.403234768,,,0.472801812
4900,Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model.,Viruses,33218024,11/22/20,pubmed,0,5,deep learning,0.956824369,0.001254594,0.038157206,0.001254596,0.001254617,0.001254618,Drug discovery,0.95032346,TRUE,61,0.709196611,48,0.673735617,1,0.537564047,,,0.640165425
4901,Prevalence of Internet Addiction during the COVID-19 Outbreak and Its Risk Factors among Junior High School Students in Taiwan.,Int J Environ Res Public Health,33218018,11/22/20,pubmed,0,1,logistic regression,0.002130719,0.002130721,0.002130788,0.002130837,0.989346127,0.002130809,Healthcare,0.84493506,TRUE,35,0.488032655,33,0.593256623,2,0.618927094,,,0.566738791
4902,COVID-19 Infection Detection and Prevention by SARS-CoV-2 Active Antigens: A Synthetic Vaccine Approach.,Vaccines (Basel),33217916,11/22/20,pubmed,0,7,"bioinformatic, genomes",0.60299233,0.215187816,0.099567468,0.079065114,0.001593533,0.001593739,Drug discovery,0.3301142,FALSE,22.28571429,0.328653596,5.142857143,0.25802783,1,0.537564047,,,0.374748491
4903,"Prevalence and associated factors of depression, anxiety, and stress among Hubei pediatric nurses during COVID-19 pandemic.",Compr Psychiatry,33217635,11/21/20,pubmed,0,9,logistic regression,0.000793409,0.000793411,0.000793461,0.000793415,0.920319376,0.076506929,Healthcare,0.98977256,TRUE,32,0.455810502,6,0.280037463,1,0.537564047,,,0.424470671
4904,Clinical and epidemiological features discriminating confirmed COVID-19 patients from SARS-CoV-2 negative patients at screening centres in Madagascar.,Int J Infect Dis,33217573,11/21/20,pubmed,0,13,logistic regression,0.001371248,0.001371341,0.087431677,0.056062778,0.337769125,0.515993831,Clinics,0.5707123,TRUE,9.153846154,0.136433917,1,0.122023013,1,0.537564047,,,0.265340326
4905,Tissue-Specific Immunopathology in Fatal COVID-19.,Am J Respir Crit Care Med,33217246,11/21/20,pubmed,0,28,sequencing,0.66309907,0.170327081,0.001112693,0.001112643,0.001112633,0.16323588,Drug discovery,0.57848275,TRUE,51.10714286,0.63893871,58.5,0.715814825,19,0.89561084,,,0.750121458
4906,The same storm but not the same boat: Effects of COVID-19 stay-at-home order on mental health in individuals with overweight.,Clin Obes,33217224,11/21/20,pubmed,0,9,logistic regression,0.0014864,0.001486398,0.001486414,0.001486455,0.992567813,0.00148652,Healthcare,0.96601176,TRUE,46.66666667,0.602510978,10.55555556,0.363727589,3,0.667819001,,,0.544685856
4907,Gross Anatomy Education in China during the Covid-19 Pandemic: A National Survey.,Anat Sci Educ,33217164,11/21/20,pubmed,0,6,active learning,0.001291235,0.001291226,0.025087215,0.107112838,0.863926263,0.001291223,Healthcare,0.88831997,TRUE,33.5,0.471890655,20.5,0.486218892,2,0.618927094,,,0.52567888
4908,The Effect of IL-6 Inhibitors on Mortality Among Hospitalized COVID-19 Patients: A Multicenter Study.,J Infect Dis,33216906,11/21/20,pubmed,0,12,"machine learning, structural model",0.038684525,0.001622771,0.145432849,0.083363653,0.001622767,0.729273436,Clinics,0.7453913,TRUE,44.16666667,0.579132909,52,0.689523682,1,0.537564047,,,0.602073546
4909,"Risk Factors and Outcomes of Hospitalized Patients with Severe COVID-19 and Secondary Bloodstream Infections: A Multicenter, Case-Control Study.",Clin Infect Dis,33216875,11/21/20,pubmed,0,12,prediction model,0.001717205,0.073106159,0.00171722,0.001717303,0.079480418,0.842261696,Clinics,0.5269207,TRUE,30.75,0.441090977,16.83333333,0.446815628,4,0.707574542,,,0.531827049
4910,Factors Associated with Mortality among Hospitalized Patients with COVID-19: A Retrospective Cohort Study.,Am J Trop Med Hyg,33215578,11/21/20,pubmed,0,9,logistic regression,0.002490406,0.002490527,0.002490432,0.050438483,0.051715397,0.890374756,Clinics,0.44692117,FALSE,52.66666667,0.651308059,18.33333333,0.464343056,1,0.537564047,,,0.551071721
4911,Saliva NMR-Based Metabolomics in the War Against COVID-19.,Anal Chem,33215503,11/21/20,pubmed,0,4,metabolom,0.353616909,0.002422422,0.002422329,0.636693499,0.002422464,0.002422377,Epidemiology,0.8956951,TRUE,45.25,0.589337621,32.25,0.587570244,0,0.403234768,,,0.526714211
4912,Deep learning applications to combat the dissemination of COVID-19 disease: a review.,Eur Rev Med Pharmacol Sci,33215473,11/21/20,pubmed,0,6,"deep learning, neural network",0.001593523,0.001593571,0.873461692,0.120164154,0.001593532,0.001593529,Imaging,0.9339886,TRUE,3.333333333,0.04044777,0,0.055525823,3,0.667819001,,,0.254597531
4913,CoVID-19 symptoms analysis of deceased and recovered cases using Chi-square test.,Eur Rev Med Pharmacol Sci,33215465,11/21/20,pubmed,0,3,dataset,0.0027202,0.002720352,0.002720238,0.293595286,0.155322138,0.542921785,Clinics,0.30851483,FALSE,6.333333333,0.089863319,0,0.055525823,0,0.403234768,,,0.182874636
4914,"Expressions of SAA, CRP, and FERR in different severities of COVID-19.",Eur Rev Med Pharmacol Sci,33215460,11/21/20,pubmed,0,9,correlation analysis,0.027349369,0.000966804,0.114222432,0.000966798,0.000966765,0.855527833,Clinics,0.9418287,TRUE,7.333333333,0.105572392,1.555555556,0.138948354,2,0.618927094,,,0.287815947
4915,"Previous psychopathology predicted severe COVID-19 concern, anxiety, and PTSD symptoms in pregnant women during "lockdown" in Italy.",Arch Womens Ment Health,33215247,11/21/20,pubmed,0,5,logistic regression,0.001141315,0.001141342,0.001141366,0.167686247,0.827748356,0.001141374,Healthcare,0.82243025,TRUE,120.6,0.907972045,74.2,0.764851485,1,0.537564047,,,0.736795859
4916,0,Futur J Pharm Sci,33215042,11/21/20,pubmed,0,4,in silico,0.992309387,0.001538097,0.001538119,0.001538133,0.001538141,0.001538123,Drug discovery,0.94839025,TRUE,8.5,0.126662131,0,0.055525823,4,0.707574542,,,0.296587499
4917,A model based on cellular automata to estimate the social isolation impact on COVID-19 spreading in Brazil.,Comput Methods Programs Biomed,33213971,11/21/20,pubmed,0,1,probabilistic,0.029483011,0.000889055,0.000889147,0.823909756,0.000889099,0.143939931,Epidemiology,0.7926811,TRUE,15,0.227596017,2,0.164302917,0,0.403234768,,,0.265044567
4918,Minimising exposure to droplet and aerosolised pathogens: a computational fluid dynamics study.,Br J Anaesth,33213833,11/21/20,pubmed,0,5,computational,0.001987228,0.125630344,0.189028528,0.441674641,0.050881106,0.190798154,Epidemiology,0.4736941,FALSE,7.2,0.103716989,2.4,0.174872893,0,0.403234768,,,0.227274883
4919,Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes.,BMC Med Inform Decis Mak,33213435,11/21/20,pubmed,0,8,"machine learning, deep learning, neural network, predictive model, logistic regression, dataset",0.001392917,0.001392838,0.595261063,0.001392921,0.00139287,0.399167391,Clinics,0.4745263,FALSE,25.375,0.372070011,15.75,0.433636607,0,0.403234768,,,0.402980462
4920,"GIS-based approaches on the accessibility of referral hospital using network analysis and the spatial distribution model of the spreading case of COVID-19 in Jakarta, Indonesia.",BMC Health Serv Res,33213404,11/21/20,pubmed,0,5,network analysis,0.001717183,0.001717279,0.001717213,0.885175451,0.001717291,0.107955582,Epidemiology,0.38928708,FALSE,7.2,0.103716989,0.4,0.075796093,0,0.403234768,,,0.194249283
4921,The COVID-19 pandemic preparedness simulation tool: CovidSIM.,BMC Infect Dis,33213360,11/21/20,pubmed,0,5,simulation model,0.001622758,0.0016227,0.001622712,0.991886408,0.001622732,0.00162269,Epidemiology,0.60090685,TRUE,54.2,0.662749706,64.8,0.737891357,4,0.707574542,,,0.702738535
4922,"COVID-19 and domestic animals: Exploring the species barrier crossing, zoonotic and reverse zoonotic transmission of SARS-CoV-2.",Curr Pharm Des,33213323,11/21/20,pubmed,0,7,sequencing,0.001072228,0.811036624,0.00107225,0.168911199,0.001072305,0.016835395,Genomics,0.86435354,TRUE,365,0.99468118,266.5714286,0.944674873,0,0.403234768,,,0.780863607
4923,Virtual screening and molecular simulation study of natural products database for lead identification of novel coronavirus main protease inhibitors.,J Biomol Struct Dyn,33213294,11/21/20,pubmed,0,6,virtual screening,0.992690583,0.001461884,0.001461889,0.001461942,0.00146185,0.001461853,Drug discovery,0.97875476,TRUE,16,0.243552477,5.5,0.267259834,0,0.403234768,,,0.30468236
4924,"RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences.",Nucleic Acids Res,33211854,11/20/20,pubmed,0,38,bioinformatic,0.592403216,0.001861779,0.001861889,0.213076943,0.188934442,0.00186173,Drug discovery,0.8113929,TRUE,72.55263158,0.775620014,408.3684211,0.971835697,6,0.764429903,,,0.837295205
4925,"Community's perceived high risk of coronavirus infections during early phase of epidemics are significantly influenced by socio-demographic background, in Gondar City, Northwest Ethiopia: A cross-sectional -study.",PLoS One,33211748,11/20/20,pubmed,0,28,logistic regression,0.001861736,0.00186178,0.001861728,0.091758363,0.900794674,0.001861719,Healthcare,0.68011785,TRUE,14.75,0.222586431,4.75,0.248260637,3,0.667819001,,,0.379555356
4926,Computed tomography characterization and outcome evaluation of COVID-19 pneumonia complicated by venous thromboembolism.,PLoS One,33211737,11/20/20,pubmed,0,9,logistic regression,0.000591815,0.00059178,0.406669288,0.000591788,0.000591778,0.590963552,Clinics,0.8965329,TRUE,129,0.920588781,50.66666667,0.683569708,2,0.618927094,,,0.741028528
4927,COVID-19 symptoms and SARS-CoV-2 infection among people living with HIV in the US: the MACS/WIHS combined cohort study.,HIV Res Clin Pract,33211636,11/20/20,pubmed,0,16,logistic regression,0.002296531,0.041281784,0.002296544,0.00229654,0.94953185,0.002296751,Healthcare,0.7628866,TRUE,185.625,0.966726452,332.375,0.962536794,3,0.667819001,,,0.865694082
4928,Emergency Medical Services Personnel Awareness and Training about Personal Protective Equipment during the COVID-19 Pandemic.,Prehosp Emerg Care,33211613,11/20/20,pubmed,0,5,logistic regression,0.025015608,0.0012727,0.163642127,0.28275898,0.526037918,0.001272666,Healthcare,0.5451913,TRUE,208.4,0.974890222,248.4,0.937650522,0,0.403234768,,,0.77192517
4929,[Chest radiological lesions in COVID-19 : from classical imaging to artificial intelligence].,Rev Med Liege,33211427,11/20/20,pubmed,0,6,artificial intelligence,0.001622739,0.001622725,0.596786246,0.001622761,0.001622785,0.396722744,Imaging,0.89826083,TRUE,26.83333333,0.392726823,2.5,0.180826866,0,0.403234768,,,0.325596152
4930,[The COVID-19 pandemic : a real opportunity for digital health].,Rev Med Liege,33211426,11/20/20,pubmed,0,2,digital health,0.002183368,0.105508271,0.172002131,0.715939527,0.002183354,0.002183349,Epidemiology,0.84448415,TRUE,129,0.920588781,14,0.412898047,0,0.403234768,,,0.578907198
4931,The potential effects of clinical antidiabetic agents on SARS-CoV-2.,J Diabetes,33210826,11/20/20,pubmed,0,12,computational,0.855369979,0.002130644,0.002130669,0.002130697,0.002130707,0.136107303,Drug discovery,0.9854064,TRUE,54.66666667,0.666151277,,,2,0.618927094,,,0.642539186
4932,Gender Disaggregation in COVID-19 and Increased Male Susceptibility.,J Nepal Health Res Counc,33210622,11/20/20,pubmed,0,7,dataset,0.27510054,0.097427806,0.035718538,0.163081221,0.165649138,0.263022758,Drug discovery,0.69496924,TRUE,23.85714286,0.352031666,4.714285714,0.24719026,2,0.618927094,,,0.406049673
4933,Structure-based identification of potential SARS-CoV-2 main protease inhibitors.,J Biomol Struct Dyn,33210561,11/20/20,pubmed,0,7,molecular dynamics simulation,0.992173519,0.001565298,0.001565304,0.001565306,0.001565292,0.001565281,Drug discovery,0.9425591,TRUE,65.57142857,0.737831653,16.42857143,0.441731335,2,0.618927094,,,0.599496694
4934,Risk factors of mortality and contribution of treatment in patients infected with COVID-19: a retrospective propensity score matched study.,Curr Med Res Opin,33210547,11/20/20,pubmed,0,7,logistic regression,0.001461888,0.001461891,0.001461896,0.16404808,0.001461926,0.830104319,Clinics,0.91704977,TRUE,65.42857143,0.736718412,,,0,0.403234768,,,0.56997659
4935,Cancer in Lockdown: Impact of the COVID-19 Pandemic on Patients with Cancer.,Oncologist,33210442,11/20/20,pubmed,0,8,artificial intelligence,0.001786649,0.001786551,0.154038016,0.362686634,0.254802461,0.224899689,Epidemiology,0.75288343,TRUE,139.5,0.93295813,57.5,0.712938186,0,0.403234768,,,0.683043694
4936,Cardiac injury is associated with inflammation in geriatric COVID-19 patients.,J Clin Lab Anal,33210392,11/20/20,pubmed,0,12,logistic regression,0.055490547,0.001141326,0.001141337,0.001141344,0.001141367,0.93994408,Clinics,0.8872175,TRUE,92.33333333,0.849836106,58.66666667,0.716149318,0,0.403234768,,,0.65640673
4937,The Impact of Previous History of Bariatric Surgery on Outcome of COVID-19. A Nationwide Medico-Administrative French Study.,Obes Surg,33210274,11/20/20,pubmed,0,7,logistic regression,0.001438102,0.00143812,0.001438094,0.001438107,0.001438124,0.992809452,Clinics,0.8043436,TRUE,159.2857143,0.950893685,135,0.869614664,5,0.739490092,,,0.853332814
4938,"Assessing healthcare workers' knowledge, emotions and perceived institutional preparedness about COVID-19 pandemic at Saudi hospitals in the early phase of the pandemic.",J Public Health Res,33209861,11/20/20,pubmed,0,6,logistic regression,0.001371281,0.001371301,0.001371296,0.001371377,0.993143452,0.001371293,Healthcare,0.86947334,TRUE,6,0.086028821,1,0.122023013,0,0.403234768,,,0.2037622
4939,Preliminary investigation of relationship between clinical indicators and CT manifestation patterns of COVID-19 pneumonia improvement.,J Thorac Dis,33209422,11/20/20,pubmed,0,15,correlation analysis,0.000793406,0.059656574,0.218439734,0.000793412,0.000793398,0.719523476,Clinics,0.90380937,TRUE,18.86666667,0.281835611,4.266666667,0.235817501,0,0.403234768,,,0.306962627
4940,The global COVID-19 pandemic at a crossroads: relevant countermeasures and ways ahead.,J Thorac Dis,33209406,11/20/20,pubmed,0,8,probabilistic,0.012892731,0.000786364,0.029794613,0.879422264,0.07631762,0.000786408,Epidemiology,0.6216103,TRUE,24.625,0.362669305,4.125,0.232204977,0,0.403234768,,,0.332703017
4941,CT imaging features of different clinical types of COVID-19 calculated by AI system: a Chinese multicenter study.,J Thorac Dis,33209367,11/20/20,pubmed,0,16,"artificial intelligence, dataset",0.001046814,0.001046845,0.505213027,0.001046825,0.001046839,0.49059965,Imaging,0.8493534,TRUE,55.5625,0.671531944,6.75,0.292079208,0,0.403234768,,,0.455615306
4942,Reduced Sleep in the Week Prior to Diagnosis of COVID-19 is Associated with the Severity of COVID-19.,Nat Sci Sleep,33209069,11/20/20,pubmed,0,5,logistic regression,0.001415289,0.001415214,0.001415199,0.082827871,0.646835051,0.266091375,Healthcare,0.61961085,TRUE,8.6,0.127280599,5.2,0.259700294,1,0.537564047,,,0.308181647
4943,Macrophage expression and prognostic significance of the long pentraxin PTX3 in COVID-19.,Nat Immunol,33208929,11/20/20,pubmed,0,29,"bioinformatic, sequencing",0.375291161,0.118858985,0.001486452,0.001486463,0.001486429,0.501390511,Clinics,0.42356408,FALSE,87.24137931,0.834250727,134.4827586,0.868811881,4,0.707574542,,,0.803545717
4944,Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning.,Nat Biomed Eng,33208927,11/20/20,pubmed,0,18,deep learning,0.04781502,0.001751275,0.649184911,0.001751287,0.001751184,0.297746323,Imaging,0.6671002,TRUE,37.94444444,0.517719092,22.94444444,0.511105165,9,0.814309525,,,0.614377927
4945,The spatiotemporal estimation of the risk and the international transmission of COVID-19: a global perspective.,Sci Rep,33208858,11/20/20,pubmed,0,4,simulation model,0.002562611,0.002562606,0.002562562,0.987186995,0.002562631,0.002562594,Epidemiology,0.21290356,FALSE,9.25,0.137918239,1,0.122023013,0,0.403234768,,,0.221058673
4946,The influence of concern about COVID-19 on mental health in the Republic of Georgia: a cross-sectional study.,Global Health,33208153,11/20/20,pubmed,0,6,probabilistic,0.001171531,0.001171545,0.001171524,0.00117161,0.994142235,0.001171555,Healthcare,0.9378384,TRUE,54.5,0.665161729,69.83333333,0.75280974,0,0.403234768,,,0.607068746
4947,CETSA MS Profiling for a Comparative Assessment of FDA-Approved Antivirals Repurposed for COVID-19 Therapy Identifies TRIP13 as a Remdesivir Off-Target.,SLAS Discov,33208020,11/20/20,pubmed,0,4,proteom,0.993814648,0.001237098,0.001237059,0.001237079,0.001237065,0.001237052,Drug discovery,0.70668584,TRUE,11.25,0.169274538,3.5,0.213607172,0,0.403234768,,,0.262038826
4948,Weight Gain in a Sample of Patients Affected by Overweight/Obesity with and without a Psychiatric Diagnosis during the Covid-19 Lockdown.,Nutrients,33207742,11/20/20,pubmed,0,6,logistic regression,0.001438099,0.001438107,0.077199241,0.001438137,0.581309128,0.337177289,Healthcare,0.9647542,TRUE,38,0.519327107,18.66666667,0.466818303,3,0.667819001,,,0.551321471
4949,Large-Scale Plasma Analysis Revealed New Mechanisms and Molecules Associated with the Host Response to SARS-CoV-2.,Int J Mol Sci,33207699,11/20/20,pubmed,0,25,"metabolom, lipidom",0.61196367,0.001717323,0.001717262,0.001717253,0.00171718,0.381167313,Drug discovery,0.84558046,TRUE,38.88,0.527738265,18.84,0.468691464,7,0.785110192,,,0.59384664
4950,"Computational Analysis of Targeting SARS-CoV-2, Viral Entry Proteins ACE2 and TMPRSS2, and Interferon Genes by Host MicroRNAs.",Genes (Basel),33207533,11/20/20,pubmed,0,6,"computational, bioinformatic, dataset",0.669296311,0.143368129,0.031124919,0.001415184,0.001415144,0.153380313,Drug discovery,0.3523638,FALSE,36.16666667,0.499165069,55.33333333,0.703371689,1,0.537564047,,,0.580033602
4951,Detection of Hate Speech in COVID-19-Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach.,J Med Internet Res,33207310,11/19/20,pubmed,0,5,"machine learning, deep learning, neural network, dataset",0.000854709,0.000854732,0.254762601,0.741818522,0.000854727,0.000854709,Epidemiology,0.002901107,FALSE,8.2,0.120477457,0,0.055525823,0,0.403234768,,,0.193079349
4952,Predicting spatial and temporal responses to non-pharmaceutical interventions on COVID-19 growth rates across 58 counties in New York State: A prospective event-based modeling study on county-level sociological predictors.,JMIR Public Health Surveill,33207309,11/19/20,pubmed,0,1,dataset,0.000822916,0.000822925,0.000822916,0.873080543,0.123627756,0.000822945,Epidemiology,0.89036363,TRUE,34,0.477766096,2,0.164302917,1,0.537564047,,,0.39321102
4953,Prevalence and associated factors of depression and anxiety among nurses during the outbreak of COVID-19 in China: A cross-sectional study.,Int J Nurs Stud,33207297,11/19/20,pubmed,0,17,logistic regression,0.001126773,0.001126795,0.00112677,0.001126806,0.994366047,0.001126809,Healthcare,0.98448646,TRUE,45.47058824,0.590265323,17.64705882,0.457184908,4,0.707574542,,,0.585008257
4954,Predictors and outcomes of healthcare-associated infections in COVID-19 patients.,Int J Infect Dis,33207271,11/19/20,pubmed,0,11,logistic regression,0.002720199,0.046185393,0.002720246,0.002720266,0.096724728,0.848929167,Clinics,0.7738505,TRUE,44.63636364,0.582843713,19.54545455,0.476117206,1,0.537564047,,,0.532174989
4955,SARS-CoV-2 Cell Entry Factors ACE2 and TMPRSS2 Are Expressed in the Microvasculature and Ducts of Human Pancreas but Are Not Enriched in β Cells.,Cell Metab,33207245,11/19/20,pubmed,0,19,dataset,0.779933839,0.063908936,0.002357813,0.002357764,0.002357774,0.149083875,Drug discovery,0.29661402,FALSE,62,0.715814212,140.1578947,0.874297565,7,0.785110192,,,0.791740656
4956,Expression of SARS-CoV-2 Entry Factors in the Pancreas of Normal Organ Donors and Individuals with COVID-19.,Cell Metab,33207244,11/19/20,pubmed,0,21,"in silico, dataset",0.69286226,0.002996469,0.091878743,0.002996445,0.002996404,0.206269679,Drug discovery,0.7700122,TRUE,77.19047619,0.795349125,103.4761905,0.828003746,9,0.814309525,,,0.812554132
4957,"SARS-CoV-2 Transmission between Mink (Neovison vison) and Humans, Denmark.",Emerg Infect Dis,33207152,11/19/20,pubmed,0,16,sequencing,0.007061702,0.964691278,0.007061464,0.007062373,0.007061621,0.007061561,Genomics,0.48718598,FALSE,86.0625,0.82924114,111.75,0.84104897,25,0.918019631,,,0.862769914
4958,"Global SNP analysis of 11,183 SARS-CoV-2 strains reveals high genetic diversity.",Transbound Emerg Dis,33207070,11/19/20,pubmed,0,4,"whole genome, genomes",0.139887648,0.856367991,0.000936123,0.000936093,0.000936068,0.000936077,Genomics,0.9197551,TRUE,144.5,0.939080957,192.25,0.91370083,1,0.537564047,,,0.796781945
4959,The IMEx coronavirus interactome: an evolving map of Coronaviridae-host molecular interactions.,Database (Oxford),33206959,11/19/20,pubmed,0,20,"proteom, interactom, dataset",0.461113119,0.002562686,0.241502208,0.002562794,0.289696625,0.002562567,Drug discovery,0.5612652,TRUE,83.5,0.819221968,311.35,0.957786995,0,0.403234768,,,0.72674791
4960,Relationship between work before the epidemic and having gone out to work during the epidemic among participants in the Brazilian Longitudinal Study of Aging: results of the ELSI-COVID-19 initiative.,Cad Saude Publica,33206834,11/19/20,pubmed,0,8,logistic regression,0.001350317,0.001350345,0.001350309,0.001350439,0.993248221,0.00135037,Healthcare,0.93255067,TRUE,66.25,0.742346465,52.625,0.69153064,0,0.403234768,,,0.612370624
4961,Clinical and molecular characterization of COVID-19 hospitalized patients.,PLoS One,33206719,11/19/20,pubmed,0,22,"sequencing, exom",0.139267398,0.344649253,0.034054626,0.001823433,0.132545542,0.347659748,Clinics,0.6276479,TRUE,46.63636364,0.60189251,24.13636364,0.522143431,0,0.403234768,,,0.509090236
4962,EGYVIR: An immunomodulatory herbal extract with potent antiviral activity against SARS-CoV-2.,PLoS One,33206688,11/19/20,pubmed,0,13,in-silico,0.989346134,0.002130658,0.002130848,0.002130762,0.002130842,0.002130756,Drug discovery,0.95475304,TRUE,32.46153846,0.460634548,26.69230769,0.543283382,0,0.403234768,,,0.469050899
4963,Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level.,PLoS Negl Trop Dis,33206644,11/19/20,pubmed,0,3,bayes,0.001272691,0.001272701,0.051008465,0.622009875,0.237623292,0.086812976,Epidemiology,0.11374414,FALSE,26,0.382398417,4,0.231469093,0,0.403234768,,,0.339034092
4964,Stability of SARS-CoV-2 phylogenies.,PLoS Genet,33206635,11/19/20,pubmed,0,14,"bioinformatic, sequencing, genome sequences",0.028946309,0.738670743,0.000956365,0.229513901,0.000956371,0.000956311,Genomics,0.33996928,FALSE,53.85714286,0.659966603,759.1428571,0.98916243,0,0.403234768,,,0.684121267
4965,Histamine receptors and COVID-19.,Inflamm Res,33206207,11/19/20,pubmed,0,2,bioinformatic,0.732411782,0.00198716,0.001987102,0.259639645,0.001987151,0.00198716,Drug discovery,0.68895805,TRUE,119,0.905683716,73,0.761974846,2,0.618927094,,,0.762195219
4966,Hypertension and renin-angiotensin system blockers are not associated with expression of angiotensin-converting enzyme 2 (ACE2) in the kidney.,Eur Heart J,33206176,11/19/20,pubmed,0,33,"sequencing, transcriptom",0.617057519,0.023798518,0.001415096,0.001415112,0.001415127,0.354898628,Drug discovery,0.944677,TRUE,112.3030303,0.894551302,253.8787879,0.939456784,5,0.739490092,,,0.857832726
4967,Flip the Clinic: A Digital Health Approach to Youth Mental Health Service Delivery During the COVID-19 Pandemic and Beyond.,JMIR Ment Health,33206051,11/19/20,pubmed,0,8,digital health,0.026595197,0.001330038,0.001330124,0.422904304,0.546510299,0.001330037,Healthcare,0.9353918,TRUE,148.875,0.943039149,275.75,0.947417715,0,0.403234768,,,0.764563877
4968,Increased susceptibility to SARS-CoV-2 infection in patients with reduced left ventricular ejection fraction.,ESC Heart Fail,33205916,11/19/20,pubmed,0,14,logistic regression,0.174846488,0.001350356,0.001350321,0.063749175,0.001350406,0.757353254,Clinics,0.9745248,TRUE,50.21428571,0.632506649,26.28571429,0.540272946,2,0.618927094,,,0.597235563
4969,Predictive Factors for Impaired Mental Health among Medical Students during the Early Stage of the COVID-19 Pandemic in Morocco.,Am J Trop Med Hyg,33205748,11/19/20,pubmed,0,9,logistic regression,0.000956288,0.000956301,0.000956306,0.000956314,0.970752344,0.025422447,Healthcare,0.5675175,TRUE,52.88888889,0.652854227,6.333333333,0.285121755,0,0.403234768,,,0.44707025
4970,Loss of orf3b in the circulating SARS-CoV-2 strains.,Emerg Microbes Infect,33205709,11/19/20,pubmed,0,7,"phylogenom, genomes",0.001254726,0.993726711,0.001254604,0.001254696,0.001254606,0.001254656,Genomics,0.40923426,FALSE,35.71428571,0.494093636,246.4285714,0.936981536,6,0.764429903,,,0.731835025
4971,Exploring the Mechanism of Covalent Inhibition: Simulating the Binding Free Energy of α-Ketoamide Inhibitors of the Main Protease of SARS-CoV-2.,Biochemistry,33205654,11/19/20,pubmed,0,2,in silico,0.899940215,0.001461907,0.094212189,0.001461974,0.001461863,0.001461852,Drug discovery,0.34979683,FALSE,64.5,0.731832519,53,0.693738293,1,0.537564047,,,0.654378286
4972,Investigational Treatments for COVID-19 may Increase Ventricular Arrhythmia Risk Through Drug Interactions.,CPT Pharmacometrics Syst Pharmacol,33205613,11/19/20,pubmed,0,7,mathematical model,0.430333164,0.001330029,0.001330072,0.203320412,0.001330103,0.362356219,Drug discovery,0.7963966,TRUE,7.571428571,0.108973963,0.857142857,0.103224512,0,0.403234768,,,0.205144414
4973,Pharmacogenomics of genetic polymorphism within the genes responsible for SARS-CoV-2 susceptibility and the drug-metabolising genes used in treatment.,Rev Med Virol,33205496,11/19/20,pubmed,0,2,pharmacogenom,0.485275247,0.351450635,0.156383917,0.00229668,0.002296613,0.002296907,Drug discovery,0.83151746,TRUE,46.5,0.601026656,3,0.199424672,1,0.537564047,,,0.446005125
4974,"Secondary attack rate of COVID-19 in household contacts in the Winnipeg Health Region, Canada.",Can J Public Health,33205377,11/19/20,pubmed,0,3,dataset,0.001392876,0.001392931,0.023321701,0.591974566,0.278340099,0.103577827,Epidemiology,0.94789547,TRUE,30.66666667,0.440225122,29,0.562884667,1,0.537564047,,,0.513557945
4975,Multidimensional Proteomic Approach of Endothelial Progenitors Demonstrate Expression of KDR Restricted to CD19 Cells.,Stem Cell Rev Rep,33205351,11/19/20,pubmed,0,26,proteom,0.468031595,0.025935889,0.078268882,0.00077943,0.000779413,0.426204791,Drug discovery,0.17677268,FALSE,98.73076923,0.866225493,39.61538462,0.631321916,0,0.403234768,,,0.633594059
4976,The combination of bromelain and curcumin as an immune-boosting nutraceutical in the prevention of severe COVID-19.,Metabol Open,33205039,11/19/20,pubmed,0,4,in silico,0.987547379,0.002490644,0.002490467,0.002490503,0.00249056,0.002490448,Drug discovery,0.90414536,TRUE,62.5,0.718164389,34.5,0.602221033,1,0.537564047,,,0.61931649
4977,An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19.,Glob Transit,33205037,11/19/20,pubmed,0,2,artificial intelligence,0.001823365,0.001823344,0.330914518,0.529517799,0.001823495,0.134097478,Epidemiology,0.9224435,TRUE,31,0.445111015,3.5,0.213607172,0,0.403234768,,,0.353984318
4978,Innate and Adaptive Immunity of Murine Neural Stem Cell-Derived piRNA Exosomes/Microvesicles against Pseudotyped SARS-CoV-2 and HIV-Based Lentivirus.,iScience,33205008,11/19/20,pubmed,0,5,genomes,0.663771085,0.272544818,0.029708785,0.001786557,0.001786677,0.030402077,Drug discovery,0.7284991,TRUE,8.2,0.120477457,3.6,0.21507894,1,0.537564047,,,0.291040148
4979,Information technology in emergency management of COVID-19 outbreak.,Inform Med Unlocked,33204821,11/19/20,pubmed,0,5,"computational, artificial intelligence",0.028352169,0.001059359,0.159387446,0.809082252,0.001059399,0.001059375,Epidemiology,0.40982813,FALSE,4.6,0.062588905,1,0.122023013,2,0.618927094,,,0.267846337
4980,"Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada.",Open Forum Infect Dis,33204755,11/19/20,pubmed,0,4,logistic regression,0.00127271,0.001272684,0.22174229,0.001272727,0.001272684,0.773166905,Clinics,0.5467771,TRUE,77.75,0.797390067,105,0.831482473,4,0.707574542,,,0.778815694
4981,Effects of information-induced behavioural changes during the COVID-19 lockdowns: the case of Italy.,R Soc Open Sci,33204488,11/19/20,pubmed,0,2,mathematical model,0.00213067,0.002130688,0.002130679,0.908482303,0.082994804,0.002130855,Epidemiology,0.33860403,FALSE,51.5,0.643267982,15,0.42594327,3,0.667819001,,,0.579010084
4982,0,F1000Res,33204411,11/19/20,pubmed,0,6,"molecular dynamics simulation, in silico",0.99292421,0.001415142,0.001415169,0.001415183,0.001415146,0.00141515,Drug discovery,0.92406636,TRUE,28.66666667,0.414125796,6,0.280037463,1,0.537564047,,,0.410575769
4983,Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care.,J Healthc Eng,33204404,11/19/20,pubmed,0,6,"machine learning, deep learning, artificial intelligence, mathematical model",0.001112636,0.00111262,0.123790042,0.871759381,0.001112653,0.001112668,Epidemiology,0.49488673,FALSE,15.16666667,0.228709258,2,0.164302917,1,0.537564047,,,0.310192074
4984,Network Pharmacology Analysis to Identify Phytochemicals in Traditional Chinese Medicines That May Regulate ACE2 for the Treatment of COVID-19.,Evid Based Complement Alternat Med,33204291,11/19/20,pubmed,0,8,genomes,0.9688026,0.02458519,0.00165304,0.001653069,0.001653075,0.001653026,Drug discovery,0.99795544,TRUE,54.625,0.665842043,41.25,0.64102221,3,0.667819001,,,0.658227752
4985,Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network.,Appl Soft Comput,33204229,11/19/20,pubmed,0,4,"neural network, classifier",0.051520902,0.001254617,0.813628393,0.10552952,0.00125464,0.026811928,Epidemiology,0.30796725,FALSE,23.25,0.34454821,3.5,0.213607172,1,0.537564047,,,0.365239809
4986,Acute Kidney Injury Can Predict In-Hospital Mortality in Elderly Patients with COVID-19 in the ICU: A Single-Center Study.,Clin Interv Aging,33204075,11/19/20,pubmed,0,8,logistic regression,0.000907296,0.000907287,0.000907403,0.02989475,0.000907304,0.96647596,Clinics,0.82219946,TRUE,78.5,0.800482405,20.75,0.488827937,0,0.403234768,,,0.564181703
4987,Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil.,Process Saf Environ Prot,33204052,11/19/20,pubmed,0,9,"artificial intelligence, forecasting model",0.001943504,0.001943539,0.175363416,0.816862323,0.001943702,0.001943514,Epidemiology,0.84338975,TRUE,13.44444444,0.203104707,2,0.164302917,0,0.403234768,,,0.256880797
4988,Upper airway gene expression reveals suppressed immune responses to SARS-CoV-2 compared with other respiratory viruses.,Nat Commun,33203890,11/19/20,pubmed,0,23,"machine learning, classifier",0.239544461,0.430204836,0.19042163,0.001415182,0.001415189,0.136998702,Genomics,0.46267977,FALSE,42.47826087,0.56181582,50.04347826,0.681830345,7,0.785110192,,,0.676252119
4989,Immune suppression in the early stage of COVID-19 disease.,Nat Commun,33203833,11/19/20,pubmed,0,13,proteom,0.3971342,0.113893391,0.262050907,0.002720239,0.002720186,0.221481077,Drug discovery,0.696003,TRUE,103,0.875378811,397.6923077,0.970832218,6,0.764429903,,,0.870213644
4990,0,Phys Biol,33203811,11/19/20,pubmed,0,3,data mining,0.940326841,0.001751187,0.052668296,0.001751227,0.001751233,0.001751216,Drug discovery,0.7597052,TRUE,65.33333333,0.736285485,64,0.735549906,0,0.403234768,,,0.625023386
4991,Love during lockdown: findings from an online survey examining the impact of COVID-19 on the sexual health of people living in Australia.,Sex Transm Infect,33203737,11/19/20,pubmed,0,13,logistic regression,0.001330031,0.001330036,0.001330052,0.23364246,0.761037336,0.001330085,Healthcare,0.81046516,TRUE,69,0.759416167,45.30769231,0.65941932,6,0.764429903,,,0.72775513
4992,"Global analysis of more than 50,000 SARS-CoV-2 genomes reveals epistasis between eight viral genes.",Proc Natl Acad Sci U S A,33203681,11/19/20,pubmed,0,5,"genome-wide, genomes",0.304468898,0.624188661,0.001823386,0.065872421,0.00182333,0.001823304,Genomics,0.46171314,FALSE,16.4,0.247572515,12.8,0.396574793,4,0.707574542,,,0.45057395
4993,Professional Quality of Life and Perceived Stress in Health Professionals before COVID-19 in Spain: Primary and Hospital Care.,Healthcare (Basel),33202750,11/19/20,pubmed,0,9,correlation analysis,0.001330017,0.001330023,0.001330025,0.001330049,0.993349757,0.00133013,Healthcare,0.95958316,TRUE,15.33333333,0.230997588,6.555555556,0.288533583,1,0.537564047,,,0.352365073
4994,"One Virus, Four Continents, Eight Countries: An Interdisciplinary and International Study on the Psychosocial Impacts of the COVID-19 Pandemic among Adults.",Int J Environ Res Public Health,33202706,11/19/20,pubmed,0,25,logistic regression,0.001415134,0.00141522,0.031410288,0.229485076,0.734859139,0.001415144,Healthcare,0.9659972,TRUE,43.24,0.569361123,27.56,0.550040139,6,0.764429903,,,0.627943722
4995,Association between frailty and changes in lifestyle and physical or psychological conditions among older adults affected by the coronavirus disease 2019 countermeasures in Japan.,Geriatr Gerontol Int,33202485,11/18/20,pubmed,0,4,logistic regression,0.001565298,0.001565303,0.001565295,0.001565348,0.992173397,0.001565359,Healthcare,0.994007,TRUE,8,0.118683901,0,0.055525823,1,0.537564047,,,0.237257924
4996,Differential diagnosis and prospective grading of COVID-19 at the early stage with simple hematological and biochemical variables.,Diagn Microbiol Infect Dis,33202303,11/18/20,pubmed,0,10,logistic regression,0.001751244,0.001751201,0.092070169,0.001751213,0.001751193,0.900924981,Clinics,0.4504223,FALSE,30.6,0.439421114,1.8,0.150120417,1,0.537564047,,,0.37570186
4997,Carbapenemase-producing Enterobacterales causing secondary infections during the COVID-19 crisis at a New York City hospital.,J Antimicrob Chemother,33202023,11/18/20,pubmed,0,12,sequencing,0.00159358,0.703782554,0.001593577,0.001593639,0.001593557,0.289843094,Genomics,0.11638686,FALSE,43.16666667,0.568433422,35.16666667,0.606234948,2,0.618927094,,,0.597865155
4998,An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images.,PLoS One,33201919,11/18/20,pubmed,0,5,"machine learning, deep learning, transfer learning, dataset",0.001059343,0.001059344,0.994703287,0.00105936,0.001059331,0.001059335,Imaging,0.91061723,TRUE,51.8,0.645061538,38.8,0.625970029,5,0.739490092,,,0.670173886
4999,"Genetic diversity of SARS-CoV-2 and clinical, epidemiological characteristics of COVID-19 patients in Hanoi, Vietnam.",PLoS One,33201914,11/18/20,pubmed,0,14,genomes,0.001171533,0.692327462,0.001171532,0.001171576,0.001171572,0.302986326,Genomics,0.6699015,TRUE,29.57142857,0.426185911,30.07142857,0.570243511,1,0.537564047,,,0.511331156
5000,Validation of Chest Computed Tomography Artificial Intelligence to Determine the Requirement for Mechanical Ventilation and Risk of Mortality in Hospitalized Coronavirus Disease-19 Patients in a Tertiary Care Center In Mexico City.,Rev Invest Clin,33201872,11/18/20,pubmed,0,8,artificial intelligence,0.00088019,0.000880203,0.600060005,0.000880218,0.000880208,0.396419176,Imaging,0.93553865,TRUE,4.125,0.055229142,0,0.055525823,2,0.618927094,,,0.243227353
5001,Prevalence and risk factors for lung involvement on low-dose chest CT (LDCT) in a paucisymptomatic population of 247 patients affected by COVID-19.,Insights Imaging,33201409,11/18/20,pubmed,0,15,logistic regression,0.001330031,0.001330016,0.134150761,0.00133002,0.001330092,0.86052908,Clinics,0.9827262,TRUE,355.4,0.9943101,453.8666667,0.976184105,0,0.403234768,,,0.791242991
5002,ceRNA analysis of SARS-CoV-2.,Arch Virol,33201341,11/18/20,pubmed,0,1,in silico,0.804272273,0.182386069,0.003335343,0.003335427,0.003335544,0.003335343,Drug discovery,0.40484136,FALSE,42,0.558537943,8,0.320511105,0,0.403234768,,,0.427427939
5003,Provider-patient communication and hospital ratings: perceived gaps and forward thinking about the effects of COVID-19.,Int J Qual Health Care,33201215,11/18/20,pubmed,0,4,correlation analysis,0.001187266,0.001187282,0.001187293,0.001187353,0.703251068,0.291999738,Healthcare,0.9773408,TRUE,11.75,0.177562001,2.5,0.180826866,2,0.618927094,,,0.325771987
5004,Racial and Ethnic Differences in Presentation and Outcomes for Patients Hospitalized with COVID-19: Findings from the American Heart Association's COVID-19 Cardiovascular Disease Registry.,Circulation,33200953,11/18/20,pubmed,0,16,logistic regression,0.000779412,0.000779449,0.013569306,0.000779439,0.000779459,0.983312935,Clinics,0.6448396,TRUE,256.625,0.985342322,438.8125,0.974177147,3,0.667819001,,,0.87577949
5005,Computational Design of 25-mer Peptide Binders of SARS-CoV-2.,J Phys Chem B,33200935,11/18/20,pubmed,0,2,computational,0.99314364,0.001371273,0.001371256,0.00137131,0.001371263,0.001371259,Drug discovery,0.706294,TRUE,10,0.15214299,0,0.055525823,0,0.403234768,,,0.203634527
5006,Natural Compounds as Inhibitors of SARS-CoV-2 Main Protease (3CLpro): A Molecular Docking and Simulation Approach to Combat COVID-19.,Curr Pharm Des,33200697,11/18/20,pubmed,0,3,computational,0.943988643,0.001861758,0.001861747,0.001861761,0.001861738,0.048564352,Drug discovery,0.89455044,TRUE,102.3333333,0.874203723,14,0.412898047,8,0.799987654,,,0.695696474
5007,Computational investigation of binding of chloroquinone and hydroxychloroquinone against PLPro of SARS-CoV-2.,J Biomol Struct Dyn,33200683,11/18/20,pubmed,0,3,"molecular dynamics simulation, computational",0.930585505,0.001438129,0.001438118,0.063661989,0.001438105,0.001438155,Drug discovery,0.8269883,TRUE,27,0.3960047,1.666666667,0.145036125,0,0.403234768,,,0.314758531
5008,0,J Biomol Struct Dyn,33200680,11/18/20,pubmed,0,8,"virtual screening, molecular dynamics simulation",0.972692122,0.001098841,0.001098847,0.022912573,0.001098802,0.001098815,Drug discovery,0.90708303,TRUE,45.5,0.591440411,31.5,0.581883864,1,0.537564047,,,0.570296107
5009,Computational investigation for identification of potential phytochemicals and antiviral drugs as potential inhibitors for RNA-dependent RNA polymerase of COVID-19.,J Biomol Struct Dyn,33200678,11/18/20,pubmed,0,3,computational,0.624826444,0.12546892,0.001272683,0.245886397,0.001272934,0.001272622,Drug discovery,0.81563675,TRUE,5.333333333,0.074339786,1.666666667,0.145036125,0,0.403234768,,,0.207536893
5010,Inhibitory potential of repurposed drugs against the SARS-CoV-2 main protease: a computational-aided approach.,J Biomol Struct Dyn,33200673,11/18/20,pubmed,0,6,"computational, in silico",0.956217367,0.001272662,0.001272658,0.038691911,0.001272669,0.001272734,Drug discovery,0.8990717,TRUE,32.5,0.461685942,6.333333333,0.285121755,0,0.403234768,,,0.383347488
5011,COVID-19 fatality prediction in people with diabetes and prediabetes using a simple score upon hospital admission.,Diabetes Obes Metab,33200501,11/18/20,pubmed,0,23,logistic regression,0.001254622,0.001254596,0.016322125,0.001254594,0.001254596,0.978659468,Clinics,0.75805855,TRUE,49.2173913,0.62564166,34.7826087,0.603692802,3,0.667819001,,,0.632384488
5012,Computational study of pomegranate peel extract polyphenols as potential inhibitors of SARS-CoV-2 virus internalization.,Mol Cell Biochem,33200379,11/18/20,pubmed,0,8,"computational, in silico",0.989836102,0.002032773,0.002032837,0.002032803,0.002032748,0.002032738,Drug discovery,0.9708593,TRUE,49.75,0.630032779,22,0.503746321,1,0.537564047,,,0.557114382
5013,Clinical features of patients with acute coronary syndrome during the COVID-19 pandemic.,J Thromb Thrombolysis,33200333,11/18/20,pubmed,0,10,dataset,0.001751207,0.001751241,0.001751296,0.113116218,0.001751238,0.879878799,Clinics,0.8482851,TRUE,45.2,0.588719154,21.1,0.49244046,0,0.403234768,,,0.494798127
5014,Modelling the structural and reactivity landscapes of tucatinib with special reference to its wavefunction-dependent properties and screening for potential antiviral activity.,J Mol Model,33200284,11/18/20,pubmed,0,6,computational,0.867368188,0.001684547,0.034245994,0.058038499,0.036978143,0.00168463,Drug discovery,0.71131045,TRUE,39,0.530521368,2,0.164302917,2,0.618927094,,,0.437917126
5015,Demographic and occupational determinants of anti-SARS-CoV-2 IgG seropositivity in hospital staff.,J Public Health (Oxf),33200200,11/18/20,pubmed,0,13,logistic regression,0.001823316,0.001823487,0.001823323,0.001823379,0.852117793,0.140588702,Healthcare,0.6577133,TRUE,57.69230769,0.686437009,26.92307692,0.545089644,9,0.814309525,,,0.681945393
5016,Host genetic liability for severe COVID-19 overlaps with alcohol drinking behavior and diabetic outcomes and in over 1 million participants.,medRxiv,33200138,11/18/20,pubmed,0,5,in-silico,0.207203711,0.51389986,0.005697797,0.005697803,0.005697903,0.261802927,Genomics,0.16784015,FALSE,57.8,0.68736471,17,0.451097137,0,0.403234768,,,0.513898872
5017,The COVID-19 epidemic in the Czech Republic: retrospective analysis of measures (not) implemented during the spring first wave.,medRxiv,33200137,11/18/20,pubmed,0,13,bayes,0.001486422,0.001486437,0.001486539,0.992567717,0.001486468,0.001486418,Epidemiology,0.06016496,FALSE,18.23076923,0.273177067,1,0.122023013,0,0.403234768,,,0.266144949
5018,"Identification of a unique TCR repertoire, consistent with a superantigen selection process in Children with Multi-system Inflammatory Syndrome.",bioRxiv,33200133,11/18/20,pubmed,0,15,in silico,0.648658688,0.095307246,0.001237125,0.001237091,0.049788606,0.203771245,Drug discovery,0.41756386,FALSE,70.6,0.766528542,95.8,0.814222638,1,0.537564047,,,0.706105076
5019,Unification of the M/ORF3-related proteins points to a diversified role for ion conductance in pathogenesis of coronaviruses and other nidoviruses.,bioRxiv,33200132,11/18/20,pubmed,0,6,"computational, structural model",0.595550202,0.401095725,0.000838506,0.000838527,0.000838525,0.000838515,Drug discovery,0.8091303,TRUE,91.33333333,0.847176696,726.1666667,0.988025154,0,0.403234768,,,0.746145539
5020,REDIAL-2020: A Suite of Machine Learning Models to Estimate Anti-SARS-CoV-2 Activities.,ChemRxiv,33200119,11/18/20,pubmed,0,8,"machine learning, classifier, dataset",0.449011845,0.001330128,0.461613943,0.085383805,0.001330167,0.001330111,Drug discovery,0.39529568,FALSE,54,0.661574618,68.25,0.748461333,0,0.403234768,,,0.604423573
5021,Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.,ChemRxiv,33200117,11/18/20,pubmed,0,49,"machine learning, in-silico, proteom",0.593051815,0.08960494,0.156577115,0.077245699,0.0019018,0.081618632,Drug discovery,0.62589777,TRUE,43.2244898,0.569299276,42.83673469,0.648715547,8,0.799987654,,,0.672667492
5022,Engineering a novel subunit vaccine against SARS-CoV-2 by exploring immunoinformatics approach.,Inform Med Unlocked,33200088,11/18/20,pubmed,0,4,"molecular dynamics simulation, in silico",0.881757863,0.001653167,0.001653076,0.111629661,0.001653097,0.001653136,Drug discovery,0.85129875,TRUE,14.5,0.219617787,1,0.122023013,2,0.618927094,,,0.320189298
5023,"Combinatorial therapeutic trial plans for COVID-19 treatment armed up with antiviral, antiparasitic, cell-entry inhibitor, and immune-boosters.",Virusdisease,33200085,11/18/20,pubmed,0,4,in silico,0.897701827,0.001486435,0.001486463,0.096352286,0.001486473,0.001486517,Drug discovery,0.8825685,TRUE,11.75,0.177562001,1,0.122023013,0,0.403234768,,,0.234273261
5024,0,Virusdisease,33200084,11/18/20,pubmed,0,10,in silico,0.947091768,0.00198714,0.001987413,0.044959098,0.001987324,0.001987257,Drug discovery,0.7401373,TRUE,134.6,0.926464222,95.2,0.813152261,0,0.403234768,,,0.71428375
5025,Enhanced expression of immune checkpoint receptors during SARS-CoV-2 viral infection.,Mol Ther Methods Clin Dev,33200082,11/18/20,pubmed,0,8,"in silico, transcriptom, dataset",0.68296709,0.199535867,0.001717228,0.001717225,0.001717216,0.112345374,Drug discovery,0.6949865,TRUE,36.375,0.501020471,51.125,0.685777362,1,0.537564047,,,0.574787293
5026,A Distinct Phylogenetic Cluster of Indian Severe Acute Respiratory Syndrome Coronavirus 2 Isolates.,Open Forum Infect Dis,33200080,11/18/20,pubmed,0,18,"sequencing, whole-genome, genomes",0.001622778,0.99188597,0.001622829,0.001622838,0.001622826,0.00162276,Genomics,0.63947666,TRUE,37.44444444,0.512895046,46.5,0.666309874,14,0.866658436,,,0.681954452
5027,Fractal-Fractional Mathematical Model Addressing the Situation of Corona Virus in Pakistan.,Results Phys,33200064,11/18/20,pubmed,0,6,mathematical model,0.008402821,0.008402784,0.008403353,0.957985443,0.008402715,0.008402884,Epidemiology,0.77447593,TRUE,61.83333333,0.714082504,12.16666667,0.387208991,2,0.618927094,,,0.573406196
5028,Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection.,Sustain Cities Soc,33200063,11/18/20,pubmed,0,4,"deep learning, transfer learning, dataset",0.00131034,0.001310335,0.805500783,0.189257821,0.001310399,0.001310322,Imaging,0.6529321,TRUE,46.25,0.598800173,20.25,0.483074659,9,0.814309525,,,0.632061452
5029,0,Comput Struct Biotechnol J,33200027,11/18/20,pubmed,0,8,"computational, in silico, transcriptom, proteom, interactom",0.994505525,0.001098847,0.00109896,0.001098852,0.001098924,0.001098892,Drug discovery,0.81963134,TRUE,36.625,0.504112808,26,0.53819909,4,0.707574542,,,0.58329548
5030,Chinese herbal compounds against SARS-CoV-2: Puerarin and quercetin impair the binding of viral S-protein to ACE2 receptor.,Comput Struct Biotechnol J,33200026,11/18/20,pubmed,0,8,bioinformatic,0.965101378,0.002032781,0.00203285,0.002032802,0.002032773,0.026767417,Drug discovery,0.9298377,TRUE,23,0.34225988,,,3,0.667819001,,,0.505039441
5031,Voice perturbations under the stress overload in young individuals: phenotyping and suboptimal health as predictors for cascading pathologies.,EPMA J,33200009,11/18/20,pubmed,0,6,"machine learning, artificial intelligence",0.214747832,0.000988421,0.229610423,0.092644418,0.240746056,0.221262849,Healthcare,0.9851817,TRUE,18.5,0.278001113,4.666666667,0.246721969,3,0.667819001,,,0.397514028
5032,Towards Using Graph Analytics for Tracking Covid-19.,Procedia Comput Sci,33200008,11/18/20,pubmed,0,4,"machine learning, bioinformatic, dataset",0.001593568,0.055295261,0.586910215,0.353013929,0.001593526,0.0015935,Epidemiology,0.8487929,TRUE,14.75,0.222586431,1.75,0.148381054,0,0.403234768,,,0.258067418
5033,The role of physical activity on mental health and quality of life during COVID-19 outbreak: A cross-sectional study.,Eur J Integr Med,33200007,11/18/20,pubmed,0,8,logistic regression,0.019960108,0.001112621,0.001112622,0.001112649,0.975589364,0.001112637,Healthcare,0.9824214,TRUE,27.875,0.405467252,13.375,0.404000535,0,0.403234768,,,0.404234185
5034,The potential antiviral effect of major royal jelly protein2 and its isoform X1 against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): Insight on their sialidase activity and molecular docking.,J Funct Foods,33199981,11/18/20,pubmed,0,2,in-silico,0.990282607,0.001943544,0.001943445,0.001943454,0.001943471,0.001943479,Drug discovery,0.7107658,TRUE,23.5,0.348506401,1.5,0.138747659,0,0.403234768,,,0.296829609
5035,AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system.,Appl Soft Comput,33199977,11/18/20,pubmed,0,31,"artificial intelligence, dataset",0.001371239,0.001371265,0.877723236,0.116791579,0.001371364,0.001371316,Imaging,0.3787994,FALSE,24.74193548,0.36390624,9.741935484,0.348809205,9,0.814309525,,,0.509008324
5036,Public opinion mining using natural language processing technique for improvisation towards smart city.,Int J Speech Technol,33199973,11/18/20,pubmed,0,2,machine learning,0.001350438,0.001350377,0.328936888,0.66566151,0.001350378,0.001350409,Epidemiology,0.3211897,FALSE,13,0.197352959,0,0.055525823,0,0.403234768,,,0.218704517
5037,Genetic analysis of the 2019 coronavirus pandemic with from real-time reverse transcriptase polymerase chain reaction.,Saudi J Biol Sci,33199970,11/18/20,pubmed,0,1,genomes,0.001684591,0.494454155,0.217893315,0.282598849,0.001684563,0.001684526,Genomics,0.7782955,TRUE,17,0.257467994,0,0.055525823,0,0.403234768,,,0.238742862
5038,Synthesis of novel coumarin analogues: Investigation of molecular docking interaction of SARS-CoV-2 proteins with natural and synthetic coumarin analogues and their pharmacokinetics studies.,Saudi J Biol Sci,33199969,11/18/20,pubmed,0,5,in silico,0.973781451,0.001034613,0.001034608,0.001034611,0.00103465,0.022080066,Drug discovery,0.93971485,TRUE,12.4,0.187148247,1.8,0.150120417,1,0.537564047,,,0.291610904
5039,Dynamics of epidemics: Impact of easing restrictions and control of infection spread.,Chaos Solitons Fractals,33199943,11/18/20,pubmed,0,5,"computational, mathematical model",0.002183361,0.115227939,0.002183222,0.876039058,0.002183225,0.002183195,Epidemiology,0.33949533,FALSE,40.2,0.541159008,5.2,0.259700294,1,0.537564047,,,0.446141116
5040,A computational study to disclose potential drugs and vaccine ensemble for COVID-19 conundrum.,J Mol Liq,33199930,11/18/20,pubmed,0,5,"virtual screening, computational",0.995057993,0.000988423,0.0009884,0.000988418,0.000988381,0.000988384,Drug discovery,0.7718236,TRUE,37.6,0.514626755,25.2,0.530371956,1,0.537564047,,,0.527520919
5041,Ranking the effectiveness of worldwide COVID-19 government interventions.,Nat Hum Behav,33199859,11/18/20,pubmed,0,9,"computational, artificial intelligence, dataset",0.002032773,0.002032752,0.002032999,0.989835963,0.002032757,0.002032755,Epidemiology,0.6361316,TRUE,53.88888889,0.66021399,55.44444444,0.703906877,1,0.537564047,,,0.633894971
5042,DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era.,Sci Rep,33199801,11/18/20,pubmed,0,4,"deep learning, predictive model, lstm",0.001291328,0.001291242,0.257338847,0.664855603,0.073931764,0.001291216,Epidemiology,0.243539,FALSE,77.5,0.79658606,60,0.720899117,1,0.537564047,,,0.685016408
5043,CovidCounties is an interactive real time tracker of the COVID19 pandemic at the level of US counties.,Sci Data,33199721,11/18/20,pubmed,0,10,dataset,0.002562905,0.002562797,0.118988949,0.87076005,0.002562658,0.00256264,Epidemiology,0.038211465,FALSE,37.9,0.517595399,103,0.827468558,2,0.618927094,,,0.654663684
5044,[Survey data as a way to estimate the prevalence of COVID-19. A pilot study in the city of Madrid.],Rev Esp Salud Publica,33199676,11/18/20,pubmed,0,1,logistic regression,0.001203429,0.001203556,0.001203453,0.178595995,0.816590128,0.00120344,Healthcare,0.8511837,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
5045,Expression Analyses of MicroRNAs in Hamster Lung Tissues Infected by SARS-CoV-2.,Mol Cells,33199671,11/18/20,pubmed,0,6,"bioinformatic, genome sequences",0.379745881,0.185422291,0.002130676,0.002130674,0.002130724,0.428439754,Clinics,0.8640195,TRUE,75.83333333,0.789721071,36.16666667,0.612122023,0,0.403234768,,,0.601692621
5046,Predicting the local COVID-19 outbreak around the world with meteorological conditions: a model-based qualitative study.,BMJ Open,33199426,11/18/20,pubmed,0,9,dataset,0.001310448,0.02557626,0.023694739,0.946797846,0.001310338,0.00131037,Epidemiology,0.45863748,FALSE,7.888888889,0.114169089,2.222222222,0.168651325,5,0.739490092,,,0.340770169
5047,Acute Kidney Injury in a National Cohort of Hospitalized US Veterans with COVID-19.,Clin J Am Soc Nephrol,33199414,11/18/20,pubmed,0,6,logistic regression,0.000988399,0.000988409,0.000988365,0.298593578,0.000988441,0.697452808,Clinics,0.9696283,TRUE,45.66666667,0.592677346,89.33333333,0.800307733,10,0.828199272,,,0.740394784
5048,"The D614G mutations in the SARS-CoV-2 spike protein: Implications for viral infectivity, disease severity and vaccine design.",Biochem Biophys Res Commun,33199022,11/18/20,pubmed,0,3,sequencing,0.00392767,0.980362634,0.003927408,0.003927551,0.003927368,0.003927369,Genomics,0.5633524,TRUE,132,0.923990352,361,0.967353492,4,0.707574542,,,0.866306129
5049,Modeling the impact of school reopening on SARS-CoV-2 transmission using contact structure data from Shanghai.,BMC Public Health,33198707,11/18/20,pubmed,0,5,mathematical model,0.001415119,0.001415147,0.001415102,0.612665114,0.381674391,0.001415127,Epidemiology,0.12670135,FALSE,127,0.917125363,52.6,0.691263045,4,0.707574542,,,0.77198765
5050,Immunodominant regions prediction of nucleocapsid protein for SARS-CoV-2 early diagnosis: a bioinformatics and immunoinformatics study.,Pathog Glob Health,33198594,11/18/20,pubmed,0,11,bioinformatic,0.327431209,0.371901623,0.19569078,0.10200351,0.001486447,0.001486431,Genomics,0.4086175,FALSE,40.36363636,0.54301441,43.72727273,0.652461868,1,0.537564047,,,0.577680108
5051,Changes in the obstetrical emergency department profile during the COVID-19 pandemic.,J Matern Fetal Neonatal Med,33198540,11/18/20,pubmed,0,9,logistic regression,0.00084655,0.000846527,0.000846522,0.000846557,0.487336561,0.509277283,Clinics,0.9195068,TRUE,48.88888889,0.621188694,19.55555556,0.476184105,2,0.618927094,,,0.572099965
5052,Machine Learning for Mortality Analysis in Patients with COVID-19.,Int J Environ Res Public Health,33198392,11/18/20,pubmed,0,5,"machine learning, classifier, logistic regression, dataset",0.032478963,0.002183304,0.382903739,0.165355192,0.002183437,0.414895366,Clinics,0.49928245,FALSE,12.6,0.190302431,2,0.164302917,1,0.537564047,,,0.297389798
5053,Associations of Medications With Lower Odds of Typical COVID-19 Symptoms: Cross-Sectional Symptom Surveillance Study.,JMIR Public Health Surveill,33197879,11/17/20,pubmed,0,6,logistic regression,0.001046882,0.144705781,0.04345398,0.001046887,0.561314809,0.248431661,Healthcare,0.86585003,TRUE,9.666666667,0.144968767,12,0.386740701,1,0.537564047,,,0.356424505
5054,Connecting the dots on vertical transmission of SARS-CoV-2 using protein-protein interaction network analysis - Potential roles of placental ACE2 and ENDOU.,Placenta,33197855,11/17/20,pubmed,0,6,network analysis,0.905448747,0.003927659,0.003927521,0.078841314,0.003927374,0.003927384,Drug discovery,0.4819154,FALSE,181.6666667,0.964809203,94.33333333,0.811412898,2,0.618927094,,,0.798383065
5055,Mental health of college students during the COVID-19 epidemic in China.,J Affect Disord,33197782,11/17/20,pubmed,0,7,logistic regression,0.001861669,0.001861664,0.001861656,0.001861766,0.990691556,0.001861688,Healthcare,0.89551187,TRUE,18.14285714,0.272311213,4.571428571,0.24337704,1,0.537564047,,,0.3510841
5056,SARS-CoV-2 microbiome dysbiosis linked disorders and possible probiotics role.,Biomed Pharmacother,33197765,11/17/20,pubmed,0,15,microbiom,0.251659931,0.112807963,0.001330047,0.21019479,0.001330131,0.422677138,Clinics,0.8862216,TRUE,22.4,0.331065619,4.2,0.234211935,5,0.739490092,,,0.434922549
5057,Diaphragmatic thickening fraction as a potential predictor of response to continuous positive airway pressure ventilation in Covid-19 pneumonia: A single-center pilot study.,Respir Physiol Neurobiol,33197604,11/17/20,pubmed,0,10,logistic regression,0.001653037,0.001653121,0.001653088,0.04455439,0.001653097,0.948833267,Clinics,0.95353043,TRUE,44.3,0.579813223,12.6,0.394300241,1,0.537564047,,,0.503892504
5058,Identifying Factors Important to Patients for Resuming Elective Imaging During the COVID-19 Pandemic.,J Am Coll Radiol,33197410,11/17/20,pubmed,0,7,logistic regression,0.001486436,0.001486418,0.310576701,0.001486479,0.453846812,0.231117153,Healthcare,0.9166977,TRUE,37.42857143,0.512771353,44,0.654401927,0,0.403234768,,,0.523469349
5059,An evaluation of two commercial deep learning-based information retrieval systems for COVID-19 literature.,J Am Med Inform Assoc,33197268,11/17/20,pubmed,0,2,"deep learning, information retrieval",0.00263902,0.002639003,0.098692431,0.890751413,0.002639144,0.00263899,Epidemiology,0.68101346,TRUE,81,0.811552972,94,0.810944608,1,0.537564047,,,0.720020542
5060,Correlation Between Early Plasma Interleukin 37 Responses With Low Inflammatory Cytokine Levels and Benign Clinical Outcomes in Severe Acute Respiratory Syndrome Coronavirus 2 Infection.,J Infect Dis,33197260,11/17/20,pubmed,0,30,prediction model,0.504005018,0.001310362,0.046714818,0.001310417,0.001310473,0.445348912,Drug discovery,0.8535625,TRUE,64.96666667,0.733935308,106.8,0.834225314,0,0.403234768,,,0.657131797
5061,PROMISCUOUS 2.0: a resource for drug-repositioning.,Nucleic Acids Res,33196798,11/17/20,pubmed,0,6,bioinformatic,0.608583368,0.001486452,0.072851969,0.314105011,0.001486493,0.001486707,Drug discovery,0.7966537,TRUE,83.5,0.819221968,56.83333333,0.709994648,0,0.403234768,,,0.644150461
5062,"Impact of the Coronavirus Disease 2019 (COVID-19) Pandemic on Invasive Pneumococcal Disease and Risk of Pneumococcal Coinfection With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): Prospective National Cohort Study, England.",Clin Infect Dis,33196783,11/17/20,pubmed,0,9,logistic regression,0.00213064,0.002130756,0.002130659,0.169600064,0.002130826,0.821877056,Clinics,0.2853034,FALSE,114.1111111,0.897829179,161.2222222,0.893029168,3,0.667819001,,,0.819559116
5063,Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation.,J Med Internet Res,33196453,11/17/20,pubmed,0,3,dataset,0.002296601,0.002296761,0.718385249,0.209645554,0.002296596,0.065079239,Epidemiology,0.065327585,FALSE,11.66666667,0.176510607,9.666666667,0.348274017,1,0.537564047,,,0.354116223
5064,Prolonged shedding of severe acute respiratory syndrome coronavirus 2 in patients with COVID-19.,Emerg Microbes Infect,33196399,11/17/20,pubmed,0,16,genomes,0.001392863,0.523979168,0.001392853,0.094930226,0.001392869,0.376912022,Genomics,0.13817623,FALSE,124.875,0.914033026,525.4375,0.980733208,11,0.840175319,,,0.911647185
5065,Artificial intelligence and COVID-19: Present state and future vision.,Intell Based Med,33196057,11/17/20,pubmed,0,1,artificial intelligence,0.004531002,0.004530705,0.753469116,0.134121632,0.004530729,0.098816816,Epidemiology,0.52458465,TRUE,155,0.948048735,147,0.880853626,1,0.537564047,,,0.788822136
5066,KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.,Patterns (N Y),33196056,11/17/20,pubmed,0,18,"machine learning, knowledge graph",0.189836746,0.002183453,0.40350642,0.400106864,0.002183324,0.002183193,Epidemiology,0.38863316,FALSE,37.72222222,0.51567815,110,0.838573722,0,0.403234768,,,0.58582888
5067,A novel framework for COVID-19 case prediction through piecewise regression in India.,Int J Inf Technol,33195969,11/17/20,pubmed,0,4,"machine learning, dataset",0.002032851,0.002032771,0.380342102,0.611526685,0.002032797,0.002032794,Epidemiology,0.5854692,TRUE,6.5,0.093512277,0.5,0.087101953,2,0.618927094,,,0.266513775
5068,Predictive modeling of COVID-19 death cases in Pakistan.,Infect Dis Model,33195884,11/17/20,pubmed,0,5,"predictive model, prediction model",0.001511963,0.001512142,0.001511858,0.632145275,0.00151193,0.361806831,Epidemiology,0.6076213,TRUE,14.6,0.220298101,0,0.055525823,0,0.403234768,,,0.226352897
5069,Predictive value of serum cystatin C for risk of mortality in severe and critically ill patients with COVID-19.,World J Clin Cases,33195640,11/17/20,pubmed,0,8,logistic regression,0.000956323,0.000956337,0.033523864,0.000956323,0.000956314,0.96265084,Clinics,0.71811306,TRUE,67.625,0.750695776,6.375,0.285322451,0,0.403234768,,,0.479750998
5070,Myocardial Injury at Early Stage and Its Association With the Risk of Death in COVID-19 Patients: A Hospital-Based Retrospective Cohort Study.,Front Cardiovasc Med,33195480,11/17/20,pubmed,0,10,logistic regression,0.001291284,0.001291393,0.001291278,0.019998718,0.00129124,0.974836086,Clinics,0.93422747,TRUE,60.8,0.707155668,32.2,0.586834359,0,0.403234768,,,0.565741598
5071,"Comorbidities, Cardiovascular Therapies, and COVID-19 Mortality: A Nationwide, Italian Observational Study (ItaliCO).",Front Cardiovasc Med,33195473,11/17/20,pubmed,0,231,logistic regression,0.001156353,0.001156256,0.001156252,0.001156288,0.001156274,0.994218577,Clinics,0.51508504,TRUE,71.71428571,0.771599975,,,10,0.828199272,,,0.799899623
5072,Investigation of the Effect of Temperature on the Structure of SARS-CoV-2 Spike Protein by Molecular Dynamics Simulations.,Front Mol Biosci,33195427,11/17/20,pubmed,0,2,"molecular dynamics simulation, bioinformatic",0.80273039,0.001156289,0.001156308,0.192644509,0.001156243,0.001156261,Drug discovery,0.5068275,TRUE,9,0.135320675,1,0.122023013,0,0.403234768,,,0.220192819
5073,Centrality of G6PD in COVID-19: The Biochemical Rationale and Clinical Implications.,Front Med (Lausanne),33195336,11/17/20,pubmed,0,4,dataset,0.362383374,0.16894689,0.080923349,0.001461965,0.001461964,0.384822458,Clinics,0.61729455,TRUE,11,0.167171748,7.5,0.307867273,2,0.618927094,,,0.364655372
5074,0,Front Med (Lausanne),33195331,11/17/20,pubmed,0,4,in silico,0.547102363,0.32154719,0.001291248,0.001291291,0.057424801,0.071343106,Drug discovery,0.7097904,TRUE,36.75,0.505968211,26.5,0.542012309,1,0.537564047,,,0.528514856
5075,The Landscape of Coronavirus Disease 2019 (COVID-19) and Integrated Analysis SARS-CoV-2 Receptors and Potential Inhibitors in Lung Adenocarcinoma Patients.,Front Cell Dev Biol,33195212,11/17/20,pubmed,0,9,bioinformatic,0.649040725,0.002238473,0.002238434,0.002238469,0.00223843,0.342005469,Drug discovery,0.85101193,TRUE,13.11111111,0.19790958,,,0,0.403234768,,,0.300572174
5076,0,Front Chem,33195080,11/17/20,pubmed,0,5,simulation experiment,0.925285262,0.02784452,0.001203513,0.043259766,0.001203495,0.001203445,Drug discovery,0.68198544,TRUE,27,0.3960047,1.8,0.150120417,1,0.537564047,,,0.361229722
5077,Hijacking SARS-CoV-2/ACE2 Receptor Interaction by Natural and Semi-synthetic Steroidal Agents Acting on Functional Pockets on the Receptor Binding Domain.,Front Chem,33195060,11/17/20,pubmed,0,13,"virtual screening, in silico",0.992567615,0.001486522,0.001486477,0.001486472,0.00148647,0.001486445,Drug discovery,0.8170736,TRUE,68.76923077,0.757869998,66.07692308,0.742641156,5,0.739490092,,,0.746667082
5078,Health-Related Quality of Life and Influencing Factors of Pediatric Medical Staff During the COVID-19 Outbreak.,Front Public Health,33194967,11/17/20,pubmed,0,8,logistic regression,0.045423438,0.001350393,0.001350393,0.001350403,0.870052516,0.080472857,Healthcare,0.88428664,TRUE,28.5,0.412579628,,,2,0.618927094,,,0.515753361
5079,0,Front Cell Infect Microbiol,33194826,11/17/20,pubmed,0,5,transcriptom,0.8268681,0.072036686,0.00229652,0.00229652,0.002296587,0.094205588,Drug discovery,0.85205257,TRUE,49.4,0.627002288,154.2,0.887075194,0,0.403234768,,,0.639104083
5080,Systems Biology Approaches for Therapeutics Development Against COVID-19.,Front Cell Infect Microbiol,33194800,11/17/20,pubmed,0,6,"computational, in silico, transcriptom, proteom, lipidom, multiom",0.936958349,0.002720185,0.052160968,0.002720308,0.00272012,0.002720071,Drug discovery,0.8961781,TRUE,63.66666667,0.725957078,16.16666667,0.438051913,0,0.403234768,,,0.522414586
5081,Does Working in a COVID-19 Receiving Health Facility Influence Seroprevalence to SARS-CoV-2?,Cureus,33194502,11/17/20,pubmed,0,10,logistic regression,0.001072183,0.067094781,0.001072275,0.001072193,0.501853337,0.427835231,Healthcare,0.7561201,TRUE,12.3,0.185292844,3.9,0.223374364,0,0.403234768,,,0.270633992
5082,Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables.,PeerJ,33194455,11/17/20,pubmed,0,8,"deep learning, neural network, deep-learning, network model",0.001072238,0.001072204,0.262465995,0.001072196,0.00107221,0.733245156,Clinics,0.9360441,TRUE,90.125,0.843218505,103.125,0.827669253,6,0.764429903,,,0.811772554
5083,Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs.,PeerJ,33194447,11/17/20,pubmed,0,5,"neural network, deep-learning, transfer learning",0.000916695,0.042744431,0.953588793,0.000916666,0.000916694,0.00091672,Imaging,0.84604573,TRUE,12,0.183190055,26.6,0.542413701,2,0.618927094,,,0.44817695
5084,Non-SARS-CoV-2 genome sequences identified in clinical samples from COVID-19 infected patients: Evidence for co-infections.,PeerJ,33194423,11/17/20,pubmed,0,1,"sequencing, genome sequences, genomes",0.086717376,0.777013975,0.001538291,0.00153815,0.001538185,0.131654023,Genomics,0.45268595,FALSE,12,0.183190055,0,0.055525823,0,0.403234768,,,0.213983548
5085,Excessive G-U transversions in novel allele variants in SARS-CoV-2 genomes.,PeerJ,33194341,11/17/20,pubmed,0,2,genomes,0.001622736,0.991886219,0.001622704,0.001622769,0.0016228,0.001622772,Genomics,0.31749135,FALSE,39,0.530521368,61.5,0.726585496,2,0.618927094,,,0.625344653
5086,"Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2, the etiologic agent of COVID-19 pandemic: an in silico approach.",PeerJ,33194329,11/17/20,pubmed,0,11,"bioinformatic, in silico",0.992032119,0.001593527,0.001593557,0.001593612,0.001593573,0.001593612,Drug discovery,0.77561057,TRUE,57.72727273,0.686684396,1128.818182,0.995116404,16,0.881782826,,,0.854527875
5087,Retrospective analysis of the effect of current clinical medications and clinicopathological factors on viral shedding in COVID-19 patients.,Biomed Rep,33194192,11/17/20,pubmed,0,11,correlation analysis,0.030242217,0.224074992,0.001438086,0.001438123,0.001438123,0.741368459,Clinics,0.9550394,TRUE,54,0.661574618,9.545454545,0.345664972,0,0.403234768,,,0.470158119
5088,Gastrointestinal pathophysiology of SARS-CoV2 - a literature review.,J Community Hosp Intern Med Perspect,33194122,11/17/20,pubmed,0,5,microbiom,0.176616879,0.152499343,0.001272641,0.001272718,0.278809331,0.389529088,Clinics,0.9694797,TRUE,17.8,0.268229328,7.4,0.305392026,2,0.618927094,,,0.397516149
5089,Serum triglyceride level and hypertension are highly associated with the recovery of COVID-19 patients.,Am J Transl Res,33194061,11/17/20,pubmed,0,7,logistic regression,0.093720682,0.063285002,0.001751173,0.001751299,0.001751261,0.837740583,Clinics,0.9352164,TRUE,20.14285714,0.298967159,,,1,0.537564047,,,0.418265603
5090,The Role of Public Trust and Media in the Psychological and Behavioral Responses to the COVID-19 Pandemic.,Iran J Psychiatry,33193767,11/17/20,pubmed,0,5,logistic regression,0.018929956,0.001059346,0.001059356,0.001059384,0.958201004,0.019690953,Healthcare,0.9864399,TRUE,29.4,0.423650195,5.2,0.259700294,3,0.667819001,,,0.45038983
5091,Identifying Effective Antiviral Drugs Against SARS-CoV-2 by Drug Repositioning Through Virus-Drug Association Prediction.,Front Genet,33193695,11/17/20,pubmed,0,8,"classifier, genome sequences",0.852115198,0.061791896,0.08243272,0.001220055,0.001220033,0.001220098,Drug discovery,0.62882465,TRUE,13.375,0.201991465,40.375,0.635670324,0,0.403234768,,,0.413632186
5092,TMPRSS2 Correlated With Immune Infiltration Serves as a Prognostic Biomarker in Prostatic Adenocarcinoma: Implication for the COVID-2019.,Front Genet,33193689,11/17/20,pubmed,0,8,dataset,0.71004038,0.00131045,0.001310428,0.001310381,0.001310369,0.284717992,Drug discovery,0.62171996,TRUE,55.125,0.668934381,17.75,0.458322184,0,0.403234768,,,0.510163777
5093,0,Front Genet,33193683,11/17/20,pubmed,0,8,"genome sequences, genomes",0.002183468,0.989083368,0.002183336,0.002183368,0.002183226,0.002183235,Genomics,0.65222865,TRUE,162.875,0.953305708,182.125,0.907679957,1,0.537564047,,,0.799516571
5094,Underlying Mechanisms and Candidate Drugs for COVID-19 Based on the Connectivity Map Database.,Front Genet,33193639,11/17/20,pubmed,0,2,transcriptom,0.803094797,0.083775947,0.001085335,0.001085358,0.001085353,0.109873211,Drug discovery,0.9631065,TRUE,41.5,0.553219123,11.5,0.378378378,2,0.618927094,,,0.516841532
5095,"Positive Selection of ORF1ab, ORF3a, and ORF8 Genes Drives the Early Evolutionary Trends of SARS-CoV-2 During the 2020 COVID-19 Pandemic.",Front Microbiol,33193132,11/17/20,pubmed,0,6,genomes,0.26288425,0.650959593,0.00218321,0.07960636,0.00218333,0.002183257,Genomics,0.6051032,TRUE,31.33333333,0.448388892,27.33333333,0.54856837,7,0.785110192,,,0.594022485
5096,Neurological Predictors of Clinical Outcomes in Hospitalized Patients With COVID-19.,Front Neurol,33193048,11/17/20,pubmed,0,24,logistic regression,0.001310303,0.001310322,0.001310319,0.001310317,0.001310359,0.99344838,Clinics,0.8693533,TRUE,32.70833333,0.463170264,28.04166667,0.554388547,2,0.618927094,,,0.545495302
5097,Anxiety and Depression Symptoms in COVID-19 Isolated Patients and in Their Relatives.,Front Psychiatry,33192727,11/17/20,pubmed,0,13,logistic regression,0.001371261,0.001371309,0.001371326,0.001371281,0.656813608,0.337701215,Healthcare,0.9300741,TRUE,106,0.881192405,65.53846154,0.740834894,1,0.537564047,,,0.719863782
5098,Mental Violence: The COVID-19 Nightmare.,Front Psychiatry,33192719,11/17/20,pubmed,0,8,logistic regression,0.0016845,0.001684572,0.001684506,0.225907905,0.76735398,0.001684536,Healthcare,0.9773513,TRUE,54.5,0.665161729,13.625,0.406408884,0,0.403234768,,,0.491601794
5099,"Association Between Depression, Health Beliefs, and Face Mask Use During the COVID-19 Pandemic.",Front Psychiatry,33192697,11/17/20,pubmed,0,7,"logistic regression, digital health",0.001203409,0.001203433,0.00120344,0.001203481,0.993982793,0.001203444,Healthcare,0.9868167,TRUE,79.14285714,0.802956274,54.14285714,0.698086701,6,0.764429903,,,0.755157626
5100,Beta-Adrenergic Receptor Stimulation Modulates the Cellular Proarrhythmic Effects of Chloroquine and Azithromycin.,Front Physiol,33192602,11/17/20,pubmed,0,2,in silico,0.668967059,0.001461988,0.001461896,0.165412411,0.00146202,0.161234625,Drug discovery,0.62699306,TRUE,11.5,0.17416043,6.5,0.288132192,1,0.537564047,,,0.333285556
5101,"Prognostic Value of Pro-Inflammatory Neutrophils and C-Reactive Protein in Cancer Patient With Coronavirus Disease 2019: A Multi-Center, Retrospective Study.",Front Pharmacol,33192519,11/17/20,pubmed,0,12,logistic regression,0.001203418,0.001203414,0.001203387,0.018929859,0.001203424,0.976256498,Clinics,0.97997165,TRUE,82.66666667,0.816871792,15.41666667,0.428819909,1,0.537564047,,,0.594418583
5102,Designing of Nucleocapsid Protein Based Novel Multi-epitope Vaccine Against SARS-COV-2 Using Immunoinformatics Approach.,Int J Pept Res Ther,33192207,11/17/20,pubmed,0,4,in silico,0.969450074,0.025148513,0.001350348,0.001350412,0.001350323,0.001350329,Drug discovery,0.8476753,TRUE,18.25,0.273609994,5.25,0.260971367,1,0.537564047,,,0.357381803
5103,The ensemble deep learning model for novel COVID-19 on CT images.,Appl Soft Comput,33192206,11/17/20,pubmed,0,6,"deep learning, neural network, classifier, network model, transfer learning",0.023734649,0.00131035,0.916838244,0.055495905,0.001310409,0.001310443,Imaging,0.90218043,TRUE,15.5,0.234028078,4.166666667,0.233074659,6,0.764429903,,,0.41051088
5104,"AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans.",Multimed Tools Appl,33192159,11/17/20,pubmed,0,6,"deep learning, dataset",0.001010953,0.001010958,0.791456871,0.204499224,0.001010991,0.001011004,Imaging,0.26098216,FALSE,5.5,0.077246583,1.5,0.138747659,0,0.403234768,,,0.20640967
5105,The Privacy Implications of Using Data Technologies in a Pandemic.,J Indian Inst Sci,33191991,11/17/20,pubmed,0,1,digital health,0.002490448,0.002490484,0.00249053,0.987547627,0.002490489,0.002490423,Epidemiology,0.6493223,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
5106,Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans.,Int J Comput Assist Radiol Surg,33191476,11/17/20,pubmed,0,3,"neural network, transfer learning, dataset",0.001059376,0.001059352,0.994703249,0.001059342,0.001059349,0.001059332,Imaging,0.47996163,FALSE,15.33333333,0.230997588,4,0.231469093,3,0.667819001,,,0.376761894
5107,"Knowledge level and factors influencing prevention of COVID-19 pandemic among residents of Dessie and Kombolcha City administrations, North-East Ethiopia: a population-based cross-sectional study.",BMJ Open,33191269,11/17/20,pubmed,0,5,logistic regression,0.001291352,0.001291242,0.001291206,0.001291248,0.993543725,0.001291227,Healthcare,0.7514043,TRUE,4.2,0.05584761,0.4,0.075796093,3,0.667819001,,,0.266487568
5108,Growth of Ambulatory Virtual Visits and Differential Use by Patient Sociodemographics at One Urban Academic Medical Center During the COVID-19 Pandemic: Retrospective Analysis.,JMIR Med Inform,33191247,11/17/20,pubmed,0,6,logistic regression,0.000740301,0.000740291,0.000740324,0.000740318,0.818253751,0.178785015,Healthcare,0.9886968,TRUE,48.83333333,0.621003154,45.33333333,0.659820712,0,0.403234768,,,0.561352878
5109,COVID-19 Infection in Men on Testosterone Replacement Therapy.,J Sex Med,33191186,11/17/20,pubmed,0,10,logistic regression,0.002130696,0.002130698,0.002130655,0.002130696,0.002130769,0.989346485,Clinics,0.8648551,TRUE,77.2,0.795472818,35.5,0.608241905,2,0.618927094,,,0.674213939
5110,"Network pharmacology, molecular docking integrated surface plasmon resonance technology reveals the mechanism of Toujie Quwen Granules against coronavirus disease 2019 pneumonia.",Phytomedicine,33191068,11/17/20,pubmed,0,7,genomes,0.985699265,0.011745641,0.000638786,0.000638774,0.000638764,0.00063877,Drug discovery,0.993521,TRUE,27.14285714,0.396932402,21,0.492239765,1,0.537564047,,,0.475578738
5111,"How loneliness is talked about in social media during COVID-19 pandemic: Text mining of 4,492 Twitter feeds.",J Psychiatr Res,33190839,11/17/20,pubmed,0,2,text mining,0.023050571,0.001593536,0.045278088,0.823838647,0.104645676,0.001593482,Epidemiology,0.68851525,TRUE,20.5,0.304966294,6,0.280037463,3,0.667819001,,,0.417607586
5112,Clinical and laboratory characteristics of patients with novel coronavirus disease-2019 infection and deep venous thrombosis.,J Vasc Surg Venous Lymphat Disord,33190816,11/17/20,pubmed,0,10,logistic regression,0.000898086,0.000898103,0.042025954,0.000898094,0.000898083,0.954381679,Clinics,0.89750546,TRUE,92.3,0.849712413,45.8,0.662697351,1,0.537564047,,,0.683324604
5113,Mortality and other adverse outcomes in patients with type 2 diabetes mellitus admitted for COVID-19 in association with glucose-lowering drugs: a nationwide cohort study.,BMC Med,33190637,11/17/20,pubmed,0,25,logistic regression,0.159615761,0.000898084,0.000898083,0.063512199,0.000898136,0.774177737,Clinics,0.90559804,TRUE,30.36,0.436390624,13.08,0.400722505,11,0.840175319,,,0.559096149
5114,Revealing the Inhibition Mechanism of RNA-Dependent RNA Polymerase (RdRp) of SARS-CoV-2 by Remdesivir and Nucleotide Analogues: A Molecular Dynamics Simulation Study.,J Phys Chem B,33190493,11/17/20,pubmed,0,3,molecular dynamics simulation,0.995885385,0.000822925,0.00082292,0.000822936,0.000822918,0.000822916,Drug discovery,0.90972614,TRUE,23,0.34225988,0.333333333,0.073187048,4,0.707574542,,,0.37434049
5115,Bioinformatic analysis of SMN1-ACE/ACE2 interactions hinted at a potential protective effect of spinal muscular atrophy against COVID-19-induced lung injury.,Brief Bioinform,33190150,11/16/20,pubmed,0,13,bioinformatic,0.399452533,0.141129446,0.002080593,0.002080668,0.098168051,0.357088708,Drug discovery,0.2565173,FALSE,43.38461538,0.571463912,28.15384615,0.555258229,0,0.403234768,,,0.509985636
5116,Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography.,Eur J Radiol,33190102,11/16/20,pubmed,0,6,"machine learning, deep learning, classifier, dataset",0.000898068,0.00089812,0.76989345,0.000898133,0.000898129,0.2265141,Imaging,0.27988255,FALSE,3.333333333,0.04044777,1.166666667,0.124565159,3,0.667819001,,,0.277610644
5117,Culinary spice bioactives as potential therapeutics against SARS-CoV-2: Computational investigation.,Comput Biol Med,33190011,11/16/20,pubmed,0,5,computational,0.944974738,0.001237146,0.001237082,0.050076914,0.001237065,0.001237055,Drug discovery,0.98332787,TRUE,6.8,0.09629538,1.8,0.150120417,0,0.403234768,,,0.216550188
5118,Early transmission of SARS-CoV-2 in South Africa: An epidemiological and phylogenetic report.,Int J Infect Dis,33189939,11/16/20,pubmed,0,19,"whole genome, genomes",0.001511917,0.299187525,0.001511821,0.694764927,0.001511831,0.001511979,Epidemiology,0.37334502,FALSE,47.21052632,0.607644258,55.68421053,0.705311747,2,0.618927094,,,0.643961033
5119,Major reduction of NKT cells in patients with severe COVID-19 pneumonia.,Clin Immunol,33189887,11/16/20,pubmed,0,12,logistic regression,0.379305228,0.00208057,0.002080575,0.002080569,0.002080566,0.612372492,Clinics,0.841751,TRUE,45.58333333,0.591811491,17.91666667,0.459860851,2,0.618927094,,,0.556866479
5120,Changed transmission epidemiology of COVID-19 at early stage: A nationwide population-based piecewise mathematical modelling study.,Travel Med Infect Dis,33189886,11/16/20,pubmed,0,16,mathematical model,0.013548912,0.013549639,0.013548986,0.932254352,0.013549089,0.013549021,Epidemiology,0.56197596,TRUE,110.375,0.89040757,43.125,0.649852823,0,0.403234768,,,0.64783172
5121,Repurposed drugs and nutraceuticals targeting envelope protein: A possible therapeutic strategy against COVID-19.,Genomics,33189776,11/16/20,pubmed,0,6,artificial intelligence,0.726245063,0.002080599,0.198811051,0.002080619,0.068701836,0.002080832,Drug discovery,0.69705904,TRUE,10,0.15214299,5.166666667,0.25849612,1,0.537564047,,,0.316067719
5122,The flexibility of ACE2 in the context of SARS-CoV-2 infection.,Biophys J,33189680,11/16/20,pubmed,0,10,molecular dynamics simulation,0.874817298,0.099253717,0.001220033,0.001220099,0.001220096,0.022268757,Drug discovery,0.5385368,TRUE,33.9,0.475168532,,,0,0.403234768,,,0.43920165
5123,Running behavior and symptoms of respiratory tract infection during the COVID-19 pandemic: A large prospective Dutch cohort study.,J Sci Med Sport,33189596,11/16/20,pubmed,0,4,logistic regression,0.001141321,0.001141342,0.001141398,0.001141416,0.880524814,0.11490971,Healthcare,0.9297757,TRUE,57.5,0.685447461,93.75,0.810409419,0,0.403234768,,,0.633030549
5124,Identifying patients with symptoms suspicious for COVID-19 at elevated risk of adverse events: The COVAS score.,Am J Emerg Med,33189516,11/16/20,pubmed,0,10,logistic regression,0.001291195,0.001291209,0.110386219,0.001291213,0.001291261,0.884448903,Clinics,0.85827714,TRUE,116.8,0.902096605,227.8,0.929020605,0,0.403234768,,,0.744783992
5125,Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF.,Sci Total Environ,33189385,11/16/20,pubmed,0,9,dataset,0.063101205,0.001593548,0.001593678,0.905870181,0.00159352,0.026247868,Epidemiology,0.52069163,TRUE,159.8888889,0.951326613,237.6666667,0.933235215,1,0.537564047,,,0.807375292
5126,Pandemic-related mental health risk among front line personnel.,J Psychiatr Res,33189356,11/16/20,pubmed,0,7,logistic regression,0.001330092,0.025131965,0.001330054,0.001330051,0.969547723,0.001330115,Healthcare,0.8849062,TRUE,63.85714286,0.727194013,93.85714286,0.810543216,5,0.739490092,,,0.759075774
5127,Cost-effectiveness of public health strategies for COVID-19 epidemic control in South Africa: a microsimulation modelling study.,Lancet Glob Health,33188729,11/15/20,pubmed,0,19,simulation model,0.023347668,0.000580178,0.000580211,0.69283433,0.248536543,0.03412107,Epidemiology,0.35089308,FALSE,89.10526316,0.83969324,122.4210526,0.854361788,1,0.537564047,,,0.743873025
5128,Nonstructural Protein 1 of SARS-CoV-2 Is a Potent Pathogenicity Factor Redirecting Host Protein Synthesis Machinery toward Viral RNA.,Mol Cell,33188728,11/15/20,pubmed,0,11,transcriptom,0.92038806,0.070878822,0.002183336,0.002183263,0.002183254,0.002183265,Drug discovery,0.30681843,FALSE,45,0.587049292,78.18181818,0.773815895,0,0.403234768,,,0.588033318
5129,Vitamin D Deficiency Is Associated with COVID-19 Incidence and Disease Severity in Chinese People [corrected].,J Nutr,33188401,11/15/20,pubmed,0,5,logistic regression,0.020755751,0.001291216,0.001291209,0.001291263,0.069459983,0.905910578,Clinics,0.9644557,TRUE,77.8,0.797699301,12.2,0.387476585,11,0.840175319,,,0.675117069
5130,Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients.,Sci Rep,33188225,11/15/20,pubmed,0,35,"predictive model, logistic regression, prediction model",0.001438109,0.001438097,0.113783928,0.001438142,0.001438099,0.880463625,Clinics,0.91890883,TRUE,56.2,0.676294143,44.6,0.657144769,2,0.618927094,,,0.650788669
5131,Mathematical Modeling and Robustness Analysis to Unravel COVID-19 Transmission Dynamics: The Italy Case.,Biology (Basel),33187109,11/15/20,pubmed,0,5,"bayes, computational, mathematical model",0.001538125,0.042037236,0.001538217,0.895824907,0.0015381,0.057523414,Epidemiology,0.29712898,FALSE,24.4,0.360442823,7.2,0.302047097,0,0.403234768,,,0.355241562
5132,Lymphocyte subsets early predict mortality in a large series of hospitalized COVID-19 patients in Spain.,Clin Exp Immunol,33187018,11/14/20,pubmed,0,6,logistic regression,0.001059441,0.001059355,0.001059349,0.001059356,0.001059349,0.994703151,Clinics,0.47624677,FALSE,106,0.881192405,127.6666667,0.860917849,1,0.537564047,,,0.759891434
5133,"Anticoagulation in COVID-19: Effect of Enoxaparin, Heparin, and Apixaban on Mortality.",Thromb Haemost,33186991,11/14/20,pubmed,0,12,logistic regression,0.001126828,0.0011268,0.016840719,0.001126847,0.0011268,0.978652007,Clinics,0.43315718,FALSE,29.25,0.421671099,11.66666667,0.38065293,15,0.874313229,,,0.558879086
5134,The effect of anticoagulation on clinical outcomes in novel Coronavirus (COVID-19) pneumonia in a U.S. cohort.,Thromb Res,33186849,11/14/20,pubmed,0,10,logistic regression,0.123005979,0.001438094,0.001438193,0.001438108,0.001438125,0.871241501,Clinics,0.41765863,FALSE,7.7,0.111880759,0.4,0.075796093,5,0.739490092,,,0.309055648
5135,A clade of SARS-CoV-2 viruses associated with lower viral loads in patient upper airways.,EBioMedicine,33186810,11/14/20,pubmed,0,11,"sequencing, whole-genome, genome sequences",0.00118727,0.750419538,0.001187291,0.001187324,0.001187298,0.244831279,Genomics,0.6522058,TRUE,68.27272727,0.755148741,,,10,0.828199272,,,0.791674006
5136,Utility of a mainstreamed genetic testing pathway in breast and ovarian cancer patients during the COVID-19 pandemic.,Eur J Med Genet,33186762,11/14/20,pubmed,0,15,sequencing,0.001438232,0.232834785,0.064584943,0.358877219,0.00143821,0.340826612,Epidemiology,0.53663033,TRUE,32.06666667,0.455934195,19.6,0.476786192,0,0.403234768,,,0.445318385
5137,A model of COVID-19 propagation based on a gamma subordinated negative binomial branching process.,J Theor Biol,33186594,11/14/20,pubmed,0,3,bayes,0.001861717,0.001861717,0.001861753,0.990691356,0.001861748,0.00186171,Epidemiology,0.42213205,FALSE,9.333333333,0.139278867,0.333333333,0.073187048,1,0.537564047,,,0.250009988
5138,"Epidemiology, risk factors and clinical course of SARS-CoV-2 infected patients in a Swiss university hospital: An observational retrospective study.",PLoS One,33186355,11/14/20,pubmed,0,21,logistic regression,0.001310334,0.001310374,0.001310324,0.001310358,0.001310337,0.993448272,Clinics,0.7196387,TRUE,26.04761905,0.382460263,16.66666667,0.444674873,3,0.667819001,,,0.498318046
5139,Evaluating sources of bias in observational studies of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker use during COVID-19: beyond confounding.,J Hypertens,33186321,11/14/20,pubmed,0,5,structural model,0.162176082,0.001751209,0.001751279,0.726213046,0.00175125,0.106357134,Epidemiology,0.80795276,TRUE,68.6,0.75688045,39.6,0.631255017,2,0.618927094,,,0.669020854
5140,Molecular epidemiology in the HIV and SARS-CoV-2 pandemics.,Curr Opin HIV AIDS,33186230,11/14/20,pubmed,0,5,"sequencing, whole genome, dataset",0.001156316,0.473691197,0.001156317,0.521683645,0.001156257,0.001156267,Epidemiology,0.83501756,TRUE,73.8,0.781124374,122.4,0.854294889,0,0.403234768,,,0.679551343
5141,Adaptation of advanced clinical virology assays from HIV-1 to SARS-CoV-2.,Curr Opin HIV AIDS,33186228,11/14/20,pubmed,0,3,sequencing,0.276950385,0.659705756,0.001538168,0.001538136,0.001538101,0.058729455,Genomics,0.33307552,FALSE,157,0.949347517,405.3333333,0.9717019,0,0.403234768,,,0.774761395
5142,An overview of cancer health disparities: new approaches and insights and why they matter.,Carcinogenesis,33185680,11/14/20,pubmed,0,4,microbiom,0.142092949,0.103867594,0.00141511,0.001415229,0.423553901,0.327655217,Healthcare,0.9622914,TRUE,58.5,0.693240151,139.25,0.873494782,0,0.403234768,,,0.656656567
5143,Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2.,J Am Med Inform Assoc,33185672,11/14/20,pubmed,0,21,"ensemble learning, prediction model",0.00125461,0.001254638,0.598212641,0.142704364,0.001254664,0.255319084,Clinics,0.23705047,FALSE,65.28571429,0.735852557,,,0,0.403234768,,,0.569543662
5144,""Depression, Anxiety and Stress" in a Cohort of Registered Practicing Ophthalmic Surgeons, Post Lockdown during COVID-19 Pandemic in India.",Ophthalmic Epidemiol,33185487,11/14/20,pubmed,0,6,correlation analysis,0.001156228,0.001156255,0.001156236,0.001156277,0.872747669,0.122627336,Healthcare,0.9991543,TRUE,9.666666667,0.144968767,2,0.164302917,0,0.403234768,,,0.237502151
5145,Residual Lung Injury in Patients Recovering From COVID-19 Critical Illness: A Prospective Longitudinal Point-of-Care Lung Ultrasound Study.,J Ultrasound Med,33185316,11/14/20,pubmed,0,15,predictive model,0.001098833,0.021211264,0.292243136,0.001098852,0.001098848,0.683249066,Clinics,0.9328519,TRUE,117.3333333,0.903395386,172.3333333,0.901391491,1,0.537564047,,,0.780783641
5146,Mental health problems and correlates among 746 217 college students during the coronavirus disease 2019 outbreak in China.,Epidemiol Psychiatr Sci,33185174,11/14/20,pubmed,0,11,logistic regression,0.00086303,0.000863046,0.000863047,0.000863071,0.987003395,0.009544412,Healthcare,0.5335187,TRUE,11.18181818,0.168223143,0.818181818,0.101752743,4,0.707574542,,,0.325850143
5147,A review of mechanistic models of viral dynamics in bat reservoirs for zoonotic disease.,Pathog Glob Health,33185145,11/14/20,pubmed,0,4,simulation model,0.001330106,0.62662693,0.001330053,0.368052779,0.001330074,0.001330058,Genomics,0.26808208,FALSE,13.25,0.199950523,5,0.257024351,0,0.403234768,,,0.286736547
5148,Non-steroidal anti-inflammatory drugs and susceptibility to COVID-19.,Arthritis Rheumatol,33185016,11/14/20,pubmed,0,24,dataset,0.127698192,0.001861723,0.00186182,0.001861833,0.044603129,0.822113303,Clinics,0.9054526,TRUE,69.625,0.76164265,67.79166667,0.74765855,2,0.618927094,,,0.709409431
5149,Navigating the risks of flying during COVID-19: a review for safe air travel.,J Travel Med,33184655,11/14/20,pubmed,0,5,digital health,0.072209866,0.001220034,0.001220036,0.834484705,0.049454874,0.041410486,Epidemiology,0.83507967,TRUE,11.8,0.177994929,5,0.257024351,4,0.707574542,,,0.380864607
5150,Mathematical modelling of the dynamics and containment of COVID-19 in Ukraine.,Sci Rep,33184338,11/14/20,pubmed,0,3,mathematical model,0.001415119,0.001415115,0.035333332,0.959006208,0.001415126,0.0014151,Epidemiology,0.20874873,FALSE,49,0.624281032,26.33333333,0.540607439,2,0.618927094,,,0.594605188
5151,Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic.,Sci Data,33184280,11/14/20,pubmed,0,6,dataset,0.001291296,0.043019327,0.001291238,0.951815589,0.001291312,0.001291238,Epidemiology,0.33419615,FALSE,21.33333333,0.31548024,6.666666667,0.290607439,7,0.785110192,,,0.463732624
5152,Collider bias undermines our understanding of COVID-19 disease risk and severity.,Nat Commun,33184277,11/14/20,pubmed,0,14,dataset,0.057372964,0.129549337,0.002183373,0.208029766,0.39156268,0.21130188,Healthcare,0.23445845,FALSE,145.3571429,0.939575731,305.7857143,0.955980733,4,0.707574542,,,0.867710335
5153,Identification of a repurposed drug as an inhibitor of Spike protein of human coronavirus SARS-CoV-2 by computational methods.,J Biosci,33184246,11/14/20,pubmed,0,4,"molecular dynamics simulation, computational, in silico",0.993248406,0.001350339,0.001350324,0.001350326,0.001350306,0.001350299,Drug discovery,0.89531446,TRUE,31,0.445111015,27.5,0.549906342,2,0.618927094,,,0.537981484
5154,"The SKI complex is a broad-spectrum, host-directed antiviral drug target for coronaviruses, influenza, and filoviruses.",Proc Natl Acad Sci U S A,33184176,11/14/20,pubmed,0,14,in silico,0.992809328,0.00143829,0.001438081,0.001438097,0.001438112,0.001438092,Drug discovery,0.75992924,TRUE,124.8571429,0.913971179,262.6428571,0.943069307,1,0.537564047,,,0.798201511
5155,Analysis of genomic distributions of SARS-CoV-2 reveals a dominant strain type with strong allelic associations.,Proc Natl Acad Sci U S A,33184173,11/14/20,pubmed,0,10,genomes,0.060765163,0.892117529,0.001486541,0.001486459,0.0426578,0.001486508,Genomics,0.5479104,TRUE,56.5,0.678891706,154.7,0.887476585,6,0.764429903,,,0.776932732
5156,Artificial Intelligence in Thoracic Radiology. A Challenge in COVID-19 Times?,Arch Bronconeumol,33183845,11/14/20,pubmed,0,3,artificial intelligence,0.025060408,0.025060375,0.874697593,0.025060923,0.025060465,0.025060236,Epidemiology,0.5177689,TRUE,141.3333333,0.93561754,66.66666667,0.744781911,0,0.403234768,,,0.694544739
5157,Prevalence and severity of malnutrition in hospitalized COVID-19 patients.,Clin Nutr ESPEN,33183539,11/14/20,pubmed,0,13,logistic regression,0.001511861,0.001511868,0.00151195,0.001511872,0.113562727,0.880389722,Clinics,0.9101004,TRUE,111.3846154,0.892572206,101,0.823989831,12,0.850299401,,,0.85562048
5158,Effect of Covid-19 on food security: A cross-sectional survey.,Clin Nutr ESPEN,33183533,11/14/20,pubmed,0,5,logistic regression,0.001203395,0.001203401,0.001203397,0.001203427,0.993982937,0.001203443,Healthcare,0.97096664,TRUE,7.2,0.103716989,0,0.055525823,3,0.667819001,,,0.275687271
5159,Performance of Nucleic Acid Amplification Tests for Detection of Severe Acute Respiratory Syndrome Coronavirus 2 in Prospectively Pooled Specimens.,Emerg Infect Dis,33183494,11/14/20,pubmed,0,11,simulation model,0.002130768,0.750395816,0.079878362,0.163333687,0.002130665,0.002130702,Genomics,0.1995849,FALSE,79.54545455,0.805059064,64.81818182,0.738025154,1,0.537564047,,,0.693549422
5160,Understanding the influence of the COVID-19 pandemic on hospital-based mortality in Burundi: a cross-sectional study comparing two time periods.,Epidemiol Infect,33183401,11/14/20,pubmed,0,10,logistic regression,0.001901666,0.001901725,0.001901683,0.261025605,0.001901782,0.731367539,Clinics,0.73255247,TRUE,6.1,0.086276208,0.7,0.096601552,0,0.403234768,,,0.195370842
5161,Development and validation of a simple risk score for diagnosing COVID-19 in the emergency room.,Epidemiol Infect,33183384,11/14/20,pubmed,0,3,"logistic regression, prediction model, dataset",0.001022616,0.001022621,0.380136886,0.06791056,0.048889009,0.501018308,Clinics,0.9292501,TRUE,1.333333333,0.01366813,0,0.055525823,0,0.403234768,,,0.15747624
5162,Community movement and COVID-19: a global study using Google's Community Mobility Reports.,Epidemiol Infect,33183366,11/14/20,pubmed,0,2,bayes,0.001593525,0.001593558,0.061289803,0.932335847,0.001593623,0.001593644,Epidemiology,0.19738516,FALSE,18,0.271569052,5,0.257024351,1,0.537564047,,,0.355385817
5163,"Identification of natural inhibitors against prime targets of SARS-CoV-2 using molecular docking, molecular dynamics simulation and MM-PBSA approaches.",J Biomol Struct Dyn,33183178,11/14/20,pubmed,0,6,"molecular dynamics simulation, computational",0.994436774,0.001112652,0.001112625,0.001112705,0.001112624,0.001112618,Drug discovery,0.93274796,TRUE,18.33333333,0.274908776,18.83333333,0.468624565,2,0.618927094,,,0.454153478
5164,Missed Opportunities of Flu Vaccination in Italian Target Categories: Insights from the Online EPICOVID 19 Survey.,Vaccines (Basel),33182426,11/14/20,pubmed,0,16,logistic regression,0.001823399,0.001823336,0.001823423,0.001823347,0.896236972,0.096469523,Healthcare,0.6939781,TRUE,121.9375,0.909580061,78.5625,0.77455178,0,0.403234768,,,0.695788869
5165,Epidemiological Impact of SARS-CoV-2 Vaccination: Mathematical Modeling Analyses.,Vaccines (Basel),33182403,11/14/20,pubmed,0,7,mathematical model,0.002898492,0.002898432,0.002898334,0.985507852,0.002898449,0.002898442,Epidemiology,0.4815683,FALSE,58.71428571,0.694353392,115.1428571,0.844728392,2,0.618927094,,,0.719336293
5166,Post-COVID-19 Action: Guarding Africa's Crops against Viral Epidemics Requires Research Capacity Building That Unifies a Trio of Transdisciplinary Interventions.,Viruses,33182262,11/14/20,pubmed,0,1,"sequencing, whole-genome",0.138317506,0.512641151,0.144507564,0.201227712,0.001653041,0.001653026,Genomics,0.74459267,TRUE,11,0.167171748,5,0.257024351,0,0.403234768,,,0.275810289
5167,Analysis of the potential impact of genomic variants in global SARS-CoV-2 genomes on molecular diagnostic assays.,Int J Infect Dis,33181329,11/13/20,pubmed,0,12,"sequencing, genomes",0.059572344,0.835959588,0.053332991,0.048760552,0.001187265,0.001187261,Genomics,0.7846986,TRUE,14.41666667,0.217700538,6.5,0.288132192,2,0.618927094,,,0.374919941
5168,Molecular docking simulation reveals ACE2 polymorphisms that may increase the affinity of ACE2 with the SARS-CoV-2 Spike protein.,Biochimie,33181224,11/13/20,pubmed,0,6,in silico,0.745200508,0.245368598,0.002357712,0.002357723,0.002357685,0.002357773,Drug discovery,0.5159816,TRUE,163.1666667,0.953553095,210.3333333,0.922531442,6,0.764429903,,,0.88017148
5169,"A Commentary on the Review Entitled, "A Scoping Review of the Human Milk Microbiome" by Groer et al.",J Hum Lact,33181051,11/13/20,pubmed,0,1,microbiom,0.019531992,0.902342773,0.019531601,0.019533167,0.019529279,0.019531188,Genomics,0.677795,TRUE,263,0.986641103,345,0.964878245,1,0.537564047,,,0.829694465
5170,Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.,PLoS One,33180877,11/13/20,pubmed,0,5,"deep learning, neural network, ensemble learning",0.000956319,0.000956331,0.995218303,0.00095637,0.000956374,0.000956302,Imaging,0.23835456,FALSE,110.8,0.891273424,82.6,0.784653465,2,0.618927094,,,0.764951328
5171,Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking.,PLoS One,33180803,11/13/20,pubmed,0,7,"bayes, machine learning, computational",0.623115003,0.001371277,0.255574494,0.117196559,0.001371275,0.001371391,Drug discovery,0.923142,TRUE,18.57142857,0.278619581,4.857142857,0.249933101,1,0.537564047,,,0.355372243
5172,"Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency.",PLoS One,33180787,11/13/20,pubmed,0,39,"machine learning, predictive model, dataset",0.001098809,0.001098824,0.393919126,0.001098857,0.001098822,0.601685563,Clinics,0.7116498,TRUE,57.53846154,0.685571155,37.61538462,0.619413968,2,0.618927094,,,0.641304072
5173,Age-determined expression of priming protease TMPRSS2 and localization of SARS-CoV-2 in lung epithelium.,J Clin Invest,33180746,11/13/20,pubmed,0,19,sequencing,0.689552364,0.089002247,0.001823369,0.001823404,0.077137508,0.140661108,Drug discovery,0.3719725,FALSE,77.05263158,0.794854351,101.3157895,0.824324324,7,0.785110192,,,0.801429622
5174,The Relationships of Deteriorating Depression and Anxiety With Longitudinal Behavioral Changes in Google and YouTube Use During COVID-19: Observational Study.,JMIR Ment Health,33180743,11/13/20,pubmed,0,5,machine learning,0.000734175,0.000734163,0.182543778,0.113930168,0.701323514,0.000734203,Healthcare,0.4120476,FALSE,5.2,0.071927763,0,0.055525823,0,0.403234768,,,0.176896118
5175,Videoconferencing-Based Telemental Health: Important Questions for the COVID-19 Era From Clinical and Patient-Centered Perspectives.,JMIR Ment Health,33180739,11/13/20,pubmed,0,3,digital health,0.001593513,0.001593533,0.001593653,0.334985424,0.474429739,0.185804139,Healthcare,0.84012705,TRUE,22.33333333,0.330261612,32.33333333,0.588239229,0,0.403234768,,,0.440578536
5176,Can machine learning optimize the efficiency of the operating room in the era of COVID-19?,Can J Surg,33180692,11/13/20,pubmed,0,2,"machine learning, artificial intelligence",0.002562626,0.002562628,0.602323214,0.387426132,0.002562722,0.002562679,Epidemiology,0.85135067,TRUE,4,0.054734368,0.5,0.087101953,0,0.403234768,,,0.181690363
5177,"Adaptation of Clinical Laboratories to COVID 19 Pandemic: Changes in Test Panels, Overcoming Problems and Preparation Suggestions for Future Pandemics Adaptation of Clinical Laboratories to COVID 19 Pandemic.",Clin Lab,33180431,11/13/20,pubmed,0,3,data mining,0.001622754,0.001622856,0.001622791,0.629465043,0.001622801,0.364043755,Epidemiology,0.94126606,TRUE,11.33333333,0.170635166,1.666666667,0.145036125,0,0.403234768,,,0.239635353
5178,"COVID-19 Pandemic in the Midst of Civil War: Planetary Health and Plant Omics Field Notes from Aden, Yemen.",OMICS,33180003,11/13/20,pubmed,0,4, omics,0.092457092,0.003101567,0.129888824,0.768349383,0.003101647,0.003101487,Epidemiology,0.38710982,FALSE,24.25,0.358092646,12,0.386740701,0,0.403234768,,,0.382689372
5179,Decoding Asymptomatic COVID-19 Infection and Transmission.,J Phys Chem Lett,33179934,11/13/20,pubmed,0,5,"artificial intelligence, sequence alignment, network analysis",0.144377261,0.736879451,0.112501265,0.002080713,0.002080661,0.002080649,Genomics,0.42611724,FALSE,37.75,0.516296617,10.75,0.366269735,9,0.814309525,,,0.565625292
5180,Reprofiling of approved drugs against SARS-CoV-2 main protease: an in-silico study.,J Biomol Struct Dyn,33179586,11/13/20,pubmed,0,8,"molecular dynamics simulation, in-silico",0.992309579,0.001538094,0.001538086,0.001538087,0.00153807,0.001538083,Drug discovery,0.86897767,TRUE,56.125,0.675737522,23,0.513513514,1,0.537564047,,,0.575605028
5181,0,J Biomol Struct Dyn,33179568,11/13/20,pubmed,0,2,"in silico, in-silico",0.989836305,0.002032759,0.002032743,0.002032761,0.002032716,0.002032717,Drug discovery,0.9786912,TRUE,55.5,0.671346404,18.5,0.46554723,4,0.707574542,,,0.614822725
5182,"COVID-19, AI enthusiasts, and toy datasets: radiology without radiologists.",Eur Radiol,33179164,11/13/20,pubmed,0,2,dataset,0.034962074,0.034961954,0.825189662,0.034962472,0.034961915,0.034961923,Imaging,0.6340874,TRUE,104.5,0.878471148,164,0.895437517,1,0.537564047,,,0.770490904
5183,PRactice of VENTilation in Patients with Novel Coronavirus Disease (PRoVENT-COVID): rationale and protocol for a national multicenter observational study in The Netherlands.,Ann Transl Med,33178783,11/13/20,pubmed,0,13,dataset,0.000926347,0.000926288,0.000926298,0.284267742,0.111797441,0.601155884,Clinics,0.71237594,TRUE,133,0.925227287,119.8461538,0.851083757,2,0.618927094,,,0.798412713
5184,Comprehensive review on the prevailing COVID-19 therapeutics and the potential of repurposing SARS-CoV-1 candidate drugs to target SARS-CoV-2 as a fast-track treatment and prevention option.,Ann Transl Med,33178779,11/13/20,pubmed,0,5,in silico,0.951797761,0.001254677,0.00125467,0.023119938,0.021318335,0.001254618,Drug discovery,0.8370771,TRUE,24.2,0.357350485,12.4,0.390286326,1,0.537564047,,,0.428400286
5185,Lymphocyte percentage and hemoglobin as a joint parameter for the prediction of severe and nonsevere COVID-19: a preliminary study.,Ann Transl Med,33178763,11/13/20,pubmed,0,8,logistic regression,0.001901728,0.001901727,0.00190185,0.094256767,0.001901768,0.89813616,Clinics,0.98007834,TRUE,36.25,0.500154617,11.25,0.374364463,0,0.403234768,,,0.425917949
5186,Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic.,Sustain Cities Soc,33178557,11/13/20,pubmed,0,3,"deep learning, dataset",0.001653077,0.001653073,0.363652157,0.629735648,0.001653039,0.001653006,Epidemiology,0.7962717,TRUE,111.6666667,0.893314367,54.33333333,0.698956382,3,0.667819001,,,0.75336325
5187,T-Cell Hyperactivation and Paralysis in Severe COVID-19 Infection Revealed by Single-Cell Analysis.,Front Immunol,33178221,11/13/20,pubmed,0,5,"in silico, dataset",0.830969203,0.049542326,0.001943607,0.00194355,0.001943529,0.113657784,Drug discovery,0.43629828,FALSE,22.6,0.334652731,30.8,0.575729195,1,0.537564047,,,0.482648657
5188,Potential Cross-Reactive Immunity to SARS-CoV-2 From Common Human Pathogens and Vaccines.,Front Immunol,33178220,11/13/20,pubmed,0,1,proteom,0.562764033,0.13246661,0.001126814,0.001126832,0.30138887,0.001126842,Drug discovery,0.45305133,FALSE,100,0.868884903,378,0.968825261,9,0.814309525,,,0.884006563
5189,Differential Expression of Viral Transcripts From Single-Cell RNA Sequencing of Moderate and Severe COVID-19 Patients and Its Implications for Case Severity.,Front Microbiol,33178176,11/13/20,pubmed,0,4,"sequencing, transcriptom",0.41494041,0.328689288,0.001310389,0.00131036,0.001310349,0.252439204,Drug discovery,0.78761905,TRUE,26.5,0.389325252,33.75,0.597337436,6,0.764429903,,,0.583697531
5190,Generalized Anxiety Disorder and Its Associated Factors Among Health Care Workers Fighting COVID-19 in Southern Ethiopia.,Psychol Res Behav Manag,33177897,11/13/20,pubmed,0,12,logistic regression,0.000977455,0.000977451,0.000977481,0.132870434,0.833169871,0.031027309,Healthcare,0.9306378,TRUE,5.25,0.072855464,1.166666667,0.124565159,1,0.537564047,,,0.24499489
5191,COVID-19 Pandemic Preparedness and Response of Chronic Disease Patients in Public Health Facilities.,Int J Gen Med,33177864,11/13/20,pubmed,0,6,logistic regression,0.001085353,0.001085343,0.001085359,0.001085379,0.803022224,0.192636343,Healthcare,0.93443054,TRUE,24.83333333,0.365823489,6.166666667,0.281977522,0,0.403234768,,,0.35034526
5192,Multiple Expression Assessments of ACE2 and TMPRSS2 SARS-CoV-2 Entry Molecules in the Urinary Tract and Their Associations with Clinical Manifestations of COVID-19.,Infect Drug Resist,33177848,11/13/20,pubmed,0,13,"sequencing, sequence alignment",0.610958081,0.131525665,0.001171542,0.001171559,0.001171556,0.254001597,Drug discovery,0.68416214,TRUE,35.46153846,0.491310533,9,0.337904736,1,0.537564047,,,0.455593105
5193,Digital health and COVID-19.,Bull World Health Organ,33177768,11/13/20,pubmed,0,1,digital health,0.011750195,0.011749802,0.011750297,0.755401404,0.197597687,0.011750616,Epidemiology,0.5714028,TRUE,42,0.558537943,15,0.42594327,0,0.403234768,,,0.462571994
5194,"SARS-CoV-2 receptor is co-expressed with elements of the kinin-kallikrein, renin-angiotensin and coagulation systems in alveolar cells.",Sci Rep,33177594,11/13/20,pubmed,0,7,transcriptom,0.897488125,0.002422289,0.002422272,0.002422274,0.002422278,0.092822762,Drug discovery,0.8113289,TRUE,100.5714286,0.870245532,119.1428571,0.85048167,5,0.739490092,,,0.820072431
5195,COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.,Sci Rep,33177550,11/13/20,pubmed,0,3,"deep learning, neural network, dataset",0.0009263,0.000926291,0.995368494,0.000926318,0.000926299,0.000926298,Imaging,0.18511146,FALSE,24,0.35574247,48,0.673735617,462,0.997654176,,,0.675710754
5196,Modelling transmission and control of the COVID-19 pandemic in Australia.,Nat Commun,33177507,11/13/20,pubmed,0,5,computational,0.001717189,0.001717191,0.001717169,0.877304928,0.115826344,0.001717179,Epidemiology,0.35584578,FALSE,19.4,0.289257221,18.8,0.468156275,220,0.992839064,,,0.58341752
5197,A Genome Epidemiological Study of SARS-CoV-2 Introduction into Japan.,mSphere,33177213,11/13/20,pubmed,0,25,"sequencing, whole-genome, genome sequences, network analysis",0.00096677,0.622545884,0.000966796,0.353292793,0.000966854,0.021260903,Genomics,0.48642075,FALSE,70.96,0.767889171,90.76,0.802649184,0,0.403234768,,,0.657924374
5198,Computational Evidences of Phytochemical Mediated Disruption of PLpro Driven Replication of SARS-CoV-2: A Therapeutic Approach Against COVID-19.,Curr Pharm Biotechnol,33176643,11/13/20,pubmed,0,3,computational,0.963521519,0.000966766,0.000966802,0.03261136,0.000966758,0.000966795,Drug discovery,0.958706,TRUE,45,0.587049292,18.66666667,0.466818303,0,0.403234768,,,0.485700788
5199,"Occupational stress, mental health, and self-efficacy among community mental health workers: A cross-sectional study during COVID-19 pandemic.",Int J Soc Psychiatry,33176527,11/13/20,pubmed,0,6,logistic regression,0.001415125,0.001415125,0.001415119,0.001415233,0.992924249,0.001415148,Healthcare,0.8433539,TRUE,20,0.298163152,9.833333333,0.351284453,2,0.618927094,,,0.422791566
5200,SARS-CoV-2 Transmission among Marine Recruits during Quarantine.,N Engl J Med,33176093,11/12/20,pubmed,0,41,genomes,0.000936073,0.218446386,0.000936117,0.342811539,0.435933764,0.00093612,Healthcare,0.5977462,TRUE,31.19512195,0.44616241,74.51219512,0.765319775,7,0.785110192,,,0.665530792
5201,"Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: The Veterans Health Administration COVID-19 (VACO) Index.",PLoS One,33175863,11/12/20,pubmed,0,14,logistic regression,0.000800568,0.02705457,0.000800603,0.151511867,0.000800599,0.819031793,Clinics,0.38384914,FALSE,70.71428571,0.767023316,53.14285714,0.693805191,4,0.707574542,,,0.722801016
5202,"Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis.",JMIR Public Health Surveill,33175693,11/12/20,pubmed,0,8,data mining,0.05062688,0.153811622,0.048639573,0.367348354,0.378656873,0.000916699,Healthcare,0.93473125,TRUE,35.5,0.492547467,15.125,0.426344661,1,0.537564047,,,0.485485392
5203,Identifying the Zoonotic Origin of SARS-CoV-2 by Modeling the Binding Affinity between the Spike Receptor-Binding Domain and Host ACE2.,J Proteome Res,33175551,11/12/20,pubmed,0,5,computational,0.693705787,0.30054156,0.001438199,0.001438169,0.001438144,0.001438142,Drug discovery,0.5049482,TRUE,144.4,0.938895417,203,0.918584426,0,0.403234768,,,0.753571537
5204,"Molecular basis for drug repurposing to study the interface of the S protein in SARS-CoV-2 and human ACE2 through docking, characterization, and molecular dynamics for natural drug candidates.",J Mol Model,33175236,11/12/20,pubmed,0,2,"virtual screening, molecular dynamics simulation",0.90814332,0.001203473,0.001203435,0.087042941,0.001203425,0.001203406,Drug discovery,0.81863576,TRUE,17.5,0.263776362,5,0.257024351,3,0.667819001,,,0.396206571
5205,Potential role of viral metagenomics as a surveillance tool for the early detection of emerging novel pathogens.,Arch Microbiol,33175192,11/12/20,pubmed,0,4,metagenom,0.001717254,0.801221685,0.001717297,0.19190928,0.001717313,0.00171717,Genomics,0.7769494,TRUE,11,0.167171748,2.25,0.170925876,1,0.537564047,,,0.291887224
5206,"Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021.",Nucleic Acids Res,33175170,11/12/20,pubmed,0,289,multi-omics,0.08483481,0.354345799,0.00162279,0.555951088,0.001622759,0.001622755,Epidemiology,0.76970446,TRUE,42.74096386,0.564351537,,,7,0.785110192,,,0.674730864
5207,Adherence to physical contact restriction measures and the spread of COVID-19 in Brazil.,Epidemiol Serv Saude,33175010,11/12/20,pubmed,0,17,logistic regression,0.00190171,0.001901842,0.001901708,0.405456931,0.586936047,0.001901762,Healthcare,0.9852075,TRUE,59.47058824,0.699115592,35.23529412,0.606569441,2,0.618927094,,,0.641537376
5208,COVID-19: What we talk about when we talk about masks.,Rev Soc Bras Med Trop,33174963,11/12/20,pubmed,0,4,computational,0.002183261,0.030590515,0.002183204,0.960676456,0.002183311,0.002183252,Epidemiology,0.15757015,FALSE,11.5,0.17416043,1.25,0.127776291,0,0.403234768,,,0.235057163
5209,Computational Hot-Spot Analysis of the SARS-CoV-2 Receptor Binding Domain/ACE2 Complex*.,Chembiochem,33174669,11/12/20,pubmed,0,2,computational,0.969792256,0.001593545,0.023833707,0.001593531,0.001593488,0.001593474,Drug discovery,0.7155156,TRUE,66,0.741356918,21.5,0.498260637,0,0.403234768,,,0.547617441
5210,"OMA orthology in 2021: website overhaul, conserved isoforms, ancestral gene order and more.",Nucleic Acids Res,33174605,11/12/20,pubmed,0,12,genomes,0.002080716,0.769696057,0.002080705,0.221981306,0.002080604,0.002080614,Genomics,0.5897025,TRUE,22,0.326056033,62.25,0.729595933,2,0.618927094,,,0.55819302
5211,Epitope-Based Potential Vaccine Candidate for Humoral and Cell-Mediated Immunity to Combat Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic.,J Phys Chem Lett,33174418,11/12/20,pubmed,0,2,computational,0.91158344,0.081411718,0.00175123,0.001751224,0.001751228,0.00175116,Drug discovery,0.7030473,TRUE,17.5,0.263776362,0.5,0.087101953,1,0.537564047,,,0.296147454
5212,The Need for Digital Health Solutions in Deep Brain Stimulation for Parkinson's Disease in the Time of COVID-19 and Beyond.,Neuromodulation,33174292,11/12/20,pubmed,0,5,digital health,0.00120345,0.001203433,0.188289639,0.459119847,0.12994131,0.220242321,Epidemiology,0.95897627,TRUE,59.8,0.701403921,32,0.585763982,0,0.403234768,,,0.563467557
5213,Comparison of clinical characteristics of patients with pandemic SARS-CoV-2-related and community-acquired pneumonias in Hungary - a pilot historical case-control study.,Geroscience,33174170,11/12/20,pubmed,0,14,"prediction model, dataset",0.001392927,0.001392885,0.253658395,0.001392869,0.001392901,0.740770023,Clinics,0.5471787,TRUE,42.85714286,0.565959552,57.21428571,0.71180091,0,0.403234768,,,0.560331743
5214,Profiling SARS-CoV-2 mutation fingerprints that range from the viral pangenome to individual infection quasispecies.,medRxiv,33173909,11/12/20,pubmed,0,11,"computational, sequencing, genomes, sequence alignment",0.000662712,0.943340457,0.018392203,0.000662705,0.000662692,0.036279231,Genomics,0.053376257,FALSE,32.09090909,0.456243429,33.27272727,0.594327,1,0.537564047,,,0.529378159
5215,Quantification of occupational and community risk factors for SARS-CoV-2 seropositivity among healthcare workers in a large U.S. healthcare system.,medRxiv,33173904,11/12/20,pubmed,0,10,logistic regression,0.001059331,0.001059373,0.001059374,0.001059418,0.956606633,0.039155871,Healthcare,0.25584447,FALSE,34.7,0.48395077,44.2,0.655137811,1,0.537564047,,,0.558884209
5216,Estimating the potential impact of COVID-19-related disruptions on HIV incidence and mortality among men who have sex with men in the United States: a modelling study.,medRxiv,33173893,11/12/20,pubmed,0,10,mathematical model,0.000445726,0.030705603,0.000445723,0.647949164,0.246402718,0.074051065,Epidemiology,0.019561201,FALSE,206.6,0.974395448,386.8,0.969694942,0,0.403234768,,,0.782441719
5217,0,bioRxiv,33173866,11/12/20,pubmed,0,17,genomes,0.173878887,0.769604928,0.001653037,0.001653063,0.001653025,0.05155706,Genomics,0.27941808,FALSE,76.23529412,0.791143546,99.05882353,0.820644902,1,0.537564047,,,0.716450832
5218,A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.,bioRxiv,33173863,11/12/20,pubmed,0,19,dataset,0.79033901,0.001486535,0.001486523,0.203714922,0.001486539,0.00148647,Drug discovery,0.42747796,FALSE,61.52631579,0.712536335,267.4210526,0.944942467,0,0.403234768,,,0.686904523
5219,"Sex differences in viral entry protein expression, host responses to SARS-CoV-2, and in vitro responses to sex steroid hormone treatment in COVID-19.",Res Sq,33173861,11/12/20,pubmed,0,29,computational,0.482951809,0.032151244,0.001486404,0.001486435,0.001486499,0.48043761,Drug discovery,0.6502414,TRUE,45.93103448,0.595336756,59.24137931,0.718022478,0,0.403234768,,,0.572198
5220,Effect of pre-existing diseases on COVID-19 infection and role of new sensors and biomaterials for its detection and treatment.,Med Devices Sens,33173852,11/12/20,pubmed,0,3,sequencing,0.348785799,0.209271444,0.249288235,0.001823454,0.001823401,0.189007668,Drug discovery,0.79391026,TRUE,7.666666667,0.111633373,0.666666667,0.096200161,1,0.537564047,,,0.24846586
5221,New putative animal reservoirs of SARS-CoV-2 in Italian fauna: A bioinformatic approach.,Heliyon,33173837,11/12/20,pubmed,0,8,"bioinformatic, in silico",0.32669005,0.667157382,0.001538085,0.001538134,0.001538205,0.001538144,Genomics,0.815546,TRUE,18.375,0.275279857,4.875,0.250066899,0,0.403234768,,,0.309527174
5222,"Through the magnifying glass: Exploring aggregations of COVID-19 datasets by county, state, and taxonomies of U.S. regions.",Proc Assoc Inf Sci Technol,33173824,11/12/20,pubmed,0,2,dataset,0.007061749,0.007061848,0.007062164,0.964690998,0.007061793,0.007061449,Epidemiology,0.5709685,TRUE,19.5,0.29117447,4.5,0.242708055,0,0.403234768,,,0.312372431
5223,Social media and COVID-19: Characterizing anti-quarantine comments on Twitter.,Proc Assoc Inf Sci Technol,33173823,11/12/20,pubmed,0,2,text mining,0.004775167,0.004775115,0.004775125,0.976124141,0.004775404,0.004775049,Epidemiology,0.5081975,TRUE,14.5,0.219617787,2,0.164302917,2,0.618927094,,,0.334282599
5224,Stigmatization in social media: Documenting and analyzing hate speech for COVID-19 on Twitter.,Proc Assoc Inf Sci Technol,33173820,11/12/20,pubmed,0,3,machine learning,0.002996452,0.002996476,0.100866092,0.650863635,0.239280841,0.002996504,Epidemiology,0.77480876,TRUE,2.333333333,0.024800544,0,0.055525823,1,0.537564047,,,0.205963471
5225,COVID-19 pandemic and information diffusion analysis on Twitter.,Proc Assoc Inf Sci Technol,33173813,11/12/20,pubmed,0,2,network analysis,0.002238499,0.002238537,0.002238486,0.988807534,0.002238491,0.002238454,Epidemiology,0.38805768,FALSE,12.5,0.189436576,0.5,0.087101953,0,0.403234768,,,0.226591099
5226,Fluid dynamics simulations show that facial masks can suppress the spread of COVID-19 in indoor environments.,ArXiv,33173803,11/12/20,pubmed,0,7,computational,0.001350416,0.001350393,0.001350409,0.931093371,0.001350377,0.063505033,Epidemiology,0.393164,FALSE,76.42857143,0.792133094,75.28571429,0.767126037,3,0.667819001,,,0.742359377
5227,Tinker-HP : Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems.,ArXiv,33173801,11/12/20,pubmed,0,12,molecular dynamics simulation,0.140478511,0.001220071,0.520537355,0.335323974,0.001220031,0.001220058,Epidemiology,0.028178483,FALSE,40.66666667,0.546292288,31.91666667,0.583823923,1,0.537564047,,,0.555893419
5228,Computational Study of the Ion and Water Permeation and Transport Mechanisms of the SARS-CoV-2 Pentameric E Protein Channel.,Front Mol Biosci,33173781,11/12/20,pubmed,0,9,"computational, in silico",0.927437195,0.002296647,0.002296699,0.063376264,0.002296621,0.002296574,Drug discovery,0.7377287,TRUE,44.33333333,0.580679077,17.77777778,0.458589778,2,0.618927094,,,0.552731983
5229,Identification of Novel SARS-CoV-2 Drug Targets by Host MicroRNAs and Transcription Factors Co-regulatory Interaction Network Analysis.,Front Genet,33173539,11/12/20,pubmed,0,3,network analysis,0.908151213,0.028737493,0.001141338,0.001141326,0.001141349,0.05968728,Drug discovery,0.80071676,TRUE,30.33333333,0.436266931,5.333333333,0.262911426,1,0.537564047,,,0.412247468
5230,Modeling aerosol transmission of SARS-CoV-2 in multi-room facility.,J Loss Prev Process Ind,33173256,11/12/20,pubmed,0,3,computational,0.002639064,0.002639078,0.002639173,0.866715659,0.002639175,0.122727851,Epidemiology,0.79603726,TRUE,70,0.764178366,12.66666667,0.39530372,0,0.403234768,,,0.520905618
5231,"Titanium (IV) complexes of some tetra-dentate symmetrical bis-Schiff bases of 1,6-hexanediamine: Synthesis, characterization, and in silico prediction of potential inhibitor against coronavirus (SARS-CoV-2).",Appl Organomet Chem,33173252,11/12/20,pubmed,0,7,in silico,0.985967496,0.002806393,0.002806603,0.002806521,0.002806448,0.002806539,Drug discovery,0.7365603,TRUE,8,0.118683901,0.285714286,0.066296495,0,0.403234768,,,0.196071721
5232,Towards a novel peptide vaccine for Middle East respiratory syndrome coronavirus and its possible use against pandemic COVID-19.,J Mol Liq,33173250,11/12/20,pubmed,0,9,"molecular dynamics simulation, in silico, proteom",0.994142084,0.00117162,0.001171546,0.00117159,0.001171566,0.001171594,Drug discovery,0.6522699,TRUE,23.55555556,0.348691941,12.88888889,0.397377576,2,0.618927094,,,0.45499887
5233,Lung transcriptome of a COVID-19 patient and systems biology predictions suggest impaired surfactant production which may be druggable by surfactant therapy.,Sci Rep,33173052,11/12/20,pubmed,0,2,transcriptom,0.884498629,0.001653105,0.001653114,0.001653111,0.001653081,0.108888961,Drug discovery,0.85896987,TRUE,21,0.312016822,12,0.386740701,10,0.828199272,,,0.508985598
5234,Transmission of SARS-CoV-2 on mink farms between humans and mink and back to humans.,Science,33172935,11/12/20,pubmed,0,22,"sequencing, whole-genome, whole genome, genomes",0.002032817,0.95670212,0.002032861,0.002032924,0.035166542,0.002032735,Genomics,0.43136132,FALSE,42.18181818,0.559280104,75.81818182,0.768597806,125,0.983270572,,,0.770382827
5235,Adoption of telemedicine applications among Saudi citizens during COVID-19 pandemic: An alternative health delivery system.,J Infect Public Health,33172819,11/12/20,pubmed,0,2,structural model,0.000772654,0.015090785,0.20617936,0.68948898,0.087695612,0.00077261,Epidemiology,0.8780463,TRUE,11.5,0.17416043,3.5,0.213607172,2,0.618927094,,,0.335564899
5236,Update in COVID-19 in the intensive care unit from the 2020 HELLENIC Athens International symposium.,Anaesth Crit Care Pain Med,33172592,11/12/20,pubmed,0,15,artificial intelligence,0.085881417,0.002490514,0.137262865,0.384002309,0.002490588,0.387872307,Clinics,0.8126005,TRUE,132.2666667,0.924052199,145.2,0.87924806,0,0.403234768,,,0.735511675
5237,Recent Patents and Advances on Nanotechnologies against Coronavirus.,Recent Pat Nanotechnol,33172382,11/12/20,pubmed,0,2,sequencing,0.131636216,0.719854978,0.077516008,0.002296723,0.066399417,0.002296658,Genomics,0.74252254,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
5238,"In-silico analysis of angiotensin converting enzyme 2 (ACE2) of livestock, pet and poultry animals to determine its susceptibility to SARS-CoV- 2 infection.",Comb Chem High Throughput Screen,33172369,11/12/20,pubmed,0,5,"in-silico, sequence alignment",0.40560287,0.520718328,0.00186176,0.001861799,0.068093432,0.001861811,Genomics,0.76007366,TRUE,1307.8,0.999752613,574.8,0.982673267,0,0.403234768,,,0.795220216
5239,"Novel biomarkers for the prediction of COVID-19 progression a retrospective, multi-center cohort study.",Virulence,33172355,11/12/20,pubmed,0,13,logistic regression,0.001861785,0.001861715,0.169445441,0.001861756,0.001861717,0.823107586,Clinics,0.9592094,TRUE,42.23076923,0.559465644,,,1,0.537564047,,,0.548514846
5240,"How Alcoholic Disinfectants Affect Coronavirus Model Membranes: Membrane Fluidity, Permeability, and Disintegration.",J Phys Chem B,33172260,11/12/20,pubmed,0,4,molecular dynamics simulation,0.618438337,0.001237098,0.001237103,0.376613197,0.001237129,0.001237136,Drug discovery,0.4209237,FALSE,87.75,0.836229822,41,0.639751137,1,0.537564047,,,0.671181669
5241,New Technologies for Influenza Vaccines.,Microorganisms,33172191,11/12/20,pubmed,0,5,computational,0.54737379,0.147159295,0.002357884,0.298393521,0.002357795,0.002357715,Drug discovery,0.06221798,FALSE,92,0.848970252,107.8,0.835964678,0,0.403234768,,,0.696056566
5242,0,Molecules,33172092,11/12/20,pubmed,0,5,"virtual screening, machine learning, in-silico",0.835300501,0.084110659,0.075720541,0.00162282,0.001622758,0.001622721,Drug discovery,0.89066625,TRUE,73.2,0.778774198,14.4,0.416644367,3,0.667819001,,,0.621079189
5243,A Multimodal Real-Time Feedback Platform Based on Spoken Interactions for Remote Active Learning Support.,Sensors (Basel),33172039,11/12/20,pubmed,0,8,active learning,0.142266064,0.001565323,0.465057735,0.189994537,0.199551035,0.001565307,Healthcare,0.33919448,FALSE,21.375,0.315913167,5.75,0.271742039,1,0.537564047,,,0.375073084
5244,Fragment Library of Natural Products and Compound Databases for Drug Discovery.,Biomolecules,33172012,11/12/20,pubmed,0,3,dataset,0.924050677,0.069336973,0.001653127,0.001653158,0.001653059,0.001653007,Drug discovery,0.46186274,FALSE,37.33333333,0.511596264,10.33333333,0.36038266,1,0.537564047,,,0.469847657
5245,Accuracy of Conventional and Machine Learning Enhanced Chest Radiography for the Assessment of COVID-19 Pneumonia: Intra-Individual Comparison with CT.,J Clin Med,33171999,11/12/20,pubmed,0,6,machine learning,0.001187259,0.001187275,0.80550976,0.00118727,0.001187293,0.189741143,Imaging,0.78395784,TRUE,42.33333333,0.560888119,6,0.280037463,0,0.403234768,,,0.414720117
5246,A Social Network Analysis of Tweets Related to Masks during the COVID-19 Pandemic.,Int J Environ Res Public Health,33171843,11/12/20,pubmed,0,4,network analysis,0.001786556,0.00178671,0.001786579,0.895274923,0.097578686,0.001786545,Epidemiology,0.7390974,TRUE,19.25,0.287896592,4,0.231469093,1,0.537564047,,,0.352309911
5247,Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm.,J Pers Med,33171723,11/12/20,pubmed,0,6,"deep learning, neural network, deep-learning, transfer learning",0.001330022,0.001330021,0.993349814,0.001330037,0.001330043,0.001330062,Imaging,0.7717583,TRUE,84.16666667,0.822376152,57.33333333,0.7122692,5,0.739490092,,,0.758045148
5248,Analysis and Evaluation of COVID-19 Web Applications for Health Professionals: Challenges and Opportunities.,Healthcare (Basel),33171711,11/12/20,pubmed,0,4,forecasting model,0.001112678,0.001112644,0.348247169,0.554720258,0.093694614,0.001112638,Epidemiology,0.48252386,FALSE,44.75,0.583771414,15.75,0.433636607,1,0.537564047,,,0.518324023
5249,Extensive Testing and Public Health Interventions for the Control of COVID-19 in the Republic of Cyprus between March and May 2020.,J Clin Med,33171651,11/12/20,pubmed,0,35,logistic regression,0.001538235,0.001538193,0.001538158,0.251626022,0.428440822,0.315318569,Healthcare,0.47146225,FALSE,25.57142857,0.374853114,,,2,0.618927094,,,0.496890104
5250,Mobility network models of COVID-19 explain inequities and inform reopening.,Nature,33171481,11/11/20,pubmed,0,7,network model,0.001310372,0.001310355,0.001310369,0.993448061,0.00131046,0.001310382,Epidemiology,0.13274142,FALSE,81.71428571,0.813532068,1069.571429,0.994112925,110,0.980739552,,,0.929461515
5251,Association of Proteinuria and Hematuria with Acute Kidney Injury and Mortality in Hospitalized Patients with COVID-19.,Kidney Blood Press Res,33171466,11/11/20,pubmed,0,15,logistic regression,0.001652997,0.001652998,0.001652992,0.001653011,0.001653021,0.991734981,Clinics,0.74754256,TRUE,62.33333333,0.717236687,60.66666667,0.723307466,3,0.667819001,,,0.702787718
5252,"AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.",Med Image Anal,33171345,11/11/20,pubmed,0,35,"deep learning, artificial intelligence",0.090499651,0.001684538,0.666208218,0.001684591,0.001684516,0.238238487,Imaging,0.6728362,TRUE,36.97142857,0.507452533,48.68571429,0.676210864,10,0.828199272,,,0.67062089
5253,Association between red blood cell distribution width and mortality of COVID-19 patients.,Anaesth Crit Care Pain Med,33171297,11/11/20,pubmed,0,23,logistic regression,0.001823398,0.001823337,0.03601363,0.001823392,0.001823338,0.956692904,Clinics,0.8992196,TRUE,62.69565217,0.719153937,34.34782609,0.600749264,2,0.618927094,,,0.646276765
5254,Simulation and Measurement of Aerosolisation in Different Chest Drainage Systems.,Semin Thorac Cardiovasc Surg,33171236,11/11/20,pubmed,0,7,simulation model,0.001717232,0.001717262,0.429447792,0.563683158,0.001717243,0.001717313,Epidemiology,0.3006255,FALSE,26.14285714,0.382955037,8.142857143,0.321447685,2,0.618927094,,,0.441109939
5255,Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19.,Cell,33171100,11/11/20,pubmed,0,55,multi-omics,0.315682985,0.001861781,0.078560626,0.001861846,0.097115067,0.504917696,Clinics,0.46748778,FALSE,67.10909091,0.748469293,280.2727273,0.948889484,32,0.933699611,,,0.877019463
5256,Age separation dramatically reduces COVID-19 mortality rate in a computational model of a large population.,Open Biol,33171068,11/11/20,pubmed,0,3,computational,0.001622781,0.001622724,0.001622731,0.80647155,0.187037394,0.00162282,Epidemiology,0.7480494,TRUE,1.666666667,0.016451234,0.666666667,0.096200161,0,0.403234768,,,0.171962054
5257,Large scale genomic analysis of 3067 SARS-CoV-2 genomes reveals a clonal geo-distribution and a rich genetic variations of hotspots mutations.,PLoS One,33170902,11/11/20,pubmed,0,22,"whole-genome, genomes",0.032012888,0.962745646,0.001310335,0.001310404,0.001310374,0.001310354,Genomics,0.7268199,TRUE,21.59090909,0.318819964,4.181818182,0.233208456,6,0.764429903,,,0.438819441
5258,Static all-atom energetic mappings of the SARS-Cov-2 spike protein and dynamic stability analysis of "Up" versus "Down" protomer states.,PLoS One,33170884,11/11/20,pubmed,0,4,molecular dynamics simulation,0.688937224,0.220402639,0.000580146,0.088919684,0.000580149,0.000580158,Drug discovery,0.44308499,FALSE,24.75,0.364710248,9.25,0.340379984,2,0.618927094,,,0.441339109
5259,Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia.,PLoS One,33170871,11/11/20,pubmed,0,2,computational,0.001901745,0.001901725,0.001901875,0.990491179,0.001901754,0.001901722,Epidemiology,0.15921763,FALSE,20,0.298163152,8,0.320511105,0,0.403234768,,,0.340636341
5260,Use Characteristics and Triage Acuity of a Digital Symptom Checker in a Large Integrated Health System: Population-Based Descriptive Study.,J Med Internet Res,33170799,11/11/20,pubmed,0,4,artificial intelligence,0.000936076,0.000936108,0.20778469,0.214661016,0.496287098,0.079395012,Healthcare,0.7106373,TRUE,5.75,0.080957388,4.75,0.248260637,0,0.403234768,,,0.244150931
5261,COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images.,IEEE J Biomed Health Inform,33170789,11/11/20,pubmed,0,13,"deep learning, neural network, dataset",0.00117157,0.001171587,0.783194167,0.065435549,0.001171581,0.147855547,Imaging,0.1916759,FALSE,13.69230769,0.207495825,14.46153846,0.417179556,11,0.840175319,,,0.488283567
5262,Step toward repurposing drug discovery for COVID-19 therapeutics through in silico approach.,Drug Dev Res,33170521,11/11/20,pubmed,0,4,"computational, in silico",0.937094938,0.002422267,0.053215846,0.002422413,0.002422309,0.002422227,Drug discovery,0.93234205,TRUE,10.5,0.157338116,0.5,0.087101953,0,0.403234768,,,0.215891612
5263,Etiology of Severe Community-Acquired Pneumonia in Adults Based on Metagenomic Next-Generation Sequencing: A Prospective Multicenter Study.,Infect Dis Ther,33170499,11/11/20,pubmed,0,11,"sequencing, metagenom",0.001901715,0.463272475,0.131612591,0.001901732,0.00190177,0.399409716,Genomics,0.61681646,TRUE,206.4545455,0.974271755,179.4545455,0.905940594,0,0.403234768,,,0.761149039
5264,A pilot study on the preventative potential of alpha-cyclodextrin and hydroxytyrosol against SARS-CoV-2 transmission.,Acta Biomed,33170176,11/11/20,pubmed,0,18,in silico,0.582275844,0.001511942,0.001511883,0.079724918,0.001511942,0.33346347,Drug discovery,0.9309075,TRUE,56.61111111,0.67938648,10.05555556,0.355699759,1,0.537564047,,,0.524216762
5265,A predictive model for the severity of COVID-19 in elderly patients.,Aging (Albany NY),33170150,11/11/20,pubmed,0,12,predictive model,0.001511808,0.001511811,0.126630643,0.001511859,0.001511861,0.867322017,Clinics,0.7448178,TRUE,49.75,0.630032779,13.5,0.405539203,2,0.618927094,,,0.551499692
5266,Using a simple open-source automated machine learning algorithm to forecast COVID-19 spread: A modelling study.,Adv Respir Med,33169811,11/11/20,pubmed,0,7,"machine learning, prediction model, dataset",0.001392822,0.001392883,0.361765906,0.632662578,0.00139285,0.00139296,Epidemiology,0.32474387,FALSE,23.14285714,0.343125734,1,0.122023013,0,0.403234768,,,0.289461172
5267,Heralding the Digitalization of Life in Post-Pandemic East Asian Societies.,J Bioeth Inq,33169256,11/11/20,pubmed,0,3,artificial intelligence,0.001823316,0.001823398,0.074369366,0.609281868,0.310878655,0.001823397,Epidemiology,0.7225289,TRUE,52.66666667,0.651308059,22,0.503746321,0,0.403234768,,,0.519429716
5268,Network Pharmacology and bioinformatics analyses identify intersection genes of niacin and COVID-19 as potential therapeutic targets.,Brief Bioinform,33169132,11/11/20,pubmed,0,6,"computational, bioinformatic",0.783600406,0.001593516,0.001593511,0.00159363,0.001593482,0.210025454,Drug discovery,0.9741982,TRUE,220,0.978415486,105.6666667,0.832419053,0,0.403234768,,,0.738023102
5269,0,Innovation (N Y),33169120,11/11/20,pubmed,0,21,correlation analysis,0.069437495,0.001461886,0.001461872,0.888268826,0.037907967,0.001461954,Epidemiology,0.5607827,TRUE,146.1904762,0.940750819,,,0,0.403234768,,,0.671992794
5270,Deep learning and its role in COVID-19 medical imaging.,Intell Based Med,33169117,11/11/20,pubmed,0,3,deep learning,0.083856239,0.169813842,0.427604235,0.315895302,0.001415191,0.001415193,Imaging,0.8465897,TRUE,51.66666667,0.644195683,91.33333333,0.804656141,0,0.403234768,,,0.617362197
5271,Impact of Gastrointestinal Symptoms in COVID-19: a Molecular Approach.,SN Compr Clin Med,33169110,11/11/20,pubmed,0,5,microbiom,0.637779105,0.204692033,0.001622804,0.00162284,0.001622801,0.152660417,Drug discovery,0.9756489,TRUE,21.4,0.316469788,12.4,0.390286326,4,0.707574542,,,0.471443552
5272,Influencing overseas Chinese by tweets: text-images as the key tactic of Chinese propaganda.,J Comput Soc Sci,33169108,11/11/20,pubmed,0,4,dataset,0.001565372,0.001565346,0.206959595,0.743501698,0.044842639,0.001565351,Epidemiology,0.67641425,TRUE,12.25,0.185107304,0.25,0.065493712,1,0.537564047,,,0.262721688
5273,Synergy of melanin and vitamin-D may play a fundamental role in preventing SARS-CoV-2 infections and halt COVID-19 by inactivating furin protease.,Transl Med Commun,33169107,11/11/20,pubmed,0,6,in silico,0.857937327,0.00175121,0.001751314,0.001751236,0.03664646,0.100162454,Drug discovery,0.7600261,TRUE,34.66666667,0.483703383,24.33333333,0.524016591,0,0.403234768,,,0.470318247
5274,Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey.,Sustain Cities Soc,33169099,11/11/20,pubmed,0,8,"deep learning, image processing",0.001187291,0.001187279,0.726816122,0.26843475,0.00118729,0.001187269,Imaging,0.5981683,TRUE,30.625,0.439544808,8.875,0.333623227,8,0.799987654,,,0.52438523
5275,Fear of COVID-19 Scale for Hospital Staff in Regional Hospitals in Mexico: a Brief Report.,Int J Ment Health Addict,33169075,11/11/20,pubmed,0,12,model fit,0.001486466,0.001486462,0.001486593,0.03164488,0.891159353,0.072736246,Healthcare,0.8630165,TRUE,15.5,0.234028078,2.916666667,0.191062349,2,0.618927094,,,0.348005841
5276,Clinical features of critically ill patients infected with SARS-CoV-2 outside Wuhan with and without diabetes.,Int J Diabetes Dev Ctries,33169053,11/11/20,pubmed,0,8,logistic regression,0.001187329,0.00118732,0.001187319,0.001187351,0.035291824,0.959958856,Clinics,0.8912333,TRUE,56.625,0.679448327,19.25,0.47310677,0,0.403234768,,,0.518596622
5277,A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis.,Mach Vis Appl,33169050,11/11/20,pubmed,0,4,"deep learning, neural network",0.00190173,0.001901694,0.990491383,0.001901852,0.001901671,0.00190167,Imaging,0.5039301,TRUE,21.5,0.318263343,5.75,0.271742039,4,0.707574542,,,0.432526641
5278,A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic.,Comput Econ,33169049,11/11/20,pubmed,0,1,"machine learning, computational, classifier, correlation analysis, prediction model, dataset",0.001392898,0.001392877,0.408640963,0.187688351,0.399492069,0.001392843,Healthcare,0.54631704,TRUE,9,0.135320675,0,0.055525823,0,0.403234768,,,0.198027089
5279,Scaling effect in COVID-19 spreading: The role of heterogeneity in a hybrid ODE-network model with restrictions on the inter-cities flow.,Physica D,33169041,11/11/20,pubmed,0,16,network model,0.002357942,0.002357785,0.002357763,0.988211053,0.002357739,0.002357718,Epidemiology,0.30172226,FALSE,8.1875,0.119982683,0.625,0.090446883,1,0.537564047,,,0.249331204
5280,0,Sci Data,33168822,11/11/20,pubmed,0,21,dataset,0.001220012,0.001220049,0.001220046,0.993899891,0.001220006,0.001219997,Epidemiology,0.67844737,TRUE,86.61904762,0.831838704,,,7,0.785110192,,,0.808474448
5281,Age-related gene expression alterations by SARS-CoV-2 infection contribute to poor prognosis in elderly.,J Genet,33168795,11/11/20,pubmed,0,2,"transcriptom, dataset",0.459562353,0.001034643,0.00103464,0.001034635,0.001034618,0.536299112,Clinics,0.60413706,TRUE,30,0.432432432,21,0.492239765,1,0.537564047,,,0.487412081
5282,"Raltegravir, Indinavir, Tipranavir, Dolutegravir, and Etravirine against main protease and RNA-dependent RNA polymerase of SARS-CoV-2: A molecular docking and drug repurposing approach.",J Infect Public Health,33168456,11/11/20,pubmed,0,6,virtual screening,0.995218475,0.000956303,0.000956299,0.000956296,0.000956296,0.000956331,Drug discovery,0.9810352,TRUE,75.16666667,0.787432742,27.33333333,0.54856837,4,0.707574542,,,0.681191885
5283,Utility of Point-of-Care Lung Ultrasound for Clinical Classification of COVID-19.,Ultrasound Med Biol,33168275,11/11/20,pubmed,0,8,correlation analysis,0.00242229,0.002422289,0.130490307,0.002422363,0.00242241,0.859820342,Clinics,0.7916077,TRUE,26,0.382398417,3.375,0.207051111,0,0.403234768,,,0.330894765
5284,Impact of Hydroxychloroquine on Mortality in Hospitalized Patients with COVID-19: Systematic Review and Meta-Analysis.,Pharmacy (Basel),33167571,11/11/20,pubmed,0,4,dataset,0.136529435,0.001415123,0.001415159,0.274558999,0.001415154,0.58466613,Clinics,0.6671215,TRUE,64,0.729173109,42.75,0.648381054,2,0.618927094,,,0.665493753
5285,Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country.,Int J Environ Res Public Health,33167564,11/11/20,pubmed,0,7,dataset,0.00182341,0.001823348,0.057920942,0.729628462,0.137344897,0.07145894,Epidemiology,0.5501071,TRUE,43,0.567629414,,,2,0.618927094,,,0.593278254
5286,"Vaccinomics and Adversomics in the Era of Precision Medicine: A Review Based on HBV, MMR, HPV, and COVID-19 Vaccines.",J Clin Med,33167413,11/11/20,pubmed,0,2,"pharmacogenom, vaccinom",0.543271309,0.092761544,0.122606137,0.001823469,0.237713944,0.001823597,Drug discovery,0.6196286,TRUE,9,0.135320675,2,0.164302917,2,0.618927094,,,0.306183562
5287,Immunoprotective potential of Ayurvedic herb Kalmegh (Andrographis paniculata) against respiratory viral infections - LC-MS/MS and network pharmacology analysis.,Phytochem Anal,33167083,11/10/20,pubmed,0,6,"data mining, metabolom",0.93168443,0.062914088,0.001350338,0.001350413,0.001350365,0.001350366,Drug discovery,0.9236423,TRUE,97.83333333,0.864246397,61.33333333,0.725715815,0,0.403234768,,,0.664398993
5288,Genome-wide mapping of SARS-CoV-2 RNA structures identifies therapeutically-relevant elements.,Nucleic Acids Res,33166999,11/10/20,pubmed,0,11,genome-wide,0.462641947,0.532339556,0.001254651,0.00125464,0.001254619,0.001254587,Genomics,0.4846579,FALSE,80.27272727,0.807965861,224.7272727,0.927749532,19,0.89561084,,,0.877108744
5289,Development of a PCR-RFLP method for detection of D614G mutation in SARS-CoV-2.,Infect Genet Evol,33166683,11/10/20,pubmed,0,7,"bioinformatic, sequencing",0.001684544,0.99157711,0.001684677,0.001684572,0.001684578,0.001684519,Genomics,0.21341613,FALSE,21.14285714,0.312758983,9.714285714,0.348608509,1,0.537564047,,,0.399643847
5290,Deletion in the C-terminal region of the envelope glycoprotein in some of the Indian SARS-CoV-2 genome.,Virus Res,33166565,11/10/20,pubmed,0,6,"in silico, whole genome, genome sequences, genomes",0.329918574,0.664135655,0.00148643,0.00148644,0.001486475,0.001486427,Genomics,0.44065303,FALSE,42.83333333,0.565650319,65.16666667,0.739496923,4,0.707574542,,,0.670907261
5291,LitCovid: an open database of COVID-19 literature.,Nucleic Acids Res,33166392,11/10/20,pubmed,0,3,"data mining, deep-learning",0.042744503,0.001987135,0.28497398,0.666320073,0.001987215,0.001987094,Epidemiology,0.28341419,FALSE,10.33333333,0.155049787,57.33333333,0.7122692,10,0.828199272,,,0.565172753
5292,COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics.,Nucleic Acids Res,33166390,11/10/20,pubmed,0,8,"text-mining, dataset",0.414327972,0.001486473,0.126566188,0.454646498,0.00148645,0.00148642,Epidemiology,0.55139995,TRUE,13.25,0.199950523,8,0.320511105,3,0.667819001,,,0.396093543
5293,Recommendations for sample pooling on the Cepheid GeneXpert® system using the Cepheid Xpert® Xpress SARS-CoV-2 assay.,PLoS One,33166373,11/10/20,pubmed,0,6,dataset,0.001438152,0.5569153,0.246999164,0.191770951,0.001438194,0.001438238,Genomics,0.21568233,FALSE,30.16666667,0.433731214,49.83333333,0.680893765,0,0.403234768,,,0.505953249
5294,Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics.,IEEE J Biomed Health Inform,33166256,11/10/20,pubmed,0,10,"deep learning, radiom, logistic regression",0.001330038,0.001330033,0.506115614,0.001330047,0.001330038,0.488564229,Imaging,0.8986416,TRUE,59.4,0.698682664,19.5,0.475983409,2,0.618927094,,,0.597864389
5295,Corrigendum: Using Machine Learning to Generate Novel Hypotheses: Increasing Optimism About COVID-19 Makes People Less Willing to Justify Unethical Behaviors.,Psychol Sci,33166227,11/10/20,pubmed,0,1,machine learning,0.025060793,0.025061039,0.874696643,0.025060935,0.025060281,0.02506031,Genomics,0.5178959,TRUE,91,0.846310842,14,0.412898047,0,0.403234768,,,0.554147885
5296,"Hydroxychloroquine vs. Azithromycin for Hospitalized Patients with COVID-19 (HAHPS): Results of a Randomized, Active Comparator Trial.",Ann Am Thorac Soc,33166179,11/10/20,pubmed,0,17,bayes,0.058161787,0.001126817,0.001126891,0.173134821,0.001126852,0.765322832,Clinics,0.9629249,TRUE,77.52941176,0.796709753,55.76470588,0.705378646,4,0.707574542,,,0.736554314
5297,Changes in Depression and Physical Activity Among College Students on a Diverse Campus After a COVID-19 Stay-at-Home Order.,J Community Health,33165765,11/10/20,pubmed,0,5,logistic regression,0.00109884,0.001098816,0.001098808,0.001098831,0.994505852,0.001098853,Healthcare,0.9046123,TRUE,51.6,0.643577216,21.8,0.501204174,0,0.403234768,,,0.516005386
5298,Development and internal validation of a diagnostic prediction model for COVID-19 at time of admission to hospital.,QJM,33165573,11/10/20,pubmed,0,11,prediction model,0.001371252,0.001371273,0.389445066,0.00137128,0.101746832,0.504694297,Clinics,0.54293716,TRUE,8.909090909,0.13068217,2,0.164302917,1,0.537564047,,,0.277516378
5299,COVID-19 Hospitalization in Adults with Type 1 Diabetes: Results from the T1D Exchange Multicenter Surveillance Study.,J Clin Endocrinol Metab,33165563,11/10/20,pubmed,0,18,logistic regression,0.031798274,0.001350361,0.001350394,0.001350386,0.265757482,0.698393103,Clinics,0.94663393,TRUE,21.55555556,0.318448884,5.944444444,0.273748997,2,0.618927094,,,0.403708325
5300,Prediction Models for COVID-19 Need Further Improvements.,JAMA Intern Med,33165518,11/10/20,pubmed,0,2,prediction model,0.034962341,0.034962113,0.825185708,0.03496345,0.034962749,0.034963638,Clinics,0.49319768,FALSE,46.5,0.601026656,14.5,0.418450629,0,0.403234768,,,0.474237351
5301,Breakthrough healthcare technologies in the COVID-19 era: a unique opportunity for cardiovascular practitioners and patients.,Panminerva Med,33165308,11/10/20,pubmed,0,5,"machine learning, deep learning",0.000966772,0.000966778,0.172031331,0.64022023,0.074202998,0.111611891,Epidemiology,0.6859304,TRUE,130,0.921763869,96,0.81509232,1,0.537564047,,,0.758140079
5302,Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections.,J Xray Sci Technol,33164982,11/10/20,pubmed,0,7,"deep learning, artificial intelligence, dataset",0.001156224,0.001156247,0.994218719,0.001156257,0.001156269,0.001156285,Imaging,0.6534277,TRUE,20.71428571,0.306759849,26.28571429,0.540272946,0,0.403234768,,,0.416755854
5303,Putting the world back to work: An expert system using big data and artificial intelligence in combating the spread of COVID-19 and similar contagious diseases.,Work,33164971,11/10/20,pubmed,0,5,"machine learning, artificial intelligence",0.001141336,0.001141329,0.422495033,0.572939651,0.001141341,0.001141311,Epidemiology,0.5157116,TRUE,12.4,0.187148247,3.6,0.21507894,0,0.403234768,,,0.268487318
5304,0,Acta Trop,33164890,11/10/20,pubmed,0,9,"machine learning, computational, dataset",0.001486464,0.001486485,0.107755044,0.886299086,0.001486437,0.001486485,Epidemiology,0.5887554,TRUE,38,0.519327107,,,0,0.403234768,,,0.461280938
5305,Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes.,Elife,33164753,11/10/20,pubmed,0,26,"transcriptom, multiom, dataset",0.814985583,0.177240181,0.001943521,0.001943568,0.001943568,0.001943579,Drug discovery,0.41473088,FALSE,36.57692308,0.503494341,48.76923077,0.67641156,6,0.764429903,,,0.648111935
5306,Multi-epitope vaccine against SARS-CoV-2 applying immunoinformatics and molecular dynamics simulation approaches.,J Biomol Struct Dyn,33164664,11/10/20,pubmed,0,3,"molecular dynamics simulation, computational, bioinformatic, in silico",0.994702864,0.00105939,0.001059468,0.001059414,0.001059435,0.001059429,Drug discovery,0.9250681,TRUE,16,0.243552477,,,1,0.537564047,,,0.390558262
5307,Biobanking in the COVID-19 Era and Beyond: Part 1. How Early Experiences Can Translate into Actionable Wisdom.,Biopreserv Biobank,33164554,11/10/20,pubmed,0,18,dataset,0.001371299,0.0013713,0.166205739,0.52192917,0.307751248,0.001371245,Epidemiology,0.12626606,FALSE,39.11111111,0.530892449,34.05555556,0.599277495,0,0.403234768,,,0.511134904
5308,Individuating Possibly Repurposable Drugs and Drug Targets for COVID-19 Treatment Through Hypothesis-Driven Systems Medicine Using CoVex.,Assay Drug Dev Technol,33164550,11/10/20,pubmed,0,6,in silico,0.926826729,0.002806452,0.06194733,0.002806644,0.002806402,0.002806443,Drug discovery,0.7023365,TRUE,20,0.298163152,4.333333333,0.237958255,1,0.537564047,,,0.357895151
5309,Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic.,SN Comput Sci,33163975,11/10/20,pubmed,0,4,deep learning,0.12188398,0.001438169,0.673179139,0.200622481,0.001438122,0.001438109,Epidemiology,0.8324212,TRUE,16.75,0.252458408,3,0.199424672,1,0.537564047,,,0.329815709
5310,Genomic exploration light on multiple origin with potential parsimony-informative sites of the severe acute respiratory syndrome coronavirus 2 in Bangladesh.,Gene Rep,33163695,11/10/20,pubmed,0,3,"genome-wide, genomes, sequence alignment",0.000999556,0.968722416,0.027279454,0.00099954,0.000999515,0.000999519,Genomics,0.92195827,TRUE,19.66666667,0.292596945,1.333333333,0.13252609,3,0.667819001,,,0.364314012
5311,A modeling informed quantitative approach to salvage clinical trials interrupted due to COVID-19.,Alzheimers Dement (N Y),33163611,11/10/20,pubmed,0,2,dataset,0.395578251,0.001392877,0.001392922,0.498771585,0.001392933,0.101471432,Epidemiology,0.976179,TRUE,92.5,0.850454574,63,0.732606369,1,0.537564047,,,0.706874996
5312,"A survey dataset to evaluate the changes in mobility and transportation due to COVID-19 travel restrictions in Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa, United States.",Data Brief,33163599,11/10/20,pubmed,0,27,dataset,0.001861723,0.049589398,0.001861719,0.606640895,0.338184584,0.001861681,Epidemiology,0.6956042,TRUE,43.62962963,0.574247016,28.25925926,0.55612791,5,0.739490092,,,0.623288339
5313,Prevalence of posttraumatic stress symptoms in health care workers after exposure to patients with COVID-19.,Neurobiol Stress,33163588,11/10/20,pubmed,0,11,logistic regression,0.001237063,0.00123706,0.001237081,0.001237127,0.677536273,0.317515396,Healthcare,0.9463341,TRUE,31.18181818,0.446038716,8.545454545,0.329341718,2,0.618927094,,,0.464769176
5314,"An Overview on SARS-CoV-2 (COVID-19) and Other Human Coronaviruses and Their Detection Capability via Amplification Assay, Chemical Sensing, Biosensing, Immunosensing, and Clinical Assays.",Nanomicro Lett,33163530,11/10/20,pubmed,0,8,bioinformatic,0.001330098,0.590927226,0.403752404,0.001330076,0.001330069,0.001330127,Genomics,0.91525537,TRUE,31.5,0.450058754,3.375,0.207051111,6,0.764429903,,,0.473846589
5315,A deep learning-based social distance monitoring framework for COVID-19.,Sustain Cities Soc,33163330,11/10/20,pubmed,0,5,"deep learning, transfer learning",0.001072196,0.088318613,0.412667183,0.495797621,0.001072225,0.001072162,Epidemiology,0.05369255,FALSE,52.4,0.649823737,16.4,0.441196147,12,0.850299401,,,0.647106428
5316,Exploring the potential therapeutic effect of traditional Chinese medicine on coronavirus disease 2019 (COVID-19) through a combination of data mining and network pharmacology analysis.,Eur J Integr Med,33163124,11/10/20,pubmed,0,4,data mining,0.431710355,0.001593598,0.08323712,0.00159362,0.34942928,0.132436026,Drug discovery,0.9828422,TRUE,83.5,0.819221968,,,0,0.403234768,,,0.611228368
5317,"The Neat Dance of COVID-19: NEAT1, DANCR, and Co-Modulated Cholinergic RNAs Link to Inflammation.",Front Immunol,33163005,11/10/20,pubmed,0,3,"bioinformatic, sequencing, transcriptom, dataset",0.632917243,0.11351686,0.000704957,0.000704945,0.050512168,0.201643827,Drug discovery,0.36439174,FALSE,187.3333333,0.967406766,213.3333333,0.923802515,0,0.403234768,,,0.764814683
5318,Impact of the Family Environment on the Emotional State of Medical Staff During the COVID-19 Outbreak: The Mediating Effect of Self-Efficacy.,Front Psychol,33162916,11/10/20,pubmed,0,7,correlation analysis,0.000822906,0.000822908,0.000822935,0.000822929,0.984110467,0.012597856,Healthcare,0.9833175,TRUE,13.57142857,0.205578576,2.714285714,0.186111854,1,0.537564047,,,0.309751493
5319,Vulnerabilities of the SARS-CoV-2 Virus to Proteotoxicity-Opportunity for Repurposed Chemotherapy of COVID-19 Infection.,Front Pharmacol,33162891,11/10/20,pubmed,0,7,proteom,0.888697624,0.001291304,0.001291239,0.001291256,0.001291244,0.106137331,Drug discovery,0.44596887,FALSE,28.57142857,0.413012555,33.85714286,0.597805727,1,0.537564047,,,0.516127443
5320,InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray.,Appl Soft Comput,33162872,11/10/20,pubmed,0,4,"deep learning, artificial intelligence",0.001330051,0.001330049,0.965806284,0.028873385,0.001330125,0.001330106,Imaging,0.78610575,TRUE,40.25,0.541715629,6.75,0.292079208,8,0.799987654,,,0.544594163
5321,Predicting psychological service providers' empowerment in the light of the COVID-19 pandemic outbreak: A structural equation modelling analysis.,Couns Psychother Res,33162833,11/10/20,pubmed,0,1,structural model,0.001059393,0.001059387,0.001059402,0.333907291,0.661855197,0.00105933,Healthcare,0.99190426,TRUE,14,0.213494959,0,0.055525823,1,0.537564047,,,0.26886161
5322,A comprehensive overview of proteomics approach for COVID 19: new perspectives in target therapy strategies.,J Proteins Proteom,33162722,11/10/20,pubmed,0,3,"proteom, metabolom",0.530060678,0.166434181,0.001461928,0.299119189,0.001461974,0.001462052,Drug discovery,0.90835613,TRUE,36.33333333,0.500773084,3,0.199424672,0,0.403234768,,,0.367810841
5323,Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia.,Process Saf Environ Prot,33162687,11/10/20,pubmed,0,10,"deep learning, artificial intelligence, neural network, forecasting model, lstm",0.001112622,0.001112638,0.216976048,0.77857344,0.00111263,0.001112621,Epidemiology,0.6069983,TRUE,90.7,0.844888367,42.7,0.648180359,2,0.618927094,,,0.703998606
5324,Strategies to exiting the COVID-19 lockdown for workplace and school: A scoping review.,Saf Sci,33162676,11/10/20,pubmed,0,12,mathematical model,0.001987138,0.001987117,0.001987148,0.990064315,0.001987184,0.001987099,Epidemiology,0.34114647,FALSE,24.83333333,0.365823489,4.916666667,0.250401391,1,0.537564047,,,0.384596309
5325,Psychological effects of the COVID-19 pandemic on Wuhan's high school students.,Child Youth Serv Rev,33162628,11/10/20,pubmed,0,4,structural model,0.0016532,0.001653157,0.001653039,0.001653069,0.991734509,0.001653026,Healthcare,0.9615746,TRUE,5.25,0.072855464,0.5,0.087101953,0,0.403234768,,,0.187730728
5326,Estimating the cumulative rate of SARS-CoV-2 infection.,Econ Lett,33162626,11/10/20,pubmed,0,2,"bayes, bayesian model",0.002996434,0.00299653,0.216108707,0.714064248,0.06083756,0.002996521,Epidemiology,0.23790523,FALSE,11,0.167171748,23.5,0.516791544,0,0.403234768,,,0.362399353
5327,Role of media coverage in mitigating COVID-19 transmission: Evidence from China.,Technol Forecast Soc Change,33162619,11/10/20,pubmed,0,3,dataset,0.002422293,0.002422314,0.002422251,0.987888533,0.002422352,0.002422257,Epidemiology,0.574777,TRUE,20,0.298163152,6.666666667,0.290607439,0,0.403234768,,,0.330668453
5328,Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images.,Pattern Recognit,33162612,11/10/20,pubmed,0,4,"image analysis, dataset",0.040699711,0.000999541,0.858102103,0.098199465,0.000999603,0.000999577,Imaging,0.68824005,TRUE,149.25,0.943533923,97.75,0.817968959,3,0.667819001,,,0.809773961
5329,"Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown.",Environ Pollut,33162213,11/10/20,pubmed,0,11,neural network,0.001461859,0.00146187,0.103203432,0.890949036,0.001461923,0.001461879,Epidemiology,0.41198173,FALSE,20.72727273,0.306821696,5.181818182,0.258830613,1,0.537564047,,,0.367738785
5330,Risk assessment of SARS-CoV-2 in Antarctic wildlife.,Sci Total Environ,33162142,11/10/20,pubmed,0,15,in silico,0.197247378,0.300525251,0.001593507,0.497446772,0.001593566,0.001593527,Epidemiology,0.81692564,TRUE,73.4,0.779268971,105.5333333,0.832151458,1,0.537564047,,,0.716328159
5331,Flow-Mediated Susceptibility and Molecular Response of Cerebral Endothelia to SARS-CoV-2 Infection.,Stroke,33161843,11/10/20,pubmed,0,12,sequencing,0.683198991,0.312293752,0.00112679,0.001126825,0.001126811,0.001126832,Drug discovery,0.89862806,TRUE,57.58333333,0.685880388,74.66666667,0.76558737,3,0.667819001,,,0.70642892
5332,Excretion of SARS-CoV-2 through faecal specimens.,Emerg Microbes Infect,33161824,11/10/20,pubmed,0,20,sequencing,0.001438298,0.782372304,0.001438126,0.00143823,0.001438197,0.211874845,Genomics,0.7451742,TRUE,87.5,0.835363968,,,3,0.667819001,,,0.751591485
5333,How the zoonotic origins of SARS-CoV-2 ensure its survival as a human disease.,Br J Community Nurs,33161739,11/10/20,pubmed,0,1,sequencing,0.002639019,0.785404291,0.002639025,0.165939077,0.002639006,0.040739582,Genomics,0.4724558,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
5334,The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose.,Comput Biol Med,33161334,11/9/20,pubmed,0,6,"deep learning, neural network, dataset",0.00165303,0.001653098,0.99173473,0.001653059,0.001653032,0.001653051,Imaging,0.33239123,FALSE,11,0.167171748,3,0.199424672,1,0.537564047,,,0.301386823
5335,Trends of mutation accumulation across global SARS-CoV-2 genomes: Implications for the evolution of the novel coronavirus.,Genomics,33161087,11/9/20,pubmed,0,7,"genome-wide, genomes",0.001943568,0.990282556,0.001943449,0.001943503,0.001943463,0.001943462,Genomics,0.6702939,TRUE,38.57142857,0.524645927,23,0.513513514,4,0.707574542,,,0.581911328
5336,0,Environ Res,33160973,11/9/20,pubmed,0,8,dataset,0.001786569,0.001786661,0.071058733,0.92179474,0.001786661,0.001786637,Epidemiology,0.34915674,FALSE,39.75,0.536829736,18,0.46180091,2,0.618927094,,,0.539185913
5337,Computational drug re-purposing targeting the spike glycoprotein of SARS-CoV-2 as an effective strategy to neutralize COVID-19.,Eur J Pharmacol,33160938,11/9/20,pubmed,0,4,"computational, in silico, proteom, sequence alignment",0.837396752,0.130689543,0.001330024,0.001330051,0.02792354,0.00133009,Drug discovery,0.73210746,TRUE,3,0.037293586,0.5,0.087101953,2,0.618927094,,,0.247774211
5338,Rapid Assessment at Hospital Admission of Mortality Risk From COVID-19: The Role of Functional Status.,J Am Med Dir Assoc,33160872,11/9/20,pubmed,0,6,logistic regression,0.034568713,0.001126798,0.001126817,0.082832421,0.001126842,0.87921841,Clinics,0.86631185,TRUE,104.5,0.878471148,167,0.897043083,2,0.618927094,,,0.798147108
5339,"The COVID-19 pandemic: How predictive analysis, artificial intelligence and GIS can be integrated into a clinical command system to improve disaster response and preparedness.",Am J Emerg Med,33160803,11/9/20,pubmed,0,3,artificial intelligence,0.218775531,0.015998925,0.717227495,0.01599961,0.015999378,0.015999061,Drug discovery,0.5336692,TRUE,51,0.63875317,5.666666667,0.270203372,3,0.667819001,,,0.525591848
5340,"COVID-19 is associated with clinically significant weight loss and risk of malnutrition, independent of hospitalisation: A post-hoc analysis of a prospective cohort study.",Clin Nutr,33160700,11/9/20,pubmed,0,9,logistic regression,0.00111265,0.001112623,0.001112676,0.001112645,0.171326286,0.824223121,Clinics,0.99249053,TRUE,30.22222222,0.434040448,25.22222222,0.530438855,5,0.739490092,,,0.567989798
5341,[Forecasting the Pandemic: The Role of Mathematical Models].,Acta Med Port,33160432,11/9/20,pubmed,0,6,mathematical model,0.034961874,0.034961874,0.034961874,0.825190631,0.034961874,0.034961874,Epidemiology,0.3085811,FALSE,31.16666667,0.445976869,14.33333333,0.415975381,0,0.403234768,,,0.421729006
5342,Shelter from the cytokine storm: pitfalls and prospects in the development of SARS-CoV-2 vaccines for an elderly population.,Semin Immunopathol,33159214,11/8/20,pubmed,0,6, omics,0.302909137,0.001486455,0.001486448,0.224769142,0.324332992,0.145015825,Healthcare,0.29420686,FALSE,270.3333333,0.987754345,519.1666667,0.98019802,7,0.785110192,,,0.917687519
5343,0,Sci Adv,33158912,11/8/20,pubmed,0,17,computational,0.923413045,0.001987148,0.068638525,0.001987117,0.001987075,0.00198709,Drug discovery,0.83527505,TRUE,44.17647059,0.579194755,30.11764706,0.570444207,20,0.900117291,,,0.683252084
5344,"Persistence of viral RNA, pneumocyte syncytia and thrombosis are hallmarks of advanced COVID-19 pathology.",EBioMedicine,33158808,11/8/20,pubmed,0,13,genomes,0.254644992,0.436613517,0.00139293,0.001392901,0.001392839,0.304562821,Genomics,0.75180876,TRUE,95.69230769,0.85830911,89.07692308,0.799438052,21,0.903944688,,,0.853897283
5345,Case report of a neonate with high viral SARSCoV-2 loads and long-term virus shedding.,J Infect Public Health,33158806,11/8/20,pubmed,0,7,sequencing,0.001943489,0.529509611,0.001943492,0.001943576,0.175794461,0.28886537,Genomics,0.8875228,TRUE,14.57142857,0.220112561,13.28571429,0.403130854,1,0.537564047,,,0.386935821
5346,"Shufeng Jiedu, a promising herbal therapy for moderate COVID-19:Antiviral and anti-inflammatory properties, pathways of bioactive compounds, and a clinical real-world pragmatic study.",Phytomedicine,33158717,11/8/20,pubmed,0,13,network analysis,0.612892026,0.001203462,0.001203495,0.074434451,0.001203438,0.309063127,Drug discovery,0.9314388,TRUE,26.30769231,0.38536706,22.46153846,0.506556061,4,0.707574542,,,0.533165888
5347,A human-centred assessment framework to prioritise heat mitigation efforts for active travel at city scale.,Sci Total Environ,33158537,11/8/20,pubmed,0,6,deep learning,0.001254644,0.001254649,0.201630048,0.740354038,0.054252013,0.001254608,Epidemiology,0.19463846,FALSE,20.16666667,0.299585627,5.833333333,0.273213808,0,0.403234768,,,0.325344734
5348,"Coevolution, Dynamics and Allostery Conspire in Shaping Cooperative Binding and Signal Transmission of the SARS-CoV-2 Spike Protein with Human Angiotensin-Converting Enzyme 2.",Int J Mol Sci,33158276,11/8/20,pubmed,0,1,"computational, network analysis",0.911715019,0.000830691,0.00083063,0.084962388,0.000830643,0.00083063,Drug discovery,0.52997047,TRUE,119,0.905683716,88,0.796895906,3,0.667819001,,,0.790132874
5349,Maternal and Perinatal Outcomes in Patients with Suspected COVID-19 and Their Relationship with a Negative RT-PCR Result.,J Clin Med,33158175,11/8/20,pubmed,0,10,predictive model,0.000926269,0.037986607,0.071954799,0.000926287,0.000926326,0.887279711,Clinics,0.94060564,TRUE,15.4,0.231616055,1.9,0.151993578,2,0.618927094,,,0.334178909
5350,Threading the Pieces Together: Integrative Perspective on SARS-CoV-2.,Pathogens,33158051,11/8/20,pubmed,0,7,"transcriptom, metagenom",0.246693696,0.243983319,0.001823572,0.368009812,0.001823457,0.137666144,Epidemiology,0.79440767,TRUE,24,0.35574247,4.571428571,0.24337704,0,0.403234768,,,0.334118093
5351,"Risk factors for Coronavirus disease 2019 pneumonia after admission outside Wuhan, China.",Medicine (Baltimore),33157941,11/8/20,pubmed,0,11,prediction model,0.00151189,0.001511853,0.248597798,0.001511894,0.057288365,0.689578201,Clinics,0.97451544,TRUE,41.18181818,0.549941246,11.72727273,0.381188119,0,0.403234768,,,0.444788044
5352,Risk factors associated with disease aggravation among 126 hospitalized patients with COVID-19 in different places in China: A retrospective observational study.,Medicine (Baltimore),33157938,11/8/20,pubmed,0,10,logistic regression,0.001438154,0.00143812,0.001438108,0.001438129,0.00143816,0.992809329,Clinics,0.95703185,TRUE,36.5,0.503123261,9.8,0.350682366,0,0.403234768,,,0.419013465
5353,SARS-CoV-2 spread across the Colombian-Venezuelan border.,Infect Genet Evol,33157300,11/7/20,pubmed,0,24,"sequencing, genomes",0.001022652,0.883632233,0.001022654,0.100441818,0.00102264,0.012858003,Genomics,0.9479766,TRUE,44.66666667,0.583462181,65.375,0.740165908,0,0.403234768,,,0.575620952
5354,Does greenery experienced indoors and outdoors provide an escape and support mental health during the COVID-19 quarantine?,Environ Res,33157110,11/7/20,pubmed,0,7,logistic regression,0.000946124,0.00094611,0.000946094,0.000946171,0.956459621,0.03975588,Healthcare,0.85484874,TRUE,60.28571429,0.704496258,30.28571429,0.571849077,3,0.667819001,,,0.648054779
5355,Analysis of the scientific production of the effect of COVID-19 on the environment: A bibliometric study.,Environ Res,33157104,11/7/20,pubmed,0,3,artificial intelligence,0.001254655,0.001254649,0.062318008,0.932663285,0.001254777,0.001254626,Epidemiology,0.33363461,FALSE,25,0.369286907,21,0.492239765,2,0.618927094,,,0.493484589
5356,A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19.,PLoS Biol,33156843,11/7/20,pubmed,0,18,"sequencing, transcriptom, proteom, metabolom, interactom, multi-omics",0.459812935,0.108970677,0.000966807,0.072881451,0.000966804,0.356401326,Drug discovery,0.71201104,TRUE,123.8888889,0.912486858,267.8333333,0.945143163,4,0.707574542,,,0.855068187
5357,Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media.,J Med Internet Res,33156805,11/7/20,pubmed,0,4,"classifier, transfer learning",0.121223819,0.000880213,0.035011142,0.246164286,0.595840346,0.000880194,Healthcare,0.001868308,FALSE,167.25,0.956521739,195,0.9151057,0,0.403234768,,,0.758287402
5358,A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT.,IEEE Trans Med Imaging,33156775,11/7/20,pubmed,0,8,"deep learning, neural network",0.000999518,0.000999521,0.995002367,0.000999545,0.000999509,0.00099954,Imaging,0.57279193,TRUE,79.5,0.80493537,209.5,0.921795558,80,0.973023026,,,0.899917985
5359,"Long-Term Existence of SARS-CoV-2 in COVID-19 Patients: Host Immunity, Viral Virulence, and Transmissibility.",Virol Sin,33156486,11/7/20,pubmed,0,11,sequencing,0.183378585,0.306528096,0.001237072,0.001237121,0.00123708,0.506382047,Clinics,0.33951318,FALSE,59.90909091,0.702146082,56.09090909,0.707118009,1,0.537564047,,,0.648942713
5360,Treatment for Anxiety and Substance Use Disorders During the COVID-19 Pandemic: Challenges and Strategies.,J Addict Med,33156267,11/7/20,pubmed,0,7,digital health,0.002183248,0.002183226,0.002183357,0.193147322,0.798119651,0.002183196,Healthcare,0.87067103,TRUE,60.71428571,0.706537201,48.14285714,0.674270805,1,0.537564047,,,0.639457351
5361,A Computational Approach for Identifying Potential Phytochemicals Against Non-structural Protein 1 (Nsp1) of SARS-CoV-2.,Comb Chem High Throughput Screen,33155887,11/7/20,pubmed,0,1,computational,0.92332031,0.001272654,0.001272697,0.001272718,0.001272707,0.071588914,Drug discovery,0.9356319,TRUE,23,0.34225988,6,0.280037463,0,0.403234768,,,0.341844037
5362,"Risk Factors for Mortality in 220 Patients With COVID-19 in Wuhan, China: A Single-Center, Retrospective Study.",Ear Nose Throat J,33155834,11/7/20,pubmed,0,7,logistic regression,0.00168446,0.001684468,0.001684465,0.001684474,0.00168451,0.991577624,Clinics,0.94291633,TRUE,111.8571429,0.893685447,,,0,0.403234768,,,0.648460108
5363,Identification and characterization of novel RdRp and Nsp15 inhibitors for SARS-COV2 using computational approach.,J Biomol Struct Dyn,33155531,11/7/20,pubmed,0,7,computational,0.992924386,0.001415115,0.001415141,0.001415145,0.001415122,0.001415092,Drug discovery,0.8809451,TRUE,203.2857143,0.973344053,90.14285714,0.801511908,1,0.537564047,,,0.77080667
5364,How could we forget immunometabolism in SARS-CoV2 infection or COVID-19?,Int Rev Immunol,33155525,11/7/20,pubmed,0,1,immunome,0.684329478,0.001511923,0.001511855,0.001511959,0.001511875,0.309622909,Drug discovery,0.7981249,TRUE,88,0.837095677,0,0.055525823,2,0.618927094,,,0.503849531
5365,"Computationally validated SARS-CoV-2 CTL and HTL Multi-Patch vaccines, designed by reverse epitomics approach, show potential to cover large ethnically distributed human population worldwide.",J Biomol Struct Dyn,33155524,11/7/20,pubmed,0,13,"computational, proteom",0.756481115,0.136263586,0.001350402,0.103204195,0.00135034,0.001350362,Drug discovery,0.42327,FALSE,33.61538462,0.472323582,14,0.412898047,1,0.537564047,,,0.474261892
5366,Living with Coronavirus (COVID-19): a brief report.,Eur Rev Med Pharmacol Sci,33155254,11/7/20,pubmed,0,4,prediction model,0.125788887,0.090015783,0.001254649,0.71792973,0.063756312,0.001254639,Epidemiology,0.43365878,FALSE,2.25,0.023439916,0,0.055525823,0,0.403234768,,,0.160733502
5367,Adaptive behavioural coping strategies as reaction to COVID-19 social distancing in Italy.,Eur Rev Med Pharmacol Sci,33155248,11/7/20,pubmed,0,6,dataset,0.031793448,0.001022687,0.001022705,0.578486904,0.386651617,0.001022641,Epidemiology,0.8847592,TRUE,29,0.41993939,3,0.199424672,2,0.618927094,,,0.412763719
5368,The Structure of the Membrane Protein of SARS-CoV-2 Resembles the Sugar Transporter SemiSWEET.,Pathog Immun,33154981,11/7/20,pubmed,0,1,in silico,0.935550572,0.04556221,0.000863031,0.00086309,0.000863075,0.016298023,Drug discovery,0.6683067,TRUE,84,0.821881378,145,0.879114263,0,0.403234768,,,0.701410136
5369,Baseline results of a living systematic review for COVID-19 clinical trial registrations.,Wellcome Open Res,33154979,11/7/20,pubmed,0,29,knowledge graph,0.11374426,0.001237153,0.001237102,0.754108218,0.128436114,0.001237153,Epidemiology,0.809556,TRUE,49.31034483,0.626136434,87.82758621,0.796427616,4,0.707574542,,,0.710046197
5370,0,Front Immunol,33154753,11/7/20,pubmed,0,24,logistic regression,0.494074744,0.001098906,0.001098833,0.001098835,0.001098824,0.501529857,Clinics,0.9862141,TRUE,22.375,0.330632692,22.45833333,0.506489162,7,0.785110192,,,0.540744016
5371,Acceptability of Vaccination Against COVID-19 Among Healthcare Workers in the Democratic Republic of the Congo.,Pragmat Obs Res,33154695,11/7/20,pubmed,0,9,logistic regression,0.00182337,0.001823374,0.001823335,0.001823377,0.904291538,0.088415006,Healthcare,0.29369932,FALSE,1.555555556,0.015585379,0,0.055525823,18,0.891474782,,,0.320861995
5372,Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.,Sci Rep,33154542,11/7/20,pubmed,0,24,"deep learning, dataset",0.001371233,0.001371244,0.697450388,0.001371295,0.00137128,0.29706456,Imaging,0.77526164,TRUE,136.625,0.928814398,,,17,0.887338725,,,0.908076561
5373,Impact of tocilizumab administration on mortality in severe COVID-19.,Sci Rep,33154452,11/7/20,pubmed,0,4,dataset,0.092866402,0.001310352,0.001310408,0.001310409,0.001310432,0.901891997,Clinics,0.80467045,TRUE,63.25,0.722926588,40.25,0.634934439,1,0.537564047,,,0.631808358
5374,Searching for target-specific and multi-targeting organics for Covid-19 in the Drugbank database with a double scoring approach.,Sci Rep,33154404,11/7/20,pubmed,0,4,computational,0.956677912,0.000786353,0.000786382,0.040176593,0.000786417,0.000786342,Drug discovery,0.7775638,TRUE,124,0.912981632,342,0.964409955,7,0.785110192,,,0.887500593
5375,"Epidemiological determinants of COVID-19 infection and mortality: A study among patients presenting with severe acute respiratory illness during the pandemic in Bihar, India.",Niger Postgrad Med J,33154281,11/7/20,pubmed,0,3,logistic regression,0.001371226,0.001371319,0.001371264,0.001371348,0.282671465,0.711843378,Clinics,0.85332215,TRUE,48.33333333,0.616921269,42.33333333,0.6462403,0,0.403234768,,,0.555465445
5376,Can quantitative RT-PCR for SARS-CoV-2 help in better management of patients and control of coronavirus disease 2019 pandemic.,Indian J Med Microbiol,33154236,11/7/20,pubmed,0,2,"whole genome, genome sequences",0.002032793,0.804802605,0.002032897,0.145095854,0.00203281,0.044003042,Genomics,0.5065748,TRUE,8.5,0.126662131,0,0.055525823,1,0.537564047,,,0.239917334
5377,Characteristics and outcomes of COVID-19 associated stroke: a UK multicentre case-control study.,J Neurol Neurosurg Psychiatry,33154179,11/7/20,pubmed,0,69,logistic regression,0.00218322,0.002183467,0.002183284,0.002183297,0.002183352,0.98908338,Clinics,0.81840086,TRUE,51.3,0.640484878,68.35,0.748795826,3,0.667819001,,,0.685699902
5378,Versatile and multivalent nanobodies efficiently neutralize SARS-CoV-2.,Science,33154108,11/7/20,pubmed,0,9,proteom,0.938719327,0.002996573,0.049294506,0.002996545,0.002996482,0.002996566,Drug discovery,0.48965558,FALSE,44.33333333,0.580679077,41.55555556,0.642226385,22,0.908142478,,,0.710349313
5379,The impact of COVID-19 on patients with asthma.,Eur Respir J,33154029,11/7/20,pubmed,0,8,artificial intelligence,0.001943505,0.040604308,0.094508721,0.001943548,0.001943604,0.859056314,Clinics,0.9904356,TRUE,61.57142857,0.712907415,79.71428571,0.778364999,15,0.874313229,,,0.788528548
5380,Outcomes of Nursing Home COVID-19 Patients by Initial Symptoms and Comorbidity: Results of Universal Testing of 1970 Residents.,J Am Med Dir Assoc,33153910,11/7/20,pubmed,0,7,logistic regression,0.001237032,0.001237065,0.001237054,0.001237087,0.477540518,0.517511244,Clinics,0.5985524,TRUE,36.14285714,0.498793988,14.57142857,0.418918919,4,0.707574542,,,0.541762483
5381,Laboratory diagnosis of COVID-19 in China: A review of challenging cases and analysis.,J Microbiol Immunol Infect,33153907,11/7/20,pubmed,0,8,sequencing,0.001330073,0.63733585,0.35734381,0.001330125,0.001330092,0.00133005,Genomics,0.8059017,TRUE,143,0.937596636,164.25,0.895571314,0,0.403234768,,,0.745467572
5382,Artificial Intelligence ECG to Detect Left Ventricular Dysfunction in COVID-19: A Case Series.,Mayo Clin Proc,33153634,11/7/20,pubmed,0,5,artificial intelligence,0.001486427,0.001486464,0.331190917,0.001486491,0.001486485,0.662863216,Clinics,0.9068234,TRUE,139.8,0.93332921,61.4,0.725849612,0,0.403234768,,,0.687471197
5383,Should we remain hopeful? The key 8 weeks: spatiotemporal epidemic characteristics of COVID-19 in Sichuan Province and its comparative analysis with other provinces in China and global epidemic trends.,BMC Infect Dis,33153445,11/7/20,pubmed,0,15,correlation analysis,0.001010962,0.001010971,0.015488547,0.935757207,0.001010969,0.045721343,Epidemiology,0.48208046,FALSE,67.66666667,0.75137609,53.33333333,0.695009366,0,0.403234768,,,0.616540074
5384,CoSinGAN: Learning COVID-19 Infection Segmentation from a Single Radiological Image.,Diagnostics (Basel),33153105,11/7/20,pubmed,0,5,"deep model, dataset",0.001112626,0.001112634,0.994436814,0.001112643,0.001112647,0.001112635,Imaging,0.3065772,FALSE,23,0.34225988,4,0.231469093,3,0.667819001,,,0.413849325
5385,Modeling the Impact of Unreported Cases of the COVID-19 in the North African Countries.,Biology (Basel),33153015,11/7/20,pubmed,0,4,mathematical model,0.002638956,0.002639035,0.002638963,0.986804978,0.002638997,0.002639071,Epidemiology,0.16988713,FALSE,15.25,0.229884347,0.75,0.099411292,1,0.537564047,,,0.288953229
5386,Post-COVID-19 Epidemic: Allostatic Load among Medical and Nonmedical Workers in China.,Psychother Psychosom,33152729,11/6/20,pubmed,0,40,logistic regression,0.001593477,0.112874003,0.001593483,0.001593535,0.880751914,0.001593588,Healthcare,0.9409306,TRUE,78.375,0.79980209,,,2,0.618927094,,,0.709364592
5387,The association between subjective impact and the willingness to adopt healthy dietary habits after experiencing the outbreak of the 2019 novel coronavirus disease (COVID-19): a cross-sectional study in China.,Aging (Albany NY),33152709,11/6/20,pubmed,0,11,logistic regression,0.001901689,0.001901702,0.001901702,0.001901824,0.990491377,0.001901705,Healthcare,0.6847536,TRUE,108.2727273,0.886449378,68.72727273,0.749464811,1,0.537564047,,,0.724492746
5388,"Molecular screening of antimalarial, antiviral, anti-inflammatory and HIV protease inhibitors against spike glycoprotein of coronavirus.",J Mol Graph Model,33152616,11/6/20,pubmed,0,11,virtual screening,0.970567873,0.000966752,0.000966772,0.00096675,0.000966767,0.025565085,Drug discovery,0.9804325,TRUE,4.363636364,0.058012246,0,0.055525823,0,0.403234768,,,0.172257612
5389,"COVID-19 in French patients with chronic inflammatory rheumatic diseases: Clinical features, risk factors and treatment adherence.",Joint Bone Spine,33152484,11/6/20,pubmed,0,8,logistic regression,0.055615727,0.001751181,0.001751162,0.001751258,0.494010731,0.445119941,Healthcare,0.8011399,TRUE,53.75,0.659348135,63.25,0.73300776,3,0.667819001,,,0.686724966
5390,SARS-CoV-2 seroprevalence and transmission risk factors among high-risk close contacts: a retrospective cohort study.,Lancet Infect Dis,33152271,11/6/20,pubmed,0,25,"bayes, bayesian model, logistic regression",0.000591781,0.000591806,0.000591779,0.569983113,0.357358996,0.070882524,Epidemiology,0.64027035,TRUE,37.68,0.515492609,51.32,0.686379449,16,0.881782826,,,0.694551628
5391,SARS-CoV-2 sequencing: The technological initiative to strengthen early warning systems for public health emergencies in Latin America and the Caribbean,Biomedica,33152203,11/6/20,pubmed,0,4,"sequencing, genomic epidemiology",0.001291231,0.718638634,0.00129124,0.276196352,0.001291329,0.001291214,Genomics,0.83781886,TRUE,19.25,0.287896592,1.25,0.127776291,0,0.403234768,,,0.272969217
5392,"Isolation and characterization of an early SARS-CoV-2 isolate from the 2020 epidemic in Medellín, Colombia",Biomedica,33152198,11/6/20,pubmed,0,9,sequencing,0.369953986,0.529804896,0.025060221,0.02506022,0.025060221,0.025060456,Genomics,0.84151286,TRUE,36.22222222,0.499597996,13.22222222,0.402394969,2,0.618927094,,,0.506973353
5393,"Variant analysis of 1,040 SARS-CoV-2 genomes.",PLoS One,33152019,11/6/20,pubmed,0,3,"sequencing, whole genome, genomes",0.002806387,0.985968037,0.002806439,0.002806419,0.002806383,0.002806335,Genomics,0.41550416,FALSE,71.66666667,0.771414435,38.33333333,0.62356168,4,0.707574542,,,0.700850219
5394,Relationship Between COVID-19 Information Sources and Attitudes in Battling the Pandemic Among the Malaysian Public: Cross-Sectional Survey Study.,J Med Internet Res,33151897,11/6/20,pubmed,0,6,logistic regression,0.001717183,0.001717193,0.001717221,0.426628442,0.566502782,0.001717178,Healthcare,0.8474213,TRUE,18.5,0.278001113,5.833333333,0.273213808,0,0.403234768,,,0.318149896
5395,Predictors of psychological distress among the public in Oman amid coronavirus disease 2019 pandemic: a cross-sectional analytical study.,Psychol Health Med,33151748,11/6/20,pubmed,0,10,logistic regression,0.001717177,0.001717186,0.001717172,0.001717232,0.940080212,0.053051022,Healthcare,0.9036081,TRUE,7,0.10179974,,,1,0.537564047,,,0.319681894
5396,"Divergent: Age, Frailty, and Atypical Presentations of COVID-19 in Hospitalized Patients.",J Gerontol A Biol Sci Med Sci,33151305,11/6/20,pubmed,0,13,logistic regression,0.00131033,0.001310335,0.001310323,0.001310334,0.111892956,0.882865722,Clinics,0.88906085,TRUE,4.692307692,0.063516606,0.076923077,0.055860316,3,0.667819001,,,0.262398641
5397,DrugCentral 2021 supports drug discovery and repositioning.,Nucleic Acids Res,33151287,11/6/20,pubmed,0,14,machine learning,0.503922621,0.002130736,0.087283872,0.359024559,0.00213079,0.045507422,Drug discovery,0.6411395,TRUE,59.71428571,0.701032841,82.78571429,0.785322451,5,0.739490092,,,0.741948461
5398,Sitagliptin: a potential drug for the treatment of COVID-19?,Acta Pharm,33151168,11/6/20,pubmed,0,3,"bioinformatic, network analysis",0.817790002,0.002238521,0.002238571,0.00223858,0.002238503,0.173255822,Drug discovery,0.7545218,TRUE,35,0.488032655,5,0.257024351,6,0.764429903,,,0.503162303
5399,Lysine 164 is critical for SARS-CoV-2 Nsp1 inhibition of host gene expression.,J Gen Virol,33151142,11/6/20,pubmed,0,6,bioinformatic,0.943651092,0.001751257,0.00175117,0.049344061,0.001751259,0.001751162,Drug discovery,0.58336943,TRUE,25.66666667,0.376461129,21.16666667,0.493310142,1,0.537564047,,,0.469111773
5400,"Viral RNA level, serum antibody responses, and transmission risk in recovered COVID-19 patients with recurrent positive SARS-CoV-2 RNA test results: a population-based observational cohort study.",Emerg Microbes Infect,33151135,11/6/20,pubmed,0,35,"sequencing, whole genome",0.001187346,0.554540082,0.001187285,0.001187348,0.001187325,0.440710613,Genomics,0.101635754,FALSE,61.54285714,0.712598182,36.11428571,0.611653733,7,0.785110192,,,0.703120702
5401,"Traditional medicinal plants against replication, maturation and transmission targets of SARS-CoV-2: computational investigation.",J Biomol Struct Dyn,33150860,11/6/20,pubmed,0,5,"computational, in silico",0.993448317,0.00131033,0.001310358,0.001310338,0.001310328,0.001310328,Drug discovery,0.98236406,TRUE,8.6,0.127280599,2.4,0.174872893,0,0.403234768,,,0.23512942
5402,0,J Biomol Struct Dyn,33150855,11/6/20,pubmed,0,9,"computational, correlation analysis",0.994886914,0.001022616,0.001022614,0.001022623,0.001022617,0.001022617,Drug discovery,0.91158617,TRUE,85.44444444,0.826272497,34,0.5990768,1,0.537564047,,,0.654304448
5403,Impact of non-pharmaceutical interventions on the incidence of respiratory infections during the COVID-19 outbreak in Korea: a nationwide surveillance study.,Clin Infect Dis,33150393,11/6/20,pubmed,0,7,prediction model,0.001538092,0.035612393,0.001538087,0.699131188,0.001538188,0.260642052,Epidemiology,0.47011587,FALSE,49.28571429,0.626074587,13.14285714,0.401525288,1,0.537564047,,,0.521721307
5404,Selection of an Optimal Combination Panel to Better Triage COVID-19 Hospitalized Patients.,J Inflamm Res,33149652,11/6/20,pubmed,0,6,logistic regression,0.000793433,0.000793409,0.107705057,0.000793418,0.000793409,0.889121273,Clinics,0.34281486,FALSE,11.5,0.17416043,,,1,0.537564047,,,0.355862239
5405,Individual Perceived Stress Mediates Psychological Distress in Medical Workers During COVID-19 Epidemic Outbreak in Wuhan.,Neuropsychiatr Dis Treat,33149594,11/6/20,pubmed,0,8,logistic regression,0.001112633,0.001112697,0.001112641,0.001112676,0.975827707,0.019721646,Healthcare,0.872656,TRUE,80.5,0.809202795,,,1,0.537564047,,,0.673383421
5406,"Depression, Anxiety and Associated Factors Among Chronic Medical Patients Amid COVID-19 Pandemic in Mettu Karl Referral Hospital, Mettu, Ethiopia, 2020.",Neuropsychiatr Dis Treat,33149592,11/6/20,pubmed,0,4,logistic regression,0.000999529,0.000999524,0.000999557,0.000999526,0.676505593,0.31949627,Healthcare,0.98861355,TRUE,4.5,0.061784897,0.25,0.065493712,1,0.537564047,,,0.221614219
5407,Targeting the GRP78-Dependant SARS-CoV-2 Cell Entry by Peptides and Small Molecules.,Bioinform Biol Insights,33149560,11/6/20,pubmed,0,9,in silico,0.994218736,0.001156278,0.001156245,0.001156262,0.001156246,0.001156233,Drug discovery,0.70655495,TRUE,33.22222222,0.468241697,5.444444444,0.264383195,0,0.403234768,,,0.378619887
5408,New Pathways of Mutational Change in SARS-CoV-2 Proteomes Involve Regions of Intrinsic Disorder Important for Virus Replication and Release.,Evol Bioinform Online,33149541,11/6/20,pubmed,0,7,"proteom, whole-genome, genomes",0.240232355,0.734630835,0.000880205,0.000880238,0.022496136,0.000880231,Genomics,0.61021465,TRUE,22,0.326056033,17.42857143,0.454776559,0,0.403234768,,,0.39468912
5409,An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot.,Sci Rep,33149198,11/6/20,pubmed,0,7,"artificial intelligence, digital health",0.002806454,0.002806438,0.540178826,0.24062071,0.210781076,0.002806496,Epidemiology,0.2996338,FALSE,15.71428571,0.236873029,7,0.299973241,0,0.403234768,,,0.313360346
5410,Independent association of meteorological characteristics with initial spread of Covid-19 in India.,Sci Total Environ,33148430,11/6/20,pubmed,0,5,dataset,0.002296579,0.050374011,0.002296546,0.940439554,0.002296632,0.002296679,Epidemiology,0.6849027,TRUE,42.6,0.563176449,34.6,0.602488627,0,0.403234768,,,0.522966615
5411,Perceived social support and compliance with stay-at-home orders during the COVID-19 outbreak: evidence from Iran.,BMC Public Health,33148209,11/6/20,pubmed,0,5,logistic regression,0.001034574,0.001034582,0.001034601,0.163166406,0.832695242,0.001034595,Healthcare,0.97337025,TRUE,98.4,0.865607026,260.2,0.941597538,1,0.537564047,,,0.781589537
5412,Parental psychological distress associated with COVID-19 outbreak: A large-scale multicenter survey from Turkey.,Int J Soc Psychiatry,33148091,11/6/20,pubmed,0,32,logistic regression,0.001486389,0.001486494,0.001486407,0.001486424,0.992567748,0.001486538,Healthcare,0.8481731,TRUE,13.0625,0.1975385,,,0,0.403234768,,,0.300386634
5413,0,J Phys Chem Lett,33147968,11/6/20,pubmed,0,2,in silico,0.86398202,0.081288202,0.001823419,0.049259638,0.001823348,0.001823374,Drug discovery,0.47798163,FALSE,87,0.833694106,128.5,0.86178753,4,0.707574542,,,0.801018726
5414,First Phylogenetic Analysis of Malian SARS-CoV-2 Sequences Provides Molecular Insights into the Genomic Diversity of the Sahel Region.,Viruses,33147840,11/6/20,pubmed,0,14,"sequencing, whole genome, genome sequences",0.002032803,0.910262761,0.002032846,0.052526182,0.002032918,0.03111249,Genomics,0.24688122,FALSE,16,0.243552477,32.14285714,0.58629917,2,0.618927094,,,0.482926247
5415,Epitope-Based Immunoinformatics Approach on Nucleocapsid Protein of Severe Acute Respiratory Syndrome-Coronavirus-2.,Molecules,33147821,11/6/20,pubmed,0,12,computational,0.757196372,0.190978949,0.001987143,0.04586318,0.001987162,0.001987194,Drug discovery,0.46209148,FALSE,31.75,0.452408931,21.41666667,0.496320578,4,0.707574542,,,0.55210135
5416,Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.,Brief Bioinform,33147627,11/5/20,pubmed,0,55,"computational, bioinformatic, sequencing",0.224415964,0.556116049,0.211996445,0.002490586,0.00249053,0.002490426,Genomics,0.52574444,TRUE,42.27272727,0.559960418,246.1454545,0.936847739,10,0.828199272,,,0.775002476
5417,A review on drug repurposing applicable to COVID-19.,Brief Bioinform,33147623,11/5/20,pubmed,0,4,"computational, artificial intelligence",0.680887911,0.001622752,0.312621102,0.001622781,0.001622725,0.00162273,Drug discovery,0.47490442,FALSE,19.5,0.29117447,5.25,0.260971367,2,0.618927094,,,0.390357644
5418,Identification of Required Host Factors for SARS-CoV-2 Infection in Human Cells.,Cell,33147445,11/5/20,pubmed,0,17,sequencing,0.710174269,0.28333475,0.001622761,0.001622742,0.001622774,0.001622703,Drug discovery,0.3094038,FALSE,50.17647059,0.632259262,183.6470588,0.908616537,59,0.963516266,,,0.834797355
5419,Genome-wide CRISPR Screens Reveal Host Factors Critical for SARS-CoV-2 Infection.,Cell,33147444,11/5/20,pubmed,0,31,genome-wide,0.774929909,0.21806549,0.001751132,0.001751162,0.001751164,0.001751144,Drug discovery,0.6161388,TRUE,49.58064516,0.62867215,108.3548387,0.836901258,40,0.947033768,,,0.804202392
5420,Streamlining follicular monitoring during controlled ovarian stimulation: a data-driven approach to efficient IVF care in the new era of social distancing.,Hum Reprod,33147345,11/5/20,pubmed,0,3,machine learning,0.000634219,0.000634211,0.227429486,0.528556067,0.061601769,0.181144248,Epidemiology,0.8677865,TRUE,2.666666667,0.029377203,0,0.055525823,0,0.403234768,,,0.162712598
5421,Liver injury with COVID-19 based on gastrointestinal symptoms and pneumonia severity.,PLoS One,33147270,11/5/20,pubmed,0,12,logistic regression,0.001126864,0.001126874,0.039989695,0.001126823,0.001126818,0.955502926,Clinics,0.9764236,TRUE,143.5,0.937782176,105.25,0.831816965,0,0.403234768,,,0.72427797
5422,Public perceptions of the effectiveness of recommended non-pharmaceutical intervention behaviors to mitigate the spread of SARS-CoV-2.,PLoS One,33147261,11/5/20,pubmed,0,5,logistic regression,0.001085343,0.001085345,0.00108538,0.26124241,0.734416129,0.001085393,Healthcare,0.5143174,TRUE,103.6,0.876677593,278.2,0.948287396,9,0.814309525,,,0.879758172
5423,Forecasting imported COVID-19 cases in South Korea using mobile roaming data.,PLoS One,33147252,11/5/20,pubmed,0,2,prediction model,0.001622711,0.001622719,0.001622765,0.685445774,0.001622792,0.308063241,Epidemiology,0.22369549,FALSE,30,0.432432432,8.5,0.329141022,0,0.403234768,,,0.388269407
5424,The impact of believing you have had COVID-19 on self-reported behaviour: Cross-sectional survey.,PLoS One,33147219,11/5/20,pubmed,0,6,logistic regression,0.001486446,0.001486456,0.001486436,0.159375241,0.834678929,0.001486491,Healthcare,0.095713764,FALSE,139.6666667,0.933205517,248,0.937583623,7,0.785110192,,,0.885299777
5425,CT and clinical assessment in asymptomatic and pre-symptomatic patients with early SARS-CoV-2 in outbreak settings.,Eur Radiol,33146796,11/5/20,pubmed,0,24,"deep learning, logistic regression",0.000889075,0.000889082,0.323833414,0.034800701,0.00088908,0.638698647,Clinics,0.37652898,FALSE,58.16666667,0.69020966,47.08333333,0.66885202,0,0.403234768,,,0.587432149
5426,Characterizing transcriptional regulatory sequences in coronaviruses and their role in recombination.,Mol Biol Evol,33146390,11/5/20,pubmed,0,4,genomes,0.00242244,0.987888344,0.002422298,0.00242235,0.002422275,0.002422293,Genomics,0.45595387,FALSE,26,0.382398417,79.25,0.776759433,0,0.403234768,,,0.520797539
5427,Modelling the spread of SARS-CoV-2 pandemic - Impact of lockdowns & interventions.,Indian J Med Res,33146155,11/5/20,pubmed,0,3,"computational, mathematical model",0.001220044,0.014312538,0.001220022,0.831357279,0.093123853,0.058766264,Epidemiology,0.5283281,TRUE,64.33333333,0.730781124,72,0.7594327,8,0.799987654,,,0.763400493
5428,A Computational Modeling Study of COVID-19 in Bangladesh.,Am J Trop Med Hyg,33146109,11/5/20,pubmed,0,4,computational,0.001786519,0.001786563,0.001786587,0.925373027,0.001786648,0.067480656,Epidemiology,0.30491874,FALSE,18.5,0.278001113,24.25,0.523414504,1,0.537564047,,,0.446326555
5429,Pathway enrichment analysis of virus-host interactome and prioritization of novel compounds targeting the spike glycoprotein receptor binding domain-human angiotensin-converting enzyme 2 interface to combat SARS-CoV-2.,J Biomol Struct Dyn,33146070,11/5/20,pubmed,0,7,"virtual screening, molecular dynamics simulation, interactom",0.994436902,0.001112637,0.00111261,0.001112617,0.001112628,0.001112606,Drug discovery,0.90608543,TRUE,15.57142857,0.23495578,2.428571429,0.175274284,1,0.537564047,,,0.31593137
5430,"Headache associated with COVID-19: Frequency, characteristics and association with anosmia and ageusia.",Cephalalgia,33146035,11/5/20,pubmed,0,2,logistic regression,0.002422287,0.002422477,0.061488584,0.00242234,0.002422402,0.928821909,Clinics,0.99696076,TRUE,22.5,0.333539489,4.5,0.242708055,6,0.764429903,,,0.446892482
5431,Correlation between emotional intelligence and negative emotions of front-line nurses during the COVID-19 epidemic: A cross-sectional study.,J Clin Nurs,33145859,11/5/20,pubmed,0,7,correlation analysis,0.001156266,0.001156286,0.177336383,0.00115633,0.818038447,0.001156288,Healthcare,0.9325281,TRUE,40.71428571,0.546354134,7.285714286,0.30331817,1,0.537564047,,,0.462412117
5432,Identifying propaganda from online social networks during COVID-19 using machine learning techniques.,Int J Inf Technol,33145473,11/5/20,pubmed,0,3,"machine learning, deep learning",0.002183322,0.00218327,0.608362357,0.382904474,0.00218338,0.002183198,Epidemiology,0.7156229,TRUE,5.666666667,0.079473066,0,0.055525823,1,0.537564047,,,0.224187645
5433,Analysis of 2019-nCoV receptor ACE2 expression in different tissues and its significance study.,Ann Transl Med,33145296,11/5/20,pubmed,0,5,transcriptom,0.69524734,0.058078081,0.001085347,0.001085413,0.001085374,0.243418445,Drug discovery,0.84248734,TRUE,195.4,0.971303111,55.4,0.703438587,3,0.667819001,,,0.780853566
5434,Understanding building-occupant-microbiome interactions toward healthy built environments: A review.,Front Environ Sci Eng,33145119,11/5/20,pubmed,0,5,microbiom,0.194549183,0.220957262,0.001538176,0.579879111,0.001538174,0.001538094,Epidemiology,0.8143739,TRUE,17.8,0.268229328,6.4,0.286058336,1,0.537564047,,,0.36395057
5435,Bioinformatic study to discover natural molecules with activity against COVID-19.,F1000Res,33145015,11/5/20,pubmed,0,2,bioinformatic,0.916170227,0.002238561,0.00223851,0.074875706,0.002238538,0.002238458,Drug discovery,0.94816434,TRUE,28.5,0.412579628,3,0.199424672,1,0.537564047,,,0.383189449
5436,[Epidemiology of SARS-CoV-2/COVID-19].,Gastroenterologe,33144889,11/5/20,pubmed,0,8,mathematical model,0.001565338,0.105631989,0.001565348,0.472461788,0.001565377,0.417210161,Epidemiology,0.29106283,FALSE,14.25,0.215535902,4.375,0.238426545,0,0.403234768,,,0.285732405
5437,Mathematical Models for COVID-19 Pandemic: A Comparative Analysis.,J Indian Inst Sci,33144763,11/5/20,pubmed,0,6,mathematical model,0.003101515,0.003101541,0.003101455,0.984492428,0.003101589,0.003101472,Epidemiology,0.45910385,FALSE,114.8333333,0.898818727,159.5,0.8914905,0,0.403234768,,,0.731181332
5438,The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia.,Sci Rep,33144676,11/5/20,pubmed,0,10,"machine learning, radiom",0.001272627,0.001272632,0.62527685,0.001272636,0.001272649,0.369632606,Imaging,0.7015428,TRUE,13.9,0.209784155,,,1,0.537564047,,,0.373674101
5439,Intra-host non-synonymous diversity at a neutralizing antibody epitope of SARS-CoV-2 spike protein N-terminal domain.,Clin Microbiol Infect,33144203,11/5/20,pubmed,0,14,"sequencing, deep sequencing",0.117809544,0.759333452,0.017868114,0.001272665,0.00127266,0.102443565,Genomics,0.3175943,FALSE,44.92857143,0.584699116,252.9285714,0.939055392,1,0.537564047,,,0.687106185
5440,Risk communication on behavioral responses during COVID-19 among general population in China: A rapid national study.,J Infect,33144190,11/5/20,pubmed,0,6,logistic regression,0.001717155,0.001717166,0.001717151,0.001717232,0.991414121,0.001717175,Healthcare,0.96932554,TRUE,53.66666667,0.658544128,54.33333333,0.698956382,2,0.618927094,,,0.658809201
5441,Prognostic implications of myocardial injury in patients with and without COVID-19 infection treated in a university hospital.,Rev Esp Cardiol (Engl Ed),33144126,11/5/20,pubmed,0,15,predictive model,0.00129121,0.001291274,0.001291262,0.001291238,0.001291284,0.993543733,Clinics,0.8260342,TRUE,29.13333333,0.420186777,11.33333333,0.375501739,0,0.403234768,,,0.399641095
5442,"Identification of natural inhibitors against Mpro of SARS-CoV-2 by molecular docking, molecular dynamics simulation, and MM/PBSA methods.",J Biomol Struct Dyn,33143552,11/5/20,pubmed,0,7,"virtual screening, molecular dynamics simulation",0.993448381,0.001310313,0.00131032,0.001310344,0.001310324,0.001310318,Drug discovery,0.9903919,TRUE,20.28571429,0.301502876,1.285714286,0.128512176,2,0.618927094,,,0.349647382
5443,Aprotinin Inhibits SARS-CoV-2 Replication.,Cells,33143316,11/5/20,pubmed,0,14,proteom,0.925815617,0.002080704,0.002080528,0.065861998,0.002080553,0.002080602,Drug discovery,0.7792774,TRUE,75.28571429,0.787989362,108.9285714,0.837503345,7,0.785110192,,,0.8035343
5444,The Impact of COVID-19 on Staff Working Practices in UK Horseracing.,Animals (Basel),33143200,11/5/20,pubmed,0,4,logistic regression,0.002080749,0.002080593,0.00208055,0.002080646,0.989596878,0.002080584,Healthcare,0.9388576,TRUE,36,0.498299215,18.75,0.467621086,1,0.537564047,,,0.501161449
5445,Motivation and Continuance Intention towards Online Instruction among Teachers during the COVID-19 Pandemic: The Mediating Effect of Burnout and Technostress.,Int J Environ Res Public Health,33143180,11/5/20,pubmed,0,5,structural model,0.002562743,0.002562664,0.002562623,0.336766283,0.652983066,0.002562621,Healthcare,0.86755407,TRUE,11.6,0.175211825,0.8,0.101351351,3,0.667819001,,,0.314794059
5446,miR-98 Regulates TMPRSS2 Expression in Human Endothelial Cells: Key Implications for COVID-19.,Biomedicines,33143053,11/5/20,pubmed,0,4,bioinformatic,0.894158919,0.00346598,0.003466184,0.003465911,0.00346599,0.091977016,Drug discovery,0.90945315,TRUE,69.25,0.760034634,45.75,0.662229061,6,0.764429903,,,0.728897866
5447,The Coronavirus Disease 2019 Pandemic in Taiwan: An Online Survey on Worry and Anxiety and Associated Factors.,Int J Environ Res Public Health,33142975,11/5/20,pubmed,0,5,logistic regression,0.001330009,0.001330053,0.001330017,0.123408145,0.871271746,0.001330029,Healthcare,0.9453557,TRUE,102.4,0.874327417,91.4,0.80472304,0,0.403234768,,,0.694095075
5448,COVID-19 and Parkinson's Disease: Shared Inflammatory Pathways Under Oxidative Stress.,Brain Sci,33142819,11/5/20,pubmed,0,5,"bioinformatic, proteom",0.666490538,0.001371338,0.036622039,0.073683615,0.114686353,0.107146117,Drug discovery,0.9507568,TRUE,9.2,0.136990537,2.2,0.16838373,1,0.537564047,,,0.280979438
5449,[Impact of the COVID-19 epidemic on anxiety among the elderly in community].,Zhonghua Yi Xue Za Zhi,33142403,11/4/20,pubmed,0,11,logistic regression,0.000999513,0.000999521,0.000999513,0.147497435,0.848504479,0.000999539,Healthcare,0.88303137,TRUE,70.72727273,0.767085163,32,0.585763982,0,0.403234768,,,0.585361304
5450,"Adherence to safety and self-isolation guidelines, conspiracy and paranoia-like beliefs during COVID-19 pandemic in Poland - associations and moderators.",Psychiatry Res,33142144,11/4/20,pubmed,0,5,probabilistic,0.00159351,0.001593565,0.001593522,0.09990853,0.893717352,0.001593522,Healthcare,0.93836355,TRUE,34,0.477766096,11.6,0.379515654,2,0.618927094,,,0.492069615
5451,Three-Dimensional Human Alveolar Stem Cell Culture Models Reveal Infection Response to SARS-CoV-2.,Cell Stem Cell,33142113,11/4/20,pubmed,0,21,transcriptom,0.783601606,0.144445141,0.067567519,0.001462002,0.00146186,0.001461871,Drug discovery,0.2826047,FALSE,59.52380952,0.699981446,96.14285714,0.815293016,9,0.814309525,,,0.776527996
5452,"Clinical characteristics and risk factors for in-hospital mortality of lung cancer patients with COVID-19: A multicenter, retrospective, cohort study.",Thorac Cancer,33142039,11/4/20,pubmed,0,16,logistic regression,0.00091668,0.000916704,0.022606988,0.000916681,0.000916677,0.973726271,Clinics,0.98779035,TRUE,62.875,0.720452718,29.3125,0.564958523,0,0.403234768,,,0.562882003
5453,Effect of mitigation measures on the spreading of COVID-19 in hard-hit states in the U.S.,PLoS One,33141823,11/4/20,pubmed,0,3,predictive model,0.00213062,0.002130636,0.002130629,0.989346766,0.002130666,0.002130682,Epidemiology,0.4982273,FALSE,57,0.68204589,20,0.481000803,5,0.739490092,,,0.634178928
5454,Trends in Lung Transplantation Practices Across the United States During the COVID-19 Pandemic.,Transplantation,33141810,11/4/20,pubmed,0,6,dataset,0.001565348,0.001565324,0.00156541,0.447521907,0.095140159,0.452641853,Clinics,0.24400562,FALSE,44.83333333,0.584389882,11.66666667,0.38065293,2,0.618927094,,,0.527989969
5455,Digital Health Technologies Respond to the COVID-19 Pandemic In a Tertiary Hospital in China: Development and Usability Study.,J Med Internet Res,33141679,11/4/20,pubmed,0,11,digital health,0.000710582,0.000710577,0.115894639,0.349499609,0.532473965,0.000710629,Healthcare,0.98075557,TRUE,121,0.908404972,33.90909091,0.598140219,1,0.537564047,,,0.681369746
5456,Proteomic Biomarkers to Guide Stratification for Covid-19 Treatment: Exemplifying a Path Forward Toward Implementation?,Proteomics,33141520,11/4/20,pubmed,0,1,proteom,0.327862014,0.001653102,0.133218042,0.342670483,0.001653172,0.192943186,Epidemiology,0.50524724,TRUE,26,0.382398417,1,0.122023013,0,0.403234768,,,0.302552066
5457,A comprehensive review on promising anti-viral therapeutic candidates identified against main protease from SARS-CoV-2 through various computational methods.,J Genet Eng Biotechnol,33141358,11/4/20,pubmed,0,8,"virtual screening, computational, in silico",0.921315383,0.00118732,0.001187312,0.073935474,0.001187249,0.001187262,Drug discovery,0.8294739,TRUE,67,0.748160059,44.625,0.657211667,3,0.667819001,,,0.691063576
5458,Network pharmacology and molecular docking analyses on Lianhua Qingwen capsule indicate Akt1 is a potential target to treat and prevent COVID-19.,Cell Prolif,33140889,11/4/20,pubmed,0,9,genomes,0.948739723,0.01764787,0.001141345,0.001141323,0.001141309,0.030188431,Drug discovery,0.931748,TRUE,69.22222222,0.7597254,85.44444444,0.791276425,6,0.764429903,,,0.771810576
5459,Blood molecular markers associated with COVID-19 immunopathology and multi-organ damage.,EMBO J,33140861,11/4/20,pubmed,0,27,"transcriptom, proteom, metabolom",0.470873812,0.001653103,0.001653052,0.001653062,0.001653135,0.522513835,Clinics,0.44254428,FALSE,65.77777778,0.738883048,,,5,0.739490092,,,0.73918657
5460,Structure-based lead optimization of herbal medicine rutin for inhibiting SARS-CoV-2's main protease.,Phys Chem Chem Phys,33140777,11/4/20,pubmed,0,3,"molecular dynamics simulation, computational, in silico",0.915214078,0.001310355,0.001310354,0.001310359,0.001310418,0.079544435,Drug discovery,0.89408934,TRUE,82,0.814769002,88.33333333,0.797364196,7,0.785110192,,,0.79908113
5461,0,J Biomol Struct Dyn,33140706,11/4/20,pubmed,0,7,in silico,0.833198692,0.089619381,0.001786532,0.001786542,0.001786574,0.071822279,Drug discovery,0.80076,TRUE,39.85714286,0.537881131,10.85714286,0.367607707,0,0.403234768,,,0.436241202
5462,Targeting SARS-CoV-2 nucleocapsid oligomerization: Insights from molecular docking and molecular dynamics simulations.,J Biomol Struct Dyn,33140703,11/4/20,pubmed,0,3,"molecular dynamics simulation, computational",0.980718086,0.001085348,0.001085329,0.001085366,0.014940544,0.001085328,Drug discovery,0.9039366,TRUE,72,0.77302245,51,0.685509767,4,0.707574542,,,0.722035586
5463,0,J Biomol Struct Dyn,33140695,11/4/20,pubmed,0,4,in-silico,0.992032175,0.001593555,0.0015935,0.00159355,0.001593718,0.001593501,Drug discovery,0.95301795,TRUE,50.5,0.635351599,21.75,0.50046829,0,0.403234768,,,0.513018219
5464,Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application.,medRxiv,33140073,11/4/20,pubmed,0,21,predictive model,0.000643398,0.000643466,0.000643501,0.708985537,0.288440658,0.00064344,Epidemiology,0.23299831,FALSE,152.6190476,0.94625518,354.2380952,0.966483811,7,0.785110192,,,0.899283061
5465,Seroprevalence of anti-SARS-CoV-2 IgG Antibodies in the Staff of a Public School System in the Midwestern United States.,medRxiv,33140066,11/4/20,pubmed,0,11,logistic regression,0.00051289,0.081514353,0.007915597,0.334407865,0.575136396,0.000512899,Healthcare,0.067596495,FALSE,230.9090909,0.98119859,128.6363636,0.861921327,2,0.618927094,,,0.820682337
5466,Symptoms at presentation for patients admitted to hospital with Covid-19: results from the ISARIC prospective multinational observational study.,medRxiv,33140062,11/4/20,pubmed,0,2,"logistic regression, dataset",0.001034593,0.001034653,0.001034659,0.081622965,0.252582047,0.662691083,Clinics,0.3496238,FALSE,28,0.408312202,30,0.570176612,0,0.403234768,,,0.460574527
5467,0,medRxiv,33140059,11/4/20,pubmed,0,5,transcriptom,0.656243301,0.253389311,0.001237142,0.001237134,0.001237126,0.086655986,Drug discovery,0.40355805,FALSE,33,0.466757375,10.33333333,0.36038266,0,0.403234768,,,0.410124934
5468,Discovery of runs-of-homozygosity diplotype clusters and their associations with diseases in UK Biobank.,medRxiv,33140058,11/4/20,pubmed,0,3,genome-wide,0.001786571,0.647987629,0.087635968,0.001786645,0.206690099,0.054113088,Genomics,0.08204028,FALSE,10,0.15214299,9.666666667,0.348274017,0,0.403234768,,,0.301217258
5469,Retrospective in silico HLA predictions from COVID-19 patients reveal alleles associated with disease prognosis.,medRxiv,33140057,11/4/20,pubmed,0,2,in silico,0.00143825,0.424163921,0.001438154,0.001438169,0.001438136,0.570083371,Clinics,0.244064,FALSE,74.5,0.784464098,232,0.930693069,3,0.667819001,,,0.794325389
5470,A cell-free antibody engineering platform rapidly generates SARS-CoV-2 neutralizing antibodies.,bioRxiv,33140055,11/4/20,pubmed,0,4,computational,0.535364214,0.300096591,0.160079824,0.001486508,0.001486464,0.001486399,Drug discovery,0.3568905,FALSE,214.25,0.976931165,2488,0.999331014,10,0.828199272,,,0.934820483
5471,Type I Interferon Transcriptional Network Regulates Expression of Coinhibitory Receptors in Human T cells.,bioRxiv,33140047,11/4/20,pubmed,0,12,sequencing,0.884884561,0.090542143,0.000977448,0.000977501,0.000977447,0.021640899,Drug discovery,0.06784892,FALSE,132.5833333,0.924485126,495.75,0.978458657,2,0.618927094,,,0.840623626
5472,Beyond Shielding: The Roles of Glycans in the SARS-CoV-2 Spike Protein.,ACS Cent Sci,33140034,11/4/20,pubmed,0,12,molecular dynamics simulation,0.906905474,0.067740328,0.021743873,0.001203479,0.001203425,0.001203421,Drug discovery,0.6724692,TRUE,51.5,0.643267982,83.25,0.786259031,27,0.92443978,,,0.784655598
5473,Fighting COVID-19 Using Molecular Dynamics Simulations.,ACS Cent Sci,33140032,11/4/20,pubmed,0,3,molecular dynamics simulation,0.902351184,0.019529539,0.019530018,0.019530475,0.019529505,0.019529279,Drug discovery,0.6148834,TRUE,22.33333333,0.330261612,6.333333333,0.285121755,2,0.618927094,,,0.41143682
5474,Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review.,Expert Syst Appl,33139966,11/4/20,pubmed,0,12,machine learning,0.000898108,0.040568408,0.000898141,0.955839117,0.00089812,0.000898106,Epidemiology,0.81187946,TRUE,25.91666667,0.379182386,17,0.451097137,6,0.764429903,,,0.531569809
5475,Hypertension management in 2030: a kaleidoscopic view.,J Hum Hypertens,33139827,11/4/20,pubmed,0,1,artificial intelligence,0.077484327,0.002422408,0.112597808,0.323884055,0.321408733,0.162202669,Epidemiology,0.6832886,TRUE,3,0.037293586,0,0.055525823,0,0.403234768,,,0.165351392
5476,Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel.,Nat Commun,33139704,11/4/20,pubmed,0,19,genome sequences,0.002638993,0.458765023,0.002638952,0.530679048,0.002639016,0.002638968,Epidemiology,0.33528274,FALSE,36.36842105,0.500958625,60,0.720899117,2,0.618927094,,,0.613594945
5477,Addressing Healthcare Gaps in Sweden during the COVID-19 Outbreak: On Community Outreach and Empowering Ethnic Minority Groups in a Digitalized Context.,Healthcare (Basel),33139619,11/4/20,pubmed,0,6,digital health,0.002357887,0.002357765,0.002357808,0.441989038,0.511549054,0.039388448,Healthcare,0.64214385,TRUE,38.33333333,0.522233904,117.6666667,0.848140219,4,0.707574542,,,0.692649555
5478,Evidence that coronavirus superspreading is fat-tailed.,Proc Natl Acad Sci U S A,33139561,11/4/20,pubmed,0,2,network model,0.00376126,0.003760734,0.003760502,0.981196247,0.003760684,0.003760574,Epidemiology,0.26755685,FALSE,283,0.989238667,1392.5,0.996721969,18,0.891474782,,,0.959145139
5479,Pattern of anticoagulation prescription for patients with Covid-19 acute respiratory distress syndrome admitted to ICU. Does it impact outcome?,Heart Lung,33138975,11/4/20,pubmed,0,14,logistic regression,0.001098819,0.001098908,0.001098836,0.024898043,0.001098834,0.97070656,Clinics,0.80027634,TRUE,6.571428571,0.093883357,1.642857143,0.1414905,0,0.403234768,,,0.212869542
5480,Predictors of In-Hospital Mortality in Older Patients With COVID-19: The COVIDAge Study.,J Am Med Dir Assoc,33138936,11/4/20,pubmed,0,17,logistic regression,0.000854756,0.00085471,0.038340942,0.000854697,0.00085474,0.958240156,Clinics,0.97775555,TRUE,53.76470588,0.659409982,43.23529412,0.650388012,14,0.866658436,,,0.725485477
5481,0,J Biomol Struct Dyn,33138699,11/4/20,pubmed,0,4,bioinformatic,0.665516549,0.25982963,0.001291255,0.001291232,0.001291256,0.070780079,Drug discovery,0.6436568,TRUE,22.5,0.333539489,9.75,0.349678887,0,0.403234768,,,0.362151048
5482,Single-Cell Transcriptome Analysis Highlights a Role for Neutrophils and Inflammatory Macrophages in the Pathogenesis of Severe COVID-19.,Cells,33138195,11/4/20,pubmed,0,4,"computational, transcriptom, dataset",0.488786171,0.106842772,0.019733556,0.000926331,0.000926282,0.382784887,Drug discovery,0.6712109,TRUE,43.5,0.573381161,46.5,0.666309874,5,0.739490092,,,0.659727043
5483,"Molecular Insights into Human Transmembrane Protease Serine-2 (TMPS2) Inhibitors against SARS-CoV2: Homology Modelling, Molecular Dynamics, and Docking Studies.",Molecules,33137894,11/4/20,pubmed,0,8,"molecular dynamics simulation, computational, in silico",0.96496813,0.001438173,0.001438149,0.029279275,0.001438138,0.001438135,Drug discovery,0.81542873,TRUE,39.375,0.532747851,37.375,0.618477388,0,0.403234768,,,0.518153336
5484,"Phylogeography of 27,000 SARS-CoV-2 Genomes: Europe as the Major Source of the COVID-19 Pandemic.",Microorganisms,33137892,11/4/20,pubmed,0,5,genomes,0.002032759,0.483357952,0.002032794,0.508510896,0.002032826,0.002032774,Epidemiology,0.4255042,FALSE,57.2,0.683097285,272.6,0.946347337,2,0.618927094,,,0.749457239
5485,Using real-world examples of the COVID-19 pandemic to increase student confidence in their scientific literacy skills.,Biochem Mol Biol Educ,33137848,11/3/20,pubmed,0,3,active learning,0.156783294,0.001291247,0.22676868,0.388542659,0.225322883,0.001291237,Epidemiology,0.8318546,TRUE,31.66666667,0.451976003,41,0.639751137,0,0.403234768,,,0.498320636
5486,Interaction of small molecules with the SARS-CoV-2 main protease in silico and in vitro validation of potential lead compounds using an enzyme-linked immunosorbent assay.,Comput Biol Chem,33137690,11/3/20,pubmed,0,8,"molecular dynamics simulation, in silico",0.993982792,0.001203451,0.001203416,0.001203447,0.001203427,0.001203468,Drug discovery,0.94782305,TRUE,42.375,0.5612592,29.125,0.563486754,5,0.739490092,,,0.621412015
5487,"Worry, avoidance, and coping during the COVID-19 pandemic: A comprehensive network analysis.",J Anxiety Disord,33137601,11/3/20,pubmed,0,5,network analysis,0.001751269,0.001751251,0.001751298,0.174649805,0.818345205,0.001751173,Healthcare,0.90343964,TRUE,94.8,0.856268167,196.6,0.915908483,6,0.764429903,,,0.845535518
5488,Functional and druggability analysis of the SARS-CoV-2 proteome.,Eur J Pharmacol,33137330,11/3/20,pubmed,0,3,"in silico, proteom",0.989835507,0.002032945,0.002032828,0.002032963,0.002032933,0.002032824,Drug discovery,0.75614893,TRUE,31.66666667,0.451976003,36.66666667,0.614998662,0,0.403234768,,,0.490069811
5489,Ensembl 2021.,Nucleic Acids Res,33137190,11/3/20,pubmed,0,79,"sequencing, genomes",0.001717301,0.592419276,0.001717268,0.400711192,0.001717447,0.001717515,Genomics,0.79800975,TRUE,16.70886076,0.251345167,141.8227848,0.876237624,4,0.707574542,,,0.611719111
5490,Impact of COVID-19-Related Stress and Lockdown on Mental Health Among People Living With HIV in Argentina.,J Acquir Immune Defic Syndr,33136748,11/3/20,pubmed,0,6,logistic regression,0.12191016,0.001371317,0.001371284,0.001371392,0.872604561,0.001371286,Healthcare,0.99373686,TRUE,46.66666667,0.602510978,23.5,0.516791544,0,0.403234768,,,0.50751243
5491,Effects of the COVID-19 Pandemic on Outpatient Providers in the United States.,Med Care,33136711,11/3/20,pubmed,0,2,dataset,0.001392849,0.025927107,0.00139286,0.285601188,0.684293143,0.001392854,Healthcare,0.72654325,TRUE,141.5,0.936050467,84,0.788399786,0,0.403234768,,,0.70922834
5492,"A Web-Based Platform on Coronavirus Disease-19 to Maintain Predicted Diagnostic, Drug, and Vaccine Candidates.",Monoclon Antib Immunodiagn Immunother,33136473,11/3/20,pubmed,0,10,in silico,0.713904175,0.093259404,0.189018387,0.001272695,0.001272702,0.001272637,Drug discovery,0.77384615,TRUE,37.4,0.512585812,14,0.412898047,0,0.403234768,,,0.442906209
5493,Association between recent oncologic treatment and mortality among patients with carcinoma who are hospitalized with COVID-19: A multicenter study.,Cancer,33136293,11/3/20,pubmed,0,13,logistic regression,0.001438415,0.001438185,0.001438115,0.00143818,0.064302418,0.929944687,Clinics,0.99081975,TRUE,48.23076923,0.615931721,19.53846154,0.476050308,0,0.403234768,,,0.498405599
5494,Remdesivir for the Treatment of Severe COVID-19: A Community Hospital's Experience.,J Am Osteopath Assoc,33136164,11/3/20,pubmed,0,6,logistic regression,0.049795499,0.029740843,0.000683174,0.000683178,0.092528963,0.826568343,Clinics,0.76231587,TRUE,21.83333333,0.322468922,8.5,0.329141022,0,0.403234768,,,0.351614904
5495,Clinical Characteristics and Outcomes of COVID-19-Infected Cancer Patients: A Systematic Review and Meta-Analysis.,J Natl Cancer Inst,33136163,11/3/20,pubmed,0,9,logistic regression,0.001751152,0.00175123,0.001751145,0.001751253,0.001751198,0.991244022,Clinics,0.7653991,TRUE,138.4444444,0.931164574,130.8888889,0.864329676,12,0.850299401,,,0.881931217
5496,Development and Performance of a Clinical Decision Support Tool to Inform Resource Utilization for Elective Operations.,JAMA Netw Open,33136133,11/3/20,pubmed,0,10,predictive model,0.000956306,0.000956317,0.172847019,0.217069612,0.000956363,0.607214383,Clinics,0.7444952,TRUE,46.9,0.60436638,37.8,0.620818839,1,0.537564047,,,0.587583089
5497,Exploring the Potential Mechanism of Shufeng Jiedu Capsule for Treating COVID-19 by Comprehensive Network Pharmacological Approaches and Molecular Docking Validation.,Comb Chem High Throughput Screen,33135607,11/3/20,pubmed,0,7,network analysis,0.993543872,0.001291221,0.001291251,0.001291242,0.00129121,0.001291204,Drug discovery,0.9687556,TRUE,25.42857143,0.372997712,12.85714286,0.39717688,0,0.403234768,,,0.391136453
5498,Factors associated with mental health outcomes among patients with COVID-19 treated in the Fangcang shelter hospital in China.,Asia Pac Psychiatry,33135397,11/3/20,pubmed,0,5,logistic regression,0.001203389,0.001203401,0.001203397,0.00120343,0.740680799,0.254505585,Healthcare,0.9432373,TRUE,10.6,0.158142124,3.6,0.21507894,16,0.881782826,,,0.41833463
5499,The noncoding and coding transcriptional landscape of the peripheral immune response in patients with COVID-19.,Clin Transl Med,33135345,11/3/20,pubmed,0,11,"sequencing, transcriptom",0.640905461,0.111235343,0.001392845,0.001392841,0.001392837,0.243680673,Drug discovery,0.92677027,TRUE,32.81818182,0.464283505,20.27272727,0.483141557,2,0.618927094,,,0.522117386
5500,Comprehensive characterization of N- and O- glycosylation of SARS-CoV-2 human receptor angiotensin converting enzyme 2.,Glycobiology,33135055,11/3/20,pubmed,0,6,"bioinformatic, proteom, glycoproteom",0.904089907,0.050140566,0.001392908,0.001392878,0.001392912,0.041590828,Drug discovery,0.10562268,FALSE,59.83333333,0.701651308,68.33333333,0.748728927,15,0.874313229,,,0.774897821
5501,"Baseline Characteristics and Associated Factors of Mortality in COVID-19 Patients; an Analysis of 16000 Cases in Tehran, Iran.",Arch Acad Emerg Med,33134966,11/3/20,pubmed,0,9,logistic regression,0.001350316,0.001350355,0.001350339,0.001350376,0.001350343,0.993248271,Clinics,0.94471484,TRUE,39.44444444,0.533180778,4.222222222,0.234278833,0,0.403234768,,,0.39023146
5502,The potential impact of the COVID-19 pandemic on the tuberculosis epidemic a modelling analysis.,EClinicalMedicine,33134905,11/3/20,pubmed,0,11,mathematical model,0.001156263,0.001156278,0.001156283,0.7982742,0.197100645,0.001156331,Epidemiology,0.1451982,FALSE,39,0.530521368,40.36363636,0.635536527,12,0.850299401,,,0.672119099
5503,A Symptom-Based Rule for Diagnosis of COVID-19.,SN Compr Clin Med,33134843,11/3/20,pubmed,0,3,"bayes, logistic regression",0.001085343,0.066408594,0.368819633,0.001085416,0.088275466,0.474325546,Clinics,0.5243026,TRUE,64,0.729173109,48,0.673735617,1,0.537564047,,,0.646824258
5504,"Insights of Novel Coronavirus (SARS-CoV-2) disease outbreak, management and treatment.",AIMS Microbiol,33134740,11/3/20,pubmed,0,3,"deep learning, artificial intelligence",0.001310434,0.340275302,0.128526661,0.527266802,0.001310422,0.001310379,Epidemiology,0.86983764,TRUE,35.66666667,0.493908096,13,0.400521809,0,0.403234768,,,0.432554891
5505,Quantitative Structure-Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections.,ACS Omega,33134697,11/3/20,pubmed,0,12,"machine learning, predictive model",0.678648053,0.001987185,0.313403223,0.001987209,0.001987124,0.001987206,Drug discovery,0.85397184,TRUE,32.91666667,0.465087513,7,0.299973241,3,0.667819001,,,0.477626585
5506,Discovery of Potential Flavonoid Inhibitors Against COVID-19 3CL Proteinase Based on Virtual Screening Strategy.,Front Mol Biosci,33134310,11/3/20,pubmed,0,8,"virtual screening, machine learning, logistic regression, dataset",0.575044069,0.001622749,0.418464532,0.001622959,0.001622845,0.001622847,Drug discovery,0.84162605,TRUE,7.625,0.109530583,0.75,0.099411292,3,0.667819001,,,0.292253626
5507,Identification of a Novel Pathogen Using Family-Wide PCR: Initial Confirmation of COVID-19 in Thailand.,Front Public Health,33134237,11/3/20,pubmed,0,18,"sequencing, whole genome",0.001861767,0.990691208,0.001861823,0.001861751,0.001861744,0.001861707,Genomics,0.26593202,FALSE,30.38888889,0.436514318,32.61111111,0.589577201,0,0.403234768,,,0.476442095
5508,Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images.,J Biomed Phys Eng,33134214,11/3/20,pubmed,0,5,"neural network, transfer learning, dataset",0.001203411,0.001203507,0.915785426,0.001203418,0.079400748,0.001203491,Imaging,0.045313,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
5509,Comparative study of ANN and Fuzzy classifier for forecasting Electrical activity of Heart to diagnose Covid-19.,Mater Today Proc,33134090,11/3/20,pubmed,0,4,"computational, artificial intelligence, neural network, classifier",0.097785183,0.001751261,0.49248072,0.1268432,0.001751233,0.279388403,Clinics,0.40828225,FALSE,7,0.10179974,1.75,0.148381054,0,0.403234768,,,0.217805187
5510,Disentangling community-level changes in crime trends during the COVID-19 pandemic in Chicago.,Crime Sci,33134029,11/3/20,pubmed,0,4,"bayes, logistic regression",0.001593563,0.001593561,0.001593521,0.992032289,0.00159355,0.001593515,Epidemiology,0.5173893,TRUE,165.75,0.955408498,429,0.973441263,3,0.667819001,,,0.865556254
5511,"Stopping the COVID-19 Pandemic: A Review on the Advances of Diagnosis, Treatment, and Control Measures.",J Pathog,33133697,11/3/20,pubmed,0,4,artificial intelligence,0.265448357,0.288879399,0.147099663,0.235183537,0.001415138,0.061973906,Genomics,0.7916547,TRUE,7.5,0.108355495,0.75,0.099411292,1,0.537564047,,,0.248443612
5512,Coronavirus disease 2019: scientific overview of the global pandemic.,New Microbes New Infect,33133611,11/3/20,pubmed,0,4,sequencing,0.002996758,0.985016702,0.002996613,0.002996681,0.002996706,0.00299654,Genomics,0.69711596,TRUE,35,0.488032655,23,0.513513514,0,0.403234768,,,0.468260312
5513,Host Genetic Variants Potentially Associated With SARS-CoV-2: A Multi-Population Analysis.,Front Genet,33133166,11/3/20,pubmed,0,4,genomes,0.058849801,0.835216596,0.001371277,0.001371331,0.001371332,0.101819663,Genomics,0.28822073,FALSE,52.25,0.648648649,67.5,0.746855767,2,0.618927094,,,0.67147717
5514,0,Front Genet,33133145,11/3/20,pubmed,0,30,dataset,0.143321635,0.657811673,0.00153819,0.001538158,0.001538153,0.194252191,Genomics,0.41807348,FALSE,24.86666667,0.366070876,2.466666667,0.17547498,4,0.707574542,,,0.416373466
5515,Lower Circulating Interferon-Gamma Is a Risk Factor for Lung Fibrosis in COVID-19 Patients.,Front Immunol,33133104,11/3/20,pubmed,0,11,"artificial intelligence, logistic regression",0.096430477,0.001415204,0.16292725,0.00141513,0.001415233,0.736396706,Clinics,0.7108699,TRUE,74,0.782113922,47.63636364,0.670992775,8,0.799987654,,,0.75103145
5516,"SARS-CoV-2 nsp1: Bioinformatics, Potential Structural and Functional Features, and Implications for Drug/Vaccine Designs.",Front Microbiol,33133055,11/3/20,pubmed,0,6,bioinformatic,0.807396097,0.150821356,0.001593472,0.037002049,0.001593559,0.001593468,Drug discovery,0.9211036,TRUE,31.66666667,0.451976003,125.5,0.858442601,0,0.403234768,,,0.571217791
5517,Socio-Cognitive Factors Associated With Lifestyle Changes in Response to the COVID-19 Epidemic in the General Population: Results From a Cross-Sectional Study in France.,Front Psychol,33132989,11/3/20,pubmed,0,4,logistic regression,0.001059355,0.001059378,0.001059359,0.173909138,0.821853415,0.001059355,Healthcare,0.96569896,TRUE,70,0.764178366,34.75,0.603559005,6,0.764429903,,,0.710722425
5518,Mental Health Outcomes in Perinatal Women During the Remission Phase of COVID-19 in China.,Front Psychiatry,33132935,11/3/20,pubmed,0,8,logistic regression,0.00127263,0.001272646,0.001272639,0.001272664,0.993636754,0.001272667,Healthcare,0.983344,TRUE,30.25,0.434782609,2.5,0.180826866,3,0.667819001,,,0.427809492
5519,"Fei Yan No. 1" as a Combined Treatment for COVID-19: An Efficacy and Potential Mechanistic Study.,Front Pharmacol,33132913,11/3/20,pubmed,0,13,genomes,0.690754588,0.036692157,0.05688943,0.090780396,0.000916753,0.123966676,Drug discovery,0.87702894,TRUE,10.38461538,0.155420867,2.769230769,0.187784319,0,0.403234768,,,0.248813318
5520,The psychosocial and clinical concerns of physicians treating patients with COVID-19.,J Taibah Univ Med Sci,33132803,11/3/20,pubmed,0,5,logistic regression,0.001098819,0.001098805,0.001098815,0.001098837,0.900296374,0.095308349,Healthcare,0.9933478,TRUE,22.6,0.334652731,7.2,0.302047097,0,0.403234768,,,0.346644865
5521,0,J Nanopart Res,33132747,11/3/20,pubmed,0,3,molecular dynamics simulation,0.78394201,0.002183398,0.002183255,0.118755676,0.002183384,0.090752277,Drug discovery,0.68520683,TRUE,41,0.549013544,7,0.299973241,0,0.403234768,,,0.417407184
5522,A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images.,Neural Comput Appl,33132536,11/3/20,pubmed,0,3,"deep learning, artificial intelligence, neural network, transfer learning, dataset",0.001371252,0.001371287,0.993143483,0.001371374,0.001371245,0.001371358,Imaging,0.50563204,TRUE,53.33333333,0.656255798,26,0.53819909,20,0.900117291,,,0.698190727
5523,ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound.,Neural Comput Appl,33132535,11/3/20,pubmed,0,9,"artificial intelligence, predictive model",0.001330064,0.001330042,0.103185877,0.870255393,0.022568605,0.001330019,Epidemiology,0.015135706,FALSE,11.66666667,0.176510607,2.111111111,0.165038801,5,0.739490092,,,0.3603465
5524,Integrated environment-occupant-pathogen information modeling to assess and communicate room-level outbreak risks of infectious diseases.,Build Environ,33132484,11/3/20,pubmed,0,5,computational,0.001565391,0.001565442,0.001565347,0.992173036,0.001565397,0.001565387,Epidemiology,0.46625084,FALSE,38.4,0.523285299,4.2,0.234211935,0,0.403234768,,,0.386910667
5525,Risk of Adverse Outcomes in Hospitalized Patients With Autoimmune Disease and COVID-19: A Matched Cohort Study From New York City.,J Rheumatol,33132221,11/3/20,pubmed,0,14,logistic regression,0.001415076,0.001415074,0.090988095,0.001415087,0.001415103,0.903351565,Clinics,0.9359876,TRUE,102.2857143,0.874018183,83.71428571,0.7877308,7,0.785110192,,,0.815619725
5526,Current status of antivirals and druggable targets of SARS CoV-2 and other human pathogenic coronaviruses.,Drug Resist Updat,33132205,11/3/20,pubmed,0,11,"virtual screening, in silico",0.710724093,0.241067974,0.000616531,0.046358316,0.000616532,0.000616554,Drug discovery,0.8542222,TRUE,78.90909091,0.80209042,54,0.697952903,10,0.828199272,,,0.776080865
5527,Overlapping host pathways between SARS-CoV-2 and its potential copathogens: An in silico analysis.,Infect Genet Evol,33132111,11/3/20,pubmed,0,1,"in silico, transcriptom",0.725584969,0.215772648,0.0014382,0.001438168,0.001438147,0.054327868,Drug discovery,0.7769445,TRUE,45,0.587049292,3,0.199424672,0,0.403234768,,,0.396569577
5528,Can pollen explain the seasonality of flu-like illnesses in the Netherlands?,Sci Total Environ,33131881,11/3/20,pubmed,0,3,predictive model,0.031452643,0.052978128,0.001565331,0.74618177,0.001565461,0.166256667,Epidemiology,0.16873673,FALSE,31.66666667,0.451976003,24.33333333,0.524016591,1,0.537564047,,,0.50451888
5529,Laboratory diagnosis of severe acute respiratory syndrome coronavirus 2.,Pathology,33131799,11/3/20,pubmed,0,5,"sequencing, whole genome",0.001486538,0.399128009,0.001486464,0.476417168,0.00148653,0.119995291,Epidemiology,0.6491016,TRUE,81.8,0.813964995,105.2,0.83161627,1,0.537564047,,,0.727715104
5530,Short-term high-dose gavage of hydroxychloroquine changes gut microbiota but not the intestinal integrity and immunological responses in mice.,Life Sci,33131749,11/3/20,pubmed,0,8,sequencing,0.358640835,0.061904427,0.001622822,0.073239373,0.00162281,0.502969734,Clinics,0.6138944,TRUE,38,0.519327107,48,0.673735617,1,0.537564047,,,0.57687559
5531,0,Eur J Pharmacol,33131721,11/3/20,pubmed,0,5,virtual screening,0.972061888,0.022366571,0.001392913,0.001392916,0.001392865,0.001392847,Drug discovery,0.77259606,TRUE,17.6,0.264456676,2.8,0.188787798,1,0.537564047,,,0.330269507
5532,Multi-omics-based identification of SARS-CoV-2 infection biology and candidate drugs against COVID-19.,Comput Biol Med,33131530,11/3/20,pubmed,0,7,"transcriptom, proteom,  omics, interactom, multi-omics",0.949029101,0.045123155,0.001461946,0.001461959,0.001461901,0.001461937,Drug discovery,0.4790546,FALSE,115.2857143,0.899313501,94.28571429,0.811212202,10,0.828199272,,,0.846241658
5533,Massive dissemination of a SARS-CoV-2 Spike Y839 variant in Portugal.,Emerg Microbes Infect,33131453,11/3/20,pubmed,0,13,"sequencing, genomes",0.114749683,0.670182279,0.001350345,0.124869844,0.001350389,0.087497461,Genomics,0.43546617,FALSE,35.69230769,0.494031789,25.07692308,0.529569173,5,0.739490092,,,0.587697018
5534,0,J Biomol Struct Dyn,33131430,11/3/20,pubmed,0,10,"molecular dynamics simulation, in silico",0.993814407,0.001237076,0.00123706,0.00123721,0.001237132,0.001237115,Drug discovery,0.89423835,TRUE,29.5,0.426000371,8.4,0.326598876,0,0.403234768,,,0.385278005
5535,Novel insight from the first lung transplant of a COVID-19 patient.,Eur J Clin Invest,33131070,11/2/20,pubmed,0,24,"transcriptom, proteom, multi-omics",0.663795847,0.109491999,0.001330098,0.001330067,0.001330028,0.222721961,Drug discovery,0.73296845,TRUE,106.5416667,0.88205826,38.91666667,0.626572117,0,0.403234768,,,0.637288381
5536,The toll of noninfected CRS patients to the COVID-19 pandemic.,Rhinology,33130830,11/2/20,pubmed,0,7,artificial intelligence,0.002422318,0.002422281,0.15082232,0.479233179,0.114131438,0.250968463,Epidemiology,0.8250705,TRUE,93.57142857,0.853299524,33.71428571,0.597002944,0,0.403234768,,,0.617845745
5537,COVID-19 detection in radiological text reports integrating entity recognition.,Comput Biol Med,33130435,11/2/20,pubmed,0,6,machine learning,0.00118728,0.001187282,0.952262801,0.00118729,0.042988037,0.00118731,Imaging,0.12267217,FALSE,62.66666667,0.719030243,23.16666667,0.514182499,3,0.667819001,,,0.633677248
5538,Unravelling high-affinity binding compounds towards transmembrane protease serine 2 enzyme in treating SARS-CoV-2 infection using molecular modelling and docking studies.,Eur J Pharmacol,33130280,11/2/20,pubmed,0,5,in silico,0.911619606,0.00178652,0.08123375,0.001786599,0.001786944,0.001786581,Drug discovery,0.8218454,TRUE,9,0.135320675,1.4,0.133864062,1,0.537564047,,,0.268916262
5539,In silico identification of strong binders of the SARS-CoV-2 receptor-binding domain.,Eur J Pharmacol,33130279,11/2/20,pubmed,0,6,"virtual screening, in silico",0.993982674,0.001203463,0.001203421,0.001203483,0.001203511,0.001203447,Drug discovery,0.9605696,TRUE,24.33333333,0.359700662,15,0.42594327,0,0.403234768,,,0.3962929
5540,Adjusting RT-qPCR conditions to avoid unspecific amplification in SARS-CoV-2 diagnosis.,Int J Infect Dis,33130201,11/2/20,pubmed,0,17,in silico,0.002357965,0.537194053,0.14411267,0.311619609,0.002357843,0.002357861,Genomics,0.84920573,TRUE,12,0.183190055,2.764705882,0.187650522,0,0.403234768,,,0.258025115
5541,A connectivity map-based drug repurposing study and integrative analysis of transcriptomic profiling of SARS-CoV-2 infection.,Infect Genet Evol,33130005,11/2/20,pubmed,0,3,"computational, transcriptom, network analysis",0.97987111,0.014727351,0.001350443,0.001350394,0.001350352,0.00135035,Drug discovery,0.91328,TRUE,32.66666667,0.462922877,12.66666667,0.39530372,0,0.403234768,,,0.420487121
5542,Association of bioelectric impedance analysis body composition and disease severity in COVID-19 hospital ward and ICU patients: The BIAC-19 study.,Clin Nutr,33129597,11/2/20,pubmed,0,7,logistic regression,0.001220105,0.001220075,0.001220056,0.00122008,0.001220088,0.993899597,Clinics,0.7183363,TRUE,7.142857143,0.102541901,11.28571429,0.374565159,3,0.667819001,,,0.381642021
5543,Digital triage: Novel strategies for population health management in response to the COVID-19 pandemic.,Healthc (Amst),33129176,11/1/20,pubmed,0,16,artificial intelligence,0.001415166,0.001415247,0.442468979,0.175974285,0.285922729,0.092803595,Healthcare,0.93081504,TRUE,75.5625,0.788731523,74.8125,0.765788065,1,0.537564047,,,0.697361212
5544,Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.,Med Image Anal,33129141,11/1/20,pubmed,0,12,"artificial intelligence, deep model, dataset",0.001010941,0.001010959,0.994945194,0.001010978,0.001010981,0.001010946,Imaging,0.24713102,FALSE,72.75,0.776300328,34.58333333,0.60235483,5,0.739490092,,,0.706048417
5545,Human Lung Stem Cell-Based Alveolospheres Provide Insights into SARS-CoV-2-Mediated Interferon Responses and Pneumocyte Dysfunction.,Cell Stem Cell,33128895,11/1/20,pubmed,0,16,transcriptom,0.991413692,0.001717253,0.001717259,0.001717289,0.001717237,0.00171727,Drug discovery,0.2294046,FALSE,116.6875,0.901849218,336.25,0.963272679,21,0.903944688,,,0.923022195
5546,0,Immunity,33128877,11/1/20,pubmed,0,19,genome-wide,0.655694799,0.335982953,0.002080563,0.002080597,0.00208055,0.002080538,Drug discovery,0.21277362,FALSE,21.31578947,0.314738079,,,31,0.931971109,,,0.623354594
5547,Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19.,Immunity,33128875,11/1/20,pubmed,0,24,proteom,0.534114357,0.001901802,0.070060018,0.001901749,0.001901792,0.390120282,Drug discovery,0.6709968,TRUE,24.83333333,0.365823489,50.54166667,0.683302114,10,0.828199272,,,0.625774958
5548,Intricate interplay between Covid-19 and cardiovascular diseases.,Rev Med Virol,33128859,11/1/20,pubmed,0,6,artificial intelligence,0.149737604,0.002080641,0.220188326,0.163193176,0.002080625,0.462719628,Clinics,0.9303099,TRUE,18.66666667,0.279670975,3.5,0.213607172,1,0.537564047,,,0.343614065
5549,Epidemic analysis of COVID-19 in Italy based on spatiotemporal geographic information and Google Trends.,Transbound Emerg Dis,33128853,11/1/20,pubmed,0,8,"machine learning, prediction model",0.001861698,0.001861742,0.248667195,0.743885879,0.001861772,0.001861714,Epidemiology,0.6082408,TRUE,22.75,0.336508133,7.625,0.309071448,0,0.403234768,,,0.349604783
5550,Psychological impact of the coronavirus disease 2019 (COVID-19) pandemic on dental students: A nationwide study.,J Dent Educ,33128397,11/1/20,pubmed,0,8,logistic regression,0.001392839,0.001392874,0.001392851,0.001392845,0.993035724,0.001392866,Healthcare,0.9687181,TRUE,11.625,0.175397365,1.5,0.138747659,8,0.799987654,,,0.371377559
5551,"Development, Implementation, and Results from a COVID-19 Messaging Campaign to Promote Health Care Seeking Behaviors Among Community Clinic Patients.",J Community Health,33128160,11/1/20,pubmed,0,8,logistic regression,0.001272669,0.001272691,0.001272736,0.074613099,0.593440655,0.328128151,Healthcare,0.9295323,TRUE,5.375,0.074525326,2.5,0.180826866,0,0.403234768,,,0.219528987
5552,Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study.,Sci Rep,33127965,11/1/20,pubmed,0,6,"machine learning, prediction model",0.001171546,0.00117159,0.326592193,0.001171632,0.106282764,0.563610274,Clinics,0.78393036,TRUE,171.5,0.959428536,131.3333333,0.864864865,15,0.874313229,,,0.899535543
5553,Genomic surveillance of COVID-19 cases in Beijing.,Nat Commun,33127911,11/1/20,pubmed,0,28,genomes,0.001943471,0.921857569,0.00194357,0.001943573,0.001943647,0.07036817,Genomics,0.3007984,FALSE,64.71428571,0.733069454,39.39285714,0.629983944,2,0.618927094,,,0.660660164
5554,Molecular Architecture of Early Dissemination and Massive Second Wave of the SARS-CoV-2 Virus in a Major Metropolitan Area.,mBio,33127862,11/1/20,pubmed,0,25,genomes,0.094682148,0.807436977,0.000863098,0.00086308,0.05372156,0.042433137,Genomics,0.17473158,FALSE,57.4,0.684828994,143.2,0.877308001,4,0.707574542,,,0.756570512
5555,Pervasive RNA Secondary Structure in the Genomes of SARS-CoV-2 and Other Coronaviruses.,mBio,33127861,11/1/20,pubmed,0,1,genomes,0.293508846,0.703758439,0.000683154,0.000683191,0.000683187,0.000683184,Genomics,0.4415067,FALSE,11,0.167171748,7,0.299973241,7,0.785110192,,,0.417418394
5556,A Founder Effect Led Early SARS-CoV-2 Transmission in Spain.,J Virol,33127745,11/1/20,pubmed,0,15,"bayes, whole-genome, genome sequences",0.000759352,0.996203199,0.000759339,0.000759389,0.000759361,0.000759359,Genomics,0.6973258,TRUE,54.26666667,0.663182633,57.26666667,0.711934707,6,0.764429903,,,0.713182414
5557,"The characteristics and clinical course of patients with COVID-19 who received invasive mechanical ventilation in Osaka, Japan.",Int J Infect Dis,33127502,11/1/20,pubmed,0,15,logistic regression,0.001438086,0.001438086,0.001438075,0.001438135,0.001438105,0.992809514,Clinics,0.9271864,TRUE,9.533333333,0.143175212,1.666666667,0.145036125,1,0.537564047,,,0.275258461
5558,In silico identification of Tretinoin as a SARS-CoV-2 envelope (E) protein ion channel inhibitor.,Comput Biol Med,33126128,10/31/20,pubmed,0,3,"molecular dynamics simulation, in silico",0.994505975,0.001098835,0.001098795,0.001098808,0.001098797,0.001098789,Drug discovery,0.6911881,TRUE,14,0.213494959,3.333333333,0.206515922,2,0.618927094,,,0.346312659
5559,Potential SARS-CoV-2 interactions with proteins involved in trophoblast functions - An in-silico study.,Placenta,33126048,10/31/20,pubmed,0,9,"in silico, in-silico, interactom, network analysis, text mining, text-mining",0.935346666,0.001622722,0.001622781,0.001622792,0.001622887,0.058162152,Drug discovery,0.91632485,TRUE,99.33333333,0.867586122,40.66666667,0.63774418,0,0.403234768,,,0.636188356
5560,Chest high-resolution computed tomography is associated to short-time progression to severe disease in patients with COVID-19 pneumonia.,Clin Imaging,33125986,10/31/20,pubmed,0,4,logistic regression,0.001392826,0.00139285,0.304350388,0.001392858,0.00139284,0.690078237,Clinics,0.9854567,TRUE,79.75,0.806110458,44,0.654401927,0,0.403234768,,,0.621249051
5561,Effect of blood analysis and immune function on the prognosis of patients with COVID-19.,PLoS One,33125396,10/31/20,pubmed,0,6,logistic regression,0.001220147,0.001220018,0.001219996,0.001220006,0.001220003,0.99389983,Clinics,0.9481312,TRUE,28,0.408312202,,,2,0.618927094,,,0.513619648
5562,Pfam: The protein families database in 2021.,Nucleic Acids Res,33125078,10/31/20,pubmed,0,12,proteom,0.116559267,0.87288427,0.002639203,0.002639138,0.002639088,0.002639034,Genomics,0.52619547,TRUE,40.91666667,0.547467376,932.4166667,0.99290875,15,0.874313229,,,0.804896452
5563,FLANNEL (Focal Loss bAsed Neural Network EnsembLe) for COVID-19 detection.,J Am Med Inform Assoc,33125051,10/31/20,pubmed,0,5,"neural network, classifier, network model, ensemble learning",0.000926281,0.00092629,0.995368543,0.000926294,0.000926296,0.000926297,Imaging,0.21800655,FALSE,22.8,0.337064754,3.4,0.208589778,4,0.707574542,,,0.417743024
5564,Transcriptomic analysis reveals novel mechanisms of SARS-CoV-2 infection in human lung cells.,Immun Inflamm Dis,33124193,10/31/20,pubmed,0,10,"bioinformatic, transcriptom, genomes",0.879214217,0.112654575,0.002032846,0.00203279,0.002032761,0.00203281,Drug discovery,0.7249947,TRUE,43.4,0.571958686,46.4,0.66604228,2,0.618927094,,,0.61897602
5565,Are Migraine Patients at Increased Risk for Symptomatic Coronavirus Disease 2019 Due to Shared Comorbidities?,Headache,33124044,10/31/20,pubmed,0,4,dataset,0.212254912,0.060697826,0.001254661,0.192677111,0.055103029,0.478012461,Clinics,0.72928137,TRUE,67.25,0.748964067,50.75,0.684238694,0,0.403234768,,,0.612145843
5566,Delayed hospital admission and high-dose corticosteroids potentially prolong SARS-CoV-2 RNA detection duration of patients with COVID-19.,Eur J Clin Microbiol Infect Dis,33123934,10/31/20,pubmed,0,8,logistic regression,0.001415114,0.223809343,0.117866049,0.001415102,0.00141512,0.654079272,Clinics,0.4786037,FALSE,19.25,0.287896592,3.375,0.207051111,0,0.403234768,,,0.299394157
5567,Possible association of vitamin D status with lung involvement and outcome in patients with COVID-19: a retrospective study.,Eur J Nutr,33123774,10/31/20,pubmed,0,7,logistic regression,0.001237074,0.001237073,0.08716235,0.001237105,0.001237084,0.907889315,Clinics,0.84244704,TRUE,23.14285714,0.343125734,3,0.199424672,18,0.891474782,,,0.478008396
5568,The role of nicotinic receptors in SARS-CoV-2 receptor ACE2 expression in intestinal epithelia.,Bioelectron Med,33123616,10/31/20,pubmed,0,12,dataset,0.916015069,0.001717202,0.036538433,0.001717249,0.00171731,0.042294737,Drug discovery,0.96790016,TRUE,37.66666667,0.515430763,31.33333333,0.580010704,0,0.403234768,,,0.499558745
5569,Carcinoembryonic Antigen: A Potential Biomarker to Evaluate the Severity and Prognosis of COVID-19.,Front Med (Lausanne),33123542,10/31/20,pubmed,0,12,logistic regression,0.001098814,0.001098855,0.045663625,0.001098821,0.001098815,0.94994107,Clinics,0.90233314,TRUE,40.08333333,0.540107613,104.75,0.83067969,0,0.403234768,,,0.59134069
5570,Prognostic Factors for COVID-19 Pneumonia Progression to Severe Symptoms Based on Earlier Clinical Features: A Retrospective Analysis.,Front Med (Lausanne),33123541,10/31/20,pubmed,0,11,logistic regression,0.001371328,0.050875704,0.075801918,0.00137142,0.001371372,0.869208259,Clinics,0.85689354,TRUE,34.36363636,0.480611046,24.90909091,0.527428419,9,0.814309525,,,0.607449663
5571,Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19.,Front Cell Dev Biol,33123533,10/31/20,pubmed,0,5,network analysis,0.821699938,0.057491607,0.117293504,0.00117163,0.001171728,0.001171594,Drug discovery,0.5150698,TRUE,48.6,0.618529284,30.2,0.571113192,3,0.667819001,,,0.619153826
5572,Within-Host Diversity of SARS-CoV-2 in COVID-19 Patients With Variable Disease Severities.,Front Cell Infect Microbiol,33123498,10/31/20,pubmed,0,8,"sequencing, genomes",0.00131035,0.59713035,0.018451149,0.001310327,0.001310398,0.380487426,Genomics,0.7511131,TRUE,45.5,0.591440411,31.375,0.580144501,4,0.707574542,,,0.626386484
5573,Correlates of Health-Protective Behavior During the Initial Days of the COVID-19 Outbreak in Norway.,Front Psychol,33123045,10/31/20,pubmed,0,5,machine learning,0.001220027,0.001220011,0.042418096,0.165670978,0.788250867,0.001220021,Healthcare,0.90899074,TRUE,57.8,0.68736471,65,0.739028633,3,0.667819001,,,0.698070781
5574,"Adherence to Lifestyle Modifications and Associated Factors Among Adult Hypertensive Patients Attending Chronic Follow-Up Units of Dessie Referral Hospital, North East Ethiopia, 2020.",Integr Blood Press Control,33122940,10/31/20,pubmed,0,3,logistic regression,0.001112657,0.001112622,0.001112655,0.001112644,0.76958008,0.225969343,Healthcare,0.9610894,TRUE,6.666666667,0.094996598,0,0.055525823,0,0.403234768,,,0.18458573
5575,Repurposing Drugs for COVID-19: Pharmacokinetics and Pharmacogenomics of Chloroquine and Hydroxychloroquine.,Pharmgenomics Pers Med,33122936,10/31/20,pubmed,0,2,pharmacogenom,0.626331412,0.001511952,0.001511861,0.274304689,0.001511943,0.094828143,Drug discovery,0.8264067,TRUE,9,0.135320675,7,0.299973241,3,0.667819001,,,0.367704306
5576,Triglyceride to High-Density Lipoprotein Cholesterol Ratio is an Important Determinant of Cardiovascular Risk and Poor Prognosis in Coronavirus Disease-19: A Retrospective Case Series Study.,Diabetes Metab Syndr Obes,33122929,10/31/20,pubmed,0,6,logistic regression,0.001098796,0.001098801,0.001098815,0.001098798,0.001098801,0.994505989,Clinics,0.8946066,TRUE,194.6666667,0.971055724,194.6666667,0.914905004,0,0.403234768,,,0.763065165
5577,"Predictors of Coronavirus Disease 2019 (COVID-19) Prevention Practices Using Health Belief Model Among Employees in Addis Ababa, Ethiopia, 2020.",Infect Drug Resist,33122922,10/31/20,pubmed,0,5,logistic regression,0.000977453,0.000977438,0.00097743,0.000977456,0.99511275,0.000977474,Healthcare,0.9745079,TRUE,11.8,0.177994929,7,0.299973241,0,0.403234768,,,0.293734312
5578,0,Sci Rep,33122844,10/31/20,pubmed,0,6,"model simulation, dataset",0.154936527,0.001987178,0.001987135,0.837114863,0.001987141,0.001987157,Epidemiology,0.4237657,FALSE,112.5,0.895479003,118.6666667,0.849946481,0,0.403234768,,,0.716220084
5579,Kinetics of SARS-CoV-2 positivity of infected and recovered patients from a single center.,Sci Rep,33122706,10/31/20,pubmed,0,26,prediction model,0.001786531,0.00178669,0.12701786,0.252688174,0.001786565,0.61493418,Clinics,0.5755969,TRUE,127.0384615,0.91718721,133.9230769,0.867607707,4,0.707574542,,,0.830789819
5580,Identification of therapeutic drugs against COVID-19 through computational investigation on drug repurposing and structural modification.,J Biomed Res,33122473,10/31/20,pubmed,0,7,computational,0.946233997,0.001861749,0.001861782,0.00186177,0.046318889,0.001861813,Drug discovery,0.78323495,TRUE,63.71428571,0.726018925,78.85714286,0.77548836,2,0.618927094,,,0.70681146
5581,Association between medical students' prior experiences and perceptions of formal online education developed in response to COVID-19: a cross-sectional study in China.,BMJ Open,33122327,10/31/20,pubmed,0,4,logistic regression,0.000863066,0.000863036,0.2234884,0.000863052,0.773059407,0.000863038,Healthcare,0.9545848,TRUE,39.75,0.536829736,15.75,0.433636607,0,0.403234768,,,0.45790037
5582,"Health Literacy, eHealth Literacy, Adherence to Infection Prevention and Control Procedures, Lifestyle Changes, and Suspected COVID-19 Symptoms Among Health Care Workers During Lockdown: Online Survey.",J Med Internet Res,33122164,10/31/20,pubmed,0,21,"logistic regression, correlation analysis",0.000956377,0.044196308,0.000956354,0.000956376,0.95197823,0.000956355,Healthcare,0.9812197,TRUE,14.14285714,0.213989733,6.761904762,0.292146107,5,0.739490092,,,0.415208644
5583,Coronavirus Detection in the Clinical Microbiology Laboratory: Are We Ready for Identifying and Diagnosing a Novel Virus?,Clin Lab Med,33121615,10/31/20,pubmed,0,3,"sequencing, metagenom, whole-genome",0.002898368,0.752603653,0.235802842,0.002898451,0.00289831,0.002898377,Genomics,0.71666354,TRUE,139.3333333,0.932587049,233.3333333,0.931295157,0,0.403234768,,,0.755705658
5584,Prediction and evolution of B cell epitopes of surface protein in SARS-CoV-2.,Virol J,33121513,10/31/20,pubmed,0,5,"whole genome, genomes",0.69264108,0.302478772,0.001220056,0.001220053,0.001220026,0.001220013,Drug discovery,0.32896638,FALSE,17.2,0.259323397,6.8,0.292881991,1,0.537564047,,,0.363256478
5585,The values and limitations of mathematical modelling to COVID-19 in the world: a follow up report.,Emerg Microbes Infect,33121387,10/31/20,pubmed,0,3,mathematical model,0.002996483,0.002996513,0.002996485,0.985017628,0.002996444,0.002996447,Epidemiology,0.286875,FALSE,44.66666667,0.583462181,45.66666667,0.661760771,0,0.403234768,,,0.549485906
5586,0,J Am Heart Assoc,33121304,10/31/20,pubmed,0,11,logistic regression,0.027115824,0.001156228,0.001156239,0.001156239,0.001156227,0.968259243,Clinics,0.9915606,TRUE,68.81818182,0.758055538,44.36363636,0.655673,2,0.618927094,,,0.677551877
5587,Risk Factors for Mortality in Hospitalized Patients with COVID-19: An Overview in a Mexican Population.,Tuberc Respir Dis (Seoul),33121231,10/31/20,pubmed,0,6,logistic regression,0.001350348,0.001350385,0.001350469,0.056820155,0.030187114,0.908941528,Clinics,0.65017277,TRUE,6.833333333,0.096852001,0.5,0.087101953,2,0.618927094,,,0.267627016
5588,"The importance of overweight in COVID-19: A retrospective analysis in a single center of Wuhan, China.",Medicine (Baltimore),33120785,10/31/20,pubmed,0,7,logistic regression,0.071139694,0.000977437,0.085381012,0.000977458,0.02607821,0.815446189,Clinics,0.97665757,TRUE,128.2857143,0.919351846,92.57142857,0.807265186,4,0.707574542,,,0.811397191
5589,Lifting mobility restrictions and the effect of superspreading events on the short-term dynamics of COVID-19.,Math Biosci Eng,33120597,10/31/20,pubmed,0,3,mathematical model,0.001272631,0.001272653,0.001272659,0.993636755,0.001272652,0.00127265,Epidemiology,0.11418852,FALSE,30,0.432432432,22,0.503746321,3,0.667819001,,,0.534665918
5590,Soft pre-rough sets and its applications in decision making.,Math Biosci Eng,33120588,10/31/20,pubmed,0,3,mathematical model,0.002806477,0.002806422,0.2811293,0.707644991,0.002806435,0.002806376,Epidemiology,0.007306069,FALSE,11,0.167171748,0.666666667,0.096200161,0,0.403234768,,,0.222202226
5591,Mathematical modeling of COVID-19 transmission: the roles of intervention strategies and lockdown.,Math Biosci Eng,33120585,10/31/20,pubmed,0,5,mathematical model,0.00141513,0.001415137,0.001415147,0.992924324,0.001415125,0.001415138,Epidemiology,0.19759408,FALSE,14.2,0.214793741,1.4,0.133864062,1,0.537564047,,,0.295407283
5592,Using a partial differential equation with Google Mobility data to predict COVID-19 in Arizona.,Math Biosci Eng,33120533,10/31/20,pubmed,0,2,forecasting model,0.001593527,0.001593511,0.00159364,0.992032288,0.001593575,0.001593459,Epidemiology,0.65025985,TRUE,6.5,0.093512277,0,0.055525823,12,0.850299401,,,0.3331125
5593,Response to Comment on Considering the Effects of Microbiome and Diet on SARS-CoV-2 Infection.,ACS Nano,33120507,10/31/20,pubmed,0,1,microbiom,0.034969345,0.825176463,0.034961924,0.034964244,0.034965705,0.034962319,Genomics,0.6004196,TRUE,284,0.989486054,227,0.928418518,0,0.403234768,,,0.773713113
5594,Comment on Considering the Effects of Microbiome and Diet on SARS-CoV-2 Infection: Nanotechnology Roles.,ACS Nano,33120503,10/31/20,pubmed,0,1,microbiom,0.057799149,0.711004256,0.057799148,0.057799148,0.057799148,0.05779915,Genomics,0.74556494,TRUE,107,0.883357041,21,0.492239765,0,0.403234768,,,0.592943858
5595,The COVID-19 pandemic and serious psychological consequences in Bangladesh: A population-based nationwide study.,J Affect Disord,33120247,10/30/20,pubmed,0,17,logistic regression,0.00159352,0.001593635,0.001593557,0.154727575,0.838898233,0.001593481,Healthcare,0.940703,TRUE,104.0588235,0.877667141,123.9411765,0.856502542,10,0.828199272,,,0.854122985
5596,Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients.,Emerg Radiol,33119835,10/30/20,pubmed,0,12,predictive model,0.001141308,0.001141317,0.398432929,0.001141338,0.001141327,0.59700178,Clinics,0.8881438,TRUE,38.25,0.520811429,25.66666667,0.534854161,1,0.537564047,,,0.531076546
5597,The values of coagulation function in COVID-19 patients.,PLoS One,33119703,10/30/20,pubmed,0,9,logistic regression,0.118661712,0.002130656,0.002130656,0.002130745,0.002130637,0.872815594,Clinics,0.9774245,TRUE,32.55555556,0.461871482,6.333333333,0.285121755,0,0.403234768,,,0.383409335
5598,SARS-CoV-2 transmission routes from genetic data: A Danish case study,PLoS One,33119657,10/30/20,pubmed,0,8,genome sequences,0.001438118,0.434137959,0.001438133,0.560109535,0.001438147,0.001438107,Epidemiology,0.6452304,TRUE,24.75,0.364710248,33.5,0.595999465,2,0.618927094,,,0.526545602
5599,Monitoring physical distancing for crowd management: Real-time trajectory and group analysis.,PLoS One,33119629,10/30/20,pubmed,0,4,computational,0.001187314,0.001187339,0.166583802,0.694728029,0.13512624,0.001187277,Epidemiology,0.52272844,TRUE,21.25,0.314614386,3.5,0.213607172,8,0.799987654,,,0.442736404
5600,On the temporal spreading of the SARS-CoV-2.,PLoS One,33119625,10/30/20,pubmed,0,3,mathematical model,0.049645063,0.002720141,0.002720198,0.939474146,0.002720274,0.002720178,Epidemiology,0.7545891,TRUE,103.6666667,0.876863133,10.33333333,0.36038266,0,0.403234768,,,0.546826854
5601,A social network model of COVID-19.,PLoS One,33119621,10/30/20,pubmed,0,1,network model,0.00105942,0.043471311,0.001059378,0.952291166,0.001059375,0.00105935,Epidemiology,0.24653667,FALSE,66,0.741356918,31,0.578204442,0,0.403234768,,,0.574265376
5602,Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach.,J Med Internet Res,33119535,10/30/20,pubmed,0,7,machine learning,0.001112621,0.001112662,0.001112739,0.809110888,0.186438467,0.001112624,Epidemiology,0.86407113,TRUE,68.71428571,0.757684458,16.71428571,0.445143163,10,0.828199272,,,0.677008964
5603,Molecular Basis of SARS-CoV-2 Infection and Rational Design of Potential Antiviral Agents: Modeling and Simulation Approaches.,J Proteome Res,33119313,10/30/20,pubmed,0,10,in silico,0.866327676,0.001330101,0.001330083,0.128352093,0.001330033,0.001330014,Drug discovery,0.6022368,TRUE,61.5,0.712350795,7.2,0.302047097,6,0.764429903,,,0.592942598
5604,0,ACS Comb Sci,33119257,10/30/20,pubmed,0,2,"neural network, network model",0.897301121,0.001022652,0.057389985,0.001022672,0.014446067,0.028817503,Drug discovery,0.6898891,TRUE,171,0.959057456,179.5,0.906007493,1,0.537564047,,,0.800876332
5605,Can Neurotropic Free-Living Amoeba Serve as a Model to Study SARS-CoV-2 Pathogenesis?,ACS Chem Neurosci,33119251,10/30/20,pubmed,0,1,bioinformatic,0.940304205,0.032476006,0.001291237,0.023346022,0.001291274,0.001291255,Drug discovery,0.7984519,TRUE,61,0.709196611,44,0.654401927,0,0.403234768,,,0.588944435
5606,Is there a role for the ACE2 receptor in SARS-CoV-2 interactions with platelets?,J Thromb Haemost,33119197,10/30/20,pubmed,0,3,"transcriptom, proteom",0.805071473,0.002080869,0.002080731,0.085939777,0.002080596,0.102746554,Drug discovery,0.33338803,FALSE,146,0.940688973,152.3333333,0.885202034,0,0.403234768,,,0.743041925
5607,Estimated Association of Construction Work With Risks of COVID-19 Infection and Hospitalization in Texas.,JAMA Netw Open,33119111,10/30/20,pubmed,0,5,mathematical model,0.013367343,0.000926286,0.000926266,0.430070797,0.415461487,0.13924782,Epidemiology,0.3136796,FALSE,144.4,0.938895417,323.2,0.960329141,2,0.618927094,,,0.839383884
5608,Unravelling the modes of transmission of SARS-CoV-2 during a nursing home outbreak: looking beyond the church super-spread event.,Clin Infect Dis,33119065,10/30/20,pubmed,0,9,"sequencing, whole genome",0.001371298,0.540606224,0.001371256,0.001371328,0.433267961,0.022011933,Genomics,0.71356326,TRUE,93.44444444,0.85280475,179,0.905739898,4,0.707574542,,,0.82203973
5609,Adaptation of a Digital Health Innovation to Prevent Relapse and Support Recovery in Youth Receiving Services for First-Episode Psychosis: Results From the Horyzons-Canada Phase 1 Study.,JMIR Form Res,33118945,10/30/20,pubmed,0,7,digital health,0.000677936,0.000677946,0.153231175,0.642249976,0.202484998,0.00067797,Epidemiology,0.8855853,TRUE,97.14285714,0.861896221,104.2857143,0.829609312,0,0.403234768,,,0.698246767
5610,Undergraduate Medical Competencies in Digital Health and Curricular Module Development: Mixed Methods Study.,J Med Internet Res,33118935,10/30/20,pubmed,0,6,digital health,0.000999535,0.000999521,0.205011253,0.380795668,0.411194461,0.000999562,Healthcare,0.9671103,TRUE,28.66666667,0.414125796,8.5,0.329141022,0,0.403234768,,,0.382167195
5611,"Severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection among hospital workers in a severely affected institution in Madrid, Spain: A surveillance cross-sectional study.",Infect Control Hosp Epidemiol,33118904,10/30/20,pubmed,0,12,logistic regression,0.001415097,0.129689273,0.001415171,0.001415175,0.689122212,0.176943072,Healthcare,0.499495,FALSE,22.83333333,0.337868761,18.83333333,0.468624565,1,0.537564047,,,0.448019125
5612,[Mental health status and its influencing factors among general population and medical personnel in Guangdong Province during COVID-19 pandemic].,Nan Fang Yi Ke Da Xue Xue Bao,33118506,10/30/20,pubmed,0,6,logistic regression,0.001046806,0.001046814,0.001046812,0.001046836,0.994765877,0.001046853,Healthcare,0.77463776,TRUE,19.5,0.29117447,7.333333333,0.304656141,0,0.403234768,,,0.333021793
5613,0,J Biomol Struct Dyn,33118480,10/30/20,pubmed,0,3,"virtual screening, molecular dynamics simulation, bioinformatic, in silico",0.994573328,0.001085331,0.001085326,0.001085355,0.001085324,0.001085337,Drug discovery,0.8788163,TRUE,18,0.271569052,1.666666667,0.145036125,2,0.618927094,,,0.345177424
5614,Biophysical analysis of SARS-CoV-2 transmission and theranostic development via N protein computational characterization.,Biotechnol Prog,33118327,10/30/20,pubmed,0,4,computational,0.513465484,0.001291322,0.085563672,0.397096804,0.001291302,0.001291415,Drug discovery,0.7186024,TRUE,2.25,0.023439916,0,0.055525823,0,0.403234768,,,0.160733502
5615,A Descriptive Study of the Implementation of Remote Occupational Rehabilitation Services Due to the COVID-19 Pandemic Within a Workers' Compensation Context.,J Occup Rehabil,33118130,10/30/20,pubmed,0,4,dataset,0.001393032,0.001392925,0.001392916,0.284798277,0.676827425,0.034195424,Healthcare,0.84627676,TRUE,41.25,0.5508071,40.5,0.637342788,0,0.403234768,,,0.530461552
5616,Upper Respiratory Tract Viral Ribonucleic Acid Load at Hospital Admission Is Associated With Coronavirus Disease 2019 Disease Severity.,Open Forum Infect Dis,33117856,10/30/20,pubmed,0,12,correlation analysis,0.001291317,0.11484228,0.001291265,0.001291263,0.033560705,0.84772317,Clinics,0.5617499,TRUE,47.5,0.610551054,14.66666667,0.420323789,0,0.403234768,,,0.478036537
5617,Geospatial Distribution and Predictors of Mortality in Hospitalized Patients With COVID-19: A Cohort Study.,Open Forum Infect Dis,33117852,10/30/20,pubmed,0,53,logistic regression,0.000916717,0.000916715,0.000916707,0.19762979,0.000916691,0.798703379,Clinics,0.74948674,TRUE,10.68518519,0.159193518,,,1,0.537564047,,,0.348378783
5618,Agile Application of Digital Health Interventions during the COVID-19 Refugee Response.,Ann Glob Health,33117656,10/30/20,pubmed,0,3,digital health,0.002562582,0.002562649,0.002562687,0.664706031,0.32504351,0.002562541,Epidemiology,0.5485217,TRUE,29.33333333,0.423093574,1.666666667,0.145036125,0,0.403234768,,,0.323788156
5619,2019 Tabletop Exercise for Laboratory Diagnosis and Analyses of Unknown Disease Outbreaks by the Korea Centers for Disease Control and Prevention.,Osong Public Health Res Perspect,33117632,10/30/20,pubmed,0,26,sequencing,0.001823379,0.205629003,0.093293318,0.492830254,0.001823447,0.204600598,Epidemiology,0.47761196,FALSE,76.76923077,0.793060795,40.46153846,0.636473107,0,0.403234768,,,0.61092289
5620,Inverse Association between Serotonin 2A Receptor Antagonist Medication Use and Mortality in Severe COVID-19 Infection.,Endocrinol Diabetes Metab J,33117497,10/30/20,pubmed,0,4,logistic regression,0.196110576,0.001203434,0.001203427,0.001203462,0.001203473,0.799075629,Clinics,0.8115101,TRUE,24.5,0.361988991,22.5,0.507492641,1,0.537564047,,,0.469015227
5621,Longitudinal Analysis of T and B Cell Receptor Repertoire Transcripts Reveal Dynamic Immune Response in COVID-19 Patients.,Front Immunol,33117392,10/30/20,pubmed,0,21,sequencing,0.283524153,0.477655952,0.001987155,0.001987231,0.001987226,0.232858283,Genomics,0.35576913,FALSE,93.33333333,0.852433669,333.4761905,0.963005084,4,0.707574542,,,0.841004432
5622,Pandemic Dreams: Network Analysis of Dream Content During the COVID-19 Lockdown.,Front Psychol,33117240,10/30/20,pubmed,0,10,"computational, network analysis",0.002422337,0.002422445,0.00242238,0.538971851,0.372831562,0.080929425,Epidemiology,0.7463691,TRUE,82.8,0.817242872,103.9,0.828739631,3,0.667819001,,,0.771267168
5623,Positive RT-PCR Test Results in 420 Patients Recovered From COVID-19 in Wuhan: An Observational Study.,Front Pharmacol,33117157,10/30/20,pubmed,0,25,logistic regression,0.00091669,0.000916724,0.000916713,0.255323796,0.000916738,0.741009339,Clinics,0.9812994,TRUE,40.88,0.547343682,9.64,0.346869146,5,0.739490092,,,0.54456764
5624,"Knowledge, Attitudes and Practices Toward Prevention and Early Detection of COVID-19 and Associated Factors Among Religious Clerics and Traditional Healers in Gondar Town, Northwest Ethiopia: A Community-Based Study.",Risk Manag Healthc Policy,33117002,10/30/20,pubmed,0,6,logistic regression,0.000988349,0.000988363,0.030571066,0.000988378,0.965475474,0.00098837,Healthcare,0.8827403,TRUE,14.33333333,0.216772837,3,0.199424672,2,0.618927094,,,0.345041534
5625,Pharmacogenomics and Pharmacogenetics: In Silico Prediction of Drug Effects in Treatments for Novel Coronavirus SARS-CoV2 Disease.,Pharmgenomics Pers Med,33116761,10/30/20,pubmed,0,12,"in silico, pharmacogenom",0.65862921,0.232711514,0.001593589,0.0716795,0.001593521,0.033792667,Drug discovery,0.71186686,TRUE,48.25,0.616364648,24.5,0.525086968,0,0.403234768,,,0.514895461
5626,"Knowledge, Attitudes, and Practices Towards COVID-19 Pandemic Among Quarantined Adults in Tigrai Region, Ethiopia.",Infect Drug Resist,33116693,10/30/20,pubmed,0,8,logistic regression,0.000926284,0.000926344,0.015671644,0.080897736,0.900651672,0.00092632,Healthcare,0.73779577,TRUE,9.875,0.147999258,4,0.231469093,4,0.707574542,,,0.362347631
5627,The mNCP-SPI Score Predicting Risk of Severe COVID-19 among Mild-Pneumonia Patients on Admission.,Infect Drug Resist,33116679,10/30/20,pubmed,0,18,logistic regression,0.001371249,0.025330515,0.001371336,0.001371268,0.00137129,0.969184342,Clinics,0.9612342,TRUE,88.38888889,0.837775991,,,0,0.403234768,,,0.620505379
5628,A Nomogram-Based Prediction for Severe Pneumonia in Patients with Coronavirus Disease 2019 (COVID-19).,Infect Drug Resist,33116677,10/30/20,pubmed,0,5,prediction model,0.034197522,0.001141336,0.076346294,0.001141378,0.001141349,0.886032121,Clinics,0.3616348,FALSE,57.6,0.686127775,4.4,0.238961734,0,0.403234768,,,0.442774759
5629,Predictors of Non-Adherence to Public Health Instructions During the COVID-19 Pandemic in the Democratic Republic of the Congo.,J Multidiscip Healthc,33116566,10/30/20,pubmed,0,12,logistic regression,0.002238468,0.00223848,0.002238451,0.002238608,0.988807436,0.002238558,Healthcare,0.6490352,TRUE,2.75,0.030304904,0.75,0.099411292,4,0.707574542,,,0.279096913
5630,"Healthcare Worker's Knowledge, Attitude, and Practice of Proper Face Mask Utilization, and Associated Factors in Police Health Facilities of Addis Ababa, Ethiopia.",J Multidiscip Healthc,33116565,10/30/20,pubmed,0,4,logistic regression,0.000977443,0.00097743,0.000977474,0.000977471,0.995112744,0.000977438,Healthcare,0.816737,TRUE,13.75,0.208299833,9.25,0.340379984,3,0.667819001,,,0.405499606
5631,Patient Satisfaction and Associated Factors During COVID-19 Pandemic in North Shoa Health Care Facilities.,Patient Prefer Adherence,33116436,10/30/20,pubmed,0,6,logistic regression,0.06625335,0.001141338,0.001141397,0.064380112,0.618393007,0.248690795,Healthcare,0.99505967,TRUE,23.16666667,0.343249428,4.333333333,0.237958255,0,0.403234768,,,0.328147484
5632,Robust neutralizing antibodies to SARS-CoV-2 infection persist for months.,Science,33115920,10/30/20,pubmed,0,18,dataset,0.253856299,0.327696903,0.002806439,0.29190917,0.120924568,0.002806621,Genomics,0.18621108,FALSE,99.55555556,0.867833509,236.8888889,0.932900723,203,0.991542688,,,0.930758973
5633,The role of case importation in explaining differences in early SARS-CoV-2 transmission dynamics in Canada-A mathematical modeling study of surveillance data.,Int J Infect Dis,33115683,10/30/20,pubmed,0,11,mathematical model,0.002296577,0.002296707,0.002296519,0.79813004,0.002296735,0.192683421,Epidemiology,0.19336858,FALSE,60.81818182,0.707279362,56.54545455,0.709191865,1,0.537564047,,,0.651345091
5634,Don't sugar coat the COVID (only the vasculature).,Biomed J,33115641,10/30/20,pubmed,0,1,artificial intelligence,0.658238467,0.003466052,0.327897369,0.00346615,0.003466044,0.003465918,Drug discovery,0.6007521,TRUE,84,0.821881378,25,0.529435376,0,0.403234768,,,0.584850507
5635,Safety and Efficacy of Imatinib for Hospitalized Adults with COVID-19: A structured summary of a study protocol for a randomised controlled trial.,Trials,33115543,10/30/20,pubmed,0,5,transcriptom,0.134545482,0.000180772,0.053939348,0.113797563,0.100645005,0.59689183,Clinics,0.9211848,TRUE,55.6,0.671841178,69.2,0.751003479,0,0.403234768,,,0.608693141
5636,"Numbers, graphs and words - do we really understand the lab test results accessible via the patient portals?",Isr J Health Policy Res,33115536,10/30/20,pubmed,0,2,digital health,0.055294044,0.001203452,0.001203459,0.324485572,0.473174208,0.144639264,Healthcare,0.87639225,TRUE,1,0.012307502,0,0.055525823,1,0.537564047,,,0.201799124
5637,Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region.,BMC Infect Dis,33115434,10/30/20,pubmed,0,9,computational,0.001310395,0.001310387,0.001310365,0.975833141,0.001310352,0.01892536,Epidemiology,0.32469383,FALSE,76.33333333,0.791576473,69.77777778,0.752609045,0,0.403234768,,,0.649140095
5638,Anger and confrontation during the COVID-19 pandemic: a national cross-sectional survey in the UK.,J R Soc Med,33115327,10/30/20,pubmed,0,6,logistic regression,0.002238438,0.036505961,0.002238436,0.002238576,0.954540124,0.002238464,Healthcare,0.90866816,TRUE,48.83333333,0.621003154,138,0.872892695,2,0.618927094,,,0.704274314
5639,"Independent Impact of Diabetes on the Severity of Coronavirus Disease 2019 in 5,307 Patients in South Korea: A Nationwide Cohort Study.",Diabetes Metab J,33115212,10/30/20,pubmed,0,7,logistic regression,0.001415091,0.001415095,0.001415146,0.001415162,0.001415196,0.992924308,Clinics,0.8894758,TRUE,74,0.782113922,27.85714286,0.55238159,2,0.618927094,,,0.651140868
5640,Integrating Datasets on Public Health and Clinical Aspects of Sickle Cell Disease for Effective Community-Based Research and Practice.,Diseases,33114600,10/30/20,pubmed,0,8,dataset,0.242078136,0.023464482,0.224624365,0.305566267,0.203079435,0.001187315,Epidemiology,0.64982027,TRUE,13.5,0.205393036,5.375,0.263446615,0,0.403234768,,,0.290691473
5641,The Determinants of Conspiracy Beliefs Related to the COVID-19 Pandemic in a Nationally Representative Sample of Internet Users.,Int J Environ Res Public Health,33114542,10/30/20,pubmed,0,1,logistic regression,0.001622694,0.001622715,0.001622742,0.140156968,0.853352132,0.001622749,Healthcare,0.81743383,TRUE,150,0.944028697,89,0.799304255,5,0.739490092,,,0.827607681
5642,Predictive Accuracy of COVID-19 World Health Organization (WHO) Severity Classification and Comparison with a Bayesian-Method-Based Severity Score (EPI-SCORE).,Pathogens,33114416,10/30/20,pubmed,0,12,"bayes, machine learning, network analysis, prediction model",0.001272638,0.001272631,0.344014543,0.001272701,0.001272642,0.650894845,Clinics,0.4658502,FALSE,33.41666667,0.47065372,26.08333333,0.538399786,2,0.618927094,,,0.5426602
5643,A Proline-Based Tectons and Supramolecular Synthons for Drug Design 2.0: A Case Study of ACEI.,Pharmaceuticals (Basel),33114370,10/30/20,pubmed,0,6,in silico,0.954069114,0.001622832,0.039439602,0.001622889,0.001622836,0.001622727,Drug discovery,0.88627887,TRUE,109.5,0.889479869,10.16666667,0.357171528,1,0.537564047,,,0.594738481
5644,COVID-19 Pandemic and Death Anxiety in Security Forces in Spain.,Int J Environ Res Public Health,33114116,10/30/20,pubmed,0,4,logistic regression,0.001330069,0.001330079,0.001330076,0.483113674,0.51156595,0.001330152,Healthcare,0.48614404,FALSE,16,0.243552477,0.75,0.099411292,1,0.537564047,,,0.293509272
5645,More than Pneumonia: Distinctive Features of SARS-Cov-2 Infection. From Autopsy Findings to Clinical Implications: A Systematic Review.,Microorganisms,33114061,10/30/20,pubmed,0,10,dataset,0.131810654,0.111626716,0.001717353,0.295117332,0.001717268,0.458010677,Clinics,0.72224903,TRUE,42.5,0.562743522,14.7,0.420457586,4,0.707574542,,,0.563591883
5646,"COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis.",Int J Environ Res Public Health,33113936,10/30/20,pubmed,0,5,simulation model,0.001438115,0.001438098,0.001438125,0.992809295,0.001438218,0.001438149,Epidemiology,0.20965987,FALSE,31.8,0.452965551,9.4,0.343256623,2,0.618927094,,,0.471716423
5647,High-Density Amplicon Sequencing Identifies Community Spread and Ongoing Evolution of SARS-CoV-2 in the Southern United States.,Cell Rep,33113345,10/29/20,pubmed,0,25,"sequencing, genomes",0.002357775,0.988210978,0.002357794,0.00235787,0.002357822,0.002357761,Genomics,0.22906911,FALSE,48.84,0.621065001,129.64,0.863459995,2,0.618927094,,,0.701150697
5648,Multidimensional Analysis of Risk Factors for the Severity and Mortality of Patients with COVID-19 and Diabetes.,Infect Dis Ther,33113119,10/29/20,pubmed,0,8,logistic regression,0.001415105,0.001415104,0.001415156,0.001415136,0.001415163,0.992924336,Clinics,0.7033164,TRUE,72.75,0.776300328,36.75,0.615466952,0,0.403234768,,,0.598334016
5649,Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach.,PLoS One,33112931,10/29/20,pubmed,0,5,machine learning,0.001538104,0.001538135,0.390608024,0.523165998,0.001538118,0.081611621,Epidemiology,0.1533745,FALSE,36.2,0.499412456,24.6,0.525689055,2,0.618927094,,,0.548009535
5650,"Bioinformatic characterization of angiotensin-converting enzyme 2, the entry receptor for SARS-CoV-2.",PLoS One,33112891,10/29/20,pubmed,0,2,"computational, bioinformatic",0.951733305,0.001272678,0.001272647,0.02201193,0.008896254,0.014813186,Drug discovery,0.5320828,TRUE,22.5,0.333539489,20,0.481000803,1,0.537564047,,,0.450701446
5651,Escape from neutralizing antibodies by SARS-CoV-2 spike protein variants.,Elife,33112236,10/29/20,pubmed,0,24,"sequencing, genomes",0.449258418,0.509910504,0.020138305,0.000504145,0.000504135,0.019684493,Genomics,0.45330837,FALSE,96.29166667,0.859793432,513.9583333,0.979662831,34,0.937156615,,,0.925537626
5652,Performance of a Severity Score on Admission Chest Radiograph in Predicting Clinical Outcomes in Hospitalized Patients with Coronavirus Disease (COVID-19).,AJR Am J Roentgenol,33112201,10/29/20,pubmed,0,11,logistic regression,0.000807867,0.000807875,0.314772825,0.000807884,0.028687654,0.654115896,Clinics,0.8372468,TRUE,27.36363636,0.399715505,28.18181818,0.555525823,1,0.537564047,,,0.497601792
5653,Angiotensin-converting enzyme 2 expression in COPD and IPF fibroblasts: the forgotten cell in COVID-19.,Am J Physiol Lung Cell Mol Physiol,33112187,10/29/20,pubmed,0,9,computational,0.535442245,0.002130677,0.002130727,0.002130789,0.002130755,0.456034808,Drug discovery,0.7570771,TRUE,84.22222222,0.822561692,141.2222222,0.875836232,1,0.537564047,,,0.745320657
5654,Laboratory markers associated with COVID-19 progression in patients with or without comorbidity: A retrospective study.,J Clin Lab Anal,33112011,10/29/20,pubmed,0,11,logistic regression,0.001684577,0.001684495,0.00168456,0.001684564,0.001684532,0.991577272,Clinics,0.64640486,TRUE,51.72727273,0.644381223,9.818181818,0.35094996,3,0.667819001,,,0.554383395
5655,Lockdown as an Intervention Measure to Mitigate the Spread of COVID-19: a modeling study.,Rev Soc Bras Med Trop,33111916,10/29/20,pubmed,0,8,mathematical model,0.001622698,0.001622729,0.00162271,0.991886392,0.001622737,0.001622735,Epidemiology,0.14965627,FALSE,9.125,0.136062836,0.375,0.073789136,1,0.537564047,,,0.249138673
5656,"Demographic and socioeconomic characteristics of Brazilian adults and COVID-19: a risk group analysis based on the Brazilian National Health Survey, 2013.",Cad Saude Publica,33111839,10/29/20,pubmed,0,2,logistic regression,0.00117156,0.00117162,0.001171536,0.001171619,0.96521194,0.030101726,Healthcare,0.8636316,TRUE,26.5,0.389325252,11,0.371287129,0,0.403234768,,,0.387949049
5657,SARS-CoV-2 isolation from the first reported patients in Brazil and establishment of a coordinated task network.,Mem Inst Oswaldo Cruz,33111751,10/29/20,pubmed,0,20,sequencing,0.179360195,0.52135798,0.001538189,0.262095102,0.001538136,0.034110398,Genomics,0.4211251,FALSE,31.65,0.451110149,19.25,0.47310677,15,0.874313229,,,0.599510049
5658,0,J Biomol Struct Dyn,33111624,10/29/20,pubmed,0,5,in-silico,0.920199623,0.001565333,0.001565324,0.073538907,0.001565398,0.001565415,Drug discovery,0.929672,TRUE,16,0.243552477,1.6,0.140687717,2,0.618927094,,,0.334389096
5659,Meeting the need: Creation of an online infection prevention course by the Golisano Institute for Developmental Disability Nursing for direct support professionals during COVID-19.,J Intellect Disabil,33111623,10/29/20,pubmed,0,2,active learning,0.001901739,0.001901732,0.147278551,0.477033642,0.369982556,0.00190178,Epidemiology,0.62472546,TRUE,17.5,0.263776362,8,0.320511105,0,0.403234768,,,0.329174078
5660,Capsule endoscopy - Recent developments and future directions.,Expert Rev Gastroenterol Hepatol,33111600,10/29/20,pubmed,0,2,artificial intelligence,0.001511863,0.246700691,0.607955936,0.001511909,0.001511889,0.140807712,Genomics,0.4816574,FALSE,16,0.243552477,1,0.122023013,0,0.403234768,,,0.256270086
5661,The COVID-19 Own Risk Appraisal Scale (CORAS): Development and validation in two samples from the United Kingdom.,J Health Psychol,33111594,10/29/20,pubmed,0,3,model fit,0.002296749,0.002296549,0.002297075,0.161985655,0.828827001,0.002296971,Healthcare,0.43034154,FALSE,132.6666667,0.924732513,131.3333333,0.864864865,3,0.667819001,,,0.819138793
5662,N-Terminomics for the Identification of In Vitro Substrates and Cleavage Site Specificity of the SARS-CoV-2 Main Protease.,Proteomics,33111431,10/29/20,pubmed,0,9,proteom,0.894600974,0.097625081,0.001943545,0.00194348,0.001943445,0.001943475,Drug discovery,0.52520645,TRUE,37,0.508936854,20.77777778,0.488961734,2,0.618927094,,,0.538941894
5663,Outcome of patients hospitalized for COVID-19 and exposure to angiotensin-converting enzyme inhibitors and angiotensin-receptor blockers in France: results of the ACE-CoV study.,Fundam Clin Pharmacol,33111329,10/29/20,pubmed,0,6,logistic regression,0.001565393,0.001565438,0.001565402,0.00156546,0.001565397,0.992172909,Clinics,0.92844975,TRUE,113.6666667,0.897087018,43.5,0.651725983,6,0.764429903,,,0.771080968
5664,Virtual Noon Conferences: Providing Resident Education and Wellness During the COVID-19 Pandemic.,PRiMER,33111044,10/29/20,pubmed,0,1,active learning,0.019918389,0.001098816,0.072793616,0.001098904,0.903991333,0.001098942,Healthcare,0.88830674,TRUE,29,0.41993939,4,0.231469093,0,0.403234768,,,0.35154775
5665,The Impact of Obesity on COVID-19 Disease Severity.,PRiMER,33111042,10/29/20,pubmed,0,3,data mining,0.001098919,0.001098842,0.001098833,0.288552091,0.120080113,0.588071202,Clinics,0.907868,TRUE,8.333333333,0.123693488,0.666666667,0.096200161,0,0.403234768,,,0.207709472
5666,Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning.,J Glob Health,33110594,10/29/20,pubmed,0,5,machine learning,0.001291208,0.001291288,0.382175442,0.532591698,0.081359088,0.001291276,Epidemiology,0.6219672,TRUE,11.2,0.168594224,3.2,0.202100615,2,0.618927094,,,0.329873978
5667,Early prediction and identification for severe patients during the pandemic of COVID-19: A severe COVID-19 risk model constructed by multivariate logistic regression analysis.,J Glob Health,33110593,10/29/20,pubmed,0,11,"logistic regression, prediction model",0.000907362,0.000907372,0.000907344,0.031404845,0.000907302,0.964965775,Clinics,0.9767353,TRUE,115.2727273,0.899251654,,,2,0.618927094,,,0.759089374
5668,"Early epidemiological indicators, outcomes, and interventions of COVID-19 pandemic: A systematic review.",J Glob Health,33110589,10/29/20,pubmed,0,10,predictive model,0.000966757,0.000966789,0.000966777,0.618334497,0.000966807,0.377798372,Epidemiology,0.4306942,FALSE,41.2,0.550126786,30,0.570176612,5,0.739490092,,,0.619931163
5669,Early psychological impact of the 2019 coronavirus disease (COVID-19) pandemic and lockdown in a large Spanish sample.,J Glob Health,33110588,10/29/20,pubmed,0,31,logistic regression,0.001371302,0.001371261,0.022835012,0.001371283,0.971679857,0.001371284,Healthcare,0.96191996,TRUE,35.61290323,0.493104088,29.06451613,0.562951565,7,0.785110192,,,0.613721948
5670,"Computational selection of flavonoid compounds as inhibitors against SARS-CoV-2 main protease, RNA-dependent RNA polymerase and spike proteins: A molecular docking study.",Saudi J Biol Sci,33110386,10/29/20,pubmed,0,6,computational,0.960724795,0.000956305,0.000956305,0.00095629,0.000956332,0.035449973,Drug discovery,0.9904834,TRUE,27.83333333,0.405219865,5.666666667,0.270203372,0,0.403234768,,,0.359552668
5671,Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach.,Chaos Solitons Fractals,33110297,10/29/20,pubmed,0,5,"deep learning, lstm",0.001072177,0.001072202,0.001072245,0.994639,0.001072199,0.001072178,Epidemiology,0.2529436,FALSE,44.8,0.584142495,11.2,0.373026492,3,0.667819001,,,0.541662663
5672,Artificial intelligence in medicine and the disclosure of risks.,AI Soc,33110296,10/29/20,pubmed,0,1,artificial intelligence,0.002996513,0.0029965,0.16624933,0.460471474,0.263296795,0.103989389,Epidemiology,0.5198004,TRUE,8,0.118683901,0,0.055525823,0,0.403234768,,,0.192481497
5673,Antibody Binding Epitope Mapping (AbMap) of Hundred Antibodies in a Single Run.,Mol Cell Proteomics,33109704,10/29/20,pubmed,0,16,sequencing,0.563475924,0.402859853,0.001392943,0.001392921,0.001392862,0.029485498,Drug discovery,0.32311827,FALSE,41,0.549013544,35.3125,0.606903934,1,0.537564047,,,0.564493842
5674,COVID-19 and 'immune boosting' on the internet: a content analysis of Google search results.,BMJ Open,33109677,10/29/20,pubmed,0,4,"microbiom, dataset",0.136209153,0.04500857,0.002422413,0.811515156,0.002422381,0.002422327,Epidemiology,0.9543621,TRUE,70,0.764178366,31.75,0.583088039,2,0.618927094,,,0.655397833
5675,"Computational studies reveal mechanism by which quinone derivatives can inhibit SARS-CoV-2. Study of embelin and two therapeutic compounds of interest, methyl prednisolone and dexamethasone.",J Infect Public Health,33109497,10/29/20,pubmed,0,4,computational,0.94777199,0.001684586,0.001684625,0.001684569,0.001684553,0.045489678,Drug discovery,0.98098123,TRUE,39.5,0.534232173,14.5,0.418450629,4,0.707574542,,,0.553419114
5676,Exploiting an early warning Nomogram for predicting the risk of ICU admission in patients with COVID-19: a multi-center study in China.,Scand J Trauma Resusc Emerg Med,33109234,10/29/20,pubmed,0,8,"logistic regression, prediction model",0.001415138,0.001415147,0.207451021,0.001415153,0.001415134,0.786888408,Clinics,0.755864,TRUE,52,0.647349867,24,0.521808938,2,0.618927094,,,0.596028633
5677,The psychological burden experienced by Chinese citizens during the COVID-19 outbreak: prevalence and determinants.,BMC Public Health,33109156,10/29/20,pubmed,0,3,logistic regression,0.001072182,0.001072188,0.001072211,0.00107223,0.984127239,0.01158395,Healthcare,0.6592705,TRUE,11,0.167171748,9.333333333,0.342253144,5,0.739490092,,,0.416304995
5678,A survival analysis of COVID-19 in the Mexican population.,BMC Public Health,33109136,10/29/20,pubmed,0,6,dataset,0.001717221,0.035822887,0.001717246,0.160298936,0.001717289,0.798726422,Clinics,0.6735497,TRUE,47.33333333,0.608633805,7.333333333,0.304656141,5,0.739490092,,,0.55092668
5679,The Binding of Remdesivir to SARS-CoV-2 RNA-Dependent RNA polymerase May Pave The Way Towards the Design of Potential Drugs for COVID-19 Treatment.,Curr Pharm Biotechnol,33109039,10/29/20,pubmed,0,2,"virtual screening, molecular dynamics simulation",0.995767257,0.00084657,0.000846549,0.000846566,0.000846528,0.000846529,Drug discovery,0.68748456,TRUE,58.5,0.693240151,7,0.299973241,0,0.403234768,,,0.46548272
5680,Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data.,PLoS One,33108386,10/28/20,pubmed,0,5,model fit,0.001653024,0.001653064,0.001653015,0.99173481,0.00165306,0.001653026,Epidemiology,0.23967555,FALSE,19.2,0.287339972,3,0.199424672,20,0.900117291,,,0.462293978
5681,"Assessing concerns for the economic consequence of the COVID-19 response and mental health problems associated with economic vulnerability and negative economic shock in Italy, Spain, and the United Kingdom.",PLoS One,33108374,10/28/20,pubmed,0,9,machine learning,0.001538096,0.001538143,0.088035009,0.32974483,0.57760582,0.001538102,Healthcare,0.30734295,FALSE,31.33333333,0.448388892,22.11111111,0.504147712,6,0.764429903,,,0.572322169
5682,"Relationship Between COVID-19 Infection and Risk Perception, Knowledge, Attitude, and Four Nonpharmaceutical Interventions During the Late Period of the COVID-19 Epidemic in China: Online Cross-Sectional Survey of 8158 Adults.",J Med Internet Res,33108317,10/28/20,pubmed,0,13,logistic regression,0.001085314,0.001085316,0.001085321,0.001085372,0.994573354,0.001085323,Healthcare,0.4467494,FALSE,142.6923077,0.937163708,,,5,0.739490092,,,0.8383269
5683,An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study.,J Med Internet Res,33108316,10/28/20,pubmed,0,11,"machine learning, prediction model",0.000916696,0.000916671,0.268830407,0.000916827,0.00091676,0.727502639,Clinics,0.9344637,TRUE,37.45454545,0.512956893,16.36363636,0.440594059,0,0.403234768,,,0.452261907
5684,The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets.,J Med Internet Res,33108315,10/28/20,pubmed,0,5,"machine learning, supervised learning, unsupervised learning",0.001220035,0.001220035,0.038862358,0.341162176,0.616315407,0.001219989,Healthcare,0.98586357,TRUE,31.8,0.452965551,14,0.412898047,0,0.403234768,,,0.423032789
5685,Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study.,JMIR Public Health Surveill,33108310,10/28/20,pubmed,0,2,data mining,0.00131035,0.001310362,0.001310386,0.879711973,0.11504658,0.00131035,Epidemiology,0.26153243,FALSE,29,0.41993939,18,0.46180091,1,0.537564047,,,0.473101449
5686,Psychological Impact of Health Risk Communication and Social Media on College Students During the COVID-19 Pandemic: Cross-Sectional Study.,J Med Internet Res,33108308,10/28/20,pubmed,0,6,logistic regression,0.001141319,0.001141315,0.001141343,0.001141378,0.965377081,0.030057564,Healthcare,0.98673046,TRUE,520.8333333,0.997897211,,,1,0.537564047,,,0.767730629
5687,Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques.,J Med Internet Res,33108306,10/28/20,pubmed,0,4,"computational, text-mining",0.000871577,0.000871566,0.081440799,0.561222091,0.35472242,0.000871546,Epidemiology,0.9882504,TRUE,27.75,0.404292164,15.5,0.430157881,0,0.403234768,,,0.412561604
5688,A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-Center Study.,IEEE J Biomed Health Inform,33108303,10/28/20,pubmed,0,9,"deep learning, neural network",0.001653024,0.00165305,0.460725702,0.001653057,0.001653036,0.532662131,Clinics,0.8226013,TRUE,65.88888889,0.739687055,23.11111111,0.513714209,1,0.537564047,,,0.596988437
5689,[Biochemical and statistical lessons from the evolution of the SARS-CoV-2 virus: paths for novel antiviral warfare].,C R Biol,33108121,10/28/20,pubmed,0,5,probabilistic,0.302948501,0.538161046,0.001046825,0.155749946,0.001046849,0.001046834,Genomics,0.70145565,TRUE,106.8,0.882738574,262.2,0.942601017,0,0.403234768,,,0.742858119
5690,Secondary metabolites from spice and herbs as potential multitarget inhibitors of SARS-CoV-2 proteins.,J Biomol Struct Dyn,33107812,10/28/20,pubmed,0,10,in silico,0.905545079,0.001987191,0.001987268,0.086505866,0.001987292,0.001987305,Drug discovery,0.888329,TRUE,29.5,0.426000371,18.4,0.464677549,1,0.537564047,,,0.476080656
5691,Awake Prone Positioning in COVID-19 Hypoxemic Respiratory Failure: Exploratory Findings in a Single-center Retrospective Cohort Study.,Acad Emerg Med,33107664,10/28/20,pubmed,0,16,logistic regression,0.028839936,0.001392907,0.00139294,0.127554113,0.001392908,0.839427195,Clinics,0.9829243,TRUE,9.25,0.137918239,2,0.164302917,7,0.785110192,,,0.362443782
5692,Comparison of VEGF-A values between pregnant women with COVID-19 and healthy pregnancies and its association with composite adverse outcomes.,J Med Virol,33107604,10/28/20,pubmed,0,9,correlation analysis,0.001593473,0.001593469,0.0015935,0.001593494,0.001593584,0.99203248,Clinics,0.98247415,TRUE,40,0.539860226,2.555555556,0.181161359,0,0.403234768,,,0.374752118
5693,Derivation and Internal Validation of a Model to Predict the Probability of Severe Acute Respiratory Syndrome Coronavirus-2 Infection in Community People.,J Gen Intern Med,33107007,10/28/20,pubmed,0,4,logistic regression,0.001622744,0.001622773,0.167744745,0.325836484,0.445618021,0.057555233,Healthcare,0.5368858,TRUE,162.75,0.953182015,239,0.933703505,0,0.403234768,,,0.763373429
5694,Associations between governor political affiliation and COVID-19 cases and deaths in the United States.,medRxiv,33106818,10/28/20,pubmed,0,5,bayes,0.001684488,0.001684567,0.001684541,0.739702767,0.253559016,0.001684621,Epidemiology,0.64706546,TRUE,43.2,0.569051889,17.2,0.452568906,0,0.403234768,,,0.474951854
5695,Modelling the Anatomical Distribution of Neurological Events in COVID-19 Patients: A Systematic Review.,medRxiv,33106811,10/28/20,pubmed,0,9,mathematical model,0.000854735,0.00085474,0.04890374,0.578349438,0.000854762,0.370182585,Epidemiology,0.16699702,FALSE,2.222222222,0.022945142,0.333333333,0.073187048,0,0.403234768,,,0.166455653
5696,Viral surface geometry shapes influenza and coronavirus spike evolution.,bioRxiv,33106808,10/28/20,pubmed,0,1,computational,0.299981282,0.693013927,0.001751161,0.001751269,0.001751167,0.001751194,Genomics,0.08230716,FALSE,8,0.118683901,0,0.055525823,0,0.403234768,,,0.192481497
5697,Adult Stem Cell-derived Complete Lung Organoid Models Emulate Lung Disease in COVID-19.,bioRxiv,33106807,10/28/20,pubmed,0,20,"computational, transcriptom",0.736916816,0.072026367,0.001272675,0.001272677,0.001272717,0.187238747,Drug discovery,0.16507903,FALSE,37,0.508936854,53.2,0.694407279,5,0.739490092,,,0.647611408
5698,Single-cell RNA-sequencing reveals dysregulation of molecular programs associated with SARS-CoV-2 severity and outcomes in patients with chronic lung disease.,bioRxiv,33106805,10/28/20,pubmed,0,21,"sequencing, transcriptom",0.675890639,0.033576325,0.000889074,0.000889063,0.000889097,0.287865801,Drug discovery,0.8993942,TRUE,55.0952381,0.668810687,79.66666667,0.7782981,2,0.618927094,,,0.688678627
5699,SARS-CoV-2 Cell Entry Factors ACE2 and TMPRSS2 are Expressed in the Pancreas but are Not Enriched in Islet Endocrine Cells.,bioRxiv,33106804,10/28/20,pubmed,0,20,"sequencing, dataset",0.814451502,0.075633478,0.001943507,0.001943486,0.001943492,0.104084535,Drug discovery,0.3687212,FALSE,68.15,0.75440658,160.35,0.892427081,5,0.739490092,,,0.795441251
5700,Analysis of SARS-CoV-2 ORF3a structure reveals chloride binding sites.,bioRxiv,33106803,10/28/20,pubmed,0,7,molecular dynamics simulation,0.928533265,0.001486462,0.001486468,0.06552093,0.001486445,0.001486429,Drug discovery,0.31318825,FALSE,50,0.632073721,36.71428571,0.615199358,2,0.618927094,,,0.622066724
5701,Identification of evolutionarily stable sites across the SARS-CoV-2 proteome.,Res Sq,33106800,10/28/20,pubmed,0,9,proteom,0.667358876,0.324318436,0.002080622,0.002080664,0.00208059,0.002080812,Drug discovery,0.54000396,TRUE,56.77777778,0.680128641,89.55555556,0.800508429,0,0.403234768,,,0.627957279
5702,Agile clinical research: A data science approach to scrumban in clinical medicine.,Intell Based Med,33106798,10/28/20,pubmed,0,6,"predictive model, dataset",0.001350398,0.001350338,0.428677586,0.522850817,0.001350397,0.044420464,Epidemiology,0.45325387,FALSE,34,0.477766096,111.6666667,0.840915173,0,0.403234768,,,0.573972012
5703,Probing infectious disease by single-cell RNA sequencing: Progresses and perspectives.,Comput Struct Biotechnol J,33106757,10/28/20,pubmed,0,4,sequencing,0.243669716,0.157237134,0.004110016,0.586763606,0.00410977,0.004109759,Epidemiology,0.5874654,TRUE,97.5,0.863318696,67.75,0.747524752,1,0.537564047,,,0.716135832
5704,"Discovery of small molecule PLpro inhibitor against COVID-19 using structure-based virtual screening, molecular dynamics simulation, and molecular mechanics/Generalized Born surface area (MM/GBSA) calculation.",Struct Chem,33106741,10/28/20,pubmed,0,4,"virtual screening, molecular dynamics simulation",0.992440813,0.00151186,0.001511856,0.001511843,0.001511826,0.001511801,Drug discovery,0.8956748,TRUE,67.5,0.750201002,260.5,0.941731335,0,0.403234768,,,0.698389035
5705,Time dependent correlations between the probability of a node being infected and its centrality measures.,Physica A,33106728,10/28/20,pubmed,0,2,model simulation,0.001684563,0.001684527,0.001684529,0.991577281,0.001684572,0.001684528,Epidemiology,0.40602848,FALSE,13,0.197352959,0,0.055525823,0,0.403234768,,,0.218704517
5706,Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data.,Proc Natl Acad Sci U S A,33106403,10/28/20,pubmed,0,6,computational,0.001438195,0.001438213,0.001438164,0.992809158,0.00143818,0.001438091,Epidemiology,0.17949972,FALSE,71.16666667,0.769559033,60.16666667,0.721768799,1,0.537564047,,,0.676297293
5707,Perceived psychosocial health and its sociodemographic correlates in times of the COVID-19 pandemic: a community-based online study in China.,Infect Dis Poverty,33106187,10/28/20,pubmed,0,2,logistic regression,0.054473243,0.001254621,0.001254598,0.001254658,0.863634471,0.078128408,Healthcare,0.91167545,TRUE,9.5,0.143051518,1,0.122023013,1,0.537564047,,,0.267546193
5708,Mechanistic Insights to the Binding of Antibody CR3022 Against RBD from SARS-CoV and HCoV-19/SARS-CoV-2: A Computational Study.,Comb Chem High Throughput Screen,33106140,10/28/20,pubmed,0,12,computational,0.880998475,0.066917073,0.000988369,0.035264073,0.00098836,0.01484365,Drug discovery,0.7820946,TRUE,25.25,0.370709382,,,0,0.403234768,,,0.386972075
5709,Ischemic Stroke Occurs Less Frequently in Patients With COVID-19: A Multicenter Cross-Sectional Study.,Stroke,33106109,10/28/20,pubmed,0,8,logistic regression,0.001751169,0.001751248,0.001751252,0.116368653,0.001751305,0.876626372,Clinics,0.6869105,TRUE,134,0.926031294,86.25,0.793484078,6,0.764429903,,,0.827981759
5710,Computer simulations of the interaction between SARS-CoV-2 spike glycoprotein and different surfaces.,Biointerphases,33105999,10/28/20,pubmed,0,2,molecular dynamics simulation,0.990882927,0.001823437,0.001823476,0.001823488,0.001823335,0.001823337,Drug discovery,0.12551418,FALSE,69.5,0.761271569,22,0.503746321,0,0.403234768,,,0.556084219
5711,Adoption of Preventive Behaviour Strategies and Public Perceptions About COVID-19 in Singapore.,Int J Health Policy Manag,33105971,10/28/20,pubmed,0,4,logistic regression,0.000734206,0.000734175,0.000734165,0.00073419,0.947930125,0.049133139,Healthcare,0.7026092,TRUE,95,0.856824788,263.5,0.943671394,0,0.403234768,,,0.734576983
5712,Health Service Utilization in Hong Kong During the COVID-19 Pandemic - A Cross-sectional Public Survey.,Int J Health Policy Manag,33105965,10/28/20,pubmed,0,7,logistic regression,0.001461933,0.001461905,0.001461905,0.085049061,0.866400645,0.04416455,Healthcare,0.8302376,TRUE,117.1428571,0.903024306,47.71428571,0.671260369,1,0.537564047,,,0.703949574
5713,Viewpoint: Origin of SARS-CoV-2.,Viruses,33105685,10/28/20,pubmed,0,17,genomes,0.422897112,0.570950504,0.001538142,0.001538086,0.001538067,0.001538088,Genomics,0.4680422,FALSE,178.5882353,0.963448574,209.3529412,0.921728659,2,0.618927094,,,0.834701443
5714,Obesity and immune status in children.,Curr Opin Pediatr,33105275,10/27/20,pubmed,0,3,sequencing,0.519314233,0.062071999,0.001330052,0.001330141,0.196292111,0.219661463,Drug discovery,0.8892514,TRUE,34,0.477766096,101,0.823989831,1,0.537564047,,,0.613106658
5715,Effect of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the United States : A Simulation Modeling Approach.,Ann Intern Med,33105091,10/27/20,pubmed,0,5,simulation model,0.001371282,0.001371268,0.001371264,0.96999017,0.001371287,0.024524728,Epidemiology,0.73849154,TRUE,170.4,0.958377141,177.4,0.904669521,2,0.618927094,,,0.827324586
5716,A comparative phylogenomic analysis of SARS-CoV-2 strains reported from non-human mammalian species and environmental samples.,Mol Biol Rep,33104993,10/27/20,pubmed,0,7,"in silico, phylogenom",0.176209829,0.819224844,0.001141324,0.001141366,0.001141323,0.001141314,Genomics,0.7707803,TRUE,46.57142857,0.60152143,32.85714286,0.591450361,1,0.537564047,,,0.576845279
5717,Repeat COVID-19 Molecular Testing: Correlation of SARS-CoV-2 Culture with Molecular Assays and Cycle Thresholds.,Clin Infect Dis,33104776,10/27/20,pubmed,0,15,"sequencing, whole genome",0.0485487,0.617455068,0.099792255,0.061795039,0.00109883,0.171310108,Genomics,0.6737776,TRUE,40.2,0.541159008,53.2,0.694407279,17,0.887338725,,,0.707635004
5718,A mathematical model of COVID-19 transmission in a tertiary hospital and assessment of the effects of different intervention strategies.,PLoS One,33104736,10/27/20,pubmed,0,13,mathematical model,0.001310354,0.001310338,0.001310377,0.313109236,0.27001718,0.412942515,Clinics,0.7591076,TRUE,71.69230769,0.771476282,26.23076923,0.53913567,2,0.618927094,,,0.643179682
5719,0,Aging (Albany NY),33104520,10/27/20,pubmed,0,5,"transcriptom, dataset",0.69102595,0.067150943,0.001511845,0.001511921,0.001511894,0.237287447,Drug discovery,0.83094925,TRUE,43,0.567629414,,,2,0.618927094,,,0.593278254
5720,COSMO-RS-Based Descriptors for the Machine Learning-Enabled Screening of Nucleotide Analogue Drugs against SARS-CoV-2.,J Phys Chem Lett,33104327,10/27/20,pubmed,0,2,machine learning,0.706775476,0.084911089,0.184437325,0.001371328,0.021133486,0.001371297,Drug discovery,0.9005996,TRUE,46.5,0.601026656,9,0.337904736,0,0.403234768,,,0.44738872
5721,Clinical stability and in-hospital mortality prediction in COVID-19 patients presenting to the Emergency Department.,Minerva Med,33104301,10/27/20,pubmed,0,14,logistic regression,0.063481389,0.00168458,0.001684597,0.001684634,0.001684675,0.929780126,Clinics,0.9615541,TRUE,31,0.445111015,6.857142857,0.293684774,1,0.537564047,,,0.425453279
5722,Evidence of Structural Protein Damage and Membrane Lipid Remodeling in Red Blood Cells from COVID-19 Patients.,J Proteome Res,33103907,10/27/20,pubmed,0,14,"proteom, metabolom, lipidom",0.383365863,0.001901851,0.001901893,0.224656409,0.001901906,0.386272078,Clinics,0.764422,TRUE,79,0.802585194,53.71428571,0.696012845,5,0.739490092,,,0.746029377
5723,"The relationship between psychological resilience, burnout, stress, and sociodemographic factors with depression in nurses and midwives during the COVID-19 pandemic: A cross-sectional study in Turkey.",Perspect Psychiatr Care,33103773,10/27/20,pubmed,0,2,logistic regression,0.002898242,0.002898274,0.002898265,0.002898248,0.985508675,0.002898296,Healthcare,0.6733716,TRUE,15.5,0.234028078,1.5,0.138747659,3,0.667819001,,,0.346864913
5724,The impact of the COVID-19 pandemic on mental health: early quarantine-related anxiety and its correlates among Jordanians.,East Mediterr Health J,33103743,10/27/20,pubmed,0,6,logistic regression,0.000907296,0.000907288,0.000907322,0.000907328,0.995463439,0.000907327,Healthcare,0.99977577,TRUE,40.83333333,0.547034449,9,0.337904736,1,0.537564047,,,0.474167744
5725,Clinical Characteristics and Risk Factors for Mortality in Very Old Patients Hospitalized With COVID-19 in Spain.,J Gerontol A Biol Sci Med Sci,33103720,10/27/20,pubmed,0,25,logistic regression,0.001141332,0.00114132,0.046273497,0.001141325,0.001141327,0.9491612,Clinics,0.5790892,TRUE,11.52,0.174222277,3.96,0.223641959,8,0.799987654,,,0.399283963
5726,"Natural phyto, compounds as possible noncovalent inhibitors against SARS-CoV2 protease: computational approach.",J Biomol Struct Dyn,33103616,10/27/20,pubmed,0,5,"virtual screening, computational",0.994142268,0.001171551,0.001171545,0.001171549,0.001171547,0.001171541,Drug discovery,0.95827675,TRUE,27,0.3960047,3.2,0.202100615,1,0.537564047,,,0.378556454
5727,High-throughput virtual screening of drug databanks for potential inhibitors of SARS-CoV-2 spike glycoprotein.,J Biomol Struct Dyn,33103586,10/27/20,pubmed,0,5,"virtual screening, in silico",0.969123809,0.001461949,0.001461846,0.025028579,0.001461949,0.001461868,Drug discovery,0.40398896,FALSE,43.2,0.569051889,5.2,0.259700294,2,0.618927094,,,0.482559759
5728,Risk analysis and hot spots detection of SARS-CoV-2 in Nigeria using demographic and environmental variables: an early assessment of transmission dynamics.,Int J Environ Health Res,33103470,10/27/20,pubmed,0,3,dataset,0.003927473,0.00392762,0.003927653,0.980362373,0.003927445,0.003927435,Epidemiology,0.7431981,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
5729,Computational Identification of Human Biological Processes and Protein Sequence Motifs Putatively Targeted by SARS-CoV-2 Proteins Using Protein-Protein Interaction Networks.,J Proteome Res,33103435,10/27/20,pubmed,0,9,"computational, interactom",0.819004603,0.175423857,0.001392903,0.001392975,0.001392858,0.001392805,Drug discovery,0.46230745,FALSE,7.111111111,0.102170821,8.222222222,0.322919454,0,0.403234768,,,0.276108347
5730,Supervised molecular dynamics for exploring the druggability of the SARS-CoV-2 spike protein.,J Comput Aided Mol Des,33103220,10/27/20,pubmed,0,3,"molecular dynamics simulation, in silico",0.99173489,0.001653026,0.001653017,0.001653047,0.001653024,0.001652995,Drug discovery,0.84405065,TRUE,72,0.77302245,21.66666667,0.499732406,4,0.707574542,,,0.660109799
5731,"Impact assessment of full and partial stay-at-home orders, face mask usage, and contact tracing: An agent-based simulation study of COVID-19 for an urban region.",Glob Epidemiol,33103108,10/27/20,pubmed,0,3,simulation model,0.055837944,0.00087157,0.000871552,0.811792477,0.10959351,0.021032947,Epidemiology,0.33306894,FALSE,11.66666667,0.176510607,5.666666667,0.270203372,1,0.537564047,,,0.328092675
5732,Protein-driven mechanism of multiorgan damage in COVID-19.,Med Drug Discov,33103107,10/27/20,pubmed,0,1,"bioinformatic, mathematical model",0.937984389,0.001717181,0.001717226,0.032417634,0.001717196,0.024446374,Drug discovery,0.18604267,FALSE,287,0.989795287,304,0.955512443,0,0.403234768,,,0.782847499
5733,Integrative Transcriptome Analyses Empower the Anti-COVID-19 Drug Arsenal.,iScience,33103068,10/27/20,pubmed,0,13,"transcriptom, dataset",0.861757189,0.002080635,0.002080727,0.101377315,0.002080581,0.030623552,Drug discovery,0.4176126,FALSE,59.92307692,0.702331622,46.53846154,0.666376773,3,0.667819001,,,0.678842465
5734,Xanthene based hybrid analogues to inhibit protease of novel corona Virus: Molecular docking and ADMET studies.,Comput Toxicol,33102987,10/27/20,pubmed,0,6,computational,0.849325857,0.070178318,0.001987198,0.074534346,0.001987186,0.001987095,Drug discovery,0.8956909,TRUE,22.83333333,0.337868761,1.833333333,0.151056998,0,0.403234768,,,0.297386842
5735,Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines.,J Comput Soc Sci,33102925,10/27/20,pubmed,0,2,"machine learning, computational, dataset",0.104932118,0.002183371,0.112276679,0.776241228,0.002183408,0.002183197,Epidemiology,0.7880868,TRUE,340,0.993567939,591,0.983409152,5,0.739490092,,,0.905489061
5736,Time-related changes in sex distribution of COVID-19 incidence proportion in Italy.,Heliyon,33102879,10/27/20,pubmed,0,5,dataset,0.001237076,0.300464011,0.001237168,0.001237161,0.384602975,0.311221609,Healthcare,0.3487458,FALSE,39,0.530521368,19.4,0.474110249,1,0.537564047,,,0.514065221
5737,Ensemble learning model for diagnosing COVID-19 from routine blood tests.,Inform Med Unlocked,33102686,10/27/20,pubmed,0,4,"bayes, classifier, logistic regression, ensemble learning, dataset",0.000772621,0.000772628,0.863676915,0.000772661,0.000772659,0.133232516,Clinics,0.19107458,FALSE,9.25,0.137918239,0.25,0.065493712,6,0.764429903,,,0.322613951
5738,"Students' self-regulated learning (SRL) profile dataset measured during Covid-19 mitigation in Yogyakarta, Indonesia.",Data Brief,33102667,10/27/20,pubmed,0,6,dataset,0.062993874,0.001022654,0.223746837,0.199293734,0.511920293,0.001022609,Healthcare,0.94902956,TRUE,35,0.488032655,1.5,0.138747659,1,0.537564047,,,0.388114787
5739,Sociodemographic and psychological study on performance of students for the COVID-19 aftermath dataset.,Data Brief,33102666,10/27/20,pubmed,0,2,dataset,0.002080546,0.03363816,0.238408221,0.002080833,0.721711674,0.002080565,Healthcare,0.9125,TRUE,21,0.312016822,0.5,0.087101953,0,0.403234768,,,0.267451181
5740,"The COVID-ASSESS dataset - COVID19 related anxiety and stress in prEgnancy, poSt-partum and breaStfeeding during lockdown in Italy.",Data Brief,33102644,10/27/20,pubmed,0,2,dataset,0.002357746,0.002357733,0.002358043,0.002357865,0.98821085,0.002357764,Healthcare,0.95540035,TRUE,128.5,0.919599233,54,0.697952903,2,0.618927094,,,0.745493077
5741,An exploratory assessment of a multidimensional healthcare and economic data on COVID-19 in Nigeria.,Data Brief,33102643,10/27/20,pubmed,0,10,dataset,0.003465889,0.003466003,0.003466114,0.932582613,0.003466068,0.053553313,Epidemiology,0.32404396,FALSE,6.9,0.097470468,0.3,0.06649719,1,0.537564047,,,0.233843902
5742,Sequence Analysis and Structure Prediction of SARS-CoV-2 Accessory Proteins 9b and ORF14: Evolutionary Analysis Indicates Close Relatedness to Bat Coronavirus.,Biomed Res Int,33102591,10/27/20,pubmed,0,3,whole-genome,0.485998072,0.444492916,0.001046884,0.066368495,0.001046826,0.001046807,Drug discovery,0.4433716,FALSE,17.66666667,0.266373925,4,0.231469093,5,0.739490092,,,0.41244437
5743,"Phytochemicals from Selective Plants Have Promising Potential against SARS-CoV-2: Investigation and Corroboration through Molecular Docking, MD Simulations, and Quantum Computations.",Biomed Res Int,33102585,10/27/20,pubmed,0,5,molecular dynamics simulation,0.858930066,0.134457711,0.001653026,0.00165306,0.001653093,0.001653044,Drug discovery,0.9871601,TRUE,23.6,0.349124869,4.6,0.244246722,3,0.667819001,,,0.420396864
5744,"COVID-19 in Human, Animal, and Environment: A Review.",Front Vet Sci,33102545,10/27/20,pubmed,0,14,sequencing,0.000988396,0.492534167,0.000988422,0.206789899,0.159323297,0.139375818,Genomics,0.6747197,TRUE,45.71428571,0.593233966,16.42857143,0.441731335,3,0.667819001,,,0.567594768
5745,"Cardiovascular Changes in Patients With COVID-19 From Wuhan, China.",Front Cardiovasc Med,33102532,10/27/20,pubmed,0,8,correlation analysis,0.095099689,0.001511923,0.001512106,0.04493058,0.001511903,0.855433798,Clinics,0.94855046,TRUE,51.375,0.641721813,5.375,0.263446615,1,0.537564047,,,0.480910825
5746,Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods.,Front Public Health,33102426,10/27/20,pubmed,0,8,"machine learning, deep learning, prediction model",0.001565299,0.001565306,0.603915357,0.001565426,0.091806271,0.299582341,Clinics,0.2560085,FALSE,21,0.312016822,3.5,0.213607172,5,0.739490092,,,0.421704695
5747,Double Burden of COVID-19 Pandemic and Military Occupation: Mental Health Among a Palestinian University Community in the West Bank.,Ann Glob Health,33102151,10/27/20,pubmed,0,14,logistic regression,0.001291207,0.001291236,0.001291227,0.001291336,0.99354376,0.001291233,Healthcare,0.9480597,TRUE,1.357142857,0.013729977,0,0.055525823,0,0.403234768,,,0.157496856
5748,Dynamics of respiratory droplets carrying SARS-CoV-2 virus in closed atmosphere.,Results Phys,33101885,10/27/20,pubmed,0,5,molecular dynamics simulation,0.212731692,0.001861731,0.001861824,0.779821104,0.001861801,0.001861848,Epidemiology,0.34417695,FALSE,24.6,0.362483765,4.8,0.249331014,0,0.403234768,,,0.338349849
5749,"A multivariate analysis of risk factors associated with death by Covid-19 in the USA, Italy, Spain, and Germany.",Z Gesundh Wiss,33101840,10/27/20,pubmed,0,6,logistic regression,0.001310316,0.001310334,0.001310392,0.365210117,0.001310385,0.629548457,Clinics,0.83044875,TRUE,19.16666667,0.28641227,2.5,0.180826866,0,0.403234768,,,0.290157968
5750,0,3 Biotech,33101829,10/27/20,pubmed,0,5,in silico,0.626800124,0.365251241,0.001987151,0.001987216,0.001987126,0.001987143,Drug discovery,0.6186237,TRUE,49.4,0.627002288,7,0.299973241,0,0.403234768,,,0.443403432
5751,The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing.,Cell Discov,33101705,10/27/20,pubmed,0,13,sequencing,0.678664008,0.04460266,0.001187285,0.001187304,0.001187275,0.273171468,Drug discovery,0.5899244,TRUE,50.61538462,0.635660832,37.76923077,0.620484346,7,0.785110192,,,0.680418457
5752,"Phenomenological Modelling of COVID-19 Epidemics in Sri Lanka, Italy, the United States, and Hebei Province of China.",Comput Math Methods Med,33101454,10/27/20,pubmed,0,3,mathematical model,0.001371255,0.001371252,0.001371266,0.993143708,0.001371258,0.001371261,Epidemiology,0.18954322,FALSE,15.33333333,0.230997588,2.333333333,0.173401124,0,0.403234768,,,0.26921116
5753,COVID-19 Epidemic in Sri Lanka: A Mathematical and Computational Modelling Approach to Control.,Comput Math Methods Med,33101453,10/27/20,pubmed,0,3,computational,0.001220002,0.001220018,0.001220018,0.825862808,0.001220164,0.169256989,Epidemiology,0.30820143,FALSE,26,0.382398417,8,0.320511105,0,0.403234768,,,0.368714763
5754,On the modeling of the interaction between tumor growth and the immune system using some new fractional and fractional-fractal operators.,Adv Differ Equ,33101402,10/27/20,pubmed,0,1,"computational, mathematical model",0.368676699,0.001486461,0.001486469,0.625377496,0.001486474,0.001486401,Epidemiology,0.76662374,TRUE,99,0.867029501,11,0.371287129,1,0.537564047,,,0.591960226
5755,Infectivity and Progression of COVID-19 Based on Selected Host Candidate Gene Variants.,Front Genet,33101356,10/27/20,pubmed,0,11,exom,0.084109014,0.57136084,0.001330055,0.065619392,0.001330109,0.276250591,Genomics,0.22052342,FALSE,4.727272727,0.063763993,1,0.122023013,0,0.403234768,,,0.196340591
5756,Genetic Spectrum and Distinct Evolution Patterns of SARS-CoV-2.,Front Microbiol,33101264,10/27/20,pubmed,0,8,"genome sequences, genomes",0.001987115,0.933213059,0.001987104,0.001987123,0.001987113,0.058838487,Genomics,0.70569664,TRUE,57.5,0.685447461,37.75,0.620350549,15,0.874313229,,,0.726703746
5757,0,Front Microbiol,33101244,10/27/20,pubmed,0,8,"sequencing, genomes",0.001538157,0.787340807,0.001538231,0.206506514,0.001538161,0.00153813,Genomics,0.8504385,TRUE,30.25,0.434782609,57.75,0.713607172,1,0.537564047,,,0.561984609
5758,SAMHD1 as the Potential Link Between SARS-CoV-2 Infection and Neurological Complications.,Front Neurol,33101175,10/27/20,pubmed,0,2,"bioinformatic, text mining",0.762150509,0.001565413,0.001565375,0.001565402,0.089601343,0.143551958,Drug discovery,0.72590417,TRUE,9,0.135320675,2,0.164302917,1,0.537564047,,,0.279062546
5759,Use of Data Mining to Determine Usage Patterns of an Online Evaluation Platform During the COVID-19 Pandemic.,Front Psychol,33101157,10/27/20,pubmed,0,6,data mining,0.001330102,0.001330121,0.132365681,0.415652334,0.447991696,0.001330066,Healthcare,0.16008145,FALSE,21.5,0.318263343,1,0.122023013,0,0.403234768,,,0.281173708
5760,Psychological Behavior of Frontline Medical Staff in the Use of Preventive Medication for COVID-19: A Cross-Sectional Study.,Front Psychol,33101103,10/27/20,pubmed,0,8,logistic regression,0.000716357,0.011616022,0.000716341,0.000716349,0.964064985,0.022169946,Healthcare,0.92311025,TRUE,36.25,0.500154617,9.25,0.340379984,1,0.537564047,,,0.459366216
5761,Anti-SARS-CoV Natural Products With the Potential to Inhibit SARS-CoV-2 (COVID-19).,Front Pharmacol,33101023,10/27/20,pubmed,0,7,computational,0.859628328,0.036492346,0.001461886,0.001461945,0.001461979,0.099493515,Drug discovery,0.9869727,TRUE,13.28571429,0.200321603,2.142857143,0.165640888,9,0.814309525,,,0.393424006
5762,"Biomolecular Simulations in the Time of COVID19, and After.",Comput Sci Eng,33100917,10/27/20,pubmed,0,2,computational,0.505480562,0.002639099,0.045904609,0.132339038,0.310997688,0.002639005,Drug discovery,0.75313413,TRUE,226.5,0.980456429,98.5,0.819842119,5,0.739490092,,,0.846596213
5763,A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell.,J Nanopart Res,33100894,10/27/20,pubmed,0,4,"machine learning, deep learning, dataset",0.123313134,0.001126825,0.872179384,0.001126871,0.00112694,0.001126845,Imaging,0.6833767,TRUE,46.25,0.598800173,20.25,0.483074659,0,0.403234768,,,0.495036533
5764,Numerical investigation of aerosol transport in a classroom with relevance to COVID-19.,Phys Fluids (1994),33100808,10/27/20,pubmed,0,4,computational,0.002357886,0.002357825,0.002357814,0.988210988,0.002357777,0.00235771,Epidemiology,0.08183265,FALSE,24.75,0.364710248,6,0.280037463,15,0.874313229,,,0.506353647
5765,A mathematical framework for estimating risk of airborne transmission of COVID-19 with application to face mask use and social distancing.,Phys Fluids (1994),33100806,10/27/20,pubmed,0,3,mathematical model,0.158915022,0.002238531,0.002238449,0.832131062,0.002238521,0.002238414,Epidemiology,0.5123976,TRUE,480.3333333,0.997093203,440.3333333,0.974578539,24,0.914439163,,,0.962036968
5766,Exploring the Immediate Effects of COVID-19 Containment Policies on Crime: an Empirical Analysis of the Short-Term Aftermath in Los Angeles.,Am J Crim Justice,33100804,10/27/20,pubmed,0,3,bayes,0.002898411,0.002898437,0.002898329,0.985507845,0.002898413,0.002898565,Epidemiology,0.96015835,TRUE,29,0.41993939,7.333333333,0.304656141,8,0.799987654,,,0.508194395
5767,0,Vegetos,33100613,10/27/20,pubmed,0,4,molecular dynamics simulation,0.890815017,0.001786515,0.001786579,0.102038564,0.001786663,0.001786662,Drug discovery,0.97224945,TRUE,17,0.257467994,29.25,0.564557131,1,0.537564047,,,0.453196391
5768,A dynamical model of SARS-CoV-2 based on people flow networks.,Saf Sci,33100582,10/27/20,pubmed,0,2,model fit,0.001717189,0.001717261,0.001717275,0.991413644,0.001717265,0.001717366,Epidemiology,0.17492726,FALSE,31,0.445111015,75,0.766590848,0,0.403234768,,,0.53831221
5769,"Building the COVID-19 Collaborative Emergency Network: a case study of COVID-19 outbreak in Hubei Province, China.",Nat Hazards (Dordr),33100580,10/27/20,pubmed,0,4,network analysis,0.001203519,0.001203443,0.27026334,0.724922826,0.001203456,0.001203417,Epidemiology,0.8589337,TRUE,59.25,0.697878657,27.75,0.551645705,0,0.403234768,,,0.55091971
5770,CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia.,Inf Process Manag,33100482,10/27/20,pubmed,0,3,"deep learning, neural network, transfer learning, dataset",0.000863029,0.000863028,0.971271131,0.000863037,0.012977898,0.013161876,Imaging,0.58879423,TRUE,72.33333333,0.774630466,78.33333333,0.774217287,2,0.618927094,,,0.722591616
5771,"Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S.",J Econom,33100476,10/27/20,pubmed,0,3,structural model,0.001022639,0.001022633,0.001022635,0.938535747,0.05737369,0.001022656,Epidemiology,0.14552402,FALSE,51.33333333,0.641288886,117.6666667,0.848140219,1,0.537564047,,,0.675664384
5772,Panel forecasts of country-level Covid-19 infections.,J Econom,33100475,10/27/20,pubmed,0,3,bayes,0.001901728,0.001901848,0.001901823,0.990491146,0.001901754,0.0019017,Epidemiology,0.09103349,FALSE,112,0.894180221,632,0.985349211,0,0.403234768,,,0.7609214
5773,Analyses the effects of COVID-19 outbreak on human sexual behaviour using ordinary least-squares based multivariate logistic regression.,Qual Quant,33100406,10/27/20,pubmed,0,3,logistic regression,0.001622737,0.001622727,0.001622761,0.123250294,0.870258726,0.001622755,Healthcare,0.8352927,TRUE,68,0.753973653,5.666666667,0.270203372,1,0.537564047,,,0.520580357
5774,0,Pattern Recognit,33100403,10/27/20,pubmed,0,2,dataset,0.001751654,0.001751289,0.991243087,0.00175137,0.001751412,0.001751188,Imaging,0.80530727,TRUE,135,0.926773455,74,0.764182499,10,0.828199272,,,0.839718409
5775,0,J Mol Struct,33100380,10/27/20,pubmed,0,4,computational,0.993636707,0.001272675,0.001272665,0.001272678,0.001272637,0.001272638,Drug discovery,0.9519015,TRUE,43.25,0.569855897,17.75,0.458322184,1,0.537564047,,,0.521914043
5776,ORF3a mutation associated with higher mortality rate in SARS-CoV-2 infection.,Epidemiol Infect,33100263,10/27/20,pubmed,0,2,"sequencing, in silico, network analysis",0.42667386,0.447842102,0.00190173,0.001901749,0.001901823,0.119778737,Genomics,0.42472374,FALSE,5.5,0.077246583,0.5,0.087101953,7,0.785110192,,,0.316486243
5777,Anxiety and depression in frontline health care workers during the outbreak of Covid-19.,Int J Soc Psychiatry,33100114,10/27/20,pubmed,0,8,logistic regression,0.001538063,0.001538065,0.001538062,0.0015381,0.992309586,0.001538125,Healthcare,0.8386228,TRUE,83.625,0.819531202,41.75,0.643296762,2,0.618927094,,,0.693918353
5778,Exploring the N95 and Surgical Mask Supply in U.S. Nursing Homes During COVID-19.,J Appl Gerontol,33100107,10/27/20,pubmed,0,3,logistic regression,0.002422256,0.002422248,0.002422283,0.248930929,0.74138,0.002422285,Healthcare,0.7815722,TRUE,19,0.285793803,2.666666667,0.185442869,2,0.618927094,,,0.363387922
5779,Exploring RdRp-remdesivir interactions to screen RdRp inhibitors for the management of novel coronavirus 2019-nCoV.,SAR QSAR Environ Res,33100032,10/27/20,pubmed,0,3,molecular dynamics simulation,0.862356956,0.001330129,0.090045403,0.001330098,0.043607217,0.001330197,Drug discovery,0.91219985,TRUE,1.666666667,0.016451234,0,0.055525823,0,0.403234768,,,0.158403941
5780,"Exploring the intrinsic dynamics of SARS-CoV-2, SARS-CoV and MERS-CoV spike glycoprotein through normal mode analysis using anisotropic network model.",J Mol Graph Model,33099199,10/26/20,pubmed,0,4,network model,0.924714003,0.001717318,0.001717222,0.068416981,0.001717224,0.001717252,Drug discovery,0.7029146,TRUE,2.75,0.030304904,0.25,0.065493712,2,0.618927094,,,0.238241903
5781,"Artificial Intelligence for clinical decision support in Critical Care, required and accelerated by COVID-19.",Anaesth Crit Care Pain Med,33099016,10/26/20,pubmed,0,4,artificial intelligence,0.01599904,0.015998777,0.920004321,0.015999378,0.015998418,0.016000065,Clinics,0.82709074,TRUE,255,0.985156781,380.25,0.969092855,0,0.403234768,,,0.785828135
5782,System biological investigations of hydroxychloroquine and azithromycin targets and their implications in QT interval prolongation.,Chem Biol Interact,33098839,10/26/20,pubmed,0,2,computational,0.811017464,0.001684576,0.001684747,0.182244068,0.001684574,0.001684571,Drug discovery,0.90029615,TRUE,52.5,0.650875131,16,0.437316029,0,0.403234768,,,0.497141976
5783,"Multivariate analysis of CT imaging, laboratory, and demographical features for prediction of acute kidney injury in COVID-19 patients: a Bi-centric analysis.",Abdom Radiol (NY),33098478,10/25/20,pubmed,0,6,"logistic regression, prediction model",0.001203445,0.00120341,0.480862034,0.001203425,0.001203425,0.514324261,Clinics,0.32531324,FALSE,93.66666667,0.853794298,92.66666667,0.80753278,0,0.403234768,,,0.688187282
5784,Proteomic characteristics of bronchoalveolar lavage fluid in critical COVID-19 patients.,FEBS J,33098359,10/25/20,pubmed,0,7,proteom,0.59046681,0.001901787,0.001901749,0.001901719,0.001901817,0.401926119,Drug discovery,0.8232001,TRUE,47.42857143,0.609066733,23.71428571,0.518263313,4,0.707574542,,,0.611634862
5785,Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study.,J Med Internet Res,33098287,10/25/20,pubmed,0,20,"machine learning, correlation analysis",0.000898136,0.000898135,0.029845639,0.914348757,0.053111197,0.000898135,Epidemiology,0.93710923,TRUE,36.25,0.500154617,22.95,0.511172063,0,0.403234768,,,0.471520483
5786,"Spatio-temporal assessment of ambient air quality, their health effects and improvement during COVID-19 lockdown in one of the most polluted cities of India.",Environ Sci Pollut Res Int,33097997,10/25/20,pubmed,0,6,correlation analysis,0.002357736,0.002357772,0.002357786,0.856427591,0.002357926,0.134141189,Epidemiology,0.75549984,TRUE,74.83333333,0.785824726,17.33333333,0.45424137,2,0.618927094,,,0.619664397
5787,Using Benford's law to assess the quality of COVID-19 register data in Brazil.,J Public Health (Oxf),33097928,10/25/20,pubmed,0,2,dataset,0.003607188,0.003607227,0.003607283,0.981963761,0.003607276,0.003607264,Epidemiology,0.20736793,FALSE,2.5,0.027459954,,,2,0.618927094,,,0.323193524
5788,Omics study reveals abnormal alterations of breastmilk proteins and metabolites in puerperant women with COVID-19.,Signal Transduct Target Ther,33097684,10/25/20,pubmed,0,10, omics,0.430074605,0.034963158,0.034961874,0.034961874,0.430075384,0.034963106,Healthcare,0.33568513,FALSE,245.5,0.98367246,176.1,0.903732941,4,0.707574542,,,0.864993314
5789,Potency and timing of antiviral therapy as determinants of duration of SARS-CoV-2 shedding and intensity of inflammatory response.,Sci Adv,33097472,10/25/20,pubmed,0,3,mathematical model,0.485809991,0.104321128,0.00194352,0.182874121,0.001943504,0.223107736,Drug discovery,0.23291051,FALSE,40.33333333,0.542890717,20.33333333,0.483810543,1,0.537564047,,,0.521421769
5790,Digital healthcare and shifting equipoise in radiation oncology: The butterfly effect of the COVID-19 pandemic.,J Med Imaging Radiat Sci,33097437,10/25/20,pubmed,0,4,digital health,0.025060466,0.025060781,0.025061294,0.025065157,0.874691953,0.025060349,Healthcare,0.36971474,FALSE,12.5,0.189436576,1,0.122023013,0,0.403234768,,,0.238231452
5791,Predictive value of initial CT scan for various adverse outcomes in patients with COVID-19 pneumonia.,Heart Lung,33097297,10/25/20,pubmed,0,14,logistic regression,0.001652992,0.001653008,0.25693313,0.001653015,0.001653036,0.736454818,Clinics,0.9693577,TRUE,23.28571429,0.344795597,39.21428571,0.628846668,6,0.764429903,,,0.579357389
5792,Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach.,Sci Total Environ,33097268,10/25/20,pubmed,0,12,"machine learning, logistic regression",0.002130873,0.002130858,0.002130697,0.526709657,0.002130813,0.464767102,Epidemiology,0.595462,TRUE,34.91666667,0.485806172,14.58333333,0.418985818,1,0.537564047,,,0.480785346
5793,Citation Network Analysis of the Novel Coronavirus Disease 2019 (COVID-19).,Int J Environ Res Public Health,33096796,10/25/20,pubmed,0,4,network analysis,0.002422317,0.002422352,0.002422383,0.987888272,0.002422328,0.002422347,Epidemiology,0.9029257,TRUE,27,0.3960047,12.25,0.388546963,5,0.739490092,,,0.508013918
5794,"Impact of obesity, fasting plasma glucose level, blood pressure, and renal function on the severity of COVID-19: A matter of sexual dimorphism?",Diabetes Res Clin Pract,33096185,10/24/20,pubmed,0,7,logistic regression,0.029726749,0.001943503,0.001943567,0.001943512,0.149118771,0.815323898,Clinics,0.8107246,TRUE,21.14285714,0.312758983,4.571428571,0.24337704,1,0.537564047,,,0.36456669
5795,Structure-altering mutations of the SARS-CoV-2 frameshifting RNA element.,Biophys J,33096082,10/24/20,pubmed,0,4,"molecular dynamics simulation, computational",0.510658151,0.344118038,0.001072285,0.142007153,0.001072203,0.00107217,Drug discovery,0.25527734,FALSE,87.5,0.835363968,78,0.773548301,2,0.618927094,,,0.742613121
5796,Large-Scale Multi-omic Analysis of COVID-19 Severity.,Cell Syst,33096026,10/24/20,pubmed,0,29,machine learning,0.46540715,0.001861841,0.215939577,0.001861781,0.001861746,0.313067905,Drug discovery,0.6021954,TRUE,55.20689655,0.669243614,103.6896552,0.828472036,4,0.707574542,,,0.735096731
5797,0,Cell,33096020,10/24/20,pubmed,0,15,transcriptom,0.702912956,0.001717257,0.001717158,0.001717191,0.001717215,0.290218223,Drug discovery,0.33038545,FALSE,76.4,0.79182386,189.4666667,0.911626974,36,0.941169208,,,0.881540014
5798,"A national cross-sectional survey of public perceptions of the COVID-19 pandemic: Self-reported beliefs, knowledge, and behaviors.",PLoS One,33095836,10/24/20,pubmed,0,16,logistic regression,0.001010939,0.001010979,0.001010971,0.207851787,0.788104367,0.001010956,Healthcare,0.6580353,TRUE,83.5625,0.819407508,89.3125,0.800107038,2,0.618927094,,,0.746147213
5799,The influence of COVID-19 on agricultural economy and emergency mitigation measures in China: A text mining analysis.,PLoS One,33095814,10/24/20,pubmed,0,4,text mining,0.043747872,0.001622891,0.00162273,0.949760935,0.001622835,0.001622736,Epidemiology,0.7733757,TRUE,10.25,0.154245779,2,0.164302917,3,0.667819001,,,0.328789232
5800,Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking.,PLoS One,33095811,10/24/20,pubmed,0,2,"machine learning, dataset",0.001034618,0.001034605,0.001034695,0.886818192,0.001034628,0.109043262,Epidemiology,0.43049258,FALSE,36,0.498299215,5.5,0.267259834,10,0.828199272,,,0.531252773
5801,Concerns About Information Regarding COVID-19 on the Internet: Cross-Sectional Study.,J Med Internet Res,33095740,10/24/20,pubmed,0,9,logistic regression,0.000966808,0.000966804,0.000966782,0.000966822,0.995166016,0.000966768,Healthcare,0.84993434,TRUE,168.4444444,0.957325747,123.3333333,0.855565962,0,0.403234768,,,0.738708825
5802,Utility of Proteomics in Emerging and Re-Emerging Infectious Diseases Caused by RNA Viruses.,J Proteome Res,33095583,10/24/20,pubmed,0,11,proteom,0.459455977,0.535729966,0.001203515,0.001203516,0.001203488,0.001203538,Genomics,0.9015212,TRUE,29.90909091,0.429401942,11.81818182,0.382793685,2,0.618927094,,,0.477040907
5803,Factors Associated With Mental Health Disorders Among University Students in France Confined During the COVID-19 Pandemic.,JAMA Netw Open,33095252,10/24/20,pubmed,0,12,logistic regression,0.000693843,0.000693852,0.000693843,0.000693863,0.996530731,0.000693867,Healthcare,0.97087085,TRUE,19.41666667,0.289628301,4.166666667,0.233074659,9,0.814309525,,,0.445670828
5804,Antiviral Peptides as Promising Therapeutics against SARS-CoV-2.,J Phys Chem B,33095007,10/24/20,pubmed,0,12,"molecular dynamics simulation, computational",0.990491606,0.001901688,0.001901673,0.001901681,0.001901676,0.001901676,Drug discovery,0.6295755,TRUE,24.08333333,0.356113551,7.916666667,0.314891624,4,0.707574542,,,0.459526572
5805,Caffeine and caffeine-containing pharmaceuticals as promising inhibitors for 3-chymotrypsin-like protease of SARS-CoV-2.,J Biomol Struct Dyn,33094705,10/24/20,pubmed,0,1,"molecular dynamics simulation, in silico",0.992924373,0.001415095,0.001415138,0.001415126,0.001415145,0.001415122,Drug discovery,0.95519674,TRUE,33,0.466757375,33,0.593256623,1,0.537564047,,,0.532526015
5806,Potential therapeutic use of corticosteroids as SARS CoV-2 main protease inhibitors: a computational study.,J Biomol Struct Dyn,33094701,10/24/20,pubmed,0,4,computational,0.952899224,0.001538102,0.001538166,0.001538164,0.001538269,0.040948074,Drug discovery,0.9828969,TRUE,48.25,0.616364648,19,0.471367407,1,0.537564047,,,0.541765368
5807,Implementation of convolutional neural network approach for COVID-19 disease detection.,Physiol Genomics,33094700,10/24/20,pubmed,0,1,neural network,0.001046824,0.001046798,0.994765932,0.001046836,0.001046796,0.001046814,Imaging,0.20049322,FALSE,1,0.012307502,0,0.055525823,1,0.537564047,,,0.201799124
5808,A multi-stage virtual screening of FDA-approved drugs reveals potential inhibitors of SARS-CoV-2 main protease.,J Biomol Struct Dyn,33094680,10/24/20,pubmed,0,3,virtual screening,0.993543973,0.001291225,0.001291211,0.001291204,0.001291193,0.001291194,Drug discovery,0.95415837,TRUE,25.33333333,0.37188447,9.666666667,0.348274017,1,0.537564047,,,0.419240845
5809,Single-cell analysis identified lung progenitor cells in COVID-19 patients.,Cell Prolif,33094537,10/24/20,pubmed,0,6,sequencing,0.822828773,0.00135036,0.001350356,0.001350352,0.001350374,0.171769784,Drug discovery,0.38674784,FALSE,117,0.902900612,54.5,0.699892962,1,0.537564047,,,0.713452541
5810,Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.,Eur J Nucl Med Mol Imaging,33094432,10/24/20,pubmed,0,10,"machine learning, deep learning, classifier, radiom, adversarial network",0.000728141,0.000728157,0.872304996,0.000728124,0.000728119,0.124782464,Imaging,0.7886125,TRUE,39.9,0.538066671,18.9,0.469092855,2,0.618927094,,,0.542028873
5811,Uncovering two phases of early intercontinental COVID-19 transmission dynamics.,J Travel Med,33094347,10/24/20,pubmed,0,14,"bayes, sequencing",0.001187285,0.370714655,0.001187272,0.624536245,0.001187274,0.00118727,Epidemiology,0.20569044,FALSE,53.42857143,0.656626879,66.64285714,0.744648113,3,0.667819001,,,0.689697998
5812,Improving the digital health of the workforce in the COVID-19 context: an opportunity to future-proof medical training.,Future Healthc J,33094221,10/24/20,pubmed,0,2,digital health,0.001461882,0.001461869,0.001461984,0.624302897,0.369849509,0.001461858,Epidemiology,0.7697709,TRUE,193,0.970128023,141.5,0.875903131,1,0.537564047,,,0.794531734
5813,Identifying the Associated Risk Factors of Sleep Disturbance During the COVID-19 Lockdown in Bangladesh: A Web-Based Survey.,Front Psychiatry,33093839,10/24/20,pubmed,0,4,logistic regression,0.001254646,0.001254604,0.001254643,0.190121211,0.782403417,0.023711479,Healthcare,0.43541887,FALSE,24,0.35574247,6,0.280037463,1,0.537564047,,,0.39111466
5814,Acute Kidney Injury in a Predominantly African American Cohort of Kidney Transplant Recipients With COVID-19 Infection.,Transplantation,33093403,10/24/20,pubmed,0,8,logistic regression,0.001751331,0.001751183,0.001751185,0.001751189,0.001751217,0.991243893,Clinics,0.87882185,TRUE,26,0.382398417,3.875,0.222972973,0,0.403234768,,,0.336202052
5815,Engineered ACE2 receptor traps potently neutralize SARS-CoV-2.,Proc Natl Acad Sci U S A,33093202,10/24/20,pubmed,0,20,computational,0.769246933,0.206058609,0.001059415,0.001059372,0.001059351,0.021516321,Drug discovery,0.15741494,FALSE,68.4,0.755890902,205.1,0.919721702,3,0.667819001,,,0.781143868
5816,Sequencing identifies multiple early introductions of SARS-CoV-2 to the New York City region.,Genome Res,33093069,10/24/20,pubmed,0,45,sequencing,0.001943488,0.521466862,0.001943519,0.470759086,0.001943538,0.001943508,Genomics,0.3286047,FALSE,34.8,0.484940318,65.86666667,0.74197217,1,0.537564047,,,0.588158845
5817,Coding-Complete Genome Sequences of 23 SARS-CoV-2 Samples from the Philippines.,Microbiol Resour Announc,33093050,10/24/20,pubmed,0,16,"genome sequences, genomes",0.006089695,0.897199395,0.006089573,0.006089472,0.006089546,0.07844232,Genomics,0.52942497,TRUE,22,0.326056033,29.6875,0.567166176,3,0.667819001,,,0.52034707
5818,Identification of tuna protein-derived peptides as potent SARS-CoV-2 inhibitors via molecular docking and molecular dynamic simulation.,Food Chem,33092925,10/24/20,pubmed,0,8,virtual screening,0.98821146,0.002357703,0.002357719,0.002357701,0.00235771,0.002357706,Drug discovery,0.7941898,TRUE,10.75,0.160244913,0.25,0.065493712,2,0.618927094,,,0.28155524
5819,Medical student support for vulnerable patients during COVID-19 - a convergent mixed-methods study.,BMC Med Educ,33092586,10/24/20,pubmed,0,24,active learning,0.000830659,0.000830648,0.13861829,0.207692211,0.618797886,0.033230306,Healthcare,0.94352305,TRUE,11.875,0.178737089,7.75,0.312215681,0,0.403234768,,,0.298062513
5820,Scale validation for the identification of falsified hand sanitizer: public and regulatory authorities perspectives from United Arab Emirates.,BMC Public Health,33092568,10/24/20,pubmed,0,5,model fit,0.00107223,0.001072208,0.001072251,0.188656471,0.807054611,0.00107223,Healthcare,0.7064444,TRUE,43.2,0.569051889,10.6,0.364262778,0,0.403234768,,,0.445516478
5821,"Detailed Molecular Interactions of Favipiravir with SARS-CoV-2, SARS-CoV, MERS-CoV, and Influenza Virus Polymerases In Silico.",Microorganisms,33092045,10/24/20,pubmed,0,11,in silico,0.771996908,0.222250704,0.001438088,0.001438091,0.001438107,0.001438102,Drug discovery,0.95303917,TRUE,100.3636364,0.869503371,85.90909091,0.792547498,3,0.667819001,,,0.77662329
5822,Uncertainty quantification in epidemiological models for the COVID-19 pandemic.,Comput Biol Med,33091766,10/23/20,pubmed,0,3,"bayes, computational, mathematical model",0.000728121,0.000728113,0.00072813,0.978406597,0.00072812,0.018680919,Epidemiology,0.36583847,FALSE,40,0.539860226,7.333333333,0.304656141,0,0.403234768,,,0.415917045
5823,Integrative analysis for COVID-19 patient outcome prediction.,Med Image Anal,33091743,10/23/20,pubmed,0,16,"deep learning, image analysis, radiom, dataset",0.001156268,0.001156472,0.707892968,0.001156304,0.001156265,0.287481724,Imaging,0.7197391,TRUE,63.875,0.727317707,36.6875,0.615065561,0,0.403234768,,,0.581872678
5824,"Molecular characterization, pathogen-host interaction pathway and in silico approaches for vaccine design against COVID-19.",J Chem Neuroanat,33091590,10/23/20,pubmed,0,4,"computational, sequencing, in silico, metagenom",0.4962106,0.26217809,0.000830638,0.21737638,0.022573622,0.00083067,Drug discovery,0.55894876,TRUE,18.5,0.278001113,2.5,0.180826866,1,0.537564047,,,0.332130676
5825,Routine use of statins and increased COVID-19 related mortality in inpatients with type 2 diabetes: Results from the CORONADO study.,Diabetes Metab,33091555,10/23/20,pubmed,0,14,logistic regression,0.039417862,0.00162272,0.001622723,0.001622769,0.001622759,0.954091168,Clinics,0.7945218,TRUE,44.35714286,0.580740924,59.5,0.718691464,4,0.707574542,,,0.66900231
5826,Variant analysis of the first Lebanese SARS-CoV-2 isolates.,Genomics,33091548,10/23/20,pubmed,0,7,"bioinformatic, genome sequences",0.002296708,0.988517021,0.002296534,0.002296532,0.002296545,0.002296659,Genomics,0.281923,FALSE,10.71428571,0.159688292,13.14285714,0.401525288,0,0.403234768,,,0.321482783
5827,Estimating the infection-fatality risk of SARS-CoV-2 in New York City during the spring 2020 pandemic wave: a model-based analysis.,Lancet Infect Dis,33091374,10/23/20,pubmed,0,12,network model,0.000926266,0.000926282,0.000926265,0.695314583,0.12888683,0.173019775,Epidemiology,0.36330014,FALSE,39.83333333,0.537571897,27.16666667,0.546829007,30,0.930057411,,,0.671486105
5828,Vaccine design based on 16 epitopes of SARS-CoV-2 spike protein.,J Med Virol,33091154,10/23/20,pubmed,0,9,in silico,0.801408768,0.193189877,0.001350316,0.001350347,0.001350355,0.001350338,Drug discovery,0.5669376,TRUE,47.22222222,0.607767951,22.66666667,0.508629917,5,0.739490092,,,0.61862932
5829,A statistical approach to white-nose syndrome surveillance monitoring using acoustic data.,PLoS One,33091068,10/23/20,pubmed,0,6,prediction model,0.000926344,0.542359732,0.033980332,0.311777262,0.110030024,0.000926307,Genomics,0.7017341,TRUE,34.83333333,0.485496939,25.83333333,0.535790741,0,0.403234768,,,0.474840816
5830,Time-lapse sentinel surveillance of SARS-CoV-2 spread in India.,PLoS One,33091066,10/23/20,pubmed,0,8,genomes,0.304236035,0.243918922,0.001072216,0.362867613,0.08683301,0.001072203,Epidemiology,0.4493662,FALSE,510,0.997773517,195.125,0.915172598,1,0.537564047,,,0.816836721
5831,Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing.,J Med Internet Res,33090964,10/23/20,pubmed,0,22,"machine learning, artificial intelligence, predictive model",0.000936107,0.00093611,0.212396639,0.095533466,0.00093612,0.689261558,Clinics,0.9558003,TRUE,120.3333333,0.907662812,163.6666667,0.894969227,4,0.707574542,,,0.836735527
5832,Perspective on Proteomics for Virus Detection in Clinical Samples.,J Proteome Res,33090795,10/23/20,pubmed,0,6,proteom,0.003335387,0.681813531,0.304844581,0.00333554,0.00333557,0.003335391,Genomics,0.8371647,TRUE,23.5,0.348506401,49,0.677481937,5,0.739490092,,,0.58849281
5833,"Clinical predictors of COVID-19 disease progression and death: Analysis of 214 hospitalised patients from Wuhan, China.",Clin Respir J,33090710,10/23/20,pubmed,0,19,prediction model,0.001141384,0.001141388,0.025521515,0.001141404,0.001141377,0.969912932,Clinics,0.9624434,TRUE,68.84210526,0.758117385,50.68421053,0.683636607,1,0.537564047,,,0.65977268
5834,A critical appraisal of evidence in the use of preprocedural mouthwash to avoid SARS-CoV-2 transmission during oral interventions.,Eur Rev Med Pharmacol Sci,33090432,10/23/20,pubmed,0,6,in silico,0.345718949,0.003101673,0.003101568,0.641874495,0.003101576,0.003101739,Epidemiology,0.84057146,TRUE,6.333333333,0.089863319,1.333333333,0.13252609,1,0.537564047,,,0.253317819
5835,Hordatines as a Potential Inhibitor of COVID-19 Main Protease and RNA Polymerase: An In-Silico Approach.,Nat Prod Bioprospect,33090359,10/23/20,pubmed,0,3,in-silico,0.97490236,0.001171579,0.020411353,0.001171574,0.001171575,0.001171561,Drug discovery,0.59156394,TRUE,6.666666667,0.094996598,0.333333333,0.073187048,0,0.403234768,,,0.190472805
5836,Telemonitoring Parkinson's disease using machine learning by combining tremor and voice analysis.,Brain Inform,33090328,10/23/20,pubmed,0,6,"bayes, machine learning",0.000889046,0.000889081,0.691789042,0.117548606,0.044722827,0.144161398,Clinics,0.7783482,TRUE,106.6666667,0.8822438,123,0.855097672,0,0.403234768,,,0.713525413
5837,A biomarker based severity progression indicator for COVID-19: the Kuwait prognosis indicator score.,Biomarkers,33090050,10/23/20,pubmed,0,9,dataset,0.001511817,0.001511836,0.001511974,0.001511869,0.001511869,0.992440635,Clinics,0.36289233,FALSE,8.111111111,0.119240522,1.666666667,0.145036125,0,0.403234768,,,0.222503805
5838,Screening of phytochemicals as potent inhibitor of 3-chymotrypsin and papain-like proteases of SARS-CoV2: an in silico approach to combat COVID-19.,J Biomol Struct Dyn,33089730,10/23/20,pubmed,0,3,"in silico, in-silico",0.974609656,0.001272658,0.001272648,0.020299757,0.001272646,0.001272636,Drug discovery,0.9687829,TRUE,19.66666667,0.292596945,2.333333333,0.173401124,3,0.667819001,,,0.377939023
5839,0,J Biomol Struct Dyn,33089728,10/23/20,pubmed,0,6,"molecular dynamics simulation, in silico",0.965264901,0.031029933,0.000926279,0.000926337,0.000926285,0.000926265,Drug discovery,0.93243307,TRUE,6.333333333,0.089863319,1.833333333,0.151056998,2,0.618927094,,,0.286615804
5840,Spine Patient Satisfaction With Telemedicine During the COVID-19 Pandemic: A Cross-Sectional Study.,Global Spine J,33089712,10/23/20,pubmed,0,9,logistic regression,0.001538144,0.001538108,0.001538291,0.001538163,0.717188705,0.276658588,Healthcare,0.98115253,TRUE,140.3333333,0.934318758,63.22222222,0.732807064,1,0.537564047,,,0.734896623
5841,Understanding the impact of interruptions to HIV services during the COVID-19 pandemic: A modelling study.,EClinicalMedicine,33089116,10/23/20,pubmed,0,3,mathematical model,0.001751155,0.001751184,0.056588788,0.691908563,0.246248989,0.001751321,Epidemiology,0.50044936,TRUE,81,0.811552972,144.3333333,0.878445277,3,0.667819001,,,0.785939083
5842,Protecting the most vulnerable during COVID-19 and beyond: a case report on the remote management of heart failure patients with cardiac implantable electronic devices.,Eur Heart J Case Rep,33089059,10/23/20,pubmed,0,4,prediction model,0.001059441,0.001059382,0.207029908,0.228808388,0.104350111,0.45769277,Clinics,0.70499897,TRUE,78.5,0.800482405,192.5,0.913901525,0,0.403234768,,,0.705872899
5843,Social Media Survey and Web Posting Assessment of the COVID-19 Response in China: Health Worker Attitudes Toward Preparedness and Personal Protective Equipment Shortages.,Open Forum Infect Dis,33088845,10/23/20,pubmed,0,9,text-mining,0.00111264,0.001112645,0.001112755,0.303716892,0.667652321,0.025292746,Healthcare,0.09326744,FALSE,86.11111111,0.829426681,107.7777778,0.835897779,0,0.403234768,,,0.689519742
5844,Online Teaching in Medical Training: Establishing Good Online Teaching Practices from Cumulative Experience.,Int J Appl Basic Med Res,33088735,10/23/20,pubmed,0,4,active learning,0.001565422,0.001565493,0.360953882,0.001565429,0.632784445,0.001565329,Healthcare,0.92008084,TRUE,139.75,0.933267363,46.25,0.665038801,1,0.537564047,,,0.711956737
5845,Positive selection within the genomes of SARS-CoV-2 and other Coronaviruses independent of impact on protein function.,PeerJ,33088633,10/23/20,pubmed,0,3,genomes,0.370173798,0.625375628,0.001112626,0.001112641,0.001112669,0.001112638,Genomics,0.530491,TRUE,75.66666667,0.789349991,207.6666667,0.921059674,1,0.537564047,,,0.74932457
5846,Impact of COVID-19 outbreak on the mental health status of undergraduate medical students in a COVID-19 treating medical college: a prospective longitudinal study.,PeerJ,33088628,10/23/20,pubmed,0,6,logistic regression,0.000704914,0.000704903,0.000704911,0.00070491,0.959557267,0.037623096,Healthcare,0.87541914,TRUE,2.5,0.027459954,0,0.055525823,1,0.537564047,,,0.206849941
5847,Modelling of COVID-19 Morbidity in Russia.,Stud Health Technol Inform,33087624,10/23/20,pubmed,0,5,predictive model,0.002562615,0.002562687,0.074208087,0.794003547,0.002562697,0.124100367,Epidemiology,0.18060723,FALSE,33.2,0.468056157,1.6,0.140687717,0,0.403234768,,,0.337326214
5848,Survival Analysis of COVID-19 Patients in Russia Using Machine Learning.,Stud Health Technol Inform,33087616,10/23/20,pubmed,0,5,machine learning,0.001565307,0.001565328,0.214766003,0.23596917,0.001565315,0.544568877,Clinics,0.4782994,FALSE,32.8,0.464221659,1.6,0.140687717,0,0.403234768,,,0.336048048
5849,The International Patient Summary Standard and the Extensibility Requirement.,Stud Health Technol Inform,33087592,10/23/20,pubmed,0,3,dataset,0.002490482,0.002490575,0.443666662,0.546371075,0.002490474,0.002490732,Epidemiology,0.26319197,FALSE,6.333333333,0.089863319,0,0.055525823,0,0.403234768,,,0.182874636
5850,Effectiveness of Face Masks in Preventing Airborne Transmission of SARS-CoV-2.,mSphere,33087517,10/23/20,pubmed,0,7,simulation experiment,0.001415164,0.001415123,0.058264947,0.836998512,0.100491132,0.001415122,Epidemiology,0.89971626,TRUE,51.14285714,0.639247944,38.28571429,0.623026492,27,0.92443978,,,0.728904739
5851,Understanding and responding to COVID-19 in Wales: protocol for a privacy-protecting data platform for enhanced epidemiology and evaluation of interventions.,BMJ Open,33087383,10/23/20,pubmed,0,38,dataset,0.030929536,0.001330101,0.001330093,0.730369613,0.001330133,0.234710525,Epidemiology,0.48447448,FALSE,71.15789474,0.769373492,192.5263158,0.913968424,4,0.707574542,,,0.796972153
5852,Dynamics of SARS-CoV-2 Transmission Among Indian Nationals Evacuated From Iran.,Disaster Med Public Health Prep,33087205,10/23/20,pubmed,0,7,model fit,0.00203275,0.002032788,0.002032794,0.782608816,0.209259949,0.002032903,Epidemiology,0.41764402,FALSE,100.7142857,0.870431072,48.85714286,0.676612256,1,0.537564047,,,0.694869125
5853,Associations of sedentary behaviours and incidence of unhealthy diet during the COVID-19 quarantine in Brazil.,Public Health Nutr,33087204,10/23/20,pubmed,0,8,logistic regression,0.001291227,0.001291226,0.0573245,0.0012913,0.860321451,0.078480296,Healthcare,0.73231566,TRUE,50.25,0.633001422,27,0.546026224,1,0.537564047,,,0.572197231
5854,Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections.,BMC Med,33087179,10/23/20,pubmed,0,52,bayes,0.000907275,0.000907296,0.00090729,0.914264813,0.000907315,0.082106011,Epidemiology,0.20132917,FALSE,54.90566038,0.667511905,,,23,0.91129082,,,0.789401363
5855,Tocilizumab for patients with COVID-19 pneumonia. The single-arm TOCIVID-19 prospective trial.,J Transl Med,33087150,10/23/20,pubmed,0,36,"logistic regression, dataset",0.24051089,0.001565322,0.001565423,0.001565408,0.001565332,0.753227625,Clinics,0.717431,TRUE,103.5833333,0.876553899,92.02777778,0.805793417,8,0.799987654,,,0.82744499
5856,Risk factors for the critical illness in SARS-CoV-2 infection: a multicenter retrospective cohort study.,Respir Res,33087114,10/23/20,pubmed,0,20,logistic regression,0.001291248,0.001291229,0.00129131,0.041359312,0.001291237,0.953475664,Clinics,0.8316408,TRUE,41.15,0.549632012,32.8,0.590982071,1,0.537564047,,,0.55939271
5857,The role of advanced technologies supplemented with traditional methods in pharmacovigilance sciences.,Recent Pat Biotechnol,33087036,10/23/20,pubmed,0,5,"bayes, data mining",0.129243668,0.00108535,0.172953981,0.601119913,0.001085388,0.094511701,Epidemiology,0.99363816,TRUE,16.4,0.247572515,2.8,0.188787798,0,0.403234768,,,0.279865027
5858,"An Assessment of the Novel COVISTRESS Questionnaire: COVID-19 Impact on Physical Activity, Sedentary Action and Psychological Emotion.",J Clin Med,33086648,10/23/20,pubmed,0,16,dataset,0.000786366,0.000786358,0.000786359,0.08691852,0.812991599,0.097730798,Healthcare,0.84577775,TRUE,98.125,0.865050405,30.8125,0.575862992,2,0.618927094,,,0.686613497
5859,Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models.,J Clin Med,33086558,10/23/20,pubmed,0,7,machine learning,0.001392967,0.047745822,0.150202351,0.001392914,0.797873073,0.001392873,Healthcare,0.82825536,TRUE,22.57142857,0.334343497,14.85714286,0.421929355,16,0.881782826,,,0.546018559
5860,An improved multivariate model that distinguishes COVID-19 from seasonal flu and other respiratory diseases.,Aging (Albany NY),33085645,10/22/20,pubmed,0,9,machine learning,0.001392917,0.001392883,0.594211863,0.00139286,0.001392911,0.400216565,Imaging,0.66008544,TRUE,81.22222222,0.811924052,48.11111111,0.674003211,0,0.403234768,,,0.629720677
5861,Thermodynamics of the Interaction between the Spike Protein of Severe Acute Respiratory Syndrome Coronavirus-2 and the Receptor of Human Angiotensin-Converting Enzyme 2. Effects of Possible Ligands.,J Phys Chem Lett,33085491,10/22/20,pubmed,0,8,computational,0.833433059,0.001511894,0.04248098,0.119550337,0.001511872,0.001511857,Drug discovery,0.49012074,FALSE,63.625,0.725647845,6.875,0.29388547,0,0.403234768,,,0.474256027
5862,Projected Utility of Pharmacogenomic Testing Among Individuals Hospitalized With COVID-19: A Retrospective Multicenter Study in the United States.,Clin Transl Sci,33085221,10/22/20,pubmed,0,4,pharmacogenom,0.001751276,0.244012742,0.001751215,0.259971367,0.001751262,0.490762139,Clinics,0.48104897,FALSE,36.25,0.500154617,7.75,0.312215681,0,0.403234768,,,0.405201688
5863,Angiotensin enzyme inhibitors and angiotensin receptor blockers as protective factors in COVID-19 mortality: a retrospective cohort study.,Intern Emerg Med,33085063,10/22/20,pubmed,0,8,logistic regression,0.164768688,0.001415115,0.001415185,0.001415132,0.001415145,0.829570736,Clinics,0.9635892,TRUE,7.875,0.114045396,2,0.164302917,6,0.764429903,,,0.347592738
5864,Transmission and prevention of SARS-CoV-2.,Biochem Soc Trans,33084885,10/22/20,pubmed,0,8,predictive model,0.001461898,0.083798318,0.001461878,0.698566209,0.126206633,0.088505064,Epidemiology,0.35116622,FALSE,40.125,0.540355,14.875,0.422130051,2,0.618927094,,,0.527137382
5865,Targeting the 3CLpro and RdRp of SARS-CoV-2 with phytochemicals from medicinal plants of the Andean Region: molecular docking and molecular dynamics simulations.,J Biomol Struct Dyn,33084512,10/22/20,pubmed,0,3,"molecular dynamics simulation, computational",0.973991078,0.001220025,0.001220005,0.001220173,0.001220016,0.021128703,Drug discovery,0.92382044,TRUE,1.666666667,0.016451234,0,0.055525823,2,0.618927094,,,0.230301384
5866,Deep mutagenesis in the study of COVID-19: a technical overview for the proteomics community.,Expert Rev Proteomics,33084449,10/22/20,pubmed,0,1,"sequencing, proteom",0.532169237,0.327202681,0.137331553,0.001098829,0.001098868,0.001098832,Drug discovery,0.61817783,TRUE,39,0.530521368,145,0.879114263,2,0.618927094,,,0.676187575
5867,The COVID-19 Pandemic from a Human Genetic Perspective.,J Proteome Res,33084340,10/22/20,pubmed,0,4, omics,0.230041068,0.54099462,0.003335457,0.218958122,0.003335304,0.003335429,Genomics,0.7210934,TRUE,57,0.68204589,29.5,0.566095799,2,0.618927094,,,0.622356261
5868,Forecasting of COVID-19 infections in E7 countries and proposing some policies based on the Stringency Index.,J Popul Ther Clin Pharmacol,33084261,10/22/20,pubmed,0,2,model fit,0.003465888,0.003465905,0.003465986,0.982670165,0.003466008,0.003466048,Epidemiology,0.5600506,TRUE,23,0.34225988,21.5,0.498260637,0,0.403234768,,,0.414585095
5869,Why Does the Novel Coronavirus Spike Protein Interact so Strongly with the Human ACE2? A Thermodynamic Answer.,Chembiochem,33084150,10/22/20,pubmed,0,3,"molecular dynamics simulation, in silico",0.990490721,0.00190179,0.001901808,0.001901979,0.00190195,0.001901752,Drug discovery,0.7259238,TRUE,28.33333333,0.410724225,3,0.199424672,0,0.403234768,,,0.337794555
5870,Estimating the impact of disruptions due to COVID-19 on HIV transmission and control among men who have sex with men in China.,medRxiv,33083811,10/22/20,pubmed,0,10,mathematical model,0.001126816,0.040449541,0.001126818,0.82514593,0.001126867,0.131024029,Epidemiology,0.33528984,FALSE,122.3,0.910260375,68.4,0.748929623,0,0.403234768,,,0.687474922
5871,Ongoing Adaptive Evolution and Globalization of Sars-Cov-2.,bioRxiv,33083804,10/22/20,pubmed,0,5,"sequencing, genomes, dataset",0.033802525,0.640932646,0.001538169,0.320650453,0.001538113,0.001538094,Genomics,0.17879942,FALSE,324.6,0.99202177,2889.4,0.99953171,3,0.667819001,,,0.886457494
5872,Progressive and accurate assembly of multi-domain protein structures from cryo-EM density maps.,bioRxiv,33083802,10/22/20,pubmed,0,6,neural network,0.36100403,0.045170613,0.58857171,0.001751273,0.001751198,0.001751176,Drug discovery,0.38725343,FALSE,89.33333333,0.840435401,200,0.917514049,0,0.403234768,,,0.720394739
5873,Reconstructing SARS-CoV-2 response signaling and regulatory networks.,bioRxiv,33083801,10/22/20,pubmed,0,5,"computational, dataset",0.988210869,0.002357921,0.002357844,0.002357822,0.002357815,0.002357729,Drug discovery,0.6219701,TRUE,83.2,0.818665347,245.2,0.936379449,4,0.707574542,,,0.820873113
5874,Longitudinal Prospective Study of Emergency Medicine Provider Wellness Across Ten Academic and Community Hospitals During the Initial Surge of the COVID-19 Pandemic.,Res Sq,33083796,10/22/20,pubmed,0,10,logistic regression,0.000936094,0.016286296,0.000936089,0.000936119,0.979969287,0.000936115,Healthcare,0.98832107,TRUE,22.4,0.331065619,11.7,0.380719829,2,0.618927094,,,0.443570847
5875,Tobacco smoking and smoking cessation in times of COVID-19.,Tob Prev Cessat,33083672,10/22/20,pubmed,0,4,digital health,0.002898345,0.002898382,0.002898319,0.194678092,0.793728287,0.002898575,Healthcare,0.96877396,TRUE,183,0.965675057,211.5,0.922732138,4,0.707574542,,,0.865327246
5876,Computer-aided drug design against spike glycoprotein of SARS-CoV-2 to aid COVID-19 treatment.,Heliyon,33083627,10/22/20,pubmed,0,3,"virtual screening, computational, in silico",0.959702431,0.035483893,0.001203407,0.001203432,0.001203429,0.001203406,Drug discovery,0.91030014,TRUE,18.33333333,0.274908776,1.333333333,0.13252609,1,0.537564047,,,0.314999638
5877,"A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients.",NPJ Digit Med,33083565,10/22/20,pubmed,0,24,prediction model,0.002422301,0.002422305,0.627441729,0.002422418,0.002422398,0.362868848,Clinics,0.6620884,TRUE,27.41666667,0.400519513,27.54166667,0.549973241,4,0.707574542,,,0.552689098
5878,Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements.,NPJ Digit Med,33083564,10/22/20,pubmed,0,9,machine learning,0.069229201,0.004310122,0.179306341,0.738534306,0.004310026,0.004310005,Epidemiology,0.21983168,FALSE,61.66666667,0.713278496,153.2222222,0.886138614,11,0.840175319,,,0.813197476
5879,Social license for the use of big data in the COVID-19 era.,NPJ Digit Med,33083563,10/22/20,pubmed,0,3,dataset,0.002996507,0.002996523,0.002996586,0.435775349,0.552238507,0.002996529,Healthcare,0.8429301,TRUE,124,0.912981632,85.33333333,0.791075729,0,0.403234768,,,0.702430709
5880,Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies.,ArXiv,33083498,10/22/20,pubmed,0,4,deep learning,0.493646017,0.448462158,0.052740306,0.001717187,0.001717178,0.001717154,Drug discovery,0.20686498,FALSE,117.25,0.903333539,32,0.585763982,3,0.667819001,,,0.718972174
5881,Prolonged prothrombin time at admission predicts poor clinical outcome in COVID-19 patients.,World J Clin Cases,33083396,10/22/20,pubmed,0,5,logistic regression,0.000822911,0.000822897,0.000822893,0.000822919,0.00082292,0.99588546,Clinics,0.9807383,TRUE,55,0.668686994,22.2,0.504816698,1,0.537564047,,,0.570355913
5882,"First Cases of SARS-CoV-2 in Iran, 2020: Case Series Report.",Iran J Public Health,33083334,10/22/20,pubmed,0,24,sequencing,0.002296686,0.988516626,0.002296749,0.002296694,0.002296645,0.0022966,Genomics,0.90654886,TRUE,16.33333333,0.247015895,9.333333333,0.342253144,1,0.537564047,,,0.375611029
5883,The impact of comprehension of disease-related information and perceptions regarding effects and controllability on protective and social solidarity behaviors with regard to COVID-19.,Z Gesundh Wiss,33083202,10/22/20,pubmed,0,3,model fit,0.001565338,0.001565289,0.001565329,0.059639836,0.934098838,0.001565369,Healthcare,0.77948916,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
5884,Mutations of SARS-CoV-2 nsp14 exhibit strong association with increased genome-wide mutation load.,PeerJ,33083157,10/22/20,pubmed,0,4,genome-wide,0.18157452,0.779355035,0.00156538,0.001565366,0.001565355,0.034374343,Genomics,0.7036833,TRUE,16.75,0.252458408,1,0.122023013,3,0.667819001,,,0.347433474
5885,"Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning.",PeerJ,33083150,10/22/20,pubmed,0,1,machine learning,0.001461873,0.001461911,0.261694615,0.705977476,0.027942155,0.001461971,Epidemiology,0.5544287,TRUE,28,0.408312202,2,0.164302917,1,0.537564047,,,0.370059722
5886,Experimental and in silico evidence suggests vaccines are unlikely to be affected by D614G mutation in SARS-CoV-2 spike protein.,NPJ Vaccines,33083031,10/22/20,pubmed,0,20,in silico,0.299861541,0.648378985,0.002639054,0.002639093,0.043842104,0.002639224,Genomics,0.30062383,FALSE,39.95,0.538437751,51.05,0.685643564,7,0.785110192,,,0.669730503
5887,Examining the effector mechanisms of Xuebijing injection on COVID-19 based on network pharmacology.,BioData Min,33082858,10/22/20,pubmed,0,8,genomes,0.922412763,0.011019777,0.046189087,0.000662696,0.000662704,0.019052973,Drug discovery,0.9811692,TRUE,56.375,0.677592925,35.25,0.606837035,1,0.537564047,,,0.607331336
5888,A Mathematical Model to Study the Effectiveness of Some of the Strategies Adopted in Curtailing the Spread of COVID-19.,Comput Math Methods Med,33082839,10/22/20,pubmed,0,3,mathematical model,0.00431016,0.004310105,0.004310122,0.862587508,0.004309958,0.120172148,Epidemiology,0.7438453,TRUE,40.25,0.541715629,12.25,0.388546963,3,0.667819001,,,0.532693864
5889,Point mutation bias in SARS-CoV-2 variants results in increased ability to stimulate inflammatory responses.,Sci Rep,33082451,10/22/20,pubmed,0,5,genomes,0.280101233,0.669567168,0.001486474,0.001486444,0.001486569,0.045872112,Genomics,0.6691008,TRUE,21.6,0.319438432,8.2,0.322785657,6,0.764429903,,,0.468884664
5890,Multiple early introductions of SARS-CoV-2 into a global travel hub in the Middle East.,Sci Rep,33082405,10/22/20,pubmed,0,17,genome sequences,0.002080617,0.958531897,0.002080732,0.002080738,0.002080607,0.033145409,Genomics,0.6039399,TRUE,48.64705882,0.618838518,37.11764706,0.616804924,2,0.618927094,,,0.618190179
5891,Expression of SARS-CoV-2 entry factors in lung epithelial stem cells and its potential implications for COVID-19.,Sci Rep,33082395,10/22/20,pubmed,0,5,dataset,0.661989745,0.005047619,0.005047785,0.133092547,0.005047473,0.189774831,Drug discovery,0.24032223,FALSE,57.6,0.686127775,43.4,0.6509232,9,0.814309525,,,0.717120167
5892,"CoVidAffect, real-time monitoring of mood variations following the COVID-19 outbreak in Spain.",Sci Data,33082338,10/22/20,pubmed,0,8,dataset,0.002639004,0.002639042,0.002639024,0.638260628,0.351183296,0.002639006,Epidemiology,0.53244716,TRUE,73.5,0.780011132,63.75,0.734211935,0,0.403234768,,,0.639152612
5893,Transcriptional and proteomic insights into the host response in fatal COVID-19 cases.,Proc Natl Acad Sci U S A,33082228,10/22/20,pubmed,0,26,"transcriptom, proteom",0.672364318,0.001622806,0.001622741,0.001622716,0.001622752,0.321144666,Drug discovery,0.62441385,TRUE,70.80769231,0.76751809,87.88461538,0.796628311,15,0.874313229,,,0.812819877
5894,COVID-19 outbreak-related psychological distress among healthcare trainees: a cross-sectional study in China.,BMJ Open,33082197,10/22/20,pubmed,0,7,logistic regression,0.00120342,0.024002652,0.001203523,0.001203495,0.97118347,0.001203439,Healthcare,0.926713,TRUE,29.42857143,0.423835735,,,2,0.618927094,,,0.521381415
5895,Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.,BMJ,33082154,10/22/20,pubmed,0,23,dataset,0.000786352,0.000786365,0.080632431,0.264508451,0.000786402,0.6525,Clinics,0.12106362,FALSE,118.826087,0.905250789,160.4347826,0.892560878,47,0.95493549,,,0.917582385
5896,Prediction models for covid-19 outcomes.,BMJ,33082149,10/22/20,pubmed,0,2,prediction model,0.057800659,0.057799149,0.710996772,0.057802975,0.057799148,0.057801298,Epidemiology,0.5987252,TRUE,35,0.488032655,304,0.955512443,6,0.764429903,,,0.735991667
5897,Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multi-epitope vaccine by utilizing immunoinformatics approaches.,Vaccine,33082015,10/22/20,pubmed,0,5,in silico,0.891412054,0.049404009,0.001098853,0.055887403,0.00109886,0.001098822,Drug discovery,0.8414343,TRUE,14.8,0.222957511,0.6,0.09011239,2,0.618927094,,,0.310665665
5898,[Analysis of the changes of inflammatory cytokine levels in patients with critical coronavirus disease 2019 undergoing invasive mechanical ventilation].,Zhonghua Wei Zhong Bing Ji Jiu Yi Xue,33081889,10/22/20,pubmed,0,7,logistic regression,0.000846523,0.00084655,0.000846572,0.014437211,0.000846528,0.982176617,Clinics,0.97953916,TRUE,169.5714286,0.958006061,259,0.941329944,0,0.403234768,,,0.767523591
5899,[Correlation between symptoms and their contribution to syndrome based on association rule combined with Bayesian network: syndrome of lung damp-heat accumulation in coronavirus disease 2019].,Zhonghua Wei Zhong Bing Ji Jiu Yi Xue,33081888,10/22/20,pubmed,0,7,"bayes, network model",0.000988419,0.000988391,0.266753413,0.000988441,0.167221283,0.563060053,Clinics,0.9951185,TRUE,62.28571429,0.71680376,13.85714286,0.40941932,0,0.403234768,,,0.509819283
5900,Income assurances are a crucial factor in determining public compliance with self-isolation regulations during the COVID-19 outbreak - cohort study in Israel.,Isr J Health Policy Res,33081833,10/22/20,pubmed,0,2,logistic regression,0.001538138,0.001538103,0.001538097,0.278029399,0.715818147,0.001538117,Healthcare,0.8694285,TRUE,140,0.933823984,68,0.748193738,2,0.618927094,,,0.766981606
5901,Comparative analysis of the main haematological indexes and RNA detection for the diagnosis of SARS-CoV-2 infection.,BMC Infect Dis,33081702,10/22/20,pubmed,0,9,logistic regression,0.031749067,0.356875391,0.213857482,0.000854711,0.000854721,0.395808628,Clinics,0.91828394,TRUE,50,0.632073721,26.44444444,0.541008831,0,0.403234768,,,0.525439107
5902,A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients.,BMC Med Imaging,33081700,10/22/20,pubmed,0,11,"deep learning, radiom, logistic regression, dataset",0.000966742,0.000966774,0.671311573,0.000966767,0.000966785,0.324821358,Imaging,0.99196434,TRUE,56.90909091,0.680623415,16.36363636,0.440594059,1,0.537564047,,,0.552927174
5903,Self-perceived general and ear-nose-throat symptoms related to the COVID-19 outbreak: a survey study during quarantine in Italy.,J Int Med Res,33081538,10/22/20,pubmed,0,5,logistic regression,0.00182333,0.001823353,0.001823339,0.001823396,0.990883178,0.001823403,Healthcare,0.8728775,TRUE,73.6,0.780258519,31,0.578204442,0,0.403234768,,,0.587232576
5904,0,Viruses,33081421,10/22/20,pubmed,0,5,"sequencing, transcriptom",0.720925861,0.266217429,0.003214158,0.003214279,0.003214157,0.003214116,Drug discovery,0.48553398,FALSE,28.4,0.411157153,40.2,0.634533048,3,0.667819001,,,0.571169734
5905,0,Microorganisms,33080900,10/22/20,pubmed,0,8,in silico,0.992924142,0.001415117,0.001415176,0.001415222,0.001415197,0.001415146,Drug discovery,0.6929576,TRUE,23,0.34225988,0.75,0.099411292,1,0.537564047,,,0.32641174
5906,Differences and prediction of imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia: A multicenter study.,Medicine (Baltimore),33080737,10/22/20,pubmed,0,9,prediction model,0.001350349,0.031021934,0.665721844,0.001350336,0.001350368,0.29920517,Imaging,0.5341233,TRUE,52,0.647349867,8.222222222,0.322919454,0,0.403234768,,,0.457834696
5907,Application of tele-podiatry in diabetic foot management: A series of illustrative cases.,Diabetes Metab Syndr,33080541,10/21/20,pubmed,0,4,artificial intelligence,0.001861714,0.001861748,0.416655125,0.101203549,0.191463369,0.286954494,Clinics,0.9730218,TRUE,32.75,0.463788732,18.5,0.46554723,0,0.403234768,,,0.444190243
5908,The impact of metabolic syndrome on morbidity and mortality among intensive care unit admitted COVID-19 patients.,Diabetes Metab Syndr,33080538,10/21/20,pubmed,0,8,logistic regression,0.001371263,0.001371235,0.001371241,0.001371249,0.001371283,0.993143729,Clinics,0.9451333,TRUE,38.25,0.520811429,12.75,0.395972705,0,0.403234768,,,0.440006301
5909,Effect of Tocilizumab vs Usual Care in Adults Hospitalized With COVID-19 and Moderate or Severe Pneumonia: A Randomized Clinical Trial.,JAMA Intern Med,33080017,10/21/20,pubmed,0,593,bayes,0.000652943,0.000652907,0.024939946,0.000652933,0.000652916,0.972448355,Clinics,0.9520345,TRUE,389.5,0.995299647,531.3333333,0.981268397,110,0.980739552,,,0.985769199
5910,Rapid Epidemiological Analysis of Comorbidities and Treatments as risk factors for COVID-19 in Scotland (REACT-SCOT): A population-based case-control study.,PLoS Med,33079969,10/21/20,pubmed,0,17,"classifier, logistic regression",0.064676509,0.000800605,0.017827443,0.000800658,0.081866178,0.834028606,Clinics,0.3380292,FALSE,121.3529412,0.908652359,121.2352941,0.853224512,15,0.874313229,,,0.878730033
5911,Proteins associated with neutrophil degranulation are upregulated in nasopharyngeal swabs from SARS-CoV-2 patients.,PLoS One,33079950,10/21/20,pubmed,0,9,proteom,0.283954616,0.47073142,0.001987143,0.001987108,0.001987145,0.239352567,Genomics,0.9495977,TRUE,29.44444444,0.423959429,11.44444444,0.376705914,4,0.707574542,,,0.502746628
5912,"Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests.",Clin Chem Lab Med,33079698,10/21/20,pubmed,0,11,"machine learning, dataset",0.001371242,0.001371362,0.68441965,0.001371354,0.00137139,0.310095003,Clinics,0.44489908,FALSE,49.45454545,0.627187829,15.63636364,0.431696548,0,0.403234768,,,0.487373048
5913,Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature.,J Med Internet Res,33079693,10/21/20,pubmed,0,6,artificial intelligence,0.000643425,0.000643418,0.317561194,0.6798651,0.00064342,0.000643444,Epidemiology,0.33657384,FALSE,85.66666667,0.827509432,51.5,0.687382927,0,0.403234768,,,0.639375709
5914,Using Chou's 5-steps rule to study pharmacophore-based virtual screening of SARS-CoV-2 Mpro inhibitors.,Mol Divers,33079314,10/21/20,pubmed,0,7,virtual screening,0.954007709,0.001461866,0.001461878,0.040144777,0.001461917,0.001461854,Drug discovery,0.9714683,TRUE,17.28571429,0.260498485,1.285714286,0.128512176,0,0.403234768,,,0.264081809
5915,Characteristics of coagulation alteration in patients with COVID-19.,Ann Hematol,33079220,10/21/20,pubmed,0,4,logistic regression,0.001861786,0.001861742,0.001861775,0.001861812,0.001861752,0.990691133,Clinics,0.8662996,TRUE,8.75,0.129383388,1.75,0.148381054,2,0.618927094,,,0.298897179
5916,Impact of COVID-19 on cardiac procedure activity in England and associated 30-day mortality.,Eur Heart J Qual Care Clin Outcomes,33079204,10/21/20,pubmed,0,11,logistic regression,0.001415119,0.001415118,0.001415152,0.384153433,0.001415182,0.610185997,Clinics,0.9616084,TRUE,150,0.944028697,87.09090909,0.79529034,5,0.739490092,,,0.82626971
5917,MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs.,Brief Bioinform,33078827,10/21/20,pubmed,0,9,"virtual screening, in silico, dataset",0.795885434,0.001350354,0.165737172,0.034326285,0.001350374,0.001350381,Drug discovery,0.8046079,TRUE,50.77777778,0.636402993,49,0.677481937,0,0.403234768,,,0.572373233
5918,Potential therapeutic effect of turmeric contents against SARS-CoV-2 compared with experimental COVID-19 therapies: in silico study.,J Biomol Struct Dyn,33078675,10/21/20,pubmed,0,1,in silico,0.994063444,0.001187335,0.001187317,0.001187337,0.001187295,0.001187273,Drug discovery,0.93864787,TRUE,27,0.3960047,1,0.122023013,2,0.618927094,,,0.378984936
5919,Group teletherapy for first-episode psychosis: Piloting its integration with coordinated specialty care during the COVID-19 pandemic.,Psychol Psychother,33078496,10/21/20,pubmed,0,4,digital health,0.001751287,0.001751224,0.001751284,0.001751308,0.838043515,0.154951382,Healthcare,0.8504579,TRUE,17,0.257467994,14.25,0.415038801,1,0.537564047,,,0.403356948
5920,Effects of the Lower Airway Secretions on Airway Opening Pressures and Suction Pressures in Critically Ill COVID-19 Patients: A Computational Simulation.,Ann Biomed Eng,33078367,10/21/20,pubmed,0,12,computational,0.15325929,0.002080617,0.002080629,0.226475517,0.002080598,0.61402335,Clinics,0.9975718,TRUE,33.58333333,0.472076195,94.75,0.812349478,0,0.403234768,,,0.56255348
5921,Epidemiological changes on the Isle of Wight after the launch of the NHS Test and Trace programme: a preliminary analysis.,Lancet Digit Health,33078140,10/21/20,pubmed,0,10,bayes,0.001622706,0.001622743,0.001622703,0.991886356,0.001622761,0.001622731,Epidemiology,0.13896835,FALSE,30.9,0.442204218,67,0.745718491,11,0.840175319,,,0.676032676
5922,"Clinical characteristics and outcomes among hospitalized adults with severe COVID-19 admitted to a tertiary medical center and receiving antiviral, antimalarials, glucocorticoids, or immunomodulation with tocilizumab or cyclosporine: A retrospective observational study (COQUIMA cohort).",EClinicalMedicine,33078138,10/21/20,pubmed,0,25,logistic regression,0.038503867,0.020047959,0.001126852,0.001126871,0.001126816,0.938067635,Clinics,0.75633484,TRUE,5.2,0.071927763,0.92,0.104495585,7,0.785110192,,,0.32051118
5923,0,Mater Today Proc,33078096,10/21/20,pubmed,0,3,in silico,0.954220692,0.039931625,0.001461886,0.001462061,0.001461874,0.001461862,Drug discovery,0.97168726,TRUE,11,0.167171748,0.666666667,0.096200161,2,0.618927094,,,0.294099668
5924,Advanced Machine Learning-Based Analytics on COVID-19 Data Using Generative Adversarial Networks.,Mater Today Proc,33078094,10/21/20,pubmed,0,4,"machine learning, deep learning, neural network, adversarial network, dataset",0.001511945,0.087119443,0.906832889,0.001511935,0.001511886,0.001511903,Imaging,0.10624075,FALSE,2.75,0.030304904,0.5,0.087101953,0,0.403234768,,,0.173547208
5925,Study of global dynamics of COVID-19 via a new mathematical model.,Results Phys,33078091,10/21/20,pubmed,0,5,mathematical model,0.003927614,0.003927474,0.003927519,0.980362582,0.003927401,0.00392741,Epidemiology,0.67529494,TRUE,116.2,0.900983363,23.4,0.515721167,2,0.618927094,,,0.678543875
5926,Factors influencing liberation from mechanical ventilation in coronavirus disease 2019: multicenter observational study in fifteen Italian ICUs.,J Intensive Care,33078076,10/21/20,pubmed,0,85,logistic regression,0.000838608,0.033453771,0.027639926,0.000838561,0.000838536,0.936390597,Clinics,0.61053944,TRUE,26.68604651,0.391056961,,,5,0.739490092,,,0.565273526
5927,Impact of COVID-19 prevalence and mode of transmission on mortality cases over WHO regions.,Health Inf Sci Syst,33078072,10/21/20,pubmed,0,4,model fit,0.001653049,0.001653164,0.001653054,0.93260385,0.060783752,0.001653131,Epidemiology,0.21104422,FALSE,16.75,0.252458408,1.75,0.148381054,1,0.537564047,,,0.31280117
5928,Stochastic mathematical model for the spread and control of Corona virus.,Adv Differ Equ,33078063,10/21/20,pubmed,0,4,mathematical model,0.001943539,0.001943505,0.001943568,0.99028241,0.001943469,0.001943508,Epidemiology,0.3671813,FALSE,17.75,0.267672707,0.25,0.065493712,1,0.537564047,,,0.290243489
5929,Numerical simulation of the novel coronavirus spreading.,Expert Syst Appl,33078047,10/21/20,pubmed,0,2,artificial intelligence,0.097432283,0.004309966,0.339305139,0.550332476,0.004310239,0.004309897,Epidemiology,0.6216134,TRUE,10,0.15214299,0,0.055525823,2,0.618927094,,,0.275531969
5930,Modeling of the adsorption of a protein-fragment on kaolinite with potential antiviral activity.,Appl Clay Sci,33078035,10/21/20,pubmed,0,5,computational,0.869312523,0.001310439,0.00131038,0.125445987,0.001310343,0.001310328,Drug discovery,0.75947803,TRUE,44.6,0.582658173,10.6,0.364262778,0,0.403234768,,,0.450051906
5931,Corona COVID-19 spread - a nonlinear modeling and simulation.,Comput Electr Eng,33078033,10/21/20,pubmed,0,2,mathematical model,0.001987283,0.001987346,0.001987099,0.85142883,0.001987204,0.140622239,Epidemiology,0.93756217,TRUE,24.5,0.361988991,22,0.503746321,1,0.537564047,,,0.467766453
5932,Is a healthy microbiome responsible for lower mortality in COVID-19?,Biologia (Bratisl),33078028,10/21/20,pubmed,0,3,microbiom,0.54305628,0.373894442,0.003335281,0.003335568,0.003335352,0.073043077,Drug discovery,0.65526986,TRUE,21.33333333,0.31548024,6.333333333,0.285121755,5,0.739490092,,,0.446697362
5933,Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method.,Sci Rep,33077837,10/21/20,pubmed,0,2,dataset,0.568308417,0.001786553,0.424545133,0.001786549,0.001786669,0.001786678,Drug discovery,0.85202736,TRUE,90,0.843094811,48.5,0.675675676,0,0.403234768,,,0.640668418
5934,Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV2 spike protein and human ACE2.,Sci Rep,33077836,10/21/20,pubmed,0,3,in silico,0.962623827,0.000988378,0.000988425,0.020452487,0.000988348,0.013958536,Drug discovery,0.9603598,TRUE,154.6666667,0.947739502,217.3333333,0.92594327,13,0.858880178,,,0.910854316
5935,"Rational approach toward COVID-19 main protease inhibitors via molecular docking, molecular dynamics simulation and free energy calculation.",Sci Rep,33077821,10/21/20,pubmed,0,3,"molecular dynamics simulation, computational",0.866652099,0.001861682,0.12590121,0.001861681,0.001861673,0.001861655,Drug discovery,0.8505645,TRUE,66,0.741356918,11.33333333,0.375501739,7,0.785110192,,,0.633989616
5936,Evidence of protective role of Ultraviolet-B (UVB) radiation in reducing COVID-19 deaths.,Sci Rep,33077792,10/21/20,pubmed,0,3,dataset,0.001538134,0.001538212,0.001538093,0.635696147,0.001538177,0.358151236,Epidemiology,0.34190136,FALSE,68,0.753973653,169.6666667,0.899250736,4,0.707574542,,,0.786932977
5937,Site-specific N-glycosylation Characterization of Recombinant SARS-CoV-2 Spike Proteins.,Mol Cell Proteomics,33077685,10/21/20,pubmed,0,12,genome sequences,0.696738536,0.297109004,0.001538104,0.001538166,0.001538098,0.001538091,Drug discovery,0.377952,FALSE,39.83333333,0.537571897,14.75,0.421327268,3,0.667819001,,,0.542239389
5938,Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis.,Sci Total Environ,33077215,10/21/20,pubmed,0,7,machine learning,0.051998301,0.001653109,0.331099057,0.61194327,0.001653112,0.001653151,Epidemiology,0.67985713,TRUE,22,0.326056033,5,0.257024351,4,0.707574542,,,0.430218309
5939,Coronavirus disease 2019 pandemic promotes the sense of professional identity among nurses.,Nurs Outlook,33077203,10/21/20,pubmed,0,7,logistic regression,0.002422275,0.002422226,0.002422373,0.002422336,0.987888486,0.002422304,Healthcare,0.7498712,TRUE,51.14285714,0.639247944,7.714285714,0.311479797,0,0.403234768,,,0.451320836
5940,The first consecutive 5000 patients with Coronavirus Disease 2019 from Qatar; a nation-wide cohort study.,BMC Infect Dis,33076848,10/21/20,pubmed,0,30,logistic regression,0.001098781,0.001098796,0.001098801,0.001098806,0.088718088,0.906886728,Clinics,0.464413,FALSE,8.4,0.124435648,3.333333333,0.206515922,0,0.403234768,,,0.244728779
5941,SARS-CoV-2 seroprevalence among health care workers in a New York City hospital: A cross-sectional analysis during the COVID-19 pandemic.,Int J Infect Dis,33075539,10/20/20,pubmed,0,13,logistic regression,0.0015935,0.21650932,0.001593488,0.089332773,0.521885031,0.169085887,Healthcare,0.39067748,FALSE,15.84615385,0.238295504,3.923076923,0.22357506,8,0.799987654,,,0.420619406
5942,Higher binding affinity of furin for SARS-CoV-2 spike (S) protein D614G mutant could be associated with higher SARS-CoV-2 infectivity.,Int J Infect Dis,33075532,10/20/20,pubmed,0,6,molecular dynamics simulation,0.704711063,0.237877344,0.001187251,0.040922866,0.001187247,0.01411423,Drug discovery,0.59100735,TRUE,35.33333333,0.490259138,38.5,0.624765855,9,0.814309525,,,0.643111506
5943,Factors associated with adverse COVID-19 outcomes in patients with psoriasis-insights from a global registry-based study.,J Allergy Clin Immunol,33075408,10/20/20,pubmed,0,140,logistic regression,0.001187265,0.001187311,0.001187279,0.093950849,0.001187325,0.901299971,Clinics,0.83601856,TRUE,43.29078014,0.570226978,,,8,0.799987654,,,0.685107316
5944,Development of a multivariate prediction model of intensive care unit transfer or death: A French prospective cohort study of hospitalized COVID-19 patients.,PLoS One,33075088,10/20/20,pubmed,0,13,"logistic regression, prediction model",0.001022628,0.020509854,0.072346862,0.001022648,0.001022648,0.90407536,Clinics,0.5267157,TRUE,212.5384615,0.976374544,160.0769231,0.891891892,10,0.828199272,,,0.898821902
5945,"After the honeymoon, the divorce: Unexpected outcomes of disease control measures against endemic infections.",PLoS Comput Biol,33075052,10/20/20,pubmed,0,3,mathematical model,0.001987189,0.001987201,0.001987257,0.99006387,0.001987235,0.001987248,Epidemiology,0.20764747,FALSE,56.33333333,0.677407385,148.6666667,0.882459192,0,0.403234768,,,0.654367115
5946,Development of Simple and Sensitive Score to Assess the Risk of Pneumonia in COVID-19 Patients.,Rev Invest Clin,33075043,10/20/20,pubmed,0,14,logistic regression,0.001330036,0.016716651,0.204816552,0.001330057,0.001330068,0.774476636,Clinics,0.9703907,TRUE,5.642857143,0.078050591,1.071428571,0.122357506,0,0.403234768,,,0.201214288
5947,Structural and molecular basis of the interaction mechanism of selected drugs towards multiple targets of SARS-CoV-2 by molecular docking and dynamic simulation studies- deciphering the scope of repurposed drugs.,Comput Biol Med,33074111,10/20/20,pubmed,0,7,computational,0.970413159,0.000956299,0.000956316,0.000956307,0.025761583,0.000956336,Drug discovery,0.8228904,TRUE,11.85714286,0.178551549,2.428571429,0.175274284,1,0.537564047,,,0.29712996
5948,Public Concerns and Mental Health Changes Related to the COVID-19 Pandemic Lockdown in Saudi Arabia.,Clin Lab,33073937,10/20/20,pubmed,0,4,logistic regression,0.001254589,0.001254606,0.001254601,0.031757936,0.963223632,0.001254636,Healthcare,0.94914305,TRUE,8.5,0.126662131,3,0.199424672,0,0.403234768,,,0.24310719
5949,Detection of SARS-CoV-2 in saliva and characterization of oral symptoms in COVID-19 patients.,Cell Prolif,33073910,10/20/20,pubmed,0,12,dataset,0.105851511,0.446184505,0.00175132,0.001751222,0.272883688,0.171577755,Genomics,0.88833797,TRUE,60.83333333,0.707464902,25.91666667,0.53632593,32,0.933699611,,,0.725830148
5950,A computational and bioinformatic analysis of ACE2: an elucidation of its dual role in COVID-19 pathology and finding its associated partners as potential therapeutic targets.,J Biomol Struct Dyn,33073716,10/20/20,pubmed,0,5,"computational, bioinformatic",0.991886073,0.001622858,0.00162277,0.001622814,0.001622765,0.001622721,Drug discovery,0.9473833,TRUE,71,0.769064259,27.2,0.547230399,1,0.537564047,,,0.617952902
5951,Antiviral activity of traditional medicinal plants from Ayurveda against SARS-CoV-2 infection.,J Biomol Struct Dyn,33073699,10/20/20,pubmed,0,4,network analysis,0.973963747,0.001203409,0.001203429,0.021222553,0.001203451,0.001203411,Drug discovery,0.94097,TRUE,62.75,0.719586864,19.25,0.47310677,2,0.618927094,,,0.603873576
5952,SARS-CoV-2 pandemic: a review of molecular diagnostic tools including sample collection and commercial response with associated advantages and limitations.,Anal Bioanal Chem,33073312,10/20/20,pubmed,0,10,sequencing,0.002357791,0.652987732,0.337580968,0.002357912,0.002357831,0.002357765,Genomics,0.86854523,TRUE,24.4,0.360442823,8.7,0.331549371,15,0.874313229,,,0.522101808
5953,A Prediction Model to Prioritize Individuals for a SARS-CoV-2 Test Built from National Symptom Surveys.,Med (N Y),33073258,10/20/20,pubmed,0,25,"prediction model, dataset",0.001141376,0.175782936,0.001141436,0.206421795,0.539810937,0.075701519,Healthcare,0.68504953,TRUE,65.84,0.739254128,92.88,0.808201766,2,0.618927094,,,0.722127663
5954,"High Tech, High Risk: Tech Ethics Lessons for the COVID-19 Pandemic Response.",Patterns (N Y),33073256,10/20/20,pubmed,0,2,machine learning,0.001987149,0.001987128,0.424134806,0.567916313,0.001987519,0.001987085,Epidemiology,0.6875061,TRUE,12,0.183190055,17.5,0.45611453,1,0.537564047,,,0.392289544
5955,"Restricted Prevalence Rates of COVID-19's Infectivity, Hospitalization, Recovery, Mortality in the USA and Their Implications.",J Healthc Inform Res,33073166,10/20/20,pubmed,0,1,"mathematical model, probabilistic",0.001156283,0.001156317,0.001156258,0.798394972,0.001156358,0.196979811,Epidemiology,0.4377278,FALSE,254,0.984847548,23,0.513513514,0,0.403234768,,,0.633865276
5956,The role of inflammation in hypertension: novel concepts.,Curr Opin Physiol,33073072,10/20/20,pubmed,0,3,microbiom,0.514821298,0.04555382,0.002422406,0.116888914,0.002422298,0.317891264,Drug discovery,0.97143483,TRUE,127.6666667,0.918114911,167.6666667,0.897578271,2,0.618927094,,,0.811540092
5957,COVID-19 prevalence estimation: Four most affected African countries.,Infect Dis Model,33073068,10/20/20,pubmed,0,6,dataset,0.001438117,0.001438129,0.001438157,0.888027277,0.106220189,0.001438132,Epidemiology,0.6049488,TRUE,20,0.298163152,3.5,0.213607172,3,0.667819001,,,0.393196441
5958,The adaptive immune receptor repertoire community as a model for FAIR stewardship of big immunology data.,Curr Opin Syst Biol,33073065,10/20/20,pubmed,0,2,"computational, bioinformatic, sequencing",0.24007636,0.386065084,0.002562751,0.366170595,0.002562581,0.002562629,Genomics,0.69096875,TRUE,90.5,0.844331746,136,0.870818839,0,0.403234768,,,0.706128451
5959,Cardiorenal Tissues Express SARS-CoV-2 Entry Genes and Basigin (BSG/CD147) Increases With Age in Endothelial Cells.,JACC Basic Transl Sci,33073064,10/20/20,pubmed,0,6,transcriptom,0.842662132,0.002996525,0.002996406,0.002996492,0.002996424,0.145352021,Drug discovery,0.5061415,TRUE,86.5,0.831653163,137.5,0.872223709,5,0.739490092,,,0.814455655
5960,Implementation of Telehealth in Radiation Oncology: Rapid Integration During COVID-19 and Its Future Role in Our Practice.,Adv Radiat Oncol,33073060,10/20/20,pubmed,0,7,logistic regression,0.001059355,0.001059367,0.033243033,0.146318015,0.610801481,0.207518749,Healthcare,0.95737076,TRUE,82.71428571,0.816933638,97.71428571,0.81790206,1,0.537564047,,,0.724133249
5961,Understand variability of COVID-19 through population and tissue variations in expression of SARS-CoV-2 host genes.,Inform Med Unlocked,33072849,10/20/20,pubmed,0,2,bioinformatic,0.53115015,0.3107239,0.001310332,0.001310354,0.001310447,0.154194818,Drug discovery,0.7078916,TRUE,143.5,0.937782176,82,0.783114798,3,0.667819001,,,0.796238658
5962,"Impact of covid-19 pandemic on dermatology practice: results of a web-based, global survey.",Int J Womens Dermatol,33072835,10/20/20,pubmed,0,3,logistic regression,0.00131039,0.001310388,0.04164292,0.001310434,0.95311544,0.001310427,Healthcare,0.8890041,TRUE,49,0.624281032,11.33333333,0.375501739,0,0.403234768,,,0.467672513
5963,Data on factors characterizing the eLearning experience of secondary school teachers and university undergraduate students in Jordan.,Data Brief,33072820,10/20/20,pubmed,0,3,dataset,0.002032821,0.002032809,0.002032913,0.38056417,0.611304501,0.002032786,Healthcare,0.7789227,TRUE,6,0.086028821,1,0.122023013,0,0.403234768,,,0.2037622
5964,"German and Chinese dataset on attitudes regarding COVID-19 policies, perception of the crisis, and belief in conspiracy theories.",Data Brief,33072819,10/20/20,pubmed,0,2,dataset,0.002032769,0.002032748,0.002032786,0.361044337,0.630824627,0.002032733,Healthcare,0.3563285,FALSE,6.5,0.093512277,2,0.164302917,1,0.537564047,,,0.265126414
5965,Protective Behavior in Course of the COVID-19 Outbreak-Survey Results From Germany.,Front Public Health,33072712,10/20/20,pubmed,0,2,logistic regression,0.001187276,0.001187292,0.001187308,0.204021042,0.791229812,0.00118727,Healthcare,0.611646,TRUE,111.5,0.892943287,79.5,0.77782981,8,0.799987654,,,0.823586917
5966,"Epidemiological and Genomic Analysis of SARS-CoV-2 in 10 Patients From a Mid-Sized City Outside of Hubei, China in the Early Phase of the COVID-19 Outbreak.",Front Public Health,33072702,10/20/20,pubmed,0,11,"sequencing, metagenom, genomes",0.001392849,0.613114658,0.05038484,0.264790046,0.0013929,0.068924707,Genomics,0.5059924,TRUE,39.18181818,0.531634609,,,0,0.403234768,,,0.467434689
5967,"COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City.",Front Public Health,33072700,10/20/20,pubmed,0,8,in silico,0.03100522,0.001098846,0.001098861,0.964599405,0.001098846,0.001098822,Epidemiology,0.24985534,FALSE,47.125,0.60684025,32.375,0.588506824,3,0.667819001,,,0.621055358
5968,Geographical Distribution of Genetic Variants and Lineages of SARS-CoV-2 in Chile.,Front Public Health,33072699,10/20/20,pubmed,0,12,genomes,0.001717197,0.935377449,0.001717196,0.057753818,0.001717185,0.001717155,Genomics,0.35468757,FALSE,93,0.851567815,76.16666667,0.76933369,6,0.764429903,,,0.795110469
5969,Considering Interim Interventions to Control COVID-19 Associated Morbidity and Mortality-Perspectives.,Front Public Health,33072682,10/20/20,pubmed,0,1,logistic regression,0.019241089,0.073100099,0.000846539,0.162638565,0.124498259,0.619675449,Clinics,0.33698636,FALSE,24,0.35574247,0,0.055525823,1,0.537564047,,,0.316277447
5970,Factors associated with the poor outcomes in diabetic patients with COVID-19.,J Diabetes Metab Disord,33072634,10/20/20,pubmed,0,16,logistic regression,0.001085306,0.001085339,0.001085321,0.001085328,0.052014922,0.943643784,Clinics,0.95214134,TRUE,48.625,0.618714825,22.5625,0.507626438,1,0.537564047,,,0.554635103
5971,Evaluation of telephone and virtual visits for routine pediatric diabetes care during the COVID-19 pandemic.,J Clin Transl Endocrinol,33072519,10/20/20,pubmed,0,6,logistic regression,0.001786612,0.00178653,0.026217538,0.001786533,0.84162259,0.126800197,Healthcare,0.83455586,TRUE,26.5,0.389325252,8.833333333,0.333154937,1,0.537564047,,,0.420014745
5972,Study of transmission dynamics of COVID-19 mathematical model under ABC fractional order derivative.,Results Phys,33072498,10/20/20,pubmed,0,4,mathematical model,0.003760563,0.003760543,0.003760589,0.981197346,0.003760482,0.003760478,Epidemiology,0.4519197,FALSE,109.25,0.888799555,46.75,0.66744715,8,0.799987654,,,0.785411453
5973,Development of a novel platform of virus-like particle (VLP)-based vaccine against COVID-19 by exposing epitopes: an immunoinformatics approach.,New Microbes New Infect,33072338,10/20/20,pubmed,0,6,in silico,0.907042066,0.001717272,0.001717234,0.086089052,0.001717217,0.001717158,Drug discovery,0.6311132,TRUE,28.16666667,0.409178057,11,0.371287129,7,0.785110192,,,0.521858459
5974,Systematic analyses on the potential immune and anti-inflammatory mechanisms of Shufeng Jiedu Capsule against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)-caused pneumonia.,J Funct Foods,33072190,10/20/20,pubmed,0,8,"molecular dynamics simulation, bioinformatic",0.885925869,0.001438125,0.074707814,0.001438142,0.001438101,0.035051948,Drug discovery,0.9535447,TRUE,13.875,0.209536768,6.25,0.283315494,0,0.403234768,,,0.298695676
5975,"Designing low-cost, accurate cervical screening strategies that take into account COVID-19: a role for self-sampled HPV typing2.",Infect Agent Cancer,33072179,10/20/20,pubmed,0,6,deep-learning,0.000830664,0.058520024,0.417579461,0.000830695,0.407128483,0.115110673,Healthcare,0.2676487,FALSE,187.8333333,0.967839693,455.5,0.976384801,1,0.537564047,,,0.827262847
5976,Proteomics Insights Into the Molecular Basis of SARS-CoV-2 Infection: What We Can Learn From the Human Olfactory Axis.,Front Microbiol,33071996,10/20/20,pubmed,0,4,"proteom, interactom",0.826884421,0.115606126,0.002720363,0.002720308,0.002720112,0.04934867,Drug discovery,0.63281417,TRUE,102,0.873523409,71.25,0.756957453,1,0.537564047,,,0.722681636
5977,When Pandemic Hits: Exercise Frequency and Subjective Well-Being During COVID-19 Pandemic.,Front Psychol,33071902,10/20/20,pubmed,0,3,prediction model,0.001330018,0.001330071,0.001330061,0.431693067,0.562986739,0.001330044,Healthcare,0.01619932,FALSE,50,0.632073721,46.33333333,0.665908483,9,0.814309525,,,0.704097243
5978,Factors Predicting Willingness to Share COVID-19 Misinformation.,Front Psychol,33071894,10/20/20,pubmed,0,4,computational,0.00411018,0.004110114,0.004110164,0.753363417,0.230196103,0.004110022,Epidemiology,0.63180536,TRUE,16.25,0.246026347,12.75,0.395972705,0,0.403234768,,,0.348411273
5979,Regulation of Angiotensin- Converting Enzyme 2 in Obesity: Implications for COVID-19.,Front Physiol,33071815,10/20/20,pubmed,0,8,"in silico, transcriptom, dataset",0.754059447,0.001059365,0.001059366,0.001059346,0.001059342,0.241703134,Drug discovery,0.915318,TRUE,78,0.798812543,52.625,0.69153064,4,0.707574542,,,0.732639241
5980,Assessing countries' performances against COVID-19 via WSIDEA and machine learning algorithms.,Appl Soft Comput,33071686,10/20/20,pubmed,0,2,machine learning,0.001751177,0.001751278,0.267059433,0.666404974,0.001751173,0.061281965,Epidemiology,0.8132602,TRUE,19,0.285793803,14,0.412898047,4,0.707574542,,,0.468755464
5981,Real-time neural network scheduling of emergency medical mask production during COVID-19.,Appl Soft Comput,33071685,10/20/20,pubmed,0,5,"computational, neural network",0.001371327,0.021943906,0.613758727,0.360183418,0.001371335,0.001371286,Epidemiology,0.45482254,FALSE,13.8,0.208980147,10.2,0.357706717,2,0.618927094,,,0.395204653
5982,Locally Informed Modeling to Predict Hospital and Intensive Care Unit Capacity During the COVID-19 Epidemic.,Ochsner J,33071661,10/20/20,pubmed,0,4,forecasting model,0.014967009,0.001141345,0.001141357,0.66793201,0.001141359,0.313676919,Epidemiology,0.0692001,FALSE,27.75,0.404292164,13.75,0.408348943,1,0.537564047,,,0.450068385
5983,A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic.,Chaos Solitons Fractals,33071481,10/20/20,pubmed,0,7,artificial intelligence,0.043424643,0.001538115,0.421356199,0.499850336,0.032292501,0.001538206,Epidemiology,0.5108283,TRUE,30.71428571,0.440534356,4,0.231469093,5,0.739490092,,,0.470497847
5984,"Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability.",Eur J Oper Res,33071441,10/20/20,pubmed,0,4,computational,0.001254664,0.001254611,0.071860743,0.923120626,0.001254698,0.001254658,Epidemiology,0.8085812,TRUE,11.75,0.177562001,1.5,0.138747659,4,0.707574542,,,0.341294734
5985,Fenoterol and dobutamine as SARS-CoV-2 main protease inhibitors: A virtual screening study.,J Mol Struct,33071354,10/20/20,pubmed,0,4,virtual screening,0.9888072,0.002238532,0.002238643,0.00223858,0.00223858,0.002238464,Drug discovery,0.5438774,TRUE,11,0.167171748,2.25,0.170925876,0,0.403234768,,,0.247110797
5986,0,J Mol Struct,33071353,10/20/20,pubmed,0,9,in silico,0.847249935,0.002562632,0.142499502,0.002562663,0.002562677,0.002562591,Drug discovery,0.617951,TRUE,47.22222222,0.607767951,2.222222222,0.168651325,0,0.403234768,,,0.393218014
5987,"Anti-COVID-19 terpenoid from marine sources: A docking, admet and molecular dynamics study.",J Mol Struct,33071352,10/20/20,pubmed,0,5,molecular dynamics simulation,0.989835961,0.00203276,0.002032866,0.002032781,0.002032765,0.002032866,Drug discovery,0.9644834,TRUE,30.6,0.439421114,4.8,0.249331014,3,0.667819001,,,0.452190377
5988,The wastewater microbiome: A novel insight for COVID-19 surveillance.,Sci Total Environ,33071116,10/20/20,pubmed,0,12,"sequencing, microbiom",0.002080558,0.577069153,0.002080599,0.002080682,0.27913037,0.137558638,Genomics,0.9137606,TRUE,42.83333333,0.565650319,19.66666667,0.477321381,4,0.707574542,,,0.583515414
5989,Mitigating the transmission of infection and death due to SARS-CoV-2 through non-pharmaceutical interventions and repurposing drugs.,ISA Trans,33070984,10/20/20,pubmed,0,4,mathematical model,0.245575961,0.001751156,0.001751181,0.747419088,0.001751466,0.001751147,Epidemiology,0.7354603,TRUE,14.25,0.215535902,3,0.199424672,4,0.707574542,,,0.374178372
5990,Cardiology on the cutting edge: updates from the European Society of Cardiology (ESC) Congress 2020.,BMC Cardiovasc Disord,33070781,10/20/20,pubmed,0,5,digital health,0.001786667,0.001786594,0.001786609,0.559642381,0.132344228,0.302653522,Epidemiology,0.93639153,TRUE,38.4,0.523285299,8.6,0.330010704,0,0.403234768,,,0.41884359
5991,"Pregnant women with COVID-19 and risk of adverse birth outcomes and maternal-fetal vertical transmission: a population-based cohort study in Wuhan, China.",BMC Med,33070775,10/20/20,pubmed,0,15,logistic regression,0.002296662,0.002296755,0.037308829,0.00229673,0.550645332,0.405155692,Healthcare,0.87753916,TRUE,41.8,0.555692993,,,5,0.739490092,,,0.647591542
5992,Current targets and drug candidates for prevention and treatment of SARS-CoV-2 (COVID-19) infection.,Rev Cardiovasc Med,33070542,10/20/20,pubmed,0,7,in silico,0.943066962,0.001112651,0.001112665,0.001112646,0.001112667,0.052482409,Drug discovery,0.82751095,TRUE,21,0.312016822,9.571428571,0.346133262,2,0.618927094,,,0.425692393
5993,Usefulness of machine learning in COVID-19 for the detection and prognosis of cardiovascular complications.,Rev Cardiovasc Med,33070540,10/20/20,pubmed,0,2,"machine learning, deep learning, artificial intelligence",0.001861914,0.001861804,0.810992539,0.001861799,0.001861763,0.181560181,Clinics,0.6843983,TRUE,39.5,0.534232173,13,0.400521809,1,0.537564047,,,0.490772676
5994,Mental health of medical personnel during the COVID-19 pandemic.,Brain Behav,33070475,10/19/20,pubmed,0,6,logistic regression,0.001059327,0.001059333,0.001059371,0.00105936,0.97718291,0.0185797,Healthcare,0.976451,TRUE,22.66666667,0.335580432,5.5,0.267259834,2,0.618927094,,,0.407255787
5995,"The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions.",Protein Sci,33070389,10/19/20,pubmed,0,15,"genome-wide, dataset",0.591339293,0.204045786,0.001098886,0.20131837,0.001098856,0.001098809,Drug discovery,0.79538536,TRUE,37.73333333,0.515739996,606.0666667,0.984211935,3,0.667819001,,,0.722590311
5996,The prevalence and risk factors of PTSD symptoms among medical assistance workers during the COVID-19 pandemic.,J Psychosom Res,33070044,10/19/20,pubmed,0,9,logistic regression,0.001486419,0.001486461,0.001486509,0.001486569,0.9925676,0.001486442,Healthcare,0.9971235,TRUE,27.33333333,0.399529965,3.777777778,0.220029435,5,0.739490092,,,0.453016497
5997,Changes in mental health symptoms from pre-COVID-19 to COVID-19 among participants with systemic sclerosis from four countries: A Scleroderma Patient-centered Intervention Network (SPIN) Cohort study.,J Psychosom Res,33070043,10/19/20,pubmed,0,101,logistic regression,0.001310361,0.001310382,0.001310452,0.001310404,0.913286954,0.081471447,Healthcare,0.70554215,TRUE,85.09708738,0.825221102,,,1,0.537564047,,,0.681392575
5998,Multiplexed detection and quantification of human antibody response to COVID-19 infection using a plasmon enhanced biosensor platform.,Biosens Bioelectron,33069957,10/19/20,pubmed,0,10,machine learning,0.195986275,0.49386482,0.142709804,0.001461885,0.001461888,0.164515328,Genomics,0.42534357,FALSE,43.7,0.575051024,20.4,0.484613326,1,0.537564047,,,0.532409466
5999,"COVID-19 Pandemic and the Lived Experience of Surgical Residents, Fellows, and Early-Career Surgeons in the American College of Surgeons.",J Am Coll Surg,33069850,10/19/20,pubmed,0,14,logistic regression,0.001371231,0.001371233,0.001371324,0.001371297,0.993143612,0.001371303,Healthcare,0.994547,TRUE,28.42857143,0.411280846,,,1,0.537564047,,,0.474422447
6000,Clustering and classification of virus sequence through music communication protocol and wavelet transform.,Genomics,33069829,10/19/20,pubmed,0,3,computational,0.133926042,0.573887173,0.229442321,0.056947434,0.002898453,0.002898577,Genomics,0.49412504,FALSE,59.66666667,0.70066176,,,0,0.403234768,,,0.551948264
6001,Assessing the consequences of environmental exposures on the expression of the human receptor and proteases involved in SARS-CoV-2 cell-entry.,Environ Res,33069705,10/19/20,pubmed,0,5,genome-wide,0.666383325,0.325485507,0.002032744,0.002032825,0.002032772,0.002032828,Drug discovery,0.55314535,TRUE,13.4,0.202548086,41.2,0.640620819,0,0.403234768,,,0.415467891
6002,ADMET profile and virtual screening of plant and microbial natural metabolites as SARS-CoV-2 S1 glycoprotein receptor binding domain and main protease inhibitors.,Eur J Pharmacol,33069672,10/19/20,pubmed,0,6,"virtual screening, in silico",0.963885398,0.001392914,0.001392881,0.001392916,0.001392856,0.030543035,Drug discovery,0.9337107,TRUE,112.5,0.895479003,52.5,0.690928552,2,0.618927094,,,0.73511155
6003,Beyond Genomes and the Covid-19 Pandemic.,Folia Biol (Praha),33069186,10/19/20,pubmed,0,1,genomes,0.057799152,0.711004254,0.057799148,0.057799148,0.057799148,0.057799148,Genomics,0.60238945,TRUE,15,0.227596017,2,0.164302917,0,0.403234768,,,0.265044567
6004,NCAM protein and SARS-COV-2 surface proteins: In-silico hypothetical evidence for the immunopathogenesis of Guillain-Barré syndrome.,Med Hypotheses,33069093,10/18/20,pubmed,0,1,in-silico,0.624015553,0.225452919,0.145171904,0.001786545,0.001786503,0.001786576,Drug discovery,0.7666403,TRUE,28,0.408312202,3,0.199424672,1,0.537564047,,,0.381766974
6005,"Stroke Care Trends During COVID-19 Pandemic in Zanjan Province, Iran. From the CASCADE Initiative: Statistical Analysis Plan and Preliminary Results.",J Stroke Cerebrovasc Dis,33069086,10/18/20,pubmed,0,47,"bayes, dataset",0.001010935,0.001010984,0.001010958,0.222751438,0.128533903,0.645681781,Clinics,0.7280592,TRUE,72.68085106,0.776114788,57.87234043,0.713874766,4,0.707574542,,,0.732521365
6006,A computational simulation platform for designing real-time monitoring systems with application to COVID-19.,Biosens Bioelectron,33068880,10/18/20,pubmed,0,4,computational,0.2124695,0.102814241,0.304745692,0.37699763,0.001486448,0.001486489,Epidemiology,0.9501276,TRUE,14.25,0.215535902,2.5,0.180826866,4,0.707574542,,,0.367979103
6007,Estimates of the rate of infection and asymptomatic COVID-19 disease in a population sample from SE England.,J Infect,33068628,10/18/20,pubmed,0,31,dataset,0.001310402,0.275601731,0.084906755,0.001310398,0.453805065,0.183065649,Healthcare,0.34863788,FALSE,74.83870968,0.785886573,180.5806452,0.906475783,3,0.667819001,,,0.786727119
6008,"The role of air conditioning in the diffusion of Sars-CoV-2 in indoor environments: A first computational fluid dynamic model, based on investigations performed at the Vatican State Children's hospital.",Environ Res,33068577,10/18/20,pubmed,0,6,computational,0.001272672,0.001272691,0.001272673,0.688628417,0.024053584,0.283499963,Epidemiology,0.20228145,FALSE,32.16666667,0.45717113,5.166666667,0.25849612,2,0.618927094,,,0.444864781
6009,The Needle in the Haystack: Identifying Credible Mobile Health Apps for Pediatric Populations during a Pandemic and beyond.,J Pediatr Psychol,33068424,10/18/20,pubmed,0,3,digital health,0.001171587,0.001171552,0.00117159,0.691512605,0.270936662,0.034036004,Epidemiology,0.8927746,TRUE,20,0.298163152,5,0.257024351,0,0.403234768,,,0.31947409
6010,Structural analysis of SARS-CoV-2 genome and predictions of the human interactome.,Nucleic Acids Res,33068416,10/18/20,pubmed,0,9,"interactom, genomes",0.708885848,0.286489028,0.001156298,0.001156269,0.001156305,0.001156252,Drug discovery,0.36021322,FALSE,12.77777778,0.191972293,6.777777778,0.292279904,3,0.667819001,,,0.384023732
6011,RASP: an atlas of transcriptome-wide RNA secondary structure probing data.,Nucleic Acids Res,33068412,10/18/20,pubmed,0,4,"transcriptom, dataset",0.519402522,0.394431814,0.082234528,0.001310422,0.001310371,0.001310343,Drug discovery,0.4167081,FALSE,37.5,0.513946441,62,0.728659352,2,0.618927094,,,0.620510962
6012,Clinical differences in chest CT characteristics between the progression and remission stages of patients with COVID-19 pneumonia.,Int J Clin Pract,33068310,10/18/20,pubmed,0,6,logistic regression,0.001371243,0.001371298,0.682764803,0.001371292,0.001371283,0.311750081,Imaging,0.76859915,TRUE,37,0.508936854,2.333333333,0.173401124,0,0.403234768,,,0.361857582
6013,Early experience with universal preoperative and pre-procedural screening for COVID-19 in low-risk pediatric surgical patients requiring general anesthesia.,Pediatr Surg Int,33068142,10/18/20,pubmed,0,3,sequencing,0.001438145,0.287104867,0.00143819,0.001438207,0.517290032,0.191290559,Healthcare,0.35195854,FALSE,16,0.243552477,9.666666667,0.348274017,1,0.537564047,,,0.376463514
6014,A fuller picture of COVID-19 prognosis: the added value of vulnerability measures to predict mortality in hospitalised older adults.,Age Ageing,33068099,10/18/20,pubmed,0,12,prediction model,0.00127264,0.001272627,0.028003981,0.001272732,0.323483909,0.644694111,Clinics,0.95747685,TRUE,66.33333333,0.742903086,82.5,0.78445277,2,0.618927094,,,0.71542765
6015,Diagnosing the novel SARS-CoV-2 by quantitative RT-PCR: variations and opportunities.,J Mol Med (Berl),33067676,10/18/20,pubmed,0,5,genome sequences,0.001415132,0.554131617,0.140708136,0.222372423,0.079957571,0.001415121,Genomics,0.60023737,TRUE,25.6,0.375162348,46.8,0.667714744,1,0.537564047,,,0.526813713
6016,"Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods.",Sci Rep,33067538,10/18/20,pubmed,0,4,dataset,0.001438105,0.001438123,0.992809438,0.001438105,0.001438086,0.001438144,Imaging,0.8407478,TRUE,28.5,0.412579628,14.25,0.415038801,4,0.707574542,,,0.51173099
6017,"Prognostic value of baseline clinical and HRCT findings in 101 patients with severe COVID-19 in Wuhan, China.",Sci Rep,33067524,10/18/20,pubmed,0,12,logistic regression,0.001717151,0.001717188,0.06595563,0.001717192,0.001717189,0.927175651,Clinics,0.971603,TRUE,83,0.818170573,64.58333333,0.737155472,1,0.537564047,,,0.697630031
6018,Development of a prognostic model for mortality in COVID-19 infection using machine learning.,Mod Pathol,33067522,10/18/20,pubmed,0,3,machine learning,0.00135043,0.0455511,0.285899388,0.129044946,0.001350385,0.536803751,Clinics,0.29117918,FALSE,15.33333333,0.230997588,0,0.055525823,7,0.785110192,,,0.357211201
6019,Quantifying the adhesive strength between the SARS-CoV-2 S-proteins and human receptor and its effect in therapeutics.,Sci Rep,33067518,10/18/20,pubmed,0,1,computational,0.832317217,0.001684498,0.001684555,0.160944746,0.001684493,0.00168449,Drug discovery,0.8625251,TRUE,23,0.34225988,4,0.231469093,0,0.403234768,,,0.32565458
6020,AKI Treated with Renal Replacement Therapy in Critically Ill Patients with COVID-19.,J Am Soc Nephrol,33067383,10/18/20,pubmed,0,301,logistic regression,0.001203424,0.001203427,0.001203402,0.00120351,0.001203415,0.993982821,Clinics,0.72562325,TRUE,80,0.807532933,80.91304348,0.781040942,18,0.891474782,,,0.826682886
6021,Overcoming barriers of retinal care delivery during a pandemic-attitudes and drivers for the implementation of digital health: a global expert survey.,Br J Ophthalmol,33067360,10/18/20,pubmed,0,12,digital health,0.001622779,0.001622764,0.001622858,0.411311664,0.519581149,0.064238787,Healthcare,0.97603035,TRUE,54.75,0.666584204,,,0,0.403234768,,,0.534909486
6022,Metagenomic Sequencing To Detect Respiratory Viruses in Persons under Investigation for COVID-19.,J Clin Microbiol,33067271,10/18/20,pubmed,0,8,"sequencing, metagenom",0.001538133,0.902482204,0.063679,0.001538123,0.001538187,0.029224354,Genomics,0.23652339,FALSE,48.75,0.62032284,32.25,0.587570244,0,0.403234768,,,0.537042617
6023,Shift in racial communities impacted by COVID-19 in California.,J Epidemiol Community Health,33067251,10/18/20,pubmed,0,1,dataset,0.001653004,0.001653069,0.001653064,0.5767859,0.416601849,0.001653112,Epidemiology,0.6131,TRUE,36,0.498299215,10,0.355632861,1,0.537564047,,,0.463832041
6024,Morphoproteomics Identifies the Vitamin D Receptor as a Potential Therapeutic Partner in Alveolar Pneumocytes for COVID-19 Infected Patients.,Ann Clin Lab Sci,33067219,10/18/20,pubmed,0,3,proteom,0.825190345,0.034962033,0.034961874,0.034961874,0.034961874,0.034962,Drug discovery,0.5016672,TRUE,109,0.888304781,51.33333333,0.686513246,0,0.403234768,,,0.659350931
6025,Risk factors associated with hospital admission in COVID-19 patients initially admitted to an observation unit.,Am J Emerg Med,33067060,10/18/20,pubmed,0,6,logistic regression,0.00131031,0.001310318,0.098327665,0.001310359,0.001310352,0.896430995,Clinics,0.866261,TRUE,15,0.227596017,14,0.412898047,1,0.537564047,,,0.392686037
6026,Caregivers' Willingness to Accept Expedited Vaccine Research During the COVID-19 Pandemic: A Cross-sectional Survey.,Clin Ther,33067013,10/18/20,pubmed,0,19,logistic regression,0.135111988,0.001220046,0.001220119,0.001220099,0.860007706,0.001220043,Healthcare,0.8732586,TRUE,58.78947368,0.694724473,26.89473684,0.544888948,4,0.707574542,,,0.649062654
6027,Observed and estimated prevalence of Covid-19 in Italy: How to estimate the total cases from medical swabs data.,Sci Total Environ,33066965,10/18/20,pubmed,0,3,probabilistic,0.001187262,0.05972475,0.001187332,0.622311963,0.294112396,0.021476296,Epidemiology,0.050745666,FALSE,6.333333333,0.089863319,3.333333333,0.206515922,3,0.667819001,,,0.321399414
6028,Outbreak minimization v.s. influence maximization: an optimization framework.,BMC Med Inform Decis Mak,33066791,10/18/20,pubmed,0,3,"computational, network analysis",0.046222528,0.001126816,0.295964098,0.65443291,0.00112684,0.001126808,Epidemiology,0.52686405,TRUE,15,0.227596017,7,0.299973241,0,0.403234768,,,0.310268008
6029,Adapting hospital capacity to meet changing demands during the COVID-19 pandemic.,BMC Med,33066777,10/18/20,pubmed,0,15,dataset,0.001171542,0.001171562,0.001171605,0.413920594,0.178663048,0.403901648,Epidemiology,0.26844108,FALSE,115.4,0.899560888,255.6,0.939858175,11,0.840175319,,,0.893198128
6030,Older age and frailty are the chief predictors of mortality in COVID-19 patients admitted to an acute medical unit in a secondary care setting- a cohort study.,BMC Geriatr,33066750,10/18/20,pubmed,0,9,logistic regression,0.001350306,0.001350345,0.001350337,0.00135036,0.001350362,0.993248289,Clinics,0.7840221,TRUE,43.44444444,0.572206073,23.44444444,0.515854964,12,0.850299401,,,0.646120146
6031,Amplicon-Based Detection and Sequencing of SARS-CoV-2 in Nasopharyngeal Swabs from Patients With COVID-19 and Identification of Deletions in the Viral Genome That Encode Proteins Involved in Interferon Antagonism.,Viruses,33066701,10/18/20,pubmed,0,46,"sequencing, genomes",0.001141348,0.920062086,0.001141361,0.001141358,0.001141332,0.075372515,Genomics,0.7026969,TRUE,58.2173913,0.690580741,89.30434783,0.799906342,4,0.707574542,,,0.732687208
6032,Assessment of Social Distancing for Controlling COVID-19 in Korea: An Age-Structured Modeling Approach.,Int J Environ Res Public Health,33066581,10/18/20,pubmed,0,5,mathematical model,0.001350343,0.001350349,0.001350324,0.838263774,0.001350426,0.156334783,Epidemiology,0.16584623,FALSE,67.6,0.750572082,26,0.53819909,0,0.403234768,,,0.56400198
6033,"Recommendations, Practices and Infrastructural Model for the Dental Radiology Set-up in Clinical and Academic Institutions in the COVID-19 Era.",Biology (Basel),33066032,10/18/20,pubmed,0,10,structural model,0.001943533,0.001943523,0.181524914,0.810700831,0.001943538,0.00194366,Epidemiology,0.7518971,TRUE,14.2,0.214793741,2.9,0.190794755,2,0.618927094,,,0.341505197
6034,Associations between Changes in Health Behaviours and Body Weight during the COVID-19 Quarantine in Lithuania: The Lithuanian COVIDiet Study.,Nutrients,33065991,10/18/20,pubmed,0,4,logistic regression,0.001511841,0.001511829,0.001511875,0.001511852,0.965142987,0.028809615,Healthcare,0.9140729,TRUE,52,0.647349867,47.25,0.669454108,11,0.840175319,,,0.718993098
6035,In silico drug discovery of major metabolites from spices as SARS-CoV-2 main protease inhibitors.,Comput Biol Med,33065388,10/17/20,pubmed,0,9,"molecular dynamics simulation, in silico",0.994142044,0.001171618,0.001171572,0.001171586,0.001171607,0.001171572,Drug discovery,0.9164649,TRUE,49.33333333,0.626507514,55.44444444,0.703906877,9,0.814309525,,,0.714907972
6036,Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation.,Comput Biol Med,33065387,10/17/20,pubmed,0,4,"deep learning, dataset",0.001371243,0.001371223,0.96668513,0.001371237,0.001371233,0.027829934,Imaging,0.48018408,FALSE,58.25,0.691384749,20,0.481000803,19,0.89561084,,,0.689332131
6037,Hyperbolic rules of the cooperative organization of eukaryotic and prokaryotic genomes.,Biosystems,33065213,10/17/20,pubmed,0,1,genomes,0.077059723,0.677485959,0.001565393,0.240758147,0.001565419,0.00156536,Genomics,0.7907176,TRUE,36,0.498299215,10,0.355632861,1,0.537564047,,,0.463832041
6038,Low dose radiation therapy for COVID-19: Effective dose and estimation of cancer risk.,Radiother Oncol,33065184,10/17/20,pubmed,0,3,computational,0.001786615,0.001786526,0.039551726,0.247784358,0.001786626,0.707304149,Clinics,0.89246774,TRUE,9.666666667,0.144968767,1.333333333,0.13252609,1,0.537564047,,,0.271686302
6039,Discriminating Multisystem Inflammatory Syndrome in Children Requiring Treatment from Common Febrile Conditions in Outpatient Settings.,J Pediatr,33065115,10/17/20,pubmed,0,15,logistic regression,0.001392874,0.00139289,0.001392868,0.001392882,0.163679842,0.830748643,Clinics,0.70913124,TRUE,24.86666667,0.366070876,11.06666667,0.371420926,0,0.403234768,,,0.38024219
6040,Use of Ivermectin Is Associated With Lower Mortality in Hospitalized Patients With Coronavirus Disease 2019: The Ivermectin in COVID Nineteen Study.,Chest,33065103,10/17/20,pubmed,0,6,logistic regression,0.046471324,0.001085324,0.001085326,0.001085349,0.001085361,0.949187315,Clinics,0.9810157,TRUE,18.5,0.278001113,7.666666667,0.310877709,31,0.931971109,,,0.506949977
6041,Computational and theoretical exploration for clinical suitability of Remdesivir drug to SARS-CoV-2.,Eur J Pharmacol,33065096,10/17/20,pubmed,0,4,computational,0.746179739,0.003101675,0.0031017,0.241413876,0.00310149,0.003101519,Drug discovery,0.8565706,TRUE,1,0.012307502,0,0.055525823,1,0.537564047,,,0.201799124
6042,Vascular Disease and Thrombosis in SARS-CoV-2-Infected Rhesus Macaques.,Cell,33065030,10/17/20,pubmed,0,27,"transcriptom, proteom",0.702710245,0.002238467,0.002238494,0.002238458,0.002238421,0.288335917,Drug discovery,0.34566635,FALSE,74.51851852,0.784587791,167.8148148,0.897778967,16,0.881782826,,,0.854716528
6043,COVID-19 in New Zealand and the impact of the national response: a descriptive epidemiological study.,Lancet Public Health,33065023,10/17/20,pubmed,0,15,logistic regression,0.000956311,0.000956352,0.024226861,0.478202058,0.304156308,0.19150211,Epidemiology,0.6572515,TRUE,57.66666667,0.686375162,88.53333333,0.798033182,32,0.933699611,,,0.806035985
6044,"COVID-19 observations and accompanying dataset of non-pharmaceutical interventions across U.S. universities, March 2020.",PLoS One,33064753,10/17/20,pubmed,0,11,dataset,0.001010944,0.001010957,0.001010969,0.763651654,0.232304521,0.001010955,Epidemiology,0.74072015,TRUE,12.18181818,0.184179603,4.818181818,0.249464811,3,0.667819001,,,0.367154472
6045,Clinical course and potential predictive factors for pneumonia of adult patients with Coronavirus Disease 2019 (COVID-19): A retrospective observational analysis of 193 confirmed cases in Thailand.,PLoS Negl Trop Dis,33064734,10/17/20,pubmed,0,9,logistic regression,0.001237054,0.001237119,0.024291454,0.001237085,0.001237084,0.970760204,Clinics,0.5752172,TRUE,47,0.606407323,29.11111111,0.563352957,0,0.403234768,,,0.524331682
6046,Review of COVID-19 Antibody Therapies.,Annu Rev Biophys,33064571,10/17/20,pubmed,0,5,"deep learning, network model",0.726033949,0.00133012,0.104152455,0.165823331,0.00133012,0.001330025,Drug discovery,0.52591664,TRUE,31,0.445111015,7,0.299973241,1,0.537564047,,,0.427549434
6047,The inherent problems with the generalizability of the CALL score: towards reliable clinical prediction models for COVID-19.,Clin Infect Dis,33064127,10/17/20,pubmed,0,3,prediction model,0.015998648,0.015998558,0.920005779,0.015999004,0.015999027,0.015998984,Healthcare,0.5390436,TRUE,59.33333333,0.698497124,22.33333333,0.505753278,2,0.618927094,,,0.607725832
6048,Twenty years of progress in angiotensin converting enzyme 2 and its link to SARS-CoV-2 disease.,Clin Sci (Lond),33063823,10/17/20,pubmed,0,3,sequencing,0.717777236,0.084609026,0.001751196,0.001751294,0.001751277,0.19235997,Drug discovery,0.7682022,TRUE,331.3333333,0.992578391,333,0.96273749,1,0.537564047,,,0.830959976
6049,0,IUCrJ,33063790,10/17/20,pubmed,0,7,computational,0.905899768,0.001786632,0.086953807,0.001786622,0.001786588,0.001786582,Drug discovery,0.96117556,TRUE,283.5714286,0.989424207,214.7142857,0.924605298,6,0.764429903,,,0.892819803
6050,"Superspreading Event of SARS-CoV-2 Infection at a Bar, Ho Chi Minh City, Vietnam.",Emerg Infect Dis,33063657,10/17/20,pubmed,0,24,whole-genome,0.006539676,0.600576073,0.006539622,0.006540024,0.006540001,0.373264603,Genomics,0.4063951,FALSE,55.25,0.669491001,87.45833333,0.79575863,5,0.739490092,,,0.734913241
6051,Ciprofloxacin and moxifloxacin could interact with SARS-CoV-2 protease: preliminary in silico analysis.,Pharmacol Rep,33063271,10/17/20,pubmed,0,5,in silico,0.991067337,0.001786526,0.001786509,0.001786574,0.001786539,0.001786516,Drug discovery,0.7314933,TRUE,54.6,0.66553281,7,0.299973241,3,0.667819001,,,0.544441684
6052,Biased and unbiased estimation of the average length of stay in intensive care units in the Covid-19 pandemic.,Ann Intensive Care,33063241,10/17/20,pubmed,0,6,forecasting model,0.00093608,0.000936084,0.000936075,0.559340208,0.000936084,0.436915469,Epidemiology,0.39008325,FALSE,171.5,0.959428536,213.6666667,0.924003211,3,0.667819001,,,0.850416916
6053,"Seroprevalence and immunity of SARS-CoV-2 infection in children and adolescents in schools in Switzerland: design for a longitudinal, school-based prospective cohort study.",Int J Public Health,33063141,10/17/20,pubmed,0,11,"bayes, logistic regression",0.00151189,0.149837985,0.001511983,0.313968937,0.480249617,0.052919587,Healthcare,0.29022074,FALSE,97.54545455,0.863566083,183.4545455,0.90848274,0,0.403234768,,,0.72509453
6054,Sofosbuvir/daclatasvir regimens for the treatment of COVID-19: an individual patient data meta-analysis.,J Antimicrob Chemother,33063117,10/17/20,pubmed,0,10,in silico,0.187784584,0.001943538,0.001943535,0.001943599,0.001943523,0.804441221,Clinics,0.5639146,TRUE,54.2,0.662749706,50.3,0.682365534,4,0.707574542,,,0.684229927
6055,COVID-19 Pandemic: ARIMA and Regression Model-Based Worldwide Death Cases Predictions.,SN Comput Sci,33063056,10/17/20,pubmed,0,2,dataset,0.001254609,0.001254604,0.073289192,0.867819987,0.001254652,0.055126956,Epidemiology,0.57347786,TRUE,53,0.654400396,88.5,0.797966283,2,0.618927094,,,0.690431258
6056,A Study on Fight Against COVID-19 from Latest Technological Intervention.,SN Comput Sci,33063054,10/17/20,pubmed,0,2,"machine learning, deep learning",0.001717366,0.001717217,0.464349561,0.528781383,0.001717273,0.0017172,Epidemiology,0.9510734,TRUE,7,0.10179974,9,0.337904736,1,0.537564047,,,0.325756175
6057,A Smartphone Enabled Approach to Manage COVID-19 Lockdown and Economic Crisis.,SN Comput Sci,33063052,10/17/20,pubmed,0,2,"machine learning, prediction model",0.001593477,0.00159351,0.001593612,0.992032361,0.001593507,0.001593533,Epidemiology,0.18825343,FALSE,44.5,0.581977859,19.5,0.475983409,6,0.764429903,,,0.607463724
6058,Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients' Recovery.,SN Comput Sci,33063049,10/17/20,pubmed,0,4,"bayes, artificial intelligence, data mining, logistic regression, dataset",0.064009093,0.001203481,0.53477045,0.230454295,0.001203458,0.168359224,Epidemiology,0.550025,TRUE,10.5,0.157338116,2.75,0.187583623,24,0.914439163,,,0.419786967
6059,Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art.,SN Comput Sci,33063048,10/17/20,pubmed,0,6,"machine learning, mathematical model, forecasting model, dataset",0.001392835,0.001392833,0.211824863,0.758711355,0.02528515,0.001392965,Epidemiology,0.27648598,FALSE,61.83333333,0.714082504,9.333333333,0.342253144,43,0.9507377,,,0.669024449
6060,How pets factor into healthcare decisions for COVID-19: A One Health perspective.,One Health,33062838,10/17/20,pubmed,0,5,logistic regression,0.001272684,0.051494551,0.001272671,0.19529075,0.749396688,0.001272657,Healthcare,0.4567511,FALSE,13.6,0.206011503,4.8,0.249331014,4,0.707574542,,,0.38763902
6061,Clinical Features of COVID-19 Patients with Diabetes and Secondary Hyperglycemia.,J Diabetes Res,33062712,10/17/20,pubmed,0,5,correlation analysis,0.053287591,0.015730742,0.064650059,0.000830645,0.000830653,0.86467031,Clinics,0.8686626,TRUE,51.2,0.639866411,22.6,0.508161627,4,0.707574542,,,0.618534193
6062,Prediction Models in Veterinary and Human Epidemiology: Our Experience With Modeling Sars-CoV-2 Spread.,Front Vet Sci,33062646,10/17/20,pubmed,0,5,prediction model,0.003466169,0.003466051,0.003466294,0.982669358,0.003466055,0.003466074,Epidemiology,0.678988,TRUE,60.8,0.707155668,42,0.644902328,0,0.403234768,,,0.585097588
6063,MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks.,PeerJ,33062453,10/17/20,pubmed,0,3,"deep learning, computational, neural network, classifier, dataset",0.000807887,0.000807893,0.995960527,0.000807895,0.000807879,0.000807919,Imaging,0.14951023,FALSE,15.66666667,0.236563795,4.333333333,0.237958255,2,0.618927094,,,0.364483048
6064,Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool.,PeerJ,33062451,10/17/20,pubmed,0,3,"machine learning, logistic regression",0.00122,0.001220052,0.625914553,0.04646844,0.073181916,0.251995038,Clinics,0.67739576,TRUE,15,0.227596017,15,0.42594327,5,0.739490092,,,0.464343126
6065,The utility of MEWS for predicting the mortality in the elderly adults with COVID-19: a retrospective cohort study with comparison to other predictive clinical scores.,PeerJ,33062437,10/17/20,pubmed,0,9,logistic regression,0.000907311,0.00090729,0.085671208,0.043379239,0.000907307,0.868227644,Clinics,0.914628,TRUE,42.77777778,0.564908158,17.66666667,0.457586299,0,0.403234768,,,0.475243075
6066,Computational perspectives revealed prospective vaccine candidates from five structural proteins of novel SARS corona virus 2019 (SARS-CoV-2).,PeerJ,33062414,10/17/20,pubmed,0,4,"computational, bioinformatic",0.996388965,0.000722216,0.000722205,0.0007222,0.000722211,0.000722204,Drug discovery,0.87525713,TRUE,17.75,0.267672707,12,0.386740701,2,0.618927094,,,0.424446834
6067,Outcome of oncological patients admitted with COVID-19: experience of a hospital center in northern Italy.,Ther Adv Med Oncol,33062065,10/17/20,pubmed,0,5,logistic regression,0.00151187,0.001511826,0.001511898,0.001511901,0.119443294,0.87450921,Clinics,0.8613725,TRUE,29.6,0.426495145,35.6,0.608442601,0,0.403234768,,,0.479390838
6068,A Mathematical Model to Investigate the Transmission of COVID-19 in the Kingdom of Saudi Arabia.,Comput Math Methods Med,33062043,10/17/20,pubmed,0,1,mathematical model,0.061865408,0.002490475,0.002490433,0.880770677,0.002490526,0.049892481,Epidemiology,0.6552515,TRUE,73.5,0.780011132,63.5,0.733676746,1,0.537564047,,,0.683750642
6069,Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases.,Int J Biomed Imaging,33061946,10/17/20,pubmed,0,3,"deep learning, artificial intelligence, transfer learning, dataset",0.0011268,0.001126807,0.956429091,0.001126844,0.039063631,0.001126825,Imaging,0.608856,TRUE,43.66666667,0.57492733,43.33333333,0.650722505,4,0.707574542,,,0.644408125
6070,Serum Cystatin C and Coronavirus Disease 2019: A Potential Inflammatory Biomarker in Predicting Critical Illness and Mortality for Adult Patients.,Mediators Inflamm,33061826,10/17/20,pubmed,0,10,logistic regression,0.001823322,0.001823333,0.001823362,0.001823335,0.001823361,0.990883287,Clinics,0.9473728,TRUE,105.9,0.880697631,37.8,0.620818839,2,0.618927094,,,0.706814521
6071,Disease knowledge and attitudes during the COVID-19 epidemic among international migrants in China: a national cross-sectional study.,Int J Biol Sci,33061804,10/17/20,pubmed,0,8,logistic regression,0.00114131,0.001141358,0.001141322,0.091912578,0.903522101,0.001141332,Healthcare,0.6686711,TRUE,87.125,0.834065186,43.875,0.653130854,0,0.403234768,,,0.630143603
6072,"The Psychological Impacts of COVID-19 Pandemic Among University Students in Bench-Sheko Zone, South-west Ethiopia: A Community-based Cross-sectional Study.",Psychol Res Behav Manag,33061696,10/17/20,pubmed,0,3,logistic regression,0.018073309,0.027852683,0.000977438,0.000977512,0.951141541,0.000977518,Healthcare,0.86137235,TRUE,1.666666667,0.016451234,0,0.055525823,6,0.764429903,,,0.27880232
6073,"Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India.",Infect Drug Resist,33061481,10/17/20,pubmed,0,7,dataset,0.069687525,0.001034619,0.089068315,0.55138957,0.112384454,0.176435517,Epidemiology,0.5618492,TRUE,21.85714286,0.322778156,,,0,0.403234768,,,0.363006462
6074,Dynamic Interplay Between Microbiota and Mucosal Immunity in Early Shaping of Asthma and its Implication for the COVID-19 Pandemic.,J Asthma Allergy,33061464,10/17/20,pubmed,0,1,microbiom,0.780000146,0.150037937,0.002032792,0.002032886,0.002032935,0.063863304,Drug discovery,0.89693356,TRUE,15,0.227596017,8,0.320511105,3,0.667819001,,,0.405308708
6075,Rapid genomic characterization of SARS-CoV-2 viruses from clinical specimens using nanopore sequencing.,Sci Rep,33060796,10/17/20,pubmed,0,13,"sequencing, whole-genome, whole genome, genome sequences, genomes",0.00190173,0.990491247,0.001901839,0.001901796,0.001901692,0.001901696,Genomics,0.63628805,TRUE,29.23076923,0.421052632,61.61538462,0.726786192,3,0.667819001,,,0.605219275
6076,Statin use is associated with lower disease severity in COVID-19 infection.,Sci Rep,33060704,10/17/20,pubmed,0,5,logistic regression,0.002183186,0.002183215,0.002183232,0.034411821,0.002183428,0.956855117,Clinics,0.92982924,TRUE,82.4,0.815634857,86.4,0.794019267,16,0.881782826,,,0.830478983
6077,Pathological features of COVID-19-associated lung injury: a preliminary proteomics report based on clinical samples.,Signal Transduct Target Ther,33060566,10/17/20,pubmed,0,20,"bioinformatic, proteom",0.790833684,0.001622776,0.114545809,0.001622741,0.001622719,0.08975227,Drug discovery,0.47719708,FALSE,53.55,0.65736904,53.4,0.695143163,6,0.764429903,,,0.705647369
6078,Unusual zwitterionic catalytic site of SARS-CoV-2 main protease revealed by neutron crystallography.,J Biol Chem,33060199,10/17/20,pubmed,0,8,computational,0.74477076,0.001438158,0.001438182,0.249476642,0.001438109,0.001438149,Drug discovery,0.8277223,TRUE,129.125,0.920650628,118.5,0.849745785,7,0.785110192,,,0.851835535
6079,Performance of prediction models for COVID-19: the Caudine Forks of the external validation.,Eur Respir J,33060155,10/17/20,pubmed,0,3,prediction model,0.019529522,0.019529277,0.902352242,0.019529631,0.019529277,0.01953005,Clinics,0.71275485,TRUE,204,0.973715134,125,0.857706717,2,0.618927094,,,0.816782982
6080,[Association of age distribution with the expression of angiotensin-converting enzyme 2 in lung tissues in severe acute respiratory syndrome coronavirus 2 infection: reflections from the study of RAS pathway expression in mice].,Zhongguo Dang Dai Er Ke Za Zhi,33059811,10/17/20,pubmed,0,8,"sequencing, transcriptom",0.43739211,0.001046849,0.001046856,0.128672082,0.071386837,0.360455265,Drug discovery,0.9892639,TRUE,95.125,0.857072175,126.25,0.85944608,0,0.403234768,,,0.706584341
6081,The Undiscovered Potential of Essential Oils for Treating SARS-CoV-2 (COVID-19).,Curr Pharm Des,33059564,10/17/20,pubmed,0,5,in silico,0.64189378,0.002032894,0.097647084,0.225158481,0.031234876,0.002032885,Drug discovery,0.9541651,TRUE,37.4,0.512585812,24.8,0.526826331,0,0.403234768,,,0.480882304
6082,COVID-19 cases in US counties: roles of racial/ethnic density and residential segregation.,Ethn Health,33059471,10/17/20,pubmed,0,3,dataset,0.00242238,0.002422316,0.002422448,0.564552735,0.425757666,0.002422455,Epidemiology,0.3858438,FALSE,30.66666667,0.440225122,19.66666667,0.477321381,8,0.799987654,,,0.572511385
6083,COVID-19-related stigma profiles and risk factors among people who are at high risk of contagion.,Soc Sci Med,33059301,10/16/20,pubmed,0,3,logistic regression,0.001415198,0.166876507,0.001415124,0.001415201,0.827462838,0.001415131,Healthcare,0.96451926,TRUE,64,0.729173109,17,0.451097137,6,0.764429903,,,0.648233383
6084,The endless quarantine: the impact of the COVID-19 outbreak on healthcare workers after three months of mandatory social isolation in Argentina.,Sleep Med,33059247,10/16/20,pubmed,0,4,logistic regression,0.001098815,0.001098863,0.106316071,0.001098867,0.889288434,0.00109895,Healthcare,0.9521111,TRUE,12,0.183190055,0.5,0.087101953,3,0.667819001,,,0.31270367
6085,Clinical Presentation of Rhegmatogenous Retinal Detachment during the COVID-19 Pandemic: A Historical Cohort Study.,Ophthalmology,33058938,10/16/20,pubmed,0,10,logistic regression,0.000988335,0.00098834,0.000988337,0.000988356,0.000988396,0.995058236,Clinics,0.99022734,TRUE,97.3,0.862638382,83.4,0.786593524,4,0.707574542,,,0.785602149
6086,Usefulness of Elevated Troponin to Predict Death in Patients With COVID-19 and Myocardial Injury.,Am J Cardiol,33058800,10/16/20,pubmed,0,6,logistic regression,0.00148642,0.001486408,0.00148643,0.001486408,0.001486413,0.992567921,Clinics,0.8106753,TRUE,57.33333333,0.68433422,61.33333333,0.725715815,6,0.764429903,,,0.724826646
6087,Genomic evidence for reinfection with SARS-CoV-2: a case study.,Lancet Infect Dis,33058797,10/16/20,pubmed,0,13,"bioinformatic, sequencing",0.017007322,0.465898852,0.001059396,0.23488795,0.108115693,0.173030787,Genomics,0.24629033,FALSE,32.92307692,0.46514936,283.0769231,0.949759165,172,0.989443793,,,0.801450773
6088,Mathematical model of Boltzmann's sigmoidal equation applicable to the spreading of the coronavirus (Covid-19) waves.,Environ Sci Pollut Res Int,33058082,10/16/20,pubmed,0,3,mathematical model,0.036299644,0.00153813,0.001538093,0.957547843,0.001538118,0.001538172,Epidemiology,0.14844409,FALSE,2.333333333,0.024800544,0,0.055525823,4,0.707574542,,,0.262633636
6089,[Telemedicine in rheumatology].,Z Rheumatol,33057786,10/16/20,pubmed,0,5,digital health,0.002562608,0.002562587,0.101713432,0.605960425,0.225976183,0.061224764,Epidemiology,0.9343536,TRUE,17,0.257467994,0.6,0.09011239,0,0.403234768,,,0.250271717
6090,IDseq-An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring.,Gigascience,33057676,10/16/20,pubmed,0,28,"computational, bioinformatic, sequencing, metagenom, dataset",0.001141384,0.659341658,0.267458115,0.069776042,0.00114141,0.001141391,Genomics,0.6407254,TRUE,17.28571429,0.260498485,45.32142857,0.659486219,0,0.403234768,,,0.441073157
6091,Web tools to fight pandemics: the COVID-19 experience.,Brief Bioinform,33057582,10/16/20,pubmed,0,3,"computational, bioinformatic, interactom",0.003335521,0.111547417,0.003335516,0.875110707,0.003335607,0.003335232,Epidemiology,0.73813635,TRUE,21,0.312016822,27.33333333,0.54856837,5,0.739490092,,,0.533358428
6092,How confidence in health care systems affects mobility and compliance during the COVID-19 pandemic.,PLoS One,33057450,10/16/20,pubmed,0,8,dataset,0.001717252,0.001717292,0.001717268,0.991413691,0.001717282,0.001717215,Epidemiology,0.31511277,FALSE,38.375,0.522481291,18.375,0.464476853,6,0.764429903,,,0.583796016
6093,"Association between COVID-19 prognosis and disease presentation, comorbidities and chronic treatment of hospitalized patients.",PLoS One,33057443,10/16/20,pubmed,0,11,logistic regression,0.001330029,0.001330067,0.001330059,0.029265194,0.065689517,0.901055135,Clinics,0.930388,TRUE,13.36363636,0.201805925,5.909090909,0.273615199,9,0.814309525,,,0.429910217
6094,"A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies.",PLoS Comput Biol,33057438,10/16/20,pubmed,0,2,network model,0.001684739,0.001684571,0.001684628,0.991576849,0.001684561,0.001684651,Epidemiology,0.42734018,FALSE,7.5,0.108355495,0.5,0.087101953,0,0.403234768,,,0.199564072
6095,"Design of novel multiepitope constructs-based peptide vaccine against the structural S, N and M proteins of human COVID-19 using immunoinformatics analysis.",PLoS One,33057358,10/16/20,pubmed,0,4,"computational, in silico",0.87580933,0.066878939,0.001272672,0.001272695,0.053493628,0.001272736,Drug discovery,0.82866156,TRUE,80,0.807532933,26.5,0.542012309,4,0.707574542,,,0.685706595
6096,Patch dynamics modeling framework from pathogens' perspective: Unified and standardized approach for complicated epidemic systems.,PLoS One,33057348,10/16/20,pubmed,0,8,"mathematical model, multiom",0.242484709,0.001085419,0.001085375,0.753173835,0.001085329,0.001085333,Epidemiology,0.45135236,FALSE,23.125,0.342940194,19.5,0.475983409,0,0.403234768,,,0.407386124
6097,"Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries.",Nat Med,33057181,10/16/20,pubmed,0,14,"bayes, bayesian model",0.002130646,0.002130668,0.002130776,0.703480688,0.081410349,0.208716873,Epidemiology,0.32165495,FALSE,152.7142857,0.94644072,499.4285714,0.978659352,43,0.9507377,,,0.958612591
6098,Modelling COVID 19 in the Basque Country from introduction to control measure response.,Sci Rep,33057119,10/16/20,pubmed,0,5,artificial intelligence,0.001220016,0.001220012,0.05352665,0.877933089,0.001220032,0.064880202,Epidemiology,0.13548991,FALSE,49.4,0.627002288,18.8,0.468156275,4,0.707574542,,,0.600911035
6099,Systematic analysis of infectious disease outcomes by age shows lowest severity in school-age children.,Sci Data,33057040,10/16/20,pubmed,0,2,dataset,0.085714132,0.001901851,0.001901776,0.337069283,0.396962716,0.176450243,Healthcare,0.4510794,FALSE,156,0.948481662,353.5,0.966149318,1,0.537564047,,,0.817398342
6100,COVID-19 Transmission in US Child Care Programs.,Pediatrics,33055228,10/16/20,pubmed,0,10,logistic regression,0.001438157,0.001438161,0.001438162,0.085252616,0.884545868,0.025887036,Healthcare,0.67864263,TRUE,64.7,0.732883914,89.8,0.801110516,12,0.850299401,,,0.79476461
6101,Predictive Models of Mortality for Hospitalized Patients With COVID-19: Retrospective Cohort Study.,JMIR Med Inform,33055061,10/16/20,pubmed,0,6,"machine learning, classifier, predictive model, logistic regression",0.000815343,0.000815333,0.276199767,0.061135831,0.000815349,0.660218377,Clinics,0.7577305,TRUE,51.16666667,0.639557177,,,1,0.537564047,,,0.588560612
6102,Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach.,JMIR Med Inform,33055059,10/16/20,pubmed,0,8,"deep learning, computational, literature mining",0.532056038,0.001098821,0.094290179,0.322291621,0.001098874,0.049164468,Drug discovery,0.8614546,TRUE,18.375,0.275279857,18.875,0.468825261,1,0.537564047,,,0.427223055
6103,Early predictors for mechanical ventilation in COVID-19 patients.,Ther Adv Respir Dis,33054630,10/16/20,pubmed,0,7,logistic regression,0.001072173,0.001072162,0.01903601,0.001072247,0.001072175,0.976675233,Clinics,0.95807135,TRUE,29.71428571,0.427979467,15.85714286,0.43443939,1,0.537564047,,,0.466660968
6104,0,Drug Dev Ind Pharm,33054436,10/16/20,pubmed,0,6,genomes,0.926858621,0.014540034,0.000838528,0.000838527,0.000838519,0.056085772,Drug discovery,0.9987707,TRUE,33.66666667,0.47312759,10.16666667,0.357171528,1,0.537564047,,,0.455954388
6105,Discovery of Ketone-Based Covalent Inhibitors of Coronavirus 3CL Proteases for the Potential Therapeutic Treatment of COVID-19.,J Med Chem,33054210,10/16/20,pubmed,0,24,genomes,0.646976945,0.205998713,0.138863989,0.002720163,0.002720096,0.002720094,Drug discovery,0.8528602,TRUE,35.83333333,0.495206877,81.45833333,0.781843725,20,0.900117291,,,0.725722631
6106,A Predictive Study of Resilience and Its Relationship with Academic and Work Dimensions during the COVID-19 Pandemic.,J Clin Med,33053785,10/16/20,pubmed,0,5,logistic regression,0.002422285,0.002422246,0.002422277,0.057239947,0.933070915,0.00242233,Healthcare,0.90171987,TRUE,42,0.558537943,4.6,0.244246722,2,0.618927094,,,0.47390392
6107,Critical Care Demand and Intensive Care Supply for Patients in Japan with COVID-19 at the Time of the State of Emergency Declaration in April 2020: A Descriptive Analysis.,Medicina (Kaunas),33053765,10/16/20,pubmed,0,2,dataset,0.001653036,0.001653025,0.001653039,0.274876306,0.001653054,0.71851154,Clinics,0.9050803,TRUE,92,0.848970252,174.5,0.902662564,0,0.403234768,,,0.718289194
6108,A Countrywide Survey in Saudi Arabia Regarding the Knowledge and Attitude of Health Care Professionals about Coronavirus Disease (COVID-19).,Int J Environ Res Public Health,33053759,10/16/20,pubmed,0,7,logistic regression,0.00137131,0.055057128,0.001371291,0.001371326,0.939457659,0.001371287,Healthcare,0.98075783,TRUE,24.71428571,0.3637207,3.857142857,0.222705379,1,0.537564047,,,0.374663375
6109,Impacts of the COVID-19 Pandemic on Food Security and Diet-Related Lifestyle Behaviors: An Analytical Study of Google Trends-Based Query Volumes.,Nutrients,33053656,10/16/20,pubmed,0,14,correlation analysis,0.072487789,0.001786626,0.001786584,0.33492754,0.587224854,0.001786606,Healthcare,0.9231877,TRUE,39.5,0.534232173,12.78571429,0.396173401,4,0.707574542,,,0.545993372
6110,Temporomandibular Disorders and Bruxism Outbreak as a Possible Factor of Orofacial Pain Worsening during the COVID-19 Pandemic-Concomitant Research in Two Countries.,J Clin Med,33053640,10/16/20,pubmed,0,8,logistic regression,0.001653046,0.001653066,0.00165308,0.243418785,0.749968955,0.001653069,Healthcare,0.9995238,TRUE,33.125,0.467375843,19.25,0.47310677,3,0.667819001,,,0.536100538
6111,Clinical features and risk factors of COVID-19-associated liver injury and function: A retrospective analysis of 830 cases.,Ann Hepatol,33053426,10/15/20,pubmed,0,7,logistic regression,0.036815255,0.001943521,0.117001832,0.001943506,0.001943492,0.840352394,Clinics,0.71813625,TRUE,88.85714286,0.838889232,13.42857143,0.40473642,1,0.537564047,,,0.5937299
6112,A Sweep of Earth's Virome Reveals Host-Guided Viral Protein Structural Mimicry and Points to Determinants of Human Disease.,Cell Syst,33053371,10/15/20,pubmed,0,3,"proteom, virom",0.585453769,0.319941784,0.068445918,0.001622785,0.022913055,0.00162269,Drug discovery,0.35860562,FALSE,13,0.197352959,57.33333333,0.7122692,0,0.403234768,,,0.437618976
6113,Erratum: Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand.,Nat Energy,33052987,10/15/20,pubmed,0,11,machine learning,0.057799812,0.057799601,0.057801681,0.711000481,0.057799162,0.057799264,Epidemiology,0.4636125,FALSE,53.09090909,0.654524089,19.45454545,0.474578539,1,0.537564047,,,0.555555558
6114,Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil.,PLoS One,33052960,10/15/20,pubmed,0,5,"logistic regression, dataset",0.000999506,0.000999545,0.230021476,0.177152043,0.09005073,0.5007767,Clinics,0.15586126,FALSE,146.2,0.940812666,211.6,0.922865935,7,0.785110192,,,0.882929598
6115,Measuring the resilience of criminogenic ecosystems to global disruption: A case-study of COVID-19 in China.,PLoS One,33052950,10/15/20,pubmed,0,4,mathematical model,0.002898309,0.002898411,0.00289841,0.985508141,0.002898401,0.002898327,Epidemiology,0.85352826,TRUE,16.5,0.249366071,4.5,0.242708055,2,0.618927094,,,0.37033374
6116,Evaluating the Need for Routine COVID-19 Testing of Emergency Department Staff: Quantitative Analysis.,JMIR Public Health Surveill,33052873,10/15/20,pubmed,0,2,mathematical model,0.001254593,0.001254654,0.001254635,0.193154937,0.70749284,0.095588341,Healthcare,0.30202052,FALSE,2.5,0.027459954,0,0.055525823,0,0.403234768,,,0.162073515
6117,"Reinfection with SARS-CoV-2: Discrete SIR (Susceptible, Infected, Recovered) Modeling Using Empirical Infection Data.",JMIR Public Health Surveill,33052872,10/15/20,pubmed,0,2,mathematical model,0.00159355,0.001593637,0.001593521,0.992032171,0.001593567,0.001593554,Epidemiology,0.29679453,FALSE,16.5,0.249366071,40,0.633395772,0,0.403234768,,,0.428665537
6118,Risk Stratification of COVID-19 Patients Using Ambulatory Oxygen Saturation in the Emergency Department.,West J Emerg Med,33052820,10/15/20,pubmed,0,9,logistic regression,0.000966747,0.00096675,0.014314685,0.000966777,0.000966767,0.981818273,Clinics,0.8767433,TRUE,11.44444444,0.171748407,3,0.199424672,1,0.537564047,,,0.302912376
6119,Discovery of COVID-19 Inhibitors Targeting the SARS-CoV-2 Nsp13 Helicase.,J Phys Chem Lett,33052685,10/15/20,pubmed,0,3,"virtual screening, structural model",0.961500526,0.002130679,0.002130667,0.00213068,0.002130642,0.029976807,Drug discovery,0.7058948,TRUE,291.6666667,0.990228214,397.6666667,0.97076532,6,0.764429903,,,0.908474479
6120,Association between meteorological indicators and COVID-19 pandemic in Pakistan.,Environ Sci Pollut Res Int,33052566,10/15/20,pubmed,0,5,correlation analysis,0.001823307,0.001823331,0.001823343,0.806647671,0.001823482,0.186058866,Epidemiology,0.66856337,TRUE,15.2,0.229142186,3.6,0.21507894,5,0.739490092,,,0.394570406
6121,Activation of ACE2 and interferon-stimulated transcriptomes in human airway epithelium is curbed by Janus Kinase inhibitors.,bioRxiv,33052350,10/15/20,pubmed,0,3,transcriptom,0.980362314,0.003927808,0.003927494,0.003927509,0.003927418,0.003927459,Drug discovery,0.59278804,TRUE,194,0.970684643,449.6666667,0.975648916,2,0.618927094,,,0.855086885
6122,The landscape of antibody binding to SARS-CoV-2.,bioRxiv,33052349,10/15/20,pubmed,0,9,proteom,0.693415094,0.241798743,0.059081028,0.001901698,0.001901677,0.001901759,Drug discovery,0.2494488,FALSE,87.1,0.833817799,120.9,0.852956917,1,0.537564047,,,0.741446255
6123,Making the invisible enemy visible.,bioRxiv,33052340,10/15/20,pubmed,0,20,structural model,0.599622453,0.078997288,0.001511885,0.316844705,0.001511852,0.001511818,Drug discovery,0.3205309,FALSE,18.33333333,0.274908776,,,1,0.537564047,,,0.406236412
6124,Alpha 1 Antitrypsin is an Inhibitor of the SARS-CoV-2-Priming Protease TMPRSS2.,bioRxiv,33052338,10/15/20,pubmed,0,12,structural model,0.993814408,0.001237162,0.001237067,0.001237163,0.001237089,0.00123711,Drug discovery,0.7429651,TRUE,107.25,0.883728122,169,0.898514851,22,0.908142478,,,0.89679515
6125,0,bioRxiv,33052337,10/15/20,pubmed,0,7,computational,0.990883242,0.001823421,0.001823383,0.001823325,0.001823312,0.001823316,Drug discovery,0.28685772,FALSE,46.14285714,0.597501392,19.71428571,0.477588975,6,0.764429903,,,0.613173423
6126,idCOV: a pipeline for quick clade identification of SARS-CoV-2 isolates.,bioRxiv,33052335,10/15/20,pubmed,0,4,"sequencing, dataset",0.003607449,0.602561286,0.314486361,0.003607511,0.072130157,0.003607236,Genomics,0.1686171,FALSE,146,0.940688973,587,0.983208456,0,0.403234768,,,0.775710732
6127,Systematic discovery and functional interrogation of SARS-CoV-2 viral RNA-host protein interactions during infection.,bioRxiv,33052334,10/15/20,pubmed,0,16,genome-wide,0.800357554,0.180966415,0.000889045,0.000889079,0.016008803,0.000889105,Drug discovery,0.15602174,FALSE,147.0625,0.941431134,485.5625,0.977789671,6,0.764429903,,,0.894550236
6128,Genome-scale identification of SARS-CoV-2 and pan-coronavirus host factor networks.,bioRxiv,33052332,10/15/20,pubmed,0,15,genome-wide,0.597266186,0.338145723,0.001461895,0.001461909,0.001461898,0.060202388,Drug discovery,0.3354051,FALSE,97.06666667,0.861710681,555.4666667,0.98207118,21,0.903944688,,,0.91590885
6129,Homologies between SARS-CoV-2 and allergen proteins may direct T cell-mediated heterologous immune responses.,Res Sq,33052330,10/15/20,pubmed,0,8,"bioinformatic, in silico, proteom",0.83839212,0.119037738,0.001462001,0.001461914,0.001461924,0.038184303,Drug discovery,0.5047412,TRUE,123.375,0.912177624,86,0.793015788,1,0.537564047,,,0.74758582
6130,Spike protein-based epitopes predicted against SARS-CoV-2 through literature mining.,Med Nov Technol Devices,33052325,10/15/20,pubmed,0,8,"sequence alignment, literature mining",0.918320778,0.076514156,0.001291306,0.001291262,0.001291251,0.001291246,Drug discovery,0.6477816,TRUE,94.5,0.85527862,41.5,0.642092588,2,0.618927094,,,0.705432767
6131,A case-control and cohort study to determine the relationship between ethnic background and severe COVID-19.,EClinicalMedicine,33052324,10/15/20,pubmed,0,18,logistic regression,0.000898069,0.000898113,0.000898074,0.000898107,0.170298634,0.826109002,Clinics,0.31572098,FALSE,89.77777778,0.841919723,160.5,0.892694675,2,0.618927094,,,0.784513831
6132,Virus database annotations assist in tracing information on patients infected with emerging pathogens.,Inform Med Unlocked,33052312,10/15/20,pubmed,0,5,structural model,0.15275351,0.397631197,0.001461927,0.401142232,0.001462041,0.045549093,Epidemiology,0.35413682,FALSE,21.4,0.316469788,89.8,0.801110516,0,0.403234768,,,0.506938357
6133,Translating evidence-based treatment for digital health delivery: a protocol for family-based treatment for anorexia nervosa using telemedicine.,J Eat Disord,33052259,10/15/20,pubmed,0,6,digital health,0.001085364,0.001085418,0.001085482,0.318568217,0.677090149,0.00108537,Healthcare,0.97681904,TRUE,62.16666667,0.716308986,97.5,0.817768263,1,0.537564047,,,0.690547099
6134,Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set.,Appl Soft Comput,33052197,10/15/20,pubmed,0,6,"deep learning, transfer learning",0.001786659,0.001786647,0.721858659,0.270994936,0.001786592,0.001786507,Epidemiology,0.196305,FALSE,37.33333333,0.511596264,7.333333333,0.304656141,3,0.667819001,,,0.494690469
6135,Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network.,Inf Fusion,33052196,10/15/20,pubmed,0,5,"artificial intelligence, neural network, network model",0.001486411,0.001486405,0.992567835,0.001486488,0.001486463,0.001486398,Imaging,0.51494396,TRUE,114.6,0.89857134,41.6,0.642493979,12,0.850299401,,,0.797121574
6136,"The design of safe classrooms of educational buildings for facing contagions and transmission of diseases: A novel approach combining audits, calibrated energy models, building performance (BPS) and computational fluid dynamic (CFD) simulations.",Energy Build,33052169,10/15/20,pubmed,0,4,computational,0.142604686,0.001310369,0.04344927,0.765272923,0.001310381,0.046052371,Epidemiology,0.6889725,TRUE,59.5,0.699857752,30,0.570176612,2,0.618927094,,,0.62965382
6137,"Gender, face mask perceptions, and face mask wearing: Are men being dangerous during the COVID-19 pandemic?",Pers Individ Dif,33052155,10/15/20,pubmed,0,1,dataset,0.001126789,0.001126784,0.001126854,0.158607928,0.836884866,0.001126779,Healthcare,0.5382724,TRUE,12,0.183190055,9,0.337904736,4,0.707574542,,,0.409556444
6138,Risk Factors Associated with Mortality Among Patients with Novel Coronavirus Disease (COVID-19) in Africa.,J Racial Ethn Health Disparities,33051749,10/15/20,pubmed,0,11,"logistic regression, dataset",0.001415133,0.00141518,0.001415138,0.274471191,0.156849452,0.564433905,Clinics,0.59802264,TRUE,7.454545455,0.106500093,0.636363636,0.090647578,1,0.537564047,,,0.244903906
6139,0,mBio,33051368,10/15/20,pubmed,0,37,genomes,0.101285661,0.894526974,0.00104682,0.001046872,0.001046832,0.001046841,Genomics,0.5034019,TRUE,46.21621622,0.598243552,66.78378378,0.744982606,1,0.537564047,,,0.626930069
6140,Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs.,Proc Natl Acad Sci U S A,33051297,10/15/20,pubmed,0,17,"virtual screening, computational",0.880123984,0.001461856,0.114028288,0.001461944,0.001461886,0.001462041,Drug discovery,0.9494613,TRUE,64,0.729173109,25.35294118,0.531643029,0,0.403234768,,,0.554683635
6141,Performance Evaluation of the SAMBA II SARS-CoV-2 Test for Point-of-Care Detection of SARS-CoV-2.,J Clin Microbiol,33051242,10/15/20,pubmed,0,11,in silico,0.001486537,0.502808762,0.27971409,0.001486499,0.001486455,0.213017656,Genomics,0.44680893,FALSE,63.81818182,0.72688478,72,0.7594327,2,0.618927094,,,0.701748191
6142,Coronavirus infection and PARP expression dysregulate the NAD metabolome: An actionable component of innate immunity.,J Biol Chem,33051211,10/15/20,pubmed,0,10,metabolom,0.991243747,0.001751293,0.001751241,0.001751265,0.001751254,0.0017512,Drug discovery,0.3065772,FALSE,56.6,0.679324634,93.5,0.809673535,1,0.537564047,,,0.675520739
6143,Predictive indicators of severe COVID-19 independent of comorbidities and advanced age: a nested case-control study.,Epidemiol Infect,33050972,10/15/20,pubmed,0,9,logistic regression,0.001392861,0.001392861,0.001392835,0.001392872,0.001392879,0.993035692,Clinics,0.88960665,TRUE,77.55555556,0.796833447,56.11111111,0.707184908,0,0.403234768,,,0.635751041
6144,Food insecurity measurement and prevalence estimates during the COVID-19 pandemic in a repeated cross-sectional survey in Mexico.,Public Health Nutr,33050968,10/15/20,pubmed,0,4,"model fit, probabilistic",0.001538062,0.001538207,0.001538082,0.001538219,0.992309329,0.0015381,Healthcare,0.79644656,TRUE,78.75,0.801533799,65.75,0.741637677,2,0.618927094,,,0.720699524
6145,Response strategies for COVID-19 epidemics in African settings: a mathematical modelling study.,BMC Med,33050951,10/15/20,pubmed,0,46,mathematical model,0.00058396,0.000583981,0.000584066,0.872671965,0.124992011,0.000584017,Epidemiology,0.12183446,FALSE,54.4893617,0.664295875,,,3,0.667819001,,,0.666057438
6146,Headache characteristics in COVID-19 pandemic-a survey study.,J Headache Pain,33050880,10/15/20,pubmed,0,8,logistic regression,0.001415111,0.001415133,0.126857685,0.001415123,0.472976954,0.395919994,Healthcare,0.99816275,TRUE,26,0.382398417,12.875,0.397243778,4,0.707574542,,,0.495738912
6147,Impact of Microbiota: A Paradigm for Evolving Herd Immunity against Viral Diseases.,Viruses,33050511,10/15/20,pubmed,0,6,microbiom,0.194764762,0.16529419,0.00090731,0.505106078,0.133020286,0.000907374,Epidemiology,0.50188357,TRUE,9.833333333,0.147628177,7.166666667,0.301311212,1,0.537564047,,,0.328834479
6148,"Genomic Diversity and Hotspot Mutations in 30,983 SARS-CoV-2 Genomes: Moving Toward a Universal Vaccine for the "Confined Virus"?",Pathogens,33050463,10/15/20,pubmed,0,24,genomes,0.217342928,0.777085692,0.001392815,0.001392851,0.001392871,0.001392843,Genomics,0.7105878,TRUE,20.16666667,0.299585627,4,0.231469093,12,0.850299401,,,0.460451374
6149,Protease Inhibitory Effect of Natural Polyphenolic Compounds on SARS-CoV-2: An In Silico Study.,Molecules,33050360,10/15/20,pubmed,0,8,"in silico, in-silico",0.993636606,0.001272689,0.00127267,0.001272727,0.001272648,0.00127266,Drug discovery,0.93519926,TRUE,7.625,0.109530583,2.375,0.173668718,1,0.537564047,,,0.273587783
6150,Evolutionary Analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Reveals Genomic Divergence with Implications for Universal Vaccine Efficacy.,Vaccines (Basel),33050053,10/15/20,pubmed,0,17,"genomes, genomic structure",0.344227752,0.651147265,0.001156238,0.001156274,0.001156245,0.001156226,Genomics,0.6685742,TRUE,26.82352941,0.392541283,21.88235294,0.501538667,1,0.537564047,,,0.477214666
6151,The impact of COVID-19 on acute ischemic stroke admissions: Analysis from a community-based tertiary care center.,J Stroke Cerebrovasc Dis,33049464,10/14/20,pubmed,0,8,dataset,0.001392836,0.001392852,0.001392923,0.001392948,0.001392875,0.993035566,Clinics,0.92009974,TRUE,217.625,0.977797019,105.75,0.832619748,1,0.537564047,,,0.782660272
6152,Non-synonymous mutations of SARS-CoV-2 leads epitope loss and segregates its variants.,Microbes Infect,33049387,10/14/20,pubmed,0,3,phylogenom,0.198392512,0.793658886,0.001987093,0.001987224,0.001987184,0.001987101,Genomics,0.43866527,FALSE,34.33333333,0.480425506,8.333333333,0.325662296,11,0.840175319,,,0.548754374
6153,Influence of the Covid-19 pandemic on out-of-hospital cardiac arrest. A Spanish nationwide prospective cohort study.,Resuscitation,33049385,10/14/20,pubmed,0,62,logistic regression,0.001538076,0.001538124,0.001538107,0.475624591,0.001538131,0.518222971,Clinics,0.70403,TRUE,3.822580645,0.047436452,,,6,0.764429903,,,0.405933178
6154,The Impact of High-Flow Nasal Cannula Use on Patient Mortality and the Availability of Mechanical Ventilators in COVID-19.,Ann Am Thorac Soc,33049156,10/14/20,pubmed,0,7,simulation model,0.001538095,0.001538124,0.001538111,0.532149087,0.001538128,0.461698454,Epidemiology,0.32316828,FALSE,122.1428571,0.909951141,171.8571429,0.900789403,4,0.707574542,,,0.839438362
6155,Co-Expression of Mitochondrial Genes and ACE2 in Cornea Involved in COVID-19.,Invest Ophthalmol Vis Sci,33049061,10/14/20,pubmed,0,5,"interactom, network analysis",0.964549131,0.001538167,0.00153815,0.001538142,0.001538108,0.029298303,Drug discovery,0.8829752,TRUE,34.8,0.484940318,16,0.437316029,1,0.537564047,,,0.486606798
6156,Problems Encountered by Nurses Due to the Use of Personal Protective Equipment During the Coronavirus Pandemic: Results of a Survey.,Wound Manag Prev,33048827,10/14/20,pubmed,0,2,logistic regression,0.001126794,0.001126814,0.001126819,0.00112686,0.901495261,0.093997452,Healthcare,0.98701596,TRUE,8.5,0.126662131,0,0.055525823,4,0.707574542,,,0.296587499
6157,Associations Between COVID-19 Misinformation Exposure and Belief With COVID-19 Knowledge and Preventive Behaviors: Cross-Sectional Online Study.,J Med Internet Res,33048825,10/14/20,pubmed,0,8,logistic regression,0.00123704,0.001237057,0.001237085,0.001237146,0.993814579,0.001237092,Healthcare,0.93216777,TRUE,40.25,0.541715629,11.75,0.381857105,10,0.828199272,,,0.583924002
6158,COVID-19 Outcome Prediction and Monitoring Solution for Military Hospitals in South Korea: Development and Evaluation of an Application.,J Med Internet Res,33048824,10/14/20,pubmed,0,8,prediction model,0.000765944,0.000765949,0.304213136,0.211684207,0.131203184,0.35136758,Clinics,0.9431663,TRUE,30.25,0.434782609,7.125,0.300240835,1,0.537564047,,,0.42419583
6159,[Commercial airline routes and international distribution of COVID-19].,Medicina (B Aires),33048796,10/14/20,pubmed,0,3,"mathematical model, predictive model",0.002898336,0.002898404,0.002898381,0.985508228,0.002898324,0.002898327,Epidemiology,0.82547575,TRUE,25.66666667,0.376461129,3.333333333,0.206515922,0,0.403234768,,,0.328737273
6160,0,IEEE J Biomed Health Inform,33048773,10/14/20,pubmed,0,7,"deep learning, dataset",0.001237067,0.001237101,0.993814382,0.001237185,0.001237088,0.001237177,Imaging,0.3197177,FALSE,39.85714286,0.537881131,65.71428571,0.74150388,2,0.618927094,,,0.632770702
6161,Renin-angiotensin-aldosterone system inhibitors and COVID-19 infection or hospitalization: a cohort study.,Am J Hypertens,33048112,10/14/20,pubmed,0,10,logistic regression,0.069720703,0.001901696,0.001901702,0.191749504,0.001901819,0.732824576,Clinics,0.061478674,FALSE,58.4,0.69231245,69.5,0.752006957,3,0.667819001,,,0.704046136
6162,Does Serum Vitamin D Level Affect COVID-19 Infection and Its Severity?-A Case-Control Study.,J Am Coll Nutr,33048028,10/14/20,pubmed,0,18,logistic regression,0.001622717,0.001622709,0.001622699,0.001622794,0.001622896,0.991886185,Clinics,0.36430433,FALSE,26.66666667,0.390995114,11.72222222,0.381054322,19,0.89561084,,,0.555886759
6163,Association between HLA gene polymorphisms and mortality of COVID-19: An in silico analysis.,Immun Inflamm Dis,33047883,10/14/20,pubmed,0,4,in silico,0.418270708,0.331824497,0.002183231,0.002183424,0.002183382,0.243354758,Drug discovery,0.36580703,FALSE,77,0.794668811,31.5,0.581883864,5,0.739490092,,,0.705347589
6164,Repositioning microbial biotechnology against COVID-19: the case of microbial production of flavonoids.,Microb Biotechnol,33047877,10/14/20,pubmed,0,10,computational,0.76601883,0.001653134,0.104340893,0.124680986,0.001653103,0.001653055,Drug discovery,0.61849546,TRUE,65.9,0.739872596,50.7,0.683770404,0,0.403234768,,,0.608959256
6165,Association of coagulopathy with liver dysfunction in patients with COVID-19.,Hepatol Res,33047431,10/14/20,pubmed,0,12,logistic regression,0.08524141,0.001291278,0.001291209,0.001291372,0.02418175,0.88670298,Clinics,0.9023667,TRUE,67.33333333,0.749396994,49.41666667,0.679288199,1,0.537564047,,,0.655416414
6166,Revisiting pharmacological potentials of Nigella sativa seed: A promising option for COVID-19 prevention and cure.,Phytother Res,33047412,10/14/20,pubmed,0,8,computational,0.821354426,0.001653052,0.001653043,0.00165306,0.00165319,0.172033229,Drug discovery,0.9711324,TRUE,11.75,0.177562001,3,0.199424672,4,0.707574542,,,0.361520405
6167,The impact of protocol-based high-intensity pharmacological thromboprophylaxis on thrombotic events in critically ill COVID-19 patients.,Anaesthesia,33047335,10/14/20,pubmed,0,9,logistic regression,0.001203445,0.001203424,0.020044385,0.001203462,0.001203477,0.975141806,Clinics,0.91739905,TRUE,13.44444444,0.203104707,2.888888889,0.190460262,4,0.707574542,,,0.367046503
6168,"Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis.",Radiol Med,33047295,10/14/20,pubmed,0,14,artificial intelligence,0.000634203,0.000634198,0.484664825,0.000634191,0.000634207,0.512798375,Clinics,0.91208637,TRUE,2.571428571,0.027707341,0,0.055525823,0,0.403234768,,,0.162155977
6169,Risk for probable post-partum depression among women during the COVID-19 pandemic.,Arch Womens Ment Health,33047207,10/14/20,pubmed,0,8,logistic regression,0.001593473,0.00159347,0.001593495,0.001593512,0.992032462,0.001593588,Healthcare,0.9725425,TRUE,141.625,0.93617416,69.25,0.751204174,7,0.785110192,,,0.824162842
6170,Designing of cytotoxic and helper T cell epitope map provides insights into the highly contagious nature of the pandemic novel coronavirus SARS-CoV-2.,R Soc Open Sci,33047062,10/14/20,pubmed,0,1,computational,0.78601006,0.11502207,0.001237099,0.09525649,0.001237153,0.001237128,Drug discovery,0.35230255,FALSE,202,0.972911126,206,0.920457586,3,0.667819001,,,0.853729238
6171,Targeting the glycan of receptor binding domain with jacalin as a novel approach to develop a treatment against COVID-19.,R Soc Open Sci,33047045,10/14/20,pubmed,0,3,in silico,0.974762145,0.005047708,0.005047547,0.005047619,0.005047543,0.005047438,Drug discovery,0.4164111,FALSE,19,0.285793803,10.33333333,0.36038266,0,0.403234768,,,0.349803743
6172,"Drug binding dynamics of the dimeric SARS-CoV-2 main protease, determined by molecular dynamics simulation.",Sci Rep,33046764,10/14/20,pubmed,0,7,molecular dynamics simulation,0.921099512,0.073239838,0.001415175,0.001415211,0.001415166,0.001415097,Drug discovery,0.8202242,TRUE,51,0.63875317,26.57142857,0.542279904,3,0.667819001,,,0.616284025
6173,"After the lockdown: simulating mobility, public health and economic recovery scenarios.",Sci Rep,33046737,10/14/20,pubmed,0,5,dataset,0.001371269,0.001371263,0.001371271,0.971796766,0.022718105,0.001371326,Epidemiology,0.39505255,FALSE,30.4,0.436823551,51.2,0.686178753,6,0.764429903,,,0.629144069
6174,Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium.,Nat Commun,33046696,10/14/20,pubmed,0,28,transcriptom,0.823427464,0.086847329,0.002032803,0.002032816,0.083626811,0.002032777,Drug discovery,0.7459247,TRUE,89.17857143,0.83987878,621,0.984880921,6,0.764429903,,,0.863063201
6175,Sensitive Recovery of Complete SARS-CoV-2 Genomes from Clinical Samples by Use of Swift Biosciences' SARS-CoV-2 Multiplex Amplicon Sequencing Panel.,J Clin Microbiol,33046529,10/14/20,pubmed,0,6,"sequencing, genomes",0.011749743,0.941249702,0.011749773,0.011750375,0.011749921,0.011750487,Genomics,0.23609132,FALSE,60.83333333,0.707464902,67.83333333,0.747792347,0,0.403234768,,,0.619497339
6176,CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients.,Acad Radiol,33046370,10/14/20,pubmed,0,10,"classifier, radiom",0.000547804,0.000547818,0.317742924,0.000547829,0.010453149,0.670160476,Clinics,0.99104285,TRUE,58.4,0.69231245,43.7,0.652328071,4,0.707574542,,,0.684071687
6177,Digital health innovation to integrate palliative care during the COVID-19 pandemic.,Am J Emerg Med,33046309,10/14/20,pubmed,0,5,digital health,0.034963217,0.034962334,0.034963783,0.825186212,0.034962523,0.034961931,Epidemiology,0.6324974,TRUE,37.8,0.516667697,13.2,0.402194273,0,0.403234768,,,0.440698913
6178,"Predicting severe outcomes in Covid-19 related illness using only patient demographics, comorbidities and symptoms.",Am J Emerg Med,33046294,10/14/20,pubmed,0,9,logistic regression,0.001593493,0.001593474,0.001593547,0.001593553,0.258824835,0.734801098,Clinics,0.7575302,TRUE,61.44444444,0.711423094,54.88888889,0.700762644,3,0.667819001,,,0.693334913
6179,Identifying and quantifying robust risk factors for mortality in critically ill patients with COVID-19 using quantile regression.,Am J Emerg Med,33046291,10/14/20,pubmed,0,5,logistic regression,0.001330125,0.001330091,0.069442101,0.00133011,0.001330057,0.925237515,Clinics,0.95034784,TRUE,60.4,0.705114726,24.6,0.525689055,0,0.403234768,,,0.544679516
6180,[The importance of transparency in building simulation models of the COVID-19 spread].,Ugeskr Laeger,33046185,10/14/20,pubmed,0,1,simulation model,0.003927624,0.00392746,0.003927571,0.93023106,0.003927569,0.054058716,Epidemiology,0.41022438,FALSE,14,0.213494959,4,0.231469093,0,0.403234768,,,0.28273294
6181,Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19.,Eur J Med Res,33046116,10/14/20,pubmed,0,10,"deep learning, dataset",0.000765915,0.000765922,0.543561604,0.000765932,0.000765928,0.453374699,Imaging,0.49641037,FALSE,12,0.183190055,1.3,0.12877977,0,0.403234768,,,0.238401531
6182,An online coronavirus analysis platform from the National Genomics Data Center.,Zool Res,33045776,10/13/20,pubmed,0,16,"sequencing, genome sequences, genomes",0.002996533,0.631537745,0.116554113,0.002996675,0.242918352,0.002996582,Genomics,0.4592575,FALSE,45.125,0.587605913,69.8125,0.752742842,2,0.618927094,,,0.65309195
6183,GESS: a database of global evaluation of SARS-CoV-2/hCoV-19 sequences.,Nucleic Acids Res,33045727,10/13/20,pubmed,0,8,genomes,0.001486496,0.76468812,0.001486521,0.229365974,0.001486454,0.001486434,Genomics,0.47510073,FALSE,52.125,0.647844641,33.875,0.598006422,9,0.814309525,,,0.686720196
6184,Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations.,Phys Med Biol,33045699,10/13/20,pubmed,0,8,"supervised learning, dataset",0.001098871,0.001098848,0.786348758,0.209255883,0.001098823,0.001098818,Imaging,0.004184693,FALSE,17.75,0.267672707,,,5,0.739490092,,,0.5035814
6185,Novel serological biomarkers for inflammation in predicting disease severity in patients with COVID-19.,Int Immunopharmacol,33045571,10/13/20,pubmed,0,10,logistic regression,0.001059372,0.001059387,0.03861503,0.013801075,0.001059348,0.944405789,Clinics,0.8202224,TRUE,68.3,0.755519822,143.3,0.8773749,3,0.667819001,,,0.766904574
6186,Changes in ambient air quality and atmospheric composition and reactivity in the South East of the UK as a result of the COVID-19 lockdown.,Sci Total Environ,33045513,10/13/20,pubmed,0,7,model simulation,0.097996895,0.001072292,0.001072222,0.868105821,0.00107222,0.030680549,Epidemiology,0.7131251,TRUE,45.57142857,0.591687798,72,0.7594327,3,0.667819001,,,0.672979833
6187,A CRISPR-Cas12a-based specific enhancer for more sensitive detection of SARS-CoV-2 infection.,EBioMedicine,33045467,10/13/20,pubmed,0,44,sequencing,0.001350395,0.429238484,0.392824963,0.173885331,0.001350392,0.001350435,Genomics,0.73456156,TRUE,59.52272727,0.699919599,,,0,0.403234768,,,0.551577183
6188,Proof of SARS-CoV-2 genomes in endomyocardial biopsy with latency after acute infection.,Int J Infect Dis,33045427,10/13/20,pubmed,0,7,genomes,0.003760631,0.355460357,0.003760558,0.003760656,0.00376064,0.629497158,Clinics,0.95663714,TRUE,48,0.614942173,36.14285714,0.611854429,2,0.618927094,,,0.615241232
6189,A single holiday was the turning point of the COVID-19 policy of Israel.,Int J Infect Dis,33045425,10/13/20,pubmed,0,4,mathematical model,0.00165303,0.001653065,0.001653072,0.991734732,0.00165306,0.00165304,Epidemiology,0.50133014,TRUE,50.25,0.633001422,23.75,0.518731603,2,0.618927094,,,0.59022004
6190,A combined approach of MALDI-TOF mass spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs.,J Virol Methods,33045283,10/13/20,pubmed,0,12,machine learning,0.001943628,0.285263781,0.672100414,0.00194361,0.001943608,0.036804958,Genomics,0.86220443,TRUE,38.33333333,0.522233904,15.75,0.433636607,1,0.537564047,,,0.497811519
6191,Universal Screening for COVID-19 in Children Undergoing Orthopaedic Surgery: A Multicenter Report.,J Pediatr Orthop,33045161,10/13/20,pubmed,0,9,logistic regression,0.001486466,0.054350643,0.106299964,0.001486527,0.472272576,0.364103824,Healthcare,0.7583841,TRUE,28.66666667,0.414125796,6.888888889,0.294153064,5,0.739490092,,,0.482589651
6192,COVID-Align: Accurate online alignment of hCoV-19 genomes using a profile HMM.,Bioinformatics,33045068,10/13/20,pubmed,0,4,"bioinformatic, sequencing, genomes",0.001622764,0.74385288,0.084913725,0.166365164,0.001622747,0.001622719,Genomics,0.16012359,FALSE,66.25,0.742346465,2052.25,0.998461333,1,0.537564047,,,0.759457282
6193,Deus Ex Machina? Predicting SARS-CoV-2 Infection from Lab Tests Using Machine Learning.,Clin Chem,33045055,10/13/20,pubmed,0,1,machine learning,0.034961874,0.034961874,0.825190394,0.034962111,0.034961874,0.034961874,Epidemiology,0.43446973,FALSE,10,0.15214299,1,0.122023013,0,0.403234768,,,0.225800257
6194,Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels.,IEEE J Biomed Health Inform,33044938,10/13/20,pubmed,0,28,"deep learning, dataset",0.001415121,0.001415162,0.78617672,0.093224015,0.001415149,0.116353833,Imaging,0.35551655,FALSE,54.57142857,0.665223576,33.57142857,0.596267059,1,0.537564047,,,0.599684894
6195,A national fight against COVID-19: lessons and experiences from China.,Aust N Z J Public Health,33044796,10/13/20,pubmed,0,3,artificial intelligence,0.001486492,0.07855369,0.001486551,0.915500244,0.001486534,0.00148649,Epidemiology,0.35130072,FALSE,29.33333333,0.423093574,5.666666667,0.270203372,0,0.403234768,,,0.365510571
6196,Effectiveness of Containment Measures Against COVID-19 in Singapore: Implications for Other National Containment Efforts.,Epidemiology,33044319,10/13/20,pubmed,0,13,bayes,0.001126788,0.001126853,0.001126838,0.974908972,0.02058366,0.001126889,Epidemiology,0.10445577,FALSE,42.23076923,0.559465644,85.30769231,0.790941932,1,0.537564047,,,0.629323874
6197,Early prediction of level-of-care requirements in patients with COVID-19.,Elife,33044170,10/13/20,pubmed,0,9,"artificial intelligence, predictive model, prediction model",0.000634249,0.000634195,0.269972973,0.045422193,0.000634243,0.682702146,Clinics,0.8529103,TRUE,93.66666667,0.853794298,40.22222222,0.634599946,4,0.707574542,,,0.731989595
6198,"Knowledge of COVID-19 and its prevention among residents of the Gedeo zone, South Ethiopia. Sources of information as a factor.",Curr Med Res Opin,33044091,10/13/20,pubmed,0,4,logistic regression,0.000871547,0.000871565,0.000871594,0.000871624,0.995642108,0.000871563,Healthcare,0.6373705,TRUE,8.25,0.121343311,0.25,0.065493712,2,0.618927094,,,0.268588039
6199,"Sewage as a Possible Transmission Vehicle During a Coronavirus Disease 2019 Outbreak in a Densely populated Community: Guangzhou, China, April 2020.",Clin Infect Dis,33043972,10/13/20,pubmed,0,16,sequencing,0.001237166,0.422213562,0.001237195,0.139905541,0.434169411,0.001237124,Healthcare,0.6291528,TRUE,82.875,0.817490259,58.3125,0.714945143,9,0.814309525,,,0.782248309
6200,Diagnostic accuracy of non-contact infrared thermometers and thermal scanners: a systematic review and meta-analysis.,J Travel Med,33043363,10/13/20,pubmed,0,8,dataset,0.001085424,0.00108541,0.57073387,0.255871662,0.001085372,0.170138262,Epidemiology,0.8738542,TRUE,12.75,0.191786752,1.875,0.151792882,3,0.667819001,,,0.337132879
6201,Zero-shot learning and its applications from autonomous vehicles to COVID-19 diagnosis: A review.,Intell Based Med,33043311,10/13/20,pubmed,0,2,"deep learning, dataset",0.002562605,0.002562596,0.936774234,0.052975194,0.002562717,0.002562654,Epidemiology,0.6403717,TRUE,32.5,0.461685942,7.5,0.307867273,1,0.537564047,,,0.435705754
6202,Digital health in electrophysiology and the COVID-19 global pandemic.,Heart Rhythm O2,33043310,10/13/20,pubmed,0,2,digital health,0.00159354,0.001593551,0.313320032,0.604019737,0.001593589,0.077879551,Epidemiology,0.767828,TRUE,302.5,0.990784835,332,0.962402997,0,0.403234768,,,0.7854742
6203,Structures and dynamics of the novel S1/S2 protease cleavage site loop of the SARS-CoV-2 spike glycoprotein.,J Struct Biol X,33043289,10/13/20,pubmed,0,5,molecular dynamics simulation,0.990691344,0.001861788,0.001861765,0.001861762,0.001861669,0.001861673,Drug discovery,0.48129588,FALSE,30.4,0.436823551,40.8,0.638346267,5,0.739490092,,,0.604886637
6204,Thromboprophylaxis with enoxaparin is associated with a lower death rate in patients hospitalized with SARS-CoV-2 infection. A cohort study.,EClinicalMedicine,33043287,10/13/20,pubmed,0,12,logistic regression,0.001237091,0.001237095,0.001237089,0.001237073,0.001237067,0.993814583,Clinics,0.68454224,TRUE,12.91666667,0.193147381,19.33333333,0.473842655,2,0.618927094,,,0.428639043
6205,SARS-CoV-2 Infection Dysregulates the Metabolomic and Lipidomic Profiles of Serum.,iScience,33043283,10/13/20,pubmed,0,19,"metabolom, lipidom",0.002562847,0.271511909,0.002562829,0.002562685,0.211702101,0.509097629,Clinics,0.6729992,TRUE,48.73684211,0.619704373,68.42105263,0.748996521,6,0.764429903,,,0.711043599
6206,Furin: A Potential Therapeutic Target for COVID-19.,iScience,33043282,10/13/20,pubmed,0,16,virtual screening,0.988210984,0.002357872,0.002357756,0.002357858,0.002357782,0.002357748,Drug discovery,0.7551838,TRUE,53.6875,0.658605974,,,42,0.949503056,,,0.804054515
6207,Redesigning COVID-19 Care With Network Medicine and Machine Learning.,Mayo Clin Proc Innov Qual Outcomes,33043272,10/13/20,pubmed,0,3,machine learning,0.352661155,0.174131538,0.140649512,0.046845147,0.163912083,0.121800563,Drug discovery,0.8927341,TRUE,82.33333333,0.815449317,38.66666667,0.625501739,1,0.537564047,,,0.659505034
6208,Exploration of surface glycoprotein to design multi-epitope vaccine for the prevention of Covid-19.,Inform Med Unlocked,33043110,10/13/20,pubmed,0,26,"molecular dynamics simulation, in silico",0.995112568,0.0009775,0.000977458,0.000977491,0.000977489,0.000977493,Drug discovery,0.65525794,TRUE,3.576923077,0.044591502,0.346153846,0.073253947,4,0.707574542,,,0.275139997
6209,Deep learning for automatic quantification of lung abnormalities in COVID-19 patients: First experience and correlation with clinical parameters.,Eur J Radiol Open,33043101,10/13/20,pubmed,0,8,deep learning,0.001072201,0.001072174,0.492307079,0.001072198,0.001072223,0.503404126,Clinics,0.9685576,TRUE,99.25,0.867462428,97.375,0.817366872,1,0.537564047,,,0.740797782
6210,"The Psychological Health Status of Healthcare Workers During the COVID-19 Outbreak: A Cross-Sectional Survey Study in Guangdong, China.",Front Public Health,33042955,10/13/20,pubmed,0,8,logistic regression,0.001511791,0.001511816,0.001511849,0.00151197,0.992440721,0.001511853,Healthcare,0.959947,TRUE,44.625,0.58272002,15.75,0.433636607,2,0.618927094,,,0.545094574
6211,Health-Related Behaviors Among School-Aged Children and Adolescents During the Spanish Covid-19 Confinement.,Front Pediatr,33042917,10/13/20,pubmed,0,8,logistic regression,0.00117159,0.001171545,0.001171532,0.001171622,0.99414214,0.001171571,Healthcare,0.9659511,TRUE,110.375,0.89040757,62.125,0.729127642,18,0.891474782,,,0.837003332
6212,Psychological impact of COVID-19 on hospital workers in nursing care hospitals.,Nurs Open,33042562,10/13/20,pubmed,0,3,logistic regression,0.001652999,0.001653018,0.001653005,0.001653015,0.758671287,0.234716676,Healthcare,0.9043138,TRUE,36.66666667,0.505102356,7.666666667,0.310877709,0,0.403234768,,,0.406404944
6213,Personal Protective Equipment Use by Healthcare Workers in Intensive Care Unit During the COVID-19 Pandemic in Japan: Comparative Analysis With the PPE-SAFE Survey.,Acute Med Surg,33042559,10/13/20,pubmed,0,11,logistic regression,0.001511845,0.001511849,0.001511869,0.001511903,0.804623165,0.18932937,Healthcare,0.81754506,TRUE,38.09090909,0.519636341,43.27272727,0.650521809,1,0.537564047,,,0.569240732
6214,"Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study.",Ann Med Surg (Lond),33042536,10/13/20,pubmed,0,10,"machine learning, prediction model, dataset",0.001072169,0.001072174,0.408914532,0.001072199,0.0010722,0.586796726,Clinics,0.59705657,TRUE,28.2,0.409734677,25.8,0.535456248,2,0.618927094,,,0.521372673
6215,Machine learning for coronavirus covid-19 detection from chest x-rays.,Procedia Comput Sci,33042308,10/13/20,pubmed,0,4,machine learning,0.00321419,0.00321426,0.983928626,0.003214308,0.003214206,0.003214409,Imaging,0.65027386,TRUE,155.25,0.948172429,35.75,0.609044688,1,0.537564047,,,0.698260388
6216,Named Entities and Their Role in Creating Context Information.,Procedia Comput Sci,33042306,10/13/20,pubmed,0,1,"supervised learning, information retrieval",0.002296607,0.053998977,0.43927247,0.499838505,0.002296556,0.002296886,Epidemiology,0.65311486,TRUE,58,0.689714887,1,0.122023013,0,0.403234768,,,0.404990889
6217,"Individualized learning in a course with a tight schedule".,Procedia Comput Sci,33042305,10/13/20,pubmed,0,2,artificial intelligence,0.001717234,0.001717312,0.34256778,0.561747135,0.090533323,0.001717215,Epidemiology,0.45601666,FALSE,35.5,0.492547467,5,0.257024351,0,0.403234768,,,0.384268862
6218,MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images.,Biomed Signal Process Control,33042210,10/13/20,pubmed,0,1,"deep learning, neural network",0.001461897,0.00146195,0.992690313,0.001461933,0.00146196,0.001461947,Imaging,0.73445904,TRUE,15,0.227596017,1,0.122023013,1,0.537564047,,,0.295727692
6219,Impact of COVID-19 on Economic Well-Being and Quality of Life of the Vietnamese During the National Social Distancing.,Front Psychol,33041928,10/13/20,pubmed,0,13,logistic regression,0.001220046,0.001220043,0.001220027,0.141108441,0.854011383,0.001220061,Healthcare,0.92878395,TRUE,58.15384615,0.690085967,42.07692308,0.645103024,10,0.828199272,,,0.721129421
6220,Systems Pharmacology and Verification of ShenFuHuang Formula in Zebrafish Model Reveal Multi-Scale Treatment Strategy for Septic Syndrome in COVID-19.,Front Pharmacol,33041827,10/13/20,pubmed,0,9,network analysis,0.859861882,0.001751227,0.001751203,0.001751243,0.001751232,0.133133212,Drug discovery,0.9461152,TRUE,21,0.312016822,8,0.320511105,0,0.403234768,,,0.345254232
6221,In Silico Prediction of Molecular Targets of Astragaloside IV for Alleviation of COVID-19 Hyperinflammation by Systems Network Pharmacology and Bioinformatic Gene Expression Analysis.,Front Pharmacol,33041797,10/13/20,pubmed,0,2,"bioinformatic, in silico, genomes, dataset",0.83458864,0.159363914,0.001511861,0.001511868,0.001511874,0.001511843,Drug discovery,0.9932555,TRUE,36.5,0.503123261,0.5,0.087101953,0,0.403234768,,,0.331153327
6222,"Glycosylation, ligand binding sites and antigenic variations between membrane glycoprotein of COVID-19 and related coronaviruses.",Vacunas,33041736,10/13/20,pubmed,0,1,"in silico, sequence alignment",0.765285027,0.228102501,0.001653082,0.001653085,0.001653166,0.001653139,Drug discovery,0.5444158,TRUE,13,0.197352959,0,0.055525823,0,0.403234768,,,0.218704517
6223,Reflections on the COVID-19 Pandemic.,Integr Med (Encinitas),33041700,10/13/20,pubmed,0,1,microbiom,0.411775313,0.28755831,0.057077127,0.178319243,0.063189263,0.002080743,Drug discovery,0.82138836,TRUE,47,0.606407323,23,0.513513514,0,0.403234768,,,0.507718535
6224,"Association between climatic variables and COVID-19 pandemic in National Capital Territory of Delhi, India.",Environ Dev Sustain,33041646,10/13/20,pubmed,0,3,correlation analysis,0.001538108,0.001538136,0.001538121,0.94802738,0.001538196,0.045820058,Epidemiology,0.7922429,TRUE,27,0.3960047,12,0.386740701,3,0.667819001,,,0.483521468
6225,"A global analysis on the effect of temperature, socio-economic and environmental factors on the spread and mortality rate of the COVID-19 pandemic.",Environ Dev Sustain,33041644,10/13/20,pubmed,0,7,correlation analysis,0.001438086,0.001438154,0.001438091,0.75012231,0.103070111,0.142493248,Epidemiology,0.82073724,TRUE,13.14285714,0.198218814,9.285714286,0.340714477,2,0.618927094,,,0.385953462
6226,"A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods.",Multimed Tools Appl,33041635,10/13/20,pubmed,0,2,"machine learning, deep learning, neural network, classifier",0.000988391,0.000988434,0.995057992,0.0009884,0.000988381,0.000988401,Imaging,0.7057098,TRUE,44.5,0.581977859,6.5,0.288132192,4,0.707574542,,,0.525894864
6227,Characterization of SARS-CoV-2 dynamics in the host.,Annu Rev Control,33041634,10/13/20,pubmed,0,5,mathematical model,0.501407967,0.002898579,0.002898347,0.486998327,0.00289841,0.00289837,Drug discovery,0.5470389,TRUE,42,0.558537943,16.4,0.441196147,6,0.764429903,,,0.588054664
6228,Transport effect of COVID-19 pandemic in France.,Annu Rev Control,33041633,10/13/20,pubmed,0,6,network model,0.002032776,0.002032841,0.083423288,0.90844558,0.002032754,0.00203276,Epidemiology,0.22283107,FALSE,66.66666667,0.745191416,21.16666667,0.493310142,2,0.618927094,,,0.619142884
6229,From the hospital scale to nationwide: observability and identification of models for the COVID-19 epidemic waves.,Annu Rev Control,33041632,10/13/20,pubmed,0,4,mathematical model,0.002130702,0.002130699,0.002130803,0.764603169,0.002130731,0.226873896,Epidemiology,0.6521914,TRUE,80.5,0.809202795,50.75,0.684238694,2,0.618927094,,,0.704122861
6230,"The Good, The Bad and The Ugly: A Mathematical Model Investigates the Differing Outcomes Among CoVID-19 Patients.",J Indian Inst Sci,33041543,10/13/20,pubmed,0,3,mathematical model,0.503425097,0.001310375,0.001310422,0.198249628,0.0013104,0.294394078,Drug discovery,0.52178955,TRUE,65.66666667,0.738450121,46,0.66416912,2,0.618927094,,,0.673848778
6231,Isfahan and Covid-19: Deep spatiotemporal representation.,Chaos Solitons Fractals,33041534,10/13/20,pubmed,0,7,"deep learning, lstm",0.001330071,0.001330028,0.156819038,0.837860781,0.001330046,0.001330035,Epidemiology,0.42488414,FALSE,32.71428571,0.463479498,9.285714286,0.340714477,0,0.403234768,,,0.402476247
6232,Applications of artificial intelligence in battling against covid-19: A literature review.,Chaos Solitons Fractals,33041533,10/13/20,pubmed,0,1,artificial intelligence,0.001593499,0.00159353,0.60693819,0.339346931,0.048934264,0.001593585,Imaging,0.6669041,TRUE,5,0.070752675,2,0.164302917,8,0.799987654,,,0.345014415
6233,A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece.,Expert Syst Appl,33041528,10/13/20,pubmed,0,1,"machine learning, neural network",0.001653037,0.001653069,0.137258558,0.803613496,0.001653077,0.054168763,Epidemiology,0.29295576,FALSE,10,0.15214299,2,0.164302917,2,0.618927094,,,0.311791
6234,Characterization of partially observed epidemics through Bayesian inference: application to COVID-19.,Comput Mech,33041410,10/13/20,pubmed,0,3,bayes,0.002080575,0.002080788,0.002080732,0.955754455,0.00208055,0.0359229,Epidemiology,0.29665548,FALSE,41.33333333,0.551549261,3.666666667,0.217621086,0,0.403234768,,,0.390801705
6235,A light CNN for detecting COVID-19 from CT scans of the chest.,Pattern Recognit Lett,33041409,10/13/20,pubmed,0,3,"neural network, dataset",0.00178656,0.001786556,0.945998355,0.046855489,0.001786532,0.001786509,Imaging,0.69823843,TRUE,113.6666667,0.897087018,21.33333333,0.495450896,25,0.918019631,,,0.770185849
6236,Promising terpenes as SARS-CoV-2 spike receptor-binding domain (RBD) attachment inhibitors to the human ACE2 receptor: Integrated computational approach.,J Mol Liq,33041407,10/13/20,pubmed,0,5,"virtual screening, computational",0.957703005,0.001112678,0.001112654,0.037846431,0.001112616,0.001112615,Drug discovery,0.97815865,TRUE,9,0.135320675,17.4,0.454642762,12,0.850299401,,,0.480087613
6237,Screening of potential drug from Azadirachta Indica (Neem) extracts for SARS-CoV-2: An insight from molecular docking and MD-simulation studies.,J Mol Struct,33041371,10/13/20,pubmed,0,5,molecular dynamics simulation,0.983323028,0.003335315,0.003335422,0.003335422,0.003335416,0.003335396,Drug discovery,0.86341304,TRUE,37.2,0.509926402,6.2,0.282512711,3,0.667819001,,,0.486752705
6238,Neuroprotective immunity by essential nutrient "Choline" for the prevention of SARS CoV2 infections: An in silico study by molecular dynamics approach.,Chem Phys Lett,33041350,10/13/20,pubmed,0,2,"virtual screening, in silico",0.976123459,0.004775203,0.004775138,0.0047752,0.004775497,0.004775503,Drug discovery,0.9719913,TRUE,15,0.227596017,0,0.055525823,2,0.618927094,,,0.300682978
6239,The Association of CT-measured Cardiac Indices with Lung Involvement and Clinical Outcome in Patients with COVID-19.,Acad Radiol,33041195,10/13/20,pubmed,0,6,logistic regression,0.001098836,0.001098806,0.116404155,0.001098816,0.00109881,0.879200577,Clinics,0.98824835,TRUE,18.83333333,0.281588224,3.5,0.213607172,3,0.667819001,,,0.387671466
6240,Radiology in the News: A Content Analysis of Radiology-Related Information Retrieved From Google Alerts.,Curr Probl Diagn Radiol,33041161,10/13/20,pubmed,0,3,artificial intelligence,0.001059352,0.00105939,0.066757432,0.702592264,0.227472178,0.001059384,Epidemiology,0.71728396,TRUE,22.66666667,0.335580432,9.666666667,0.348274017,0,0.403234768,,,0.362363072
6241,"Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques.",Disaster Med Public Health Prep,33040775,10/13/20,pubmed,0,2,machine learning,0.002562584,0.002562653,0.166399475,0.823349805,0.00256262,0.002562864,Epidemiology,0.8429564,TRUE,29.5,0.426000371,2.5,0.180826866,0,0.403234768,,,0.336687335
6242,Importance of Environmental Factors on Production of Computationally- Defined Natural Molecules against COVID-19 Pandemic.,Curr Top Med Chem,33040729,10/13/20,pubmed,0,4,computational,0.483305841,0.013550146,0.013549191,0.462495714,0.013549245,0.013549863,Drug discovery,0.42113766,FALSE,23.5,0.348506401,3.75,0.21982874,0,0.403234768,,,0.323856636
6243,Digital cardiovascular care in COVID-19 pandemic: A potential alternative?,J Card Surg,33040399,10/12/20,pubmed,0,3,"machine learning, artificial intelligence, digital health",0.001987105,0.001987099,0.210202077,0.393329989,0.134102994,0.258390736,Epidemiology,0.97373134,TRUE,10.66666667,0.159131672,0.333333333,0.073187048,2,0.618927094,,,0.283748605
6244,Antibody dynamics to SARS-CoV-2 in asymptomatic COVID-19 infections.,Allergy,33040337,10/12/20,pubmed,0,25,proteom,0.001156308,0.630896401,0.001156294,0.001156305,0.087016526,0.278618167,Genomics,0.61692894,TRUE,43.2,0.569051889,68.32,0.74859513,19,0.89561084,,,0.73775262
6245,Air quality assessment among populous sites of major metropolitan cities in India during COVID-19 pandemic confinement.,Environ Sci Pollut Res Int,33040289,10/12/20,pubmed,0,7,correlation analysis,0.002422493,0.1356627,0.002422302,0.763966287,0.002422449,0.09310377,Epidemiology,0.962057,TRUE,16,0.243552477,7.285714286,0.30331817,0,0.403234768,,,0.316701805
6246,SARS-CoV-2 has the advantage of competing the iMet-tRNAs with human hosts to allow efficient translation.,Mol Genet Genomics,33040198,10/12/20,pubmed,0,6,genome sequences,0.472570581,0.470885404,0.001486501,0.052084653,0.001486438,0.001486424,Drug discovery,0.12761068,FALSE,11,0.167171748,,,1,0.537564047,,,0.352367898
6247,"A rapidly deployed, interactive, online visualization system to support fatality management during the coronavirus disease 2019 (COVID-19) pandemic.",J Am Med Inform Assoc,33040152,10/12/20,pubmed,0,3,dataset,0.00198712,0.001987133,0.001987233,0.812840856,0.179210527,0.001987131,Epidemiology,0.5083588,TRUE,15.66666667,0.236563795,4.666666667,0.246721969,0,0.403234768,,,0.295506844
6248,"Factors Affecting Infection Control Behaviors to Prevent COVID-19: An Online Survey of Nursing Students in Anhui, China in March and April 2020.",Med Sci Monit,33040074,10/12/20,pubmed,0,4,logistic regression,0.00137125,0.001371229,0.001371248,0.001371268,0.993143765,0.00137124,Healthcare,0.94103146,TRUE,87,0.833694106,38.75,0.625769334,1,0.537564047,,,0.665675829
6249,Decreased complement C3 levels are associated with poor prognosis in patients with COVID-19: A retrospective cohort study.,Int Immunopharmacol,33039965,10/12/20,pubmed,0,5,logistic regression,0.08570342,0.00168455,0.031022057,0.00168452,0.001684497,0.878220956,Clinics,0.8781299,TRUE,53.8,0.659719216,23.8,0.519199893,4,0.707574542,,,0.628831217
6250,Effects of COVID-19 lockdown on global air quality and health.,Sci Total Environ,33039885,10/12/20,pubmed,0,3,dataset,0.001371332,0.001371312,0.021132329,0.973382375,0.001371329,0.001371323,Epidemiology,0.62859535,TRUE,37.33333333,0.511596264,9,0.337904736,7,0.785110192,,,0.544870398
6251,A linear prognostic score based on the ratio of interleukin-6 to interleukin-10 predicts outcomes in COVID-19.,EBioMedicine,33039714,10/12/20,pubmed,0,12,logistic regression,0.001126808,0.001126817,0.013065911,0.142607898,0.001126836,0.840945729,Clinics,0.8160703,TRUE,75,0.787061661,62.91666667,0.73207118,13,0.858880178,,,0.792671006
6252,The effects of the COVID-19 pandemic on the physical activity of the Thai population: Evidence from Thailand's Surveillance on Physical Activity 2020.,J Sport Health Sci,33039655,10/12/20,pubmed,0,5,dataset,0.001415113,0.001415116,0.001415127,0.217640234,0.776699275,0.001415136,Healthcare,0.80707616,TRUE,10.2,0.153627312,3.8,0.221501204,2,0.618927094,,,0.33135187
6253,Deep vein thrombosis in hospitalized patients with coronavirus disease 2019.,J Vasc Surg Venous Lymphat Disord,33039545,10/12/20,pubmed,0,10,logistic regression,0.000916725,0.000916702,0.01826879,0.00091674,0.000916723,0.97806432,Clinics,0.64991355,TRUE,45.8,0.593852434,15.6,0.431428954,1,0.537564047,,,0.520948478
6254,Coronavirus disease 2019 and first-trimester spontaneous abortion: a case-control study of 225 pregnant patients.,Am J Obstet Gynecol,33039396,10/12/20,pubmed,0,11,logistic regression,0.001415112,0.265356381,0.00141515,0.087146457,0.21990164,0.42476526,Clinics,0.9366995,TRUE,165.1818182,0.955037417,69.27272727,0.751271073,9,0.814309525,,,0.840206005
6255,Four-tier response system and spatial propagation of COVID-19 in China by a network model.,Math Biosci,33039365,10/12/20,pubmed,0,5,network model,0.001593519,0.001593482,0.001593495,0.992032498,0.001593522,0.001593484,Epidemiology,0.39142808,FALSE,83.2,0.818665347,46.8,0.667714744,5,0.739490092,,,0.741956728
6256,Characterizing COVID-19: A chief complaint based approach.,Am J Emerg Med,33039233,10/12/20,pubmed,0,4,"computational, data mining",0.001538204,0.122120941,0.001538215,0.132936025,0.313507384,0.428359231,Clinics,0.9551083,TRUE,10.75,0.160244913,0.5,0.087101953,1,0.537564047,,,0.261636971
6257,Racial/ethnic disparities in COVID-19 disease burden & mortality among emergency department patients in a safety net health system.,Am J Emerg Med,33039228,10/12/20,pubmed,0,8,logistic regression,0.001511811,0.001511811,0.001511839,0.001511934,0.266454812,0.727497793,Clinics,0.58604765,TRUE,11.375,0.170820706,5.375,0.263446615,2,0.618927094,,,0.351064805
6258,The role for the metagenome in the pathogenesis of COVID-19.,EBioMedicine,33038769,10/11/20,pubmed,0,2,metagenom,0.71100092,0.057802181,0.057799148,0.057799148,0.057799148,0.057799454,Drug discovery,0.64251375,TRUE,34.5,0.482404601,79.5,0.77782981,2,0.618927094,,,0.626387169
6259,SARS-CoV-2 receptor networks in diabetic and COVID-19-associated kidney disease.,Kidney Int,33038424,10/11/20,pubmed,0,32,"bayes, dataset",0.736418986,0.001156299,0.001156249,0.001156313,0.001156263,0.25895589,Drug discovery,0.8569216,TRUE,59.28125,0.697940503,,,4,0.707574542,,,0.702757523
6260,A comprehensive risk assessment of toxic elements in international brands of face foundation powders.,Environ Res,33038363,10/11/20,pubmed,0,2,computational,0.001272705,0.064898628,0.001272676,0.776952004,0.154331252,0.001272735,Epidemiology,0.7640325,TRUE,69,0.759416167,27,0.546026224,0,0.403234768,,,0.569559053
6261,Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach.,JMIR Med Inform,33038076,10/11/20,pubmed,0,14,"machine learning, neural network, prediction model",0.00156528,0.00156529,0.640855958,0.053273648,0.001565299,0.301174525,Imaging,0.40422565,FALSE,33.85714286,0.474611912,17.78571429,0.458656676,0,0.403234768,,,0.445501119
6262,"Depression, Anxiety, and Lifestyle Among Essential Workers: A Web Survey From Brazil and Spain During the COVID-19 Pandemic.",J Med Internet Res,33038075,10/11/20,pubmed,0,8,logistic regression,0.001046828,0.017938805,0.00104686,0.045083579,0.91732196,0.017561968,Healthcare,0.9574945,TRUE,87.125,0.834065186,85.625,0.791811614,3,0.667819001,,,0.764565267
6263,A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks.,Sci Rep,33037291,10/11/20,pubmed,0,1,"artificial intelligence, neural network, deep-learning, transfer learning",0.00115626,0.001156286,0.994218713,0.001156263,0.001156232,0.001156247,Imaging,0.2468706,FALSE,258,0.985466015,142,0.87643832,8,0.799987654,,,0.887297329
6264,Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2.,Nat Commun,33037213,10/11/20,pubmed,0,13,"bayes, genomes",0.002562557,0.556314496,0.036693514,0.399304134,0.002562653,0.002562646,Genomics,0.20880845,FALSE,117.1538462,0.903086153,1842.846154,0.997993043,11,0.840175319,,,0.913751505
6265,Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.,Nat Commun,33037212,10/11/20,pubmed,0,11,"artificial intelligence, neural network, dataset",0.001371313,0.017217494,0.951331257,0.001371275,0.001371264,0.027337398,Imaging,0.30249113,FALSE,54.81818182,0.666831591,64.54545455,0.737021675,13,0.858880178,,,0.754244481
6266,A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic.,Nat Commun,33037190,10/11/20,pubmed,0,13,network model,0.001901703,0.001901726,0.001901744,0.990491345,0.001901732,0.00190175,Epidemiology,0.6067886,TRUE,12.84615385,0.192467067,6.153846154,0.281375435,23,0.91129082,,,0.461711107
6267,Toddler With New Onset Diabetes and Atypical Hemolytic-Uremic Syndrome in the Setting of COVID-19.,Pediatrics,33037119,10/11/20,pubmed,0,7,"sequencing, exom",0.002996659,0.114822932,0.002996553,0.366620417,0.00299676,0.50956668,Clinics,0.80014753,TRUE,13.28571429,0.200321603,3.857142857,0.222705379,0,0.403234768,,,0.275420583
6268,Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins.,Biomed J,33036956,10/11/20,pubmed,0,3,"machine learning, classifier",0.673825853,0.001461999,0.320326471,0.001461904,0.00146187,0.001461904,Drug discovery,0.6675072,TRUE,22.66666667,0.335580432,9,0.337904736,5,0.739490092,,,0.470991753
6269,Prehospital management of acute respiratory distress in suspected COVID-19 patients.,Am J Emerg Med,33036861,10/11/20,pubmed,0,10,logistic regression,0.002238493,0.002238558,0.002238556,0.002238601,0.092395661,0.898650131,Clinics,0.9924042,TRUE,31.5,0.450058754,3.5,0.213607172,0,0.403234768,,,0.355633565
6270,Corrigendum to "Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020" [Sci. Total Environ. 728 (2020) 1-8/138860].,Sci Total Environ,33036768,10/11/20,pubmed,0,3,prediction model,0.034962643,0.034963167,0.034965993,0.825183135,0.034962093,0.034962969,Epidemiology,0.4156021,FALSE,5.333333333,0.074339786,3.333333333,0.206515922,0,0.403234768,,,0.228030158
6271,Postpartum consciousness disturbance: can covid-19 cause posterior reversible encephalopathy syndrome?,Rev Esp Anestesiol Reanim,33036761,10/11/20,pubmed,0,6,sequencing,0.001786555,0.109655055,0.193807025,0.153345039,0.185830755,0.355575572,Clinics,0.98578113,TRUE,2.5,0.027459954,0,0.055525823,1,0.537564047,,,0.206849941
6272,[Nomadism of patients treated by radiotherapy in Île-de-France: Does our health system have as much money to waste?],Bull Cancer,33036742,10/11/20,pubmed,0,7,probabilistic,0.001371274,0.001371299,0.00137132,0.845060071,0.001371297,0.149454739,Epidemiology,0.4572248,FALSE,42.28571429,0.560207805,9.285714286,0.340714477,0,0.403234768,,,0.434719016
6273,Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.,Hum Genomics,33036646,10/11/20,pubmed,0,2,"machine learning, classifier, dataset",0.001371296,0.32822532,0.438504455,0.001371303,0.001371336,0.229156292,Genomics,0.39782667,FALSE,24,0.35574247,25.5,0.533382392,1,0.537564047,,,0.47556297
6274,0,J Biomol Struct Dyn,33036548,10/11/20,pubmed,0,6,"molecular dynamics simulation, in silico",0.934242566,0.001538144,0.001538102,0.059604896,0.001538167,0.001538126,Drug discovery,0.84524834,TRUE,3,0.037293586,0.5,0.087101953,0,0.403234768,,,0.175876769
6275,0,Molecules,33036293,10/11/20,pubmed,0,6,molecular dynamics simulation,0.987547543,0.00249045,0.002490511,0.002490629,0.002490434,0.002490433,Drug discovery,0.32727545,FALSE,57,0.68204589,12.5,0.392761573,5,0.739490092,,,0.604765852
6276,Crystal Structure of Non-Structural Protein 10 from Severe Acute Respiratory Syndrome Coronavirus-2.,Int J Mol Sci,33036230,10/11/20,pubmed,0,11,sequencing,0.687468699,0.30334486,0.002296588,0.00229665,0.002296693,0.00229651,Drug discovery,0.70344216,TRUE,26.72727273,0.391428041,25.63636364,0.534051378,2,0.618927094,,,0.514802171
6277,First COVID-19 case in Zambia - Comparative phylogenomic analyses of SARS-CoV-2 detected in African countries.,Int J Infect Dis,33035675,10/10/20,pubmed,0,39,"phylogenom, whole genome",0.001593531,0.690665485,0.001593494,0.178896623,0.077054136,0.050196731,Genomics,0.47238296,FALSE,44.79487179,0.583895108,56.58974359,0.709258764,2,0.618927094,,,0.637360322
6278,"StopCOVID cohort: An observational study of 3,480 patients admitted to the Sechenov University hospital network in Moscow city for suspected COVID-19 infection.",Clin Infect Dis,33035307,10/10/20,pubmed,0,28,logistic regression,0.001330024,0.001330084,0.11105014,0.001330079,0.038225023,0.846734651,Clinics,0.9271256,TRUE,36.35714286,0.500896778,6.571428571,0.288734279,6,0.764429903,,,0.51802032
6279,Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic.,PLoS One,33035253,10/10/20,pubmed,0,6,probabilistic,0.001220066,0.001220054,0.001219996,0.792839661,0.001220031,0.202280191,Epidemiology,0.4509458,FALSE,77.83333333,0.797822995,115.1666667,0.84479529,7,0.785110192,,,0.809242826
6280,Cost-effectiveness and return on investment of protecting health workers in low- and middle-income countries during the COVID-19 pandemic.,PLoS One,33035244,10/10/20,pubmed,0,6,bayes,0.001565311,0.00156531,0.001565352,0.746025955,0.247712603,0.001565469,Epidemiology,0.5830699,TRUE,80.33333333,0.808213248,50.83333333,0.68443939,2,0.618927094,,,0.703859911
6281,"Knowledge, attitude, and practice regarding COVID-19 outbreak in Bangladesh: An online-based cross-sectional study.",PLoS One,33035219,10/10/20,pubmed,0,6,logistic regression,0.0010468,0.001046821,0.029100494,0.001046857,0.966712212,0.001046816,Healthcare,0.97830296,TRUE,170.8333333,0.958933762,292.1666667,0.951966818,27,0.92443978,,,0.945113454
6282,Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation.,J Med Internet Res,33035175,10/10/20,pubmed,0,14,"machine learning, logistic regression, correlation analysis, prediction model",0.017180988,0.001010958,0.526445693,0.001010993,0.001010966,0.453340401,Clinics,0.84518754,TRUE,24.64285714,0.362792999,10.92857143,0.3679422,0,0.403234768,,,0.377989989
6283,Whole genome sequencing and phylogenetic classification of Tunisian SARS-CoV-2 strains from patients of the Military Hospital in Tunis.,Virus Genes,33034798,10/10/20,pubmed,0,9,"sequencing, whole genome, genome sequences, genomes",0.002996407,0.894046155,0.002996438,0.002996527,0.002996563,0.093967908,Genomics,0.5440003,TRUE,24.22222222,0.357474179,62.44444444,0.730398716,2,0.618927094,,,0.56893333
6284,The CHA2DS2-VASc score and in-hospital mortality in patients with COVID-19: A multicenter retrospective cohort study.,Turk Kardiyol Dern Ars,33034573,10/10/20,pubmed,0,8,logistic regression,0.002130706,0.002130688,0.002130637,0.002130714,0.002130678,0.989346578,Clinics,0.9727948,TRUE,36,0.498299215,2.875,0.190259566,1,0.537564047,,,0.408707609
6285,Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19.,Brief Bioinform,33034338,10/10/20,pubmed,0,5,"machine learning, prediction model, dataset",0.393261555,0.00143812,0.484714687,0.001438145,0.001438109,0.117709384,Drug discovery,0.77017987,TRUE,26.2,0.383820892,8.8,0.332753546,2,0.618927094,,,0.445167177
6286,Potential zoonotic sources of SARS-CoV-2 infections.,Transbound Emerg Dis,33034151,10/10/20,pubmed,0,6,sequencing,0.001392954,0.871315191,0.001392892,0.09968174,0.024824373,0.001392849,Genomics,0.34432572,FALSE,69.66666667,0.762075577,132.6666667,0.866537329,9,0.814309525,,,0.814307477
6287,Predicting susceptibility for SARS-CoV-2 infection in domestic and wildlife animals using ACE2 protein sequence homology.,Zoo Biol,33034084,10/10/20,pubmed,0,6,bioinformatic,0.004310241,0.842828951,0.139930052,0.004310279,0.004310262,0.004310215,Genomics,0.4688495,FALSE,100.6666667,0.870369225,21.16666667,0.493310142,5,0.739490092,,,0.701056486
6288,Combining fragment docking with graph theory to improve ligand docking for homology model structures.,J Comput Aided Mol Des,33034007,10/10/20,pubmed,0,3,computational,0.806887261,0.001156312,0.060929365,0.128714528,0.001156289,0.001156246,Drug discovery,0.18861178,FALSE,61,0.709196611,13.33333333,0.403866738,0,0.403234768,,,0.505432705
6289,Advancing Digital Health Equity: A Policy Paper of the Infectious Diseases Society of America and the HIV Medicine Association.,Clin Infect Dis,33033829,10/10/20,pubmed,0,9,digital health,0.002720249,0.002720233,0.002720209,0.40023079,0.588888377,0.002720142,Healthcare,0.9210278,TRUE,57.55555556,0.685818542,158.5555556,0.890955312,1,0.537564047,,,0.7047793
6290,"Predictors of Severity in Covid-19 Patients in Casablanca, Morocco.",Cureus,33033687,10/10/20,pubmed,0,10,logistic regression,0.000638797,0.04690904,0.020848001,0.000638792,0.000638773,0.930326597,Clinics,0.97002494,TRUE,5.8,0.081266621,0,0.055525823,3,0.667819001,,,0.268203815
6291,Transaminitis is an indicator of mortality in patients with COVID-19: A retrospective cohort study.,World J Hepatol,33033568,10/10/20,pubmed,0,9,logistic regression,0.022725352,0.001565417,0.001565332,0.001565457,0.041499722,0.931078721,Clinics,0.82163084,TRUE,30.11111111,0.432803513,10.22222222,0.35804121,0,0.403234768,,,0.398026497
6292,Cancer patients in SARS-CoV-2 infection: a single-center experience from Wuhan.,J Cancer,33033507,10/10/20,pubmed,0,9,logistic regression,0.001237071,0.001237123,0.001237113,0.201963092,0.001237166,0.793088435,Clinics,0.9341247,TRUE,67.66666667,0.75137609,76,0.769132994,2,0.618927094,,,0.713145393
6293,Digital Health Solutions for Mental Health Disorders During COVID-19.,Front Psychiatry,33033487,10/10/20,pubmed,0,9,digital health,0.019529601,0.019529277,0.019530804,0.415193813,0.506687119,0.019529386,Healthcare,0.4863051,FALSE,16.55555556,0.249613458,15.44444444,0.428953706,2,0.618927094,,,0.432498086
6294,The specific metabolome profiling of patients infected by SARS-COV-2 supports the key role of tryptophan-nicotinamide pathway and cytosine metabolism.,Sci Rep,33033346,10/10/20,pubmed,0,18,metabolom,0.265392987,0.10533632,0.24330108,0.001823408,0.001823357,0.382322849,Clinics,0.69646007,TRUE,48,0.614942173,24.22222222,0.523146909,9,0.814309525,,,0.650799536
6295,Two distinct immunopathological profiles in autopsy lungs of COVID-19.,Nat Commun,33033248,10/10/20,pubmed,0,20,transcriptom,0.441062631,0.141900048,0.001461939,0.001461959,0.001461891,0.412651531,Drug discovery,0.6683656,TRUE,104.8,0.878842229,189.65,0.911894568,34,0.937156615,,,0.909297804
6296,"Metagenome of a Bronchoalveolar Lavage Fluid Sample from a Confirmed COVID-19 Case in Quito, Ecuador, Obtained Using Oxford Nanopore MinION Technology.",Microbiol Resour Announc,33033138,10/10/20,pubmed,0,13,"sequencing, metagenom",0.019529887,0.902350529,0.019530177,0.019530513,0.019529514,0.019529379,Genomics,0.3730659,FALSE,24.92307692,0.366503804,25.76923077,0.535322451,1,0.537564047,,,0.479796767
6297,Metagenomics of Wastewater Influent from Southern California Wastewater Treatment Facilities in the Era of COVID-19.,Microbiol Resour Announc,33033132,10/10/20,pubmed,0,6,"sequencing, metagenom",0.006089635,0.969550241,0.006090112,0.006089846,0.006090194,0.006089973,Genomics,0.7633151,TRUE,35.83333333,0.495206877,84.5,0.789202569,0,0.403234768,,,0.562548071
6298,"Anxiety levels, precautionary behaviours and public perceptions during the early phase of the COVID-19 outbreak in China: a population-based cross-sectional survey.",BMJ Open,33033099,10/10/20,pubmed,0,7,logistic regression,0.001371235,0.001371263,0.00137125,0.001371321,0.993143652,0.001371279,Healthcare,0.83878446,TRUE,150.4285714,0.944337931,197.1428571,0.916309874,6,0.764429903,,,0.875025903
6299,Predicting Coronavirus Disease 2019 Infection Risk and Related Risk Drivers in Nursing Homes: A Machine Learning Approach.,J Am Med Dir Assoc,33032935,10/10/20,pubmed,0,8,"machine learning, dataset",0.001219998,0.001220031,0.504240436,0.052562101,0.360160269,0.080597164,Healthcare,0.33051842,FALSE,10.125,0.152575917,3.625,0.215346535,3,0.667819001,,,0.345247151
6300,Mechanisms linking the human gut microbiome to prophylactic and treatment strategies for COVID-19.,Br J Nutr,33032673,10/10/20,pubmed,0,3,microbiom,0.215408135,0.268938976,0.00280651,0.423048818,0.086990902,0.002806658,Epidemiology,0.93649095,TRUE,209.3333333,0.975199456,843.3333333,0.991303184,3,0.667819001,,,0.878107214
6301,"Factors associated with psychological distress, fear and coping strategies during the COVID-19 pandemic in Australia.",Global Health,33032629,10/10/20,pubmed,0,10,logistic regression,0.001141304,0.001141312,0.00114132,0.001141332,0.962006889,0.033427842,Healthcare,0.9211712,TRUE,21.7,0.320860907,29,0.562884667,7,0.785110192,,,0.556285255
6302,Response to COVID-19 in South Korea and implications for lifting stringent interventions.,BMC Med,33032601,10/10/20,pubmed,0,41,bayes,0.000871535,0.000871607,0.00087157,0.995642141,0.000871576,0.000871571,Epidemiology,0.67856383,TRUE,62.41463415,0.717484074,242.2439024,0.934640086,14,0.866658436,,,0.839594199
6303,Severity assessment of COVID-19 using CT image features and laboratory indices.,Phys Med Biol,33032267,10/9/20,pubmed,0,8,radiom,0.001141316,0.001141331,0.7230633,0.001141368,0.001141361,0.272371324,Imaging,0.70986193,TRUE,72.85714286,0.777042489,85.85714286,0.792279904,6,0.764429903,,,0.777917432
6304,Tissue distributions of antiviral drugs affect their capabilities of reducing viral loads in COVID-19 treatment.,Eur J Pharmacol,33031797,10/9/20,pubmed,0,2,sequencing,0.79500158,0.171479339,0.001310355,0.001310351,0.001310392,0.029587983,Drug discovery,0.6956473,TRUE,19,0.285793803,,,6,0.764429903,,,0.525111853
6305,Mining the Antibody Repertoire for Solutions to SARS-CoV-2.,Cell Host Microbe,33031765,10/9/20,pubmed,0,3,sequencing,0.297245351,0.603756632,0.005353468,0.005353367,0.005353596,0.082937586,Genomics,0.5658177,TRUE,35.33333333,0.490259138,34,0.5990768,0,0.403234768,,,0.497523568
6306,Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers During the COVID-19 Pandemic: A Modeling Analysis.,PLoS Comput Biol,33031368,10/9/20,pubmed,0,2,"model simulation, mathematical model",0.703338593,0.001291234,0.001291219,0.083745274,0.001291242,0.209042438,Drug discovery,0.8849981,TRUE,102,0.873523409,63,0.732606369,2,0.618927094,,,0.741685624
6307,[Predictive modeling to estimate the demand for intensive care hospital beds nationwide in the context of the COVID-19 pandemic].,Medwave,33031358,10/9/20,pubmed,0,2,predictive model,0.001203408,0.00120345,0.001203483,0.514947018,0.001203466,0.480239176,Epidemiology,0.80422086,TRUE,23,0.34225988,33.5,0.595999465,0,0.403234768,,,0.447164704
6308,The Direct and Indirect Impact of SARS-CoV-2 Infections on Neonates: A Series of 26 Cases in Bangladesh.,Pediatr Infect Dis J,33031143,10/9/20,pubmed,0,20,sequencing,0.001684522,0.081498501,0.153604937,0.001684647,0.170272206,0.591255187,Clinics,0.93784577,TRUE,38.55,0.524336694,,,1,0.537564047,,,0.53095037
6309,Can the novel coronavirus be transmitted via RNAs without protein capsids?,J Infect Dev Ctries,33031088,10/9/20,pubmed,0,1,genomes,0.203412266,0.604135176,0.001203474,0.149148838,0.0408968,0.001203447,Genomics,0.5911686,TRUE,3,0.037293586,0,0.055525823,1,0.537564047,,,0.210127819
6310,Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models.,J Infect Dev Ctries,33031083,10/9/20,pubmed,0,8,"bayes, prediction model",0.001565313,0.001565312,0.036495504,0.957243158,0.00156535,0.001565364,Epidemiology,0.43506226,FALSE,5.875,0.081761395,2.25,0.170925876,3,0.667819001,,,0.306835424
6311,Digital Phenotyping to Enhance Substance Use Treatment During the COVID-19 Pandemic.,JMIR Ment Health,33031044,10/9/20,pubmed,0,3,machine learning,0.002080668,0.002080604,0.29920869,0.521528441,0.173020938,0.002080659,Epidemiology,0.92477286,TRUE,72.66666667,0.776052941,64,0.735549906,2,0.618927094,,,0.710176647
6312,Molecular dynamics analysis predicts ritonavir and naloxegol strongly block the SARS-CoV-2 spike protein-hACE2 binding.,J Biomol Struct Dyn,33030105,10/9/20,pubmed,0,2,virtual screening,0.995509459,0.000898118,0.000898089,0.00089813,0.000898101,0.000898103,Drug discovery,0.92441845,TRUE,7,0.10179974,2.5,0.180826866,0,0.403234768,,,0.228620458
6313,A multiscale absorption and transit model for oral delivery of hydroxychloroquine: Pharmacokinetic modeling and intestinal concentration prediction to assess toxicity and drug-induced damage in healthy subjects.,Int J Numer Method Biomed Eng,33029911,10/9/20,pubmed,0,2,computational,0.472755757,0.001786544,0.001786659,0.520097894,0.001786585,0.001786561,Epidemiology,0.43563545,FALSE,46.5,0.601026656,10,0.355632861,0,0.403234768,,,0.453298095
6314,COVID-19 and beliefs about tobacco use: an online cross-sectional study in Iran.,Environ Sci Pollut Res Int,33029777,10/9/20,pubmed,0,8,logistic regression,0.001684546,0.001684541,0.001684604,0.001684635,0.922827377,0.070434298,Healthcare,0.78393006,TRUE,58.625,0.693611231,75.25,0.76699224,2,0.618927094,,,0.693176855
6315,"Covid-19 Estimating the burden of symptomatic disease in the community and the impact of public health measures on physical, mental and social wellbeing: a study protocol.",HRB Open Res,33029573,10/9/20,pubmed,0,18,network model,0.00099951,0.000999514,0.000999527,0.510669347,0.485332594,0.000999507,Epidemiology,0.24623764,FALSE,48.38888889,0.617230503,31.5,0.581883864,0,0.403234768,,,0.534116378
6316,Molecular docking between human TMPRSS2 and SARS-CoV-2 spike protein: conformation and intermolecular interactions.,AIMS Microbiol,33029570,10/9/20,pubmed,0,8,structural model,0.99436604,0.001126835,0.001126779,0.001126795,0.001126772,0.001126779,Drug discovery,0.5770019,TRUE,20.5,0.304966294,11.5,0.378378378,7,0.785110192,,,0.489484955
6317,Rapid and inexpensive whole-genome sequencing of SARS-CoV-2 using 1200 bp tiled amplicons and Oxford Nanopore Rapid Barcoding.,Biol Methods Protoc,33029559,10/9/20,pubmed,0,4,"sequencing, whole-genome, genomes",0.002422341,0.954376678,0.002422628,0.002422501,0.002422539,0.035933313,Genomics,0.19321719,FALSE,20.5,0.304966294,34.25,0.600280974,6,0.764429903,,,0.556559057
6318,0,Biomed Res Int,33029513,10/9/20,pubmed,0,13,in silico,0.993814539,0.001237135,0.001237069,0.00123712,0.001237072,0.001237065,Drug discovery,0.94103754,TRUE,3.923076923,0.048116767,0.846153846,0.102622424,3,0.667819001,,,0.272852731
6319,Intra-genome variability in the dinucleotide composition of SARS-CoV-2.,Virus Evol,33029383,10/9/20,pubmed,0,5,genomes,0.159038267,0.811388139,0.024421933,0.001717247,0.001717225,0.00171719,Genomics,0.62939507,TRUE,22.2,0.327787742,30.6,0.573655339,1,0.537564047,,,0.479669043
6320,A Mathematical Description of the Dynamics of Coronavirus Disease 2019 (COVID-19): A Case Study of Brazil.,Comput Math Methods Med,33029196,10/9/20,pubmed,0,3,mathematical model,0.003101486,0.003101501,0.003101476,0.984492656,0.003101438,0.003101443,Epidemiology,0.5971205,TRUE,16.66666667,0.251159626,6,0.280037463,10,0.828199272,,,0.45313212
6321,Assessment of the Potential Adverse Events Related to Ribavirin-Interferon Combination for Novel Coronavirus Therapy.,Comput Math Methods Med,33029193,10/9/20,pubmed,0,4,"bayes, neural network, data mining",0.242464248,0.001141416,0.134132153,0.001141415,0.155621988,0.465498779,Clinics,0.9761332,TRUE,7,0.10179974,1.5,0.138747659,0,0.403234768,,,0.214594055
6322,0,Evid Based Complement Alternat Med,33029165,10/9/20,pubmed,0,6,"molecular dynamics simulation, computational, bioinformatic, in silico",0.992567766,0.001486432,0.001486431,0.001486486,0.001486437,0.001486449,Drug discovery,0.9112265,TRUE,31.5,0.450058754,7.833333333,0.314088841,0,0.403234768,,,0.389127454
6323,Association of procalcitonin levels with the progression and prognosis of hospitalized patients with COVID-19.,Int J Med Sci,33029089,10/9/20,pubmed,0,13,logistic regression,0.00095631,0.000956327,0.084472856,0.000956341,0.000956336,0.91170183,Clinics,0.96226287,TRUE,36.69230769,0.505287897,6.846153846,0.293550977,2,0.618927094,,,0.472588656
6324,Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis.,Fed Pract,33029064,10/9/20,pubmed,0,6,"machine learning, deep learning, computational, artificial intelligence, neural network, dataset",0.000728131,0.00072813,0.836807008,0.000728159,0.078740291,0.082268282,Imaging,0.43574035,FALSE,13.33333333,0.201558538,18,0.46180091,0,0.403234768,,,0.355531405
6325,A novel severity score to predict inpatient mortality in COVID-19 patients.,Sci Rep,33028914,10/9/20,pubmed,0,7,logistic regression,0.001272672,0.001272651,0.137052919,0.001272694,0.00127284,0.857856225,Clinics,0.51900005,TRUE,45.42857143,0.590141629,108.1428571,0.836432968,9,0.814309525,,,0.746961374
6326,Prevalence of asymptomatic SARS-CoV-2-positive individuals in the general population of northern Italy and evaluation of a diagnostic serological ELISA test: a cross-sectional study protocol.,BMJ Open,33028562,10/9/20,pubmed,0,5,dataset,0.001392923,0.128239236,0.186493767,0.367808753,0.183637461,0.13242786,Epidemiology,0.81855303,TRUE,119,0.905683716,58.8,0.716416912,1,0.537564047,,,0.719888225
6327,Asian-Pacific perspective on the psychological well-being of healthcare workers during the evolution of the COVID-19 pandemic.,BJPsych Open,33028449,10/9/20,pubmed,0,33,logistic regression,0.001022614,0.037874051,0.028803822,0.00102269,0.930254166,0.001022657,Healthcare,0.75846756,TRUE,54.39393939,0.663862948,32.21212121,0.586901258,9,0.814309525,,,0.68835791
6328,A predictive model for Covid-19 spread - with application to eight US states and how to end the pandemic.,Epidemiol Infect,33028445,10/9/20,pubmed,0,3,predictive model,0.002490419,0.002490556,0.002490517,0.987547586,0.002490476,0.002490445,Epidemiology,0.18801469,FALSE,2,0.022141134,0.333333333,0.073187048,3,0.667819001,,,0.254382395
6329,Intimate partner violence against reproductive age women during COVID-19 pandemic in northern Ethiopia 2020: a community-based cross-sectional study.,Reprod Health,33028424,10/9/20,pubmed,0,3,logistic regression,0.001371318,0.001371296,0.001371266,0.129378359,0.865136452,0.00137131,Healthcare,0.8906596,TRUE,11.66666667,0.176510607,6,0.280037463,5,0.739490092,,,0.398679387
6330,Estimation of the basic reproduction number (R0) for the novel coronavirus disease in Sri Lanka.,Virol J,33028382,10/9/20,pubmed,0,6,bayes,0.001717167,0.001717225,0.0017172,0.89655112,0.001717295,0.096579994,Epidemiology,0.04948899,FALSE,30.33333333,0.436266931,91.33333333,0.804656141,0,0.403234768,,,0.548052613
6331,The role of computational fluid dynamics tools on investigation of pathogen transmission: Prevention and control.,Sci Total Environ,33027870,10/9/20,pubmed,0,3,computational,0.0013504,0.14901118,0.001350408,0.817666855,0.001350339,0.029270818,Epidemiology,0.7725544,TRUE,12,0.183190055,1.333333333,0.13252609,6,0.764429903,,,0.360048683
6332,The Stress of Bayesian Medicine - Uncomfortable Uncertainty in the Face of Covid-19.,N Engl J Med,33027563,10/8/20,pubmed,0,1,bayes,0.015999324,0.015998445,0.015998789,0.525616124,0.410388771,0.015998547,Epidemiology,0.6527937,TRUE,32,0.455810502,126,0.859245384,1,0.537564047,,,0.617539978
6333,Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset.,J Am Med Inform Assoc,33027509,10/8/20,pubmed,0,4,"machine learning, dataset",0.001786578,0.228885713,0.626676476,0.001786626,0.001786684,0.139077924,Genomics,0.5724195,TRUE,20.25,0.301317336,5,0.257024351,4,0.707574542,,,0.421972076
6334,Flavonoid glycosides and their putative human metabolites as potential inhibitors of the SARS-CoV-2 main protease (Mpro) and RNA-dependent RNA polymerase (RdRp).,Mem Inst Oswaldo Cruz,33027419,10/8/20,pubmed,0,8,"virtual screening, in silico",0.992309388,0.001538146,0.001538117,0.001538134,0.001538136,0.001538079,Drug discovery,0.9806666,TRUE,17.375,0.261549879,5.25,0.260971367,8,0.799987654,,,0.4408363
6335,"Risk factors associated with delay in diagnosis and mortality in patients with COVID-19 in the city of Rio de Janeiro, Brazil.",Cien Saude Colet,33027349,10/8/20,pubmed,0,7,logistic regression,0.001565305,0.001565425,0.001565354,0.001565449,0.146621766,0.847116701,Clinics,0.81650525,TRUE,54.71428571,0.666213124,18.42857143,0.464878245,1,0.537564047,,,0.556218472
6336,Barriers and facilitators of adherence to social distancing recommendations during COVID-19 among a large international sample of adults.,PLoS One,33027281,10/8/20,pubmed,0,4,logistic regression,0.000772614,0.000772623,0.000772605,0.344273416,0.652636132,0.00077261,Healthcare,0.77697515,TRUE,138.75,0.931659348,158.5,0.890888413,17,0.887338725,,,0.903295495
6337,How Data Analytics and Big Data Can Help Scientists in Managing COVID-19 Diffusion: Modeling Study to Predict the COVID-19 Diffusion in Italy and the Lombardy Region.,J Med Internet Res,33027038,10/8/20,pubmed,0,2,predictive model,0.001861659,0.036176209,0.00186178,0.956376899,0.001861725,0.001861729,Epidemiology,0.1960305,FALSE,98.5,0.86591626,28.5,0.558402462,1,0.537564047,,,0.653960923
6338,Health Perceptions and Misconceptions Regarding COVID-19 in China: Online Survey Study.,J Med Internet Res,33027037,10/8/20,pubmed,0,9,logistic regression,0.000956329,0.000956321,0.11482648,0.140796392,0.741508154,0.000956324,Healthcare,0.9515737,TRUE,26.55555556,0.389696332,19.11111111,0.4717019,1,0.537564047,,,0.46632076
6339,"Physical Distancing Measures and Walking Activity in Middle-aged and Older Residents in Changsha, China, During the COVID-19 Epidemic Period: Longitudinal Observational Study.",J Med Internet Res,33027035,10/8/20,pubmed,0,12,logistic regression,0.000677912,0.000677929,0.00067792,0.54601329,0.421374353,0.030578597,Epidemiology,0.98886347,TRUE,100.5,0.869936298,,,2,0.618927094,,,0.744431696
6340,Feasibility of Asynchronous and Automated Telemedicine in Otolaryngology: Prospective Cross-Sectional Study.,JMIR Med Inform,33027033,10/8/20,pubmed,0,11,machine learning,0.001330062,0.001330053,0.833709291,0.05677195,0.029607076,0.077251568,Imaging,0.83654845,TRUE,38.09090909,0.519636341,10.45454545,0.361386139,0,0.403234768,,,0.428085749
6341,Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.,J Med Internet Res,33027032,10/8/20,pubmed,0,48,"machine learning, classifier",0.000765952,0.000765946,0.225885505,0.000765967,0.000765922,0.771050708,Clinics,0.98620677,TRUE,107.9166667,0.88527429,280.9583333,0.949023281,4,0.707574542,,,0.847290704
6342,"Clinical characteristics and risk factors of liver injury in COVID-19: a retrospective cohort study from Wuhan, China.",Hepatol Int,33026573,10/8/20,pubmed,0,21,logistic regression,0.001156247,0.001156238,0.00115622,0.00115625,0.001156253,0.994218793,Clinics,0.64871633,TRUE,254.2380952,0.984971241,204.4761905,0.91932031,2,0.618927094,,,0.841072882
6343,Analysis of Genomic Characteristics and Transmission Routes of Patients With Confirmed SARS-CoV-2 in Southern California During the Early Stage of the US COVID-19 Pandemic.,JAMA Netw Open,33026453,10/8/20,pubmed,0,9,"sequencing, genomes",0.00090728,0.698582514,0.000907353,0.000907351,0.171944294,0.126751207,Genomics,0.84222555,TRUE,15.11111111,0.227967098,32.33333333,0.588239229,4,0.707574542,,,0.507926956
6344,"Discovery of potent inhibitors for SARS-CoV-2's main protease by ligand-based/structure-based virtual screening, MD simulations, and binding energy calculations.",Phys Chem Chem Phys,33025993,10/8/20,pubmed,0,4,"virtual screening, in silico",0.96655,0.00099953,0.000999555,0.029451833,0.000999557,0.000999525,Drug discovery,0.8455333,TRUE,56.25,0.676788917,4.75,0.248260637,2,0.618927094,,,0.514658883
6345,"Drug Information Association (DIA) 2020 Virtual Global Annual Meeting (June 14-18, 2020).",Drugs Today (Barc),33025949,10/8/20,pubmed,0,1,digital health,0.19767228,0.003607358,0.003607335,0.471121037,0.003607316,0.320384673,Epidemiology,0.7630823,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
6346,Integrated network pharmacology and molecular docking strategy to explore the mechanism of medicinal and edible Astragali Radix-Atractylodis Macrocephalae Rhizoma acting on pneumonia via immunomodulation.,J Food Biochem,33025599,10/8/20,pubmed,0,2,bioinformatic,0.7928599,0.001187314,0.202390841,0.001187327,0.001187288,0.001187329,Drug discovery,0.9907427,TRUE,3,0.037293586,0,0.055525823,0,0.403234768,,,0.165351392
6347,Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays.,Phys Eng Sci Med,33025386,10/8/20,pubmed,0,4,"machine learning, neural network, transfer learning",0.00108539,0.001085406,0.994573098,0.001085404,0.001085325,0.001085377,Imaging,0.35006562,FALSE,3.25,0.039148989,0,0.055525823,0,0.403234768,,,0.16596986
6348,Network-based identification genetic effect of SARS-CoV-2 infections to Idiopathic pulmonary fibrosis (IPF) patients.,Brief Bioinform,33024988,10/8/20,pubmed,0,7,"bioinformatic, transcriptom, genomes, dataset",0.663724697,0.063521064,0.001622828,0.036297445,0.001622781,0.233211184,Drug discovery,0.70636034,TRUE,35.14285714,0.488465582,3.857142857,0.222705379,9,0.814309525,,,0.508493495
6349,Viral genomes reveal patterns of the SARS-CoV-2 outbreak in Washington State.,medRxiv,33024981,10/8/20,pubmed,0,43,genomes,0.001943462,0.719368803,0.001943466,0.207540417,0.001943522,0.067260332,Genomics,0.24340498,FALSE,24.37209302,0.359948049,73.13953488,0.762309339,7,0.785110192,,,0.635789193
6350,"Wrong person, place and time: viral load and contact network structure predict SARS-CoV-2 transmission and super-spreading events.",medRxiv,33024978,10/8/20,pubmed,0,5,mathematical model,0.001371323,0.397624187,0.001371304,0.55835194,0.03990994,0.001371306,Epidemiology,0.11100677,FALSE,13.2,0.199270208,61,0.724578539,22,0.908142478,,,0.610663742
6351,0,bioRxiv,33024971,10/8/20,pubmed,0,15,sequence alignment,0.380539294,0.555899768,0.001059348,0.025102862,0.001059367,0.036339361,Genomics,0.19028968,FALSE,96.6,0.860350053,101.0666667,0.824123629,0,0.403234768,,,0.695902816
6352,Ultrafast Sample Placement on Existing Trees (UShER) Empowers Real-Time Phylogenetics for the SARS-CoV-2 Pandemic.,bioRxiv,33024970,10/8/20,pubmed,0,8,genome sequences,0.00143814,0.748573865,0.001438139,0.245673599,0.001438149,0.001438108,Genomics,0.18325758,FALSE,87.25,0.83437442,1297.875,0.996052984,6,0.764429903,,,0.864952436
6353,SARS-CoV-2 gene content and COVID-19 mutation impact by comparing 44 Sarbecovirus genomes.,Res Sq,33024961,10/8/20,pubmed,0,3,"whole-genome, genomes",0.213432317,0.782700661,0.000966759,0.000966769,0.000966748,0.000966747,Genomics,0.35900787,FALSE,157.3333333,0.94947121,2016,0.998327535,1,0.537564047,,,0.828454264
6354,Chinese medical students' interest in COVID-19 pandemic.,World J Virol,33024718,10/8/20,pubmed,0,14,logistic regression,0.001156282,0.001156258,0.040222536,0.001156322,0.955152329,0.001156272,Healthcare,0.39558703,FALSE,51.21428571,0.639928258,20.28571429,0.48347605,0,0.403234768,,,0.508879692
6355,Perception and practices during the COVID-19 pandemic in an urban community in Nigeria: a cross-sectional study.,PeerJ,33024646,10/8/20,pubmed,0,2,logistic regression,0.00123705,0.001237048,0.001237074,0.001237111,0.993814637,0.001237081,Healthcare,0.8848473,TRUE,63,0.721998887,60,0.720899117,7,0.785110192,,,0.742669399
6356,Benchmarking evolutionary tinkering underlying human-viral molecular mimicry shows multiple host pulmonary-arterial peptides mimicked by SARS-CoV-2.,Cell Death Discov,33024578,10/8/20,pubmed,0,6,"computational, proteom",0.652807737,0.317416987,0.02552994,0.001415136,0.001415109,0.001415091,Drug discovery,0.13569936,FALSE,49.16666667,0.62545612,71.33333333,0.75709125,5,0.739490092,,,0.70734582
6357,A text mining analysis of perceptions of the COVID-19 pandemic among final-year medical students.,Acute Med Surg,33024569,10/8/20,pubmed,0,5,text mining,0.05617601,0.001684533,0.321365664,0.001684591,0.617404599,0.001684604,Healthcare,0.7567076,TRUE,138.6,0.931350114,35.4,0.607639818,0,0.403234768,,,0.647408233
6358,A new emergency response of spherical intelligent fuzzy decision process to diagnose of COVID19.,Soft comput,33024412,10/8/20,pubmed,0,3,mathematical model,0.002032806,0.002032803,0.133985356,0.857883484,0.002032788,0.002032762,Epidemiology,0.6899794,TRUE,78.66666667,0.801224566,15.33333333,0.428351619,1,0.537564047,,,0.589046744
6359,[Incubation period of COVID-19: A systematic review and meta-analysis].,Rev Clin Esp,33024342,10/8/20,pubmed,0,6,predictive model,0.001220049,0.001220023,0.001220039,0.60166334,0.001220072,0.393456478,Epidemiology,0.3724193,FALSE,17.33333333,0.261178799,2.666666667,0.185442869,2,0.618927094,,,0.355182921
6360,The age distribution of mortality from novel coronavirus disease (COVID-19) suggests no large difference of susceptibility by age.,Sci Rep,33024235,10/8/20,pubmed,0,3,"mathematical model, dataset",0.001684544,0.001684606,0.001684487,0.802566618,0.001684527,0.190695218,Epidemiology,0.5500195,TRUE,27.66666667,0.403488156,8.666666667,0.331415574,18,0.891474782,,,0.542126171
6361,Spatial and temporal variations of air pollution over 41 cities of India during the COVID-19 lockdown period.,Sci Rep,33024128,10/8/20,pubmed,0,7,dataset,0.001684521,0.001684548,0.001684501,0.991577394,0.001684507,0.001684527,Epidemiology,0.8971181,TRUE,19.28571429,0.288020286,11.57142857,0.378712871,4,0.707574542,,,0.458102566
6362,Inference of person-to-person transmission of COVID-19 reveals hidden super-spreading events during the early outbreak phase.,Nat Commun,33024095,10/8/20,pubmed,0,8,"bayes, genome sequences",0.002490427,0.388798096,0.002490438,0.557486918,0.046243463,0.002490657,Epidemiology,0.410551,FALSE,204.5,0.973838827,441.75,0.974712336,16,0.881782826,,,0.943444663
6363,Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.,Nat Commun,33024092,10/8/20,pubmed,0,32,"machine learning, neural network, logistic regression, prediction model",0.002296526,0.002296548,0.562350809,0.002296652,0.002296563,0.428462901,Clinics,0.46051815,FALSE,50.5,0.635351599,,,14,0.866658436,,,0.751005017
6364,Development and validation of a prediction model for early identification of critically ill elderly COVID-19 patients.,Aging (Albany NY),33024057,10/8/20,pubmed,0,5,prediction model,0.001622702,0.001622718,0.100675579,0.001622774,0.001622735,0.892833492,Clinics,0.857112,TRUE,47.8,0.612777537,454.8,0.976251003,1,0.537564047,,,0.708864196
6365,COVID-19 epidemic outside China: 34 founders and exponential growth.,J Investig Med,33023916,10/8/20,pubmed,0,8,mathematical model,0.001751162,0.001751206,0.112953141,0.76997334,0.035170443,0.078400708,Epidemiology,0.2814029,FALSE,30.625,0.439544808,,,1,0.537564047,,,0.488554428
6366,Psychological distress in Nepalese residents during COVID-19 pandemic: a community level survey.,BMC Psychiatry,33023563,10/8/20,pubmed,0,10,logistic regression,0.000977429,0.000977473,0.000977458,0.000977516,0.937255694,0.058834429,Healthcare,0.8998053,TRUE,10.2,0.153627312,0.5,0.087101953,3,0.667819001,,,0.302849422
6367,miRNAs in SARS-CoV 2: A Spoke in the Wheel of Pathogenesis.,Curr Pharm Des,33023438,10/8/20,pubmed,0,9,"computational, genomes",0.78973852,0.205381366,0.001220005,0.001220031,0.001220008,0.001220069,Drug discovery,0.82080865,TRUE,85.55555556,0.826767271,35.44444444,0.607706717,2,0.618927094,,,0.684467027
6368,In silico exploration of small-molecule α-helix mimetics as inhibitors of SARS-COV-2 attachment to ACE2.,J Biomol Struct Dyn,33023417,10/8/20,pubmed,0,7,"virtual screening, molecular dynamics simulation, in silico, in-silico",0.940575946,0.001330041,0.054103897,0.001330071,0.001330011,0.001330035,Drug discovery,0.71135896,TRUE,37,0.508936854,5,0.257024351,1,0.537564047,,,0.434508418
6369,State Actions and Shortages of Personal Protective Equipment and Staff in U.S. Nursing Homes.,J Am Geriatr Soc,33022757,10/7/20,pubmed,0,2,logistic regression,0.00098836,0.000988366,0.000988368,0.093758773,0.902287784,0.000988349,Healthcare,0.7744552,TRUE,96.5,0.860226359,142.5,0.87690661,4,0.707574542,,,0.814902503
6370,"1,2,4 triazolo[1,5-a] pyrimidin-7-ones as novel SARS-CoV-2 Main protease inhibitors: In silico screening and molecular dynamics simulation of potential COVID-19 drug candidates.",Biophys Chem,33022567,10/7/20,pubmed,0,3,"molecular dynamics simulation, in silico",0.991886508,0.001622695,0.001622689,0.001622698,0.001622709,0.001622701,Drug discovery,0.9388993,TRUE,16.33333333,0.247015895,6.333333333,0.285121755,0,0.403234768,,,0.311790806
6371,Implementing a negative pressure isolation space within a skilled nursing facility to control SARS-CoV-2 transmission.,Am J Infect Control,33022331,10/7/20,pubmed,0,5,computational,0.00101098,0.001011004,0.001010954,0.859802078,0.136154035,0.001010949,Epidemiology,0.86135745,TRUE,61.2,0.709815078,61.6,0.726719294,0,0.403234768,,,0.61325638
6372,Microbe hunting in the modern era: reflecting on a decade of microbial genomic epidemiology.,Curr Biol,33022254,10/7/20,pubmed,0,1,"sequencing, whole-genome, genomic epidemiology",0.002183343,0.521247689,0.074733986,0.397468295,0.002183409,0.002183278,Genomics,0.4248635,FALSE,59,0.696579875,62,0.728659352,0,0.403234768,,,0.609491332
6373,Changes in the clustering of unhealthy movement behaviors during the COVID-19 quarantine and the association with mental health indicators among Brazilian adults.,Transl Behav Med,33021631,10/7/20,pubmed,0,7,logistic regression,0.001415087,0.001415183,0.001415134,0.063973956,0.930365511,0.001415129,Healthcare,0.42954692,FALSE,78.14285714,0.798998083,61.71428571,0.727321381,5,0.739490092,,,0.755269852
6374,Association between renin-angiotensin-aldosterone system blockers and outcome in coronavirus disease 2019: analysing in-hospital exposure generates a biased seemingly protective effect of treatment.,J Hypertens,33021511,10/7/20,pubmed,0,10,logistic regression,0.020935174,0.001350336,0.001350348,0.073871499,0.001350406,0.901142237,Clinics,0.95446557,TRUE,68.2,0.754653967,47.7,0.671193471,5,0.739490092,,,0.721779177
6375,COVID-19 and myeloid cells: complex interplay correlates with lung severity.,J Clin Invest,33021506,10/7/20,pubmed,0,2,immunome,0.490322433,0.002183459,0.002183423,0.002183405,0.0021834,0.500943881,Clinics,0.47836617,FALSE,53,0.654400396,65.5,0.740767996,1,0.537564047,,,0.644244146
6376,A risk score to predict admission to the intensive care unit in patients with Covid-19: the ABC-GOALS score.,Salud Publica Mex,33021362,10/7/20,pubmed,0,6,"predictive model, logistic regression",0.002080545,0.002080526,0.068609158,0.002080617,0.002080542,0.923068612,Clinics,0.99574554,TRUE,37.83333333,0.516853238,10.5,0.363459995,8,0.799987654,,,0.560100295
6377,The community psychosocial burden during the COVID-19 pandemic in Indonesia.,Heliyon,33020744,10/7/20,pubmed,0,7,logistic regression,0.001438114,0.001438107,0.001438103,0.027642442,0.966605091,0.001438144,Healthcare,0.9582157,TRUE,16.42857143,0.248067289,0.428571429,0.076665775,4,0.707574542,,,0.344102535
6378,Scenarios for handling the impact of COVID-19 based on food supply network through regional food hubs under uncertainty.,Heliyon,33020743,10/7/20,pubmed,0,4,optimization model,0.001330038,0.001330035,0.001330111,0.8376883,0.15699143,0.001330085,Epidemiology,0.3620594,FALSE,26.5,0.389325252,0.75,0.099411292,0,0.403234768,,,0.297323771
6379,Novel hybrid antiviral VTRRT-13V2.1 against SARS-CoV2 main protease: retro-combinatorial synthesis and molecular dynamics analysis.,Heliyon,33020742,10/7/20,pubmed,0,1,"molecular dynamics simulation, in-silico",0.876015099,0.017075572,0.103805551,0.001034589,0.001034564,0.001034626,Drug discovery,0.6130823,TRUE,40,0.539860226,11,0.371287129,0,0.403234768,,,0.438127374
6380,0,Comput Struct Biotechnol J,33020707,10/7/20,pubmed,0,3,molecular dynamics simulation,0.946430448,0.001861812,0.046122475,0.001861839,0.001861722,0.001861705,Drug discovery,0.73208815,TRUE,54,0.661574618,9.333333333,0.342253144,2,0.618927094,,,0.540918286
6381,In-host Mathematical Modelling of COVID-19 in Humans.,Annu Rev Control,33020692,10/7/20,pubmed,0,2,"model simulation, mathematical model",0.432275517,0.072749487,0.00165317,0.432702152,0.00165316,0.058966513,Epidemiology,0.2995345,FALSE,53,0.654400396,36,0.611118544,12,0.850299401,,,0.70527278
6382,Comparing the impact of Hydroxychloroquine based regimens and standard treatment on COVID-19 patient outcomes: A retrospective cohort study.,Saudi Pharm J,33020690,10/7/20,pubmed,0,5,logistic regression,0.039888707,0.000999516,0.000999535,0.000999571,0.023218981,0.933893691,Clinics,0.8064323,TRUE,10,0.15214299,1,0.122023013,3,0.667819001,,,0.313995001
6383,How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms.,Int J Environ Res Public Health,33020395,10/7/20,pubmed,0,9,machine learning,0.001392877,0.001392842,0.175004829,0.001392884,0.819423705,0.001392863,Healthcare,0.95420945,TRUE,58.22222222,0.690766281,33.77777778,0.597404335,15,0.874313229,,,0.720827948
6384,Potential repurposing of drugs with anti-SARS-CoV-2 activity in preclinical trials: a systematic review.,Curr Med Chem,33019921,10/7/20,pubmed,0,5,in silico,0.661053865,0.002238538,0.002238542,0.236063042,0.096167542,0.002238471,Drug discovery,0.6794739,TRUE,10.8,0.161110768,3,0.199424672,0,0.403234768,,,0.254590069
6385,The Human Respiratory System and its Microbiome at a Glimpse.,Biology (Basel),33019595,10/7/20,pubmed,0,7,microbiom,0.410082942,0.470009633,0.002639376,0.002639166,0.111989693,0.00263919,Genomics,0.9392698,TRUE,59.14285714,0.697136496,12.42857143,0.390420123,13,0.858880178,,,0.648812266
6386,Using the Past to Maximize the Success Probability of Future Anti-Viral Vaccines.,Vaccines (Basel),33019507,10/7/20,pubmed,0,1,genomes,0.156536301,0.487703635,0.002238465,0.349044463,0.002238605,0.002238532,Genomics,0.65540826,TRUE,186,0.966911992,91,0.803987155,0,0.403234768,,,0.724711305
6387,Confidence intervals for the COVID-19 neutralizing antibody retention rate in the Korean population.,Genomics Inform,33017875,10/6/20,pubmed,0,3,bayes,0.181695148,0.188924111,0.002238502,0.592039296,0.002238563,0.032864381,Epidemiology,0.17960408,FALSE,107.3333333,0.884161049,208,0.921327268,2,0.618927094,,,0.80813847
6388,"Computational analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV genome using MEGA.",Genomics Inform,33017874,10/6/20,pubmed,0,1,"bayes, computational",0.001653046,0.634749721,0.001653155,0.311340658,0.001653146,0.048950273,Genomics,0.36945742,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
6389,Sampling manholes to home in on SARS-CoV-2 infections.,PLoS One,33017438,10/6/20,pubmed,0,3,computational,0.001330122,0.235348072,0.211477801,0.549183803,0.001330105,0.001330098,Epidemiology,0.031830043,FALSE,47,0.606407323,95.66666667,0.814021943,1,0.537564047,,,0.652664438
6390,Prediction and analysis of Corona Virus Disease 2019.,PLoS One,33017421,10/6/20,pubmed,0,5,"neural network, lstm",0.002080575,0.002080544,0.156291643,0.83538615,0.002080535,0.002080552,Epidemiology,0.65167856,TRUE,83.2,0.818665347,46,0.66416912,6,0.764429903,,,0.749088123
6391,"Origin, structural characteristics, prevention measures, diagnosis and potential drugs to prevent and COVID-19.",Medwave,33017386,10/6/20,pubmed,0,2,in silico,0.343501533,0.321503462,0.106877419,0.001310448,0.001310418,0.22549672,Drug discovery,0.87735367,TRUE,11.5,0.17416043,6,0.280037463,1,0.537564047,,,0.330587314
6392,VIRTUS: a pipeline for comprehensive virus analysis from conventional RNA-seq data.,Bioinformatics,33017003,10/6/20,pubmed,0,4,"bioinformatic, sequencing",0.32969067,0.661986401,0.002080912,0.002080786,0.002080695,0.002080536,Genomics,0.53234386,TRUE,128.25,0.919289999,940.25,0.993109446,0,0.403234768,,,0.771878071
6393,Frequent neurologic manifestations and encephalopathy-associated morbidity in Covid-19 patients.,Ann Clin Transl Neurol,33016619,10/6/20,pubmed,0,7,logistic regression,0.001461965,0.001461897,0.001462026,0.042725682,0.00146192,0.951426509,Clinics,0.8948539,TRUE,47.71428571,0.611911683,49.85714286,0.681027562,44,0.952157541,,,0.748365595
6394,"Structural analysis, virtual screening and molecular simulation to identify potential inhibitors targeting 2'-O-ribose methyltransferase of SARS-CoV-2 coronavirus.",J Biomol Struct Dyn,33016237,10/6/20,pubmed,0,7,"virtual screening, molecular dynamics simulation",0.992440736,0.001511871,0.001511809,0.001511883,0.001511842,0.001511859,Drug discovery,0.7832381,TRUE,17.71428571,0.266621312,30,0.570176612,3,0.667819001,,,0.501538975
6395,"Identification, protein antiglycation, antioxidant, antiproliferative, and molecular docking of novel bioactive peptides produced from hydrolysis of Lens culinaris.",J Food Biochem,33015836,10/6/20,pubmed,0,8,in silico,0.957399484,0.00098842,0.000988396,0.000988412,0.000988361,0.038646927,Drug discovery,0.79327697,TRUE,32.375,0.459459459,14.75,0.421327268,0,0.403234768,,,0.428007165
6396,Italian SARS-CoV-2 patients in intensive care: towards an identikit for subjects at risk?,Eur Rev Med Pharmacol Sci,33015815,10/6/20,pubmed,0,15,logistic regression,0.001330005,0.001330019,0.001330009,0.001330033,0.001330051,0.993349882,Clinics,0.7664892,TRUE,33.86666667,0.474735605,6.933333333,0.29435376,1,0.537564047,,,0.435551137
6397,Computational Evaluation of the Inhibition Efficacies of HIV Antivirals on SARS-CoV-2 (COVID-19) Protease and Identification of 3D Pharmacophore and Hit Compounds.,Adv Pharmacol Pharm Sci,33015628,10/6/20,pubmed,0,2,"computational, in silico",0.911165902,0.000907307,0.060325948,0.000907341,0.000907322,0.02578618,Drug discovery,0.89614326,TRUE,14.5,0.219617787,0,0.055525823,0,0.403234768,,,0.226126126
6398,PPARγ Cistrome Repression during Activation of Lung Monocyte-Macrophages in Severe COVID-19.,iScience,33015591,10/6/20,pubmed,0,4,transcriptom,0.798269277,0.00229684,0.002296604,0.002296579,0.002296682,0.192544018,Drug discovery,0.7703651,TRUE,59.5,0.699857752,37.75,0.620350549,2,0.618927094,,,0.646378465
6399,The SARS-CoV-2 pandemic course in Saudi Arabia: A dynamic epidemiological model.,Infect Dis Model,33015424,10/6/20,pubmed,0,4,prediction model,0.002490486,0.002490499,0.002490467,0.883934029,0.10610395,0.002490568,Epidemiology,0.2651533,FALSE,30,0.432432432,29.25,0.564557131,2,0.618927094,,,0.538638886
6400,Identification of twenty-five mutations in surface glycoprotein (Spike) of SARS-CoV-2 among Indian isolates and their impact on protein dynamics.,Gene Rep,33015411,10/6/20,pubmed,0,3,in silico,0.437316269,0.556309732,0.001593475,0.00159354,0.001593508,0.001593475,Genomics,0.76964736,TRUE,25.66666667,0.376461129,8.666666667,0.331415574,6,0.764429903,,,0.490768869
6401,0,Heliyon,33015402,10/6/20,pubmed,0,13,"sequencing, genomes",0.182683367,0.580686456,0.017585416,0.120839754,0.000966764,0.097238243,Genomics,0.36584264,FALSE,31.30769231,0.447584885,40.69230769,0.637811078,2,0.618927094,,,0.568107686
6402,Readiness for voice assistants to support healthcare delivery during a health crisis and pandemic.,NPJ Digit Med,33015374,10/6/20,pubmed,0,4,digital health,0.002806523,0.002806417,0.002806479,0.353021085,0.635753054,0.002806443,Healthcare,0.9098064,TRUE,3.25,0.039148989,0,0.055525823,7,0.785110192,,,0.293261668
6403,WHO Digital Health Guidelines: a milestone for global health.,NPJ Digit Med,33015373,10/6/20,pubmed,0,4,digital health,0.00333529,0.003335261,0.003335321,0.983323508,0.003335383,0.003335237,Epidemiology,0.6562444,TRUE,81.5,0.8129136,84.25,0.788801177,0,0.403234768,,,0.668316515
6404,Improving supply chain sustainability in the context of COVID-19 pandemic in an emerging economy: Exploring drivers using an integrated model.,Sustain Prod Consum,33015267,10/6/20,pubmed,0,6,structural model,0.001823414,0.001823421,0.202783027,0.789923433,0.00182338,0.001823324,Epidemiology,0.7777903,TRUE,41.66666667,0.554703445,23,0.513513514,5,0.739490092,,,0.602569017
6405,COVID-19 and Media datasets: Period- and location-specific textual data mining.,Data Brief,33015251,10/6/20,pubmed,0,1,"machine learning, data mining, dataset",0.005352877,0.005352913,0.268051738,0.710536704,0.005352962,0.005352805,Epidemiology,0.64520234,TRUE,70,0.764178366,16,0.437316029,0,0.403234768,,,0.534909721
6406,Survey data on government risk communication and citizen compliance during the COVID-19 pandemic in Vietnam.,Data Brief,33015250,10/6/20,pubmed,0,2,dataset,0.001987154,0.001987148,0.001987198,0.377629918,0.614421485,0.001987097,Healthcare,0.71707886,TRUE,45.5,0.591440411,12,0.386740701,1,0.537564047,,,0.505248386
6407,The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias.,Front Med (Lausanne),33015100,10/6/20,pubmed,0,9,"deep learning, neural network",0.001272636,0.001272659,0.951175535,0.001272689,0.043733782,0.001272699,Imaging,0.588768,TRUE,79.33333333,0.803822129,43.88888889,0.653465347,3,0.667819001,,,0.708368825
6408,Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic.,Front Public Health,33014985,10/6/20,pubmed,0,6,dataset,0.001684514,0.001684537,0.02541625,0.96784564,0.001684518,0.001684542,Epidemiology,0.59029305,TRUE,25.16666667,0.369843528,4.5,0.242708055,1,0.537564047,,,0.383371876
6409,Characteristic of 523 COVID-19 in Henan Province and a Death Prediction Model.,Front Public Health,33014973,10/6/20,pubmed,0,23,prediction model,0.001350372,0.001350334,0.111413372,0.001350377,0.001350348,0.883185198,Clinics,0.5787738,TRUE,26.47826087,0.38746985,,,1,0.537564047,,,0.462516948
6410,Facemasks simple but powerful weapons to protect against COVID-19 spread: Can they have sides effects?,Results Phys,33014697,10/6/20,pubmed,0,2,mathematical model,0.046963748,0.000800582,0.000800604,0.913860017,0.036774443,0.000800605,Epidemiology,0.9524586,TRUE,37.5,0.513946441,7.5,0.307867273,26,0.920859312,,,0.580891009
6411,Using Nominal Group Technique to Elucidate a COVID-19 Research Agenda for Maternal and Child Health (MCH) Populations.,Int J MCH AIDS,33014627,10/6/20,pubmed,0,6,artificial intelligence,0.00392771,0.003927653,0.338074404,0.533878125,0.116264455,0.003927653,Epidemiology,0.47005677,FALSE,22.16666667,0.327107428,0.833333333,0.102488627,0,0.403234768,,,0.277610274
6412,Clinical Factors Associated with Progression and Prolonged Viral Shedding in COVID-19 Patients: A Multicenter Study.,Aging Dis,33014523,10/6/20,pubmed,0,15,logistic regression,0.090393071,0.001156324,0.052848848,0.001156237,0.001156247,0.853289273,Clinics,0.9580609,TRUE,38.93333333,0.528047498,40.13333333,0.633730265,7,0.785110192,,,0.648962652
6413,A novel reusable anti-COVID-19 transparent face respirator with optimized airflow.,Biodes Manuf,33014512,10/6/20,pubmed,0,3,computational,0.082100714,0.001593575,0.001593567,0.911525163,0.001593492,0.00159349,Epidemiology,0.6919934,TRUE,60.33333333,0.704743645,24.66666667,0.526090447,5,0.739490092,,,0.656774728
6414,The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection.,Health Inf Sci Syst,33014355,10/6/20,pubmed,0,2,"machine learning, deep learning, neural network, classifier, dataset",0.026248128,0.000786344,0.943424513,0.027968295,0.000786339,0.000786382,Imaging,0.8319228,TRUE,14.5,0.219617787,0.5,0.087101953,3,0.667819001,,,0.324846247
6415,"Effective Anti-SARS-CoV-2 RNA Dependent RNA Polymerase Drugs Based on Docking Methods: The Case of Milbemycin, Ivermectin, and Baloxavir Marboxil.",Avicenna J Med Biotechnol,33014317,10/6/20,pubmed,0,1,computational,0.939745844,0.002296634,0.002296564,0.002296539,0.002296781,0.051067638,Drug discovery,0.91482854,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
6416,Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence.,Comput Math Methods Med,33014121,10/6/20,pubmed,0,5,"deep learning, artificial intelligence, neural network",0.001786524,0.001786574,0.930740169,0.062113536,0.001786584,0.001786612,Imaging,0.7527312,TRUE,22.2,0.327787742,5,0.257024351,10,0.828199272,,,0.471003788
6417,A numerical solution by alternative Legendre polynomials on a model for novel coronavirus (COVID-19).,Adv Differ Equ,33014025,10/6/20,pubmed,0,2,mathematical model,0.092508135,0.004309995,0.00431028,0.890251751,0.004309926,0.004309914,Epidemiology,0.66179633,TRUE,13,0.197352959,7.5,0.307867273,0,0.403234768,,,0.302818333
6418,Variations in SARS-CoV-2 Spike Protein Cell Epitopes and Glycosylation Profiles During Global Transmission Course of COVID-19.,Front Immunol,33013929,10/6/20,pubmed,0,4,"whole genome, genome sequences",0.511863179,0.48311836,0.001254586,0.001254618,0.001254598,0.00125466,Drug discovery,0.41464883,FALSE,13.5,0.205393036,23,0.513513514,4,0.707574542,,,0.475493697
6419,Excessive Neutrophils and Neutrophil Extracellular Traps in COVID-19.,Front Immunol,33013872,10/6/20,pubmed,0,10,"bayes, sequencing, transcriptom, network analysis",0.264127211,0.074355556,0.155656555,0.001098892,0.001098818,0.503662968,Clinics,0.8076566,TRUE,69.8,0.762694044,34.2,0.60001338,39,0.945737391,,,0.769481605
6420,Identification of Novel Candidate Epitopes on SARS-CoV-2 Proteins for South America: A Review of HLA Frequencies by Country.,Front Immunol,33013857,10/6/20,pubmed,0,5,dataset,0.516141256,0.346272792,0.064089238,0.071718543,0.000889097,0.000889074,Drug discovery,0.08184129,FALSE,35,0.488032655,11,0.371287129,4,0.707574542,,,0.522298108
6421,"Individual Differences, Economic Stability, and Fear of Contagion as Risk Factors for PTSD Symptoms in the COVID-19 Emergency.",Front Psychol,33013604,10/6/20,pubmed,0,11,logistic regression,0.001350365,0.001350354,0.001350324,0.330350452,0.664248173,0.001350333,Healthcare,0.9451713,TRUE,18.90909091,0.282206692,7,0.299973241,19,0.89561084,,,0.492596924
6422,"Differences Between Health Workers and General Population in Risk Perception, Behaviors, and Psychological Distress Related to COVID-19 Spread in Italy.",Front Psychol,33013555,10/6/20,pubmed,0,2,logistic regression,0.001593506,0.001593499,0.001593498,0.061741526,0.931884428,0.001593543,Healthcare,0.6178649,TRUE,8.5,0.126662131,11.5,0.378378378,28,0.926168282,,,0.477069597
6423,Screening of FDA Approved Drugs Against SARS-CoV-2 Main Protease: Coronavirus Disease.,Int J Pept Res Ther,33013255,10/6/20,pubmed,0,2,virtual screening,0.667282758,0.049892784,0.001187423,0.279262415,0.001187285,0.001187334,Drug discovery,0.9387256,TRUE,5,0.070752675,10.5,0.363459995,1,0.537564047,,,0.323925572
6424,Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.,Appl Soft Comput,33013254,10/6/20,pubmed,0,6,"deep learning, classifier, dataset",0.001438116,0.001438147,0.204558484,0.667425434,0.123701685,0.001438134,Epidemiology,0.29450184,FALSE,38,0.519327107,8.5,0.329141022,10,0.828199272,,,0.558889134
6425,0,Atmos Environ (1994),33013178,10/6/20,pubmed,0,7,"model simulation, dataset",0.001593553,0.001593538,0.001593495,0.992032313,0.001593553,0.001593549,Epidemiology,0.4849223,FALSE,90.14285714,0.843280351,92.71428571,0.807733476,0,0.403234768,,,0.684749532
6426,"Epidemiology, Genomic Structure, the Molecular Mechanism of Injury, Diagnosis and Clinical Manifestations of Coronavirus Infection: An Overview.",Indian J Nephrol,33013059,10/6/20,pubmed,0,5,"sequencing, genomic structure",0.109327138,0.581901619,0.001330063,0.001330117,0.001330082,0.304780981,Genomics,0.60358036,TRUE,78,0.798812543,22.4,0.506087771,1,0.537564047,,,0.614154787
6427,A Web Resource for Exploring the CORD-19 Dataset Using Root- and Rule-Based Phrases.,J Indian Inst Sci,33013023,10/6/20,pubmed,0,7,dataset,0.176722336,0.002130691,0.002130753,0.814754883,0.002130688,0.002130649,Epidemiology,0.7442794,TRUE,60.85714286,0.707526749,54,0.697952903,0,0.403234768,,,0.602904807
6428,Health is the Motive and Digital is the Instrument.,J Indian Inst Sci,33013022,10/6/20,pubmed,0,2,"artificial intelligence, digital health",0.00162283,0.036314991,0.001622869,0.957193719,0.001622855,0.001622736,Epidemiology,0.72298706,TRUE,5,0.070752675,0,0.055525823,1,0.537564047,,,0.221280848
6429,Deep learning approaches for COVID-19 detection based on chest X-ray images.,Expert Syst Appl,33013005,10/6/20,pubmed,0,2,"deep learning, neural network, classifier, deep-learning, dataset",0.000779431,0.000779437,0.996102814,0.000779469,0.000779411,0.000779439,Imaging,0.47118264,FALSE,40.5,0.544746119,52.5,0.690928552,13,0.858880178,,,0.69818495
6430,How can process safety and a risk management approach guide pandemic risk management?,J Loss Prev Process Ind,33013002,10/6/20,pubmed,0,6,"neural network, mathematical model",0.001593574,0.001593541,0.285720972,0.707904904,0.001593504,0.001593506,Epidemiology,0.43365547,FALSE,70,0.764178366,23.83333333,0.519467487,2,0.618927094,,,0.634190983
6431,Can digitization mitigate the economic damage of a pandemic? Evidence from SARS.,Telecomm Policy,33012958,10/6/20,pubmed,0,3,dataset,0.001653066,0.001653045,0.045969596,0.94741819,0.001653098,0.001653006,Epidemiology,0.7353588,TRUE,33,0.466757375,12.66666667,0.39530372,1,0.537564047,,,0.466541714
6432,Mathematical model and COVID-19.,Colomb Med (Cali),33012889,10/6/20,pubmed,0,4,mathematical model,0.002357842,0.002357865,0.002357751,0.903260081,0.002357893,0.087308568,Epidemiology,0.5099269,TRUE,67.75,0.751932711,59.25,0.718089377,0,0.403234768,,,0.624418952
6433,Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation.,Ann Emerg Med,33012378,10/6/20,pubmed,0,8,predictive model,0.001010918,0.001010921,0.087026962,0.001010955,0.001010944,0.908929301,Clinics,0.91898024,TRUE,29.375,0.423340961,26.25,0.539670859,35,0.939317242,,,0.634109687
6434,The Prognostic Value of Electrocardiogram at Presentation to Emergency Department in Patients With COVID-19.,Mayo Clin Proc,33012341,10/6/20,pubmed,0,33,logistic regression,0.001371234,0.030151537,0.053633847,0.001371241,0.001371267,0.912100874,Clinics,0.9507983,TRUE,164.8484848,0.954728184,214.4242424,0.924270805,6,0.764429903,,,0.881142964
6435,SARS-CoV-2 outbreak investigation in a German meat processing plant.,EMBO Mol Med,33012091,10/5/20,pubmed,0,10,"sequencing, genome sequences",0.001786611,0.540801852,0.001786583,0.420676142,0.001786562,0.03316225,Genomics,0.41450164,FALSE,48.9,0.621250541,88.1,0.796962804,6,0.764429903,,,0.72754775
6436,Risk factors for adverse outcomes among pregnant and postpartum women with acute respiratory distress syndrome due to COVID-19 in Brazil.,Int J Gynaecol Obstet,33011966,10/5/20,pubmed,0,12,logistic regression,0.00198709,0.00198716,0.001987092,0.001987244,0.394953384,0.597098029,Clinics,0.35149634,FALSE,22.5,0.333539489,11.91666667,0.383730265,1,0.537564047,,,0.418277934
6437,Predicting the response of the dental pulp to SARS-CoV2 infection: a transcriptome-wide effect cross-analysis.,Genes Immun,33011745,10/5/20,pubmed,0,4,"transcriptom, interactom, dataset",0.916799167,0.002898391,0.002898421,0.002898429,0.002898558,0.071607034,Drug discovery,0.9327251,TRUE,97.75,0.864060857,58.5,0.715814825,4,0.707574542,,,0.762483408
6438,Identification and validation of predictive factors for progression to severe COVID-19 pneumonia by proteomics.,Signal Transduct Target Ther,33011738,10/5/20,pubmed,0,10,proteom,0.01599888,0.015998865,0.920004017,0.015998932,0.015998387,0.016000919,Clinics,0.3265019,FALSE,26.2,0.383820892,21.3,0.494447418,2,0.618927094,,,0.499065135
6439,Novel cyclohexanone compound as a potential ligand against SARS-CoV-2 main-protease.,Microb Pathog,33011363,10/5/20,pubmed,0,4,molecular dynamics simulation,0.991734845,0.001653031,0.001653052,0.001653034,0.00165303,0.001653008,Drug discovery,0.83508223,TRUE,106.5,0.881996413,22.5,0.507492641,1,0.537564047,,,0.642351034
6440,A database resource and online analysis tools for coronaviruses on a historical and global scale.,Database (Oxford),33009914,10/4/20,pubmed,0,4,"proteom, genomes",0.33502429,0.62511375,0.00213078,0.002130848,0.033469702,0.00213063,Genomics,0.5138392,TRUE,14,0.213494959,15.25,0.427682633,4,0.707574542,,,0.449584045
6441,Clinical Characteristics and Outcomes of Patients Hospitalized for COVID-19 in Africa: Early Insights from the Democratic Republic of the Congo.,Am J Trop Med Hyg,33009770,10/4/20,pubmed,0,25,structural model,0.041241581,0.001538121,0.001538102,0.001538116,0.001538187,0.952605892,Clinics,0.8722054,TRUE,108.56,0.886944152,181.68,0.907412363,17,0.887338725,,,0.893898413
6442,"Knowledge, Attitude and Practice toward COVID-19 among Egyptians.",J Epidemiol Glob Health,33009730,10/4/20,pubmed,0,7,logistic regression,0.001022627,0.001022604,0.001022603,0.001022627,0.994886908,0.00102263,Healthcare,0.756376,TRUE,10,0.15214299,0.857142857,0.103224512,4,0.707574542,,,0.320980681
6443,Telehealth Capability Among Substance Use Disorder Treatment Facilities in Counties With High Versus Low COVID-19 Social Distancing.,J Addict Med,33009166,10/4/20,pubmed,0,3,logistic regression,0.001511797,0.001511816,0.001511828,0.646621317,0.347331437,0.001511805,Epidemiology,0.3963203,FALSE,157,0.949347517,173.6666667,0.902127375,1,0.537564047,,,0.796346313
6444,In silico molecular docking: Evaluation of coumarin based derivatives against SARS-CoV-2.,J Infect Public Health,33008777,10/4/20,pubmed,0,5,in silico,0.912571866,0.000889097,0.000889096,0.083871765,0.000889104,0.000889072,Drug discovery,0.96580917,TRUE,10.6,0.158142124,3.6,0.21507894,3,0.667819001,,,0.347013355
6445,Effectiveness of COVID-19 diagnosis and management tools: A review.,Radiography (Lond),33008761,10/4/20,pubmed,0,2,artificial intelligence,0.001010966,0.069356488,0.863585451,0.064025092,0.001011,0.001011004,Imaging,0.6987304,TRUE,3.5,0.044344115,0,0.055525823,2,0.618927094,,,0.239599011
6446,Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis.,Hum Genomics,33008459,10/4/20,pubmed,0,1,"machine learning, artificial intelligence, metabolom, multi-omics",0.130074614,0.13041756,0.454813974,0.000871603,0.064835957,0.218986291,Clinics,0.758535,TRUE,56,0.675304595,5,0.257024351,1,0.537564047,,,0.489964331
6447,A comparative study of international and Chinese public health emergency management from the perspective of knowledge domains mapping.,Environ Health Prev Med,33008319,10/4/20,pubmed,0,10,network analysis,0.00090735,0.000907306,0.000907362,0.995463374,0.000907318,0.00090729,Epidemiology,0.9013895,TRUE,8.8,0.129630775,13.3,0.403331549,0,0.403234768,,,0.312065697
6448,Digital Healthy Diet Literacy and Self-Perceived Eating Behavior Change during COVID-19 Pandemic among Undergraduate Nursing and Medical Students: A Rapid Online Survey.,Int J Environ Res Public Health,33008102,10/4/20,pubmed,0,12,digital health,0.001511915,0.001511999,0.001511883,0.001511952,0.907311469,0.086640782,Healthcare,0.8989719,TRUE,16.91666667,0.25394273,6.666666667,0.290607439,5,0.739490092,,,0.42801342
6449,Bullying and Harassment in Downsized Workplaces: What Can We Learn from the 2008 Icelandic Economic Collapse?,Int J Environ Res Public Health,33008097,10/4/20,pubmed,0,3,dataset,0.00235774,0.002357807,0.002357922,0.787483609,0.203085093,0.00235783,Epidemiology,0.8910898,TRUE,4.333333333,0.057950399,0.333333333,0.073187048,1,0.537564047,,,0.222900498
6450,The Adoption of Preventive Behaviors during the COVID-19 Pandemic in China and Israel.,Int J Environ Res Public Health,33007985,10/4/20,pubmed,0,2,model fit,0.001126825,0.001126828,0.001126809,0.315278607,0.680214107,0.001126824,Healthcare,0.519284,TRUE,46.5,0.601026656,95,0.813085363,6,0.764429903,,,0.726180641
6451,Blockchain in Healthcare: Insights on COVID-19.,Int J Environ Res Public Health,33007951,10/4/20,pubmed,0,4,"artificial intelligence, prediction model, digital health",0.002562614,0.002562642,0.213861682,0.684872757,0.093577719,0.002562586,Epidemiology,0.8274809,TRUE,23,0.34225988,4.25,0.235750602,5,0.739490092,,,0.439166858
6452,The Disproportionate Burden of the COVID-19 Pandemic Among Pregnant Black Women.,Psychiatry Res,33007683,10/3/20,pubmed,0,16,logistic regression,0.001511843,0.001511895,0.001511855,0.00151185,0.992440713,0.001511845,Healthcare,0.8950951,TRUE,95.375,0.857690643,176.8125,0.904067434,4,0.707574542,,,0.823110873
6453,An in-silico evaluation of COVID-19 main protease with clinically approved drugs.,J Mol Graph Model,33007575,10/3/20,pubmed,0,4,in-silico,0.922196711,0.072050612,0.00143811,0.001438307,0.001438136,0.001438124,Drug discovery,0.9784694,TRUE,33,0.466757375,10,0.355632861,2,0.618927094,,,0.48043911
6454,SARS Coronavirus-2 variant tracing within the first Coronavirus Disease 19 clusters in northern Germany.,Clin Microbiol Infect,33007476,10/3/20,pubmed,0,17,"sequencing, metagenom",0.001291243,0.932095726,0.00129121,0.001291263,0.01888405,0.045146508,Genomics,0.39521486,FALSE,67.47058824,0.749829921,112.7058824,0.842052448,1,0.537564047,,,0.709815472
6455,Psychological Impact of COVID-19 on ICU Caregivers.,Anaesth Crit Care Pain Med,33007463,10/3/20,pubmed,0,4,logistic regression,0.001538086,0.001538094,0.068072975,0.001538152,0.603833059,0.323479634,Healthcare,0.4846778,FALSE,21.25,0.314614386,5.75,0.271742039,4,0.707574542,,,0.431310322
6456,Zoonotic evolution and implications of microbiome in viral transmission and infection.,Virus Res,33007342,10/3/20,pubmed,0,4,"phylogenom, microbiom",0.002130703,0.830430251,0.002130738,0.161046833,0.002130682,0.002130793,Genomics,0.767465,TRUE,28.75,0.415115344,26.25,0.539670859,0,0.403234768,,,0.452673657
6457,"The association of smoking status with SARS-CoV-2 infection, hospitalization and mortality from COVID-19: a living rapid evidence review with Bayesian meta-analyses (version 7).",Addiction,33007104,10/3/20,pubmed,0,4,bayes,0.001310425,0.001310356,0.001310439,0.174514547,0.223223608,0.598330625,Clinics,0.3806781,FALSE,130.25,0.922011256,91,0.803987155,36,0.941169208,,,0.889055873
6458,Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis.,PLoS One,33007054,10/3/20,pubmed,0,4,probabilistic,0.002996447,0.002996428,0.002996916,0.985017359,0.002996428,0.002996422,Epidemiology,0.2385312,FALSE,29.5,0.426000371,20.25,0.483074659,0,0.403234768,,,0.437436599
6459,Impact of the COVID-19 epidemic on patterns of pregnant women's perception of threat and its relationship to mental state: A latent class analysis.,PLoS One,33007020,10/3/20,pubmed,0,5,logistic regression,0.001538123,0.19422989,0.068399349,0.001538233,0.686438211,0.047856193,Healthcare,0.99139607,TRUE,34.8,0.484940318,18.4,0.464677549,0,0.403234768,,,0.450950878
6460,In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm.,PLoS One,33006999,10/3/20,pubmed,0,13,"artificial intelligence, in-silico",0.474186237,0.001593581,0.18899659,0.001593598,0.001593541,0.332036454,Drug discovery,0.7841572,TRUE,27.07692308,0.396313934,76,0.769132994,4,0.707574542,,,0.62434049
6461,Similarity between mutation spectra in hypermutated genomes of rubella virus and in SARS-CoV-2 genomes accumulated during the COVID-19 pandemic.,PLoS One,33006981,10/3/20,pubmed,0,5,"genomes, dataset",0.001538179,0.992309122,0.001538238,0.001538168,0.001538112,0.001538181,Genomics,0.5926562,TRUE,47.2,0.607520564,214.8,0.924739096,0,0.403234768,,,0.645164809
6462,Mental Health During the COVID-19 Pandemic in the United States: Online Survey.,JMIR Form Res,33006939,10/3/20,pubmed,0,6,logistic regression,0.000786364,0.000786367,0.000786358,0.037279718,0.959574807,0.000786386,Healthcare,0.90930116,TRUE,15.16666667,0.228709258,11.33333333,0.375501739,0,0.403234768,,,0.335815255
6463,Molecular Simulations and Network Modeling Reveal an Allosteric Signaling in the SARS-CoV-2 Spike Proteins.,J Proteome Res,33006900,10/3/20,pubmed,0,1,"computational, network model",0.911160616,0.03734254,0.000999533,0.048498305,0.000999506,0.0009995,Drug discovery,0.91396654,TRUE,119,0.905683716,87,0.795223441,9,0.814309525,,,0.838405561
6464,Bentonite Clay: A Potential Natural Sanitizer for Preventing Neurological Disorders.,ACS Chem Neurosci,33006886,10/3/20,pubmed,0,2,microbiom,0.002898359,0.456854442,0.002898476,0.216073844,0.318376356,0.002898523,Genomics,0.79918814,TRUE,61,0.709196611,18.5,0.46554723,0,0.403234768,,,0.52599287
6465,Application of the Health Action Process Approach to Social Distancing Behavior During COVID-19.,Appl Psychol Health Well Being,33006814,10/3/20,pubmed,0,5,model fit,0.001203457,0.001203493,0.001203423,0.416749099,0.57843709,0.001203438,Healthcare,0.65528166,TRUE,155.6,0.948296122,276.8,0.947685309,0,0.403234768,,,0.7664054
6466,Systemic effects of missense mutations on SARS-CoV-2 spike glycoprotein stability and receptor-binding affinity.,Brief Bioinform,33006605,10/3/20,pubmed,0,5,computational,0.764982699,0.211649302,0.00127263,0.001272654,0.001272622,0.019550093,Drug discovery,0.95548636,TRUE,25.2,0.370214608,21.4,0.496052984,2,0.618927094,,,0.495064895
6467,Risk factors for myocardial injury in patients with coronavirus disease 2019 in China.,ESC Heart Fail,33006440,10/3/20,pubmed,0,11,"predictive model, logistic regression",0.000999513,0.000999516,0.000999535,0.000999559,0.000999523,0.995002355,Clinics,0.9436203,TRUE,68.09090909,0.754159194,26.27272727,0.539804656,1,0.537564047,,,0.610509299
6468,A Novel Strategy for the Development of Vaccines for SARS-CoV-2 (COVID-19) and Other Viruses Using AI and Viral Shell Disorder.,J Proteome Res,33006287,10/3/20,pubmed,0,4,"artificial intelligence, neural network",0.269045966,0.437314847,0.108674805,0.001126854,0.086611296,0.097226231,Genomics,0.7081665,TRUE,37.75,0.516296617,21.75,0.50046829,3,0.667819001,,,0.561527969
6469,Molecular characterization of canine coronaviruses: an enteric and pantropic approach.,Arch Virol,33005986,10/3/20,pubmed,0,4,"bioinformatic, sequencing",0.145118729,0.814214238,0.001511847,0.001511842,0.001511829,0.036131514,Genomics,0.7858671,TRUE,25.5,0.37435834,14.25,0.415038801,0,0.403234768,,,0.39754397
6470,Θ-SEIHRD mathematical model of Covid19-stability analysis using fast-slow decomposition.,PeerJ,33005495,10/3/20,pubmed,0,3,mathematical model,0.002562677,0.002562655,0.002562827,0.987186566,0.002562677,0.002562598,Epidemiology,0.2994775,FALSE,15.66666667,0.236563795,1.333333333,0.13252609,1,0.537564047,,,0.302217978
6471,"Genomic diversity and evolution, diagnosis, prevention, and therapeutics of the pandemic COVID-19 disease.",PeerJ,33005486,10/3/20,pubmed,0,5,"whole-genome, genomes",0.326775408,0.618405399,0.001254665,0.001254683,0.001254733,0.051055111,Genomics,0.68092525,TRUE,19.6,0.291854784,12.6,0.394300241,2,0.618927094,,,0.435027373
6472,Policy-aware data lakes: a flexible approach to achieve legal interoperability for global research collaborations.,J Law Biosci,33005429,10/3/20,pubmed,0,1,dataset,0.001717294,0.001717239,0.454260627,0.538870349,0.001717245,0.001717246,Epidemiology,0.34559268,FALSE,7,0.10179974,1,0.122023013,1,0.537564047,,,0.2537956
6473,0,Future Virol,33005212,10/3/20,pubmed,0,1,"computational, bioinformatic",0.515988383,0.475278393,0.002183251,0.002183265,0.002183381,0.002183327,Drug discovery,0.8967801,TRUE,6,0.086028821,7,0.299973241,2,0.618927094,,,0.334976385
6474,"Prevalence and Correlation of Anxiety, Insomnia and Somatic Symptoms in a Chinese Population During the COVID-19 Epidemic.",Front Psychiatry,33005165,10/3/20,pubmed,0,14,logistic regression,0.0013503,0.001350312,0.001350318,0.001350355,0.94564122,0.048957496,Healthcare,0.98714024,TRUE,59.78571429,0.701218381,,,6,0.764429903,,,0.732824142
6475,Designing a multi-epitope peptide based vaccine against SARS-CoV-2.,Sci Rep,33004978,10/3/20,pubmed,0,4,in-silico,0.630973371,0.002183239,0.002183302,0.360293533,0.002183339,0.002183217,Drug discovery,0.5404558,TRUE,939.75,0.999505226,369,0.968089377,4,0.707574542,,,0.891723048
6476,The turning point and end of an expanding epidemic cannot be precisely forecast.,Proc Natl Acad Sci U S A,33004629,10/3/20,pubmed,0,4,"bayes, dataset",0.028938493,0.001751207,0.001751212,0.964056777,0.001751149,0.001751162,Epidemiology,0.19733474,FALSE,168.25,0.957202053,188.75,0.911359379,27,0.92443978,,,0.931000404
6477,"Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19.",Eur Respir Rev,33004526,10/3/20,pubmed,0,3,"machine learning, deep learning, artificial intelligence, predictive model",0.001622741,0.001622713,0.704837396,0.234601598,0.05569279,0.001622763,Epidemiology,0.5582145,TRUE,97.33333333,0.862762076,76.66666667,0.770404067,0,0.403234768,,,0.678800304
6478,Cardiometabolic multimorbidity is associated with a worse Covid-19 prognosis than individual cardiometabolic risk factors: a multicentre retrospective study (CoViDiab II).,Cardiovasc Diabetol,33004045,10/3/20,pubmed,0,79,logistic regression,0.001415088,0.0014152,0.00141511,0.001415099,0.001415161,0.992924342,Clinics,0.907729,TRUE,49.15,0.625146886,,,11,0.840175319,,,0.732661103
6479,Performance of Targeted Library Preparation Solutions for SARS-CoV-2 Whole Genome Analysis.,Diagnostics (Basel),33003465,10/3/20,pubmed,0,6,"sequencing, whole genome, genome sequences",0.003101554,0.811697677,0.17589611,0.003101673,0.003101518,0.003101467,Genomics,0.45839697,FALSE,42.16666667,0.55915641,9.333333333,0.342253144,6,0.764429903,,,0.555279819
6480,Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19.,Healthcare (Basel),33003439,10/3/20,pubmed,0,5,machine learning,0.11020168,0.003101538,0.074501116,0.639061767,0.00310207,0.170031829,Epidemiology,0.8424056,TRUE,21.2,0.313686684,2.2,0.16838373,2,0.618927094,,,0.36699917
6481,The Avon Longitudinal Study of Parents and Children - A resource for COVID-19 research: Questionnaire data capture May-July 2020.,Wellcome Open Res,32995559,10/3/20,pubmed,0,7,dataset,0.001392903,0.001392922,0.11197693,0.23978857,0.644055787,0.001392887,Healthcare,0.42942774,FALSE,74,0.782113922,110.4285714,0.839042012,2,0.618927094,,,0.746694343
6482,"A computational toolset for rapid identification of SARS-CoV-2, other viruses and microorganisms from sequencing data.",Brief Bioinform,33003197,10/2/20,pubmed,0,5,"computational, sequencing, genomes",0.002080595,0.766908212,0.224769483,0.00208061,0.002080576,0.002080524,Genomics,0.7399217,TRUE,46.6,0.60170697,22.8,0.509967889,0,0.403234768,,,0.504969875
6483,SARS-CoV-2 infection serology validation of different methods: Usefulness of IgA in the early phase of infection.,Clin Chim Acta,33002475,10/2/20,pubmed,0,11,genomes,0.11949973,0.580951287,0.211110283,0.00203283,0.002032882,0.084372988,Genomics,0.43210858,FALSE,23.81818182,0.351289505,17.36363636,0.454308269,4,0.707574542,,,0.504390772
6484,Microbial cell-free DNA in plasma of patients with sepsis: a potential diagnostic methodology.,Discov Med,33002409,10/2/20,pubmed,0,11,sequencing,0.001034703,0.673275678,0.116358806,0.001034627,0.001034575,0.207261612,Genomics,0.7649938,TRUE,77,0.794668811,74.18181818,0.764516992,0,0.403234768,,,0.65414019
6485,In-silico design of a potential inhibitor of SARS-CoV-2 S protein.,PLoS One,33002032,10/2/20,pubmed,0,2,"computational, in-silico",0.990691238,0.00186172,0.001861769,0.001861753,0.001861714,0.001861805,Drug discovery,0.8049834,TRUE,11.5,0.17416043,11,0.371287129,5,0.739490092,,,0.42831255
6486,Can routine laboratory variables predict survival in COVID-19? An artificial neural network-based approach.,Clin Chem Lab Med,33001844,10/2/20,pubmed,0,9,neural network,0.011749776,0.011749855,0.514874637,0.011749922,0.011750239,0.438125572,Clinics,0.655314,TRUE,127.8888889,0.918609685,165.3333333,0.896106503,1,0.537564047,,,0.784093412
6487,Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study.,JMIR Public Health Surveill,33001839,10/2/20,pubmed,0,8,mathematical model,0.001272646,0.001272652,0.00127266,0.993636714,0.001272648,0.001272679,Epidemiology,0.8590152,TRUE,52.125,0.647844641,16,0.437316029,0,0.403234768,,,0.496131812
6488,Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study.,J Med Internet Res,33001832,10/2/20,pubmed,0,13,artificial intelligence,0.013994204,0.000772619,0.565711646,0.190831082,0.227917769,0.000772681,Imaging,0.80775464,TRUE,77.84615385,0.797884841,70.30769231,0.754281509,0,0.403234768,,,0.651800373
6489,No evidence that prefrontal HD-tDCS influences cue-induced food craving.,Behav Neurosci,33001679,10/2/20,pubmed,0,4,bayes,0.26852676,0.001823403,0.14440388,0.123795967,0.459626611,0.001823379,Healthcare,0.81602967,TRUE,84,0.821881378,200.5,0.917781643,0,0.403234768,,,0.714299263
6490,First results of the "Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS)".,Infection,33001409,10/2/20,pubmed,0,35,logistic regression,0.001187396,0.001187309,0.045457629,0.001187308,0.121020407,0.829959951,Clinics,0.9261981,TRUE,51.85714286,0.645370771,32.11428571,0.586098475,9,0.814309525,,,0.681926257
6491,Dynamically evolving novel overlapping gene as a factor in the SARS-CoV-2 pandemic.,Elife,33001029,10/2/20,pubmed,0,8,genomes,0.002562715,0.987186991,0.002562607,0.002562586,0.002562578,0.002562524,Genomics,0.82967657,TRUE,63.5,0.72496753,85,0.790406743,0,0.403234768,,,0.639536347
6492,Analysis of Chinese Herbal Formulae Recommended for COVID-19 in Dif- ferent Schemes in China: A Data Mining Approach,Comb Chem High Throughput Screen,33001008,10/2/20,pubmed,0,5,"data mining, correlation analysis",0.185732402,0.001141394,0.001141391,0.606023506,0.082133664,0.123827643,Epidemiology,0.99063396,TRUE,9,0.135320675,3.2,0.202100615,0,0.403234768,,,0.246885353
6493,"Workplace health and safety training, employees' risk perceptions, behavioral safety compliance, and perceived job insecurity during COVID-19: Data of Vietnam.",Data Brief,32999913,10/2/20,pubmed,0,5,dataset,0.002898311,0.002898397,0.153698518,0.002898497,0.834707947,0.00289833,Healthcare,0.6354439,TRUE,6.8,0.09629538,0.4,0.075796093,1,0.537564047,,,0.23655184
6494,Role of Biochemical Markers in Invasive Ventilation of Coronavirus Disease 2019 Patients: Multinomial Regression and Survival Analysis.,Cureus,32999777,10/2/20,pubmed,0,10,logistic regression,0.000871606,0.000871604,0.000871538,0.000871576,0.000871559,0.995642116,Clinics,0.7333784,TRUE,24.6,0.362483765,11,0.371287129,2,0.618927094,,,0.450899329
6495,0,PeerJ,32999768,10/2/20,pubmed,0,5,data mining,0.932773571,0.001861721,0.059779408,0.001861759,0.001861777,0.001861764,Drug discovery,0.8954267,TRUE,53.6,0.657987507,54.2,0.698220498,7,0.785110192,,,0.713772732
6496,A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks.,Inf Sci (N Y),32999505,10/2/20,pubmed,0,2,"supervised learning, neural network, dataset",0.002080559,0.002080555,0.989597093,0.002080643,0.002080547,0.002080603,Imaging,0.6381731,TRUE,71,0.769064259,19.5,0.475983409,8,0.799987654,,,0.68167844
6497,0,mSphere,32999084,10/2/20,pubmed,0,1,microbiom,0.003214262,0.295522031,0.003214157,0.003214367,0.691621009,0.003214174,Healthcare,0.5228774,TRUE,17,0.257467994,10,0.355632861,0,0.403234768,,,0.338778541
6498,COVID-19 and Parent Intention to Vaccinate Their Children Against Influenza.,Pediatrics,32999011,10/2/20,pubmed,0,2,logistic regression,0.001511852,0.001511851,0.051383066,0.001511911,0.942569468,0.001511853,Healthcare,0.3454947,FALSE,40.5,0.544746119,21.5,0.498260637,2,0.618927094,,,0.55397795
6499,"CURB-65 may serve as a useful prognostic marker in COVID-19 patients within Wuhan, China: a retrospective cohort study.",Epidemiol Infect,32998791,10/2/20,pubmed,0,13,logistic regression,0.001156237,0.001156232,0.03049695,0.001156276,0.001156335,0.964877969,Clinics,0.88464296,TRUE,154.1538462,0.947182881,74.61538462,0.765520471,2,0.618927094,,,0.777210149
6500,The inhibitory effect of some natural bioactive compounds against SARS-CoV-2 main protease: insights from molecular docking analysis and molecular dynamic simulation.,J Environ Sci Health A Tox Hazard Subst Environ Eng,32998618,10/2/20,pubmed,0,7,sequence alignment,0.994218736,0.001156312,0.001156278,0.001156239,0.001156217,0.001156218,Drug discovery,0.9721122,TRUE,11.28571429,0.169460078,1.571428571,0.139416644,3,0.667819001,,,0.325565241
6501,"Renin Angiotensin System, COVID-19 and Male Fertility: Any Risk for Conceiving?",Microorganisms,32998451,10/2/20,pubmed,0,9,bioinformatic,0.552550634,0.00146192,0.001462102,0.238194808,0.001461981,0.204868555,Drug discovery,0.9674927,TRUE,86,0.829117447,49.33333333,0.678953706,1,0.537564047,,,0.6818784
6502,Frontiers of Robotic Gastroscopy: A Comprehensive Review of Robotic Gastroscopes and Technologies.,Cancers (Basel),32998213,10/2/20,pubmed,0,10,artificial intelligence,0.001415178,0.001415187,0.586030594,0.288511805,0.060097689,0.062529547,Epidemiology,0.9887569,TRUE,109.2,0.888490321,37,0.616671127,2,0.618927094,,,0.708029514
6503,Predicting the evolution and control of the COVID-19 pandemic in Portugal.,F1000Res,32983416,10/2/20,pubmed,0,2,mathematical model,0.002357719,0.002357723,0.00235776,0.988211016,0.002357837,0.002357945,Epidemiology,0.48226476,FALSE,16,0.243552477,24,0.521808938,1,0.537564047,,,0.434308487
6504,"Early prediction of mortality risk among patients with severe COVID-19, using machine learning.",Int J Epidemiol,32997743,10/1/20,pubmed,0,23,"machine learning, phenomics, predictive model, logistic regression",0.001486496,0.001486425,0.218680874,0.001486432,0.001486409,0.775373364,Clinics,0.74630463,TRUE,41.17391304,0.549755705,15.91304348,0.434706984,5,0.739490092,,,0.574650927
6505,Development and external validation of a prediction risk model for short-term mortality among hospitalized U.S. COVID-19 patients: A proposal for the COVID-AID risk tool.,PLoS One,32997700,10/1/20,pubmed,0,19,prediction model,0.014350741,0.001046809,0.224733553,0.001046865,0.017915973,0.740906059,Clinics,0.9684318,TRUE,110.7368421,0.891087884,107.0526316,0.834626706,5,0.739490092,,,0.821734894
6506,IL6-mediated HCoV-host interactome regulatory network and GO/Pathway enrichment analysis.,PLoS Comput Biol,32997660,10/1/20,pubmed,0,2,interactom,0.617535451,0.002130816,0.002130847,0.373941485,0.002130693,0.002130709,Drug discovery,0.70917726,TRUE,131.5,0.923495578,44.5,0.656743377,1,0.537564047,,,0.705934334
6507,The Need for Sustainable Teleconsultation Systems in the Aftermath of the First COVID-19 Wave.,J Med Internet Res,32997642,10/1/20,pubmed,0,6,digital health,0.002183275,0.002183238,0.002183284,0.509580207,0.446843,0.037026996,Epidemiology,0.6679715,TRUE,38.16666667,0.520378502,31.5,0.581883864,0,0.403234768,,,0.501832378
6508,A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19.,IEEE J Biomed Health Inform,32997638,10/1/20,pubmed,0,12,"computational, predictive model",0.0010722,0.001072215,0.001072248,0.994638899,0.00107219,0.001072248,Epidemiology,0.304617,FALSE,22.27272727,0.328468056,6.818181818,0.293082687,1,0.537564047,,,0.386371597
6509,External validation of a clinical risk score to predict hospital admission and in-hospital mortality in COVID-19 patients.,Ann Med,32997542,10/1/20,pubmed,0,7,logistic regression,0.000907296,0.000907304,0.000907334,0.000907313,0.000907293,0.995463461,Clinics,0.64968455,TRUE,25.14285714,0.369781681,10.14285714,0.356636339,2,0.618927094,,,0.448448372
6510,"Risk factors for mortality in critically ill patients with COVID-19 in Huanggang, China: A single-center multivariate pattern analysis.",J Med Virol,32997344,10/1/20,pubmed,0,9,prediction model,0.001098864,0.001098814,0.106793869,0.001098854,0.113331195,0.776578404,Clinics,0.96827155,TRUE,12.88888889,0.193023687,8,0.320511105,1,0.537564047,,,0.35036628
6511,A multimodal deep learning-based drug repurposing approach for treatment of COVID-19.,Mol Divers,32997257,10/1/20,pubmed,0,6,"deep learning, computational",0.590797598,0.001565428,0.402940934,0.001565383,0.001565316,0.001565341,Drug discovery,0.8293947,TRUE,51,0.63875317,21.5,0.498260637,3,0.667819001,,,0.601610936
6512,The association of treatment with hydroxychloroquine and hospital mortality in COVID-19 patients.,Intern Emerg Med,32997237,10/1/20,pubmed,0,3,logistic regression,0.031012175,0.001861687,0.0018617,0.001861726,0.001861753,0.961540958,Clinics,0.5800345,TRUE,66.33333333,0.742903086,107.3333333,0.835094996,10,0.828199272,,,0.802065785
6513,"Factors associated with COVID-19 hospital deaths in Espírito Santo, Brazil, 2020.",Epidemiol Serv Saude,32997070,10/1/20,pubmed,0,9,logistic regression,0.002080571,0.002080606,0.002080564,0.073412373,0.002080676,0.918265211,Clinics,0.9669556,TRUE,64.55555556,0.732018059,22.77777778,0.509298903,1,0.537564047,,,0.592960336
6514,Forecast UTI: application for predicting intensive care unit beds in the context of the COVID-19 pandemic.,Epidemiol Serv Saude,32997068,10/1/20,pubmed,0,11,computational,0.001861704,0.001861686,0.001861746,0.805307517,0.001861704,0.187245643,Epidemiology,0.69865584,TRUE,18.18181818,0.27274414,13.90909091,0.410021408,1,0.537564047,,,0.406776532
6515,"Genomic and phylogenetic characterisation of an imported case of SARS-CoV-2 in Amazonas State, Brazil.",Mem Inst Oswaldo Cruz,32997001,10/1/20,pubmed,0,11,sequencing,0.002238514,0.988807155,0.00223868,0.0022386,0.002238624,0.002238428,Genomics,0.18314463,FALSE,17.09090909,0.257715381,22,0.503746321,10,0.828199272,,,0.529886991
6516,Current Procedural Terminology Codes for Medication Therapy Management in Administrative Data.,J Manag Care Spec Pharm,32996390,10/1/20,pubmed,0,9,dataset,0.001141348,0.001141352,0.231112739,0.131024017,0.481928019,0.153652525,Healthcare,0.057952195,FALSE,53.88888889,0.66021399,23.77777778,0.518932299,0,0.403234768,,,0.527460352
6517,Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review.,J Prim Care Community Health,32996368,10/1/20,pubmed,0,4,"machine learning, deep learning, artificial intelligence, data mining",0.048349924,0.001022644,0.563054929,0.292763313,0.093786534,0.001022657,Epidemiology,0.55644184,TRUE,3.25,0.039148989,1.25,0.127776291,3,0.667819001,,,0.278248094
6518,The impact of COVID-19 pandemic on rheumatology practice: a cross-sectional multinational study.,Clin Rheumatol,32996071,10/1/20,pubmed,0,10,logistic regression,0.122341685,0.001310357,0.001310377,0.001310432,0.755128011,0.118599138,Healthcare,0.9537214,TRUE,21,0.312016822,2.3,0.171394166,5,0.739490092,,,0.407633694
6519,"Identification of potential inhibitors of coronavirus hemagglutinin-esterase using molecular docking, molecular dynamics simulation and binding free energy calculation.",Mol Divers,32996011,10/1/20,pubmed,0,4,"virtual screening, molecular dynamics simulation, computational",0.991244001,0.001751172,0.001751167,0.001751292,0.001751226,0.001751142,Drug discovery,0.92020226,TRUE,19.5,0.29117447,1.75,0.148381054,2,0.618927094,,,0.352827539
6520,#Covid4Rheum: an analytical twitter study in the time of the COVID-19 pandemic.,Rheumatol Int,32995894,10/1/20,pubmed,0,3,data mining,0.002720182,0.002720178,0.002720248,0.943780961,0.04533833,0.002720102,Epidemiology,0.75829613,TRUE,16,0.243552477,0.666666667,0.096200161,1,0.537564047,,,0.292438895
6521,Outcomes associated with SARS-CoV-2 viral clades in COVID-19.,medRxiv,32995827,10/1/20,pubmed,0,13,"machine learning, whole genome",0.001034578,0.500622871,0.10081735,0.00103463,0.001034591,0.395455978,Genomics,0.4386861,FALSE,24.30769231,0.35858742,26.76923077,0.543751672,4,0.707574542,,,0.536637878
6522,Characterizing COVID-19 Clinical Phenotypes and Associated Comorbidities and Complication Profiles.,medRxiv,32995813,10/1/20,pubmed,0,17,dataset,0.000772641,0.000772661,0.092613805,0.00077265,0.00077265,0.904295592,Clinics,0.36179265,FALSE,26.47058824,0.387408003,13.23529412,0.402595665,1,0.537564047,,,0.442522572
6523,Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19.,medRxiv,32995811,10/1/20,pubmed,0,16,"deep learning, neural network, dataset",0.00105933,0.001059362,0.609081836,0.001059404,0.001059371,0.386680696,Imaging,0.41570348,FALSE,16.125,0.244170944,4.9375,0.250535189,2,0.618927094,,,0.371211076
6524,Estimating Risk of Mechanical Ventilation and Mortality Among Adult COVID-19 patients Admitted to Mass General Brigham: The VICE and DICE Scores.,medRxiv,32995802,10/1/20,pubmed,0,8,logistic regression,0.000966809,0.000966789,0.013148393,0.016598417,0.000966821,0.96735277,Clinics,0.37273085,FALSE,4.75,0.064506154,1.375,0.132927482,7,0.785110192,,,0.327514609
6525,Fixed single-cell RNA sequencing for understanding virus infection and host response.,bioRxiv,32995793,10/1/20,pubmed,0,6,"sequencing, transcriptom",0.661431082,0.330246402,0.002080644,0.002080717,0.002080614,0.002080541,Drug discovery,0.313461,FALSE,29.33333333,0.423093574,126,0.859245384,0,0.403234768,,,0.561857909
6526,AI-guided discovery of the invariant host response to viral pandemics.,bioRxiv,32995790,10/1/20,pubmed,0,22,"transcriptom, dataset",0.649679037,0.059036092,0.063185451,0.001254689,0.001254628,0.225590103,Drug discovery,0.41620582,FALSE,38.63636364,0.525326242,90.86363636,0.803050575,1,0.537564047,,,0.621980288
6527,"Ebselen, disulfiram, carmofur, PX-12, tideglusib, and shikonin are non-specific promiscuous SARS-CoV-2 main protease inhibitors.",bioRxiv,32995786,10/1/20,pubmed,0,7,molecular dynamics simulation,0.994505807,0.001098869,0.001098835,0.001098839,0.001098824,0.001098827,Drug discovery,0.86106026,TRUE,45.85714286,0.594656441,26,0.53819909,4,0.707574542,,,0.613476691
6528,An evolutionary portrait of the progenitor SARS-CoV-2 and its dominant offshoots in COVID-19 pandemic.,bioRxiv,32995781,10/1/20,pubmed,0,8,"sequencing, genomes",0.001684561,0.85959645,0.001684517,0.133665338,0.001684615,0.001684519,Genomics,0.22665933,FALSE,63.75,0.726390006,279.375,0.948688788,9,0.814309525,,,0.829796106
6529,Immune transcriptomes of highly exposed SARS-CoV-2 asymptomatic seropositive versus seronegative individuals from the Ischgl community.,Res Sq,32995765,10/1/20,pubmed,0,16,transcriptom,0.311580142,0.231825901,0.001786532,0.19753713,0.001786655,0.25548364,Drug discovery,0.3301628,FALSE,77,0.794668811,166.3333333,0.89670859,0,0.403234768,,,0.698204056
6530,Distinct B cell subsets give rise to antigen-specific antibody responses against SARS-CoV-2.,Res Sq,32995763,10/1/20,pubmed,0,27,sequencing,0.574387787,0.318128678,0.001272681,0.001272666,0.001272713,0.103665476,Drug discovery,0.15395254,FALSE,75.03703704,0.787123508,133.8518519,0.86747391,4,0.707574542,,,0.787390653
6531,Harm reduction via online platforms for people who use drugs in Russia: A qualitative analysis of web outreach work.,Res Sq,32995761,10/1/20,pubmed,0,4,dataset,0.063140027,0.000907283,0.000907314,0.317918142,0.61621987,0.000907364,Healthcare,0.83987653,TRUE,23.75,0.350547344,6.5,0.288132192,0,0.403234768,,,0.347304768
6532,Unsupervised learning for county-level typological classification for COVID-19 research.,Intell Based Med,32995759,10/1/20,pubmed,0,4,"supervised learning, unsupervised learning, computational",0.002562583,0.002562633,0.002562564,0.823646486,0.166103028,0.002562706,Epidemiology,0.18343166,FALSE,67,0.748160059,96.5,0.816229596,1,0.537564047,,,0.700651234
6533,"ReScan, a Multiplex Diagnostic Pipeline, Pans Human Sera for SARS-CoV-2 Antigens.",Cell Rep Med,32995758,10/1/20,pubmed,0,22,proteom,0.420740618,0.455754814,0.117673776,0.001943533,0.001943463,0.001943796,Genomics,0.4742641,FALSE,99.5,0.867709815,356.8636364,0.966885202,6,0.764429903,,,0.86634164
6534,Projection of the Effects of the COVID-19 Pandemic on the Welfare of Remittance-Dependent Households in the Philippines.,Econ Disaster Clim Chang,32995703,10/1/20,pubmed,0,3,dataset,0.004310011,0.004310005,0.004310024,0.449131141,0.5336288,0.004310019,Healthcare,0.5178467,TRUE,46.33333333,0.599418641,2.666666667,0.185442869,2,0.618927094,,,0.467929534
6535,Estimative of real number of infections by COVID-19 in Brazil and possible scenarios.,Infect Dis Model,32995682,10/1/20,pubmed,0,2,mathematical model,0.04992148,0.003214218,0.003214178,0.893560653,0.003214138,0.046875332,Epidemiology,0.30169195,FALSE,2.5,0.027459954,0,0.055525823,0,0.403234768,,,0.162073515
6536,Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases.,Infect Dis Model,32995681,10/1/20,pubmed,0,6,"bayes, bayesian model",0.002562577,0.002562607,0.002562628,0.987186725,0.002562882,0.00256258,Epidemiology,0.547363,TRUE,7.666666667,0.111633373,1,0.122023013,0,0.403234768,,,0.212297051
6537,Single cell sequencing unraveling genetic basis of severe COVID19 in obesity.,Obes Med,32995660,10/1/20,pubmed,0,29,sequencing,0.474826239,0.129777103,0.001371484,0.001371311,0.001371336,0.391282527,Drug discovery,0.87650347,TRUE,7,0.10179974,4.724137931,0.247324057,4,0.707574542,,,0.35223278
6538,Integrated genomic view of SARS-CoV-2 in India.,Wellcome Open Res,32995557,10/1/20,pubmed,0,37,"sequencing, genomes",0.001538142,0.992309165,0.001538202,0.001538222,0.001538139,0.00153813,Genomics,0.7809636,TRUE,22.81081081,0.3371266,10.21621622,0.357840514,2,0.618927094,,,0.437964736
6539,"Data on early assessment of knowledge, attitudes, and behavioral responses to COVID-19 among Connecticut residents.",Data Brief,32995388,10/1/20,pubmed,0,2,dataset,0.002130648,0.002130649,0.002130804,0.084830229,0.906646992,0.002130678,Healthcare,0.7688834,TRUE,38.5,0.524089307,46,0.66416912,0,0.403234768,,,0.530497731
6540,Variational Disentanglement for Rare Event Modeling.,ArXiv,32995365,10/1/20,pubmed,0,6,"machine learning, dataset",0.001751158,0.001751239,0.712964017,0.082254451,0.140785272,0.060493863,Healthcare,0.016387016,FALSE,61.83333333,0.714082504,61.66666667,0.727187584,0,0.403234768,,,0.614834952
6541,"Estimating the impact of lock-down, quarantine and sensitization in a COVID-19 outbreak: lessons from the COVID-19 outbreak in China.",PeerJ,32995089,10/1/20,pubmed,0,2,mathematical model,0.004775188,0.366479531,0.004775193,0.614419037,0.004775746,0.004775305,Epidemiology,0.86348486,TRUE,31.5,0.450058754,24.5,0.525086968,0,0.403234768,,,0.459460163
6542,Tailoring time series models for forecasting coronavirus spread: Case studies of 187 countries.,Comput Struct Biotechnol J,32994886,10/1/20,pubmed,0,5,forecasting model,0.001943461,0.001943471,0.131746102,0.860479951,0.001943536,0.001943479,Epidemiology,0.44172147,FALSE,86.4,0.830787309,57.8,0.71367407,2,0.618927094,,,0.721129491
6543,"Corrigendum to "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan" [Chaos Solitons Fractals 135 (2020), 109846].",Chaos Solitons Fractals,32994672,10/1/20,pubmed,0,5,mathematical model,0.006540301,0.006539867,0.140155579,0.833684941,0.006539705,0.006539607,Epidemiology,0.61218387,TRUE,109,0.888304781,42.2,0.645571314,0,0.403234768,,,0.645703621
6544,Genopo: a nanopore sequencing analysis toolkit for portable Android devices.,Commun Biol,32994472,10/1/20,pubmed,0,9,"computational, bioinformatic, sequencing, genomes",0.001717263,0.647331131,0.345799883,0.001717268,0.00171721,0.001717244,Genomics,0.64339,TRUE,13.55555556,0.205454883,2.111111111,0.165038801,2,0.618927094,,,0.329806926
6545,Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity.,Science,32994364,10/1/20,pubmed,0,151,"machine learning, proteom",0.360725928,0.155855492,0.175260185,0.001901724,0.001901807,0.304354864,Drug discovery,0.27324194,FALSE,68,0.753973653,232.3611111,0.930759968,85,0.975121921,,,0.886618514
6546,The Emerging Role of Artificial Intelligence in the Fight Against COVID-19.,Eur Urol,32994064,10/1/20,pubmed,0,5,artificial intelligence,0.533711033,0.005047684,0.292317354,0.158828651,0.005047804,0.005047472,Drug discovery,0.6519904,TRUE,52.8,0.652173913,18.2,0.462938186,0,0.403234768,,,0.506115622
6547,"Expression of SARS-CoV-2 entry receptors in the respiratory tract of healthy individuals, smokers and asthmatics.",Respir Res,32993656,10/1/20,pubmed,0,2,dataset,0.71961707,0.123540641,0.004110116,0.004110139,0.004109964,0.144512069,Drug discovery,0.7643715,TRUE,5.5,0.077246583,0.5,0.087101953,9,0.814309525,,,0.326219354
6548,Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.,BMC Med Inform Decis Mak,32993652,10/1/20,pubmed,0,19,"machine learning, computational, dataset",0.043707244,0.001486541,0.434013724,0.001486493,0.107727509,0.411578489,Clinics,0.5289295,TRUE,24.84210526,0.365885336,8.157894737,0.321514584,4,0.707574542,,,0.464991487
6549,Knowledge and practice of physicians during COVID-19 pandemic: a cross-sectional study in Lebanon.,BMC Public Health,32993603,10/1/20,pubmed,0,6,logistic regression,0.001046862,0.021934847,0.001046822,0.001046843,0.973877805,0.001046821,Healthcare,0.64521104,TRUE,11.83333333,0.178304162,1.333333333,0.13252609,1,0.537564047,,,0.2827981
6550,The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model.,BMC Infect Dis,32993524,10/1/20,pubmed,0,6,predictive model,0.001438094,0.001438124,0.114869784,0.879377566,0.001438118,0.001438314,Epidemiology,0.25018632,FALSE,159.1666667,0.950708145,97.16666667,0.81696548,0,0.403234768,,,0.723636131
6551,Role of SARS-CoV-2 in Altering the RNA-Binding Protein and miRNA-Directed Post-Transcriptional Regulatory Networks in Humans.,Int J Mol Sci,32993015,10/1/20,pubmed,0,4,"computational, dataset",0.802592572,0.192782435,0.001156251,0.00115626,0.00115624,0.001156243,Drug discovery,0.59641325,TRUE,42,0.558537943,92.5,0.80706449,0,0.403234768,,,0.5896124
6552,Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19.,Int J Environ Res Public Health,32993005,10/1/20,pubmed,0,14,machine learning,0.001717257,0.001717291,0.23589279,0.54165615,0.217299295,0.001717217,Epidemiology,0.9549375,TRUE,30.21428571,0.433978601,46,0.66416912,4,0.707574542,,,0.601907421
6553,Identification of Novel Hypothalamic MicroRNAs as Promising Therapeutics for SARS-CoV-2 by Regulating ACE2 and TMPRSS2 Expression: An In Silico Analysis.,Brain Sci,32992681,10/1/20,pubmed,0,2,"bioinformatic, in silico",0.839115688,0.002238524,0.070633964,0.002238575,0.002238594,0.083534655,Drug discovery,0.9454509,TRUE,6.5,0.093512277,1,0.122023013,2,0.618927094,,,0.278154128
6554,Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations.,Int J Environ Res Public Health,32992643,10/1/20,pubmed,0,4,neural network,0.002490479,0.002490412,0.433883699,0.55615431,0.002490527,0.002490574,Epidemiology,0.61269563,TRUE,86.5,0.831653163,182.75,0.90801445,1,0.537564047,,,0.75907722
6555,Host Immune Response Driving SARS-CoV-2 Evolution.,Viruses,32992592,10/1/20,pubmed,0,5,genome sequences,0.158218424,0.683914398,0.001823335,0.001823487,0.084156191,0.070064166,Genomics,0.5195386,TRUE,36.4,0.501329705,11.6,0.379515654,0,0.403234768,,,0.428026709
6556,Obstetric Hemorrhage Risk Associated with Novel COVID-19 Diagnosis from a Single-Institution Cohort in the United States.,Am J Perinatol,32992351,9/30/20,pubmed,0,4,logistic regression,0.001098802,0.001098806,0.001098898,0.001098802,0.354119504,0.641485186,Clinics,0.9799876,TRUE,30.25,0.434782609,7.75,0.312215681,2,0.618927094,,,0.455308461
6557,Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms.,Int J Med Inform,32992136,9/30/20,pubmed,0,6,"deep learning, neural network, transfer learning, dataset",0.001010925,0.001010968,0.994945287,0.001010945,0.001010945,0.00101093,Imaging,0.40128976,FALSE,27.92307692,0.405652792,24,0.521808938,22,0.908142478,,,0.611868069
6558,The Neurosphere Simulator: An educational online tool for modeling neural stem cell behavior and tissue growth.,Dev Biol,32991866,9/30/20,pubmed,0,3,computational,0.38545235,0.001901769,0.041203357,0.445196845,0.12434399,0.001901688,Epidemiology,0.81233156,TRUE,38,0.519327107,76.66666667,0.770404067,0,0.403234768,,,0.564321981
6559,Lung Expression of Human Angiotensin-Converting Enzyme 2 Sensitizes the Mouse to SARS-CoV-2 Infection.,Am J Respir Cell Mol Biol,32991819,9/30/20,pubmed,0,25,"transcriptom, genomes",0.841528505,0.065760277,0.001371281,0.001371267,0.001371253,0.088597417,Drug discovery,0.4689349,FALSE,55.88,0.67388212,83.68,0.787663902,4,0.707574542,,,0.723040188
6560,Overexpression of the SARS-CoV-2 receptor ACE2 is induced by cigarette smoke in bronchial and alveolar epithelia.,J Pathol,32991738,9/30/20,pubmed,0,11,dataset,0.725339012,0.089437875,0.001371281,0.001371356,0.001371388,0.181109088,Drug discovery,0.800205,TRUE,35.81818182,0.495021337,22.72727273,0.509098207,5,0.739490092,,,0.581203212
6561,Sodium-glucose co-transporter-2 inhibitors and susceptibility to COVID-19: A population-based retrospective cohort study.,Diabetes Obes Metab,32991065,9/30/20,pubmed,0,25,dataset,0.323824991,0.002183252,0.002183365,0.002183478,0.165721927,0.503902987,Clinics,0.90571606,TRUE,61.36,0.710742779,42.04,0.644969227,4,0.707574542,,,0.687762183
6562,Flight-Associated Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 Corroborated by Whole-Genome Sequencing.,Emerg Infect Dis,32990563,9/30/20,pubmed,0,11,"sequencing, whole-genome",0.003607158,0.777420348,0.003607184,0.003607456,0.003607562,0.208150292,Genomics,0.11414808,FALSE,73.54545455,0.780072979,75.72727273,0.76839711,11,0.840175319,,,0.796215136
6563,Mathematical modelling on COVID-19 transmission impacts with preventive measures: a case study of Tanzania.,J Biol Dyn,32990177,9/30/20,pubmed,0,2,mathematical model,0.002806571,0.002806542,0.002806451,0.985967385,0.002806502,0.00280655,Epidemiology,0.92348075,TRUE,5.5,0.077246583,0,0.055525823,1,0.537564047,,,0.223445484
6564,Derivation and Validation of a Clinical Model to Predict Intensive Care Unit Length of Stay After Cardiac Surgery.,J Am Heart Assoc,32990156,9/30/20,pubmed,0,5,logistic regression,0.001330054,0.001330056,0.060365088,0.139332359,0.001330046,0.796312396,Clinics,0.9542693,TRUE,97.2,0.862143608,66.6,0.744514316,1,0.537564047,,,0.714740657
6565,"Correlation analysis of coagulation dysfunction and liver damage in patients with novel coronavirus pneumonia: a single-center, retrospective, observational study.",Ups J Med Sci,32990149,9/30/20,pubmed,0,6,correlation analysis,0.001565379,0.001565328,0.001565309,0.001565354,0.001565335,0.992173296,Clinics,0.73668057,TRUE,129.3333333,0.921021708,21.33333333,0.495450896,1,0.537564047,,,0.651345551
6566,COVID-19 Patients Upregulate Toll-like Receptor 4-mediated Inflammatory Signaling That Mimics Bacterial Sepsis.,J Korean Med Sci,32989935,9/30/20,pubmed,0,14,transcriptom,0.573543758,0.001220048,0.001220039,0.001220044,0.00122002,0.421576091,Drug discovery,0.5399414,TRUE,34,0.477766096,28.5,0.558402462,0,0.403234768,,,0.479801108
6567,"Obstructive Sleep Apnea and Risk of COVID-19 Infection, Hospitalization and Respiratory Failure.",Sleep Breath,32989673,9/30/20,pubmed,0,5,logistic regression,0.001717166,0.001717188,0.001717193,0.001717195,0.001717265,0.991413993,Clinics,0.9027425,TRUE,139.6,0.933081823,131.4,0.864998662,9,0.814309525,,,0.87079667
6568,Reaction order and neural network approaches for the simulation of COVID-19 spreading kinetic in India.,Infect Dis Model,32989426,9/30/20,pubmed,0,4,neural network,0.001237073,0.001237096,0.174297713,0.820753954,0.00123709,0.001237074,Epidemiology,0.70230377,TRUE,19.25,0.287896592,8.75,0.332218357,0,0.403234768,,,0.341116572
6569,Genomic Epidemiology and Recent Update on Nucleic Acid-Based Diagnostics for COVID-19.,Curr Trop Med Rep,32989413,9/30/20,pubmed,0,11,"whole genome, genomic epidemiology",0.200988983,0.653678581,0.077883699,0.001371373,0.064705985,0.001371379,Genomics,0.78536093,TRUE,180.8181818,0.964685509,114.3636364,0.843992507,1,0.537564047,,,0.782080688
6570,Leveraging Computational Modeling to Understand Infectious Diseases.,Curr Pathobiol Rep,32989410,9/30/20,pubmed,0,5,"machine learning, computational, mathematical model",0.506283956,0.001237102,0.050499842,0.439504866,0.001237128,0.001237106,Drug discovery,0.4343235,FALSE,25.4,0.372626631,83.6,0.787329409,1,0.537564047,,,0.565840029
6571,Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN.,Appl Soft Comput,32989379,9/30/20,pubmed,0,3,"deep learning, neural network",0.000977498,0.000977508,0.923038692,0.07305122,0.000977536,0.000977546,Imaging,0.17464927,FALSE,15.33333333,0.230997588,0.666666667,0.096200161,2,0.618927094,,,0.315374948
6572,MAIT cell activation and dynamics associated with COVID-19 disease severity.,Sci Immunol,32989174,9/30/20,pubmed,0,88,transcriptom,0.800629354,0.001987162,0.001987152,0.001987218,0.001987226,0.191421887,Drug discovery,0.8186214,TRUE,43.42857143,0.572144227,56.95238095,0.71039604,23,0.91129082,,,0.731277029
6573,Global COVID-19 pandemic demands joint interventions for the suppression of future waves.,Proc Natl Acad Sci U S A,32989148,9/30/20,pubmed,0,31,mathematical model,0.001861935,0.001861782,0.001861713,0.990691113,0.00186172,0.001861737,Epidemiology,0.5730643,TRUE,57.38709677,0.684581607,149.8709677,0.883328873,3,0.667819001,,,0.74524316
6574,Superantigenic character of an insert unique to SARS-CoV-2 spike supported by skewed TCR repertoire in patients with hyperinflammation.,Proc Natl Acad Sci U S A,32989130,9/30/20,pubmed,0,9,computational,0.668909034,0.149871383,0.001187266,0.00118727,0.106898235,0.071946813,Drug discovery,0.09101266,FALSE,53,0.654400396,83.33333333,0.786459727,20,0.900117291,,,0.780325805
6575,"Endovascular thrombectomy in acute ischemic stroke patients with COVID-19: prevalence, demographics, and outcomes.",J Neurointerv Surg,32989032,9/30/20,pubmed,0,11,logistic regression,0.001156219,0.001156287,0.001156243,0.001156256,0.001156293,0.994218702,Clinics,0.8014786,TRUE,70.54545455,0.766157462,68.45454545,0.74906342,4,0.707574542,,,0.740931808
6576,Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study.,BMJ Open,32988953,9/30/20,pubmed,0,5,dataset,0.001254646,0.055134044,0.050814885,0.113865946,0.567945627,0.210984852,Healthcare,0.40092632,FALSE,8.6,0.127280599,2,0.164302917,0,0.403234768,,,0.231606094
6577,The obesity paradox: Analysis from the SMAtteo COvid-19 REgistry (SMACORE) cohort.,Nutr Metab Cardiovasc Dis,32988724,9/30/20,pubmed,0,11,logistic regression,0.001310334,0.001310338,0.001310318,0.018828068,0.001310353,0.97593059,Clinics,0.93512475,TRUE,71.54545455,0.770795968,62.18181818,0.729194541,6,0.764429903,,,0.754806804
6578,[HLA genetic polymorphisms and prognosis of patients with COVID-19].,Med Intensiva,32988645,9/30/20,pubmed,0,36,logistic regression,0.001461894,0.344419246,0.001461889,0.001461946,0.001461879,0.649733146,Clinics,0.68463665,TRUE,50.89189189,0.637021461,,,5,0.739490092,,,0.688255776
6579,Increase in prevalence of current mental disorders in the context of COVID-19: analysis of repeated nationwide cross-sectional surveys.,Epidemiol Psychiatr Sci,32988427,9/30/20,pubmed,0,7,logistic regression,0.001046814,0.001046865,0.001046867,0.001046903,0.994765721,0.00104683,Healthcare,0.84162664,TRUE,37.42857143,0.512771353,11.71428571,0.380987423,6,0.764429903,,,0.55272956
6580,Italian Response to Coronavirus Pandemic in Dental Care Access: The DeCADE Study.,Int J Environ Res Public Health,32987661,9/30/20,pubmed,0,7,logistic regression,0.00198717,0.001987231,0.001987172,0.001987245,0.990064038,0.001987143,Healthcare,0.9625944,TRUE,24.71428571,0.3637207,8.571428571,0.329810008,2,0.618927094,,,0.437485934
6581,Modeling the transmission dynamics and the impact of the control interventions for the COVID-19 epidemic outbreak.,Math Biosci Eng,32987574,9/30/20,pubmed,0,4,bayes,0.002357702,0.002357715,0.002357729,0.988211311,0.002357862,0.00235768,Epidemiology,0.37528646,FALSE,14.75,0.222586431,1.25,0.127776291,8,0.799987654,,,0.383450125
6582,Predictive value of CT in the short-term mortality of Coronavirus Disease 2019 (COVID-19) pneumonia in nonelderly patients: A case-control study.,Eur J Radiol,32987252,9/29/20,pubmed,0,4,logistic regression,0.000926267,0.000926268,0.281010361,0.000926287,0.000926291,0.715284526,Clinics,0.7939858,TRUE,14.5,0.219617787,2.5,0.180826866,3,0.667819001,,,0.356087885
6583,Clinical management and mortality among COVID-19 cases in sub-Saharan Africa: A retrospective study from Burkina Faso and simulated case analysis.,Int J Infect Dis,32987177,9/29/20,pubmed,0,14,"logistic regression, probabilistic",0.001022691,0.001022657,0.001022643,0.312112814,0.00102268,0.683796515,Clinics,0.3021046,FALSE,41.21428571,0.550188633,30.64285714,0.573722237,5,0.739490092,,,0.621133654
6584,Design of novel viral attachment inhibitors of the spike glycoprotein (S) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) through virtual screening and dynamics.,Int J Antimicrob Agents,32987103,9/29/20,pubmed,0,6,"virtual screening, molecular dynamics simulation, computational",0.99372694,0.001254596,0.001254631,0.00125462,0.001254598,0.001254615,Drug discovery,0.982554,TRUE,13,0.197352959,1.833333333,0.151056998,5,0.739490092,,,0.36263335
6585,Survey of antibiotic and antifungal prescribing in patients with suspected and confirmed COVID-19 in Scottish hospitals.,J Infect,32987097,9/29/20,pubmed,0,13,logistic regression,0.001141362,0.001141369,0.001141358,0.0011414,0.17053879,0.824895722,Clinics,0.5930431,TRUE,37.30769231,0.510668563,69.38461538,0.751538667,8,0.799987654,,,0.687398295
6586,Biased Mutation and Selection in RNA Viruses.,Mol Biol Evol,32986832,9/29/20,pubmed,0,2,genomes,0.028150527,0.967284118,0.001141364,0.001141352,0.001141323,0.001141316,Genomics,0.5045133,TRUE,14.5,0.219617787,37.5,0.619213273,1,0.537564047,,,0.458798369
6587,"Probable delirium is a presenting symptom of COVID-19 in frail, older adults: a cohort study of 322 hospitalised and 535 community-based older adults.",Age Ageing,32986799,9/29/20,pubmed,0,21,logistic regression,0.001034586,0.001034636,0.001034657,0.001034614,0.391583805,0.604277702,Clinics,0.90136003,TRUE,52.42857143,0.65013297,,,12,0.850299401,,,0.750216186
6588,A Bayesian framework for estimating the risk ratio of hospitalization for people with comorbidity infected by SARS-CoV-2 virus.,J Am Med Inform Assoc,32986795,9/29/20,pubmed,0,2,"bayes, dataset",0.001415111,0.001415136,0.001415161,0.285477554,0.001415188,0.708861851,Clinics,0.304159,FALSE,28,0.408312202,56,0.706850415,0,0.403234768,,,0.506132462
6589,A bioinformatic prediction of antigen presentation from SARS-CoV-2 spike protein revealed a theoretical correlation of HLA-DRB1*01 with COVID-19 fatality in Mexican population: An ecological approach.,J Med Virol,32986250,9/29/20,pubmed,0,12,bioinformatic,0.553254227,0.166405817,0.045031604,0.002130827,0.002130761,0.231046765,Drug discovery,0.6846286,TRUE,17.5,0.263776362,9.333333333,0.342253144,2,0.618927094,,,0.408318867
6590,Clinical Characteristics and Mortality of Healthcare Workers with SARS-CoV-2 infection in Mexico City.,Clin Infect Dis,32986118,9/29/20,pubmed,0,4,logistic regression,0.00190172,0.001901717,0.001901709,0.001901797,0.264349707,0.72804335,Clinics,0.7392961,TRUE,61.75,0.713711423,23.75,0.518731603,3,0.667819001,,,0.633420676
6591,COVID-19: A Greek Perspective.,J Card Surg,32985717,9/29/20,pubmed,0,1,computational,0.002032835,0.002032778,0.128267028,0.756265911,0.109368495,0.002032954,Epidemiology,0.76159894,TRUE,72,0.77302245,56,0.706850415,0,0.403234768,,,0.627702544
6592,"Immune dysfunction following COVID-19, especially in severe patients.",Sci Rep,32985562,9/29/20,pubmed,0,4,logistic regression,0.199469893,0.001254634,0.064473192,0.0012546,0.001254646,0.732293035,Clinics,0.8297201,TRUE,269,0.987506958,118.75,0.85001338,9,0.814309525,,,0.883943288
6593,Modeling lung perfusion abnormalities to explain early COVID-19 hypoxemia.,Nat Commun,32985528,9/29/20,pubmed,0,4,mathematical model,0.002562724,0.002562694,0.002562776,0.360790413,0.002562606,0.628958786,Clinics,0.54900527,TRUE,185.75,0.966788299,131.75,0.865667648,8,0.799987654,,,0.8774812
6594,Structural and functional modelling of SARS-CoV-2 entry in animal models.,Sci Rep,32985513,9/29/20,pubmed,0,2,computational,0.80188821,0.192790971,0.001330296,0.001330143,0.001330242,0.001330139,Drug discovery,0.50514436,TRUE,20,0.298163152,10,0.355632861,8,0.799987654,,,0.484594555
6595,Genomic characterization and phylogenetic evolution of the SARS-CoV-2.,Acta Virol,32985209,9/29/20,pubmed,0,5,"genome sequences, genomes",0.001653056,0.991734747,0.001653012,0.00165309,0.001653002,0.001653092,Genomics,0.83417875,TRUE,43.2,0.569051889,38.2,0.622758898,1,0.537564047,,,0.576458278
6596,Virtual screening for functional foods against the main protease of SARS-CoV-2.,J Food Biochem,32984999,9/29/20,pubmed,0,4,virtual screening,0.928383277,0.001254646,0.001254662,0.001254677,0.066598099,0.001254639,Drug discovery,0.92532766,TRUE,27.75,0.404292164,4.25,0.235750602,0,0.403234768,,,0.347759178
6597,0,Drug Dev Res,32984987,9/29/20,pubmed,0,2,virtual screening,0.952749211,0.001511866,0.001511824,0.041203443,0.001511845,0.001511811,Drug discovery,0.8122832,TRUE,4.5,0.061784897,0,0.055525823,1,0.537564047,,,0.218291589
6598,Angiotensin-converting enzyme 2 (ACE2) levels in relation to risk factors for COVID-19 in two large cohorts of patients with atrial fibrillation.,Eur Heart J,32984892,9/29/20,pubmed,0,11,"proteom, genome-wide",0.279998616,0.142970952,0.001350315,0.001350373,0.001350337,0.572979408,Clinics,0.8464346,TRUE,379.7272727,0.995175954,824.4545455,0.990767996,7,0.785110192,,,0.923684714
6599,Predicted Impact of COVID-19 on Neglected Tropical Disease Programs and the Opportunity for Innovation.,Clin Infect Dis,32984870,9/29/20,pubmed,0,47,mathematical model,0.060252705,0.003101615,0.003101577,0.927340894,0.003101682,0.003101527,Epidemiology,0.86245596,TRUE,59.85106383,0.701775002,59.40425532,0.718423869,6,0.764429903,,,0.728209591
6600,Digital health for primary prevention of cardiovascular disease: Promise to practice.,Cardiovasc Digit Health J,32984862,9/29/20,pubmed,0,3,digital health,0.013550847,0.01354884,0.202173747,0.013550895,0.530716318,0.226459353,Healthcare,0.41857108,FALSE,16,0.243552477,15.66666667,0.432365534,0,0.403234768,,,0.359717593
6601,Long-term hydroxychloroquine use in patients with rheumatic conditions and development of SARS-CoV-2 infection: a retrospective cohort study.,Lancet Rheumatol,32984847,9/29/20,pubmed,0,6,logistic regression,0.000977481,0.000977439,0.016694209,0.000977459,0.000977512,0.9793959,Clinics,0.7225216,TRUE,59.33333333,0.698497124,89,0.799304255,18,0.891474782,,,0.796425387
6602,What can we learn from previous pandemics to reduce the frequency of emerging infectious diseases like COVID-19?,Glob Transit,32984800,9/29/20,pubmed,0,3,"mathematical model, structural model",0.053686922,0.383271686,0.001187486,0.559479333,0.00118731,0.001187263,Epidemiology,0.8907767,TRUE,63,0.721998887,66.33333333,0.743510838,0,0.403234768,,,0.622914831
6603,Clinical features of COVID-19 mortality: development and validation of a clinical prediction model.,Lancet Digit Health,32984797,9/29/20,pubmed,0,6,"machine learning, computational, prediction model, dataset",0.001034581,0.001034576,0.491537269,0.001034615,0.00103459,0.504324368,Clinics,0.39323798,FALSE,30.16666667,0.433731214,15.33333333,0.428351619,20,0.900117291,,,0.587400041
6604,Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation.,Lancet Digit Health,32984796,9/29/20,pubmed,0,21,"deep learning, artificial intelligence",0.000648115,0.000648142,0.576409029,0.026956957,0.000648161,0.394689596,Imaging,0.58607674,TRUE,97.52380952,0.863380543,87.14285714,0.795357238,7,0.785110192,,,0.814615991
6605,Prediction models for COVID-19 clinical decision making.,Lancet Digit Health,32984794,9/29/20,pubmed,0,2,prediction model,0.034962238,0.034961874,0.825186445,0.034964612,0.034961915,0.034962916,Epidemiology,0.37807482,FALSE,97,0.861586987,47.5,0.670591383,3,0.667819001,,,0.733332457
6606,Artificial intelligence in COVID-19 drug repurposing.,Lancet Digit Health,32984792,9/29/20,pubmed,0,5,artificial intelligence,0.52708683,0.001684509,0.466174922,0.001684648,0.00168455,0.001684542,Drug discovery,0.8795323,TRUE,229.2,0.980703816,341.6,0.964276157,39,0.945737391,,,0.963572455
6607,The spectrum of biochemical alterations associated with organ dysfunction and inflammatory status and their association with disease outcomes in severe COVID-19: A longitudinal cohort and time-series design study.,EClinicalMedicine,32984786,9/29/20,pubmed,0,24,dataset,0.001350363,0.053846117,0.001350427,0.001350404,0.001350408,0.940752281,Clinics,0.8064507,TRUE,53.45833333,0.656812419,29.29166667,0.564891624,3,0.667819001,,,0.629841015
6608,Challenges and Opportunities of Preclinical Medical Education: COVID-19 Crisis and Beyond.,SN Compr Clin Med,32984766,9/29/20,pubmed,0,6,artificial intelligence,0.001034598,0.0010346,0.312002305,0.233376073,0.451517826,0.001034598,Healthcare,0.97169256,TRUE,17.66666667,0.266373925,8.833333333,0.333154937,5,0.739490092,,,0.446339652
6609,Identification of Geographic Specific SARS-Cov-2 Mutations by Random Forest Classification and Variable Selection Methods.,Stat Appl,32984664,9/29/20,pubmed,0,2,"genomes, dataset",0.001901779,0.731841949,0.260550818,0.001901878,0.00190173,0.001901846,Genomics,0.3054393,FALSE,85.5,0.82658173,233.5,0.931428954,1,0.537564047,,,0.765191577
6610,Gut microbiome dysbiosis and endotoxemia - Additional pathophysiological explanation for increased COVID-19 severity in obesity.,Obes Med,32984641,9/29/20,pubmed,0,1,microbiom,0.474396786,0.213122467,0.003760512,0.003760652,0.060049263,0.24491032,Drug discovery,0.6204914,TRUE,67,0.748160059,1,0.122023013,8,0.799987654,,,0.556723575
6611,A SARS-CoV-2 host infection model network based on genomic human Transcription Factors (TFs) depletion.,Heliyon,32984567,9/29/20,pubmed,0,5,"bioinformatic, sequencing, in silico",0.380274765,0.541941248,0.000807917,0.016644513,0.000807931,0.059523625,Genomics,0.5095266,TRUE,36.6,0.503865421,27.6,0.550441531,2,0.618927094,,,0.557744682
6612,Selective pressure on SARS-CoV-2 protein coding genes and glycosylation site prediction.,Heliyon,32984566,9/29/20,pubmed,0,3,genomes,0.446077476,0.549297395,0.001156264,0.001156276,0.001156283,0.001156305,Genomics,0.5882496,TRUE,266.3333333,0.987074031,308.6666667,0.956984212,3,0.667819001,,,0.870625748
6613,Fragility of a multilayer network of intranational supply chains.,Appl Netw Sci,32984501,9/29/20,pubmed,0,5,dataset,0.06476096,0.001350436,0.001350413,0.900891366,0.030296458,0.001350367,Epidemiology,0.7522048,TRUE,28,0.408312202,3.6,0.21507894,2,0.618927094,,,0.414106079
6614,Importance of Dietary Changes During the Coronavirus Pandemic: How to Upgrade Your Immune Response.,Front Public Health,32984253,9/29/20,pubmed,0,4,microbiom,0.367175761,0.067174367,0.002639019,0.406557112,0.153814478,0.002639264,Epidemiology,0.926785,TRUE,9.5,0.143051518,1.75,0.148381054,3,0.667819001,,,0.319750525
6615,COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models.,Front Public Health,32984242,9/29/20,pubmed,0,3,"neural network, network model, probabilistic, dataset",0.002080573,0.002080548,0.579897196,0.360323833,0.002080591,0.053537258,Epidemiology,0.49272037,FALSE,35.33333333,0.490259138,3,0.199424672,6,0.764429903,,,0.484704571
6616,"Machine Learning and Image Analysis Applications in the Fight against COVID-19 Pandemic: Datasets, Research Directions, Challenges and Opportunities.",Mater Today Proc,32983909,9/29/20,pubmed,0,4,"machine learning, neural network, image analysis, transfer learning, dataset",0.001717167,0.001717178,0.864021939,0.072469485,0.001717214,0.058357018,Imaging,0.6012404,TRUE,17.75,0.267672707,0.5,0.087101953,1,0.537564047,,,0.297446236
6617,Potential role of cellular miRNAs in coronavirus-host interplay.,PeerJ,32983652,9/29/20,pubmed,0,6,"computational, bioinformatic, sequencing",0.631681655,0.205798348,0.001653056,0.0016531,0.001653057,0.157560785,Drug discovery,0.8769594,TRUE,30.66666667,0.440225122,31.83333333,0.58348943,3,0.667819001,,,0.563844518
6618,Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries.,PeerJ,32983643,9/29/20,pubmed,0,2,"bayes, bayesian model",0.000999516,0.000999534,0.000999512,0.945478232,0.05052366,0.000999546,Epidemiology,0.13272533,FALSE,116.5,0.901478137,466,0.977120685,0,0.403234768,,,0.760611197
6619,PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images.,Health Inf Sci Syst,32983419,9/29/20,pubmed,0,3,neural network,0.001034602,0.001034616,0.994827016,0.001034597,0.001034577,0.001034592,Imaging,0.6464336,TRUE,29.33333333,0.423093574,11,0.371287129,1,0.537564047,,,0.443981583
6620,"The 21st annual Bioinformatics Open Source Conference (BOSC 2020, part of BCC2020).",F1000Res,32983415,9/29/20,pubmed,0,8,bioinformatic,0.002130783,0.355943776,0.045233849,0.59243013,0.002130738,0.002130725,Epidemiology,0.65669566,TRUE,47,0.606407323,227.25,0.928552315,0,0.403234768,,,0.646064802
6621,Computational biophysical characterization of the SARS-CoV-2 spike protein binding with the ACE2 receptor and implications for infectivity.,Comput Struct Biotechnol J,32983400,9/29/20,pubmed,0,3,computational,0.867141875,0.128569263,0.001072202,0.001072292,0.001072198,0.001072171,Drug discovery,0.09006676,FALSE,89.33333333,0.840435401,217.3333333,0.92594327,6,0.764429903,,,0.843602858
6622,Modeling the effects of contact tracing on COVID-19 transmission.,Adv Differ Equ,32983238,9/29/20,pubmed,0,2,mathematical model,0.009285286,0.009284236,0.009284304,0.953577734,0.009284281,0.009284159,Epidemiology,0.76692915,TRUE,5.5,0.077246583,1,0.122023013,0,0.403234768,,,0.200834788
6623,Neutrophil-to-Lymphocyte Ratios Are Closely Associated With the Severity and Course of Non-mild COVID-19.,Front Immunol,32983180,9/29/20,pubmed,0,21,logistic regression,0.00127269,0.00127268,0.020951654,0.001272661,0.001272689,0.973957625,Clinics,0.9483988,TRUE,42.71428571,0.564165997,,,7,0.785110192,,,0.674638094
6624,What's Sex Got to Do With COVID-19? Gender-Based Differences in the Host Immune Response to Coronaviruses.,Front Immunol,32983176,9/29/20,pubmed,0,4,microbiom,0.371658861,0.06319781,0.001565303,0.001565376,0.267779804,0.294232846,Drug discovery,0.6078833,TRUE,16.25,0.246026347,9.5,0.345531175,15,0.874313229,,,0.488623584
6625,Re-analysis of Single Cell Transcriptome Reveals That the NR3C1-CXCL8-Neutrophil Axis Determines the Severity of COVID-19.,Front Immunol,32983174,9/29/20,pubmed,0,2,transcriptom,0.506496903,0.001861728,0.001861761,0.001861696,0.001861745,0.486056168,Drug discovery,0.88654727,TRUE,48,0.614942173,247.5,0.937382927,12,0.850299401,,,0.800874834
6626,Obesity-Driven Deficiencies of Specialized Pro-resolving Mediators May Drive Adverse Outcomes During SARS-CoV-2 Infection.,Front Immunol,32983141,9/29/20,pubmed,0,5,immunome,0.471792468,0.001861801,0.00186173,0.159818366,0.001861808,0.362803826,Drug discovery,0.7466246,TRUE,77.8,0.797699301,102,0.82592989,4,0.707574542,,,0.777067911
6627,0,Front Immunol,32983097,9/29/20,pubmed,0,13,proteom,0.801685072,0.120238452,0.000677944,0.000677938,0.000677947,0.076042645,Drug discovery,0.45362777,FALSE,66.07692308,0.741418764,,,1,0.537564047,,,0.639491406
6628,An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization.,Appl Soft Comput,32982615,9/29/20,pubmed,0,3,"deep learning, neural network",0.001461876,0.001461876,0.947675659,0.046476841,0.001461903,0.001461845,Imaging,0.74841845,TRUE,332,0.992825778,107.3333333,0.835094996,4,0.707574542,,,0.845165105
6629,Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning.,Neuropsychiatr Dis Treat,32982249,9/29/20,pubmed,0,4,machine learning,0.001126786,0.001126804,0.199979627,0.001126818,0.795513107,0.001126857,Healthcare,0.877081,TRUE,21,0.312016822,7.25,0.302983677,1,0.537564047,,,0.384188182
6630,Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic.,Chaos Solitons Fractals,32982084,9/29/20,pubmed,0,3,"mathematical model, dataset",0.001717275,0.044522887,0.001717222,0.948608173,0.001717194,0.001717249,Epidemiology,0.33646658,FALSE,107.3333333,0.884161049,44,0.654401927,3,0.667819001,,,0.735460659
6631,Forecasting of COVID-19 pandemic: From integer derivatives to fractional derivatives.,Chaos Solitons Fractals,32982078,9/29/20,pubmed,0,3,mathematical model,0.006539593,0.006539546,0.006539885,0.967301675,0.00653974,0.00653956,Epidemiology,0.7300816,TRUE,51,0.63875317,20.66666667,0.487958255,8,0.799987654,,,0.642233026
6632,COVID-19 pandemic in India: a mathematical model study.,Nonlinear Dyn,32982061,9/29/20,pubmed,0,4,mathematical model,0.001461895,0.00146188,0.001461904,0.992690561,0.001461909,0.001461852,Epidemiology,0.45478287,FALSE,29.25,0.421671099,7.75,0.312215681,5,0.739490092,,,0.491125624
6633,Examining risk and crisis communications of government agencies and stakeholders during early-stages of COVID-19 on Twitter.,Comput Human Behav,32982038,9/29/20,pubmed,0,3,"network analysis, text mining",0.001653077,0.001653096,0.001653364,0.991734249,0.001653143,0.001653072,Epidemiology,0.850225,TRUE,6,0.086028821,,,9,0.814309525,,,0.450169173
6634,Design of a nonlinear model for the propagation of COVID-19 and its efficient nonstandard computational implementation.,Appl Math Model,32982020,9/29/20,pubmed,0,4,"computational, mathematical model",0.001943591,0.001943534,0.001943503,0.990282373,0.001943489,0.001943509,Epidemiology,0.15917826,FALSE,56.5,0.678891706,21.5,0.498260637,1,0.537564047,,,0.57157213
6635,Gynecologic oncology care during the COVID-19 pandemic at three affiliated New York City hospitals.,Gynecol Oncol,32981694,9/29/20,pubmed,0,16,logistic regression,0.001156242,0.001156269,0.001156255,0.357217075,0.001156315,0.638157844,Clinics,0.9932446,TRUE,95.125,0.857072175,74.1875,0.764583891,1,0.537564047,,,0.719740038
6636,Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone.,Biochem Biophys Res Commun,32981683,9/29/20,pubmed,0,9,"machine learning, logistic regression",0.225930274,0.481740633,0.287177544,0.001717202,0.001717161,0.001717187,Genomics,0.41331205,FALSE,80.11111111,0.807656627,67.33333333,0.746320578,0,0.403234768,,,0.652403991
6637,Shortages of Staff in Nursing Homes During the COVID-19 Pandemic: What are the Driving Factors?,J Am Med Dir Assoc,32981663,9/29/20,pubmed,0,3,logistic regression,0.001034581,0.001034575,0.001034595,0.001034617,0.994827033,0.001034599,Healthcare,0.94805366,TRUE,66,0.741356918,31,0.578204442,6,0.764429903,,,0.694663754
6638,"Pharmacophore based virtual screening, molecular docking, molecular dynamics and MM-GBSA approach for identification of prospective SARS-CoV-2 inhibitor from natural product databases.",J Biomol Struct Dyn,32981461,9/29/20,pubmed,0,7,"virtual screening, computational",0.91925405,0.001272638,0.001272636,0.04796678,0.001272697,0.028961199,Drug discovery,0.944121,TRUE,32.42857143,0.460387161,7.714285714,0.311479797,1,0.537564047,,,0.436477001
6639,Clinical and Proteomic Correlates of Plasma ACE2 (Angiotensin-Converting Enzyme 2) in Human Heart Failure.,Hypertension,32981365,9/29/20,pubmed,0,21,proteom,0.578452415,0.001085365,0.001085314,0.001085339,0.001085382,0.417206184,Drug discovery,0.8004937,TRUE,84.71428571,0.823860474,70.85714286,0.755686379,10,0.828199272,,,0.802582042
6640,Covid-19.bioreproducibility.org: A web resource for SARS-CoV-2-related structural models.,Protein Sci,32981130,9/28/20,pubmed,0,12,structural model,0.506878139,0.002996826,0.481135319,0.002996634,0.002996647,0.002996434,Drug discovery,0.48013955,FALSE,174.6666667,0.961778712,186.0833333,0.910021408,1,0.537564047,,,0.803121389
6641,Perceived risk of infection and smoking behavior change during COVID-19 in Ohio.,Public Health Nurs,32981125,9/28/20,pubmed,0,1,logistic regression,0.002490455,0.002490536,0.04705005,0.002490556,0.901419214,0.044059189,Healthcare,0.8676447,TRUE,66,0.741356918,30,0.570176612,3,0.667819001,,,0.659784177
6642,Half of children entitled to free school meals did not have access to the scheme during COVID-19 lockdown in the UK.,Public Health,32980783,9/28/20,pubmed,0,4,logistic regression,0.001219999,0.001220023,0.001219999,0.001220028,0.993899939,0.001220012,Healthcare,0.34192866,FALSE,233.5,0.981631517,295.25,0.952635804,2,0.618927094,,,0.851064805
6643,Investigation on sleep and mental health of patients with Parkinson's disease during the Coronavirus disease 2019 pandemic.,Sleep Med,32980664,9/28/20,pubmed,0,14,logistic regression,0.001350314,0.001350323,0.001350354,0.001350349,0.594581041,0.400017619,Healthcare,0.994836,TRUE,41.64285714,0.554270518,43.71428571,0.652394969,1,0.537564047,,,0.581409845
6644,"Psychological distress surveillance and related impact analysis of hospital staff during the COVID-19 epidemic in Chongqing, China.",Compr Psychiatry,32980595,9/28/20,pubmed,0,11,logistic regression,0.000634173,0.000634179,0.000634178,0.000634186,0.892356698,0.105106584,Healthcare,0.9868771,TRUE,28.45454545,0.411528233,1.181818182,0.124765855,5,0.739490092,,,0.425261393
6645,Evolutionary artificial intelligence based peptide discoveries for effective Covid-19 therapeutics.,Biochim Biophys Acta Mol Basis Dis,32980462,9/28/20,pubmed,0,2,"computational, artificial intelligence",0.476905471,0.181500255,0.224790261,0.113727757,0.001538137,0.001538118,Drug discovery,0.568195,TRUE,53.5,0.657245346,5,0.257024351,1,0.537564047,,,0.483944581
6646,Evaluation of the potency of FDA-approved drugs on wild type and mutant SARS-CoV-2 helicase (Nsp13).,Int J Biol Macromol,32980406,9/28/20,pubmed,0,8,"in silico, genome sequences",0.692517282,0.300613557,0.001717197,0.001717378,0.001717235,0.001717351,Drug discovery,0.8407227,TRUE,8.375,0.123940875,1.625,0.141222906,2,0.618927094,,,0.294696958
6647,Intention of nurses to accept coronavirus disease 2019 vaccination and change of intention to accept seasonal influenza vaccination during the coronavirus disease 2019 pandemic: A cross-sectional survey.,Vaccine,32980199,9/28/20,pubmed,0,7,logistic regression,0.001350348,0.001350355,0.001350357,0.081919195,0.893413833,0.020615913,Healthcare,0.8697966,TRUE,71.14285714,0.769311646,55.42857143,0.703706181,20,0.900117291,,,0.791045039
6648,Robust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19.,Cell,32979941,9/28/20,pubmed,0,78,dataset,0.774353492,0.001823411,0.001823379,0.001823421,0.068986405,0.151189892,Drug discovery,0.3576138,FALSE,27.43037975,0.400705053,,,77,0.972097043,,,0.686401048
6649,"Anxiety Severity Levels and Coping Strategies during the COVID-19 Pandemic among People Aged 15 Years and Above in Gonabad, Iran.",Arch Iran Med,32979912,9/28/20,pubmed,0,6,logistic regression,0.001350341,0.001350327,0.001350357,0.001350377,0.993248152,0.001350446,Healthcare,0.9547671,TRUE,16,0.243552477,2.166666667,0.166845063,2,0.618927094,,,0.343108211
6650,Identification of novel human USP2 inhibitor and its putative role in treatment of COVID-19 by inhibiting SARS-CoV-2 papain-like (PLpro) protease.,Comput Biol Chem,32979815,9/27/20,pubmed,0,4,"virtual screening, in-silico",0.966051008,0.001415202,0.001415145,0.00141521,0.028288328,0.001415107,Drug discovery,0.8844062,TRUE,38.75,0.526748717,8,0.320511105,4,0.707574542,,,0.518278121
6651,Status and influential factors of anxiety depression and insomnia symptoms in the work resumption period of COVID-19 epidemic: A multicenter cross-sectional study.,J Psychosom Res,32979696,9/27/20,pubmed,0,9,logistic regression,0.00133002,0.001330016,0.001330028,0.001330061,0.95937876,0.035301115,Healthcare,0.9766828,TRUE,34.33333333,0.480425506,5.666666667,0.270203372,2,0.618927094,,,0.456518657
6652,High-speed large-scale automated isolation of SARS-CoV-2 from clinical samples using miniaturized co-culture coupled to high-content screening.,Clin Microbiol Infect,32979576,9/27/20,pubmed,0,7,image analysis,0.067065794,0.510871301,0.193532879,0.225160838,0.001684598,0.00168459,Genomics,0.5813293,TRUE,126.5714286,0.916135815,243.8571429,0.935710463,0,0.403234768,,,0.751693682
6653,Effects of tocilizumab on mortality in hospitalized patients with COVID-19: a multicentre cohort study.,Clin Microbiol Infect,32979572,9/27/20,pubmed,0,7,structural model,0.093603617,0.001126826,0.001126903,0.00112687,0.001126807,0.901888977,Clinics,0.6177172,TRUE,131.2857143,0.923186344,130,0.863928285,12,0.850299401,,,0.87913801
6654,Incidence of venous thromboembolism in coronavirus disease 2019: An experience from a single large academic center.,J Vasc Surg Venous Lymphat Disord,32979557,9/27/20,pubmed,0,243,logistic regression,0.00137123,0.001371281,0.059571095,0.001371322,0.001371279,0.934943793,Clinics,0.37751505,FALSE,45.12280702,0.587482219,,,4,0.707574542,,,0.64752838
6655,Signal hotspot mutations in SARS-CoV-2 genomes evolve as the virus spreads and actively replicates in different parts of the world.,Virus Res,32979477,9/27/20,pubmed,0,3,genomes,0.245517738,0.748821501,0.001415202,0.001415188,0.001415184,0.001415188,Genomics,0.42964193,FALSE,176.6666667,0.9629538,111,0.839844795,9,0.814309525,,,0.872369374
6656,Current approaches for target-specific drug discovery using natural compounds against SARS-CoV-2 infection.,Virus Res,32979476,9/27/20,pubmed,0,4,computational,0.925753749,0.047011914,0.00146188,0.001461919,0.022848693,0.001461845,Drug discovery,0.96772623,TRUE,20.5,0.304966294,9.25,0.340379984,5,0.739490092,,,0.461612123
6657,Myocardial injury and risk factors for mortality in patients with COVID-19 pneumonia.,Int J Cardiol,32979425,9/27/20,pubmed,0,10,logistic regression,0.001171603,0.001171537,0.001171576,0.001171562,0.001171556,0.994142167,Clinics,0.9529355,TRUE,37.9,0.517595399,9.1,0.338105432,5,0.739490092,,,0.531730308
6658,SARS-CoV-2 Infection of Pluripotent Stem Cell-Derived Human Lung Alveolar Type 2 Cells Elicits a Rapid Epithelial-Intrinsic Inflammatory Response.,Cell Stem Cell,32979316,9/27/20,pubmed,0,22,transcriptom,0.954834031,0.001823467,0.001823535,0.001823503,0.001823513,0.037871951,Drug discovery,0.16716534,FALSE,39.59090909,0.534603253,60.36363636,0.722103291,9,0.814309525,,,0.69033869
6659,"Impacts of Type 2 Diabetes on Disease Severity, Therapeutic Effect, and Mortality of Patients With COVID-19.",J Clin Endocrinol Metab,32979271,9/27/20,pubmed,0,11,logistic regression,0.001310479,0.001310343,0.001310309,0.020160929,0.001310384,0.974597557,Clinics,0.9520354,TRUE,39.90909091,0.538128518,18.27272727,0.463406476,2,0.618927094,,,0.540154029
6660,PharmGKB Tutorial for Pharmacogenomics of Drugs Potentially Used in the Context of COVID-19.,Clin Pharmacol Ther,32978778,9/27/20,pubmed,0,4,pharmacogenom,0.321607651,0.002996504,0.359620338,0.309782275,0.00299656,0.002996672,Drug discovery,0.7702129,TRUE,246.5,0.983981693,623.5,0.985014718,2,0.618927094,,,0.862641168
6661,Can medical practitioners rely on prediction models for COVID-19? A systematic review.,Evid Based Dent,32978532,9/27/20,pubmed,0,1,"predictive model, prediction model",0.030358011,0.000759383,0.416384875,0.148579688,0.109208812,0.294709232,Imaging,0.53501207,TRUE,3,0.037293586,0,0.055525823,0,0.403234768,,,0.165351392
6662,Increased Serum Levels of Hepcidin and Ferritin Are Associated with Severity of COVID-19.,Med Sci Monit,32978363,9/27/20,pubmed,0,5,logistic regression,0.001684487,0.001684534,0.245225825,0.001684523,0.001684504,0.748036127,Clinics,0.89568913,TRUE,51,0.63875317,15.2,0.427214343,6,0.764429903,,,0.610132472
6663,"Impact of solid cancer on in-hospital mortality overall and among different subgroups of patients with COVID-19: a nationwide, population-based analysis.",ESMO Open,32978251,9/27/20,pubmed,0,50,logistic regression,0.000916668,0.000916683,0.013349343,0.037863748,0.000916781,0.946036777,Clinics,0.8559493,TRUE,63.83333333,0.727008473,47.16666667,0.669253412,15,0.874313229,,,0.756858371
6664,Preliminary predictive criteria for COVID-19 cytokine storm.,Ann Rheum Dis,32978237,9/27/20,pubmed,0,230,logistic regression,0.204634965,0.032343076,0.183499609,0.001310362,0.001310381,0.576901607,Clinics,0.8256302,TRUE,143.625,0.937905869,154.75,0.887543484,16,0.881782826,,,0.902410726
6665,How to rapidly design and operationalise PPE donning and doffing areas for a COVID-19 care facility: quality improvement initiative.,BMJ Open Qual,32978176,9/27/20,pubmed,0,4,sequencing,0.001486483,0.031030569,0.2107885,0.460462948,0.29474499,0.00148651,Epidemiology,0.96283543,TRUE,21.75,0.321603068,3.25,0.203572384,2,0.618927094,,,0.381367515
6666,"Systems pharmacological study illustrates the immune regulation, anti-infection, anti-inflammation, and multi-organ protection mechanism of Qing-Fei-Pai-Du decoction in the treatment of COVID-19.",Phytomedicine,32978039,9/27/20,pubmed,0,14,"bioinformatic, text mining",0.976830058,0.001022629,0.001022675,0.001022677,0.001022621,0.01907934,Drug discovery,0.9561379,TRUE,153.6428571,0.946997341,61.71428571,0.727321381,7,0.785110192,,,0.819809638
6667,Preventing the transmission of COVID-19 and other coronaviruses in older adults aged 60 years and above living in long-term care: a rapid review.,Syst Rev,32977848,9/27/20,pubmed,0,10,active learning,0.001438219,0.101877911,0.001438197,0.510629488,0.383178021,0.001438164,Epidemiology,0.7341772,TRUE,104.8,0.878842229,176.3,0.903866738,5,0.739490092,,,0.84073302
6668,Mental health impacts among health workers during COVID-19 in a low resource setting: a cross-sectional survey from Nepal.,Global Health,32977818,9/27/20,pubmed,0,5,logistic regression,0.000907269,0.000907265,0.000907267,0.000907274,0.995463625,0.000907301,Healthcare,0.99492085,TRUE,6.2,0.087574989,0,0.055525823,12,0.850299401,,,0.331133404
6669,The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19.,J Clin Med,32977606,9/27/20,pubmed,0,15,"predictive model, logistic regression",0.001461902,0.001461904,0.190204115,0.001461898,0.001461876,0.803948305,Clinics,0.5842952,TRUE,35.6,0.493042241,12.26666667,0.388613861,3,0.667819001,,,0.516491701
6670,Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study.,J Clin Med,32977578,9/27/20,pubmed,0,4,dataset,0.001187381,0.020945133,0.001187274,0.649395613,0.034185392,0.293099207,Epidemiology,0.45450905,FALSE,93.5,0.85317583,176.75,0.904000535,2,0.618927094,,,0.792034487
6671,0,Pharmaceuticals (Basel),32977547,9/27/20,pubmed,0,12,"sequencing, whole-genome",0.817596381,0.12967884,0.047254541,0.001823564,0.001823348,0.001823325,Drug discovery,0.37722123,FALSE,39.16666667,0.531510916,15,0.42594327,0,0.403234768,,,0.453562985
6672,The Recommended and Excessive Preventive Behaviors during the COVID-19 Pandemic: A Community-Based Online Survey in China.,Int J Environ Res Public Health,32977538,9/27/20,pubmed,0,8,logistic regression,0.001371273,0.001371251,0.001371266,0.001371312,0.993143648,0.001371249,Healthcare,0.98277855,TRUE,22.5,0.333539489,11.75,0.381857105,4,0.707574542,,,0.474323712
6673,Older People's Nonphysical Contacts and Depression During the COVID-19 Lockdown.,Gerontologist,32977334,9/26/20,pubmed,0,4,logistic regression,0.001511802,0.001511832,0.00151181,0.515717405,0.478235327,0.001511824,Epidemiology,0.778462,TRUE,35.25,0.489331437,24,0.521808938,3,0.667819001,,,0.559653125
6674,Biomarkers for the prediction of venous thromboembolism in critically ill COVID-19 patients.,Thromb Res,32977128,9/26/20,pubmed,0,8,prediction model,0.000977443,0.000977425,0.073510001,0.000977438,0.000977433,0.92258026,Clinics,0.79975533,TRUE,85.875,0.828251593,86.25,0.793484078,5,0.739490092,,,0.787075254
6675,Short-term and long-term health impacts of air pollution reductions from COVID-19 lockdowns in China and Europe: a modelling study.,Lancet Planet Health,32976757,9/26/20,pubmed,0,6,model simulation,0.01257146,0.000710595,0.000710576,0.960667318,0.000710594,0.024629457,Epidemiology,0.67730236,TRUE,38.16666667,0.520378502,31.66666667,0.582686647,17,0.887338725,,,0.663467958
6676,"COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology.",Bioinformatics,32976572,9/26/20,pubmed,0,9,knowledge graph,0.002490568,0.002490519,0.002490634,0.750976037,0.239061765,0.002490477,Epidemiology,0.10905272,FALSE,26.33333333,0.386047375,8.444444444,0.327134065,6,0.764429903,,,0.492537114
6677,Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter.,PLoS One,32976519,9/26/20,pubmed,0,6,machine learning,0.002898276,0.002898498,0.111941493,0.686041695,0.193321596,0.002898443,Epidemiology,0.7470699,TRUE,75.16666667,0.787432742,17,0.451097137,11,0.840175319,,,0.692901733
6678,Clinical Predictive Models for COVID-19: Systematic Study.,J Med Internet Res,32976111,9/26/20,pubmed,0,4,"machine learning, neural network, predictive model, logistic regression",0.001330137,0.001330178,0.40558179,0.065334251,0.001330045,0.5250936,Clinics,0.60348946,TRUE,9.75,0.146267549,3.25,0.203572384,7,0.785110192,,,0.378316708
6679,Evolution of SARS-CoV-2 genome from December 2019 to late March 2020: Emerged haplotypes and informative Tag nucleotide variations.,J Med Virol,32975856,9/26/20,pubmed,0,3,"genome sequences, genomes",0.058003154,0.936425303,0.001392855,0.0013929,0.001392881,0.001392907,Genomics,0.2106595,FALSE,42.33333333,0.560888119,43,0.649785925,1,0.537564047,,,0.58274603
6680,Development and validation of a simplified nomogram predicting individual critical illness of risk in COVID-19: A retrospective study.,J Med Virol,32975825,9/26/20,pubmed,0,8,"logistic regression, prediction model",0.001653057,0.001653017,0.093782628,0.001653074,0.001653029,0.899605194,Clinics,0.8008579,TRUE,30.875,0.442080524,,,2,0.618927094,,,0.530503809
6681,Risk factors associated with adverse fetal outcomes in pregnancies affected by Coronavirus disease 2019 (COVID-19): a secondary analysis of the WAPM study on COVID-19.,J Perinat Med,32975205,9/26/20,pubmed,0,205,logistic regression,0.001112618,0.161723826,0.001112736,0.001112715,0.217812578,0.617125527,Clinics,0.9867109,TRUE,67.59512195,0.750262849,28.16585366,0.555325127,18,0.891474782,,,0.732354253
6682,"The COVID-19 Crisis in Sub-Saharan Africa: Knowledge, Attitudes, and Practices of the Nigerian Public.",Am J Trop Med Hyg,32975179,9/26/20,pubmed,0,12,logistic regression,0.001237078,0.001237047,0.001237055,0.00123709,0.993814658,0.001237071,Healthcare,0.3745767,FALSE,4.833333333,0.065372008,1.333333333,0.13252609,4,0.707574542,,,0.301824213
6683,Quantitative Proteomic Analysis of the Expression of SARS-CoV-2 Receptors in the Gut of Patients with Chronic Enterocolitis.,Yonsei Med J,32975064,9/26/20,pubmed,0,6,proteom,0.772899115,0.003101837,0.003101574,0.003101431,0.003101617,0.214694425,Drug discovery,0.6153338,TRUE,21.16666667,0.31319191,10.16666667,0.357171528,0,0.403234768,,,0.357866069
6684,Lung Mechanics of Mechanically Ventilated Patients With COVID-19: Analytics With High-Granularity Ventilator Waveform Data.,Front Med (Lausanne),32974375,9/26/20,pubmed,0,11,deep learning,0.175957155,0.001187344,0.16512607,0.001187353,0.144263313,0.512278764,Clinics,0.9930113,TRUE,71.09090909,0.769187952,26,0.53819909,1,0.537564047,,,0.614983697
6685,"Exploring the Demographics and Clinical Characteristics Related to the Expression of Angiotensin-Converting Enzyme 2, a Receptor of SARS-CoV-2.",Front Med (Lausanne),32974373,9/26/20,pubmed,0,8,logistic regression,0.318357336,0.001059382,0.001059371,0.001059372,0.103584302,0.574880237,Clinics,0.7350145,TRUE,30.875,0.442080524,9.25,0.340379984,3,0.667819001,,,0.483426503
6686,SARS-CoV-2 Codon Usage Bias Downregulates Host Expressed Genes With Similar Codon Usage.,Front Cell Dev Biol,32974353,9/26/20,pubmed,0,2,proteom,0.629587738,0.363542877,0.001717305,0.001717293,0.001717419,0.001717368,Drug discovery,0.52138454,TRUE,65.5,0.737646113,49.5,0.679622692,1,0.537564047,,,0.651610951
6687,"Lions, tigers and kittens too: ACE2 and susceptibility to COVID-19.",Evol Med Public Health,32974030,9/26/20,pubmed,0,2,computational,0.592593469,0.397156277,0.002562531,0.002562635,0.002562533,0.002562555,Drug discovery,0.33940786,FALSE,67.5,0.750201002,157,0.889884934,7,0.785110192,,,0.808398709
6688,Single Cell RNA-seq Data Analysis Reveals the Potential Risk of SARS-CoV-2 Infection Among Different Respiratory System Conditions.,Front Genet,32973879,9/26/20,pubmed,0,6,"transcriptom, dataset",0.678136282,0.002032932,0.002032922,0.002032913,0.002032799,0.313732153,Drug discovery,0.87046987,TRUE,25.66666667,0.376461129,4.5,0.242708055,1,0.537564047,,,0.385577744
6689,Immunomodulatory and Antiviral Activity of Metformin and Its Potential Implications in Treating Coronavirus Disease 2019 and Lung Injury.,Front Immunol,32973814,9/26/20,pubmed,0,6,proteom,0.703354285,0.002238505,0.002238638,0.002238486,0.002238434,0.287691652,Drug discovery,0.9137388,TRUE,15.16666667,0.228709258,3.333333333,0.206515922,10,0.828199272,,,0.421141484
6690,Acute Ischemic Stroke and COVID-19: Experience From a Comprehensive Stroke Center in Midwest US.,Front Neurol,32973666,9/26/20,pubmed,0,12,logistic regression,0.001593501,0.074013673,0.00159356,0.051752638,0.00159359,0.869453038,Clinics,0.62099916,TRUE,21.58333333,0.318758117,9.5,0.345531175,3,0.667819001,,,0.444036098
6691,Glimpsing the Impact of COVID19 Lock-Down on People With Epilepsy: A Text Mining Approach.,Front Neurol,32973656,9/26/20,pubmed,0,9,text mining,0.173747363,0.001943577,0.001943679,0.278950475,0.332310011,0.211104894,Healthcare,0.6931659,TRUE,41.55555556,0.553528357,23.66666667,0.518062617,2,0.618927094,,,0.563506023
6692,The C-C Chemokine Receptor Type 4 Is an Immunomodulatory Target of Hydroxychloroquine.,Front Pharmacol,32973504,9/26/20,pubmed,0,6,in silico,0.88940741,0.001330062,0.050983266,0.001330139,0.001330077,0.055619046,Drug discovery,0.68956614,TRUE,32,0.455810502,33.33333333,0.594995986,0,0.403234768,,,0.484680418
6693,Sequence analysis of SARS-CoV-2 genome reveals features important for vaccine design.,Sci Rep,32973171,9/26/20,pubmed,0,9,"in silico, transcriptom",0.354581346,0.637811619,0.001901799,0.001901739,0.001901782,0.001901715,Genomics,0.6418262,TRUE,47.11111111,0.606778403,97.44444444,0.817500669,2,0.618927094,,,0.681068722
6694,"Clinical Characteristics and Risk Factors of Acute Respiratory Distress Syndrome (ARDS) in COVID-19 Patients in Beijing, China: A Retrospective Study.",Med Sci Monit,32973126,9/26/20,pubmed,0,18,logistic regression,0.001392886,0.001392908,0.075799349,0.001392945,0.001392866,0.918629047,Clinics,0.91179305,TRUE,79.38888889,0.803883976,50.05555556,0.681897244,1,0.537564047,,,0.674448422
6695,The rise of COVID-19 cases is associated with support for world leaders.,Proc Natl Acad Sci U S A,32973100,9/26/20,pubmed,0,6,dataset,0.002898371,0.002898531,0.002898317,0.667309843,0.321096499,0.002898439,Epidemiology,0.64934886,TRUE,46,0.596882924,37.33333333,0.618209794,3,0.667819001,,,0.62763724
6696,Whole-Genome Sequence of SARS-CoV-2 Isolate Siena-1/2020.,Microbiol Resour Announc,32972948,9/26/20,pubmed,0,6,"sequencing, whole-genome",0.004109786,0.979451211,0.004109724,0.004109766,0.004109783,0.00410973,Genomics,0.6299722,TRUE,56.5,0.678891706,48.33333333,0.675073588,1,0.537564047,,,0.630509781
6697,Coding-Complete Genome Sequences of Three SARS-CoV-2 Strains from Bangladesh.,Microbiol Resour Announc,32972934,9/26/20,pubmed,0,20,"sequencing, genome sequences, genomes",0.0045309,0.977345589,0.004530591,0.00453133,0.004530936,0.004530654,Genomics,0.5249473,TRUE,18.3,0.273795535,16.9,0.447551512,0,0.403234768,,,0.374860605
6698,Poor appetite and overeating reported by adults in Australia during the coronavirus-19 disease pandemic: a population-based study.,Public Health Nutr,32972479,9/26/20,pubmed,0,5,logistic regression,0.00182329,0.001823307,0.001823315,0.001823367,0.959730636,0.032976084,Healthcare,0.8772168,TRUE,63.6,0.725462304,56.6,0.709392561,1,0.537564047,,,0.657472971
6699,"Social network analysis of COVID-19 transmission in Karnataka, India.",Epidemiol Infect,32972463,9/26/20,pubmed,0,4,network analysis,0.002130809,0.002130744,0.002130759,0.781501681,0.209975283,0.002130723,Epidemiology,0.7433828,TRUE,9.75,0.146267549,1,0.122023013,0,0.403234768,,,0.223841777
6700,Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data.,Int J Environ Res Public Health,32971995,9/26/20,pubmed,0,5,"deep learning, dataset",0.001538121,0.036505515,0.728678978,0.230201089,0.001538113,0.001538183,Imaging,0.24073377,FALSE,49,0.624281032,37.4,0.618678084,45,0.953206988,,,0.732055368
6701,The Epidemiological Signature of Pathogen Populations That Vary in the Relationship between Free-Living Parasite Survival and Virulence.,Viruses,32971954,9/26/20,pubmed,0,4,mathematical model,0.001254659,0.263381727,0.001254639,0.510298165,0.001254645,0.222556166,Epidemiology,0.52963156,TRUE,23,0.34225988,16.75,0.446012845,1,0.537564047,,,0.441945591
6702,Imaging Diagnostics and Pathology in SARS-CoV-2-Related Diseases.,Int J Mol Sci,32971906,9/26/20,pubmed,0,7,artificial intelligence,0.37802118,0.001291308,0.312557416,0.230308164,0.001291272,0.07653066,Drug discovery,0.9621617,TRUE,112.2857143,0.894489455,33.71428571,0.597002944,2,0.618927094,,,0.703473164
6703,Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software.,Int J Environ Res Public Health,32971756,9/26/20,pubmed,0,20,correlation analysis,0.00098835,0.00098841,0.793397205,0.000988391,0.000988384,0.20264926,Imaging,0.9731544,TRUE,39.6,0.53503618,9.2,0.339577201,4,0.707574542,,,0.527395974
6704,Heterogeneous expression of the SARS-Coronavirus-2 receptor ACE2 in the human respiratory tract.,EBioMedicine,32971472,9/25/20,pubmed,0,10,sequencing,0.613190492,0.262503669,0.001350364,0.001350399,0.037322601,0.084282476,Drug discovery,0.39313665,FALSE,58.9,0.695404787,118.9,0.850147177,11,0.840175319,,,0.795242428
6705,Efficacy of corticosteroid treatment for hospitalized patients with severe COVID-19: a multicentre study.,Clin Microbiol Infect,32971254,9/25/20,pubmed,0,26,logistic regression,0.021137466,0.001330061,0.001330123,0.023607147,0.001330061,0.951265142,Clinics,0.8679043,TRUE,54.76923077,0.666646051,73.69230769,0.762978325,7,0.785110192,,,0.738244856
6706,Comprehensive assessment of side effects in COVID-19 drug pipeline from a network perspective.,Food Chem Toxicol,32971210,9/25/20,pubmed,0,9,data mining,0.823415679,0.001350364,0.001350439,0.001350415,0.042117486,0.130415617,Drug discovery,0.9064995,TRUE,45.88888889,0.595027522,49.55555556,0.679823388,1,0.537564047,,,0.604138319
6707,Clinical features of patients with type 2 diabetes with and without Covid-19: A case control study (CoViDiab I).,Diabetes Res Clin Pract,32971157,9/25/20,pubmed,0,16,logistic regression,0.021800822,0.001371259,0.00137125,0.001371254,0.001371315,0.972714101,Clinics,0.82913303,TRUE,109.875,0.889850949,70.0625,0.753545625,6,0.764429903,,,0.802608826
6708,Deciphering the SSR incidences across viral members of Coronaviridae family.,Chem Biol Interact,32971122,9/25/20,pubmed,0,10,"in-silico, genomes",0.00162277,0.962640902,0.001622751,0.001622786,0.030868052,0.00162274,Genomics,0.8066391,TRUE,84.3,0.822809079,40,0.633395772,0,0.403234768,,,0.619813206
6709,Integrative transcriptomics analysis of lung epithelial cells and identification of repurposable drug candidates for COVID-19.,Eur J Pharmacol,32971089,9/25/20,pubmed,0,6,"bioinformatic, in silico, transcriptom, network analysis",0.994365423,0.001127113,0.001127003,0.001126838,0.001126804,0.001126819,Drug discovery,0.9078577,TRUE,14.16666667,0.214175274,5.5,0.267259834,3,0.667819001,,,0.383084703
6710,"Indirect effects of the COVID-19 pandemic on malaria intervention coverage, morbidity, and mortality in Africa: a geospatial modelling analysis.",Lancet Infect Dis,32971006,9/25/20,pubmed,0,21,bayes,0.065552146,0.000956324,0.000956331,0.89864346,0.032935355,0.000956384,Epidemiology,0.7493359,TRUE,96.28571429,0.859607892,296.5714286,0.952970297,12,0.850299401,,,0.887625863
6711,Identifying #addiction concerns on twitter during the COVID-19 pandemic: A text mining analysis.,Subst Abus,32970973,9/25/20,pubmed,0,3,text mining,0.026871196,0.000916719,0.000916696,0.969461944,0.000916746,0.000916698,Epidemiology,0.1755088,FALSE,17.66666667,0.266373925,12.33333333,0.389550442,3,0.667819001,,,0.441247789
6712,Preliminary estimation of temporal and spatiotemporal dynamic measures of COVID-19 transmission in Thailand.,PLoS One,32970773,9/25/20,pubmed,0,4,mathematical model,0.001350338,0.00135034,0.00135034,0.993248287,0.001350375,0.00135032,Epidemiology,0.7033489,TRUE,64.5,0.731832519,47,0.668718223,2,0.618927094,,,0.673159279
6713,Loneliness in the UK during the COVID-19 pandemic: Cross-sectional results from the COVID-19 Psychological Wellbeing Study.,PLoS One,32970764,9/25/20,pubmed,0,6,logistic regression,0.001653072,0.001653031,0.001653061,0.001653151,0.991734613,0.001653072,Healthcare,0.9633925,TRUE,54.33333333,0.663677407,64.16666667,0.735884399,25,0.918019631,,,0.772527146
6714,A web visualization tool using T cell subsets as the predictor to evaluate COVID-19 patient's severity.,PLoS One,32970753,9/25/20,pubmed,0,10,logistic regression,0.162385506,0.001126819,0.00112684,0.152484803,0.001126889,0.681749144,Clinics,0.8464619,TRUE,91.8,0.848166244,57.6,0.713138881,1,0.537564047,,,0.699623058
6715,Clinical and imaging features predict mortality in COVID-19 infection in Iran.,PLoS One,32970733,9/25/20,pubmed,0,10,"logistic regression, prediction model",0.000936083,0.000936096,0.414090723,0.000936097,0.000936112,0.582164889,Clinics,0.8333251,TRUE,15.9,0.238913971,,,1,0.537564047,,,0.388239009
6716,The impact of social distancing and public behavior changes on COVID-19 transmission dynamics in the Republic of Korea.,PLoS One,32970716,9/25/20,pubmed,0,4,mathematical model,0.054113101,0.001220028,0.00121999,0.941006806,0.001220055,0.00122002,Epidemiology,0.28779274,FALSE,36.75,0.505968211,7.75,0.312215681,3,0.667819001,,,0.495334298
6717,An investigation into the molecular basis of cancer comorbidities in coronavirus infection.,FEBS Open Bio,32970391,9/25/20,pubmed,0,3,transcriptom,0.370138155,0.001126806,0.001126801,0.001126793,0.001126784,0.625354662,Clinics,0.7932686,TRUE,74.66666667,0.785268106,55.66666667,0.705244849,1,0.537564047,,,0.676025667
6718,Global lockdown: An effective safeguard in responding to the threat of COVID-19.,J Eval Clin Pract,32970386,9/25/20,pubmed,0,9,bayes,0.029536599,0.001291237,0.001291209,0.965298459,0.001291262,0.001291234,Epidemiology,0.7872326,TRUE,36.22222222,0.499597996,12.22222222,0.387811078,5,0.739490092,,,0.542299722
6719,Genomic and proteomic mutation landscapes of SARS-CoV-2.,J Med Virol,32970329,9/25/20,pubmed,0,3,"proteom, genomes",0.276596061,0.687701634,0.001461879,0.001462004,0.031316402,0.00146202,Genomics,0.72668135,TRUE,3.333333333,0.04044777,0,0.055525823,7,0.785110192,,,0.293694595
6720,Handling immune-suppressive therapies during SARS-CoV-2 pandemic: insights from pediatric datasets.,J Nephrol,32970281,9/25/20,pubmed,0,3,dataset,0.567939802,0.019530266,0.353936357,0.019531366,0.019532499,0.019529711,Drug discovery,0.32391304,FALSE,212.6666667,0.976436391,206,0.920457586,0,0.403234768,,,0.766709582
6721,Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs.,J Thorac Imaging,32969949,9/25/20,pubmed,0,16,"deep learning, classifier",0.001141317,0.001141344,0.85280025,0.001141318,0.001141324,0.142634448,Imaging,0.580111,TRUE,13.375,0.201991465,22.625,0.508228526,1,0.537564047,,,0.415928013
6722,Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence.,Radiology,32969761,9/25/20,pubmed,0,13,"artificial intelligence, neural network",0.000800564,0.000800587,0.791605375,0.000800577,0.000800626,0.20519227,Imaging,0.87310755,TRUE,77.92307692,0.798070382,62.92307692,0.732138079,7,0.785110192,,,0.771772884
6723,"A novel risk score to predict cardiovascular complications in patients with coronavirus disease 2019 (COVID-19): A retrospective, multicenter, observational study.",Immun Inflamm Dis,32969605,9/25/20,pubmed,0,6,logistic regression,0.001415121,0.001415087,0.053842528,0.001415118,0.001415132,0.940497013,Clinics,0.96329594,TRUE,46.33333333,0.599418641,14.5,0.418450629,1,0.537564047,,,0.518477772
6724,The natural way forward: Molecular dynamics simulation analysis of phytochemicals from Indian medicinal plants as potential inhibitors of SARS-CoV-2 targets.,Phytother Res,32969524,9/25/20,pubmed,0,3,molecular dynamics simulation,0.971371088,0.001415132,0.022968352,0.00141516,0.00141512,0.001415148,Drug discovery,0.8983451,TRUE,7.666666667,0.111633373,4,0.231469093,3,0.667819001,,,0.336973822
6725,Intersectionality and inequalities in medical risk for severe COVID-19 in the Canadian Longitudinal Study on Aging.,Gerontologist,32969470,9/25/20,pubmed,0,1,logistic regression,0.001254627,0.001254609,0.001254613,0.001254676,0.584409467,0.410572009,Healthcare,0.7672019,TRUE,10,0.15214299,0,0.055525823,2,0.618927094,,,0.275531969
6726,Investigating the Impact of COVID-19 Lockdown on the Psychological Health of University Students and Their Attitudes Toward Mobile Mental Health Solutions: Two-Part Questionnaire Study.,JMIR Form Res,32969340,9/25/20,pubmed,0,6,digital health,0.000740314,0.000740306,0.000740315,0.057387305,0.928714298,0.011677462,Healthcare,0.88332903,TRUE,8.333333333,0.123693488,0,0.055525823,2,0.618927094,,,0.266048802
6727,Repurposing of the approved small molecule drugs in order to inhibit SARS-CoV-2 S protein and human ACE2 interaction through virtual screening approaches.,J Biomol Struct Dyn,32969333,9/25/20,pubmed,0,5,virtual screening,0.994063617,0.001187273,0.001187304,0.001187288,0.001187256,0.001187263,Drug discovery,0.93647397,TRUE,17.6,0.264456676,2.2,0.16838373,4,0.707574542,,,0.380138316
6728,"Proteomic Analysis Reveals Upregulation of ACE2 (Angiotensin-Converting Enzyme 2), the Putative SARS-CoV-2 Receptor in Pressure-but Not Volume-Overloaded Human Hearts.",Hypertension,32969280,9/25/20,pubmed,0,17,proteom,0.650761572,0.009284501,0.00928566,0.009284561,0.009284164,0.312099542,Drug discovery,0.36378032,FALSE,77.05882353,0.794916198,49.41176471,0.679221301,0,0.403234768,,,0.625790755
6729,Serum Endocan Levels on Admission Are Associated With Worse Clinical Outcomes in COVID-19 Patients: A Pilot Study.,Angiology,32969233,9/25/20,pubmed,0,10,logistic regression,0.028394693,0.001538107,0.001538191,0.00153818,0.001538109,0.96545272,Clinics,0.8089824,TRUE,26.5,0.389325252,15.4,0.42875301,1,0.537564047,,,0.45188077
6730,Azithromycin in viral infections.,Rev Med Virol,32969125,9/25/20,pubmed,0,2,in silico,0.935458624,0.001565353,0.00156533,0.001565318,0.00156531,0.058280065,Drug discovery,0.93861866,TRUE,22.5,0.333539489,5,0.257024351,8,0.799987654,,,0.463517165
6731,Global stability of COVID-19 model involving the quarantine strategy and media coverage effects.,AIMS Public Health,32968680,9/25/20,pubmed,0,4,mathematical model,0.003465946,0.003465981,0.281957277,0.704178871,0.003465987,0.003465939,Epidemiology,0.5018645,TRUE,26.5,0.389325252,3.75,0.21982874,6,0.764429903,,,0.457861298
6732,"Possible effects of mixed prevention strategy for COVID-19 epidemic: massive testing, quarantine and social distancing.",AIMS Public Health,32968673,9/25/20,pubmed,0,2,mathematical model,0.001156271,0.001156261,0.001156273,0.934838146,0.06053677,0.001156279,Epidemiology,0.22667,FALSE,99,0.867029501,32.5,0.589376505,5,0.739490092,,,0.731965366
6733,Modeling the Epidemiological Trend and Behavior of COVID-19 in Italy.,Cureus,32968550,9/25/20,pubmed,0,3,computational,0.002080654,0.055253088,0.032645404,0.905859448,0.002080651,0.002080755,Epidemiology,0.57357085,TRUE,13,0.197352959,2,0.164302917,1,0.537564047,,,0.299739974
6734,A simulated single ventilator/dual patient ventilation strategy for acute respiratory distress syndrome during the COVID-19 pandemic.,R Soc Open Sci,32968521,9/25/20,pubmed,0,9,mathematical model,0.033561258,0.001751222,0.142014484,0.347387041,0.001751285,0.473534711,Clinics,0.41121274,FALSE,51.66666667,0.644195683,61.22222222,0.725247525,3,0.667819001,,,0.679087403
6735,Advancing COVID-19 differentiation with a robust preprocessing and integration of multi-institutional open-repository computer tomography datasets for deep learning analysis.,Exp Ther Med,32968435,9/25/20,pubmed,0,10,"deep learning, artificial intelligence, image analysis, transfer learning, dataset",0.00153816,0.001538167,0.907957814,0.085889514,0.001538194,0.001538152,Imaging,0.68035924,TRUE,135.4,0.927082689,75.9,0.768731603,1,0.537564047,,,0.744459446
6736,Correlations of CT scan with high-sensitivity C-reactive protein and D-dimer in patients with coronavirus disease 2019.,Pak J Med Sci,32968416,9/25/20,pubmed,0,4,"logistic regression, correlation analysis",0.000846532,0.00084653,0.292361704,0.000846528,0.000846534,0.704252173,Clinics,0.95999837,TRUE,104.75,0.878656689,34,0.5990768,2,0.618927094,,,0.698886861
6737,Risk assessment of airborne transmission of COVID-19 by asymptomatic individuals under different practical settings.,J Aerosol Sci,32968325,9/25/20,pubmed,0,9,computational,0.002357856,0.002357841,0.002357896,0.927307984,0.002357826,0.063260596,Epidemiology,0.33423078,FALSE,8.444444444,0.124868576,0.111111111,0.056663099,6,0.764429903,,,0.315320526
6738,0,mSphere,32968009,9/25/20,pubmed,0,3,"sequencing, metagenom",0.156262448,0.839783965,0.000988362,0.000988408,0.000988476,0.000988342,Genomics,0.82092726,TRUE,96.66666667,0.860411899,289.6666667,0.951297832,2,0.618927094,,,0.810212275
6739,Template-dependent inhibition of coronavirus RNA-dependent RNA polymerase by remdesivir reveals a second mechanism of action.,J Biol Chem,32967965,9/25/20,pubmed,0,8,structural model,0.568634623,0.390436588,0.001565324,0.001565486,0.001565322,0.036232658,Drug discovery,0.3552273,FALSE,36.625,0.504112808,104.5,0.8302114,12,0.850299401,,,0.72820787
6740,Early impact of COVID-19 social distancing measures on reported sexual behaviour of HIV pre-exposure prophylaxis users in Wales.,Sex Transm Infect,32967930,9/25/20,pubmed,0,10,logistic regression,0.001291219,0.001291273,0.001291215,0.264782887,0.730052138,0.001291269,Healthcare,0.30814487,FALSE,86.5,0.831653163,74.7,0.765654268,2,0.618927094,,,0.738744842
6741,Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study.,BMJ Open,32967887,9/25/20,pubmed,0,14,prediction model,0.001291274,0.001291267,0.001291327,0.326408442,0.00129131,0.66842638,Clinics,0.6834958,TRUE,66.5,0.743830787,77.64285714,0.772477923,2,0.618927094,,,0.711745268
6742,Pandemic number five - Latest insights into the COVID-19 crisis.,Biomed J,32967801,9/25/20,pubmed,0,1,artificial intelligence,0.183379453,0.110069262,0.213237632,0.486381111,0.003466342,0.003466199,Epidemiology,0.5485756,TRUE,83,0.818170573,25,0.529435376,1,0.537564047,,,0.628389999
6743,"TMPRSS2, a SARS-CoV-2 internalization protease is downregulated in head and neck cancer patients.",J Exp Clin Cancer Res,32967703,9/25/20,pubmed,0,18,dataset,0.630964662,0.001717202,0.001717255,0.00171721,0.001717232,0.362166439,Drug discovery,0.9436792,TRUE,112.1666667,0.894242068,94.55555556,0.811747391,0,0.403234768,,,0.703074742
6744,A persistently replicating SARS-CoV-2 variant derived from an asymptomatic individual.,J Transl Med,32967693,9/25/20,pubmed,0,11,"sequencing, in silico, whole genome",0.052854915,0.909414205,0.001098822,0.001098869,0.034434374,0.001098814,Genomics,0.7604006,TRUE,58.45454545,0.69249799,34.72727273,0.60329141,7,0.785110192,,,0.693633197
6745,Molecular Features of Non-Selective Small Molecule Antagonists of the Bradykinin Receptors.,Pharmaceuticals (Basel),32967280,9/25/20,pubmed,0,3,in silico,0.823323665,0.002080581,0.002080625,0.002080587,0.002080591,0.168353951,Drug discovery,0.89596885,TRUE,12.33333333,0.186467932,3,0.199424672,2,0.618927094,,,0.3349399
6746,Tracking and Analyzing Public Emotion Evolutions During COVID-19: A Case Study from the Event-Driven Perspective on Microblogs.,Int J Environ Res Public Health,32967163,9/25/20,pubmed,0,5,dataset,0.001901722,0.152608465,0.00190178,0.839784524,0.001901764,0.001901745,Epidemiology,0.97421145,TRUE,35,0.488032655,6.8,0.292881991,1,0.537564047,,,0.439492898
6747,Recognition of Potential COVID-19 Drug Treatments through the Study of Existing Protein-Drug and Protein-Protein Structures: An Analysis of Kinetically Active Residues.,Biomolecules,32967116,9/25/20,pubmed,0,1,in silico,0.986398888,0.002720199,0.002720205,0.002720234,0.002720339,0.002720134,Drug discovery,0.78175384,TRUE,14,0.213494959,10,0.355632861,1,0.537564047,,,0.368897289
6748,Phylogenetic analysis of SARS-CoV-2 in the first few months since its emergence.,J Med Virol,32966646,9/24/20,pubmed,0,6,bayes,0.001371304,0.993143444,0.001371352,0.001371363,0.00137127,0.001371268,Genomics,0.48821676,FALSE,22,0.326056033,6.5,0.288132192,2,0.618927094,,,0.41103844
6749,Evaluating aerosol and splatter following dental procedures: Addressing new challenges for oral health care and rehabilitation.,J Oral Rehabil,32966633,9/24/20,pubmed,0,12,image analysis,0.001511864,0.130927607,0.054379803,0.758791928,0.001511918,0.05287688,Epidemiology,0.83162403,TRUE,26.75,0.391799122,35.25,0.606837035,1,0.537564047,,,0.512066735
6750,SARS-CoV-2 infects and induces cytotoxic effects in human cardiomyocytes.,Cardiovasc Res,32966582,9/24/20,pubmed,0,22,sequencing,0.659452964,0.156906352,0.001098818,0.001098811,0.001098835,0.180344221,Drug discovery,0.5998492,TRUE,94.04545455,0.854660152,129,0.86272411,5,0.739490092,,,0.818958118
6751,"Serial Testing for SARS-CoV-2 and Virus Whole Genome Sequencing Inform Infection Risk at Two Skilled Nursing Facilities with COVID-19 Outbreaks - Minnesota, April-June 2020.",MMWR Morb Mortal Wkly Rep,32966272,9/24/20,pubmed,0,47,"sequencing, whole genome, genomes",0.001461883,0.37253493,0.001461906,0.100062695,0.523016679,0.001461907,Healthcare,0.81767434,TRUE,27.23404255,0.397798256,60.93617021,0.723842655,11,0.840175319,,,0.653938743
6752,Neither ACEIs nor ARBs are associated with respiratory distress or mortality in COVID-19 results of a prospective study on a hospital-based cohort.,Intern Emerg Med,32965603,9/24/20,pubmed,0,16,logistic regression,0.324617904,0.001786567,0.001786549,0.001786598,0.001786621,0.668235762,Clinics,0.96000457,TRUE,8.75,0.129383388,3.4375,0.209124967,5,0.739490092,,,0.359332815
6753,Management of differentiated thyroid cancer through nuclear medicine facilities during Covid-19 emergency: the telemedicine challenge.,Eur J Nucl Med Mol Imaging,32965559,9/24/20,pubmed,0,10,radiom,0.04245713,0.001943534,0.208659816,0.001943617,0.445812934,0.299182969,Healthcare,0.9061186,TRUE,53.1,0.654585936,19.3,0.473508162,1,0.537564047,,,0.555219382
6754,Insights into the biased activity of dextromethorphan and haloperidol towards SARS-CoV-2 NSP6: in silico binding mechanistic analysis.,J Mol Med (Berl),32965508,9/24/20,pubmed,0,4,"molecular dynamics simulation, in silico",0.994366007,0.001126802,0.001126784,0.001126802,0.001126831,0.001126775,Drug discovery,0.4034745,FALSE,30.5,0.438493413,1.25,0.127776291,4,0.707574542,,,0.424614749
6755,Rationale and design of the "Tocilizumab in patients with moderate to severe COVID-19: an open-label multicentre randomized controlled" trial (TOCIBRAS).,Rev Bras Ter Intensiva,32965395,9/24/20,pubmed,0,19,logistic regression,0.178372655,0.00099957,0.000999548,0.094784147,0.000999589,0.723844491,Clinics,0.9726931,TRUE,101.2631579,0.871482466,94.78947368,0.812416377,5,0.739490092,,,0.807796312
6756,Ethical dilemmas in COVID-19 times: how to decide who lives and who dies?,Rev Assoc Med Bras (1992),32965367,9/24/20,pubmed,0,3,artificial intelligence,0.001565348,0.001565329,0.186877502,0.684212958,0.124213359,0.001565504,Epidemiology,0.76117873,TRUE,13.33333333,0.201558538,1,0.122023013,1,0.537564047,,,0.287048533
6757,Impact of antihypertensive agents on clinical course and in-hospital mortality: analysis of 169 hypertensive patients hospitalized for COVID-19.,Rev Assoc Med Bras (1992),32965360,9/24/20,pubmed,0,6,logistic regression,0.376233723,0.001987136,0.001987118,0.00198716,0.001987133,0.615817729,Clinics,0.802457,TRUE,33.16666667,0.46780877,4,0.231469093,4,0.707574542,,,0.468950801
6758,Bioethical aspects of artificial intelligence: COVID-19 & end of life.,Rev Assoc Med Bras (1992),32965345,9/24/20,pubmed,0,3,artificial intelligence,0.015999146,0.015998802,0.920004143,0.015999996,0.015999439,0.015998474,Epidemiology,0.55188704,TRUE,63.66666667,0.725957078,15,0.42594327,1,0.537564047,,,0.563154799
6759,[; PROBLEMS OF MONITORING THE QUALITY OF HOSPITALS IN GEORGIA IN THE CONTEXT OF THE COVID 19 PANDEMIC (REVIEW)].,Georgian Med News,32965269,9/24/20,pubmed,0,3,mathematical model,0.000657794,0.000657795,0.46735264,0.304548715,0.164327938,0.062455118,Epidemiology,0.24621221,FALSE,5.333333333,0.074339786,0.333333333,0.073187048,0,0.403234768,,,0.183587201
6760,Secondary structure of the SARS-CoV-2 5'-UTR.,RNA Biol,32965173,9/24/20,pubmed,0,4,sequence alignment,0.606815438,0.38631572,0.001717202,0.00171723,0.001717211,0.001717198,Drug discovery,0.5107899,TRUE,41.25,0.5508071,34.5,0.602221033,11,0.840175319,,,0.664401151
6761,Artificial intelligence technology for diagnosing COVID-19 cases: a review of substantial issues.,Eur Rev Med Pharmacol Sci,32965018,9/24/20,pubmed,0,6,artificial intelligence,0.002562613,0.002562597,0.828390879,0.161358675,0.002562619,0.002562617,Epidemiology,0.5773985,TRUE,23,0.34225988,1.833333333,0.151056998,0,0.403234768,,,0.298850548
6762,Design and in-silico screening of Peptide Nucleic Acid (PNA) inspired novel pronucleotide scaffolds targeting COVID-19.,Curr Comput Aided Drug Des,32964827,9/24/20,pubmed,0,8,in-silico,0.828359238,0.167502382,0.001034604,0.001034593,0.001034596,0.001034587,Drug discovery,0.627274,TRUE,14.75,0.222586431,1.875,0.151792882,0,0.403234768,,,0.259204693
6763,Repurposing drugs for treatment of SARS-CoV-2 infection: computational design insights into mechanisms of action.,J Biomol Struct Dyn,32964805,9/24/20,pubmed,0,2,computational,0.908911245,0.085336042,0.001438197,0.001438224,0.001438127,0.001438165,Drug discovery,0.79241943,TRUE,23,0.34225988,8.5,0.329141022,2,0.618927094,,,0.430109332
6764,Identification of destabilizing SNPs in SARS-CoV2-ACE2 protein and spike glycoprotein: implications for virus entry mechanisms.,J Biomol Struct Dyn,32964802,9/24/20,pubmed,0,2,"bioinformatic, in silico",0.72693843,0.267214104,0.001461895,0.001461877,0.001461846,0.001461848,Drug discovery,0.53640705,TRUE,34,0.477766096,13,0.400521809,1,0.537564047,,,0.471950651
6765,Association of subjective olfactory dysfunction and 12-item odor identification testing in ambulatory COVID-19 patients.,Int Forum Allergy Rhinol,32964657,9/24/20,pubmed,0,7,logistic regression,0.001171582,0.001171569,0.395135273,0.001171576,0.299172228,0.302177771,Clinics,0.99720466,TRUE,36.28571429,0.500463851,24.85714286,0.527093926,2,0.618927094,,,0.54882829
6766,Aframomum melegueta secondary metabolites exhibit polypharmacology against SARS-CoV-2 drug targets: in vitro validation of furin inhibition.,Phytother Res,32964551,9/24/20,pubmed,0,11,computational,0.72676795,0.001861863,0.001861838,0.147250128,0.001861822,0.120396398,Drug discovery,0.8723333,TRUE,23.27272727,0.344610056,6.636363636,0.289470163,2,0.618927094,,,0.417669105
6767,The Paradox of the Low Prevalence of Current Smokers Among Covid-19 Patients Hospitalized in Non-Intensive Care Wards: Results From an Italian Multicenter Case-Control Study.,Nicotine Tob Res,32964233,9/24/20,pubmed,0,5,logistic regression,0.001538131,0.001538249,0.001538145,0.106830115,0.205031673,0.683523687,Clinics,0.50911885,TRUE,67.4,0.749706228,35,0.605298368,6,0.764429903,,,0.706478166
6768,0,J Immunol Res,32964056,9/24/20,pubmed,0,10,"computational, in silico",0.943517472,0.001141357,0.028352942,0.024705519,0.001141359,0.00114135,Drug discovery,0.65446925,TRUE,4.7,0.063578453,0.4,0.075796093,0,0.403234768,,,0.180869771
6769,Controlling the Spread of COVID-19: Optimal Control Analysis.,Comput Math Methods Med,32963585,9/24/20,pubmed,0,3,mathematical model,0.001486648,0.001486439,0.001486495,0.99256732,0.001486581,0.001486518,Epidemiology,0.64719254,TRUE,6.333333333,0.089863319,0,0.055525823,0,0.403234768,,,0.182874636
6770,Determinants of nurse job dissatisfaction - findings from a cross-sectional survey analysis in the UK.,BMC Nurs,32963498,9/24/20,pubmed,0,7,logistic regression,0.001717234,0.001717219,0.001717186,0.076254309,0.894224693,0.024369359,Healthcare,0.78381073,TRUE,126.4285714,0.916012122,187.8571429,0.910690393,2,0.618927094,,,0.81520987
6771,[Risk factors for in-hospital mortality in patients with acute myocardial infarction during the COVID-19 outbreak].,Rev Esp Cardiol,32963419,9/24/20,pubmed,0,17,logistic regression,0.001593485,0.001593502,0.053770374,0.001593519,0.001593509,0.939855611,Clinics,0.9767461,TRUE,60.17647059,0.703939638,33.88235294,0.598073321,0,0.403234768,,,0.568415909
6772,Consensus transcriptional regulatory networks of coronavirus-infected human cells.,Sci Data,32963239,9/24/20,pubmed,0,3,"transcriptom, dataset",0.99088308,0.001823421,0.001823451,0.001823419,0.001823318,0.001823311,Drug discovery,0.5644996,TRUE,39.33333333,0.532562311,122.6666667,0.854830078,1,0.537564047,,,0.641652145
6773,Changes in Psychological Distress During the COVID-19 Pandemic in Japan: A Longitudinal Study.,J Epidemiol,32963212,9/24/20,pubmed,0,9,logistic regression,0.001461862,0.00146193,0.001461863,0.001461979,0.992690457,0.001461909,Healthcare,0.7525115,TRUE,111,0.892015585,56.22222222,0.707653198,7,0.785110192,,,0.794926325
6774,0,mSystems,32963099,9/24/20,pubmed,0,12,molecular dynamics simulation,0.924904769,0.00081534,0.023959108,0.048690061,0.000815366,0.000815357,Drug discovery,0.8459948,TRUE,48.83333333,0.621003154,20.16666667,0.481803586,2,0.618927094,,,0.573911278
6775,Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19.,BMJ Support Palliat Care,32963059,9/24/20,pubmed,0,8,"machine learning, classifier, prediction model",0.001219983,0.001219986,0.303336763,0.001220024,0.001220015,0.691783228,Clinics,0.32241678,FALSE,54,0.661574618,48.375,0.675341183,2,0.618927094,,,0.651947632
6776,"Estimated surge in hospital and intensive care admission because of the coronavirus disease 2019 pandemic in the Greater Toronto Area, Canada: a mathematical modelling study.",CMAJ Open,32963024,9/24/20,pubmed,0,16,mathematical model,0.001219993,0.00122001,0.001220047,0.617640431,0.001220013,0.377479507,Epidemiology,0.30310434,FALSE,92.8125,0.850887501,135.9375,0.870618143,0,0.403234768,,,0.708246804
6777,How COVID-19 has changed the unselected medical take: an observational study.,Clin Med (Lond),32962974,9/24/20,pubmed,0,6,logistic regression,0.0021307,0.002130828,0.002130791,0.002130849,0.257680719,0.733796113,Clinics,0.92852217,TRUE,25.66666667,0.376461129,16.83333333,0.446815628,1,0.537564047,,,0.453613601
6778,Targeting SARS-CoV-2 Main Protease: A Computational Drug Repurposing Study.,Arch Med Res,32962867,9/24/20,pubmed,0,6,"virtual screening, molecular dynamics simulation, computational",0.973107317,0.001220006,0.001220085,0.022012589,0.001220009,0.001219995,Drug discovery,0.9652141,TRUE,31.83333333,0.453151092,7.666666667,0.310877709,11,0.840175319,,,0.534734707
6779,A nosocomial cluster of vancomycin resistant enterococci among COVID-19 patients in an intensive care unit.,Antimicrob Resist Infect Control,32962759,9/24/20,pubmed,0,5,whole genome,0.001684541,0.607958519,0.001684582,0.129442551,0.073709221,0.185520585,Genomics,0.92026645,TRUE,51.2,0.639866411,113.6,0.843457319,2,0.618927094,,,0.700750275
6780,Mathematical modeling and cellular automata simulation of infectious disease dynamics: Applications to the understanding of herd immunity.,J Chem Phys,32962383,9/24/20,pubmed,0,3,mathematical model,0.166271623,0.001593579,0.001593576,0.827354007,0.001593567,0.001593647,Epidemiology,0.24650812,FALSE,187.6666667,0.967716,27,0.546026224,1,0.537564047,,,0.683768757
6781,Hypoxemia Index Associated with Prehospital Intubation in COVID-19 Patients.,J Clin Med,32962227,9/24/20,pubmed,0,9,logistic regression,0.001415244,0.001415112,0.001415154,0.049818088,0.001415208,0.944521193,Clinics,0.9794029,TRUE,57.11111111,0.682478817,5,0.257024351,1,0.537564047,,,0.492355739
6782,A Putative Prophylactic Solution for COVID-19: Development of Novel Multiepitope Vaccine Candidate against SARS-COV-2 by Comprehensive Immunoinformatic and Molecular Modelling Approach.,Biology (Basel),32962156,9/24/20,pubmed,0,11,"molecular dynamics simulation, in silico",0.993349545,0.001330074,0.001330076,0.001330209,0.001330059,0.001330038,Drug discovery,0.4532565,FALSE,41,0.549013544,13.54545455,0.405739898,1,0.537564047,,,0.497439163
6783,Patients with Initial Negative RT-PCR and Typical Imaging of COVID-19: Clinical Implications.,J Clin Med,32962092,9/24/20,pubmed,0,12,bayes,0.001237043,0.001237135,0.143427379,0.001237104,0.001237098,0.851624241,Clinics,0.59057856,TRUE,17.16666667,0.258890469,1.583333333,0.139483543,2,0.618927094,,,0.339100369
6784,Characteristics and Symptoms of App Users Seeking COVID-19-Related Digital Health Information and Remote Services: Retrospective Cohort Study.,J Med Internet Res,32961527,9/23/20,pubmed,0,12,"artificial intelligence, digital health",0.000916725,0.000916706,0.151705959,0.141428218,0.704115602,0.00091679,Healthcare,0.9194782,TRUE,13.41666667,0.202671779,11.5,0.378378378,0,0.403234768,,,0.328094975
6785,COVID-19 survey among people who use drugs in three cities in Norway.,Drug Alcohol Depend,32961453,9/23/20,pubmed,0,5,logistic regression,0.159392134,0.002032782,0.002032805,0.002032858,0.803456242,0.031053179,Healthcare,0.94733745,TRUE,85.8,0.827818665,117.2,0.847069842,1,0.537564047,,,0.737484185
6786,Body composition on low dose chest CT is a significant predictor of poor clinical outcome in COVID-19 disease - A multicenter feasibility study.,Eur J Radiol,32961451,9/23/20,pubmed,0,8,logistic regression,0.001254597,0.00125465,0.07235574,0.185150538,0.001254683,0.738729792,Clinics,0.96670187,TRUE,86.125,0.829488527,34,0.5990768,2,0.618927094,,,0.682497474
6787,Anxiety and depression symptoms of medical staff under COVID-19 epidemic in China.,J Affect Disord,32961409,9/23/20,pubmed,0,9,logistic regression,0.001330018,0.001330019,0.001330065,0.001330077,0.980857894,0.013821928,Healthcare,0.73108137,TRUE,40,0.539860226,14.22222222,0.414236018,8,0.799987654,,,0.584694633
6788,Vaccines for COVID-19: perspectives from nucleic acid vaccines to BCG as delivery vector system.,Microbes Infect,32961274,9/23/20,pubmed,0,7,bioinformatic,0.879115305,0.004775357,0.004775373,0.004775701,0.101782717,0.004775546,Drug discovery,0.4867201,FALSE,23.42857143,0.346836539,28,0.554321648,3,0.667819001,,,0.522992396
6789,Outcomes Among Patients Hospitalized With COVID-19 and Acute Kidney Injury.,Am J Kidney Dis,32961245,9/23/20,pubmed,0,45,logistic regression,0.001085381,0.001085328,0.077792541,0.001085349,0.001085354,0.917866046,Clinics,0.9881391,TRUE,70.36363636,0.765415301,72.54545455,0.760636874,17,0.887338725,,,0.804463633
6790,Viral pandemic preparedness: A pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing.,Stem Cells Transl Med,32961040,9/23/20,pubmed,0,2,"supervised learning, unsupervised learning",0.807586628,0.037608896,0.150753399,0.001350429,0.001350336,0.001350313,Drug discovery,0.2512849,FALSE,9.5,0.143051518,3.5,0.213607172,2,0.618927094,,,0.325195261
6791,A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity.,PLoS One,32960917,9/23/20,pubmed,0,20,machine learning,0.001622709,0.001622769,0.771071847,0.001622765,0.001622751,0.222437159,Clinics,0.6294526,TRUE,34.05,0.477827942,94.4,0.811546695,5,0.739490092,,,0.676288243
6792,Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.,J Med Internet Res,32960774,9/23/20,pubmed,0,8,machine learning,0.057799812,0.057799601,0.057801681,0.711000481,0.057799162,0.057799264,Epidemiology,0.49247506,FALSE,66.625,0.744634795,228.625,0.929221301,4,0.707574542,,,0.793810212
6793,Urinary Proteomics Associates with COVID-19 Severity: Pilot Proof-of-Principle Data and Design of a Multicentric Diagnostic Study.,Proteomics,32960510,9/23/20,pubmed,0,12,proteom,0.123145168,0.002032828,0.143471924,0.315897039,0.002032966,0.413420076,Clinics,0.5488052,TRUE,62.83333333,0.720205331,33.5,0.595999465,2,0.618927094,,,0.645043963
6794,Ethnic differences in alpha-1 antitrypsin deficiency allele frequencies may partially explain national differences in COVID-19 fatality rates.,FASEB J,32960480,9/23/20,pubmed,0,3,dataset,0.17097327,0.213747424,0.001371258,0.483895755,0.001371322,0.128640971,Epidemiology,0.32749736,FALSE,116.3333333,0.901168903,153.3333333,0.886205512,8,0.799987654,,,0.862454023
6795,Differential microRNA expression in the peripheral blood from human patients with COVID-19.,J Clin Lab Anal,32960473,9/23/20,pubmed,0,4,"sequencing, correlation analysis",0.514286371,0.102852234,0.001059352,0.001059355,0.001059362,0.379683326,Drug discovery,0.9175967,TRUE,34.5,0.482404601,21.25,0.494046026,5,0.739490092,,,0.57198024
6796,Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index.,JMIR Public Health Surveill,32960177,9/23/20,pubmed,0,4,correlation analysis,0.000977448,0.000977447,0.000977447,0.884150485,0.041655559,0.071261613,Epidemiology,0.6945932,TRUE,3.25,0.039148989,0,0.055525823,1,0.537564047,,,0.210746286
6797,[Adaptive immunity against SARS-CoV-2].,Med Sci (Paris),32960167,9/23/20,pubmed,0,1,bioinformatic,0.568847976,0.074767158,0.001141339,0.001141404,0.211133052,0.14296907,Drug discovery,0.94837964,TRUE,53,0.654400396,118,0.848876104,0,0.403234768,,,0.635503756
6798,"The potential role of procyanidin as a therapeutic agent against SARS-CoV-2: a text mining, molecular docking and molecular dynamics simulation approach.",J Biomol Struct Dyn,32960159,9/23/20,pubmed,0,4,"molecular dynamics simulation, network analysis, text mining",0.993726845,0.001254717,0.001254607,0.001254612,0.001254625,0.001254594,Drug discovery,0.9446495,TRUE,40.5,0.544746119,7,0.299973241,4,0.707574542,,,0.5174313
6799,Thrombo-inflammatory features predicting mortality in patients with COVID-19: The FAD-85 score.,J Int Med Res,32960106,9/23/20,pubmed,0,9,logistic regression,0.001786495,0.001786522,0.073374332,0.001786533,0.001786547,0.919479571,Clinics,0.9484029,TRUE,63.33333333,0.723545055,22.11111111,0.504147712,6,0.764429903,,,0.66404089
6800,Theoretical Insights into the Anti-SARS-CoV-2 Activity of Chloroquine and Its Analogs and In Silico Screening of Main Protease Inhibitors.,J Proteome Res,32960061,9/23/20,pubmed,0,3,in silico,0.99069165,0.001861676,0.001861698,0.001861682,0.001861649,0.001861645,Drug discovery,0.93180835,TRUE,1.333333333,0.01366813,0,0.055525823,1,0.537564047,,,0.202252667
6801,"Quantitative proteomics reveals a broad-spectrum antiviral property of ivermectin, benefiting for COVID-19 treatment.",J Cell Physiol,32959892,9/23/20,pubmed,0,3,"proteom, network analysis",0.953762409,0.001112664,0.041786912,0.001112664,0.001112651,0.0011127,Drug discovery,0.65497273,TRUE,141.3333333,0.93561754,76.33333333,0.769668183,7,0.785110192,,,0.830131972
6802,COVID-19 mortality among migrants living in Italy.,Ann Ist Super Sanita,32959804,9/23/20,pubmed,0,49,dataset,0.002183231,0.002183337,0.002183294,0.258187108,0.216510867,0.518752163,Clinics,0.35123613,FALSE,102,0.873523409,103.6666667,0.828338239,5,0.739490092,,,0.813783913
6803,Using posterior predictive distributions to analyse epidemic models: COVID-19 in Mexico City.,Phys Biol,32959788,9/23/20,pubmed,0,7,bayes,0.00148642,0.001486426,0.001486468,0.992567663,0.001486508,0.001486514,Epidemiology,0.20426145,FALSE,25.42857143,0.372997712,25.85714286,0.535991437,8,0.799987654,,,0.569658934
6804,Epidemic Landscape and Forecasting of SARS-CoV-2 in India.,J Epidemiol Glob Health,32959618,9/23/20,pubmed,0,9,bayes,0.000822907,0.000822926,0.000822916,0.962658967,0.034049319,0.000822965,Epidemiology,0.08545998,FALSE,3.555555556,0.044529656,0.888888889,0.10422799,1,0.537564047,,,0.228773898
6805,COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.,Interdiscip Sci,32959234,9/23/20,pubmed,0,11,"neural network, transfer learning, dataset",0.001538177,0.00153812,0.992309362,0.001538113,0.001538083,0.001538146,Imaging,0.7239988,TRUE,32.27272727,0.458222525,16,0.437316029,2,0.618927094,,,0.504821883
6806,Machine Learning Maps Research Needs in COVID-19 Literature.,Patterns (N Y),32959032,9/23/20,pubmed,0,6,"machine learning, dataset",0.002422411,0.167753939,0.265969439,0.527572414,0.033859454,0.002422343,Epidemiology,0.42775145,FALSE,25.66666667,0.376461129,25.66666667,0.534854161,0,0.403234768,,,0.438183353
6807,"Nature rejuvenation: Long-term (1989-2016) vs short-term memory approach based appraisal of water quality of the upper part of Ganga River, India.",Environ Technol Innov,32959018,9/23/20,pubmed,0,5,dataset,0.003465959,0.003465992,0.003466075,0.879523011,0.003466145,0.106612818,Epidemiology,0.9544769,TRUE,56,0.675304595,17.6,0.456984212,2,0.618927094,,,0.583738634
6808,Discrimination of pulmonary ground-glass opacity changes in COVID-19 and non-COVID-19 patients using CT radiomics analysis.,Eur J Radiol Open,32959017,9/23/20,pubmed,0,7,radiom,0.001371245,0.001371308,0.573439086,0.001371276,0.001371306,0.421075778,Imaging,0.5900758,TRUE,10.71428571,0.159688292,15,0.42594327,4,0.707574542,,,0.431068701
6809,An overview of COVID-19 with an emphasis on computational approach for its preventive intervention.,3 Biotech,32959007,9/23/20,pubmed,0,4,"computational, in silico, proteom",0.720275513,0.215333653,0.001751219,0.001751264,0.001751182,0.059137169,Drug discovery,0.5026662,TRUE,3.75,0.046817985,0,0.055525823,3,0.667819001,,,0.256720936
6810,COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images.,Pattern Recognit Lett,32958971,9/23/20,pubmed,0,6,"neural network, transfer learning, dataset",0.000946096,0.000946108,0.886633005,0.109582611,0.0009461,0.000946081,Imaging,0.32462427,FALSE,15.33333333,0.230997588,5.5,0.267259834,110,0.980739552,,,0.492998991
6811,COVID-19 image classification using deep features and fractional-order marine predators algorithm.,Sci Rep,32958781,9/23/20,pubmed,0,6,"computational, neural network, dataset",0.001034601,0.016512079,0.979349463,0.00103465,0.001034585,0.001034623,Imaging,0.71043515,TRUE,72,0.77302245,14.16666667,0.41370083,12,0.850299401,,,0.67900756
6812,A model of COVID-19 transmission to understand the effectiveness of the containment measures: application to data from France.,Epidemiol Infect,32958091,9/23/20,pubmed,0,5,"simulation experiment, mathematical model",0.001486417,0.001486473,0.001486544,0.942706376,0.001486493,0.051347696,Epidemiology,0.19337052,FALSE,17.6,0.264456676,6.4,0.286058336,0,0.403234768,,,0.317916593
6813,Immuno-Informatics Quest against COVID-19/SARS-COV-2: Determining Putative T-Cell Epitopes for Vaccine Prediction.,Infect Disord Drug Targets,32957905,9/23/20,pubmed,0,4,proteom,0.935523934,0.00117166,0.001171543,0.059789709,0.001171592,0.001171562,Drug discovery,0.8583267,TRUE,11.75,0.177562001,0.5,0.087101953,1,0.537564047,,,0.267409334
6814,Drug Repurposing Approaches: Existing Leads For Novel Threats And Drug Targets.,Curr Protein Pept Sci,32957901,9/23/20,pubmed,0,9,"computational, literature mining",0.575572095,0.001653084,0.001653107,0.273626047,0.14584257,0.001653098,Drug discovery,0.68165934,TRUE,15.66666667,0.236563795,4,0.231469093,1,0.537564047,,,0.335198978
6815,Identification of Main Protease of Coronavirus SARS-CoV-2 (Mpro)Inhibitors from Melissa officinalis.,Curr Drug Discov Technol,32957889,9/23/20,pubmed,0,5,computational,0.932528129,0.001901833,0.059864549,0.001901867,0.001901843,0.00190178,Drug discovery,0.9629049,TRUE,29.8,0.428474241,17.6,0.456984212,1,0.537564047,,,0.474340833
6816,Consumers' Fears Regarding Food Availability and Purchasing Behaviors during the COVID-19 Pandemic: The Importance of Trust and Perceived Stress.,Nutrients,32957585,9/23/20,pubmed,0,3,logistic regression,0.001010926,0.001010938,0.001010989,0.001010971,0.994945238,0.001010938,Healthcare,0.64776665,TRUE,33.66666667,0.47312759,4.666666667,0.246721969,3,0.667819001,,,0.462556187
6817,Complex Immunometabolic Profiling Reveals the Activation of Cellular Immunity and Biliary Lesions in Patients with Severe COVID-19.,J Clin Med,32957548,9/23/20,pubmed,0,14,immunome,0.327051184,0.001684508,0.001684578,0.001684501,0.001684488,0.666210741,Clinics,0.7082059,TRUE,38.57142857,0.524645927,14.92857143,0.422330747,0,0.403234768,,,0.450070481
6818,Statin Use Is Associated with Decreased Risk of Invasive Mechanical Ventilation in COVID-19 Patients: A Preliminary Study.,Pathogens,32957539,9/23/20,pubmed,0,7,logistic regression,0.001751287,0.00175115,0.00175116,0.001751171,0.001751221,0.991244011,Clinics,0.9542029,TRUE,60.28571429,0.704496258,101.5714286,0.82499331,9,0.814309525,,,0.781266365
6819,Novel Binding Mechanisms of Fusion Broad Range Anti-Infective Protein Ricin A Chain Mutant-Pokeweed Antiviral Protein 1 (RTAM-PAP1) against SARS-CoV-2 Key Proteins in Silico.,Toxins (Basel),32957454,9/23/20,pubmed,0,3,in silico,0.987888418,0.00242237,0.002422353,0.002422314,0.002422287,0.002422258,Drug discovery,0.86336416,TRUE,9.333333333,0.139278867,0,0.055525823,1,0.537564047,,,0.244122912
6820,Telemedicine in myocarditis: Evolution of a mutidisciplinary "disease unit" at the time of COVID-19 pandemic.,Am Heart J,32957030,9/22/20,pubmed,0,10,digital health,0.006540109,0.188854791,0.006540115,0.468258873,0.006539862,0.32326625,Epidemiology,0.7747401,TRUE,50.7,0.63609376,21.5,0.498260637,2,0.618927094,,,0.584427164
6821,Socio-economic and psychological impact of the COVID-19 outbreak on private practice and public hospital radiologists.,Eur J Radiol,32957001,9/22/20,pubmed,0,8,logistic regression,0.001291236,0.001291226,0.089680965,0.001291322,0.905153934,0.001291318,Healthcare,0.94270754,TRUE,32.125,0.456552663,25.5,0.533382392,4,0.707574542,,,0.565836532
6822,Assessment of effective imidazole derivatives against SARS-CoV-2 main protease through computational approach.,Life Sci,32956664,9/22/20,pubmed,0,5,computational,0.953974045,0.001786618,0.038879726,0.001786558,0.001786524,0.00178653,Drug discovery,0.87821174,TRUE,19,0.285793803,1,0.122023013,1,0.537564047,,,0.315126954
6823,"Two Pandemics, One Challenge-Leveraging Molecular Test Capacity of Tuberculosis Laboratories for Rapid COVID-19 Case-Finding.",Emerg Infect Dis,32956612,9/22/20,pubmed,0,10,sequencing,0.002357805,0.382582941,0.166059701,0.247977476,0.198664173,0.002357903,Genomics,0.35168064,FALSE,39.6,0.53503618,69.5,0.752006957,2,0.618927094,,,0.635323411
6824,Spotlight on the Shift to Remote Anatomical Teaching During Covid-19 Pandemic: Perspectives and Experiences from the University of Malta.,Anat Sci Educ,32956579,9/22/20,pubmed,0,2,logistic regression,0.001126817,0.001126828,0.049490572,0.001126862,0.946001945,0.001126975,Healthcare,0.9251709,TRUE,45.5,0.591440411,2.5,0.180826866,3,0.667819001,,,0.480028759
6825,"Availability, Use, and Satisfaction of Personal Protective Equipment Among Healthcare Workers: A Cross-Sectional Assessment of Low- and Middle-Income Countries.",J Occup Environ Med,32956236,9/22/20,pubmed,0,5,logistic regression,0.00310143,0.00310148,0.003101484,0.003101597,0.984492387,0.003101622,Healthcare,0.6727777,TRUE,6.4,0.090667326,0,0.055525823,1,0.537564047,,,0.227919065
6826,Digital Clinical Trials for Substance Use Disorders in the Age of Covid-19.,J Addict Med,32956164,9/22/20,pubmed,0,4,digital health,0.071540803,0.001291314,0.001291268,0.750525384,0.117501399,0.057849832,Epidemiology,0.94743884,TRUE,92.5,0.850454574,104,0.828806529,0,0.403234768,,,0.69416529
6827,The Relationship Between Media Involvement and Death Anxiety of Self-Quarantined People in the COVID-19 Outbreak in China: The Mediating Roles of Empathy and Sympathy.,Omega (Westport),32955991,9/22/20,pubmed,0,5,correlation analysis,0.002130825,0.002130672,0.002130689,0.118042309,0.655530876,0.220034629,Healthcare,0.9327589,TRUE,42,0.558537943,7,0.299973241,1,0.537564047,,,0.46535841
6828,How do arbidol and its analogs inhibit the SARS-CoV-2?,Bratisl Lek Listy,32955901,9/22/20,pubmed,0,5,in silico,0.844103697,0.002562648,0.002562824,0.068638185,0.002562901,0.079569746,Drug discovery,0.7326241,TRUE,18.8,0.281278991,6,0.280037463,2,0.618927094,,,0.393414516
6829,Pandemic Politics: Timing State-Level Social Distancing Responses to COVID-19.,J Health Polit Policy Law,32955556,9/22/20,pubmed,0,5,dataset,0.00175117,0.001751174,0.001751176,0.991243824,0.001751321,0.001751335,Epidemiology,0.787216,TRUE,35.2,0.488898509,99.4,0.821180091,12,0.850299401,,,0.720126001
6830,"Virtual Trauma-Focused Therapy for Military Members, Veterans, and Public Safety Personnel With Posttraumatic Stress Injury: Systematic Scoping Review.",JMIR Mhealth Uhealth,32955456,9/22/20,pubmed,0,17,digital health,0.120794927,0.000710581,0.00071059,0.352303194,0.456231987,0.069248721,Healthcare,0.9942806,TRUE,22.47058824,0.331745934,7,0.299973241,0,0.403234768,,,0.344984647
6831,Racial and Ethnic Disparities in COVID-19 Infections and Deaths Across U.S. Nursing Homes.,J Am Geriatr Soc,32955105,9/22/20,pubmed,0,4,logistic regression,0.00089806,0.000898066,0.000898068,0.000898121,0.778473905,0.217933781,Healthcare,0.7806858,TRUE,100,0.868884903,38,0.622223709,7,0.785110192,,,0.758739601
6832,0,J Biomol Struct Dyn,32954984,9/22/20,pubmed,0,4,molecular dynamics simulation,0.916982536,0.001943526,0.001943529,0.075243216,0.001943581,0.001943612,Drug discovery,0.8995881,TRUE,105.25,0.879522543,72.5,0.760436179,3,0.667819001,,,0.769259241
6833,Population Risk Factors for COVID-19 Mortality in 93 Countries.,J Epidemiol Glob Health,32954710,9/22/20,pubmed,0,3,correlation analysis,0.001653114,0.035098995,0.001653083,0.32575329,0.134725715,0.501115804,Clinics,0.7824478,TRUE,51.33333333,0.641288886,16,0.437316029,2,0.618927094,,,0.565844003
6834,Comprehensive annotations of the mutational spectra of SARS-CoV-2 spike protein: a fast and accurate pipeline.,Transbound Emerg Dis,32954666,9/22/20,pubmed,0,10,"sequence alignment, dataset",0.130514464,0.833473685,0.001022709,0.001022671,0.001022655,0.032943815,Genomics,0.66916525,TRUE,11.8,0.177994929,2.6,0.18256623,3,0.667819001,,,0.342793386
6835,The role of methylprednisolone on preventing disease progression for hospitalized patients with severe COVID-19.,Eur J Clin Invest,32954492,9/22/20,pubmed,0,5,logistic regression,0.001272676,0.00127265,0.001272655,0.068553553,0.001272709,0.926355756,Clinics,0.8962924,TRUE,34.8,0.484940318,152.4,0.885268932,4,0.707574542,,,0.692594597
6836,Whole-genome sequencing to track SARS-CoV-2 transmission in nosocomial outbreaks.,Clin Infect Dis,32954414,9/22/20,pubmed,0,10,"sequencing, whole-genome",0.001371264,0.302328019,0.001371301,0.371891161,0.232565944,0.090472311,Epidemiology,0.31041035,FALSE,43.4,0.571958686,46.8,0.667714744,13,0.858880178,,,0.69951787
6837,Genetic characterization of structural and open reading Fram-8 proteins of SARS-CoV-2 isolates from different countries.,Gene Rep,32954047,9/22/20,pubmed,0,5,"sequencing, sequence alignment",0.061677496,0.934033778,0.00107218,0.001072193,0.001072172,0.001072181,Genomics,0.6990671,TRUE,86.4,0.830787309,14.4,0.416644367,4,0.707574542,,,0.651668739
6838,Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK.,Wellcome Open Res,32954015,9/22/20,pubmed,0,25,dataset,0.001330012,0.001330057,0.001330038,0.993349814,0.00133005,0.001330029,Epidemiology,0.18562624,FALSE,80.84,0.810316037,220.48,0.926679154,14,0.866658436,,,0.867884542
6839,COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis.,Inform Med Unlocked,32953971,9/22/20,pubmed,0,7,"deep learning, dataset",0.001034596,0.001034605,0.853277021,0.105420083,0.001034607,0.038199088,Imaging,0.10626531,FALSE,57.28571429,0.683715752,18.71428571,0.467152796,3,0.667819001,,,0.606229183
6840,Implications of myocardial injury in Mexican hospitalized patients with coronavirus disease 2019 (COVID-19).,Int J Cardiol Heart Vasc,32953968,9/22/20,pubmed,0,8,logistic regression,0.001350312,0.001350332,0.001350319,0.00135033,0.001350365,0.993248342,Clinics,0.93494684,TRUE,1,0.012307502,0,0.055525823,4,0.707574542,,,0.258469289
6841,Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study.,Ann Transl Med,32953735,9/22/20,pubmed,0,24,artificial intelligence,0.000936076,0.000936091,0.174064802,0.000936136,0.000936097,0.822190799,Clinics,0.89238274,TRUE,31.08333333,0.445234708,12.54166667,0.392895371,1,0.537564047,,,0.458564709
6842,It's all in the recipe: How to increase domestic leisure tourists' experiential loyalty to local food.,Tour Manag Perspect,32953431,9/22/20,pubmed,0,2,logistic regression,0.005353116,0.005353096,0.00535303,0.496018987,0.48256855,0.00535322,Epidemiology,0.8013963,TRUE,11.5,0.17416043,0,0.055525823,0,0.403234768,,,0.210973674
6843,Making Waves Perspectives of Modelling and Monitoring of SARS-CoV-2 in Aquatic Environment for COVID-19 Pandemic.,Curr Pollut Rep,32953402,9/22/20,pubmed,0,11,predictive model,0.083935349,0.141898743,0.002357882,0.767091883,0.002358066,0.002358076,Epidemiology,0.35871947,FALSE,41.18181818,0.549941246,30.81818182,0.57592989,9,0.814309525,,,0.646726887
6844,Identification of risk factors for mortality associated with COVID-19.,PeerJ,32953279,9/22/20,pubmed,0,8,"neural network, network model, logistic regression",0.001171581,0.018416636,0.342197375,0.001171574,0.001171584,0.635871251,Clinics,0.89253485,TRUE,96.125,0.859236811,43,0.649785925,2,0.618927094,,,0.70931661
6845,Similarity Studies of Corona Viruses through Chaos Game Representation.,Comput Mol Biosci,32953249,9/22/20,pubmed,0,4,genomes,0.002296595,0.753162444,0.092210865,0.147736994,0.002296545,0.002296558,Genomics,0.26705647,FALSE,36.25,0.500154617,8.5,0.329141022,0,0.403234768,,,0.410843469
6846,"Epidemiology, Clinical Aspects, Laboratory Diagnosis and Treatment of Rickettsial Diseases in the Mediterranean Area During COVID-19 Pandemic: A Review of the Literature.",Mediterr J Hematol Infect Dis,32952967,9/22/20,pubmed,0,12,sequencing,0.001538175,0.252373179,0.441959163,0.301053174,0.00153813,0.001538179,Epidemiology,0.8969979,TRUE,54.75,0.666584204,23,0.513513514,1,0.537564047,,,0.572553922
6847,Key Challenges in Modelling an Epidemic - What have we Learned from the COVID-19 Epidemic so Far.,Zdr Varst,32952711,9/22/20,pubmed,0,4,mathematical model,0.001823345,0.001823346,0.00182338,0.990883232,0.001823361,0.001823336,Epidemiology,0.45263597,FALSE,35.5,0.492547467,6.25,0.283315494,1,0.537564047,,,0.437809003
6848,Modeling and forecasting the spread tendency of the COVID-19 in China.,Adv Differ Equ,32952537,9/22/20,pubmed,0,4,mathematical model,0.001438085,0.001438122,0.00143818,0.992809396,0.001438104,0.001438113,Epidemiology,0.541563,TRUE,46.75,0.603314985,7,0.299973241,0,0.403234768,,,0.435507665
6849,Epidemic trends of COVID-19 in 10 countries compared with Turkey.,Vacunas,32952488,9/22/20,pubmed,0,1,dataset,0.001310346,0.001310359,0.001310353,0.818414988,0.176343539,0.001310415,Epidemiology,0.2497226,FALSE,7,0.10179974,0,0.055525823,0,0.403234768,,,0.186853444
6850,Pandemic programming: How COVID-19 affects software developers and how their organizations can help.,Empir Softw Eng,32952438,9/22/20,pubmed,0,17,structural model,0.002183351,0.002183315,0.002183387,0.216764601,0.774502117,0.00218323,Healthcare,0.88071895,TRUE,68.11764706,0.754282887,77.58823529,0.772277228,9,0.814309525,,,0.78028988
6851,Social distancing in airplane seat assignments.,J Air Transp Manag,32952319,9/22/20,pubmed,0,5,simulation experiment,0.001350407,0.00135036,0.001350375,0.935506113,0.059092404,0.001350341,Epidemiology,0.74300474,TRUE,45.8,0.593852434,9.8,0.350682366,5,0.739490092,,,0.56134163
6852,Ultrasensitive supersandwich-type electrochemical sensor for SARS-CoV-2 from the infected COVID-19 patients using a smartphone.,Sens Actuators B Chem,32952300,9/22/20,pubmed,0,12,in silico,0.001861833,0.543937416,0.369231481,0.001861832,0.001861711,0.081245728,Genomics,0.64216053,TRUE,50.91666667,0.637330695,22.5,0.507492641,19,0.89561084,,,0.680144725
6853,The relationship between air pollution and COVID-19-related deaths: An application to three French cities.,Appl Energy,32952266,9/22/20,pubmed,0,3,"machine learning, neural network",0.002357804,0.002357796,0.307362266,0.683206579,0.002357754,0.002357801,Epidemiology,0.71649617,TRUE,49.66666667,0.629166924,22,0.503746321,24,0.914439163,,,0.682450803
6854,Short-term forecasting of the coronavirus pandemic.,Int J Forecast,32952247,9/22/20,pubmed,0,3,machine learning,0.002806341,0.002806354,0.00280668,0.985967895,0.00280635,0.002806381,Epidemiology,0.24605656,FALSE,237,0.982311831,933.3333333,0.992975649,4,0.707574542,,,0.894287341
6855,"Prevalence of SARS-CoV-2 infection in India: Findings from the national serosurvey, May-June 2020.",Indian J Med Res,32952144,9/22/20,pubmed,0,74,logistic regression,0.001141314,0.049429088,0.035597924,0.235461757,0.581908251,0.096461666,Healthcare,0.23921975,FALSE,40.60810811,0.545364587,,,35,0.939317242,,,0.742340914
6856,Early Psychological Correlates Associated With COVID-19 in A Spanish Older Adult Sample.,Am J Geriatr Psychiatry,32951996,9/22/20,pubmed,0,10,logistic regression,0.001392896,0.001392849,0.001392824,0.001392842,0.993035719,0.001392871,Healthcare,0.9903921,TRUE,91.5,0.847547777,88.2,0.7971635,3,0.667819001,,,0.770843426
6857,Predictions of coronavirus COVID-19 distinct cases in Pakistan through an artificial neural network.,Epidemiol Infect,32951626,9/22/20,pubmed,0,2,neural network,0.002638992,0.002639154,0.397261284,0.592182345,0.002639015,0.002639211,Epidemiology,0.65918523,TRUE,35.5,0.492547467,5,0.257024351,2,0.618927094,,,0.456166304
6858,Psychoanalytic Perspectives on Moral Injury in Nurses on the Frontlines of the COVID-19 Pandemic.,J Am Psychiatr Nurses Assoc,32951499,9/22/20,pubmed,0,1,structural model,0.001653054,0.001653031,0.001653099,0.243377568,0.644439724,0.107223523,Healthcare,0.9628898,TRUE,21,0.312016822,6,0.280037463,0,0.403234768,,,0.331763018
6859,Electrocardiogram analysis of patients with different types of COVID-19.,Ann Noninvasive Electrocardiol,32951316,9/21/20,pubmed,0,7,logistic regression,0.001486543,0.001486453,0.001486479,0.001486502,0.001486491,0.992567531,Clinics,0.9847146,TRUE,61.42857143,0.711361247,,,2,0.618927094,,,0.665144171
6860,"Concerns, perceived impact, and preparedness of oral healthcare workers in their working environment during COVID-19 pandemic.",J Occup Health,32951286,9/21/20,pubmed,0,6,logistic regression,0.001751213,0.001751157,0.001751395,0.001751284,0.991243723,0.001751227,Healthcare,0.9459428,TRUE,10.33333333,0.155049787,3.5,0.213607172,2,0.618927094,,,0.329194684
6861,Electrocardiograhic characteristics in patients with coronavirus infection: A single-center observational study.,Ann Noninvasive Electrocardiol,32951285,9/21/20,pubmed,0,7,logistic regression,0.001593452,0.001593477,0.001593476,0.001593489,0.001593492,0.992032614,Clinics,0.9891763,TRUE,269.7142857,0.987630651,144.1428571,0.87831148,5,0.739490092,,,0.868477408
6862,Sleep characteristics in health workers exposed to the COVID-19 pandemic.,Sleep Med,32950884,9/21/20,pubmed,0,7,logistic regression,0.001350325,0.001350334,0.00135032,0.001350349,0.911390822,0.08320785,Healthcare,0.96849614,TRUE,2.571428571,0.027707341,0.285714286,0.066296495,2,0.618927094,,,0.237643643
6863,Predictors of Outcomes of COVID-19 in Patients with Chronic Liver Disease: US Multi-center Study.,Clin Gastroenterol Hepatol,32950749,9/21/20,pubmed,0,31,logistic regression,0.001622707,0.001622794,0.001622806,0.001622761,0.001622754,0.991886179,Clinics,0.8483559,TRUE,195.6451613,0.971426804,107.516129,0.835496387,8,0.799987654,,,0.868970282
6864,Hypokalemia as a sensitive biomarker of disease severity and the requirement for invasive mechanical ventilation requirement in COVID-19 pneumonia: A case series of 306 Mediterranean patients.,Int J Infect Dis,32950739,9/21/20,pubmed,0,76,logistic regression,0.001653042,0.001653038,0.001653029,0.001653034,0.001653024,0.991734834,Clinics,0.79177165,TRUE,26.8961039,0.393097903,,,7,0.785110192,,,0.589104048
6865,Substitutions in Spike and Nucleocapsid proteins of SARS-CoV-2 circulating in South America.,Infect Genet Evol,32950697,9/21/20,pubmed,0,15,genomes,0.132154609,0.863158773,0.001171831,0.001171609,0.001171586,0.001171592,Genomics,0.8358812,TRUE,14.4,0.217576845,3.2,0.202100615,11,0.840175319,,,0.419950927
6866,Prediction of potential inhibitors for RNA-dependent RNA polymerase of SARS-CoV-2 using comprehensive drug repurposing and molecular docking approach.,Int J Biol Macromol,32950529,9/21/20,pubmed,0,8,"virtual screening, computational",0.972057713,0.001511857,0.001511862,0.001511832,0.021894917,0.001511819,Drug discovery,0.98362434,TRUE,23.375,0.346032531,16,0.437316029,9,0.814309525,,,0.532552695
6867,"Characteristics of immune cells and cytokines in patients with coronavirus disease 2019 in Guangzhou, China.",Hum Immunol,32950268,9/21/20,pubmed,0,7,logistic regression,0.268323198,0.001059382,0.001059355,0.001059416,0.001059398,0.727439251,Clinics,0.94789344,TRUE,37.28571429,0.510606717,10.85714286,0.367607707,1,0.537564047,,,0.471926157
6868,"Network pharmacology analysis of the therapeutic mechanisms of the traditional Chinese herbal formula Lian Hua Qing Wen in Corona virus disease 2019 (COVID-19), gives fundamental support to the clinical use of LHQW.",Phytomedicine,32949888,9/20/20,pubmed,0,8,network analysis,0.938799645,0.000907295,0.000907309,0.000907298,0.000907344,0.05757111,Drug discovery,0.966634,TRUE,130.125,0.921887563,63.625,0.733810543,6,0.764429903,,,0.806709336
6869,"Delay in the diagnosis of pulmonary tuberculosis in The Gambia, West Africa: A cross-sectional study.",Int J Infect Dis,32949776,9/20/20,pubmed,0,11,logistic regression,0.001717157,0.001717253,0.114176241,0.001717248,0.001717312,0.878954789,Clinics,0.98144513,TRUE,47.54545455,0.610798441,52.90909091,0.692734814,5,0.739490092,,,0.681007782
6870,Viable SARS-CoV-2 in the air of a hospital room with COVID-19 patients.,Int J Infect Dis,32949774,9/20/20,pubmed,0,19,genomes,0.001511976,0.492573115,0.001511849,0.229979447,0.001511824,0.272911788,Genomics,0.2662283,FALSE,51.05263158,0.638815016,47.94736842,0.672464544,9,0.814309525,,,0.708529695
6871,Microdroplet and spatter contamination during phacoemulsification cataract surgery in the era of COVID-19.,Clin Exp Ophthalmol,32949452,9/20/20,pubmed,0,8,image analysis,0.002080658,0.00208069,0.197678315,0.543845905,0.25223376,0.002080671,Epidemiology,0.692088,TRUE,45.875,0.594841982,28.125,0.555057533,5,0.739490092,,,0.629796535
6872,Ultrasensitive high-resolution profiling of early seroconversion in patients with COVID-19.,Nat Biomed Eng,32948854,9/20/20,pubmed,0,19,logistic regression,0.0378815,0.610709074,0.107093269,0.001901758,0.001901737,0.240512662,Genomics,0.43896553,FALSE,72.10526316,0.773331684,74.89473684,0.766189457,10,0.828199272,,,0.789240137
6873,Development and implementation of a COVID-19 near real-time traffic light system in an acute hospital setting.,Emerg Med J,32948623,9/20/20,pubmed,0,150,bioinformatic,0.195431942,0.001126835,0.129863645,0.001126871,0.001126891,0.671323815,Clinics,0.9770146,TRUE,81.76470588,0.813717608,61.17647059,0.725046829,3,0.667819001,,,0.735527813
6874,"Community knowledge, perceptions and practices around COVID-19 in Sierra Leone: a nationwide, cross-sectional survey.",BMJ Open,32948576,9/20/20,pubmed,0,8,logistic regression,0.001187308,0.001187285,0.001187274,0.001187305,0.994063553,0.001187275,Healthcare,0.91312575,TRUE,19.75,0.293215412,11.5,0.378378378,1,0.537564047,,,0.403052613
6875,Caffeic acid derivatives (CAFDs) as inhibitors of SARS-CoV-2: CAFDs-based functional foods as a potential alternative approach to combat COVID-19.,Phytomedicine,32948420,9/20/20,pubmed,0,9,molecular dynamics simulation,0.965887944,0.001291289,0.001291246,0.001291271,0.028946976,0.001291275,Drug discovery,0.94896257,TRUE,23.22222222,0.343620508,10,0.355632861,4,0.707574542,,,0.468942637
6876,Differential diagnosis for suspected cases of coronavirus disease 2019: a retrospective study.,BMC Infect Dis,32948121,9/20/20,pubmed,0,8,correlation analysis,0.0009073,0.089521097,0.204721759,0.000907292,0.000907299,0.703035252,Clinics,0.88522553,TRUE,2.5,0.027459954,0,0.055525823,0,0.403234768,,,0.162073515
6877,Identification of potential inhibitors of SARS-CoV-2 main protease and spike receptor from 10 important spices through structure-based virtual screening and molecular dynamic study.,J Biomol Struct Dyn,32948116,9/20/20,pubmed,0,5,virtual screening,0.938627281,0.001310356,0.025967481,0.001310377,0.01905615,0.013728355,Drug discovery,0.9788715,TRUE,27.4,0.400395819,3.8,0.221501204,5,0.739490092,,,0.453795705
6878,A computational approach to drug repurposing against SARS-CoV-2 RNA dependent RNA polymerase (RdRp).,J Biomol Struct Dyn,32948103,9/20/20,pubmed,0,6,computational,0.992173232,0.001565388,0.001565368,0.00156538,0.001565345,0.001565288,Drug discovery,0.9137995,TRUE,37.33333333,0.511596264,11.66666667,0.38065293,12,0.850299401,,,0.580849532
6879,Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks.,SLAS Technol,32948098,9/20/20,pubmed,0,2,"machine learning, deep learning, neural network, classifier, transfer learning",0.001187264,0.00118727,0.994063629,0.001187288,0.001187264,0.001187286,Imaging,0.5982746,TRUE,29,0.41993939,6,0.280037463,4,0.707574542,,,0.469183798
6880,Development and validation of risk prediction models for COVID-19 positivity in a hospital setting.,Int J Infect Dis,32947055,9/19/20,pubmed,0,28,"logistic regression, prediction model",0.001486501,0.001486445,0.191906553,0.073590473,0.001486474,0.730043554,Clinics,0.6114481,TRUE,18.46428571,0.275960171,29.46428571,0.56556061,1,0.537564047,,,0.459694943
6881,Semiparametric Bayesian inference for the transmission dynamics of COVID-19 with a state-space model.,Contemp Clin Trials,32947047,9/19/20,pubmed,0,2,"bayes, probabilistic, dataset",0.001684494,0.001684718,0.079147523,0.914114098,0.001684575,0.001684591,Epidemiology,0.17979649,FALSE,23,0.34225988,4,0.231469093,5,0.739490092,,,0.437739688
6882,INTESTINAL MICROBIOME MODULATION DURING COVID-19: ANOTHER CHANCE TO MANAGE THE DISEASE?,Gastroenterology,32946905,9/19/20,pubmed,0,2,microbiom,0.034962201,0.435580729,0.034964189,0.424564992,0.034964337,0.034963551,Genomics,0.61364967,TRUE,47.5,0.610551054,7,0.299973241,0,0.403234768,,,0.437919688
6883,Relation of Statin Use Prior to Admission to Severity and Recovery Among COVID-19 Inpatients.,Am J Cardiol,32946859,9/19/20,pubmed,0,9,logistic regression,0.14180803,0.001350347,0.001350361,0.001350356,0.001350341,0.852790566,Clinics,0.8347925,TRUE,156.8888889,0.949223823,213.2222222,0.923668718,25,0.918019631,,,0.930304057
6884,A Single-Cell RNA Expression Map of Human Coronavirus Entry Factors.,Cell Rep,32946807,9/19/20,pubmed,0,3,transcriptom,0.901499077,0.002238684,0.002238472,0.002238534,0.002238514,0.089546719,Drug discovery,0.46932942,FALSE,23,0.34225988,11.66666667,0.38065293,5,0.739490092,,,0.487467634
6885,Multisystem inflammatory syndrome associated with COVID-19 from the pediatric emergency physician's point of view.,J Pediatr (Rio J),32946801,9/19/20,pubmed,0,7,immunome,0.10552448,0.001187308,0.050000324,0.185636642,0.073745324,0.583905921,Clinics,0.9644085,TRUE,19.14285714,0.28622673,4.714285714,0.24719026,3,0.667819001,,,0.400411997
6886,SARS-CoV-2 infection of Chinese hamsters (Cricetulus griseus) reproduces COVID-19 pneumonia in a well-established small animal model.,Transbound Emerg Dis,32946664,9/19/20,pubmed,0,7,transcriptom,0.416431924,0.182080565,0.093711741,0.153411698,0.152233321,0.00213075,Drug discovery,0.45355046,FALSE,111.4285714,0.89288144,132,0.866002141,6,0.764429903,,,0.841104495
6887,Telepsychiatry and other cutting-edge technologies in COVID-19 pandemic: Bridging the distance in mental health assistance.,Int J Clin Pract,32946641,9/19/20,pubmed,0,8,artificial intelligence,0.029685007,0.001126823,0.163189562,0.42631473,0.378557034,0.001126844,Epidemiology,0.84992856,TRUE,51.375,0.641721813,31,0.578204442,7,0.785110192,,,0.668345482
6888,Disentangling primer interactions improves SARS-CoV-2 genome sequencing by multiplex tiling PCR.,PLoS One,32946527,9/19/20,pubmed,0,5,"sequencing, whole-genome",0.083112572,0.805861035,0.104927842,0.002032878,0.002032843,0.00203283,Genomics,0.21796381,FALSE,109.4,0.889108788,143.6,0.877709393,27,0.92443978,,,0.897085987
6889,"Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and social vulnerability.",PLoS One,32946442,9/19/20,pubmed,0,10,probabilistic,0.001901712,0.001901759,0.001901771,0.946693525,0.00190181,0.045699423,Epidemiology,0.33714965,FALSE,50.9,0.637145154,,,4,0.707574542,,,0.672359848
6890,Artificial Intelligence for COVID-19: Rapid Review.,J Med Internet Res,32946413,9/19/20,pubmed,0,2,artificial intelligence,0.042578996,0.00148653,0.397141553,0.478576628,0.001486519,0.078729773,Epidemiology,0.9199134,TRUE,41.5,0.553219123,17.5,0.45611453,3,0.667819001,,,0.559050885
6891,Delirium in COVID-19: epidemiology and clinical correlations in a large group of patients admitted to an academic hospital.,Aging Clin Exp Res,32946031,9/19/20,pubmed,0,9,logistic regression,0.032436404,0.001272649,0.06329826,0.046410588,0.001272674,0.855309425,Clinics,0.7954479,TRUE,102.3333333,0.874203723,113.1111111,0.842654536,8,0.799987654,,,0.838948637
6892,Molecular mimicry between SARS-CoV-2 spike glycoprotein and mammalian proteomes: implications for the vaccine.,Immunol Res,32946016,9/19/20,pubmed,0,2,proteom,0.932253327,0.013549865,0.013548712,0.013549482,0.013549573,0.013549041,Drug discovery,0.37918675,FALSE,251.5,0.984600161,235.5,0.932499331,19,0.89561084,,,0.937570111
6893,Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients.,Eur Radiol,32945968,9/19/20,pubmed,0,14,"deep learning, artificial intelligence",0.000800562,0.000800573,0.291292132,0.00080057,0.000800579,0.705505584,Clinics,0.9498279,TRUE,151.2142857,0.945327479,101.5,0.824859513,10,0.828199272,,,0.866128754
6894,"Clinical Impact, Costs, and Cost-Effectiveness of Expanded SARS-CoV-2 Testing in Massachusetts.",Clin Infect Dis,32945845,9/19/20,pubmed,0,17,simulation model,0.000988365,0.000988405,0.000988429,0.545984103,0.08097039,0.370080308,Epidemiology,0.09931862,FALSE,95.29411765,0.857319562,159.2352941,0.891289805,2,0.618927094,,,0.78917882
6895,Clinical features predicting mortality risk in older patients with COVID-19.,Curr Med Res Opin,32945707,9/19/20,pubmed,0,8,logistic regression,0.001085333,0.00108535,0.10086561,0.001085411,0.001085346,0.89479295,Clinics,0.8316717,TRUE,49.25,0.625889047,61.125,0.724846133,10,0.828199272,,,0.726311484
6896,Usefulness of biological markers in the early prediction of corona virus disease-2019 severity.,Scand J Clin Lab Invest,32945705,9/19/20,pubmed,0,8,logistic regression,0.001203432,0.001203435,0.064643349,0.001203502,0.001203428,0.930542853,Clinics,0.9007852,TRUE,5.125,0.071185602,0.75,0.099411292,5,0.739490092,,,0.303362329
6897,Single-cell RNA sequencing analysis of SARS-CoV-2 entry receptors in human organoids.,J Cell Physiol,32944935,9/19/20,pubmed,0,8,sequencing,0.960029519,0.032196532,0.001943476,0.001943511,0.001943452,0.00194351,Drug discovery,0.86871994,TRUE,44.5,0.581977859,24,0.521808938,2,0.618927094,,,0.574237964
6898,Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology.,J Thorac Dis,32944361,9/19/20,pubmed,0,17,artificial intelligence,0.001072301,0.001072219,0.487643915,0.242489187,0.26665014,0.001072238,Healthcare,0.6772741,TRUE,91.47058824,0.847424083,53.17647059,0.694072786,0,0.403234768,,,0.648243879
6899,Design an Efficient Multi-Epitope Peptide Vaccine Candidate Against SARS-CoV-2: An in silico Analysis.,Infect Drug Resist,32943888,9/19/20,pubmed,0,4,in silico,0.962163683,0.001272671,0.001272657,0.032745508,0.001272778,0.001272703,Drug discovery,0.6705097,TRUE,47.5,0.610551054,17.5,0.45611453,0,0.403234768,,,0.489966784
6900,Engaging the next generation of plant geneticists through sustained research: an overview of a post-16 project.,Heredity (Edinb),32943768,9/19/20,pubmed,0,1,genomes,0.002806458,0.182762844,0.002806418,0.43690421,0.371913655,0.002806415,Epidemiology,0.40795654,FALSE,3,0.037293586,0,0.055525823,1,0.537564047,,,0.210127819
6901,Systemic complement activation is associated with respiratory failure in COVID-19 hospitalized patients.,Proc Natl Acad Sci U S A,32943538,9/19/20,pubmed,0,32,logistic regression,0.3664132,0.064523967,0.00114133,0.001141332,0.001141327,0.565638845,Clinics,0.95703125,TRUE,49.15625,0.62527058,,,27,0.92443978,,,0.77485518
6902,"Impact of COVID-19 pandemic response on uptake of routine immunizations in Sindh, Pakistan: An analysis of provincial electronic immunization registry data.",Vaccine,32943265,9/19/20,pubmed,0,13,logistic regression,0.001254647,0.027682317,0.001254632,0.355431001,0.613122758,0.001254646,Healthcare,0.6081346,TRUE,19.76923077,0.293339106,12.30769231,0.388747659,8,0.799987654,,,0.494024806
6903,Social network analysis methods for exploring SARS-CoV-2 contact tracing data.,BMC Med Res Methodol,32942988,9/19/20,pubmed,0,4,network analysis,0.000846584,0.00084655,0.000846568,0.715811556,0.000846543,0.2808022,Epidemiology,0.75316715,TRUE,19.5,0.29117447,15.75,0.433636607,6,0.764429903,,,0.49641366
6904,COVID-19 Pandemic: Implications on Interventional Pain Practice-a Narrative Review.,Pain Physician,32942791,9/19/20,pubmed,0,2,pharmacogenom,0.186716427,0.074162324,0.000765963,0.444324682,0.07883467,0.215195934,Epidemiology,0.955995,TRUE,43.5,0.573381161,74,0.764182499,2,0.618927094,,,0.652163585
6905,Early Psychological Impact of the COVID-19 Pandemic in Brazil: A National Survey.,J Clin Med,32942647,9/19/20,pubmed,0,6,logistic regression,0.001237094,0.001237099,0.001237153,0.001237145,0.993814419,0.00123709,Healthcare,0.88975763,TRUE,134.6666667,0.926587915,142.3333333,0.876505218,8,0.799987654,,,0.867693596
6906,The prognostic value of pneumonia severity score and pectoralis muscle Area on chest CT in adult COVID-19 patients.,Eur J Radiol,32942198,9/18/20,pubmed,0,6,logistic regression,0.001219991,0.00121998,0.20576982,0.001219993,0.001220014,0.789350202,Clinics,0.99331975,TRUE,43.66666667,0.57492733,4.5,0.242708055,5,0.739490092,,,0.519041826
6907,Candesartan could ameliorate the COVID-19 cytokine storm.,Biomed Pharmacother,32942152,9/18/20,pubmed,0,2,transcriptom,0.882167043,0.002238843,0.002238478,0.002238436,0.002238652,0.108878548,Drug discovery,0.77586293,TRUE,35.5,0.492547467,59,0.717554188,5,0.739490092,,,0.649863916
6908,Burnout and career satisfaction among attending neurosurgeons during the COVID-19 pandemic.,Clin Neurol Neurosurg,32942135,9/18/20,pubmed,0,7,logistic regression,0.001486417,0.045842542,0.00148649,0.001486489,0.94821158,0.001486481,Healthcare,0.99568176,TRUE,45.85714286,0.594656441,22.71428571,0.50896441,5,0.739490092,,,0.614370314
6909,Potential Pathogenicity Determinants Identified from Structural Proteomics of SARS-CoV and SARS-CoV-2.,Mol Biol Evol,32941612,9/18/20,pubmed,0,9,"molecular dynamics simulation, proteom",0.816926574,0.177753292,0.001330034,0.001330049,0.00133004,0.001330012,Drug discovery,0.7029097,TRUE,25.11111111,0.369472447,43.33333333,0.650722505,1,0.537564047,,,0.519253
6910,COVID-19 pandemic and Farr's law: A global comparison and prediction of outbreak acceleration and deceleration rates.,PLoS One,32941485,9/18/20,pubmed,0,4,"predictive model, correlation analysis",0.001203426,0.001203446,0.001203449,0.95402223,0.041163959,0.00120349,Epidemiology,0.14303505,FALSE,36.25,0.500154617,82.5,0.78445277,3,0.667819001,,,0.650808796
6911,Assessing the potential impact of COVID-19 on life expectancy.,PLoS One,32941467,9/18/20,pubmed,0,3,simulation model,0.001254619,0.001254649,0.001254608,0.709666937,0.28531447,0.001254717,Epidemiology,0.2595837,FALSE,99.66666667,0.868142742,84.33333333,0.788934975,5,0.739490092,,,0.798855936
6912,Genetic grouping of SARS-CoV-2 coronavirus sequences using informative subtype markers for pandemic spread visualization.,PLoS Comput Biol,32941419,9/18/20,pubmed,0,5,genomes,0.001141382,0.823283351,0.001141384,0.172151225,0.001141328,0.00114133,Genomics,0.33272097,FALSE,33.6,0.472261735,33.4,0.595129783,2,0.618927094,,,0.562106204
6913,Effect of Intraocular Pressure on Aerosol Density Generated by Noncontact Tonometer Measurement.,J Glaucoma,32941321,9/18/20,pubmed,0,11,correlation analysis,0.001987164,0.001987185,0.00198722,0.678595952,0.039822862,0.275619617,Epidemiology,0.96008456,TRUE,48.45454545,0.61766343,25.72727273,0.535054857,1,0.537564047,,,0.563427445
6914,Digging metagenomic data of pangolins revealed SARS-CoV-2 related viruses and other significant viruses.,J Med Virol,32940906,9/18/20,pubmed,0,11,"metagenom, genomes",0.004109879,0.97945089,0.004109761,0.004109895,0.004109806,0.004109769,Genomics,0.29522344,FALSE,53.27272727,0.655389944,,,0,0.403234768,,,0.529312356
6915,Molecular Basis of Pathogenesis of Coronaviruses: A Comparative Genomics Approach to Planetary Health to Prevent Zoonotic Outbreaks in the 21st Century.,OMICS,32940573,9/18/20,pubmed,0,4,multiom,0.444905948,0.408276999,0.118203049,0.00127276,0.001272735,0.026068509,Drug discovery,0.9113884,TRUE,30.5,0.438493413,2.5,0.180826866,6,0.764429903,,,0.461250061
6916,A dynamic simulation study of FDA drug from zinc database against COVID-19 main protease receptor.,J Biomol Struct Dyn,32940134,9/18/20,pubmed,0,7,in silico,0.939735882,0.001330027,0.00133004,0.054943939,0.001330058,0.001330054,Drug discovery,0.8309839,TRUE,41.28571429,0.550930793,11,0.371287129,2,0.618927094,,,0.513715005
6917,Investigating the capabilities of information technologies to support policymaking in COVID-19 crisis management; a systematic review and expert opinions.,Eur J Clin Invest,32939771,9/18/20,pubmed,0,3,machine learning,0.001987153,0.001987114,0.287691895,0.650195002,0.056151741,0.001987096,Epidemiology,0.8619293,TRUE,45.33333333,0.589646855,8.333333333,0.325662296,1,0.537564047,,,0.484291066
6918,Development of a volumetric pancreas segmentation CT dataset for AI applications through trained technologists: a study during the COVID 19 containment phase.,Abdom Radiol (NY),32939632,9/18/20,pubmed,0,8,dataset,0.001622739,0.001622725,0.943009685,0.001622757,0.001622741,0.050499353,Imaging,0.53203833,TRUE,98.125,0.865050405,228.5,0.929154402,0,0.403234768,,,0.732479858
6919,Structural models of human ACE2 variants with SARS-CoV-2 Spike protein for structure-based drug design.,Sci Data,32938937,9/18/20,pubmed,0,7,"computational, genome-wide, structural model, dataset",0.704159521,0.240717492,0.050254776,0.001622802,0.001622714,0.001622694,Drug discovery,0.27407247,FALSE,25.71428571,0.376894057,24.85714286,0.527093926,3,0.667819001,,,0.523935661
6920,The Social Distancing Imposed To Contain COVID-19 Can Affect Our Microbiome: a Double-Edged Sword in Human Health.,mSphere,32938697,9/18/20,pubmed,0,5,microbiom,0.002720277,0.581401577,0.002720194,0.279017434,0.002720177,0.131420341,Genomics,0.7840426,TRUE,14.2,0.214793741,13.2,0.402194273,6,0.764429903,,,0.460472639
6921,"SARS-CoV-2 in Quarantined Domestic Cats from COVID-19 Households or Close Contacts, Hong Kong, China.",Emerg Infect Dis,32938527,9/18/20,pubmed,0,10,genomes,0.006540099,0.724734786,0.006540301,0.006540255,0.24910448,0.006540079,Genomics,0.3278712,FALSE,27.7,0.403673697,77.4,0.771808938,10,0.828199272,,,0.667893969
6922,Reverse vaccinology assisted designing of multiepitope-based subunit vaccine against SARS-CoV-2.,Infect Dis Poverty,32938504,9/18/20,pubmed,0,9,"computational, in silico",0.968223394,0.001272715,0.001272679,0.026685852,0.00127265,0.00127271,Drug discovery,0.6736297,TRUE,17.33333333,0.261178799,10.44444444,0.36131924,8,0.799987654,,,0.474161898
6923,0,J Biomol Struct Dyn,32938313,9/18/20,pubmed,0,3,computational,0.994293415,0.001141318,0.001141317,0.001141336,0.001141314,0.0011413,Drug discovery,0.86282194,TRUE,17.66666667,0.266373925,1.666666667,0.145036125,5,0.739490092,,,0.383633381
6924,Spatio-Temporal Mutational Profile Appearances of Swedish SARS-CoV-2 during the Early Pandemic.,Viruses,32937868,9/18/20,pubmed,0,7,bayes,0.043563762,0.949698119,0.001684489,0.001684577,0.001684528,0.001684524,Genomics,0.65011513,TRUE,64.42857143,0.731152205,15.85714286,0.43443939,4,0.707574542,,,0.624388712
6925,Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19.,Sensors (Basel),32937867,9/18/20,pubmed,0,2,"deep learning, dataset",0.001310333,0.001310335,0.74255336,0.252205236,0.001310407,0.001310328,Imaging,0.1928297,FALSE,34.5,0.482404601,17.5,0.45611453,14,0.866658436,,,0.601725856
6926,Prediction of repurposed drugs for treating lung injury in COVID-19.,F1000Res,32934806,9/18/20,pubmed,0,2,bioinformatic,0.818511692,0.000907291,0.000907294,0.000907278,0.000907276,0.17785917,Drug discovery,0.9348178,TRUE,54.5,0.665161729,43.5,0.651725983,2,0.618927094,,,0.645271602
6927,A dataset for the perceived vulnerability to disease scale in Japan before the spread of COVID-19.,F1000Res,32934802,9/18/20,pubmed,0,3,dataset,0.004310048,0.004310147,0.004310392,0.446466428,0.536292982,0.004310003,Healthcare,0.21252075,FALSE,57,0.68204589,15,0.42594327,2,0.618927094,,,0.575638752
6928,Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia.,Sci Adv,32937591,9/17/20,pubmed,0,25,sequencing,0.891230055,0.002639371,0.002638964,0.00263902,0.098213585,0.002639006,Drug discovery,0.8328396,TRUE,45.16,0.587976993,289.2,0.951097137,50,0.957775171,,,0.8322831
6929,Quasi-species nature and differential gene expression of severe acute respiratory syndrome coronavirus 2 and phylogenetic analysis of a novel Iranian strain.,Infect Genet Evol,32937193,9/17/20,pubmed,0,8,"sequencing, transcriptom",0.209643266,0.774342031,0.001046845,0.001046823,0.001046805,0.012874231,Genomics,0.7742471,TRUE,21,0.312016822,7.875,0.314557131,2,0.618927094,,,0.415167016
6930,Use of antimicrobial mouthwashes (gargling) and nasal sprays by healthcare workers to protect them when treating patients with suspected or confirmed COVID-19 infection.,Cochrane Database Syst Rev,32936949,9/17/20,pubmed,0,8,microbiom,0.05373421,0.038652422,0.000740312,0.548111005,0.107455047,0.251307005,Epidemiology,0.9621327,TRUE,135.875,0.92745377,,,3,0.667819001,,,0.797636385
6931,Antimicrobial mouthwashes (gargling) and nasal sprays administered to patients with suspected or confirmed COVID-19 infection to improve patient outcomes and to protect healthcare workers treating them.,Cochrane Database Syst Rev,32936948,9/17/20,pubmed,0,8,microbiom,0.05888386,0.090613054,0.000704935,0.446039744,0.061833279,0.341925128,Epidemiology,0.98018974,TRUE,139.125,0.932277816,171.875,0.9009232,2,0.618927094,,,0.817376037
6932,Antimicrobial mouthwashes (gargling) and nasal sprays to protect healthcare workers when undertaking aerosol-generating procedures (AGPs) on patients without suspected or confirmed COVID-19 infection.,Cochrane Database Syst Rev,32936947,9/17/20,pubmed,0,8,microbiom,0.04738962,0.098210183,0.000838527,0.436713689,0.044515976,0.372332006,Epidemiology,0.9732076,TRUE,139.125,0.932277816,171.875,0.9009232,5,0.739490092,,,0.857563703
6933,Flaws in the development and validation of a covid-19 prediction model.,Clin Infect Dis,32936916,9/17/20,pubmed,0,3,prediction model,0.034962719,0.034961874,0.825186772,0.034962924,0.034961874,0.034963837,Clinics,0.51743954,TRUE,226,0.980394582,331.3333333,0.9622692,0,0.403234768,,,0.781966183
6934,Isolation and characterization of severe acute respiratory syndrome coronavirus 2 in Turkey.,PLoS One,32936826,9/17/20,pubmed,0,9,"whole genome, genome sequences",0.182559988,0.724519483,0.002490472,0.00249055,0.002490537,0.08544897,Genomics,0.55682576,TRUE,10.44444444,0.155977488,0.666666667,0.096200161,1,0.537564047,,,0.263247232
6935,Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study.,J Med Internet Res,32936777,9/17/20,pubmed,0,6,"machine learning, dataset",0.000728139,0.061512749,0.166484013,0.335096882,0.435450079,0.000728138,Healthcare,0.8499791,TRUE,109.5,0.889479869,154,0.887008295,4,0.707574542,,,0.828020902
6936,Associations of Mental Health and Personal Preventive Measure Compliance With Exposure to COVID-19 Information During Work Resumption Following the COVID-19 Outbreak in China: Cross-Sectional Survey Study.,J Med Internet Res,32936776,9/17/20,pubmed,0,12,logistic regression,0.00075289,0.000752898,0.000752903,0.000752935,0.975106789,0.021881584,Healthcare,0.97527826,TRUE,43.66666667,0.57492733,14.25,0.415038801,0,0.403234768,,,0.4644003
6937,COVID-19 and the "Film Your Hospital" Conspiracy Theory: Social Network Analysis of Twitter Data.,J Med Internet Res,32936771,9/17/20,pubmed,0,4,network analysis,0.001220074,0.001220112,0.06774172,0.927378015,0.001220052,0.001220027,Epidemiology,0.98418427,TRUE,22.25,0.328344363,5.25,0.260971367,3,0.667819001,,,0.41904491
6938,Identification of Risk Factors and Symptoms of COVID-19: Analysis of Biomedical Literature and Social Media Data.,J Med Internet Res,32936770,9/17/20,pubmed,0,4,machine learning,0.001438161,0.001438179,0.03475136,0.516484566,0.276555807,0.169331928,Epidemiology,0.542779,TRUE,6.5,0.093512277,0.75,0.099411292,0,0.403234768,,,0.198719446
6939,Territories Under Siege: Risks of the Decimation of Indigenous and Quilombolas Peoples in the Context of COVID-19 in South Brazil.,J Racial Ethn Health Disparities,32936443,9/17/20,pubmed,0,7,correlation analysis,0.003101554,0.003101638,0.003101534,0.98449232,0.003101508,0.003101446,Epidemiology,0.3038473,FALSE,52.71428571,0.651679139,37.28571429,0.61747391,2,0.618927094,,,0.629360048
6940,Molecular basis of the potential interaction of SARS-CoV-2 spike protein to CD147 in COVID-19 associated-lymphopenia.,J Biomol Struct Dyn,32936048,9/17/20,pubmed,0,8,in-silico,0.992924296,0.001415109,0.001415181,0.001415116,0.001415093,0.001415206,Drug discovery,0.46963596,FALSE,12.75,0.191786752,3.875,0.222972973,8,0.799987654,,,0.404915793
6941,COVID-19 in Italy: An Analysis of Death Registry Data.,J Public Health (Oxf),32935849,9/17/20,pubmed,0,2,dataset,0.041319256,0.001823432,0.001823355,0.588050034,0.264136673,0.10284725,Epidemiology,0.65016294,TRUE,8,0.118683901,2,0.164302917,22,0.908142478,,,0.397043099
6942,Identification of the RNase-binding site of SARS-CoV-2 RNA for anchor primer-PCR detection of viral loading in 306 COVID-19 patients.,Brief Bioinform,32935831,9/17/20,pubmed,0,19,bioinformatic,0.088986187,0.805155931,0.001203482,0.001203411,0.001203454,0.102247535,Genomics,0.86203516,TRUE,34.57894737,0.482899375,39.21052632,0.62877977,0,0.403234768,,,0.504971304
6943,"Phylogeography of SARS-CoV-2 pandemic in Spain: a story of multiple introductions, micro-geographic stratification, founder effects, and super-spreaders.",Zool Res,32935498,9/17/20,pubmed,0,6,genomes,0.001415092,0.99292427,0.001415118,0.001415235,0.001415138,0.001415146,Genomics,0.65073097,TRUE,67.83333333,0.752674872,62.33333333,0.729997324,5,0.739490092,,,0.740720763
6944,Kynurenic acid underlies sex-specific immune responses to COVID-19.,medRxiv,32935119,9/17/20,pubmed,0,19,metabolom,0.604045151,0.001751185,0.001751132,0.001751174,0.001751242,0.388950115,Drug discovery,0.5888529,TRUE,61.47368421,0.711546787,169.4736842,0.898983142,3,0.667819001,,,0.759449643
6945,Potential impact on coagulopathy of gene variants of coagulation related proteins that interact with SARS-CoV-2.,bioRxiv,32935103,9/17/20,pubmed,0,9,computational,0.475775467,0.340498871,0.000999546,0.000999543,0.000999539,0.180727035,Drug discovery,0.3219223,FALSE,44.22222222,0.579627683,95.33333333,0.813620551,0,0.403234768,,,0.598827667
6946,Immunologically distinct responses occur in the CNS of COVID-19 patients.,bioRxiv,32935102,9/17/20,pubmed,0,18,sequencing,0.409880407,0.282598928,0.001823348,0.001823358,0.001823498,0.302050462,Drug discovery,0.32686567,FALSE,68.27777778,0.755210588,210.5555556,0.922665239,3,0.667819001,,,0.781898276
6947,Response to the COVID-19 Outbreak in Urban Settings in China.,Res Sq,32935093,9/17/20,pubmed,0,12,logistic regression,0.000936086,0.000936099,0.000936086,0.000936147,0.995319492,0.000936089,Healthcare,0.8462158,TRUE,73.58333333,0.780134826,49.66666667,0.680157881,1,0.537564047,,,0.665952251
6948,Estimation of the case fatality rate of COVID-19 epidemiological data in Nigeria using statistical regression analysis.,Biosaf Health,32935084,9/17/20,pubmed,0,4,dataset,0.002183226,0.002183258,0.00218323,0.879167134,0.002183308,0.112099843,Epidemiology,0.2313062,FALSE,6.25,0.088564537,2.25,0.170925876,0,0.403234768,,,0.220908394
6949,Evolution of changes in physical activity over lockdown time: Physical activity datasets of four independent adult sample groups corresponding to each of the last four of the six COVID-19 lockdown weeks in Greece.,Data Brief,32934972,9/17/20,pubmed,0,2,dataset,0.030608318,0.134889768,0.001010948,0.327402151,0.505077807,0.001011009,Healthcare,0.6030233,TRUE,5.5,0.077246583,1,0.122023013,4,0.707574542,,,0.302281379
6950,Molecular neuro-biological and systemic health benefits of achieving dopamine homeostasis in the face of a catastrophic pandemic (COVID- 19): A mechanistic exploration.,J Syst Integr Neurosci,32934824,9/17/20,pubmed,0,13,nutrigenom,0.186282171,0.001717298,0.001717288,0.621195118,0.187370765,0.00171736,Epidemiology,0.9578943,TRUE,17,0.257467994,4.461538462,0.239697618,0,0.403234768,,,0.30013346
6951,Tuberculosis and COVID-19: Lessons from the Past Viral Outbreaks and Possible Future Outcomes.,Can Respir J,32934758,9/17/20,pubmed,0,7,"data mining, dataset",0.094962753,0.264572536,0.090245674,0.547518129,0.001350368,0.00135054,Epidemiology,0.8213134,TRUE,15.57142857,0.23495578,0,0.055525823,7,0.785110192,,,0.358530598
6952,Molecular diagnosis of COVID-19: Current situation and trend in China (Review).,Exp Ther Med,32934678,9/17/20,pubmed,0,6,sequencing,0.109422236,0.299114206,0.453659604,0.135397058,0.001203463,0.001203434,Genomics,0.9081477,TRUE,75.33333333,0.788113056,33.16666667,0.593658014,3,0.667819001,,,0.68319669
6953,A Potent Postentry Restriction to Primate Lentiviruses in a Yinpterochiropteran Bat.,mBio,32934084,9/17/20,pubmed,0,6,genomes,0.28074816,0.417649534,0.00098841,0.29863707,0.000988392,0.000988434,Genomics,0.65629876,TRUE,52.83333333,0.652297607,151.6666667,0.884533048,3,0.667819001,,,0.734883219
6954,C-reactive protein and albumin association with mortality of hospitalised SARS-CoV-2 patients: A tertiary hospital experience.,Clin Med (Lond),32934038,9/17/20,pubmed,0,13,logistic regression,0.0014381,0.001438084,0.001438099,0.001438099,0.001438167,0.992809451,Clinics,0.9448899,TRUE,23.23076923,0.343991589,7.230769231,0.30238159,5,0.739490092,,,0.461954423
6955,Transmissibility and Epidemicity of COVID-19 in India: A Case Study.,Recent Pat Antiinfect Drug Discov,32933464,9/17/20,pubmed,0,2,dataset,0.001415124,0.001415202,0.001415146,0.949969214,0.001415175,0.044370139,Epidemiology,0.066613555,FALSE,2.5,0.027459954,0,0.055525823,0,0.403234768,,,0.162073515
6956,Mortality Rate and Predictors of Mortality in Hospitalized COVID-19 Patients with Diabetes.,Healthcare (Basel),32933191,9/17/20,pubmed,0,5,logistic regression,0.001511819,0.00151183,0.001511816,0.00151184,0.00151186,0.992440836,Clinics,0.5310501,TRUE,52.8,0.652173913,25.6,0.533716885,4,0.707574542,,,0.631155113
6957,Incidence and case fatality rate of COVID-19 in patients with inflammatory articular diseases.,Int J Clin Pract,32931643,9/16/20,pubmed,0,12,logistic regression,0.001823294,0.001823319,0.001823344,0.001823389,0.00182333,0.990883324,Clinics,0.87522644,TRUE,22.08333333,0.32630342,14.5,0.418450629,3,0.667819001,,,0.470857683
6958,Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study.,J Med Internet Res,32931446,9/16/20,pubmed,0,4,prediction model,0.000734162,0.000734164,0.012665211,0.693549509,0.291582786,0.000734168,Epidemiology,0.69330454,TRUE,53.25,0.655328097,25.5,0.533382392,0,0.403234768,,,0.530648419
6959,"CoV-Seq, a New Tool for SARS-CoV-2 Genome Analysis and Visualization: Development and Usability Study.",J Med Internet Res,32931441,9/16/20,pubmed,0,6,"bioinformatic, genomes",0.001220048,0.660409948,0.071611748,0.264318148,0.001220078,0.00122003,Genomics,0.63362825,TRUE,50,0.632073721,5.333333333,0.262911426,0,0.403234768,,,0.432739972
6960,Digital Health in Physicians' and Pharmacists' Office: A Comparative Study of e-Prescription Systems' Architecture and Digital Security in Eight Countries.,OMICS,32931378,9/16/20,pubmed,0,2,digital health,0.001438162,0.001438176,0.127609031,0.525529136,0.19893793,0.145047565,Epidemiology,0.81489414,TRUE,58.5,0.693240151,42,0.644902328,1,0.537564047,,,0.625235509
6961,Generation of SARS-CoV-2 S1 Spike Glycoprotein Putative Antigenic Epitopes in Vitro by Intracellular Aminopeptidases.,J Proteome Res,32931291,9/16/20,pubmed,0,5,computational,0.769104136,0.225654269,0.00131043,0.001310403,0.001310357,0.001310405,Drug discovery,0.23873916,FALSE,53.2,0.65508071,44,0.654401927,0,0.403234768,,,0.570905801
6962,Derivation and validation of the clinical prediction model for COVID-19.,Intern Emerg Med,32930963,9/16/20,pubmed,0,10,"predictive model, prediction model",0.001371257,0.001371392,0.001371327,0.198139668,0.001371272,0.796375085,Clinics,0.5109091,TRUE,3,0.037293586,0.6,0.09011239,4,0.707574542,,,0.278326839
6963,Characteristics and Outcomes of Patients Undergoing Endoscopy During the COVID-19 Pandemic: A Multicenter Study from New York City.,Dig Dis Sci,32930898,9/16/20,pubmed,0,19,logistic regression,0.001751137,0.001751205,0.001751209,0.001751277,0.2968984,0.696096773,Clinics,0.9838569,TRUE,105.9473684,0.880759478,38.10526316,0.622357506,2,0.618927094,,,0.707348026
6964,"Risk of severe coronavirus disease in imported and secondary cases in Zhejiang province, China.",J Public Health (Oxf),32930793,9/16/20,pubmed,0,8,logistic regression,0.001987199,0.25095143,0.001987129,0.00198727,0.129786586,0.613300386,Clinics,0.26136273,FALSE,79.25,0.803512895,52.25,0.689858175,0,0.403234768,,,0.632201946
6965,Pharmacological hypothesis: TPC2 antagonist tetrandrine as a potential therapeutic agent for COVID-19.,Pharmacol Res Perspect,32930523,9/16/20,pubmed,0,2,dataset,0.581205066,0.002720235,0.154516466,0.165790181,0.002720477,0.093047574,Drug discovery,0.69029856,TRUE,59,0.696579875,44.5,0.656743377,1,0.537564047,,,0.630295766
6966,The dynamics of entropy in the COVID-19 outbreaks.,Nonlinear Dyn,32929304,9/16/20,pubmed,0,4,"bayes, mathematical model, dataset",0.001072205,0.001072231,0.001072214,0.9788059,0.016905277,0.001072173,Epidemiology,0.568082,TRUE,53,0.654400396,30,0.570176612,2,0.618927094,,,0.614501367
6967,Structural dynamics of COVID-19 main protease.,J Mol Struct,32929291,9/16/20,pubmed,0,2,"molecular dynamics simulation, in silico",0.634892515,0.002639141,0.002639087,0.354551069,0.002639091,0.002639097,Drug discovery,0.76308,TRUE,33,0.466757375,0.5,0.087101953,0,0.403234768,,,0.319031365
6968,Changes in network centrality of psychopathology symptoms between the COVID-19 outbreak and after peak.,Mol Psychiatry,32929212,9/16/20,pubmed,0,5,"network analysis, network model",0.113030785,0.001461869,0.079540472,0.24694084,0.557563926,0.001462107,Healthcare,0.9877735,TRUE,21.4,0.316469788,,,4,0.707574542,,,0.512022165
6969,Analysis of the SARS-CoV-2 spike protein glycan shield reveals implications for immune recognition.,Sci Rep,32929138,9/16/20,pubmed,0,4,glycomics,0.991243882,0.001751245,0.001751201,0.001751219,0.00175126,0.001751193,Drug discovery,0.13452166,FALSE,132.75,0.9249799,138,0.872892695,33,0.936045435,,,0.91130601
6970,Patient characteristics and admitting vital signs associated with coronavirus disease 2019 (COVID-19)-related mortality among patients admitted with noncritical illness.,Infect Control Hosp Epidemiol,32928319,9/16/20,pubmed,0,10,logistic regression,0.001156234,0.001156247,0.020873658,0.001156302,0.001156332,0.974501227,Clinics,0.8046557,TRUE,93.6,0.853361371,167.9,0.897912764,2,0.618927094,,,0.790067076
6971,"Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA.",Int J Health Geogr,32928236,9/16/20,pubmed,0,7,computational,0.001291234,0.001291236,0.001291285,0.875656146,0.067170548,0.053299551,Epidemiology,0.8485523,TRUE,14.28571429,0.215783289,3.142857143,0.200561948,0,0.403234768,,,0.273193335
6972,Characterization of codon usage pattern in SARS-CoV-2.,Virol J,32928234,9/16/20,pubmed,0,1,"bioinformatic, genome-wide",0.001751236,0.991243917,0.001751242,0.001751221,0.001751237,0.001751147,Genomics,0.43075353,FALSE,584,0.998453831,263,0.943203104,5,0.739490092,,,0.893715676
6973,In silico modeling of small molecule carboxamides as inhibitors of SARS-CoV 3CL protease: An approach towards combating COVID-19.,Comb Chem High Throughput Screen,32928086,9/16/20,pubmed,0,3,in silico,0.638262832,0.001330055,0.245107764,0.001330175,0.11263905,0.001330123,Drug discovery,0.6465812,TRUE,140.6666667,0.934689839,93.33333333,0.809272143,1,0.537564047,,,0.760508676
6974,Physical Activity Changes and Its Risk Factors among Community-Dwelling Japanese Older Adults during the COVID-19 Epidemic: Associations with Subjective Well-Being and Health-Related Quality of Life.,Int J Environ Res Public Health,32927829,9/16/20,pubmed,0,5,logistic regression,0.00194353,0.001943496,0.0019435,0.001943633,0.990282299,0.001943542,Healthcare,0.9750148,TRUE,65.4,0.736656565,12.8,0.396574793,15,0.874313229,,,0.669181529
6975,Impact of HVAC-Systems on the Dispersion of Infectious Aerosols in a Cardiac Intensive Care Unit.,Int J Environ Res Public Health,32927583,9/16/20,pubmed,0,10,computational,0.037506681,0.001622788,0.001622736,0.664352142,0.001622717,0.293272937,Epidemiology,0.63720727,TRUE,23,0.34225988,2.1,0.164771207,1,0.537564047,,,0.348198378
6976,Viral genomics to inform infection control response in occupational COVID-19 transmission.,Clin Infect Dis,32927479,9/15/20,pubmed,0,10,sequencing,0.004775283,0.400781538,0.004775107,0.004775381,0.580117525,0.004775166,Healthcare,0.2628492,FALSE,75.8,0.789659224,83.6,0.787329409,0,0.403234768,,,0.660074467
6977,"Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning.",Eur J Radiol,32927416,9/15/20,pubmed,0,15,"machine learning, neural network, image analysis",0.001059403,0.001059425,0.878662985,0.001059403,0.001059372,0.117099413,Imaging,0.5194853,TRUE,27.26666667,0.39829303,16.73333333,0.445210062,3,0.667819001,,,0.503774031
6978,Vulnerability and resilience to pandemic-related stress among U.S. women pregnant at the start of the COVID-19 pandemic.,Soc Sci Med,32927382,9/15/20,pubmed,0,4,logistic regression,0.001126808,0.001126801,0.001126836,0.001126865,0.994365792,0.001126899,Healthcare,0.9920077,TRUE,25.5,0.37435834,8,0.320511105,9,0.814309525,,,0.503059657
6979,"Public perceptions, anxiety and the perceived efficacy of health-protective behaviours to mitigate the spread of the SARS-Cov-2/ COVID-19 pandemic.",Public Health,32927291,9/15/20,pubmed,0,1,logistic regression,0.000846521,0.00084655,0.02053695,0.016511551,0.960411883,0.000846545,Healthcare,0.7818048,TRUE,26,0.382398417,4,0.231469093,5,0.739490092,,,0.451119201
6980,"Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator.",Int J Med Inform,32927229,9/15/20,pubmed,0,3,"predictive model, logistic regression",0.024515496,0.000746573,0.227091041,0.269461292,0.000746576,0.477439022,Clinics,0.17088956,FALSE,237.3333333,0.982435525,350,0.965614129,13,0.858880178,,,0.935643277
6981,"Is oseltamivir suitable for fighting against COVID-19: In silico assessment, in vitro and retrospective study.",Bioorg Chem,32927129,9/15/20,pubmed,0,19,in silico,0.764058357,0.001126833,0.001126815,0.001126824,0.043886476,0.188674696,Drug discovery,0.94772327,TRUE,22,0.326056033,,,12,0.850299401,,,0.588177717
6982,Molecular phylogeny and missense mutations at envelope proteins across coronaviruses.,Genomics,32927009,9/15/20,pubmed,0,3,genomes,0.004110145,0.979450791,0.004109736,0.004109774,0.00410977,0.004109785,Genomics,0.61984694,TRUE,79.66666667,0.805924918,54,0.697952903,2,0.618927094,,,0.707601639
6983,Using Machine Learning to Generate Novel Hypotheses: Increasing Optimism About COVID-19 Makes People Less Willing to Justify Unethical Behaviors.,Psychol Sci,32926807,9/15/20,pubmed,0,3,"machine learning, deep-learning",0.002130676,0.002130738,0.288641231,0.173816419,0.531150274,0.002130661,Healthcare,0.54910284,TRUE,19,0.285793803,17.33333333,0.45424137,1,0.537564047,,,0.425866407
6984,Impact of Reduced Elective Ophthalmic Surgical Volume on U.S. Hospitals During the Early COVID-19 Pandemic.,J Cataract Refract Surg,32925656,9/15/20,pubmed,0,7,dataset,0.001653005,0.00165305,0.123454636,0.208382373,0.373729941,0.291126995,Healthcare,0.9906861,TRUE,105.5714286,0.880264704,65.71428571,0.74150388,0,0.403234768,,,0.675001117
6985,Self-Reported Compliance With Personal Preventive Measures Among Chinese Factory Workers at the Beginning of Work Resumption Following the COVID-19 Outbreak: Cross-Sectional Survey Study.,J Med Internet Res,32924947,9/15/20,pubmed,0,12,logistic regression,0.000759342,0.000759358,0.000759376,0.00075939,0.996203179,0.000759355,Healthcare,0.85755146,TRUE,63.41666667,0.724039829,32.41666667,0.588640621,5,0.739490092,,,0.684056847
6986,Potential protease inhibitors and their combinations to block SARS-CoV-2.,J Biomol Struct Dyn,32924827,9/15/20,pubmed,0,5,molecular dynamics simulation,0.994436799,0.001112704,0.001112637,0.001112637,0.001112614,0.001112609,Drug discovery,0.86581135,TRUE,63.4,0.723977983,39.4,0.630050843,3,0.667819001,,,0.673949276
6987,Computational insight of dexamethasone against potential targets of SARS-CoV-2.,J Biomol Struct Dyn,32924825,9/15/20,pubmed,0,4,computational,0.994365843,0.001126798,0.001126886,0.001126848,0.001126815,0.001126809,Drug discovery,0.94353443,TRUE,34.25,0.479559651,6.5,0.288132192,3,0.667819001,,,0.478503615
6988,A tale of two instructional experiences: student engagement in active learning and emergency remote learning of biomechanics.,Sports Biomech,32924795,9/15/20,pubmed,0,1,active learning,0.001272692,0.001272634,0.211375142,0.001272681,0.783534187,0.001272664,Healthcare,0.83415526,TRUE,24,0.35574247,13,0.400521809,0,0.403234768,,,0.386499682
6989,Clinical characteristics and outcome of hemodialysis patients with COVID-19: a large cohort study in a single Chinese center.,Ren Fail,32924707,9/15/20,pubmed,0,5,logistic regression,0.001237101,0.001237064,0.001237157,0.001237134,0.001237106,0.993814438,Clinics,0.8623947,TRUE,57.2,0.683097285,16.6,0.443537597,1,0.537564047,,,0.554732976
6990,Trend of COVID-19 spreads and status of household handwashing practice and its determinants in Bangladesh - situation analysis using national representative data.,Int J Environ Health Res,32924565,9/15/20,pubmed,0,2,dataset,0.002996581,0.002996531,0.002996606,0.291716934,0.696296803,0.002996545,Healthcare,0.4712218,FALSE,19.5,0.29117447,1,0.122023013,2,0.618927094,,,0.344041526
6991,Identification of Hub genes associated with infection of three lung cell lines by SARS-CoV-2 with integrated bioinformatics analysis.,J Cell Mol Med,32924263,9/15/20,pubmed,0,6,bioinformatic,0.932254528,0.013549484,0.013549344,0.013548828,0.013548873,0.013548943,Drug discovery,0.54571474,TRUE,21.5,0.318263343,22.66666667,0.508629917,0,0.403234768,,,0.410042676
6992,The future is now? Clinical and translational aspects of "Omics" technologies.,Immunol Cell Biol,32924178,9/15/20,pubmed,0,3,"machine learning, artificial intelligence, proteom,  omics, microbiom",0.001751258,0.001751229,0.770692621,0.222302362,0.001751292,0.001751239,Epidemiology,0.8479198,TRUE,12.33333333,0.186467932,4.666666667,0.246721969,4,0.707574542,,,0.380254815
6993,Origin-independent analysis links SARS-CoV-2 local genomes with COVID-19 incidence and mortality.,Brief Bioinform,32924062,9/15/20,pubmed,0,2,"genome sequences, genomes",0.233490898,0.507405584,0.001156244,0.130676458,0.001156248,0.126114568,Genomics,0.43910792,FALSE,33,0.466757375,15.5,0.430157881,0,0.403234768,,,0.433383341
6994,Understanding clinical decision-making during the COVID-19 pandemic: A cross-sectional worldwide survey.,EClinicalMedicine,32923995,9/15/20,pubmed,0,5,logistic regression,0.095974381,0.001156346,0.001156276,0.409138857,0.410223719,0.082350422,Healthcare,0.94425786,TRUE,171.4,0.959181149,182.2,0.907746856,1,0.537564047,,,0.801497351
6995,"Investigation of SARS-CoV-2 outbreaks in six care homes in London, April 2020.",EClinicalMedicine,32923993,9/15/20,pubmed,0,30,"sequencing, whole genome",0.001156242,0.321996558,0.001156255,0.001156326,0.559484139,0.11505048,Healthcare,0.25845948,FALSE,59.96666667,0.702455316,61.86666667,0.727990367,25,0.918019631,,,0.782821771
6996,An immunoinformatics study on the spike protein of SARS-CoV-2 revealing potential epitopes as vaccine candidates.,Heliyon,32923731,9/15/20,pubmed,0,6,computational,0.995058063,0.000988417,0.000988374,0.000988374,0.00098839,0.000988382,Drug discovery,0.8387952,TRUE,11.83333333,0.178304162,2.666666667,0.185442869,1,0.537564047,,,0.300437026
6997,"C-C chemokine receptor type 5 links COVID-19, rheumatoid arthritis, and Hydroxychloroquine: in silico analysis.",Transl Med Commun,32923679,9/15/20,pubmed,0,6,"in silico, transcriptom",0.854748799,0.001684538,0.001684611,0.001684552,0.001684499,0.138513002,Drug discovery,0.7194304,TRUE,23.5,0.348506401,4,0.231469093,0,0.403234768,,,0.327736754
6998,Subjective wellbeing and mental health during the COVID-19 pandemic: Data from three population groups in Colombia.,Data Brief,32923550,9/15/20,pubmed,0,3,"correlation analysis, dataset",0.001438128,0.001438138,0.001438164,0.306538172,0.687709285,0.001438112,Healthcare,0.457442,FALSE,7.666666667,0.111633373,0,0.055525823,0,0.403234768,,,0.190131321
6999,Impacts of COVID-19 on global tourism industry: A cross-regional comparison.,Tour Manag Perspect,32923356,9/15/20,pubmed,0,2,text mining,0.003335313,0.003335377,0.003335355,0.983323318,0.003335351,0.003335286,Epidemiology,0.66084576,TRUE,8.5,0.126662131,0.5,0.087101953,9,0.814309525,,,0.342691203
7000,"Transportation, germs, culture: a dynamic graph model of COVID-19 outbreak.",Quant Biol,32923014,9/15/20,pubmed,0,7,model simulation,0.001823359,0.001823391,0.001823463,0.79449224,0.198214142,0.001823405,Epidemiology,0.6462101,TRUE,152.4285714,0.946131486,64,0.735549906,0,0.403234768,,,0.694972053
7001,Potential Treatment of Chinese and Western Medicine Targeting Nsp14 of SARS-CoV-2.,J Pharm Anal,32923004,9/15/20,pubmed,0,6,virtual screening,0.991067279,0.001786536,0.001786534,0.001786563,0.001786546,0.001786542,Drug discovery,0.9440796,TRUE,39.83333333,0.537571897,23.66666667,0.518062617,6,0.764429903,,,0.606688139
7002,COVID-19: Rational discovery of the therapeutic potential of Melatonin as a SARS-CoV-2 main Protease Inhibitor.,Int J Med Sci,32922174,9/15/20,pubmed,0,7,in silico,0.927529842,0.001126795,0.001126793,0.049070511,0.001126869,0.02001919,Drug discovery,0.98472285,TRUE,4.857142857,0.065557548,0.428571429,0.076665775,8,0.799987654,,,0.314070326
7003,Strategic design of precautionary measures for airport passengers in times of global health crisis Covid 19: Parametric modelling and processing algorithms.,J Air Transp Manag,32921936,9/15/20,pubmed,0,5,simulation model,0.002720168,0.002720197,0.002720249,0.986399029,0.002720226,0.002720131,Epidemiology,0.3904358,FALSE,30.2,0.433916754,2,0.164302917,1,0.537564047,,,0.378594573
7004,CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images.,Chaos Solitons Fractals,32921934,9/15/20,pubmed,0,3,"deep learning, artificial intelligence, neural network, dataset",0.001098815,0.001098868,0.929130498,0.066474179,0.001098828,0.001098813,Imaging,0.46172583,FALSE,10,0.15214299,1,0.122023013,11,0.840175319,,,0.371447107
7005,A deep learning approach to detect Covid-19 coronavirus with X-Ray images.,Biocybern Biomed Eng,32921862,9/15/20,pubmed,0,4,"deep learning, network model, dataset",0.018439678,0.000846553,0.978174069,0.000846594,0.000846567,0.00084654,Imaging,0.26200235,FALSE,10.5,0.157338116,2.75,0.187583623,13,0.858880178,,,0.401267306
7006,Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data.,Socioecon Plann Sci,32921839,9/15/20,pubmed,0,4,data mining,0.001717227,0.001717219,0.271229194,0.721901813,0.001717313,0.001717235,Epidemiology,0.77749544,TRUE,22.5,0.333539489,4.25,0.235750602,1,0.537564047,,,0.368951379
7007,Fighting fake news in the COVID-19 era: policy insights from an equilibrium model.,Policy Sci,32921821,9/15/20,pubmed,0,2,mathematical model,0.001622771,0.030255049,0.001622733,0.963253891,0.001622778,0.001622778,Epidemiology,0.4088195,FALSE,17,0.257467994,1,0.122023013,3,0.667819001,,,0.349103336
7008,Probiotics and COVID-19: Think about the link.,Br J Nutr,32921328,9/15/20,pubmed,0,2,microbiom,0.769720226,0.076676736,0.002898402,0.002898429,0.002898468,0.14490774,Drug discovery,0.8468642,TRUE,71,0.769064259,9,0.337904736,5,0.739490092,,,0.615486362
7009,Assessing Fear and Anxiety of Corona Virus Among Dental Practitioners.,Disaster Med Public Health Prep,32921326,9/15/20,pubmed,0,5,logistic regression,0.001350363,0.001350379,0.001350335,0.001350379,0.99324817,0.001350374,Healthcare,0.91998816,TRUE,15.4,0.231616055,7,0.299973241,2,0.618927094,,,0.383505463
7010,Highly conserved binding region of ACE2 as a receptor for SARS-CoV-2 between humans and mammals.,Vet Q,32921279,9/15/20,pubmed,0,5,sequence alignment,0.633413684,0.362801513,0.00094626,0.000946138,0.000946246,0.000946159,Drug discovery,0.31510174,FALSE,300,0.990599295,257.2,0.940527161,5,0.739490092,,,0.890205516
7011,There are no shortcuts in the development and validation of a COVID-19 prediction model.,Transbound Emerg Dis,32920970,9/14/20,pubmed,0,3,prediction model,0.034962719,0.034961874,0.825186772,0.034962924,0.034961874,0.034963837,Clinics,0.6615821,TRUE,123.3333333,0.912115777,143,0.877174204,0,0.403234768,,,0.730841583
7012,Dynamic analysis of the mathematical model of COVID-19 with demographic effects.,Z Naturforsch C J Biosci,32920544,9/14/20,pubmed,0,5,mathematical model,0.004310012,0.004310193,0.004310025,0.978449315,0.004310103,0.004310352,Epidemiology,0.81984156,TRUE,4,0.054734368,0.2,0.061145304,5,0.739490092,,,0.285123255
7013,"Importance of meteorology in air pollution events during the city lockdown for COVID-19 in Hubei Province, Central China.",Sci Total Environ,32920418,9/14/20,pubmed,0,9,model simulation,0.028568575,0.001371344,0.001371249,0.91885695,0.048460579,0.001371304,Epidemiology,0.95858616,TRUE,56.22222222,0.67635599,28.77777778,0.560074926,7,0.785110192,,,0.673847036
7014,Access to healthcare and prevalence of anxiety and depression in persons with epilepsy during the COVID-19 pandemic: A multicountry online survey.,Epilepsy Behav,32920373,9/14/20,pubmed,0,8,logistic regression,0.001187295,0.001187266,0.001187273,0.00118735,0.973064796,0.02218602,Healthcare,0.9915452,TRUE,46.25,0.598800173,48.125,0.674137008,4,0.707574542,,,0.660170574
7015,"Targeting the SARS-CoV-2 main protease using FDA-approved Isavuconazonium, a P2-P3 α-ketoamide derivative and Pentagastrin: An in-silico drug discovery approach.",J Mol Graph Model,32920239,9/14/20,pubmed,0,5,"virtual screening, molecular dynamics simulation, computational, in-silico",0.972846187,0.001415168,0.001415137,0.00141512,0.001415126,0.021493262,Drug discovery,0.9125098,TRUE,33.6,0.472261735,7,0.299973241,1,0.537564047,,,0.436599674
7016,Risk factors for non-invasive/invasive ventilatory support in patients with COVID-19 pneumonia: A retrospective study within a multidisciplinary approach.,Int J Infect Dis,32920232,9/14/20,pubmed,0,15,logistic regression,0.001022622,0.001022615,0.001022625,0.045923948,0.001022642,0.949985549,Clinics,0.67303836,TRUE,11.26666667,0.169336384,1.933333333,0.152127375,1,0.537564047,,,0.286342602
7017,SARS-CoV-2 transcriptome analysis and molecular cataloguing of immunodominant epitopes for multi-epitope based vaccine design.,Genomics,32920121,9/14/20,pubmed,0,5,transcriptom,0.969347214,0.001565356,0.001565312,0.024391558,0.001565286,0.001565274,Drug discovery,0.6020485,TRUE,12.8,0.192343373,3.8,0.221501204,0,0.403234768,,,0.272359782
7018,Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data.,Math Biosci,32920095,9/14/20,pubmed,0,4,bayes,0.001156225,0.001156254,0.001156278,0.804987845,0.00115627,0.190387128,Epidemiology,0.37880442,FALSE,30.25,0.434782609,16.25,0.438988493,5,0.739490092,,,0.537753731
7019,Characterization of the cytokine storm reflects hyperinflammatory endothelial dysfunction in COVID-19.,J Allergy Clin Immunol,32920092,9/14/20,pubmed,0,17,proteom,0.323659727,0.073362344,0.001034589,0.001034616,0.001034613,0.599874112,Clinics,0.79433113,TRUE,95.11764706,0.856886635,150.2352941,0.883596468,14,0.866658436,,,0.869047179
7020,Magnetic particle targeting for diagnosis and therapy of lung cancers.,J Control Release,32920079,9/14/20,pubmed,0,7,computational,0.626799303,0.001072209,0.278835234,0.001072241,0.001072217,0.091148796,Drug discovery,0.66364646,TRUE,32.42857143,0.460387161,8.285714286,0.324324324,0,0.403234768,,,0.395982084
7021,Safety assessment of drug combinations used in COVID-19 treatment: in silico toxicogenomic data-mining approach.,Toxicol Appl Pharmacol,32920000,9/14/20,pubmed,0,8,"in silico, toxicogenom",0.719628033,0.001786593,0.001786719,0.001786655,0.001786754,0.273225246,Drug discovery,0.8929268,TRUE,17.75,0.267672707,4.25,0.235750602,2,0.618927094,,,0.374116801
7022,Immediate psychological impact on nurses working at 42 government-designated hospitals during COVID-19 outbreak in China: A cross-sectional study.,Nurs Outlook,32919788,9/14/20,pubmed,0,8,logistic regression,0.001901717,0.001901749,0.001901743,0.001901809,0.99049127,0.001901712,Healthcare,0.96430933,TRUE,93,0.851567815,32.875,0.591584158,12,0.850299401,,,0.764483792
7023,Association of insomnia disorder with sociodemographic factors and poor mental health in COVID-19 inpatients in China.,Sleep Med,32919351,9/13/20,pubmed,0,12,logistic regression,0.001987094,0.001987283,0.001987139,0.001987153,0.714930954,0.277120377,Healthcare,0.9791986,TRUE,49.08333333,0.624713959,,,6,0.764429903,,,0.694571931
7024,"A correlation study between meteorological parameters and COVID-19 pandemic in Mumbai, India.",Diabetes Metab Syndr,32919321,9/13/20,pubmed,0,2,neural network,0.00218329,0.002183239,0.195755597,0.695826682,0.002183317,0.101867874,Epidemiology,0.91983634,TRUE,12.5,0.189436576,0.5,0.087101953,1,0.537564047,,,0.271367526
7025,Can CT performed in the early disease phase predict outcome of patients with COVID 19 pneumonia? Analysis of a cohort of 64 patients from Germany.,Eur J Radiol,32919265,9/13/20,pubmed,0,11,logistic regression,0.020027462,0.001254631,0.175764375,0.001254658,0.001254617,0.800444257,Clinics,0.9403226,TRUE,110.0909091,0.890098336,42.36363636,0.646307198,3,0.667819001,,,0.734741512
7026,Clinical and laboratory features of COVID-19: Predictors of severe prognosis.,Int Immunopharmacol,32919217,9/13/20,pubmed,0,12,logistic regression,0.0012727,0.001272673,0.001272696,0.001272713,0.001272652,0.993636566,Clinics,0.68850607,TRUE,36.33333333,0.500773084,13.91666667,0.410155205,7,0.785110192,,,0.56534616
7027,COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review.,Comput Biol Med,32919186,9/13/20,pubmed,0,43,artificial intelligence,0.046027573,0.001653066,0.453272434,0.131352261,0.032934034,0.334760631,Imaging,0.7956693,TRUE,80.95348837,0.810748964,44.13953488,0.654803318,12,0.850299401,,,0.771950561
7028,A graph-based approach identifies dynamic H-bond communication networks in spike protein S of SARS-CoV-2.,J Struct Biol,32919067,9/13/20,pubmed,0,8,bioinformatic,0.82161428,0.173295021,0.001272665,0.001272678,0.001272687,0.001272669,Drug discovery,0.5577314,TRUE,32,0.455810502,61.25,0.725381322,0,0.403234768,,,0.528142197
7029,Parameters predicting COVID-19-induced myocardial injury and mortality.,Life Sci,32918975,9/13/20,pubmed,0,19,immunome,0.067780242,0.001237186,0.001237066,0.001237088,0.001237063,0.927271355,Clinics,0.18675452,FALSE,93.21052632,0.852000742,94.31578947,0.811279101,9,0.814309525,,,0.825863123
7030,SARS-CoV-2 host tropism: An in silico analysis of the main cellular factors.,Virus Res,32918944,9/13/20,pubmed,0,5,"bioinformatic, in silico",0.937221224,0.056517127,0.001565385,0.001565505,0.001565461,0.001565298,Drug discovery,0.5879319,TRUE,2.2,0.022759602,1.2,0.126103827,1,0.537564047,,,0.228809158
7031,Gastrointestinal Infection is the Risk Factor for COVID-19 Inpatients with Assisted Ventilation.,Liver Transpl,32918858,9/13/20,pubmed,0,10,logistic regression,0.001254717,0.001254638,0.001254743,0.001254687,0.001254665,0.99372655,Clinics,0.94413793,TRUE,22.6,0.334652731,14.3,0.415172598,0,0.403234768,,,0.384353365
7032,Bronchoscopy safety precautions for diagnosing COVID-19 associated pulmonary aspergillosis-A simulation study.,Mycoses,32918497,9/13/20,pubmed,0,3,simulation model,0.196641358,0.00162281,0.178346585,0.376332751,0.245433644,0.001622851,Epidemiology,0.83813715,TRUE,193.6666667,0.970437256,231.3333333,0.929957185,4,0.707574542,,,0.869322994
7033,SCOR: A secure international informatics infrastructure to investigate COVID-19.,J Am Med Inform Assoc,32918447,9/13/20,pubmed,0,34,machine learning,0.002032825,0.00203292,0.50667346,0.485195083,0.002032878,0.002032834,Epidemiology,0.4773622,FALSE,81.88235294,0.814150535,93.88235294,0.810610115,7,0.785110192,,,0.803290281
7034,Mental health and quality of life among healthcare professionals during the COVID-19 pandemic in India.,Brain Behav,32918403,9/13/20,pubmed,0,8,logistic regression,0.00159352,0.001593679,0.001593488,0.001593527,0.897375575,0.096250211,Healthcare,0.9466251,TRUE,58.125,0.69002412,26,0.53819909,7,0.785110192,,,0.671111134
7035,"COVID-19, smoking, vaping and quitting: a representative population survey in England.",Addiction,32918300,9/13/20,pubmed,0,6,bayes,0.001291257,0.001291229,0.001291233,0.001291303,0.993543679,0.001291299,Healthcare,0.53678566,TRUE,143.8333333,0.938153256,188.5,0.911225582,3,0.667819001,,,0.839065946
7036,Prognostic implications of neutrophil-lymphocyte ratio in COVID-19.,Eur J Clin Invest,32918295,9/13/20,pubmed,0,8,logistic regression,0.001272729,0.001272682,0.045834166,0.130348705,0.001272865,0.819998853,Clinics,0.6771665,TRUE,3.25,0.039148989,0.25,0.065493712,2,0.618927094,,,0.241189932
7037,Molecular characterization of ebselen binding activity to SARS-CoV-2 main protease.,Sci Adv,32917717,9/13/20,pubmed,0,5,computational,0.989346475,0.002130856,0.002130646,0.002130672,0.002130707,0.002130643,Drug discovery,0.8246397,TRUE,108.2,0.886263838,20.6,0.487021675,9,0.814309525,,,0.729198346
7038,"Overexpression of the Severe Acute Respiratory Syndrome Coronavirus-2 Receptor, Angiotensin-Converting Enzyme 2, in Diabetic Kidney Disease: Implications for Kidney Injury in Novel Coronavirus Disease 2019.",Can J Diabetes,32917504,9/13/20,pubmed,0,7,sequencing,0.371118619,0.202681284,0.00165306,0.001653038,0.001653025,0.421240974,Clinics,0.9526042,TRUE,162,0.952749088,508.8571429,0.979462135,4,0.707574542,,,0.879928588
7039,Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19.,Emerg Infect Dis,32917290,9/13/20,pubmed,0,21,mathematical model,0.002490403,0.002490424,0.002490441,0.911531653,0.002490535,0.078506543,Epidemiology,0.65619355,TRUE,69.28571429,0.760158328,752.3809524,0.989028633,10,0.828199272,,,0.859128744
7040,Analysis of ACE2 genetic variants in 131 Italian SARS-CoV-2-positive patients.,Hum Genomics,32917283,9/13/20,pubmed,0,14,"sequencing, in silico, exom",0.001171661,0.656821473,0.001171548,0.001171564,0.088415097,0.251248656,Genomics,0.24997026,FALSE,63.28571429,0.723112128,40,0.633395772,11,0.840175319,,,0.73222774
7041,Immune Response to COVID-19: Can We Benefit from the SARS-CoV and MERS-CoV Pandemic Experience?,Pathogens,32916812,9/13/20,pubmed,0,5,sequencing,0.542086906,0.305932075,0.001461881,0.09912061,0.001461925,0.049936603,Drug discovery,0.6427874,TRUE,19.2,0.287339972,2.4,0.174872893,0,0.403234768,,,0.288482544
7042,Anticoagulation and bleeding risk in patients with COVID-19.,Thromb Res,32916565,9/12/20,pubmed,0,13,logistic regression,0.084293889,0.001565383,0.001565435,0.001565392,0.001565327,0.909444574,Clinics,0.9251354,TRUE,159.1538462,0.950646298,243,0.935242173,15,0.874313229,,,0.920067234
7043,Identification of angiotensin-converting enzyme 2 (ACE2) protein as the potential biomarker in SARS-CoV-2 infection-related lung cancer using computational analyses.,Genomics,32916258,9/12/20,pubmed,0,3,computational,0.688320014,0.023212131,0.001237081,0.001237071,0.001237077,0.284756626,Drug discovery,0.7794424,TRUE,3,0.037293586,1,0.122023013,5,0.739490092,,,0.299602231
7044,Estimating the Binding of Sars-CoV-2 Peptides to HLA Class I in Human Subpopulations Using Artificial Neural Networks.,Cell Syst,32916095,9/12/20,pubmed,0,2,neural network,0.731509674,0.001901854,0.119523582,0.092840143,0.001901861,0.052322886,Drug discovery,0.12156296,FALSE,192.5,0.970004329,96,0.81509232,4,0.707574542,,,0.830890397
7045,Intestinal Receptor of SARS-CoV-2 in Inflamed IBD Tissue Seems Downregulated by HNF4A in Ileum and Upregulated by Interferon Regulating Factors in Colon.,J Crohns Colitis,32915959,9/12/20,pubmed,0,10,sequencing,0.454844451,0.183196337,0.001593483,0.001593499,0.001593521,0.357178709,Drug discovery,0.95759064,TRUE,65.2,0.735543324,79.2,0.77642494,8,0.799987654,,,0.770651972
7046,A new advanced in silico drug discovery method for novel coronavirus (SARS-CoV-2) with tensor decomposition-based unsupervised feature extraction.,PLoS One,32915876,9/12/20,pubmed,0,2,in silico,0.853091649,0.001751177,0.13990338,0.00175119,0.001751196,0.001751408,Drug discovery,0.95892954,TRUE,42.5,0.562743522,14.5,0.418450629,10,0.828199272,,,0.603131141
7047,Early estimation of the risk factors for hospitalization and mortality by COVID-19 in Mexico.,PLoS One,32915872,9/12/20,pubmed,0,5,logistic regression,0.001392919,0.001393036,0.001392856,0.001392926,0.001392926,0.993035336,Clinics,0.53764033,TRUE,46.2,0.598181706,24.2,0.522946214,10,0.828199272,,,0.64977573
7048,Measuring Italian citizens' engagement in the first wave of the COVID-19 pandemic containment measures: A cross-sectional study.,PLoS One,32915822,9/12/20,pubmed,0,7,logistic regression,0.00109884,0.001098839,0.001098855,0.265205598,0.655892839,0.075605028,Healthcare,0.98510146,TRUE,67.71428571,0.751685324,26.28571429,0.540272946,1,0.537564047,,,0.609840772
7049,"Prevalence of Perceived Stress, Anxiety, Depression, and Obsessive-Compulsive Symptoms in Health Care Workers and Other Workers in Alberta During the COVID-19 Pandemic: Cross-Sectional Survey.",JMIR Ment Health,32915764,9/12/20,pubmed,0,11,logistic regression,0.00060805,0.000608056,0.000608044,0.000608069,0.996959739,0.000608042,Healthcare,0.98356223,TRUE,43.54545455,0.573628548,25.45454545,0.532445812,1,0.537564047,,,0.547879469
7050,Investigating the Prevalence of Reactive Online Searching in the COVID-19 Pandemic: Infoveillance Study.,J Med Internet Res,32915763,9/12/20,pubmed,0,5,correlation analysis,0.000752904,0.026046416,0.000752911,0.970941852,0.00075295,0.000752967,Epidemiology,0.117542416,FALSE,25.8,0.378316532,36.6,0.614664169,1,0.537564047,,,0.510181583
7051,Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT Classification.,IEEE J Biomed Health Inform,32915751,9/12/20,pubmed,0,3,"machine learning, dataset",0.001187268,0.001187273,0.963191742,0.032059214,0.001187258,0.001187245,Imaging,0.28466308,FALSE,14.66666667,0.221163956,35,0.605298368,11,0.840175319,,,0.555545881
7052,Are open-source approaches the most efficient way forward for COVID-19 drug discovery?,Expert Opin Drug Discov,32915657,9/12/20,pubmed,0,4,dataset,0.374163747,0.030848532,0.00143819,0.590673156,0.001438232,0.001438143,Epidemiology,0.3002373,FALSE,71.75,0.771909209,5,0.257024351,0,0.403234768,,,0.477389443
7053,Moderate or Severe Impairment in Pulmonary Function is Associated with Mortality in Sarcoidosis Patients Infected with SARS‑CoV‑2.,Lung,32915271,9/12/20,pubmed,0,5,logistic regression,0.001717196,0.001717246,0.001717297,0.001717211,0.001717234,0.991413816,Clinics,0.866632,TRUE,101.6,0.872348321,89.4,0.800374632,5,0.739490092,,,0.804071015
7054,Medical Student Training in eHealth: Scoping Review.,JMIR Med Educ,32915154,9/12/20,pubmed,0,4,artificial intelligence,0.000822927,0.000822916,0.476157781,0.298327384,0.223046064,0.000822928,Epidemiology,0.7555295,TRUE,20.25,0.301317336,17.25,0.453104094,1,0.537564047,,,0.430661826
7055,Coronavirus disease 2019 (COVID-19) in domestic animals and wildlife: advances and prospects in the development of animal models for vaccine and therapeutic research.,Hum Vaccin Immunother,32915100,9/12/20,pubmed,0,11,"computational, in silico",0.363857001,0.588283378,0.002238643,0.002238564,0.041143873,0.00223854,Genomics,0.78189474,TRUE,133.5454545,0.925598367,79,0.776023548,5,0.739490092,,,0.813704003
7056,What Are the Odds of Finding a COVID-19 Drug from a Lab Repurposing Screen?,J Chem Inf Model,32914973,9/12/20,pubmed,0,1,virtual screening,0.873446113,0.004109795,0.004109825,0.004109797,0.110114333,0.004110137,Drug discovery,0.61304975,TRUE,1723,0.999938153,26,0.53819909,2,0.618927094,,,0.719021446
7057,"The Prognostic Nutritional Index is associated with mortality of COVID-19 patients in Wuhan, China.",J Clin Lab Anal,32914892,9/12/20,pubmed,0,13,logistic regression,0.06774799,0.001392871,0.001392922,0.057115305,0.001392863,0.870958049,Clinics,0.91341704,TRUE,45.23076923,0.588842847,10.07692308,0.355900455,3,0.667819001,,,0.537520768
7058,Community seroprevalence of COVID-19 in probable and possible cases at primary health care centres in Spain.,Fam Pract,32914857,9/12/20,pubmed,0,13,logistic regression,0.00165304,0.209991188,0.001653069,0.001653134,0.259360323,0.525689246,Clinics,0.7396916,TRUE,36.61538462,0.503989115,50.84615385,0.684506288,1,0.537564047,,,0.57535315
7059,Synthesis of exfoliated multilayer graphene and its putative interactions with SARS-CoV-2 virus investigated through computational studies.,J Biomol Struct Dyn,32914690,9/12/20,pubmed,0,7,computational,0.869301426,0.001272715,0.059533278,0.001272658,0.050126631,0.01849329,Drug discovery,0.8376559,TRUE,48.42857143,0.617416043,10.42857143,0.361118544,1,0.537564047,,,0.505366211
7060,Computational Immune Proteomics Approach to Target COVID-19.,J Proteome Res,32914632,9/12/20,pubmed,0,4,"computational, bioinformatic, in silico, proteom,  omics",0.18929107,0.15685809,0.255133996,0.395347704,0.001684554,0.001684585,Epidemiology,0.5124219,TRUE,139.5,0.93295813,88,0.796895906,4,0.707574542,,,0.812476192
7061,Risk factors for depression and anxiety in healthcare workers deployed during the COVID-19 outbreak in China.,Soc Psychiatry Psychiatr Epidemiol,32914298,9/12/20,pubmed,0,14,logistic regression,0.001511797,0.001511832,0.001511822,0.001511849,0.992440862,0.001511838,Healthcare,0.93762267,TRUE,78.5,0.800482405,44.71428571,0.657813754,9,0.814309525,,,0.757535228
7062,The impact of community containment implementation timing on the spread of COVID-19: A simulation study.,F1000Res,32913638,9/12/20,pubmed,0,2,simulation model,0.001786564,0.001786524,0.001786529,0.945726779,0.001786518,0.047127087,Epidemiology,0.28541982,FALSE,10.5,0.157338116,1.5,0.138747659,0,0.403234768,,,0.233106847
7063,Single-cell transcriptomic atlas of primate cardiopulmonary aging.,Cell Res,32913304,9/12/20,pubmed,0,22,transcriptom,0.768007932,0.001684528,0.001684501,0.001684517,0.001684597,0.225253924,Drug discovery,0.6408624,TRUE,84.54545455,0.823613087,92.68181818,0.807599679,7,0.785110192,,,0.805440986
7064,Cryptic transmission of SARS-CoV-2 in Washington state.,Science,32913002,9/12/20,pubmed,0,87,"sequencing, genomes",0.003607634,0.90726306,0.003607214,0.00360751,0.003607379,0.078307203,Genomics,0.39115304,FALSE,58.96428571,0.695590327,182.6607143,0.907947552,22,0.908142478,,,0.837226786
7065,"Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study.",Nutr Metab Cardiovasc Dis,32912793,9/12/20,pubmed,0,106,machine learning,0.001126855,0.001126892,0.159084607,0.001126914,0.001126883,0.836407849,Clinics,0.88736033,TRUE,93.50943396,0.853237677,71.86792453,0.758763714,29,0.928020248,,,0.84667388
7066,COVID-19 and diabetes; Possible role of polymorphism and rise of telemedicine.,Prim Care Diabetes,32912711,9/12/20,pubmed,0,1,in-silico,0.626348365,0.002080623,0.002080622,0.161004836,0.002080691,0.206404862,Drug discovery,0.82164633,TRUE,3,0.037293586,0,0.055525823,1,0.537564047,,,0.210127819
7067,The landscape of cognitive function in recovered COVID-19 patients.,J Psychiatr Res,32912598,9/12/20,pubmed,0,8,correlation analysis,0.001203471,0.001203497,0.027170556,0.10385047,0.063004617,0.803567389,Clinics,0.97692466,TRUE,25.625,0.375286041,5.375,0.263446615,27,0.92443978,,,0.521057479
7068,Anxiety and public responses to covid-19: Early data from Thailand.,J Psychiatr Res,32912591,9/12/20,pubmed,0,5,logistic regression,0.001291232,0.001291217,0.001291218,0.001291305,0.993543821,0.001291208,Healthcare,0.249405,FALSE,37.2,0.509926402,20.2,0.482204977,5,0.739490092,,,0.577207157
7069,"Descriptive epidemiology of coronavirus disease 2019 in Nigeria, 27 February-6 June 2020.",Epidemiol Infect,32912370,9/12/20,pubmed,0,67,dataset,0.001861708,0.145078859,0.001861833,0.537470407,0.001861833,0.311865361,Epidemiology,0.8654252,TRUE,9.074626866,0.135444369,4.880597015,0.250133797,5,0.739490092,,,0.375022753
7070,Duplex Shiny app quantification of the sepsis biomarkers C-reactive protein and interleukin-6 in a fast quantum dot labeled lateral flow assay.,J Nanobiotechnology,32912236,9/12/20,pubmed,0,6,image processing,0.256405587,0.14779763,0.379713995,0.089745253,0.001098866,0.12523867,Imaging,0.82544315,TRUE,56.33333333,0.677407385,23.83333333,0.519467487,1,0.537564047,,,0.578146306
7071,COVID-19 clinical outcomes and nationality: results from a Nationwide registry in Kuwait.,BMC Public Health,32912230,9/12/20,pubmed,0,8,logistic regression,0.001823377,0.001823424,0.001823405,0.24416893,0.234628104,0.51573276,Clinics,0.49096674,FALSE,15.125,0.228090791,6.5,0.288132192,3,0.667819001,,,0.394680661
7072,Current State of Evidence: Influence of Nutritional and Nutrigenetic Factors on Immunity in the COVID-19 Pandemic Framework.,Nutrients,32911778,9/12/20,pubmed,0,3,"genome-wide, genomes",0.168527823,0.161910489,0.000863076,0.474966565,0.192868969,0.000863079,Epidemiology,0.7017364,TRUE,182.3333333,0.965180283,115,0.844594595,11,0.840175319,,,0.883316732
7073,The Effect of Strict State Measures on the Epidemiologic Curve of COVID-19 Infection in the Context of a Developing Country: A Simulation from Jordan.,Int J Environ Res Public Health,32911738,9/12/20,pubmed,0,7,simulation model,0.00114133,0.001141321,0.001141334,0.869660575,0.001141407,0.125774034,Epidemiology,0.35457355,FALSE,36.42857143,0.501762632,21,0.492239765,8,0.799987654,,,0.597996683
7074,"Comorbidities associated with mortality in 31,461 adults with COVID-19 in the United States: A federated electronic medical record analysis.",PLoS Med,32911500,9/11/20,pubmed,0,5,logistic regression,0.000854706,0.000854723,0.000854722,0.000854747,0.109658676,0.886922426,Clinics,0.9397694,TRUE,191,0.969138475,261.2,0.941932031,34,0.937156615,,,0.94940904
7075,Site mapping and small molecule blind docking reveal a possible target site on the SARS-CoV-2 main protease dimer interface.,Comput Biol Chem,32911432,9/11/20,pubmed,0,7,molecular dynamics simulation,0.970473964,0.001203452,0.001203404,0.024712277,0.001203477,0.001203426,Drug discovery,0.7760917,TRUE,24,0.35574247,8.142857143,0.321447685,2,0.618927094,,,0.432039083
7076,Predictive model with analysis of the initial spread of COVID-19 in India.,Int J Med Inform,32911257,9/11/20,pubmed,0,1,"simulation model, mathematical model, predictive model, prediction model",0.000815331,0.000815348,0.030754302,0.814828031,0.038782381,0.114004606,Epidemiology,0.20187178,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
7077,Deep vein thrombosis in non-critically ill patients with coronavirus disease 2019 pneumonia: deep vein thrombosis in non-intensive care unit patients.,J Vasc Surg Venous Lymphat Disord,32911110,9/11/20,pubmed,0,6,logistic regression,0.001310484,0.104751805,0.071930278,0.001310341,0.001310414,0.819386678,Clinics,0.75907767,TRUE,12.33333333,0.186467932,0.5,0.087101953,0,0.403234768,,,0.225601551
7078,"Factors associated with asymptomatic infection in health-care workers with severe acute respiratory syndrome coronavirus 2 infection in Wuhan, China: a multicentre retrospective cohort study.",Clin Microbiol Infect,32911080,9/11/20,pubmed,0,10,logistic regression,0.001622723,0.235783559,0.051777076,0.001622823,0.509374958,0.199818861,Healthcare,0.7700461,TRUE,60.4,0.705114726,42.5,0.647578271,4,0.707574542,,,0.686755846
7079,Analysis of the predictive factors for a critical illness of COVID-19 during treatment － relationship between serum zinc level and critical illness of COVID-19.,Int J Infect Dis,32911042,9/11/20,pubmed,0,10,logistic regression,0.001901723,0.001901735,0.151962714,0.085006332,0.001901777,0.757325719,Clinics,0.87385046,TRUE,25.8,0.378316532,2.9,0.190794755,6,0.764429903,,,0.44451373
7080,Structural-based virtual screening and in vitro assays for small molecules inhibiting the feline coronavirus 3CL protease as a surrogate platform for coronaviruses.,Antiviral Res,32910955,9/11/20,pubmed,0,6,virtual screening,0.920900657,0.072838001,0.001565343,0.00156533,0.001565374,0.001565295,Drug discovery,0.90038675,TRUE,17.83333333,0.268476715,21,0.492239765,1,0.537564047,,,0.432760175
7081,Comparative Antiviral Efficacy of Viral Protease Inhibitors against the Novel SARS-CoV-2 In Vitro.,Virol Sin,32910347,9/11/20,pubmed,0,25,in silico,0.991734615,0.001653053,0.001653116,0.001653113,0.001653054,0.001653049,Drug discovery,0.6903043,TRUE,91.4,0.84730039,247.56,0.937449826,3,0.667819001,,,0.817523072
7082,0,J Biomol Struct Dyn,32909528,9/11/20,pubmed,0,4,"virtual screening, molecular dynamics simulation",0.98036277,0.003927465,0.003927528,0.00392759,0.003927338,0.00392731,Drug discovery,0.9143694,TRUE,30.5,0.438493413,7.75,0.312215681,14,0.866658436,,,0.53912251
7083,Intermittent demand forecasting for medical consumables with short life cycle using a dynamic neural network during the COVID-19 epidemic.,Health Informatics J,32909495,9/11/20,pubmed,0,1,"neural network, network model, forecasting model",0.001538102,0.001538117,0.433641267,0.56020627,0.001538121,0.001538123,Epidemiology,0.75208163,TRUE,55,0.668686994,25,0.529435376,1,0.537564047,,,0.578562139
7084,Preliminary assessment of patient and physician satisfaction with the use of teleconsultation in urology during the COVID-19 pandemic.,World J Urol,32909174,9/11/20,pubmed,0,9,logistic regression,0.001622695,0.001622712,0.001622782,0.001622801,0.69932045,0.294188561,Healthcare,0.90925765,TRUE,223.2222222,0.979405034,116.1111111,0.846200161,1,0.537564047,,,0.787723081
7085,Swab-Seq: A high-throughput platform for massively scaled up SARS-CoV-2 testing.,medRxiv,32909008,9/11/20,pubmed,0,31,sequencing,0.001203463,0.739237143,0.255949019,0.001203501,0.001203453,0.001203422,Genomics,0.18806604,FALSE,70.06451613,0.764425753,486.7419355,0.977923468,14,0.866658436,,,0.869669219
7086,Cytotoxic lymphocytes are dysregulated in multisystem inflammatory syndrome in children.,medRxiv,32909006,9/11/20,pubmed,0,83,"bayes, transcriptom",0.724108133,0.081003962,0.002238545,0.002238745,0.034455692,0.155954923,Drug discovery,0.43877894,FALSE,41.04819277,0.549137238,,,2,0.618927094,,,0.584032166
7087,SARS-CoV-2-specific IgA and limited inflammatory cytokines are present in the stool of select patients with acute COVID-19.,medRxiv,32909002,9/11/20,pubmed,0,25,microbiom,0.286195461,0.239249277,0.001653023,0.00165301,0.001653053,0.469596175,Clinics,0.8530596,TRUE,46.72,0.602696518,86,0.793015788,10,0.828199272,,,0.741303859
7088,Serological Responses to Human Virome Define Clinical Outcomes of Italian Patients Infected with SARS-CoV-2.,medRxiv,32908997,9/11/20,pubmed,0,12,virom,0.203155744,0.397574874,0.076809249,0.001751242,0.001751291,0.3189576,Genomics,0.32763568,FALSE,92.91666667,0.851196734,120.4166667,0.85188654,3,0.667819001,,,0.790300759
7089,An intestinal cell type in zebrafish is the nexus for the SARS-CoV-2 receptor and the Renin-Angiotensin-Aldosterone System that contributes to COVID-19 comorbidities.,bioRxiv,32908984,9/11/20,pubmed,0,3,transcriptom,0.720888183,0.045102004,0.001237103,0.001237076,0.001237062,0.23029857,Drug discovery,0.2973274,FALSE,136,0.92763931,646.3333333,0.985951298,2,0.618927094,,,0.844172567
7090,"The immune vulnerability landscape of the 2019 Novel Coronavirus, SARS-CoV-2.",bioRxiv,32908981,9/11/20,pubmed,0,17,"bioinformatic, genomes",0.48131827,0.408647567,0.001861727,0.104448916,0.001861777,0.001861744,Drug discovery,0.4696058,FALSE,115.6153846,0.900055662,,,4,0.707574542,,,0.803815102
7091,Induction of a regulatory myeloid program in bacterial sepsis and severe COVID-19.,bioRxiv,32908980,9/11/20,pubmed,0,18,transcriptom,0.525244688,0.001511876,0.001511872,0.001511943,0.001511869,0.468707752,Drug discovery,0.41265577,FALSE,6.888888889,0.097346775,,,2,0.618927094,,,0.358136934
7092,Improvements to the ARTIC multiplex PCR method for SARS-CoV-2 genome sequencing using nanopore.,bioRxiv,32908977,9/11/20,pubmed,0,17,"sequencing, genomic epidemiology, genome sequences, genomes",0.002357746,0.886739283,0.002357908,0.103829498,0.002357806,0.002357759,Genomics,0.19204038,FALSE,40.76470588,0.546725215,97.47058824,0.817634466,32,0.933699611,,,0.766019764
7093,Technology-based Health Solutions for Cancer Caregivers to Better Shoulder the Impact of COVID-19: A Systematic Review Protocol.,Res Sq,32908975,9/11/20,pubmed,0,9,digital health,0.000815352,0.000815339,0.000815364,0.711551305,0.060717568,0.225285073,Epidemiology,0.9283551,TRUE,27.55555556,0.401880141,8.444444444,0.327134065,0,0.403234768,,,0.377416324
7094,In silico dynamics of COVID-19 phenotypes for optimizing clinical management.,Res Sq,32908974,9/11/20,pubmed,0,10,in silico,0.478803291,0.001291281,0.001291253,0.2538385,0.001291228,0.263484448,Drug discovery,0.6924366,TRUE,168.2,0.957016513,464,0.977053786,2,0.618927094,,,0.850999131
7095,"Using multiple data streams to estimate and forecast SARS-CoV-2 transmission dynamics, with application to the virus spread in Orange County, California.",ArXiv,32908946,9/11/20,pubmed,0,16,"bayes, bayesian model",0.00129121,0.001291245,0.001291237,0.993543807,0.001291222,0.001291279,Epidemiology,0.13370523,FALSE,30.125,0.433174593,32.75,0.590647578,4,0.707574542,,,0.577132238
7096,Utility-Based Multicriteria Model for Screening Patients under the COVID-19 Pandemic.,Comput Math Methods Med,32908584,9/11/20,pubmed,0,5,mathematical model,0.001861706,0.001861682,0.503114455,0.285662522,0.001861766,0.20563787,Epidemiology,0.48373333,FALSE,53.8,0.659719216,21.4,0.496052984,2,0.618927094,,,0.591566431
7097,A Model for SARS-CoV-2 Infection with Treatment.,Comput Math Methods Med,32908574,9/11/20,pubmed,0,2,mathematical model,0.757751934,0.091912754,0.002490493,0.142863846,0.002490532,0.002490441,Drug discovery,0.7271942,TRUE,29.5,0.426000371,1.5,0.138747659,6,0.764429903,,,0.443059311
7098,#stayhome to contain Covid-19: Neuro-SIR - Neurodynamical epidemic modeling of infection patterns in social networks.,Expert Syst Appl,32908331,9/11/20,pubmed,0,1,simulation experiment,0.001237113,0.001237114,0.00123708,0.993814558,0.001237067,0.001237068,Epidemiology,0.2518278,FALSE,7,0.10179974,3,0.199424672,1,0.537564047,,,0.279596153
7099,Substantial underestimation of SARS-CoV-2 infection in the United States.,Nat Commun,32908126,9/11/20,pubmed,0,13,"bayes, probabilistic",0.002357733,0.00235778,0.259663889,0.649195983,0.084066764,0.002357851,Epidemiology,0.4164615,FALSE,32.30769231,0.458655452,31,0.578204442,47,0.95493549,,,0.663931795
7100,De novo design of picomolar SARS-CoV-2 miniprotein inhibitors.,Science,32907861,9/11/20,pubmed,0,15,computational,0.966574675,0.001786594,0.001786579,0.001786634,0.001786509,0.026279009,Drug discovery,0.3771535,FALSE,79.73333333,0.805986765,256.1333333,0.940125769,4,0.707574542,,,0.817895692
7101,Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.,BMJ,32907855,9/11/20,pubmed,0,357,dataset,0.000863038,0.000863035,0.112640467,0.000863054,0.000863061,0.883907345,Clinics,0.6582846,TRUE,86.48571429,0.830849156,120.0285714,0.851351351,28,0.926168282,,,0.869456263
7102,Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication.,Disaster Med Public Health Prep,32907685,9/11/20,pubmed,0,3,network analysis,0.001786704,0.001786629,0.001786576,0.99106703,0.001786568,0.001786494,Epidemiology,0.5876112,TRUE,44,0.578390748,35,0.605298368,0,0.403234768,,,0.528974628
7103,Risk Factors of Fatal Outcome in Patients With COVID-19 Pneumonia.,Disaster Med Public Health Prep,32907676,9/11/20,pubmed,0,6,logistic regression,0.00139282,0.001392837,0.306722183,0.001392858,0.001392835,0.687706466,Clinics,0.6895401,TRUE,44.33333333,0.580679077,9.666666667,0.348274017,1,0.537564047,,,0.488839047
7104,The proteomics contribution to the counter-bioterrorism toolbox in the post-COVID-19 era.,Expert Rev Proteomics,32907407,9/11/20,pubmed,0,1,proteom,0.05780383,0.057803573,0.057805376,0.710986571,0.057800206,0.057800445,Epidemiology,0.4923431,FALSE,180,0.964252582,141,0.875702435,1,0.537564047,,,0.792506355
7105,Repurposing of FDA-Approved Toremifene to Treat COVID-19 by Blocking the Spike Glycoprotein and NSP14 of SARS-CoV-2.,J Proteome Res,32907334,9/11/20,pubmed,0,2,molecular dynamics simulation,0.991734755,0.001653093,0.001653024,0.001653086,0.001653016,0.001653025,Drug discovery,0.7185846,TRUE,138.5,0.931288268,408,0.971768799,8,0.799987654,,,0.901014907
7106,Restrictercise! Preferences Regarding Digital Home Training Programs during Confinements Associated with the COVID-19 Pandemic.,Int J Environ Res Public Health,32906788,9/11/20,pubmed,0,23,logistic regression,0.002357746,0.002357823,0.002357771,0.116471236,0.874097621,0.002357802,Healthcare,0.9363404,TRUE,67.91304348,0.753045952,59.52173913,0.718758362,14,0.866658436,,,0.779487583
7107,The New Coronavirus (SARS-CoV-2): A Comprehensive Review on Immunity and the Application of Bioinformatics and Molecular Modeling to the Discovery of Potential Anti-SARS-CoV-2 Agents.,Molecules,32906733,9/11/20,pubmed,0,27,"bioinformatic, sequencing, structural model",0.632441649,0.080484628,0.001653183,0.282114372,0.001653108,0.001653059,Drug discovery,0.9228695,TRUE,13.14814815,0.198280661,1.740740741,0.145905807,0,0.403234768,,,0.249140412
7108,The coding capacity of SARS-CoV-2.,Nature,32906143,9/10/20,pubmed,0,17,computational,0.460696479,0.532812473,0.001622802,0.001622791,0.001622732,0.001622723,Genomics,0.81457996,TRUE,26.52941176,0.389448946,34.29411765,0.600548568,10,0.828199272,,,0.606065595
7109,Emerging algebraic growth trends in SARS-CoV-2 pandemic data.,Phys Biol,32906094,9/10/20,pubmed,0,2,mathematical model,0.001987295,0.001987171,0.001987123,0.990064098,0.00198713,0.001987183,Epidemiology,0.56367403,TRUE,19,0.285793803,5,0.257024351,2,0.618927094,,,0.387248416
7110,Sapphire-Based Clustering.,J Chem Theory Comput,32905698,9/10/20,pubmed,0,3,molecular dynamics simulation,0.258456968,0.001987252,0.198898722,0.536682367,0.001987527,0.001987164,Epidemiology,0.75575703,TRUE,176,0.962644567,203.3333333,0.918785122,1,0.537564047,,,0.806331245
7111,"Susceptibility to COVID-19 in populations with health disparities: Posited involvement of mitochondrial disorder, socioeconomic stress, and pollutants.",J Biochem Mol Toxicol,32905655,9/10/20,pubmed,0,2,metabolom,0.421381175,0.113096344,0.002032794,0.002032913,0.196304346,0.265152427,Drug discovery,0.9265779,TRUE,82.5,0.816129631,85.5,0.791410222,2,0.618927094,,,0.742155649
7112,Customer experiences in the age of artificial intelligence.,Comput Human Behav,32905175,9/10/20,pubmed,0,4,artificial intelligence,0.001565436,0.001565349,0.264354087,0.184706883,0.546242959,0.001565287,Healthcare,0.8926655,TRUE,25.5,0.37435834,19.5,0.475983409,2,0.618927094,,,0.489756281
7113,Survey of prevalence of anxiety and depressive symptoms among 1124 healthcare workers during the coronavirus disease 2019 pandemic across India.,Med J Armed Forces India,32905170,9/10/20,pubmed,0,6,logistic regression,0.001254598,0.001254609,0.001254588,0.001254629,0.993726913,0.001254662,Healthcare,0.95006406,TRUE,11.5,0.17416043,0.5,0.087101953,5,0.739490092,,,0.333584159
7114,Fractional order mathematical modeling of COVID-19 transmission.,Chaos Solitons Fractals,32905156,9/10/20,pubmed,0,6,mathematical model,0.003465909,0.003465918,0.003465959,0.982670393,0.003465881,0.003465939,Epidemiology,0.8792155,TRUE,66.83333333,0.745933577,14.83333333,0.421862457,11,0.840175319,,,0.669323784
7115,COVID-19 pandemic changes the food consumption patterns.,Trends Food Sci Technol,32905099,9/10/20,pubmed,0,5,artificial intelligence,0.046200424,0.001461919,0.20076983,0.345724275,0.404381686,0.001461867,Healthcare,0.93945503,TRUE,46.8,0.603686066,32.6,0.589510302,3,0.667819001,,,0.620338456
7116,"Whatever it takes? The global financial safety net, Covid-19, and developing countries.",World Dev,32905064,9/10/20,pubmed,0,5,dataset,0.002638986,0.002639017,0.002639103,0.98680485,0.002639041,0.002639003,Epidemiology,0.71535707,TRUE,63.8,0.726822933,51.4,0.686914637,3,0.667819001,,,0.69385219
7117,The SARS-CoV-2 host cell receptor ACE2 correlates positively with immunotherapy response and is a potential protective factor for cancer progression.,Comput Struct Biotechnol J,32905022,9/10/20,pubmed,0,4,"computational, dataset",0.524990225,0.028520184,0.001565333,0.001565349,0.001565365,0.441793544,Drug discovery,0.48929623,FALSE,68.25,0.755086895,29.75,0.567835162,0,0.403234768,,,0.575385608
7118,Current approaches used in treating COVID-19 from a molecular mechanisms and immune response perspective.,Saudi Pharm J,32905015,9/10/20,pubmed,0,2,genomic structure,0.523453832,0.150194883,0.001171604,0.163165346,0.160842675,0.00117166,Drug discovery,0.4699822,FALSE,7.5,0.108355495,0,0.055525823,3,0.667819001,,,0.27723344
7119,Classification of Coronavirus (COVID-19) from X-ray and CT images using shrunken features.,Int J Imaging Syst Technol,32904960,9/10/20,pubmed,0,3,"machine learning, dataset",0.001220013,0.001220044,0.890475475,0.104644427,0.001220016,0.001220025,Imaging,0.4745421,FALSE,12,0.183190055,4.666666667,0.246721969,6,0.764429903,,,0.398113976
7120,Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy.,Nonlinear Dyn,32904911,9/10/20,pubmed,0,5,logistic regression,0.002490528,0.002490615,0.002490589,0.987547382,0.002490463,0.002490423,Epidemiology,0.4244361,FALSE,117.8,0.904013854,40.4,0.636138614,1,0.537564047,,,0.692572172
7121,Enhancing India's Health Care during COVID Era: Role of Artificial Intelligence and Algorithms.,Indian J Otolaryngol Head Neck Surg,32904904,9/10/20,pubmed,0,3,artificial intelligence,0.003335329,0.003335339,0.813323621,0.173334876,0.003335482,0.003335352,Epidemiology,0.5114435,TRUE,6.333333333,0.089863319,0,0.055525823,0,0.403234768,,,0.182874636
7122,Mathematical modeling for infectious viral disease: The COVID-19 perspective.,J Public Aff,32904838,9/10/20,pubmed,0,5,mathematical model,0.001901933,0.00190175,0.100798409,0.891594358,0.00190177,0.00190178,Epidemiology,0.8472837,TRUE,15,0.227596017,0,0.055525823,1,0.537564047,,,0.273561962
7123,Temporal Detection and Phylogenetic Assessment of SARS-CoV-2 in Municipal Wastewater.,Cell Rep Med,32904687,9/10/20,pubmed,0,9,sequencing,0.002898277,0.838903839,0.002898269,0.00289851,0.149502471,0.002898634,Genomics,0.26670605,FALSE,8.333333333,0.123693488,10.33333333,0.36038266,16,0.881782826,,,0.455286325
7124,"Awareness of Health Professionals on COVID-19 and Factors Affecting It Before and During Index Case in North Shoa Zone, Ethiopia, 2020.",Infect Drug Resist,32904682,9/10/20,pubmed,0,3,logistic regression,0.001415223,0.001415177,0.035299795,0.174374758,0.786079872,0.001415176,Healthcare,0.45054764,FALSE,9,0.135320675,1.333333333,0.13252609,1,0.537564047,,,0.268470271
7125,"Knowledge, Attitudes, and Practice Regarding COVID-19 among Patients with Musculoskeletal and Rheumatic Diseases in Nepal: A Web-Based Cross-Sectional Study.",Open Access Rheumatol,32904652,9/10/20,pubmed,0,4,logistic regression,0.001291258,0.001291256,0.02667239,0.001291296,0.814728878,0.154724922,Healthcare,0.89683545,TRUE,20.5,0.304966294,1.75,0.148381054,3,0.667819001,,,0.373722116
7126,The Effect and Mechanism of Adverse Childhood Experience on Suicide Ideation in Young Cancer Patients During Coronavirus Disease 2019 (COVID-19) Pandemic.,Risk Manag Healthc Policy,32904621,9/10/20,pubmed,0,4,correlation analysis,0.069329662,0.001098803,0.001098798,0.00109883,0.57851593,0.348857977,Healthcare,0.98884094,TRUE,25.75,0.377698064,17,0.451097137,0,0.403234768,,,0.410676656
7127,Mass Spectrometry Analysis of Newly Emerging Coronavirus HCoV-19 Spike Protein and Human ACE2 Reveals Camouflaging Glycans and Unique Post-Translational Modifications.,Engineering (Beijing),32904601,9/10/20,pubmed,0,9,structural model,0.987547535,0.002490533,0.002490481,0.002490512,0.002490479,0.00249046,Drug discovery,0.47749028,FALSE,31.33333333,0.448388892,26.11111111,0.538466685,8,0.799987654,,,0.59561441
7128,"The impact of COVID-19 lockdown on the air quality of Eastern Province, Saudi Arabia.",Air Qual Atmos Health,32904471,9/10/20,pubmed,0,2,dataset,0.00194362,0.001943613,0.001943512,0.702317654,0.18203027,0.109821333,Epidemiology,0.9317284,TRUE,17,0.257467994,4.5,0.242708055,6,0.764429903,,,0.421535317
7129,Is it safe to lift COVID-19 travel bans? The Newfoundland story.,Comput Mech,32904431,9/10/20,pubmed,0,4,machine learning,0.002490481,0.002490529,0.002490708,0.987547286,0.002490533,0.002490463,Epidemiology,0.3125881,FALSE,166,0.955594038,171,0.900120417,1,0.537564047,,,0.797759501
7130,The effect of oral and nasal breathing on the deposition of inhaled particles in upper and tracheobronchial airways.,J Aerosol Sci,32904428,9/10/20,pubmed,0,13,computational,0.222880365,0.001291297,0.059813543,0.713432245,0.00129129,0.00129126,Epidemiology,0.65243316,TRUE,62.30769231,0.716927454,11.61538462,0.379582553,1,0.537564047,,,0.544691351
7131,A data-driven understanding of COVID-19 dynamics using sequential genetic algorithm based probabilistic cellular automata.,Appl Soft Comput,32904415,9/10/20,pubmed,0,2,probabilistic,0.094975078,0.048964249,0.001330142,0.852070445,0.00133006,0.001330026,Epidemiology,0.3801746,FALSE,25,0.369286907,3.5,0.213607172,7,0.785110192,,,0.456001424
7132,BioAider: An efficient tool for viral genome analysis and its application in tracing SARS-CoV-2 transmission.,Sustain Cities Soc,32904401,9/10/20,pubmed,0,5,"sequencing, genome sequences",0.137725818,0.765591172,0.001350346,0.092631954,0.001350373,0.001350336,Genomics,0.56896746,TRUE,21.8,0.322283382,30.2,0.571113192,6,0.764429903,,,0.552608826
7133,Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm.,Soft comput,32904395,9/10/20,pubmed,0,7,"deep learning, neural network, dataset",0.001987326,0.001987238,0.905793302,0.086257783,0.001987248,0.001987104,Imaging,0.5583339,TRUE,173.7142857,0.961036551,41.57142857,0.642293283,11,0.840175319,,,0.814501718
7134,Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning.,Mater Today Proc,32904353,9/10/20,pubmed,0,6,machine learning,0.002032785,0.002032758,0.002032969,0.989835918,0.002032768,0.002032802,Epidemiology,0.34348,FALSE,24.33333333,0.359700662,13.33333333,0.403866738,4,0.707574542,,,0.490380647
7135,"Community's Knowledge of COVID-19 and Its Associated Factors in Mizan-Aman Town, Southwest Ethiopia, 2020.",Int J Gen Med,32903877,9/10/20,pubmed,0,7,logistic regression,0.00103459,0.039331548,0.022878953,0.001034635,0.866353491,0.069366783,Healthcare,0.57528114,TRUE,13.42857143,0.202795473,13.85714286,0.40941932,1,0.537564047,,,0.383259613
7136,"Clinical Characteristics and Risk Factors for Disease Severity and Death in Patients With Coronavirus Disease 2019 in Wuhan, China.",Front Med (Lausanne),32903644,9/10/20,pubmed,0,7,sequencing,0.001291324,0.056488787,0.001291205,0.001291231,0.001291245,0.938346209,Clinics,0.8825955,TRUE,52.71428571,0.651679139,,,9,0.814309525,,,0.732994332
7137,A Simple Bayesian Method for Evaluating Whether Data From Patients With Rheumatic Diseases Who Have Been Under Chronic Hydroxychloroquine Medication Since Before the COVID-19 Outbreak Can Speak to Hydroxychloroquine's Prophylactic Effect Against Infection With SARS-CoV-2.,Front Med (Lausanne),32903552,9/10/20,pubmed,0,1,bayes,0.001943521,0.001943507,0.220147463,0.160175942,0.547224724,0.068564844,Healthcare,0.46934837,FALSE,23,0.34225988,14,0.412898047,0,0.403234768,,,0.386130898
7138,Localization of Cell Receptor-Related Genes of SARS-CoV-2 in the Kidney through Single-Cell Transcriptome Analysis.,Kidney Dis (Basel),32903321,9/10/20,pubmed,0,6,"transcriptom, dataset",0.911870009,0.01830006,0.000926304,0.000926279,0.000926303,0.067051046,Drug discovery,0.94492817,TRUE,22.16666667,0.327107428,15.83333333,0.434238694,2,0.618927094,,,0.460091072
7139,"COVID-19 pandemic in resource-poor countries: challenges, experiences and opportunities in Ghana.",J Infect Dev Ctries,32903226,9/10/20,pubmed,0,4,genomes,0.043702317,0.063416464,0.001565317,0.610785865,0.196611009,0.083919028,Epidemiology,0.45102555,FALSE,122,0.909889294,59.75,0.719761841,9,0.814309525,,,0.814653554
7140,Heterogeneous groups of alveolar type II cells in lung homeostasis and repair.,Am J Physiol Cell Physiol,32903031,9/10/20,pubmed,0,2,sequencing,0.608095333,0.124334143,0.001901751,0.1638308,0.022432602,0.079405372,Drug discovery,0.6596314,TRUE,42.5,0.562743522,42.5,0.647578271,1,0.537564047,,,0.582628613
7141,Rhinology on the move despite Covid-19!,Rhinology,32902518,9/10/20,pubmed,0,1,proteom,0.085939014,0.0032143,0.003214383,0.47094873,0.299420694,0.137262879,Epidemiology,0.8251263,TRUE,5,0.070752675,5,0.257024351,0,0.403234768,,,0.243670598
7142,Predicting the recombination potential of severe acute respiratory syndrome coronavirus 2 and Middle East respiratory syndrome coronavirus.,J Gen Virol,32902372,9/10/20,pubmed,0,10,computational,0.273183291,0.686933074,0.002130688,0.002130695,0.002130664,0.033491587,Genomics,0.35846356,FALSE,49.5,0.628362917,110.5,0.839309607,4,0.707574542,,,0.725082355
7143,Recent biotechnological tools for diagnosis of corona virus disease: A review.,Biotechnol Prog,32902193,9/10/20,pubmed,0,3,sequencing,0.267848035,0.338685161,0.389648633,0.001272726,0.001272715,0.001272731,Imaging,0.77175915,TRUE,25.66666667,0.376461129,19.33333333,0.473842655,1,0.537564047,,,0.46262261
7144,Factors associated with the use and reuse of face masks among Brazilian individuals during the COVID-19 pandemic.,Rev Lat Am Enfermagem,32901772,9/10/20,pubmed,0,10,logistic regression,0.002032745,0.002032772,0.002032797,0.177264359,0.814604559,0.002032768,Healthcare,0.97399855,TRUE,51.3,0.640484878,12.5,0.392761573,0,0.403234768,,,0.478827073
7145,"Ribonucleocapsid assembly/packaging signals in the genomes of the coronaviruses SARS-CoV and SARS-CoV-2: detection, comparison and implications for therapeutic targeting.",J Biomol Struct Dyn,32901577,9/10/20,pubmed,0,2,genomes,0.317737577,0.62490989,0.001786825,0.051992304,0.001786738,0.001786666,Genomics,0.69610566,TRUE,61.5,0.712350795,12.5,0.392761573,2,0.618927094,,,0.574679821
7146,In search of a vaccine against COVID-19: implications for nursing practice.,Br J Nurs,32901559,9/10/20,pubmed,0,1,sequencing,0.131582192,0.17479716,0.002996824,0.506667406,0.133828169,0.050128249,Epidemiology,0.7543961,TRUE,11,0.167171748,1,0.122023013,0,0.403234768,,,0.230809843
7147,Evaluation of COVID-19 Surveillance Strategy in Ecuador.,Disaster Med Public Health Prep,32900417,9/10/20,pubmed,0,3,bayes,0.002357702,0.00235789,0.042615388,0.805917723,0.002357927,0.144393371,Epidemiology,0.10737297,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
7148,Assessing coronavirus disease 2019 (COVID-19) transmission to healthcare personnel: The global ACT-HCP case-control study.,Infect Control Hosp Epidemiol,32900402,9/10/20,pubmed,0,13,logistic regression,0.001392849,0.001392888,0.001392913,0.001392921,0.821293264,0.173135165,Healthcare,0.56701034,TRUE,87.30769231,0.834498114,51.38461538,0.686713942,5,0.739490092,,,0.753567382
7149,Modeling of COVID-19 Outbreak Indicators in China Between January and June.,Disaster Med Public Health Prep,32900401,9/10/20,pubmed,0,3,bayes,0.002080609,0.002080593,0.002080739,0.945234588,0.002080583,0.046442888,Epidemiology,0.3503276,FALSE,108,0.886078298,22.33333333,0.505753278,1,0.537564047,,,0.643131874
7150,COVID-19 in Somalia: Adherence to Preventive Measures and Evolution of the Disease Burden.,Pathogens,32899931,9/10/20,pubmed,0,8,logistic regression,0.00146188,0.001461893,0.001461871,0.001461939,0.992690496,0.001461922,Healthcare,0.45591468,FALSE,49,0.624281032,51.875,0.688921595,6,0.764429903,,,0.692544177
7151,Anosmia and Ageusia as Predictive Signs of COVID-19 in Healthcare Workers in Italy: A Prospective Case-Control Study.,J Clin Med,32899778,9/10/20,pubmed,0,14,logistic regression,0.001751152,0.001751189,0.493732582,0.001751185,0.499262592,0.0017513,Healthcare,0.7560824,TRUE,94.57142857,0.855340466,43.14285714,0.649919722,2,0.618927094,,,0.708062427
7152,Challenges of Testing COVID-19 Cases in Bangladesh.,Int J Environ Res Public Health,32899619,9/10/20,pubmed,0,3,dataset,0.002183251,0.10031482,0.002183386,0.890951586,0.002183469,0.002183488,Epidemiology,0.19868776,FALSE,17,0.257467994,,,2,0.618927094,,,0.438197544
7153,Students' Acceptance of the COVID-19 Impact on Shifting Higher Education to Distance Learning in Poland.,Int J Environ Res Public Health,32899478,9/10/20,pubmed,0,2,dataset,0.001330056,0.001330051,0.204475553,0.29337112,0.498163206,0.001330013,Healthcare,0.1776464,FALSE,26.5,0.389325252,2,0.164302917,6,0.764429903,,,0.439352691
7154,Genetic Hypothesis and Pharmacogenetics Side of Renin-Angiotensin-System in COVID-19.,Genes (Basel),32899439,9/10/20,pubmed,0,2,genome-wide,0.12068282,0.486522116,0.001310378,0.001310411,0.001310409,0.388863867,Genomics,0.61523,TRUE,69,0.759416167,44.5,0.656743377,5,0.739490092,,,0.718549879
7155,Analyzing the effects of social distancing on the COVID-19 pandemic in Korea using mathematical modeling.,Epidemiol Health,32898942,9/9/20,pubmed,0,2,mathematical model,0.001684466,0.001684503,0.001684547,0.938425937,0.001684587,0.054835959,Epidemiology,0.3432998,FALSE,70.5,0.766033768,49,0.677481937,1,0.537564047,,,0.660359918
7156,Viral disease spreading in grouped population.,Comput Methods Programs Biomed,32898813,9/9/20,pubmed,0,1,"computational, probabilistic",0.03860606,0.001220041,0.001220048,0.956513831,0.001220014,0.001220005,Epidemiology,0.24629802,FALSE,49,0.624281032,3,0.199424672,0,0.403234768,,,0.408980157
7157,"Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective.",Prog Retin Eye Res,32898686,9/9/20,pubmed,0,17,artificial intelligence,0.001415133,0.025884242,0.156456751,0.765449058,0.049379712,0.001415105,Epidemiology,0.9612225,TRUE,79.11764706,0.802894428,82.52941176,0.784519668,7,0.785110192,,,0.790841429
7158,Surface electrocardiographic characteristics in coronavirus disease 2019: repolarization abnormalities associated with cardiac involvement.,ESC Heart Fail,32898341,9/9/20,pubmed,0,12,logistic regression,0.0477703,0.001220062,0.001220007,0.001220064,0.001220016,0.947349551,Clinics,0.95428157,TRUE,80.75,0.809759416,39.33333333,0.629649451,3,0.667819001,,,0.70240929
7159,"In vivo antiviral host transcriptional response to SARS-CoV-2 by viral load, sex, and age.",PLoS Biol,32898168,9/9/20,pubmed,0,15,sequencing,0.551806424,0.215606303,0.000871526,0.000871551,0.000871559,0.229972636,Drug discovery,0.5775268,TRUE,47.53333333,0.610736595,49.6,0.679890286,39,0.945737391,,,0.745454757
7160,Social Listening as a Rapid Approach to Collecting and Analyzing COVID-19 Symptoms and Disease Natural Histories Reported by Large Numbers of Individuals.,Popul Health Manag,32897820,9/9/20,pubmed,0,10,machine learning,0.001371327,0.001371278,0.130291156,0.406535985,0.408275848,0.052154407,Healthcare,0.5222368,TRUE,5,0.070752675,4.8,0.249331014,2,0.618927094,,,0.313003594
7161,[Interval between entry of mainland China and diagnosis in imported COVID-19 cases and factors contributing to delayed diagnosis in Guangdong Province].,Nan Fang Yi Ke Da Xue Xue Bao,32897209,9/9/20,pubmed,0,2,logistic regression,0.091785178,0.123139478,0.078938669,0.487876325,0.001461957,0.216798393,Epidemiology,0.7373174,TRUE,81,0.811552972,14,0.412898047,0,0.403234768,,,0.542561929
7162,"Molecular docking, validation, dynamics simulations, and pharmacokinetic prediction of natural compounds against the SARS-CoV-2 main-protease.",J Biomol Struct Dyn,32897178,9/9/20,pubmed,0,6,molecular dynamics simulation,0.993448381,0.001310329,0.001310328,0.001310324,0.001310322,0.001310316,Drug discovery,0.96671456,TRUE,1.333333333,0.01366813,,,11,0.840175319,,,0.426921725
7163,Drug similarity and structure-based screening of medicinal compounds to target macrodomain-I from SARS-CoV-2 to rescue the host immune system: a molecular dynamics study.,J Biomol Struct Dyn,32897173,9/9/20,pubmed,0,11,"virtual screening, molecular dynamics simulation, computational",0.992924102,0.001415186,0.001415211,0.001415199,0.001415169,0.001415133,Drug discovery,0.67792726,TRUE,28.63636364,0.413569176,4.363636364,0.238025154,1,0.537564047,,,0.396386126
7164,0,J Biomol Struct Dyn,32897138,9/9/20,pubmed,0,2,molecular dynamics simulation,0.99389988,0.001220057,0.00122001,0.001220008,0.001220003,0.001220043,Drug discovery,0.91010445,TRUE,122.5,0.910755149,128,0.861386139,5,0.739490092,,,0.83721046
7165,METATRYP v 2.0: Metaproteomic Least Common Ancestor Analysis for Taxonomic Inference Using Specialized Sequence Assemblies-Standalone Software and Web Servers for Marine Microorganisms and Coronaviruses.,J Proteome Res,32897080,9/9/20,pubmed,0,8,"sequencing, transcriptom, proteom, metagenom, genomes, metatranscriptom",0.452470164,0.365038864,0.177437282,0.001684588,0.001684547,0.001684555,Drug discovery,0.8583657,TRUE,37.25,0.51035933,82.375,0.783716885,1,0.537564047,,,0.610546754
7166,COVID-19 patients in a tertiary US hospital: Assessment of clinical course and predictors of the disease severity.,Respir Med,32896798,9/9/20,pubmed,0,11,logistic regression,0.00137123,0.001371235,0.001371223,0.001371228,0.001371259,0.993143824,Clinics,0.95788217,TRUE,39.63636364,0.535159874,18.63636364,0.466082419,6,0.764429903,,,0.588557399
7167,Impacts of transportation and meteorological factors on the transmission of COVID-19.,Int J Hyg Environ Health,32896785,9/9/20,pubmed,0,21,logistic regression,0.002422373,0.002422282,0.002422265,0.733187322,0.002422418,0.257123339,Epidemiology,0.9422897,TRUE,25.95238095,0.37930608,,,5,0.739490092,,,0.559398086
7168,0,J Biomol Struct Dyn,32896226,9/9/20,pubmed,0,5,"molecular dynamics simulation, in-silico",0.927829635,0.001098842,0.001098878,0.06777489,0.001098896,0.00109886,Drug discovery,0.9693718,TRUE,31,0.445111015,6.4,0.286058336,4,0.707574542,,,0.479581297
7169,"Epidemiology and factors associated with diarrhoea among children under five years of age in the Engela District in the Ohangwena Region, Namibia.",Afr J Prim Health Care Fam Med,32896151,9/9/20,pubmed,0,3,logistic regression,0.001823357,0.001823462,0.001823318,0.260427301,0.732278995,0.001823566,Healthcare,0.84747547,TRUE,7,0.10179974,0.666666667,0.096200161,0,0.403234768,,,0.200411556
7170,Genomic characterization of SARS-CoV-2 identified in a reemerging COVID-19 outbreak in Beijing's Xinfadi market in 2020.,Biosaf Health,32895643,9/9/20,pubmed,0,14,"genome sequences, genomes",0.001310403,0.993447981,0.001310368,0.001310414,0.001310348,0.001310486,Genomics,0.22519904,FALSE,76.85714286,0.793431876,361.8571429,0.967487289,4,0.707574542,,,0.822831236
7171,"Dataset of knowledge, attitude, practices and psychological implications of healthcare workers in Pakistan during COVID-19 pandemic.",Data Brief,32895632,9/9/20,pubmed,0,6,dataset,0.001861799,0.001861784,0.152023126,0.001861828,0.840529787,0.001861676,Healthcare,0.9035193,TRUE,30.66666667,0.440225122,8,0.320511105,2,0.618927094,,,0.459887774
7172,Predicting Health Care Workers' Tolerance of Personal Protective Equipment: An Observational Simulation Study.,Clin Simul Nurs,32895609,9/9/20,pubmed,0,7,predictive model,0.164013414,0.001565388,0.044275761,0.149718494,0.570647098,0.069779846,Healthcare,0.9742026,TRUE,27.85714286,0.405281712,2.714285714,0.186111854,0,0.403234768,,,0.331542778
7173,COVID-19 and stock market volatility: An industry level analysis.,Financ Res Lett,32895607,9/9/20,pubmed,0,3,machine learning,0.003760524,0.003760725,0.321387033,0.663570405,0.003760686,0.003760627,Epidemiology,0.60910296,TRUE,22.66666667,0.335580432,1,0.122023013,11,0.840175319,,,0.432592921
7174,Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier.,Biocybern Biomed Eng,32895587,9/9/20,pubmed,0,2,"bayes, neural network, classifier, dataset",0.001330017,0.001330029,0.99334984,0.001330039,0.001330043,0.001330032,Imaging,0.59041715,TRUE,57,0.68204589,31.5,0.581883864,7,0.785110192,,,0.683013315
7175,Quantitative proteomic analysis of the tizoxanide effect in vero cells.,Sci Rep,32895447,9/9/20,pubmed,0,7,proteom,0.954098599,0.038127224,0.001943507,0.001943652,0.001943515,0.001943503,Drug discovery,0.6600006,TRUE,28.71428571,0.41462057,42.28571429,0.645905807,1,0.537564047,,,0.532696808
7176,Developing a COVID-19 mortality risk prediction model when individual-level data are not available.,Nat Commun,32895375,9/9/20,pubmed,0,15,prediction model,0.002898313,0.002898301,0.290842322,0.002898553,0.002898579,0.697563933,Clinics,0.36666536,FALSE,40.26666667,0.541777475,68.53333333,0.749197217,13,0.858880178,,,0.71661829
7177,"Disease Prevention Knowledge, Anxiety, and Professional Identity during COVID-19 Pandemic in Nursing Students in Zhengzhou, China.",J Korean Acad Nurs,32895340,9/9/20,pubmed,0,6,logistic regression,0.001987089,0.001987155,0.001987076,0.001987144,0.990064389,0.001987146,Healthcare,0.9389609,TRUE,7.166666667,0.102851135,0,0.055525823,2,0.618927094,,,0.259101351
7178,A nomogram for predicting mortality in patients with COVID-19 and solid tumors: a multicenter retrospective cohort study.,J Immunother Cancer,32895296,9/9/20,pubmed,0,13,logistic regression,0.001350341,0.001350318,0.024286129,0.00135037,0.001350321,0.970312522,Clinics,0.7908356,TRUE,60.30769231,0.704681799,72.07692308,0.759499599,2,0.618927094,,,0.694369497
7179,[A systematic pharmacological investigation of pharmacologically active ingredients in Toujie Quwen granules for treatment of COVID-19].,Nan Fang Yi Ke Da Xue Xue Bao,32895172,9/9/20,pubmed,0,6,"virtual screening, literature mining",0.994945266,0.001010942,0.001010947,0.001010961,0.001010938,0.001010946,Drug discovery,0.9879569,TRUE,162.5,0.953058321,91.5,0.805124431,1,0.537564047,,,0.765248933
7180,[Clinical features of severe or critical ill patients with COVID-19].,Nan Fang Yi Ke Da Xue Xue Bao,32895171,9/9/20,pubmed,0,8,logistic regression,0.001751339,0.001751211,0.001751246,0.001751147,0.001751166,0.991243891,Clinics,0.9863453,TRUE,52.75,0.651802833,16.75,0.446012845,0,0.403234768,,,0.500350148
7181,Racial and ethnic differences in self-reported telehealth use during the COVID-19 pandemic: a secondary analysis of a US survey of internet users from late March.,J Am Med Inform Assoc,32894772,9/8/20,pubmed,0,2,logistic regression,0.001438122,0.001438132,0.026446371,0.00143817,0.924326135,0.04491307,Healthcare,0.8490346,TRUE,48.5,0.618158204,80.5,0.780572652,4,0.707574542,,,0.702101799
7182,Three critical clinicobiological phases of the human SARS-associated coronavirus infections.,Eur Rev Med Pharmacol Sci,32894568,9/8/20,pubmed,0,4,sequencing,0.27560278,0.305127558,0.001171616,0.185554118,0.001171635,0.231372293,Genomics,0.97372943,TRUE,113.25,0.896468551,70.25,0.754147712,4,0.707574542,,,0.786063602
7183,Baseline chest X-ray in coronavirus disease 19 (COVID-19) patients: association with clinical and laboratory data.,Radiol Med,32894449,9/8/20,pubmed,0,20,logistic regression,0.001098816,0.001098843,0.364489895,0.001098819,0.001098828,0.631114799,Clinics,0.95802206,TRUE,41.3,0.55099264,13.8,0.408884132,4,0.707574542,,,0.555817105
7184,Health equity considerations in COVID-19: geospatial network analysis of the COVID-19 outbreak in the migrant population in Singapore.,J Travel Med,32894286,9/8/20,pubmed,0,6,network analysis,0.001203448,0.001203445,0.001203453,0.490188068,0.504998184,0.001203403,Healthcare,0.81323665,TRUE,7.333333333,0.105572392,1.833333333,0.151056998,10,0.828199272,,,0.361609554
7185,Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset.,Elife,32894217,9/8/20,pubmed,0,8,mathematical model,0.001987121,0.571865082,0.00198718,0.223567467,0.062408493,0.138184657,Genomics,0.22105706,FALSE,45.375,0.589894242,262.375,0.942868611,16,0.881782826,,,0.80484856
7186,In Silico Drug Repurposing for SARS-CoV-2 Main Proteinase and Spike Proteins.,J Proteome Res,32893632,9/8/20,pubmed,0,2,"virtual screening, in silico",0.98392894,0.003214232,0.00321416,0.003214363,0.003214197,0.003214108,Drug discovery,0.6693891,TRUE,43,0.567629414,12.5,0.392761573,6,0.764429903,,,0.574940297
7187,[Investigation of modulating effect of Qingfei Paidu Decoction on host metabolism and gut microbiome in rats].,Zhongguo Zhong Yao Za Zhi,32893565,9/8/20,pubmed,0,11,"sequencing, metabolom, microbiom",0.519186829,0.386540044,0.00235771,0.002357945,0.002357792,0.08719968,Drug discovery,0.9713086,TRUE,71.18181818,0.769620879,27.09090909,0.546360717,1,0.537564047,,,0.617848548
7188,Estimating the Effectiveness of Non-Pharmaceutical Interventions on COVID-19 Control in Korea.,J Korean Med Sci,32893522,9/8/20,pubmed,0,5,simulation model,0.001371268,0.001371316,0.001371349,0.866661818,0.127852948,0.0013713,Epidemiology,0.48070288,FALSE,32.4,0.45983054,19,0.471367407,4,0.707574542,,,0.546257496
7189,Predictive criteria of severe cases in COVID-19 patients of early stage: A retrospective observational study.,J Clin Lab Anal,32893398,9/8/20,pubmed,0,5,logistic regression,0.001653065,0.001653095,0.029776317,0.001653057,0.054840939,0.910423527,Clinics,0.9519087,TRUE,6.8,0.09629538,2.4,0.174872893,1,0.537564047,,,0.26957744
7190,Child Maltreatment during the COVID-19 Pandemic: Consequences of Parental Job Loss on Psychological and Physical Abuse Towards Children.,Child Abuse Negl,32893003,9/8/20,pubmed,0,3,logistic regression,0.001565394,0.001565295,0.001565304,0.001565324,0.992173352,0.00156533,Healthcare,0.99477756,TRUE,16.33333333,0.247015895,5.666666667,0.270203372,20,0.900117291,,,0.472445519
7191,"Obesity, COVID-19 and innate immunometabolism.",Br J Nutr,32892755,9/8/20,pubmed,0,3,immunome,0.74549982,0.003101623,0.003101511,0.003101579,0.003101548,0.242093919,Drug discovery,0.6952447,TRUE,137,0.929371019,244.6666667,0.936111854,2,0.618927094,,,0.828136656
7192,"Characteristics and outcomes of patients with COVID-19 admitted to ICU in a tertiary hospital in Stockholm, Sweden.",Acta Anaesthesiol Scand,32892337,9/7/20,pubmed,0,12,logistic regression,0.001350312,0.001350317,0.001350314,0.001350348,0.00135034,0.993248369,Clinics,0.93893594,TRUE,42.91666667,0.566392479,45.58333333,0.661091785,8,0.799987654,,,0.675823973
7193,The potential impact of enhanced hygienic measures during the COVID-19 outbreak on hospital-acquired infections: A pragmatic study in neurological units.,J Neurol Sci,32892033,9/7/20,pubmed,0,12,logistic regression,0.001901726,0.001901745,0.001901743,0.001901808,0.175900127,0.81649285,Clinics,0.62074506,TRUE,15.33333333,0.230997588,5.333333333,0.262911426,2,0.618927094,,,0.37094537
7194,Improved binding of SARS-CoV-2 Envelope protein to tight junction-associated PALS1 could play a key role in COVID-19 pathogenesis.,Microbes Infect,32891874,9/7/20,pubmed,0,11,"computational, in silico, genomes",0.762670145,0.230838948,0.001622716,0.001622758,0.00162274,0.001622693,Drug discovery,0.436509,FALSE,150.5454545,0.944647164,90.54545455,0.802047097,12,0.850299401,,,0.865664554
7195,"Mathematical models for devising the optimal SARS-CoV-2 strategy for eradication in China, South Korea, and Italy.",J Transl Med,32891155,9/7/20,pubmed,0,11,mathematical model,0.001823358,0.00182335,0.001823457,0.99088304,0.001823376,0.00182342,Epidemiology,0.8094289,TRUE,34.18181818,0.47881749,36.09090909,0.611586834,2,0.618927094,,,0.56977714
7196,"The mental health of neurological doctors and nurses in Hunan Province, China during the initial stages of the COVID-19 outbreak.",BMC Psychiatry,32891124,9/7/20,pubmed,0,7,logistic regression,0.001010912,0.001010923,0.001010925,0.001010928,0.994945361,0.001010951,Healthcare,0.99276185,TRUE,110.1428571,0.890160183,58.42857143,0.715279636,7,0.785110192,,,0.796850004
7197,Quantitative phylogenomic evidence reveals a spatially structured SARS-CoV-2 diversity.,Virology,32890979,9/6/20,pubmed,0,2,"phylogenom, genomes",0.003214199,0.890448743,0.003214214,0.09669429,0.003214394,0.00321416,Genomics,0.6266247,TRUE,56.5,0.678891706,32,0.585763982,4,0.707574542,,,0.657410077
7198,Position paper on COVID-19 imaging and AI: From the clinical needs and technological challenges to initial AI solutions at the lab and national level towards a new era for AI in healthcare.,Med Image Anal,32890777,9/6/20,pubmed,0,5,predictive model,0.002238466,0.002238516,0.528703349,0.462342482,0.002238518,0.002238669,Epidemiology,0.37825787,FALSE,232.2,0.981322283,333.4,0.962938186,4,0.707574542,,,0.883945004
7199,Managing gestational diabetes mellitus using a smartphone application with artificial intelligence (SineDie) during the COVID-19 pandemic: Much more than just telemedicine.,Diabetes Res Clin Pract,32890548,9/6/20,pubmed,0,6,artificial intelligence,0.005697463,0.005697492,0.323905319,0.005697827,0.401383412,0.257618488,Healthcare,0.8565755,TRUE,35.33333333,0.490259138,16.83333333,0.446815628,4,0.707574542,,,0.548216436
7200,Scholarly Publishing in the Wake of COVID-19.,Int J Radiat Oncol Biol Phys,32890542,9/6/20,pubmed,0,2,artificial intelligence,0.002183279,0.002183276,0.00218345,0.989083285,0.002183392,0.002183318,Epidemiology,0.068087906,FALSE,114.5,0.8983858,84.5,0.789202569,1,0.537564047,,,0.741717472
7201,Commercial Transport During a Pandemic: Network Analysis to Reconcile COVID-19 Diffusion and Vital Supply Chain Resilience.,J Occup Environ Med,32890226,9/6/20,pubmed,0,4,network analysis,0.019530722,0.019530556,0.591369098,0.330509842,0.019529752,0.019530029,Epidemiology,0.64567316,TRUE,40.5,0.544746119,60.5,0.722772277,0,0.403234768,,,0.556917721
7202,Predictive Computed Tomography and Clinical Features for Diagnosis of COVID-19 Pneumonia: Compared With Common Viral Pneumonia.,J Comput Assist Tomogr,32889972,9/6/20,pubmed,0,4,logistic regression,0.002639043,0.002639148,0.675525894,0.002639033,0.002639,0.313917881,Imaging,0.87203765,TRUE,14.25,0.215535902,5.75,0.271742039,0,0.403234768,,,0.29683757
7203,Drug repurposing approach to fight COVID-19.,Pharmacol Rep,32889701,9/6/20,pubmed,0,6,"computational, in-silico",0.902747659,0.019976028,0.001310346,0.073345302,0.001310344,0.001310322,Drug discovery,0.7071463,TRUE,76,0.790586926,37.83333333,0.620952636,34,0.937156615,,,0.782898725
7204,Assessing oligonucleotide designs from early lab developed PCR diagnostic tests for SARS-CoV-2 using the PCR_strainer pipeline.,J Clin Virol,32889496,9/6/20,pubmed,0,3,"bioinformatic, genome sequences, genomes",0.001171561,0.914552727,0.001171597,0.080760985,0.001171586,0.001171544,Genomics,0.41864595,FALSE,23.33333333,0.345723298,13.66666667,0.407412363,2,0.618927094,,,0.457354252
7205,Machine learning-based mortality rate prediction using optimized hyper-parameter.,Comput Methods Programs Biomed,32889405,9/6/20,pubmed,0,3,"machine learning, prediction model",0.002238484,0.002238524,0.242589539,0.452741312,0.002238477,0.297953664,Epidemiology,0.18233305,FALSE,6.333333333,0.089863319,0.333333333,0.073187048,0,0.403234768,,,0.188761712
7206,Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques.,Infect Genet Evol,32889094,9/6/20,pubmed,0,5,"genome-wide, genomes, sequence alignment",0.001943565,0.751218869,0.107426176,0.13552423,0.00194359,0.00194357,Genomics,0.65569544,TRUE,16.4,0.247572515,3.4,0.208589778,4,0.707574542,,,0.387912278
7207,Current state of vaccine development and targeted therapies for COVID-19: impact of basic science discoveries.,Cardiovasc Pathol,32889088,9/6/20,pubmed,0,1,sequencing,0.69588135,0.224155917,0.001511825,0.07542721,0.001511889,0.001511809,Drug discovery,0.511052,TRUE,278,0.988682046,431,0.97357506,2,0.618927094,,,0.860394733
7208,Conserved HLA binding peptides from five non-structural proteins of SARS-CoV-2-An in silico glance.,Hum Immunol,32888767,9/6/20,pubmed,0,1,"bioinformatic, in silico",0.989835944,0.00203289,0.002032765,0.00203279,0.002032879,0.002032733,Drug discovery,0.4556239,FALSE,3,0.037293586,0,0.055525823,1,0.537564047,,,0.210127819
7209,The Epistemology of a Positive SARS-CoV-2 Test.,Acta Biotheor,32888175,9/6/20,pubmed,0,2,bayes,0.001538142,0.183071784,0.115072723,0.412391631,0.088747491,0.199178228,Epidemiology,0.32095292,FALSE,77,0.794668811,55.5,0.704575863,1,0.537564047,,,0.67893624
7210,An epidemiological modelling approach for COVID-19 via data assimilation.,Eur J Epidemiol,32888169,9/6/20,pubmed,0,5,model fit,0.001987185,0.026714302,0.001987194,0.965337097,0.001987108,0.001987113,Epidemiology,0.12296659,FALSE,64.6,0.73238914,8.2,0.322785657,8,0.799987654,,,0.618387483
7211,Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions.,Bull Math Biol,32888118,9/6/20,pubmed,0,2,mathematical model,0.00168452,0.001684523,0.001684552,0.991577269,0.00168452,0.001684615,Epidemiology,0.2126362,FALSE,19.5,0.29117447,3,0.199424672,10,0.828199272,,,0.439599471
7212,GC usage of SARS-CoV-2 genes might adapt to the environment of human lung expressed genes.,Mol Genet Genomics,32888056,9/6/20,pubmed,0,7,genomes,0.656144707,0.338102631,0.001438136,0.001438232,0.001438136,0.001438157,Drug discovery,0.59401757,TRUE,124.2857143,0.913290865,88.71428571,0.798702168,4,0.707574542,,,0.806522525
7213,An Early Pandemic Analysis of SARS-CoV-2 Population Structure and Dynamics in Arizona.,mBio,32887735,9/6/20,pubmed,0,23,genomes,0.000977454,0.542839026,0.000977435,0.453251097,0.00097748,0.000977509,Genomics,0.090866655,FALSE,64.56521739,0.732079906,488.3478261,0.977990367,7,0.785110192,,,0.831726822
7214,A living WHO guideline on drugs for covid-19.,BMJ,32887691,9/6/20,pubmed,0,58,network analysis,0.104134993,0.00081534,0.00081534,0.507624084,0.143950733,0.242659511,Epidemiology,0.5538135,TRUE,105.5384615,0.880079164,118.9230769,0.850214075,83,0.974381135,,,0.901558125
7215,Prevalence and Socio-Demographic Predictors of Food Insecurity in Australia during the COVID-19 Pandemic.,Nutrients,32887422,9/6/20,pubmed,0,7,logistic regression,0.001371253,0.001371239,0.001371292,0.001371284,0.993143673,0.001371259,Healthcare,0.9464156,TRUE,24.14285714,0.356608325,4.857142857,0.249933101,3,0.667819001,,,0.424786809
7216,Rationale for the Use of Radiation-Activated Mesenchymal Stromal/Stem Cells in Acute Respiratory Distress Syndrome.,Cells,32887260,9/6/20,pubmed,0,5,proteom,0.61406648,0.001943541,0.00194355,0.001943606,0.001943601,0.378159223,Drug discovery,0.17646399,FALSE,39,0.530521368,20.4,0.484613326,3,0.667819001,,,0.560984565
7217,Site-specific characterization of SARS-CoV-2 spike glycoprotein receptor-binding domain.,Glycobiology,32886791,9/5/20,pubmed,0,9,"proteom, glycoproteom",0.986399136,0.002720254,0.002720152,0.002720217,0.002720107,0.002720135,Drug discovery,0.4348096,FALSE,87.44444444,0.834992888,,,6,0.764429903,,,0.799711395
7218,Modeling the spread of COVID-19 in Germany: Early assessment and possible scenarios.,PLoS One,32886696,9/5/20,pubmed,0,7,mathematical model,0.002183213,0.002183284,0.002183288,0.793050086,0.002183241,0.198216887,Epidemiology,0.23412251,FALSE,41.42857143,0.552229575,34.71428571,0.603224512,15,0.874313229,,,0.676589105
7219,"Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence.",Environ Sci Pollut Res Int,32886309,9/5/20,pubmed,0,2,machine learning,0.00299649,0.002996559,0.002996799,0.926082782,0.002996468,0.061930903,Epidemiology,0.48984796,FALSE,73.5,0.780011132,33,0.593256623,15,0.874313229,,,0.749193662
7220,Access to intensive care in 14 European countries: a spatial analysis of intensive care need and capacity in the light of COVID-19.,Intensive Care Med,32886208,9/5/20,pubmed,0,7,correlation analysis,0.001059344,0.001059359,0.036088913,0.728701605,0.00105939,0.23203139,Epidemiology,0.8986161,TRUE,105.2857143,0.87958439,112.7142857,0.842119347,15,0.874313229,,,0.865338989
7221,Depression and Psychological-Behavioral Responses Among the General Public in China During the Early Stages of the COVID-19 Pandemic: Survey Study.,J Med Internet Res,32886066,9/5/20,pubmed,0,7,logistic regression,0.001291212,0.00129121,0.001291239,0.001291257,0.947666826,0.047168255,Healthcare,0.98228604,TRUE,13,0.197352959,3.571428571,0.21414236,2,0.618927094,,,0.343474138
7222,The Answer Lies in the Energy: How Simple Atomistic Molecular Dynamics Simulations May Hold the Key to Epitope Prediction on the Fully Glycosylated SARS-CoV-2 Spike Protein.,J Phys Chem Lett,32885971,9/5/20,pubmed,0,9,molecular dynamics simulation,0.785929757,0.205115767,0.002238587,0.002238679,0.002238663,0.002238547,Drug discovery,0.24763551,FALSE,24.77777778,0.365019482,14.55555556,0.418785122,1,0.537564047,,,0.440456217
7223,The keys to control a COVID-19 outbreak in a haemodialysis unit.,Clin Kidney J,32885797,9/5/20,pubmed,0,12,logistic regression,0.001141341,0.257436448,0.001141404,0.00114139,0.212550381,0.526589036,Clinics,0.4205565,FALSE,29.25,0.421671099,14.33333333,0.415975381,12,0.850299401,,,0.562648627
7224,0,J Biomol Struct Dyn,32885740,9/5/20,pubmed,0,8,in silico,0.994638876,0.001072202,0.001072215,0.001072342,0.001072184,0.001072181,Drug discovery,0.93911874,TRUE,13.75,0.208299833,9.75,0.349678887,10,0.828199272,,,0.46205933
7225,COVID-19 and ocular implications: an update.,J Ophthalmic Inflamm Infect,32885277,9/5/20,pubmed,0,3,correlation analysis,0.099493756,0.001462006,0.001461928,0.409464507,0.227020062,0.26109774,Epidemiology,0.94013166,TRUE,2.666666667,0.029377203,0,0.055525823,1,0.537564047,,,0.207489024
7226,Online dashboard and data analysis approach for assessing COVID-19 case and death data.,F1000Res,32884676,9/5/20,pubmed,0,2,mathematical model,0.003465915,0.003465947,0.003466105,0.982670013,0.003466072,0.003465949,Epidemiology,0.32268757,FALSE,41,0.549013544,10.5,0.363459995,2,0.618927094,,,0.510466878
7227,Nonadherence to Treatment and Patient-Reported Outcomes of Psoriasis During the COVID-19 Epidemic: A Web-Based Survey.,Patient Prefer Adherence,32884243,9/5/20,pubmed,0,8,logistic regression,0.001511833,0.001511818,0.001511793,0.00151184,0.8185882,0.175364516,Healthcare,0.99664307,TRUE,49.375,0.626631208,5.75,0.271742039,2,0.618927094,,,0.50576678
7228,Mutations in SARS-CoV-2 Leading to Antigenic Variations in Spike Protein: A Challenge in Vaccine Development.,J Lab Physicians,32884216,9/5/20,pubmed,0,5,"sequencing, whole genome, genomes, sequence alignment",0.205645194,0.771459494,0.019791466,0.001034608,0.001034604,0.001034633,Genomics,0.64208287,TRUE,26.6,0.390191106,6,0.280037463,3,0.667819001,,,0.446015857
7229,Predicted peptide patterns from the SARS-CoV-2 proteome for MS-MS based diagnosis.,Bioinformation,32884213,9/5/20,pubmed,0,2,"proteom, dataset",0.210525224,0.779512458,0.002490731,0.002490589,0.002490473,0.002490525,Genomics,0.46643326,FALSE,20,0.298163152,13.5,0.405539203,0,0.403234768,,,0.368979041
7230,Genomic evolution of severe acute respiratory syndrome Coronavirus 2 in India and vaccine impact.,Indian J Med Microbiol,32883935,9/5/20,pubmed,0,5,genome sequences,0.00229674,0.961190966,0.002296556,0.00229663,0.002296557,0.02962255,Genomics,0.33546856,FALSE,65.8,0.739068588,11.2,0.373026492,1,0.537564047,,,0.549886376
7231,"Gender and occupation predict Coronavirus Disease 2019 knowledge, attitude and practices of a cohort of a South Indian state population.",Indian J Med Microbiol,32883926,9/5/20,pubmed,0,3,logistic regression,0.001511881,0.001511808,0.001511848,0.001511882,0.992440702,0.001511879,Healthcare,0.9609225,TRUE,9,0.135320675,0.666666667,0.096200161,1,0.537564047,,,0.256361628
7232,Strategies to reduce severe diabetic foot infections and complications during epidemics (STRIDE).,J Diabetes Complications,32883566,9/5/20,pubmed,0,5,data mining,0.001291293,0.001291235,0.001291357,0.2019508,0.35209293,0.442082384,Clinics,0.9925431,TRUE,52.2,0.648339415,58.8,0.716416912,0,0.403234768,,,0.589330365
7233,A protein interaction map identifies existing drugs targeting SARS-CoV-2.,BMC Pharmacol Toxicol,32883368,9/5/20,pubmed,0,3,"bioinformatic, in silico",0.974255472,0.001371259,0.001371312,0.00137128,0.001371262,0.020259415,Drug discovery,0.7570568,TRUE,23,0.34225988,23.33333333,0.515386674,5,0.739490092,,,0.532378882
7234,An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City.,BMC Med,32883276,9/5/20,pubmed,0,2,bayes,0.00122002,0.001220073,0.001220026,0.367023091,0.628096717,0.001220073,Healthcare,0.655221,TRUE,8,0.118683901,0,0.055525823,1,0.537564047,,,0.237257924
7235,Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2.,Viruses,32883050,9/5/20,pubmed,0,28,artificial intelligence,0.001291347,0.301512307,0.620731025,0.073882831,0.001291257,0.001291233,Imaging,0.8827431,TRUE,39.25,0.532253077,32.39285714,0.588573722,4,0.707574542,,,0.609467114
7236,Potential of Ocular Transmission of SARS-CoV-2: A Review.,Vision (Basel),32883010,9/5/20,pubmed,0,7,dataset,0.814490233,0.002183333,0.002183252,0.176776552,0.0021833,0.002183329,Drug discovery,0.6702522,TRUE,66.28571429,0.742470159,78.14285714,0.773748997,4,0.707574542,,,0.741264566
7237,Computational and Transcriptome Analyses Revealed Preferential Induction of Chemotaxis and Lipid Synthesis by SARS-CoV-2.,Biology (Basel),32882823,9/5/20,pubmed,0,2,"computational, bioinformatic, transcriptom",0.734864719,0.259189448,0.001486447,0.001486478,0.001486471,0.001486437,Drug discovery,0.53125054,TRUE,9.5,0.143051518,0,0.055525823,2,0.618927094,,,0.272501478
7238,Characteristics of Newborns Born to SARS-CoV-2-Positive Mothers: A Retrospective Cohort Study.,Am J Perinatol,32882743,9/4/20,pubmed,0,4,logistic regression,0.001330024,0.001330151,0.001330042,0.096652411,0.361802429,0.537554942,Clinics,0.9702061,TRUE,5.75,0.080957388,0.5,0.087101953,5,0.739490092,,,0.302516478
7239,"Prevalence of anxiety and depression symptoms, and association with epidemic-related factors during the epidemic period of COVID-19 among 123,768 workers in China: A large cross-sectional study.",J Affect Disord,32882506,9/4/20,pubmed,0,14,logistic regression,0.001156224,0.001156292,0.001156242,0.001156287,0.978579763,0.016795193,Healthcare,0.9929018,TRUE,28.92857143,0.416537819,11.64285714,0.379783249,4,0.707574542,,,0.501298537
7240,Machine learning reveals that prolonged exposure to air pollution is associated with SARS-CoV-2 mortality and infectivity in Italy.,Environ Pollut,32882464,9/4/20,pubmed,0,5,"machine learning, artificial intelligence",0.001653095,0.161731929,0.06897976,0.717769043,0.001653131,0.048213041,Epidemiology,0.6479092,TRUE,27.4,0.400395819,11.8,0.38252609,3,0.667819001,,,0.483580304
7241,New technologies and Amyotrophic Lateral Sclerosis - Which step forward rushed by the COVID-19 pandemic?,J Neurol Sci,32882437,9/4/20,pubmed,0,3,artificial intelligence,0.001622871,0.001622734,0.196901631,0.483362491,0.17974859,0.136741683,Epidemiology,0.86174214,TRUE,156.3333333,0.948852743,275,0.947016323,6,0.764429903,,,0.886766323
7242,Genome based evolutionary lineage of SARS-CoV-2 towards the development of novel chimeric vaccine.,Infect Genet Evol,32882432,9/4/20,pubmed,0,9,"whole genome, sequence alignment",0.806722268,0.189139268,0.001034618,0.00103467,0.001034605,0.001034571,Drug discovery,0.76854813,TRUE,13.77777778,0.208423527,14,0.412898047,3,0.667819001,,,0.429713525
7243,Reassessing the operative threshold for abdominal aortic aneurysm repair in the context of COVID-19.,J Vasc Surg,32882347,9/4/20,pubmed,0,5,probabilistic,0.001098843,0.035052025,0.00109887,0.40099646,0.001098853,0.560654949,Clinics,0.71767616,TRUE,7.2,0.103716989,1.2,0.126103827,1,0.537564047,,,0.255794954
7244,"Angiotensin-converting enzymes (ACE, ACE2) gene variants and COVID-19 outcome.",Gene,32882331,9/4/20,pubmed,0,17,"sequencing, logistic regression",0.22586081,0.130479604,0.001486399,0.00148641,0.001486467,0.63920031,Clinics,0.6753404,TRUE,58.41176471,0.692374296,56.29411765,0.707920792,16,0.881782826,,,0.760692638
7245,Job Tension and Emotional Sensitivity to COVID-19 Public Messaging and Risk Perception.,Popul Health Manag,32882155,9/4/20,pubmed,0,4,correlation analysis,0.001653033,0.001653105,0.001653101,0.523049726,0.470337956,0.001653079,Epidemiology,0.35799676,FALSE,22.25,0.328344363,20.75,0.488827937,1,0.537564047,,,0.451578782
7246,Kidney function on admission predicts in-hospital mortality in COVID-19.,PLoS One,32881976,9/4/20,pubmed,0,13,logistic regression,0.001291206,0.001291213,0.001291201,0.001291224,0.001291245,0.993543912,Clinics,0.704563,TRUE,45.15384615,0.587915146,13.23076923,0.402461868,1,0.537564047,,,0.509313687
7247,A cross-sectional study of psychological wellbeing of Indian adults during the Covid-19 lockdown: Different strokes for different folks.,PLoS One,32881946,9/4/20,pubmed,0,2,logistic regression,0.001126788,0.001126844,0.001126828,0.095000377,0.833126161,0.068493002,Healthcare,0.9362006,TRUE,26.5,0.389325252,48,0.673735617,3,0.667819001,,,0.576959957
7248,Comparative genome analysis of novel coronavirus (SARS-CoV-2) from different geographical locations and the effect of mutations on major target proteins: An in silico insight.,PLoS One,32881907,9/4/20,pubmed,0,7,"in silico, genome sequences, genomes",0.420942819,0.574549986,0.001126795,0.001126839,0.001126781,0.00112678,Genomics,0.93726575,TRUE,17.42857143,0.261982807,3.285714286,0.203973776,17,0.887338725,,,0.451098436
7249,"Risk factors associated with mortality in hospitalized patients with SARS-CoV-2 infection. A prospective, longitudinal, unicenter study in Reus, Spain.",PLoS One,32881860,9/4/20,pubmed,0,11,logistic regression,0.001823354,0.001823536,0.001823412,0.138650274,0.001823428,0.854055996,Clinics,0.81877923,TRUE,81.63636364,0.81309914,52.81818182,0.692400321,1,0.537564047,,,0.68102117
7250,Renin-angiotensin system (RAS) and immune system profile in specific subgroups with COVID-19.,Curr Med Chem,32881654,9/4/20,pubmed,0,6,"proteom, metabolom",0.453209367,0.001461949,0.001461969,0.001461924,0.085035832,0.457368959,Clinics,0.8816012,TRUE,25.33333333,0.37188447,22.83333333,0.510235483,1,0.537564047,,,0.473228
7251,Modulation of endothelial organelle size as an antithrombotic strategy.,J Thromb Haemost,32881285,9/4/20,pubmed,0,6,image analysis,0.750080305,0.00156538,0.134508923,0.001565409,0.001565331,0.110714652,Drug discovery,0.8558449,TRUE,64.33333333,0.730781124,91.33333333,0.804656141,1,0.537564047,,,0.691000438
7252,Molecular dynamics study with mutation shows that N-terminal domain structural re-orientation in Niemann-Pick type C1 is required for proper alignment of cholesterol transport.,J Neurochem,32880929,9/4/20,pubmed,0,4,molecular dynamics simulation,0.661378676,0.308852102,0.001254616,0.001254654,0.026005265,0.001254687,Drug discovery,0.8509662,TRUE,46.75,0.603314985,41.75,0.643296762,0,0.403234768,,,0.549948838
7253,Transcriptomic analyses suggest that mucopolysaccharidosis patients may be less susceptible to COVID-19.,FEBS Lett,32880920,9/4/20,pubmed,0,3,transcriptom,0.810582396,0.002996598,0.002996583,0.089865188,0.002996566,0.090562669,Drug discovery,0.82285243,TRUE,157.6666667,0.949780444,77.66666667,0.772544822,0,0.403234768,,,0.708520011
7254,Loneliness and COVID-19 preventive behaviours among Japanese adults.,J Public Health (Oxf),32880635,9/4/20,pubmed,0,3,logistic regression,0.00208058,0.002080587,0.002080547,0.002080648,0.989597067,0.002080571,Healthcare,0.8082291,TRUE,103.6666667,0.876863133,63.33333333,0.733409152,3,0.667819001,,,0.759363762
7255,Current Challenges of Digital Health Interventions in Pakistan: Mixed Methods Analysis.,J Med Internet Res,32880584,9/4/20,pubmed,0,18,"machine learning, artificial intelligence, digital health",0.049746476,0.000956333,0.162804784,0.747376569,0.03815951,0.000956327,Epidemiology,0.99249727,TRUE,7.5,0.108355495,2.111111111,0.165038801,2,0.618927094,,,0.297440464
7256,Mass Spectrometry Techniques in Emerging Pathogens Studies: COVID-19 Perspectives.,J Am Soc Mass Spectrom,32880453,9/4/20,pubmed,0,2,"proteom, metabolom, lipidom, glycomics",0.119202623,0.432965303,0.444691505,0.001046875,0.001046848,0.001046845,Genomics,0.7144903,TRUE,154.5,0.947492115,153,0.88587102,4,0.707574542,,,0.846979225
7257,Perils of Precipitate Publication: Fraudulent and Substandard COVID-19 Research.,J Law Med,32880397,9/4/20,pubmed,0,1,dataset,0.070464793,0.004110077,0.004109965,0.913095497,0.00410992,0.004109749,Epidemiology,0.30136412,FALSE,47,0.606407323,3,0.199424672,0,0.403234768,,,0.403022254
7258,COVID-19: The role of artificial intelligence in empowering the healthcare sector and enhancing social distancing measures during a pandemic.,S Afr Med J,32880332,9/4/20,pubmed,0,1,artificial intelligence,0.001538088,0.001538103,0.00153814,0.625586719,0.349991697,0.019807253,Epidemiology,0.43117863,FALSE,12,0.183190055,4,0.231469093,1,0.537564047,,,0.317407732
7259,"Therapeutics for COVID-19: from computation to practices-where we are, where we are heading to.",Mol Divers,32880078,9/4/20,pubmed,0,5,computational,0.691218099,0.001653077,0.001653154,0.231314148,0.001653217,0.072508305,Drug discovery,0.90150136,TRUE,0,0.006432061,,,9,0.814309525,,,0.410370793
7260,Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network.,Eur Radiol,32879987,9/4/20,pubmed,0,14,"deep learning, neural network",0.014389186,0.000710608,0.733272894,0.000710606,0.000710605,0.250206101,Imaging,0.62982357,TRUE,0,0.006432061,,,8,0.799987654,,,0.403209857
7261,"The challenge of COVID-19 low disease prevalence for artificial intelligence models: report of 1,610 patients.",Quant Imaging Med Surg,32879867,9/4/20,pubmed,0,7,artificial intelligence,0.015998428,0.015998704,0.728949619,0.015998663,0.207052539,0.016002047,Healthcare,0.58037156,TRUE,0,0.006432061,,,1,0.537564047,,,0.271998054
7262,Mutation density changes in SARS-CoV-2 are related to the pandemic stage but to a lesser extent in the dominant strain with mutations in spike and RdRp.,PeerJ,32879797,9/4/20,pubmed,0,4,genomes,0.001717212,0.910980995,0.001717191,0.082150142,0.001717232,0.001717227,Genomics,0.4280113,FALSE,18,0.271569052,1,0.122023013,0,0.403234768,,,0.265608944
7263,Epidemic features of coronavirus disease 2019 in Henan Province.,Zhong Nan Da Xue Xue Bao Yi Xue Ban,32879111,9/4/20,pubmed,0,5,correlation analysis,0.000936063,0.000936089,0.000936082,0.533303477,0.091854634,0.372033656,Epidemiology,0.59599656,TRUE,35.2,0.488898509,5.6,0.267995718,0,0.403234768,,,0.386709665
7264,Clinical characteristics and the risk factors for severe events of elderly coronavirus disease 2019 patients.,Zhong Nan Da Xue Xue Bao Yi Xue Ban,32879104,9/4/20,pubmed,0,9,logistic regression,0.000699341,0.000699337,0.000699333,0.000699343,0.00069936,0.996503287,Clinics,0.9834256,TRUE,97.22222222,0.862329148,45.44444444,0.6603559,0,0.403234768,,,0.641973272
7265,Predictive role of clinical features in patients with coronavirus disease 2019 for severe disease.,Zhong Nan Da Xue Xue Bao Yi Xue Ban,32879103,9/4/20,pubmed,0,10,logistic regression,0.00056175,0.000561781,0.000561753,0.084602029,0.000561748,0.91315094,Clinics,0.9667415,TRUE,69.9,0.763065125,40.3,0.635202034,0,0.403234768,,,0.600500642
7266,Mapping genome variation of SARS-CoV-2 worldwide highlights the impact of COVID-19 super-spreaders.,Genome Res,32878977,9/4/20,pubmed,0,5,genomes,0.001684503,0.912296921,0.001684576,0.080964898,0.00168457,0.001684532,Genomics,0.3998037,FALSE,70,0.764178366,67.4,0.746521274,24,0.914439163,,,0.808379601
7267,SARS-CoV-2/COVID-19 and advances in developing potential therapeutics and vaccines to counter this emerging pandemic.,Ann Clin Microbiol Antimicrob,32878641,9/4/20,pubmed,0,15,genomes,0.279013268,0.404472783,0.001415201,0.226967938,0.001415191,0.086715619,Genomics,0.8313551,TRUE,166.6666667,0.956088812,92.33333333,0.806529302,20,0.900117291,,,0.887578468
7268,Impact of lockdown on Covid-19 case fatality rate and viral mutations spread in 7 countries in Europe and North America.,J Transl Med,32878627,9/4/20,pubmed,0,9,genomes,0.000815347,0.422354755,0.000815341,0.499415254,0.000815351,0.075783952,Epidemiology,0.5307819,TRUE,0,0.006432061,,,25,0.918019631,,,0.462225846
7269,Risk factors of non-adherence to guidelines for the prevention of COVID-19 among young adults with asthma in a region with a high risk of a COVID-19 outbreak.,J Asthma,32878518,9/4/20,pubmed,0,6,logistic regression,0.001861739,0.001861702,0.001861764,0.366803507,0.553655275,0.073956013,Healthcare,0.9403435,TRUE,11.5,0.17416043,4.166666667,0.233074659,0,0.403234768,,,0.270156619
7270,Neurological complications in a predominantly African American sample of COVID-19 predict worse outcomes during hospitalization.,Clin Neurol Neurosurg,32877769,9/3/20,pubmed,0,7,logistic regression,0.000722225,0.000722183,0.000722182,0.000722201,0.082759087,0.914352123,Clinics,0.9605019,TRUE,8.714285714,0.128764921,,,8,0.799987654,,,0.464376287
7271,"Investigating the Effects of Meteorological Parameters on COVID-19: Case Study of New Jersey, United States.",Environ Res,32877703,9/3/20,pubmed,0,5,correlation analysis,0.001823357,0.001823368,0.001823371,0.99088293,0.001823482,0.001823493,Epidemiology,0.5679214,TRUE,20.4,0.302863504,5.8,0.272678619,11,0.840175319,,,0.471905814
7272,Growth Factor Receptor Signaling Inhibition Prevents SARS-CoV-2 Replication.,Mol Cell,32877642,9/3/20,pubmed,0,6,"proteom, phosphoproteom",0.991577093,0.001684683,0.001684484,0.001684617,0.001684538,0.001684587,Drug discovery,0.58965516,TRUE,85.66666667,0.827509432,97,0.816697886,4,0.707574542,,,0.783927286
7273,Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation.,J Med Internet Res,32877350,9/3/20,pubmed,0,4,"deep learning, neural network, mathematical model",0.00118734,0.001187385,0.239592029,0.755658608,0.001187336,0.001187302,Epidemiology,0.7135972,TRUE,0,0.006432061,,,2,0.618927094,,,0.312679578
7274,A public-private partnership for the express development of antiviral leads: a perspective view.,Expert Opin Drug Discov,32877233,9/3/20,pubmed,0,1,"machine learning, computational, artificial intelligence, in-silico",0.173832216,0.236163915,0.279741234,0.252074572,0.056649814,0.001538248,Epidemiology,0.7689607,TRUE,7,0.10179974,2,0.164302917,0,0.403234768,,,0.223112475
7275,Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.,JAMA,32876697,9/3/20,pubmed,0,1064,bayes,0.171563238,0.000599814,0.0005998,0.11529538,0.000599836,0.711341932,Clinics,0.97773397,TRUE,225.4285714,0.979961655,195.75,0.915440193,110,0.980739552,,,0.9587138
7276,Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings.,Diagn Interv Radiol,32876569,9/3/20,pubmed,0,11,deep learning,0.001010923,0.001010944,0.512319571,0.001010959,0.001010942,0.483636661,Imaging,0.8610103,TRUE,26.36363636,0.386294762,9.818181818,0.35094996,4,0.707574542,,,0.481606421
7277,Long-term forecasts of the COVID-19 epidemic: a dangerous idea.,Rev Soc Bras Med Trop,32876321,9/3/20,pubmed,0,3,"mathematical model, predictive model",0.00310145,0.003101537,0.003101508,0.984492461,0.003101463,0.00310158,Epidemiology,0.5181897,TRUE,76.66666667,0.792689715,15.33333333,0.428351619,1,0.537564047,,,0.586201794
7278,Estimated conditions to control the covid-19 pandemic in peruvian pre- and post-quarantine scenarios.,Rev Peru Med Exp Salud Publica,32876206,9/3/20,pubmed,0,5,simulation model,0.001622719,0.001622759,0.001622723,0.802501032,0.001622795,0.191007972,Epidemiology,0.6594672,TRUE,31,0.445111015,4.8,0.249331014,0,0.403234768,,,0.365892266
7279,0,J Biomol Struct Dyn,32875950,9/3/20,pubmed,0,7,computational,0.991577261,0.001684594,0.001684527,0.001684582,0.001684549,0.001684486,Drug discovery,0.84696746,TRUE,29,0.41993939,15.71428571,0.432833824,4,0.707574542,,,0.520115919
7280,Online laboratory exercise on computational biology: Phylogenetic analyses and protein modeling based on SARS-CoV-2 data during COVID-19 remote instruction.,Biochem Mol Biol Educ,32875662,9/3/20,pubmed,0,2,"computational, bioinformatic",0.382411335,0.00453092,0.472213034,0.004530798,0.131782758,0.004531155,Drug discovery,0.6561969,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
7281,Association of COVID-19 with skin diseases and relevant biologics: a cross-sectional study using nationwide claim data in South Korea.,Br J Dermatol,32875557,9/3/20,pubmed,0,3,dataset,0.002080607,0.002080664,0.149684234,0.002080711,0.002080655,0.84199313,Clinics,0.96834135,TRUE,5,0.070752675,0.333333333,0.073187048,2,0.618927094,,,0.254288939
7282,Capacity-need gap in hospital resources for varying mitigation and containment strategies in India in the face of COVID-19 pandemic.,Infect Dis Model,32875175,9/3/20,pubmed,0,5,mathematical model,0.000977432,0.000977438,0.099580287,0.743241602,0.000977469,0.154245773,Epidemiology,0.7406164,TRUE,3,0.037293586,0,0.055525823,2,0.618927094,,,0.237248835
7283,0,Gene Rep,32875166,9/3/20,pubmed,0,1,"virtual screening, in silico",0.994218484,0.001156257,0.001156288,0.001156346,0.001156298,0.001156327,Drug discovery,0.88330686,TRUE,29,0.41993939,13,0.400521809,11,0.840175319,,,0.553545506
7284,Prediction of daily COVID-19 cases in European countries using automatic ARIMA model.,J Public Health Res,32874964,9/3/20,pubmed,0,2,predictive model,0.002562546,0.026712485,0.045743316,0.919856328,0.002562654,0.002562671,Epidemiology,0.2884695,FALSE,16,0.243552477,5,0.257024351,0,0.403234768,,,0.301270532
7285,COVID-19 pandemic in Finland - Preliminary analysis on health system response and economic consequences.,Health Policy Technol,32874860,9/3/20,pubmed,0,8,digital health,0.001751232,0.001751193,0.001751286,0.863480171,0.129514843,0.001751276,Epidemiology,0.6108979,TRUE,77.625,0.797080834,37.375,0.618477388,5,0.739490092,,,0.718349438
7286,The first months of the COVID-19 pandemic in Spain.,Health Policy Technol,32874852,9/3/20,pubmed,0,4,dataset,0.001684496,0.001684536,0.001684512,0.869810991,0.123450939,0.001684526,Epidemiology,0.3341518,FALSE,30.5,0.438493413,9.75,0.349678887,4,0.707574542,,,0.498582281
7287,Discovery of human coronaviruses pan-papain-like protease inhibitors using computational approaches.,J Pharm Anal,32874702,9/3/20,pubmed,0,6,"virtual screening, computational",0.99450584,0.001098861,0.001098798,0.001098883,0.001098814,0.001098804,Drug discovery,0.95751655,TRUE,25.33333333,0.37188447,11.33333333,0.375501739,6,0.764429903,,,0.503938704
7288,Dynamics of COVID-19 mathematical model with stochastic perturbation.,Adv Differ Equ,32874186,9/3/20,pubmed,0,4,mathematical model,0.004775282,0.004775247,0.004775316,0.976123871,0.004775169,0.004775116,Epidemiology,0.5415908,TRUE,34.75,0.484507391,2.25,0.170925876,12,0.850299401,,,0.501910889
7289,Tracking the COVID-19 pandemic in Australia using genomics.,Nat Commun,32873808,9/3/20,pubmed,0,21,sequencing,0.001987102,0.477240416,0.001987126,0.458640832,0.001987208,0.058157316,Genomics,0.22777346,FALSE,0,0.006432061,,,6,0.764429903,,,0.385430982
7290,Low Baseline Pulmonary Levels of Cytotoxic Lymphocytes as a Predisposing Risk Factor for Severe COVID-19.,mSystems,32873611,9/3/20,pubmed,0,1,in silico,0.715602063,0.000889064,0.000889072,0.000889073,0.077891811,0.203838917,Drug discovery,0.88238996,TRUE,13,0.197352959,2,0.164302917,0,0.403234768,,,0.254963548
7291,Clearing the surgical backlog caused by COVID-19 in Ontario: a time series modelling study.,CMAJ,32873541,9/3/20,pubmed,0,11,probabilistic,0.001461848,0.001461879,0.00146191,0.82321753,0.001461897,0.170934935,Epidemiology,0.73714876,TRUE,64.72727273,0.733193147,52.09090909,0.689590581,10,0.828199272,,,0.750327667
7292,Confronting the Challenges of Anatomy Education in a Competency-Based Medical Curriculum During Normal and Unprecedented Times (COVID-19 Pandemic): Pedagogical Framework Development and Implementation.,JMIR Med Educ,32873536,9/3/20,pubmed,0,3,"interactom, active learning",0.000889101,0.03871083,0.244492974,0.395889557,0.319128482,0.000889055,Epidemiology,0.7757219,TRUE,17.66666667,0.266373925,5.333333333,0.262911426,3,0.667819001,,,0.399034784
7293,Covid-19 and Priorities for Research in Aging.,Can J Aging,32873348,9/3/20,pubmed,0,5,digital health,0.053214424,0.121895115,0.00310162,0.414120383,0.404566887,0.003101571,Epidemiology,0.49739566,FALSE,43.8,0.575916878,41,0.639751137,0,0.403234768,,,0.539634261
7294,0,J Biomol Struct Dyn,32873185,9/3/20,pubmed,0,6,"virtual screening, molecular dynamics simulation",0.994827018,0.00103458,0.001034644,0.001034612,0.001034566,0.00103458,Drug discovery,0.92559767,TRUE,6.5,0.093512277,3.5,0.213607172,4,0.707574542,,,0.33823133
7295,"Potential inhibitors of SARS-cov-2 RNA dependent RNA polymerase protein: molecular docking, molecular dynamics simulations and MM-PBSA analyses.",J Biomol Struct Dyn,32873176,9/3/20,pubmed,0,5,molecular dynamics simulation,0.993248269,0.001350398,0.001350333,0.001350368,0.001350328,0.001350304,Drug discovery,0.9458649,TRUE,0,0.006432061,,,3,0.667819001,,,0.337125531
7296,Characteristics and Outcomes of Mechanically Ventilated COVID-19 Patients-An Observational Cohort Study.,J Intensive Care Med,32873103,9/3/20,pubmed,0,5,logistic regression,0.00104681,0.001046849,0.001046872,0.078120804,0.034875245,0.883863421,Clinics,0.7473442,TRUE,70,0.764178366,89.8,0.801110516,2,0.618927094,,,0.728071992
7297,Fractional diffusion on the human proteome as an alternative to the multi-organ damage of SARS-CoV-2.,Chaos,32872802,9/3/20,pubmed,0,1,proteom,0.93364438,0.002996713,0.002996559,0.002996608,0.002996418,0.054369322,Drug discovery,0.54764944,TRUE,290,0.989918981,305,0.955913835,5,0.739490092,,,0.895107636
7298,Update on association between exposure to renin-angiotensin-aldosterone system inhibitors and coronavirus disease 2019 in South Korea.,Korean J Intern Med,32872736,9/3/20,pubmed,0,2,logistic regression,0.14009299,0.002490639,0.002490537,0.002490648,0.14463833,0.707796855,Clinics,0.8153081,TRUE,8.5,0.126662131,2,0.164302917,4,0.707574542,,,0.33284653
7299,Effects of renin-angiotensin system blockers on the risk and outcomes of severe acute respiratory syndrome coronavirus 2 infection in patients with hypertension.,Korean J Intern Med,32872731,9/3/20,pubmed,0,8,dataset,0.001987101,0.001987132,0.001987246,0.001987184,0.001987202,0.990064135,Clinics,0.77823186,TRUE,106.75,0.882367493,45.75,0.662229061,2,0.618927094,,,0.721174549
7300,"A Decision Aide for the Risk Stratification of GU Cancer Patients at Risk of SARS-CoV-2 Infection, COVID-19 Related Hospitalization, Intubation, and Mortality.",J Clin Med,32872607,9/3/20,pubmed,0,6,machine learning,0.001565326,0.001565319,0.192083978,0.001565397,0.001565306,0.801654674,Clinics,0.9341054,TRUE,6.666666667,0.094996598,,,0,0.403234768,,,0.249115683
7301,The Virome of Acute Respiratory Diseases in Individuals at Risk of Zoonotic Infections.,Viruses,32872469,9/3/20,pubmed,0,17,"sequencing, metagenom, genomes, virom",0.00137128,0.993143634,0.001371277,0.001371284,0.00137127,0.001371255,Genomics,0.3747391,FALSE,0,0.006432061,,,1,0.537564047,,,0.271998054
7302,"Δ9-Tetrahydrocannabinol Prevents Mortality from Acute Respiratory Distress Syndrome through the Induction of Apoptosis in Immune Cells, Leading to Cytokine Storm Suppression.",Int J Mol Sci,32872332,9/3/20,pubmed,0,9,"sequencing, transcriptom, metabolom, dataset",0.7545366,0.067463295,0.001203431,0.001203413,0.00120341,0.174389851,Drug discovery,0.4161581,FALSE,0,0.006432061,,,3,0.667819001,,,0.337125531
7303,0,Int J Environ Res Public Health,32872261,9/3/20,pubmed,0,5,correlation analysis,0.001254611,0.001254663,0.001254664,0.993726743,0.001254663,0.001254657,Epidemiology,0.59119296,TRUE,82.4,0.815634857,20.6,0.487021675,3,0.667819001,,,0.656825178
7304,Health Behavior Changes During COVID-19 Pandemic and Subsequent "Stay-at-Home" Orders.,Int J Environ Res Public Health,32872179,9/3/20,pubmed,0,5,logistic regression,0.018759157,0.001310347,0.00131033,0.001310379,0.975999416,0.001310372,Healthcare,0.9880347,TRUE,71.4,0.7701775,27.2,0.547230399,16,0.881782826,,,0.733063575
7305,Efficacy and safety of traditional Chinese medicine combined with routine western medicine for the asymptomatic novel coronavirus disease (COVID-19): A Bayesian network meta-analysis protocol.,Medicine (Baltimore),32871931,9/3/20,pubmed,0,5,bayes,0.25720612,0.001943596,0.001943559,0.33350996,0.001943569,0.403453195,Clinics,0.81134295,TRUE,65.2,0.735543324,16.8,0.446548033,1,0.537564047,,,0.573218468
7306,"Impaired immune and coagulation systems may be early risk factors for COVID-19 patients: A retrospective study of 118 inpatients from Wuhan, China.",Medicine (Baltimore),32871887,9/3/20,pubmed,0,7,"logistic regression, prediction model",0.001291267,0.001291227,0.056364651,0.001291249,0.001291227,0.938470378,Clinics,0.8225138,TRUE,121.5714286,0.908899746,154,0.887008295,1,0.537564047,,,0.77782403
7307,The psychological status of 8817 hospital workers during COVID-19 Epidemic: A cross-sectional study in Chongqing.,J Affect Disord,32871686,9/3/20,pubmed,0,27,logistic regression,0.001203417,0.001203504,0.001203544,0.08493149,0.762100272,0.149357772,Healthcare,0.95602226,TRUE,49.07407407,0.624652112,7.407407407,0.305458924,13,0.858880178,,,0.596330405
7308,COVID-19 infection outbreak increases anxiety level of general public in China: involved mechanisms and influencing factors.,J Affect Disord,32871676,9/3/20,pubmed,0,11,"logistic regression, correlation analysis",0.001622707,0.001622731,0.001622758,0.00162283,0.991886166,0.001622809,Healthcare,0.796473,TRUE,55.27272727,0.669676542,65.45454545,0.740433503,12,0.850299401,,,0.753469815
7309,Depressive symptoms in the front-line non-medical workers during the COVID-19 outbreak in Wuhan.,J Affect Disord,32871675,9/3/20,pubmed,0,8,logistic regression,0.001461917,0.049302218,0.001461978,0.00146198,0.91599392,0.030317988,Healthcare,0.9796077,TRUE,91,0.846310842,31.375,0.580144501,3,0.667819001,,,0.698091448
7310,Prevalence of depression and its impact on quality of life among frontline nurses in emergency departments during the COVID-19 outbreak.,J Affect Disord,32871661,9/3/20,pubmed,0,10,logistic regression,0.001751135,0.001751142,0.001751155,0.001751169,0.866994863,0.126000535,Healthcare,0.97461504,TRUE,77.5,0.79658606,44.9,0.658348943,14,0.866658436,,,0.773864479
7311,Toward automated severe pharyngitis detection with smartphone camera using deep learning networks.,Comput Biol Med,32871294,9/2/20,pubmed,0,5,"deep learning, classifier, adversarial network, transfer learning, dataset",0.001254685,0.001254703,0.922256226,0.024614417,0.001254659,0.049365309,Imaging,0.90616167,TRUE,25.4,0.372626631,13,0.400521809,0,0.403234768,,,0.392127736
7312,Potential of Plant Proteins Digested In Silico by Gastrointestinal Enzymes as Nutritional Supplement for COVID-19 Patients.,Plant Foods Hum Nutr,32870435,9/2/20,pubmed,0,3,in silico,0.963131374,0.00203288,0.002032841,0.002032809,0.002032748,0.028737348,Drug discovery,0.66567254,TRUE,25,0.369286907,5,0.257024351,1,0.537564047,,,0.387958435
7313,Community Outbreak Investigation of SARS-CoV-2 Transmission Among Bus Riders in Eastern China.,JAMA Intern Med,32870239,9/2/20,pubmed,0,22,sequencing,0.001098822,0.256898401,0.001098845,0.196256024,0.217993586,0.326654322,Clinics,0.41010377,FALSE,91.95454545,0.848599171,51.04545455,0.685576666,49,0.956787456,,,0.830321098
7314,Are older adults also at higher psychological risk from COVID-19?,Aging Ment Health,32870024,9/2/20,pubmed,0,10,logistic regression,0.001438105,0.001438106,0.001438124,0.001438128,0.992809398,0.001438139,Healthcare,0.8903848,TRUE,49,0.624281032,52.9,0.692667915,4,0.707574542,,,0.674841163
7315,The unintended consequences of inconsistent pandemic control policies.,medRxiv,32869043,9/2/20,pubmed,0,7,mathematical model,0.001203416,0.001203415,0.001203399,0.934232988,0.060953352,0.001203429,Epidemiology,0.052061737,FALSE,38.71428571,0.52650133,38.42857143,0.624163768,9,0.814309525,,,0.654991541
7316,Mask Wearing and Control of SARS-CoV-2 Transmission in the United States.,medRxiv,32869039,9/2/20,pubmed,0,13,logistic regression,0.000830622,0.000830661,0.000830655,0.613740325,0.382937093,0.000830643,Epidemiology,0.7252032,TRUE,56.25,0.676788917,125.9166667,0.858777094,8,0.799987654,,,0.778517888
7317,Genomic surveillance revealed prevalence of unique SARS-CoV-2 variants bearing mutation in the RdRp gene among Nevada patients.,medRxiv,32869037,9/2/20,pubmed,0,12,"sequencing, genomes, structural model",0.058915076,0.802063613,0.001291236,0.001291253,0.001291217,0.135147605,Genomics,0.5328649,TRUE,8,0.118683901,,,4,0.707574542,,,0.413129221
7318,Elucidation of remdesivir cytotoxicity pathways through genome-wide CRISPR-Cas9 screening and transcriptomics.,bioRxiv,32869031,9/2/20,pubmed,0,20,"sequencing, transcriptom, genome-wide",0.777831713,0.125635788,0.001901716,0.001901787,0.001901773,0.090827224,Drug discovery,0.108948916,FALSE,32.15,0.45698559,79.45,0.777428419,4,0.707574542,,,0.647329517
7319,Genome-wide CRISPR screen reveals host genes that regulate SARS-CoV-2 infection.,bioRxiv,32869025,9/2/20,pubmed,0,27,genome-wide,0.945787661,0.046081124,0.002032737,0.002032865,0.002032756,0.002032858,Drug discovery,0.2960215,FALSE,57.2962963,0.683839446,134.3333333,0.868611185,35,0.939317242,,,0.830589291
7320,SARS-CoV-2 genomic and quasispecies analyses in cancer patients reveal relaxed intrahost virus evolution.,bioRxiv,32869023,9/2/20,pubmed,0,9,"sequencing, genomes, deep sequencing",0.014005278,0.61008631,0.036046767,0.000612252,0.084292478,0.254956914,Genomics,0.30957985,FALSE,36.77777778,0.506091904,42,0.644902328,6,0.764429903,,,0.638474712
7321,Structure-Altering Mutations of the SARS-CoV-2 Frame Shifting RNA Element.,bioRxiv,32869017,9/2/20,pubmed,0,4,"molecular dynamics simulation, computational",0.498826055,0.348607121,0.001112733,0.149228832,0.001112646,0.001112614,Drug discovery,0.26859674,FALSE,1.25,0.013173356,,,5,0.739490092,,,0.376331724
7322,SARS-CoV-2 infection in the COPD population is associated with increased healthcare utilization: An analysis of Cleveland clinic's COVID-19 registry.,EClinicalMedicine,32869011,9/2/20,pubmed,0,3,logistic regression,0.001684459,0.001684454,0.001684501,0.001684517,0.001684653,0.991577417,Clinics,0.4777369,FALSE,31.66666667,0.451976003,5.333333333,0.262911426,9,0.814309525,,,0.509732318
7323,Health-related quality of life and behavior-related lifestyle changes due to the COVID-19 home confinement: Dataset from a Moroccan sample.,Data Brief,32868996,9/2/20,pubmed,0,9,dataset,0.001022636,0.001022639,0.001022642,0.530002324,0.448709676,0.018220082,Epidemiology,0.8827257,TRUE,8.444444444,0.124868576,15.55555556,0.430759968,4,0.707574542,,,0.421067695
7324,A Bayesian approach for monitoring epidemics in presence of undetected cases.,Chaos Solitons Fractals,32868967,9/2/20,pubmed,0,2,bayes,0.004109729,0.004109912,0.004109903,0.979450885,0.00410979,0.004109782,Epidemiology,0.33360255,FALSE,13.5,0.205393036,3.5,0.213607172,6,0.764429903,,,0.394476704
7325,Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble.,Expert Syst Appl,32868966,9/2/20,pubmed,0,5,"classifier, radiom",0.040875937,0.001350375,0.875098036,0.00135037,0.001350391,0.079974892,Imaging,0.8912386,TRUE,48,0.614942173,7.4,0.305392026,13,0.858880178,,,0.593071459
7326,Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures.,J Popul Econ,32868965,9/2/20,pubmed,0,3,machine learning,0.098453674,0.001823429,0.094045856,0.802030358,0.001823355,0.001823328,Epidemiology,0.34324294,FALSE,3,0.037293586,0,0.055525823,15,0.874313229,,,0.322377546
7327,Segmenting areas of potential contamination for adaptive robotic disinfection in built environments.,Build Environ,32868961,9/2/20,pubmed,0,5,deep-learning,0.002080592,0.002080687,0.272301288,0.719375963,0.002080736,0.002080734,Epidemiology,0.7758157,TRUE,38.4,0.523285299,3.2,0.202100615,5,0.739490092,,,0.488292002
7328,0,Tetrahedron Lett,32868957,9/2/20,pubmed,0,8,in silico,0.873275958,0.002562695,0.002562719,0.116473339,0.002562663,0.002562627,Drug discovery,0.86756706,TRUE,70.5,0.766033768,5.25,0.260971367,5,0.739490092,,,0.588831743
7329,Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays.,Pattern Recognit,32868956,9/2/20,pubmed,0,7,"deep learning, dataset",0.001141319,0.001141327,0.93864152,0.001141333,0.00114135,0.056793151,Imaging,0.8856257,TRUE,42,0.558537943,59.42857143,0.718490768,10,0.828199272,,,0.701742661
7330,Nonalcoholic fatty liver disease is associated with COVID-19 severity independently of metabolic syndrome: a retrospective case-control study.,Eur J Gastroenterol Hepatol,32868652,9/2/20,pubmed,0,10,logistic regression,0.00203284,0.002032804,0.08254066,0.002032857,0.002032848,0.909327992,Clinics,0.97854173,TRUE,47.9,0.613643392,13.3,0.403331549,3,0.667819001,,,0.561597981
7331,A SARS-CoV-2 vaccine candidate would likely match all currently circulating variants.,Proc Natl Acad Sci U S A,32868447,9/2/20,pubmed,0,12,"bioinformatic, genomes",0.185584645,0.79017026,0.001310323,0.001310354,0.001310353,0.020314065,Genomics,0.3825919,FALSE,5.25,0.072855464,,,63,0.965985555,,,0.519420509
7332,Emerging of a SARS-CoV-2 viral strain with a deletion in nsp1.,J Transl Med,32867854,9/2/20,pubmed,0,7,"genome sequences, genomes, prediction model",0.365227323,0.598889448,0.000898087,0.000898114,0.000898087,0.033188939,Genomics,0.66629064,TRUE,217.1428571,0.977549632,297.2857143,0.953505486,12,0.850299401,,,0.927118173
7333,"An outbreak of intestinal schistosomiasis, alongside increasing urogenital schistosomiasis prevalence, in primary school children on the shoreline of Lake Malawi, Mangochi District, Malawi.",Infect Dis Poverty,32867849,9/2/20,pubmed,0,12,logistic regression,0.00127268,0.284254965,0.001272676,0.001272716,0.710654267,0.001272696,Healthcare,0.649009,TRUE,12.16666667,0.184117756,4.666666667,0.246721969,1,0.537564047,,,0.322801258
7334,Neutralization of SARS-CoV-2 Spike Protein via Natural Compounds: A Multilayered High Throughput Virtual Screening Approach.,Curr Pharm Des,32867645,9/2/20,pubmed,0,9,virtual screening,0.722911614,0.001220061,0.001220059,0.249412116,0.001220042,0.024016108,Drug discovery,0.95817935,TRUE,89.11111111,0.839816934,42.88888889,0.648849344,2,0.618927094,,,0.702531124
7335,COVID-19 and Genetic Variants of Protein Involved in the SARS-CoV-2 Entry into the Host Cells.,Genes (Basel),32867305,9/2/20,pubmed,0,14,"sequencing, exom",0.246128077,0.654081981,0.001461853,0.001461877,0.001461948,0.095404263,Genomics,0.4424436,FALSE,66.85714286,0.745995423,40,0.633395772,11,0.840175319,,,0.739855505
7336,Acceptance of COVID-19 Vaccination during the COVID-19 Pandemic in China.,Vaccines (Basel),32867224,9/2/20,pubmed,0,7,logistic regression,0.001187285,0.001187281,0.001187277,0.121535078,0.873715818,0.001187262,Healthcare,0.92165285,TRUE,32.42857143,0.460387161,20,0.481000803,39,0.945737391,,,0.629041785
7337,Using All-Atom Potentials to Refine RNA Structure Predictions of SARS-CoV-2 Stem Loops.,Int J Mol Sci,32867123,9/2/20,pubmed,0,2,molecular dynamics simulation,0.855834389,0.134475947,0.00242245,0.002422503,0.002422365,0.002422345,Drug discovery,0.37100434,FALSE,0,0.006432061,,,1,0.537564047,,,0.271998054
7338,"Study of a SARS-CoV-2 Outbreak in a Belgian Military Education and Training Center in Maradi, Niger.",Viruses,32867108,9/2/20,pubmed,0,17,genomes,0.00198712,0.5130345,0.114774521,0.001987285,0.366229313,0.001987262,Genomics,0.478,FALSE,0,0.006432061,,,4,0.707574542,,,0.357003301
7339,Coronavirus discovery by metagenomic sequencing: a tool for pandemic preparedness.,J Clin Virol,32866812,9/1/20,pubmed,0,8,"sequencing, metagenom",0.067587865,0.838737626,0.089623498,0.001350359,0.001350313,0.001350339,Genomics,0.5648719,TRUE,36.125,0.498670295,43,0.649785925,3,0.667819001,,,0.605425074
7340,Prevalence and correlates of somatization in anxious individuals in a Chinese online crisis intervention during COVID-19 epidemic.,J Affect Disord,32866802,9/1/20,pubmed,0,7,logistic regression,0.00133005,0.001330129,0.001330079,0.001330073,0.928346521,0.066333147,Healthcare,0.98259413,TRUE,33.28571429,0.469107551,5.428571429,0.264249398,2,0.618927094,,,0.450761348
7341,Virtual screening and molecular dynamics study of approved drugs as inhibitors of spike protein S1 domain and ACE2 interaction in SARS-CoV-2.,J Mol Graph Model,32866780,9/1/20,pubmed,0,11,"virtual screening, molecular dynamics simulation",0.981526947,0.000759371,0.015435533,0.0007594,0.000759374,0.000759374,Drug discovery,0.98538756,TRUE,52.09090909,0.647597254,17.81818182,0.458924271,8,0.799987654,,,0.635503059
7342,The mental state and risk factors of Chinese medical staff and medical students in early stages of the COVID-19 epidemic.,Compr Psychiatry,32866693,9/1/20,pubmed,0,7,logistic regression,0.001220011,0.001220014,0.001220035,0.001220105,0.993899779,0.001220057,Healthcare,0.8457079,TRUE,11.85714286,0.178551549,0.428571429,0.076665775,1,0.537564047,,,0.264260457
7343,Comprehensive evolution and molecular characteristics of a large number of SARS-CoV-2 genomes reveal its epidemic trends.,Int J Infect Dis,32866640,9/1/20,pubmed,0,10,"whole genome, genomes",0.002238531,0.778150643,0.002238452,0.212895326,0.002238533,0.002238514,Genomics,0.59398574,TRUE,12.3,0.185292844,7.5,0.307867273,14,0.866658436,,,0.453272851
7344,"Predicting the second wave of COVID-19 in Washtenaw County, MI.",J Theor Biol,32866493,9/1/20,pubmed,0,3,dataset,0.001098843,0.001098819,0.001098858,0.967451532,0.028153132,0.001098817,Epidemiology,0.42773628,FALSE,18,0.271569052,18.5,0.46554723,2,0.618927094,,,0.452014459
7345,Outcomes following SARS-CoV-2 infection in liver transplant recipients: an international registry study.,Lancet Gastroenterol Hepatol,32866433,9/1/20,pubmed,0,22,logistic regression,0.000740281,0.000740303,0.000740289,0.000740362,0.069684875,0.927353889,Clinics,0.7733433,TRUE,45.86363636,0.594780135,34.45454545,0.601351351,30,0.930057411,,,0.708729632
7346,Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19.,J Am Med Inform Assoc,32866264,9/1/20,pubmed,0,7,digital health,0.044357545,0.001085352,0.001085393,0.001085406,0.667082154,0.285304149,Healthcare,0.80234563,TRUE,170.4285714,0.958438988,144.5714286,0.878579074,7,0.785110192,,,0.874042751
7347,Characteristics of telehealth users in NYC for COVID-related care during the coronavirus pandemic.,J Am Med Inform Assoc,32866249,9/1/20,pubmed,0,5,logistic regression,0.001823332,0.001823342,0.00182333,0.169285929,0.480495919,0.344748149,Healthcare,0.31805235,FALSE,38.4,0.523285299,49.2,0.678150923,0,0.403234768,,,0.53489033
7348,Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development.,JMIR Med Inform,32866109,9/1/20,pubmed,0,16,prediction model,0.001203437,0.001203471,0.054116191,0.001203489,0.001203468,0.941069944,Clinics,0.79272836,TRUE,39.9375,0.538375905,17.8125,0.458857372,0,0.403234768,,,0.466822681
7349,The role of neutrophil-lymphocyte ratio and lymphocyte-monocyte ratio in the prognosis of type 2 diabetics with COVID-19.,Scott Med J,32865157,9/1/20,pubmed,0,5,correlation analysis,0.001392922,0.001392956,0.001392947,0.001392898,0.001392839,0.993035438,Clinics,0.66205007,TRUE,42,0.558537943,14,0.412898047,3,0.667819001,,,0.54641833
7350,"COVID-19 drug repurposing: A review of computational screening methods, clinical trials, and protein interaction assays.",Med Res Rev,32864815,8/31/20,pubmed,0,2,computational,0.75469076,0.001751235,0.18361564,0.001751309,0.001751271,0.056439785,Drug discovery,0.44669265,FALSE,48.5,0.618158204,,,9,0.814309525,,,0.716233865
7351,COVID-19-associated hyperinflammation and escalation of patient care: a retrospective longitudinal cohort study.,Lancet Rheumatol,32864628,8/31/20,pubmed,0,36,logistic regression,0.000838504,0.037678103,0.0008385,0.000838534,0.000838527,0.958967832,Clinics,0.7041642,TRUE,26.69444444,0.391118808,41.52777778,0.642159486,36,0.941169208,,,0.658149167
7352,"Time from COVID-19 shutdown, gender-based violence exposure, and mental health outcomes among a state representative sample of California residents.",EClinicalMedicine,32864593,8/31/20,pubmed,0,4,logistic regression,0.001141366,0.020009821,0.001141328,0.034774366,0.941791721,0.001141399,Healthcare,0.7214664,TRUE,11.75,0.177562001,16,0.437316029,1,0.537564047,,,0.384147359
7353,"Clinical features, diagnostics, and outcomes of patients presenting with acute respiratory illness: A retrospective cohort study of patients with and without COVID-19.",EClinicalMedicine,32864588,8/31/20,pubmed,0,45,"sequencing, metagenom",0.00118739,0.130749292,0.001187428,0.001187287,0.0011873,0.864501303,Clinics,0.5734376,TRUE,22.42222222,0.33125116,36.2,0.612322719,13,0.858880178,,,0.600818019
7354,Exploring Epidemiological Behavior of Novel Coronavirus (COVID-19) Outbreak in Bangladesh.,SN Compr Clin Med,32864577,8/31/20,pubmed,0,4,dataset,0.002032823,0.11604765,0.002032774,0.828028953,0.002032922,0.049824878,Epidemiology,0.7803432,TRUE,15.75,0.237862577,2.25,0.170925876,1,0.537564047,,,0.315450833
7355,Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt.,Infect Dis Model,32864516,8/31/20,pubmed,0,3,"machine learning, dataset",0.001392842,0.001392844,0.001392873,0.993035748,0.001392855,0.001392836,Epidemiology,0.4711993,FALSE,5.666666667,0.079473066,0.333333333,0.073187048,5,0.739490092,,,0.297383402
7356,"Dataset of mutational analysis, miRNAs targeting SARS-CoV-2 genes and host gene expression in SARS-CoV and SARS-CoV-2 infections.",Data Brief,32864402,8/31/20,pubmed,0,4,"genome sequences, genomes, dataset",0.468496811,0.455985034,0.001786628,0.001786594,0.034368215,0.037576717,Drug discovery,0.5047626,TRUE,28,0.408312202,6.25,0.283315494,1,0.537564047,,,0.409730581
7357,"The Current Mental Health Crisis of COVID-19 Pandemic Among Communities Living in Gedeo Zone Dilla, SNNP, Ethiopia, April 2020.",J Psychosoc Rehabil Ment Health,32864302,8/31/20,pubmed,0,2,logistic regression,0.042477962,0.001751243,0.001751227,0.001751242,0.950517082,0.001751242,Healthcare,0.71833265,TRUE,9,0.135320675,0.5,0.087101953,0,0.403234768,,,0.208552465
7358,Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning.,Cureus,32864270,8/31/20,pubmed,0,11,"deep learning, neural network, network model, prediction model, dataset",0.001291218,0.001291258,0.857312446,0.00129125,0.00129124,0.137522587,Imaging,0.7879976,TRUE,90.63636364,0.844702826,2260.545455,0.998996521,44,0.952157541,,,0.931952296
7359,Risk factors and outcome of COVID-19 in patients with hematological malignancies.,Exp Hematol Oncol,32864192,8/31/20,pubmed,0,45,logistic regression,0.001310422,0.017687258,0.016048963,0.001310321,0.001310348,0.962332688,Clinics,0.92809016,TRUE,36.42222222,0.501453398,22.84444444,0.510302382,9,0.814309525,,,0.608688435
7360,How COVID-19 Transformed Problem-Based Learning at Carle Illinois College of Medicine.,Med Sci Educ,32864181,8/31/20,pubmed,0,4,active learning,0.002238488,0.002238458,0.226466924,0.205115146,0.56170256,0.002238424,Healthcare,0.39260587,FALSE,15.5,0.234028078,16.25,0.438988493,2,0.618927094,,,0.430647889
7361,Pharmacogenomics of COVID-19 therapies.,NPJ Genom Med,32864162,8/31/20,pubmed,0,4,pharmacogenom,0.541688388,0.150295602,0.001461871,0.095456231,0.001461959,0.20963595,Drug discovery,0.9086933,TRUE,42.75,0.564598924,26,0.53819909,9,0.814309525,,,0.639035846
7362,A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept.,Chaos Solitons Fractals,32863614,8/31/20,pubmed,0,2,"predictive model, probabilistic",0.06292432,0.0024905,0.002490476,0.927113665,0.002490502,0.002490536,Epidemiology,0.26994255,FALSE,19,0.285793803,2,0.164302917,2,0.618927094,,,0.356341271
7363,Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries.,Chaos Solitons Fractals,32863611,8/31/20,pubmed,0,5,dataset,0.002422237,0.002422383,0.002422364,0.987888453,0.002422278,0.002422284,Epidemiology,0.6872056,TRUE,74.2,0.782608696,13,0.400521809,4,0.707574542,,,0.630235015
7364,A non-central beta model to forecast and evaluate pandemics time series.,Chaos Solitons Fractals,32863610,8/31/20,pubmed,0,4,probabilistic,0.001486421,0.001486418,0.001486489,0.992567792,0.001486472,0.001486408,Epidemiology,0.1783264,FALSE,13.75,0.208299833,0.75,0.099411292,1,0.537564047,,,0.281758391
7365,Not all interventions are equal for the height of the second peak.,Chaos Solitons Fractals,32863609,8/31/20,pubmed,0,3,network model,0.00109885,0.001098848,0.001098816,0.97934305,0.001098813,0.016261624,Epidemiology,0.055675,FALSE,20.33333333,0.302059497,5.333333333,0.262911426,3,0.667819001,,,0.410929975
7366,Suffering in silence: How COVID-19 school closures inhibit the reporting of child maltreatment.,J Public Econ,32863462,8/31/20,pubmed,0,3,dataset,0.00178653,0.001786532,0.00178655,0.545051387,0.447802487,0.001786513,Epidemiology,0.6557868,TRUE,4,0.054734368,1.666666667,0.145036125,30,0.930057411,,,0.376609301
7367,Molecular dynamics simulation of docking structures of SARS-CoV-2 main protease and HIV protease inhibitors.,J Mol Struct,32863430,8/31/20,pubmed,0,2,"molecular dynamics simulation, in silico",0.791010011,0.00410977,0.004109871,0.192550716,0.004109855,0.004109777,Drug discovery,0.87692076,TRUE,45,0.587049292,2.5,0.180826866,4,0.707574542,,,0.4918169
7368,0,Curr Ther Res Clin Exp,32863400,8/31/20,pubmed,0,2,in silico,0.779273584,0.001717214,0.001717208,0.055168732,0.16040598,0.001717283,Drug discovery,0.697311,TRUE,1,0.012307502,0,0.055525823,2,0.618927094,,,0.22892014
7369,Indicators of Acute Kidney Injury as Biomarkers to Differentiate Heatstroke from Coronavirus Disease 2019: A Retrospective Multicenter Analysis.,J Nippon Med Sch,32863339,8/31/20,pubmed,0,11,"machine learning, logistic regression",0.001565406,0.001565301,0.228986767,0.001565302,0.001565328,0.764751896,Clinics,0.7369268,TRUE,42.54545455,0.562990909,12.27272727,0.38868076,1,0.537564047,,,0.496411905
7370,BCG vaccine may generate cross-reactive T cells against SARS-CoV-2: In silico analyses and a hypothesis.,Vaccine,32863070,8/31/20,pubmed,0,4,in silico,0.627166167,0.085398017,0.043226538,0.001901853,0.240405687,0.001901739,Drug discovery,0.4959062,FALSE,70,0.764178366,39.25,0.629114263,10,0.828199272,,,0.7404973
7371,Digital health for patients with chronic pain during the COVID-19 pandemic.,Br J Anaesth,32863018,8/31/20,pubmed,0,3,digital health,0.02506022,0.025060222,0.025060876,0.308271972,0.025061466,0.591485243,Clinics,0.43163797,FALSE,41,0.549013544,38.66666667,0.625501739,2,0.618927094,,,0.597814126
7372,Transmammary transmission of Troglostrongylus brevior feline lungworm: a lesson from our gardens.,Vet Parasitol,32862125,8/31/20,pubmed,0,9,sequencing,0.150550988,0.494447661,0.001392907,0.001392946,0.121057832,0.231157666,Genomics,0.66693753,TRUE,144.1111111,0.93864803,225,0.927883329,2,0.618927094,,,0.828486151
7373,Molecular modelling investigation for drugs and nutraceuticals against protease of SARS-CoV-2.,J Mol Graph Model,32861974,8/31/20,pubmed,0,4,"molecular dynamics simulation, computational, in silico",0.946009235,0.050206264,0.000946121,0.000946126,0.00094614,0.000946114,Drug discovery,0.9308947,TRUE,165.25,0.955099264,137,0.87168852,3,0.667819001,,,0.831535595
7374,Peripheral blood CD4+ cell counts but not CD3+ and CD8+ cell counts are reduced in SARS-CoV-2 infection.,J Affect Disord,32861838,8/31/20,pubmed,0,6,logistic regression,0.315325202,0.001511851,0.001511925,0.001511946,0.034234138,0.645904937,Clinics,0.46279946,FALSE,34.33333333,0.480425506,9.666666667,0.348274017,0,0.403234768,,,0.410644763
7375,Assessment of the Modified CHA2DS2VASc Risk Score in Predicting Mortality in Patients Hospitalized With COVID-19.,Am J Cardiol,32861734,8/31/20,pubmed,0,10,logistic regression,0.001126801,0.001126799,0.001126813,0.001126865,0.001126815,0.994365906,Clinics,0.9113227,TRUE,20.4,0.302863504,1.6,0.140687717,1,0.537564047,,,0.327038423
7376,Prognostic Value of Elevated Cardiac Troponin I in Hospitalized Covid-19 Patients.,Am J Cardiol,32861733,8/31/20,pubmed,0,8,logistic regression,0.001653024,0.001653008,0.001653038,0.001653016,0.001653066,0.991734848,Clinics,0.2643621,FALSE,18.375,0.275279857,7.5,0.307867273,6,0.764429903,,,0.449192344
7377,Pathophysiology of COVID-19-associated acute respiratory distress syndrome: a multicentre prospective observational study.,Lancet Respir Med,32861276,8/31/20,pubmed,0,38,dataset,0.000765942,0.000765924,0.087775783,0.000765973,0.106886587,0.80303979,Clinics,0.89499754,TRUE,163.1724138,0.953614942,317.4137931,0.958924271,86,0.975307118,,,0.962615444
7378,Prevalence of phenotypes of acute respiratory distress syndrome in critically ill patients with COVID-19: a prospective observational study.,Lancet Respir Med,32861275,8/31/20,pubmed,0,18,classifier,0.026743288,0.022694109,0.014949291,0.000977465,0.000977579,0.933658268,Clinics,0.5516387,TRUE,84.5,0.823489393,121.5555556,0.853492106,29,0.928020248,,,0.868333916
7379,Sunlight ultraviolet radiation dose is negatively correlated with the percent positive of SARS-CoV-2 and four other common human coronaviruses in the U.S.,Sci Total Environ,32861186,8/30/20,pubmed,0,7,correlation analysis,0.001943543,0.332889484,0.001943489,0.254542909,0.001943573,0.406737003,Clinics,0.49934685,FALSE,19.85714286,0.294514194,2,0.164302917,10,0.828199272,,,0.429005461
7380,Sleep disturbances among Chinese residents during the Coronavirus Disease 2019 outbreak and associated factors.,Sleep Med,32861011,8/30/20,pubmed,0,10,logistic regression,0.040960194,0.001622733,0.001622846,0.001622931,0.952548355,0.00162294,Healthcare,0.985221,TRUE,72.6,0.775867401,20.7,0.48822585,4,0.707574542,,,0.657222597
7381,Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19).,Clin Microbiol Infect,32860964,8/30/20,pubmed,0,345,logistic regression,0.001538112,0.001538085,0.001538115,0.115051633,0.001538118,0.878795937,Clinics,0.548845,TRUE,9.764705882,0.146391243,,,25,0.918019631,,,0.532205437
7382,Effect of hydroxychloroquine with or without azithromycin on the mortality of coronavirus disease 2019 (COVID-19) patients: a systematic review and meta-analysis.,Clin Microbiol Infect,32860962,8/30/20,pubmed,0,6,bayes,0.06100495,0.00122002,0.001220034,0.26564148,0.001220109,0.669693407,Clinics,0.45449662,FALSE,30.66666667,0.440225122,25.5,0.533382392,62,0.965553429,,,0.646386981
7383,"Modeling return of the epidemic: Impact of population structure, asymptomatic infection, case importation and personal contacts.",Travel Med Infect Dis,32860959,8/30/20,pubmed,0,1,mathematical model,0.001330116,0.001330067,0.001330018,0.814029957,0.001330133,0.180649711,Epidemiology,0.48500204,FALSE,14,0.213494959,1,0.122023013,0,0.403234768,,,0.246250913
7384,Does the COVID-19 Pandemic Spell the End for the Direct Ophthalmoscope?,Ophthalmol Ther,32860621,8/30/20,pubmed,0,5,deep-learning,0.002639072,0.002639092,0.319034338,0.220197579,0.452850873,0.002639045,Healthcare,0.7727728,TRUE,30.8,0.441523904,5.6,0.267995718,0,0.403234768,,,0.37091813
7385,Management of patients with hidradenitis suppurativa during the COVID-19 pandemic: Risk and benefit of immunomodulatory therapy.,Dermatol Ther,32860474,8/30/20,pubmed,0,8,immunome,0.047866284,0.002238517,0.002238707,0.4826566,0.002238532,0.46276136,Epidemiology,0.7961069,TRUE,84.125,0.822128765,22.25,0.505284988,0,0.403234768,,,0.57688284
7386,Clinical features and potential risk factors for discerning the critical cases and predicting the outcome of patients with COVID-19.,J Clin Lab Anal,32860454,8/30/20,pubmed,0,8,logistic regression,0.001203388,0.001203389,0.154270166,0.001203398,0.00120341,0.840916248,Clinics,0.96667165,TRUE,134.375,0.926402375,79.75,0.778498796,6,0.764429903,,,0.823110358
7387,The modifications brought about by the COVID-19 pandemic to Nuclear Medicine practice.,Hell J Nucl Med,32860389,8/30/20,pubmed,0,1,artificial intelligence,0.000677922,0.000677937,0.035634237,0.602562567,0.209290176,0.15115716,Epidemiology,0.8672723,TRUE,11,0.167171748,0,0.055525823,0,0.403234768,,,0.208644113
7388,"Associations between serum amyloid A, interleukin-6, and COVID-19: A cross-sectional study.",J Clin Lab Anal,32860278,8/30/20,pubmed,0,6,logistic regression,0.001538083,0.001538123,0.089911546,0.001538094,0.00153808,0.903936074,Clinics,0.99464726,TRUE,34.83333333,0.485496939,6.5,0.288132192,4,0.707574542,,,0.493734557
7389,Risk factors for Covid-19 severity and fatality: a structured literature review.,Infection,32860214,8/30/20,pubmed,0,4,dataset,0.053234867,0.001486434,0.001486445,0.3047107,0.001486488,0.637595065,Clinics,0.7577727,TRUE,48.75,0.62032284,19.5,0.475983409,32,0.933699611,,,0.67666862
7390,Compartmentalized Replication of SARS-Cov-2 in Upper vs. Lower Respiratory Tract Assessed by Whole Genome Quasispecies Analysis.,Microorganisms,32858978,8/30/20,pubmed,0,11,"bioinformatic, sequencing, whole-genome, whole genome",0.001512039,0.806816285,0.001511825,0.001511822,0.001511811,0.187136217,Genomics,0.62823594,TRUE,134.0909091,0.926093141,97.90909091,0.818637945,4,0.707574542,,,0.817435209
7391,The Impact and Consequences of SARS-CoV-2 Pandemic on a Single University Dermatology Outpatient Clinic in Germany.,Int J Environ Res Public Health,32858870,8/30/20,pubmed,0,10,dataset,0.001593516,0.001593592,0.001593608,0.188733257,0.129231496,0.677254531,Clinics,0.4259803,FALSE,65.4,0.736656565,47.5,0.670591383,0,0.403234768,,,0.603494239
7392,"Interplay between oxidative damage, the redox status, and metabolic biomarkers during long-term fasting.",Food Chem Toxicol,32858131,8/29/20,pubmed,0,8,machine learning,0.123480423,0.00143822,0.09199155,0.470829458,0.001438247,0.310822102,Epidemiology,0.9241968,TRUE,23.125,0.342940194,10.25,0.358375702,2,0.618927094,,,0.440080997
7393,Telemedicine is an important aspect of healthcare services amid COVID-19 outbreak: Its barriers in Bangladesh and strategies to overcome.,Int J Health Plann Manage,32857887,8/29/20,pubmed,0,3,digital health,0.002422303,0.00242237,0.002422506,0.764486595,0.225823959,0.002422266,Epidemiology,0.8923603,TRUE,6,0.086028821,0.666666667,0.096200161,6,0.764429903,,,0.315552961
7394,Knowledge and attitude towards COVID-19 and associated factors among health care providers in Northwest Ethiopia.,PLoS One,32857811,8/29/20,pubmed,0,6,logistic regression,0.001291232,0.001291205,0.025106528,0.001291251,0.969728574,0.00129121,Healthcare,0.8655198,TRUE,7.833333333,0.113798009,3.5,0.213607172,8,0.799987654,,,0.375797611
7395,"Adolescents' health literacy, health protective measures, and health-related quality of life during the Covid-19 pandemic.",PLoS One,32857806,8/29/20,pubmed,0,5,logistic regression,0.001593538,0.001593568,0.001593554,0.191663406,0.801962342,0.001593592,Healthcare,0.90881586,TRUE,21.6,0.319438432,17,0.451097137,2,0.618927094,,,0.463154221
7396,Coronavirus Disease-2019 Treatment Strategies Targeting Interleukin-6 Signaling and Herbal Medicine.,OMICS,32857671,8/29/20,pubmed,0,4, omics,0.895151985,0.001438154,0.048724992,0.001438227,0.001438112,0.05180853,Drug discovery,0.7049334,TRUE,43.5,0.573381161,35.75,0.609044688,0,0.403234768,,,0.528553539
7397,Modeling the Impact of COVID-19 on Dental Insurance Coverage and Utilization.,J Dent Res,32857641,8/29/20,pubmed,0,4,simulation model,0.00137126,0.001371309,0.00137137,0.516578953,0.477935839,0.00137127,Epidemiology,0.5790177,TRUE,33.25,0.468860165,61.5,0.726585496,2,0.618927094,,,0.604790918
7398,The psychological impact of COVID-19 pandemic on patients included in a bariatric surgery program.,Eat Weight Disord,32857287,8/29/20,pubmed,0,8,logistic regression,0.001310357,0.001310377,0.001310329,0.001310368,0.789889129,0.20486944,Healthcare,0.98402584,TRUE,25.125,0.369534294,11,0.371287129,4,0.707574542,,,0.482798655
7399,Discriminating Metabolic Health Status in a Cohort of Nursing Students: Protocol for a Cross-Sectional Study.,JMIR Res Protoc,32857058,8/29/20,pubmed,0,4,logistic regression,0.000688468,0.0006885,0.049818634,0.000688513,0.717611875,0.230504009,Healthcare,0.96067125,TRUE,18.5,0.278001113,11.5,0.378378378,0,0.403234768,,,0.353204753
7400,The Essential Role of Technology in the Public Health Battle Against COVID-19.,Popul Health Manag,32857014,8/29/20,pubmed,0,3,artificial intelligence,0.043426009,0.00125465,0.228735308,0.724074649,0.001254642,0.001254741,Epidemiology,0.8242247,TRUE,140,0.933823984,195.6666667,0.915373294,1,0.537564047,,,0.795587108
7401,Blood use and transfusion needs at a large health care system in Washington state during the SARS-CoV-2 pandemic.,Transfusion,32856307,8/29/20,pubmed,0,10,logistic regression,0.001291313,0.001291243,0.001291231,0.114929103,0.001291286,0.879905823,Clinics,0.9626268,TRUE,35.3,0.48945513,42.2,0.645571314,4,0.707574542,,,0.614200329
7402,"Effect of Weather on COVID-19 Transmission and Mortality in Lagos, Nigeria.",Scientifica (Cairo),32855836,8/29/20,pubmed,0,7,correlation analysis,0.002130662,0.002130751,0.00213069,0.78289097,0.002130811,0.208586115,Epidemiology,0.49558198,FALSE,8.142857143,0.119426062,1.857142857,0.151525288,2,0.618927094,,,0.296626148
7403,The value of serum amyloid A for predicting the severity and recovery of COVID-19.,Exp Ther Med,32855710,8/29/20,pubmed,0,8,logistic regression,0.001392896,0.00139286,0.001392907,0.001392943,0.001392868,0.993035527,Clinics,0.83230174,TRUE,35.375,0.490568372,,,5,0.739490092,,,0.615029232
7404,A structured open dataset of government interventions in response to COVID-19.,Sci Data,32855430,8/29/20,pubmed,0,43,dataset,0.00162281,0.001622779,0.00162283,0.991886133,0.001622715,0.001622734,Epidemiology,0.40462404,FALSE,89.51162791,0.841177562,113.7906977,0.843591116,4,0.707574542,,,0.79744774
7405,Exposure to health misinformation about COVID-19 and increased tobacco and alcohol use: a population-based survey in Hong Kong.,Tob Control,32855353,8/29/20,pubmed,0,8,logistic regression,0.001823429,0.05826699,0.001823349,0.001823432,0.934439407,0.001823393,Healthcare,0.9158867,TRUE,58.875,0.695281093,30.75,0.57499331,7,0.785110192,,,0.685128199
7406,"Four SARS-CoV-2 Genome Sequences from Late April in Stockholm, Sweden, Reveal a Rare Mutation in the Spike Protein.",Microbiol Resour Announc,32855259,8/29/20,pubmed,0,7,genome sequences,0.006539845,0.967301476,0.00653956,0.006539716,0.006539695,0.006539707,Genomics,0.49826205,FALSE,22.42857143,0.331498547,124.4285714,0.857037731,1,0.537564047,,,0.575366775
7407,Material hardship and parenting stress among grandparent kinship providers during the COVID-19 pandemic: The mediating role of grandparents' mental health.,Child Abuse Negl,32854948,8/29/20,pubmed,0,4,logistic regression,0.001901705,0.001901706,0.001901711,0.001901768,0.990491302,0.001901808,Healthcare,0.98984903,TRUE,52,0.647349867,73,0.761974846,4,0.707574542,,,0.705633085
7408,"Longitudinal changes of inflammatory parameters and their correlation with disease severity and outcomes in patients with COVID-19 from Wuhan, China.",Crit Care,32854750,8/29/20,pubmed,0,11,logistic regression,0.08553628,0.000946167,0.000946072,0.02056685,0.000946105,0.891058526,Clinics,0.89798963,TRUE,307.3636364,0.991094069,253.7272727,0.939322986,24,0.914439163,,,0.948285406
7409,Characterization of accessory genes in coronavirus genomes.,Virol J,32854725,8/29/20,pubmed,0,4,"computational, bioinformatic, genomes",0.236675902,0.757922635,0.001350378,0.001350386,0.001350354,0.001350344,Genomics,0.4527961,FALSE,128,0.919104459,2371.25,0.999130318,0,0.403234768,,,0.773823182
7410,"Coping style, social support and psychological distress in the general Chinese population in the early stages of the COVID-19 epidemic.",BMC Psychiatry,32854656,8/29/20,pubmed,0,8,logistic regression,0.0013929,0.001392875,0.001392854,0.001392963,0.993035541,0.001392866,Healthcare,0.96313286,TRUE,44.5,0.581977859,35.5,0.608241905,16,0.881782826,,,0.69066753
7411,Decreased prealbumin level is associated with increased risk for mortality in elderly hospitalized patients with COVID-19.,Nutrition,32854020,8/28/20,pubmed,0,10,logistic regression,0.017225741,0.001034564,0.001034595,0.00103462,0.001034641,0.978635839,Clinics,0.98952055,TRUE,67.6,0.750572082,16.9,0.447551512,2,0.618927094,,,0.605683563
7412,"Clinical, immunological and virological characterization of COVID-19 patients that test re-positive for SARS-CoV-2 by RT-PCR.",EBioMedicine,32853988,8/28/20,pubmed,0,18,"sequencing, genomes",0.001717494,0.689977338,0.001717288,0.089316545,0.001717313,0.215554021,Genomics,0.4396815,FALSE,58.27777778,0.691446595,154.8888889,0.887610383,9,0.814309525,,,0.797788834
7413,J-shaped association between fasting blood glucose levels and COVID-19 severity in patients without diabetes.,Diabetes Res Clin Pract,32853687,8/28/20,pubmed,0,10,logistic regression,0.14117702,0.002639131,0.00263915,0.002639245,0.002639222,0.848266232,Clinics,0.8494264,TRUE,122.9,0.911435463,34,0.5990768,5,0.739490092,,,0.750000785
7414,Immune dysregulation and multisystem inflammatory syndrome in children (MIS-C) in individuals with haploinsufficiency of SOCS1.,J Allergy Clin Immunol,32853638,8/28/20,pubmed,0,12,"sequencing, exom",0.455600309,0.237316627,0.001538132,0.001538115,0.085989965,0.218016852,Drug discovery,0.95592886,TRUE,87,0.833694106,151.4166667,0.884198555,7,0.785110192,,,0.834334284
7415,Screening and evaluation of approved drugs as inhibitors of main protease of SARS-CoV-2.,Int J Biol Macromol,32853604,8/28/20,pubmed,0,7,in-silico,0.990490986,0.001901803,0.001901741,0.001902007,0.001901736,0.001901727,Drug discovery,0.67303556,TRUE,17.42857143,0.261982807,15.14285714,0.426545357,4,0.707574542,,,0.465367568
7416,Coronavirus RNA Proofreading: Molecular Basis and Therapeutic Targeting.,Mol Cell,32853546,8/28/20,pubmed,0,8,genomes,0.599242196,0.275045699,0.00263907,0.117794929,0.00263914,0.002638965,Drug discovery,0.7055496,TRUE,14.125,0.213804193,16.25,0.438988493,36,0.941169208,,,0.531320632
7417,HLA predictions from the bronchoalveolar lavage fluid and blood samples of eight COVID-19 patients at the pandemic onset.,Bioinformatics,32853340,8/28/20,pubmed,0,2,"bioinformatic, sequencing, transcriptom, metatranscriptom",0.268840204,0.415584512,0.001371294,0.00137129,0.001371415,0.311461285,Genomics,0.38063756,FALSE,155,0.948048735,1289.5,0.995919187,1,0.537564047,,,0.827177323
7418,Simulation of the COVID-19 epidemic on the social network of Slovenia: Estimating the intrinsic forecast uncertainty.,PLoS One,32853292,8/28/20,pubmed,0,3,network model,0.00137129,0.001371329,0.043919213,0.879129687,0.072837196,0.001371285,Epidemiology,0.20810899,FALSE,8.333333333,0.123693488,0.333333333,0.073187048,3,0.667819001,,,0.288233179
7419,Development and evaluation of a rapid CRISPR-based diagnostic for COVID-19.,PLoS Pathog,32853291,8/28/20,pubmed,0,15,"sequencing, metagenom",0.002806552,0.529448013,0.459326082,0.002806532,0.002806452,0.002806369,Genomics,0.739116,TRUE,31.53333333,0.450182448,,,22,0.908142478,,,0.679162463
7420,"Predicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models.",PLoS One,32853285,8/28/20,pubmed,0,12,"neural network, lstm",0.001486417,0.001486462,0.129141768,0.86491248,0.00148643,0.001486442,Epidemiology,0.7142615,TRUE,65.91666667,0.739934442,,,4,0.707574542,,,0.723754492
7421,Getting back to the "new normal": Autonomy restoration during a global pandemic.,J Appl Psychol,32852985,8/28/20,pubmed,0,5,dataset,0.001330191,0.001330076,0.001330057,0.733073169,0.187106633,0.075829875,Epidemiology,0.42516014,FALSE,14.4,0.217576845,7.6,0.308536259,1,0.537564047,,,0.35455905
7422,Increased Risk of COVID-19 Among Users of Proton Pump Inhibitors.,Am J Gastroenterol,32852340,8/28/20,pubmed,0,3,logistic regression,0.194174689,0.001861819,0.001861684,0.001861727,0.798378264,0.001861817,Healthcare,0.7772389,TRUE,323,0.991898077,329.6666667,0.962001606,38,0.944132354,,,0.966010679
7423,Impact of Famotidine Use on Clinical Outcomes of Hospitalized Patients With COVID-19.,Am J Gastroenterol,32852338,8/28/20,pubmed,0,3,logistic regression,0.00107219,0.001072187,0.001072192,0.057545754,0.001072203,0.938165474,Clinics,0.830112,TRUE,199,0.972354506,96,0.81509232,21,0.903944688,,,0.897130505
7424,Single-cell RNA expression profiling of SARS-CoV-2-related ACE2 and TMPRSS2 in human trophectoderm and placenta.,Ultrasound Obstet Gynecol,32851697,8/28/20,pubmed,0,7,"bioinformatic, transcriptom, dataset",0.866974803,0.001565389,0.001565328,0.001565454,0.126763538,0.001565489,Drug discovery,0.8630258,TRUE,9.571428571,0.143422599,3.428571429,0.208991169,4,0.707574542,,,0.353329437
7425,Four Challenges Associated With Current Mathematical Modeling Paradigm of Infectious Diseases and Call for a Shift.,Open Forum Infect Dis,32851113,8/28/20,pubmed,0,4,mathematical model,0.002130727,0.002130714,0.002130724,0.959805795,0.002130733,0.031671307,Epidemiology,0.29355672,FALSE,246.25,0.983858,188,0.910891089,1,0.537564047,,,0.810771045
7426,The Correlation Between Clinical Features and Viral RNA Shedding in Outpatients With COVID-19.,Open Forum Infect Dis,32851112,8/28/20,pubmed,0,8,logistic regression,0.001098828,0.312008672,0.001098811,0.001098818,0.001098862,0.683596009,Clinics,0.87644607,TRUE,33.625,0.472447276,20.875,0.48963072,1,0.537564047,,,0.499880681
7427,Insights Into Dynamics of Inhibitor and Ubiquitin-Like Protein Binding in SARS-CoV-2 Papain-Like Protease.,Front Mol Biosci,32850963,8/28/20,pubmed,0,5,computational,0.980260882,0.000999529,0.000999541,0.000999572,0.015740858,0.000999619,Drug discovery,0.50241905,TRUE,28.4,0.411157153,13.6,0.406208188,4,0.707574542,,,0.508313294
7428,"Identify the Risk Factors of COVID-19-Related Acute Kidney Injury: A Single-Center, Retrospective Cohort Study.",Front Med (Lausanne),32850917,8/28/20,pubmed,0,14,logistic regression,0.043274388,0.002490521,0.113937535,0.002490651,0.002490483,0.835316423,Clinics,0.92550564,TRUE,82.57142857,0.816500711,,,1,0.537564047,,,0.677032379
7429,Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests.,Front Cell Dev Biol,32850809,8/28/20,pubmed,0,13,machine learning,0.126398073,0.001987186,0.721537973,0.001987185,0.001987217,0.146102366,Clinics,0.8632749,TRUE,72.61538462,0.775929247,32.61538462,0.589710998,5,0.739490092,,,0.701710112
7430,Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019.,Front Bioeng Biotechnol,32850746,8/28/20,pubmed,0,15,"deep learning, artificial intelligence, neural network, dataset",0.001085323,0.001085324,0.566284877,0.001085377,0.001085363,0.429373735,Imaging,0.6421607,TRUE,48.06666667,0.615065867,18,0.46180091,6,0.764429903,,,0.61376556
7431,A Predicting Nomogram for Mortality in Patients With COVID-19.,Front Public Health,32850612,8/28/20,pubmed,0,7,"logistic regression, prediction model",0.0015382,0.00153813,0.001538205,0.23914102,0.001538113,0.754706333,Clinics,0.8353849,TRUE,36.42857143,0.501762632,12.14285714,0.387008295,5,0.739490092,,,0.542753673
7432,Case Report: Benign Infantile Seizures Temporally Associated With COVID-19.,Front Pediatr,32850563,8/28/20,pubmed,0,11,"sequencing, exom",0.084703904,0.371721927,0.03663914,0.001059416,0.165777452,0.340098162,Genomics,0.81499267,TRUE,30,0.432432432,79.18181818,0.776224244,7,0.785110192,,,0.664588956
7433,COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings.,Int J Biomed Imaging,32849861,8/28/20,pubmed,0,4,"deep learning, neural network, prediction model",0.001861712,0.001861712,0.966624336,0.001861704,0.001861807,0.02592873,Imaging,0.6429305,TRUE,21.25,0.314614386,1,0.122023013,7,0.785110192,,,0.407249197
7434,Overview of Immune Response During SARS-CoV-2 Infection: Lessons From the Past.,Front Immunol,32849654,8/28/20,pubmed,0,5,sequencing,0.614759598,0.179746141,0.000907321,0.090439382,0.000907349,0.11324021,Drug discovery,0.48208869,FALSE,19.8,0.293957573,19.8,0.478257961,34,0.937156615,,,0.569790716
7435,Contriving Multi-Epitope Subunit of Vaccine for COVID-19: Immunoinformatics Approaches.,Front Immunol,32849643,8/28/20,pubmed,0,4,in silico,0.902383799,0.001034672,0.001034628,0.001034661,0.001034692,0.093477549,Drug discovery,0.6633903,TRUE,6.25,0.088564537,19.75,0.477990367,9,0.814309525,,,0.460288143
7436,CoronaVR: A Computational Resource and Analysis of Epitopes and Therapeutics for Severe Acute Respiratory Syndrome Coronavirus-2.,Front Microbiol,32849449,8/28/20,pubmed,0,16,"computational, bioinformatic, genomes",0.519464468,0.353617577,0.00105942,0.098449993,0.00105939,0.026349151,Drug discovery,0.6340514,TRUE,11.875,0.178737089,3.125,0.200160557,3,0.667819001,,,0.348905549
7437,Directly Acting Antivirals for COVID-19: Where Do We Stand?,Front Microbiol,32849448,8/28/20,pubmed,0,4,genomic structure,0.873783234,0.04722312,0.001511876,0.001511919,0.001511917,0.074457934,Drug discovery,0.84074986,TRUE,33,0.466757375,17.75,0.458322184,3,0.667819001,,,0.530966187
7438,Oral Microbiome and SARS-CoV-2: Beware of Lung Co-infection.,Front Microbiol,32849438,8/28/20,pubmed,0,6,microbiom,0.398884681,0.280435553,0.002032861,0.002032949,0.002032918,0.314581038,Drug discovery,0.96958447,TRUE,85.66666667,0.827509432,7.333333333,0.304656141,10,0.828199272,,,0.653454948
7439,"Social Distancing and Stigma: Association Between Compliance With Behavioral Recommendations, Risk Perception, and Stigmatizing Attitudes During the COVID-19 Outbreak.",Front Psychol,32849073,8/28/20,pubmed,0,3,logistic regression,0.001237121,0.001237227,0.001237096,0.001237163,0.993814331,0.001237063,Healthcare,0.9685666,TRUE,11,0.167171748,15.66666667,0.432365534,7,0.785110192,,,0.461549158
7440,COVID-19 Pandemic and Lockdown Measures Impact on Mental Health Among the General Population in Italy.,Front Psychiatry,32848952,8/28/20,pubmed,0,10,logistic regression,0.001438102,0.001438112,0.001438094,0.001438116,0.992809468,0.001438109,Healthcare,0.9755384,TRUE,44.5,0.581977859,30.4,0.572451164,128,0.983826162,,,0.712751728
7441,DPP4 and ACE2 in Diabetes and COVID-19: Therapeutic Targets for Cardiovascular Complications?,Front Pharmacol,32848769,8/28/20,pubmed,0,6,in silico,0.654757122,0.001511869,0.001511905,0.001511903,0.001511877,0.339195323,Drug discovery,0.85611165,TRUE,52,0.647349867,125.3333333,0.858241905,17,0.887338725,,,0.797643499
7442,"Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing.",Nat Hum Behav,32848231,8/28/20,pubmed,0,36,predictive model,0.002238513,0.002238593,0.002238566,0.414260874,0.576784711,0.002238744,Healthcare,0.23469877,FALSE,45.86111111,0.594718288,82.66666667,0.78492106,17,0.887338725,,,0.755659357
7443,Dynamics of the ACE2-SARS-CoV-2/SARS-CoV spike protein interface reveal unique mechanisms.,Sci Rep,32848162,8/28/20,pubmed,0,2,molecular dynamics simulation,0.994142172,0.001171561,0.001171565,0.001171605,0.001171566,0.00117153,Drug discovery,0.5173206,TRUE,34,0.477766096,20.5,0.486218892,37,0.942712513,,,0.635565834
7444,Development and Clinical Application of a Rapid and Sensitive Loop-Mediated Isothermal Amplification Test for SARS-CoV-2 Infection.,mSphere,32848011,8/28/20,pubmed,0,15,sequencing,0.000871562,0.599483384,0.190278162,0.000871587,0.000871592,0.207623713,Genomics,0.73535013,TRUE,42.8,0.565279238,11.8,0.38252609,11,0.840175319,,,0.595993549
7445,Diabetes as a Risk Factor for Poor Early Outcomes in Patients Hospitalized With COVID-19.,Diabetes Care,32847827,8/28/20,pubmed,0,13,logistic regression,0.001219985,0.001219993,0.001219991,0.001220014,0.001220011,0.993900006,Clinics,0.9775273,TRUE,101.3076923,0.871544313,313.1538462,0.957987691,11,0.840175319,,,0.889902441
7446,Vascular underpinning of COVID-19.,Open Biol,32847471,8/28/20,pubmed,0,4,sequencing,0.687508011,0.030628293,0.001310353,0.001310435,0.001310461,0.277932446,Drug discovery,0.38911378,FALSE,12.25,0.185107304,9.5,0.345531175,4,0.707574542,,,0.412737673
7447,Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling.,SAR QSAR Environ Res,32847369,8/28/20,pubmed,0,5,"predictive model, dataset",0.573021273,0.030406421,0.264131207,0.103526486,0.026833964,0.002080647,Drug discovery,0.7505379,TRUE,32,0.455810502,8.2,0.322785657,1,0.537564047,,,0.438720069
7448,Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional survey.,PLoS One,32845928,8/28/20,pubmed,0,5,logistic regression,0.001786529,0.001786557,0.001786543,0.001786595,0.991067198,0.001786578,Healthcare,0.8384526,TRUE,6.6,0.094007051,2.4,0.174872893,23,0.91129082,,,0.393390255
7449,Association of Socioeconomic Changes due to the COVID-19 Pandemic With Health Outcomes in Patients With Skin Diseases: Cross-Sectional Survey Study.,J Med Internet Res,32845850,8/28/20,pubmed,0,10,logistic regression,0.001171539,0.001171549,0.001171733,0.001171723,0.856375345,0.138938111,Healthcare,0.9906907,TRUE,67.3,0.749087761,,,0,0.403234768,,,0.576161264
7450,Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT.,IEEE J Biomed Health Inform,32845849,8/28/20,pubmed,0,18,"machine learning, dataset",0.001171529,0.001171542,0.994142296,0.001171548,0.001171534,0.001171551,Imaging,0.15109995,FALSE,33.41176471,0.470591873,25,0.529435376,24,0.914439163,,,0.638155471
7451,Central Nervous System Targets and Routes for SARS-CoV-2: Current Views and New Hypotheses.,ACS Chem Neurosci,32845609,8/28/20,pubmed,0,1,transcriptom,0.888059001,0.001415141,0.001415132,0.001415158,0.001415179,0.106280388,Drug discovery,0.36228552,FALSE,250,0.984538314,129,0.86272411,6,0.764429903,,,0.870564109
7452,"The diagnostic methods in the COVID-19 pandemic, today and in the future.",Expert Rev Mol Diagn,32845192,8/28/20,pubmed,0,12,sequencing,0.001901761,0.648338835,0.29730387,0.001901834,0.001901731,0.048651968,Genomics,0.51065516,TRUE,30.83333333,0.441833137,20.16666667,0.481803586,3,0.667819001,,,0.530485241
7453,Severe clinical spectrum with high mortality in pediatric patients with COVID-19 and multisystem inflammatory syndrome.,Clinics (Sao Paulo),32844958,8/28/20,pubmed,0,30,logistic regression,0.001291254,0.094751633,0.001291238,0.001291219,0.02246473,0.878909926,Clinics,0.5862758,TRUE,42.8,0.565279238,18.63333333,0.46601552,9,0.814309525,,,0.615201428
7454,Histopathological findings and viral tropism in UK patients with severe fatal COVID-19: a post-mortem study.,Lancet Microbe,32844161,8/28/20,pubmed,0,18,genomes,0.122056937,0.190029976,0.00095634,0.000956333,0.000956351,0.685044063,Clinics,0.763444,TRUE,85.83333333,0.828127899,105.9444444,0.832954241,76,0.971664918,,,0.877582353
7455,Evaluation of the effect of the state of emergency for the first wave of COVID-19 in Japan.,Infect Dis Model,32844135,8/28/20,pubmed,0,1,mathematical model,0.005047524,0.005047466,0.005047488,0.9747624,0.005047582,0.005047539,Epidemiology,0.71640795,TRUE,92,0.848970252,36,0.611118544,6,0.764429903,,,0.741506233
7456,Mathematical modelling on diffusion and control of COVID-19.,Infect Dis Model,32844134,8/28/20,pubmed,0,1,"mathematical model, dataset",0.001350322,0.001350375,0.001350341,0.993248271,0.001350339,0.001350353,Epidemiology,0.26255786,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
7457,"Integrative analyses of SARS-CoV-2 genomes from different geographical locations reveal unique features potentially consequential to host-virus interaction, pathogenesis and clues for novel therapies.",Heliyon,32844125,8/28/20,pubmed,0,4,"bioinformatic, genome sequences, genomes",0.679588348,0.314259049,0.00153812,0.001538265,0.001538096,0.001538122,Drug discovery,0.67556953,TRUE,28,0.408312202,5.25,0.260971367,16,0.881782826,,,0.517022132
7458,ACE2 coding variants in different populations and their potential impact on SARS-CoV-2 binding affinity.,Biochem Biophys Rep,32844124,8/28/20,pubmed,0,4,computational,0.283886474,0.698872959,0.004309991,0.004310285,0.004310217,0.004310074,Genomics,0.4279283,FALSE,27.5,0.401570907,5.75,0.271742039,1,0.537564047,,,0.403625665
7459,A first public dataset from Brazilian twitter and news on COVID-19 in Portuguese.,Data Brief,32844106,8/28/20,pubmed,0,2,dataset,0.002720244,0.002720196,0.002720312,0.986398816,0.002720343,0.002720088,Epidemiology,0.29883063,FALSE,5.5,0.077246583,4,0.231469093,1,0.537564047,,,0.282093241
7460,Covid-19 Dataset: Worldwide spread log including countries first case and first death.,Data Brief,32844105,8/28/20,pubmed,0,4,"machine learning, dataset",0.001415105,0.001415141,0.144283604,0.778004929,0.001415227,0.073465994,Epidemiology,0.050156772,FALSE,26.5,0.389325252,4.5,0.242708055,0,0.403234768,,,0.345089358
7461,Increasing access to care: telehealth during COVID-19.,J Law Biosci,32843985,8/28/20,pubmed,0,1,artificial intelligence,0.002032869,0.002032786,0.213461038,0.554446846,0.225993457,0.002033003,Epidemiology,0.6167303,TRUE,96,0.858927577,35,0.605298368,6,0.764429903,,,0.742885283
7462,HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images.,Appl Soft Comput,32843887,8/28/20,pubmed,0,3,machine learning,0.000907284,0.000907293,0.995463407,0.000907332,0.000907316,0.000907369,Imaging,0.5495806,TRUE,102.6666667,0.874760344,32.66666667,0.59011239,5,0.739490092,,,0.734787608
7463,"A systematic review on recent trends in transmission, diagnosis, prevention and imaging features of COVID-19.",Process Biochem,32843849,8/28/20,pubmed,0,6,"deep learning, artificial intelligence",0.001310366,0.194724654,0.437048216,0.251901806,0.113704621,0.001310337,Imaging,0.7188225,TRUE,90.83333333,0.8451976,57.16666667,0.711667113,16,0.881782826,,,0.812882513
7464,Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study.,Chaos Solitons Fractals,32843824,8/28/20,pubmed,0,5,"deep learning, neural network, lstm, dataset",0.001861784,0.04474353,0.300965374,0.648705823,0.001861695,0.001861794,Epidemiology,0.6386991,TRUE,29,0.41993939,5,0.257024351,11,0.840175319,,,0.50571302
7465,Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm.,Chaos Solitons Fractals,32843823,8/28/20,pubmed,0,1,"machine learning, dataset",0.001943437,0.001943503,0.343549354,0.615238282,0.035381947,0.001943477,Epidemiology,0.44643655,FALSE,25,0.369286907,6,0.280037463,11,0.840175319,,,0.496499897
7466,Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome.,Sci Rep,32843695,8/28/20,pubmed,0,4,"computational, proteom",0.86544416,0.001415207,0.128895136,0.001415198,0.001415171,0.001415129,Drug discovery,0.47233933,FALSE,215.5,0.977116705,169,0.898514851,2,0.618927094,,,0.83151955
7467,Origin and cross-species transmission of bat coronaviruses in China.,Nat Commun,32843626,8/28/20,pubmed,0,15,bayes,0.002357805,0.988210931,0.002357819,0.00235794,0.00235776,0.002357745,Genomics,0.61426747,TRUE,93.86666667,0.854165378,400.2,0.97123361,7,0.785110192,,,0.870169727
7468,Psychological impact of COVID-19 pandemic on postgraduate trainees: a cross-sectional survey.,Postgrad Med J,32843485,8/28/20,pubmed,0,4,logistic regression,0.001461911,0.001461887,0.001461937,0.001461994,0.949842928,0.044309343,Healthcare,0.96862984,TRUE,15.5,0.234028078,10,0.355632861,5,0.739490092,,,0.443050344
7469,What has changed during the state of emergency due to COVID-19 on an Academic Urology Department of a Tertiary Hospital in Portugal.,Actas Urol Esp,32843150,8/28/20,pubmed,0,7,logistic regression,0.001415137,0.00141515,0.001415206,0.06394268,0.206105584,0.725706243,Clinics,0.8828915,TRUE,10,0.15214299,,,0,0.403234768,,,0.277688879
7470,Application of records theory on the COVID-19 pandemic in Lebanon: prediction and prevention.,Epidemiol Infect,32843111,8/28/20,pubmed,0,4,"machine learning, mathematical model",0.001538097,0.001538137,0.140397983,0.853449527,0.001538136,0.001538119,Epidemiology,0.18452972,FALSE,5.75,0.080957388,0.25,0.065493712,0,0.403234768,,,0.183228622
7471,A five-compartment model of age-specific transmissibility of SARS-CoV-2.,Infect Dis Poverty,32843094,8/28/20,pubmed,0,24,"model fit, mathematical model, dataset",0.001112629,0.068550549,0.00111265,0.213143352,0.046607828,0.669472992,Clinics,0.63345075,TRUE,44.29166667,0.579751376,,,4,0.707574542,,,0.643662959
7472,Differential diagnosis of coronavirus disease 2019 from community-acquired-pneumonia by computed tomography scan and follow-up.,Infect Dis Poverty,32843064,8/28/20,pubmed,0,9,correlation analysis,0.000977522,0.000977451,0.463381567,0.000977486,0.000977461,0.532708512,Clinics,0.95010746,TRUE,34.55555556,0.482590142,19,0.471367407,0,0.403234768,,,0.452397439
7473,In Silico Identification of Potential Natural Product Inhibitors of Human Proteases Key to SARS-CoV-2 Infection.,Molecules,32842606,8/28/20,pubmed,0,5,"virtual screening, in silico",0.971798716,0.001187311,0.00118732,0.023452095,0.001187278,0.001187281,Drug discovery,0.9549824,TRUE,28.2,0.409734677,9.2,0.339577201,8,0.799987654,,,0.516433177
7474,0,Molecules,32842509,8/28/20,pubmed,0,6,"virtual screening, in silico",0.926470664,0.001943574,0.001943566,0.065755246,0.001943505,0.001943446,Drug discovery,0.7630031,TRUE,54,0.661574618,43.16666667,0.650187316,12,0.850299401,,,0.720687112
7475,The Association between Influenza and Pneumococcal Vaccinations and SARS-Cov-2 Infection: Data from the EPICOVID19 Web-Based Survey.,Vaccines (Basel),32842505,8/28/20,pubmed,0,16,logistic regression,0.002238482,0.284778396,0.002238525,0.002238532,0.70626749,0.002238574,Healthcare,0.8217336,TRUE,129.75,0.921392789,85.625,0.791811614,10,0.828199272,,,0.847134558
7476,Viral Vectors Applied for RNAi-Based Antiviral Therapy.,Viruses,32842491,8/28/20,pubmed,0,1,bioinformatic,0.860156777,0.080122082,0.001823432,0.00182344,0.054250943,0.001823325,Drug discovery,0.6795093,TRUE,69,0.759416167,56,0.706850415,2,0.618927094,,,0.695064559
7477,[Construction of urban scale-free network model and its epidemiological significance in the prevention and control of COVID-19].,Zhonghua Yu Fang Yi Xue Za Zhi,32842308,8/26/20,pubmed,0,8,network model,0.003335414,0.003335407,0.161573006,0.681384686,0.147036163,0.003335324,Epidemiology,0.59698576,TRUE,13.5,0.205393036,0.25,0.065493712,0,0.403234768,,,0.224707172
7478,[Predictive value of neutrophil/lymphocyte ratio on myocardial injury in severe COVID-19 patients].,Zhonghua Xin Xue Guan Bing Za Zhi,32842269,8/26/20,pubmed,0,8,logistic regression,0.000871554,0.000871548,0.000871544,0.000871555,0.000871542,0.995642257,Clinics,0.99010956,TRUE,12.875,0.192961841,2.75,0.187583623,0,0.403234768,,,0.261260077
7479,Computed Tomography Radiomics Can Predict Disease Severity and Outcome in Coronavirus Disease 2019 Pneumonia.,J Comput Assist Tomogr,32842058,8/26/20,pubmed,0,9,"radiom, logistic regression",0.000838554,0.000838689,0.405312445,0.00083856,0.01570726,0.576464492,Clinics,0.96089137,TRUE,11.55555556,0.174655204,16.77777778,0.446280439,2,0.618927094,,,0.413287579
7480,COVID-19 surveillance in Southeastern Virginia using wastewater-based epidemiology.,Water Res,32841929,8/26/20,pubmed,0,10,dataset,0.074449741,0.65939607,0.001943632,0.260323433,0.001943606,0.001943518,Genomics,0.35568577,FALSE,40.6,0.54530274,54.7,0.70049505,50,0.957775171,,,0.73452432
7481,A genetic barcode of SARS-CoV-2 for monitoring global distribution of different clades during the COVID-19 pandemic.,Int J Infect Dis,32841689,8/26/20,pubmed,0,13,"sequencing, genomes",0.001438178,0.992809316,0.001438198,0.001438103,0.001438095,0.001438109,Genomics,0.35776246,FALSE,25,0.369286907,30.76923077,0.575127107,11,0.840175319,,,0.594863111
7482,Virus-Receptor Interactions of Glycosylated SARS-CoV-2 Spike and Human ACE2 Receptor.,Cell Host Microbe,32841605,8/26/20,pubmed,0,16,"molecular dynamics simulation, bioinformatic, proteom, glycomics, glycoproteom",0.79500433,0.154902122,0.001751235,0.044839946,0.001751232,0.001751134,Drug discovery,0.30762345,FALSE,72.5625,0.77568186,80.25,0.779636072,15,0.874313229,,,0.809877054
7483,Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding.,Cell,32841599,8/26/20,pubmed,0,13,dataset,0.610289821,0.382564096,0.001786546,0.001786537,0.001786507,0.001786493,Drug discovery,0.38543612,FALSE,54.61538462,0.665594656,120.4615385,0.852020337,23,0.91129082,,,0.809635271
7484,Association between Body Mass Index and Risk of COVID-19: A Nationwide Case-Control Study in South Korea.,Clin Infect Dis,32841322,8/26/20,pubmed,0,8,logistic regression,0.001943479,0.001943484,0.001943513,0.001943529,0.22124042,0.770985574,Clinics,0.7320025,TRUE,53.375,0.656441338,16.875,0.44715012,5,0.739490092,,,0.614360517
7485,Human angiotensin-converting enzyme 2 transgenic mice infected with SARS-CoV-2 develop severe and fatal respiratory disease.,JCI Insight,32841215,8/26/20,pubmed,0,17,transcriptom,0.600878872,0.051854094,0.001653108,0.00165308,0.120647002,0.223313844,Drug discovery,0.5672097,TRUE,20.11764706,0.298596079,,,6,0.764429903,,,0.531512991
7486,COVID-19 re-infection by a phylogenetically distinct SARS-coronavirus-2 strain confirmed by whole genome sequencing.,Clin Infect Dis,32840608,8/26/20,pubmed,0,20,"sequencing, whole genome, genomes",0.04790883,0.760375429,0.017450295,0.0011873,0.001187299,0.171890847,Genomics,0.39374822,FALSE,41.95,0.556373307,131.45,0.865065561,220,0.992839064,,,0.804759311
7487,Socio-demographic heterogeneity in the prevalence of COVID-19 during lockdown is associated with ethnicity and household size: Results from an observational cohort study.,EClinicalMedicine,32840492,8/26/20,pubmed,0,19,logistic regression,0.001141347,0.072461584,0.001141345,0.00114141,0.561045191,0.363069123,Healthcare,0.39879334,FALSE,112.2631579,0.894427608,124.6315789,0.857238427,26,0.920859312,,,0.890841782
7488,"Risk factors for developing into critical COVID-19 patients in Wuhan, China: A multicenter, retrospective, cohort study.",EClinicalMedicine,32840491,8/26/20,pubmed,0,24,logistic regression,0.001141363,0.001141383,0.001141366,0.001141393,0.001141454,0.994293041,Clinics,0.8199218,TRUE,21.16666667,0.31319191,,,13,0.858880178,,,0.586036044
7489,Alkamides and Piperamides as Potential Antivirals against the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2).,J Phys Chem Lett,32840378,8/26/20,pubmed,0,5,molecular dynamics simulation,0.98959716,0.002080621,0.002080555,0.00208057,0.002080551,0.002080542,Drug discovery,0.9493135,TRUE,23.8,0.351227658,21.8,0.501204174,5,0.739490092,,,0.530640641
7490,Correction to: A machine learning forecasting model for COVID-19 pandemic in India.,Stoch Environ Res Risk Assess,32840243,8/26/20,pubmed,0,3,"machine learning, forecasting model",0.057799812,0.057799601,0.057801681,0.711000481,0.057799162,0.057799264,Epidemiology,0.771267,TRUE,26.66666667,0.390995114,3,0.199424672,4,0.707574542,,,0.432664776
7491,[Research on coronavirus disease 2019 (COVID-19) detection method based on depthwise separable DenseNet in chest X-ray images].,Sheng Wu Yi Xue Gong Cheng Xue Za Zhi,32840070,8/26/20,pubmed,0,6,"deep learning, dataset",0.001415142,0.001415111,0.992923916,0.001415246,0.00141526,0.001415325,Imaging,0.022406816,FALSE,98.16666667,0.865112252,62.83333333,0.731736687,0,0.403234768,,,0.666694569
7492,Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity.,bioRxiv,32839779,8/26/20,pubmed,0,36,"genomes, dataset",0.589442264,0.195597292,0.001291285,0.001291252,0.111430463,0.100947444,Drug discovery,0.59447753,TRUE,91.19444444,0.846558229,,,10,0.828199272,,,0.83737875
7493,SARS-CoV-2 ORF9c Is a Membrane-Associated Protein that Suppresses Antiviral Responses in Cells.,bioRxiv,32839770,8/26/20,pubmed,0,13,"bioinformatic, transcriptom, proteom, interactom",0.945252303,0.047742983,0.001751163,0.001751162,0.00175118,0.00175121,Drug discovery,0.17442721,FALSE,54.84615385,0.667140825,236.1538462,0.932633128,7,0.785110192,,,0.794961382
7494,Technology-Based Interventions for Nursing Home Residents: Implications for Nursing Home Practice Amid and Beyond the Influence of COVID-19: A Systematic Review Protocol.,Res Sq,32839768,8/26/20,pubmed,0,13,digital health,0.00087155,0.00087155,0.000871564,0.668543213,0.32797058,0.000871543,Epidemiology,0.9505684,TRUE,53.38461538,0.656503185,22,0.503746321,0,0.403234768,,,0.521161424
7495,Feasibility of using alternative swabs and storage solutions for paired SARS-CoV-2 detection and microbiome analysis in the hospital environment.,Res Sq,32839765,8/26/20,pubmed,0,14,microbiom,0.081670371,0.547974602,0.083994784,0.153242359,0.023624388,0.109493497,Genomics,0.8675239,TRUE,69.57142857,0.761333416,252.8571429,0.938988493,2,0.618927094,,,0.773083001
7496,Snapshot Impact of COVID-19 on Mental Wellness in Nonphysician Otolaryngology Health Care Workers: A National Study.,OTO Open,32839747,8/26/20,pubmed,0,21,logistic regression,0.001171562,0.001171648,0.001171573,0.001171578,0.907432402,0.087881237,Healthcare,0.97598565,TRUE,63.28571429,0.723112128,31.14285714,0.578806529,5,0.739490092,,,0.680469583
7497,"miRNA target prediction might explain the reduced transmission of SARS-CoV-2 in Jordan, Middle East.",Noncoding RNA Res,32839745,8/26/20,pubmed,0,2,transcriptom,0.610268043,0.362954437,0.001237067,0.001237072,0.001237134,0.023066248,Drug discovery,0.38779187,FALSE,3.5,0.044344115,0,0.055525823,1,0.537564047,,,0.212477995
7498,Preventing COVID-19 from the perspective of industrial information integration: Evaluation and continuous improvement of information networks for sustainable epidemic prevention.,J Ind Inf Integr,32839741,8/26/20,pubmed,0,3,network analysis,0.002183368,0.002183245,0.1970938,0.717123967,0.079232315,0.002183305,Epidemiology,0.6560503,TRUE,59,0.696579875,11.66666667,0.38065293,13,0.858880178,,,0.645370994
7499,AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app.,Inform Med Unlocked,32839734,8/26/20,pubmed,0,9,"artificial intelligence, transfer learning",0.001310384,0.001310338,0.795321009,0.001310375,0.199437521,0.001310372,Healthcare,0.49421778,FALSE,12.375,0.186839013,1.25,0.127776291,73,0.970677202,,,0.428430835
7500,Generated time-series prediction data of COVID-19's daily infections in Brazil by using recurrent neural networks.,Data Brief,32839733,8/26/20,pubmed,0,1,"neural network, forecasting model",0.001538108,0.001538104,0.365208334,0.628639172,0.001538168,0.001538114,Epidemiology,0.5759998,TRUE,7,0.10179974,1,0.122023013,3,0.667819001,,,0.297213918
7501,When pandemics impact economies and climate change: Exploring the impacts of COVID-19 on oil and electricity demand in China.,Energy Res Soc Sci,32839693,8/26/20,pubmed,0,4,"neural network, network model",0.067825414,0.002996549,0.062187682,0.860997201,0.002996677,0.002996477,Epidemiology,0.60077065,TRUE,15.5,0.234028078,2.5,0.180826866,24,0.914439163,,,0.443098036
7502,"The Need for Developing Technology-Enabled, Safe, and Ethical Workforce for Healthcare Delivery.",Saf Health Work,32839672,8/26/20,pubmed,0,2,"artificial intelligence, digital health",0.003101593,0.003101549,0.213051569,0.437762103,0.33988175,0.003101436,Epidemiology,0.83731174,TRUE,25.5,0.37435834,7.5,0.307867273,0,0.403234768,,,0.361820127
7503,Optimal multiparametric set-up modelled for best survival outcomes in palliative treatment of liver malignancies: unsupervised machine learning and 3 PM recommendations.,EPMA J,32839667,8/26/20,pubmed,0,6,"machine learning, predictive model",0.13557165,0.126777159,0.100296181,0.11446035,0.000880222,0.522014439,Clinics,0.77002287,TRUE,28.33333333,0.410724225,6.166666667,0.281977522,4,0.707574542,,,0.466758763
7504,"COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine.",EPMA J,32839666,8/26/20,pubmed,0,8,digital health,0.000625241,0.000625237,0.104892464,0.793090764,0.100141043,0.00062525,Epidemiology,0.9297097,TRUE,14.875,0.223328592,3.75,0.21982874,10,0.828199272,,,0.423785534
7505,"Factors determining the knowledge and prevention practice of healthcare workers towards COVID-19 in Amhara region, Ethiopia: a cross-sectional survey.",Trop Med Health,32839649,8/26/20,pubmed,0,1,logistic regression,0.000936069,0.000936079,0.000936087,0.000936089,0.979201984,0.017053693,Healthcare,0.98236096,TRUE,22,0.326056033,13,0.400521809,11,0.840175319,,,0.522251054
7506,Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities.,Chaos Solitons Fractals,32839644,8/26/20,pubmed,0,3,"deep learning, lstm",0.002357871,0.002357852,0.399417199,0.591151315,0.002357894,0.00235787,Epidemiology,0.12027657,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
7507,"Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran.",Chaos Solitons Fractals,32839643,8/26/20,pubmed,0,5,"deep learning, forecasting model, lstm",0.001461895,0.001461875,0.087645469,0.906506991,0.001461908,0.001461861,Epidemiology,0.15871775,FALSE,26.4,0.386913229,3.6,0.21507894,3,0.667819001,,,0.42327039
7508,"Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM.",Chaos Solitons Fractals,32839642,8/26/20,pubmed,0,3,"deep learning, prediction model, lstm",0.001392858,0.001392845,0.196172071,0.798256429,0.001392855,0.001392942,Epidemiology,0.6324091,TRUE,21.66666667,0.320737213,3,0.199424672,15,0.874313229,,,0.464825038
7509,"COVID-19 outbreak, social response, and early economic effects: a global VAR analysis of cross-country interdependencies.",J Popul Econ,32839640,8/26/20,pubmed,0,1,dataset,0.002080604,0.002080685,0.002080605,0.81895997,0.172717584,0.002080552,Epidemiology,0.52644855,TRUE,13,0.197352959,6,0.280037463,2,0.618927094,,,0.365439172
7510,Identification of potential SARS-CoV-2 inhibitors from South African medicinal plant extracts using molecular modelling approaches.,S Afr J Bot,32839635,8/26/20,pubmed,0,5,"molecular dynamics simulation, in silico",0.857535038,0.138123509,0.001085339,0.001085367,0.00108537,0.001085378,Drug discovery,0.9089016,TRUE,35.8,0.49495949,6.2,0.282512711,1,0.537564047,,,0.438345416
7511,Investigating the potential antiviral activity drugs against SARS-CoV-2 by molecular docking simulation.,J Mol Liq,32839634,8/26/20,pubmed,0,1,"computational, bioinformatic",0.85333264,0.082274555,0.060519008,0.001291298,0.001291282,0.001291217,Drug discovery,0.7879597,TRUE,30,0.432432432,4,0.231469093,5,0.739490092,,,0.467797206
7512,"Association between renin-angiotensin-aldosterone system inhibitor treatment, neutrophil-lymphocyte ratio, D-Dimer and clinical severity of COVID-19 in hospitalized patients: a multicenter, observational study.",J Hum Hypertens,32839534,8/26/20,pubmed,0,12,bayes,0.057753625,0.001126798,0.001126782,0.001126851,0.001126827,0.937739116,Clinics,0.82872355,TRUE,45.83333333,0.594223514,11.66666667,0.38065293,7,0.785110192,,,0.586662212
7513,The Antiviral and Antimalarial Drug Repurposing in Quest of Chemotherapeutics to Combat COVID-19 Utilizing Structure-Based Molecular Docking.,Comb Chem High Throughput Screen,32838713,8/26/20,pubmed,0,4,"in silico, in-silico",0.941537452,0.001823471,0.001823383,0.051168928,0.001823407,0.001823358,Drug discovery,0.923913,TRUE,79.75,0.806110458,19.5,0.475983409,1,0.537564047,,,0.606552638
7514,Structure-based drug repurposing for targeting Nsp9 replicase and spike proteins of severe acute respiratory syndrome coronavirus 2.,J Biomol Struct Dyn,32838660,8/26/20,pubmed,0,6,"virtual screening, bioinformatic",0.994703243,0.001059353,0.001059361,0.001059364,0.001059332,0.001059346,Drug discovery,0.9808972,TRUE,39.16666667,0.531510916,7.666666667,0.310877709,7,0.785110192,,,0.542499606
7515,"Will COVID-19 impact upon pregnancy, childhood and adult outcomes? A call to establish national longitudinal datasets.",J Psychosom Obstet Gynaecol,32838631,8/26/20,pubmed,0,2,dataset,0.013549352,0.013549527,0.377099837,0.013550163,0.568701273,0.013549848,Healthcare,0.33301544,FALSE,67.5,0.750201002,115,0.844594595,0,0.403234768,,,0.666010121
7516,Psychometric properties of the Pandemic-Related Pregnancy Stress Scale (PREPS).,J Psychosom Obstet Gynaecol,32838629,8/26/20,pubmed,0,3,model fit,0.001717302,0.001717213,0.001717264,0.001717304,0.991413728,0.001717189,Healthcare,0.98017037,TRUE,27.66666667,0.403488156,10.33333333,0.36038266,5,0.739490092,,,0.501120303
7517,Dynamic causal modelling of COVID-19.,Wellcome Open Res,32832701,8/26/20,pubmed,0,12,bayes,0.002422325,0.002422292,0.075066261,0.915244497,0.002422383,0.002422241,Epidemiology,0.25233015,FALSE,280.9166667,0.988867586,1434.75,0.996855767,36,0.941169208,,,0.975630854
7518,Radiomics-based model for accurately distinguishing between severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) and influenza A infected pneumonia.,MedComm (Beijing),32838396,8/25/20,pubmed,0,11,radiom,0.001684545,0.00168499,0.635531011,0.001684515,0.001684518,0.35773042,Imaging,0.48317438,FALSE,57.09090909,0.682416971,22.45454545,0.506422264,2,0.618927094,,,0.602588776
7519,Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients.,J Am Coll Emerg Physicians Open,32838390,8/25/20,pubmed,0,8,"artificial intelligence, deep-learning, prediction model, dataset",0.000936097,0.000936068,0.400447504,0.000936091,0.000936093,0.595808147,Clinics,0.8986913,TRUE,25.375,0.372070011,11.875,0.383596468,8,0.799987654,,,0.518551377
7520,Reducing aerosol dispersion by High Flow Therapy in COVID-19: High Resolution Computational Fluid Dynamics Simulations of Particle Behavior during High Velocity Nasal Insufflation with a Simple Surgical Mask.,J Am Coll Emerg Physicians Open,32838373,8/25/20,pubmed,0,8,"computational, in-silico",0.001310407,0.001310382,0.001310368,0.719853124,0.070552807,0.205662912,Epidemiology,0.26998895,FALSE,32.875,0.464716433,16.25,0.438988493,2,0.618927094,,,0.507544007
7521,Patient factors associated with SARS-CoV-2 in an admitted emergency department population.,J Am Coll Emerg Physicians Open,32838371,8/25/20,pubmed,0,10,logistic regression,0.001751199,0.001751235,0.00175123,0.02691572,0.00175126,0.966079357,Clinics,0.7400337,TRUE,37.8,0.516667697,24.1,0.521942735,4,0.707574542,,,0.582061658
7522,Epidemiology of the 2020 pandemic of COVID-19 in the state of Georgia: Inadequate critical care resources and impact after 7 weeks of community spread.,J Am Coll Emerg Physicians Open,32838368,8/25/20,pubmed,0,4,dataset,0.001511811,0.001511848,0.001511823,0.516092503,0.34889887,0.130473145,Epidemiology,0.65217996,TRUE,118,0.904632321,112.25,0.841584158,5,0.739490092,,,0.828568857
7523,Virus-Host Interactome and Proteomic Survey Reveal Potential Virulence Factors Influencing SARS-CoV-2 Pathogenesis.,Med (N Y),32838362,8/25/20,pubmed,0,17,"proteom, interactom, genome-wide",0.54118999,0.024801583,0.018217141,0.290250194,0.000854757,0.124686335,Drug discovery,0.83527434,TRUE,22.64705882,0.334776424,7.941176471,0.315025421,42,0.949503056,,,0.533101634
7524,Investigating Ketone Bodies as Immunometabolic Countermeasures against Respiratory Viral Infections.,Med (N Y),32838361,8/25/20,pubmed,0,7,immunome,0.675496844,0.002296678,0.002296654,0.1577168,0.002296604,0.15989642,Drug discovery,0.83520794,TRUE,26.85714286,0.392912363,90.28571429,0.801712604,7,0.785110192,,,0.65991172
7525,Artificial Intelligence-Based Clinical Decision Support for COVID-19-Where Art Thou?,Adv Intell Syst,32838300,8/25/20,pubmed,0,5,artificial intelligence,0.004310004,0.004310058,0.643262473,0.339497293,0.00431017,0.004310002,Epidemiology,0.79698616,TRUE,142.6,0.937040015,256,0.940058871,2,0.618927094,,,0.83200866
7526,Addressing COVID-19 Drug Development with Artificial Intelligence.,Adv Intell Syst,32838299,8/25/20,pubmed,0,1,artificial intelligence,0.599806348,0.001684629,0.048090768,0.347049126,0.00168457,0.001684559,Drug discovery,0.4288615,FALSE,171,0.959057456,138,0.872892695,12,0.850299401,,,0.894083184
7527,Risk factors for severe acute respiratory syndrome coronavirus 2 infection in pregnant women.,Am J Obstet Gynecol MFM,32838274,8/25/20,pubmed,0,6,logistic regression,0.001330095,0.028910572,0.034913781,0.001330112,0.829867488,0.103647951,Healthcare,0.76948965,TRUE,168.1666667,0.956954666,94.16666667,0.811078405,4,0.707574542,,,0.825202538
7528,Applications of predictive modelling early in the COVID-19 epidemic.,Lancet Digit Health,32838252,8/25/20,pubmed,0,3,predictive model,0.034961875,0.034962077,0.034965599,0.825180753,0.034962479,0.034967217,Epidemiology,0.32867306,FALSE,65.33333333,0.736285485,141,0.875702435,6,0.764429903,,,0.792139274
7529,Incidence and Risk Factors for Acute Kidney Injury and Its Effect on Mortality in Patients Hospitalized From COVID-19.,Mayo Clin Proc Innov Qual Outcomes,32838205,8/25/20,pubmed,0,11,logistic regression,0.001438075,0.001438081,0.001438076,0.001438114,0.001438138,0.992809517,Clinics,0.84997773,TRUE,17.72727273,0.266806853,1.181818182,0.124765855,5,0.739490092,,,0.377020933
7530,Sex Differences in Age and Comorbidities for COVID-19 Mortality in Urban New York City.,SN Compr Clin Med,32838184,8/25/20,pubmed,0,8,logistic regression,0.001350295,0.001350301,0.0013503,0.001350327,0.352858726,0.641740051,Clinics,0.9677757,TRUE,87.75,0.836229822,78.875,0.775622157,2,0.618927094,,,0.743593025
7531,Can HCQ Be Considered a "Safe Weapon" for COVID-19 in the Indian Population?,SN Compr Clin Med,32838163,8/25/20,pubmed,0,5,pharmacogenom,0.402167641,0.040854967,0.001823378,0.125045979,0.155804346,0.274303688,Drug discovery,0.66268456,TRUE,26,0.382398417,0.6,0.09011239,1,0.537564047,,,0.336691618
7532,Compartmental Models of the COVID-19 Pandemic for Physicians and Physician-Scientists.,SN Compr Clin Med,32838137,8/25/20,pubmed,0,1,mathematical model,0.004530681,0.004530639,0.004531156,0.977344649,0.004531628,0.004531247,Epidemiology,0.46093574,FALSE,4,0.054734368,3,0.199424672,8,0.799987654,,,0.351382231
7533,Machine learning based approaches for detecting COVID-19 using clinical text data.,Int J Inf Technol,32838125,8/25/20,pubmed,0,5,"bayes, machine learning, artificial intelligence, neural network, classifier, logistic regression",0.001861696,0.081624546,0.910928266,0.001861857,0.001861892,0.001861743,Genomics,0.5255306,TRUE,3.2,0.038468675,0,0.055525823,16,0.881782826,,,0.325259108
7534,Data analysis of COVID-2019 epidemic using machine learning methods: a case study of India.,Int J Inf Technol,32838124,8/25/20,pubmed,0,1,"machine learning, dataset",0.00146187,0.001461887,0.001461956,0.992690429,0.001461986,0.001461872,Epidemiology,0.39002365,FALSE,15,0.227596017,9,0.337904736,13,0.858880178,,,0.474793644
7535,The Economic Cost of COVID Lockdowns: An Out-of-Equilibrium Analysis.,Econ Disaster Clim Chang,32838118,8/25/20,pubmed,0,2,"computational, network model",0.001220045,0.024211843,0.001220037,0.970908043,0.001220026,0.001220006,Epidemiology,0.05379814,FALSE,30,0.432432432,1,0.122023013,31,0.931971109,,,0.495475518
7536,Liver Care and Surveillance: The Global Impact of the COVID-19 Pandemic.,Hepatol Commun,32838107,8/25/20,pubmed,0,4,logistic regression,0.001112649,0.001112681,0.113375916,0.228311038,0.234529799,0.421557917,Clinics,0.415864,FALSE,203.25,0.973282207,105.25,0.831816965,4,0.707574542,,,0.837557905
7537,Stochastic modelling for predicting COVID-19 prevalence in East Africa Countries.,Infect Dis Model,32838091,8/25/20,pubmed,0,1,predictive model,0.001010929,0.001010956,0.001010966,0.886192871,0.109763338,0.001010939,Epidemiology,0.27804232,FALSE,3,0.037293586,1,0.122023013,5,0.739490092,,,0.299602231
7538,Hepatic steatosis as an independent risk factor for severe disease in patients with COVID-19: A computed tomography study.,JGH Open,32838045,8/25/20,pubmed,0,10,logistic regression,0.001237054,0.001237056,0.194715337,0.001237047,0.036006574,0.765566931,Clinics,0.95002025,TRUE,20.6,0.305832148,7.1,0.300040139,2,0.618927094,,,0.40826646
7539,Rapid translation of clinical guidelines into executable knowledge: a case study of COVID-19 and on-line demonstration.,Learn Health Syst,32838035,8/25/20,pubmed,0,9,artificial intelligence,0.030543195,0.001415128,0.573663313,0.228545354,0.16441779,0.00141522,Epidemiology,0.6408167,TRUE,5,0.070752675,0.222222222,0.061412898,1,0.537564047,,,0.223243207
7540,Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention.,Adv Ther (Weinh),32838027,8/25/20,pubmed,0,15,artificial intelligence,0.632506556,0.001187291,0.111168177,0.212364519,0.001187304,0.041586154,Drug discovery,0.4851183,FALSE,25.8,0.378316532,24.4,0.524284185,6,0.764429903,,,0.555676873
7541,Spatial prediction of COVID-19 epidemic using ARIMA techniques in India.,Model Earth Syst Environ,32838022,8/25/20,pubmed,0,3,prediction model,0.00151185,0.001511933,0.088642552,0.819467809,0.087354018,0.001511838,Epidemiology,0.7865075,TRUE,53.33333333,0.656255798,21.33333333,0.495450896,16,0.881782826,,,0.67782984
7542,Significance of geographical factors to the COVID-19 outbreak in India.,Model Earth Syst Environ,32838021,8/25/20,pubmed,0,3,model fit,0.002130821,0.002130756,0.002130845,0.989345768,0.00213085,0.00213096,Epidemiology,0.7181106,TRUE,5.666666667,0.079473066,0,0.055525823,19,0.89561084,,,0.343536576
7543,SARS-CoV-2-human protein-protein interaction network.,Inform Med Unlocked,32838020,8/25/20,pubmed,0,3,computational,0.717164202,0.048744667,0.172410309,0.059482947,0.001098957,0.001098918,Drug discovery,0.7400712,TRUE,70.66666667,0.766837776,43.66666667,0.652261172,3,0.667819001,,,0.695639316
7544,Amino acid variation analysis of surface spike glycoprotein at 614 in SARS-CoV-2 strains.,Genes Dis,32837981,8/25/20,pubmed,0,10,"genomic epidemiology, genomes",0.001350413,0.971182365,0.001350341,0.001350374,0.001350396,0.023416111,Genomics,0.6635814,TRUE,28,0.408312202,15,0.42594327,6,0.764429903,,,0.532895125
7545,Data-driven modeling of COVID-19-Lessons learned.,Extreme Mech Lett,32837980,8/25/20,pubmed,0,1,"machine learning, mathematical model",0.001486424,0.00148644,0.001486563,0.992567715,0.001486427,0.001486431,Epidemiology,0.4115261,FALSE,17,0.257467994,0,0.055525823,9,0.814309525,,,0.375767781
7546,COVID-19: A scholarly production dataset report for research analysis.,Data Brief,32837978,8/25/20,pubmed,0,6,dataset,0.002898498,0.002898382,0.296817488,0.691588759,0.002898474,0.002898399,Epidemiology,0.66775256,TRUE,34.5,0.482404601,10.66666667,0.365333155,1,0.537564047,,,0.461767268
7547,Dataset on the Acceptance of e-learning System among Universities Students' under the COVID-19 Pandemic Conditions.,Data Brief,32837976,8/25/20,pubmed,0,5,dataset,0.001901781,0.001901754,0.001901754,0.253638956,0.738754045,0.001901711,Healthcare,0.42951593,FALSE,10.8,0.161110768,0.6,0.09011239,2,0.618927094,,,0.290050084
7548,The most important challenges ahead of microbiome pattern in the post era of the COVID-19 pandemic.,J Diabetes Metab Disord,32837956,8/25/20,pubmed,0,4,microbiom,0.001901826,0.224441874,0.00190171,0.392871286,0.376981539,0.001901764,Epidemiology,0.98590887,TRUE,53.25,0.655328097,8,0.320511105,2,0.618927094,,,0.531588765
7549,Drug repurposing using computational methods to identify therapeutic options for COVID-19.,J Diabetes Metab Disord,32837954,8/25/20,pubmed,0,3,"virtual screening, computational",0.937483863,0.001098821,0.001098819,0.001098968,0.058120698,0.001098831,Drug discovery,0.8932877,TRUE,42.33333333,0.560888119,9.666666667,0.348274017,10,0.828199272,,,0.579120469
7550,Anxiety and depression and the related factors in nurses of Guilan University of Medical Sciences hospitals during COVID-19: A web-based cross-sectional study.,Int J Afr Nurs Sci,32837911,8/25/20,pubmed,0,7,logistic regression,0.0013929,0.001392836,0.001392885,0.001392912,0.905437753,0.088990715,Healthcare,0.96525085,TRUE,12,0.183190055,1,0.122023013,8,0.799987654,,,0.368400241
7551,"COVID-19 Pandemic: from Molecular Biology, Pathogenesis, Detection, and Treatment to Global Societal Impact.",Curr Pharmacol Rep,32837855,8/25/20,pubmed,0,9,sequencing,0.185432994,0.583643096,0.001371418,0.164265424,0.063915684,0.001371384,Genomics,0.75101316,TRUE,18.33333333,0.274908776,7.666666667,0.310877709,8,0.799987654,,,0.461924713
7552,Coronavirus in Continuous Flux: From SARS-CoV to SARS-CoV-2.,Adv Sci (Weinh),32837848,8/25/20,pubmed,0,5,whole-genome,0.188399873,0.339617362,0.00333556,0.128995256,0.082228977,0.257422972,Genomics,0.72338575,TRUE,33,0.466757375,12.2,0.387476585,3,0.667819001,,,0.507350987
7553,COVID-19 and social distancing.,Z Gesundh Wiss,32837835,8/25/20,pubmed,0,2,prediction model,0.001823347,0.001823362,0.065207578,0.568677388,0.36064487,0.001823456,Epidemiology,0.5439737,TRUE,27,0.3960047,8,0.320511105,16,0.881782826,,,0.532766211
7554,Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic.,Environ Syst Decis,32837820,8/25/20,pubmed,0,3,network analysis,0.001291254,0.001291249,0.327065917,0.667769023,0.001291236,0.001291321,Epidemiology,0.5543387,TRUE,31.33333333,0.448388892,15.66666667,0.432365534,38,0.944132354,,,0.608295593
7555,0,Engineering (Beijing),32837754,8/25/20,pubmed,0,10,transcriptom,0.599242594,0.002296675,0.002296689,0.17442404,0.219443342,0.00229666,Drug discovery,0.7585545,TRUE,90,0.843094811,68.6,0.749331014,3,0.667819001,,,0.753414942
7556,A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia.,Engineering (Beijing),32837749,8/25/20,pubmed,0,21,"bayes, deep learning, dataset",0.00090732,0.14740217,0.653604001,0.000907311,0.000907301,0.196271897,Imaging,0.4656353,FALSE,50.28571429,0.633186963,75.57142857,0.76792882,256,0.993641583,,,0.798252455
7557,Robust inference for nonlinear regression models from the Tsallis score: application to COVID-19 contagion in Italy.,Stat (Int Stat Inst),32837719,8/25/20,pubmed,0,7,bayes,0.008403403,0.008402806,0.008403066,0.841504325,0.008403319,0.124883081,Epidemiology,0.1867845,FALSE,39.85714286,0.537881131,19.42857143,0.474310945,1,0.537564047,,,0.516585374
7558,Structural insights into the mechanism of RNA recognition by the N-terminal RNA-binding domain of the SARS-CoV-2 nucleocapsid phosphoprotein.,Comput Struct Biotechnol J,32837710,8/25/20,pubmed,0,8,"computational, proteom",0.854646992,0.111444149,0.029040542,0.001622739,0.001622746,0.001622832,Drug discovery,0.63482714,TRUE,43.125,0.568000495,6,0.280037463,7,0.785110192,,,0.544382717
7559,Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays.,Ing Rech Biomed,32837679,8/25/20,pubmed,0,5,"deep learning, transfer learning, dataset",0.000966741,0.000966751,0.995166247,0.000966747,0.000966751,0.000966764,Imaging,0.37568593,FALSE,61,0.709196611,11.6,0.379515654,32,0.933699611,,,0.674137292
7560,Deep Transfer Learning Based Classification Model for COVID-19 Disease.,Ing Rech Biomed,32837678,8/25/20,pubmed,0,6,"supervised learning, transfer learning, dataset",0.001751186,0.001751223,0.946676583,0.001751244,0.001751249,0.046318516,Imaging,0.78397316,TRUE,132.3333333,0.924299586,40.16666667,0.634131656,39,0.945737391,,,0.834722878
7561,The impact of COVID-19 related 'stay-at-home' restrictions on food prices in Europe: findings from a preliminary analysis.,Food Secur,32837638,8/25/20,pubmed,0,1,dataset,0.001653022,0.001653071,0.001653028,0.684147858,0.30923986,0.001653161,Epidemiology,0.68653417,TRUE,37,0.508936854,54,0.697952903,12,0.850299401,,,0.68572972
7562,Environmental pollution and COVID-19 outbreak: insights from Germany.,Air Qual Atmos Health,32837621,8/25/20,pubmed,0,9,correlation analysis,0.00208069,0.051480275,0.002080582,0.940196809,0.002080792,0.002080853,Epidemiology,0.6641569,TRUE,12.77777778,0.191972293,7.333333333,0.304656141,16,0.881782826,,,0.45947042
7563,Social Group Optimization-Assisted Kapur's Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images.,Cognit Comput,32837591,8/25/20,pubmed,0,5,"machine learning, classifier, dataset",0.00127268,0.001272719,0.920484287,0.028261327,0.001272729,0.047436258,Imaging,0.4693822,FALSE,132.8,0.925041747,28.4,0.556997592,19,0.89561084,,,0.792550059
7564,"Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19.",Cell Mol Bioeng,32837582,8/25/20,pubmed,0,7,"artificial intelligence, mathematical model",0.001987167,0.001987159,0.28249934,0.500019813,0.211519365,0.001987155,Epidemiology,0.86314464,TRUE,57,0.68204589,92.57142857,0.807265186,23,0.91129082,,,0.800200632
7565,The novel coronavirus 2019-nCoV: Its evolution and transmission into humans causing global COVID-19 pandemic.,Int J Environ Sci Technol (Tehran),32837521,8/25/20,pubmed,0,5,bioinformatic,0.003335433,0.62238669,0.003335819,0.31671861,0.050888066,0.003335382,Genomics,0.5591776,TRUE,5.4,0.074710866,0.2,0.061145304,11,0.840175319,,,0.32534383
7566,Process integration for emerging challenges: optimal allocation of antivirals under resource constraints.,Clean Technol Environ Policy,32837502,8/25/20,pubmed,0,12,"computational, mathematical model",0.204763508,0.001461868,0.001461959,0.714346188,0.001461959,0.076504518,Epidemiology,0.24265528,FALSE,66.41666667,0.743150473,22.08333333,0.503947016,1,0.537564047,,,0.594887179
7567,Lower COVID-19 mortality in Italian forested areas suggests immunoprotection by Mediterranean plants.,Environ Chem Lett,32837486,8/25/20,pubmed,0,2,in silico,0.443277833,0.001565412,0.14627445,0.265673768,0.073449995,0.069758542,Drug discovery,0.7685971,TRUE,28,0.408312202,1,0.122023013,15,0.874313229,,,0.468216148
7568,Mathematical modeling of COVID-19 spreading with asymptomatic infected and interacting peoples.,J Appl Math Comput,32837466,8/25/20,pubmed,0,2,mathematical model,0.004530657,0.004530711,0.004530766,0.977345765,0.004530811,0.004531289,Epidemiology,0.6828283,TRUE,6,0.086028821,0,0.055525823,5,0.739490092,,,0.293681578
7569,A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization.,Appl Soft Comput,32837453,8/25/20,pubmed,0,3,"bayes, machine learning, neural network, classifier, transfer learning",0.001046828,0.001046873,0.994765729,0.001046893,0.001046861,0.001046817,Imaging,0.47461933,FALSE,63,0.721998887,89.66666667,0.800642226,40,0.947033768,,,0.82322496
7570,Gender and Fear of COVID-19 in a Cuban Population Sample.,Int J Ment Health Addict,32837428,8/25/20,pubmed,0,5,logistic regression,0.001350321,0.001350334,0.001350319,0.001350323,0.993248342,0.001350361,Healthcare,0.9435762,TRUE,13,0.197352959,1.6,0.140687717,24,0.914439163,,,0.41749328
7571,Assessing Coronavirus Fear in Indian Population Using the Fear of COVID-19 Scale.,Int J Ment Health Addict,32837422,8/25/20,pubmed,0,5,logistic regression,0.001461885,0.00146188,0.001461915,0.001461966,0.992690432,0.001461923,Healthcare,0.5822242,TRUE,28,0.408312202,11.6,0.379515654,24,0.914439163,,,0.56742234
7572,"Fear of COVID-19 and Positivity: Mediating Role of Intolerance of Uncertainty, Depression, Anxiety, and Stress.",Int J Ment Health Addict,32837421,8/25/20,pubmed,0,3,correlation analysis,0.002357688,0.002357698,0.002357715,0.002357835,0.988211334,0.00235773,Healthcare,0.9227212,TRUE,13.66666667,0.207310285,4.333333333,0.237958255,30,0.930057411,,,0.458441984
7573,Psychometric Properties of the Greek Version of FCV-19S.,Int J Ment Health Addict,32837420,8/25/20,pubmed,0,10,model fit,0.00289861,0.002898645,0.077082664,0.002898549,0.911323121,0.002898411,Healthcare,0.80584383,TRUE,9.8,0.147071557,2.7,0.185576666,25,0.918019631,,,0.416889284
7574,The role of ESG performance during times of financial crisis: Evidence from COVID-19 in China.,Financ Res Lett,32837385,8/25/20,pubmed,0,4,dataset,0.003607411,0.003607354,0.453015349,0.532555262,0.003607482,0.003607142,Epidemiology,0.7722484,TRUE,51.5,0.643267982,47,0.668718223,1,0.537564047,,,0.616516751
7575,Stock markets and the COVID-19 fractal contagion effects.,Financ Res Lett,32837366,8/25/20,pubmed,0,2,correlation analysis,0.007674152,0.12137955,0.007674464,0.847923175,0.007674665,0.007673993,Epidemiology,0.79324394,TRUE,221.5,0.979033954,192,0.913633931,19,0.89561084,,,0.929426242
7576,The Influence of the COVID-19 Pandemic on Technology: Adoption in Health Care.,Nurse Lead,32837346,8/25/20,pubmed,0,1,artificial intelligence,0.002130828,0.002130701,0.20859083,0.695215705,0.08980112,0.002130815,Epidemiology,0.5589535,TRUE,12,0.183190055,10,0.355632861,6,0.764429903,,,0.434417606
7577,A machine learning forecasting model for COVID-19 pandemic in India.,Stoch Environ Res Risk Assess,32837309,8/25/20,pubmed,0,3,"machine learning, forecasting model",0.002562713,0.002562709,0.0025628,0.671844695,0.071026677,0.249440406,Epidemiology,0.8275686,TRUE,20.66666667,0.306512462,3,0.199424672,43,0.9507377,,,0.485558278
7578,"Strategic assessment of COVID-19 pandemic in Bangladesh: comparative lockdown scenario analysis, public perception, and management for sustainability.",Environ Dev Sustain,32837281,8/25/20,pubmed,0,4,dataset,0.000699353,0.000699345,0.000699357,0.646531937,0.35067064,0.000699369,Epidemiology,0.21497592,FALSE,32.25,0.458098831,9,0.337904736,24,0.914439163,,,0.570147577
7579,"Understanding COVID-19 transmission, health impacts and mitigation: timely social distancing is the key.",Environ Dev Sustain,32837280,8/25/20,pubmed,0,5,bayes,0.001034606,0.001034591,0.001034576,0.969510676,0.001034707,0.026350845,Epidemiology,0.5318231,TRUE,46.2,0.598181706,13.2,0.402194273,11,0.840175319,,,0.6135171
7580,Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective.,Environ Dev Sustain,32837277,8/25/20,pubmed,0,8,"bayes, probabilistic",0.001350357,0.001350593,0.001350355,0.857270616,0.001350403,0.137327676,Epidemiology,0.5929932,TRUE,39.5,0.534232173,16.625,0.443738293,20,0.900117291,,,0.626029252
7581,A secure location-based alert system with tunable privacy-performance trade-off.,Geoinformatica,32837253,8/25/20,pubmed,0,4,computational,0.001392863,0.001392858,0.345126694,0.649301911,0.00139285,0.001392823,Epidemiology,0.4865802,FALSE,191.5,0.969385862,362.75,0.967754884,3,0.667819001,,,0.868319916
7582,An optimal predictive control strategy for COVID-19 (SARS-CoV-2) social distancing policies in Brazil.,Annu Rev Control,32837241,8/25/20,pubmed,0,4,dataset,0.00115623,0.001156241,0.001156331,0.964272082,0.031102831,0.001156285,Epidemiology,0.1586856,FALSE,86.75,0.832580865,64.75,0.737824458,22,0.908142478,,,0.8261826
7583,0,Saudi J Biol Sci,32837219,8/25/20,pubmed,0,5,genomes,0.857838716,0.135292444,0.001717186,0.001717229,0.001717222,0.001717203,Drug discovery,0.9609919,TRUE,40.2,0.541159008,15,0.42594327,3,0.667819001,,,0.54497376
7584,Assessing the prevalence of self-medication among healthcare workers before and during the 2019 SARS-CoV-2 (COVID-19) pandemic in Kenya.,Saudi Pharm J,32837218,8/25/20,pubmed,0,3,logistic regression,0.016842906,0.001187257,0.001187256,0.001187267,0.978408011,0.001187303,Healthcare,0.98223126,TRUE,4.666666667,0.063392912,0,0.055525823,2,0.618927094,,,0.24594861
7585,Knowledge and beliefs towards universal safety precautions during the coronavirus disease (COVID-19) pandemic among the Indian public: a web-based cross-sectional survey.,Drugs Ther Perspect,32837191,8/25/20,pubmed,0,7,logistic regression,0.001330154,0.001330066,0.0013301,0.054210135,0.94046947,0.001330076,Healthcare,0.9571825,TRUE,20.57142857,0.305399221,3.142857143,0.200561948,3,0.667819001,,,0.391260057
7586,The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges.,Arch Comput Methods Eng,32837183,8/25/20,pubmed,0,6,machine learning,0.002996419,0.002996471,0.721107363,0.266906559,0.002996588,0.002996599,Epidemiology,0.39418638,FALSE,47.66666667,0.611787989,27.66666667,0.550909821,6,0.764429903,,,0.642375904
7587,Cybercrime in America amid COVID-19: the Initial Results from a Natural Experiment.,Am J Crim Justice,32837157,8/25/20,pubmed,0,3,dataset,0.002357899,0.002357841,0.002357875,0.679968832,0.310599734,0.00235782,Epidemiology,0.7312056,TRUE,36,0.498299215,36,0.611118544,5,0.739490092,,,0.616302617
7588,Genomics insights of SARS-CoV-2 (COVID-19) into target-based drug discovery.,Med Chem Res,32837137,8/25/20,pubmed,0,2,bioinformatic,0.714005666,0.230378618,0.001156272,0.052146838,0.001156229,0.001156376,Drug discovery,0.93521106,TRUE,29.5,0.426000371,7,0.299973241,8,0.799987654,,,0.508653755
7589,Repurposing metocurine as main protease inhibitor to develop novel antiviral therapy for COVID-19.,Struct Chem,32837119,8/25/20,pubmed,0,2,computational,0.822909249,0.002898474,0.002898538,0.165496728,0.002898402,0.002898608,Drug discovery,0.70313305,TRUE,10,0.15214299,0.5,0.087101953,3,0.667819001,,,0.302354648
7590,Destabilizing the structural integrity of COVID-19 by caulerpin and its derivatives along with some antiviral drugs: An in silico approaches for a combination therapy.,Struct Chem,32837118,8/25/20,pubmed,0,7,in silico,0.993814467,0.001237061,0.001237068,0.001237075,0.001237145,0.001237184,Drug discovery,0.98269975,TRUE,13.14285714,0.198218814,1.142857143,0.123628579,4,0.707574542,,,0.343140645
7591,Structure-based virtual screening of phytochemicals and repurposing of FDA approved antiviral drugs unravels lead molecules as potential inhibitors of coronavirus 3C-like protease enzyme.,J King Saud Univ Sci,32837113,8/25/20,pubmed,0,5,"virtual screening, computational",0.994573255,0.00108537,0.001085337,0.001085331,0.001085346,0.00108536,Drug discovery,0.9549479,TRUE,15.6,0.235450554,7.6,0.308536259,5,0.739490092,,,0.427825635
7592,An Ayurvedic Perspective along with in Silico Study of the Drugs for the Management of Sars-Cov-2.,J Ayurveda Integr Med,32837100,8/25/20,pubmed,0,5,"virtual screening, in silico, in-silico",0.765629129,0.001203434,0.001203469,0.145533379,0.085227139,0.00120345,Drug discovery,0.9376134,TRUE,13.2,0.199270208,0.8,0.101351351,1,0.537564047,,,0.279395202
7593,Factors that predict passengers willingness to fly during and after the COVID-19 pandemic.,J Air Transp Manag,32837029,8/25/20,pubmed,0,5,predictive model,0.00242229,0.002422297,0.002422312,0.483314372,0.506996391,0.002422338,Healthcare,0.7672726,TRUE,48.6,0.618529284,8.2,0.322785657,4,0.707574542,,,0.549629828
7594,A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images.,Chaos Solitons Fractals,32836918,8/25/20,pubmed,0,6,"deep learning, classifier, transfer learning, dataset",0.001141329,0.001141356,0.925130038,0.070304526,0.001141342,0.001141408,Imaging,0.63798153,TRUE,42,0.558537943,7.666666667,0.310877709,16,0.881782826,,,0.583732826
7595,Effect of the early use of antivirals on the COVID-19 pandemic. A computational network modeling approach.,Chaos Solitons Fractals,32836917,8/25/20,pubmed,0,5,"computational, network model",0.176315793,0.002490463,0.002490589,0.662345064,0.153867326,0.002490765,Epidemiology,0.4661124,FALSE,4.4,0.058321479,0,0.055525823,3,0.667819001,,,0.260555434
7596,Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review.,Chaos Solitons Fractals,32836916,8/25/20,pubmed,0,4,"machine learning, deep learning, prediction model",0.001653052,0.001653032,0.595099062,0.39828879,0.001653014,0.00165305,Epidemiology,0.87318826,TRUE,35.25,0.489331437,,,28,0.926168282,,,0.707749859
7597,Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models.,Chaos Solitons Fractals,32836915,8/25/20,pubmed,0,3,"computational, neural network, mathematical model, network model",0.002357762,0.002357728,0.462872894,0.527696179,0.002357724,0.002357712,Epidemiology,0.62743604,TRUE,3.333333333,0.04044777,0,0.055525823,40,0.947033768,,,0.34766912
7598,Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour.,J Clean Prod,32836914,8/25/20,pubmed,0,7,bayes,0.001022634,0.001022639,0.001022637,0.945277431,0.050632041,0.001022617,Epidemiology,0.566148,TRUE,52.42857143,0.65013297,80.71428571,0.780907145,14,0.866658436,,,0.765899517
7599,A new model for the spread of COVID-19 and the improvement of safety.,Saf Sci,32836873,8/25/20,pubmed,0,2,simulation experiment,0.044605213,0.00186172,0.001861726,0.947947813,0.001861749,0.001861779,Epidemiology,0.6216412,TRUE,58,0.689714887,34.5,0.602221033,9,0.814309525,,,0.702081815
7600,The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach.,Environ Resour Econ (Dordr),32836865,8/25/20,pubmed,0,3,machine learning,0.004530708,0.004530708,0.136095377,0.845781371,0.004530851,0.004530984,Epidemiology,0.7472887,TRUE,258.3333333,0.985527862,402.3333333,0.971434306,23,0.91129082,,,0.956084329
7601,In the Name of COVID-19: Is the ECB Fuelling the Climate Crisis?,Environ Resour Econ (Dordr),32836830,8/25/20,pubmed,0,6,dataset,0.162960538,0.003466044,0.003465998,0.823175288,0.003466128,0.003466004,Epidemiology,0.18940732,FALSE,30.83333333,0.441833137,12.83333333,0.396775488,0,0.403234768,,,0.413947798
7602,"Using traveller-derived cases in Henan Province to quantify the spread of COVID-19 in Wuhan, China.",Nonlinear Dyn,32836816,8/25/20,pubmed,0,5,mathematical model,0.003214116,0.003214204,0.003214088,0.983929282,0.003214157,0.003214152,Epidemiology,0.5147763,TRUE,103.4,0.875997279,78,0.773548301,0,0.403234768,,,0.684260116
7603,Complete dimensional collapse in the continuum limit of a delayed SEIQR network model with separable distributed infectivity.,Nonlinear Dyn,32836812,8/25/20,pubmed,0,2,network model,0.246175988,0.006090256,0.006090103,0.729464551,0.006089568,0.006089533,Epidemiology,0.172844,FALSE,85,0.825035562,87,0.795223441,1,0.537564047,,,0.71927435
7604,Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov's complexity and Shannon's information theories.,Nonlinear Dyn,32836811,8/25/20,pubmed,0,3,"computational, dataset",0.002720249,0.327601968,0.002720329,0.661517283,0.002720099,0.002720073,Epidemiology,0.42199105,FALSE,18.33333333,0.274908776,0.666666667,0.096200161,2,0.618927094,,,0.33001201
7605,"Investigating time, strength, and duration of measures in controlling the spread of COVID-19 using a networked meta-population model.",Nonlinear Dyn,32836809,8/25/20,pubmed,0,6,dataset,0.0021832,0.002183197,0.002183231,0.989083932,0.002183244,0.002183197,Epidemiology,0.40123358,FALSE,89.83333333,0.842105263,26.16666667,0.53866738,4,0.707574542,,,0.696115728
7606,A fractional-order model for the novel coronavirus (COVID-19) outbreak.,Nonlinear Dyn,32836806,8/25/20,pubmed,0,6,mathematical model,0.002562564,0.002562576,0.002562636,0.987187103,0.002562566,0.002562555,Epidemiology,0.573542,TRUE,43.33333333,0.571154679,9.166666667,0.338774418,16,0.881782826,,,0.597237308
7607,A new SAIR model on complex networks for analysing the 2019 novel coronavirus (COVID-19).,Nonlinear Dyn,32836802,8/25/20,pubmed,0,5,mathematical model,0.001593515,0.001593538,0.001593553,0.992032309,0.00159353,0.001593556,Epidemiology,0.3039459,FALSE,9.8,0.147071557,2.6,0.18256623,10,0.828199272,,,0.385945686
7608,"Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions.",Eur J Oper Res,32836717,8/25/20,pubmed,0,5,deep-learning,0.002490429,0.002490432,0.160969278,0.829068962,0.002490441,0.002490457,Epidemiology,0.8338014,TRUE,27.4,0.400395819,13.4,0.404602622,19,0.89561084,,,0.566869761
7609,Predictive models of COVID-19 in India: A rapid review.,Med J Armed Forces India,32836710,8/25/20,pubmed,0,5,"mathematical model, predictive model",0.001098823,0.029465827,0.001098823,0.863557269,0.001098875,0.103680383,Epidemiology,0.1952216,FALSE,49,0.624281032,15.8,0.434104897,3,0.667819001,,,0.575401643
7610,The dynamical model for COVID-19 with asymptotic analysis and numerical implementations.,Appl Math Model,32836696,8/25/20,pubmed,0,4,mathematical model,0.001438149,0.001438213,0.145053139,0.717973103,0.001438146,0.132659249,Epidemiology,0.22096995,FALSE,41.75,0.555383759,73.5,0.762710731,2,0.618927094,,,0.645673861
7611,[Impact of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers on COVID-19 in a western population. CARDIOVID registry].,Rev Esp Cardiol,32836666,8/25/20,pubmed,0,16,logistic regression,0.096760301,0.001272658,0.001272652,0.034595706,0.00127271,0.864825973,Clinics,0.93839777,TRUE,71,0.769064259,70.5,0.755351887,10,0.828199272,,,0.784205139
7612,CovidSens: a vision on reliable social sensing for COVID-19.,Artif Intell Rev,32836651,8/25/20,pubmed,0,2,"machine learning, computational",0.000956311,0.013424202,0.198006147,0.78570067,0.000956367,0.000956303,Epidemiology,0.12818143,FALSE,71,0.769064259,9.5,0.345531175,10,0.828199272,,,0.647598235
7613,Who do you trust? The digital destruction of shared situational awareness and the COVID-19 infodemic.,Int J Inf Manage,32836649,8/25/20,pubmed,0,1,artificial intelligence,0.001622804,0.016144772,0.127391628,0.551755929,0.301462165,0.001622702,Epidemiology,0.8217283,TRUE,88,0.837095677,73,0.761974846,6,0.764429903,,,0.787833475
7614,Will COVID-19 be the tipping point for the Intelligent Automation of work? A review of the debate and implications for research.,Int J Inf Manage,32836639,8/25/20,pubmed,0,1,artificial intelligence,0.002357847,0.002357838,0.314295877,0.460739198,0.217891534,0.002357707,Epidemiology,0.8867587,TRUE,68,0.753973653,60,0.720899117,10,0.828199272,,,0.767690681
7615,Considerations for development and use of AI in response to COVID-19.,Int J Inf Manage,32836632,8/25/20,pubmed,0,1,artificial intelligence,0.044237676,0.045590666,0.547715227,0.356659691,0.002898471,0.002898269,Epidemiology,0.6214628,TRUE,163,0.953491249,102,0.82592989,13,0.858880178,,,0.879433772
7616,Non-Invasive Technique-Based Novel Corona(COVID-19) Virus Detection Using CNN.,Natl Acad Sci Lett,32836613,8/25/20,pubmed,0,3,"neural network, dataset",0.001622733,0.121784687,0.800559208,0.001622831,0.001622811,0.07278773,Imaging,0.6656285,TRUE,13.33333333,0.201558538,3.666666667,0.217621086,2,0.618927094,,,0.346035573
7617,"Diffusion-reaction compartmental models formulated in a continuum mechanics framework: application to COVID-19, mathematical analysis, and numerical study.",Comput Mech,32836602,8/25/20,pubmed,0,10,mathematical model,0.002080622,0.039576156,0.002080664,0.952101401,0.002080567,0.00208059,Epidemiology,0.4125752,FALSE,159.3,0.950955532,365.8,0.967955579,10,0.828199272,,,0.915703461
7618,Bayesian-based predictions of COVID-19 evolution in Texas using multispecies mixture-theoretic continuum models.,Comput Mech,32836598,8/25/20,pubmed,0,3,bayes,0.003214111,0.003214217,0.242578733,0.744564491,0.003214118,0.00321433,Epidemiology,0.3532092,FALSE,211,0.975817923,319.6666667,0.959593257,12,0.850299401,,,0.928570194
7619,The reproduction number of COVID-19 and its correlation with public health interventions.,Comput Mech,32836597,8/25/20,pubmed,0,3,machine learning,0.001987106,0.001987116,0.001987252,0.990064332,0.001987102,0.001987094,Epidemiology,0.302506,FALSE,9.333333333,0.139278867,1,0.122023013,5,0.739490092,,,0.333597324
7620,Characteristics of scientific articles on COVID-19 published during the initial 3 months of the pandemic.,Scientometrics,32836530,8/25/20,pubmed,0,2,in silico,0.102909574,0.072672287,0.001272689,0.713594215,0.108278506,0.001272729,Epidemiology,0.8593023,TRUE,49,0.624281032,10,0.355632861,8,0.799987654,,,0.593300515
7621,Social Network Analysis for Coronavirus (COVID-19) in the United States.,Soc Sci Q,32836475,8/25/20,pubmed,0,1,network analysis,0.002183275,0.002183258,0.002183323,0.846116854,0.145149948,0.002183342,Epidemiology,0.72499084,TRUE,13,0.197352959,0,0.055525823,6,0.764429903,,,0.339102895
7622,Sleep problems among Chinese adolescents and young adults during the coronavirus-2019 pandemic.,Sleep Med,32836185,8/25/20,pubmed,0,9,logistic regression,0.001254609,0.001254588,0.001254627,0.001254688,0.954741063,0.040240425,Healthcare,0.98213065,TRUE,8.444444444,0.124868576,2.888888889,0.190460262,20,0.900117291,,,0.40514871
7623,Prevalence of sleep disturbances during COVID-19 outbreak in an urban Chinese population: a cross-sectional study.,Sleep Med,32836181,8/25/20,pubmed,0,10,logistic regression,0.002080569,0.002080578,0.00208058,0.002080618,0.989596918,0.002080737,Healthcare,0.9234108,TRUE,29.7,0.427793927,16.6,0.443537597,7,0.785110192,,,0.552147239
7624,Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data.,Sci Total Environ,32835964,8/25/20,pubmed,0,7,dataset,0.047779784,0.001291244,0.001291225,0.94705528,0.001291254,0.001291212,Epidemiology,0.8836086,TRUE,22.85714286,0.337992455,13.42857143,0.40473642,9,0.814309525,,,0.5190128
7625,Assessing the susceptibility to acute respiratory illness COVID-19-related in a cohort of multiple sclerosis patients.,Mult Scler Relat Disord,32835900,8/25/20,pubmed,0,6,logistic regression,0.001861717,0.001861754,0.001861772,0.158369918,0.131745945,0.704298895,Clinics,0.6860894,TRUE,26.16666667,0.383078731,8.666666667,0.331415574,4,0.707574542,,,0.474022949
7626,Identification B and T-Cell epitopes and functional exposed amino acids of S protein as a potential vaccine candidate against SARS-CoV-2/COVID-19.,Microb Pathog,32835775,8/25/20,pubmed,0,2,bioinformatic,0.652274778,0.118618754,0.001350359,0.001350395,0.046042495,0.180363219,Drug discovery,0.74440217,TRUE,3,0.037293586,0,0.055525823,1,0.537564047,,,0.210127819
7627,Association of obesity and its genetic predisposition with the risk of severe COVID-19: Analysis of population-based cohort data.,Metabolism,32835759,8/25/20,pubmed,0,6,genome-wide,0.001653062,0.098186939,0.001653068,0.001653062,0.001653095,0.895200774,Clinics,0.48022527,FALSE,64.33333333,0.730781124,35.33333333,0.607439122,2,0.618927094,,,0.652382447
7628,0,J Biomol Struct Dyn,32835632,8/25/20,pubmed,0,2,"virtual screening, in silico",0.995269474,0.000946122,0.000946088,0.000946159,0.000946078,0.000946079,Drug discovery,0.8294792,TRUE,44,0.578390748,4.5,0.242708055,2,0.618927094,,,0.480008632
7629,Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data.,Patterns (N Y),32835314,8/25/20,pubmed,0,3,computational,0.002639123,0.002639151,0.201750466,0.242064412,0.002639082,0.548267766,Clinics,0.8492298,TRUE,2.666666667,0.029377203,0,0.055525823,12,0.850299401,,,0.311734142
7630,Human iPSC-Derived Cardiomyocytes Are Susceptible to SARS-CoV-2 Infection.,Cell Rep Med,32835305,8/25/20,pubmed,0,7,sequencing,0.849833888,0.002080734,0.002080538,0.002080537,0.002080637,0.141843666,Drug discovery,0.6168541,TRUE,121.2857143,0.908528666,,,17,0.887338725,,,0.897933695
7631,Identification of SARS-CoV-2 Vaccine Epitopes Predicted to Induce Long-Term Population-Scale Immunity.,Cell Rep Med,32835302,8/25/20,pubmed,0,4,proteom,0.677757006,0.316195466,0.001511928,0.001511933,0.001511858,0.001511808,Drug discovery,0.11585811,FALSE,165,0.954851877,274.75,0.946882526,21,0.903944688,,,0.935226364
7632,Time-series analyses of directional sequence changes in SARS-CoV-2 genomes and an efficient search method for candidates for advantageous mutations for growth in human cells.,Gene X,32835214,8/25/20,pubmed,0,3,"genomes, sequence alignment",0.194429721,0.630360329,0.002296558,0.168320308,0.002296527,0.002296557,Genomics,0.25713745,FALSE,35,0.488032655,63.33333333,0.733409152,4,0.707574542,,,0.643005449
7633,Machine learning for COVID-19-asking the right questions.,Lancet Digit Health,32835197,8/25/20,pubmed,0,3,machine learning,0.025060247,0.02506144,0.874688391,0.025064221,0.025065215,0.025060485,Healthcare,0.4600206,FALSE,164,0.95423341,81.33333333,0.781709928,9,0.814309525,,,0.850084288
7634,Nano-enabled biosensing systems for intelligent healthcare: towards COVID-19 management.,Mater Today Chem,32835155,8/25/20,pubmed,0,6,bioinformatic,0.162342459,0.001486527,0.514169974,0.223465412,0.001486455,0.097049173,Epidemiology,0.9485347,TRUE,20.66666667,0.306512462,3,0.199424672,19,0.89561084,,,0.467182658
7635,Predictive modelling of COVID-19 confirmed cases in Nigeria.,Infect Dis Model,32835145,8/25/20,pubmed,0,5,"model fit, predictive model, dataset",0.001901703,0.001901855,0.033151851,0.906335608,0.05480723,0.001901754,Epidemiology,0.849045,TRUE,12.6,0.190302431,2.2,0.16838373,10,0.828199272,,,0.395628477
7636,Adequacy of Logistic models for describing the dynamics of COVID-19 pandemic.,Infect Dis Model,32835144,8/25/20,pubmed,0,3,model fit,0.004310472,0.00431017,0.28799816,0.69476086,0.004310232,0.004310106,Epidemiology,0.6729281,TRUE,13.66666667,0.207310285,14.66666667,0.420323789,2,0.618927094,,,0.41552039
7637,Will an imperfect vaccine curtail the COVID-19 pandemic in the U.S.?,Infect Dis Model,32835142,8/25/20,pubmed,0,3,mathematical model,0.028897099,0.001046832,0.001046854,0.966915506,0.001046864,0.001046845,Epidemiology,0.20211408,FALSE,22,0.326056033,11.66666667,0.38065293,4,0.707574542,,,0.471427835
7638,Lung microbiome and coronavirus disease 2019 (COVID-19): Possible link and implications.,Hum Microb J,32835135,8/25/20,pubmed,0,2,microbiom,0.504272162,0.375865103,0.002080629,0.002080616,0.113620752,0.002080739,Drug discovery,0.7936254,TRUE,3,0.037293586,0,0.055525823,13,0.858880178,,,0.317233196
7639,Phylogenomic proximity and comparative proteomic analysis of SARS-CoV-2.,Gene Rep,32835133,8/25/20,pubmed,0,3,"proteom, phylogenom, dataset",0.002720142,0.986398202,0.002720486,0.002720424,0.002720288,0.002720458,Genomics,0.48185444,FALSE,36,0.498299215,32.33333333,0.588239229,3,0.667819001,,,0.584785815
7640,In Silico Identification of Potential Inhibitors of ADP-Ribose Phosphatase of SARS-CoV-2 nsP3 by Combining E-Pharmacophore- and Receptor-Based Virtual Screening of Database.,ChemistrySelect,32835090,8/25/20,pubmed,0,4,"virtual screening, in silico",0.992690253,0.00146189,0.001461993,0.001462,0.00146193,0.001461933,Drug discovery,0.87837386,TRUE,34,0.477766096,5,0.257024351,1,0.537564047,,,0.424118165
7641,A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images.,Inform Med Unlocked,32835084,8/25/20,pubmed,0,3,"deep learning, neural network, lstm, dataset",0.001291228,0.001291208,0.953604948,0.041230154,0.001291201,0.001291261,Imaging,0.66461647,TRUE,9,0.135320675,1.333333333,0.13252609,9,0.814309525,,,0.360718764
7642,COVID faster R-CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images.,Inform Med Unlocked,32835082,8/25/20,pubmed,0,4,"neural network, dataset",0.001059486,0.00105938,0.880210241,0.11555209,0.001059403,0.0010594,Imaging,0.4567421,FALSE,16.5,0.249366071,1.5,0.138747659,4,0.707574542,,,0.365229424
7643,A case-based reasoning framework for early detection and diagnosis of novel coronavirus.,Inform Med Unlocked,32835080,8/25/20,pubmed,0,2,mathematical model,0.00127265,0.0012728,0.633612865,0.301961613,0.00127275,0.060607323,Epidemiology,0.28839257,FALSE,27.5,0.401570907,3,0.199424672,3,0.667819001,,,0.422938194
7644,0,Inform Med Unlocked,32835079,8/25/20,pubmed,0,9,in-silico,0.903406005,0.001786513,0.001786593,0.089447863,0.001786514,0.001786512,Drug discovery,0.71710485,TRUE,23.66666667,0.34986703,13.22222222,0.402394969,17,0.887338725,,,0.546533574
7645,Structure-based drug designing towards the identification of potential anti-viral for COVID-19 by targeting endoribonuclease NSP15.,Inform Med Unlocked,32835078,8/25/20,pubmed,0,5,in silico,0.92046979,0.001461896,0.001461953,0.07368246,0.001461946,0.001461955,Drug discovery,0.81237936,TRUE,13.2,0.199270208,5.2,0.259700294,7,0.785110192,,,0.414693565
7646,Hybrid deep learning for detecting lung diseases from X-ray images.,Inform Med Unlocked,32835077,8/25/20,pubmed,0,3,"machine learning, deep learning, neural network, image processing, dataset",0.000999515,0.000999536,0.975821039,0.000999539,0.000999563,0.020180808,Imaging,0.6465894,TRUE,24,0.35574247,5,0.257024351,14,0.866658436,,,0.493141752
7647,On the role of governmental action and individual reaction on COVID-19 dynamics in South Africa: A mathematical modelling study.,Inform Med Unlocked,32835076,8/25/20,pubmed,0,3,mathematical model,0.001823406,0.00182338,0.001823446,0.940618654,0.001823332,0.052087783,Epidemiology,0.601315,TRUE,12,0.183190055,2.666666667,0.185442869,7,0.785110192,,,0.384581039
7648,Similarity maps and pairwise predictions for transmission dynamics of COVID-19 with neural networks.,Inform Med Unlocked,32835075,8/25/20,pubmed,0,1,neural network,0.001943511,0.055159265,0.001943543,0.878347953,0.060662107,0.00194362,Epidemiology,0.68466413,TRUE,66,0.741356918,9,0.337904736,2,0.618927094,,,0.566062916
7649,Identification and classification of differentially expressed genes reveal potential molecular signature associated with SARS-CoV-2 infection in lung adenocarcinomal cells.,Inform Med Unlocked,32835074,8/25/20,pubmed,0,3,sequencing,0.829398639,0.092361333,0.044967688,0.001237089,0.00123709,0.030798161,Drug discovery,0.88066816,TRUE,27.66666667,0.403488156,4,0.231469093,2,0.618927094,,,0.417961448
7650,Data analytics for novel coronavirus disease.,Inform Med Unlocked,32835073,8/25/20,pubmed,0,4,"logistic regression, dataset",0.002357765,0.228316148,0.424793142,0.237531855,0.104643084,0.002358005,Epidemiology,0.23933524,FALSE,20,0.298163152,3.75,0.21982874,16,0.881782826,,,0.466591572
7651,Potential of age distribution profiles for the prediction of COVID-19 infection origin in a patient group.,Inform Med Unlocked,32835072,8/25/20,pubmed,0,1,dataset,0.001171576,0.377551885,0.001171584,0.277539441,0.001171625,0.341393888,Genomics,0.24264577,FALSE,97,0.861586987,172,0.901190795,2,0.618927094,,,0.793901625
7652,The state of One Health research across disciplines and sectors - a bibliometric analysis.,One Health,32835067,8/25/20,pubmed,0,3,network analysis,0.001415186,0.056444379,0.00141516,0.937895004,0.001415162,0.001415109,Epidemiology,0.5031151,TRUE,13.33333333,0.201558538,4.333333333,0.237958255,4,0.707574542,,,0.382363778
7653,"Survey data regarding perceived air quality in Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa, United States before and during Covid-19 restrictions.",Data Brief,32835042,8/25/20,pubmed,0,27,dataset,0.002422256,0.002422317,0.002422398,0.583047116,0.407263658,0.002422255,Epidemiology,0.7728418,TRUE,28.25925926,0.410167605,9.703703704,0.348340915,3,0.667819001,,,0.475442507
7654,"Survey data of COVID-19 awareness, knowledge, preparedness and related behaviors among breast cancer patients in Indonesia.",Data Brief,32835041,8/25/20,pubmed,0,8,dataset,0.002238488,0.002238496,0.002238672,0.002238716,0.75174405,0.239301579,Healthcare,0.69948393,TRUE,66.875,0.74624281,7.625,0.309071448,1,0.537564047,,,0.530959435
7655,RCOVID19: Recurrence-based SARS-CoV-2 features using chaos game representation.,Data Brief,32835040,8/25/20,pubmed,0,4,"whole genome, genome sequences, dataset",0.00289834,0.632575216,0.355831505,0.002898372,0.002898258,0.002898308,Genomics,0.1460272,FALSE,14.25,0.215535902,1,0.122023013,0,0.403234768,,,0.246931228
7656,Impact of female students' perceptions on behavioral intention to use video conferencing tools in COVID-19: Data of Vietnam.,Data Brief,32835039,8/25/20,pubmed,0,5,dataset,0.002562594,0.002562609,0.285954874,0.002562801,0.703794596,0.002562526,Healthcare,0.78321075,TRUE,2.2,0.022759602,0,0.055525823,2,0.618927094,,,0.232404173
7657,Acquired infection after intubating patients with COVID-19: Datasets.,Data Brief,32835037,8/25/20,pubmed,0,8,dataset,0.00146188,0.001461874,0.090744151,0.001461929,0.497941463,0.406928702,Healthcare,0.9828913,TRUE,41.625,0.554146824,12.625,0.394434038,0,0.403234768,,,0.45060521
7658,Quantitative proteomic dataset from oro- and naso-pharyngeal swabs used for COVID-19 diagnosis: Detection of viral proteins and host's biological processes altered by the infection.,Data Brief,32835036,8/25/20,pubmed,0,7,"proteom, dataset",0.327446017,0.505227194,0.102801196,0.061865464,0.001330084,0.001330046,Genomics,0.415998,FALSE,28.57142857,0.413012555,32.71428571,0.590246187,1,0.537564047,,,0.513607596
7659,The COVID-19 global fear index and the predictability of commodity price returns.,J Behav Exp Finance,32835013,8/25/20,pubmed,0,3,predictive model,0.002080595,0.002080671,0.085095095,0.644806935,0.263856058,0.002080646,Epidemiology,0.28664818,FALSE,31.66666667,0.451976003,11,0.371287129,8,0.799987654,,,0.541083595
7660,Analysing the behaviour of doubling rates in 8 major countries affected by COVID-19 virus.,J Oral Biol Craniofac Res,32834981,8/25/20,pubmed,0,3,dataset,0.069246625,0.000916734,0.023118134,0.680487429,0.225314353,0.000916724,Epidemiology,0.6764833,TRUE,54,0.661574618,7,0.299973241,4,0.707574542,,,0.556374133
7661,Preventive healthcare policies in the US: solutions for disease management using Big Data Analytics.,J Big Data,32834926,8/25/20,pubmed,0,4,machine learning,0.037106751,0.001272687,0.249860568,0.435889106,0.274598124,0.001272764,Epidemiology,0.57334685,TRUE,10.25,0.154245779,1.25,0.127776291,0,0.403234768,,,0.228418946
7662,Molecular docking suggests repurposing of brincidofovir as a potential drug targeting SARS-CoV-2 ACE2 receptor and main protease.,Netw Model Anal Health Inform Bioinform,32834922,8/25/20,pubmed,0,2,virtual screening,0.989083525,0.002183308,0.00218324,0.002183336,0.002183324,0.002183266,Drug discovery,0.7870194,TRUE,11.5,0.17416043,9,0.337904736,3,0.667819001,,,0.393294723
7663,Nullane salus extra ecclesiam.,New Microbes New Infect,32834899,8/25/20,pubmed,0,2,in silico,0.00840303,0.008403151,0.008402923,0.957984219,0.008403003,0.008403675,Epidemiology,0.42829597,FALSE,446.5,0.996660276,705.5,0.987356168,1,0.537564047,,,0.84052683
7664,Genome evolution of SARS-CoV-2 and its virological characteristics.,Inflamm Regen,32834891,8/25/20,pubmed,0,2,"computational, genome sequences",0.00289868,0.821903389,0.002898476,0.002898492,0.002898463,0.1665025,Genomics,0.49610397,FALSE,190.5,0.968829241,207,0.920792079,13,0.858880178,,,0.916167166
7665,Logistics Flow Optimization for Advanced Management of the Crisis Situation.,Procedia Comput Sci,32834880,8/25/20,pubmed,0,3,"artificial intelligence, mathematical model",0.002032809,0.002032846,0.156308543,0.835560216,0.002032819,0.002032767,Epidemiology,0.5243181,TRUE,2,0.022141134,0,0.055525823,1,0.537564047,,,0.205077001
7666,An IoT-based framework for early identification and monitoring of COVID-19 cases.,Biomed Signal Process Control,32834831,8/25/20,pubmed,0,5,"bayes, machine learning, neural network, dataset",0.00122006,0.050026546,0.730832077,0.071098595,0.145602676,0.001220046,Healthcare,0.51624954,TRUE,15.6,0.235450554,1.2,0.126103827,10,0.828199272,,,0.396584551
7667,The dynamics of COVID-19 with quarantined and isolation.,Adv Differ Equ,32834821,8/25/20,pubmed,0,4,mathematical model,0.007062055,0.00706154,0.0070616,0.964691637,0.007061732,0.007061436,Epidemiology,0.34295195,FALSE,56.75,0.680004948,6,0.280037463,32,0.933699611,,,0.631247341
7668,Dynamics of a fractional order mathematical model for COVID-19 epidemic.,Adv Differ Equ,32834820,8/25/20,pubmed,0,4,mathematical model,0.001901781,0.001901811,0.162175421,0.830217276,0.001901848,0.001901862,Epidemiology,0.31220686,FALSE,21.75,0.321603068,3,0.199424672,3,0.667819001,,,0.396282247
7669,Analysis of Caputo fractional-order model for COVID-19 with lockdown.,Adv Differ Equ,32834819,8/25/20,pubmed,0,5,mathematical model,0.00256262,0.002562567,0.002562691,0.987186938,0.002562635,0.002562549,Epidemiology,0.54322946,TRUE,169.2,0.957696827,41,0.639751137,19,0.89561084,,,0.831019602
7670,A mathematical model of COVID-19 using fractional derivative: outbreak in India with dynamics of transmission and control.,Adv Differ Equ,32834815,8/25/20,pubmed,0,3,mathematical model,0.001901829,0.104269064,0.001901766,0.888123916,0.001901714,0.001901711,Epidemiology,0.83449125,TRUE,122,0.909889294,5,0.257024351,43,0.9507377,,,0.705883782
7671,Study of transmission dynamics of novel COVID-19 by using mathematical model.,Adv Differ Equ,32834812,8/25/20,pubmed,0,4,mathematical model,0.002996608,0.002996487,0.002996547,0.985017357,0.002996526,0.002996475,Epidemiology,0.53555834,TRUE,81.75,0.813655761,41,0.639751137,13,0.858880178,,,0.770762359
7672,Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks.,Appl Soft Comput,32834800,8/25/20,pubmed,0,2,prediction model,0.001593621,0.001593553,0.284906887,0.687297933,0.001593487,0.023014518,Epidemiology,0.4605109,FALSE,8,0.118683901,0,0.055525823,11,0.840175319,,,0.338128348
7673,A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the COVID-19 outbreak.,Appl Soft Comput,32834799,8/25/20,pubmed,0,4,digital health,0.001622773,0.001622731,0.284279257,0.646127165,0.06472535,0.001622724,Epidemiology,0.8716598,TRUE,44.5,0.581977859,10.5,0.363459995,9,0.814309525,,,0.58658246
7674,The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy.,Inf Fusion,32834796,8/25/20,pubmed,0,6,mathematical model,0.002806451,0.002806383,0.002806464,0.985967717,0.002806644,0.00280634,Epidemiology,0.6824401,TRUE,29.83333333,0.428907168,30.5,0.573187048,5,0.739490092,,,0.580528103
7675,On the uncertainty of real-time predictions of epidemic growths: A COVID-19 case study for China and Italy.,Commun Nonlinear Sci Numer Simul,32834701,8/25/20,pubmed,0,2,dataset,0.001786505,0.001786545,0.00178654,0.991067352,0.001786526,0.001786531,Epidemiology,0.22617778,FALSE,79.5,0.80493537,18.5,0.46554723,10,0.828199272,,,0.699560624
7676,Simulation model of security control lane operation in the state of the COVID-19 epidemic.,J Air Transp Manag,32834694,8/25/20,pubmed,0,2,simulation model,0.002806872,0.002806429,0.222181544,0.766592216,0.002806514,0.002806424,Epidemiology,0.60367835,TRUE,52.5,0.650875131,16.5,0.44293551,4,0.707574542,,,0.600461728
7677,Neural network powered COVID-19 spread forecasting model.,Chaos Solitons Fractals,32834663,8/25/20,pubmed,0,3,"neural network, network model, forecasting model",0.002720187,0.061175286,0.519918059,0.410745949,0.002720235,0.002720284,Epidemiology,0.34262252,FALSE,72.66666667,0.776052941,15,0.42594327,18,0.891474782,,,0.697823664
7678,Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models.,Chaos Solitons Fractals,32834662,8/25/20,pubmed,0,1,bayes,0.001415152,0.001415166,0.001415216,0.938622414,0.055716901,0.00141515,Epidemiology,0.43056333,FALSE,47,0.606407323,12,0.386740701,7,0.785110192,,,0.592752739
7679,Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using Group Method of Data Handling.,Chaos Solitons Fractals,32834660,8/25/20,pubmed,0,2,data mining,0.001901734,0.001901918,0.106031668,0.886361008,0.001901869,0.001901802,Epidemiology,0.21554506,FALSE,7,0.10179974,0,0.055525823,4,0.707574542,,,0.288300035
7680,Data science and the role of Artificial Intelligence in achieving the fast diagnosis of Covid-19.,Chaos Solitons Fractals,32834658,8/25/20,pubmed,0,2,"artificial intelligence, sequencing, classifier, dataset",0.00109884,0.062635018,0.874234466,0.059833937,0.001098887,0.001098852,Imaging,0.46084887,FALSE,3,0.037293586,0,0.055525823,4,0.707574542,,,0.266797984
7681,Probabilistic approximation of effective reproduction number of COVID-19 using daily death statistics.,Chaos Solitons Fractals,32834657,8/25/20,pubmed,0,5,"in-silico, probabilistic",0.001461849,0.001461863,0.0014619,0.894650317,0.099502121,0.00146195,Epidemiology,0.26276684,FALSE,91,0.846310842,39.8,0.632124699,2,0.618927094,,,0.699120878
7682,Mathematical model of Ebola and Covid-19 with fractional differential operators: Non-Markovian process and class for virus pathogen in the environment.,Chaos Solitons Fractals,32834655,8/25/20,pubmed,0,2,mathematical model,0.002996771,0.002996488,0.096915551,0.891098138,0.00299652,0.002996532,Epidemiology,0.6668236,TRUE,23,0.34225988,37.5,0.619213273,9,0.814309525,,,0.591927559
7683,A mathematical model for COVID-19 transmission dynamics with a case study of India.,Chaos Solitons Fractals,32834653,8/25/20,pubmed,0,3,"model simulation, mathematical model",0.099739353,0.001653098,0.001653127,0.893648067,0.00165307,0.001653285,Epidemiology,0.6556641,TRUE,13.66666667,0.207310285,2.666666667,0.185442869,24,0.914439163,,,0.435730772
7684,On the dynamical modeling of COVID-19 involving Atangana-Baleanu fractional derivative and based on Daubechies framelet simulations.,Chaos Solitons Fractals,32834652,8/25/20,pubmed,0,2,mathematical model,0.004310018,0.004309965,0.004310085,0.978449973,0.00430999,0.004309968,Epidemiology,0.6866431,TRUE,15,0.227596017,0,0.055525823,5,0.739490092,,,0.340870644
7685,Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches.,Chaos Solitons Fractals,32834651,8/25/20,pubmed,0,3,"deep learning, neural network",0.001486456,0.001486442,0.967784324,0.026269897,0.001486401,0.00148648,Imaging,0.54832876,TRUE,5.333333333,0.074339786,0.666666667,0.096200161,13,0.858880178,,,0.343140041
7686,An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast.,Chaos Solitons Fractals,32834649,8/25/20,pubmed,0,3,mathematical model,0.002296561,0.002296625,0.002296565,0.948674067,0.042139556,0.002296626,Epidemiology,0.5601754,TRUE,12.66666667,0.191044592,0.666666667,0.096200161,4,0.707574542,,,0.331606431
7687,Occurrence of backward bifurcation and prediction of disease transmission with imperfect lockdown: A case study on COVID-19.,Chaos Solitons Fractals,32834647,8/25/20,pubmed,0,2,mathematical model,0.0013929,0.001392862,0.001392837,0.993035609,0.001392874,0.001392917,Epidemiology,0.66497695,TRUE,124,0.912981632,56.5,0.709124967,11,0.840175319,,,0.820760639
7688,Target specific mining of COVID-19 scholarly articles using one-class approach.,Chaos Solitons Fractals,32834643,8/25/20,pubmed,0,3,"machine learning, dataset",0.001171635,0.134265136,0.54032734,0.321892735,0.0011716,0.001171554,Epidemiology,0.7925918,TRUE,62,0.715814212,23.66666667,0.518062617,12,0.850299401,,,0.69472541
7689,Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods.,Chaos Solitons Fractals,32834642,8/25/20,pubmed,0,4,mathematical model,0.002562643,0.002562576,0.002562606,0.987187023,0.002562568,0.002562584,Epidemiology,0.6289245,TRUE,6.75,0.095862453,0,0.055525823,9,0.814309525,,,0.321899267
7690,Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans.,Chaos Solitons Fractals,32834641,8/25/20,pubmed,0,6,"artificial intelligence, neural network, dataset",0.001538096,0.001538094,0.992309435,0.001538108,0.001538091,0.001538175,Imaging,0.5433059,TRUE,24.83333333,0.365823489,8.166666667,0.321915975,16,0.881782826,,,0.523174097
7691,Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques.,Chaos Solitons Fractals,32834640,8/25/20,pubmed,0,2,forecasting model,0.001203429,0.157205735,0.096644896,0.742539094,0.00120343,0.001203416,Epidemiology,0.59643495,TRUE,45.5,0.591440411,4.5,0.242708055,1,0.537564047,,,0.457237504
7692,Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models.,Chaos Solitons Fractals,32834639,8/25/20,pubmed,0,4,dataset,0.001717235,0.058587278,0.001717279,0.93454376,0.001717233,0.001717215,Epidemiology,0.6983907,TRUE,55,0.668686994,3.5,0.213607172,7,0.785110192,,,0.555801452
7693,Corrigendum to a novel covid-19 mathematical model with fractional derivatives: Singular and nonsingular kernels [Chaos Solitons & Fractals 139 (2020) 110060].,Chaos Solitons Fractals,32834637,8/25/20,pubmed,0,1,mathematical model,0.057799148,0.057799148,0.057799148,0.711004258,0.057799148,0.057799148,Epidemiology,0.6925409,TRUE,16,0.243552477,2,0.164302917,0,0.403234768,,,0.270363387
7694,A numerical simulation of fractional order mathematical modeling of COVID-19 disease in case of Wuhan China.,Chaos Solitons Fractals,32834636,8/25/20,pubmed,0,2,mathematical model,0.002080615,0.002080597,0.002080686,0.989596939,0.002080596,0.002080566,Epidemiology,0.7568563,TRUE,8,0.118683901,0,0.055525823,10,0.828199272,,,0.334136332
7695,Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks.,Chaos Solitons Fractals,32834634,8/25/20,pubmed,0,3,"deep learning, neural network",0.001653009,0.001653041,0.970223817,0.023164093,0.001653043,0.001652998,Imaging,0.6075015,TRUE,9.333333333,0.139278867,1.666666667,0.145036125,34,0.937156615,,,0.407157202
7696,Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study.,Chaos Solitons Fractals,32834633,8/25/20,pubmed,0,4,"deep learning, neural network, lstm",0.0014381,0.001438104,0.388704244,0.60554321,0.001438117,0.001438225,Epidemiology,0.67497724,TRUE,43.25,0.569855897,10,0.355632861,25,0.918019631,,,0.614502796
7697,Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.,Chaos Solitons Fractals,32834632,8/25/20,pubmed,0,3,"prediction model, dataset",0.001438134,0.057090932,0.001438206,0.937156497,0.001438096,0.001438135,Epidemiology,0.5374544,TRUE,54,0.661574618,35.33333333,0.607439122,7,0.785110192,,,0.684707977
7698,"Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique.",Chaos Solitons Fractals,32834627,8/25/20,pubmed,0,2,deep learning,0.001330018,0.001330074,0.877691488,0.116988281,0.001330045,0.001330094,Imaging,0.5675707,TRUE,6,0.086028821,0.5,0.087101953,43,0.9507377,,,0.374622825
7699,Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries.,Chaos Solitons Fractals,32834622,8/25/20,pubmed,0,6,mathematical model,0.002032778,0.002032805,0.16535906,0.791506617,0.037036003,0.002032738,Epidemiology,0.74034876,TRUE,18.5,0.278001113,2,0.164302917,7,0.785110192,,,0.409138074
7700,Assessment of lockdown effect in some states and overall India: A predictive mathematical study on COVID-19 outbreak.,Chaos Solitons Fractals,32834620,8/25/20,pubmed,0,4,mathematical model,0.001565456,0.026632798,0.001565363,0.967105763,0.001565323,0.001565297,Epidemiology,0.34299755,FALSE,70.25,0.76504422,26.25,0.539670859,55,0.960800049,,,0.75517171
7701,Modeling the impact of non-pharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study.,Chaos Solitons Fractals,32834618,8/25/20,pubmed,0,2,mathematical model,0.001717217,0.001717161,0.001717175,0.99141396,0.001717276,0.00171721,Epidemiology,0.36327356,FALSE,24.5,0.361988991,7,0.299973241,46,0.954009507,,,0.538657246
7702,Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia.,Chaos Solitons Fractals,32834616,8/25/20,pubmed,0,5,simulation model,0.002720306,0.002720181,0.002720119,0.986399143,0.00272016,0.002720091,Epidemiology,0.43835244,FALSE,21,0.312016822,2,0.164302917,29,0.928020248,,,0.468113329
7703,A novel covid-19 mathematical model with fractional derivatives: Singular and nonsingular kernels.,Chaos Solitons Fractals,32834613,8/25/20,pubmed,0,1,mathematical model,0.003101577,0.003101549,0.003101618,0.984492307,0.003101482,0.003101468,Epidemiology,0.6726995,TRUE,16,0.243552477,2,0.164302917,15,0.874313229,,,0.427389541
7704,Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review.,Chaos Solitons Fractals,32834612,8/25/20,pubmed,0,3,"machine learning, artificial intelligence",0.043664756,0.000988361,0.490884503,0.399061472,0.064412516,0.000988392,Epidemiology,0.7183019,TRUE,4.333333333,0.057950399,0.666666667,0.096200161,78,0.972405704,,,0.375518755
7705,Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics.,Chaos Solitons Fractals,32834611,8/25/20,pubmed,0,4,"machine learning, prediction model",0.002562546,0.002562577,0.002562796,0.987187004,0.002562529,0.002562548,Epidemiology,0.260721,FALSE,17.75,0.267672707,6.75,0.292079208,39,0.945737391,,,0.501829769
7706,Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19.,Chaos Solitons Fractals,32834609,8/25/20,pubmed,0,4,"machine learning, computational, dataset",0.001823393,0.00182338,0.736833417,0.255873141,0.001823328,0.001823341,Epidemiology,0.5337747,TRUE,47,0.606407323,22,0.503746321,11,0.840175319,,,0.650109654
7707,An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data.,Chaos Solitons Fractals,32834608,8/25/20,pubmed,0,2,machine learning,0.001943581,0.001943632,0.700904258,0.291321529,0.001943524,0.001943475,Epidemiology,0.52715933,TRUE,4.5,0.061784897,1,0.122023013,9,0.814309525,,,0.332705812
7708,Analysis on novel coronavirus (COVID-19) using machine learning methods.,Chaos Solitons Fractals,32834604,8/25/20,pubmed,0,3,"machine learning, dataset",0.001486471,0.00148647,0.334871646,0.587304185,0.001486482,0.073364746,Epidemiology,0.6081059,TRUE,13.66666667,0.207310285,1.666666667,0.145036125,15,0.874313229,,,0.408886547
7709,Modeling and forecasting the COVID-19 pandemic in India.,Chaos Solitons Fractals,32834603,8/25/20,pubmed,0,3,"model simulation, mathematical model",0.001237199,0.001237112,0.001237097,0.968665411,0.026385899,0.001237282,Epidemiology,0.6346963,TRUE,16,0.243552477,6,0.280037463,85,0.975121921,,,0.49957062
7710,A novel mathematical approach of COVID-19 with non-singular fractional derivative.,Chaos Solitons Fractals,32834602,8/25/20,pubmed,0,3,mathematical model,0.015999241,0.015998571,0.015998603,0.920006335,0.015998505,0.015998745,Epidemiology,0.52010113,TRUE,85.33333333,0.825901416,7.333333333,0.304656141,4,0.707574542,,,0.6127107
7711,Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19.,Chaos Solitons Fractals,32834597,8/25/20,pubmed,0,2,network analysis,0.05708253,0.001220078,0.216550804,0.645054693,0.078871868,0.001220028,Epidemiology,0.022581369,FALSE,33,0.466757375,4,0.231469093,3,0.667819001,,,0.45534849
7712,Dynamic analysis of a mathematical model with health care capacity for COVID-19 pandemic.,Chaos Solitons Fractals,32834594,8/25/20,pubmed,0,1,mathematical model,0.003335368,0.003335279,0.00333531,0.983323048,0.003335343,0.003335652,Epidemiology,0.2563115,FALSE,11,0.167171748,0,0.055525823,15,0.874313229,,,0.365670267
7713,"Analysis of a mathematical model for COVID-19 population dynamics in Lagos, Nigeria.",Chaos Solitons Fractals,32834593,8/25/20,pubmed,0,2,mathematical model,0.001861734,0.001861741,0.001861794,0.955247725,0.037305215,0.00186179,Epidemiology,0.4570054,FALSE,11,0.167171748,3.5,0.213607172,26,0.920859312,,,0.433879411
7714,"HIV and shifting epicenters for COVID-19, an alert for some countries.",Chaos Solitons Fractals,32834592,8/25/20,pubmed,0,3,mathematical model,0.001653065,0.001653105,0.019133933,0.927192174,0.048714666,0.001653057,Epidemiology,0.3350411,FALSE,18,0.271569052,1.333333333,0.13252609,22,0.908142478,,,0.43741254
7715,Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables.,Chaos Solitons Fractals,32834591,8/25/20,pubmed,0,4,"bayes, artificial intelligence, neural network, forecasting model",0.001098814,0.00109881,0.256523201,0.739081397,0.001098864,0.001098914,Epidemiology,0.5533995,TRUE,146.25,0.94093636,117.5,0.847872625,22,0.908142478,,,0.898983821
7716,A novel adaptive deep learning model of Covid-19 with focus on mortality reduction strategies.,Chaos Solitons Fractals,32834586,8/25/20,pubmed,0,2,"deep learning, neural network",0.001486455,0.001486502,0.387138826,0.569515522,0.001486505,0.03888619,Epidemiology,0.27612793,FALSE,6.5,0.093512277,0,0.055525823,6,0.764429903,,,0.304489334
7717,Going by the numbers : Learning and modeling COVID-19 disease dynamics.,Chaos Solitons Fractals,32834585,8/25/20,pubmed,0,2,lstm,0.001823349,0.001823344,0.001823565,0.990883011,0.001823374,0.001823357,Epidemiology,0.26219338,FALSE,29.5,0.426000371,19,0.471367407,0,0.403234768,,,0.433534182
7718,Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches.,Chaos Solitons Fractals,32834583,8/25/20,pubmed,0,6,"machine learning, dataset",0.001415138,0.001415155,0.242263801,0.752075551,0.001415139,0.001415215,Epidemiology,0.4106039,FALSE,9.833333333,0.147628177,0.333333333,0.073187048,25,0.918019631,,,0.379611619
7719,Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19.,Chaos Solitons Fractals,32834580,8/25/20,pubmed,0,6,computational,0.039302499,0.001203473,0.001203479,0.955883601,0.001203478,0.001203469,Epidemiology,0.46409586,FALSE,100,0.868884903,55,0.702167514,12,0.850299401,,,0.807117273
7720,"Forecasting the cumulative number of confirmed cases of COVID-19 in Italy, UK and USA using fractional nonlinear grey Bernoulli model.",Chaos Solitons Fractals,32834578,8/25/20,pubmed,0,2,prediction model,0.002357735,0.002357723,0.09493397,0.89563505,0.002357762,0.002357761,Epidemiology,0.12272951,FALSE,11.5,0.17416043,1.5,0.138747659,16,0.881782826,,,0.398230305
7721,Insights into the dynamics and control of COVID-19 infection rates.,Chaos Solitons Fractals,32834573,8/25/20,pubmed,0,4,model simulation,0.00289833,0.002898324,0.002898317,0.985508452,0.00289827,0.002898306,Epidemiology,0.21230939,FALSE,20,0.298163152,2.25,0.170925876,7,0.785110192,,,0.418066407
7722,A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients.,Expert Syst Appl,32834556,8/25/20,pubmed,0,9,"machine learning, predictive model",0.00139284,0.001392856,0.475467739,0.136382353,0.001392935,0.383971278,Clinics,0.9786776,TRUE,15.66666667,0.236563795,16.33333333,0.440460262,14,0.866658436,,,0.514560831
7723,A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier.,Knowl Based Syst,32834553,8/25/20,pubmed,0,4,classifier,0.018789417,0.021913998,0.924436331,0.000916733,0.033026811,0.00091671,Imaging,0.84711605,TRUE,23.5,0.348506401,4.25,0.235750602,14,0.866658436,,,0.48363848
7724,Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19.,Mach Vis Appl,32834523,8/25/20,pubmed,0,3,"deep learning, image analysis",0.050665744,0.001511849,0.79426246,0.067744073,0.027906103,0.057909771,Imaging,0.6025446,TRUE,12.66666667,0.191044592,2.333333333,0.173401124,9,0.814309525,,,0.392918414
7725,Human and organizational factors within the public sectors for the prevention and control of epidemic.,Saf Sci,32834517,8/25/20,pubmed,0,5,bayes,0.002720491,0.002720221,0.352513063,0.636605826,0.002720187,0.002720211,Epidemiology,0.5679001,TRUE,53,0.654400396,143.6,0.877709393,1,0.537564047,,,0.689891279
7726,0,Appl Math Lett,32834475,8/25/20,pubmed,0,9,mathematical model,0.001943512,0.001943542,0.025231659,0.966994321,0.001943482,0.001943484,Epidemiology,0.59090626,TRUE,136.2222222,0.928072237,101.3333333,0.824591919,19,0.89561084,,,0.882758332
7727,COVID-19 in Italy and extreme data mining.,Physica A,32834435,8/25/20,pubmed,0,5,"artificial intelligence, data mining, dataset",0.001461874,0.030597153,0.280872036,0.684145107,0.001461922,0.001461909,Epidemiology,0.25296497,FALSE,37.6,0.514626755,16,0.437316029,1,0.537564047,,,0.496502277
7728,"COVID-19 and SARS-CoV-2. Modeling the present, looking at the future.",Phys Rep,32834430,8/25/20,pubmed,0,1,"virtual screening, vaccinom",0.374043414,0.021095509,0.000988377,0.601895947,0.000988377,0.000988376,Epidemiology,0.1508638,FALSE,287,0.989795287,304,0.955512443,31,0.931971109,,,0.959092947
7729,A Review of Multi-Compartment Infectious Disease Models.,Int Stat Rev,32834402,8/25/20,pubmed,0,8,mathematical model,0.001786569,0.001786575,0.001786611,0.991067054,0.00178668,0.001786511,Epidemiology,0.26447183,FALSE,339.625,0.993382398,223.5,0.927481937,7,0.785110192,,,0.901991509
7730,Statistical Implementations of Agent-Based Demographic Models.,Int Stat Rev,32834401,8/25/20,pubmed,0,3,"bayes, computational",0.11580926,0.001901762,0.001901881,0.844925305,0.033559793,0.001901999,Epidemiology,0.32349658,FALSE,135.3333333,0.926897149,290,0.95143163,3,0.667819001,,,0.848715927
7731,A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate.,Ecol Modell,32834369,8/25/20,pubmed,0,1,dataset,0.001461935,0.001461946,0.001462013,0.992690329,0.001461898,0.001461878,Epidemiology,0.53619695,TRUE,59,0.696579875,36,0.611118544,11,0.840175319,,,0.715957913
7732,Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models.,Cities,32834328,8/25/20,pubmed,0,5,"supervised learning, deep learning",0.002080558,0.002080584,0.183029672,0.808647867,0.002080693,0.002080626,Epidemiology,0.2285169,FALSE,94.6,0.8556497,51.8,0.688587102,8,0.799987654,,,0.781408152
7733,A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic.,Measurement (Lond),32834324,8/25/20,pubmed,0,4,"machine learning, classifier, transfer learning, dataset",0.001653034,0.001653021,0.768090188,0.225297656,0.001653109,0.001652991,Epidemiology,0.50957054,TRUE,32,0.455810502,14.75,0.421327268,26,0.920859312,,,0.599332361
7734,Immunoinformatics characterization of SARS-CoV-2 spike glycoprotein for prioritization of epitope based multivalent peptide vaccine.,J Mol Liq,32834259,8/25/20,pubmed,0,3,molecular dynamics simulation,0.994141955,0.001171665,0.001171581,0.001171633,0.001171618,0.001171548,Drug discovery,0.9032134,TRUE,19.66666667,0.292596945,6.333333333,0.285121755,3,0.667819001,,,0.415179234
7735,Deep learning disease prediction model for use with intelligent robots.,Comput Electr Eng,32834174,8/25/20,pubmed,0,3,"machine learning, deep learning, prediction model",0.001717217,0.001717194,0.921292113,0.001717297,0.001717212,0.071838967,Clinics,0.8762064,TRUE,19.33333333,0.288638753,1.666666667,0.145036125,0,0.403234768,,,0.278969882
7736,Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective.,Technol Forecast Soc Change,32834137,8/25/20,pubmed,0,4,neural network,0.002130753,0.002130847,0.059535237,0.480198606,0.453873894,0.002130663,Epidemiology,0.8490989,TRUE,87.75,0.836229822,53,0.693738293,16,0.881782826,,,0.80391698
7737,Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors.,J Mol Struct,32834115,8/25/20,pubmed,0,4,data mining,0.92754286,0.00115628,0.067832063,0.001156288,0.001156263,0.001156247,Drug discovery,0.8683589,TRUE,46.25,0.598800173,5,0.257024351,10,0.828199272,,,0.561341265
7738,Computational evaluation of major components from plant essential oils as potent inhibitors of SARS-CoV-2 spike protein.,J Mol Struct,32834111,8/25/20,pubmed,0,6,computational,0.988807532,0.002238515,0.002238499,0.002238521,0.002238485,0.002238448,Drug discovery,0.9278797,TRUE,18.16666667,0.272682293,1,0.122023013,17,0.887338725,,,0.42734801
7739,Inhibitory activity of hydroxychloroquine on COVID-19 main protease: An insight from MD-simulation studies.,J Mol Struct,32834108,8/25/20,pubmed,0,3,molecular dynamics simulation,0.846065054,0.002238581,0.002238553,0.087576491,0.059642843,0.002238479,Drug discovery,0.9094373,TRUE,52.33333333,0.649452656,8,0.320511105,16,0.881782826,,,0.617248863
7740,Analogies between SARS-CoV-2 infection dynamics and batch chemical reactor behavior.,Chem Eng Sci,32834062,8/25/20,pubmed,0,7,predictive model,0.303296098,0.00198725,0.001987149,0.68875492,0.001987289,0.001987295,Epidemiology,0.5017486,TRUE,9.714285714,0.145401695,3.714285714,0.218022478,3,0.667819001,,,0.343747725
7741,Therapeutic Options for the Treatment of Coronavirus Disease (COVID-19).,Crit Care Nurs Q,32833772,8/25/20,pubmed,0,5,sequencing,0.799159411,0.186411068,0.003607492,0.003607414,0.003607377,0.003607239,Drug discovery,0.9168119,TRUE,25.8,0.378316532,5.8,0.272678619,2,0.618927094,,,0.423307415
7742,Advances in Viral Diagnostic Technologies for Combating COVID-19 and Future Pandemics.,SLAS Technol,32833548,8/25/20,pubmed,0,2,sequencing,0.108385439,0.769750681,0.117083304,0.001593602,0.001593497,0.001593477,Genomics,0.7452029,TRUE,84,0.821881378,80,0.779033984,1,0.537564047,,,0.71282647
7743,Computational Alanine Scanning and Structural Analysis of the SARS-CoV-2 Spike Protein/Angiotensin-Converting Enzyme 2 Complex.,ACS Nano,32833435,8/25/20,pubmed,0,5,"computational, in silico",0.882515029,0.037732393,0.026566239,0.001272771,0.001272647,0.05064092,Drug discovery,0.83697236,TRUE,219.2,0.97835364,42.4,0.646641691,10,0.828199272,,,0.817731534
7744,Various Facets of Pathogenic Lipids in Infectious Diseases: Exploring Virulent Lipid-Host Interactome and Their Druggability.,J Membr Biol,32833058,8/25/20,pubmed,0,2,interactom,0.992440585,0.001511912,0.001511838,0.001511893,0.001511888,0.001511885,Drug discovery,0.7113396,TRUE,34,0.477766096,13,0.400521809,1,0.537564047,,,0.471950651
7745,Anti-COVID-19 multi-epitope vaccine designs employing global viral genome sequences.,PeerJ,32832263,8/25/20,pubmed,0,12,"in silico, genome sequences, genomes",0.645247292,0.349432521,0.001330035,0.001330029,0.001330085,0.001330039,Drug discovery,0.78549665,TRUE,28.16666667,0.409178057,9.25,0.340379984,1,0.537564047,,,0.429040696
7746,Putative Natural History of CoViD-19.,Bioinformation,32831521,8/25/20,pubmed,0,1,prediction model,0.207412805,0.001786626,0.001786584,0.614065865,0.001786593,0.173161526,Epidemiology,0.5034271,TRUE,1,0.012307502,0,0.055525823,2,0.618927094,,,0.22892014
7747,The big challenge of SARS-CoV-2 latency: testes as reservoir.,Gac Med Mex,32831326,8/25/20,pubmed,0,5,"transcriptom, proteom",0.810703567,0.181849571,0.001861718,0.001861763,0.001861676,0.001861704,Drug discovery,0.7943722,TRUE,10,0.15214299,3,0.199424672,2,0.618927094,,,0.323498252
7748,Risk estimation and prediction of the transmission of coronavirus disease-2019 (COVID-19) in the mainland of China excluding Hubei province.,Infect Dis Poverty,32831142,8/25/20,pubmed,0,3,mathematical model,0.000815353,0.000815338,0.000815329,0.995923279,0.000815372,0.000815329,Epidemiology,0.22507998,FALSE,68.66666667,0.757313377,43.33333333,0.650722505,4,0.707574542,,,0.705203475
7749,"Public Awareness, Individual Prevention Practice, and Psychological Effect at the Beginning of the COVID-19 Outbreak in China.",J Epidemiol,32830167,8/25/20,pubmed,0,9,logistic regression,0.001461852,0.001461877,0.001461897,0.001462,0.992690476,0.001461898,Healthcare,0.9091585,TRUE,75.44444444,0.788236749,52.77777778,0.692132727,1,0.537564047,,,0.672644508
7750,Perceived Discrimination and Mental Distress Amid the COVID-19 Pandemic: Evidence From the Understanding America Study.,Am J Prev Med,32829968,8/25/20,pubmed,0,5,logistic regression,0.001085321,0.001085339,0.001085357,0.001085395,0.823268564,0.172390024,Healthcare,0.93103904,TRUE,82.8,0.817242872,75.6,0.768062617,11,0.840175319,,,0.808493603
7751,Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space-time modelling.,Epidemiol Infect,32829732,8/25/20,pubmed,0,12,bayes,0.002080562,0.00208067,0.04006724,0.95161008,0.002080647,0.002080802,Epidemiology,0.80690193,TRUE,7.833333333,0.113798009,0.416666667,0.075996789,5,0.739490092,,,0.30976163
7752,[The role of large-scale testing platform in the prevention and control of the COVID-19 pandemic: an empirical study based on a novel numerical model].,Zhonghua Yi Xue Za Zhi,32829601,8/25/20,pubmed,0,20,"mathematical model, sequencing",0.000916683,0.16447597,0.143269621,0.637142806,0.000916719,0.053278201,Epidemiology,0.7970634,TRUE,12.26315789,0.185169151,42.78947368,0.648447953,0,0.403234768,,,0.412283957
7753,Environmental Factors and Hyperacute Stroke Care Activity During the COVID-19 Pandemic: An Interrupted Time-Series Analysis.,J Stroke Cerebrovasc Dis,32828638,8/24/20,pubmed,0,11,dataset,0.001126858,0.001126862,0.001126833,0.687219629,0.001126865,0.308272952,Epidemiology,0.5303688,TRUE,24.45454545,0.360813903,27.90909091,0.552716082,3,0.667819001,,,0.527116329
7754,Hydroxychloroquine in the Treatment of COVID-19: A Multicenter Randomized Controlled Study.,Am J Trop Med Hyg,32828135,8/24/20,pubmed,0,10,logistic regression,0.001371255,0.001371249,0.001371262,0.062059425,0.001371299,0.93245551,Clinics,0.8975977,TRUE,18.9,0.282144845,2.4,0.174872893,27,0.92443978,,,0.460485839
7755,Prevalence and factors for anxiety during the coronavirus disease 2019 (COVID-19) epidemic among the teachers in China.,J Affect Disord,32828002,8/23/20,pubmed,0,6,logistic regression,0.001511811,0.001511844,0.001511835,0.142541319,0.851411352,0.001511839,Healthcare,0.9306355,TRUE,21,0.312016822,1.166666667,0.124565159,4,0.707574542,,,0.381385508
7756,Gastrointestinal symptoms associated with COVID-19: impact on the gut microbiome.,Transl Res,32827705,8/23/20,pubmed,0,1,microbiom,0.222933697,0.183030666,0.001751287,0.001751291,0.194954385,0.395578673,Clinics,0.92360467,TRUE,4,0.054734368,0,0.055525823,33,0.936045435,,,0.348768542
7757,Black/African American Communities are at highest risk of COVID-19: spatial modeling of New York City ZIP Code-level testing results.,Ann Epidemiol,32827672,8/23/20,pubmed,0,4,bayes,0.001461843,0.001461921,0.001461879,0.317959499,0.459279325,0.218375533,Healthcare,0.60488737,TRUE,60.75,0.706784588,42,0.644902328,13,0.858880178,,,0.736855698
7758,Emerging strategies on in silico drug development against COVID-19: challenges and opportunities.,Eur J Pharm Sci,32827661,8/23/20,pubmed,0,3,in silico,0.973589895,0.001203434,0.001203478,0.001203442,0.001203444,0.021596307,Drug discovery,0.88341963,TRUE,19.33333333,0.288638753,1.666666667,0.145036125,3,0.667819001,,,0.367164627
7759,ACE2 and SCARF expression in human dorsal root ganglion nociceptors: implications for SARS-CoV-2 virus neurological effects.,Pain,32826754,8/23/20,pubmed,0,10,sequencing,0.908381311,0.002130904,0.083095207,0.002130812,0.002130908,0.002130858,Drug discovery,0.4277585,FALSE,71.2,0.769744573,110.3,0.838774418,4,0.707574542,,,0.772031178
7760,Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates.,Proc Natl Acad Sci U S A,32826334,8/23/20,pubmed,0,19,dataset,0.461693759,0.474345751,0.059645857,0.001438172,0.001438227,0.001438235,Genomics,0.43625087,FALSE,85.42105263,0.826148803,478.7894737,0.977655874,31,0.931971109,,,0.911925262
7761,Neutralizing Antibodies Correlate with Protection from SARS-CoV-2 in Humans during a Fishery Vessel Outbreak with a High Attack Rate.,J Clin Microbiol,32826322,8/23/20,pubmed,0,9,"sequencing, metagenom, genomes",0.001392879,0.63204103,0.027287196,0.001392892,0.226971412,0.110914591,Genomics,0.23011893,FALSE,62.55555556,0.718349929,78.66666667,0.774886272,49,0.956787456,,,0.816674552
7762,Predicting Psychological Distress Amid the COVID-19 Pandemic by Machine Learning: Discrimination and Coping Mechanisms of Korean Immigrants in the U.S.,Int J Environ Res Public Health,32825349,8/23/20,pubmed,0,4,"machine learning, neural network",0.051923958,0.00208059,0.263985861,0.002080685,0.677848199,0.002080707,Healthcare,0.95624113,TRUE,23.5,0.348506401,7,0.299973241,2,0.618927094,,,0.422468912
7763,Whole Genome Sequencing of SARS-CoV-2: Adapting Illumina Protocols for Quick and Accurate Outbreak Investigation during a Pandemic.,Genes (Basel),32824573,8/23/20,pubmed,0,17,"bioinformatic, sequencing, whole genome, genomes",0.001511842,0.746990608,0.098894792,0.088179612,0.001511925,0.062911221,Genomics,0.5038308,TRUE,20.11764706,0.298596079,19.58823529,0.476451699,3,0.667819001,,,0.480955593
7764,0,Molecules,32824454,8/23/20,pubmed,0,4,virtual screening,0.96840134,0.026580109,0.00125466,0.00125468,0.001254609,0.001254601,Drug discovery,0.88554776,TRUE,86,0.829117447,12.75,0.395972705,5,0.739490092,,,0.654860081
7765,"A Comparison of Whole Genome Sequencing of SARS-CoV-2 Using Amplicon-Based Sequencing, Random Hexamers, and Bait Capture.",Viruses,32824272,8/23/20,pubmed,0,17,"bioinformatic, sequencing, whole genome",0.001085384,0.949177462,0.025364471,0.001085365,0.022201931,0.001085387,Genomics,0.7720667,TRUE,52.76470588,0.651864679,92.41176471,0.806729997,7,0.785110192,,,0.747901623
7766,Molecular Epidemiology Analysis of SARS-CoV-2 Strains Circulating in Romania during the First Months of the Pandemic.,Life (Basel),32823907,8/23/20,pubmed,0,12,sequencing,0.001350345,0.809742362,0.001350312,0.184856334,0.001350339,0.001350308,Genomics,0.68995404,TRUE,41.33333333,0.551549261,24.75,0.526491838,2,0.618927094,,,0.565656065
7767,Influencing Factors of Understanding COVID-19 Risks and Coping Behaviors among the Elderly Population.,Int J Environ Res Public Health,32823740,8/23/20,pubmed,0,4,logistic regression,0.001330068,0.001330055,0.001330057,0.001330124,0.993349634,0.001330061,Healthcare,0.6528255,TRUE,26.5,0.389325252,3.75,0.21982874,9,0.814309525,,,0.474487839
7768,Nicotinic Cholinergic System and COVID-19: In Silico Identification of an Interaction between SARS-CoV-2 and Nicotinic Receptors with Potential Therapeutic Targeting Implications.,Int J Mol Sci,32823591,8/23/20,pubmed,0,6,"computational, in silico",0.862866706,0.089453277,0.001392832,0.001392857,0.001392949,0.04350138,Drug discovery,0.7685935,TRUE,133.3333333,0.925412827,77.83333333,0.772745518,17,0.887338725,,,0.861832356
7769,Hesperidin and SARS-CoV-2: New Light on the Healthy Function of Citrus Fruits.,Antioxidants (Basel),32823497,8/23/20,pubmed,0,2,computational,0.834468503,0.001565347,0.001565346,0.116714816,0.001565454,0.044120535,Drug discovery,0.92989993,TRUE,131.5,0.923495578,55.5,0.704575863,13,0.858880178,,,0.828983873
7770,"Skin is a potential host of SARS-CoV-2: A clinical, single-cell transcriptome-profiling and histologic study.",J Am Acad Dermatol,32822795,8/22/20,pubmed,0,7,transcriptom,0.920006913,0.015999187,0.015998497,0.015998406,0.015998404,0.015998592,Drug discovery,0.84183824,TRUE,21.14285714,0.312758983,3.714285714,0.218022478,3,0.667819001,,,0.399533487
7771,Missense mutations in SARS-CoV2 genomes from Indian patients.,Genomics,32822756,8/22/20,pubmed,0,4,genomes,0.002238595,0.958376592,0.002238465,0.002238546,0.002238441,0.03266936,Genomics,0.19343013,FALSE,73,0.778464964,55.25,0.7028365,17,0.887338725,,,0.789546729
7772,Comparison of molecular testing strategies for COVID-19 control: a mathematical modelling study.,Lancet Infect Dis,32822577,8/22/20,pubmed,0,43,mathematical model,0.000677925,0.185139101,0.09270268,0.449204771,0.271597583,0.00067794,Epidemiology,0.12221637,FALSE,47.20454545,0.607582411,,,39,0.945737391,,,0.776659901
7773,Effects of a major deletion in the SARS-CoV-2 genome on the severity of infection and the inflammatory response: an observational cohort study.,Lancet,32822564,8/22/20,pubmed,0,29,logistic regression,0.001371272,0.371336927,0.001371276,0.001371283,0.00137132,0.623177922,Clinics,0.6967587,TRUE,55.31034483,0.669862082,90.17241379,0.801578807,103,0.978949318,,,0.816796735
7774,Real-time public health communication of local SARS-CoV-2 genomic epidemiology.,PLoS Biol,32822393,8/22/20,pubmed,0,12,genomic epidemiology,0.002806525,0.28844632,0.002807023,0.700327318,0.002806465,0.002806349,Epidemiology,0.32071394,FALSE,19.58333333,0.291607397,18.08333333,0.462001606,2,0.618927094,,,0.457512032
7775,Virtual Imaging Trials for Coronavirus Disease (COVID-19).,AJR Am J Roentgenol,32822224,8/22/20,pubmed,0,4,computational,0.177301814,0.001392866,0.771081088,0.001392895,0.001392882,0.047438455,Imaging,0.7571941,TRUE,156.25,0.948729049,98,0.819106235,2,0.618927094,,,0.79558746
7776,Gut Microbiome Imbalance and Neuroinflammation: Impact of COVID-19 on Parkinson's Disease.,Mov Disord,32822087,8/22/20,pubmed,0,1,microbiom,0.057799149,0.711004256,0.057799148,0.057799148,0.057799148,0.05779915,Genomics,0.8761364,TRUE,49,0.624281032,68,0.748193738,1,0.537564047,,,0.636679606
7777,Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.,Clin Chem,32821907,8/22/20,pubmed,0,12,"machine learning, dataset",0.001156245,0.126393431,0.500714633,0.00115627,0.001156288,0.369423133,Clinics,0.79278666,TRUE,127.1666667,0.917310904,200.1666667,0.917647846,1,0.537564047,,,0.790840932
7778,The impact of delirium on outcomes for older adults hospitalised with COVID-19.,Age Ageing,32821901,8/22/20,pubmed,0,5,logistic regression,0.001237065,0.126979049,0.001237092,0.001237059,0.033239071,0.836070664,Clinics,0.89592993,TRUE,175.8,0.962520873,435.6,0.973842655,11,0.840175319,,,0.925512949
7779,A modelling framework to assess the likely effectiveness of facemasks in combination with 'lock-down' in managing the COVID-19 pandemic.,Proc Math Phys Eng Sci,32821237,8/22/20,pubmed,0,5,mathematical model,0.017303388,0.000956341,0.000956326,0.908548534,0.071279067,0.000956344,Epidemiology,0.22659644,FALSE,65,0.734801163,84.2,0.788734279,82,0.973578616,,,0.832371353
7780,Simulations and Virtual Learning Supporting Clinical Education During the COVID 19 Pandemic.,Adv Med Educ Pract,32821192,8/22/20,pubmed,0,1,active learning,0.001861768,0.001861686,0.0953398,0.304697653,0.594377354,0.001861739,Healthcare,0.5401283,TRUE,14,0.213494959,1,0.122023013,5,0.739490092,,,0.358336022
7781,"SARS-CoV-2 growth, furin-cleavage-site adaptation and neutralization using serum from acutely infected hospitalized COVID-19 patients.",J Gen Virol,32821033,8/22/20,pubmed,0,15,sequencing,0.555644711,0.342187961,0.001684594,0.001684632,0.001684918,0.097113184,Drug discovery,0.70074993,TRUE,54.06666667,0.661636465,89.06666667,0.799371153,12,0.850299401,,,0.770435673
7782,A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.,Sci Rep,32820210,8/21/20,pubmed,0,7,"machine learning, logistic regression, prediction model",0.130584935,0.002183278,0.535065775,0.00218326,0.002183217,0.327799535,Clinics,0.9412031,TRUE,43.57142857,0.573814089,,,0,0.403234768,,,0.488524428
7783,Evolutionary and structural analyses of SARS-CoV-2 D614G spike protein mutation now documented worldwide.,Sci Rep,32820179,8/21/20,pubmed,0,12,"bioinformatic, in silico, genomes",0.165135406,0.828918601,0.001486452,0.00148652,0.001486453,0.001486567,Genomics,0.57887113,TRUE,56.91666667,0.680685262,93.66666667,0.810008028,8,0.799987654,,,0.763560314
7784,"GRL-0920, an Indole Chloropyridinyl Ester, Completely Blocks SARS-CoV-2 Infection.",mBio,32820005,8/21/20,pubmed,0,16,structural model,0.915678031,0.062022471,0.01883078,0.00115625,0.001156227,0.001156242,Drug discovery,0.89403594,TRUE,138.5,0.931288268,99.5625,0.821648381,6,0.764429903,,,0.839122184
7785,Angiotensin Converting Enzyme Inhibitor and Angiotensin II Receptor Blocker Use Among Outpatients Diagnosed With COVID-19.,Am J Cardiol,32819683,8/21/20,pubmed,0,17,logistic regression,0.170269996,0.001392891,0.001392841,0.00139286,0.001392856,0.824158556,Clinics,0.94771636,TRUE,72.82352941,0.776609562,60.52941176,0.722839176,10,0.828199272,,,0.77588267
7786,Lead Finding from Selected Flavonoids with Antiviral (SARS-CoV-2) Potentials against COVID-19: An in-silico Evaluation.,Comb Chem High Throughput Screen,32819226,8/21/20,pubmed,0,5,"in silico, in-silico",0.946124832,0.001187262,0.001187285,0.001187294,0.049125985,0.001187342,Drug discovery,0.99095595,TRUE,13.4,0.202548086,0.4,0.075796093,6,0.764429903,,,0.347591361
7787,SARS-CoV-2 exhibits intra-host genomic plasticity and low-frequency polymorphic quasispecies.,J Clin Virol,32818852,8/21/20,pubmed,0,6,bioinformatic,0.065149211,0.907055667,0.000999555,0.00099961,0.000999543,0.024796415,Genomics,0.40827012,FALSE,52.5,0.650875131,47.5,0.670591383,1,0.537564047,,,0.619676854
7788,Risk of stroke in hospitalized SARS-CoV-2 infected patients: A multinational study.,EBioMedicine,32818804,8/21/20,pubmed,0,44,logistic regression,0.001237048,0.001237134,0.025912146,0.243691656,0.001237127,0.726684889,Clinics,0.9443325,TRUE,41.40909091,0.551982188,12.20454545,0.387543484,20,0.900117291,,,0.613214321
7789,Could masks curtail the post-lockdown resurgence of COVID-19 in the US?,Math Biosci,32818515,8/21/20,pubmed,0,3,mathematical model,0.000547805,0.013105261,0.000547855,0.860165586,0.125085641,0.000547852,Epidemiology,0.1612578,FALSE,13.66666667,0.207310285,6,0.280037463,6,0.764429903,,,0.417259217
7790,Structural basis of SARS-CoV-2 spike protein induced by ACE2.,Bioinformatics,32818261,8/21/20,pubmed,0,3,bioinformatic,0.853328068,0.078854555,0.064676847,0.001046896,0.001046826,0.001046808,Drug discovery,0.5863731,TRUE,63.66666667,0.725957078,72,0.7594327,0,0.403234768,,,0.629541515
7791,Rapid Ventilator Splitting During COVID-19 Pandemic Using 3D Printed Devices and Numerical Modeling of 200 Million Patient Specific Air Flow Scenarios.,Res Sq,32818206,8/21/20,pubmed,0,14,computational,0.001371318,0.001371263,0.269609396,0.524182346,0.001371296,0.202094381,Epidemiology,0.6015296,TRUE,82.64285714,0.816748098,70.35714286,0.754482205,0,0.403234768,,,0.658155024
7792,Digestive symptoms of COVID-19 and expression of ACE2 in digestive tract organs.,Cell Death Discov,32818075,8/21/20,pubmed,0,10,sequencing,0.538429142,0.071849057,0.023685895,0.001486426,0.001486477,0.363063003,Drug discovery,0.90621114,TRUE,28.2,0.409734677,7.8,0.313286058,16,0.881782826,,,0.534934521
7793,[Use of point-of-care technologies for the management of the COVID-19 pandemic in Colombia].,Rev Panam Salud Publica,32818035,8/21/20,pubmed,0,1,artificial intelligence,0.001310423,0.146697054,0.304822843,0.334858788,0.129112019,0.083198874,Epidemiology,0.5472928,TRUE,2,0.022141134,0,0.055525823,1,0.537564047,,,0.205077001
7794,Neutralizing antibodies correlate with protection from SARS-CoV-2 in humans during a fishery vessel outbreak with high attack rate.,medRxiv,32817980,8/21/20,pubmed,0,9,"sequencing, metagenom, genomes",0.001538146,0.627520833,0.033077883,0.001538167,0.227252909,0.109072063,Genomics,0.17787555,FALSE,34.22222222,0.479374111,49.22222222,0.67828472,114,0.981418606,,,0.713025812
7795,"Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number.",medRxiv,32817970,8/21/20,pubmed,0,7,"computational, mathematical model",0.001538129,0.001538125,0.001538195,0.992309175,0.001538171,0.001538206,Epidemiology,0.104757756,FALSE,68.28571429,0.755396128,161.8571429,0.893698154,8,0.799987654,,,0.816360645
7796,Management Strategies for People Experiencing Sheltered Homelessness during the COVID-19 Pandemic: Clinical Outcomes and Costs.,medRxiv,32817967,8/21/20,pubmed,0,18,simulation model,0.000889099,0.000889125,0.000889096,0.562380177,0.204950791,0.230001711,Epidemiology,0.34292763,FALSE,68.5,0.756571217,111.0555556,0.839978592,2,0.618927094,,,0.738492301
7797,Genomic Diversity of SARS-CoV-2 During Early Introduction into the United States National Capital Region.,medRxiv,32817965,8/21/20,pubmed,0,22,"sequencing, whole genome, genomes",0.001415137,0.992924149,0.001415195,0.001415232,0.001415132,0.001415155,Genomics,0.27628967,FALSE,34.81818182,0.485187705,123.0909091,0.855298368,10,0.828199272,,,0.722895115
7798,Maraviroc inhibits SARS-CoV-2 multiplication and s-protein mediated cell fusion in cell culture.,bioRxiv,32817953,8/21/20,pubmed,0,10,mathematical model,0.845907371,0.001220089,0.001220013,0.149212478,0.001220028,0.001220021,Drug discovery,0.35895306,FALSE,21.2,0.313686684,,,5,0.739490092,,,0.526588388
7799,0,bioRxiv,32817944,8/21/20,pubmed,0,2,"computational, genomes",0.547270067,0.355622687,0.091853671,0.001751214,0.001751192,0.001751169,Drug discovery,0.3138923,FALSE,25.5,0.37435834,46,0.66416912,2,0.618927094,,,0.552484851
7800,A 3.4-Å cryo-EM structure of the human coronavirus spike trimer computationally derived from vitrified NL63 virus particles.,bioRxiv,32817943,8/21/20,pubmed,0,8,computational,0.855488021,0.002080721,0.05319463,0.00208063,0.08507521,0.002080788,Drug discovery,0.55260813,TRUE,93.5,0.85317583,100.25,0.822852556,1,0.537564047,,,0.737864144
7801,SRSF protein kinases 1 and 2 are essential host factors for human coronaviruses including SARS-CoV-2.,bioRxiv,32817937,8/21/20,pubmed,0,9,genome-wide,0.804378186,0.188617005,0.001751183,0.001751204,0.001751196,0.001751226,Drug discovery,0.42949784,FALSE,21.33333333,0.31548024,47.77777778,0.671594862,16,0.881782826,,,0.622952643
7802,"Public Behavior Change, Perceptions, Depression, and Anxiety in Relation to the COVID-19 Outbreak.",Open Forum Infect Dis,32817845,8/21/20,pubmed,0,10,logistic regression,0.001112634,0.001112621,0.001112627,0.001112667,0.979490955,0.016058495,Healthcare,0.91168725,TRUE,56.2,0.676294143,,,2,0.618927094,,,0.647610619
7803,A predictive model of the temperature-dependent inactivation of coronaviruses.,Appl Phys Lett,32817726,8/21/20,pubmed,0,4,predictive model,0.115071632,0.059142001,0.001593608,0.656413832,0.166185421,0.001593507,Epidemiology,0.2421461,FALSE,56,0.675304595,8.25,0.323789136,11,0.840175319,,,0.613089683
7804,Age could be driving variable SARS-CoV-2 epidemic trajectories worldwide.,PLoS One,32817662,8/21/20,pubmed,0,6,mathematical model,0.00165301,0.001653043,0.00165301,0.730134954,0.063568823,0.201337159,Epidemiology,0.48297158,FALSE,64.5,0.731832519,125.5,0.858442601,0,0.403234768,,,0.664503296
7805,In situ structural analysis of SARS-CoV-2 spike reveals flexibility mediated by three hinges.,Science,32817270,8/21/20,pubmed,0,20,molecular dynamics simulation,0.837328898,0.003101769,0.150264573,0.003101707,0.003101526,0.003101527,Drug discovery,0.2338289,FALSE,41.8,0.555692993,65.95,0.742039069,28,0.926168282,,,0.741300115
7806,Reply to "COVID-19 prediction models should adhere to methodological and reporting standards".,Eur Respir J,32817260,8/21/20,pubmed,0,4,prediction model,0.034962574,0.034962219,0.825186136,0.034963704,0.034962175,0.034963192,Epidemiology,0.5867361,TRUE,84,0.821881378,107.75,0.83583088,0,0.403234768,,,0.686982342
7807,Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank.,IEEE J Biomed Health Inform,32816680,8/21/20,pubmed,0,13,"supervised learning, neural network",0.001237049,0.15484375,0.840207932,0.001237075,0.001237119,0.001237075,Imaging,0.4067152,FALSE,28.53846154,0.412641474,10.07692308,0.355900455,7,0.785110192,,,0.51788404
7808,Health Inequalities in the Use of Telehealth in the United States in the Lens of COVID-19.,Popul Health Manag,32816644,8/21/20,pubmed,0,4,logistic regression,0.001538246,0.001538188,0.001538218,0.230376501,0.702616215,0.062392633,Healthcare,0.94341826,TRUE,22.5,0.333539489,4.75,0.248260637,7,0.785110192,,,0.455636773
7809,The mental health of immigrants and refugees: Canadian evidence from a nationally linked database.,Health Rep,32816413,8/21/20,pubmed,0,2,logistic regression,0.001461943,0.00146197,0.001461942,0.093319445,0.753267044,0.149027657,Healthcare,0.16497323,FALSE,37,0.508936854,48,0.673735617,2,0.618927094,,,0.600533189
7810,Geographical reconstruction of the SARS-CoV-2 outbreak in Lombardy (Italy) during the early phase.,J Med Virol,32816316,8/21/20,pubmed,0,11,genomes,0.001254613,0.833141001,0.001254614,0.001254678,0.001254645,0.161840449,Genomics,0.47328457,FALSE,42.18181818,0.559280104,18.81818182,0.468223174,1,0.537564047,,,0.521689108
7811,Antitussive noscapine and antiviral drug conjugates as arsenal against COVID-19: a comprehensive chemoinformatics analysis.,J Biomol Struct Dyn,32815796,8/21/20,pubmed,0,9,"molecular dynamics simulation, in silico",0.994365855,0.001126814,0.001126819,0.001126847,0.001126798,0.001126866,Drug discovery,0.6707843,TRUE,47.11111111,0.606778403,8.555555556,0.329542414,3,0.667819001,,,0.534713273
7812,Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images.,Diagn Interv Radiol,32815519,8/21/20,pubmed,0,19,"deep learning, artificial intelligence, image analysis",0.000822926,0.000822929,0.728126812,0.000822908,0.021808311,0.247596113,Imaging,0.8607167,TRUE,20.84210526,0.308058631,,,8,0.799987654,,,0.554023142
7813,0,J Biomol Struct Dyn,32815481,8/21/20,pubmed,0,4,"molecular dynamics simulation, computational",0.903284496,0.001187293,0.001187257,0.091966391,0.001187286,0.001187276,Drug discovery,0.9939904,TRUE,16,0.243552477,1,0.122023013,6,0.764429903,,,0.376668464
7814,Risks and features of secondary infections in severe and critical ill COVID-19 patients.,Emerg Microbes Infect,32815458,8/21/20,pubmed,0,15,"sequencing, metagenom",0.002032773,0.187476924,0.029138125,0.002032908,0.002032796,0.777286474,Clinics,0.91650814,TRUE,101.5333333,0.872162781,66.46666667,0.74384533,14,0.866658436,,,0.827555516
7815,"Anti-Thrombotic Therapy to Ameliorate Complications of COVID-19 (ATTACC): Study design and methodology for an international, adaptive Bayesian randomized controlled trial.",Clin Trials,32815416,8/21/20,pubmed,0,27,bayes,0.26894566,0.00115626,0.001156292,0.150333635,0.001156271,0.577251882,Clinics,0.58109653,TRUE,175.3703704,0.961964253,333.3703704,0.962871287,5,0.739490092,,,0.888108544
7816,0,Expert Rev Vaccines,32815406,8/21/20,pubmed,0,6,"molecular dynamics simulation, computational, in-silico",0.992440613,0.001511907,0.001511856,0.001511887,0.001511889,0.001511847,Drug discovery,0.7839762,TRUE,22.5,0.333539489,4.166666667,0.233074659,2,0.618927094,,,0.395180414
7817,Risk factors for positive and negative COVID-19 tests: a cautious and in-depth analysis of UK biobank data.,Int J Epidemiol,32814959,8/21/20,pubmed,0,9,logistic regression,0.001786615,0.001786647,0.001786612,0.155765318,0.492710065,0.346164743,Healthcare,0.7397757,TRUE,140.2222222,0.934133218,197.3333333,0.916443671,10,0.828199272,,,0.892925387
7818,Genome-wide analysis of SARS-CoV-2 virus strains circulating worldwide implicates heterogeneity.,Sci Rep,32814791,8/21/20,pubmed,0,10,"genome-wide, genome sequences",0.234061663,0.761543075,0.001098812,0.001098842,0.001098807,0.001098802,Genomics,0.66996,TRUE,41.7,0.554888985,1125.1,0.994982606,66,0.967652324,,,0.839174639
7819,Comparison of nonhuman primates identified the suitable model for COVID-19.,Signal Transduct Target Ther,32814760,8/21/20,pubmed,0,34,genomes,0.299837669,0.416802669,0.04291308,0.002080604,0.002080599,0.236285379,Genomics,0.36347693,FALSE,65.11764706,0.73504855,53.79411765,0.696347337,38,0.944132354,,,0.791842747
7820,On-target versus off-target effects of drugs inhibiting the replication of SARS-CoV-2.,Cell Death Dis,32814759,8/21/20,pubmed,0,11,bioinformatic,0.808815203,0.002080734,0.037464831,0.147477937,0.002080695,0.002080601,Drug discovery,0.5023754,TRUE,342.5454545,0.993753479,1277.454545,0.995785389,9,0.814309525,,,0.934616131
7821,Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning.,Biomed Eng Online,32814568,8/21/20,pubmed,0,5,"machine learning, image analysis, classifier, logistic regression",0.000807936,0.0008079,0.940036615,0.000807909,0.000807901,0.056731739,Imaging,0.89376664,TRUE,12.8,0.192343373,7.2,0.302047097,3,0.667819001,,,0.387403157
7822,Considerations for target oxygen saturation in COVID-19 patients: are we under-shooting?,BMC Med,32814566,8/21/20,pubmed,0,3,"transcriptom, dataset",0.288077724,0.00102266,0.001022647,0.200027662,0.001022659,0.508826649,Clinics,0.8958926,TRUE,8.333333333,0.123693488,0,0.055525823,11,0.840175319,,,0.33979821
7823,COVID-19 infection and cardiac arrhythmias.,Trends Cardiovasc Med,32814095,8/20/20,pubmed,0,6,digital health,0.154469943,0.002422614,0.002422383,0.37875266,0.039117128,0.422815272,Clinics,0.94693893,TRUE,32.33333333,0.459273919,2.666666667,0.185442869,8,0.799987654,,,0.481568147
7824,Anemia is associated with severe illness in COVID-19: A retrospective cohort study.,J Med Virol,32813298,8/20/20,pubmed,0,12,logistic regression,0.001486543,0.001486444,0.001486435,0.00148644,0.061523068,0.93253107,Clinics,0.97480494,TRUE,64.5,0.731832519,31.5,0.581883864,4,0.707574542,,,0.673763642
7825,Telemedicine in ophthalmology in view of the emerging COVID-19 outbreak.,Graefes Arch Clin Exp Ophthalmol,32813110,8/20/20,pubmed,0,2,image processing,0.001330185,0.00133005,0.5256087,0.25615653,0.00133013,0.214244406,Imaging,0.8987894,TRUE,73.5,0.780011132,36,0.611118544,2,0.618927094,,,0.670018924
7826,Dynamical properties of enzyme-substrate complexes disclose substrate specificity of the SARS-CoV-2 main protease as characterized by the electron density descriptors.,Phys Chem Chem Phys,32812956,8/20/20,pubmed,0,3,molecular dynamics simulation,0.807542866,0.001823351,0.124908213,0.001823452,0.001823356,0.062078762,Drug discovery,0.5928017,TRUE,168.6666667,0.95744944,31.33333333,0.580010704,2,0.618927094,,,0.718795746
7827,COVID-19 Forecasts for Cuba Using Logistic Regression and Gompertz Curves.,MEDICC Rev,32812897,8/20/20,pubmed,0,3,logistic regression,0.002032831,0.002032767,0.002032785,0.989835881,0.002032808,0.002032928,Epidemiology,0.48065215,FALSE,1.666666667,0.016451234,0,0.055525823,0,0.403234768,,,0.158403941
7828,Applying active learning in a virtual classroom such as a molecular biology escape room.,Biochem Mol Biol Educ,32812701,8/20/20,pubmed,0,2,active learning,0.280898372,0.004109898,0.262765549,0.219562187,0.22855424,0.004109753,Drug discovery,0.721821,TRUE,7,0.10179974,0,0.055525823,0,0.403234768,,,0.186853444
7829,Nonrespiratory Complications and Obesity in Patients Dying with COVID-19 in Italy.,Obesity (Silver Spring),32812383,8/20/20,pubmed,0,71,logistic regression,0.001438094,0.001438088,0.001438139,0.001438175,0.001438165,0.992809338,Clinics,0.70026064,TRUE,135.4,0.927082689,156.2,0.888814557,3,0.667819001,,,0.827905416
7830,Interest of the cellular population data analysis as an aid in the early diagnosis of SARS-CoV-2 infection.,Int J Lab Hematol,32812365,8/20/20,pubmed,0,10,classifier,0.052393245,0.001438203,0.459791482,0.001438226,0.001438187,0.483500657,Clinics,0.8984972,TRUE,32.3,0.458593605,13.9,0.40988761,3,0.667819001,,,0.512100072
7831,Vitamin B12 may inhibit RNA-dependent-RNA polymerase activity of nsp12 from the SARS-CoV-2 virus.,IUBMB Life,32812340,8/20/20,pubmed,0,2,"computational, in silico",0.805847511,0.160949079,0.001291193,0.001291212,0.029329799,0.001291207,Drug discovery,0.7532538,TRUE,73,0.778464964,56,0.706850415,1,0.537564047,,,0.674293142
7832,Utilization of machine-learning models to accurately predict the risk for critical COVID-19.,Intern Emerg Med,32812204,8/20/20,pubmed,0,13,artificial intelligence,0.000966775,0.000966742,0.261640147,0.000966767,0.000966756,0.734492812,Clinics,0.95699394,TRUE,38.38461538,0.522728678,23.46153846,0.515921862,13,0.858880178,,,0.63251024
7833,The impact of sofosbuvir/daclatasvir or ribavirin in patients with severe COVID-19.,J Antimicrob Chemother,32812051,8/20/20,pubmed,0,22,in silico,0.099052594,0.001141384,0.108332508,0.001141391,0.0011414,0.789190723,Clinics,0.821347,TRUE,24.72727273,0.363844394,18.86363636,0.468758362,21,0.903944688,,,0.578849148
7834,COVID-19: relationship between atmospheric temperature and daily new cases growth rate.,Epidemiol Infect,32811577,8/20/20,pubmed,0,5,correlation analysis,0.001987118,0.070601974,0.001987179,0.921449261,0.00198717,0.001987297,Epidemiology,0.8808718,TRUE,106,0.881192405,21.6,0.498795826,5,0.739490092,,,0.706492774
7835,The current landscape of coronavirus-host protein-protein interactions.,J Transl Med,32811513,8/20/20,pubmed,0,9,"proteom, interactom, dataset",0.886228668,0.108823066,0.001237071,0.001237078,0.001237061,0.001237055,Drug discovery,0.4716992,FALSE,22.33333333,0.330261612,44.66666667,0.65754616,12,0.850299401,,,0.612702391
7836,"COVID-19: Underpinning Research for Detection, Therapeutics, and Vaccines Development.",Pharm Nanotechnol,32811406,8/20/20,pubmed,0,15,interactom,0.631484774,0.001156314,0.097272694,0.267773503,0.001156318,0.001156397,Drug discovery,0.8670479,TRUE,77.86666667,0.797946688,17.4,0.454642762,5,0.739490092,,,0.664026514
7837,An in-silico evaluation of dietary components for structural inhibition of SARS-Cov-2 main protease.,J Biomol Struct Dyn,32811367,8/20/20,pubmed,0,2,in-silico,0.990691414,0.001861722,0.0018617,0.001861756,0.001861726,0.001861682,Drug discovery,0.91839135,TRUE,35,0.488032655,8.5,0.329141022,2,0.618927094,,,0.478700257
7838,Possible role of HLA class-I genotype in SARS-CoV-2 infection and progression: A pilot study in a cohort of Covid-19 Spanish patients.,Clin Immunol,32810602,8/19/20,pubmed,0,10,in silico,0.6595518,0.001593612,0.001593526,0.001593582,0.001593531,0.334073949,Drug discovery,0.3136052,FALSE,11.6,0.175211825,6.7,0.290808135,9,0.814309525,,,0.426776495
7839,Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19.,Cell,32810439,8/19/20,pubmed,0,45,sequencing,0.310800576,0.253780962,0.002032871,0.002032841,0.002032773,0.429319977,Clinics,0.45454937,FALSE,84.13333333,0.822252458,234.7777778,0.932031041,122,0.982838447,,,0.912373982
7840,Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment.,Cell,32810438,8/19/20,pubmed,0,138,"sequencing, proteom",0.427023162,0.088615214,0.001751146,0.001751168,0.001751188,0.479108122,Clinics,0.7610811,TRUE,89.08633094,0.839631393,,,156,0.987715291,,,0.913673342
7841,Prognostic machine learning models for COVID-19 to facilitate decision making.,Int J Clin Pract,32810316,8/19/20,pubmed,0,3,machine learning,0.029050335,0.00272024,0.603504235,0.14845419,0.002720253,0.213550747,Clinics,0.41186836,FALSE,11,0.167171748,5.333333333,0.262911426,2,0.618927094,,,0.34967009
7842,Oxytocin's anti-inflammatory and proimmune functions in COVID-19: a transcriptomic signature-based approach.,Physiol Genomics,32809918,8/19/20,pubmed,0,9,transcriptom,0.875113735,0.001565329,0.001565312,0.001565327,0.001565305,0.118624992,Drug discovery,0.84730333,TRUE,19.11111111,0.286103037,17.77777778,0.458589778,5,0.739490092,,,0.494727636
7843,The therapeutic potential of colchicine in the complications of COVID19. Could the immunometabolic properties of an old and cheap drug help?,Metabol Open,32808940,8/19/20,pubmed,0,4,immunome,0.801075463,0.002996506,0.002996605,0.002996624,0.00299655,0.186938252,Drug discovery,0.37973768,FALSE,16.25,0.246026347,62.75,0.731669789,1,0.537564047,,,0.505086727
7844,Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath.,ACS Nano,32808759,8/19/20,pubmed,0,22,artificial intelligence,0.001593562,0.001593539,0.662809137,0.084750304,0.001593542,0.247659917,Clinics,0.7261392,TRUE,40.22727273,0.541282701,,,23,0.91129082,,,0.726286761
7845,Bacterial infections and patterns of antibiotic use in patients with COVID-19.,J Med Virol,32808695,8/19/20,pubmed,0,13,logistic regression,0.001717194,0.1476629,0.001717295,0.001717244,0.001717227,0.845468139,Clinics,0.7462915,TRUE,159.0769231,0.950584452,243,0.935242173,5,0.739490092,,,0.875105572
7846,Minimizing endoscopist facial exposure to droplets: Optimal patient-endoscopist distance and use of a barrier device.,J Gastroenterol Hepatol,32808303,8/19/20,pubmed,0,5,simulation model,0.001371298,0.001371313,0.001371401,0.649980654,0.001371356,0.344533979,Epidemiology,0.7838079,TRUE,134.2,0.926216835,120.2,0.851618946,0,0.403234768,,,0.727023516
7847,Effect of common medications on the expression of SARS-CoV-2 entry receptors in liver tissue.,Arch Toxicol,32808185,8/19/20,pubmed,0,10,"transcriptom, toxicogenom, dataset",0.722207988,0.00129125,0.001291258,0.001291305,0.00129125,0.272626949,Drug discovery,0.95151436,TRUE,21.9,0.323149236,20.7,0.48822585,1,0.537564047,,,0.449646378
7848,An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors.,Sci Rep,32807895,8/19/20,pubmed,0,5,"virtual screening, in silico",0.992809322,0.001438172,0.001438123,0.001438161,0.001438116,0.001438106,Drug discovery,0.7143698,TRUE,7.2,0.103716989,1.2,0.126103827,17,0.887338725,,,0.372386514
7849,Public Perceptions of Resuming Elective Surgery During the COVID-19 Pandemic.,J Arthroplasty,32807565,8/19/20,pubmed,0,6,logistic regression,0.002033029,0.002032782,0.002032881,0.002032895,0.789773011,0.202095403,Healthcare,0.95639044,TRUE,78.66666667,0.801224566,38,0.622223709,2,0.618927094,,,0.68079179
7850,Analytics and Prediction Modeling During the COVID-19 Pandemic.,Mayo Clin Proc,32807519,8/19/20,pubmed,0,3,prediction model,0.057800659,0.057799149,0.710996772,0.057802975,0.057799148,0.057801298,Epidemiology,0.57642746,TRUE,43,0.567629414,11.66666667,0.38065293,1,0.537564047,,,0.495282131
7851,"Integrative Modeling of Quantitative Plasma Lipoprotein, Metabolic, and Amino Acid Data Reveals a Multiorgan Pathological Signature of SARS-CoV-2 Infection.",J Proteome Res,32806897,8/19/20,pubmed,0,16,predictive model,0.083895329,0.312166143,0.053002909,0.00129128,0.001291249,0.548353091,Clinics,0.68302447,TRUE,52.375,0.649638197,,,7,0.785110192,,,0.717374194
7852,Evaluation of the Ion AmpliSeq SARS-CoV-2 Research Panel by Massive Parallel Sequencing.,Genes (Basel),32806776,8/19/20,pubmed,0,9,"sequencing, whole genome",0.000822957,0.925842936,0.059089211,0.000822946,0.00082298,0.012598968,Genomics,0.5724974,TRUE,46.77777778,0.603438679,31.22222222,0.579341718,3,0.667819001,,,0.616866466
7853,Analysis of Risk Factors on Readmission Cases of COVID-19 in the Republic of Korea: Using Nationwide Health Claims Data.,Int J Environ Res Public Health,32806775,8/19/20,pubmed,0,4,logistic regression,0.00171754,0.001717279,0.229957995,0.00171724,0.001717279,0.763172668,Clinics,0.952971,TRUE,38,0.519327107,18.5,0.46554723,6,0.764429903,,,0.583101414
7854,Mathematical Parameters of the COVID-19 Epidemic in Brazil and Evaluation of the Impact of Different Public Health Measures.,Biology (Basel),32806613,8/19/20,pubmed,0,3,"bayes, dataset",0.000838526,0.000838533,0.000838542,0.995807346,0.000838518,0.000838535,Epidemiology,0.11357865,FALSE,17.66666667,0.266373925,,,9,0.814309525,,,0.540341725
7855,Enhanced Binding of SARS-CoV-2 Spike Protein to Receptor by Distal Polybasic Cleavage Sites.,ACS Nano,32806067,8/19/20,pubmed,0,2,molecular dynamics simulation,0.941540684,0.053368721,0.001272632,0.001272687,0.001272657,0.001272619,Drug discovery,0.6730922,TRUE,95,0.856824788,17,0.451097137,22,0.908142478,,,0.738688134
7856,Risk factors for severe cases of COVID-19: a retrospective cohort study.,Aging (Albany NY),32805730,8/18/20,pubmed,0,13,logistic regression,0.001901893,0.001901704,0.001901671,0.001901675,0.001901756,0.990491301,Clinics,0.87523186,TRUE,57.53846154,0.685571155,23.92307692,0.519935777,3,0.667819001,,,0.624441978
7857,Revealing the targets and mechanisms of vitamin A in the treatment of COVID-19.,Aging (Albany NY),32805728,8/18/20,pubmed,0,7,bioinformatic,0.743428772,0.00182345,0.001823477,0.106453503,0.001823342,0.144647456,Drug discovery,0.6618469,TRUE,247.1428571,0.98422908,105,0.831482473,8,0.799987654,,,0.871899735
7858,Elevated Lactate Dehydrogenase (LDH) level as an independent risk factor for the severity and mortality of COVID-19.,Aging (Albany NY),32805722,8/18/20,pubmed,0,14,logistic regression,0.002422256,0.002422257,0.093836163,0.002422256,0.002422251,0.896474818,Clinics,0.7384749,TRUE,91.42857143,0.847362236,36.92857143,0.616069039,4,0.707574542,,,0.723668606
7859,Caseload is increased by resequencing cases before and on the day of surgery at ambulatory surgery centers where initial patient recovery is in operating rooms and cleanup times are longer than typical.,J Clin Anesth,32805684,8/18/20,pubmed,0,3,sequencing,0.026752124,0.001511955,0.001511926,0.582749958,0.001511919,0.385962118,Epidemiology,0.79840124,TRUE,236.3333333,0.981940751,197.3333333,0.916443671,1,0.537564047,,,0.811982823
7860,Hospital indoor air quality monitoring for the detection of SARS-CoV-2 (COVID-19) virus.,Sci Total Environ,32805566,8/18/20,pubmed,0,7,genomes,0.001987439,0.457122757,0.00198722,0.365968653,0.00198711,0.170946821,Genomics,0.6236907,TRUE,30,0.432432432,7.142857143,0.300709125,19,0.89561084,,,0.542917466
7861,Unravelling host-pathogen interactions: ceRNA network in SARS-CoV-2 infection (COVID-19).,Gene,32805314,8/18/20,pubmed,0,5,dataset,0.977796853,0.001203449,0.001203446,0.001203439,0.001203451,0.017389361,Drug discovery,0.8516727,TRUE,22,0.326056033,2.2,0.16838373,3,0.667819001,,,0.387419588
7862,"The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.",J Am Med Inform Assoc,32805036,8/18/20,pubmed,0,56,"machine learning, dataset",0.001272706,0.001272677,0.50623125,0.459085702,0.030864704,0.001272961,Epidemiology,0.52853996,TRUE,9.333333333,0.139278867,1.333333333,0.13252609,22,0.908142478,,,0.393315812
7863,Mechanistic insights into SARS-CoV-2 epidemic via revealing the features of SARS-CoV-2 coding proteins and host responses upon its infection.,Bioinformatics,32805023,8/18/20,pubmed,0,6,bioinformatic,0.243989915,0.440357838,0.001717253,0.001717268,0.001717198,0.310500529,Genomics,0.22061315,FALSE,41.66666667,0.554703445,7.5,0.307867273,1,0.537564047,,,0.466711588
7864,CoV-AbDab: the Coronavirus Antibody Database.,Bioinformatics,32805021,8/18/20,pubmed,0,4,bioinformatic,0.37094336,0.565948636,0.001684574,0.001684633,0.001684679,0.058054118,Genomics,0.15014353,FALSE,17.25,0.260189251,5.5,0.267259834,5,0.739490092,,,0.422313059
7865,Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.,J Am Med Inform Assoc,32805004,8/18/20,pubmed,0,3,artificial intelligence,0.002639108,0.002639056,0.246189789,0.683910612,0.002639244,0.061982192,Epidemiology,0.40791684,FALSE,34.33333333,0.480425506,19.66666667,0.477321381,4,0.707574542,,,0.555107143
7866,Personal Protective Equipment and Mental Health Symptoms Among Nurses During the COVID-19 Pandemic.,J Occup Environ Med,32804747,8/18/20,pubmed,0,6,logistic regression,0.002130615,0.002130634,0.002130622,0.00213064,0.989346831,0.002130659,Healthcare,0.9560015,TRUE,80.83333333,0.81025419,125.1666667,0.857974311,9,0.814309525,,,0.827512675
7867,Effects of COVID-19 pandemic on acute ischemic stroke care. A single-centre retrospective analysis of medical collateral damage.,Orv Hetil,32804669,8/18/20,pubmed,0,5,logistic regression,0.001943546,0.001943502,0.001943472,0.140202708,0.00194358,0.852023192,Clinics,0.9784837,TRUE,62,0.715814212,31.8,0.583288734,1,0.537564047,,,0.612222331
7868,"AI-Driven COVID-19 Tools to Interpret, Quantify Lung Images.",IEEE Pulse,32804639,8/18/20,pubmed,0,1,artificial intelligence,0.003607212,0.003607493,0.981962895,0.003607496,0.003607431,0.003607473,Imaging,0.6603579,TRUE,91,0.846310842,8,0.320511105,1,0.537564047,,,0.568128665
7869,0,SLAS Discov,32804597,8/18/20,pubmed,0,7,bayes,0.574199918,0.001717221,0.001717203,0.418931231,0.001717219,0.001717208,Drug discovery,0.8639606,TRUE,2.857142857,0.031541839,0.571428571,0.088038534,2,0.618927094,,,0.246169156
7870,Utility of the neutrophil-to-lymphocyte ratio and C-reactive protein level for coronavirus disease 2019 (COVID-19).,Scand J Clin Lab Invest,32804580,8/18/20,pubmed,0,8,logistic regression,0.001220036,0.001220019,0.088035721,0.001219997,0.001220006,0.907084221,Clinics,0.92700815,TRUE,77.125,0.795163585,17,0.451097137,4,0.707574542,,,0.651278421
7871,Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms.,J Xray Sci Technol,32804113,8/18/20,pubmed,0,2,"deep learning, image analysis, network model, transfer learning, dataset",0.001112615,0.001112621,0.994436889,0.00111263,0.001112614,0.001112632,Imaging,0.62792957,TRUE,10.5,0.157338116,1,0.122023013,3,0.667819001,,,0.31572671
7872,A retrospective study of the initial chest CT imaging findings in 50 COVID-19 patients stratified by gender and age.,J Xray Sci Technol,32804112,8/18/20,pubmed,0,7,correlation analysis,0.001126773,0.001126798,0.42910018,0.024207064,0.001126859,0.543312326,Clinics,0.97269213,TRUE,36,0.498299215,7.857142857,0.314222638,0,0.403234768,,,0.405252207
7873,Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine.,J Med Internet Res,32804086,8/18/20,pubmed,0,6,artificial intelligence,0.154970345,0.001538126,0.297822237,0.542593032,0.001538121,0.00153814,Epidemiology,0.48656297,FALSE,78.16666667,0.799059929,157,0.889884934,0,0.403234768,,,0.697393211
7874,"Epidemiology, outcomes and associated factors of COVID-19 RT-PCR confirmed cases in the San Pedro Sula Metropolitan Area, Honduras.",Clin Infect Dis,32803236,8/18/20,pubmed,0,16,logistic regression,0.001593503,0.021667398,0.001593568,0.001593666,0.135231355,0.838320509,Clinics,0.6683965,TRUE,9.625,0.143917373,9.4375,0.343658014,2,0.618927094,,,0.36883416
7875,Correlates of access to hand hygiene resources in Ghanaian households: An exploratory analysis of the 2014 demographic and health survey.,Heliyon,32802991,8/18/20,pubmed,0,6,dataset,0.000907304,0.000907306,0.000907321,0.213874892,0.782495872,0.000907306,Healthcare,0.6043234,TRUE,9.833333333,0.147628177,0.333333333,0.073187048,0,0.403234768,,,0.208016664
7876,"Virtual screening, molecular docking studies and DFT calculations of FDA approved compounds similar to the non-nucleoside reverse transcriptase inhibitor (NNRTI) efavirenz.",Heliyon,32802982,8/18/20,pubmed,0,4,virtual screening,0.991067227,0.001786545,0.001786578,0.001786573,0.001786549,0.001786528,Drug discovery,0.97692883,TRUE,6,0.086028821,0,0.055525823,2,0.618927094,,,0.253493913
7877,Predicting novel drugs for SARS-CoV-2 using machine learning from a >10 million chemical space.,Heliyon,32802980,8/18/20,pubmed,0,2,machine learning,0.833929392,0.001593537,0.15969622,0.001593743,0.001593537,0.001593571,Drug discovery,0.513997,TRUE,30.5,0.438493413,54,0.697952903,6,0.764429903,,,0.633625407
7878,Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity.,Mol Biol Res Commun,32802902,8/18/20,pubmed,0,6,"bayes, computational, genome-wide",0.63336706,0.331785441,0.029793937,0.001684541,0.001684501,0.001684521,Drug discovery,0.88054854,TRUE,8.166666667,0.119735296,0.5,0.087101953,5,0.739490092,,,0.315442447
7879,The Application of Single-Cell RNA Sequencing in Vaccinology.,J Immunol Res,32802896,8/18/20,pubmed,0,5,sequencing,0.418913174,0.369110326,0.001684585,0.206922879,0.001684532,0.001684504,Drug discovery,0.7054813,TRUE,20.4,0.302863504,11.6,0.379515654,0,0.403234768,,,0.361871309
7880,Bibliometric analysis of researches on traditional Chinese medicine for coronavirus disease 2019 (COVID-19).,Integr Med Res,32802744,8/18/20,pubmed,0,7,data mining,0.174402935,0.001461936,0.001461922,0.547744651,0.273466664,0.001461893,Epidemiology,0.8257178,TRUE,197.2857143,0.971797885,101.5714286,0.82499331,3,0.667819001,,,0.821536732
7881,Potential chimeric peptides to block the SARS-CoV-2 spike receptor-binding domain.,F1000Res,32802318,8/18/20,pubmed,0,10,bioinformatic,0.99500238,0.000999561,0.000999519,0.000999513,0.000999516,0.000999512,Drug discovery,0.8497696,TRUE,88.3,0.837590451,59,0.717554188,10,0.828199272,,,0.79444797
7882,The Predictive Effectiveness of Blood Biochemical Indexes for the Severity of COVID-19.,Can J Infect Dis Med Microbiol,32802219,8/18/20,pubmed,0,4,logistic regression,0.001034604,0.001034563,0.016941137,0.001034635,0.001034594,0.978920466,Clinics,0.9657924,TRUE,271.5,0.988001732,98,0.819106235,2,0.618927094,,,0.808678354
7883,0,Infect Drug Resist,32801801,8/18/20,pubmed,0,11,sequencing,0.00321413,0.679368646,0.003214394,0.081435083,0.003214222,0.229553524,Genomics,0.8953587,TRUE,49.63636364,0.628981384,,,1,0.537564047,,,0.583272716
7884,Translation of genomic epidemiology of infectious pathogens: Enhancing African genomics hubs for outbreaks.,Int J Infect Dis,32800861,8/18/20,pubmed,0,7,"genomic epidemiology, genomes",0.001392875,0.362666491,0.001392985,0.6317619,0.001392884,0.001392867,Epidemiology,0.84083414,TRUE,10.28571429,0.154431319,2.285714286,0.171193471,2,0.618927094,,,0.314850628
7885,"In silico molecular investigations of pyridine N-Oxide compounds as potential inhibitors of SARS-CoV-2: 3D QSAR, molecular docking modeling, and ADMET screening.",J Biomol Struct Dyn,32799761,8/18/20,pubmed,0,6,in silico,0.897558945,0.00156534,0.001565415,0.096179533,0.001565358,0.00156541,Drug discovery,0.7737682,TRUE,22.83333333,0.337868761,1.5,0.138747659,2,0.618927094,,,0.365181171
7886,Effect of Common Medications on the Expression of SARS-CoV-2 Entry Receptors in Kidney Tissue.,Clin Transl Sci,32799423,8/18/20,pubmed,0,13,"in silico, transcriptom",0.628411671,0.001392836,0.001392826,0.001392848,0.001392885,0.366016933,Drug discovery,0.93360114,TRUE,50.69230769,0.636031913,37.30769231,0.617674605,2,0.618927094,,,0.624211204
7887,Clinical characteristics and risk factors for mortality in obstetric patients with severe COVID-19 in Brazil: a surveillance database analysis.,BJOG,32799381,8/18/20,pubmed,0,12,logistic regression,0.001593609,0.001593598,0.001593498,0.173431046,0.209886422,0.611901828,Clinics,0.8159189,TRUE,35.53846154,0.492609314,18.69230769,0.466885202,10,0.828199272,,,0.595897929
7888,Epilepsy in time of COVID-19: A survey-based study.,Acta Neurol Scand,32799337,8/18/20,pubmed,0,8,logistic regression,0.06267964,0.001653073,0.001653065,0.001653128,0.545736142,0.386624953,Healthcare,0.98151284,TRUE,21.375,0.315913167,7.625,0.309071448,12,0.850299401,,,0.491761339
7889,Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.,Comput Biol Med,32798922,8/18/20,pubmed,0,14,machine learning,0.001237058,0.001237046,0.50651081,0.001237088,0.001237064,0.488540934,Clinics,0.9758009,TRUE,22.28571429,0.328653596,16.07142857,0.437583623,13,0.858880178,,,0.541705799
7890,Response to Surgical Triage in an Evolving Pandemic Based on Disease Classification and Predictive Modeling.,World Neurosurg,32798783,8/18/20,pubmed,0,1,predictive model,0.013550284,0.013550197,0.431994384,0.513805938,0.013549472,0.013549726,Epidemiology,0.33881262,FALSE,14,0.213494959,7,0.299973241,0,0.403234768,,,0.305567656
7891,Clinical characteristics and risk factors for mortality of patients with COVID-19 in a large data set from Mexico.,Ann Epidemiol,32798701,8/18/20,pubmed,0,3,logistic regression,0.001684509,0.001684533,0.001684496,0.080680068,0.001684561,0.912581832,Clinics,0.50832266,TRUE,122,0.909889294,75.33333333,0.767326733,11,0.840175319,,,0.839130449
7892,Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: a whole-population study.,Lancet Diabetes Endocrinol,32798472,8/18/20,pubmed,0,13,logistic regression,0.000863036,0.034302656,0.000863048,0.000863074,0.000863102,0.962245084,Clinics,0.9124944,TRUE,46.84615385,0.60418084,38.92307692,0.626639015,119,0.982344589,,,0.737721481
7893,Molecular Docking and Virtual Screening based prediction of drugs for COVID-19.,Comb Chem High Throughput Screen,32798373,8/18/20,pubmed,0,1,virtual screening,0.913916046,0.019531311,0.04517155,0.019155818,0.001112616,0.00111266,Drug discovery,0.92134756,TRUE,10,0.15214299,0,0.055525823,7,0.785110192,,,0.330926335
7894,Appraisal of Critically Ill COVID-19 Patients at a Dedicated COVID Hospital.,J Assoc Physicians India,32798339,8/17/20,pubmed,0,9,logistic regression,0.001565465,0.046899659,0.033829435,0.001565368,0.001565345,0.914574728,Clinics,0.9901767,TRUE,,,,,0,0.403234768,,,0.403234768
7895,Reducing Aerosolized Particles and Droplet Spread in Endoscopic Sinus Surgery during COVID-19.,Laryngoscope,32798323,8/17/20,pubmed,0,3,image processing,0.001823407,0.238418592,0.344327769,0.291163875,0.122442971,0.001823386,Imaging,0.45560938,FALSE,45.33333333,0.589646855,16.66666667,0.444674873,0,0.403234768,,,0.479185499
7896,"Association Between Youth Smoking, Electronic Cigarette Use, and COVID-19.",J Adolesc Health,32798097,8/17/20,pubmed,0,3,logistic regression,0.001943437,0.001943474,0.001943626,0.001943596,0.802400198,0.18982567,Healthcare,0.7166421,TRUE,91.66666667,0.84785701,65,0.739028633,39,0.945737391,,,0.844207678
7897,Potential antigenic cross-reactivity between SARS-CoV-2 and Dengue viruses.,Clin Infect Dis,32797228,8/17/20,pubmed,0,12,in-silico,0.196408411,0.490866101,0.001415187,0.001415143,0.001415156,0.308480002,Genomics,0.37895322,FALSE,43.58333333,0.573875935,88.25,0.797230399,25,0.918019631,,,0.763041988
7898,Footprint of the host restriction factors APOBEC3 on the genome of human viruses.,PLoS Pathog,32797103,8/17/20,pubmed,0,4,"genome-wide, genomes",0.068646143,0.895728032,0.001187297,0.032063903,0.001187351,0.001187275,Genomics,0.8625685,TRUE,13.25,0.199950523,15.75,0.433636607,4,0.707574542,,,0.44705389
7899,Mask or no mask for COVID-19: A public health and market study.,PLoS One,32797067,8/17/20,pubmed,0,5,mathematical model,0.03560339,0.001022701,0.001022668,0.960305895,0.001022685,0.001022661,Epidemiology,0.5638845,TRUE,50.8,0.636588534,28.2,0.555860316,22,0.908142478,,,0.700197109
7900,Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.,Nat Commun,32796848,8/17/20,pubmed,0,39,"deep learning, artificial intelligence, dataset",0.001751149,0.00175115,0.868240429,0.001751164,0.001751143,0.124754965,Imaging,0.56980145,TRUE,90,0.843094811,86.12820513,0.793149585,40,0.947033768,,,0.861092721
7901,Adaptive immune responses to SARS-CoV-2 infection in severe versus mild individuals.,Signal Transduct Target Ther,32796814,8/17/20,pubmed,0,12,sequencing,0.510441235,0.229377709,0.001901709,0.001901747,0.001901685,0.254475916,Drug discovery,0.5381396,TRUE,40.08333333,0.540107613,46.58333333,0.66651057,29,0.928020248,,,0.711546144
7902,Big Data Analytics + Virtual Clinical Semantic Network (vCSN): An Approach to Addressing the Increasing Clinical Nuances and Organ Involvement of COVID-19.,ASAIO J,32796159,8/17/20,pubmed,0,5,artificial intelligence,0.002130828,0.077143566,0.234205753,0.587130118,0.00213072,0.097259016,Epidemiology,0.40343353,FALSE,1.4,0.013915517,,,0,0.403234768,,,0.208575142
7903,Clinical outcomes of hospitalised patients with COVID-19 and chronic inflammatory and autoimmune rheumatic diseases: a multicentric matched cohort study.,Ann Rheum Dis,32796045,8/17/20,pubmed,0,23,logistic regression,0.001461899,0.001461875,0.001461851,0.001461849,0.001461863,0.992690663,Clinics,0.76975375,TRUE,129.3333333,0.921021708,99.73333333,0.822049773,27,0.92443978,,,0.88917042
7904,0,J Biomol Struct Dyn,32795148,8/17/20,pubmed,0,7,"virtual screening, molecular dynamics simulation, computational, in-silico",0.968516637,0.001098809,0.001098857,0.02708799,0.001098821,0.001098887,Drug discovery,0.87030673,TRUE,184.1428571,0.966107984,56,0.706850415,3,0.667819001,,,0.780259133
7905,Biological Rationale for the Repurposing of BCG Vaccine against SARS-CoV-2.,J Proteome Res,32794723,8/17/20,pubmed,0,5,"bioinformatic, proteom",0.424843945,0.065152249,0.002491041,0.387907723,0.117114323,0.002490718,Drug discovery,0.38296413,FALSE,35.2,0.488898509,30.8,0.575729195,2,0.618927094,,,0.561184933
7906,Missed takes towards a pandemic of COVID-19? A systematic literature review of Coronavirus related diseases in Pakistan.,J Infect Dev Ctries,32794461,8/15/20,pubmed,0,3,dataset,0.001987242,0.333015732,0.001987171,0.611236346,0.0497864,0.001987109,Epidemiology,0.6055243,TRUE,8,0.118683901,0.333333333,0.073187048,0,0.403234768,,,0.198368572
7907,"Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020.",Euro Surveill,32794443,8/15/20,pubmed,0,587,sequencing,0.004530658,0.977346124,0.004530732,0.00453116,0.004530728,0.004530599,Genomics,0.6352271,TRUE,39.11111111,0.530892449,,,53,0.959195012,,,0.74504373
7908,Characteristics of peripheral blood differential counts in hospitalized patients with COVID-19.,Eur J Haematol,32794205,8/15/20,pubmed,0,12,logistic regression,0.001786507,0.001786552,0.001786522,0.00178656,0.001786572,0.991067286,Clinics,0.6853734,TRUE,172.25,0.959799617,263.25,0.9434038,4,0.707574542,,,0.870259319
7909,Coronavirus disease 2019 (COVID-19) in autoimmune and inflammatory conditions: clinical characteristics of poor outcomes.,Rheumatol Int,32794113,8/15/20,pubmed,0,8,logistic regression,0.001330027,0.001330064,0.021756039,0.001330268,0.026249446,0.948004156,Clinics,0.9764724,TRUE,48.75,0.62032284,23.5,0.516791544,18,0.891474782,,,0.676196389
7910,COVID-19 Prediction Models and Unexploited Data.,J Med Syst,32794042,8/15/20,pubmed,0,1,"predictive model, prediction model",0.001565358,0.001565329,0.146498633,0.847239928,0.001565345,0.001565407,Epidemiology,0.41134176,FALSE,173,0.960603624,34,0.5990768,7,0.785110192,,,0.781596872
7911,A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19.,Brief Bioinform,32793981,8/15/20,pubmed,0,4,"neural network, classifier",0.001511871,0.307514886,0.424982179,0.262967362,0.001511863,0.001511838,Genomics,0.5214629,TRUE,24.75,0.364710248,4.75,0.248260637,0,0.403234768,,,0.338735217
7912,Magnitude and Dynamics of the T-Cell Response to SARS-CoV-2 Infection at Both Individual and Population Levels.,medRxiv,32793919,8/15/20,pubmed,0,53,"sequencing, classifier",0.526378715,0.227022548,0.10508003,0.000999569,0.000999542,0.139519596,Drug discovery,0.13017845,FALSE,50.37735849,0.634052817,68.11320755,0.748327535,35,0.939317242,,,0.773899198
7913,0,bioRxiv,32793910,8/15/20,pubmed,0,30,computational,0.968119406,0.001593594,0.001593494,0.025506291,0.001593678,0.001593537,Drug discovery,0.23267156,FALSE,36.43333333,0.501886326,,,1,0.537564047,,,0.519725186
7914,Robotic High-Throughput Biomanufacturing and Functional Differentiation of Human Pluripotent Stem Cells.,bioRxiv,32793899,8/15/20,pubmed,0,17,transcriptom,0.729082412,0.001291297,0.200322704,0.001291304,0.001291381,0.066720903,Drug discovery,0.627844,TRUE,26,0.382398417,62.47058824,0.730465614,0,0.403234768,,,0.505366266
7915,An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department.,ArXiv,32793769,8/15/20,pubmed,0,19,"artificial intelligence, neural network",0.001371243,0.001371298,0.874774431,0.024959738,0.001371336,0.096151953,Imaging,0.22547919,FALSE,45.05263158,0.587172985,25.63157895,0.53398448,4,0.707574542,,,0.609577336
7916,Trove: Ontology-driven weak supervision for medical entity classification.,ArXiv,32793768,8/15/20,pubmed,0,7,"machine learning, supervised learning, classifier, dataset",0.001098865,0.00109885,0.951323247,0.044281296,0.001098926,0.001098816,Epidemiology,0.001079232,FALSE,32.14285714,0.456861896,21.14285714,0.492841852,2,0.618927094,,,0.522876947
7917,Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study.,Ann Transl Med,32793703,8/15/20,pubmed,0,24,"machine learning, radiom, logistic regression, dataset",0.00088904,0.000889044,0.382350185,0.000889078,0.000889059,0.614093593,Clinics,0.86479986,TRUE,34.75,0.484507391,,,16,0.881782826,,,0.683145108
7918,Computational-based drug repurposing methods in COVID-19.,Bioimpacts,32793443,8/15/20,pubmed,0,1,computational,0.579295267,0.002996528,0.234577829,0.177137382,0.002996527,0.002996468,Drug discovery,0.83058417,TRUE,10,0.15214299,0,0.055525823,3,0.667819001,,,0.291829271
7919,The Effects of Type 2 Diabetes Mellitus on Organ Metabolism and the Immune System.,Front Immunol,32793223,8/15/20,pubmed,0,5,immunome,0.529021914,0.0017866,0.001786561,0.001786612,0.001786665,0.463831648,Drug discovery,0.77342695,TRUE,173.4,0.960974705,203,0.918584426,10,0.828199272,,,0.902586134
7920,Geographic and Genomic Distribution of SARS-CoV-2 Mutations.,Front Microbiol,32793182,8/15/20,pubmed,0,2,genomes,0.087878171,0.870231311,0.001593565,0.001593533,0.00159355,0.03710987,Genomics,0.46085134,FALSE,17.5,0.263776362,36,0.611118544,171,0.989196864,,,0.621363923
7921,0,Front Microbiol,32793181,8/15/20,pubmed,0,4,in silico,0.891678463,0.001823427,0.001823378,0.027166233,0.075685066,0.001823432,Drug discovery,0.991365,TRUE,62,0.715814212,56,0.706850415,18,0.891474782,,,0.771379803
7922,"Sleep Disturbance and Psychological Profiles of Medical Staff and Non-Medical Staff During the Early Outbreak of COVID-19 in Hubei Province, China.",Front Psychiatry,32793014,8/15/20,pubmed,0,7,logistic regression,0.001272646,0.001272694,0.001272652,0.001272681,0.993636654,0.001272673,Healthcare,0.93858457,TRUE,42,0.558537943,16.28571429,0.439256088,8,0.799987654,,,0.599260561
7923,Association Between Current Physical Activity and Current Perceived Anxiety and Mood in the Initial Phase of COVID-19 Confinement.,Front Psychiatry,32793013,8/15/20,pubmed,0,7,logistic regression,0.00104681,0.001046824,0.00104682,0.001046852,0.99476583,0.001046865,Healthcare,0.8826354,TRUE,147.5714286,0.941740367,130.7142857,0.864195879,20,0.900117291,,,0.902017846
7924,Face mask use in the general population and optimal resource allocation during the COVID-19 pandemic.,Nat Commun,32792562,8/15/20,pubmed,0,2,mathematical model,0.001786535,0.001786565,0.001786551,0.9566577,0.036196041,0.001786608,Epidemiology,0.35108683,FALSE,13,0.197352959,8,0.320511105,5,0.739490092,,,0.419118052
7925,E-mental health mindfulness-based and skills-based 'CoPE It' intervention to reduce psychological distress in times of COVID-19: study protocol for a bicentre longitudinal study.,BMJ Open,32792455,8/15/20,pubmed,0,11,logistic regression,0.001022692,0.001022632,0.001022709,0.227575229,0.768334026,0.001022713,Healthcare,0.9568337,TRUE,36.72727273,0.505473437,16.90909091,0.44761841,3,0.667819001,,,0.540303616
7926,A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany.,Int J Health Geogr,32791994,8/15/20,pubmed,0,6,"bayes, machine learning",0.026775284,0.000766002,0.050726208,0.768147937,0.152818593,0.000765976,Epidemiology,0.8572978,TRUE,26,0.382398417,11.5,0.378378378,9,0.814309525,,,0.525028773
7927,Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes.,Genome Med,32791978,8/15/20,pubmed,0,11,"bioinformatic, genomes",0.798295431,0.161005469,0.000699355,0.000699354,0.000699367,0.038601024,Drug discovery,0.1691289,FALSE,27.72727273,0.403859237,109,0.837637142,12,0.850299401,,,0.69726526
7928,A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil.,Trop Med Int Health,32790891,8/14/20,pubmed,0,6,"logistic regression, dataset",0.000946079,0.000946144,0.399984679,0.235502598,0.000946111,0.36167439,Clinics,0.7964518,TRUE,41,0.549013544,30.5,0.573187048,0,0.403234768,,,0.508478453
7929,No more business as usual: Agile and effective responses to emerging pathogen threats require open data and open analytics.,PLoS Pathog,32790776,8/14/20,pubmed,0,18,computational,0.002422405,0.061104331,0.168376633,0.763251956,0.002422397,0.002422277,Epidemiology,0.43983203,FALSE,47.44444444,0.60931412,189.7777778,0.911961466,3,0.667819001,,,0.729698196
7930,A simulation modelling toolkit for organising outpatient dialysis services during the COVID-19 pandemic.,PLoS One,32790773,8/14/20,pubmed,0,6,simulation model,0.001072267,0.001072248,0.001072196,0.507796359,0.045906807,0.443080122,Epidemiology,0.73365194,TRUE,36.33333333,0.500773084,33.16666667,0.593658014,1,0.537564047,,,0.543998382
7931,Digital Response During the COVID-19 Pandemic in Saudi Arabia.,J Med Internet Res,32790642,8/14/20,pubmed,0,3,artificial intelligence,0.001141392,0.001141337,0.05614073,0.85495499,0.085480231,0.00114132,Epidemiology,0.9081515,TRUE,7.666666667,0.111633373,3.333333333,0.206515922,8,0.799987654,,,0.372712316
7932,Does SARS-CoV-2 Bind to Human ACE2 More Strongly Than Does SARS-CoV?,J Phys Chem B,32790406,8/14/20,pubmed,0,6,molecular dynamics simulation,0.854229808,0.03563559,0.001350332,0.106083405,0.001350361,0.001350504,Drug discovery,0.613313,TRUE,22.16666667,0.327107428,17.33333333,0.45424137,12,0.850299401,,,0.543882733
7933,COVID-19: Nanomedicine Uncovers Blood-Clot Mystery.,J Proteome Res,32790309,8/14/20,pubmed,0,3,proteom,0.167232122,0.002032833,0.079261,0.069000043,0.002032852,0.680441149,Clinics,0.5775763,TRUE,34,0.477766096,12.33333333,0.389550442,4,0.707574542,,,0.524963693
7934,Automated quantification of COVID-19 severity and progression using chest CT images.,Eur Radiol,32789756,8/14/20,pubmed,0,13,deep learning,0.000863052,0.000863088,0.95381868,0.000863074,0.000863059,0.042729047,Imaging,0.75859153,TRUE,127,0.917125363,208.3076923,0.921461065,7,0.785110192,,,0.87456554
7935,Computational analysis of complement inhibitor compstatin using molecular dynamics.,J Mol Model,32789582,8/14/20,pubmed,0,3,"molecular dynamics simulation, computational",0.99106709,0.001786538,0.001786621,0.001786646,0.001786596,0.001786509,Drug discovery,0.7860714,TRUE,127,0.917125363,311.3333333,0.957720096,0,0.403234768,,,0.759360076
7936,Dataset of Jordanian university students' psychological health impacted by using e-learning tools during COVID-19.,Data Brief,32789158,8/14/20,pubmed,0,2,dataset,0.001350369,0.001350322,0.104265203,0.3146844,0.576999367,0.00135034,Healthcare,0.89246875,TRUE,16,0.243552477,3.5,0.213607172,4,0.707574542,,,0.38824473
7937,"Data for understanding trust in varied information sources, use of news media, and perception of misinformation regarding COVID-19 in Pakistan.",Data Brief,32789157,8/14/20,pubmed,0,2,dataset,0.002422393,0.002422299,0.002422355,0.396095438,0.594215229,0.002422287,Healthcare,0.8196622,TRUE,7.5,0.108355495,0,0.055525823,2,0.618927094,,,0.260936137
7938,The hidden Markov chain modelling of the COVID-19 spreading using Moroccan dataset.,Data Brief,32789156,8/14/20,pubmed,0,8,"mathematical model, dataset",0.001901711,0.001901779,0.001901809,0.99049126,0.001901742,0.001901699,Epidemiology,0.3398312,FALSE,8.5,0.126662131,17.5,0.45611453,2,0.618927094,,,0.400567919
7939,"Current coronavirus (SARS-CoV-2) epidemiological, diagnostic and therapeutic approaches: An updated review until June 2020.",EXCLI J,32788913,8/14/20,pubmed,0,7,genomes,0.47702585,0.106880221,0.095665841,0.001538237,0.1424543,0.176435551,Drug discovery,0.8103441,TRUE,31.57142857,0.450553528,9.857142857,0.35141825,8,0.799987654,,,0.533986477
7940,Single-cell landscape of immunological responses in patients with COVID-19.,Nat Immunol,32788748,8/14/20,pubmed,0,21,sequencing,0.731141111,0.035844741,0.001438126,0.001438111,0.001438122,0.228699789,Drug discovery,0.85350645,TRUE,95.38095238,0.857752489,155.7142857,0.888480064,57,0.962281622,,,0.902838059
7941,Comparing different deep learning architectures for classification of chest radiographs.,Sci Rep,32788602,8/14/20,pubmed,0,6,"deep learning, neural network, dataset",0.001392885,0.001392893,0.993035589,0.001392888,0.00139288,0.001392865,Imaging,0.11861983,FALSE,66.66666667,0.745191416,32.33333333,0.588239229,16,0.881782826,,,0.73840449
7942,Serum Protein Profiling Reveals a Landscape of Inflammation and Immune Signaling in Early-stage COVID-19 Infection.,Mol Cell Proteomics,32788344,8/14/20,pubmed,0,13,proteom,0.586834093,0.090003196,0.001987131,0.00198719,0.001987239,0.317201152,Drug discovery,0.88345647,TRUE,88.07692308,0.83721937,52.76923077,0.69199893,0,0.403234768,,,0.644151023
7943,Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans.,Science,32788292,8/14/20,pubmed,0,31,transcriptom,0.764119771,0.001823579,0.001823494,0.001823382,0.001823377,0.228586397,Drug discovery,0.89972234,TRUE,52.64516129,0.650998825,122.483871,0.854428686,157,0.987900488,,,0.831109333
7944,Clinical activity of pembrolizumab in metastatic prostate cancer with microsatellite instability high (MSI-H) detected by circulating tumor DNA.,J Immunother Cancer,32788235,8/14/20,pubmed,0,16,sequencing,0.196405028,0.156933843,0.001272764,0.080561152,0.073458105,0.491369108,Clinics,0.57267034,TRUE,109.5,0.889479869,78,0.773548301,6,0.764429903,,,0.809152691
7945,Rapid identification of COVID-19 severity in CT scans through classification of deep features.,Biomed Eng Online,32787937,8/14/20,pubmed,0,9,"neural network, classifier, deep model",0.020461831,0.001350322,0.97413676,0.001350349,0.001350334,0.001350403,Imaging,0.5375652,TRUE,28.88888889,0.416414126,12.77777778,0.396039604,5,0.739490092,,,0.517314607
7946,The status and high risk factors of severe psychological distress in migraine patients during nCOV-2019 outbreak in Southwest China: a cross-sectional study.,J Headache Pain,32787771,8/14/20,pubmed,0,8,logistic regression,0.001538213,0.001538357,0.001538114,0.001538127,0.826861683,0.166985505,Healthcare,0.9270705,TRUE,77.25,0.795658359,10.875,0.367741504,2,0.618927094,,,0.594108986
7947,Drug Repurposing: A Strategy for Discovering Inhibitors against Emerging Viral Infections.,Curr Med Chem,32787752,8/14/20,pubmed,0,3,computational,0.577500948,0.000854761,0.000854727,0.419080101,0.000854723,0.000854741,Drug discovery,0.40323055,FALSE,10,0.15214299,1.666666667,0.145036125,1,0.537564047,,,0.278247721
7948,Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19.,Radiology,32787701,8/14/20,pubmed,0,12,logistic regression,0.000734162,0.000734172,0.197997019,0.000734215,0.000734189,0.799066243,Clinics,0.92722124,TRUE,19.25,0.287896592,26.91666667,0.545022746,12,0.850299401,,,0.561072913
7949,0,Future Med Chem,32787684,8/14/20,pubmed,0,4,virtual screening,0.990491262,0.001901744,0.001901878,0.001901761,0.001901674,0.001901681,Drug discovery,0.89827,TRUE,3.5,0.044344115,0,0.055525823,13,0.858880178,,,0.319583372
7950,Dynamic changes of throat swabs RNA and serum antibodies for SARS-CoV-2 and their diagnostic performances in patients with COVID-19.,Emerg Microbes Infect,32787527,8/14/20,pubmed,0,16,logistic regression,0.001622769,0.531222728,0.001622756,0.001622818,0.001622742,0.462286187,Genomics,0.7487411,TRUE,33.4375,0.470715567,47.1875,0.66932031,3,0.667819001,,,0.602618293
7951,Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies.,J Phys Chem Lett,32787328,8/14/20,pubmed,0,6,machine learning,0.482767285,0.01174978,0.470233795,0.011749761,0.011749695,0.011749684,Drug discovery,0.73571825,TRUE,57,0.68204589,17,0.451097137,8,0.799987654,,,0.644376894
7952,Structure of Furin Protease Binding to SARS-CoV-2 Spike Glycoprotein and Implications for Potential Targets and Virulence.,J Phys Chem Lett,32787225,8/14/20,pubmed,0,1,"sequencing, exom",0.72889227,0.26484656,0.00156529,0.001565316,0.001565286,0.001565276,Drug discovery,0.6737169,TRUE,10,0.15214299,15,0.42594327,11,0.840175319,,,0.47275386
7953,Interaction of Drug Candidates with Various SARS-CoV-2 Receptors: An in Silico Study to Combat COVID-19.,J Proteome Res,32786890,8/14/20,pubmed,0,5,in silico,0.758054003,0.002898331,0.002898358,0.23035212,0.002898572,0.002898616,Drug discovery,0.70327526,TRUE,3,0.037293586,0,0.055525823,10,0.828199272,,,0.307006227
7954,Computational Prediction of Mutational Effects on SARS-CoV-2 Binding by Relative Free Energy Calculations.,J Chem Inf Model,32786709,8/14/20,pubmed,0,8,computational,0.888406497,0.043353372,0.032606045,0.002130714,0.031372743,0.002130629,Drug discovery,0.7885318,TRUE,40.375,0.543138104,12.375,0.389751137,4,0.707574542,,,0.546821261
7955,0,J Chem Inf Model,32786695,8/14/20,pubmed,0,18,"virtual screening, in silico",0.782914805,0.173029447,0.001415186,0.001415167,0.039810199,0.001415195,Drug discovery,0.8091538,TRUE,25.27777778,0.370833076,24.16666667,0.522544822,5,0.739490092,,,0.54428933
7956,"Studying the Effects of ACE2 Mutations on the Stability, Dynamics, and Dissociation Process of SARS-CoV-2 S1/hACE2 Complexes.",J Proteome Res,32786692,8/14/20,pubmed,0,2,computational,0.782561788,0.206212539,0.002806395,0.002806579,0.002806369,0.002806331,Drug discovery,0.29106423,FALSE,16,0.243552477,2,0.164302917,3,0.667819001,,,0.358558132
7957,Serum Proteomics in COVID-19 Patients: Altered Coagulation and Complement Status as a Function of IL-6 Level.,J Proteome Res,32786691,8/14/20,pubmed,0,10,proteom,0.37048769,0.001653077,0.001653029,0.001653075,0.001653082,0.622900046,Clinics,0.95535445,TRUE,122.6,0.910878842,87.4,0.795557934,9,0.814309525,,,0.840248767
7958,Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces.,J Chem Inf Model,32786511,8/14/20,pubmed,0,3,virtual screening,0.635533886,0.000916696,0.316056046,0.045659957,0.000916699,0.000916716,Drug discovery,0.4525036,FALSE,80,0.807532933,74,0.764182499,4,0.707574542,,,0.759763325
7959,Anti-SARS-CoV-2 Potential of Artemisinins In Vitro.,ACS Infect Dis,32786284,8/14/20,pubmed,0,18,prediction model,0.969040111,0.001653094,0.001653103,0.001653064,0.001653175,0.024347453,Drug discovery,0.93044025,TRUE,33.38888889,0.470158946,,,17,0.887338725,,,0.678748835
7960,Risk factors for anxiety of otolaryngology healthcare workers in Hubei province fighting coronavirus disease 2019 (COVID-19).,Soc Psychiatry Psychiatr Epidemiol,32785756,8/14/20,pubmed,0,4,logistic regression,0.001254617,0.0012546,0.001254598,0.001254657,0.993726886,0.001254642,Healthcare,0.9618107,TRUE,116.75,0.901911064,34.5,0.602221033,6,0.764429903,,,0.756187333
7961,A SARS-CoV-2 Prediction Model from Standard Laboratory Tests.,Clin Infect Dis,32785701,8/14/20,pubmed,0,9,"machine learning, prediction model",0.001901853,0.175789146,0.477381082,0.001901825,0.001901815,0.341124279,Clinics,0.73490137,TRUE,48.22222222,0.615869874,54.33333333,0.698956382,0,0.403234768,,,0.572687008
7962,COVID-19 in Americans aboard the Diamond Princess cruise ship.,Clin Infect Dis,32785683,8/14/20,pubmed,0,35,"whole genome, genome sequences",0.002032759,0.433165082,0.002032792,0.217342369,0.148275729,0.19715127,Genomics,0.83625305,TRUE,52.94285714,0.652977921,77.25714286,0.771407546,6,0.764429903,,,0.729605123
7963,Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning.,J Gerontol B Psychol Sci Soc Sci,32785620,8/14/20,pubmed,0,7,machine learning,0.001438141,0.001438146,0.001438275,0.478391644,0.515855622,0.001438172,Healthcare,0.81497586,TRUE,20.28571429,0.301502876,6.714285714,0.291142628,6,0.764429903,,,0.452358469
7964,Structural insight into the role of novel SARS-CoV-2 E protein: A potential target for vaccine development and other therapeutic strategies.,PLoS One,32785274,8/14/20,pubmed,0,2,"bioinformatic, structural model",0.78739058,0.207037762,0.001392853,0.001392948,0.001393014,0.001392843,Drug discovery,0.524166,TRUE,24,0.35574247,4,0.231469093,11,0.840175319,,,0.475795627
7965,Children's Anxiety and Factors Related to the COVID-19 Pandemic: An Exploratory Study Using the Children's Anxiety Questionnaire and the Numerical Rating Scale.,Int J Environ Res Public Health,32784898,8/14/20,pubmed,0,8,logistic regression,0.001823344,0.00182332,0.001823322,0.03672728,0.955979377,0.001823356,Healthcare,0.9443897,TRUE,18,0.271569052,5.75,0.271742039,7,0.785110192,,,0.442807094
7966,0,Biology (Basel),32784802,8/14/20,pubmed,0,6,sequencing,0.674014188,0.048307115,0.001593474,0.001593505,0.001593621,0.272898096,Drug discovery,0.38652843,FALSE,33.83333333,0.474488218,32.33333333,0.588239229,11,0.840175319,,,0.634300922
7967,Racial and ethnic disparities in SARS-CoV-2 pandemic: analysis of a COVID-19 observational registry for a diverse US metropolitan population.,BMJ Open,32784264,8/14/20,pubmed,0,12,logistic regression,0.039470831,0.001022739,0.001022661,0.103563059,0.609818748,0.245101962,Healthcare,0.48863918,FALSE,62.58333333,0.718411776,31,0.578204442,49,0.956787456,,,0.751134558
7968,SARS-CoV-2 serological testing changes disease management in a PCR-negative patient.,BMJ Case Rep,32784236,8/14/20,pubmed,0,2,bayes,0.00242244,0.002422554,0.358402712,0.631907289,0.002422415,0.002422591,Epidemiology,0.35919726,FALSE,11,0.167171748,2,0.164302917,1,0.537564047,,,0.289679571
7969,Early Stage Machine Learning-Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach.,JMIR Public Health Surveill,32784193,8/14/20,pubmed,0,4,machine learning,0.001350321,0.001350368,0.099332505,0.765150901,0.077754004,0.055061901,Epidemiology,0.24486282,FALSE,69.75,0.762446657,59,0.717554188,2,0.618927094,,,0.699642646
7970,Efficacy of Hydroxychloroquine and Tocilizumab in Patients With COVID-19: Single-Center Retrospective Chart Review.,J Med Internet Res,32784192,8/14/20,pubmed,0,11,logistic regression,0.045072718,0.000916703,0.000916712,0.000916763,0.000916728,0.951260376,Clinics,0.9515337,TRUE,43.72727273,0.575298411,16.18181818,0.438118812,5,0.739490092,,,0.584302438
7971,Public Perceptions and Attitudes Toward COVID-19 Nonpharmaceutical Interventions Across Six Countries: A Topic Modeling Analysis of Twitter Data.,J Med Internet Res,32784190,8/14/20,pubmed,0,4,correlation analysis,0.00095631,0.000956338,0.000956306,0.772627437,0.223547308,0.000956302,Epidemiology,0.74756235,TRUE,34.75,0.484507391,9.25,0.340379984,5,0.739490092,,,0.521459156
7972,Single-Cell Sequencing of Peripheral Mononuclear Cells Reveals Distinct Immune Response Landscapes of COVID-19 and Influenza Patients.,Immunity,32783921,8/14/20,pubmed,0,44,sequencing,0.729381365,0.002238536,0.002238427,0.002238504,0.002238434,0.261664733,Drug discovery,0.6505691,TRUE,48.11363636,0.615436947,84.54545455,0.789269467,46,0.954009507,,,0.78623864
7973,ACE2 (Angiotensin-Converting Enzyme 2) in Cardiopulmonary Diseases: Ramifications for the Control of SARS-CoV-2.,Hypertension,32783758,8/14/20,pubmed,0,9,microbiom,0.904869051,0.031757715,0.001565314,0.058677268,0.001565341,0.001565311,Drug discovery,0.91105664,TRUE,152.6666667,0.946317026,,,11,0.840175319,,,0.893246173
7974,Associations Between Chronic Health Conditions and COVID-19 Preventive Behaviors Among a Nationally Representative Sample of U.S. Adults: An Analysis of the COVID Impact Survey.,Health Equity,32783017,8/13/20,pubmed,0,3,logistic regression,0.019307473,0.001272668,0.001272639,0.001272703,0.975601797,0.00127272,Healthcare,0.5150832,TRUE,49.33333333,0.626507514,13,0.400521809,4,0.707574542,,,0.578201288
7975,COVID-19 Outbreak in a Hemodialysis Center: A Retrospective Monocentric Case Series.,Can J Kidney Health Dis,32782814,8/13/20,pubmed,0,10,logistic regression,0.061992167,0.000889062,0.000889057,0.000889087,0.148013947,0.78732668,Clinics,0.87296265,TRUE,24.2,0.357350485,18.1,0.462135403,4,0.707574542,,,0.509020143
7976,Internet Public Opinion Evolution in the COVID-19 Event and Coping Strategies.,Disaster Med Public Health Prep,32782053,8/13/20,pubmed,0,1,correlation analysis,0.001861693,0.001861709,0.001861717,0.910462754,0.082090386,0.00186174,Epidemiology,0.65089124,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
7977,Key questions for modelling COVID-19 exit strategies.,Proc Biol Sci,32781946,8/13/20,pubmed,0,43,mathematical model,0.001593491,0.001593513,0.001593531,0.992032437,0.001593551,0.001593478,Epidemiology,0.33743197,FALSE,74.37209302,0.783598244,204,0.919186513,33,0.936045435,,,0.879610064
7978,Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning.,Med Image Anal,32781377,8/12/20,pubmed,0,5,"deep learning, neural network, transfer learning, dataset",0.001156238,0.001156258,0.994218723,0.001156288,0.001156234,0.001156259,Imaging,0.15650648,FALSE,132.6,0.924608819,263.4,0.943537597,92,0.976233101,,,0.948126506
7979,Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study).,Clin Microbiol Infect,32781244,8/12/20,pubmed,0,93,"logistic regression, prediction model",0.001098834,0.001098806,0.001098866,0.001098815,0.001098817,0.994505862,Clinics,0.8801133,TRUE,36.41489362,0.501391552,,,15,0.874313229,,,0.68785239
7980,Pediatric patients with COVID-19 admitted to intensive care units in Brazil: a prospective multicenter study.,J Pediatr (Rio J),32781034,8/12/20,pubmed,0,31,logistic regression,0.001112607,0.001112608,0.001112609,0.001112609,0.017515301,0.978034266,Clinics,0.92003715,TRUE,11.38709677,0.170882553,3.741935484,0.218156275,9,0.814309525,,,0.401116118
7981,Using an integrated social cognition model to predict COVID-19 preventive behaviours.,Br J Health Psychol,32780891,8/12/20,pubmed,0,8,model fit,0.000752931,0.000752897,0.000752918,0.256860902,0.740127448,0.000752904,Healthcare,0.98966503,TRUE,152.375,0.946069639,197.5,0.91651057,14,0.866658436,,,0.909746215
7982,"Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19.",PLoS One,32780765,8/12/20,pubmed,0,8,logistic regression,0.015900114,0.000779458,0.056924154,0.000779467,0.199821781,0.725795026,Clinics,0.5846286,TRUE,146.5,0.941060053,205.375,0.919922398,21,0.903944688,,,0.92164238
7983,Distance learning during social seclusion by COVID-19: Improving the quality of life of undergraduate dentistry students.,Eur J Dent Educ,32780535,8/12/20,pubmed,0,8,logistic regression,0.083439283,0.001098854,0.001098845,0.001098875,0.912165312,0.001098831,Healthcare,0.9768494,TRUE,43.25,0.569855897,14.625,0.41932031,2,0.618927094,,,0.536034434
7984,Evaluation of the prothrombin fragment 1.2 in patients with coronavirus disease 2019 (COVID-19).,Am J Hematol,32780525,8/12/20,pubmed,0,5,logistic regression,0.001392922,0.023426363,0.042189727,0.001392876,0.001392842,0.930205269,Clinics,0.6918936,TRUE,159.6,0.951264766,130.8,0.864262778,1,0.537564047,,,0.784363864
7985,Pharmacokinetic Basis of the Hydroxychloroquine Response in COVID-19: Implications for Therapy and Prevention.,Eur J Drug Metab Pharmacokinet,32780273,8/12/20,pubmed,0,2,mathematical model,0.420396041,0.001622784,0.001622718,0.548011402,0.001622802,0.026724253,Epidemiology,0.8677746,TRUE,30.5,0.438493413,49,0.677481937,6,0.764429903,,,0.626801751
7986,A case study in model failure? COVID-19 daily deaths and ICU bed utilisation predictions in New York state.,Eur J Epidemiol,32780189,8/12/20,pubmed,0,7,forecasting model,0.00133001,0.001330021,0.261711299,0.703706243,0.001330028,0.030592399,Epidemiology,0.1131407,FALSE,91.28571429,0.846991156,353.5714286,0.966283115,12,0.850299401,,,0.887857891
7987,Mutational spectra of SARS-CoV-2 orf1ab polyprotein and signature mutations in the United States of America.,J Med Virol,32779784,8/12/20,pubmed,0,5,sequence alignment,0.354106744,0.531961467,0.002183271,0.002183368,0.002183259,0.107381891,Genomics,0.5409996,TRUE,10.6,0.158142124,3.4,0.208589778,3,0.667819001,,,0.344850301
7988,Clinical characteristics of moderate COVID-19 patients aggravation in Wuhan Stadium Cabin Hospital: A 571 cases of retrospective cohort study.,J Med Virol,32779760,8/12/20,pubmed,0,13,logistic regression,0.001684578,0.001684501,0.001684621,0.00168456,0.001684523,0.991577217,Clinics,0.8967588,TRUE,50.92307692,0.637392541,16.61538462,0.443604496,6,0.764429903,,,0.615142313
7989,Profiling of Initial Available SARS-CoV-2 Sequences from Iranian Related COVID-19 Patients.,Cell J,32779445,8/12/20,pubmed,0,3,bioinformatic,0.269372698,0.705085673,0.00153811,0.001538133,0.001538101,0.020927284,Genomics,0.55642855,TRUE,44.33333333,0.580679077,15,0.42594327,0,0.403234768,,,0.469952372
7990,Drugs against SARS-CoV-2: What do we know about their mode of action?,Rev Med Virol,32779326,8/12/20,pubmed,0,8,artificial intelligence,0.641386123,0.001593536,0.104912863,0.158262325,0.001593557,0.092251595,Drug discovery,0.75515544,TRUE,46.875,0.604304533,70.5,0.755351887,7,0.785110192,,,0.714922204
7991,High affinity interaction of Solanum tuberosum and Brassica juncea residue smoke water compounds with proteins involved in coronavirus infection.,Phytother Res,32779305,8/12/20,pubmed,0,7,"virtual screening, in silico",0.899826099,0.002183213,0.002183218,0.002183236,0.002183429,0.091440807,Drug discovery,0.6987165,TRUE,19,0.285793803,4.142857143,0.232472572,1,0.537564047,,,0.351943474
7992,MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm.,Brief Bioinform,32778891,8/12/20,pubmed,0,6,"deep learning, artificial intelligence",0.537015146,0.00095642,0.459159469,0.000956354,0.00095631,0.0009563,Drug discovery,0.7999526,TRUE,34.16666667,0.47863195,23.83333333,0.519467487,3,0.667819001,,,0.555306146
7993,Pathogenetic profiling of COVID-19 and SARS-like viruses.,Brief Bioinform,32778874,8/12/20,pubmed,0,6,"whole-genome, dataset",0.63094319,0.266713115,0.001330089,0.025654103,0.001330104,0.074029399,Drug discovery,0.8775728,TRUE,29.66666667,0.42773208,25.16666667,0.53017126,8,0.799987654,,,0.585963665
7994,The effect of delaying transperineal fusion biopsy of the prostate for patients with suspicious MRI findings-Implications for the COVID-19 era.,Urol Oncol,32778478,8/12/20,pubmed,0,10,logistic regression,0.001486435,0.001486429,0.286351899,0.001486535,0.001486543,0.707702159,Clinics,0.9776814,TRUE,64.8,0.733626075,42.9,0.648916243,0,0.403234768,,,0.595259028
7995,Vaccine Efficacy Needed for a COVID-19 Coronavirus Vaccine to Prevent or Stop an Epidemic as the Sole Intervention.,Am J Prev Med,32778354,8/12/20,pubmed,0,11,"simulation experiment, computational",0.001565389,0.001565348,0.001565294,0.9921733,0.001565392,0.001565278,Epidemiology,0.16528633,FALSE,56.36363636,0.677531078,49.54545455,0.679756489,46,0.954009507,,,0.770432358
7996,Massive migration promotes the early spread of COVID-19 in China: a study based on a scale-free network.,Infect Dis Poverty,32778160,8/12/20,pubmed,0,10,correlation analysis,0.001371304,0.00137129,0.049565308,0.944949519,0.001371298,0.001371281,Epidemiology,0.5553571,TRUE,25.2,0.370214608,7.6,0.308536259,1,0.537564047,,,0.405438305
7997,A Public Website for the Automated Assessment and Validation of SARS-CoV-2 Diagnostic PCR Assays.,Bioinformatics,32777813,8/11/20,pubmed,0,11,bioinformatic,0.001684512,0.681947163,0.311314701,0.001684583,0.001684513,0.001684528,Genomics,0.53352576,TRUE,52.18181818,0.648277568,135.4545455,0.870149853,1,0.537564047,,,0.685330489
7998,Impact of COVID-19 on global HCV elimination efforts.,J Hepatol,32777322,8/11/20,pubmed,0,14,mathematical model,0.000988383,0.024810021,0.000988398,0.644627857,0.102188141,0.2263972,Epidemiology,0.9637381,TRUE,145.5,0.939884965,182.5,0.907813754,11,0.840175319,,,0.895958013
7999,"Anti-HCV and anti-malaria agent, potential candidates to repurpose for coronavirus infection: Virtual screening, molecular docking, and molecular dynamics simulation study.",Life Sci,32777300,8/11/20,pubmed,0,2,"virtual screening, molecular dynamics simulation, computational, dataset",0.900765804,0.001126844,0.001126839,0.070944744,0.024908969,0.001126799,Drug discovery,0.95841515,TRUE,114.5,0.8983858,28,0.554321648,7,0.785110192,,,0.745939213
8000,Modeling the role of asymptomatics in infection spread with application to SARS-CoV-2.,PLoS One,32776963,8/11/20,pubmed,0,1,mathematical model,0.003214193,0.075459042,0.003214142,0.863432076,0.003214213,0.051466333,Epidemiology,0.38803318,FALSE,44,0.578390748,15,0.42594327,7,0.785110192,,,0.596481403
8001,Timing and delay of radical prostatectomy do not lead to adverse oncologic outcomes: results from a large European cohort at the times of COVID-19 pandemic.,World J Urol,32776243,8/11/20,pubmed,0,16,logistic regression,0.001861735,0.00186173,0.037228926,0.216778614,0.149694465,0.592574531,Clinics,0.95022255,TRUE,122.375,0.910507762,29.5,0.566095799,1,0.537564047,,,0.671389203
8002,Impact of diabetes on COVID-19-related in-hospital mortality: a retrospective study from Northern Italy.,J Endocrinol Invest,32776197,8/11/20,pubmed,0,12,logistic regression,0.001538121,0.037603173,0.001538096,0.001538135,0.001538115,0.956244359,Clinics,0.7765523,TRUE,15.91666667,0.239037665,4.416666667,0.239095531,8,0.799987654,,,0.426040283
8003,Structural Basis for Designing Multiepitope Vaccines Against COVID-19 Infection: In Silico Vaccine Design and Validation.,JMIR Bioinform Biotech,32776022,8/11/20,pubmed,0,9,"in silico, proteom",0.960139134,0.036304581,0.000889057,0.000889058,0.000889082,0.000889087,Drug discovery,0.86884034,TRUE,36.88888889,0.507143299,16.11111111,0.43771742,1,0.537564047,,,0.494141589
8004,Optimal control on a mathematical model to pattern the progression of coronavirus disease 2019 (COVID-19) in Indonesia.,Glob Health Res Policy,32775696,8/11/20,pubmed,0,4,mathematical model,0.002032792,0.002032788,0.002032838,0.989835979,0.002032785,0.002032818,Epidemiology,0.79755247,TRUE,114.5,0.8983858,77.25,0.771340648,4,0.707574542,,,0.792433663
8005,0,Data Brief,32775582,8/11/20,pubmed,0,8,"interactom, dataset",0.621788628,0.345400573,0.001987162,0.00198719,0.026849348,0.001987098,Drug discovery,0.5471511,TRUE,15.75,0.237862577,10.75,0.366269735,1,0.537564047,,,0.380565453
8006,Anxiety in Chinese pediatric medical staff during the outbreak of Coronavirus Disease 2019: a cross-sectional study.,Transl Pediatr,32775241,8/11/20,pubmed,0,8,correlation analysis,0.001371222,0.001371225,0.001371275,0.001371263,0.897593898,0.096921117,Healthcare,0.9918472,TRUE,20.875,0.308120477,2.125,0.165306396,4,0.707574542,,,0.393667138
8007,Epidemiology of COVID-19 and Predictors of Recovery in the Republic of Korea.,Pulm Med,32774918,8/11/20,pubmed,0,2,logistic regression,0.001219991,0.001220031,0.001220009,0.176870023,0.265234296,0.55423565,Clinics,0.4175451,FALSE,29,0.41993939,38,0.622223709,0,0.403234768,,,0.481799289
8008,Genome sequencing and the diagnosis of novel coronavirus (SARS-COV-2) in Africa: how far are we?,Pan Afr Med J,32774639,8/11/20,pubmed,0,11,sequencing,0.001823498,0.465203558,0.52750276,0.001823457,0.001823376,0.001823351,Genomics,0.541096,TRUE,78.54545455,0.800729792,61.27272727,0.72544822,4,0.707574542,,,0.744584185
8009,Repurposing of known anti-virals as potential inhibitors for SARS-CoV-2 main protease using molecular docking analysis.,Bioinformation,32773989,8/11/20,pubmed,0,5,virtual screening,0.910213355,0.001823408,0.001823503,0.052046216,0.001823379,0.032270139,Drug discovery,0.7828492,TRUE,21,0.312016822,4.8,0.249331014,10,0.828199272,,,0.463182369
8010,Projections for novel coronavirus (COVID-19) and evaluation of epidemic response strategies for India.,Med J Armed Forces India,32773928,8/11/20,pubmed,0,5,mathematical model,0.001112628,0.001112627,0.001112618,0.896057742,0.001112672,0.099491714,Epidemiology,0.1374166,FALSE,15.8,0.238109964,6.4,0.286058336,12,0.850299401,,,0.4581559
8011,"Attitude, practice, behavior, and mental health impact of COVID-19 on doctors.",Indian J Psychiatry,32773868,8/11/20,pubmed,0,6,logistic regression,0.001141356,0.001141327,0.001141419,0.080269674,0.855745478,0.060560746,Healthcare,0.9482086,TRUE,43.33333333,0.571154679,13.33333333,0.403866738,38,0.944132354,,,0.639717924
8012,[Investigation of protective exposure risk events in nurses against corona virus disease 2019 in Wuhan].,Beijing Da Xue Xue Bao Yi Xue Ban,32773807,8/11/20,pubmed,0,6,logistic regression,0.000966772,0.000966812,0.0009668,0.233079121,0.763053692,0.000966803,Healthcare,0.7996166,TRUE,,,,,0,0.403234768,,,0.403234768
8013,Genomic variance of Open Reading Frames (ORFs) and Spike protein in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).,J Chin Med Assoc,32773643,8/11/20,pubmed,0,9,sequence alignment,0.118886885,0.827053592,0.001046857,0.00104687,0.024849935,0.027115861,Genomics,0.70582485,TRUE,35,0.488032655,20.66666667,0.487958255,2,0.618927094,,,0.531639335
8014,Transcriptome & viral growth analysis of SARS-CoV-2-infected Vero CCL-81 cells.,Indian J Med Res,32773420,8/11/20,pubmed,0,11,"sequencing, transcriptom",0.49699013,0.477876715,0.0215712,0.001187314,0.001187318,0.001187324,Drug discovery,0.8125527,TRUE,22.81818182,0.337188447,8.272727273,0.323922933,1,0.537564047,,,0.399558476
8015,Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.,J Xray Sci Technol,32773400,8/11/20,pubmed,0,10,"deep learning, transfer learning, dataset",0.001330033,0.001330056,0.933921858,0.060757862,0.001330078,0.001330113,Imaging,0.51222676,TRUE,34.4,0.480982126,,,10,0.828199272,,,0.654590699
8016,Viruses and viral epidemics in the metabolic theory of evolution.,Eur Ann Otorhinolaryngol Head Neck Dis,32773332,8/11/20,pubmed,0,1,metagenom,0.39515026,0.497568754,0.001538112,0.071083284,0.03312152,0.001538071,Genomics,0.37330395,FALSE,25,0.369286907,39,0.62784319,2,0.618927094,,,0.53868573
8017,0,J Biomol Struct Dyn,32772892,8/11/20,pubmed,0,2,"molecular dynamics simulation, in silico",0.979062755,0.001098841,0.001098828,0.001098855,0.001098826,0.016541895,Drug discovery,0.63655823,TRUE,10,0.15214299,0.5,0.087101953,2,0.618927094,,,0.286057346
8018,History of Stroke Is Independently Associated With In-Hospital Death in Patients With COVID-19.,Stroke,32772679,8/11/20,pubmed,0,5,logistic regression,0.001350304,0.001350406,0.001350332,0.001350346,0.001350344,0.993248268,Clinics,0.94444776,TRUE,72.2,0.773950152,20.2,0.482204977,5,0.739490092,,,0.665215074
8019,Prolonged social lockdown during COVID-19 pandemic and hip fracture epidemiology.,Int Orthop,32772318,8/11/20,pubmed,0,14,logistic regression,0.001237095,0.001237091,0.173120572,0.095057348,0.00123714,0.728110754,Clinics,0.7968888,TRUE,36.64285714,0.504236502,13.64285714,0.406542681,6,0.764429903,,,0.558403029
8020,Anthraquinone Derivatives as an Immune Booster and their Therapeutic Option Against COVID-19.,Nat Prod Bioprospect,32772313,8/11/20,pubmed,0,4,genomes,0.973445455,0.021069474,0.001371271,0.001371252,0.001371271,0.001371277,Drug discovery,0.96806484,TRUE,8.25,0.121343311,0,0.055525823,9,0.814309525,,,0.330392886
8021,Patient-reported treatment delays in breast cancer care during the COVID-19 pandemic.,Breast Cancer Res Treat,32772225,8/11/20,pubmed,0,2,logistic regression,0.001237144,0.001237124,0.001237074,0.149954295,0.391633175,0.454701187,Clinics,0.9251032,TRUE,19.5,0.29117447,8,0.320511105,11,0.840175319,,,0.483953631
8022,Prevalence and associated factors of poor sleep quality among Chinese returning workers during the COVID-19 pandemic.,Sleep Med,32771927,8/11/20,pubmed,0,7,logistic regression,0.018442631,0.001350323,0.001350336,0.001350369,0.976155983,0.001350357,Healthcare,0.97221696,TRUE,30.14285714,0.433360134,3.428571429,0.208991169,6,0.764429903,,,0.468927069
8023,Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model.,Diabetes Metab Syndr,32771920,8/11/20,pubmed,0,3,prediction model,0.001350378,0.001350378,0.062237264,0.932361142,0.001350394,0.001350444,Epidemiology,0.25124457,FALSE,9.666666667,0.144968767,0,0.055525823,9,0.814309525,,,0.338268039
8024,Clinical course and prognostic factors of COVID-19 infection in an elderly hospitalized population.,Arch Gerontol Geriatr,32771883,8/11/20,pubmed,0,52,logistic regression,0.00137142,0.001371255,0.001371276,0.001371283,0.001371292,0.993143475,Clinics,0.9408777,TRUE,12.86792453,0.1927763,,,10,0.828199272,,,0.510487786
8025,Functional prediction and comparative population analysis of variants in genes for proteases and innate immunity related to SARS-CoV-2 infection.,Infect Genet Evol,32771700,8/11/20,pubmed,0,7,"in silico, genomes",0.388775231,0.576355935,0.00133009,0.001330111,0.001330064,0.030878569,Genomics,0.65559447,TRUE,42.28571429,0.560207805,9.428571429,0.343591116,1,0.537564047,,,0.480454323
8026,Association Between Time to Operation and Pathologic Stage in Ductal Carcinoma in Situ and Early-Stage Hormone Receptor-Positive Breast Cancer.,J Am Coll Surg,32771654,8/11/20,pubmed,0,6,logistic regression,0.001415132,0.001415116,0.078870348,0.001415176,0.001415145,0.915469083,Clinics,0.9417329,TRUE,94.33333333,0.855154926,129.5,0.863326197,0,0.403234768,,,0.70723863
8027,Transmission onset distribution of COVID-19.,Int J Infect Dis,32771633,8/11/20,pubmed,0,3,bayes,0.001291196,0.001291241,0.001291213,0.655934683,0.001291304,0.338900361,Epidemiology,0.35755846,FALSE,10.33333333,0.155049787,8.666666667,0.331415574,9,0.814309525,,,0.433591629
8028,Global multi-omics and systems pharmacological strategy unravel the multi-targeted therapeutic potential of natural bioactive molecules against COVID-19: An in silico approach.,Genomics,32771622,8/11/20,pubmed,0,9,"in silico, transcriptom, multi-omics, dataset",0.989083476,0.002183223,0.002183313,0.00218326,0.002183287,0.00218344,Drug discovery,0.8486203,TRUE,35.11111111,0.488341889,28.77777778,0.560074926,5,0.739490092,,,0.595968969
8029,The effect of preventing subclinical transmission on the containment of COVID-19: Mathematical modeling and experience in Taiwan.,Contemp Clin Trials,32771432,8/11/20,pubmed,0,13,mathematical model,0.001622739,0.066584779,0.09510329,0.702089441,0.132976981,0.00162277,Epidemiology,0.11781868,FALSE,39.46153846,0.533366318,27.30769231,0.547966283,4,0.707574542,,,0.596302381
8030,Analysis of codon usage of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) and its adaptability in dog.,Virus Res,32771430,8/11/20,pubmed,0,3,genome-wide,0.173829664,0.818876657,0.001823379,0.00182345,0.00182345,0.0018234,Genomics,0.8077034,TRUE,9,0.135320675,0,0.055525823,5,0.739490092,,,0.310112197
8031,Modeling the viral dynamics of SARS-CoV-2 infection.,Math Biosci,32771304,8/11/20,pubmed,0,6,"model simulation, mathematical model",0.405535169,0.192783915,0.001538155,0.352291027,0.00153809,0.046313645,Drug discovery,0.15013635,FALSE,33.16666667,0.46780877,36.16666667,0.612122023,14,0.866658436,,,0.648863076
8032,Potential effects of disruption to HIV programmes in sub-Saharan Africa caused by COVID-19: results from multiple mathematical models.,Lancet HIV,32771089,8/11/20,pubmed,0,19,mathematical model,0.074409328,0.001112626,0.001112687,0.779047859,0.143204798,0.001112702,Epidemiology,0.2888084,FALSE,35.31578947,0.48964067,40.05263158,0.633596468,41,0.948144947,,,0.690460695
8033,Staffing Levels and COVID-19 Cases and Outbreaks in U.S. Nursing Homes.,J Am Geriatr Soc,32770832,8/10/20,pubmed,0,2,"logistic regression, dataset",0.000988355,0.000988357,0.00098838,0.080776671,0.669448114,0.246810124,Healthcare,0.7779703,TRUE,16.5,0.249366071,10,0.355632861,21,0.903944688,,,0.502981206
8034,Bayesian latent class models to estimate diagnostic test accuracies of COVID-19 tests.,J Med Virol,32770741,8/10/20,pubmed,0,3,bayes,0.013548845,0.013549174,0.522917814,0.422886234,0.013548992,0.013548942,Epidemiology,0.29308778,FALSE,94,0.854598305,64,0.735549906,0,0.403234768,,,0.664460993
8035,Epigallocatechin gallate and theaflavin gallate interaction in SARS-CoV-2 spike-protein central channel with reference to the hydroxychloroquine interaction: Bioinformatics and molecular docking study.,Drug Dev Res,32770567,8/10/20,pubmed,0,2,bioinformatic,0.801262529,0.065384118,0.001046848,0.130212779,0.001046854,0.001046871,Drug discovery,0.5778973,TRUE,77,0.794668811,38,0.622223709,11,0.840175319,,,0.752355946
8036,Digital technologies in the public-health response to COVID-19.,Nat Med,32770165,8/10/20,pubmed,0,17,"machine learning, dataset",0.001861815,0.001862106,0.176190892,0.816361687,0.001861817,0.001861684,Epidemiology,0.7933244,TRUE,83.70588235,0.820025976,176.8823529,0.904201231,54,0.959812334,,,0.894679847
8037,COVID-19 salivary signature: diagnostic and research opportunities.,J Clin Pathol,32769214,8/10/20,pubmed,0,10,metabolom,0.001622768,0.372571901,0.385991777,0.236567829,0.001622846,0.001622878,Genomics,0.7181103,TRUE,36.8,0.506339291,27.9,0.552649184,16,0.881782826,,,0.646923767
8038,Risk factors for hospital admissions related to COVID-19 in patients with autoimmune inflammatory rheumatic diseases.,Ann Rheum Dis,32769150,8/10/20,pubmed,0,10,logistic regression,0.048695374,0.001371254,0.001371297,0.001371352,0.001371388,0.945819335,Clinics,0.9915321,TRUE,61.4,0.71111386,36.6,0.614664169,29,0.928020248,,,0.751266092
8039,Repurposing approved drugs as potential inhibitors of 3CL-protease of SARS-CoV-2: Virtual screening and structure based drug design.,Comput Biol Chem,32769050,8/10/20,pubmed,0,1,virtual screening,0.954720977,0.002238537,0.002238432,0.036325056,0.00223853,0.002238467,Drug discovery,0.8681189,TRUE,78,0.798812543,20,0.481000803,11,0.840175319,,,0.706662888
8040,COVID-19: Time to exonerate the pangolin from the transmission of SARS-CoV-2 to humans.,Infect Genet Evol,32768565,8/10/20,pubmed,0,4,in silico,0.560867675,0.429945847,0.002296626,0.00229677,0.00229656,0.002296521,Drug discovery,0.5231512,TRUE,72.5,0.77549632,85.75,0.792213005,17,0.887338725,,,0.81834935
8041,In silico pharmacokinetic and molecular docking studies of natural flavonoids and synthetic indole chalcones against essential proteins of SARS-CoV-2.,Eur J Pharmacol,32768503,8/10/20,pubmed,0,5,in silico,0.992567817,0.001486472,0.00148643,0.001486451,0.001486432,0.001486398,Drug discovery,0.9486254,TRUE,3.4,0.040942544,0,0.055525823,10,0.828199272,,,0.308222546
8042,Theoretical Study of the Molecular Mechanism of Maxingyigan Decoction Against COVID-19: Network Pharmacology-based Strategy.,Comb Chem High Throughput Screen,32767929,8/10/20,pubmed,0,4,bioinformatic,0.994142201,0.001171537,0.001171572,0.001171545,0.001171547,0.001171597,Drug discovery,0.9987372,TRUE,101.5,0.872100934,,,3,0.667819001,,,0.769959968
8043,Physiologically-Based Pharmacokinetic Modeling to Predict the Clinical Efficacy of the Coadministration of Lopinavir and Ritonavir against SARS-CoV-2.,Clin Pharmacol Ther,32767755,8/9/20,pubmed,0,3,dataset,0.459169359,0.001291254,0.0012913,0.258270473,0.001291322,0.278686293,Drug discovery,0.50213397,TRUE,4.666666667,0.063392912,0.333333333,0.073187048,2,0.618927094,,,0.251835685
8044,Analysis of clinical features and imaging signs of COVID-19 with the assistance of artificial intelligence.,Eur Rev Med Pharmacol Sci,32767351,8/9/20,pubmed,0,7,artificial intelligence,0.000521984,0.000521985,0.562025522,0.000521971,0.000521965,0.435886573,Imaging,0.947713,TRUE,7.285714286,0.10445915,1.285714286,0.128512176,0,0.403234768,,,0.212068698
8045,Prevalence and Influencing Factors on Fatigue of First-line Nurses Combating with COVID-19 in China: A Descriptive Cross-Sectional Study.,Curr Med Sci,32767264,8/9/20,pubmed,0,12,correlation analysis,0.000716333,0.000716326,0.000716327,0.000716349,0.939879202,0.057255464,Healthcare,0.9964914,TRUE,22.25,0.328344363,4.666666667,0.246721969,6,0.764429903,,,0.446498745
8046,"Coronavirus (Covid-19) sepsis: revisiting mitochondrial dysfunction in pathogenesis, aging, inflammation, and mortality.",Inflamm Res,32767095,8/9/20,pubmed,0,1,immunome,0.786545521,0.030060777,0.001310437,0.072254558,0.001310363,0.108518346,Drug discovery,0.75727177,TRUE,30,0.432432432,11,0.371287129,18,0.891474782,,,0.565064781
8047,COVID-19 in solid organ transplant: A multi-center cohort study.,Clin Infect Dis,32766815,8/9/20,pubmed,0,34,logistic regression,0.001653025,0.001653026,0.057227107,0.001653036,0.001653121,0.936160686,Clinics,0.9532331,TRUE,50.58823529,0.635537139,43.14705882,0.64998662,44,0.952157541,,,0.745893767
8048,High SARS-CoV-2 seroprevalence in Health Care Workers but relatively low numbers of deaths in urban Malawi.,medRxiv,32766597,8/9/20,pubmed,0,22,mathematical model,0.001187267,0.193916573,0.001187352,0.537591348,0.165431178,0.100686283,Epidemiology,0.38456926,FALSE,8.045454545,0.118931288,4.045454545,0.231736687,27,0.92443978,,,0.425035919
8049,Baseline Cardiometabolic Profiles and SARS-CoV-2 Risk in the UK Biobank.,medRxiv,32766593,8/9/20,pubmed,0,6,logistic regression,0.002130912,0.002130799,0.002130705,0.06906839,0.30854594,0.615993254,Clinics,0.47918242,FALSE,11.16666667,0.168161296,6.333333333,0.285121755,3,0.667819001,,,0.373700684
8050,Massive-scale biological activity-based modeling identifies novel antiviral leads against SARS-CoV-2.,bioRxiv,32766591,8/9/20,pubmed,0,16,in silico,0.965470138,0.001098876,0.030134469,0.001098858,0.001098856,0.001098802,Drug discovery,0.39313242,FALSE,64.25,0.729791576,115.9375,0.845598073,4,0.707574542,,,0.760988064
8051,0,bioRxiv,32766590,8/9/20,pubmed,0,17,computational,0.95066205,0.001254622,0.044319527,0.001254615,0.001254596,0.001254591,Drug discovery,0.49519217,FALSE,39.52941176,0.534355866,27.05882353,0.546160021,18,0.891474782,,,0.657330223
8052,Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories.,bioRxiv,32766585,8/9/20,pubmed,0,48,"transcriptom, multiom",0.690802444,0.21227691,0.002032935,0.002032823,0.002032761,0.090822128,Drug discovery,0.4980269,FALSE,66.5625,0.744387408,311.1666667,0.957653198,10,0.828199272,,,0.843413292
8053,"Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.",bioRxiv,32766581,8/9/20,pubmed,0,8,machine learning,0.456266148,0.237760156,0.050406574,0.068334279,0.000956356,0.186276487,Drug discovery,0.2911418,FALSE,72.25,0.774135692,64.625,0.737423067,2,0.618927094,,,0.710161951
8054,Oligonucleotide capture sequencing of the SARS-CoV-2 genome and subgenomic fragments from COVID-19 individuals.,bioRxiv,32766579,8/9/20,pubmed,0,21,"sequencing, transcriptom, genomes",0.071430697,0.881889931,0.001622754,0.001622769,0.001622716,0.041811134,Genomics,0.31007105,FALSE,87.38095238,0.834745501,375.9047619,0.968624565,6,0.764429903,,,0.855933323
8055,COVID-19 in Wuhan: Sociodemographic characteristics and hospital support measures associated with the immediate psychological impact on healthcare workers.,EClinicalMedicine,32766545,8/9/20,pubmed,0,16,logistic regression,0.00093607,0.000936087,0.000936073,0.000936096,0.995319531,0.000936143,Healthcare,0.8446573,TRUE,101.25,0.87142062,32.3125,0.587837838,27,0.92443978,,,0.794566079
8056,Scoring systems for predicting mortality for severe patients with COVID-19.,EClinicalMedicine,32766541,8/9/20,pubmed,0,12,"logistic regression, prediction model",0.000898107,0.054197942,0.021764314,0.039479104,0.000898101,0.882762432,Clinics,0.88136536,TRUE,29.16666667,0.420619704,14.41666667,0.416711266,29,0.928020248,,,0.588450406
8057,Economic resilience dataset in facing physical distancing during COVID-19 global pandemic.,Data Brief,32766414,8/9/20,pubmed,0,2,dataset,0.001371272,0.001371265,0.001371292,0.64068901,0.353825895,0.001371266,Epidemiology,0.8347688,TRUE,29,0.41993939,0,0.055525823,0,0.403234768,,,0.292899994
8058,COVID TV-UNet: Segmenting COVID-19 Chest CT Images Using Connectivity Imposed U-Net.,ArXiv,32766387,8/9/20,pubmed,0,5,dataset,0.001272664,0.0012727,0.99363655,0.001272739,0.001272667,0.001272681,Imaging,0.006267071,FALSE,136.2,0.927948544,269.4,0.94574525,6,0.764429903,,,0.879374566
8059,"Renin-Angiotensin-Aldosterone System Blockers Are Not Associated With Coronavirus Disease 2019 (COVID-19) Hospitalization: Study of 1,439 UK Biobank Cases.",Front Cardiovasc Med,32766285,8/9/20,pubmed,0,8,logistic regression,0.043449369,0.001371241,0.001371251,0.00137123,0.288514135,0.663922774,Clinics,0.76860976,TRUE,64.375,0.730904818,59.875,0.72029703,10,0.828199272,,,0.759800373
8060,COVID 19 - Clinical Picture in the Elderly Population: A Qualitative Systematic Review.,Aging Dis,32765959,8/9/20,pubmed,0,6,predictive model,0.00114136,0.071632414,0.059881225,0.207050541,0.001141379,0.659153081,Clinics,0.13673604,FALSE,25.5,0.37435834,6.833333333,0.293484078,10,0.828199272,,,0.498680563
8061,Clinical features and risk factors for ICU admission in COVID-19 patients with cardiovascular diseases.,Aging Dis,32765943,8/9/20,pubmed,0,10,logistic regression,0.00165305,0.001653035,0.001653045,0.001653053,0.001653058,0.991734759,Clinics,0.928175,TRUE,24,0.35574247,2.5,0.180826866,3,0.667819001,,,0.401462779
8062,0,F1000Res,32765844,8/9/20,pubmed,0,4,in silico,0.993814523,0.001237097,0.001237092,0.001237116,0.001237065,0.001237107,Drug discovery,0.9145485,TRUE,3.25,0.039148989,3.75,0.21982874,4,0.707574542,,,0.32218409
8063,Genus-specific pattern of intrinsically disordered central regions in the nucleocapsid protein of coronaviruses.,Comput Struct Biotechnol J,32765822,8/9/20,pubmed,0,1,bioinformatic,0.001823463,0.881880253,0.001823329,0.001823387,0.110826201,0.001823367,Genomics,0.8952857,TRUE,167,0.956274352,297,0.953438587,3,0.667819001,,,0.859177313
8064,Natural Polymorphisms Are Present in the Furin Cleavage Site of the SARS-CoV-2 Spike Glycoprotein.,Front Genet,32765596,8/9/20,pubmed,0,4,genome sequences,0.414342631,0.573671478,0.002996401,0.002996463,0.002996572,0.002996455,Genomics,0.33785018,FALSE,42.5,0.562743522,12,0.386740701,11,0.840175319,,,0.596553181
8065,"Epigenetic Regulator miRNA Pattern Differences Among SARS-CoV, SARS-CoV-2, and SARS-CoV-2 World-Wide Isolates Delineated the Mystery Behind the Epic Pathogenicity and Distinct Clinical Characteristics of Pandemic COVID-19.",Front Genet,32765592,8/9/20,pubmed,0,4,"computational, in silico, genomes",0.743535324,0.150589778,0.000946141,0.000946144,0.00094611,0.103036504,Drug discovery,0.888991,TRUE,13.25,0.199950523,6,0.280037463,3,0.667819001,,,0.382602329
8066,Dynamic Interleukin-6 Level Changes as a Prognostic Indicator in Patients With COVID-19.,Front Pharmacol,32765283,8/9/20,pubmed,0,13,logistic regression,0.00108535,0.001085326,0.050318854,0.001085369,0.001085355,0.945339746,Clinics,0.9369334,TRUE,40.07692308,0.53998392,10.46153846,0.361453037,10,0.828199272,,,0.57654541
8067,The actual implementation status of self-isolation among Japanese workers during the COVID-19 outbreak.,Trop Med Health,32765185,8/9/20,pubmed,0,13,logistic regression,0.03157828,0.000999513,0.000999507,0.000999606,0.964423531,0.000999564,Healthcare,0.3190118,FALSE,84.53846154,0.82355124,34.15384615,0.599678887,2,0.618927094,,,0.680719074
8068,"Characteristics, Outcomes and Indicators of Severity for COVID-19 Among Sample of ESNA Quarantine Hospital's Patients, Egypt: A Retrospective Study.",Infect Drug Resist,32765012,8/9/20,pubmed,0,8,logistic regression,0.001010978,0.02545863,0.184423271,0.001010947,0.001010958,0.787085216,Clinics,0.90908134,TRUE,14.625,0.220421795,0.875,0.103826599,12,0.850299401,,,0.391515932
8069,Single-cell analysis of two severe COVID-19 patients reveals a monocyte-associated and tocilizumab-responding cytokine storm.,Nat Commun,32764665,8/9/20,pubmed,0,24,dataset,0.898534427,0.001943509,0.001943523,0.001943509,0.001943471,0.093691561,Drug discovery,0.6848499,TRUE,83.54166667,0.819283815,65.04166667,0.739095531,43,0.9507377,,,0.836372349
8070,"Phosphate Derivatives of 3-Carboxyacylbetulin: SynThesis, In Vitro Anti-HIV and Molecular Docking Study.",Biomolecules,32764519,8/9/20,pubmed,0,7,in silico,0.990883045,0.00182337,0.001823326,0.001823366,0.001823408,0.001823484,Drug discovery,0.6603881,TRUE,30.14285714,0.433360134,2.857142857,0.189925074,0,0.403234768,,,0.342173325
8071,Comprehensive Analyses of SARS-CoV-2 Transmission in a Public Health Virology Laboratory.,Viruses,32764372,8/9/20,pubmed,0,9,genomes,0.00120344,0.514933069,0.001203423,0.001203493,0.480253142,0.001203433,Genomics,0.4493779,FALSE,65.33333333,0.736285485,42,0.644902328,3,0.667819001,,,0.683002271
8072,Winter Is Coming: A Southern Hemisphere Perspective of the Environmental Drivers of SARS-CoV-2 and the Potential Seasonality of COVID-19.,Int J Environ Res Public Health,32764257,8/9/20,pubmed,0,6,dataset,0.00137129,0.223789999,0.001371288,0.770724838,0.001371302,0.001371282,Epidemiology,0.61700386,TRUE,34.83333333,0.485496939,55.5,0.704575863,28,0.926168282,,,0.705413695
8073,Marked Elevation of Lipase in COVID-19 Disease: A Cohort Study.,Clin Transl Gastroenterol,32764201,8/9/20,pubmed,0,7,logistic regression,0.001538201,0.001538098,0.001538108,0.034182532,0.048886405,0.912316656,Clinics,0.97971356,TRUE,46.42857143,0.600098955,32.14285714,0.58629917,9,0.814309525,,,0.66690255
8074,"Sex, age, and hospitalization drive antibody responses in a COVID-19 convalescent plasma donor population.",J Clin Invest,32764200,8/9/20,pubmed,0,23,predictive model,0.263776885,0.484229783,0.001486421,0.001486443,0.001486468,0.247534001,Genomics,0.39561725,FALSE,117,0.902900612,169.2173913,0.89858175,12,0.850299401,,,0.883927255
8075,"Genome Sequences of Six SARS-CoV-2 Strains Isolated in Morocco, Obtained Using Oxford Nanopore MinION Technology.",Microbiol Resour Announc,32763945,8/9/20,pubmed,0,22,"genome sequences, genomes",0.005352848,0.909810093,0.00535288,0.005352892,0.005352768,0.06877852,Genomics,0.74399745,TRUE,26.36363636,0.386294762,5.5,0.267259834,1,0.537564047,,,0.397039548
8076,Dynamics and Development of the COVID-19 Epidemic in the United States: A Compartmental Model Enhanced With Deep Learning Techniques.,J Med Internet Res,32763892,8/9/20,pubmed,0,1,deep learning,0.001291201,0.00129121,0.158703611,0.836131531,0.001291196,0.001291251,Epidemiology,0.58150804,TRUE,34,0.477766096,0,0.055525823,0,0.403234768,,,0.312175562
8077,Regulatory Sandboxes: A Cure for mHealth Pilotitis?,J Med Internet Res,32763889,8/9/20,pubmed,0,4,digital health,0.001461886,0.001461878,0.001461955,0.99269045,0.001461945,0.001461886,Epidemiology,0.78177214,TRUE,19,0.285793803,8,0.320511105,0,0.403234768,,,0.336513225
8078,Existing highly accumulating lysosomotropic drugs with potential for repurposing to target COVID-19.,Biomed Pharmacother,32763818,8/9/20,pubmed,0,4,in silico,0.954879566,0.001438156,0.001438184,0.001438232,0.001438197,0.039367665,Drug discovery,0.76839316,TRUE,7.25,0.104149917,1,0.122023013,3,0.667819001,,,0.29799731
8079,Coronavirus diseases 2019: Current biological situation and potential therapeutic perspective.,Eur J Pharmacol,32763302,8/9/20,pubmed,0,10,genome sequences,0.593091386,0.317171587,0.002422279,0.082469844,0.002422318,0.002422587,Drug discovery,0.6759878,TRUE,31.6,0.451048302,23.1,0.513580412,4,0.707574542,,,0.557401085
8080,Do olfactory and gustatory psychophysical scores have prognostic value in COVID-19 patients? A prospective study of 106 patients.,J Otolaryngol Head Neck Surg,32762737,8/9/20,pubmed,0,11,logistic regression,0.001156243,0.001156264,0.001156293,0.03080754,0.001156288,0.964567371,Clinics,0.98207456,TRUE,76.27272727,0.791390933,40.45454545,0.636406208,15,0.874313229,,,0.767370124
8081,0,J Biomol Struct Dyn,32762537,8/9/20,pubmed,0,2,virtual screening,0.959417925,0.033969786,0.00165305,0.001653165,0.001653042,0.001653032,Drug discovery,0.809716,TRUE,17.5,0.263776362,5,0.257024351,4,0.707574542,,,0.409458418
8082,0,J Biomol Struct Dyn,32762511,8/9/20,pubmed,0,1,in silico,0.968844534,0.001653081,0.001653059,0.001653103,0.001653077,0.024543145,Drug discovery,0.9901271,TRUE,29,0.41993939,0,0.055525823,17,0.887338725,,,0.454267979
8083,"Effect of mutation on structure, function and dynamics of receptor binding domain of human SARS-CoV-2 with host cell receptor ACE2: a molecular dynamics simulations study.",J Biomol Struct Dyn,32762417,8/9/20,pubmed,0,4,"molecular dynamics simulation, computational, in silico",0.960943874,0.016551802,0.00101094,0.001011008,0.001010959,0.019471417,Drug discovery,0.6450719,TRUE,41.5,0.553219123,16.75,0.446012845,9,0.814309525,,,0.604513831
8084,0,J Biomol Struct Dyn,32762411,8/9/20,pubmed,0,4,computational,0.978159895,0.001220041,0.001219999,0.001220024,0.001220009,0.016960033,Drug discovery,0.7228834,TRUE,49.5,0.628362917,20,0.481000803,10,0.828199272,,,0.64585433
8085,Communication for Awareness and Action on Inequitable Impacts of COVID-19 on Latinos.,Health Promot Pract,32762369,8/9/20,pubmed,0,4,digital health,0.035730018,0.001538184,0.00153811,0.958117394,0.001538214,0.00153808,Epidemiology,0.94486,TRUE,89.25,0.840064321,79.75,0.778498796,0,0.403234768,,,0.673932628
8086,"Fear and Psychopathology During the COVID-19 Crisis: Neuroticism, Hypochondriasis, Reassurance-Seeking, and Coronaphobia as Fear Factors.",Omega (Westport),32762291,8/9/20,pubmed,0,2,logistic regression,0.003101624,0.003101595,0.003101621,0.003101647,0.924239386,0.063354127,Healthcare,0.89731985,TRUE,30.5,0.438493413,37.5,0.619213273,8,0.799987654,,,0.619231447
8087,"Clinical Course and Factors Associated With Hospitalization and Critical Illness Among COVID-19 Patients in Chicago, Illinois.",Acad Emerg Med,32762106,8/8/20,pubmed,0,5,logistic regression,0.001220038,0.001220021,0.001220011,0.001220037,0.001220039,0.993899854,Clinics,0.9858953,TRUE,89.8,0.84198157,56.4,0.708455981,11,0.840175319,,,0.796870957
8088,The arrival and spread of SARS-CoV-2 in Colombia.,J Med Virol,32761908,8/8/20,pubmed,0,24,phylogenom,0.00928409,0.95357861,0.009284172,0.009284476,0.00928436,0.009284292,Genomics,0.6296086,TRUE,29,0.41993939,27.75,0.551645705,10,0.828199272,,,0.599928122
8089,COVID-19 outbreak: The impact of stress on seizures in patients with epilepsy.,Epilepsia,32761900,8/8/20,pubmed,0,9,logistic regression,0.019817954,0.001220033,0.001219989,0.001220039,0.635556342,0.340965642,Healthcare,0.9841208,TRUE,104.2222222,0.877914528,32.44444444,0.588841317,12,0.850299401,,,0.772351749
8090,Abdominopelvic CT findings in patients with novel coronavirus disease 2019 (COVID-19).,Abdom Radiol (NY),32761402,8/8/20,pubmed,0,6,logistic regression,0.001415114,0.026461927,0.24675945,0.001415114,0.001415165,0.722533229,Clinics,0.67609787,TRUE,26.66666667,0.390995114,19,0.471367407,8,0.799987654,,,0.554116725
8091,"A citizen science initiative for open data and visualization of COVID-19 outbreak in Kerala, India.",J Am Med Inform Assoc,32761211,8/8/20,pubmed,0,16,dataset,0.001684549,0.001684509,0.0016846,0.991577286,0.001684545,0.001684511,Epidemiology,0.7372581,TRUE,7.5625,0.108788422,1,0.122023013,1,0.537564047,,,0.256125161
8092,Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging.,Front Med (Lausanne),32760732,8/8/20,pubmed,0,12,"deep learning, neural network, classifier",0.001272617,0.00127268,0.993636752,0.001272664,0.001272641,0.001272645,Imaging,0.27511093,FALSE,76.08333333,0.790710619,38.25,0.622892695,18,0.891474782,,,0.768359365
8093,Acceptance of a COVID-19 Vaccine in Southeast Asia: A Cross-Sectional Study in Indonesia.,Front Public Health,32760691,8/8/20,pubmed,0,10,logistic regression,0.063788812,0.001861699,0.001861704,0.00186178,0.928764318,0.001861686,Healthcare,0.383935,FALSE,44.8,0.584142495,16.3,0.439322986,42,0.949503056,,,0.657656179
8094,The Reference Model: An Initial Use Case for COVID-19.,Cureus,32760637,8/8/20,pubmed,0,1,"machine learning, computational",0.001786611,0.001786597,0.173879661,0.775352409,0.001786564,0.045408159,Epidemiology,0.45739612,FALSE,38,0.519327107,13,0.400521809,0,0.403234768,,,0.441027895
8095,Bioinformatic Analysis of Correlation between Immune Infiltration and COVID-19 in Cancer Patients.,Int J Biol Sci,32760213,8/8/20,pubmed,0,7,bioinformatic,0.598347217,0.001751216,0.001751227,0.05608253,0.001751209,0.340316602,Drug discovery,0.8538718,TRUE,91.85714286,0.848475478,85.42857143,0.791209526,3,0.667819001,,,0.769168002
8096,Glucocorticoids improve severe or critical COVID-19 by activating ACE2 and reducing IL-6 levels.,Int J Biol Sci,32760206,8/8/20,pubmed,0,11,bioinformatic,0.677521904,0.001220058,0.096909201,0.020906263,0.001220082,0.202222492,Drug discovery,0.60591066,TRUE,63.18181818,0.722493661,31.27272727,0.579609312,15,0.874313229,,,0.725472067
8097,Feasibility study of mitigation and suppression strategies for controlling COVID-19 outbreaks in London and Wuhan.,PLoS One,32760081,8/8/20,pubmed,0,8,"mathematical model, dataset",0.000830666,0.00083066,0.015541298,0.981136024,0.000830675,0.000830677,Epidemiology,0.2017861,FALSE,49,0.624281032,20.875,0.48963072,10,0.828199272,,,0.647370341
8098,Factors Associated with Mental Health Results among Workers with Income Losses Exposed to COVID-19 in China.,Int J Environ Res Public Health,32759877,8/8/20,pubmed,0,5,logistic regression,0.001684469,0.001684502,0.001684499,0.001684548,0.967676663,0.025585319,Healthcare,0.8581835,TRUE,56.8,0.680314181,25.6,0.533716885,6,0.764429903,,,0.65948699
8099,Use of Whole Genome Sequencing Data for a First in Silico Specificity Evaluation of the RT-qPCR Assays Used for SARS-CoV-2 Detection.,Int J Mol Sci,32759818,8/8/20,pubmed,0,8,"bioinformatic, sequencing, in silico, whole genome, genomes",0.097438778,0.752713555,0.145067079,0.001593551,0.001593527,0.00159351,Genomics,0.7734118,TRUE,35,0.488032655,50,0.681763447,1,0.537564047,,,0.56912005
8100,Racial and/or Ethnic and Socioeconomic Disparities of SARS-CoV-2 Infection Among Children.,Pediatrics,32759379,8/8/20,pubmed,0,7,logistic regression,0.043061741,0.001593542,0.001593518,0.001593565,0.886763827,0.065393807,Healthcare,0.24713415,FALSE,66.28571429,0.742470159,65.14285714,0.739363126,21,0.903944688,,,0.795259324
8101,Immunoinformatic approach to assess SARS-CoV-2 protein S epitopes recognised by the most frequent MHC-I alleles in the Brazilian population.,J Clin Pathol,32759312,8/8/20,pubmed,0,8,in silico,0.628199571,0.196901435,0.033497326,0.097757636,0.001330071,0.04231396,Drug discovery,0.80883986,TRUE,73.5,0.780011132,39.75,0.631924003,1,0.537564047,,,0.649833061
8102,Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic.,J Med Internet Res,32759101,8/8/20,pubmed,0,70,text mining,0.001220094,0.001220065,0.285646976,0.498291516,0.001220071,0.212401277,Epidemiology,0.35767612,FALSE,55.81818182,0.673387346,32.63636364,0.589777897,6,0.764429903,,,0.675865049
8103,"Viruses in wastewater: occurrence, abundance and detection methods.",Sci Total Environ,32758747,8/8/20,pubmed,0,9,"sequencing, metagenom",0.092297601,0.629716915,0.001861835,0.272399948,0.001861799,0.001861901,Genomics,0.8828238,TRUE,77.33333333,0.795967592,49.11111111,0.677548836,21,0.903944688,,,0.792487039
8104,In silico molecular docking analysis for repurposing therapeutics against multiple proteins from SARS-CoV-2.,Eur J Pharmacol,32758569,8/8/20,pubmed,0,4,in silico,0.993814707,0.001237074,0.001237055,0.001237081,0.001237036,0.001237047,Drug discovery,0.87059903,TRUE,15.75,0.237862577,15,0.42594327,11,0.840175319,,,0.501327055
8105,Evidence-based crisis standards of care for out-of-hospital cardiac arrests in a pandemic.,Resuscitation,32758516,8/8/20,pubmed,0,7,dataset,0.046366911,0.000946123,0.00094615,0.422648371,0.000946141,0.528146304,Clinics,0.5205553,TRUE,36.85714286,0.506710372,17.85714286,0.459325662,0,0.403234768,,,0.456423601
8106,Using Virus Sequencing to Determine Source of SARS-CoV-2 Transmission for Healthcare Worker.,Emerg Infect Dis,32758345,8/8/20,pubmed,0,5,sequencing,0.004530615,0.116889815,0.004530865,0.004530702,0.707374522,0.162143482,Healthcare,0.50572616,TRUE,211.2,0.975941617,256.4,0.940259566,4,0.707574542,,,0.874591908
8107,"Imaging of COVID-19 pneumonia: Patterns, pathogenesis, and advances.",Br J Radiol,32758014,8/8/20,pubmed,0,7,artificial intelligence,0.001371266,0.02481841,0.883216851,0.001371313,0.0013713,0.08785086,Imaging,0.35409498,FALSE,124,0.912981632,129.2857143,0.862924806,4,0.707574542,,,0.827826993
8108,0,OMICS,32757981,8/8/20,pubmed,0,6,in silico,0.989083548,0.002183245,0.002183238,0.002183329,0.002183237,0.002183404,Drug discovery,0.95204926,TRUE,39.5,0.534232173,8.5,0.329141022,7,0.785110192,,,0.549494462
8109,Smoking and COVID-19: Similar bronchial ACE2 and TMPRSS2 expression and higher TMPRSS4 expression in current versus never smokers.,Drug Dev Res,32757420,8/7/20,pubmed,0,2,"data mining, genome-wide",0.628559378,0.053055848,0.00171724,0.190046416,0.001717226,0.124903892,Drug discovery,0.4751588,FALSE,89,0.839384006,102.5,0.826933369,6,0.764429903,,,0.810249093
8110,Unique SARS-CoV-2 clusters causing a large COVID-19 outbreak in Hong Kong.,Clin Infect Dis,32756996,8/7/20,pubmed,0,19,genomes,0.006539579,0.641376908,0.006539605,0.33246448,0.006539821,0.006539606,Genomics,0.56410015,TRUE,36.68421053,0.505164203,197.8947368,0.916845063,11,0.840175319,,,0.754061528
8111,Bayesian inference of COVID-19 spreading rates in South Africa.,PLoS One,32756608,8/7/20,pubmed,0,2,bayes,0.001622746,0.001622851,0.050562383,0.942946182,0.001622762,0.001623076,Epidemiology,0.26399282,FALSE,269,0.987506958,66,0.742574257,3,0.667819001,,,0.799300072
8112,Development and simulation of fully glycosylated molecular models of ACE2-Fc fusion proteins and their interaction with the SARS-CoV-2 spike protein binding domain.,PLoS One,32756606,8/7/20,pubmed,0,7,"molecular dynamics simulation, computational",0.885330867,0.00333528,0.10132797,0.003335364,0.003335284,0.003335236,Drug discovery,0.8151186,TRUE,57.14285714,0.682726204,23,0.513513514,1,0.537564047,,,0.577934588
8113,Dietary and Lifestyle Changes During COVID-19 and the Subsequent Lockdowns among Polish Adults: A Cross-Sectional Online Survey PLifeCOVID-19 Study.,Nutrients,32756458,8/7/20,pubmed,0,4,logistic regression,0.001717217,0.001717264,0.001717448,0.156210894,0.836919944,0.001717232,Healthcare,0.88710785,TRUE,63.5,0.72496753,13.25,0.402997056,26,0.920859312,,,0.6829413
8114,What Happened to People with Non-Communicable Diseases during COVID-19: Implications of H-EDRM Policies.,Int J Environ Res Public Health,32756382,8/7/20,pubmed,0,10,dataset,0.001987151,0.00198712,0.001987206,0.184747351,0.80730397,0.001987201,Healthcare,0.92193615,TRUE,56.2,0.676294143,50.1,0.681964142,2,0.618927094,,,0.659061793
8115,Gut Microbiota and Liver Interaction through Immune System Cross-Talk: A Comprehensive Review at the Time of the SARS-CoV-2 Pandemic.,J Clin Med,32756323,8/7/20,pubmed,0,9,"microbiom, virom",0.616005766,0.069717179,0.001622782,0.001622858,0.001622771,0.309408644,Drug discovery,0.9802488,TRUE,136.1111111,0.927701157,52.44444444,0.690527161,3,0.667819001,,,0.762015773
8116,How artificial intelligence (AI) could have helped our medical education during the COVID-19 pandemic - A student's perspective.,Med Teach,32755431,8/7/20,pubmed,0,2,artificial intelligence,0.013549021,0.013548946,0.541220785,0.013550027,0.404582382,0.013548839,Healthcare,0.686563,TRUE,9.5,0.143051518,6,0.280037463,0,0.403234768,,,0.27544125
8117,MDM2-Mediated Ubiquitination of Angiotensin-Converting Enzyme 2 Contributes to the Development of Pulmonary Arterial Hypertension.,Circulation,32755395,8/7/20,pubmed,0,15,bioinformatic,0.687294803,0.038980146,0.001593521,0.001593561,0.001593509,0.26894446,Drug discovery,0.893846,TRUE,53.33333333,0.656255798,87.66666667,0.796293819,1,0.537564047,,,0.663371221
8118,SARS-CoV-2 may regulate cellular responses through depletion of specific host miRNAs.,Am J Physiol Lung Cell Mol Physiol,32755307,8/7/20,pubmed,0,7,"bioinformatic, genomes",0.2009745,0.788775016,0.002562585,0.002562699,0.002562668,0.002562532,Genomics,0.7233554,TRUE,102.8571429,0.875007731,81.71428571,0.782378914,9,0.814309525,,,0.823898723
8119,Multisystem Assessment of the Imaging Manifestations of Coagulopathy in Hospitalized Patients With Coronavirus Disease (COVID-19).,AJR Am J Roentgenol,32755217,8/7/20,pubmed,0,4,logistic regression,0.000936112,0.000936107,0.362807376,0.000936085,0.000936087,0.633448233,Clinics,0.94946015,TRUE,66,0.741356918,24.75,0.526491838,2,0.618927094,,,0.628925283
8120,CT Manifestations of Coronavirus Disease (COVID-19) Pneumonia and Influenza Virus Pneumonia: A Comparative Study.,AJR Am J Roentgenol,32755175,8/7/20,pubmed,0,7,artificial intelligence,0.000977459,0.079091409,0.537074887,0.00097748,0.000977473,0.380901294,Imaging,0.9563699,TRUE,11.85714286,0.178551549,2.428571429,0.175274284,9,0.814309525,,,0.389378453
8121,Datasets on how misinformation promotes immune perception of COVID-19 pandemic in Africa.,Data Brief,32754628,8/6/20,pubmed,0,2,dataset,0.165763525,0.003466026,0.003466231,0.312491665,0.511346588,0.003465965,Healthcare,0.7303341,TRUE,21,0.312016822,5,0.257024351,0,0.403234768,,,0.32409198
8122,Potential mechanism prediction of Cold-Damp Plague Formula against COVID-19 via network pharmacology analysis and molecular docking.,Chin Med,32754224,8/6/20,pubmed,0,9,genomes,0.962469784,0.015625444,0.019412819,0.000830643,0.000830646,0.000830663,Drug discovery,0.8633944,TRUE,20.66666667,0.306512462,6.111111111,0.280438855,3,0.667819001,,,0.418256773
8123,0,Front Immunol,32754161,8/6/20,pubmed,0,3,virtual screening,0.995058118,0.000988374,0.000988398,0.000988388,0.000988373,0.000988348,Drug discovery,0.98419094,TRUE,101.6666667,0.872595708,43,0.649785925,45,0.953206988,,,0.825196207
8124,SARS-CoV-2 Molecular Network Structure.,Front Physiol,32754056,8/6/20,pubmed,0,1,"interactom, network model",0.868120298,0.002562705,0.002562717,0.002562844,0.121628691,0.002562745,Drug discovery,0.42740268,FALSE,325,0.992083617,22,0.503746321,7,0.785110192,,,0.760313376
8125,Leveraging IoTs and Machine Learning for Patient Diagnosis and Ventilation Management in the Intensive Care Unit.,IEEE Pervasive Comput,32754005,8/6/20,pubmed,0,8,machine learning,0.00229655,0.002296564,0.654145137,0.002296673,0.065546114,0.273418962,Clinics,0.28939107,FALSE,142.875,0.937287402,231.5,0.930224779,4,0.707574542,,,0.858362241
8126,Expansion of Single Cell Transcriptomics Data of SARS-CoV Infection in Human Bronchial Epithelial Cells to COVID-19.,Biol Proced Online,32754004,8/6/20,pubmed,0,5,transcriptom,0.93623399,0.001085371,0.001085363,0.059424444,0.001085411,0.001085421,Drug discovery,0.9222079,TRUE,29,0.41993939,4.2,0.234211935,3,0.667819001,,,0.440656775
8127,Public Trust and Compliance with the Precautionary Measures Against COVID-19 Employed by Authorities in Saudi Arabia.,Risk Manag Healthc Policy,32753988,8/6/20,pubmed,0,5,logistic regression,0.001350376,0.001350345,0.001350328,0.158975893,0.835622681,0.001350377,Healthcare,0.8166488,TRUE,12,0.183190055,13,0.400521809,12,0.850299401,,,0.478003755
8128,Psychological Distress Amongst Health Workers and the General Public During the COVID-19 Pandemic in Saudi Arabia.,Risk Manag Healthc Policy,32753986,8/6/20,pubmed,0,7,logistic regression,0.001511875,0.00151186,0.001511939,0.11090186,0.883050575,0.001511891,Healthcare,0.90906495,TRUE,11.71428571,0.1766343,4.142857143,0.232472572,19,0.89561084,,,0.434905904
8129,A novel Monte Carlo simulation procedure for modelling COVID-19 spread over time.,Sci Rep,32753639,8/6/20,pubmed,0,1,simulation model,0.002130763,0.002130654,0.028018478,0.963458769,0.002130659,0.002130677,Epidemiology,0.18247646,FALSE,1,0.012307502,0,0.055525823,4,0.707574542,,,0.258469289
8130,Is Nigeria really on top of COVID-19? Message from effective reproduction number.,Epidemiol Infect,32753078,8/6/20,pubmed,0,4,bayes,0.001538178,0.001538112,0.001538073,0.992309376,0.001538118,0.001538142,Epidemiology,0.15273458,FALSE,69.25,0.760034634,26.75,0.543617875,3,0.667819001,,,0.65715717
8131,Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2.,J R Soc Interface,32752997,8/6/20,pubmed,0,3,"deep learning, mathematical model",0.00263899,0.002639053,0.322522945,0.666921058,0.002638979,0.002638975,Epidemiology,0.51098806,TRUE,145.6666667,0.940194199,180.3333333,0.906341986,10,0.828199272,,,0.891578485
8132,"Molecular docking, molecular dynamics simulations and reactivity, studies on approved drugs library targeting ACE2 and SARS-CoV-2 binding with ACE2.",J Biomol Struct Dyn,32752951,8/6/20,pubmed,0,3,molecular dynamics simulation,0.964298989,0.001684556,0.001684537,0.028962922,0.00168447,0.001684526,Drug discovery,0.9056446,TRUE,8.333333333,0.123693488,0,0.055525823,3,0.667819001,,,0.282346104
8133,"Predictive modeling by deep learning, virtual screening and molecular dynamics study of natural compounds against SARS-CoV-2 main protease.",J Biomol Struct Dyn,32752947,8/6/20,pubmed,0,6,"virtual screening, deep learning, predictive model, deep-learning, dataset",0.759920364,0.001171578,0.235393348,0.001171582,0.001171558,0.00117157,Drug discovery,0.5152964,TRUE,20.83333333,0.307996784,1,0.122023013,3,0.667819001,,,0.365946266
8134,In silico identification of widely used and well-tolerated drugs as potential SARS-CoV-2 3C-like protease and viral RNA-dependent RNA polymerase inhibitors for direct use in clinical trials.,J Biomol Struct Dyn,32752938,8/6/20,pubmed,0,6,in silico,0.922145578,0.001511844,0.00151181,0.071807081,0.001511884,0.001511804,Drug discovery,0.9636264,TRUE,13.33333333,0.201558538,4.333333333,0.237958255,5,0.739490092,,,0.393002295
8135,Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India.,Chaos,32752627,8/6/20,pubmed,0,2,mathematical model,0.07109212,0.001987115,0.001987107,0.920959472,0.001987105,0.001987081,Epidemiology,0.49308723,FALSE,18,0.271569052,6,0.280037463,27,0.92443978,,,0.492015432
8136,Association of Comorbidities With Pneumonia and Death Among COVID-19 Patients in Mexico: A Nationwide Cross-sectional Study.,J Prev Med Public Health,32752589,8/6/20,pubmed,0,4,logistic regression,0.00146186,0.001461872,0.001461882,0.074435485,0.001461951,0.91971695,Clinics,0.8096825,TRUE,45,0.587049292,5.5,0.267259834,4,0.707574542,,,0.520627889
8137,Food and COVID-19: Preventive/Co-therapeutic Strategies Explored by Current Clinical Trials and in Silico Studies.,Foods,32752217,8/6/20,pubmed,0,8,in silico,0.71273696,0.001565355,0.001565382,0.046167525,0.236399484,0.001565293,Drug discovery,0.811093,TRUE,87.75,0.836229822,92.875,0.808134868,5,0.739490092,,,0.794618261
8138,"Tobacco, but Not Nicotine and Flavor-Less Electronic Cigarettes, Induces ACE2 and Immune Dysregulation.",Int J Mol Sci,32752138,8/6/20,pubmed,0,7,dataset,0.758779906,0.002996645,0.002996541,0.22923339,0.00299661,0.002996909,Drug discovery,0.84572697,TRUE,70.57142857,0.766343002,40.28571429,0.635068237,0,0.403234768,,,0.601548669
8139,Point-of-Care Diagnostics of COVID-19: From Current Work to Future Perspectives.,Sensors (Basel),32752043,8/6/20,pubmed,0,4,proteom,0.026686075,0.544619269,0.354928374,0.001310388,0.001310382,0.071145513,Genomics,0.8371528,TRUE,28.5,0.412579628,8,0.320511105,11,0.840175319,,,0.524422017
8140,Risk for Depressive Symptoms among Hospitalized Women in High-Risk Pregnancy Units during the COVID-19 Pandemic.,J Clin Med,32751804,8/6/20,pubmed,0,9,logistic regression,0.00171716,0.001717185,0.001717172,0.001717259,0.620067948,0.373063277,Healthcare,0.9592805,TRUE,125.7777778,0.915022574,53.88888889,0.69668183,8,0.799987654,,,0.803897353
8141,COVID-19-Related Coagulopathy-Is Transferrin a Missing Link?,Diagnostics (Basel),32751741,8/6/20,pubmed,0,8,"proteom, dataset",0.740082481,0.001901752,0.001901742,0.001901763,0.001901779,0.252310483,Drug discovery,0.5757257,TRUE,86.5,0.831653163,154.5,0.887409687,1,0.537564047,,,0.752208966
8142,Changes of Physical Activity and Ultra-Processed Food Consumption in Adolescents from Different Countries during Covid-19 Pandemic: An Observational Study.,Nutrients,32751721,8/6/20,pubmed,0,21,logistic regression,0.001684493,0.001684562,0.001684481,0.141004674,0.85225726,0.00168453,Healthcare,0.8116454,TRUE,28.33333333,0.410724225,16.52380952,0.443002408,9,0.814309525,,,0.556012053
8143,Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia.,Int J Environ Res Public Health,32751669,8/6/20,pubmed,0,11,mathematical model,0.001751157,0.001751154,0.001751158,0.991244201,0.001751177,0.001751154,Epidemiology,0.22816202,FALSE,15.63636364,0.235574247,,,4,0.707574542,,,0.471574394
8144,Coronavirus-Related Health Literacy: A Cross-Sectional Study in Adults during the COVID-19 Infodemic in Germany.,Int J Environ Res Public Health,32751484,8/6/20,pubmed,0,6,model fit,0.002996614,0.002996567,0.201137052,0.002996665,0.78687666,0.002996442,Healthcare,0.80581295,TRUE,188.6666667,0.968396314,31,0.578204442,21,0.903944688,,,0.816848481
8145,"Face Masks and Respirators in the Fight against the COVID-19 Pandemic: A Review of Current Materials, Advances and Future Perspectives.",Materials (Basel),32751260,8/6/20,pubmed,0,9,artificial intelligence,0.130222975,0.001310389,0.079682759,0.786163125,0.001310402,0.001310351,Epidemiology,0.8533758,TRUE,38.33333333,0.522233904,24.88888889,0.527294621,12,0.850299401,,,0.633275976
8146,Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models.,IEEE J Biomed Health Inform,32750973,8/6/20,pubmed,0,7,"deep learning, dataset",0.001310404,0.001310344,0.957130543,0.001310389,0.037627852,0.001310467,Imaging,0.6472032,TRUE,52.14285714,0.647968334,14.42857143,0.41697886,2,0.618927094,,,0.56129143
8147,α-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.,IEEE J Biomed Health Inform,32750960,8/6/20,pubmed,0,10,"artificial intelligence, dataset",0.001203428,0.001203429,0.267325524,0.727860748,0.001203455,0.001203416,Epidemiology,0.41803753,FALSE,78.9,0.802028573,43.6,0.651926679,3,0.667819001,,,0.707258085
8148,Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach.,IEEE J Biomed Health Inform,32750931,8/6/20,pubmed,0,4,"computational, neural network, lstm",0.001786565,0.00178654,0.532068216,0.460785528,0.001786611,0.00178654,Epidemiology,0.32699183,FALSE,34.5,0.482404601,,,3,0.667819001,,,0.575111801
8149,Deep bidirectional classification model for COVID-19 disease infected patients.,IEEE/ACM Trans Comput Biol Bioinform,32750891,8/6/20,pubmed,0,3,dataset,0.001291217,0.00129128,0.804336587,0.19049846,0.001291215,0.001291239,Imaging,0.6499385,TRUE,45,0.587049292,3.333333333,0.206515922,3,0.667819001,,,0.487128072
8150,Approaches to screening for hyperglycaemia in pregnant women during and after the COVID-19 pandemic.,Diabet Med,32750184,8/5/20,pubmed,0,10,logistic regression,0.014386365,0.001034589,0.214415438,0.001034627,0.001034647,0.768094335,Clinics,0.98008573,TRUE,58.5,0.693240151,53.2,0.694407279,4,0.707574542,,,0.698407324
8151,Closing the COVID-19 Psychological Treatment Gap for Cancer Patients in Alberta: Protocol for the Implementation and Evaluation of Text4Hope-Cancer Care.,JMIR Res Protoc,32750012,8/5/20,pubmed,0,9,"machine learning, data mining",0.0006728,0.000672835,0.06800852,0.122149591,0.604016958,0.204479295,Healthcare,0.98975694,TRUE,30.77777778,0.44121467,14.66666667,0.420323789,2,0.618927094,,,0.493488518
8152,Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation.,J Med Internet Res,32750010,8/5/20,pubmed,0,15,dataset,0.001098856,0.001098889,0.21339115,0.12509548,0.06166871,0.597646915,Clinics,0.79556775,TRUE,38.06666667,0.519450801,17.6,0.456984212,3,0.667819001,,,0.548084671
8153,"Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram.",JMIR Public Health Surveill,32750006,8/5/20,pubmed,0,8,deep learning,0.035973879,0.00084656,0.224078798,0.737407664,0.000846565,0.000846534,Epidemiology,0.57924324,TRUE,54.25,0.663120787,26.125,0.538600482,2,0.618927094,,,0.606882788
8154,Digital Inequality During a Pandemic: Quantitative Study of Differences in COVID-19-Related Internet Uses and Outcomes Among the General Population.,J Med Internet Res,32750005,8/5/20,pubmed,0,1,dataset,0.00088021,0.000880213,0.00088023,0.103591493,0.892887646,0.000880208,Healthcare,0.9270347,TRUE,18,0.271569052,39,0.62784319,8,0.799987654,,,0.566466632
8155,Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence.,J Med Internet Res,32750001,8/5/20,pubmed,0,10,"machine learning, artificial intelligence, network analysis",0.001786551,0.001786625,0.122874167,0.682382047,0.189384079,0.001786531,Epidemiology,0.86876214,TRUE,34.7,0.48395077,25.9,0.536192133,10,0.828199272,,,0.616114058
8156,[Suicidality in adults].,Ned Tijdschr Geneeskd,32749822,8/5/20,pubmed,0,2,machine learning,0.002562825,0.002562616,0.13378903,0.540703075,0.317819776,0.002562678,Epidemiology,0.88122004,TRUE,18.5,0.278001113,11.5,0.378378378,0,0.403234768,,,0.353204753
8157,Exploring the growth of COVID-19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning.,Transbound Emerg Dis,32749759,8/5/20,pubmed,0,6,"machine learning, logistic regression",0.001511979,0.117249303,0.27570383,0.602511118,0.001511908,0.001511862,Epidemiology,0.4846759,FALSE,3.5,0.044344115,1,0.122023013,3,0.667819001,,,0.278062043
8158,Statistical issues in the development of COVID-19 prediction models.,J Med Virol,32749704,8/5/20,pubmed,0,2,prediction model,0.025060701,0.025060341,0.025063071,0.874693584,0.025061153,0.025061149,Epidemiology,0.5140265,TRUE,205.5,0.974024368,220.5,0.926812952,1,0.537564047,,,0.812800455
8159,SARS-CoV-2 nucleocapsid and Nsp3 binding: an in silico study.,Arch Microbiol,32749662,8/5/20,pubmed,0,13,in silico,0.682958365,0.297032143,0.001112622,0.001112643,0.001112688,0.016671539,Drug discovery,0.7777344,TRUE,29.92307692,0.429463789,9.230769231,0.339710998,4,0.707574542,,,0.492249776
8160,Neurological comorbidity and severity of COVID-19.,J Neurol,32749601,8/5/20,pubmed,0,14,logistic regression,0.001350511,0.001350532,0.001350392,0.001350372,0.078604902,0.915993291,Clinics,0.7884805,TRUE,81.92857143,0.814274228,78.07142857,0.773615199,8,0.799987654,,,0.795959027
8161,Evaluation of medicinal herbs as a potential therapeutic option against SARS-CoV-2 targeting its main protease.,Phytother Res,32748969,8/5/20,pubmed,0,6,in-silico,0.842552043,0.001987197,0.068555039,0.08293124,0.001987265,0.001987215,Drug discovery,0.7360208,TRUE,14,0.213494959,2,0.164302917,2,0.618927094,,,0.332241657
8162,Measurement Method for Evaluating the Lockdown Policies during the COVID-19 Pandemic.,Int J Environ Res Public Health,32748822,8/5/20,pubmed,0,4,machine learning,0.001823383,0.001823348,0.099417524,0.89328893,0.0018234,0.001823416,Epidemiology,0.6637247,TRUE,12.25,0.185107304,0.75,0.099411292,2,0.618927094,,,0.301148564
8163,Evidence-Based Considerations Exploring Relations between SARS-CoV-2 Pandemic and Air Pollution: Involvement of PM2.5-Mediated Up-Regulation of the Viral Receptor ACE-2.,Int J Environ Res Public Health,32748812,8/5/20,pubmed,0,8,"bioinformatic, correlation analysis",0.3348012,0.153839917,0.001350318,0.35207658,0.001350367,0.156581617,Epidemiology,0.38618582,FALSE,96.5,0.860226359,79.5,0.77782981,15,0.874313229,,,0.837456466
8164,Discovery of Potent SARS-CoV-2 Inhibitors from Approved Antiviral Drugs via Docking and Virtual Screening.,Comb Chem High Throughput Screen,32748740,8/5/20,pubmed,0,6,virtual screening,0.914755376,0.001034577,0.001034617,0.001034628,0.023174403,0.058966398,Drug discovery,0.9938328,TRUE,30.83333333,0.441833137,2,0.164302917,6,0.764429903,,,0.456855319
8165,Nanotechnology-based antiviral therapeutics.,Drug Deliv Transl Res,32748035,8/5/20,pubmed,0,2,mathematical model,0.719703373,0.001203483,0.077801126,0.198885183,0.001203429,0.001203406,Drug discovery,0.94215643,TRUE,4.5,0.061784897,0,0.055525823,14,0.866658436,,,0.327989719
8166,Increased Risk of Hospitalization and Death in Patients with COVID-19 and Pre-existing Noncommunicable Diseases and Modifiable Risk Factors in Mexico.,Arch Med Res,32747155,8/5/20,pubmed,0,7,logistic regression,0.001717191,0.001717204,0.001717205,0.12989908,0.00171734,0.86323198,Clinics,0.66486377,TRUE,8,0.118683901,1.285714286,0.128512176,17,0.887338725,,,0.378178267
8167,Development and validation of prognosis model of mortality risk in patients with COVID-19.,Epidemiol Infect,32746957,8/5/20,pubmed,0,17,logistic regression,0.001622746,0.001622824,0.354344873,0.001622707,0.001622717,0.639164133,Clinics,0.8028355,TRUE,79.64705882,0.805306451,73,0.761974846,8,0.799987654,,,0.78908965
8168,Abnormal immunity of non-survivors with COVID-19: predictors for mortality.,Infect Dis Poverty,32746940,8/5/20,pubmed,0,10,logistic regression,0.224630567,0.000759373,0.009766611,0.000759362,0.000759357,0.763324731,Clinics,0.9354745,TRUE,47.9,0.613643392,75.8,0.768530907,19,0.89561084,,,0.759261713
8169,A consideration of publication-derived immune-related associations in Coronavirus and related lung damaging diseases.,J Transl Med,32746922,8/5/20,pubmed,0,2,network analysis,0.422811429,0.110283379,0.001187348,0.258996456,0.06934343,0.137377959,Drug discovery,0.80456704,TRUE,29.5,0.426000371,10.5,0.363459995,1,0.537564047,,,0.442341471
8170,Dynamic changes in clinical and CT characteristics of COVID-19 cases with different exposure histories: a retrospective study.,BMC Infect Dis,32746805,8/5/20,pubmed,0,9,correlation analysis,0.000916724,0.000916773,0.180821944,0.081935749,0.000916777,0.734492033,Clinics,0.72504026,TRUE,36,0.498299215,10.22222222,0.35804121,1,0.537564047,,,0.464634824
8171,Deciphering the protein motion of S1 subunit in SARS-CoV-2 spike glycoprotein through integrated computational methods.,J Biomol Struct Dyn,32746720,8/5/20,pubmed,0,2,computational,0.524821139,0.001861778,0.041870675,0.427722947,0.001861762,0.0018617,Drug discovery,0.5547349,TRUE,11.5,0.17416043,0.5,0.087101953,4,0.707574542,,,0.322945642
8172,The Ethics of Emergent Health Technologies: Implications of the 21st Century Cures Act for Nursing.,Policy Polit Nurs Pract,32746711,8/5/20,pubmed,0,1,digital health,0.105550854,0.001310347,0.001310392,0.84204743,0.048470649,0.001310328,Epidemiology,0.6945193,TRUE,173,0.960603624,91,0.803987155,0,0.403234768,,,0.722608516
8173,Flavonoids with inhibitory activity against SARS-CoV-2 3CLpro.,J Enzyme Inhib Med Chem,32746637,8/5/20,pubmed,0,5,in silico,0.985017378,0.00299682,0.002996429,0.002996443,0.002996547,0.002996384,Drug discovery,0.97695065,TRUE,46,0.596882924,42.4,0.646641691,14,0.866658436,,,0.70339435
8174,Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data.,IEEE Trans Ultrason Ferroelectr Freq Control,32746195,8/4/20,pubmed,0,12,classifier,0.001565323,0.001565316,0.992173031,0.001565358,0.001565538,0.001565434,Imaging,0.97067547,TRUE,105.1666667,0.87939885,179.6666667,0.90614129,8,0.799987654,,,0.861842598
8175,Orientation of immobilized antigens on common surfaces by a simple computational model: Exposition of SARS-CoV-2 Spike protein RBD epitopes.,Biophys Chem,32745829,8/4/20,pubmed,0,4,computational,0.683255668,0.001786645,0.00178666,0.309597922,0.001786578,0.001786527,Drug discovery,0.2888841,FALSE,83.75,0.820211516,20,0.481000803,2,0.618927094,,,0.640046471
8176,Was school closure effective in mitigating coronavirus disease 2019 (COVID-19)? Time series analysis using Bayesian inference.,Int J Infect Dis,32745628,8/4/20,pubmed,0,3,bayes,0.001684489,0.001684497,0.001684506,0.991577418,0.001684578,0.001684511,Epidemiology,0.16228965,FALSE,53.66666667,0.658544128,14.33333333,0.415975381,14,0.866658436,,,0.647059315
8177,Transcriptomics reveal immune downregulation of newts overwhelmed by chytrid co-infection.,Mol Ecol,32745298,8/4/20,pubmed,0,2,"sequencing, transcriptom",0.482712483,0.424721774,0.002130746,0.055794334,0.002130706,0.032509957,Drug discovery,0.413842,FALSE,44.5,0.581977859,56.5,0.709124967,0,0.403234768,,,0.564779198
8178,The impact of COVID-19 on doctors' well-being: results of a web survey during the lockdown in Italy.,Eur Rev Med Pharmacol Sci,32744715,8/4/20,pubmed,0,8,logistic regression,0.001461878,0.001461908,0.001461882,0.195057712,0.777777476,0.022779144,Healthcare,0.8998134,TRUE,26,0.382398417,2.5,0.180826866,10,0.828199272,,,0.463808185
8179,Single-cell RNA sequencing analysis of human kidney reveals the presence of ACE2 receptor: A potential pathway of COVID-19 infection.,Mol Genet Genomic Med,32744436,8/4/20,pubmed,0,6,sequencing,0.578571085,0.216218506,0.001461924,0.001462016,0.001461902,0.200824567,Drug discovery,0.9697002,TRUE,5.833333333,0.081514008,1.5,0.138747659,9,0.814309525,,,0.344857064
8180,Application of Bayesian phylogenetic inference modelling for evolutionary genetic analysis and dynamic changes in 2019-nCoV.,Brief Bioinform,32743639,8/4/20,pubmed,0,8,"bayes, bioinformatic",0.147410854,0.846436468,0.001538197,0.00153817,0.001538214,0.001538097,Genomics,0.4995625,FALSE,55,0.668686994,34.5,0.602221033,0,0.403234768,,,0.558047598
8181,UV-C decontamination for N95 emergency reuse: Quantitative dose validation with photochromic indicators.,medRxiv,32743615,8/4/20,pubmed,0,5,radiom,0.00208075,0.002080649,0.398362923,0.422565297,0.00208062,0.17282976,Epidemiology,0.39872032,FALSE,43.4,0.571958686,20.6,0.487021675,3,0.667819001,,,0.575599788
8182,"Recent smell loss is the best predictor of COVID-19: a preregistered, cross-sectional study.",medRxiv,32743605,8/4/20,pubmed,0,129,logistic regression,0.001371313,0.0013714,0.183028081,0.001371368,0.406113882,0.406743956,Clinics,0.45545107,FALSE,42.89147287,0.566268786,,,10,0.828199272,,,0.697234029
8183,Mapping physical access to healthcare for older adults in sub-Saharan Africa: A cross-sectional analysis with implications for the COVID-19 response.,medRxiv,32743597,8/4/20,pubmed,0,7,dataset,0.000648129,0.000648139,0.00064815,0.283226662,0.5857998,0.12902912,Healthcare,0.48053107,FALSE,100.5714286,0.870245532,173,0.901659085,3,0.667819001,,,0.813241206
8184,Elucidation of cryptic and allosteric pockets within the SARS-CoV-2 protease.,bioRxiv,32743587,8/4/20,pubmed,0,3,"virtual screening, computational, in silico",0.984491721,0.003101829,0.003101598,0.003101636,0.00310181,0.003101406,Drug discovery,0.74351054,TRUE,71,0.769064259,36,0.611118544,7,0.785110192,,,0.721764332
8185,A deep learning framework for high-throughput mechanism-driven phenotype compound screening.,bioRxiv,32743586,8/4/20,pubmed,0,5,"deep learning, neural network,  omics, predictive model, dataset",0.645404545,0.000846532,0.351209298,0.000846538,0.000846525,0.000846562,Drug discovery,0.17071176,FALSE,31.2,0.446471643,23.6,0.517259834,0,0.403234768,,,0.455655415
8186,Progenitor identification and SARS-CoV-2 infection in long-term human distal lung organoid cultures.,bioRxiv,32743583,8/4/20,pubmed,0,34,sequencing,0.849503849,0.117042858,0.00093615,0.000936173,0.030644852,0.000936118,Drug discovery,0.23044744,FALSE,60.97058824,0.707959676,296.0588235,0.9528365,11,0.840175319,,,0.833657165
8187,Single-dose intranasal vaccination elicits systemic and mucosal immunity against SARS-CoV-2.,bioRxiv,32743568,8/4/20,pubmed,0,10,sequencing,0.855360187,0.137493431,0.001786558,0.001786653,0.00178655,0.001786621,Drug discovery,0.388628,FALSE,24.2,0.357350485,8,0.320511105,0,0.403234768,,,0.360365453
8188,Immuno-informatics approach for multi-epitope vaccine designing against SARS-CoV-2.,bioRxiv,32743567,8/4/20,pubmed,0,5,in silico,0.96515464,0.029965225,0.00122001,0.001220037,0.001220015,0.001220073,Drug discovery,0.6213999,TRUE,5,0.070752675,0.2,0.061145304,4,0.707574542,,,0.279824173
8189,Severe SARS-CoV-2 infection in humans is defined by a shift in the serum lipidome resulting in dysregulation of eicosanoid immune mediators.,Res Sq,32743565,8/4/20,pubmed,0,11,lipidom,0.564344566,0.002996701,0.002996652,0.00299662,0.002996577,0.423668884,Drug discovery,0.35518122,FALSE,63.90909091,0.727688787,98.18181818,0.819373829,0,0.403234768,,,0.650099128
8190,Prediction of Small Molecule Inhibitors Targeting the Severe Acute Respiratory Syndrome Coronavirus-2 RNA-dependent RNA Polymerase.,ACS Omega,32743211,8/4/20,pubmed,0,7,"computational, bioinformatic",0.987187167,0.002562648,0.002562591,0.002562544,0.00256252,0.002562529,Drug discovery,0.91117114,TRUE,25.85714286,0.378811306,6.714285714,0.291142628,4,0.707574542,,,0.459176158
8191,Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty Quantification.,ArXiv,32743021,8/4/20,pubmed,0,10,"bayes, mathematical model",0.00213066,0.002130708,0.002130787,0.989346297,0.002130783,0.002130764,Epidemiology,0.15091982,FALSE,28.7,0.414373183,31.5,0.581883864,3,0.667819001,,,0.554692016
8192,Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays.,IEEE Access,32742893,8/4/20,pubmed,0,6,"deep learning, neural network, ensemble learning",0.001565325,0.001565382,0.992173361,0.001565331,0.001565292,0.001565309,Imaging,0.60477614,TRUE,101.6666667,0.872595708,60.16666667,0.721768799,51,0.958392493,,,0.850919
8193,RdRp mutations are associated with SARS-CoV-2 genome evolution.,PeerJ,32742818,8/4/20,pubmed,0,4,genome sequences,0.002032905,0.949210159,0.002032757,0.042658503,0.002032926,0.00203275,Genomics,0.39488223,FALSE,18,0.271569052,1,0.122023013,1,0.537564047,,,0.310385371
8194,"Understanding genomic diversity, pan-genome, and evolution of SARS-CoV-2.",PeerJ,32742815,8/4/20,pubmed,0,7,"whole-genome, genomes",0.001272651,0.993636612,0.001272697,0.001272722,0.001272666,0.001272651,Genomics,0.66424334,TRUE,8.571428571,0.126785825,1.714285714,0.14577201,4,0.707574542,,,0.326710792
8195,Presence of mismatches between diagnostic PCR assays and coronavirus SARS-CoV-2 genome.,R Soc Open Sci,32742701,8/4/20,pubmed,0,2,"bioinformatic, in silico",0.001751273,0.739808487,0.196363541,0.058574106,0.001751283,0.00175131,Genomics,0.8245716,TRUE,78,0.798812543,115.5,0.845129783,33,0.936045435,,,0.85999592
8196,Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays.,Exp Ther Med,32742318,8/4/20,pubmed,0,11,"artificial intelligence, transfer learning, dataset",0.00143811,0.001438132,0.967525515,0.001438194,0.026721961,0.001438087,Imaging,0.5483978,TRUE,98.18181818,0.865174099,51.45454545,0.687048435,18,0.891474782,,,0.814565772
8197,Roadmap to strengthen global mental health systems to tackle the impact of the COVID-19 pandemic.,Int J Ment Health Syst,32742305,8/4/20,pubmed,0,3,"artificial intelligence, predictive model",0.000936103,0.000936079,0.0527959,0.478408208,0.465987625,0.000936085,Epidemiology,0.38326362,FALSE,369.6666667,0.994928567,754,0.989095531,3,0.667819001,,,0.8839477
8198,SARS-CoV-2 Infection of Ocular Cells from Human Adult Donor Eyes and hESC-Derived Eye Organoids.,SSRN,32742243,8/4/20,pubmed,0,5,sequencing,0.590612503,0.18780731,0.001415122,0.088872841,0.129877074,0.001415151,Drug discovery,0.9038596,TRUE,32.2,0.45754221,124.4,0.856903934,8,0.799987654,,,0.704811266
8199,0,SSRN,32742242,8/4/20,pubmed,0,14,transcriptom,0.759233922,0.001392898,0.001392845,0.001392935,0.001392928,0.235194472,Drug discovery,0.651806,TRUE,29.21428571,0.420928938,58.42857143,0.715279636,12,0.850299401,,,0.662169325
8200,Variant analysis of SARS-CoV-2 genomes.,Bull World Health Organ,32742035,8/4/20,pubmed,0,3,"bayes, genomes",0.033978635,0.741556117,0.000591777,0.000591785,0.000591813,0.222689874,Genomics,0.9992325,TRUE,42,0.558537943,38.33333333,0.62356168,123,0.983085376,,,0.721728333
8201,Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2.,Infect Dis Poverty,32741372,8/4/20,pubmed,0,6,bioinformatic,0.89529259,0.065363175,0.001156293,0.035875379,0.001156286,0.001156277,Drug discovery,0.7895738,TRUE,74.33333333,0.783227163,34.16666667,0.599879583,10,0.828199272,,,0.737102006
8202,Identification of promising drug candidates against NSP16 of SARS-CoV-2 through computational drug repurposing study.,J Biomol Struct Dyn,32741322,8/4/20,pubmed,0,7,"molecular dynamics simulation, computational",0.975353816,0.001272718,0.001272688,0.001272691,0.019555402,0.001272684,Drug discovery,0.98000777,TRUE,126.8571429,0.916506896,62.28571429,0.729796628,2,0.618927094,,,0.755076873
8203,Identification of potential drug candidates to combat COVID-19: a structural study using the main protease (mpro) of SARS-CoV-2.,J Biomol Struct Dyn,32741313,8/4/20,pubmed,0,8,"virtual screening, computational",0.993814633,0.001237068,0.00123708,0.001237067,0.001237081,0.00123707,Drug discovery,0.9746208,TRUE,111.75,0.893499907,55.625,0.704776559,7,0.785110192,,,0.794462219
8204,Anthocyanin derivatives as potent inhibitors of SARS-CoV-2 main protease: An in-silico perspective of therapeutic targets against COVID-19 pandemic.,J Biomol Struct Dyn,32741312,8/4/20,pubmed,0,4,"virtual screening, molecular dynamics simulation, computational, in-silico",0.9746994,0.001237058,0.001237071,0.001237075,0.001237044,0.020352352,Drug discovery,0.9664142,TRUE,40.75,0.546539675,17.75,0.458322184,5,0.739490092,,,0.58145065
8205,"Identification of potential anti-TMPRSS2 natural products through homology modelling, virtual screening and molecular dynamics simulation studies.",J Biomol Struct Dyn,32741259,8/4/20,pubmed,0,6,"virtual screening, molecular dynamics simulation",0.994505887,0.001098806,0.001098836,0.001098859,0.001098803,0.00109881,Drug discovery,0.65095115,TRUE,184.1666667,0.966169831,191.5,0.913031844,8,0.799987654,,,0.893063109
8206,The impact of the COVID-19 pandemic on the mental health of the adult population in Bangladesh: a nationwide cross-sectional study.,Int J Environ Health Res,32741205,8/4/20,pubmed,0,11,logistic regression,0.001943468,0.001943497,0.001943547,0.098209507,0.894016478,0.001943503,Healthcare,0.8801957,TRUE,13.36363636,0.201805925,2,0.164302917,25,0.918019631,,,0.428042824
8207,Is loss of sense of smell a diagnostic marker in COVID-19: A systematic review and meta-analysis.,Clin Otolaryngol,32741085,8/3/20,pubmed,0,4,logistic regression,0.001350365,0.176218495,0.0013504,0.231540642,0.069708422,0.519831676,Clinics,0.75494796,TRUE,129.5,0.921269095,100.5,0.823454643,3,0.667819001,,,0.804180913
8208,In Silico Screening of Potential Spike Glycoprotein Inhibitors of SARS-CoV-2 with Drug Repurposing Strategy.,Chin J Integr Med,32740825,8/3/20,pubmed,0,12,"virtual screening, in silico",0.995002317,0.000999514,0.000999557,0.000999547,0.000999527,0.000999539,Drug discovery,0.91569316,TRUE,8.75,0.129383388,4.25,0.235750602,12,0.850299401,,,0.405144464
8209,COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings.,Eur Radiol,32740817,8/3/20,pubmed,0,4,"bayes, classifier",0.001046807,0.001046835,0.938879787,0.001046886,0.001046859,0.056932827,Imaging,0.78901374,TRUE,19.75,0.293215412,6.75,0.292079208,10,0.828199272,,,0.471164631
8210,Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review.,J Med Syst,32740678,8/3/20,pubmed,0,6,"machine learning, artificial intelligence",0.002806404,0.002806445,0.985967804,0.00280658,0.002806397,0.00280637,Epidemiology,0.50495076,TRUE,86.16666667,0.829797761,93,0.808670056,3,0.667819001,,,0.768762273
8211,Baseline use of angiotensin-converting enzyme inhibitor/AT1 blocker and outcomes in hospitalized coronavirus disease 2019 African-American patients.,J Hypertens,32740406,8/3/20,pubmed,0,6,logistic regression,0.064936195,0.001350326,0.001350299,0.001350312,0.026732393,0.904280475,Clinics,0.97576463,TRUE,52.16666667,0.648153875,31,0.578204442,8,0.799987654,,,0.675448657
8212,Prioritizing potential ACE2 inhibitors in the COVID-19 pandemic: Insights from a molecular mechanics-assisted structure-based virtual screening experiment.,J Mol Graph Model,32739642,8/3/20,pubmed,0,3,"virtual screening, in silico",0.975103312,0.001272666,0.001272686,0.00127276,0.001272679,0.019805897,Drug discovery,0.6031655,TRUE,14.33333333,0.216772837,4.666666667,0.246721969,7,0.785110192,,,0.416201666
8213,Reliability and Validity of 2 Surgical Prioritization Systems for Reinstating Nonemergent Benign Gynecologic Surgery during the COVID-19 Pandemic.,J Minim Invasive Gynecol,32739612,8/3/20,pubmed,0,6,logistic regression,0.001823387,0.001823466,0.001823461,0.297072317,0.053679588,0.643777781,Clinics,0.6603382,TRUE,22.5,0.333539489,11.66666667,0.38065293,2,0.618927094,,,0.444373171
8214,Self-assessed health among older people in Europe and internet use.,Int J Med Inform,32739610,8/3/20,pubmed,0,1,logistic regression,0.001653025,0.001653087,0.118609981,0.640373634,0.236057129,0.001653144,Epidemiology,0.9523653,TRUE,40,0.539860226,6,0.280037463,0,0.403234768,,,0.407710819
8215,Nox2 activation in Covid-19.,Redox Biol,32738789,8/2/20,pubmed,0,16,logistic regression,0.335893508,0.204723468,0.002996426,0.002996468,0.002996989,0.450393142,Clinics,0.5488593,TRUE,106.375,0.881749026,44.75,0.657947552,15,0.874313229,,,0.804669936
8216,In silico ADMET and molecular docking study on searching potential inhibitors from limonoids and triterpenoids for COVID-19.,Comput Biol Med,32738628,8/2/20,pubmed,0,2,"virtual screening, computational, in silico",0.993143774,0.001371259,0.001371252,0.001371272,0.001371223,0.001371221,Drug discovery,0.9794562,TRUE,91,0.846310842,6.5,0.288132192,13,0.858880178,,,0.66444107
8217,ACE2 Co-evolutionary Pattern Suggests Targets for Pharmaceutical Intervention in the COVID-19 Pandemic.,iScience,32738617,8/2/20,pubmed,0,8,computational,0.986804747,0.002639092,0.002639092,0.002639115,0.002639014,0.002638941,Drug discovery,0.36717844,FALSE,37.375,0.512029192,22.375,0.505887075,4,0.707574542,,,0.575163603
8218,Computational guided drug repurposing for targeting 2'-O-ribose methyltransferase of SARS-CoV-2.,Life Sci,32738360,8/2/20,pubmed,0,4,"molecular dynamics simulation, computational",0.922430993,0.071721205,0.001461935,0.001461919,0.001461996,0.001461952,Drug discovery,0.9285066,TRUE,27.75,0.404292164,6,0.280037463,7,0.785110192,,,0.489813273
8219,"In silico study of azithromycin, chloroquine and hydroxychloroquine and their potential mechanisms of action against SARS-CoV-2 infection.",Int J Antimicrob Agents,32738306,8/2/20,pubmed,0,7,in silico,0.937941752,0.001330068,0.030916327,0.001330046,0.001330082,0.027151725,Drug discovery,0.90096974,TRUE,18.57142857,0.278619581,11.14285714,0.372089912,3,0.667819001,,,0.439509498
8220,Online conferencing platform provides opportunity for reciprocal teaching.,Biochem Mol Biol Educ,32738009,8/2/20,pubmed,0,2,active learning,0.004110096,0.004109872,0.358464759,0.004109948,0.625095613,0.004109713,Healthcare,0.71995354,TRUE,29.5,0.426000371,33.5,0.595999465,0,0.403234768,,,0.475078201
8221,Identification of potential Mpro inhibitors for the treatment of COVID-19 by using systematic virtual screening approach.,Mol Divers,32737681,8/2/20,pubmed,0,6,virtual screening,0.908998373,0.085836471,0.001291312,0.001291345,0.001291277,0.001291224,Drug discovery,0.8403854,TRUE,45.66666667,0.592677346,7.833333333,0.314088841,7,0.785110192,,,0.563958793
8222,"Clinical outcomes of COVID-19 in Wuhan, China: a large cohort study.",Ann Intensive Care,32737627,8/2/20,pubmed,0,17,logistic regression,0.018168013,0.000966747,0.014028293,0.00096676,0.000966753,0.964903433,Clinics,0.6950076,TRUE,48.29411765,0.616612035,62.88235294,0.731870484,20,0.900117291,,,0.74953327
8223,A palindromic RNA sequence as a common breakpoint contributor to copy-choice recombination in SARS-COV-2.,Arch Virol,32737584,8/2/20,pubmed,0,1,bioinformatic,0.305577412,0.689736196,0.00117163,0.001171648,0.001171564,0.001171551,Genomics,0.31919062,FALSE,51,0.63875317,58,0.714476853,11,0.840175319,,,0.731135114
8224,Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil.,Nat Hum Behav,32737472,8/2/20,pubmed,0,42,bayes,0.00310145,0.31168608,0.003101796,0.675906912,0.003101893,0.003101869,Epidemiology,0.5318963,TRUE,37.76190476,0.516358464,98.83333333,0.820176612,68,0.968393111,,,0.768309396
8225,A modular framework for the development of targeted Covid-19 blood transcript profiling panels.,J Transl Med,32736569,8/2/20,pubmed,0,18,transcriptom,0.608762432,0.002080653,0.13357873,0.002080628,0.002080582,0.251416975,Drug discovery,0.55572337,TRUE,49.72222222,0.629538005,59.94444444,0.720363928,0,0.403234768,,,0.5843789
8226,[Inference of start time of resurgent COVID-19 epidemic in Beijing with SEIR dynamics model and evaluation of control measure effect].,Zhonghua Liu Xing Bing Xue Za Zhi,32736418,8/1/20,pubmed,0,5,model fit,0.00156532,0.00156532,0.001565316,0.992173439,0.001565311,0.001565293,Epidemiology,0.38192135,FALSE,10.4,0.155606407,0.8,0.101351351,2,0.618927094,,,0.291961618
8227,Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment.,Comput Methods Programs Biomed,32736332,8/1/20,pubmed,0,4,mathematical model,0.001171577,0.001171566,0.001171598,0.994142116,0.00117158,0.001171562,Epidemiology,0.6096482,TRUE,98.25,0.865483332,7,0.299973241,15,0.874313229,,,0.679923267
8228,Spatiotemporal transmission dynamics of the COVID-19 pandemic and its impact on critical healthcare capacity.,Health Place,32736312,8/1/20,pubmed,0,7,mathematical model,0.003101518,0.00310154,0.003101476,0.984492225,0.003101739,0.003101503,Epidemiology,0.7309014,TRUE,66.57142857,0.744511101,27.28571429,0.547832486,9,0.814309525,,,0.702217704
8229,Maternal psychological distress & mental health service use during the COVID-19 pandemic.,J Affect Disord,32736186,8/1/20,pubmed,0,6,logistic regression,0.001291215,0.001291237,0.001291219,0.001291311,0.993543794,0.001291224,Healthcare,0.88975865,TRUE,17.16666667,0.258890469,15,0.42594327,20,0.900117291,,,0.52831701
8230,"A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?",Ann Epidemiol,32736059,8/1/20,pubmed,0,4,model fit,0.001438113,0.001438135,0.001438106,0.803559892,0.190687656,0.001438098,Epidemiology,0.16653898,FALSE,62.75,0.719586864,68.75,0.749598609,21,0.903944688,,,0.791043387
8231,Increased risk of SARS-CoV-2 infection in staff working across different care homes: enhanced CoVID-19 outbreak investigations in London care Homes.,J Infect,32735893,8/1/20,pubmed,0,30,"sequencing, whole genome",0.001486413,0.248851754,0.001486441,0.001486555,0.745202404,0.001486433,Healthcare,0.20795119,FALSE,54.5,0.665161729,60.73333333,0.723441263,15,0.874313229,,,0.754305407
8232,Network perturbation analysis in human bronchial epithelial cells following SARS-CoV2 infection.,Exp Cell Res,32735892,8/1/20,pubmed,0,8,"machine learning, dataset",0.912054157,0.001272709,0.049319244,0.001272686,0.001272668,0.034808535,Drug discovery,0.79728156,TRUE,148.75,0.942977302,80,0.779033984,12,0.850299401,,,0.857436896
8233,Roles of flavonoids against coronavirus infection.,Chem Biol Interact,32735799,8/1/20,pubmed,0,5,in silico,0.721964097,0.120996953,0.001330066,0.083822851,0.070555938,0.001330094,Drug discovery,0.88900477,TRUE,47.2,0.607520564,66.8,0.745116404,27,0.92443978,,,0.759025583
8234,Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation.,J Med Internet Res,32735549,8/1/20,pubmed,0,6,"deep learning, artificial intelligence, neural network",0.001371293,0.001371337,0.411272776,0.001371313,0.00137128,0.583242,Clinics,0.9184803,TRUE,18.66666667,0.279670975,12.5,0.392761573,1,0.537564047,,,0.403332199
8235,Lessons of COVID-19: Virtual conferences.,J Exp Med,32735327,8/1/20,pubmed,0,1,immunome,0.238953713,0.005352907,0.005352863,0.739634419,0.005353212,0.005352886,Epidemiology,0.6708988,TRUE,15,0.227596017,8,0.320511105,2,0.618927094,,,0.389011405
8236,Deploying Randomized Controlled Trials during the COVID-19 Pandemic: Reason and Bayesian Designs.,Ann Am Thorac Soc,32735169,8/1/20,pubmed,0,1,bayes,0.019531318,0.019529837,0.019531539,0.902345072,0.019530959,0.019531275,Epidemiology,0.63390344,TRUE,131,0.922815264,57,0.711198823,3,0.667819001,,,0.767277696
8237,"Prevalence of anxiety and depression symptom, and the demands for psychological knowledge and interventions in college students during COVID-19 epidemic: A large cross-sectional study.",J Affect Disord,32734907,8/1/20,pubmed,0,14,logistic regression,0.001219986,0.001220005,0.017779583,0.001220065,0.977340337,0.001220024,Healthcare,0.6987333,TRUE,22.42857143,0.331498547,8.714285714,0.33161627,27,0.92443978,,,0.529184866
8238,"Tackling COVID-19: identification of potential main protease inhibitors via structural analysis, virtual screening, molecular docking and MM-PBSA calculations.",J Biomol Struct Dyn,32734828,8/1/20,pubmed,0,1,virtual screening,0.994505815,0.001098848,0.001098835,0.001098857,0.00109883,0.001098815,Drug discovery,0.9399613,TRUE,24,0.35574247,1,0.122023013,1,0.537564047,,,0.338443177
8239,Association Between Pulmonary Embolism and COVID-19 in Emergency Department Patients Undergoing Computed Tomography Pulmonary Angiogram: The PEPCOV International Retrospective Study.,Acad Emerg Med,32734624,8/1/20,pubmed,0,29,logistic regression,0.001593517,0.00159354,0.08508301,0.073094863,0.001593585,0.837041484,Clinics,0.969172,TRUE,46.06896552,0.597006618,22.82758621,0.510034787,14,0.866658436,,,0.657899947
8240,A logistic model for age-specific COVID-19 case-fatality rates.,JAMIA Open,32734152,8/1/20,pubmed,0,2,mathematical model,0.003760659,0.003760738,0.003760626,0.695194852,0.289762326,0.003760799,Epidemiology,0.3325296,FALSE,18.5,0.278001113,50.5,0.683235215,0,0.403234768,,,0.454823699
8241,High Inflammatory Burden: A Potential Cause of Myocardial Injury in Critically Ill Patients With COVID-19.,Front Cardiovasc Med,32733921,8/1/20,pubmed,0,8,logistic regression,0.013266537,0.000871524,0.00087153,0.000871522,0.000871533,0.983247355,Clinics,0.9812525,TRUE,68.875,0.758302925,20.5,0.486218892,9,0.814309525,,,0.686277114
8242,Role of Autophagy in Lung Inflammation.,Front Immunol,32733448,8/1/20,pubmed,0,3,immunome,0.762034541,0.022011863,0.000966788,0.000966807,0.039554337,0.174465664,Drug discovery,0.81753486,TRUE,17.33333333,0.261178799,101,0.823989831,3,0.667819001,,,0.584329211
8243,"Predictors of Health-Related Quality of Life and Influencing Factors for COVID-19 Patients, a Follow-Up at One Month.",Front Psychiatry,32733299,8/1/20,pubmed,0,5,logistic regression,0.001141347,0.001141333,0.108606416,0.001141376,0.536743259,0.351226269,Healthcare,0.9877879,TRUE,40.8,0.546972602,6.4,0.286058336,9,0.814309525,,,0.549113488
8244,The double-edged relationship between COVID-19 stress and smoking: Implications for smoking cessation.,Tob Induc Dis,32733178,8/1/20,pubmed,0,8,logistic regression,0.001823349,0.001823371,0.001823343,0.114069358,0.878637181,0.001823397,Healthcare,0.75574183,TRUE,115.5,0.899684582,148.875,0.882659888,16,0.881782826,,,0.888042432
8245,Detection of SARS-CoV-2 in nasal swabs using MALDI-MS.,Nat Biotechnol,32733106,8/1/20,pubmed,0,4,machine learning,0.001786528,0.22374509,0.769108668,0.001786586,0.001786562,0.001786567,Genomics,0.68978614,TRUE,28,0.408312202,10.5,0.363459995,17,0.887338725,,,0.553036974
8246,The relationship between coronary artery disease and clinical outcomes in COVID-19: a single-center retrospective analysis.,Coron Artery Dis,32732512,8/1/20,pubmed,0,11,logistic regression,0.001350371,0.001350336,0.001350318,0.001350299,0.001350327,0.993248349,Clinics,0.80547595,TRUE,172.2727273,0.959861463,285.6363636,0.950361252,1,0.537564047,,,0.815928921
8247,Adaptation of SARS-CoV-2 in BALB/c mice for testing vaccine efficacy.,Science,32732280,8/1/20,pubmed,0,32,"sequencing, deep sequencing",0.425680947,0.395801467,0.059385587,0.002080592,0.114970766,0.002080642,Drug discovery,0.26523504,FALSE,77,0.794668811,,,117,0.981912464,,,0.888290637
8248,Genome Sequences of SARS-CoV-2 Strains Detected in Hong Kong.,Microbiol Resour Announc,32732237,8/1/20,pubmed,0,8,"sequencing, genomic epidemiology, genome sequences, genomes",0.009284084,0.779584112,0.18327922,0.009284312,0.009284094,0.009284176,Genomics,0.6981713,TRUE,12.375,0.186839013,12.5,0.392761573,2,0.618927094,,,0.399509227
8249,Reports of Coding-Complete Genome Sequences of Five 2019 Novel Coronavirus (SARS-CoV-2) Strains Isolated in Bangladesh.,Microbiol Resour Announc,32732236,8/1/20,pubmed,0,12,genome sequences,0.007673777,0.809787293,0.007674123,0.007673947,0.007673998,0.159516862,Genomics,0.3069411,FALSE,12.25,0.185107304,2.5,0.180826866,0,0.403234768,,,0.256389646
8250,Drug treatments for covid-19: living systematic review and network meta-analysis.,BMJ,32732190,8/1/20,pubmed,0,59,bayes,0.050382822,0.000710593,0.000710602,0.4495094,0.000710622,0.497975961,Clinics,0.15320262,FALSE,137.0740741,0.929432865,,,110,0.980739552,,,0.955086209
8251,Pseudoscientific beliefs and psychopathological risks increase after COVID-19 social quarantine.,Global Health,32731864,8/1/20,pubmed,0,4,bayes,0.001511864,0.001511845,0.001511818,0.528960263,0.373306283,0.093197927,Epidemiology,0.99147964,TRUE,11.75,0.177562001,4,0.231469093,4,0.707574542,,,0.372201879
8252,Experiential Learning Program to Strengthen Self-Reflection and Critical Thinking in Freshmen Nursing Students during COVID-19: A Quasi-Experimental Study.,Int J Environ Res Public Health,32731648,8/1/20,pubmed,0,4,correlation analysis,0.111415135,0.001653097,0.159859612,0.001653172,0.723765861,0.001653123,Healthcare,0.892189,TRUE,4.5,0.061784897,2.75,0.187583623,2,0.618927094,,,0.289431872
8253,Computational Studies of SARS-CoV-2 3CLpro: Insights from MD Simulations.,Int J Mol Sci,32731361,8/1/20,pubmed,0,9,computational,0.850413074,0.001438209,0.001438128,0.143834318,0.001438185,0.001438086,Drug discovery,0.88882005,TRUE,38.77777778,0.526934257,262.2222222,0.942667915,9,0.814309525,,,0.761303899
8254,Analyzing Spanish News Frames on Twitter during COVID-19-A Network Study of El País and El Mundo.,Int J Environ Res Public Health,32731359,8/1/20,pubmed,0,3,network analysis,0.002296557,0.002296636,0.060518593,0.930295124,0.002296588,0.002296502,Epidemiology,0.30504715,FALSE,19.33333333,0.288638753,5.666666667,0.270203372,3,0.667819001,,,0.408887042
8255,Usage Patterns of a Web-Based Palliative Care Content Platform (PalliCOVID) During the COVID-19 Pandemic.,J Pain Symptom Manage,32730951,7/31/20,pubmed,0,9,digital health,0.000926277,0.000926287,0.062122415,0.84152804,0.000926343,0.093570639,Epidemiology,0.86526334,TRUE,32.22222222,0.457604057,11.22222222,0.373227188,4,0.707574542,,,0.512801929
8256,Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages.,Eur J Pharm Sci,32730844,7/31/20,pubmed,0,4,"virtual screening, structural model",0.913022427,0.001310412,0.001310367,0.08173604,0.001310409,0.001310344,Drug discovery,0.57330173,TRUE,91.75,0.848042551,131,0.864597271,10,0.828199272,,,0.846946364
8257,Estimating Shortages in Capacity to Deliver Continuous Kidney Replacement Therapy During the COVID-19 Pandemic in the United States.,Am J Kidney Dis,32730812,7/31/20,pubmed,0,5,mathematical model,0.000838496,0.000838497,0.09718002,0.716581652,0.000838517,0.183722817,Epidemiology,0.8397587,TRUE,80.6,0.809326489,120,0.851284453,4,0.707574542,,,0.789395161
8258,Differential Tropism of SARS-CoV and SARS-CoV-2 in Bat Cells.,Emerg Infect Dis,32730733,7/31/20,pubmed,0,13,structural model,0.608631936,0.367009733,0.006089517,0.006089753,0.006089546,0.006089516,Drug discovery,0.40170327,FALSE,68.38461538,0.755767209,192.4615385,0.913834627,1,0.537564047,,,0.735721961
8259,Persistent minimal sequences of SARS-CoV-2.,Bioinformatics,32730589,7/31/20,pubmed,0,2,"computational, bioinformatic, transcriptom, genomes",0.422541082,0.470642035,0.001392942,0.10263822,0.00139285,0.001392872,Genomics,0.40704817,FALSE,30.5,0.438493413,19.5,0.475983409,1,0.537564047,,,0.484013623
8260,Prediction model and risk scores of ICU admission and mortality in COVID-19.,PLoS One,32730358,7/31/20,pubmed,0,9,"logistic regression, prediction model, dataset",0.001112622,0.001112637,0.16279557,0.001112688,0.001112692,0.832753791,Clinics,0.75536364,TRUE,182.6666667,0.965551364,211.5555556,0.922799037,33,0.936045435,,,0.941465278
8261,Enacting national social distancing policies corresponds with dramatic reduction in COVID19 infection rates.,PLoS One,32730356,7/31/20,pubmed,0,4,mathematical model,0.001622705,0.0016227,0.001622722,0.955567259,0.037941919,0.001622695,Epidemiology,0.22965357,FALSE,109.25,0.888799555,677.5,0.986687182,7,0.785110192,,,0.886865643
8262,Global Research on Coronaviruses: An R Package.,J Med Internet Res,32730218,7/31/20,pubmed,0,1,"machine learning, network analysis, text mining",0.001046858,0.158742128,0.41476526,0.423351933,0.001046949,0.001046872,Epidemiology,0.57341754,TRUE,16,0.243552477,1,0.122023013,1,0.537564047,,,0.301046512
8263,Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.,J Med Internet Res,32730217,7/31/20,pubmed,0,8,machine learning,0.00139288,0.001392892,0.183319929,0.811108594,0.001392882,0.001392823,Epidemiology,0.73049235,TRUE,61.125,0.709567691,,,6,0.764429903,,,0.736998797
8264,Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans.,IEEE Trans Med Imaging,32730216,7/31/20,pubmed,0,4,"neural network, transfer learning",0.046296593,0.001392875,0.915554723,0.001392901,0.001392931,0.033969976,Imaging,0.7790916,TRUE,73,0.778464964,86.75,0.794621354,0,0.403234768,,,0.658773695
8265,A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images.,IEEE Trans Med Imaging,32730215,7/31/20,pubmed,0,10,deep learning,0.00094608,0.000946081,0.908726053,0.00094615,0.087489558,0.000946078,Imaging,0.69716966,TRUE,62.3,0.716865607,65.8,0.741771474,39,0.945737391,,,0.801458158
8266,"A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis.",IEEE Trans Med Imaging,32730214,7/31/20,pubmed,0,16,"deep learning, artificial intelligence, dataset",0.001156289,0.062527418,0.826463649,0.107540121,0.001156255,0.001156268,Imaging,0.5774413,TRUE,43.125,0.568000495,20.6875,0.488025154,22,0.908142478,,,0.654722709
8267,Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images.,IEEE Trans Med Imaging,32730213,7/31/20,pubmed,0,8,deep model,0.021087302,0.001141354,0.923901596,0.051587075,0.001141347,0.001141327,Imaging,0.40308025,FALSE,76.375,0.791762014,159.875,0.891691196,108,0.980060498,,,0.887837902
8268,Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia.,IEEE Trans Med Imaging,32730212,7/31/20,pubmed,0,18,dataset,0.001072175,0.001072193,0.994638922,0.001072228,0.00107225,0.001072232,Imaging,0.49533927,FALSE,98.11111111,0.864926712,56.88888889,0.710329141,61,0.964936107,,,0.846730653
8269,Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images.,IEEE Trans Med Imaging,32730210,7/31/20,pubmed,0,9,classifier,0.001098848,0.018457939,0.977146768,0.001098829,0.001098809,0.001098807,Imaging,0.34009537,FALSE,47.55555556,0.610860288,17.55555556,0.456382125,29,0.928020248,,,0.665087554
8270,Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence.,Radiology,32729810,7/31/20,pubmed,0,42,"artificial intelligence, deep-learning",0.000871545,0.000871557,0.632071536,0.000871551,0.000871563,0.364442247,Imaging,0.8858826,TRUE,30.45238095,0.437194632,43.9047619,0.653599144,17,0.887338725,,,0.6593775
8271,Clinically practiced and commercially viable nanobio engineered analytical methods for COVID-19 diagnosis.,Biosens Bioelectron,32729494,7/31/20,pubmed,0,2,proteom,0.160382209,0.12935792,0.290901034,0.416658022,0.001350429,0.001350385,Epidemiology,0.6335084,TRUE,34.5,0.482404601,23,0.513513514,27,0.92443978,,,0.640119298
8272,Antiviral potential of some novel structural analogs of standard drugs repurposed for the treatment of COVID-19.,J Biomol Struct Dyn,32729392,7/31/20,pubmed,0,7,"molecular dynamics simulation, computational",0.993543869,0.001291255,0.001291205,0.001291227,0.00129125,0.001291193,Drug discovery,0.88958544,TRUE,45.14285714,0.587791453,6.428571429,0.286259031,1,0.537564047,,,0.470538177
8273,COVID-19 Intervention Scenarios for a Long-term Disease Management.,Int J Health Policy Manag,32729281,7/31/20,pubmed,0,3,simulation model,0.001310366,0.001310353,0.001310346,0.993448245,0.001310369,0.001310321,Epidemiology,0.10909617,FALSE,11.33333333,0.170635166,3.333333333,0.206515922,5,0.739490092,,,0.372213727
8274,Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19.,Korean J Radiol,32729263,7/31/20,pubmed,0,5,deep learning,0.001098785,0.001098802,0.779616244,0.001098858,0.001098875,0.215988435,Imaging,0.7739474,TRUE,140.6,0.934566145,123.6,0.855967353,6,0.764429903,,,0.851654467
8275,In vitro and in vivo identification of clinically approved drugs that modify ACE2 expression.,Mol Syst Biol,32729248,7/31/20,pubmed,0,6,dataset,0.843691791,0.002422245,0.002422315,0.002422269,0.002422245,0.146619134,Drug discovery,0.9216597,TRUE,124.8333333,0.913909333,666.6666667,0.986419588,4,0.707574542,,,0.869301154
8276,Epidemiology of paediatric Middle East respiratory syndrome coronavirus and implications for the control of coronavirus virus disease 2019.,J Paediatr Child Health,32729192,7/31/20,pubmed,0,4,dataset,0.001622755,0.283571021,0.001622866,0.001622793,0.575911715,0.135648849,Healthcare,0.60782486,TRUE,199,0.972354506,145,0.879114263,0,0.403234768,,,0.751567845
8277,Safety and potential efficacy of cyclooxygenase-2 inhibitors in coronavirus disease 2019.,Clin Transl Immunology,32728438,7/31/20,pubmed,0,9,logistic regression,0.133252865,0.001272628,0.001272632,0.00127267,0.001272726,0.861656478,Clinics,0.8677123,TRUE,94.66666667,0.856020781,161.7777778,0.893497458,3,0.667819001,,,0.80577908
8278,Development and validation of a risk stratification model for screening suspected cases of COVID-19 in China.,Aging (Albany NY),32727933,7/31/20,pubmed,0,10,prediction model,0.001861714,0.001861748,0.296265809,0.140319693,0.001861728,0.557829308,Clinics,0.3859718,FALSE,117.5,0.903642773,91.5,0.805124431,1,0.537564047,,,0.748777084
8279,Association between cytokine profiles and lung injury in COVID-19 pneumonia.,Respir Res,32727465,7/31/20,pubmed,0,8,correlation analysis,0.001461877,0.001461865,0.113934945,0.001461832,0.001461872,0.880217609,Clinics,0.8970318,TRUE,25,0.369286907,,,9,0.814309525,,,0.591798216
8280,[Clinical characteristics and death risk factors of severe COVID-19].,Zhonghua Jie He He Hu Xi Za Zhi,32727175,7/31/20,pubmed,0,9,logistic regression,0.000977495,0.000977413,0.028308934,0.000977432,0.000977437,0.967781288,Clinics,0.9901567,TRUE,4.777777778,0.064753541,67.66666667,0.74719026,2,0.618927094,,,0.476956965
8281,Expected immune recognition of COVID-19 virus by memory from earlier infections with common coronaviruses in a large part of the world population.,F1000Res,32595955,7/31/20,pubmed,0,2,in silico,0.578147681,0.135745433,0.002032786,0.002032896,0.240563976,0.041477229,Drug discovery,0.36618888,FALSE,69.5,0.761271569,137.5,0.872223709,11,0.840175319,,,0.824556866
8282,Estimation of COVID-19 spread curves integrating global data and borrowing information.,PLoS One,32726361,7/30/20,pubmed,0,3,bayes,0.182062251,0.001987148,0.1823853,0.629590896,0.001987275,0.001987131,Epidemiology,0.21973377,FALSE,67,0.748160059,129.3333333,0.863125502,3,0.667819001,,,0.759701521
8283,"Knowledge, attitude, and perceptions towards the 2019 Coronavirus Pandemic: A bi-national survey in Africa.",PLoS One,32726340,7/30/20,pubmed,0,6,logistic regression,0.001098822,0.001098886,0.001098845,0.224014237,0.771590395,0.001098815,Healthcare,0.57652587,TRUE,9,0.135320675,3.833333333,0.222103291,2,0.618927094,,,0.325450354
8284,Integrative analysis of miRNA and mRNA sequencing data reveals potential regulatory mechanisms of ACE2 and TMPRSS2.,PLoS One,32726325,7/30/20,pubmed,0,5,sequencing,0.872036255,0.118274531,0.002422274,0.002422345,0.00242231,0.002422286,Drug discovery,0.89260423,TRUE,45.4,0.590017936,38.4,0.623896173,13,0.858880178,,,0.690931429
8285,"The ongoing COVID-19 epidemic in Minas Gerais, Brazil: insights from epidemiological data and SARS-CoV-2 whole genome sequencing.",Emerg Microbes Infect,32726185,7/30/20,pubmed,0,28,"sequencing, whole genome, genomes",0.001717165,0.321065165,0.00171716,0.672066111,0.001717203,0.001717196,Epidemiology,0.32038164,FALSE,46.53571429,0.601088503,157.2142857,0.890018732,8,0.799987654,,,0.763698296
8286,[Analysis of medication regularity and pharmacodynamic characteristics of traditional Chinese medicine treatment in 444 severe cases of COVID-19].,Zhongguo Zhong Yao Za Zhi,32726005,7/30/20,pubmed,0,7,correlation analysis,0.576107202,0.000846574,0.000846577,0.150344916,0.076235358,0.195619373,Drug discovery,0.99947774,TRUE,51.42857143,0.642154741,24,0.521808938,2,0.618927094,,,0.594296924
8287,COVID-19 in Patients With Inflammatory Arthritis: A Prospective Study on the Effects of Comorbidities and Disease-Modifying Antirheumatic Drugs on Clinical Outcomes.,Arthritis Rheumatol,32725762,7/30/20,pubmed,0,16,logistic regression,0.001717211,0.001717196,0.001717254,0.00171723,0.080280948,0.912850162,Clinics,0.99019027,TRUE,40.1875,0.540726081,44.375,0.655739898,14,0.866658436,,,0.687708138
8288,Clinical characteristics and outcomes of coronavirus disease 2019 infections among diabetics: A retrospective and multicenter study in China.,J Diabetes,32725691,7/30/20,pubmed,0,10,sequencing,0.001438136,0.164816907,0.118117302,0.00143812,0.00143813,0.712751405,Clinics,0.9250541,TRUE,49.1,0.624837652,227.3,0.92875301,4,0.707574542,,,0.753721735
8289,Prediction of the Receptorome for the Human-Infecting Virome.,Virol Sin,32725480,7/30/20,pubmed,0,5,virom,0.822537876,0.001486561,0.17151632,0.001486432,0.001486406,0.001486405,Drug discovery,0.5444658,TRUE,44.4,0.580864617,62.4,0.730264918,1,0.537564047,,,0.616231194
8290,Comparison of Face-Touching Behaviors Before and During the Coronavirus Disease 2019 Pandemic.,JAMA Netw Open,32725247,7/30/20,pubmed,0,7,logistic regression,0.000740282,0.000740309,0.000740283,0.679605599,0.317433186,0.000740342,Epidemiology,0.55302316,TRUE,52.85714286,0.652544994,38.71428571,0.625702435,13,0.858880178,,,0.712375869
8291,"Mathematical models for COVID-19: applications, limitations, and potentials.",J Public Health Emerg,32724894,7/30/20,pubmed,0,1,mathematical model,0.025060381,0.025060635,0.02506167,0.874695821,0.025060972,0.025060521,Epidemiology,0.56126297,TRUE,27,0.3960047,5,0.257024351,14,0.866658436,,,0.506562496
8292,AeDES: a next-generation monitoring and forecasting system for environmental suitability of Aedes-borne disease transmission.,Sci Rep,32724218,7/30/20,pubmed,0,8,probabilistic,0.031650192,0.234136875,0.001823348,0.728742846,0.001823372,0.001823367,Epidemiology,0.79931104,TRUE,24.875,0.366256417,29.25,0.564557131,3,0.667819001,,,0.532877516
8293,Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic.,Nat Microbiol,32724171,7/30/20,pubmed,0,8,bayes,0.093219646,0.900168038,0.001653054,0.001653173,0.001653054,0.001653035,Genomics,0.40429863,FALSE,65.25,0.735728864,196.375,0.915774686,44,0.952157541,,,0.86788703
8294,0,Proc Natl Acad Sci U S A,32723824,7/30/20,pubmed,0,17,"sequencing, whole-genome",0.003101498,0.710038558,0.003101529,0.277555453,0.003101517,0.003101445,Genomics,0.49161303,FALSE,107.9411765,0.885459831,166.2941176,0.896574793,4,0.707574542,,,0.829869722
8295,Comparative Genomic Analysis of Rapidly Evolving SARS-CoV-2 Reveals Mosaic Pattern of Phylogeographical Distribution.,mSystems,32723797,7/30/20,pubmed,0,17,"interactom, genomes",0.439764331,0.556783421,0.000863056,0.000863077,0.000863048,0.000863067,Genomics,0.78826964,TRUE,19.41176471,0.289319067,6.058823529,0.280104362,9,0.814309525,,,0.461244318
8296,Characterisation of the transcriptome and proteome of SARS-CoV-2 reveals a cell passage induced in-frame deletion of the furin-like cleavage site from the spike glycoprotein.,Genome Med,32723359,7/30/20,pubmed,0,11,"sequencing, transcriptom, proteom, phosphoproteom",0.482597285,0.512777522,0.001156257,0.00115634,0.00115625,0.001156346,Genomics,0.33261836,FALSE,58.63636364,0.693734925,91.27272727,0.804388547,88,0.975554047,,,0.824559173
8297,Evaluation of the mRNA-1273 Vaccine against SARS-CoV-2 in Nonhuman Primates.,N Engl J Med,32722908,7/30/20,pubmed,0,74,genomes,0.457520004,0.345934625,0.00109883,0.001098852,0.042073171,0.152274518,Drug discovery,0.31032312,FALSE,74.48648649,0.784031171,208.8378378,0.921594862,215,0.992345206,,,0.899323746
8298,In silico Drug Repurposing for COVID-19: Targeting SARS-CoV-2 Proteins through Docking and Consensus Ranking.,Mol Inform,32722864,7/30/20,pubmed,0,2,in silico,0.956139053,0.00141512,0.001415123,0.001415139,0.001415148,0.038200417,Drug discovery,0.90594804,TRUE,47,0.606407323,55,0.702167514,21,0.903944688,,,0.737506508
8299,"Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models.",PLoS One,32722716,7/30/20,pubmed,0,7,machine learning,0.001461908,0.001461915,0.24466877,0.749483586,0.001461938,0.001461882,Epidemiology,0.6896931,TRUE,44.57142857,0.582348939,71.42857143,0.757425743,5,0.739490092,,,0.693088258
8300,Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs.,PLoS One,32722697,7/30/20,pubmed,0,6,"artificial intelligence, neural network, deep-learning, correlation analysis, transfer learning, dataset",0.000898149,0.000898105,0.918425891,0.000898142,0.000898129,0.077981583,Imaging,0.7388308,TRUE,9.333333333,0.139278867,20,0.481000803,16,0.881782826,,,0.500687499
8301,In Silico Insights into the SARS CoV-2 Main Protease Suggest NADH Endogenous Defences in the Control of the Pandemic Coronavirus Infection.,Viruses,32722574,7/30/20,pubmed,0,3,in silico,0.851143896,0.0017513,0.001751256,0.001751281,0.141851002,0.001751267,Drug discovery,0.82333124,TRUE,85.33333333,0.825901416,11.66666667,0.38065293,4,0.707574542,,,0.638042963
8302,Molecular Tracing of SARS-CoV-2 in Italy in the First Three Months of the Epidemic.,Viruses,32722343,7/30/20,pubmed,0,20,genomes,0.001538094,0.652020656,0.001538143,0.341826879,0.001538115,0.001538113,Genomics,0.30478138,FALSE,103.1,0.875440658,72.5,0.760436179,0,0.403234768,,,0.679703868
8303,The Role of Health Preconditions on COVID-19 Deaths in Portugal: Evidence from Surveillance Data of the First 20293 Infection Cases.,J Clin Med,32722159,7/30/20,pubmed,0,10,"logistic regression, dataset",0.001901744,0.001901727,0.001901832,0.218970601,0.158026844,0.617297252,Clinics,0.95572793,TRUE,46.1,0.597315851,54,0.697952903,4,0.707574542,,,0.667614432
8304,"Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing.",Int J Environ Res Public Health,32722154,7/30/20,pubmed,0,4,artificial intelligence,0.001438159,0.001438194,0.483280005,0.510967409,0.001438138,0.001438096,Epidemiology,0.7519081,TRUE,25.25,0.370709382,10.75,0.366269735,12,0.850299401,,,0.529092839
8305,0,Chemosphere,32721685,7/30/20,pubmed,0,7,neural network,0.002080605,0.002080641,0.225613476,0.766063854,0.002080667,0.002080757,Epidemiology,0.9091591,TRUE,63.28571429,0.723112128,36.42857143,0.613727589,0,0.403234768,,,0.580024828
8306,Isolation and phylogenetic analysis of SARS-CoV-2 variants collected in Russia during the COVID-19 outbreak.,Int J Infect Dis,32721529,7/30/20,pubmed,0,13,whole genome,0.00242244,0.987888268,0.002422246,0.002422438,0.002422281,0.002422327,Genomics,0.60213333,TRUE,15.23076923,0.229451419,14,0.412898047,6,0.764429903,,,0.468926456
8307,"A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data.",Braz J Infect Dis,32721387,7/30/20,pubmed,0,11,predictive model,0.001593508,0.0015937,0.393806385,0.171413152,0.001593654,0.429999601,Clinics,0.7152413,TRUE,16.27272727,0.24615004,6.272727273,0.283449291,3,0.667819001,,,0.399139444
8308,Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions.,Cell Syst,32721383,7/30/20,pubmed,0,7,"machine learning, computational, genomes",0.403364642,0.169965477,0.183567902,0.239127594,0.001987195,0.001987188,Drug discovery,0.021246046,FALSE,91.71428571,0.847918857,458,0.976719294,15,0.874313229,,,0.89965046
8309,Immunometabolic Status of COVID-19 Cancer Patients.,Physiol Rev,32721181,7/30/20,pubmed,0,6,immunome,0.529812597,0.001310396,0.001310406,0.086084865,0.00131042,0.380171315,Drug discovery,0.81359434,TRUE,224,0.979652421,378.3333333,0.968892159,1,0.537564047,,,0.828702876
8310,A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS-CoV-2.,Mol Inform,32721082,7/29/20,pubmed,0,6,"molecular dynamics simulation, computational, in silico",0.985507869,0.002898425,0.002898463,0.00289856,0.002898312,0.002898371,Drug discovery,0.76370203,TRUE,31.5,0.450058754,9.5,0.345531175,6,0.764429903,,,0.520006611
8311,In silico study to evaluate the antiviral activity of novel structures against 3C-like protease of Novel Coronavirus (COVID-19) and SARS-CoV.,Med Chem,32720605,7/29/20,pubmed,0,9,in silico,0.956666818,0.001392925,0.001392865,0.020111966,0.001392834,0.019042592,Drug discovery,0.8991296,TRUE,21.66666667,0.320737213,6.555555556,0.288533583,0,0.403234768,,,0.337501855
8312,"Identification of some novel oxazine substituted 9-anilinoacridines as SARS-CoV-2 inhibitors for COVID-19 by molecular docking, free energy calculation and molecular dynamics studies.",J Biomol Struct Dyn,32720578,7/29/20,pubmed,0,5,in silico,0.993448178,0.001310322,0.001310382,0.001310391,0.001310394,0.001310332,Drug discovery,0.9911626,TRUE,10.4,0.155606407,0,0.055525823,2,0.618927094,,,0.276686441
8313,Determinants of COVID-19 disease severity in patients with underlying rheumatic disease.,Clin Rheumatol,32720259,7/29/20,pubmed,0,6,logistic regression,0.035254812,0.000599809,0.000599792,0.000599852,0.00059983,0.962345905,Clinics,0.9466849,TRUE,13.16666667,0.198528047,1.166666667,0.124565159,14,0.866658436,,,0.396583881
8314,Estimating the risk of SARS-CoV-2 transmission to pediatric anesthesiologists: a microsimulation model.,Can J Anaesth,32720258,7/29/20,pubmed,0,10,simulation model,0.034961906,0.034963595,0.034961874,0.825188076,0.034962381,0.034962168,Epidemiology,0.206649,FALSE,33.8,0.474117138,12,0.386740701,0,0.403234768,,,0.421364202
8315,General surgery and COVID-19: review of practical recommendations in the first pandemic phase.,Surg Today,32720009,7/29/20,pubmed,0,7,dataset,0.034746323,0.00146187,0.001461971,0.572945708,0.22546164,0.163922488,Epidemiology,0.9913638,TRUE,56.57142857,0.679077247,15.57142857,0.430960664,2,0.618927094,,,0.576321668
8316,The impact of SARS-CoV-2 (COVID-19) pandemic on trauma bay management and guideline adherence in a European level-one-trauma centre.,Int Orthop,32719932,7/29/20,pubmed,0,6,dataset,0.000988357,0.000988401,0.072368923,0.163831707,0.156650836,0.605171775,Clinics,0.9891342,TRUE,67.83333333,0.752674872,33.66666667,0.596802248,2,0.618927094,,,0.656134738
8317,Epidemiology of invasive pulmonary aspergillosis among COVID-19 intubated patients: a prospective study.,Clin Infect Dis,32719848,7/29/20,pubmed,0,22,logistic regression,0.001350426,0.073988568,0.001350394,0.001350356,0.001350375,0.920609881,Clinics,0.7146533,TRUE,86.77272727,0.832642711,112.6363636,0.841918651,37,0.942712513,,,0.872424625
8318,Is a COVID-19 prediction model based on symptom tracking through an app applicable in primary care?,Fam Pract,32719842,7/29/20,pubmed,0,4,prediction model,0.015998824,0.015999229,0.016000063,0.454613809,0.481388122,0.015999953,Healthcare,0.5590878,TRUE,29.5,0.426000371,11,0.371287129,2,0.618927094,,,0.472071531
8319,"Non-pharmaceutical Interventions for Pandemic COVID-19: A Cross-Sectional Investigation of US General Public Beliefs, Attitudes, and Actions.",Front Med (Lausanne),32719807,7/29/20,pubmed,0,2,logistic regression,0.001593584,0.001593508,0.026618099,0.157692892,0.810908404,0.001593514,Healthcare,0.76904666,TRUE,45.5,0.591440411,33.5,0.595999465,8,0.799987654,,,0.662475843
8320,Clinical Characteristics of Adult Fevered COVID-19 Patients and Predictors for Developing Severe Events.,Front Med (Lausanne),32719804,7/29/20,pubmed,0,9,logistic regression,0.001022614,0.001022608,0.001022622,0.001022663,0.001022648,0.994886845,Clinics,0.98152995,TRUE,42.66666667,0.563856763,14.22222222,0.414236018,3,0.667819001,,,0.548637261
8321,COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm.,Front Public Health,32719767,7/29/20,pubmed,0,9,"artificial intelligence, dataset",0.00153808,0.001538151,0.442295273,0.36019145,0.001538169,0.192898876,Epidemiology,0.77130914,TRUE,27.11111111,0.396499474,3.111111111,0.199959861,29,0.928020248,,,0.508159861
8322,Establishing a Unified COVID-19 "Immunome": Integrating Coronavirus Pathogenesis and Host Immunopathology.,Front Immunol,32719686,7/29/20,pubmed,0,16,immunome,0.90234923,0.019530475,0.019530331,0.019530507,0.019529559,0.019529898,Drug discovery,0.4661754,FALSE,112.9375,0.89591193,244.8125,0.936178753,4,0.707574542,,,0.846555075
8323,COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.,Front Immunol,32719684,7/29/20,pubmed,0,4,"machine learning, proteom",0.794875915,0.100352372,0.057711983,0.001112631,0.044834418,0.001112681,Drug discovery,0.47640267,FALSE,17.75,0.267672707,27.5,0.549906342,10,0.828199272,,,0.548592774
8324,0,Front Microbiol,32719673,7/29/20,pubmed,0,11,genomes,0.436856327,0.556652815,0.001622724,0.001622754,0.001622701,0.001622678,Genomics,0.9161669,TRUE,16,0.243552477,5.818181818,0.272812416,11,0.840175319,,,0.452180071
8325,"Lung involvement in macrophage activation syndrome and severe COVID-19: results from a cross-sectional study to assess clinical, laboratory and artificial intelligence-radiological differences.",Ann Rheum Dis,32719039,7/29/20,pubmed,0,16,artificial intelligence,0.002130828,0.002130687,0.486397448,0.00213066,0.002130633,0.505079743,Clinics,0.84611005,TRUE,107,0.883357041,43.625,0.651993578,11,0.840175319,,,0.791841979
8326,Predictive model of increased mortality and bed occupancy if thrombolysis becomes the initial treatment strategy for STEMI during the SARS-CoV-2 pandemic.,Clin Med (Lond),32719037,7/29/20,pubmed,0,5,predictive model,0.00218353,0.002183209,0.002183256,0.705291941,0.00218337,0.285974694,Epidemiology,0.7345035,TRUE,162.2,0.952810935,257,0.940460262,1,0.537564047,,,0.810278415
8327,A colorimetric RT-LAMP assay and LAMP-sequencing for detecting SARS-CoV-2 RNA in clinical samples.,Sci Transl Med,32719001,7/29/20,pubmed,0,19,sequencing,0.001461876,0.778468497,0.215683951,0.001461903,0.001461878,0.001461895,Genomics,0.31777412,FALSE,53.52631579,0.657307193,155.2105263,0.888279368,72,0.970121612,,,0.838569391
8328,"Epidemiological and clinical characteristics of 1663 hospitalized patients infected with COVID-19 in Wuhan, China: a single-center experience.",J Infect Public Health,32718894,7/29/20,pubmed,0,6,logistic regression,0.000907282,0.000907314,0.147502567,0.000907297,0.040442171,0.809333369,Clinics,0.9928226,TRUE,49.16666667,0.62545612,29,0.562884667,5,0.739490092,,,0.642610293
8329,In Silico Identification of a Potent Arsenic Based Approved Drug Darinaparsin against SARS-CoV-2: Inhibitor of RNA Dependent RNA polymerase (RdRp) and Essential Proteases.,Infect Disord Drug Targets,32718300,7/29/20,pubmed,0,3,in silico,0.992567899,0.001486437,0.001486437,0.001486426,0.001486398,0.001486402,Drug discovery,0.9512414,TRUE,64.66666667,0.732822067,30,0.570176612,8,0.799987654,,,0.700995444
8330,Immunogenic SARS-CoV-2 Epitopes: In Silico Study Towards Better Understanding of COVID-19 Disease-Paving the Way for Vaccine Development.,Vaccines (Basel),32717854,7/29/20,pubmed,0,6,in silico,0.882229821,0.073579702,0.002032784,0.002032881,0.002032793,0.038092018,Drug discovery,0.25281549,FALSE,27.5,0.401570907,24.83333333,0.526960128,2,0.618927094,,,0.515819377
8331,Evolution of viral quasispecies during SARS-CoV-2 infection.,Clin Microbiol Infect,32717416,7/28/20,pubmed,0,15,sequencing,0.037702399,0.818198186,0.001171531,0.001171559,0.001171556,0.140584768,Genomics,0.5644623,TRUE,38.86666667,0.527614571,25.06666667,0.529502275,18,0.891474782,,,0.649530543
8332,Identification of high-affinity inhibitors of SARS-CoV-2 main protease: Towards the development of effective COVID-19 therapy.,Virus Res,32717346,7/28/20,pubmed,0,9,molecular dynamics simulation,0.946361802,0.00135035,0.001350337,0.048236841,0.001350353,0.001350317,Drug discovery,0.9194814,TRUE,72.11111111,0.773393531,10.55555556,0.363727589,8,0.799987654,,,0.645702924
8333,Peptidyl Acyloxymethyl Ketones as Activity-Based Probes for the Main Protease of SARS-CoV-2*.,Chembiochem,32717117,7/28/20,pubmed,0,3,proteom,0.823423831,0.167843076,0.00218339,0.002183246,0.002183191,0.002183265,Drug discovery,0.73913926,TRUE,3.666666667,0.045952131,0.333333333,0.073187048,2,0.618927094,,,0.246022091
8334,Association between NSAIDs use and adverse clinical outcomes among adults hospitalized with COVID-19 in South Korea: A nationwide study.,Clin Infect Dis,32717066,7/28/20,pubmed,0,6,logistic regression,0.145379023,0.001565311,0.001565321,0.105478883,0.001565443,0.744446019,Clinics,0.34730417,FALSE,94,0.854598305,63.66666667,0.734145036,2,0.618927094,,,0.735890145
8335,Automated EHR score to predict COVID-19 outcomes at US Department of Veterans Affairs.,PLoS One,32716922,7/28/20,pubmed,0,5,logistic regression,0.096124708,0.001310417,0.130386271,0.250772307,0.00131042,0.520095877,Clinics,0.96801114,TRUE,26.4,0.386913229,6.4,0.286058336,1,0.537564047,,,0.403511871
8336,Mental Health Disorders and Associated Risk Factors in Quarantined Adults During the COVID-19 Outbreak in China: Cross-Sectional Study.,J Med Internet Res,32716899,7/28/20,pubmed,0,11,logistic regression,0.001538081,0.001538084,0.001538068,0.001538103,0.924668182,0.069179483,Healthcare,0.9160638,TRUE,96.45454545,0.859978972,116,0.846133262,19,0.89561084,,,0.867241025
8337,"Impact of the COVID-19 Pandemic on Partner Relationships and Sexual and Reproductive Health: Cross-Sectional, Online Survey Study.",J Med Internet Res,32716895,7/28/20,pubmed,0,10,logistic regression,0.00171721,0.001717229,0.001717184,0.229516794,0.763614371,0.001717212,Healthcare,0.9352168,TRUE,92,0.848970252,40.3,0.635202034,15,0.874313229,,,0.786161838
8338,Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model.,Big Data,32716641,7/28/20,pubmed,0,9,deep learning,0.001653045,0.028823119,0.276406689,0.635360039,0.001653063,0.056104045,Epidemiology,0.3899002,FALSE,53.55555556,0.657492733,26.66666667,0.543216484,14,0.866658436,,,0.689122551
8339,Anxiety and depression in the Republic of Ireland during the COVID-19 pandemic.,Acta Psychiatr Scand,32716520,7/28/20,pubmed,0,8,logistic regression,0.001653028,0.001653053,0.001653038,0.001653085,0.991734746,0.00165305,Healthcare,0.78976405,TRUE,164.5,0.95441895,298,0.953639283,35,0.939317242,,,0.949125158
8340,Estimation of Viral Aerosol Emissions From Simulated Individuals With Asymptomatic to Moderate Coronavirus Disease 2019.,JAMA Netw Open,32716517,7/28/20,pubmed,0,2,mathematical model,0.000988395,0.315761048,0.000988396,0.500228129,0.000988454,0.181045577,Epidemiology,0.261507,FALSE,65,0.734801163,85.5,0.791410222,30,0.930057411,,,0.818756265
8341,Hypertension is a risk factor for adverse outcomes in patients with coronavirus disease 2019: a cohort study.,Ann Med,32716217,7/28/20,pubmed,0,12,logistic regression,0.001219986,0.00122,0.001219977,0.001219996,0.00122003,0.993900011,Clinics,0.94302297,TRUE,66.25,0.742346465,20.75,0.488827937,6,0.764429903,,,0.665201435
8342,"Virtual screening, ADMET prediction and dynamics simulation of potential compounds targeting the main protease of SARS-CoV-2.",J Biomol Struct Dyn,32715956,7/28/20,pubmed,0,5,"virtual screening, molecular dynamics simulation",0.948080324,0.001438111,0.046167167,0.001438148,0.00143811,0.001438141,Drug discovery,0.9875729,TRUE,15,0.227596017,7,0.299973241,0,0.403234768,,,0.310268008
8343,Leveraging the United States Epicenter to Provide Insights on COVID-19 in Patients With Systemic Lupus Erythematosus.,Arthritis Rheumatol,32715660,7/28/20,pubmed,0,18,logistic regression,0.001350449,0.001350382,0.001350373,0.001350364,0.112134284,0.882464149,Clinics,0.49000293,FALSE,51.16666667,0.639557177,62.61111111,0.731201499,15,0.874313229,,,0.748357302
8344,The protein expression profile of ACE2 in human tissues.,Mol Syst Biol,32715618,7/28/20,pubmed,0,6,dataset,0.910561265,0.001593552,0.001593577,0.001593608,0.023911641,0.060746357,Drug discovery,0.7843765,TRUE,197.6666667,0.971921578,445.8333333,0.97497993,41,0.948144947,,,0.965015485
8345,Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research.,Brief Bioinform,32715315,7/28/20,pubmed,0,6,"computational, bioinformatic",0.956564314,0.001823389,0.001823466,0.001823442,0.001823493,0.036141896,Drug discovery,0.6617403,TRUE,62.5,0.718164389,10.5,0.363459995,3,0.667819001,,,0.583147795
8346,SARS-CoV-2 Whole Genome Amplification and Sequencing for Effective Population-Based Surveillance and Control of Viral Transmission.,Clin Chem,32715310,7/28/20,pubmed,0,11,"sequencing, transcriptom, whole genome, genome sequences, genomes, sequence alignment",0.001022643,0.824871633,0.02724068,0.144819509,0.001022634,0.001022902,Genomics,0.32398498,FALSE,43.81818182,0.575978725,29,0.562884667,1,0.537564047,,,0.558809146
8347,Extended-Interval Dosing Strategy of Immune Checkpoint Inhibitors in Lung Cancer: Will it Outlast the COVID-19 Pandemic?,Front Oncol,32714874,7/28/20,pubmed,0,3,in silico,0.294355699,0.00168461,0.001684538,0.20559654,0.142008838,0.354669775,Clinics,0.8734231,TRUE,79.66666667,0.805924918,164.3333333,0.895705111,0,0.403234768,,,0.701621599
8348,"COVID-19 and Inflammatory Bowel Diseases: Risk Assessment, Shared Molecular Pathways, and Therapeutic Challenges.",Gastroenterol Res Pract,32714386,7/28/20,pubmed,0,5,"artificial intelligence, dataset",0.636687789,0.001203463,0.105894257,0.085969298,0.001203508,0.169041685,Drug discovery,0.88313806,TRUE,41.8,0.555692993,7.4,0.305392026,0,0.403234768,,,0.421439929
8349,Summoning a New Artificial Intelligence Patent Model: In the Age of Pandemic.,SSRN,32714121,7/28/20,pubmed,0,2,artificial intelligence,0.027005127,0.000786369,0.516352056,0.454283705,0.000786393,0.00078635,Epidemiology,0.33852363,FALSE,13,0.197352959,1.5,0.138747659,0,0.403234768,,,0.246445128
8350,0,SSRN,32714116,7/28/20,pubmed,0,2,computational,0.830497605,0.101675218,0.001653062,0.001653141,0.001653073,0.062867901,Drug discovery,0.8452036,TRUE,286.5,0.989671594,600.5,0.98394434,0,0.403234768,,,0.792283567
8351,Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis.,SSRN,32714115,7/28/20,pubmed,0,5,"interactom, multiom, probabilistic",0.800429582,0.001272695,0.001272687,0.194479628,0.001272686,0.001272722,Drug discovery,0.7193047,TRUE,20.8,0.30768755,31,0.578204442,2,0.618927094,,,0.501606362
8352,A Pharmacological Interactome between COVID-19 Patient Samples and Human Sensory Neurons Reveals Potential Drivers of Neurogenic Pulmonary Dysfunction.,SSRN,32714114,7/28/20,pubmed,0,15,"sequencing, interactom, genome-wide, dataset",0.627767033,0.137565149,0.001538201,0.001538189,0.001538152,0.230053276,Drug discovery,0.7341091,TRUE,37.33333333,0.511596264,56.4,0.708455981,12,0.850299401,,,0.690117215
8353,A SARS-CoV-2 Vaccination Strategy Focused on Population-Scale Immunity.,SSRN,32714112,7/28/20,pubmed,0,4,proteom,0.661885593,0.141376457,0.001415154,0.001415218,0.151203564,0.042704014,Drug discovery,0.62077117,TRUE,167.25,0.956521739,307.5,0.956649719,0,0.403234768,,,0.772135409
8354,Candidate Targets for Immune Responses to 2019-Novel Coronavirus (nCoV): Sequence Homology- and Bioinformatic-Based Predictions.,SSRN,32714104,7/28/20,pubmed,0,6,bioinformatic,0.573078874,0.418598584,0.002080669,0.002080679,0.00208057,0.002080624,Drug discovery,0.33604875,FALSE,143,0.937596636,327.8333333,0.961399518,0,0.403234768,,,0.767410307
8355,A Quantitative and Radiomics approach to monitoring ARDS in COVID-19 patients based on chest CT: a retrospective cohort study.,Int J Med Sci,32714080,7/28/20,pubmed,0,10,"radiom, dataset",0.000916683,0.000916714,0.728398275,0.000916713,0.000916706,0.26793491,Imaging,0.93465036,TRUE,13.9,0.209784155,,,6,0.764429903,,,0.487107029
8356,Computational search for potential COVID-19 drugs from FDAapproved drugs and small molecules of natural origin identifies several anti-virals and plant products.,J Biosci,32713863,7/28/20,pubmed,0,3,"virtual screening, computational",0.665267082,0.109136133,0.001272708,0.001272735,0.118639028,0.104412313,Drug discovery,0.59772813,TRUE,86.66666667,0.832209784,38.66666667,0.625501739,7,0.785110192,,,0.747607239
8357,"Understanding COVID-19 via comparative analysis of dark proteomes of SARS-CoV-2, human SARS and bat SARS-like coronaviruses.",Cell Mol Life Sci,32712910,7/28/20,pubmed,0,8,"computational, proteom, sequence alignment",0.577456238,0.336111219,0.001203443,0.001203466,0.050513702,0.033511932,Drug discovery,0.698623,TRUE,138.625,0.931473808,294.125,0.952502007,28,0.926168282,,,0.936714699
8358,PEEP/ FIO2 ARDSNet Scale Grouping of a Single Ventilator for Two Patients: Modeling Tidal Volume Response.,Respir Care,32712582,7/28/20,pubmed,0,6,computational,0.001203469,0.001203467,0.313399321,0.301384253,0.001203472,0.381606018,Clinics,0.21239889,FALSE,41.83333333,0.555878533,18.33333333,0.464343056,2,0.618927094,,,0.546382894
8359,Decoding the proteome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for cell-penetrating peptides involved in pathogenesis or applicable as drug delivery vectors.,Infect Genet Evol,32712315,7/28/20,pubmed,0,3,"computational, proteom",0.91377386,0.001291272,0.0012913,0.001291268,0.081061033,0.001291268,Drug discovery,0.8296814,TRUE,27.33333333,0.399529965,3.333333333,0.206515922,3,0.667819001,,,0.424621629
8360,Hyperglycemia is a strong predictor of poor prognosis in COVID-19.,Diabetes Res Clin Pract,32712122,7/28/20,pubmed,0,9,logistic regression,0.001461869,0.001461876,0.001461912,0.001461882,0.001461916,0.992690545,Clinics,0.83365047,TRUE,140.2222222,0.934133218,234.8888889,0.93209794,12,0.850299401,,,0.905510186
8361,Reverse genetic systems: Rational design of coronavirus live attenuated vaccines with immune sequelae.,Adv Virus Res,32711735,7/28/20,pubmed,0,5,genomes,0.464779207,0.436210208,0.002422387,0.091743608,0.002422352,0.002422238,Drug discovery,0.49289355,FALSE,28.8,0.415548271,16,0.437316029,4,0.707574542,,,0.520146281
8362,Spatial and temporal dynamics of SARS-CoV-2 in COVID-19 patients: A systematic review and meta-analysis.,EBioMedicine,32711256,7/28/20,pubmed,0,3,mathematical model,0.001085426,0.304517755,0.001085365,0.164968147,0.001085337,0.527257971,Clinics,0.4500272,FALSE,16.66666667,0.251159626,4.666666667,0.246721969,20,0.900117291,,,0.465999629
8363,Effect of lockdown due to SARS COVID-19 on aerosol optical depth (AOD) over urban and mining regions in India.,Sci Total Environ,32711073,7/28/20,pubmed,0,3,radiom,0.001415129,0.001415147,0.026237178,0.968102237,0.001415172,0.001415137,Epidemiology,0.97697437,TRUE,10.66666667,0.159131672,2.666666667,0.185442869,24,0.914439163,,,0.419671234
8364,"Knowledge, attitudes and practices towards COVID-19 among young adults with Type 1 Diabetes Mellitus amid the nationwide lockdown in India: A cross-sectional survey.",Diabetes Res Clin Pract,32710997,7/28/20,pubmed,0,6,logistic regression,0.001330037,0.001330051,0.001330052,0.00133011,0.950925181,0.04375457,Healthcare,0.9928405,TRUE,96.5,0.860226359,51.5,0.687382927,8,0.799987654,,,0.782532313
8365,Mutations Strengthened SARS-CoV-2 Infectivity.,J Mol Biol,32710986,7/28/20,pubmed,0,4,"machine learning, sequence alignment",0.60107356,0.330045286,0.064566689,0.001438201,0.001438151,0.001438114,Drug discovery,0.30020902,FALSE,36.5,0.503123261,17.75,0.458322184,60,0.964503982,,,0.641983142
8366,IL-6-based mortality risk model for hospitalized patients with COVID-19.,J Allergy Clin Immunol,32710975,7/28/20,pubmed,0,17,predictive model,0.001156254,0.001156252,0.037144045,0.001156299,0.001156233,0.958230917,Clinics,0.24895954,FALSE,1.470588235,0.014039211,0,0.055525823,5,0.739490092,,,0.269685042
8367,Clinical molecular genetics evaluation in women with reproductive failures.,Am J Reprod Immunol,32710571,7/28/20,pubmed,0,6,"sequencing, pharmacogenom, nutrigenom",0.084866151,0.498940381,0.168209991,0.220449128,0.026096177,0.001438172,Genomics,0.8716413,TRUE,31.16666667,0.445976869,9.5,0.345531175,1,0.537564047,,,0.44302403
8368,Mental health of healthcare workers during the COVID-19 pandemic in Italy.,J Eval Clin Pract,32710481,7/28/20,pubmed,0,4,dataset,0.001593506,0.00159349,0.00159362,0.001593541,0.917592934,0.076032909,Healthcare,0.96554315,TRUE,49.25,0.625889047,26.25,0.539670859,5,0.739490092,,,0.635016666
8369,Covid-19 mortality is negatively associated with test number and government effectiveness.,Sci Rep,32709854,7/28/20,pubmed,0,4,dataset,0.001371242,0.001371277,0.023985279,0.406732313,0.001371356,0.565168532,Clinics,0.72586906,TRUE,57.5,0.685447461,89.75,0.800709125,38,0.944132354,,,0.810096313
8370,Patient-reported Outcomes of Patients With Breast Cancer During the COVID-19 Outbreak in the Epicenter of China: A Cross-sectional Survey Study.,Clin Breast Cancer,32709505,7/28/20,pubmed,0,16,logistic regression,0.030725379,0.001059414,0.001059371,0.001059394,0.596076884,0.370019558,Healthcare,0.993067,TRUE,21.75,0.321603068,2.0625,0.164369815,13,0.858880178,,,0.448284354
8371,Multivariate Analysis of Black Race and Environmental Temperature on COVID-19 in the US.,Am J Med Sci,32709397,7/28/20,pubmed,0,11,logistic regression,0.00131038,0.001310384,0.001310333,0.381040891,0.199139834,0.415888178,Clinics,0.48461,FALSE,62.18181818,0.716370833,18.72727273,0.467219695,18,0.891474782,,,0.691688437
8372,"Clinical Characteristics and Predictors of Disease Progression in Severe Patients with COVID-19 Infection in Jiangsu Province, China: A Descriptive Study.",Am J Med Sci,32709280,7/28/20,pubmed,0,17,logistic regression,0.001272812,0.001272776,0.001272666,0.063261564,0.001272669,0.931647513,Clinics,0.98299897,TRUE,69.82352941,0.762755891,34.05882353,0.599344394,10,0.828199272,,,0.730099852
8373,"Simulating the effect of school closure during COVID-19 outbreaks in Ontario, Canada.",BMC Med,32709232,7/28/20,pubmed,0,5,simulation model,0.001203418,0.001203416,0.001203408,0.752161605,0.097746552,0.1464816,Epidemiology,0.5138456,TRUE,108,0.886078298,70.4,0.754616002,13,0.858880178,,,0.833191493
8374,Polymorphism and Selection Pressure of SARS-CoV-2 Vaccine and Diagnostic Antigens: Implications for Immune Evasion and Serologic Diagnostic Performance.,Pathogens,32709055,7/28/20,pubmed,0,2,genome sequences,0.001823466,0.965688207,0.027018142,0.001823362,0.001823473,0.001823349,Genomics,0.59575295,TRUE,93.5,0.85317583,99.5,0.821581482,0,0.403234768,,,0.692664027
8375,Demographic and Comorbidities Data Description of Population in Mexico with SARS-CoV-2 Infected Patients(COVID19): An Online Tool Analysis.,Int J Environ Res Public Health,32709027,7/28/20,pubmed,0,8,dataset,0.001622742,0.001622787,0.069584774,0.826817189,0.001622853,0.098729654,Epidemiology,0.07210821,FALSE,32.75,0.463788732,1.625,0.141222906,0,0.403234768,,,0.336082135
8376,Baseline Chronic Comorbidity and Mortality in Laboratory-Confirmed COVID-19 Cases: Results from the PRECOVID Study in Spain.,Int J Environ Res Public Health,32709002,7/28/20,pubmed,0,16,logistic regression,0.117576375,0.001823426,0.001823414,0.06754345,0.001823446,0.80940989,Clinics,0.8120464,TRUE,20.3125,0.301626569,11.8125,0.382726786,24,0.914439163,,,0.532930839
8377,Genetic Diversity Among SARS-CoV2 Strains in South America may Impact Performance of Molecular Detection.,Pathogens,32708840,7/28/20,pubmed,0,9,"in silico, whole genome, genome sequences, genomes",0.201932847,0.75393075,0.039821855,0.001438202,0.001438148,0.001438199,Genomics,0.45173734,FALSE,38,0.519327107,26.77777778,0.54388547,5,0.739490092,,,0.60090089
8378,Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections.,Viruses,32708803,7/28/20,pubmed,0,9,"machine learning, deep learning, neural network, lstm",0.00092629,0.000926301,0.633649516,0.3626453,0.00092631,0.000926283,Epidemiology,0.62620294,TRUE,11.55555556,0.174655204,2.111111111,0.165038801,18,0.891474782,,,0.410389596
8379,From Anti-SARS-CoV-2 Immune Responses to COVID-19 via Molecular Mimicry.,Antibodies (Basel),32708525,7/28/20,pubmed,0,1,proteom,0.760138324,0.228980582,0.002720153,0.002720213,0.002720456,0.002720272,Drug discovery,0.66178846,TRUE,184,0.965922444,65,0.739028633,17,0.887338725,,,0.8640966
8380,A Comprehensive Mapping of the Druggable Cavities within the SARS-CoV-2 Therapeutically Relevant Proteins by Combining Pocket and Docking Searches as Implemented in Pockets 2.0.,Int J Mol Sci,32708196,7/28/20,pubmed,0,10,"virtual screening, in silico",0.915766428,0.002080635,0.075911197,0.002080658,0.002080546,0.002080535,Drug discovery,0.76714265,TRUE,63.8,0.726822933,270.7,0.945945946,7,0.785110192,,,0.819293024
8381,"COVID-19: A Review on Diagnosis, Treatment, and Prophylaxis.",Int J Mol Sci,32708112,7/28/20,pubmed,0,3,bioinformatic,0.56004004,0.262109739,0.001350379,0.001350423,0.141819471,0.033329947,Drug discovery,0.6295073,TRUE,52,0.647349867,20.66666667,0.487958255,4,0.707574542,,,0.614294221
8382,Two Examples of RNA Aptamers with Antiviral Activity. Are Aptamers the Wished Antiviral Drugs?,Pharmaceuticals (Basel),32707768,7/28/20,pubmed,0,2,genomes,0.583010557,0.410836913,0.001538178,0.001538146,0.0015381,0.001538106,Drug discovery,0.9429474,TRUE,8,0.118683901,0,0.055525823,1,0.537564047,,,0.237257924
8383,"Social, ethical and behavioural aspects of COVID-19.",Wellcome Open Res,32704548,7/28/20,pubmed,0,20,mathematical model,0.017571003,0.001156249,0.001156249,0.790221129,0.188739136,0.001156235,Epidemiology,0.64121985,TRUE,21.45,0.316717175,21.4,0.496052984,0,0.403234768,,,0.405334975
8384,"Modeling the effect of area deprivation on COVID-19 incidences: a study of Chennai megacity, India.",Public Health,32707468,7/25/20,pubmed,0,6,bayes,0.001350372,0.00135037,0.001350362,0.532528471,0.462070019,0.001350407,Epidemiology,0.28924274,FALSE,17,0.257467994,2.666666667,0.185442869,10,0.828199272,,,0.423703378
8385,Lack of antibody-mediated cross-protection between SARS-CoV-2 and SARS-CoV infections.,EBioMedicine,32707445,7/25/20,pubmed,0,11,whole genome,0.521760207,0.331029146,0.001684514,0.109760675,0.001684625,0.034080832,Drug discovery,0.26934344,FALSE,65.45454545,0.736842105,41.27272727,0.641156007,6,0.764429903,,,0.714142672
8386,Deciphering the co-adaptation of codon usage between respiratory coronaviruses and their human host uncovers candidate therapeutics for COVID-19.,Infect Genet Evol,32707288,7/25/20,pubmed,0,2,"genomes, correlation analysis",0.639884695,0.335089208,0.001010946,0.001011021,0.021993128,0.001011002,Drug discovery,0.90806293,TRUE,6.5,0.093512277,3.5,0.213607172,5,0.739490092,,,0.348869847
8387,"Risk factors associated with mental illness in hospital discharged patients infected with COVID-19 in Wuhan, China.",Psychiatry Res,32707218,7/25/20,pubmed,0,7,logistic regression,0.001861677,0.0018617,0.001861696,0.00186174,0.691257504,0.301295684,Healthcare,0.9383694,TRUE,120.4,0.907724658,82.2,0.783382392,20,0.900117291,,,0.863741447
8388,Remdesivir for Severe COVID-19 versus a Cohort Receiving Standard of Care.,Clin Infect Dis,32706859,7/25/20,pubmed,0,33,logistic regression,0.11417953,0.001126815,0.00112683,0.001126867,0.001126819,0.88131314,Clinics,0.65590817,TRUE,82.57575758,0.816562558,132.2727273,0.866135938,35,0.939317242,,,0.874005246
8389,Quantitative lung lesion features and temporal changes on chest CT in patients with common and severe SARS-CoV-2 pneumonia.,PLoS One,32706819,7/25/20,pubmed,0,4,artificial intelligence,0.001046814,0.001046904,0.428453921,0.055875573,0.001046828,0.51252996,Clinics,0.9785793,TRUE,292.75,0.990290061,281.75,0.949157078,1,0.537564047,,,0.825670395
8390,Quantifying early COVID-19 outbreak transmission in South Africa and exploring vaccine efficacy scenarios.,PLoS One,32706790,7/25/20,pubmed,0,6,mathematical model,0.042394877,0.001593539,0.001593507,0.951230918,0.001593548,0.001593611,Epidemiology,0.14124322,FALSE,29.66666667,0.42773208,17.33333333,0.45424137,1,0.537564047,,,0.473179166
8391,"In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation.",PLoS One,32706783,7/25/20,pubmed,0,4,"molecular dynamics simulation, in silico",0.862174266,0.000926299,0.00092634,0.055206718,0.000926323,0.079840055,Drug discovery,0.7745397,TRUE,41.5,0.553219123,11.25,0.374364463,6,0.764429903,,,0.564004497
8392,Presence of Genetic Variants Among Young Men With Severe COVID-19.,JAMA,32706371,7/25/20,pubmed,0,30,"sequencing, exom",0.115546449,0.217977622,0.01175197,0.00068316,0.156596404,0.497444395,Clinics,0.9194954,TRUE,72.1,0.773269837,145.1333333,0.879181161,151,0.987036237,,,0.879829079
8393,Molecular docking and dynamics study of natural compound for potential inhibition of main protease of SARS-CoV-2.,J Biomol Struct Dyn,32705962,7/25/20,pubmed,0,11,"molecular dynamics simulation, bioinformatic",0.906588053,0.001684586,0.086673727,0.001684609,0.001684505,0.001684519,Drug discovery,0.9499463,TRUE,4.636363636,0.062836292,0.363636364,0.073454643,5,0.739490092,,,0.291927009
8394,Identification of a potential SARS-CoV2 inhibitor via molecular dynamics simulations and amino acid decomposition analysis.,J Biomol Struct Dyn,32705953,7/25/20,pubmed,0,4,"molecular dynamics simulation, in silico",0.99650322,0.000699346,0.000699331,0.000699414,0.000699353,0.000699336,Drug discovery,0.7957606,TRUE,21.75,0.321603068,2,0.164302917,3,0.667819001,,,0.384574995
8395,0,J Biomol Struct Dyn,32705952,7/25/20,pubmed,0,4,"molecular dynamics simulation, in silico, in-silico",0.972419191,0.022179431,0.001350314,0.001350341,0.001350313,0.00135041,Drug discovery,0.9798282,TRUE,79.5,0.80493537,67.5,0.746855767,13,0.858880178,,,0.803557105
8396,Computational drug repurposing for the identification of SARS-CoV-2 main protease inhibitors.,J Biomol Struct Dyn,32705942,7/25/20,pubmed,0,6,"virtual screening, computational",0.937144484,0.001684498,0.001684582,0.001684541,0.031762667,0.026039228,Drug discovery,0.8790788,TRUE,82.33333333,0.815449317,11,0.371287129,2,0.618927094,,,0.601887847
8397,COVID-19 and associations with frailty and multimorbidity: a prospective analysis of UK Biobank participants.,Aging Clin Exp Res,32705587,7/25/20,pubmed,0,9,logistic regression,0.001861712,0.001861755,0.129437446,0.001861871,0.551511572,0.313465644,Healthcare,0.40814677,FALSE,8,0.118683901,1.222222222,0.126304522,2,0.618927094,,,0.287971839
8398,Small molecule therapeutics for COVID-19: repurposing of inhaled furosemide.,PeerJ,32704455,7/25/20,pubmed,0,14,in silico,0.952117174,0.00120343,0.001203423,0.001203435,0.001203417,0.043069122,Drug discovery,0.9109441,TRUE,40.64285714,0.545550127,,,3,0.667819001,,,0.606684564
8399,Coronavirus disease 2019 drug discovery through molecular docking.,F1000Res,32704354,7/25/20,pubmed,0,2,virtual screening,0.78549863,0.002562599,0.002562598,0.204250961,0.002562598,0.002562613,Drug discovery,0.88561165,TRUE,43,0.567629414,10.5,0.363459995,7,0.785110192,,,0.572066534
8400,Human SARS-CoV-2 has evolved to reduce CG dinucleotide in its open reading frames.,Sci Rep,32704018,7/25/20,pubmed,0,7,genomes,0.550551212,0.387707707,0.056035636,0.001901824,0.001901858,0.001901763,Drug discovery,0.43432713,FALSE,55.57142857,0.671655637,,,15,0.874313229,,,0.772984433
8401,Can Dietary Fatty Acids Affect the COVID-19 Infection Outcome in Vulnerable Populations?,mBio,32703911,7/25/20,pubmed,0,3,microbiom,0.517823754,0.046255473,0.002422309,0.002422341,0.045953463,0.385122659,Drug discovery,0.76422906,TRUE,130,0.921763869,328.6666667,0.961734011,3,0.667819001,,,0.850438961
8402,Evolution and epidemic spread of SARS-CoV-2 in Brazil.,Science,32703910,7/25/20,pubmed,0,78,"sequencing, genomes, dataset",0.00182331,0.298685106,0.001823349,0.694021488,0.001823362,0.001823385,Epidemiology,0.21245328,FALSE,60,0.703444864,252.0769231,0.938654001,25,0.918019631,,,0.853372832
8403,COVID-19 prediction models should adhere to methodological and reporting standards.,Eur Respir J,32703773,7/25/20,pubmed,0,3,prediction model,0.034962574,0.034962219,0.825186136,0.034963704,0.034962175,0.034963192,Epidemiology,0.5151906,TRUE,226,0.980394582,331.3333333,0.9622692,8,0.799987654,,,0.914217145
8404,Current Landscape of Imaging and the Potential Role for Artificial Intelligence in the Management of COVID-19.,Curr Probl Diagn Radiol,32703538,7/25/20,pubmed,0,9,artificial intelligence,0.098766863,0.002357861,0.89180179,0.002357793,0.002357861,0.002357833,Imaging,0.8710166,TRUE,55.44444444,0.670727936,31.88888889,0.583690126,2,0.618927094,,,0.624448385
8405,SEIR model for COVID-19 dynamics incorporating the environment and social distancing.,BMC Res Notes,32703315,7/25/20,pubmed,0,5,mathematical model,0.002183274,0.002183299,0.002183226,0.989083486,0.002183302,0.002183413,Epidemiology,0.3142622,FALSE,22,0.326056033,6.8,0.292881991,23,0.91129082,,,0.510076281
8406,Potentially repurposable drugs for COVID-19 identified from SARS-CoV-2 Host Protein Interactome.,Res Sq,32702734,7/24/20,pubmed,0,3,"computational, transcriptom, interactom",0.99141403,0.001717197,0.001717206,0.001717195,0.001717167,0.001717205,Drug discovery,0.49617684,FALSE,29,0.41993939,14.66666667,0.420323789,1,0.537564047,,,0.459275742
8407,In silico Drug Repurposing to combat COVID-19 based on Pharmacogenomics of Patient Transcriptomic Data.,Res Sq,32702730,7/24/20,pubmed,0,3,"in silico, in-silico, transcriptom, pharmacogenom",0.848273982,0.001622767,0.048606578,0.001622839,0.001622729,0.098251105,Drug discovery,0.8074519,TRUE,137.3333333,0.929680252,177.3333333,0.904602622,1,0.537564047,,,0.790615641
8408,State-level impact of social distancing and testing on COVID-19 in the United States.,Res Sq,32702727,7/24/20,pubmed,0,3,bayes,0.001272664,0.001272679,0.001272647,0.99363671,0.001272665,0.001272635,Epidemiology,0.29103005,FALSE,74.33333333,0.783227163,59.33333333,0.718290072,3,0.667819001,,,0.723112079
8409,Structure of human steroid 5α-reductase 2 with anti-androgen drug finasteride.,Res Sq,32702725,7/24/20,pubmed,0,10,"molecular dynamics simulation, computational",0.925848355,0.067147016,0.001751147,0.001751166,0.001751135,0.00175118,Drug discovery,0.8424519,TRUE,68.3,0.755519822,26.7,0.543350281,1,0.537564047,,,0.612144717
8410,Can Hyperperfusion of Nonaerated Lung Explain COVID-19 Hypoxia?,Res Sq,32702716,7/24/20,pubmed,0,4,mathematical model,0.00289849,0.00289846,0.002898535,0.502531591,0.00289833,0.485874593,Epidemiology,0.4594931,FALSE,43.5,0.573381161,45.5,0.660957988,0,0.403234768,,,0.545857972
8411,Worldwide inverse correlation between Bacille Calmette-Guérin immunization and COVID-19 morbidity and mortality.,Res Sq,32702715,7/24/20,pubmed,0,1,correlation analysis,0.002032835,0.002032854,0.002032743,0.67885023,0.002032902,0.313018436,Epidemiology,0.26678437,FALSE,65,0.734801163,138,0.872892695,3,0.667819001,,,0.758504286
8412,Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs.,Res Sq,32702714,7/24/20,pubmed,0,3,"computational, transcriptom, proteom, interactom, genomes",0.902387133,0.043973839,0.048943077,0.001565359,0.001565288,0.001565302,Drug discovery,0.4238624,FALSE,29,0.41993939,14.66666667,0.420323789,5,0.739490092,,,0.526584424
8413,An in-silico approach to develop of a multi-epitope vaccine candidate against SARS-CoV-2 envelope (E) protein.,Res Sq,32702713,7/24/20,pubmed,0,5,"computational, in-silico",0.857300769,0.135092245,0.001901776,0.001901768,0.001901741,0.0019017,Drug discovery,0.118632674,FALSE,31.6,0.451048302,19.6,0.476786192,2,0.618927094,,,0.515587196
8414,COVID-19 and stroke: Experience in a Ghanaian healthcare system.,J Neurol Sci,32702560,7/24/20,pubmed,0,6,logistic regression,0.001684548,0.001684554,0.001684485,0.253409806,0.001684699,0.739851908,Clinics,0.9190737,TRUE,53,0.654400396,41.66666667,0.643029168,5,0.739490092,,,0.678973219
8415,"The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study.",Lancet Oncol,32702310,7/24/20,pubmed,0,8,dataset,0.000662742,0.000662692,0.158474858,0.383723393,0.000662705,0.45581361,Clinics,0.746857,TRUE,125.125,0.91434226,134.625,0.869012577,171,0.989196864,,,0.9241839
8416,Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS.,Res Sq,32702077,7/24/20,pubmed,0,15,"bioinformatic, transcriptom",0.951186161,0.001943549,0.041039641,0.001943554,0.001943484,0.001943611,Drug discovery,0.8047267,TRUE,14.13333333,0.21386604,7.866666667,0.314289537,2,0.618927094,,,0.38236089
8417,Pre-existing traits associated with Covid-19 illness severity.,PLoS One,32702044,7/24/20,pubmed,0,29,logistic regression,0.001098825,0.001098819,0.001098806,0.001098821,0.001098896,0.994505832,Clinics,0.88810134,TRUE,102.5862069,0.874574804,151.137931,0.883997859,37,0.942712513,,,0.900428392
8418,Analysis of prediction and early warning indexes of patients with COVID-19.,Expert Rev Respir Med,32701376,7/24/20,pubmed,0,8,logistic regression,0.001486422,0.001486516,0.00148652,0.001486433,0.001486458,0.992567651,Clinics,0.972306,TRUE,32.125,0.456552663,26.875,0.544554455,2,0.618927094,,,0.540011404
8419,Emergence of European and North American mutant variants of SARS-CoV-2 in South-East Asia.,Transbound Emerg Dis,32701194,7/24/20,pubmed,0,6,"genome sequences, sequence alignment",0.00099954,0.995002203,0.000999516,0.000999596,0.000999544,0.000999602,Genomics,0.6661713,TRUE,22,0.326056033,6.333333333,0.285121755,14,0.866658436,,,0.492612075
8420,Targeting SARS-CoV-2 RBD Interface: a Supervised Computational Data-Driven Approach to Identify Potential Modulators.,ChemMedChem,32700795,7/24/20,pubmed,0,8,computational,0.992690527,0.001461893,0.00146189,0.001461941,0.001461897,0.001461852,Drug discovery,0.8682139,TRUE,37.5,0.513946441,3.125,0.200160557,0,0.403234768,,,0.372447255
8421,Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients.,Environ Sci Pollut Res Int,32700269,7/24/20,pubmed,0,1,machine learning,0.001486505,0.126788825,0.867265212,0.001486503,0.001486463,0.001486491,Imaging,0.58196104,TRUE,24,0.35574247,1,0.122023013,10,0.828199272,,,0.435321585
8422,Public Opinions towards COVID-19 in California and New York on Twitter.,medRxiv,32699856,7/24/20,pubmed,0,4,dataset,0.001371268,0.001371275,0.00137131,0.835982565,0.15853221,0.001371372,Epidemiology,0.2350221,FALSE,5.25,0.072855464,2.25,0.170925876,6,0.764429903,,,0.336070415
8423,Single-nucleotide conservation state annotation of SARS-CoV-2 genome.,bioRxiv,32699851,7/24/20,pubmed,0,2,sequence alignment,0.001987181,0.906659269,0.085392006,0.001987242,0.001987102,0.0019872,Genomics,0.20339757,FALSE,30,0.432432432,253.5,0.939256088,0,0.403234768,,,0.591641096
8424,Comparative multiplexed interactomics of SARS-CoV-2 and homologous coronavirus non-structural proteins identifies unique and shared host-cell dependencies.,bioRxiv,32699849,7/24/20,pubmed,0,4,"proteom, interactom",0.693856472,0.301578156,0.001141332,0.001141387,0.001141335,0.001141318,Drug discovery,0.5645831,TRUE,0,0.006432061,,,5,0.739490092,,,0.372961077
8425,Predicting Mechanical Ventilation Requirement and Mortality in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study.,ArXiv,32699815,7/24/20,pubmed,0,7,"machine learning, deep learning, computational, image analysis, classifier, radiom",0.00129123,0.001291212,0.60135392,0.001291292,0.001291227,0.393481118,Imaging,0.04333839,FALSE,23.85714286,0.352031666,,,2,0.618927094,,,0.48547938
8426,Dynamics of B-cell repertoires and emergence of cross-reactive responses in COVID-19 patients with different disease severity.,ArXiv,32699813,7/24/20,pubmed,0,15,sequencing,0.27520896,0.421881757,0.002296618,0.002296687,0.002296574,0.296019404,Genomics,0.2841317,FALSE,137.7333333,0.93011318,434.2666667,0.973708857,0,0.403234768,,,0.769018935
8427,SARS-CoV-2 genomic variations associated with mortality rate of COVID-19.,J Hum Genet,32699345,7/24/20,pubmed,0,5,genome sequences,0.001538103,0.534569735,0.001538099,0.255948104,0.001538193,0.204867767,Genomics,0.3687596,FALSE,136.6,0.928752551,338.6,0.963807867,95,0.976912155,,,0.956490858
8428,Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial.,BMJ Open,32699167,7/24/20,pubmed,0,12,artificial intelligence,0.001022673,0.001022695,0.001022705,0.675368293,0.001022712,0.320540923,Epidemiology,0.9456017,TRUE,11.58333333,0.174717051,12.58333333,0.393631255,7,0.785110192,,,0.451152833
8429,Utilization of COVID-19 Treatments and Clinical Outcomes among Patients with Cancer: A COVID-19 and Cancer Consortium (CCC19) Cohort Study.,Cancer Discov,32699031,7/24/20,pubmed,0,45,logistic regression,0.034212129,0.001653055,0.001653086,0.08545296,0.001653123,0.875375646,Clinics,0.85376346,TRUE,129.2,0.920712474,134.8444444,0.86934707,40,0.947033768,,,0.912364437
8430,Dual inhibitors of SARS-CoV-2 proteases: pharmacophore and molecular dynamics based drug repositioning and phytochemical leads.,J Biomol Struct Dyn,32698693,7/24/20,pubmed,0,6,virtual screening,0.929919109,0.001565385,0.00156541,0.063819355,0.001565383,0.001565358,Drug discovery,0.9407611,TRUE,49.66666667,0.629166924,32,0.585763982,1,0.537564047,,,0.584164984
8431,"Anti-COVID drugs: repurposing existing drugs or search for new complex entities, strategies and perspectives.",Future Med Chem,32698626,7/24/20,pubmed,0,2,computational,0.986804688,0.002639028,0.002639127,0.002639158,0.002638974,0.002639025,Drug discovery,0.92388284,TRUE,46,0.596882924,4.5,0.242708055,2,0.618927094,,,0.486172691
8432,Point-of-Use Rapid Detection of SARS-CoV-2: Nanotechnology-Enabled Solutions for the COVID-19 Pandemic.,Int J Mol Sci,32698479,7/24/20,pubmed,0,11,"sequencing, whole genome",0.001622841,0.17426825,0.406455757,0.414407684,0.001622745,0.001622722,Epidemiology,0.59232455,TRUE,150.0909091,0.944090544,47.36363636,0.669922398,13,0.858880178,,,0.824297706
8433,Tiotropium is Predicted to be a Promising Drug for COVID-19 Through Transcriptome-Based Comprehensive Molecular Pathway Analysis.,Viruses,32698440,7/24/20,pubmed,0,3,"bioinformatic, transcriptom, deep-learning, prediction model",0.838742764,0.00139285,0.07193495,0.00139285,0.00139284,0.085143745,Drug discovery,0.8151238,TRUE,35.33333333,0.490259138,27,0.546026224,3,0.667819001,,,0.568034788
8434,Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China.,Wellcome Open Res,32685698,7/24/20,pubmed,0,4,mathematical model,0.001371265,0.001371346,0.001371281,0.993143535,0.001371284,0.001371289,Epidemiology,0.27622774,FALSE,137,0.929371019,209.75,0.922130051,222,0.993024261,,,0.94817511
8435,Panic and generalized anxiety during the COVID-19 pandemic among Bangladeshi people: An online pilot survey early in the outbreak.,J Affect Disord,32697713,7/23/20,pubmed,0,3,logistic regression,0.001415098,0.001415141,0.001415161,0.001415254,0.992924207,0.001415138,Healthcare,0.887064,TRUE,225.6666667,0.980085349,522.3333333,0.980465614,31,0.931971109,,,0.964174024
8436,"Transmission route and introduction of pandemic SARS-CoV-2 between China, Italy, and Spain.",J Med Virol,32697346,7/23/20,pubmed,0,3,"bayes, genome sequences",0.001538128,0.411551033,0.023151417,0.560683179,0.001538101,0.001538142,Epidemiology,0.29791075,FALSE,1.333333333,0.01366813,0,0.055525823,1,0.537564047,,,0.202252667
8437,Proteotyping SARS-CoV-2 Virus from Nasopharyngeal Swabs: A Proof-of-Concept Focused on a 3 Min Mass Spectrometry Window.,J Proteome Res,32697082,7/23/20,pubmed,0,11,proteom,0.130689949,0.756626636,0.106722012,0.001987172,0.001987104,0.001987127,Genomics,0.50277585,TRUE,54.09090909,0.661822005,30.18181818,0.570845598,7,0.785110192,,,0.672592598
8438,Can the neutrophil/lymphocyte ratio (NLR) have a role in the diagnosis of coronavirus 2019 disease (COVID-19)?,Rev Assoc Med Bras (1992),32696861,7/23/20,pubmed,0,6,logistic regression,0.001786556,0.001786663,0.001786683,0.001786559,0.001786605,0.991066933,Clinics,0.7808906,TRUE,64.5,0.731832519,6.5,0.288132192,6,0.764429903,,,0.594798205
8439,Paper spray mass spectrometry utilizing Teslin® substrate for rapid detection of lipid metabolite changes during COVID-19 infection.,Analyst,32696763,7/23/20,pubmed,0,6,metabolom,0.582342689,0.002806598,0.084958754,0.002806571,0.002806656,0.324278733,Drug discovery,0.7642124,TRUE,44,0.578390748,19.33333333,0.473842655,1,0.537564047,,,0.529932483
8440,"Associations between psychiatric disorders, COVID-19 testing probability and COVID-19 testing results: findings from a population-based study.",BJPsych Open,32696734,7/23/20,pubmed,0,7,logistic regression,0.001717154,0.001717237,0.001717309,0.001717246,0.727609426,0.265521626,Healthcare,0.744431,TRUE,46.42857143,0.600098955,12.85714286,0.39717688,6,0.764429903,,,0.587235246
8441,Comparative molecular investigation of the potential inhibitors against SARS-CoV-2 main protease: a molecular docking study.,J Biomol Struct Dyn,32696718,7/23/20,pubmed,0,6,molecular dynamics simulation,0.991413652,0.001717182,0.001717262,0.001717331,0.001717255,0.001717318,Drug discovery,0.87917835,TRUE,62.66666667,0.719030243,20.16666667,0.481803586,4,0.707574542,,,0.636136123
8442,Mathematical modelling on phase based transmissibility of Coronavirus.,Infect Dis Model,32695940,7/23/20,pubmed,0,2,mathematical model,0.065691702,0.166827742,0.001862003,0.720364943,0.043391674,0.001861936,Epidemiology,0.45672718,FALSE,4.5,0.061784897,0,0.055525823,4,0.707574542,,,0.274961754
8443,Regulatory Cross Talk Between SARS-CoV-2 Receptor Binding and Replication Machinery in the Human Host.,Front Physiol,32695025,7/23/20,pubmed,0,6,interactom,0.885521757,0.001653092,0.001653069,0.001653074,0.001653024,0.107865984,Drug discovery,0.55149275,TRUE,14,0.213494959,5.166666667,0.25849612,4,0.707574542,,,0.39318854
8444,Artificial intelligence in ophthalmology during COVID-19 and in the post COVID-19 era.,Curr Opin Ophthalmol,32694268,7/23/20,pubmed,0,4,artificial intelligence,0.001538128,0.001538176,0.768952703,0.103424775,0.057799824,0.066746394,Epidemiology,0.5832133,TRUE,156.5,0.949038283,97,0.816697886,2,0.618927094,,,0.794887754
8445,Update to living systematic review on prediction models for diagnosis and prognosis of covid-19.,BMJ,32694132,7/23/20,pubmed,0,1,prediction model,0.011750097,0.01175024,0.546035536,0.406959386,0.011752961,0.01175178,Epidemiology,0.521491,TRUE,459,0.996845816,2,0.164302917,2,0.618927094,,,0.593358609
8446,SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases.,mSystems,32694130,7/23/20,pubmed,0,19,sequencing,0.001461953,0.527604237,0.029004356,0.439005546,0.001461974,0.001461933,Genomics,0.53005594,TRUE,34.94736842,0.485868019,128,0.861386139,25,0.918019631,,,0.755091263
8447,COVID-19 and the Gut Microbiome: More than a Gut Feeling.,mSystems,32694127,7/23/20,pubmed,0,2,microbiom,0.249360473,0.500166988,0.059890685,0.002806636,0.002806534,0.184968684,Genomics,0.8439373,TRUE,27.5,0.401570907,96.5,0.816229596,7,0.785110192,,,0.667636898
8448,"COVID-19 Docking Server: a meta server for docking small molecules, peptides and antibodies against potential targets of COVID-19.",Bioinformatics,32692801,7/22/20,pubmed,0,8,bioinformatic,0.989083142,0.002183336,0.002183363,0.002183431,0.002183341,0.002183387,Drug discovery,0.8083242,TRUE,184.125,0.966046138,111.125,0.840045491,44,0.952157541,,,0.91941639
8449,"Impact of Social Distancing Measures on Coronavirus Disease Healthcare Demand, Central Texas, USA.",Emerg Infect Dis,32692648,7/22/20,pubmed,0,9,mathematical model,0.001717171,0.00171754,0.001717193,0.850382608,0.001717246,0.142748242,Epidemiology,0.30546603,FALSE,65.44444444,0.736780259,92.22222222,0.806261707,20,0.900117291,,,0.814386419
8450,"Virtual screening, molecular dynamics and structure-activity relationship studies to identify potent approved drugs for Covid-19 treatment.",J Biomol Struct Dyn,32692306,7/22/20,pubmed,0,13,"virtual screening, computational",0.980387829,0.000871556,0.016125987,0.000871553,0.000871544,0.000871532,Drug discovery,0.96581745,TRUE,15.15384615,0.228338178,3.923076923,0.22357506,6,0.764429903,,,0.405447714
8451,Analysis of clinical characteristics and outcomes in patients with COVID-19 based on a series of 1000 patients treated in Spanish emergency departments.,Emergencias,32692000,7/22/20,pubmed,0,30,logistic regression,0.001098829,0.001098819,0.00109883,0.001098847,0.042168543,0.953436131,Clinics,0.9980239,TRUE,60.8,0.707155668,9.933333333,0.352154134,13,0.858880178,,,0.63939666
8452,"ACE2, TMPRSS2, and Furin variants and SARS-CoV-2 infection in Madrid, Spain.",J Med Virol,32691890,7/22/20,pubmed,0,8,"sequencing, exom",0.041499562,0.853295471,0.002238465,0.002238531,0.002238676,0.098489296,Genomics,0.5119578,TRUE,55.375,0.670295009,17,0.451097137,6,0.764429903,,,0.62860735
8453,Natural derivatives with dual binding potential against SARS-CoV-2 main protease and human ACE2 possess low oral bioavailability: a brief computational analysis.,J Biomol Struct Dyn,32691697,7/22/20,pubmed,0,2,"computational, in silico",0.814994853,0.001330071,0.001330082,0.055194171,0.086875191,0.040275632,Drug discovery,0.7071071,TRUE,29.5,0.426000371,3.5,0.213607172,1,0.537564047,,,0.39239053
8454,Dysregulation in Akt/mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells.,Emerg Microbes Infect,32691695,7/22/20,pubmed,0,15,transcriptom,0.988807316,0.002238484,0.002238478,0.00223859,0.002238518,0.002238615,Drug discovery,0.6627842,TRUE,61.86666667,0.714329891,216.5333333,0.925675676,29,0.928020248,,,0.856008605
8455,0,J Biomol Struct Dyn,32691680,7/22/20,pubmed,0,5,"molecular dynamics simulation, in silico",0.992809394,0.001438115,0.001438125,0.001438148,0.001438107,0.00143811,Drug discovery,0.9723327,TRUE,17.6,0.264456676,35.2,0.606368745,6,0.764429903,,,0.545085108
8456,Chest CT for triage during COVID-19 on the emergency department: myth or truth?,Emerg Radiol,32691211,7/22/20,pubmed,0,9,"machine learning, logistic regression, prediction model",0.001022637,0.001022619,0.826857454,0.00102261,0.001022638,0.169052042,Imaging,0.7793199,TRUE,12.11111111,0.183499289,17.22222222,0.452635804,3,0.667819001,,,0.434651365
8457,"Why lockdown? Why national unity? Why global solidarity? Simplified arithmetic tools for decision-makers, health professionals, journalists and the general public to explore containment options for the 2019 novel coronavirus.",Infect Dis Model,32691016,7/22/20,pubmed,0,2,predictive model,0.001272656,0.001272708,0.001272656,0.947477466,0.047431812,0.001272703,Epidemiology,0.5923883,TRUE,83,0.818170573,168.5,0.898180359,9,0.814309525,,,0.843553486
8458,Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example.,Infect Dis Model,32691015,7/22/20,pubmed,0,19,mathematical model,0.003214189,0.003214254,0.003214145,0.922424146,0.064719056,0.003214211,Epidemiology,0.524678,TRUE,24.78947368,0.365143175,21.36842105,0.495651592,13,0.858880178,,,0.573224982
8459,Structure-based drug designing and immunoinformatics approach for SARS-CoV-2.,Sci Adv,32691011,7/22/20,pubmed,0,9,virtual screening,0.927977397,0.001486509,0.001486467,0.03684048,0.030722631,0.001486516,Drug discovery,0.8705884,TRUE,105.3333333,0.879708083,26.44444444,0.541008831,41,0.948144947,,,0.78962062
8460,Herbal medicine for the management of COVID-19 during the medical observation period: A review of guidelines.,Integr Med Res,32691000,7/22/20,pubmed,0,4,network analysis,0.001901766,0.00190177,0.001901814,0.632288948,0.303189955,0.058815747,Epidemiology,0.9177178,TRUE,94.25,0.854907539,31,0.578204442,5,0.739490092,,,0.724200691
8461,Depicting SARS-CoV-2 faecal viral activity in association with gut microbiota composition in patients with COVID-19.,Gut,32690600,7/22/20,pubmed,0,11,"sequencing, metagenom, microbiom",0.079802926,0.714789396,0.001098821,0.001098858,0.001098833,0.202111165,Genomics,0.9264276,TRUE,47.81818182,0.612901231,59.45454545,0.718624565,51,0.958392493,,,0.763306096
8462,A Recurrent Mutation at Position 26340 of SARS-CoV-2 Is Associated with Failure of the E Gene Quantitative Reverse Transcription-PCR Utilized in a Commercial Dual-Target Diagnostic Assay.,J Clin Microbiol,32690547,7/22/20,pubmed,0,11,"sequencing, whole-genome, genomes",0.001461892,0.959223126,0.001461924,0.001461887,0.034929244,0.001461928,Genomics,0.56603664,TRUE,119.4545455,0.906240336,83.81818182,0.787998394,25,0.918019631,,,0.870752787
8463,0,J Clin Pathol,32690544,7/22/20,pubmed,0,2,"computational, sequencing",0.27129055,0.619621157,0.001461874,0.079834403,0.001461955,0.026330062,Genomics,0.5529254,TRUE,26,0.382398417,15.5,0.430157881,2,0.618927094,,,0.477161131
8464,Experience with tocilizumab in severe COVID-19 pneumonia after 80 days of follow-up: A retrospective cohort study.,J Autoimmun,32690352,7/22/20,pubmed,0,10,logistic regression,0.001171649,0.001171638,0.001171543,0.001171603,0.00117162,0.994141946,Clinics,0.98659563,TRUE,50.9,0.637145154,27.3,0.547899385,15,0.874313229,,,0.686452589
8465,The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemic in the final phase of the current outbreak in China.,Int J Infect Dis,32689711,7/22/20,pubmed,0,9,dataset,0.001823339,0.001823353,0.217237486,0.775469089,0.001823364,0.00182337,Epidemiology,0.5000129,TRUE,61.22222222,0.709938772,42.55555556,0.647712069,3,0.667819001,,,0.675156614
8466,Non Pharmaceutical Interventions for Optimal Control of COVID-19.,Comput Methods Programs Biomed,32688137,7/21/20,pubmed,0,6,mathematical model,0.000786376,0.106983345,0.000786399,0.872297474,0.000786389,0.018360016,Epidemiology,0.56198806,TRUE,129,0.920588781,33.83333333,0.597671929,12,0.850299401,,,0.789520037
8467,Low prevalence of active COVID-19 in Slovenia: a nationwide population study of a probability-based sample.,Clin Microbiol Infect,32688068,7/21/20,pubmed,0,12,bayes,0.00104684,0.278069703,0.001046874,0.204782232,0.514007463,0.001046889,Healthcare,0.5286652,TRUE,63.75,0.726390006,93.91666667,0.810677014,15,0.874313229,,,0.803793416
8468,Phylogenetic and phylodynamic analyses of SARS-CoV-2.,Virus Res,32687861,7/21/20,pubmed,0,10,"bayes, genomes",0.002296576,0.5274582,0.002296581,0.463355513,0.00229659,0.002296541,Genomics,0.6474257,TRUE,68.5,0.756571217,81.9,0.782713406,29,0.928020248,,,0.822434957
8469,Routine childhood immunisation during the COVID-19 pandemic in Africa: a benefit-risk analysis of health benefits versus excess risk of SARS-CoV-2 infection.,Lancet Glob Health,32687792,7/21/20,pubmed,0,46,probabilistic,0.00077944,0.021825518,0.000779451,0.378543801,0.506593132,0.091478658,Healthcare,0.9052188,TRUE,63.76595745,0.726451852,,,56,0.961540836,,,0.843996344
8470,Teleconsultation Between Patients and Health Care Professionals in the Catalan Primary Care Service: Message Annotation Analysis in a Retrospective Cross-Sectional Study.,J Med Internet Res,32687477,7/21/20,pubmed,0,10,artificial intelligence,0.000863062,0.000863084,0.250334844,0.206839444,0.540236446,0.000863119,Healthcare,0.89934033,TRUE,27.4,0.400395819,7.6,0.308536259,0,0.403234768,,,0.370722282
8471,Structure-Based Virtual Screening to Discover Potential Lead Molecules for the SARS-CoV-2 Main Protease.,J Chem Inf Model,32687345,7/21/20,pubmed,0,8,virtual screening,0.848503803,0.147100859,0.001098867,0.001098842,0.001098815,0.001098815,Drug discovery,0.934299,TRUE,58.125,0.69002412,104.625,0.830412095,7,0.785110192,,,0.768515469
8472,"Evidence for Strong Mutation Bias toward, and Selection against, U Content in SARS-CoV-2: Implications for Vaccine Design.",Mol Biol Evol,32687176,7/21/20,pubmed,0,10,"whole-genome, genome sequences",0.203484428,0.790762948,0.001438196,0.001438199,0.00143813,0.0014381,Genomics,0.115397215,FALSE,28.5,0.412579628,100,0.822584961,17,0.887338725,,,0.707501104
8473,Pan-Family Assays for Rapid Viral Screening: Reducing Delays in Public Health Responses During Pandemics.,Clin Infect Dis,32687168,7/21/20,pubmed,0,5,genomes,0.001059365,0.938677294,0.001059401,0.001059384,0.001059434,0.057085121,Genomics,0.31385523,FALSE,67.4,0.749706228,17,0.451097137,1,0.537564047,,,0.579455804
8474,"Network analysis, sequence and structure dynamics of key proteins of coronavirus and human host, and molecular docking of selected phytochemicals of nine medicinal plants.",J Biomol Struct Dyn,32686993,7/21/20,pubmed,0,9,network analysis,0.993035634,0.001392939,0.001392822,0.001392891,0.001392831,0.001392882,Drug discovery,0.96261835,TRUE,18.66666667,0.279670975,7.333333333,0.304656141,3,0.667819001,,,0.417382039
8475,Evaluation of a novel blood microsampling device for clinical trial sample collection and protein biomarker analysis.,Bioanalysis,32686955,7/21/20,pubmed,0,5,proteom,0.174339335,0.264782731,0.298112635,0.002639024,0.002639132,0.257487142,Genomics,0.9090275,TRUE,41.6,0.553961284,18.6,0.465881723,3,0.667819001,,,0.562554003
8476,SARS-CoV-2-Encoded Proteome and Human Genetics: From Interaction-Based to Ribosomal Biology Impact on Disease and Risk Processes.,J Proteome Res,32686937,7/21/20,pubmed,0,11,proteom,0.779267911,0.125674111,0.001220033,0.061608566,0.001220065,0.031009313,Drug discovery,0.117637336,FALSE,10.90909091,0.162038469,17.27272727,0.45330479,3,0.667819001,,,0.427720753
8477,Virtual care: Enhancing access or harming care?,Healthc Manage Forum,32686506,7/21/20,pubmed,0,2,artificial intelligence,0.001786575,0.001786546,0.225988958,0.001786657,0.766864508,0.001786756,Healthcare,0.8990774,TRUE,43,0.567629414,16.5,0.44293551,6,0.764429903,,,0.591664942
8478,Obesity is a risk factor for developing critical condition in COVID-19 patients: A systematic review and meta-analysis.,Obes Rev,32686331,7/21/20,pubmed,0,16,dataset,0.001653082,0.00165309,0.001653147,0.091592975,0.001653064,0.901794642,Clinics,0.5335373,TRUE,37.0625,0.508998701,6.0625,0.28017126,31,0.931971109,,,0.57371369
8479,Novel coronavirus SARS-CoV-2 (Covid-19) dynamics inside the human body.,Rev Med Virol,32686248,7/21/20,pubmed,0,2,in silico,0.458024115,0.00159353,0.001593578,0.423007225,0.001593511,0.114188042,Drug discovery,0.5599836,TRUE,1.5,0.015523533,0,0.055525823,2,0.618927094,,,0.22999215
8480,FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network.,Brief Bioinform,32685972,7/21/20,pubmed,0,3,"computational, information retrieval, dataset",0.411865719,0.001371386,0.504796408,0.00137131,0.001371321,0.079223856,Drug discovery,0.62758565,TRUE,17.66666667,0.266373925,1.333333333,0.13252609,7,0.785110192,,,0.394670069
8481,Exploring COVID-19 stress and its factors in Bangladesh: A perception-based study.,Heliyon,32685726,7/21/20,pubmed,0,5,dataset,0.0713607,0.001010983,0.001010959,0.27303074,0.652575679,0.001010938,Healthcare,0.80348015,TRUE,26,0.382398417,8.2,0.322785657,38,0.944132354,,,0.549772143
8482,Germany's digital health reforms in the COVID-19 era: lessons and opportunities for other countries.,NPJ Digit Med,32685700,7/21/20,pubmed,0,3,digital health,0.00310156,0.003101541,0.003101544,0.984492394,0.003101533,0.003101427,Epidemiology,0.69008106,TRUE,64.33333333,0.730781124,7.666666667,0.310877709,6,0.764429903,,,0.602029579
8483,Identification of COVID-19 Infection-Related Human Genes Based on a Random Walk Model in a Virus-Human Protein Interaction Network.,Biomed Res Int,32685484,7/21/20,pubmed,0,6,computational,0.752488591,0.189601258,0.002238664,0.002238604,0.051194315,0.002238568,Drug discovery,0.88277197,TRUE,87.66666667,0.835611355,93.33333333,0.809272143,1,0.537564047,,,0.727482515
8484,Mathematical Model for Coronavirus Disease 2019 (COVID-19) Containing Isolation Class.,Biomed Res Int,32685469,7/21/20,pubmed,0,4,mathematical model,0.002562725,0.002562682,0.00256278,0.930012899,0.002562677,0.059736238,Epidemiology,0.7983116,TRUE,46.5,0.601026656,13,0.400521809,23,0.91129082,,,0.637613095
8485,The significance of case detection ratios for predictions on the outcome of an epidemic - a message from mathematical modelers.,Arch Public Health,32685147,7/21/20,pubmed,0,2,mathematical model,0.003214215,0.240973207,0.003214341,0.746169897,0.0032142,0.00321414,Epidemiology,0.2859444,FALSE,15,0.227596017,5,0.257024351,5,0.739490092,,,0.40803682
8486,A mathematical model to guide the re-opening of economies during the COVID-19 pandemic.,Ann Med Surg (Lond),32685143,7/21/20,pubmed,0,1,mathematical model,0.001538095,0.001538127,0.001538241,0.866045545,0.100012322,0.02932767,Epidemiology,0.4304248,FALSE,15,0.227596017,1,0.122023013,1,0.537564047,,,0.295727692
8487,Sneezing and asymptomatic virus transmission.,Phys Fluids (1994),32684746,7/21/20,pubmed,0,4,computational,0.158150215,0.001511921,0.001511943,0.835802269,0.001511825,0.001511828,Epidemiology,0.23574618,FALSE,8.25,0.121343311,0.5,0.087101953,35,0.939317242,,,0.382587502
8488,Vaccines based on virus-like nano-particles for use against Middle East Respiratory Syndrome (MERS) coronavirus.,Vaccine,32684497,7/21/20,pubmed,0,4,sequencing,0.143107374,0.444805673,0.001717262,0.312190456,0.001717343,0.096461893,Genomics,0.72545743,TRUE,149,0.943224689,29.75,0.567835162,3,0.667819001,,,0.726292951
8489,"Mathematical model describing CoViD-19 in São Paulo, Brazil - evaluating isolation as control mechanism and forecasting epidemiological scenarios of release.",Epidemiol Infect,32684175,7/21/20,pubmed,0,4,mathematical model,0.001310326,0.001310381,0.001310324,0.837985877,0.156772715,0.001310377,Epidemiology,0.15353459,FALSE,2.5,0.027459954,0,0.055525823,6,0.764429903,,,0.282471893
8490,Work at inpatient care units is associated with an increased risk of SARS-CoV-2 infection; a cross-sectional study of 8679 healthcare workers in Sweden.,Ups J Med Sci,32684119,7/21/20,pubmed,0,5,logistic regression,0.002032747,0.002032858,0.002032862,0.133758359,0.682700756,0.177442418,Healthcare,0.4427102,FALSE,30,0.432432432,28.8,0.560543216,3,0.667819001,,,0.553598217
8491,0,J Biomol Struct Dyn,32684109,7/21/20,pubmed,0,7,computational,0.953477826,0.001085334,0.001085341,0.001085359,0.042180699,0.00108544,Drug discovery,0.933529,TRUE,14,0.213494959,1.142857143,0.123628579,5,0.739490092,,,0.35887121
8492,Characteristics and outcomes of COVID-19 in hospitalized patients with and without diabetes.,Diabetes Metab Res Rev,32683744,7/20/20,pubmed,0,17,logistic regression,0.001438106,0.001438105,0.001438097,0.001438098,0.001438122,0.992809473,Clinics,0.84349483,TRUE,41.58823529,0.553775744,17.17647059,0.451966818,11,0.840175319,,,0.61530596
8493,From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans.,Eur Radiol,32683550,7/20/20,pubmed,0,13,deep learning,0.000916677,0.000916692,0.887275362,0.000916704,0.000916705,0.10905786,Imaging,0.95441425,TRUE,26.92307692,0.393283444,7.230769231,0.30238159,1,0.537564047,,,0.41107636
8494,Data-driven modelling and prediction of COVID-19 infection in India and correlation analysis of the virus transmission with socio-economic factors.,Diabetes Metab Syndr,32683321,7/20/20,pubmed,0,5,correlation analysis,0.06526659,0.001717183,0.001717202,0.90299246,0.001717298,0.026589267,Epidemiology,0.69338495,TRUE,38.8,0.527119797,9.4,0.343256623,4,0.707574542,,,0.525983654
8495,Immune and bioinformatics identification of T cell and B cell epitopes in the protein structure of SARS-CoV-2: A systematic review.,Int Immunopharmacol,32683296,7/20/20,pubmed,0,6,bioinformatic,0.892020339,0.099025592,0.00223856,0.002238623,0.002238467,0.002238419,Drug discovery,0.21777332,FALSE,11.16666667,0.168161296,1,0.122023013,17,0.887338725,,,0.392507678
8496,Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study.,Lancet Public Health,32682487,7/20/20,pubmed,0,6,mathematical model,0.000587829,0.000587839,0.000587845,0.923052576,0.074596064,0.000587848,Epidemiology,0.14909399,FALSE,52,0.647349867,85.16666667,0.790741236,172,0.989443793,,,0.809178299
8497,"Risk Factors for Hospitalization and Mortality due to COVID-19 in Espírito Santo State, Brazil.",Am J Trop Med Hyg,32682453,7/20/20,pubmed,0,3,logistic regression,0.001565284,0.001565388,0.001565311,0.001565585,0.001565358,0.992173074,Clinics,0.6635781,TRUE,8.666666667,0.12839384,0.333333333,0.073187048,21,0.903944688,,,0.368508525
8498,Towards explainable deep neural networks (xDNN).,Neural Netw,32682084,7/19/20,pubmed,0,2,"deep learning, computational, neural network, classifier, dataset",0.001220025,0.001220026,0.730174615,0.264945329,0.00122001,0.001219996,Imaging,0.6328194,TRUE,63,0.721998887,12.5,0.392761573,21,0.903944688,,,0.672901716
8499,Design of a multi-epitope vaccine against SARS-CoV-2 using immunoinformatics approach.,Int J Biol Macromol,32682041,7/19/20,pubmed,0,9,"in silico, proteom",0.989596725,0.002080601,0.002080705,0.002080637,0.002080766,0.002080566,Drug discovery,0.8122982,TRUE,12.55555556,0.189683963,2.777777778,0.187985015,9,0.814309525,,,0.397326168
8500,"Diagnostic value of peripheral hematologic markers for coronavirus disease 2019 (COVID-19): A multicenter, cross-sectional study.",J Clin Lab Anal,32681559,7/19/20,pubmed,0,7,correlation analysis,0.001034586,0.00103458,0.115946312,0.001034577,0.001034573,0.879915372,Clinics,0.93374085,TRUE,13,0.197352959,4,0.231469093,8,0.799987654,,,0.409603235
8501,ACE2 gene variants may underlie interindividual variability and susceptibility to COVID-19 in the Italian population.,Eur J Hum Genet,32681121,7/19/20,pubmed,0,138,"sequencing, exom, genomes",0.356206404,0.503955965,0.001350376,0.001350442,0.001350434,0.135786379,Genomics,0.66284776,TRUE,65.50359712,0.73770796,,,60,0.964503982,,,0.851105971
8502,0,Appl Environ Microbiol,32680860,7/19/20,pubmed,0,11,mathematical model,0.001786583,0.312221593,0.001786544,0.680632084,0.001786521,0.001786675,Epidemiology,0.4752264,FALSE,60,0.703444864,61.09090909,0.724779235,12,0.850299401,,,0.759507833
8503,Modelling insights into the COVID-19 pandemic.,Paediatr Respir Rev,32680824,7/19/20,pubmed,0,10,mathematical model,0.001717209,0.001717217,0.001717215,0.991413783,0.001717227,0.001717349,Epidemiology,0.2743221,FALSE,38.55555556,0.52439854,15,0.42594327,10,0.828199272,,,0.592847027
8504,Older adults' strategies for obtaining medication refills in hypothetical scenarios in the face of COVID-19 risk.,J Am Pharm Assoc (2003),32680780,7/19/20,pubmed,0,2,logistic regression,0.001141365,0.001141329,0.001141444,0.084609096,0.881886551,0.030080215,Healthcare,0.9879061,TRUE,109,0.888304781,157,0.889884934,0,0.403234768,,,0.727141494
8505,[Predictive models of the COVID-19 epidemic in Spain with Gompertz curves].,Gac Sanit,32680658,7/19/20,pubmed,0,2,predictive model,0.002898263,0.002898366,0.002898363,0.98550807,0.002898327,0.002898611,Epidemiology,0.16551402,FALSE,15.5,0.234028078,7.5,0.307867273,0,0.403234768,,,0.315043373
8506,"Mental health status among family members of health care workers in Ningbo, China, during the coronavirus disease 2019 (COVID-19) outbreak: a cross-sectional study.",BMC Psychiatry,32680478,7/19/20,pubmed,0,6,logistic regression,0.000977426,0.000977432,0.000977428,0.000977454,0.958929883,0.037160376,Healthcare,0.940586,TRUE,8,0.118683901,1.166666667,0.124565159,1,0.537564047,,,0.260271036
8507,Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19.,Int J Environ Res Public Health,32679925,7/19/20,pubmed,0,5,prediction model,0.001538101,0.001538135,0.081262271,0.912585233,0.001538119,0.00153814,Epidemiology,0.6424739,TRUE,19.2,0.287339972,11.2,0.373026492,1,0.537564047,,,0.39931017
8508,Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach.,Int J Environ Res Public Health,32679861,7/19/20,pubmed,0,8,"network model, lstm",0.001126803,0.001126821,0.196621361,0.798871415,0.001126807,0.001126793,Epidemiology,0.046358943,FALSE,25.75,0.377698064,11.25,0.374364463,3,0.667819001,,,0.473293843
8509,Deamidated Human Triosephosphate Isomerase is a Promising Druggable Target.,Biomolecules,32679775,7/19/20,pubmed,0,8,"in silico, proteom",0.924308959,0.069745131,0.00148645,0.001486495,0.00148646,0.001486505,Drug discovery,0.6454827,TRUE,20.375,0.302245037,9,0.337904736,1,0.537564047,,,0.392571273
8510,0,Med Hypotheses,32679426,7/18/20,pubmed,0,2,bioinformatic,0.72816777,0.001786634,0.001786568,0.103622223,0.102138458,0.062498346,Drug discovery,0.5014264,TRUE,27,0.3960047,0.5,0.087101953,5,0.739490092,,,0.407532249
8511,Matrix metallopeptidase 9 as a host protein target of chloroquine and melatonin for immunoregulation in COVID-19: A network-based meta-analysis.,Life Sci,32679150,7/18/20,pubmed,0,4,interactom,0.927272018,0.001350337,0.001350356,0.001350334,0.019367566,0.04930939,Drug discovery,0.7992812,TRUE,8,0.118683901,1.75,0.148381054,6,0.764429903,,,0.34383162
8512,COVID-19: from rapid genome sequencing to fast decisions.,Lancet Infect Dis,32679087,7/18/20,pubmed,0,2,sequencing,0.019529423,0.532908226,0.388972627,0.019530713,0.019529337,0.019529674,Genomics,0.26649293,FALSE,23.5,0.348506401,41,0.639751137,0,0.403234768,,,0.463830769
8513,Rapid implementation of SARS-CoV-2 sequencing to investigate cases of health-care associated COVID-19: a prospective genomic surveillance study.,Lancet Infect Dis,32679081,7/18/20,pubmed,0,28,"sequencing, genomes",0.001010943,0.519982617,0.001010951,0.218363154,0.001010979,0.258621355,Genomics,0.24608019,FALSE,34.10714286,0.478013483,,,72,0.970121612,,,0.724067548
8514,0,J Biomol Struct Dyn,32679006,7/18/20,pubmed,0,7,in silico,0.956529132,0.001141398,0.001141416,0.038905391,0.001141343,0.001141321,Drug discovery,0.9368057,TRUE,26.42857143,0.387160616,11.85714286,0.383328873,7,0.785110192,,,0.518533227
8515,Correction to: Strengths and limitations of mathematical models in pandemicsthe case of COVID-19 in Chile.,Medwave,32678810,7/18/20,pubmed,0,1,mathematical model,0.025060381,0.025060635,0.02506167,0.874695821,0.025060972,0.025060521,Epidemiology,0.6743399,TRUE,8,0.118683901,0,0.055525823,0,0.403234768,,,0.192481497
8516,SARS-CoV-2 Main Protease: A Molecular Dynamics Study.,J Chem Inf Model,32678588,7/18/20,pubmed,0,2,computational,0.944334133,0.001565399,0.049404382,0.001565451,0.001565341,0.001565294,Drug discovery,0.93670046,TRUE,0,0.006432061,,,11,0.840175319,,,0.42330369
8517,Rapid SARS-CoV-2 whole-genome sequencing and analysis for informed public health decision-making in the Netherlands.,Nat Med,32678356,7/18/20,pubmed,0,87,"sequencing, whole-genome",0.002238481,0.718713692,0.002238653,0.272332207,0.00223845,0.002238517,Genomics,0.3655662,FALSE,38.625,0.525202548,,,72,0.970121612,,,0.74766208
8518,VOC fingerprints: metabolomic signatures of biothreat agents with and without antibiotic resistance.,Sci Rep,32678173,7/18/20,pubmed,0,8,metabolom,0.177826977,0.523569512,0.002238648,0.2918877,0.002238573,0.002238591,Genomics,0.5499187,TRUE,6.375,0.090172552,8.75,0.332218357,1,0.537564047,,,0.319984986
8519,"Author Correction: COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.",Sci Data,32678106,7/18/20,pubmed,0,25,computational,0.013549827,0.013549418,0.013549729,0.932252223,0.013549873,0.01354893,Epidemiology,0.22925436,FALSE,122.44,0.910631455,316.12,0.958589778,0,0.403234768,,,0.757485334
8520,"Detection and Genetic Characterization of Community-Based SARS-CoV-2 Infections - New York City, March 2020.",MMWR Morb Mortal Wkly Rep,32678072,7/18/20,pubmed,0,30,sequencing,0.001141334,0.528485238,0.001141323,0.12860069,0.223281238,0.117350177,Genomics,0.24274686,FALSE,41.9,0.556187767,39.06666667,0.628043885,3,0.667819001,,,0.617350218
8521,Anxiety symptoms and preventive measures during the COVID-19 outbreak in Taiwan.,BMC Psychiatry,32677926,7/18/20,pubmed,0,4,logistic regression,0.000977424,0.000977444,0.000977434,0.0009775,0.995112725,0.000977473,Healthcare,0.9869602,TRUE,65.75,0.738759354,43,0.649785925,11,0.840175319,,,0.742906866
8522,"Correlation analysis between disease severity and clinical and biochemical characteristics of 143 cases of COVID-19 in Wuhan, China: a descriptive study.",BMC Infect Dis,32677918,7/18/20,pubmed,0,8,correlation analysis,0.0009263,0.000926283,0.000926266,0.000926303,0.000926269,0.995368578,Clinics,0.8197276,TRUE,111.375,0.892510359,61,0.724578539,22,0.908142478,,,0.841743792
8523,Synthetic repurposing of drugs against hypertension: a datamining method based on association rules and a novel discrete algorithm.,BMC Bioinformatics,32677879,7/18/20,pubmed,0,2,"computational, data mining, dataset",0.44014488,0.001272654,0.333516392,0.190076981,0.001272672,0.033716421,Drug discovery,0.7429824,TRUE,77,0.794668811,52.5,0.690928552,3,0.667819001,,,0.717805455
8524,Visceral Adiposity and High Intramuscular Fat Deposition Independently Predict Critical Illness in Patients with SARS-CoV-2.,Obesity (Silver Spring),32677752,7/18/20,pubmed,0,8,logistic regression,0.002080689,0.002080567,0.136195218,0.002080593,0.00208056,0.855482372,Clinics,0.95191896,TRUE,133.75,0.925783907,120.5,0.852421729,18,0.891474782,,,0.889893473
8525,Data-driven inference of the reproduction number for COVID-19 before and after interventions for 51 European countries.,Swiss Med Wkly,32677705,7/18/20,pubmed,0,7,"bayes, model fit",0.00159348,0.00159351,0.001593536,0.992032476,0.001593522,0.001593476,Epidemiology,0.09571904,FALSE,57.85714286,0.687797637,23.85714286,0.519534386,8,0.799987654,,,0.669106559
8526,Designing a multi-epitope vaccine against SARS-CoV-2: an immunoinformatics approach.,J Biomol Struct Dyn,32677533,7/18/20,pubmed,0,7,molecular dynamics simulation,0.779205184,0.001059367,0.001059407,0.059524234,0.001059382,0.158092426,Drug discovery,0.6890082,TRUE,10.85714286,0.161729235,1.714285714,0.14577201,21,0.903944688,,,0.403815311
8527,The Palliative Performance Scale predicts mortality in hospitalized patients with COVID-19.,Palliat Med,32677509,7/18/20,pubmed,0,4,logistic regression,0.001034584,0.001034592,0.214071901,0.001034637,0.202992512,0.579831773,Clinics,0.97341955,TRUE,52.5,0.650875131,34,0.5990768,5,0.739490092,,,0.663147341
8528,"Value-based medicine, a compass to guide healthcare decisions in the COVID-19 aftermath.",Liver Int,32677111,7/18/20,pubmed,0,1,computational,0.001901743,0.001901732,0.001901799,0.699585852,0.182767042,0.111941832,Epidemiology,0.752879,TRUE,0,0.006432061,,,0,0.403234768,,,0.204833414
8529,Re-visiting preoperative SARS-CoV-2 testing using a Bayesian approach.,Can J Anaesth,32676995,7/18/20,pubmed,0,2,bayes,0.025060267,0.025061242,0.025061616,0.425062606,0.474693551,0.025060718,Healthcare,0.13961959,FALSE,3.5,0.044344115,2,0.164302917,2,0.618927094,,,0.275858042
8530,Forecasting efforts from prior epidemics and COVID-19 predictions.,Eur J Epidemiol,32676971,7/18/20,pubmed,0,3,prediction model,0.004109938,0.004109791,0.004110504,0.979450108,0.004109842,0.004109818,Epidemiology,0.50783515,TRUE,7,0.10179974,0.666666667,0.096200161,2,0.618927094,,,0.272308998
8531,Transmission of droplet-conveyed infectious agents such as SARS-CoV-2 by speech and vocal exercises during speech therapy: preliminary experiment concerning airflow velocity.,Eur Arch Otorhinolaryngol,32676677,7/18/20,pubmed,0,7,computational,0.165826758,0.00165312,0.001653075,0.827560733,0.001653157,0.001653158,Epidemiology,0.38216096,FALSE,30.28571429,0.434968149,5.714285714,0.270537865,5,0.739490092,,,0.481665369
8532,Continuous flexibility analysis of SARS-CoV-2 Spike prefusion structures.,bioRxiv,32676604,7/18/20,pubmed,0,18,"image processing, dataset",0.595752078,0.001486505,0.263460306,0.136328222,0.001486444,0.001486445,Drug discovery,0.008957177,FALSE,49.27777778,0.62601274,98.72222222,0.819975917,11,0.840175319,,,0.762054659
8533,0,bioRxiv,32676602,7/18/20,pubmed,0,11,transcriptom,0.587214306,0.075256959,0.001415186,0.001415119,0.001415149,0.33328328,Drug discovery,0.27398008,FALSE,30.54545455,0.4387408,63.81818182,0.734546428,5,0.739490092,,,0.63759244
8534,Conserved Genomic Terminals of SARS-CoV-2 as Co-evolving Functional Elements and Potential Therapeutic Targets.,bioRxiv,32676601,7/18/20,pubmed,0,3,"genome-wide, genomes",0.235721965,0.723656368,0.001565331,0.001565315,0.001565344,0.035925676,Genomics,0.34327787,FALSE,209.3333333,0.975199456,739.6666667,0.988961734,6,0.764429903,,,0.909530364
8535,Unique transcriptional changes in coagulation cascade genes in SARS-CoV-2-infected lung epithelial cells: A potential factor in COVID-19 coagulopathies.,bioRxiv,32676594,7/18/20,pubmed,0,2,"sequencing, transcriptom, dataset",0.673428217,0.07660872,0.000728134,0.000728137,0.00072814,0.247778652,Drug discovery,0.53500473,TRUE,39,0.530521368,139.5,0.873628579,3,0.667819001,,,0.690656316
8536,Adaptive Evolution of Peptide Inhibitors for Mutating SARS-CoV-2.,ChemRxiv,32676578,7/18/20,pubmed,0,4,"molecular dynamics simulation, computational",0.74981755,0.192584225,0.052544609,0.001684608,0.001684519,0.001684489,Drug discovery,0.18635133,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
8537,"GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research.",ArXiv,32676519,7/18/20,pubmed,0,15,"computational, in silico",0.748578448,0.001751205,0.244416585,0.001751268,0.001751335,0.001751159,Drug discovery,0.40142602,FALSE,27.13333333,0.396685015,14.4,0.416644367,4,0.707574542,,,0.506967974
8538,An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time.,ArXiv,32676518,7/18/20,pubmed,0,17,"bayes, bayesian model",0.001254622,0.001254634,0.001254656,0.970911013,0.001254648,0.024070427,Epidemiology,0.4548023,FALSE,66.41176471,0.743088626,184.0588235,0.90895103,18,0.891474782,,,0.847838146
8539,Psychosocial and Socio-Economic Crisis in Bangladesh Due to COVID-19 Pandemic: A Perception-Based Assessment.,Front Public Health,32676492,7/18/20,pubmed,0,5,dataset,0.000854715,0.000854712,0.000854738,0.2022198,0.794361317,0.000854717,Healthcare,0.9560429,TRUE,28.2,0.409734677,9.8,0.350682366,31,0.931971109,,,0.564129384
8540,""Small" Intestinal Immunopathology Plays a "Big" Role in Lethal Cytokine Release Syndrome, and Its Modulation by Interferon-γ, IL-17A, and a Janus Kinase Inhibitor.",Front Immunol,32676080,7/18/20,pubmed,0,13,sequencing,0.835846013,0.020329845,0.000946094,0.000946135,0.000946103,0.14098581,Drug discovery,0.28225112,FALSE,42.61538462,0.563361989,32.84615385,0.591316564,1,0.537564047,,,0.564080867
8541,Musculoskeletal Consequences of COVID-19.,J Bone Joint Surg Am,32675661,7/18/20,pubmed,0,10,computational,0.236463716,0.002422493,0.002422334,0.281220258,0.002422474,0.475048724,Clinics,0.93981326,TRUE,63.2,0.722679201,,,27,0.92443978,,,0.823559491
8542,Cancer Surgery Scheduling During and After the COVID-19 First Wave: The MD Anderson Cancer Center Experience.,Ann Surg,32675511,7/18/20,pubmed,0,23,sequencing,0.045167885,0.035394834,0.001622762,0.561238303,0.001622836,0.354953381,Epidemiology,0.9960985,TRUE,174.5652174,0.961655019,175.4347826,0.903331549,4,0.707574542,,,0.85752037
8543,Tailoring steroids in the treatment of COVID-19 pneumonia assisted by CT scans: three case reports.,J Xray Sci Technol,32675436,7/18/20,pubmed,0,11,artificial intelligence,0.002638975,0.002639019,0.489425769,0.002639107,0.002639046,0.500018085,Clinics,0.86653423,TRUE,63.63636364,0.725709691,,,2,0.618927094,,,0.672318393
8544,0,J Immunother Cancer,32675312,7/18/20,pubmed,0,4,"sequencing, microbiom",0.632840894,0.121120426,0.000786351,0.000786364,0.000786372,0.243679593,Drug discovery,0.51839525,TRUE,131.25,0.923124497,151.5,0.884265454,2,0.618927094,,,0.808772348
8545,0,Eur Respir J,32675206,7/18/20,pubmed,0,24,"sequencing, proteom, dataset",0.894578413,0.10133087,0.001022664,0.001022726,0.00102267,0.001022657,Drug discovery,0.6186732,TRUE,73.75,0.780753293,76.33333333,0.769668183,14,0.866658436,,,0.805693304
8546,Medical Vulnerability of Young Adults to Severe COVID-19 Illness-Data From the National Health Interview Survey.,J Adolesc Health,32674964,7/18/20,pubmed,0,5,logistic regression,0.001085321,0.00108538,0.001085333,0.001085367,0.954330729,0.04132787,Healthcare,0.56798506,TRUE,28.6,0.413321789,28.6,0.559071448,18,0.891474782,,,0.62128934
8547,Frailty and Mortality in Hospitalized Older Adults With COVID-19: Retrospective Observational Study.,J Am Med Dir Assoc,32674821,7/18/20,pubmed,0,12,logistic regression,0.001187329,0.001187428,0.086968399,0.001187313,0.088950486,0.820519044,Clinics,0.83074147,TRUE,9.416666667,0.140206568,3.5,0.213607172,4,0.707574542,,,0.353796094
8548,"The Effects of ARBs, ACEis, and Statins on Clinical Outcomes of COVID-19 Infection Among Nursing Home Residents.",J Am Med Dir Assoc,32674818,7/18/20,pubmed,0,14,logistic regression,0.141289124,0.001237135,0.001237077,0.001237094,0.245458824,0.609540747,Clinics,0.8657974,TRUE,52.28571429,0.648957882,28,0.554321648,10,0.828199272,,,0.677159601
8549,Predicting SARS-CoV-2 Infection Trend Using Technical Analysis Indicators.,Disaster Med Public Health Prep,32674742,7/18/20,pubmed,0,2,dataset,0.00223857,0.066966991,0.002238575,0.860559272,0.002238441,0.065758151,Epidemiology,0.46877593,FALSE,51.5,0.643267982,35.5,0.608241905,0,0.403234768,,,0.551581552
8550,Endpoints for randomized controlled clinical trials for COVID-19 treatments.,Clin Trials,32674594,7/18/20,pubmed,0,15,simulation model,0.001059475,0.001059442,0.0010594,0.572852363,0.00105939,0.422909929,Epidemiology,0.6011826,TRUE,71.46666667,0.770424887,,,11,0.840175319,,,0.805300103
8551,Validation of Variant Assembly Using HAPHPIPE with Next-Generation Sequence Data from Viruses.,Viruses,32674515,7/18/20,pubmed,0,6,"computational, bioinformatic, sequencing, dataset",0.001350369,0.750018212,0.117873473,0.128057188,0.001350441,0.001350318,Genomics,0.3217341,FALSE,90.5,0.844331746,2077.333333,0.99859513,0,0.403234768,,,0.748720548
8552,An Epidemiological Study on the Prevalence of the Clinical Features of SARS-CoV-2 Infection in Romanian People.,Int J Environ Res Public Health,32674479,7/18/20,pubmed,0,2,"logistic regression, prediction model",0.001653065,0.214500653,0.336267134,0.001653127,0.444272965,0.001653056,Healthcare,0.42357227,FALSE,23.5,0.348506401,2,0.164302917,1,0.537564047,,,0.350124455
8553,"Social Distancing among Medical Students during the 2019 Coronavirus Disease Pandemic in China: Disease Awareness, Anxiety Disorder, Depression, and Behavioral Activities.",Int J Environ Res Public Health,32674285,7/18/20,pubmed,0,10,logistic regression,0.00097746,0.01671721,0.000977442,0.000977466,0.967238344,0.013112078,Healthcare,0.9071191,TRUE,108.5,0.886882306,122.4,0.854294889,12,0.850299401,,,0.863825532
8554,"How important is obesity as a risk factor for respiratory failure, intensive care admission and death in hospitalised COVID-19 patients? Results from a single Italian centre.",Eur J Endocrinol,32674071,7/17/20,pubmed,0,17,logistic regression,0.001653054,0.001653044,0.001653035,0.00165318,0.001653072,0.991734615,Clinics,0.42880452,FALSE,83.70588235,0.820025976,126.2941176,0.859646775,28,0.926168282,,,0.868613678
8555,RNA-GPS Predicts SARS-CoV-2 RNA Residency to Host Mitochondria and Nucleolus.,Cell Syst,32673562,7/17/20,pubmed,0,5,"computational, transcriptom, genomes",0.575332869,0.356487629,0.001786581,0.062819834,0.001786519,0.001786568,Drug discovery,0.7302631,TRUE,99.8,0.868451976,592.4,0.98347605,19,0.89561084,,,0.915846289
8556,Modelling scenarios of the epidemic of COVID-19 in Canada.,Can Commun Dis Rep,32673384,7/17/20,pubmed,0,12,predictive model,0.001823368,0.075113852,0.001823338,0.752323167,0.001823463,0.167092812,Epidemiology,0.106809825,FALSE,79.16666667,0.803141815,110.5,0.839309607,11,0.840175319,,,0.827542247
8557,Negative cognitive and psychological correlates of mandatory quarantine during the initial COVID-19 outbreak in China.,Am Psychol,32673008,7/17/20,pubmed,0,21,model fit,0.001112731,0.001112666,0.001112658,0.001112709,0.948273955,0.047275282,Healthcare,0.8710111,TRUE,48.0952381,0.61518956,,,23,0.91129082,,,0.76324019
8558,Clinical characteristics and predictors of mortality in African-Americans with COVID-19 from an inner-city community teaching hospital in New York.,J Med Virol,32672844,7/17/20,pubmed,0,6,logistic regression,0.001098817,0.00109881,0.001098794,0.001098822,0.001098843,0.994505913,Clinics,0.7778491,TRUE,24,0.35574247,2.166666667,0.166845063,17,0.887338725,,,0.469975419
8559,"Computer-aided screening for potential TMPRSS2 inhibitors: a combination of pharmacophore modeling, molecular docking and molecular dynamics simulation approaches.",J Biomol Struct Dyn,32672528,7/17/20,pubmed,0,4,molecular dynamics simulation,0.994945323,0.001010944,0.001010954,0.00101093,0.001010922,0.001010927,Drug discovery,0.90186834,TRUE,0,0.006432061,,,5,0.739490092,,,0.372961077
8560,Bioactivity Profile Similarities to Expand the Repertoire of COVID-19 Drugs.,J Chem Inf Model,32672454,7/17/20,pubmed,0,18,computational,0.746231218,0.001593549,0.001593614,0.247394475,0.001593639,0.001593505,Drug discovery,0.2579323,FALSE,3.944444444,0.048302307,,,1,0.537564047,,,0.292933177
8561,Social Determinants of Health and Risk of SARS-CoV-2 Infection in Community-Dwelling Older Adults Living in a Rural Latin American Setting.,J Community Health,32671516,7/17/20,pubmed,0,4,logistic regression,0.001593504,0.001593568,0.001593523,0.135240213,0.858385547,0.001593645,Healthcare,0.30266732,FALSE,122.75,0.911188076,84,0.788399786,5,0.739490092,,,0.813025985
8562,Prealbumin as a Predictor of Prognosis in Patients With Coronavirus Disease 2019.,Front Med (Lausanne),32671085,7/17/20,pubmed,0,9,prediction model,0.001511886,0.001511824,0.113565832,0.001511824,0.001511803,0.880386831,Clinics,0.894949,TRUE,60.55555556,0.7056095,60.22222222,0.721902596,6,0.764429903,,,0.730647333
8563,"Epidemiologic Characteristics, Transmission Chain, and Risk Factors of Severe Infection of COVID-19 in Tianjin, a Representative Municipality City of China.",Front Public Health,32671007,7/17/20,pubmed,0,8,logistic regression,0.001901759,0.00190184,0.001901712,0.244057834,0.238676664,0.51156019,Clinics,0.85085565,TRUE,30,0.432432432,38.375,0.623695478,2,0.618927094,,,0.558351668
8564,Immune and Metabolic Signatures of COVID-19 Revealed by Transcriptomics Data Reuse.,Front Immunol,32670298,7/17/20,pubmed,0,4,"transcriptom, dataset",0.857536127,0.001254638,0.001254654,0.00125463,0.001254603,0.137445349,Drug discovery,0.8016998,TRUE,21.75,0.321603068,11.75,0.381857105,35,0.939317242,,,0.547592471
8565,MicroGMT: A Mutation Tracker for SARS-CoV-2 and Other Microbial Genome Sequences.,Front Microbiol,32670259,7/17/20,pubmed,0,4,"genome sequences, genomes",0.004109898,0.97945039,0.004110256,0.004109979,0.00410974,0.004109736,Genomics,0.39618093,FALSE,46.75,0.603314985,56.25,0.707853893,8,0.799987654,,,0.703718844
8566,Accelerating Telemedicine for Cerebral Palsy During the COVID-19 Pandemic and Beyond.,Front Neurol,32670193,7/17/20,pubmed,0,3,digital health,0.032368819,0.001330076,0.001330083,0.46133116,0.318457824,0.185182039,Epidemiology,0.8409565,TRUE,36.66666667,0.505102356,33,0.593256623,8,0.799987654,,,0.632782211
8567,"A comprehensive investigation of the mRNA and protein level of ACE2, the putative receptor of SARS-CoV-2, in human tissues and blood cells.",Int J Med Sci,32669955,7/17/20,pubmed,0,12,"proteom, dataset",0.732278038,0.102883865,0.001022678,0.001022701,0.001022659,0.16177006,Drug discovery,0.538095,TRUE,50.33333333,0.63380543,,,17,0.887338725,,,0.760572077
8568,A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology.,Nat Microbiol,32669681,7/17/20,pubmed,0,8,"genomic epidemiology, genome sequences",0.08678482,0.901621198,0.002898709,0.002898572,0.00289836,0.002898341,Genomics,0.3140429,FALSE,138.875,0.931783042,674.375,0.986486486,335,0.995863942,,,0.971377823
8569,How computational immunology changed the face of COVID-19 vaccine development.,Nat Med,32669667,7/17/20,pubmed,0,1,computational,0.025062792,0.025061182,0.025061162,0.87469176,0.025062517,0.025060587,Epidemiology,0.42279297,FALSE,118,0.904632321,17,0.451097137,0,0.403234768,,,0.586321408
8570,Early triage of critically ill COVID-19 patients using deep learning.,Nat Commun,32669540,7/17/20,pubmed,0,33,deep learning,0.002032855,0.0020328,0.27187689,0.002032902,0.002033,0.719991552,Clinics,0.7306047,TRUE,63.36363636,0.723668749,161.4545455,0.893363661,42,0.949503056,,,0.855511822
8571,"Aging, Male Sex, Obesity, and Metabolic Inflammation Create the Perfect Storm for COVID-19.",Diabetes,32669390,7/17/20,pubmed,0,1,immunome,0.288656035,0.003466018,0.003466136,0.200394699,0.003465955,0.500551158,Clinics,0.6455149,TRUE,124,0.912981632,217,0.925742574,39,0.945737391,,,0.928153866
8572,"Investigation on knowledge, attitudes and practices about food safety and nutrition in the China during the epidemic of corona virus disease 2019.",Public Health Nutr,32669149,7/17/20,pubmed,0,6,logistic regression,0.001187253,0.001187244,0.001187277,0.001187283,0.99406365,0.001187294,Healthcare,0.9986414,TRUE,37.66666667,0.515430763,18.16666667,0.462603693,1,0.537564047,,,0.505199501
8573,High Throughput Virtual Screening to Discover Inhibitors of the Main Protease of the Coronavirus SARS-CoV-2.,Molecules,32668701,7/17/20,pubmed,0,5,virtual screening,0.945610756,0.001126834,0.049881846,0.001126826,0.001126833,0.001126904,Drug discovery,0.60886216,TRUE,29,0.41993939,8.4,0.326598876,15,0.874313229,,,0.540283832
8574,An Integrated Blueprint for Digital Mental Health Services Amidst COVID-19.,JMIR Ment Health,32668402,7/16/20,pubmed,0,2,"machine learning, digital health",0.001156257,0.001156272,0.271591806,0.221193089,0.503746355,0.00115622,Healthcare,0.8997371,TRUE,0,0.006432061,,,6,0.764429903,,,0.385430982
8575,Next-Generation Sequencing of T and B Cell Receptor Repertoires from COVID-19 Patients Showed Signatures Associated with Severity of Disease.,Immunity,32668194,7/16/20,pubmed,0,14,sequencing,0.671058721,0.193636511,0.001717223,0.001717307,0.001717205,0.130153033,Drug discovery,0.45103347,FALSE,36.78571429,0.506153751,59,0.717554188,49,0.956787456,,,0.726831798
8576,Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US.,JAMA Intern Med,32667668,7/16/20,pubmed,0,254,logistic regression,0.000793398,0.000793466,0.000793395,0.000793426,0.000793412,0.996032904,Clinics,0.92913914,TRUE,92.23529412,0.849465026,120.6176471,0.852555526,193,0.99104883,,,0.897689794
8577,Transcriptome-based drug repositioning for coronavirus disease 2019 (COVID-19).,Pathog Dis,32667665,7/16/20,pubmed,0,5,transcriptom,0.783490033,0.001943519,0.001943589,0.001943558,0.001943552,0.208735749,Drug discovery,0.73131686,TRUE,24,0.35574247,10.4,0.360917849,5,0.739490092,,,0.48538347
8578,SARS-CoV-2 diagnostic diary: from rumors to the first case. Early reports of molecular tests from the military research and diagnostic institute of Rio de Janeiro.,Mem Inst Oswaldo Cruz,32667461,7/16/20,pubmed,0,11,in silico,0.098405406,0.420021931,0.002898488,0.390084004,0.085691791,0.00289838,Genomics,0.48437637,FALSE,2.272727273,0.023563609,1.636363636,0.141356703,2,0.618927094,,,0.261282469
8579,"A putative new SARS-CoV protein, 3c, encoded in an ORF overlapping ORF3a.",J Gen Virol,32667280,7/16/20,pubmed,0,1,computational,0.549897075,0.441580185,0.002130779,0.002130716,0.00213062,0.002130625,Drug discovery,0.6571717,TRUE,149,0.943224689,506,0.979127642,14,0.866658436,,,0.929670256
8580,Social Distancing and Outdoor Physical Activity During the COVID-19 Outbreak in South Korea: Implications for Physical Distancing Strategies.,Asia Pac J Public Health,32667221,7/16/20,pubmed,0,3,dataset,0.001653086,0.00165305,0.001653063,0.839633429,0.153754296,0.001653075,Epidemiology,0.589211,TRUE,95,0.856824788,25.66666667,0.534854161,10,0.828199272,,,0.739959407
8581,"Computational investigation of potential inhibitors of novel coronavirus 2019 through structure-based virtual screening, molecular dynamics and density functional theory studies.",J Biomol Struct Dyn,32666910,7/16/20,pubmed,0,6,"virtual screening, computational",0.978213001,0.001085353,0.001085345,0.001085394,0.001085345,0.017445562,Drug discovery,0.90119994,TRUE,22.66666667,0.335580432,2.166666667,0.166845063,3,0.667819001,,,0.390081499
8582,Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.,Eur J Nucl Med Mol Imaging,32666395,7/16/20,pubmed,0,18,deep learning,0.001461946,0.001461915,0.684643209,0.001461904,0.001461947,0.309509078,Imaging,0.86333793,TRUE,37.88888889,0.517162471,14.94444444,0.422397645,14,0.866658436,,,0.602072851
8583,SARS-CoV-2 Antibody Responses Do Not Predict COVID-19 Disease Severity.,Am J Clin Pathol,32666092,7/16/20,pubmed,0,13,proteom,0.001511827,0.549654676,0.044242762,0.001511837,0.001511915,0.401566983,Genomics,0.37121788,FALSE,27.15384615,0.397056095,21.23076923,0.493711533,7,0.785110192,,,0.55862594
8584,Mathematical modeling and the transmission dynamics in predicting the Covid-19 - What next in combating the pandemic.,Infect Dis Model,32666005,7/16/20,pubmed,0,1,"mathematical prediction, mathematical model",0.002806513,0.002806439,0.002806458,0.985967811,0.002806413,0.002806367,Epidemiology,0.74702406,TRUE,4,0.054734368,0,0.055525823,12,0.850299401,,,0.320186531
8585,Teleurology and digital health app in COVID-19 pandemic.,Investig Clin Urol,32665988,7/16/20,pubmed,0,2,digital health,0.034961874,0.034961874,0.034962151,0.82518733,0.034964898,0.034961874,Epidemiology,0.236487,FALSE,168,0.95689282,93.5,0.809673535,1,0.537564047,,,0.768043467
8586,"A one-step, one-tube real-time RT-PCR based assay with an automated analysis for detection of SARS-CoV-2.",Heliyon,32665985,7/16/20,pubmed,0,13,computational,0.001593621,0.656514026,0.247136194,0.001593573,0.0015935,0.091569086,Genomics,0.51555854,TRUE,10.07692308,0.152266683,3.076923077,0.199759165,9,0.814309525,,,0.388778458
8587,A dataset for emotional reactions and family resilience during COVID-19 isolation period among Indonesian families.,Data Brief,32665969,7/16/20,pubmed,0,1,dataset,0.002720179,0.152189339,0.002720533,0.002720336,0.836929387,0.002720227,Healthcare,0.97569245,TRUE,14,0.213494959,0,0.055525823,0,0.403234768,,,0.224085183
8588,Dataset to support the adoption of social media and emerging technologies for students' continuous engagement.,Data Brief,32665968,7/16/20,pubmed,0,4,dataset,0.032145775,0.001220009,0.128928468,0.421872099,0.414613655,0.001219994,Epidemiology,0.8944576,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
8589,Machine learning to assist clinical decision-making during the COVID-19 pandemic.,Bioelectron Med,32665967,7/16/20,pubmed,0,20,"machine learning, dataset",0.0019019,0.001901739,0.420095472,0.154438799,0.05133294,0.37032915,Clinics,0.79559386,TRUE,39.66666667,0.536211268,32,0.585763982,14,0.866658436,,,0.662877895
8590,D-dimer as a biomarker for disease severity and mortality in COVID-19 patients: a case control study.,J Intensive Care,32665858,7/16/20,pubmed,0,11,logistic regression,0.001046801,0.001046817,0.055329582,0.001046808,0.00104681,0.940483183,Clinics,0.9434861,TRUE,62.90909091,0.720576412,37.72727273,0.620082954,68,0.968393111,,,0.769684159
8591,A stochastic agent-based model of the SARS-CoV-2 epidemic in France.,Nat Med,32665655,7/16/20,pubmed,0,8,simulation model,0.001786579,0.02760828,0.001786504,0.828248988,0.001786632,0.138783018,Epidemiology,0.2950185,FALSE,155.25,0.948172429,432.875,0.973641959,60,0.964503982,,,0.962106123
8592,SARS-CoV-2 proteome microarray for global profiling of COVID-19 specific IgG and IgM responses.,Nat Commun,32665645,7/16/20,pubmed,0,10,proteom,0.392438408,0.411751287,0.002183251,0.002183309,0.002183377,0.189260368,Genomics,0.4832259,FALSE,167.9,0.956830973,119.6,0.850816163,58,0.963022409,,,0.923556515
8593,Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing.,Nat Commun,32665542,7/16/20,pubmed,0,17,interactom,0.814382612,0.001684517,0.127899609,0.052664087,0.001684711,0.001684464,Drug discovery,0.6001521,TRUE,20.94117647,0.308800792,30.58823529,0.573454643,38,0.944132354,,,0.608795929
8594,COVID-19 Disease Severity Risk Factors for Pediatric Patients in Italy.,Pediatrics,32665373,7/16/20,pubmed,0,15,logistic regression,0.00159356,0.079620637,0.001593488,0.00159364,0.236332127,0.679266548,Clinics,0.4430347,FALSE,103.5333333,0.876430206,102.2666667,0.826331282,21,0.903944688,,,0.868902059
8595,Covid-19: disinfectants and sanitisers are changing microbiomes.,BMJ,32665219,7/16/20,pubmed,0,1,microbiom,0.034962561,0.825184356,0.034962027,0.034963862,0.034964477,0.034962717,Genomics,0.48090285,FALSE,41,0.549013544,9,0.337904736,4,0.707574542,,,0.531497607
8596,COVID-19 Pandemic Impact on Decreased Imaging Utilization: A Single Institutional Experience.,Acad Radiol,32665091,7/16/20,pubmed,0,8,predictive model,0.001010943,0.001010942,0.38266828,0.305448919,0.138783884,0.171077032,Imaging,0.8347187,TRUE,56.75,0.680004948,37.125,0.616938721,11,0.840175319,,,0.712372996
8597,SARS-CoV-2 induces transcriptional signatures in human lung epithelial cells that promote lung fibrosis.,Respir Res,32664949,7/16/20,pubmed,0,6,"bioinformatic, dataset",0.969919373,0.001461869,0.001461903,0.001461922,0.001461841,0.024233093,Drug discovery,0.7292993,TRUE,111.6666667,0.893314367,64.83333333,0.738092052,23,0.91129082,,,0.847565747
8598,New insights into genetic susceptibility of COVID-19: an ACE2 and TMPRSS2 polymorphism analysis.,BMC Med,32664879,7/16/20,pubmed,0,10,genomes,0.119333363,0.581249623,0.001717227,0.001717253,0.001717215,0.29426532,Genomics,0.8141383,TRUE,159,0.950522605,328.1,0.961533315,54,0.959812334,,,0.957289418
8599,"Early epidemiological assessment of the transmission potential and virulence of coronavirus disease 2019 (COVID-19) in Wuhan City, China, January-February, 2020.",BMC Med,32664866,7/16/20,pubmed,0,3,dataset,0.000956298,0.000956331,0.000956302,0.91660777,0.000956317,0.079566981,Epidemiology,0.46296978,FALSE,108.3333333,0.886573072,231.6666667,0.930358576,25,0.918019631,,,0.911650426
8600,Lactoferrin as Protective Natural Barrier of Respiratory and Intestinal Mucosa against Coronavirus Infection and Inflammation.,Int J Mol Sci,32664543,7/16/20,pubmed,0,8,sequencing,0.759501845,0.231975026,0.002130743,0.002130828,0.00213077,0.002130788,Drug discovery,0.82275885,TRUE,102,0.873523409,43.375,0.650789403,13,0.858880178,,,0.794397663
8601,Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining.,Int J Environ Res Public Health,32664388,7/16/20,pubmed,0,3,"data mining, dataset",0.001220011,0.001220041,0.165651063,0.487112976,0.343575836,0.001220074,Epidemiology,0.901781,TRUE,1,0.012307502,0,0.055525823,7,0.785110192,,,0.284314506
8602,A virtual ELISA to quantitate COVID-19 antibodies in patient serum.,Biochem Mol Biol Educ,32663903,7/15/20,pubmed,0,2,mathematical model,0.31507162,0.100257962,0.25370419,0.325169067,0.002898521,0.00289864,Epidemiology,0.47646406,FALSE,18,0.271569052,2,0.164302917,1,0.537564047,,,0.324478672
8603,Adherence to social distancing and use of personal protective equipment and the risk of SARS-CoV-2 infection in a cohort of patients with multiple sclerosis.,Mult Scler Relat Disord,32663793,7/15/20,pubmed,0,9,logistic regression,0.000815363,0.00081534,0.043115593,0.250910318,0.470018559,0.234324828,Healthcare,0.5534982,TRUE,109.7777778,0.889665409,142.5555556,0.876973508,1,0.537564047,,,0.768067655
8604,"Association of hypertension, diabetes, stroke, cancer, kidney disease, and high-cholesterol with COVID-19 disease severity and fatality: A systematic review.",Diabetes Metab Syndr,32663789,7/15/20,pubmed,0,3,dataset,0.001461883,0.00146193,0.041511299,0.40385169,0.001461903,0.550251296,Clinics,0.5304441,TRUE,37.66666667,0.515430763,16,0.437316029,4,0.707574542,,,0.553440444
8605,0,Biophys Chem,32663708,7/15/20,pubmed,0,7,computational,0.916181801,0.001653093,0.001653041,0.077205868,0.001653102,0.001653094,Drug discovery,0.9114045,TRUE,32.14285714,0.456861896,16.42857143,0.441731335,6,0.764429903,,,0.554341045
8606,"Intestinal microbiome transfer, a novel therapeutic strategy for COVID-19 induced hyperinflammation?: In reply to, 'COVID-19: Immunology and treatment options', Felsenstein, Herbert McNamara et al. 2020'.",Clin Immunol,32663514,7/15/20,pubmed,0,5,microbiom,0.637819917,0.298179656,0.016003954,0.015998797,0.015998503,0.015999173,Drug discovery,0.7163241,TRUE,63.6,0.725462304,52.4,0.690393364,2,0.618927094,,,0.678260921
8607,Detection of SARS-CoV-2 RNA in commercial passenger aircraft and cruise ship wastewater: a surveillance tool for assessing the presence of COVID-19 infected travellers.,J Travel Med,32662867,7/15/20,pubmed,0,24,sequencing,0.001187344,0.802146359,0.069055461,0.125236233,0.001187313,0.00118729,Genomics,0.872862,TRUE,107,0.883357041,283.875,0.949959861,19,0.89561084,,,0.909642581
8608,Integrative pharmacological mechanism of vitamin C combined with glycyrrhizic acid against COVID-19: findings of bioinformatics analyses.,Brief Bioinform,32662814,7/15/20,pubmed,0,6,"bioinformatic, genomes",0.780845071,0.028175567,0.122248272,0.065945104,0.001392956,0.00139303,Drug discovery,0.89447033,TRUE,278.1666667,0.988743893,137.6666667,0.872491303,9,0.814309525,,,0.89184824
8609,The combination of artificial intelligence and systems biology for intelligent vaccine design.,Expert Opin Drug Discov,32662677,7/15/20,pubmed,0,4,"artificial intelligence, in silico",0.523012841,0.001350384,0.31354263,0.159393428,0.001350355,0.001350361,Drug discovery,0.7715574,TRUE,94,0.854598305,102.25,0.826264383,0,0.403234768,,,0.694699152
8610,"Clinical, radiological, and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients.",Allergy,32662525,7/15/20,pubmed,0,11,logistic regression,0.000772637,0.000772624,0.038057655,0.000772669,0.000772634,0.958851781,Clinics,0.99016416,TRUE,113.9090909,0.897458099,235.4545455,0.932298635,16,0.881782826,,,0.90384652
8611,Does the human placenta express the canonical cell entry mediators for SARS-CoV-2?,Elife,32662421,7/15/20,pubmed,0,9,sequencing,0.743116323,0.188068485,0.00263902,0.00263913,0.060898008,0.002639034,Drug discovery,0.5023748,TRUE,236,0.981755211,349.3333333,0.965480332,45,0.953206988,,,0.966814177
8612,Computational discovery of small drug-like compounds as potential inhibitors of SARS-CoV-2 main protease.,J Biomol Struct Dyn,32662333,7/15/20,pubmed,0,5,"virtual screening, molecular dynamics simulation, computational, in silico",0.99505825,0.000988343,0.000988362,0.000988355,0.000988347,0.000988343,Drug discovery,0.88108575,TRUE,50.4,0.634547591,2.2,0.16838373,5,0.739490092,,,0.514140471
8613,Therapeutic dilemma in the repression of severe acute respiratory syndrome coronavirus-2 proteome.,Drug Dev Res,32662099,7/15/20,pubmed,0,2,proteom,0.860786025,0.132344867,0.001717246,0.001717313,0.001717306,0.001717244,Drug discovery,0.90409994,TRUE,15,0.227596017,3.5,0.213607172,0,0.403234768,,,0.281479319
8614,Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection.,J Clin Immunol,32661797,7/15/20,pubmed,0,9,prediction model,0.190019519,0.001392836,0.025550788,0.001392829,0.001392852,0.780251176,Clinics,0.71171916,TRUE,132.8888889,0.92516544,137.1111111,0.871889216,5,0.739490092,,,0.845514916
8615,Computational View toward the Inhibition of SARS-CoV-2 Spike Glycoprotein and the 3CL Protease.,Computation (Basel),32661494,7/15/20,pubmed,0,4,computational,0.894004283,0.003607364,0.003607542,0.003607452,0.003607581,0.091565778,Drug discovery,0.96439505,TRUE,35,0.488032655,23,0.513513514,7,0.785110192,,,0.59555212
8616,First results of a national external quality assessment scheme for the detection of SARS-CoV-2 genome sequences.,J Clin Virol,32659712,7/14/20,pubmed,0,8,"genome sequences, genomes",0.00125461,0.561711924,0.322476182,0.00125467,0.112047957,0.001254657,Genomics,0.580143,TRUE,27.25,0.398169336,13.625,0.406408884,4,0.707574542,,,0.504050921
8617,"Data mining and analysis of scientific research data records on Covid-19 mortality, immunity, and vaccine development - In the first wave of the Covid-19 pandemic.",Diabetes Metab Syndr,32659695,7/14/20,pubmed,0,3,data mining,0.001059426,0.026770491,0.001059365,0.90835006,0.001059391,0.061701267,Epidemiology,0.12582499,FALSE,24.33333333,0.359700662,7.666666667,0.310877709,6,0.764429903,,,0.478336091
8618,Defining the true impact of coronavirus disease 2019 in the at-risk population of patients with cancer.,Eur J Cancer,32659475,7/14/20,pubmed,0,20,logistic regression,0.001237103,0.001237108,0.001237104,0.24974604,0.001237146,0.7453055,Clinics,0.87315476,TRUE,70.05,0.764240213,67.25,0.746052984,8,0.799987654,,,0.770093617
8619,"Coronavirus disease 2019 (COVID-19) associated coagulopathy and its impact on outcomes in Shenzhen, China: A retrospective cohort study.",Thromb Res,32659462,7/14/20,pubmed,0,13,logistic regression,0.001652995,0.001653085,0.022115274,0.028396961,0.001653111,0.944528574,Clinics,0.9935961,TRUE,57.07692308,0.682293277,17.15384615,0.451766123,1,0.537564047,,,0.557207816
8620,Genome-wide analysis of Indian SARS-CoV-2 genomes for the identification of genetic mutation and SNP.,Infect Genet Evol,32659347,7/14/20,pubmed,0,6,"genome-wide, genomes, sequence alignment",0.001112662,0.762723292,0.001112734,0.232825964,0.001112699,0.001112649,Genomics,0.31297892,FALSE,13.83333333,0.209227534,2.666666667,0.185442869,12,0.850299401,,,0.414989935
8621,Investigating the Trajectory of the COVID-19 Outbreak in Milwaukee County and Projected Effects of Relaxed Distancing.,WMJ,32659059,7/14/20,pubmed,0,6,predictive model,0.001254664,0.052438071,0.001254609,0.847478705,0.037209358,0.060364592,Epidemiology,0.2660084,FALSE,36.66666667,0.505102356,25.5,0.533382392,0,0.403234768,,,0.480573172
8622,[Information technology and digital health to support health in the time of CoViD-19.],Recenti Prog Med,32658876,7/14/20,pubmed,0,1,"artificial intelligence, digital health",0.001438135,0.001438151,0.16135714,0.380387412,0.40928875,0.046090412,Healthcare,0.5518044,TRUE,160,0.951697693,63,0.732606369,0,0.403234768,,,0.695846276
8623,Belief in a COVID-19 Conspiracy Theory as a Predictor of Mental Health and Well-Being of Health Care Workers in Ecuador: Cross-Sectional Survey Study.,JMIR Public Health Surveill,32658859,7/14/20,pubmed,0,7,logistic regression,0.001392933,0.001392922,0.001392929,0.068224052,0.926204334,0.00139283,Healthcare,0.92873085,TRUE,24.28571429,0.358340033,3.428571429,0.208991169,0,0.403234768,,,0.32352199
8624,[Recommendations for resource management in intensive care units during the COVID-19 pandemic].,Medicina (B Aires),32658850,7/14/20,pubmed,0,8,predictive model,0.053408606,0.001786526,0.001786619,0.657905001,0.18660039,0.098512858,Epidemiology,0.93202865,TRUE,2.625,0.028573196,0,0.055525823,1,0.537564047,,,0.207221022
8625,Forecasting the effect of social distancing on COVID-19 autumn-winter outbreak in the metropolitan area of Buenos Aires.,Medicina (B Aires),32658842,7/14/20,pubmed,0,2,"simulation model, mathematical model",0.001237045,0.001237052,0.001237059,0.97109193,0.001237076,0.023959838,Epidemiology,0.9693936,TRUE,112.5,0.895479003,12.5,0.392761573,3,0.667819001,,,0.652019859
8626,Prevalence of depression and its association with quality of life in clinically stable patients with COVID-19.,J Affect Disord,32658818,7/14/20,pubmed,0,15,logistic regression,0.001943528,0.001943476,0.00194347,0.001943487,0.835156007,0.157070031,Healthcare,0.94388145,TRUE,65.86666667,0.739439669,,,17,0.887338725,,,0.813389197
8627,"Prevalence, risk factors and clinical correlates of depression in quarantined population during the COVID-19 outbreak.",J Affect Disord,32658813,7/14/20,pubmed,0,12,correlation analysis,0.001486399,0.001486423,0.001486426,0.001486561,0.924968167,0.069086025,Healthcare,0.994564,TRUE,26.66666667,0.390995114,8.833333333,0.333154937,12,0.850299401,,,0.524816484
8628,CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization.,Comput Biol Med,32658740,7/14/20,pubmed,0,3,"deep learning, neural network, dataset",0.000822925,0.000822947,0.995885384,0.000822919,0.000822899,0.000822925,Imaging,0.6077479,TRUE,60,0.703444864,11,0.371287129,48,0.955614544,,,0.676782179
8629,Bioinformatics studies on a function of the SARS-CoV-2 spike glycoprotein as the binding of host sialic acid glycans.,Comput Biol Med,32658736,7/14/20,pubmed,0,1,bioinformatic,0.720635941,0.272873028,0.00162281,0.001622783,0.001622718,0.001622719,Drug discovery,0.7240866,TRUE,4,0.054734368,1,0.122023013,16,0.881782826,,,0.352846736
8630,Identification of potential inhibitors of three key enzymes of SARS-CoV2 using computational approach.,Comput Biol Med,32658735,7/14/20,pubmed,0,5,computational,0.957138372,0.001034611,0.038723086,0.001034634,0.001034714,0.001034584,Drug discovery,0.8672099,TRUE,13.6,0.206011503,6.6,0.28913567,15,0.874313229,,,0.456486801
8631,Identification of potential drugs against SARS-CoV-2 non-structural protein 1 (nsp1).,J Biomol Struct Dyn,32657643,7/14/20,pubmed,0,2,"virtual screening, molecular dynamics simulation, in silico",0.933751783,0.001684527,0.001684592,0.001684561,0.001684593,0.059509944,Drug discovery,0.96363235,TRUE,8,0.118683901,0,0.055525823,3,0.667819001,,,0.280676242
8632,Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease.,J Proteome Res,32657586,7/14/20,pubmed,0,4,"artificial intelligence, proteom",0.388454964,0.195393149,0.133487499,0.001823474,0.001823388,0.279017526,Drug discovery,0.5407507,TRUE,17,0.257467994,5.25,0.260971367,11,0.840175319,,,0.45287156
8633,Using informatics to guide public health policy during the COVID-19 pandemic in the USA.,J Public Health (Oxf),32657332,7/14/20,pubmed,0,3,dataset,0.002183222,0.002183245,0.002183288,0.922144262,0.069122653,0.00218333,Epidemiology,0.6273711,TRUE,37.66666667,0.515430763,22,0.503746321,1,0.537564047,,,0.51891371
8634,"Increased generalized anxiety, depression and distress during the COVID-19 pandemic: a cross-sectional study in Germany.",J Public Health (Oxf),32657323,7/14/20,pubmed,0,9,dataset,0.002080572,0.002080566,0.002080752,0.002080702,0.989596866,0.002080543,Healthcare,0.94413614,TRUE,38.66666667,0.525882862,17.88888889,0.459526358,50,0.957775171,,,0.647728131
8635,Covid-19 in kidney transplant recipients: a systematic review of the case series available three months into the pandemic.,Infect Dis (Lond),32657186,7/14/20,pubmed,0,9,logistic regression,0.06527476,0.001593506,0.001593539,0.001593626,0.001593549,0.92835102,Clinics,0.75248456,TRUE,33.66666667,0.47312759,14.11111111,0.413098742,6,0.764429903,,,0.550218745
8636,Information and Misinformation on COVID-19: a Cross-Sectional Survey Study.,J Korean Med Sci,32657090,7/14/20,pubmed,0,6,artificial intelligence,0.001486463,0.00148646,0.20074646,0.453046789,0.34174739,0.001486438,Epidemiology,0.9228133,TRUE,65.16666667,0.735110396,25.33333333,0.531576131,23,0.91129082,,,0.725992449
8637,SARS-CoV-2 will constantly sweep its tracks: a vaccine containing CpG motifs in 'lasso' for the multi-faced virus.,Inflamm Res,32656668,7/14/20,pubmed,0,6,"genome sequences, genomes",0.435821774,0.387423864,0.000740319,0.155211232,0.020062445,0.000740366,Drug discovery,0.14030299,FALSE,11.5,0.17416043,2.833333333,0.189523682,5,0.739490092,,,0.367724735
8638,Blood type and outcomes in patients with COVID-19.,Ann Hematol,32656591,7/14/20,pubmed,0,8,logistic regression,0.000662686,0.000662705,0.00066279,0.000662717,0.052978455,0.944370647,Clinics,0.9274074,TRUE,106.75,0.882367493,41.25,0.64102221,72,0.970121612,,,0.831170439
8639,Impact of Thiol-Disulfide Balance on the Binding of Covid-19 Spike Protein with Angiotensin-Converting Enzyme 2 Receptor.,ACS Omega,32656452,7/14/20,pubmed,0,2,"molecular dynamics simulation, computational",0.992032309,0.001593557,0.001593475,0.001593507,0.001593612,0.001593539,Drug discovery,0.7651357,TRUE,28,0.408312202,7,0.299973241,4,0.707574542,,,0.471953328
8640,Emerging coronavirus diseases and future perspectives.,Virusdisease,32656308,7/14/20,pubmed,0,2,"artificial intelligence, sequencing",0.001901775,0.113954282,0.255671394,0.624668926,0.00190175,0.001901874,Epidemiology,0.7720355,TRUE,1,0.012307502,0,0.055525823,1,0.537564047,,,0.201799124
8641,"Liver Injury in Critically Ill and Non-critically Ill COVID-19 Patients: A Multicenter, Retrospective, Observational Study.",Front Med (Lausanne),32656222,7/14/20,pubmed,0,16,logistic regression,0.068753923,0.000722197,0.000722169,0.00072218,0.000722177,0.928357354,Clinics,0.9866048,TRUE,25.75,0.377698064,14.875,0.422130051,4,0.707574542,,,0.502467552
8642,A Predictive Model for Patient Census and Ventilator Requirements at Individual Hospitals During the Coronavirus Disease 2019 (COVID-19) Pandemic: A Preliminary Technical Report.,Cureus,32656017,7/14/20,pubmed,0,2,predictive model,0.000716329,0.000716327,0.000716336,0.601665171,0.000716334,0.395469504,Epidemiology,0.1585404,FALSE,451,0.996783969,267.5,0.945009366,0,0.403234768,,,0.781676034
8643,Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios.,PeerJ,32656001,7/14/20,pubmed,0,4,"bayes, machine learning",0.000988377,0.000988386,0.426965402,0.442175673,0.073966356,0.054915805,Epidemiology,0.281829,FALSE,26,0.382398417,3.75,0.21982874,1,0.537564047,,,0.379930401
8644,Lymphocyte-to-C-Reactive Protein Ratio: A Novel Predictor of Adverse Outcomes in COVID-19.,J Clin Med Res,32655735,7/14/20,pubmed,0,11,logistic regression,0.001126803,0.001126779,0.001126779,0.001126777,0.001126812,0.99436605,Clinics,0.90841794,TRUE,62.81818182,0.720081638,21.27272727,0.494179823,2,0.618927094,,,0.611062852
8645,Risk-prediction model for COVID-19 infection in dialysis patients.,Saudi J Kidney Dis Transpl,32655060,7/14/20,pubmed,0,2,prediction model,0.034963243,0.034961876,0.034969446,0.034965345,0.034961879,0.825178211,Clinics,0.7299726,TRUE,300.5,0.990661142,9.5,0.345531175,0,0.403234768,,,0.579809028
8646,"Development of a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation: e-Outbreak Platform.",Biomed J,32654885,7/14/20,pubmed,0,13,knowledge graph,0.001461945,0.001461986,0.001461971,0.932166449,0.061985785,0.001461864,Epidemiology,0.2890755,FALSE,7.153846154,0.102665595,,,2,0.618927094,,,0.360796344
8647,COVID-19 Pandemic and Africa: From the Situation in Zimbabwe to a Case for Precision Herbal Medicine.,OMICS,32654634,7/14/20,pubmed,0,3," omics, pharmacogenom",0.378221432,0.002130845,0.06686107,0.548525131,0.002130848,0.002130674,Epidemiology,0.5881924,TRUE,85.33333333,0.825901416,59,0.717554188,6,0.764429903,,,0.769295169
8648,Association Between Renin-Angiotensin-Aldosterone System Inhibitors and COVID-19 Infection in South Korea.,Hypertension,32654557,7/14/20,pubmed,0,3,logistic regression,0.349209693,0.001622755,0.001622694,0.001622792,0.055880416,0.590041649,Clinics,0.9104742,TRUE,11.66666667,0.176510607,2.666666667,0.185442869,15,0.874313229,,,0.412088902
8649,Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning.,J Proteome Res,32654489,7/14/20,pubmed,0,10,"deep learning, transcriptom, proteom, deep-learning, knowledge graph",0.676975602,0.001622735,0.316533274,0.001622837,0.00162279,0.001622761,Drug discovery,0.5749042,TRUE,86.77777778,0.832766405,569.4444444,0.982405673,17,0.887338725,,,0.900836934
8650,Architecture and self-assembly of the SARS-CoV-2 nucleocapsid protein.,Protein Sci,32654247,7/13/20,pubmed,0,4,genome sequences,0.454522221,0.396902426,0.065697695,0.001272669,0.080332367,0.001272623,Drug discovery,0.45448083,FALSE,68.5,0.756571217,105,0.831482473,32,0.933699611,,,0.840584433
8651,0,Life Sci,32653520,7/13/20,pubmed,0,4,"molecular dynamics simulation, computational",0.993726894,0.001254606,0.001254635,0.00125464,0.001254615,0.001254609,Drug discovery,0.9495785,TRUE,37.25,0.51035933,6.75,0.292079208,16,0.881782826,,,0.561407121
8652,Declined serum high density lipoprotein cholesterol is associated with the severity of COVID-19 infection.,Clin Chim Acta,32653486,7/13/20,pubmed,0,5,correlation analysis,0.001291259,0.001291324,0.11466922,0.02562846,0.001291247,0.855828489,Clinics,0.9353117,TRUE,56,0.675304595,27.8,0.552047097,19,0.89561084,,,0.707654177
8653,Characterization of heparin and severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) spike glycoprotein binding interactions.,Antiviral Res,32653452,7/13/20,pubmed,0,11,computational,0.964299238,0.00168454,0.00168451,0.00168454,0.00168451,0.028962662,Drug discovery,0.6945597,TRUE,345.6363636,0.994062713,321.2727273,0.959994648,53,0.959195012,,,0.971084124
8654,Recurrent pneumonia in a patient with new coronavirus infection after discharge from hospital for insufficient antibody production: a case report.,BMC Infect Dis,32652938,7/13/20,pubmed,0,3,sequencing,0.073641286,0.274021439,0.130438678,0.070961159,0.001486498,0.44945094,Clinics,0.31200567,FALSE,44.33333333,0.580679077,448.3333333,0.975381322,13,0.858880178,,,0.804980192
8655,In Silico Investigation of the SARS CoV2 Protease with Thymoquinone Major Constituent of Nigella Sativa.,Curr Drug Discov Technol,32652915,7/13/20,pubmed,0,3,in silico,0.986398613,0.002720191,0.002720144,0.002720298,0.002720367,0.002720387,Drug discovery,0.94100493,TRUE,2.666666667,0.029377203,0,0.055525823,3,0.667819001,,,0.250907342
8656,Comparative in silico design and validation of GPS™ CoVID-19 dtec-RT-qPCR test.,J Appl Microbiol,32652813,7/12/20,pubmed,0,6,"in silico, sequence alignment",0.002080576,0.38301976,0.48010001,0.002080816,0.002080725,0.130638114,Genomics,0.49277756,FALSE,20.16666667,0.299585627,40.83333333,0.638546963,2,0.618927094,,,0.519019895
8657,A case-based learning approach to online biochemistry labs during COVID-19.,Biochem Mol Biol Educ,32652794,7/12/20,pubmed,0,2,active learning,0.00392756,0.003927466,0.427758377,0.250659626,0.309799386,0.003927584,Healthcare,0.3751595,FALSE,2,0.022141134,0,0.055525823,1,0.537564047,,,0.205077001
8658,Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population.,Int Immunopharmacol,32652499,7/12/20,pubmed,0,8,"machine learning, artificial intelligence, neural network, dataset",0.000800608,0.000800641,0.413201658,0.000800624,0.000800623,0.583595846,Clinics,0.4736541,FALSE,15,0.227596017,10.375,0.360650254,15,0.874313229,,,0.487519833
8659,Addressing Reduced Laboratory-Based Pulmonary Function Testing During a Pandemic.,Chest,32652095,7/12/20,pubmed,0,10,"machine learning, digital health",0.00131037,0.001310372,0.385995981,0.395068651,0.001310462,0.215004165,Epidemiology,0.7825931,TRUE,158.2,0.950089678,171.2,0.900254215,6,0.764429903,,,0.871591265
8660,Association of Padua prediction score with in-hospital prognosis in COVID-19 patients.,QJM,32652021,7/12/20,pubmed,0,7,logistic regression,0.001538104,0.001538177,0.001538178,0.001538118,0.00153809,0.992309333,Clinics,0.91419667,TRUE,127.1428571,0.917249057,77.57142857,0.772143431,4,0.707574542,,,0.79898901
8661,The Importance of Cellular Immunity in the Development of Vaccines and Therapeutics for COVID-19.,J Infect Dis,32651586,7/12/20,pubmed,0,2,immunome,0.767731076,0.015998792,0.015998719,0.015998603,0.015998942,0.168273869,Drug discovery,0.40374568,FALSE,490,0.997278743,446.5,0.975113728,1,0.537564047,,,0.836652173
8662,Predictive factors of COVID-19 in patients with negative RT-qPCR.,Semergen,32651152,7/12/20,pubmed,0,9,logistic regression,0.00208052,0.002080597,0.216351117,0.002080617,0.229509136,0.547898013,Clinics,0.8105278,TRUE,15.55555556,0.234522852,6.555555556,0.288533583,2,0.618927094,,,0.380661177
8663,"How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19.",Infect Dis Poverty,32650840,7/12/20,pubmed,0,3,artificial intelligence,0.001220014,0.001220046,0.154743358,0.840376418,0.001220067,0.001220098,Epidemiology,0.2518955,FALSE,19,0.285793803,,,6,0.764429903,,,0.525111853
8664,"Transmissibility of COVID-19 in 11 major cities in China and its association with temperature and humidity in Beijing, Shanghai, Guangzhou, and Chengdu.",Infect Dis Poverty,32650838,7/12/20,pubmed,0,3,correlation analysis,0.001438141,0.001438183,0.001438101,0.992809137,0.00143818,0.001438257,Epidemiology,0.4361263,FALSE,17,0.257467994,1,0.122023013,13,0.858880178,,,0.412790395
8665,Rapid Large-Scale COVID-19 Testing During Shortages.,Diagnostics (Basel),32650631,7/12/20,pubmed,0,17,genomes,0.001022653,0.334152659,0.180980882,0.241510302,0.24131087,0.001022633,Genomics,0.354757,FALSE,28.05882353,0.408374049,23.76470588,0.518798501,6,0.764429903,,,0.563867485
8666,Self-Reported Symptoms of SARS-CoV-2 Infection in a Nonhospitalized Population in Italy: Cross-Sectional Study of the EPICOVID19 Web-Based Survey.,JMIR Public Health Surveill,32650305,7/11/20,pubmed,0,21,logistic regression,0.001156246,0.00115632,0.040189911,0.001156298,0.603912813,0.352428412,Healthcare,0.9084103,TRUE,79.23809524,0.803389202,51.80952381,0.688654001,11,0.840175319,,,0.777406174
8667,Development and Validation of a Nomogram for Assessing Survival in Patients With COVID-19 Pneumonia.,Clin Infect Dis,32649738,7/11/20,pubmed,0,17,"predictive model, prediction model",0.001085334,0.001085322,0.296998187,0.001085371,0.001085339,0.698660448,Clinics,0.86667895,TRUE,78.47058824,0.800049477,62.11764706,0.729060744,10,0.828199272,,,0.785769831
8668,"Mentality and behavior in COVID-19 emergency status in Japan: Influence of personality, morality and ideology.",PLoS One,32649687,7/11/20,pubmed,0,2,dataset,0.0024906,0.002490511,0.002490636,0.249071047,0.740966711,0.002490494,Healthcare,0.95620596,TRUE,86,0.829117447,54,0.697952903,14,0.866658436,,,0.797909595
8669,Help-Seeking Behavior of Returning to Work in Healthcare Workers and its Influencing Factors During COVID-19 Subsiding.,J Occup Environ Med,32649650,7/11/20,pubmed,0,5,logistic regression,0.001987146,0.001987125,0.068309612,0.001987166,0.923741827,0.001987124,Healthcare,0.94951487,TRUE,190.8,0.968952935,117,0.847002944,1,0.537564047,,,0.784506642
8670,"Virtual" Boot Camp: Orthopaedic Intern Education in the Time of COVID-19 and Beyond.,J Am Acad Orthop Surg,32649439,7/11/20,pubmed,0,2,mathematical model,0.002358108,0.064090679,0.217671388,0.226419143,0.487102946,0.002357737,Healthcare,0.91578865,TRUE,49,0.624281032,31.5,0.581883864,3,0.667819001,,,0.624661299
8671,A Comprehensive Appraisal of Laboratory Biochemistry Tests as Major Predictors of COVID-19 Severity.,Arch Pathol Lab Med,32649222,7/11/20,pubmed,0,7,logistic regression,0.00115634,0.001156263,0.074171314,0.001156298,0.001156289,0.921203496,Clinics,0.9026332,TRUE,120.5714286,0.907910199,71,0.756556061,12,0.850299401,,,0.83825522
8672,Therapeutic Targets and Computational Approaches on Drug Development for COVID-19.,Curr Top Med Chem,32648845,7/11/20,pubmed,0,4,computational,0.987547397,0.00249047,0.002490566,0.002490628,0.002490462,0.002490476,Drug discovery,0.41667426,FALSE,164.75,0.954666337,106.5,0.833823923,2,0.618927094,,,0.802472451
8673,Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling.,Nat Med,32647358,7/11/20,pubmed,0,24,"computational, sequencing",0.001392886,0.420694892,0.001392936,0.516535306,0.001392871,0.058591109,Epidemiology,0.18277875,FALSE,88.5,0.837961531,212.1666667,0.923200428,83,0.974381135,,,0.911847698
8674,"Psychological health, sleep quality, and coping styles to stress facing the COVID-19 in Wuhan, China.",Transl Psychiatry,32647160,7/11/20,pubmed,0,7,logistic regression,0.00168447,0.001684511,0.001684501,0.001684537,0.969888029,0.023373951,Healthcare,0.91875875,TRUE,52.42857143,0.65013297,13.14285714,0.401525288,40,0.947033768,,,0.666230675
8675,Coding-Complete Genome Sequences of Two SARS-CoV-2 Isolates from Early Manifestations of COVID-19 in Israel.,Microbiol Resour Announc,32646911,7/11/20,pubmed,0,14,genome sequences,0.006089544,0.837960162,0.006089685,0.006089795,0.006089725,0.137681089,Genomics,0.4704738,FALSE,51.35714286,0.64141258,47.57142857,0.670658282,1,0.537564047,,,0.61654497
8676,Coding-Complete Genome Sequence of SARS-CoV-2 Isolate from Bangladesh by Sanger Sequencing.,Microbiol Resour Announc,32646908,7/11/20,pubmed,0,12,sequencing,0.009284117,0.61341556,0.00928448,0.009284166,0.173744303,0.184987373,Genomics,0.18084475,FALSE,13.16666667,0.198528047,1.75,0.148381054,5,0.739490092,,,0.362133065
8677,"Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects.",J Infect Public Health,32646771,7/11/20,pubmed,0,14,artificial intelligence,0.000846531,0.00084652,0.964907883,0.031705992,0.000846534,0.00084654,Imaging,0.8839739,TRUE,10.85714286,0.161729235,1.642857143,0.1414905,17,0.887338725,,,0.39685282
8678,Prevalence of SARS-CoV-2 infection in general practitioners and nurses in primary care and nursing homes in the Healthcare Area of León and associated factors.,Semergen,32646731,7/11/20,pubmed,0,12,logistic regression,0.001538096,0.12811582,0.001538203,0.044574467,0.755226944,0.06900647,Healthcare,0.6191025,TRUE,10.5,0.157338116,3.666666667,0.217621086,5,0.739490092,,,0.371483098
8679,0,J Transl Med,32646487,7/11/20,pubmed,0,11,"in silico, genomes, sequence alignment",0.305204412,0.664277799,0.000977506,0.000977459,0.000977427,0.027585397,Genomics,0.64463687,TRUE,51.36363636,0.641474426,25.09090909,0.529769869,9,0.814309525,,,0.661851274
8680,"Mutation Patterns of Human SARS-CoV-2 and Bat RaTG13 Coronavirus Genomes Are Strongly Biased Towards C>U Transitions, Indicating Rapid Evolution in Their Hosts.",Genes (Basel),32646049,7/11/20,pubmed,0,2,genomes,0.025723858,0.967538058,0.001684509,0.001684557,0.001684517,0.0016845,Genomics,0.56014574,TRUE,118.5,0.905003402,231.5,0.930224779,27,0.92443978,,,0.91988932
8681,Protein Coding and Long Noncoding RNA (lncRNA) Transcriptional Landscape in SARS-CoV-2 Infected Bronchial Epithelial Cells Highlight a Role for Interferon and Inflammatory Response.,Genes (Basel),32646047,7/11/20,pubmed,0,3,"computational, bioinformatic, transcriptom, network analysis",0.894236075,0.001085513,0.001085373,0.001085349,0.001085367,0.101422324,Drug discovery,0.57215184,TRUE,5.333333333,0.074339786,,,14,0.866658436,,,0.470499111
8682,Implications of SARS-CoV-2 Mutations for Genomic RNA Structure and Host microRNA Targeting.,Int J Mol Sci,32645951,7/11/20,pubmed,0,2,genomes,0.277022242,0.663926572,0.001272651,0.001272671,0.001272662,0.055233202,Genomics,0.3303147,FALSE,36,0.498299215,49.5,0.679622692,5,0.739490092,,,0.639137333
8683,Analysis of User Satisfaction with Online Education Platforms in China during the COVID-19 Pandemic.,Healthcare (Basel),32645911,7/11/20,pubmed,0,6,"neural network, network model",0.001717189,0.001717188,0.095998492,0.3959931,0.502856872,0.001717159,Healthcare,0.49473715,FALSE,20.16666667,0.299585627,3.333333333,0.206515922,24,0.914439163,,,0.473513571
8684,Development and validation of the HNC-LL score for predicting the severity of coronavirus disease 2019.,EBioMedicine,32645614,7/10/20,pubmed,0,10,logistic regression,0.001461896,0.00146188,0.13314002,0.00146193,0.001461886,0.861012388,Clinics,0.8925251,TRUE,58.5,0.693240151,18.8,0.468156275,10,0.828199272,,,0.663198566
8685,SmartPDT®: Smartphone enabled real-time dosimetry via satellite observation for daylight photodynamic therapy.,Photodiagnosis Photodyn Ther,32645436,7/10/20,pubmed,0,6,computational,0.001291363,0.001291236,0.269094215,0.337971331,0.135261142,0.255090713,Epidemiology,0.8939753,TRUE,51.66666667,0.644195683,28.5,0.558402462,2,0.618927094,,,0.60717508
8686,The Global Phosphorylation Landscape of SARS-CoV-2 Infection.,Cell,32645325,7/10/20,pubmed,0,78,"proteom, phosphoproteom",0.952956577,0.002639072,0.002639015,0.002639005,0.036487363,0.002638968,Drug discovery,0.87589514,TRUE,49.8974359,0.63077494,178.3717949,0.905070912,166,0.988764739,,,0.841536864
8687,"2020 ACR Presidential Address: Quality, Ownership, and Our Role as Physicians.",J Am Coll Radiol,32645287,7/10/20,pubmed,0,1,artificial intelligence,0.002183226,0.002183261,0.218819882,0.128389808,0.515697011,0.132726812,Healthcare,0.50095254,TRUE,55,0.668686994,52,0.689523682,0,0.403234768,,,0.587148481
8688,The COVID-19 pandemic.,Crit Rev Clin Lab Sci,32645276,7/10/20,pubmed,0,6,artificial intelligence,0.288444694,0.236933249,0.130393181,0.340655736,0.001786571,0.00178657,Epidemiology,0.60555905,TRUE,72.5,0.77549632,55.66666667,0.705244849,3,0.667819001,,,0.716186723
8689,"Cerebrovascular disease in patients with COVID-19: neuroimaging, histological and clinical description.",Brain,32645151,7/10/20,pubmed,0,13,logistic regression,0.001438115,0.057621351,0.180611623,0.001438168,0.001438178,0.757452565,Clinics,0.86560047,TRUE,15.07692308,0.227719711,3.846153846,0.222303987,72,0.970121612,,,0.47338177
8690,Advances in Telemedicine in Ophthalmology.,Semin Ophthalmol,32644878,7/10/20,pubmed,0,4,artificial intelligence,0.002490552,0.002490526,0.447567593,0.130136016,0.334452606,0.082862707,Healthcare,0.9794682,TRUE,36.75,0.505968211,15,0.42594327,2,0.618927094,,,0.516946192
8691,0,J Biomol Struct Dyn,32643552,7/10/20,pubmed,0,6,computational,0.994886568,0.001022627,0.001022745,0.001022657,0.001022721,0.001022682,Drug discovery,0.9476757,TRUE,265.1666667,0.986826644,68.83333333,0.749866203,19,0.89561084,,,0.877434562
8692,0,J Biomol Struct Dyn,32643550,7/10/20,pubmed,0,8,in silico,0.991577466,0.001684509,0.001684528,0.001684563,0.001684468,0.001684465,Drug discovery,0.9312365,TRUE,53,0.654400396,2.25,0.170925876,3,0.667819001,,,0.497715091
8693,0,J Biomol Struct Dyn,32643529,7/10/20,pubmed,0,4,in-silico,0.968423759,0.001171561,0.00117157,0.026889907,0.001171564,0.001171639,Drug discovery,0.9555565,TRUE,9.5,0.143051518,0.75,0.099411292,13,0.858880178,,,0.36711433
8694,Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Huashi Baidu formula in the treatment of COVID-19.,Drug Dev Ind Pharm,32643448,7/10/20,pubmed,0,8,genomes,0.916417367,0.018135207,0.001171556,0.001171553,0.020633631,0.042470686,Drug discovery,0.99650085,TRUE,49.5,0.628362917,11.625,0.379649451,9,0.814309525,,,0.607440631
8695,Successful sequencing of the first SARS-CoV-2 genomes from Croatian patients.,Croat Med J,32643351,7/10/20,pubmed,0,3,"sequencing, genomes",0.019530031,0.671141947,0.019530303,0.019530764,0.019529742,0.250737214,Genomics,0.7989849,TRUE,40.33333333,0.542890717,53.66666667,0.695812149,3,0.667819001,,,0.635507289
8696,Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers.,Croat Med J,32643346,7/10/20,pubmed,0,5,artificial intelligence,0.011752116,0.011749655,0.475317261,0.011750061,0.477681012,0.011749894,Healthcare,0.62636024,TRUE,62,0.715814212,86.2,0.793350281,4,0.707574542,,,0.738913012
8697,Excavating SARS-coronavirus 2 genome for epitope-based subunit vaccine synthesis using immunoinformatics approach.,J Cell Physiol,32643158,7/10/20,pubmed,0,6,"computational, in silico",0.902379865,0.091867435,0.001438157,0.001438224,0.001438162,0.001438158,Drug discovery,0.53289276,TRUE,43.83333333,0.576287958,9.833333333,0.351284453,2,0.618927094,,,0.515499835
8698,COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care.,Health Care Manag Sci,32642878,7/10/20,pubmed,0,5,simulation model,0.00120342,0.001203416,0.001203419,0.818385335,0.00120344,0.176800969,Epidemiology,0.52090454,TRUE,17,0.257467994,6.2,0.282512711,9,0.814309525,,,0.451430077
8699,Proteome-wide data analysis reveals tissue-specific network associated with SARS-CoV-2 infection.,J Mol Cell Biol,32642770,7/10/20,pubmed,0,7,"proteom, dataset",0.725130167,0.001987139,0.120985237,0.001987167,0.001987084,0.147923205,Drug discovery,0.82782936,TRUE,465.8571429,0.99696951,97.85714286,0.818437249,4,0.707574542,,,0.840993767
8700,"A Confirmed Case of SARS-CoV-2 Pneumonia with Routine RT-PCR Negative and Virus Variation in Guangzhou, China.",Clin Infect Dis,32642757,7/10/20,pubmed,0,13,sequencing,0.003465894,0.424040039,0.562096321,0.003465851,0.003465948,0.003465948,Genomics,0.8117038,TRUE,48.76923077,0.620446533,86.53846154,0.794219963,3,0.667819001,,,0.694161832
8701,"Clinical characteristics of COVID-19 infection in chronic obstructive pulmonary disease: a multicenter, retrospective, observational study.",J Thorac Dis,32642086,7/10/20,pubmed,0,41,sequencing,0.001350331,0.048483217,0.129920113,0.001350359,0.001350349,0.817545631,Clinics,0.66735214,TRUE,52.82926829,0.65223576,99.12195122,0.820711801,12,0.850299401,,,0.774415654
8702,Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19.,Theranostics,32641989,7/10/20,pubmed,0,14,radiom,0.000469391,0.000469385,0.335430432,0.000469388,0.000469371,0.662692034,Clinics,0.55248755,TRUE,36.5,0.503123261,28.07142857,0.554522344,12,0.850299401,,,0.635981669
8703,Risk and predictors of in-hospital mortality from COVID-19 in patients with diabetes and cardiovascular disease.,Diabetol Metab Syndr,32641974,7/10/20,pubmed,0,15,logistic regression,0.001330022,0.001330007,0.001330018,0.001330032,0.021115714,0.973564207,Clinics,0.6559472,TRUE,51.93333333,0.645680005,23.26666667,0.514650789,11,0.840175319,,,0.666835371
8704,"COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown.",Sci Data,32641758,7/10/20,pubmed,0,7,dataset,0.001786527,0.00178663,0.001786602,0.991067181,0.001786548,0.001786512,Epidemiology,0.8607837,TRUE,3.142857143,0.037912054,2.857142857,0.189925074,55,0.960800049,,,0.396212392
8705,Single cell transcriptome revealed SARS-CoV-2 entry genes enriched in colon tissues and associated with coronavirus infection and cytokine production.,Signal Transduct Target Ther,32641705,7/10/20,pubmed,0,11,transcriptom,0.953579152,0.009284351,0.009284124,0.009284131,0.00928407,0.009284172,Drug discovery,0.27531385,FALSE,66.45454545,0.743336013,54.27272727,0.698488092,0,0.403234768,,,0.615019624
8706,Genomic Analysis of Early SARS-CoV-2 Variants Introduced in Mexico.,J Virol,32641486,7/10/20,pubmed,0,31,phylogenom,0.00097745,0.995112688,0.000977438,0.000977506,0.000977476,0.000977442,Genomics,0.21865681,FALSE,25.77419355,0.377759911,28.12903226,0.555124431,5,0.739490092,,,0.557458145
8707,Serpentoviruses: More than Respiratory Pathogens.,J Virol,32641481,7/10/20,pubmed,0,9,"sequencing, genomes",0.232852286,0.552590104,0.047390158,0.000815349,0.000815342,0.165536762,Genomics,0.45021164,FALSE,54.66666667,0.666151277,52,0.689523682,1,0.537564047,,,0.631079669
8708,SARS-CoV-2 infection risk assessment in the endometrium: viral infection-related gene expression across the menstrual cycle.,Fertil Steril,32641214,7/10/20,pubmed,0,5,transcriptom,0.828194737,0.001717284,0.001717218,0.001717316,0.001717254,0.164936191,Drug discovery,0.8926863,TRUE,19,0.285793803,12.6,0.394300241,12,0.850299401,,,0.510131148
8709,Higher level of neutrophil-to-lymphocyte is associated with severe COVID-19.,Epidemiol Infect,32641174,7/10/20,pubmed,0,5,logistic regression,0.001653121,0.001653056,0.001653013,0.001653086,0.001653076,0.991734647,Clinics,0.6300184,TRUE,30,0.432432432,12,0.386740701,25,0.918019631,,,0.579064255
8710,"Clinical characteristics of different subtypes and risk factors for the severity of illness in patients with COVID-19 in Zhejiang, China.",Infect Dis Poverty,32641121,7/10/20,pubmed,0,15,logistic regression,0.001156261,0.001156272,0.027389157,0.001156273,0.001156271,0.967985766,Clinics,0.96850264,TRUE,47.8,0.612777537,122.6,0.854763179,21,0.903944688,,,0.790495135
8711,Factors associated with COVID-19-related death using OpenSAFELY.,Nature,32640463,7/9/20,pubmed,0,30,dataset,0.002238467,0.002238483,0.002238525,0.369553637,0.00223857,0.621492317,Clinics,0.4831234,FALSE,102.1333333,0.873770796,131.9333333,0.865868344,257,0.993765047,,,0.911134729
8712,"Identification of potential inhibitors against SARS-CoV-2 by targeting proteins responsible for envelope formation and virion assembly using docking based virtual screening, and pharmacokinetics approaches.",Infect Genet Evol,32640381,7/9/20,pubmed,0,6,virtual screening,0.993982887,0.001203441,0.00120342,0.001203439,0.001203415,0.001203397,Drug discovery,0.9571572,TRUE,15.5,0.234028078,2.166666667,0.166845063,15,0.874313229,,,0.425062124
8713,SARS-CoV2 envelope protein: non-synonymous mutations and its consequences.,Genomics,32640274,7/9/20,pubmed,0,3,"genome sequences, genomes",0.14411083,0.839450134,0.004109754,0.004109798,0.004109749,0.004109735,Genomics,0.19977629,FALSE,79.66666667,0.805924918,54,0.697952903,17,0.887338725,,,0.797072182
8714,Generation of Complete Multi-Cell Type Lung Organoids From Human Embryonic and Patient-Specific Induced Pluripotent Stem Cells for Infectious Disease Modeling and Therapeutics Validation.,Curr Protoc Stem Cell Biol,32640120,7/9/20,pubmed,0,5,microbiom,0.789448283,0.126605958,0.078692075,0.0017513,0.001751171,0.001751213,Drug discovery,0.4711384,FALSE,60,0.703444864,158.4,0.890821515,3,0.667819001,,,0.75402846
8715,Association of a Public Health Campaign About Coronavirus Disease 2019 Promoted by News Media and a Social Influencer With Self-reported Personal Hygiene and Physical Distancing in the Netherlands.,JAMA Netw Open,32639569,7/9/20,pubmed,0,13,logistic regression,0.031729,0.000648154,0.000648164,0.036498928,0.929827614,0.00064814,Healthcare,0.89994884,TRUE,31.38461538,0.448574433,26.07692308,0.538265989,6,0.764429903,,,0.583756775
8716,Concentration-dependent mortality of chloroquine in overdose.,Elife,32639233,7/9/20,pubmed,0,7,"bayes, mathematical model",0.201550319,0.000815356,0.000815336,0.232918158,0.044115075,0.519785756,Clinics,0.3805841,FALSE,59,0.696579875,99.28571429,0.821113192,1,0.537564047,,,0.685085705
8717,Systematic profiling of ACE2 expression in diverse physiological and pathological conditions for COVID-19/SARS-CoV-2.,J Cell Mol Med,32639084,7/9/20,pubmed,0,7,multi-omics,0.392613067,0.001538148,0.001538133,0.001538169,0.034294004,0.568478479,Clinics,0.44748127,FALSE,359.8571429,0.994433793,149.1428571,0.882860583,9,0.814309525,,,0.897201301
8718,Confirmed Invasive Pulmonary Aspergillosis and COVID-19: the value of postmortem findings to support antemortem management.,Rev Soc Bras Med Trop,32638890,7/9/20,pubmed,0,20,sequencing,0.073962405,0.310452521,0.005697572,0.005697547,0.005697594,0.598492362,Clinics,0.38506272,FALSE,51.25,0.640113798,41.45,0.641758095,16,0.881782826,,,0.72121824
8719,"Seroprevalence of anti-SARS-CoV-2 among blood donors in Rio de Janeiro, Brazil.",Rev Saude Publica,32638883,7/9/20,pubmed,0,10,logistic regression,0.001330048,0.081104006,0.034005979,0.235410969,0.520665188,0.12748381,Healthcare,0.53327173,TRUE,89,0.839384006,97.8,0.818169655,21,0.903944688,,,0.853832783
8720,Risk factors for myocardial injury and death in patients with COVID-19: insights from a cohort study with chest computed tomography.,Cardiovasc Res,32637999,7/9/20,pubmed,0,19,logistic regression,0.001085305,0.001085311,0.100699658,0.00108532,0.001085328,0.894959078,Clinics,0.9845131,TRUE,61.05263158,0.709258458,51.68421053,0.687985015,4,0.707574542,,,0.701606005
8721,Evidence for structural protein damage and membrane lipid remodeling in red blood cells from COVID-19 patients.,medRxiv,32637980,7/9/20,pubmed,0,14,"proteom, metabolom, lipidom",0.328742529,0.001943615,0.001943648,0.301398075,0.001943509,0.364028623,Clinics,0.6783569,TRUE,78,0.798812543,57.07142857,0.71133262,19,0.89561084,,,0.801918667
8722,Cost-effectiveness of public health strategies for COVID-19 epidemic control in South Africa.,medRxiv,32637979,7/9/20,pubmed,0,19,simulation model,0.001072215,0.001072238,0.082289331,0.740160153,0.174333796,0.001072267,Epidemiology,0.59996796,TRUE,82.16666667,0.81520193,107.5,0.835429489,9,0.814309525,,,0.821646981
8723,Social determinants of COVID-19 mortality at the county level.,medRxiv,32637976,7/9/20,pubmed,0,3,dataset,0.002080609,0.002080652,0.002080581,0.416553366,0.263335558,0.313869233,Epidemiology,0.049320668,FALSE,24.33333333,0.359700662,21,0.492239765,11,0.840175319,,,0.564038582
8724,Citizen Scientists Create an Exascale Computer to Combat COVID-19.,bioRxiv,32637963,7/9/20,pubmed,0,18,proteom,0.657135764,0.002080647,0.002080834,0.334541609,0.002080624,0.002080523,Drug discovery,0.19554532,FALSE,30.55555556,0.438864494,28.11111111,0.554990634,19,0.89561084,,,0.629821989
8725,Discovery of Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 In Vitro.,bioRxiv,32637956,7/9/20,pubmed,0,13,in silico,0.990490934,0.001901793,0.001901842,0.001901795,0.001901819,0.001901817,Drug discovery,0.5033145,TRUE,83.38461538,0.818850888,148.6923077,0.882592989,14,0.866658436,,,0.856034104
8726,Hidden genomic diversity of SARS-CoV-2: implications for qRT-PCR diagnostics and transmission.,bioRxiv,32637955,7/9/20,pubmed,0,20,dataset,0.002422371,0.987888204,0.002422467,0.002422396,0.002422269,0.002422292,Genomics,0.19301862,FALSE,58.7,0.694291546,897.65,0.99244046,9,0.814309525,,,0.83368051
8727,Snapshot of the evolution and mutation patterns of SARS-CoV-2.,bioRxiv,32637950,7/9/20,pubmed,0,3,"whole genome, genome sequences",0.231747,0.694943201,0.001126831,0.069929365,0.001126798,0.001126805,Genomics,0.23038921,FALSE,86.5,0.831653163,843.0833333,0.991236286,6,0.764429903,,,0.862439784
8728,Genetic architecture of host proteins interacting with SARS-CoV-2.,bioRxiv,32637948,7/9/20,pubmed,0,18,"in silico, proteom, omicscience",0.68661907,0.267183692,0.001046883,0.001046835,0.001046855,0.043056666,Drug discovery,0.3634912,FALSE,61.5,0.712350795,290.1666667,0.951699224,6,0.764429903,,,0.809493307
8729,SARS-CoV-2 Spike protein variant D614G increases infectivity and retains sensitivity to antibodies that target the receptor binding domain.,bioRxiv,32637944,7/9/20,pubmed,0,22,"genomes, structural model",0.398848371,0.597326334,0.00095632,0.000956345,0.000956312,0.000956319,Genomics,0.1475082,FALSE,68.55555556,0.756756757,,,51,0.958392493,,,0.857574625
8730,COVID-19 Pandemic: How to Use Artificial Intelligence to Choose Non-Vulnerable Workers for Positions with the Highest Possible Levels of Exposure to the Novel Coronavirus.,J Biomed Phys Eng,32637383,7/9/20,pubmed,0,3,artificial intelligence,0.002238519,0.002238483,0.002238651,0.988807385,0.002238546,0.002238415,Epidemiology,0.78259623,TRUE,8.333333333,0.123693488,6.666666667,0.290607439,1,0.537564047,,,0.317288325
8731,0,Respir Care,32636278,7/9/20,pubmed,0,2,logistic regression,0.001112631,0.001112632,0.001112659,0.415632969,0.001112654,0.579916455,Clinics,0.5929639,TRUE,75,0.787061661,69,0.750535189,0,0.403234768,,,0.646943872
8732,Clinical analysis of risk factors for severe COVID-19 patients with type 2 diabetes.,J Diabetes Complications,32636061,7/9/20,pubmed,0,5,logistic regression,0.002183334,0.00218324,0.002183253,0.002183295,0.002183213,0.989083665,Clinics,0.8299882,TRUE,37,0.508936854,15.4,0.42875301,20,0.900117291,,,0.612602385
8733,Analysis of SARS-CoV-2 RNA-dependent RNA polymerase as a potential therapeutic drug target using a computational approach.,J Transl Med,32635935,7/9/20,pubmed,0,7,"virtual screening, computational, whole-genome, genome sequences",0.786414406,0.207433188,0.001538102,0.001538127,0.001538092,0.001538085,Drug discovery,0.9400681,TRUE,8.428571429,0.124621189,1.571428571,0.139416644,16,0.881782826,,,0.38194022
8734,Point-of-Care Diagnostic Services as an Integral Part of Health Services during the Novel Coronavirus 2019 Era.,Diagnostics (Basel),32635234,7/9/20,pubmed,0,2,artificial intelligence,0.002562558,0.002562621,0.783652498,0.002562729,0.206096998,0.002562596,Healthcare,0.87505203,TRUE,90,0.843094811,52,0.689523682,1,0.537564047,,,0.690060847
8735,Clusters of COVID-19 in long-term care hospitals and facilities in Japan from 16 January to 9 May 2020.,Geriatr Gerontol Int,32634849,7/8/20,pubmed,0,9,logistic regression,0.001141319,0.22042558,0.001141346,0.076057165,0.360064888,0.341169702,Healthcare,0.43691924,FALSE,27.77777778,0.404601398,7.111111111,0.300173936,1,0.537564047,,,0.414113127
8736,Optimizing Benefits of Testing Key Workers for Infection with SARS-CoV-2: A Mathematical Modeling Analysis.,Clin Infect Dis,32634823,7/8/20,pubmed,0,4,mathematical model,0.001112642,0.001112687,0.069234773,0.304044068,0.623383182,0.001112649,Healthcare,0.09818974,FALSE,180.25,0.964314429,293.5,0.952301311,0,0.403234768,,,0.773283503
8737,Development and Delivery of a Real-time Hospital-onset COVID-19 Surveillance System Using Network Analysis.,Clin Infect Dis,32634822,7/8/20,pubmed,0,16,network analysis,0.082964648,0.001310404,0.001310368,0.787685594,0.001310387,0.1254186,Epidemiology,0.83507645,TRUE,37.75,0.516296617,47.75,0.671394166,4,0.707574542,,,0.631755108
8738,Application of Artificial Intelligence in COVID-19 drug repurposing.,Diabetes Metab Syndr,32634717,7/8/20,pubmed,0,5,"virtual screening, computational, artificial intelligence, prediction model",0.424106686,0.001415155,0.34530584,0.153207824,0.001415202,0.074549292,Drug discovery,0.52681273,TRUE,10.8,0.161110768,9.6,0.346735349,20,0.900117291,,,0.469321136
8739,Estimation of the probable outbreak size of novel coronavirus (COVID-19) in social gathering events and industrial activities.,Int J Infect Dis,32634588,7/8/20,pubmed,0,7,mathematical model,0.001461913,0.001461959,0.001461911,0.992690451,0.001461881,0.001461885,Epidemiology,0.54629457,TRUE,10.42857143,0.155853794,1.714285714,0.14577201,4,0.707574542,,,0.336400115
8740,"Spatiotemporal pattern of COVID-19 and government response in South Korea (as of May 31, 2020).",Int J Infect Dis,32634584,7/8/20,pubmed,0,2,correlation analysis,0.00171717,0.001717312,0.063544619,0.9295865,0.001717201,0.001717198,Epidemiology,0.5997788,TRUE,253,0.984723854,265,0.944139684,16,0.881782826,,,0.936882122
8741,Update on therapeutic approaches and emerging therapies for SARS-CoV-2 virus.,Eur J Pharmacol,32634438,7/8/20,pubmed,0,5,"machine learning, artificial intelligence, bioinformatic",0.755677038,0.001438098,0.238570404,0.001438172,0.001438159,0.001438129,Drug discovery,0.8524599,TRUE,44.2,0.579503989,22.6,0.508161627,26,0.920859312,,,0.669508309
8742,Pediatric Mental and Behavioral Health in the Period of Quarantine and Social Distancing With COVID-19.,JMIR Pediatr Parent,32634105,7/8/20,pubmed,0,1,artificial intelligence,0.017841402,0.000662688,0.000662748,0.396731376,0.583439106,0.00066268,Healthcare,0.9734471,TRUE,5,0.070752675,0,0.055525823,8,0.799987654,,,0.308755384
8743,Mortality and the Use of Antithrombotic Therapies Among Nursing Home Residents with COVID-19.,J Am Geriatr Soc,32633418,7/8/20,pubmed,0,10,logistic regression,0.001291212,0.001291197,0.001291206,0.00129125,0.482253376,0.512581758,Clinics,0.7120947,TRUE,73.9,0.781309914,91.5,0.805124431,14,0.866658436,,,0.817697594
8744,"Rapid and sensitive diagnostic procedure for multiple detection of pandemic Coronaviridae family members SARS-CoV-2, SARS-CoV, MERS-CoV and HCoV: a translational research and cooperation between the Phan Chau Trinh University in Vietnam and University of Bari "Aldo Moro" in Italy.",Eur Rev Med Pharmacol Sci,32633414,7/8/20,pubmed,0,15,sequencing,0.000988377,0.210928329,0.520112221,0.154060243,0.000988437,0.112922392,Imaging,0.979243,TRUE,27.4,0.400395819,6.533333333,0.28819909,9,0.814309525,,,0.500968145
8745,In Silico Screening of Potential Chinese Herbal Medicine Against COVID-19 by Targeting SARS-CoV-2 3CLpro and Angiotensin Converting Enzyme II Using Molecular Docking.,Chin J Integr Med,32632717,7/8/20,pubmed,0,3,"virtual screening, in silico, genomes",0.945814084,0.028284821,0.001112733,0.022563092,0.001112639,0.001112631,Drug discovery,0.9740205,TRUE,336,0.993135011,151.6666667,0.884533048,4,0.707574542,,,0.861747534
8746,Dataset of ex-pat teachers in Southeast Asia's intention to leave due to the COVID-19 pandemic.,Data Brief,32632376,7/8/20,pubmed,0,10,dataset,0.002490513,0.002490497,0.002490483,0.619846148,0.370191845,0.002490513,Epidemiology,0.7936896,TRUE,9.1,0.135753603,0.8,0.101351351,2,0.618927094,,,0.285344016
8747,"A joint dataset of official COVID-19 reports and the governance, trade and competitiveness indicators of World Bank group platforms.",Data Brief,32632375,7/8/20,pubmed,0,1,dataset,0.00223848,0.002238497,0.002238529,0.988807254,0.002238566,0.002238673,Epidemiology,0.11734915,FALSE,8,0.118683901,1,0.122023013,3,0.667819001,,,0.302841972
8748,Artificial intelligence and COVID-19: A multidisciplinary approach.,Integr Med Res,32632356,7/8/20,pubmed,0,3,"deep learning, artificial intelligence, dataset",0.311570249,0.001901717,0.427613666,0.255110972,0.001901725,0.001901671,Drug discovery,0.71113175,TRUE,69,0.759416167,34.33333333,0.600682366,15,0.874313229,,,0.74480392
8749,SIR HELMET (Safety In Radiology HEalthcare Localised Metrological EnviromenT): a low-cost negative-pressure isolation barrier for shielding MRI frontline workers from COVID-19 exposure.,Clin Radiol,32631627,7/8/20,pubmed,0,9,virom,0.009284319,0.009284718,0.009285172,0.121437211,0.841423952,0.009284629,Healthcare,0.3316886,FALSE,6.222222222,0.087760529,5.777777778,0.272009633,1,0.537564047,,,0.299111403
8750,Application of ordinal logistic regression analysis to identify the determinants of illness severity of COVID-19 in China.,Epidemiol Infect,32631458,7/8/20,pubmed,0,8,logistic regression,0.001141361,0.00114135,0.001141405,0.001141422,0.001141395,0.994293067,Clinics,0.90832126,TRUE,38.5,0.524089307,91.625,0.805258229,3,0.667819001,,,0.665722179
8751,SARS-CoV-2: Repurposed Drugs and Novel Therapeutic Approaches-Insights into Chemical Structure-Biological Activity and Toxicological Screening.,J Clin Med,32630746,7/8/20,pubmed,0,10,genomic structure,0.787363074,0.124591843,0.001593558,0.001593625,0.001593583,0.083264317,Drug discovery,0.8436687,TRUE,65.3,0.735914404,20.9,0.489831416,10,0.828199272,,,0.684648364
8752,Preliminary Virtual Screening Studies to Identify GRP78 Inhibitors Which May Interfere with SARS-CoV-2 Infection.,Pharmaceuticals (Basel),32630514,7/8/20,pubmed,0,9,virtual screening,0.956478853,0.001392876,0.001392924,0.001392812,0.001392817,0.037949718,Drug discovery,0.6833854,TRUE,49.88888889,0.630713093,50.55555556,0.683369013,11,0.840175319,,,0.718085808
8753,Putative Roles for Peptidylarginine Deiminases in COVID-19.,Int J Mol Sci,32629995,7/8/20,pubmed,0,3,"in silico, transcriptom",0.781395516,0.001046925,0.001046827,0.001046852,0.001046845,0.214417034,Drug discovery,0.92324805,TRUE,67.33333333,0.749396994,21.66666667,0.499732406,3,0.667819001,,,0.6389828
8754,An Overview of the Genetic Variations of the SARS-CoV-2 Genomes Isolated in Southeast Asian Countries.,J Microbiol Biotechnol,32627759,7/7/20,pubmed,0,6,genomes,0.003335375,0.824179147,0.003335243,0.162479622,0.00333532,0.003335292,Genomics,0.7994361,TRUE,85.33333333,0.825901416,98.5,0.819842119,2,0.618927094,,,0.75489021
8755,0,J Biomol Struct Dyn,32627715,7/7/20,pubmed,0,8,molecular dynamics simulation,0.992440584,0.001511838,0.001511982,0.001511882,0.001511891,0.001511824,Drug discovery,0.9509679,TRUE,2.625,0.028573196,0,0.055525823,9,0.814309525,,,0.299469515
8756,"Possibility of HIV-1 protease inhibitors-clinical trial drugs as repurposed drugs for SARS-CoV-2 main protease: a molecular docking, molecular dynamics and binding free energy simulation study.",J Biomol Struct Dyn,32627689,7/7/20,pubmed,0,3,in silico,0.994218825,0.001156242,0.001156223,0.001156255,0.001156236,0.00115622,Drug discovery,0.9779178,TRUE,29.33333333,0.423093574,3.666666667,0.217621086,6,0.764429903,,,0.468381521
8757,Considerations around the SARS-CoV-2 Spike Protein with Particular Attention to COVID-19 Brain Infection and Neurological Symptoms.,ACS Chem Neurosci,32627524,7/7/20,pubmed,0,8,computational,0.992309245,0.001538253,0.001538151,0.001538158,0.00153808,0.001538114,Drug discovery,0.80549884,TRUE,13.25,0.199950523,4.25,0.235750602,13,0.858880178,,,0.431527101
8758,The Correlation of Comorbidities on the Mortality in Patients with COVID-19: an Observational Study Based on the Korean National Health Insurance Big Data.,J Korean Med Sci,32627443,7/7/20,pubmed,0,5,logistic regression,0.000956387,0.000956331,0.02554828,0.179686935,0.129992074,0.662859993,Clinics,0.39334732,FALSE,10.8,0.161110768,1.6,0.140687717,9,0.814309525,,,0.372036003
8759,"COVID stress syndrome: Concept, structure, and correlates.",Depress Anxiety,32627255,7/7/20,pubmed,0,6,network analysis,0.00141518,0.001415163,0.141208131,0.001415169,0.853131188,0.001415169,Healthcare,0.8905449,TRUE,129,0.920588781,339.8333333,0.964008563,53,0.959195012,,,0.947930785
8760,Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models.,Front Med (Lausanne),32626719,7/7/20,pubmed,0,26,mathematical model,0.001187267,0.001187293,0.00118728,0.994063526,0.001187307,0.001187327,Epidemiology,0.5085667,TRUE,32.61538462,0.46230441,29.88461538,0.568236553,16,0.881782826,,,0.637441263
8761,Leucocyte Subsets Effectively Predict the Clinical Outcome of Patients With COVID-19 Pneumonia: A Retrospective Case-Control Study.,Front Public Health,32626680,7/7/20,pubmed,0,4,logistic regression,0.142980116,0.000880202,0.024370127,0.000880203,0.000880197,0.830009156,Clinics,0.9663923,TRUE,12.75,0.191786752,25.5,0.533382392,13,0.858880178,,,0.528016441
8762,"Development and Validation of a Rapid, Single-Step Reverse Transcriptase Loop-Mediated Isothermal Amplification (RT-LAMP) System Potentially to Be Used for Reliable and High-Throughput Screening of COVID-19.",Front Cell Infect Microbiol,32626666,7/7/20,pubmed,0,12,in-silico,0.001220071,0.494861453,0.500258259,0.001220102,0.001220038,0.001220076,Genomics,0.5850302,TRUE,81.25,0.812109592,70.41666667,0.754682901,5,0.739490092,,,0.768760862
8763,Psychological Symptoms During the Two Stages of Lockdown in Response to the COVID-19 Outbreak: An Investigation in a Sample of Citizens in Northern Spain.,Front Psychol,32625157,7/7/20,pubmed,0,4,probabilistic,0.00137127,0.001371275,0.001371272,0.070145221,0.924369693,0.001371269,Healthcare,0.89542997,TRUE,9.5,0.143051518,4.25,0.235750602,49,0.956787456,,,0.445196525
8764,Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia.,Int J Med Sci,32624700,7/7/20,pubmed,0,1,"deep learning, neural network, image processing, network model",0.057454523,0.001072247,0.891008978,0.001072263,0.001072287,0.048319702,Imaging,0.81053185,TRUE,25,0.369286907,2,0.164302917,6,0.764429903,,,0.432673242
8765,Protein structure analysis of the interactions between SARS-CoV-2 spike protein and the human ACE2 receptor: from conformational changes to novel neutralizing antibodies.,Cell Mol Life Sci,32623480,7/6/20,pubmed,0,5,in silico,0.739283378,0.228065519,0.001112707,0.001112685,0.029313076,0.001112633,Drug discovery,0.6623036,TRUE,16.4,0.247572515,38,0.622223709,7,0.785110192,,,0.551635472
8766,Long-term ACE Inhibitor/ARB Use Is Associated With Severe Renal Dysfunction and Acute Kidney Injury in Patients With Severe COVID-19: Results From a Referral Center Cohort in the Northeast of France.,Clin Infect Dis,32623470,7/6/20,pubmed,0,25,logistic regression,0.089695367,0.00146197,0.001461863,0.039011103,0.001461893,0.866907804,Clinics,0.9438454,TRUE,55.56,0.671470097,31.24,0.579408617,22,0.908142478,,,0.719673731
8767,The most cited and co-cited COVID-19 articles: Knowledge base for rehabilitation team members.,Work,32623414,7/6/20,pubmed,0,1,network analysis,0.001987175,0.00198715,0.00198724,0.990064071,0.001987251,0.001987113,Epidemiology,0.9105695,TRUE,16,0.243552477,1,0.122023013,0,0.403234768,,,0.256270086
8768,Virtual screening of approved drugs as potential SARS-CoV-2 main protease inhibitors.,Comput Biol Chem,32623357,7/6/20,pubmed,0,4,"virtual screening, molecular dynamics simulation",0.992309232,0.001538106,0.001538196,0.001538154,0.001538126,0.001538187,Drug discovery,0.8191507,TRUE,19.5,0.29117447,6.75,0.292079208,22,0.908142478,,,0.497132052
8769,Multiplex detection and dynamics of IgG antibodies to SARS-CoV2 and the highly pathogenic human coronaviruses SARS-CoV and MERS-CoV.,J Clin Virol,32623350,7/6/20,pubmed,0,13,logistic regression,0.001392877,0.695895465,0.059388236,0.001392911,0.085896596,0.156033914,Genomics,0.38352263,FALSE,79,0.802585194,88.69230769,0.79856837,23,0.91129082,,,0.837481462
8770,Baseline characteristics and risk factors for short-term outcomes in 132 COVID-19 patients with diabetes in Wuhan China: A retrospective study.,Diabetes Res Clin Pract,32623030,7/6/20,pubmed,0,10,logistic regression,0.001330002,0.001330005,0.056861375,0.001330008,0.001330025,0.937818585,Clinics,0.9335028,TRUE,61.2,0.709815078,45.6,0.661292481,7,0.785110192,,,0.71873925
8771,Predictors of progression from moderate to severe coronavirus disease 2019: a retrospective cohort.,Clin Microbiol Infect,32622952,7/6/20,pubmed,0,9,logistic regression,0.001141324,0.001141316,0.055100054,0.001141319,0.00114133,0.940334656,Clinics,0.91815984,TRUE,11.22222222,0.168717917,3.888888889,0.223240567,13,0.858880178,,,0.416946221
8772,Coronavirus disease-19 and fertility: viral host entry protein expression in male and female reproductive tissues.,Fertil Steril,32622411,7/6/20,pubmed,0,4,"sequencing, transcriptom, proteom, dataset",0.676641695,0.126648262,0.001085371,0.001085425,0.12039175,0.074147496,Drug discovery,0.9630976,TRUE,61.25,0.710248005,90.75,0.802582285,47,0.95493549,,,0.822588594
8773,Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study.,Lancet Glob Health,32622400,7/6/20,pubmed,0,5,dataset,0.000815332,0.000815378,0.000815347,0.41528972,0.07220719,0.510057033,Clinics,0.5185149,TRUE,12.8,0.192343373,7.6,0.308536259,126,0.983517501,,,0.494799044
8774,COVID-19 in health-care workers in three hospitals in the south of the Netherlands: a cross-sectional study.,Lancet Infect Dis,32622380,7/6/20,pubmed,0,28,"sequencing, whole-genome, genome sequences",0.001171552,0.498794717,0.001171563,0.001171614,0.328173309,0.169517245,Genomics,0.56798095,TRUE,73.64285714,0.780382213,545.9642857,0.981669789,60,0.964503982,,,0.908851994
8775,Current Status of COVID-19 Therapies and Drug Repositioning Applications.,iScience,32622261,7/6/20,pubmed,0,8,"computational, in silico",0.986398939,0.002720154,0.002720184,0.002720234,0.002720223,0.002720266,Drug discovery,0.91300076,TRUE,192.625,0.970066176,423.375,0.972839176,25,0.918019631,,,0.953641661
8776,"Ancestral origin, antigenic resemblance and epidemiological insights of novel coronavirus (SARS-CoV-2): Global burden and Bangladesh perspective.",Infect Genet Evol,32622082,7/6/20,pubmed,0,6,bioinformatic,0.218650226,0.694172491,0.024738871,0.001653158,0.059132189,0.001653065,Genomics,0.56463784,TRUE,17,0.257467994,16,0.437316029,5,0.739490092,,,0.478091372
8777,Integrated approaches to reveal mechanisms by which RNA viruses reprogram the cellular environment.,Methods,32622045,7/6/20,pubmed,0,3,genomes,0.413410508,0.517798567,0.035374816,0.001112685,0.00111267,0.031190755,Genomics,0.78581417,TRUE,19.66666667,0.292596945,8.333333333,0.325662296,2,0.618927094,,,0.412395445
8778,Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study.,Lancet Infect Dis,32621869,7/6/20,pubmed,0,6,mathematical model,0.013355115,0.000772619,0.000772616,0.983554385,0.000772635,0.00077263,Epidemiology,0.34416732,FALSE,5.5,0.077246583,25.33333333,0.531576131,142,0.985616396,,,0.531479703
8779,Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation.,Curr Top Med Chem,32621718,7/6/20,pubmed,0,8,"molecular dynamics simulation, machine learning, artificial intelligence",0.720239192,0.000988408,0.238978418,0.037817227,0.000988393,0.000988361,Drug discovery,0.9746913,TRUE,43.75,0.575545798,6.5,0.288132192,5,0.739490092,,,0.53438936
8780,Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2.,Eur Radiol,32621237,7/6/20,pubmed,0,5,"radiom, logistic regression",0.001010976,0.001011006,0.717679084,0.001010951,0.001010947,0.278277036,Imaging,0.53477794,TRUE,130.6,0.922382337,51.4,0.686914637,6,0.764429903,,,0.791242292
8781,Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through COVID-19 and future crises: the PathLAKE consortium perspective.,J Clin Pathol,32620678,7/6/20,pubmed,0,9,artificial intelligence,0.200950529,0.003607265,0.34466226,0.003607519,0.443564614,0.003607813,Healthcare,0.8850348,TRUE,109.6666667,0.889541716,140.3333333,0.874632058,6,0.764429903,,,0.842867892
8782,"The timeline and risk factors of clinical progression of COVID-19 in Shenzhen, China.",J Transl Med,32620125,7/6/20,pubmed,0,12,logistic regression,0.001415202,0.023869683,0.001415162,0.001415142,0.001415151,0.970469661,Clinics,0.97462356,TRUE,81.16666667,0.811800359,161.1666667,0.892962269,13,0.858880178,,,0.854547602
8783,Effect of COVID-19 pandemic on stroke admission rates in a Norwegian population.,Acta Neurol Scand,32620027,7/4/20,pubmed,0,4,logistic regression,0.002130615,0.002130635,0.00213067,0.226126494,0.249112348,0.518369237,Clinics,0.10016999,FALSE,46.25,0.598800173,33.25,0.594193203,16,0.881782826,,,0.691592067
8784,S2 subunit of SARS-nCoV-2 interacts with tumor suppressor protein p53 and BRCA: an in silico study.,Transl Oncol,32619819,7/4/20,pubmed,0,2,"bioinformatic, in silico",0.499744867,0.003335456,0.00333531,0.174634977,0.003335477,0.315613913,Drug discovery,0.4044491,FALSE,10,0.15214299,5.5,0.267259834,4,0.707574542,,,0.375659122
8785,Virtual screening based on molecular docking of possible inhibitors of Covid-19 main protease.,Microb Pathog,32619669,7/4/20,pubmed,0,7,"virtual screening, in silico",0.986804705,0.002639058,0.002639129,0.002639149,0.002638987,0.002638971,Drug discovery,0.7529756,TRUE,25.85714286,0.378811306,5.428571429,0.264249398,11,0.840175319,,,0.494412008
8786,Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection.,Cell Syst,32619549,7/4/20,pubmed,0,33,"classifier, proteom",0.150464113,0.224316897,0.381620064,0.002032906,0.002032825,0.239533195,Clinics,0.8081924,TRUE,108.9090909,0.887686313,214.3636364,0.924203907,75,0.971417989,,,0.927769403
8787,Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning.,J Biomol Struct Dyn,32619398,7/4/20,pubmed,0,5,"deep learning, neural network, transfer learning, dataset",0.04549981,0.001486397,0.948554589,0.001486403,0.001486392,0.001486408,Imaging,0.55605644,TRUE,134.8,0.926649762,62.4,0.730264918,48,0.955614544,,,0.870843075
8788,Shotgun proteomics analysis of SARS-CoV-2-infected cells and how it can optimize whole viral particle antigen production for vaccines.,Emerg Microbes Infect,32619390,7/4/20,pubmed,0,20,proteom,0.457645434,0.345201892,0.002720261,0.188992063,0.002720194,0.002720156,Drug discovery,0.58235854,TRUE,21.85,0.322530769,11.75,0.381857105,14,0.866658436,,,0.523682103
8789,COVID-19 drug repurposing: Summary statistics on current clinical trials and promising untested candidates.,Transbound Emerg Dis,32619318,7/4/20,pubmed,0,2,text-mining,0.80271913,0.001786659,0.001786606,0.190134481,0.001786556,0.001786568,Drug discovery,0.48938137,FALSE,25,0.369286907,70.5,0.755351887,8,0.799987654,,,0.641542149
8790,Bacterial protein azurin and derived peptides as potential anti-SARS-CoV-2 agents: insights from molecular docking and molecular dynamics simulations.,J Biomol Struct Dyn,32619162,7/4/20,pubmed,0,7,molecular dynamics simulation,0.968636421,0.001653073,0.001653055,0.001653078,0.001653097,0.024751276,Drug discovery,0.9640699,TRUE,89.42857143,0.840868328,36.28571429,0.612857907,6,0.764429903,,,0.73938538
8791,Epitope based peptide vaccine against SARS-COV2: an immune-informatics approach.,J Biomol Struct Dyn,32619134,7/4/20,pubmed,0,4,in-silico,0.811280575,0.001272719,0.001272715,0.157072899,0.027828445,0.001272647,Drug discovery,0.50499374,TRUE,34.5,0.482404601,42,0.644902328,11,0.840175319,,,0.655827416
8792,Using Machine Learning to Estimate Unobserved COVID-19 Infections in North America.,J Bone Joint Surg Am,32618918,7/4/20,pubmed,0,3,"bayes, machine learning",0.001786555,0.001786621,0.001786732,0.798161841,0.001786571,0.19469168,Epidemiology,0.19500145,FALSE,345.3333333,0.994000866,315,0.958255285,0,0.403234768,,,0.785163639
8793,"Microstructure, pathophysiology, and potential therapeutics of COVID-19: A comprehensive review.",J Med Virol,32617987,7/4/20,pubmed,0,4,genomic structure,0.479123593,0.462765281,0.053997221,0.001371306,0.001371266,0.001371332,Drug discovery,0.5126339,TRUE,34.25,0.479559651,3.5,0.213607172,3,0.667819001,,,0.453661941
8794,Characteristics and clinical outcomes of COVID-19 patients in an underserved-inner city population: A single tertiary center cohort.,J Med Virol,32617986,7/4/20,pubmed,0,12,logistic regression,0.00139281,0.001392867,0.001392852,0.001392863,0.001392924,0.993035684,Clinics,0.6111394,TRUE,172.3333333,0.959985157,263.25,0.9434038,20,0.900117291,,,0.934502083
8795,Phylogenetic Analysis and Structural Perspectives of RNA-Dependent RNA-Polymerase Inhibition from SARs-CoV-2 with Natural Products.,Interdiscip Sci,32617855,7/4/20,pubmed,0,10,"virtual screening, molecular dynamics simulation, computational",0.877255889,0.100770869,0.018099447,0.001291295,0.001291262,0.001291239,Drug discovery,0.8224175,TRUE,35.3,0.48945513,5.8,0.272678619,12,0.850299401,,,0.537477717
8796,A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.,Eur Radiol,32617690,7/4/20,pubmed,0,14,"deep learning, dataset",0.000765922,0.000765961,0.884251997,0.000765929,0.078739045,0.034711145,Imaging,0.82874024,TRUE,168.2142857,0.95707836,68.28571429,0.748528231,25,0.918019631,,,0.874542074
8797,Whole Genome Analysis and Targeted Drug Discovery Using Computational Methods and High Throughput Screening Tools for Emerged Novel Coronavirus (2019-nCoV).,J Pharm Drug Res,32617527,7/4/20,pubmed,0,2,"computational, whole genome",0.692385409,0.301668591,0.001486511,0.00148656,0.001486442,0.001486486,Drug discovery,0.66696167,TRUE,62.5,0.718164389,33.5,0.595999465,5,0.739490092,,,0.684551315
8798,COVID-19: A master stroke of Nature.,AIMS Public Health,32617365,7/4/20,pubmed,0,1,"machine learning, prediction model",0.00194363,0.001943582,0.001943626,0.895090293,0.097135413,0.001943456,Epidemiology,0.54757196,TRUE,9,0.135320675,0,0.055525823,1,0.537564047,,,0.242803515
8799,Rich at risk: socio-economic drivers of COVID-19 pandemic spread.,Clin Mol Allergy,32617078,7/4/20,pubmed,0,3,correlation analysis,0.002490566,0.002490587,0.002490525,0.897505826,0.092531908,0.002490588,Epidemiology,0.32902378,FALSE,155,0.948048735,69.33333333,0.75140487,12,0.850299401,,,0.849917669
8800,A candidate multi-epitope vaccine against SARS-CoV-2.,Sci Rep,32616763,7/4/20,pubmed,0,7,"molecular dynamics simulation, computational, in silico",0.948169469,0.001593521,0.001593513,0.001593601,0.045456406,0.001593489,Drug discovery,0.78572583,TRUE,19.42857143,0.289751995,13,0.400521809,35,0.939317242,,,0.543197015
8801,Genome Sequencing of a Severe Acute Respiratory Syndrome Coronavirus 2 Isolate Obtained from a South African Patient with Coronavirus Disease 2019.,Microbiol Resour Announc,32616644,7/4/20,pubmed,0,16,sequencing,0.006540128,0.885989874,0.006539787,0.00654084,0.006539643,0.087849727,Genomics,0.5877543,TRUE,24.5,0.361988991,19.75,0.477990367,2,0.618927094,,,0.486302151
8802,Coronavirus disease 2019 (COVID-19): an evidence map of medical literature.,BMC Med Res Methodol,32615936,7/4/20,pubmed,0,18,artificial intelligence,0.001565358,0.001565336,0.279378556,0.645739663,0.070185692,0.001565396,Epidemiology,0.77804977,TRUE,70.38888889,0.765477148,40.33333333,0.635469628,5,0.739490092,,,0.713478956
8803,Clinical Implications of SARS-CoV-2 Infection in the Viable Preterm Period.,Am J Perinatol,32615621,7/3/20,pubmed,0,6,logistic regression,0.001010939,0.001011009,0.001010965,0.001010977,0.413895912,0.582060198,Clinics,0.9642011,TRUE,66.16666667,0.741666151,13.66666667,0.407412363,7,0.785110192,,,0.644729569
8804,Effects of ambient temperature and humidity on droplet lifetime - A perspective of exhalation sneeze droplets with COVID-19 virus transmission.,Int J Hyg Environ Health,32615522,7/3/20,pubmed,0,1,mathematical model,0.002996625,0.002996503,0.0029966,0.940176501,0.047837132,0.002996638,Epidemiology,0.67366135,TRUE,30,0.432432432,19,0.471367407,10,0.828199272,,,0.577333037
8805,Gene signatures of SARS-CoV/SARS-CoV-2-infected ferret lungs in short- and long-term models.,Infect Genet Evol,32615317,7/3/20,pubmed,0,13,bioinformatic,0.547522594,0.064758253,0.001565333,0.323741801,0.001565312,0.060846706,Drug discovery,0.56914777,TRUE,37,0.508936854,19.61538462,0.476919989,4,0.707574542,,,0.564477128
8806,"Characterizations of SARS-CoV-2 mutational profile, spike protein stability and viral transmission.",Infect Genet Evol,32615316,7/3/20,pubmed,0,6,"sequencing, whole-genome, genomes, dataset",0.339153267,0.640665326,0.000977472,0.000977524,0.000977438,0.017248973,Genomics,0.7375214,TRUE,17.66666667,0.266373925,10.5,0.363459995,14,0.866658436,,,0.498830785
8807,"Main Routes of Entry and Genomic Diversity of SARS-CoV-2, Uganda.",Emerg Infect Dis,32614767,7/3/20,pubmed,0,14,sequencing,0.005047467,0.814240348,0.005047854,0.165568559,0.005047758,0.005048013,Genomics,0.47030354,FALSE,27.92857143,0.405714639,36.85714286,0.615868344,7,0.785110192,,,0.602231058
8808,Review of the SARS-CoV-2 in Wuhan and Analysis as Well as Prediction of Therapeutic Drugs.,Viral Immunol,32614684,7/3/20,pubmed,0,2,mathematical model,0.242759728,0.003607313,0.003607319,0.634700989,0.003607552,0.111717098,Epidemiology,0.69320726,TRUE,18.5,0.278001113,1,0.122023013,0,0.403234768,,,0.267752965
8809,0,J Dent Res,32614681,7/3/20,pubmed,0,6,mathematical model,0.00103458,0.106609606,0.001034603,0.633345029,0.149955384,0.108020798,Epidemiology,0.5703884,TRUE,59.16666667,0.697322036,63.66666667,0.734145036,11,0.840175319,,,0.757214131
8810,Risk of Ischemic Stroke in Patients With Coronavirus Disease 2019 (COVID-19) vs Patients With Influenza.,JAMA Neurol,32614385,7/3/20,pubmed,0,23,logistic regression,0.000889091,0.052260175,0.000889058,0.00088907,0.189082922,0.755989685,Clinics,0.66484547,TRUE,90.95652174,0.845444987,126.826087,0.86038266,118,0.982159393,,,0.89599568
8811,Insights into pathogenesis of fatal COVID-19 pneumonia from histopathology with immunohistochemical and viral RNA studies.,Histopathology,32614086,7/3/20,pubmed,0,18,sequencing,0.238422618,0.382288913,0.001392878,0.00139289,0.001392853,0.375109849,Genomics,0.8852916,TRUE,130.7222222,0.92250603,319.7222222,0.959660155,24,0.914439163,,,0.932201783
8812,Evaluation of Nutrition Risk and Its Association With Mortality Risk in Severely and Critically Ill COVID-19 Patients.,JPEN J Parenter Enteral Nutr,32613660,7/3/20,pubmed,0,15,logistic regression,0.001653047,0.001653071,0.001653007,0.001653006,0.001653046,0.991734823,Clinics,0.99513626,TRUE,57.06666667,0.682231431,22.4,0.506087771,2,0.618927094,,,0.602415432
8813,Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics.,Eur Radiol,32613287,7/3/20,pubmed,0,5,"radiom, logistic regression, correlation analysis, prediction model",0.001254606,0.001254605,0.534792419,0.001254616,0.001254607,0.460189147,Imaging,0.88219833,TRUE,25,0.369286907,3.8,0.221501204,10,0.828199272,,,0.472995794
8814,Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies.,PeerJ,32612894,7/3/20,pubmed,0,7,mathematical model,0.00186171,0.001861715,0.001861708,0.990691407,0.00186172,0.001861741,Epidemiology,0.26495302,FALSE,18.42857143,0.275836477,7.285714286,0.30331817,4,0.707574542,,,0.42890973
8815,"Knowledge, Attitude and Practice Towards COVID-19 Among Chronic Disease Patients at Addis Zemen Hospital, Northwest Ethiopia.",Infect Drug Resist,32612371,7/3/20,pubmed,0,3,logistic regression,0.00103457,0.001034598,0.001034578,0.001034606,0.872650564,0.123211083,Healthcare,0.9614215,TRUE,16.33333333,0.247015895,4,0.231469093,41,0.948144947,,,0.475543312
8816,"Modeling, state estimation, and optimal control for the US COVID-19 outbreak.",Sci Rep,32612204,7/3/20,pubmed,0,4,"computational, sequencing",0.001901737,0.031680159,0.001901749,0.960712897,0.001901748,0.001901711,Epidemiology,0.36290973,FALSE,52,0.647349867,30,0.570176612,26,0.920859312,,,0.712795264
8817,Chest CT Evaluation of 11 Persistent Asymptomatic Patients with SARS-CoV-2 Infection.,Jpn J Infect Dis,32611980,7/3/20,pubmed,0,9,deep learning,0.001415099,0.094969466,0.580927517,0.029781149,0.001415159,0.29149161,Imaging,0.82119083,TRUE,79.66666667,0.805924918,40.44444444,0.63633931,1,0.537564047,,,0.659942758
8818,Risk Factor Analysis and Nomogram Construction for Non-Survivors among Critical Patients with COVID-19.,Jpn J Infect Dis,32611979,7/3/20,pubmed,0,6,logistic regression,0.002639032,0.00263902,0.085058835,0.002639136,0.002639026,0.90438495,Clinics,0.9396271,TRUE,11.5,0.17416043,20.5,0.486218892,0,0.403234768,,,0.35453803
8819,COVID-19 pandemic from an ophthalmology point of view.,Indian J Med Res,32611912,7/3/20,pubmed,0,3,artificial intelligence,0.088867747,0.001717334,0.075014618,0.250095723,0.269509015,0.314795564,Clinics,0.99033093,TRUE,160.6666667,0.952068774,46.33333333,0.665908483,1,0.537564047,,,0.718513768
8820,Increasing Temperature and Relative Humidity Accelerates Inactivation of SARS-CoV-2 on Surfaces.,mSphere,32611701,7/3/20,pubmed,0,23,mathematical model,0.159027047,0.001187323,0.001187332,0.836223682,0.001187308,0.001187308,Epidemiology,0.5345822,TRUE,31.56521739,0.450306141,45.30434783,0.659352422,66,0.967652324,,,0.692436962
8821,Identification of a druggable binding pocket in the spike protein reveals a key site for existing drugs potentially capable of combating Covid-19 infectivity.,BMC Mol Cell Biol,32611313,7/3/20,pubmed,0,2,"virtual screening, in silico",0.956218788,0.00129126,0.001291261,0.038616245,0.001291241,0.001291206,Drug discovery,0.70121914,TRUE,49,0.624281032,24.5,0.525086968,6,0.764429903,,,0.637932634
8822,Characteristics of YouTube Videos in Spanish on How to Prevent COVID-19.,Int J Environ Res Public Health,32610523,7/3/20,pubmed,0,2,logistic regression,0.002357755,0.002357872,0.002357817,0.681638086,0.308930623,0.002357847,Epidemiology,0.80251086,TRUE,12.5,0.189436576,5,0.257024351,7,0.785110192,,,0.410523706
8823,0,Microorganisms,32610445,7/3/20,pubmed,0,8,virtual screening,0.992309429,0.001538105,0.001538138,0.001538126,0.00153809,0.001538113,Drug discovery,0.95913446,TRUE,52.375,0.649638197,19.125,0.471902596,10,0.828199272,,,0.649913355
8824,"The Association of Obesity, Type 2 Diabetes, and Hypertension with Severe Coronavirus Disease 2019 on Admission Among Mexican Patients.",Obesity (Silver Spring),32610364,7/2/20,pubmed,0,14,logistic regression,0.001786513,0.035569484,0.001786536,0.001786501,0.001786513,0.957284453,Clinics,0.80896264,TRUE,38.14285714,0.520069268,78.71428571,0.774953171,11,0.840175319,,,0.711732586
8825,Omics-Driven Systems Interrogation of Metabolic Dysregulation in COVID-19 Pathogenesis.,Cell Metab,32610096,7/2/20,pubmed,0,23,"metabolom, lipidom",0.42159406,0.110853959,0.002490646,0.002490573,0.002490476,0.460080285,Clinics,0.851231,TRUE,29.08695652,0.420063084,35.73913043,0.608777094,51,0.958392493,,,0.66241089
8826,"Rapid, Sensitive, Full-Genome Sequencing of Severe Acute Respiratory Syndrome Coronavirus 2.",Emerg Infect Dis,32610037,7/2/20,pubmed,0,7,"sequencing, genomes",0.003927375,0.980362753,0.003927433,0.003927411,0.003927478,0.003927551,Genomics,0.34862322,FALSE,47,0.606407323,111.5714286,0.840647578,22,0.908142478,,,0.785065793
8827,Oropharyngeal candidiasis in hospitalised COVID-19 patients from Iran: Species identification and antifungal susceptibility pattern.,Mycoses,32609906,7/2/20,pubmed,0,15,sequencing,0.163730706,0.416360448,0.001823451,0.001823436,0.060035422,0.356226537,Genomics,0.9026368,TRUE,47.8,0.612777537,68.8,0.749799304,13,0.858880178,,,0.740485673
8828,"Prediction of the Transition From Subexponential to the Exponential Transmission of SARS-CoV-2 in Chennai, India: Epidemic Nowcasting.",JMIR Public Health Surveill,32609621,7/2/20,pubmed,0,4,probabilistic,0.001392839,0.001392879,0.00139293,0.993035648,0.001392847,0.001392858,Epidemiology,0.56437135,TRUE,37,0.508936854,10,0.355632861,0,0.403234768,,,0.422601494
8829,Prevalence of and Risk Factors Associated With Mental Health Symptoms Among the General Population in China During the Coronavirus Disease 2019 Pandemic.,JAMA Netw Open,32609353,7/2/20,pubmed,0,13,logistic regression,0.000541099,0.00054111,0.000541104,0.000541127,0.915257428,0.082578132,Healthcare,0.83662915,TRUE,45.69230769,0.592924733,20.53846154,0.486285791,83,0.974381135,,,0.684530553
8830,Estimation and prediction of COVID-19 cases in Brazilian metropolises.,Rev Lat Am Enfermagem,32609282,7/2/20,pubmed,0,6,model fit,0.002490424,0.002490461,0.002490437,0.987547666,0.002490453,0.002490558,Epidemiology,0.55797005,TRUE,76.33333333,0.791576473,9.166666667,0.338774418,4,0.707574542,,,0.612641811
8831,Pure-tone audiometry without bone-conduction thresholds: using the digits-in-noise test to detect conductive hearing loss.,Int J Audiol,32609044,7/2/20,pubmed,0,5,logistic regression,0.002490462,0.002490622,0.309293123,0.27819332,0.287633516,0.119898958,Healthcare,0.8820275,TRUE,53,0.654400396,54,0.697952903,2,0.618927094,,,0.657093464
8832,In silico analysis and identification of promising hits against 2019 novel coronavirus 3C-like main protease enzyme.,J Biomol Struct Dyn,32608329,7/2/20,pubmed,0,6,"virtual screening, in silico",0.990064558,0.00198709,0.00198709,0.001987118,0.001987081,0.001987064,Drug discovery,0.9713105,TRUE,7.166666667,0.102851135,0.5,0.087101953,7,0.785110192,,,0.325021093
8833,Risk factors associated with COVID-19 infection: a retrospective cohort study based on contacts tracing.,Emerg Microbes Infect,32608325,7/2/20,pubmed,0,32,logistic regression,0.001823332,0.001823556,0.001823573,0.31673764,0.675968438,0.00182346,Healthcare,0.47393024,FALSE,48.53125,0.618343744,39.5,0.630920524,27,0.92443978,,,0.724568016
8834,Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study.,J Med Syst,32607737,7/2/20,pubmed,0,6,machine learning,0.001098912,0.121263312,0.541858901,0.001098871,0.001098859,0.333581144,Clinics,0.44731125,FALSE,105.5,0.880017317,44.5,0.656743377,6,0.764429903,,,0.767063532
8835,DVT incidence and risk factors in critically ill patients with COVID-19.,J Thromb Thrombolysis,32607652,7/2/20,pubmed,0,5,logistic regression,0.025088218,0.001461924,0.033551214,0.001461953,0.001461959,0.936974731,Clinics,0.9850362,TRUE,19.2,0.287339972,38.6,0.625100348,19,0.89561084,,,0.60268372
8836,A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19.,Eur Radiol,32607634,7/2/20,pubmed,0,9,"predictive model, logistic regression",0.000889061,0.037306035,0.561577677,0.000889099,0.000889073,0.398449055,Imaging,0.6871046,TRUE,31.77777778,0.452594471,29.77777778,0.56790206,10,0.828199272,,,0.616231934
8837,A Streamlined CyTOF Workflow To Facilitate Standardized Multi-Site Immune Profiling of COVID-19 Patients.,medRxiv,32607524,7/2/20,pubmed,0,12,proteom,0.126786456,0.545395727,0.209989634,0.001098887,0.00109888,0.115630417,Genomics,0.36497828,FALSE,11.75,0.177562001,23.5,0.516791544,2,0.618927094,,,0.437760213
8838,COVID-19 Outpatient Screening: a Prediction Score for Adverse Events.,medRxiv,32607523,7/2/20,pubmed,0,27,logistic regression,0.00108535,0.001085327,0.075485623,0.001085361,0.001085355,0.920172983,Clinics,0.50454843,TRUE,48.51851852,0.618281897,153.1481481,0.886004817,2,0.618927094,,,0.707737936
8839,Observational Study of Metformin and Risk of Mortality in Patients Hospitalized with Covid-19.,medRxiv,32607520,7/2/20,pubmed,0,16,logistic regression,0.069008822,0.001438136,0.001438105,0.001438206,0.253204855,0.673471876,Clinics,0.79054564,TRUE,39.125,0.531263529,20.8125,0.489363126,48,0.955614544,,,0.658747066
8840,Predicting the Trajectory of Any COVID19 Epidemic From the Best Straight Line.,medRxiv,32607515,7/2/20,pubmed,0,3,mathematical model,0.329364681,0.001310409,0.12729705,0.539407062,0.00131039,0.001310408,Epidemiology,0.040073425,FALSE,22.33333333,0.330261612,4.666666667,0.246721969,8,0.799987654,,,0.458990412
8841,Factors Associated with Hospitalization and Disease Severity in a Racially and Ethnically Diverse Population of COVID-19 Patients.,medRxiv,32607513,7/2/20,pubmed,0,4,logistic regression,0.026999134,0.001653049,0.001653037,0.001653066,0.001653191,0.966388525,Clinics,0.4194412,FALSE,16.25,0.246026347,28.75,0.559941129,40,0.947033768,,,0.584333748
8842,Female reproductive tract has low concentration of SARS-CoV2 receptors.,bioRxiv,32607512,7/2/20,pubmed,0,3,"sequencing, dataset",0.513921031,0.140517002,0.002238526,0.168368843,0.172716012,0.002238585,Drug discovery,0.52915853,TRUE,70.66666667,0.766837776,162,0.893898849,8,0.799987654,,,0.820241426
8843,0,bioRxiv,32607510,7/2/20,pubmed,0,15,sequencing,0.540588006,0.236612121,0.000977421,0.000977448,0.000977462,0.219867541,Drug discovery,0.438012,FALSE,38.93333333,0.528047498,38.2,0.622758898,22,0.908142478,,,0.686316291
8844,"Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms.",Harv Data Sci Rev,32607504,7/2/20,pubmed,0,18,"bayes, forecasting model",0.001415096,0.001415113,0.001415137,0.992924403,0.001415145,0.001415106,Epidemiology,0.74157083,TRUE,31.05555556,0.445172862,19.83333333,0.478458657,11,0.840175319,,,0.587935613
8845,Demographics and self-reported well-being of Brazilian adults as a function of pet ownership: A pilot study.,Heliyon,32607453,7/2/20,pubmed,0,3,logistic regression,0.002720265,0.002720213,0.002720167,0.16650282,0.822616348,0.002720187,Healthcare,0.80701816,TRUE,45.33333333,0.589646855,23.33333333,0.515386674,1,0.537564047,,,0.547532525
8846,"Survey data of COVID-19-related knowledge, attitude, and practices among indonesian undergraduate students.",Data Brief,32607405,7/2/20,pubmed,0,10,dataset,0.00229657,0.00229652,0.108708257,0.002296669,0.882105417,0.002296568,Healthcare,0.9575989,TRUE,11,0.167171748,0.2,0.061145304,10,0.828199272,,,0.352172108
8847,Molecular Aspects of Co-morbidities in COVID-19 Infection.,Arch Bone Jt Surg,32607393,7/2/20,pubmed,0,3,genomes,0.521621124,0.214813736,0.002296638,0.002296625,0.002296677,0.256675201,Drug discovery,0.76483023,TRUE,27.66666667,0.403488156,9.666666667,0.348274017,4,0.707574542,,,0.486445572
8848,Coronaviruses' sugar shields as vaccine candidates.,Curr Trends Immunol,32606565,7/2/20,pubmed,0,1,proteom,0.598540051,0.230804134,0.003466181,0.160257422,0.003466304,0.003465908,Drug discovery,0.64264214,TRUE,54,0.661574618,44,0.654401927,5,0.739490092,,,0.685155546
8849,Multiple approaches for massively parallel sequencing of SARS-CoV-2 genomes directly from clinical samples.,Genome Med,32605661,7/2/20,pubmed,0,34,"sequencing, transcriptom, whole-genome, genomes, metatranscriptom",0.022051002,0.844142376,0.130968306,0.000946148,0.000946089,0.000946078,Genomics,0.16991472,FALSE,23.41176471,0.346589152,,,32,0.933699611,,,0.640144382
8850,Saxifraga spinulosa-Derived Components Rapidly Inactivate Multiple Viruses Including SARS-CoV-2.,Viruses,32605306,7/2/20,pubmed,0,8,genomes,0.571188887,0.417929953,0.002720383,0.002720338,0.002720172,0.002720266,Drug discovery,0.7690689,TRUE,66.125,0.741542458,28.875,0.561145304,3,0.667819001,,,0.656835588
8851,Psycho-Emotional Approach to the Psychological Distress Related to the COVID-19 Pandemic in Spain: A Cross-Sectional Observational Study.,Healthcare (Basel),32605264,7/2/20,pubmed,0,6,logistic regression,0.001901708,0.001901713,0.001901783,0.001901808,0.990491134,0.001901854,Healthcare,0.9171996,TRUE,43.33333333,0.571154679,7.166666667,0.301311212,13,0.858880178,,,0.577115356
8852,0,Mar Drugs,32605149,7/2/20,pubmed,0,6,virtual screening,0.883093779,0.002898752,0.105312041,0.002898401,0.002898389,0.002898637,Drug discovery,0.5920229,TRUE,23.5,0.348506401,5.5,0.267259834,1,0.537564047,,,0.384443427
8853,Data and Text Mining Help Identify Key Proteins Involved in the Molecular Mechanisms Shared by SARS-CoV-2 and HIV-1.,Molecules,32604797,7/2/20,pubmed,0,4,"data mining, text mining",0.730639921,0.261411148,0.001987149,0.00198728,0.001987399,0.001987102,Drug discovery,0.6696905,TRUE,125.25,0.9145278,44.5,0.656743377,8,0.799987654,,,0.79041961
8854,0,Int J Mol Sci,32604724,7/2/20,pubmed,0,6,"bioinformatic, sequencing",0.17026504,0.820780931,0.002238559,0.002238534,0.002238459,0.002238478,Genomics,0.89741254,TRUE,34.16666667,0.47863195,25.83333333,0.535790741,8,0.799987654,,,0.604803448
8855,Setting up an Easy-to-Use Machine Learning Pipeline for Medical Decision Support: A Case Study for COVID-19 Diagnosis Based on Deep Learning with CT Scans.,Stud Health Technol Inform,32604588,7/2/20,pubmed,0,5,"machine learning, deep learning, dataset",0.001901705,0.001901717,0.902722556,0.001901774,0.00190171,0.089670539,Imaging,0.87366897,TRUE,7.2,0.103716989,0,0.055525823,5,0.739490092,,,0.299577635
8856,Application of Social Network Analysis of COVID-19 Related Tweets Mentioning Cannabis and Opioids to Gain Insights for Drug Abuse Research.,Stud Health Technol Inform,32604586,7/2/20,pubmed,0,10,network analysis,0.039483347,0.002130748,0.002130902,0.829021243,0.125103076,0.002130684,Epidemiology,0.48702493,FALSE,22,0.326056033,9.8,0.350682366,0,0.403234768,,,0.359991055
8857,Unsupervised Machine Learning for the Discovery of Latent Clusters in COVID-19 Patients Using Electronic Health Records.,Stud Health Technol Inform,32604585,7/2/20,pubmed,0,3,machine learning,0.00256276,0.00256277,0.196803423,0.430779788,0.119210128,0.24808113,Epidemiology,0.82914376,TRUE,40.66666667,0.546292288,14,0.412898047,2,0.618927094,,,0.526039143
8858,0,F1000Res,32528664,7/2/20,pubmed,0,11,in silico,0.049144718,0.001786598,0.001786528,0.570440516,0.149318514,0.227523125,Epidemiology,0.9269041,TRUE,10.45454545,0.156039335,0.636363636,0.090647578,7,0.785110192,,,0.343932368
8859,Tele-Neuro-Ophthalmology: Vision for 20/20 and Beyond.,J Neuroophthalmol,32604247,7/1/20,pubmed,0,2,artificial intelligence,0.001415173,0.001415129,0.142468924,0.393200469,0.395088206,0.066412099,Healthcare,0.9921866,TRUE,54,0.661574618,23,0.513513514,2,0.618927094,,,0.598005075
8860,When science goes viral: The research response during three months of the COVID-19 outbreak.,Biomed Pharmacother,32603887,7/1/20,pubmed,0,5,mathematical model,0.001203515,0.148706973,0.001203444,0.826075544,0.021607096,0.001203428,Epidemiology,0.867138,TRUE,31.4,0.44882182,10.6,0.364262778,12,0.850299401,,,0.554461333
8861,"Large SARS-CoV-2 Outbreak Caused by Asymptomatic Traveler, China.",Emerg Infect Dis,32603652,7/1/20,pubmed,0,3,"genome sequences, genomes",0.00453062,0.899171053,0.004530658,0.004531003,0.004530937,0.082705729,Genomics,0.86573076,TRUE,8,0.118683901,1.333333333,0.13252609,8,0.799987654,,,0.350399215
8862,Correction to Considering the Effects of Microbiome and Diet on SARS-CoV-2 Infection: Nanotechnology Roles.,ACS Nano,32603582,7/1/20,pubmed,0,4,microbiom,0.057799149,0.711004256,0.057799148,0.057799148,0.057799148,0.05779915,Genomics,0.75311816,TRUE,336,0.993135011,573,0.982472572,1,0.537564047,,,0.837723877
8863,Telehealth Availability in US Hospitals in the Face of the COVID-19 Pandemic.,J Rural Health,32603017,7/1/20,pubmed,0,2,logistic regression,0.001310371,0.001310383,0.001310424,0.503189303,0.148771888,0.344107631,Epidemiology,0.78348935,TRUE,16,0.243552477,0,0.055525823,2,0.618927094,,,0.306001798
8864,Progression of COVID-19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates.,J Rural Health,32602983,7/1/20,pubmed,0,5,bayes,0.001486415,0.00148644,0.001486515,0.544041682,0.3730776,0.078421349,Epidemiology,0.887444,TRUE,29,0.41993939,20.2,0.482204977,20,0.900117291,,,0.600753886
8865,Life satisfaction among adolescents before and during the COVID-19 pandemic.,Tidsskr Nor Laegeforen,32602316,7/1/20,pubmed,0,4,logistic regression,0.001438128,0.00143811,0.001438106,0.165236449,0.829011078,0.001438128,Healthcare,0.81031585,TRUE,63,0.721998887,58.5,0.715814825,3,0.667819001,,,0.701877571
8866,Compliance with infection control rules among adolescents in Oslo during the COVID-19 pandemic.,Tidsskr Nor Laegeforen,32602306,7/1/20,pubmed,0,4,logistic regression,0.001392847,0.001392871,0.001392858,0.001392888,0.993035662,0.001392874,Healthcare,0.9273207,TRUE,63,0.721998887,58.5,0.715814825,3,0.667819001,,,0.701877571
8867,Obesity as a Potential Predictor of Disease Severity in Young COVID-19 Patients: A Retrospective Study.,Obesity (Silver Spring),32602202,7/1/20,pubmed,0,9,logistic regression,0.039001708,0.001593505,0.001593624,0.001593584,0.001593537,0.954624041,Clinics,0.94421613,TRUE,117.7777778,0.90389016,45.77777778,0.662295959,30,0.930057411,,,0.832081177
8868,Alcohol use in Australia during the early days of the COVID-19 pandemic: Initial results from the COLLATE project.,Psychiatry Clin Neurosci,32602150,7/1/20,pubmed,0,7,logistic regression,0.001987164,0.001987122,0.001987099,0.001987195,0.990064294,0.001987127,Healthcare,0.52177036,TRUE,127.7142857,0.918238605,78.85714286,0.77548836,21,0.903944688,,,0.865890551
8869,Structure-based screening of novel lichen compounds against SARS Coronavirus main protease (Mpro) as potentials inhibitors of COVID-19.,Mol Divers,32602074,7/1/20,pubmed,0,6,"virtual screening, molecular dynamics simulation",0.975755867,0.001171556,0.019557788,0.001171633,0.001171601,0.001171556,Drug discovery,0.97843313,TRUE,13.66666667,0.207310285,1.333333333,0.13252609,7,0.785110192,,,0.374982189
8870,COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database.,ChemRxiv,32601612,7/1/20,pubmed,0,11,knowledge graph,0.557671075,0.005697707,0.419538078,0.005697857,0.00569775,0.005697532,Drug discovery,0.5407147,TRUE,395.9090909,0.995423341,222.7272727,0.927281242,1,0.537564047,,,0.820089543
8871,A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19.,ArXiv,32601600,7/1/20,pubmed,0,2,"transfer learning, dataset",0.058128356,0.00107222,0.277656914,0.600310212,0.061760127,0.00107217,Epidemiology,0.061478615,FALSE,1.5,0.015523533,,,2,0.618927094,,,0.317225313
8872,Numerical evaluation of spray position for improved nasal drug delivery.,Sci Rep,32601278,7/1/20,pubmed,0,19,computational,0.289608432,0.001717285,0.262464288,0.442775429,0.00171734,0.001717226,Epidemiology,0.51184034,TRUE,80.68421053,0.809635723,64.21052632,0.735951298,13,0.858880178,,,0.801489066
8873,Features of Mild-to-Moderate COVID-19 Patients With Dysphonia.,J Voice,32600873,7/1/20,pubmed,0,41,bayes,0.001486397,0.001486454,0.022593871,0.001486483,0.190385883,0.782560911,Clinics,0.94468755,TRUE,54.09756098,0.661883852,48.34146341,0.675140487,24,0.914439163,,,0.750487834
8874,Laboratory findings and a combined multifactorial approach to predict death in critically ill patients with COVID-19: a retrospective study.,Epidemiol Infect,32600484,7/1/20,pubmed,0,5,"logistic regression, prediction model",0.00159348,0.00159355,0.00159362,0.029644708,0.0015935,0.963981143,Clinics,0.8915907,TRUE,4.8,0.065248315,1.4,0.133864062,6,0.764429903,,,0.32118076
8875,"Treatment of coronavirus disease 2019 in Shandong, China: a cost and affordability analysis.",Infect Dis Poverty,32600426,7/1/20,pubmed,0,8,logistic regression,0.049476067,0.001220019,0.05751235,0.46367876,0.119641883,0.308470921,Epidemiology,0.9036929,TRUE,86.375,0.830478075,35.25,0.606837035,7,0.785110192,,,0.740808434
8876,The factors predicting pneumonia in COVID-19 patients: preliminary results from a university hospital in Turkey,Turk J Med Sci,32599972,7/1/20,pubmed,0,34,logistic regression,0.001987073,0.001987109,0.073269522,0.001987088,0.001987133,0.918782076,Clinics,0.8983091,TRUE,53.14705882,0.65489517,17.88235294,0.459392561,1,0.537564047,,,0.550617259
8877,Class A G Protein-Coupled Receptor Antagonist Famotidine as a Therapeutic Alternative Against SARS-CoV2: An In Silico Analysis.,Biomolecules,32599963,7/1/20,pubmed,0,3,"computational, in silico",0.822501709,0.001438177,0.001438202,0.15083507,0.001438142,0.022348701,Drug discovery,0.85974884,TRUE,39.66666667,0.536211268,25.33333333,0.531576131,14,0.866658436,,,0.644815278
8878,On a Coupled Time-Dependent SIR Models Fitting with New York and New-Jersey States COVID-19 Data.,Biology (Basel),32599867,7/1/20,pubmed,0,2,model fit,0.001901723,0.001901711,0.001901704,0.99049132,0.001901773,0.00190177,Epidemiology,0.14801282,FALSE,59.5,0.699857752,44.5,0.656743377,1,0.537564047,,,0.631388392
8879,Which type of cancer patients are more susceptible to the SARS-COX-2: Evidence from a meta-analysis and bioinformatics analysis.,Crit Rev Oncol Hematol,32599375,7/1/20,pubmed,0,2,bioinformatic,0.139062176,0.049824337,0.044207647,0.001987281,0.001987234,0.762931325,Clinics,0.8434055,TRUE,29,0.41993939,8,0.320511105,9,0.814309525,,,0.51825334
8880,Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.,Comput Methods Programs Biomed,32599338,7/1/20,pubmed,0,4,deep learning,0.001438135,0.001438228,0.863400481,0.001438199,0.00143818,0.130846776,Imaging,0.7047894,TRUE,182,0.965118437,40.25,0.634934439,62,0.965553429,,,0.855202102
8881,Targeting hub genes and pathways of innate immune response in COVID-19: A network biology perspective.,Int J Biol Macromol,32599245,7/1/20,pubmed,0,9,"transcriptom, interactom",0.989835786,0.002032959,0.002032785,0.002032793,0.002032836,0.002032842,Drug discovery,0.48884115,FALSE,17.11111111,0.257839075,5.777777778,0.272009633,10,0.828199272,,,0.45268266
8882,"COVID-19 fatalities, latitude, sunlight, and vitamin D.",Am J Infect Control,32599103,7/1/20,pubmed,0,1,correlation analysis,0.049654549,0.001593518,0.001593508,0.652048712,0.001593555,0.293516157,Epidemiology,0.556077,TRUE,5,0.070752675,0,0.055525823,33,0.936045435,,,0.354107978
8883,Alterations in Fecal Fungal Microbiome of Patients With COVID-19 During Time of Hospitalization until Discharge.,Gastroenterology,32598884,7/1/20,pubmed,0,12,"sequencing, metagenom, microbiom",0.001237119,0.464768922,0.020788751,0.001237072,0.001237074,0.510731063,Clinics,0.8617742,TRUE,77.5,0.79658606,92.41666667,0.806796896,29,0.928020248,,,0.843801068
8884,Absence of SARS-CoV-2 in semen of a COVID-19 patient cohort.,Andrology,32598557,7/1/20,pubmed,0,12,bioinformatic,0.180979872,0.272881078,0.088930825,0.001237146,0.001237106,0.454733973,Clinics,0.9826095,TRUE,32.83333333,0.464592739,,,23,0.91129082,,,0.68794178
8885,Rapid identification of SARS-CoV-2-infected patients at the emergency department using routine testing.,Clin Chem Lab Med,32598302,7/1/20,pubmed,0,10,computational,0.001141384,0.001141419,0.601271964,0.001141407,0.001141386,0.39416244,Imaging,0.68709886,TRUE,16.3,0.246273734,13.4,0.404602622,8,0.799987654,,,0.483621337
8886,Predicting human microbe-drug associations via graph convolutional network with conditional random field.,Bioinformatics,32597948,7/1/20,pubmed,0,5,"computational, bioinformatic, dataset",0.526577749,0.001072187,0.46913342,0.001072212,0.001072234,0.001072198,Drug discovery,0.7009409,TRUE,91.8,0.848166244,90,0.801445009,6,0.764429903,,,0.804680385
8887,[Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19].,Sheng Wu Yi Xue Gong Cheng Xue Za Zhi,32597076,7/1/20,pubmed,0,6,"machine learning, supervised learning, unsupervised learning",0.00143817,0.001438173,0.454290978,0.220280859,0.321113679,0.001438141,Healthcare,0.69773674,TRUE,51,0.63875317,16.33333333,0.440460262,2,0.618927094,,,0.566046842
8888,Effect of Underlying Comorbidities on the Infection and Severity of COVID-19 in Korea: a Nationwide Case-Control Study.,J Korean Med Sci,32597048,7/1/20,pubmed,0,13,logistic regression,0.001717223,0.001717373,0.001717353,0.001717384,0.135547237,0.85758343,Clinics,0.75725377,TRUE,18.23076923,0.273177067,3.461538462,0.209459459,37,0.942712513,,,0.475116347
8889,AI Surveillance during Pandemics: Ethical Implementation Imperatives.,Hastings Cent Rep,32596887,7/1/20,pubmed,0,3,artificial intelligence,0.002422296,0.002422474,0.450493135,0.493641123,0.00242244,0.048598533,Epidemiology,0.2579924,FALSE,149.6666667,0.943905003,94.66666667,0.812014985,1,0.537564047,,,0.764494679
8890,Stress and sleep: a survey based on wearable sleep trackers among medical and nursing staff in Wuhan during the COVID-19 pandemic.,Gen Psychiatr,32596641,7/1/20,pubmed,0,5,"logistic regression, correlation analysis",0.001350336,0.00135037,0.065706882,0.001350398,0.805207783,0.125034231,Healthcare,0.9852489,TRUE,67.8,0.752303791,21.2,0.493644635,5,0.739490092,,,0.661812839
8891,Psychological impact of the COVID-19 pandemic on healthcare workers: a cross-sectional study in China.,Gen Psychiatr,32596640,7/1/20,pubmed,0,14,logistic regression,0.0008802,0.000880197,0.000880225,0.000880211,0.986049298,0.010429869,Healthcare,0.95951927,TRUE,37.35714286,0.511719958,16.71428571,0.445143163,58,0.963022409,,,0.639961843
8892,Evidence for host-dependent RNA editing in the transcriptome of SARS-CoV-2.,Sci Adv,32596474,7/1/20,pubmed,0,5,"transcriptom, genomes",0.323243351,0.588374243,0.002296629,0.002296822,0.002296608,0.081492346,Genomics,0.6221986,TRUE,24.2,0.357350485,45.8,0.662697351,41,0.948144947,,,0.656064261
8893,Data stream dataset of SARS-CoV-2 genome.,Data Brief,32596428,7/1/20,pubmed,0,2,"bioinformatic, dataset",0.003465957,0.561477345,0.306944015,0.121180863,0.00346586,0.00346596,Genomics,0.49020347,FALSE,43.5,0.573381161,3,0.199424672,0,0.403234768,,,0.392013534
8894,"After Less Than 2 Months, the Simulations That Drove the World to Strict Lockdown Appear to be Wrong, the Same of the Policies They Generated.",Health Serv Res Manag Epidemiol,32596417,7/1/20,pubmed,0,1,model simulation,0.001254621,0.00125461,0.001254661,0.88299651,0.001254657,0.11198494,Epidemiology,0.5422072,TRUE,179,0.963695961,32,0.585763982,9,0.814309525,,,0.787923156
8895,Qualitative Analysis of a Mathematical Model in the Time of COVID-19.,Biomed Res Int,32596319,7/1/20,pubmed,0,4,mathematical model,0.002898586,0.002898337,0.002898783,0.985507285,0.002898412,0.002898596,Epidemiology,0.2781148,FALSE,107.5,0.884593976,51.5,0.687382927,29,0.928020248,,,0.833332384
8896,Sars-CoV-2 Envelope and Membrane Proteins: Structural Differences Linked to Virus Characteristics?,Biomed Res Int,32596311,7/1/20,pubmed,0,6,structural model,0.52077326,0.468346332,0.002720085,0.002720155,0.002720076,0.002720093,Drug discovery,0.54682577,TRUE,79,0.802585194,71,0.756556061,43,0.9507377,,,0.836626318
8897,Erratum: Author Correction: Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing.,Cell Discov,32595980,7/1/20,pubmed,0,18,sequencing,0.057799812,0.057799601,0.057801681,0.711000481,0.057799162,0.057799264,Epidemiology,0.6257132,TRUE,63.44444444,0.724287216,46.33333333,0.665908483,9,0.814309525,,,0.734835075
8898,Interferon-Induced Transmembrane Protein (IFITM3) Is Upregulated Explicitly in SARS-CoV-2 Infected Lung Epithelial Cells.,Front Immunol,32595654,7/1/20,pubmed,0,7,"transcriptom, toxicogenom, dataset",0.840370786,0.00171728,0.047053492,0.107424026,0.001717221,0.001717196,Drug discovery,0.7643763,TRUE,55.85714286,0.673634733,51,0.685509767,17,0.887338725,,,0.748827742
8899,"Prevalence and Demographic Correlates of Poor Sleep Quality Among Frontline Health Professionals in Liaoning Province, China During the COVID-19 Outbreak.",Front Psychiatry,32595534,7/1/20,pubmed,0,13,logistic regression,0.001461842,0.001461853,0.001461863,0.001461914,0.939906853,0.054245675,Healthcare,0.95633924,TRUE,53.30769231,0.655637331,44.76923077,0.65801445,9,0.814309525,,,0.709320435
8900,Integration of transcriptomic profile of SARS-CoV-2 infected normal human bronchial epithelial cells with metabolic and protein-protein interaction networks.,Turk J Biol,32595353,7/1/20,pubmed,0,2,transcriptom,0.764216763,0.07783868,0.002032908,0.115506533,0.002032807,0.038372309,Drug discovery,0.5002445,TRUE,9,0.135320675,12,0.386740701,5,0.739490092,,,0.420517156
8901,An updated analysis of variations in SARS-CoV-2 genome.,Turk J Biol,32595352,7/1/20,pubmed,0,3,"genome sequences, genomes",0.001861936,0.99069107,0.001861705,0.001861787,0.001861783,0.001861719,Genomics,0.6238269,TRUE,12,0.183190055,4.666666667,0.246721969,21,0.903944688,,,0.444618904
8902,Phylogenetic analysis of SARS-CoV-2 genomes in Turkey.,Turk J Biol,32595351,7/1/20,pubmed,0,7,genomes,0.001943592,0.904157294,0.08806859,0.001943586,0.001943475,0.001943463,Genomics,0.2815663,FALSE,2.285714286,0.023687303,0,0.055525823,3,0.667819001,,,0.249010709
8903,Healthcare utilization among breast cancer patients during the COVID-19 outbreak.,Palliat Support Care,32594966,7/1/20,pubmed,0,3,logistic regression,0.001254614,0.001254622,0.001254609,0.001254686,0.792407322,0.202574146,Healthcare,0.93496156,TRUE,6.666666667,0.094996598,1.666666667,0.145036125,8,0.799987654,,,0.346673459
8904,"Taking the inner route: spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil.",Epidemiol Infect,32594926,7/1/20,pubmed,0,5,predictive model,0.001653011,0.001653096,0.059120895,0.800543773,0.001653086,0.135376138,Epidemiology,0.24298,FALSE,3.6,0.044838889,0.2,0.061145304,14,0.866658436,,,0.32421421
8905,The ACE2 expression in Sertoli cells and germ cells may cause male reproductive disorder after SARS-CoV-2 infection.,J Cell Mol Med,32594644,7/1/20,pubmed,0,7,sequencing,0.638610795,0.002130891,0.002130674,0.087360529,0.267636352,0.002130759,Drug discovery,0.91371435,TRUE,20.14285714,0.298967159,9,0.337904736,45,0.953206988,,,0.530026295
8906,SARS-CoV-2 Infection in Healthcare Personnel With High-risk Occupational Exposure: Evaluation of 7-Day Exclusion From Work Policy.,Clin Infect Dis,32594160,7/1/20,pubmed,0,11,logistic regression,0.001717163,0.001717214,0.001717188,0.001717326,0.731179672,0.261951436,Healthcare,0.86949515,TRUE,31.90909091,0.453955099,29.72727273,0.567500669,16,0.881782826,,,0.634412865
8907,Testing of natural products in clinical trials targeting the SARS-CoV-2 (Covid-19) viral spike protein-angiotensin converting enzyme-2 (ACE2) interaction.,Biochem Pharmacol,32593613,7/1/20,pubmed,0,2,in silico,0.982670107,0.003465949,0.00346591,0.003466027,0.003466026,0.00346598,Drug discovery,0.85955626,TRUE,46.5,0.601026656,45.5,0.660957988,14,0.866658436,,,0.709547693
8908,Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study.,Lancet Psychiatry,32593341,7/1/20,pubmed,0,115,dataset,0.000716346,0.000716361,0.000716365,0.27867958,0.212062639,0.507108709,Clinics,0.77055293,TRUE,62.07758621,0.715937906,,,297,0.994937959,,,0.855437933
8909,"COVID-19 in children and adolescents in Europe: a multinational, multicentre cohort study.",Lancet Child Adolesc Health,32593339,7/1/20,pubmed,0,115,logistic regression,0.105689937,0.000846579,0.000846557,0.158033711,0.083851195,0.65073202,Clinics,0.63896376,TRUE,31.84482759,0.453212938,,,245,0.993394654,,,0.723303796
8910,Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach.,Wellcome Open Res,32587900,7/1/20,pubmed,0,2,machine learning,0.001254613,0.001254686,0.182090134,0.701975232,0.001254728,0.112170608,Epidemiology,0.37587422,FALSE,8,0.118683901,0.5,0.087101953,15,0.874313229,,,0.360033028
8911,Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods.,Comput Methods Programs Biomed,32593060,6/28/20,pubmed,0,14,machine learning,0.000916769,0.094031062,0.306984229,0.181024137,0.000916707,0.416127095,Clinics,0.97403944,TRUE,7.642857143,0.109654277,0.5,0.087101953,14,0.866658436,,,0.354471555
8912,Comparative analysis of protein synthesis rate in COVID-19 with other human coronaviruses.,Infect Genet Evol,32592845,6/28/20,pubmed,0,2,proteom,0.147002167,0.848760306,0.001059368,0.001059433,0.001059358,0.001059366,Genomics,0.5564003,TRUE,12.5,0.189436576,1,0.122023013,1,0.537564047,,,0.283007879
8913,In-Hospital Use of Statins Is Associated with a Reduced Risk of Mortality among Individuals with COVID-19.,Cell Metab,32592657,6/28/20,pubmed,0,49,structural model,0.399275735,0.002639114,0.002639155,0.002639235,0.002639056,0.590167705,Clinics,0.8160699,TRUE,66.44897959,0.743274167,,,130,0.983949627,,,0.863611897
8914,Identification of a Potential Peptide Inhibitor of SARS-CoV-2 Targeting its Entry into the Host Cells.,Drugs R D,32592145,6/28/20,pubmed,0,4,computational,0.968005685,0.001653036,0.001653013,0.025382053,0.001653072,0.001653142,Drug discovery,0.5621844,TRUE,14,0.213494959,3.75,0.21982874,17,0.887338725,,,0.440220808
8915,The examination of sleep quality for frontline healthcare workers during the outbreak of COVID-19.,Sleep Breath,32592021,6/28/20,pubmed,0,12,logistic regression,0.001046794,0.00104681,0.001046804,0.001046803,0.994765954,0.001046836,Healthcare,0.99732894,TRUE,33.16666667,0.46780877,15.33333333,0.428351619,13,0.858880178,,,0.585013522
8916,Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation.,Eur Radiol,32591888,6/28/20,pubmed,0,10,logistic regression,0.001310331,0.001310315,0.465910735,0.001310352,0.00131036,0.528847907,Clinics,0.7811798,TRUE,22.2,0.327787742,21.6,0.498795826,23,0.91129082,,,0.579291463
8917,The short-term impact of COVID-19 pandemic on spine surgeons: a cross-sectional global study.,Eur Spine J,32591880,6/28/20,pubmed,0,9,logistic regression,0.001538107,0.001538108,0.001538155,0.001538211,0.992309272,0.001538147,Healthcare,0.9403931,TRUE,37.44444444,0.512895046,10.33333333,0.36038266,5,0.739490092,,,0.537589266
8918,COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis.,Nat Biotechnol,32591762,6/28/20,pubmed,0,36,sequencing,0.717673433,0.059393876,0.001461876,0.001461897,0.001461891,0.218547026,Drug discovery,0.7067331,TRUE,86.08333333,0.829302987,142.3611111,0.876572117,192,0.990740169,,,0.898871758
8919,Could there be a link between oral hygiene and the severity of SARS-CoV-2 infections?,Br Dent J,32591714,6/28/20,pubmed,0,3,microbiom,0.00280639,0.142873961,0.002806588,0.066844017,0.002806512,0.781862532,Clinics,0.71999454,TRUE,2,0.022141134,0,0.055525823,17,0.887338725,,,0.321668561
8920,Can orthodontic care be safely delivered during the COVID-19 pandemic? Recommendations from a literature review.,Evid Based Dent,32591665,6/28/20,pubmed,0,1,dataset,0.001022695,0.001022667,0.072134795,0.778714285,0.124260516,0.022845043,Epidemiology,0.84050065,TRUE,1,0.012307502,1,0.122023013,1,0.537564047,,,0.223964854
8921,"Data, Reagents, Assays and Merits of Proteomics for SARS-CoV-2 Research and Testing.",Mol Cell Proteomics,32591346,6/28/20,pubmed,0,11,"proteom, genomes",0.529664295,0.327899449,0.034419815,0.086109873,0.001237096,0.020669471,Drug discovery,0.4704194,FALSE,29.45454545,0.424083122,51.09090909,0.685710463,18,0.891474782,,,0.667089456
8922,Mental health circumstances among health care workers and general public under the pandemic situation of COVID-19 (HOME-COVID-19).,Medicine (Baltimore),32590751,6/27/20,pubmed,0,14,logistic regression,0.000672791,0.000672776,0.00067276,0.148854346,0.848454546,0.00067278,Healthcare,0.6504296,TRUE,60.07142857,0.70350671,36.28571429,0.612857907,10,0.828199272,,,0.71485463
8923,Analysis of clinical features and early warning signs in patients with severe COVID-19: A retrospective cohort study.,PLoS One,32589691,6/27/20,pubmed,0,9,logistic regression,0.001237099,0.001237112,0.099189508,0.00123718,0.001237138,0.895861963,Clinics,0.9714331,TRUE,5.555555556,0.07749397,6.888888889,0.294153064,7,0.785110192,,,0.385585742
8924,New machine learning method for image-based diagnosis of COVID-19.,PLoS One,32589673,6/27/20,pubmed,0,6,"machine learning, computational, dataset",0.001717198,0.001717348,0.924281927,0.068849152,0.001717168,0.001717207,Imaging,0.859546,TRUE,31.66666667,0.451976003,9.5,0.345531175,41,0.948144947,,,0.581884042
8925,Ocular Surface Expression of SARS-CoV-2 Receptors.,Ocul Immunol Inflamm,32589459,6/27/20,pubmed,0,3,"sequencing, transcriptom",0.786712351,0.205156372,0.002032759,0.002032797,0.002032926,0.002032794,Drug discovery,0.7408873,TRUE,48.66666667,0.619333292,18.66666667,0.466818303,13,0.858880178,,,0.648343924
8926,COVID-19 Screening with Chest CT in Acute Stroke Imaging: A Clinical Decision Model.,J Neuroimaging,32589348,6/27/20,pubmed,0,6,probabilistic,0.001237099,0.001237081,0.59716738,0.104066014,0.295055247,0.001237178,Imaging,0.8706887,TRUE,223,0.979281341,82.66666667,0.78492106,3,0.667819001,,,0.810673801
8927,Myocardial injury determination improves risk stratification and predicts mortality in COVID-19 patients.,Cardiol J,32589258,6/27/20,pubmed,0,11,prediction model,0.001310432,0.001310348,0.027230018,0.001310407,0.001310365,0.96752843,Clinics,0.906409,TRUE,102.2727273,0.873956336,246.6363636,0.93724913,16,0.881782826,,,0.897662764
8928,Clinical Characteristics and Outcomes in Patients With Coronavirus Disease 2019 and Multiple Sclerosis.,JAMA Neurol,32589189,6/27/20,pubmed,0,90,logistic regression,0.000889068,0.000889102,0.048040867,0.000889076,0.047716936,0.901574951,Clinics,0.62585604,TRUE,97.45,0.863133156,80.45,0.780104362,83,0.974381135,,,0.872539551
8929,"Global Changes and Factors of Increase in Caloric/Salty Food Intake, Screen Use, and Substance Use During the Early COVID-19 Containment Phase in the General Population in France: Survey Study.",JMIR Public Health Surveill,32589149,6/27/20,pubmed,0,6,logistic regression,0.001034571,0.001034587,0.01463573,0.00103464,0.981225865,0.001034607,Healthcare,0.96552545,TRUE,79.5,0.80493537,39.5,0.630920524,18,0.891474782,,,0.775776892
8930,"Primary Symptoms, Comorbidities, and Outcomes of 431 Hospitalized Patients with Confirmative RT-PCR Results for COVID-19.",Am J Trop Med Hyg,32588801,6/27/20,pubmed,0,6,logistic regression,0.001684477,0.001684515,0.319570813,0.001684478,0.001684523,0.673691195,Clinics,0.6769451,TRUE,49.5,0.628362917,13.16666667,0.401792882,5,0.739490092,,,0.589881964
8931,Truncated inception net: COVID-19 outbreak screening using chest X-rays.,Phys Eng Sci Med,32588200,6/27/20,pubmed,0,3,"deep learning, neural network, dataset",0.001291216,0.001291234,0.939683792,0.055151301,0.001291237,0.001291221,Imaging,0.6211146,TRUE,86.33333333,0.830354382,22,0.503746321,54,0.959812334,,,0.764637679
8932,Distinct genetic spectrums and evolution patterns of SARS-CoV-2.,medRxiv,32588000,6/27/20,pubmed,0,8,"genome sequences, genomes",0.002296553,0.903919819,0.002296548,0.002296573,0.002296572,0.086893935,Genomics,0.64258724,TRUE,97.2,0.862143608,56.2,0.707586299,11,0.840175319,,,0.803301742
8933,A Digital Protein Microarray for COVID-19 Cytokine Storm Monitoring.,medRxiv,32587979,6/27/20,pubmed,0,11,machine learning,0.133400368,0.201437056,0.203144932,0.00131046,0.001310441,0.459396743,Clinics,0.6666271,TRUE,30.72727273,0.440719896,17.27272727,0.45330479,2,0.618927094,,,0.50431726
8934,Decoding of persistent multiscale structures in complex biological networks.,bioRxiv,32587977,6/27/20,pubmed,0,6,transcriptom,0.586090811,0.00321432,0.003214384,0.401052061,0.003214237,0.003214187,Drug discovery,0.4190918,FALSE,64.83333333,0.733811615,776.1666667,0.98963072,0,0.403234768,,,0.708892367
8935,The D614G mutation in SARS-CoV-2 Spike increases transduction of multiple human cell types.,bioRxiv,32587969,6/27/20,pubmed,0,7,"phylogenom, genomes",0.328909246,0.666640155,0.001112663,0.001112635,0.001112644,0.001112656,Genomics,0.17439348,FALSE,38.33333333,0.522233904,143.6666667,0.877776291,52,0.958701154,,,0.786237117
8936,Comparative analysis of coronavirus genomic RNA structure reveals conservation in SARS-like coronaviruses.,bioRxiv,32587967,6/27/20,pubmed,0,8,genomes,0.608139728,0.385122149,0.001684478,0.001684607,0.001684522,0.001684516,Drug discovery,0.29351264,FALSE,85.125,0.825406642,240.125,0.934104897,17,0.887338725,,,0.882283421
8937,0,bioRxiv,32587963,6/27/20,pubmed,0,14,transcriptom,0.821601846,0.001622766,0.001622699,0.00162273,0.00162278,0.17190718,Drug discovery,0.58448756,TRUE,32,0.455810502,61.5,0.726585496,33,0.936045435,,,0.706147144
8938,SARS-CoV-2 (COVID-19) structural and evolutionary dynamicome: Insights into functional evolution and human genomics.,J Biol Chem,32587094,6/27/20,pubmed,0,22,"molecular dynamics simulation, proteom",0.468172398,0.513056391,0.001059376,0.001059366,0.001059398,0.015593072,Genomics,0.55408067,TRUE,48.36363636,0.616983116,86.18181818,0.793283382,14,0.866658436,,,0.758974978
8939,Acute necrotizing encephalopathy with SARS-CoV-2 RNA confirmed in cerebrospinal fluid.,Neurology,32586897,6/27/20,pubmed,0,18,proteom,0.229268507,0.193017177,0.002422327,0.002422433,0.002422484,0.570447071,Clinics,0.7396849,TRUE,108.9444444,0.88774816,99.66666667,0.821849077,69,0.968763504,,,0.892786914
8940,"Genome Sequence of SARS-CoV-2 Isolate Cali-01, from Colombia, Obtained Using Oxford Nanopore MinION Sequencing.",Microbiol Resour Announc,32586872,6/27/20,pubmed,0,3,sequencing,0.008402468,0.578609621,0.008402529,0.008402739,0.00840297,0.387779673,Genomics,0.48705322,FALSE,34,0.477766096,57.33333333,0.7122692,5,0.739490092,,,0.643175129
8941,Advanced bioinformatics rapidly identifies existing therapeutics for patients with coronavirus disease-2019 (COVID-19).,J Transl Med,32586380,6/27/20,pubmed,0,10,"computational, bioinformatic",0.943853685,0.001203489,0.051332418,0.00120349,0.001203468,0.001203449,Drug discovery,0.80475426,TRUE,2.2,0.022759602,0.7,0.096601552,26,0.920859312,,,0.346740155
8942,A Human Support Robot for the Cleaning and Maintenance of Door Handles Using a Deep-Learning Framework.,Sensors (Basel),32585864,6/27/20,pubmed,0,7,deep-learning,0.001901799,0.001901849,0.650903341,0.341489498,0.001901815,0.001901698,Imaging,0.91137254,TRUE,10.14285714,0.15269961,0.857142857,0.103224512,7,0.785110192,,,0.347011438
8943,"Effects of tobacco cigarettes, e-cigarettes, and waterpipe smoking on endothelial function and clinical outcomes.",Eur Heart J,32585699,6/26/20,pubmed,0,6,"transcriptom, proteom, microbiom",0.2511408,0.137522016,0.002238502,0.332267429,0.002238591,0.274592662,Epidemiology,0.6702111,TRUE,171.6666667,0.959614076,341.8333333,0.964343056,12,0.850299401,,,0.924752178
8944,The Adoption and Implementation of Digital Health Care in the Post-COVID-19 Era.,J Allergy Clin Immunol Pract,32585407,6/26/20,pubmed,0,4,digital health,0.02506022,0.025060311,0.025060928,0.87469692,0.0250614,0.02506022,Epidemiology,0.37222382,FALSE,83.75,0.820211516,34.5,0.602221033,2,0.618927094,,,0.680453214
8945,COVID-19 and cardiac arrhythmias.,Heart Rhythm,32585191,6/26/20,pubmed,0,28,logistic regression,0.001392813,0.001392848,0.001392804,0.001392873,0.00139291,0.993035753,Clinics,0.5345299,TRUE,106.3214286,0.881625332,100.7142857,0.823722237,73,0.970677202,,,0.892008257
8946,Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review.,J Med Internet Res,32584780,6/26/20,pubmed,0,3,artificial intelligence,0.096660643,0.000907284,0.517149937,0.360349929,0.024024887,0.000907321,Epidemiology,0.8815667,TRUE,9,0.135320675,1.333333333,0.13252609,1,0.537564047,,,0.268470271
8947,Primary Care Practice Finances In The United States Amid The COVID-19 Pandemic.,Health Aff (Millwood),32584605,6/26/20,pubmed,0,5,simulation model,0.003214103,0.0032142,0.003214123,0.397140671,0.590002651,0.003214252,Healthcare,0.9328935,TRUE,159.2,0.950831839,221.4,0.926946749,13,0.858880178,,,0.912219588
8948,Characterization of the Inflammatory Response to Severe COVID-19 Illness.,Am J Respir Crit Care Med,32584597,6/26/20,pubmed,0,36,immunome,0.090007088,0.001371279,0.001371273,0.001371275,0.001371277,0.904507808,Clinics,0.9855032,TRUE,45.55555556,0.591625951,37.02777778,0.616738025,70,0.969072165,,,0.725812047
8949,Antibody tests for identification of current and past infection with SARS-CoV-2.,Cochrane Database Syst Rev,32584464,6/26/20,pubmed,0,17,logistic regression,0.000305631,0.330680637,0.168757769,0.190243604,0.201707326,0.108305032,Genomics,0.0565359,FALSE,71.88235294,0.772342136,70.17647059,0.753880118,271,0.99413544,,,0.840119231
8950,Predictive value of National Early Warning Score 2 (NEWS2) for intensive care unit admission in patients with SARS-CoV-2 infection.,Infect Dis (Lond),32584161,6/26/20,pubmed,0,4,logistic regression,0.001438086,0.001438115,0.00143821,0.029442537,0.001438115,0.964804937,Clinics,0.62540334,TRUE,43.75,0.575545798,12.25,0.388546963,25,0.918019631,,,0.627370797
8951,Immunoinformatics study to search epitopes of spike glycoprotein from SARS-CoV-2 as potential vaccine.,J Biomol Struct Dyn,32583729,6/26/20,pubmed,0,4,in silico,0.823032952,0.080007464,0.001861841,0.091374191,0.001861804,0.001861748,Drug discovery,0.46697244,FALSE,3,0.037293586,0,0.055525823,5,0.739490092,,,0.2774365
8952,Post-mortem surveillance of bovine tuberculosis in Ireland: herd-level variation in the probability of herds disclosed with lesions at routine slaughter to have skin test reactors at follow-up test.,Vet Res Commun,32583301,6/26/20,pubmed,0,6,dataset,0.001653091,0.167004099,0.065171081,0.507354667,0.001653087,0.257163976,Epidemiology,0.41274557,FALSE,37.33333333,0.511596264,26.83333333,0.544286861,3,0.667819001,,,0.574567376
8953,Does Early Childhood Vaccination Protect Against COVID-19?,Front Mol Biosci,32582766,6/26/20,pubmed,0,4,computational,0.527767712,0.225391235,0.002490499,0.00249066,0.2014729,0.040386994,Drug discovery,0.39020625,FALSE,14.75,0.222586431,47.5,0.670591383,21,0.903944688,,,0.599040834
8954,"Risk Factors Associated With Long-Term Hospitalization in Patients With COVID-19: A Single-Centered, Retrospective Study.",Front Med (Lausanne),32582749,6/26/20,pubmed,0,5,logistic regression,0.000765946,0.000765933,0.000765961,0.045218114,0.000766026,0.95171802,Clinics,0.9490248,TRUE,26.4,0.386913229,5.4,0.263914905,10,0.828199272,,,0.493009135
8955,Laboratory Testing Methods for Novel Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2).,Front Cell Dev Biol,32582718,6/26/20,pubmed,0,3,proteom,0.001126822,0.590331036,0.2919054,0.087742051,0.001126829,0.027767861,Genomics,0.6512352,TRUE,8.666666667,0.12839384,16.66666667,0.444674873,27,0.92443978,,,0.499169498
8956,Hyperlocal Postcode Based Crowdsourced Surveillance Systems in the COVID-19 Pandemic Response.,Front Public Health,32582620,6/26/20,pubmed,0,3,dataset,0.002639072,0.002639055,0.262937212,0.726506483,0.002639205,0.002638972,Epidemiology,0.11960477,FALSE,13.66666667,0.207310285,3.333333333,0.206515922,2,0.618927094,,,0.3442511
8957,A Novel Scoring System for Prediction of Disease Severity in COVID-19.,Front Cell Infect Microbiol,32582575,6/26/20,pubmed,0,16,logistic regression,0.028740052,0.001461881,0.127023109,0.062274677,0.001461872,0.779038409,Clinics,0.83239484,TRUE,98.25,0.865483332,72,0.7594327,29,0.928020248,,,0.85097876
8958,Herbs that might be effective for the management of COVID-19: A bioinformatics analysis on anti-tyrosine kinase property.,J Res Med Sci,32582350,6/26/20,pubmed,0,2,bioinformatic,0.825188076,0.03496395,0.034962053,0.034962106,0.034961941,0.034961874,Drug discovery,0.66145504,TRUE,127,0.917125363,4.5,0.242708055,3,0.667819001,,,0.609217473
8959,"Neutrophils, Crucial, or Harmful Immune Cells Involved in Coronavirus Infection: A Bioinformatics Study.",Front Genet,32582303,6/26/20,pubmed,0,6,"bioinformatic, network analysis, dataset",0.771043358,0.002490789,0.002490691,0.002490749,0.002490693,0.21899372,Drug discovery,0.8907199,TRUE,58.16666667,0.69020966,15.33333333,0.428351619,23,0.91129082,,,0.676617367
8960,SARS-CoV-2 Genome Analysis of Japanese Travelers in Nile River Cruise.,Front Microbiol,32582136,6/26/20,pubmed,0,15,"genome-wide, network analysis",0.004775408,0.918447943,0.00477616,0.004775624,0.00477513,0.062449735,Genomics,0.41032228,FALSE,38.4,0.523285299,28.8,0.560543216,7,0.785110192,,,0.622979569
8961,COVID-19 Confinement and Health Risk Behaviors in Spain.,Front Psychol,32581985,6/26/20,pubmed,0,8,logistic regression,0.00135036,0.001350376,0.001350322,0.001350405,0.99324817,0.001350367,Healthcare,0.56010985,TRUE,137.125,0.929494712,152.875,0.885536527,45,0.953206988,,,0.922746076
8962,Rampant C→U Hypermutation in the Genomes of SARS-CoV-2 and Other Coronaviruses: Causes and Consequences for Their Short- and Long-Term Evolutionary Trajectories.,mSphere,32581081,6/26/20,pubmed,0,1,genomes,0.085454466,0.911427714,0.000779441,0.000779478,0.000779439,0.000779461,Genomics,0.46377853,FALSE,11,0.167171748,7,0.299973241,60,0.964503982,,,0.477216324
8963,COVID-19 Hyperinflammation: What about Neutrophils?,mSphere,32581077,6/26/20,pubmed,0,1,network analysis,0.806020268,0.002080639,0.002080595,0.002080705,0.002080573,0.185657221,Drug discovery,0.799023,TRUE,19,0.285793803,9,0.337904736,25,0.918019631,,,0.513906057
8964,Morphoproteomics and Etiopathogenic Features of Pulmonary COVID-19 with Therapeutic Implications: A Case Study.,Ann Clin Lab Sci,32581017,6/26/20,pubmed,0,5,proteom,0.762885143,0.001291371,0.001291359,0.001291287,0.001291275,0.231949564,Drug discovery,0.3509813,FALSE,172.4,0.960047004,106.2,0.833422531,0,0.403234768,,,0.732234768
8965,Covid-19 and lack of linked datasets for care homes.,BMJ,32581008,6/26/20,pubmed,0,5,dataset,0.02506125,0.025060779,0.414511633,0.025062099,0.485243502,0.025060736,Healthcare,0.4997102,FALSE,91.6,0.847733317,56,0.706850415,16,0.881782826,,,0.812122186
8966,Coronavirus Disease 2019-COVID-19.,Clin Microbiol Rev,32580969,6/26/20,pubmed,0,10,sequencing,0.170078032,0.79035569,0.001272689,0.001272711,0.035748164,0.001272713,Genomics,0.6957181,TRUE,208.3,0.974828375,105.8,0.832753546,225,0.993209457,,,0.933597126
8967,Analysis of factors affecting the prognosis of COVID-19 patients and viral shedding duration.,Epidemiol Infect,32580792,6/26/20,pubmed,0,8,logistic regression,0.001717209,0.094683374,0.00171729,0.001717277,0.001717235,0.898447615,Clinics,0.9234748,TRUE,14.75,0.222586431,11.5,0.378378378,8,0.799987654,,,0.466984154
8968,"Individual-based simulation model for COVID-19 transmission in Daegu, Korea.",Epidemiol Health,32580535,6/26/20,pubmed,0,1,simulation model,0.001751166,0.001751229,0.001751193,0.748097541,0.166611536,0.080037335,Epidemiology,0.7055367,TRUE,9,0.135320675,0.5,0.087101953,3,0.667819001,,,0.29674721
8969,Intervention effects in the transmission of COVID-19 depending on the detection rate and extent of isolation.,Epidemiol Health,32580532,6/26/20,pubmed,0,4,mathematical model,0.001438171,0.040981516,0.026951376,0.820837968,0.10835278,0.001438187,Epidemiology,0.1621648,FALSE,30.5,0.438493413,15,0.42594327,2,0.618927094,,,0.494454593
8970,Diagnostic accuracy of the FebriDx host response point-of-care test in patients hospitalised with suspected COVID-19.,J Infect,32579983,6/25/20,pubmed,0,9,predictive model,0.001310493,0.304439966,0.26771087,0.001310388,0.001310384,0.4239179,Clinics,0.7150554,TRUE,23.11111111,0.342692807,19,0.471367407,11,0.840175319,,,0.551411845
8971,Paromomycin: A potential dual targeted drug effectively inhibits both spike (S1) and main protease of COVID-19.,Int J Infect Dis,32579907,6/25/20,pubmed,0,4,"molecular dynamics simulation, in silico",0.992924275,0.001415109,0.00141511,0.001415244,0.001415145,0.001415117,Drug discovery,0.82721865,TRUE,39,0.530521368,14.5,0.418450629,6,0.764429903,,,0.571133967
8972,SARS-CoV-2 and SARS-CoV: Virtual screening of potential inhibitors targeting RNA-dependent RNA polymerase activity (NSP12).,J Med Virol,32579254,6/25/20,pubmed,0,7,virtual screening,0.902966032,0.001861767,0.001861701,0.089586969,0.001861775,0.001861754,Drug discovery,0.87094605,TRUE,110.2857143,0.890283877,70,0.753344929,22,0.908142478,,,0.850590428
8973,"Insights into SARS-CoV-2, the Coronavirus Underlying COVID-19: Recent Genomic Data and the Development of Reverse Genetics Systems.",J Gen Virol,32579100,6/25/20,pubmed,0,6,genome sequences,0.001751317,0.991243874,0.001751229,0.001751248,0.001751155,0.001751177,Genomics,0.46291485,FALSE,39.83333333,0.537571897,57.5,0.712938186,0,0.403234768,,,0.551248283
8974,0,J Biomol Struct Dyn,32579064,6/25/20,pubmed,0,10,in-silico,0.944787601,0.001415145,0.0014151,0.001415124,0.001415139,0.049551892,Drug discovery,0.94623077,TRUE,28.8,0.415548271,8.1,0.320912497,16,0.881782826,,,0.539414531
8975,"Screening of Chloroquine, Hydroxychloroquine and its derivatives for their binding affinity to multiple SARS-CoV-2 protein drug targets.",J Biomol Struct Dyn,32579059,6/25/20,pubmed,0,3,in silico,0.991243392,0.001751201,0.001751417,0.001751373,0.001751277,0.00175134,Drug discovery,0.946113,TRUE,9.333333333,0.139278867,3.666666667,0.217621086,6,0.764429903,,,0.373776619
8976,"Mental health status of the general population, healthcare professionals, and university students during 2019 coronavirus disease outbreak in Jordan: A cross-sectional study.",Brain Behav,32578943,6/25/20,pubmed,0,16,logistic regression,0.000999523,0.00099965,0.015862572,0.000999564,0.952268948,0.028869743,Healthcare,0.98156905,TRUE,7.5,0.108355495,1.1875,0.124832754,6,0.764429903,,,0.332539384
8977,Nanopore Targeted Sequencing for the Accurate and Comprehensive Detection of SARS-CoV-2 and Other Respiratory Viruses.,Small,32578378,6/25/20,pubmed,0,18,sequencing,0.001462,0.670160132,0.292230626,0.001461957,0.001461874,0.033223411,Genomics,0.6824224,TRUE,61.5,0.712350795,66.5,0.744246722,30,0.930057411,,,0.795551643
8978,Could SARS-CoV-2 affect male fertility?,Andrologia,32578263,6/25/20,pubmed,0,2,"sequencing, transcriptom",0.509484785,0.339078865,0.001786657,0.001786621,0.001786574,0.146076498,Drug discovery,0.94496775,TRUE,56.5,0.678891706,46.5,0.666309874,12,0.850299401,,,0.731833661
8979,Impact of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the US: A Simulation Modeling Approach.,medRxiv,32577703,6/25/20,pubmed,0,5,simulation model,0.001511847,0.001511837,0.001511805,0.99244088,0.001511841,0.001511791,Epidemiology,0.64244556,TRUE,3.8,0.047374606,0.2,0.061145304,7,0.785110192,,,0.2978767
8980,0,medRxiv,32577690,6/25/20,pubmed,0,13,whole-genome,0.183367225,0.743653702,0.003760771,0.003760636,0.003760581,0.061697085,Genomics,0.2384355,FALSE,10.53846154,0.15746181,10.23076923,0.358175007,1,0.537564047,,,0.351066954
8981,Development and Prospective Validation of a Transparent Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.,medRxiv,32577682,6/25/20,pubmed,0,13,"deep learning, deep-learning",0.000815328,0.000815325,0.312954457,0.000815335,0.000815495,0.683784061,Clinics,0.655313,TRUE,22.69230769,0.335704125,15,0.42594327,5,0.739490092,,,0.500379162
8982,"Population-scale Longitudinal Mapping of COVID-19 Symptoms, Behavior, and Testing Identifies Contributors to Continued Disease Spread in the United States.",medRxiv,32577674,6/25/20,pubmed,0,36,predictive model,0.001653074,0.001653124,0.001653139,0.464249082,0.529138392,0.001653189,Healthcare,0.22312579,FALSE,12.41666667,0.18727194,9.555555556,0.345999465,10,0.828199272,,,0.453823559
8983,Proteomic Profiling in Biracial Cohorts Implicates DC-SIGN as a Mediator of Genetic Risk in COVID-19.,medRxiv,32577670,6/25/20,pubmed,0,21,proteom,0.260908496,0.367086186,0.002130649,0.002130738,0.173161591,0.194582339,Genomics,0.12591472,FALSE,46.80952381,0.603809759,95.28571429,0.813419855,5,0.739490092,,,0.718906569
8984,COVID-19 infections and outcomes in patients with multiple myeloma in New York City: a cohort study from five academic centers.,medRxiv,32577667,6/25/20,pubmed,0,28,logistic regression,0.001046925,0.00104686,0.001046834,0.001046893,0.00104688,0.994765608,Clinics,0.30951956,FALSE,142.1428571,0.936545241,132.75,0.866671127,22,0.908142478,,,0.903786282
8985,"Evidence of significant natural selection in the evolution of SARS-CoV-2 in bats, not humans.",bioRxiv,32577659,6/25/20,pubmed,0,8,sequencing,0.041773926,0.952073484,0.001538117,0.001538268,0.001538067,0.001538137,Genomics,0.13305336,FALSE,63,0.721998887,186.6666667,0.910222103,32,0.933699611,,,0.855306867
8986,Landscape and Selection of Vaccine Epitopes in SARS-CoV-2.,bioRxiv,32577654,6/25/20,pubmed,0,20,computational,0.841332057,0.133827255,0.000946137,0.000946178,0.000946089,0.022002284,Drug discovery,0.2641808,FALSE,41.65,0.554394211,94.75,0.812349478,8,0.799987654,,,0.722243781
8987,Analysis of Rapidly Emerging Variants in Structured Regions of the SARS-CoV-2 Genome.,bioRxiv,32577650,6/25/20,pubmed,0,1,"sequencing, genome sequences",0.172353307,0.810329476,0.001291213,0.001291248,0.001291232,0.013443524,Genomics,0.45059887,FALSE,41.66666667,0.554703445,58.66666667,0.716149318,6,0.764429903,,,0.678427555
8988,Morphological Cell Profiling of SARS-CoV-2 Infection Identifies Drug Repurposing Candidates for COVID-19.,bioRxiv,32577649,6/25/20,pubmed,0,20,machine learning,0.898865739,0.001059392,0.096896771,0.001059374,0.001059365,0.001059359,Drug discovery,0.6389173,TRUE,59.2,0.697507576,98.3,0.819574525,31,0.931971109,,,0.81635107
8989,Shielding and Beyond: The Roles of Glycans in SARS-CoV-2 Spike Protein.,bioRxiv,32577644,6/25/20,pubmed,0,12,molecular dynamics simulation,0.972439993,0.001291313,0.022394937,0.001291292,0.001291236,0.00129123,Drug discovery,0.6176607,TRUE,49.5,0.628362917,79.41666667,0.777294621,90,0.975800975,,,0.793819504
8990,A Rare Deletion in SARS-CoV-2 ORF6 Dramatically Alters the Predicted Three-Dimensional Structure of the Resultant Protein.,bioRxiv,32577643,6/25/20,pubmed,0,12,sequencing,0.19524238,0.757976327,0.002357756,0.002357784,0.002357782,0.039707971,Genomics,0.25302133,FALSE,27.58333333,0.402065681,73.83333333,0.763446615,12,0.850299401,,,0.671937232
8991,Sarbecovirus comparative genomics elucidates gene content of SARS-CoV-2 and functional impact of COVID-19 pandemic mutations.,bioRxiv,32577641,6/25/20,pubmed,0,3,"whole-genome, genomes",0.232090581,0.76533572,0.000643422,0.000643422,0.000643438,0.000643417,Genomics,0.09169689,FALSE,136.3333333,0.928443317,1712,0.997591651,15,0.874313229,,,0.933449399
8992,Protein covariance networks reveal interactions important to the emergence of SARS coronaviruses as human pathogens.,bioRxiv,32577639,6/25/20,pubmed,0,2,computational,0.448108497,0.544444781,0.001861689,0.001861718,0.001861662,0.001861653,Genomics,0.46095616,FALSE,179.5,0.964005195,1071,0.994246722,0,0.403234768,,,0.787162228
8993,"Clinical Characteristics of Patients Infected With the Novel 2019 Coronavirus (SARS-Cov-2) in Guangzhou, China.",Open Forum Infect Dis,32577426,6/25/20,pubmed,0,30,logistic regression,0.001461842,0.001461906,0.031678479,0.00146203,0.048627424,0.915308319,Clinics,0.67888266,TRUE,47.86666667,0.613210464,,,14,0.866658436,,,0.73993445
8994,COVID-19 Government Response Event Dataset (CoronaNet v.1.0).,Nat Hum Behav,32576982,6/25/20,pubmed,0,5,"bayes, dataset",0.002080749,0.002080643,0.002080618,0.989596783,0.002080621,0.002080587,Epidemiology,0.3492574,FALSE,9.8,0.147071557,1.2,0.126103827,54,0.959812334,,,0.410995906
8995,DREAM-in-CDM Approach and Identification of a New Generation of Anti-inflammatory Drugs Targeting mPGES-1.,Sci Rep,32576928,6/25/20,pubmed,0,7,"computational, data mining",0.964795741,0.027597318,0.00190175,0.001901798,0.001901705,0.001901689,Drug discovery,0.9281118,TRUE,108.8571429,0.88756262,49.85714286,0.681027562,3,0.667819001,,,0.745469728
8996,A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2.,Science,32576668,6/25/20,pubmed,0,3,mathematical model,0.002898588,0.002898339,0.002898341,0.985507671,0.00289855,0.00289851,Epidemiology,0.14504263,FALSE,194,0.970684643,208.6666667,0.921527964,188,0.990431508,,,0.960881372
8997,Efficacy of contact tracing for the containment of the 2019 novel coronavirus (COVID-19).,J Epidemiol Community Health,32576605,6/25/20,pubmed,0,3,predictive model,0.001511809,0.001511856,0.001511849,0.909641289,0.084311368,0.001511829,Epidemiology,0.17035055,FALSE,104.3333333,0.878223762,169.3333333,0.898715547,15,0.874313229,,,0.883750846
8998,[Logistic regression analysis of death risk factors of patients with severe and critical coronavirus disease 2019 and their predictive value].,Zhonghua Wei Zhong Bing Ji Jiu Yi Xue,32576344,6/25/20,pubmed,0,2,"logistic regression, prediction model",0.000854702,0.00085469,0.000854721,0.00085475,0.000854703,0.995726435,Clinics,0.96449304,TRUE,271,0.987939885,62,0.728659352,2,0.618927094,,,0.778508777
8999,Assessment of Countries' Preparedness and Lockdown Effectiveness in Fighting COVID-19.,Disaster Med Public Health Prep,32576332,6/25/20,pubmed,0,5,model simulation,0.002357733,0.002357816,0.075549206,0.799781799,0.002357737,0.117595709,Epidemiology,0.38603824,FALSE,99.8,0.868451976,10.2,0.357706717,3,0.667819001,,,0.631325898
9000,Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19.,Disaster Med Public Health Prep,32576328,6/25/20,pubmed,0,5,dataset,0.001751155,0.001751165,0.091136572,0.317482293,0.001751265,0.586127549,Clinics,0.9309963,TRUE,150.8,0.944894551,67,0.745718491,2,0.618927094,,,0.769846712
9001,Inverse correlation between average monthly high temperatures and COVID-19-related death rates in different geographical areas.,J Transl Med,32576227,6/25/20,pubmed,0,7,dataset,0.000772613,0.000772657,0.00077262,0.83564808,0.000772628,0.161261402,Epidemiology,0.40203843,FALSE,230,0.98101305,342.8571429,0.96461065,10,0.828199272,,,0.924607657
9002,Investigation of COVID-19-related symptoms based on factor analysis.,Ann Palliat Med,32576016,6/25/20,pubmed,0,12,correlation analysis,0.149830973,0.002130752,0.002130873,0.002130841,0.47701261,0.366763951,Healthcare,0.98052835,TRUE,30.08333333,0.432556126,10.08333333,0.356034252,3,0.667819001,,,0.485469793
9003,Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico.,Healthcare (Basel),32575622,6/25/20,pubmed,0,4,"neural network, network model",0.001684633,0.001684562,0.761458204,0.231803422,0.00168463,0.001684548,Epidemiology,0.09315309,FALSE,401,0.995608881,116,0.846133262,27,0.92443978,,,0.922060641
9004,Re-analysis of SARS-CoV-2-infected host cell proteomics time-course data by impact pathway analysis and network analysis: a potential link with inflammatory response.,Aging (Albany NY),32575076,6/24/20,pubmed,0,2,"proteom, network analysis",0.788640239,0.002130762,0.002130714,0.002130803,0.00213079,0.202836693,Drug discovery,0.57759047,TRUE,21,0.312016822,10.5,0.363459995,2,0.618927094,,,0.43146797
9005,"Super-factors associated with transmission of occupational COVID-19 infection among healthcare staff in Wuhan, China.",J Hosp Infect,32574702,6/24/20,pubmed,0,9,network analysis,0.001392861,0.048045179,0.001392949,0.127833593,0.781013513,0.040321905,Healthcare,0.9184401,TRUE,41,0.549013544,,,5,0.739490092,,,0.644251818
9006,Relationship between chest CT manifestations and immune response in COVID-19 patients.,Int J Infect Dis,32574694,6/24/20,pubmed,0,12,correlation analysis,0.001438202,0.001438085,0.100787311,0.001438118,0.001438103,0.893460182,Clinics,0.90556,TRUE,9.416666667,0.140206568,2.166666667,0.166845063,6,0.764429903,,,0.357160511
9007,"Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020).",Int J Infect Dis,32574693,6/24/20,pubmed,0,16,machine learning,0.000880248,0.00088032,0.156649193,0.839829729,0.000880229,0.000880282,Epidemiology,0.29257554,FALSE,24.6875,0.363473313,31.0625,0.578271341,21,0.903944688,,,0.615229781
9008,Metabolomics to Predict Antiviral Drug Efficacy in COVID-19.,Am J Respir Cell Mol Biol,32574504,6/24/20,pubmed,0,6,metabolom,0.874697007,0.025060375,0.025060414,0.025060755,0.025060984,0.025060465,Drug discovery,0.50051856,TRUE,112,0.894180221,155,0.887944876,9,0.814309525,,,0.865478207
9009,Textile Masks and Surface Covers-A Spray Simulation Method and a "Universal Droplet Reduction Model" Against Respiratory Pandemics.,Front Med (Lausanne),32574342,6/24/20,pubmed,0,5,simulation model,0.001310402,0.001310386,0.090816576,0.777183404,0.128068796,0.001310436,Epidemiology,0.55154026,TRUE,62,0.715814212,54,0.697952903,22,0.908142478,,,0.773969865
9010,The Rise and Impact of COVID-19 in India.,Front Med (Lausanne),32574338,6/24/20,pubmed,0,4,computational,0.002183253,0.002183348,0.002183278,0.989083504,0.00218342,0.002183198,Epidemiology,0.8527496,TRUE,30.5,0.438493413,7.5,0.307867273,28,0.926168282,,,0.557509656
9011,Can a toilet promote virus transmission? From a fluid dynamics perspective.,Phys Fluids (1994),32574232,6/24/20,pubmed,0,3,computational,0.001823467,0.166852829,0.001823367,0.825853619,0.001823392,0.001823325,Epidemiology,0.87112546,TRUE,44,0.578390748,10.66666667,0.365333155,40,0.947033768,,,0.630252557
9012,COVID-19 is rapidly changing: Examining public perceptions and behaviors in response to this evolving pandemic.,PLoS One,32574184,6/24/20,pubmed,0,8,logistic regression,0.00122,0.001220105,0.001220006,0.116017457,0.879102423,0.001220009,Healthcare,0.7610243,TRUE,128.875,0.919908467,96.375,0.815828204,6,0.764429903,,,0.833388858
9013,Elucidation of cellular targets and exploitation of the receptor-binding domain of SARS-CoV-2 for vaccine and monoclonal antibody synthesis.,J Med Virol,32573788,6/24/20,pubmed,0,3,sequence alignment,0.887126572,0.08187266,0.001237062,0.001237132,0.001237065,0.027289509,Drug discovery,0.3407255,FALSE,25,0.369286907,29.33333333,0.565226117,5,0.739490092,,,0.558001039
9014,Underestimation of COVID-19 cases in Japan: an analysis of RT-PCR testing for COVID-19 among 47 prefectures in Japan.,QJM,32573730,6/24/20,pubmed,0,7,correlation analysis,0.001438096,0.001438226,0.239395687,0.275486938,0.001438186,0.480802867,Clinics,0.8197689,TRUE,48.14285714,0.615560641,11.14285714,0.372089912,3,0.667819001,,,0.551823185
9015,Platelet gene expression and function in patients with COVID-19.,Blood,32573711,6/24/20,pubmed,0,14,sequencing,0.571186146,0.163405125,0.001310337,0.001310346,0.001310322,0.261477723,Drug discovery,0.79412127,TRUE,69.92857143,0.763126971,88.35714286,0.797431094,104,0.979134514,,,0.846564193
9016,Ethnomedicines of Indian origin for combating COVID-19 infection by hampering the viral replication: using structure-based drug discovery approach.,J Biomol Struct Dyn,32573351,6/24/20,pubmed,0,4,molecular dynamics simulation,0.94200354,0.001171593,0.001171561,0.03006091,0.001171666,0.02442073,Drug discovery,0.9720035,TRUE,9.25,0.137918239,5,0.257024351,10,0.828199272,,,0.407713954
9017,Molecular Diagnosis of COVID-19: Challenges and Research Needs.,Anal Chem,32573207,6/24/20,pubmed,0,16,sequencing,0.000746583,0.771589795,0.225423942,0.000746577,0.000746548,0.000746554,Genomics,0.67192996,TRUE,95.375,0.857690643,88.4375,0.797765587,36,0.941169208,,,0.865541813
9018,Using the kalman filter with Arima for the COVID-19 pandemic dataset of Pakistan.,Data Brief,32572378,6/24/20,pubmed,0,1,dataset,0.002996478,0.002996489,0.002996451,0.949341904,0.038672176,0.002996502,Epidemiology,0.38538438,FALSE,316,0.991465149,99,0.820444207,6,0.764429903,,,0.858779753
9019,"Application of System Biology to Explore the Association of Neprilysin, Angiotensin-Converting Enzyme 2 (ACE2), and Carbonic Anhydrase (CA) in Pathogenesis of SARS-CoV-2.",Biol Proced Online,32572334,6/24/20,pubmed,0,3,bioinformatic,0.427230326,0.001141369,0.031087029,0.080687743,0.001141421,0.458712112,Clinics,0.97176754,TRUE,43.66666667,0.57492733,8.666666667,0.331415574,7,0.785110192,,,0.563817699
9020,Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India.,Chaos Solitons Fractals,32572310,6/24/20,pubmed,0,3,"deep learning, neural network, lstm, dataset",0.001823322,0.028134511,0.343827311,0.622568164,0.001823364,0.001823329,Epidemiology,0.3099556,FALSE,17.33333333,0.261178799,3,0.199424672,41,0.948144947,,,0.469582806
9021,Life under lockdown: Notes on Covid-19 in Silicon Valley.,Anthropol Today,32572297,6/24/20,pubmed,0,2,in silico,0.00289829,0.002898331,0.002898308,0.939242469,0.049164315,0.002898287,Epidemiology,0.60954267,TRUE,39.5,0.534232173,15.5,0.430157881,1,0.537564047,,,0.500651367
9022,Identification of Common Deletions in the Spike Protein of Severe Acute Respiratory Syndrome Coronavirus 2.,J Virol,32571797,6/24/20,pubmed,0,21,sequencing,0.355276732,0.640898069,0.000956321,0.000956298,0.000956288,0.000956293,Genomics,0.32576334,FALSE,112.9047619,0.895850083,719.5714286,0.987824458,17,0.887338725,,,0.923671089
9023,Chest CT in COVID-19 pneumonia: A review of current knowledge.,Diagn Interv Imaging,32571748,6/24/20,pubmed,0,6,artificial intelligence,0.001943511,0.001943496,0.712120509,0.001943503,0.001943501,0.280105481,Imaging,0.6550215,TRUE,59.5,0.699857752,13,0.400521809,15,0.874313229,,,0.65823093
9024,"Predictive factors of severe coronavirus disease 2019 in previously healthy young adults: a single-center, retrospective study.",Respir Res,32571410,6/24/20,pubmed,0,9,logistic regression,0.025828443,0.001310316,0.073401213,0.001310319,0.001310365,0.896839344,Clinics,0.83955264,TRUE,185.3333333,0.966540912,443,0.974779235,12,0.850299401,,,0.930539849
9025,"Drug repurposing against SARS-CoV-2 using E-pharmacophore based virtual screening, molecular docking and molecular dynamics with main protease as the target.",J Biomol Struct Dyn,32571168,6/24/20,pubmed,0,5,"virtual screening, molecular dynamics simulation",0.993035644,0.00139284,0.001392848,0.001392949,0.001392868,0.001392851,Drug discovery,0.9464846,TRUE,22.8,0.337064754,25.4,0.532044421,12,0.850299401,,,0.573136192
9026,The Perception of COVID-19 among Italian Dentists: An Orthodontic Point of View.,Int J Environ Res Public Health,32570842,6/24/20,pubmed,0,5,logistic regression,0.001901699,0.001901695,0.001901706,0.001901814,0.905086213,0.087306872,Healthcare,0.96618867,TRUE,20.2,0.300327788,2.8,0.188787798,8,0.799987654,,,0.42970108
9027,Global Comparison of Changes in the Number of Test-Positive Cases and Deaths by Coronavirus Infection (COVID-19) in the World.,J Clin Med,32570833,6/24/20,pubmed,0,6,machine learning,0.002183208,0.109124612,0.00218344,0.747558253,0.136767129,0.002183357,Epidemiology,0.3711313,FALSE,78.5,0.800482405,61.33333333,0.725715815,0,0.403234768,,,0.643144329
9028,"Clinical prediction model for mortality of adult diabetes inpatients with COVID-19 in Wuhan, China: A retrospective pilot study.",J Clin Anesth,32570072,6/23/20,pubmed,0,6,prediction model,0.011749866,0.011750245,0.011750346,0.011750646,0.011750347,0.94124855,Clinics,0.69106764,TRUE,43.16666667,0.568433422,22.16666667,0.504549104,2,0.618927094,,,0.563969873
9029,"Characteristics of 1573 healthcare workers who underwent nasopharyngeal swab testing for SARS-CoV-2 in Milan, Lombardy, Italy.",Clin Microbiol Infect,32569835,6/23/20,pubmed,0,16,logistic regression,0.001486422,0.137702407,0.001486473,0.001486464,0.57525584,0.282582394,Healthcare,0.9931968,TRUE,90.75,0.845073907,65.3125,0.739764517,35,0.939317242,,,0.841385222
9030,Can artificial intelligence identify effective COVID-19 therapies?,EMBO Mol Med,32569446,6/23/20,pubmed,0,3,artificial intelligence,0.523206946,0.004309985,0.278157577,0.004310079,0.004310044,0.185705369,Drug discovery,0.59439075,TRUE,103.3333333,0.875935432,528.3333333,0.981000803,4,0.707574542,,,0.854836925
9031,COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation.,J Med Internet Res,32568730,6/23/20,pubmed,0,11,"deep learning, artificial intelligence, transfer learning",0.000740289,0.000740315,0.996298409,0.000740334,0.000740326,0.000740326,Imaging,0.39226928,FALSE,32.27272727,0.458222525,,,29,0.928020248,,,0.693121386
9032,"Flexible, Freely Available Stochastic Individual Contact Model for Exploring COVID-19 Intervention and Control Strategies: Development and Simulation.",JMIR Public Health Surveill,32568729,6/23/20,pubmed,0,2,simulation model,0.000854753,0.000854715,0.000854714,0.995726369,0.00085473,0.000854718,Epidemiology,0.22668472,FALSE,13.5,0.205393036,2.5,0.180826866,5,0.739490092,,,0.375236665
9033,Racial and Ethnic Digital Divides in Posting COVID-19 Content on Social Media Among US Adults: Secondary Survey Analysis.,J Med Internet Res,32568726,6/23/20,pubmed,0,2,logistic regression,0.000547815,0.012818383,0.000547818,0.172051459,0.813486703,0.000547821,Healthcare,0.5990622,TRUE,25.5,0.37435834,12,0.386740701,0,0.403234768,,,0.38811127
9034,The Role of Health Technology and Informatics in a Global Public Health Emergency: Practices and Implications From the COVID-19 Pandemic.,JMIR Med Inform,32568725,6/23/20,pubmed,0,1,artificial intelligence,0.002183302,0.053425864,0.246431885,0.370352522,0.325422948,0.002183479,Epidemiology,0.9521684,TRUE,4,0.054734368,0,0.055525823,12,0.850299401,,,0.320186531
9035,"COVID-19 Coronavirus spike protein analysis for synthetic vaccines, a peptidomimetic antagonist, and therapeutic drugs, and analysis of a proposed achilles' heel conserved region to minimize probability of escape mutations and drug resistance.",Comput Biol Med,32568687,6/23/20,pubmed,0,1,"computational, bioinformatic",0.678924649,0.151478338,0.001171629,0.119918294,0.0011716,0.047335491,Drug discovery,0.7243698,TRUE,4,0.054734368,1,0.122023013,52,0.958701154,,,0.378486179
9036,COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches.,Comput Biol Med,32568679,6/23/20,pubmed,0,3,"deep learning, artificial intelligence, dataset",0.001653082,0.099050444,0.86635766,0.029632704,0.001653111,0.001653,Imaging,0.7041306,TRUE,27.33333333,0.399529965,4.666666667,0.246721969,85,0.975121921,,,0.540457952
9037,Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks.,Comput Biol Med,32568676,6/23/20,pubmed,0,5,"deep learning, artificial intelligence, neural network",0.001203423,0.001203466,0.796694798,0.001203427,0.001203426,0.19849146,Imaging,0.78699136,TRUE,107.4,0.884346589,174.6,0.902729462,106,0.979628372,,,0.922234808
9038,Automated detection of COVID-19 cases using deep neural networks with X-ray images.,Comput Biol Med,32568675,6/23/20,pubmed,0,6,"artificial intelligence, neural network, classifier",0.001330038,0.001330048,0.907221385,0.087458425,0.001330044,0.00133006,Imaging,0.35417208,FALSE,83,0.818170573,146.1666667,0.880318437,354,0.996110871,,,0.898199961
9039,An agent-based model to evaluate the COVID-19 transmission risks in facilities.,Comput Biol Med,32568667,6/23/20,pubmed,0,1,mathematical model,0.149497344,0.00117158,0.019459267,0.827528673,0.001171581,0.001171554,Epidemiology,0.038394004,FALSE,128,0.919104459,14,0.412898047,42,0.949503056,,,0.760501854
9040,0,J Biomol Struct Dyn,32568620,6/23/20,pubmed,0,8,in silico,0.859284641,0.123263032,0.014028299,0.001141328,0.00114132,0.001141381,Drug discovery,0.9131953,TRUE,9.5,0.143051518,2.625,0.182766925,13,0.858880178,,,0.39489954
9041,0,J Biomol Struct Dyn,32568618,6/23/20,pubmed,0,4,"virtual screening, data mining",0.835156228,0.001538112,0.033522893,0.126706558,0.001538125,0.001538083,Drug discovery,0.6545076,TRUE,57.75,0.68699363,6.75,0.292079208,24,0.914439163,,,0.631170667
9042,0,J Biomol Struct Dyn,32568613,6/23/20,pubmed,0,4,molecular dynamics simulation,0.993248144,0.001350338,0.001350325,0.001350387,0.001350484,0.001350323,Drug discovery,0.992,TRUE,45.5,0.591440411,15.75,0.433636607,45,0.953206988,,,0.659428002
9043,Mass Spectrometric Identification of SARS-CoV-2 Proteins from Gargle Solution Samples of COVID-19 Patients.,J Proteome Res,32568543,6/23/20,pubmed,0,7,proteom,0.155793054,0.413168018,0.422878394,0.002720232,0.002720169,0.002720133,Genomics,0.19866502,FALSE,50,0.632073721,41.42857143,0.641624298,12,0.850299401,,,0.70799914
9044,Differentiating pneumonia with and without COVID-19 using chest CT images: from qualitative to quantitative.,J Xray Sci Technol,32568167,6/23/20,pubmed,0,8,dataset,0.000946097,0.000946098,0.702361195,0.000946126,0.000946126,0.293854358,Imaging,0.9947577,TRUE,40.625,0.54548828,28.375,0.556863794,6,0.764429903,,,0.622260659
9045,Targeting infectious Coronavirus Disease 2019 (COVID-19) with Artificial Intelligence (AI) applications: Evidence based opinion.,Infect Disord Drug Targets,32568026,6/23/20,pubmed,0,2,artificial intelligence,0.095680297,0.002720184,0.210337072,0.685822178,0.00272017,0.002720099,Epidemiology,0.94296,TRUE,30,0.432432432,7.5,0.307867273,2,0.618927094,,,0.4530756
9046,Virtual screening and dynamics of potential inhibitors targeting RNA binding domain of nucleocapsid phosphoprotein from SARS-CoV-2.,J Biomol Struct Dyn,32568013,6/23/20,pubmed,0,5,"virtual screening, molecular dynamics simulation",0.994365798,0.001126916,0.00112685,0.001126809,0.001126821,0.001126806,Drug discovery,0.90381265,TRUE,16.8,0.253138722,7.8,0.313286058,13,0.858880178,,,0.475101653
9047,Promising inhibitors of main protease of novel corona virus to prevent the spread of COVID-19 using docking and molecular dynamics simulation.,J Biomol Struct Dyn,32567995,6/23/20,pubmed,0,11,"molecular dynamics simulation, in silico",0.839435681,0.001254631,0.001254601,0.076972876,0.079827553,0.001254658,Drug discovery,0.9126327,TRUE,54.27272727,0.663306327,9.636363636,0.346802248,13,0.858880178,,,0.622996251
9048,0,J Biomol Struct Dyn,32567989,6/23/20,pubmed,0,6,in silico,0.989346346,0.002130841,0.002130739,0.002130776,0.002130653,0.002130645,Drug discovery,0.92490995,TRUE,17.83333333,0.268476715,2.166666667,0.166845063,19,0.89561084,,,0.443644206
9049,Structure-based virtual screening and molecular dynamics simulation of SARS-CoV-2 Guanine-N7 methyltransferase (nsp14) for identifying antiviral inhibitors against COVID-19.,J Biomol Struct Dyn,32567979,6/23/20,pubmed,0,6,"virtual screening, molecular dynamics simulation, computational",0.87016054,0.083256162,0.042973001,0.001203444,0.001203435,0.001203418,Drug discovery,0.85036373,TRUE,55.33333333,0.670171315,16,0.437316029,10,0.828199272,,,0.645228872
9050,"Social networks, machine learning and cladistics in the time of COVID-19.",Cir Cir,32567584,6/23/20,pubmed,0,3,machine learning,0.019529482,0.019529294,0.650937159,0.270943642,0.019531145,0.019529277,Epidemiology,0.41906053,FALSE,56.33333333,0.677407385,18.33333333,0.464343056,0,0.403234768,,,0.514995069
9051,A cross-sectional survey on the psychological impact of the COVID-19 pandemic on inflammatory bowel disease patients in Saudi Arabia.,Saudi J Gastroenterol,32567580,6/23/20,pubmed,0,14,logistic regression,0.001171614,0.001171567,0.001171589,0.001171562,0.64307723,0.352236438,Healthcare,0.9732419,TRUE,22.21428571,0.327849589,,,6,0.764429903,,,0.546139746
9052,0,J Biomol Struct Dyn,32567501,6/23/20,pubmed,0,5,"molecular dynamics simulation, in silico",0.947497334,0.001622772,0.001622735,0.046011674,0.00162276,0.001622726,Drug discovery,0.79735756,TRUE,10,0.15214299,1.2,0.126103827,3,0.667819001,,,0.315355272
9053,0,J Biomol Struct Dyn,32567487,6/23/20,pubmed,0,15,whole genome,0.857513097,0.136639215,0.00146198,0.001461947,0.001461867,0.001461893,Drug discovery,0.5658734,TRUE,19.46666667,0.290061228,12.4,0.390286326,8,0.799987654,,,0.493445069
9054,"Depression, anxiety, and stress and socio-demographic correlates among general Indian public during COVID-19.",Int J Soc Psychiatry,32567466,6/23/20,pubmed,0,2,logistic regression,0.001350318,0.001350326,0.001350388,0.025154387,0.969444248,0.001350334,Healthcare,0.96159923,TRUE,3,0.037293586,1,0.122023013,27,0.92443978,,,0.361252127
9055,Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan.,J Korean Med Sci,32567261,6/23/20,pubmed,0,7,predictive model,0.001538084,0.001538083,0.001538101,0.68691072,0.098587476,0.209887536,Epidemiology,0.76763165,TRUE,11,0.167171748,0.428571429,0.076665775,7,0.785110192,,,0.342982572
9056,End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT.,Eur J Nucl Med Mol Imaging,32567006,6/23/20,pubmed,0,10,"classifier, adversarial network, dataset",0.000907276,0.000907319,0.809672295,0.000907337,0.000907322,0.186698452,Imaging,0.61954314,TRUE,31.6,0.451048302,14.5,0.418450629,17,0.887338725,,,0.585612552
9057,Epidemiological control measures and predicted number of infections for SARS-CoV-2 pandemic: case study Serbia march-april 2020.,Heliyon,32566795,6/23/20,pubmed,0,1,predictive model,0.000800584,0.015024918,0.000800589,0.981772649,0.000800646,0.000800614,Epidemiology,0.18130738,FALSE,6,0.086028821,14,0.412898047,1,0.537564047,,,0.345496971
9058,Host transcriptome-guided drug repurposing for COVID-19 treatment: a meta-analysis based approach.,PeerJ,32566414,6/23/20,pubmed,0,5,"computational, transcriptom",0.929734334,0.001511871,0.00151189,0.02770329,0.038026732,0.001511882,Drug discovery,0.85866,TRUE,10,0.15214299,0.2,0.061145304,7,0.785110192,,,0.332799495
9059,Phyloevolutionary analysis of SARS-CoV-2 in Nigeria.,New Microbes New Infect,32566234,6/23/20,pubmed,0,6,genomes,0.00146191,0.93923881,0.001461919,0.001461926,0.054913567,0.001461869,Genomics,0.6882615,TRUE,2.333333333,0.024800544,0.166666667,0.058736955,4,0.707574542,,,0.263704014
9060,Serial interval and time-varying reproduction number estimation for COVID-19 in western Iran.,New Microbes New Infect,32566233,6/23/20,pubmed,0,6,bayes,0.001717165,0.001717333,0.001717266,0.78205746,0.001717283,0.211073493,Epidemiology,0.4858069,FALSE,26.33333333,0.386047375,6.333333333,0.285121755,12,0.850299401,,,0.507156177
9061,"Knowledge, attitudes and practices of COVID-19 among income-poor households in the Philippines: A cross-sectional study.",J Glob Health,32566169,6/23/20,pubmed,0,7,logistic regression,0.00105937,0.001059373,0.001059366,0.086624629,0.90913792,0.001059342,Healthcare,0.8889088,TRUE,18.42857143,0.275836477,6,0.280037463,33,0.936045435,,,0.497306458
9062,Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron.,Comput Math Methods Med,32565882,6/23/20,pubmed,0,5,"neural network, dataset",0.001392859,0.001392843,0.506282539,0.281303299,0.001392833,0.208235627,Epidemiology,0.34363678,FALSE,25.8,0.378316532,0.2,0.061145304,25,0.918019631,,,0.452493822
9063,"Air Microbiome and Pollution: Composition and Potential Effects on Human Health, Including SARS Coronavirus Infection.",J Environ Public Health,32565838,6/23/20,pubmed,0,2,"microbiom, virom",0.001461989,0.429088612,0.001461992,0.472707828,0.001461959,0.093817619,Epidemiology,0.7607788,TRUE,41.5,0.553219123,18.5,0.46554723,5,0.739490092,,,0.586085482
9064,Can Users Search Trends Predict People Scares or Disease Breakout? An Examination of Infectious Skin Diseases in the United States.,Infect Dis (Auckl),32565678,6/23/20,pubmed,0,4,dataset,0.001022638,0.001022653,0.065927136,0.782257028,0.1487479,0.001022645,Epidemiology,0.22353643,FALSE,21.5,0.318263343,4.5,0.242708055,0,0.403234768,,,0.321402055
9065,Modeling and prediction of COVID-19 pandemic using Gaussian mixture model.,Chaos Solitons Fractals,32565627,6/23/20,pubmed,0,4,mathematical model,0.001538142,0.001538113,0.001538154,0.992309282,0.001538205,0.001538105,Epidemiology,0.24818885,FALSE,99,0.867029501,33.75,0.597337436,20,0.900117291,,,0.78816141
9066,Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model.,Chaos Solitons Fractals,32565626,6/23/20,pubmed,0,3,"neural network, lstm, dataset",0.001330099,0.632230407,0.255879062,0.001330114,0.001330033,0.107900286,Genomics,0.24324352,FALSE,65.66666667,0.738450121,16.66666667,0.444674873,20,0.900117291,,,0.694414095
9067,"Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches.",Chaos Solitons Fractals,32565625,6/23/20,pubmed,0,4,"neural network, lstm",0.003465932,0.003465907,0.295991426,0.690144919,0.003465896,0.00346592,Epidemiology,0.35488245,FALSE,46,0.596882924,12.25,0.388546963,22,0.908142478,,,0.631190788
9068,Novel fractional order SIDARTHE mathematical model of COVID-19 pandemic.,Chaos Solitons Fractals,32565624,6/23/20,pubmed,0,1,mathematical model,0.002238541,0.002238517,0.137076531,0.853969516,0.002238461,0.002238435,Epidemiology,0.8787451,TRUE,16,0.243552477,0,0.055525823,21,0.903944688,,,0.401007663
9069,A novel mathematics model of covid-19 with fractional derivative. Stability and numerical analysis.,Chaos Solitons Fractals,32565623,6/23/20,pubmed,0,2,mathematical model,0.003927537,0.003927513,0.003927424,0.980362851,0.003927349,0.003927326,Epidemiology,0.8416419,TRUE,8,0.118683901,0,0.055525823,11,0.840175319,,,0.338128348
9070,A nonlinear epidemiological model considering asymptotic and quarantine classes for SARS CoV-2 virus.,Chaos Solitons Fractals,32565620,6/23/20,pubmed,0,4,mathematical model,0.002806476,0.002806442,0.167914521,0.820859507,0.002806466,0.002806588,Epidemiology,0.78857446,TRUE,2.5,0.027459954,0,0.055525823,20,0.900117291,,,0.327701023
9071,Exploration and correlation analysis of changes in Krebs von den Lungen-6 levels in COVID-19 patients with different types in China.,Biosci Trends,32565512,6/23/20,pubmed,0,12,correlation analysis,0.001653022,0.001653024,0.187877775,0.001653024,0.001653055,0.8055101,Clinics,0.97647524,TRUE,59.91666667,0.702269775,88.41666667,0.79763179,5,0.739490092,,,0.746463886
9072,Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II): protocol for an observational study using linked Scottish national data.,BMJ Open,32565483,6/23/20,pubmed,0,13,"logistic regression, dataset",0.141526835,0.001126832,0.001126798,0.325619913,0.260891993,0.269707628,Epidemiology,0.22151765,FALSE,89.46153846,0.840992022,113.4615385,0.842989029,4,0.707574542,,,0.797185197
9073,Modelling the pandemic: attuning models to their contexts.,BMJ Glob Health,32565430,6/23/20,pubmed,0,4,mathematical model,0.002806501,0.063066057,0.002806496,0.925708015,0.002806546,0.002806385,Epidemiology,0.038023025,FALSE,93,0.851567815,156.75,0.889416644,9,0.814309525,,,0.851764662
9074,Digital health and care in pandemic times: impact of COVID-19.,BMJ Health Care Inform,32565418,6/23/20,pubmed,0,3,digital health,0.057799148,0.057799148,0.057800273,0.711001505,0.057800776,0.057799148,Epidemiology,0.74852693,TRUE,55.33333333,0.670171315,18.66666667,0.466818303,9,0.814309525,,,0.650433048
9075,Prompt Predicting of Early Clinical Deterioration of Moderate-to-Severe COVID-19 Patients: Usefulness of a Combined Score Using IL-6 in a Preliminary Study.,J Allergy Clin Immunol Pract,32565226,6/23/20,pubmed,0,28,logistic regression,0.001237096,0.001237094,0.080930291,0.001237098,0.001237069,0.914121352,Clinics,0.9551133,TRUE,71.60714286,0.771167048,126.0357143,0.859312283,27,0.92443978,,,0.851639704
9076,Targeting the SARS-CoV-2 spike glycoprotein prefusion conformation: virtual screening and molecular dynamics simulations applied to the identification of potential fusion inhibitors.,Virus Res,32565126,6/23/20,pubmed,0,3,"virtual screening, molecular dynamics simulation, sequence alignment",0.964645317,0.001538245,0.02920208,0.001538129,0.001538163,0.001538066,Drug discovery,0.9152842,TRUE,11.66666667,0.176510607,15,0.42594327,10,0.828199272,,,0.476884383
9077,"Clinical Characteristics, Associated Factors, and Predicting COVID-19 Mortality Risk: A Retrospective Study in Wuhan, China.",Am J Prev Med,32564974,6/23/20,pubmed,0,7,logistic regression,0.001511788,0.001511811,0.001511848,0.001511824,0.001511821,0.992440908,Clinics,0.95159054,TRUE,36.85714286,0.506710372,28,0.554321648,31,0.931971109,,,0.664334376
9078,First case of SARS-COV-2 sequencing in cerebrospinal fluid of a patient with suspected demyelinating disease.,J Neurol,32564153,6/22/20,pubmed,0,11,"sequencing, deep sequencing",0.002720139,0.535052573,0.082869731,0.002720126,0.00272021,0.373917222,Genomics,0.59020567,TRUE,54.27272727,0.663306327,31.81818182,0.583355633,35,0.939317242,,,0.728659734
9079,Correlates of symptoms of anxiety and depression and mental wellbeing associated with COVID-19: a cross-sectional study of UK-based respondents.,Psychiatry Res,32562931,6/21/20,pubmed,0,10,logistic regression,0.001511786,0.001511831,0.001511796,0.001511865,0.992440879,0.001511843,Healthcare,0.73937064,TRUE,79.1,0.802770734,38.6,0.625100348,44,0.952157541,,,0.793342874
9080,SARS-CoV-2 and co-infections detection in nasopharyngeal throat swabs of COVID-19 patients by metagenomics.,J Infect,32562797,6/21/20,pubmed,0,22,metagenom,0.019529277,0.672127084,0.01953097,0.019529359,0.019529301,0.249754009,Genomics,0.5944036,TRUE,39.45454545,0.533242625,37.45454545,0.618945678,7,0.785110192,,,0.645766165
9081,"Optical techniques, computed tomography and deep learning role in the diagnosis of COVID-19 pandemic towards increasing the survival rate of vulnerable populations.",Photodiagnosis Photodyn Ther,32562732,6/21/20,pubmed,0,2,deep learning,0.011750047,0.011749717,0.725818335,0.01175062,0.011752935,0.227178346,Imaging,0.6009467,TRUE,14,0.213494959,4,0.231469093,1,0.537564047,,,0.327509367
9082,"Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study.",Lancet Infect Dis,32562601,6/21/20,pubmed,0,16,dataset,0.000603896,0.031855207,0.000603883,0.59697417,0.36935893,0.000603915,Epidemiology,0.6171605,TRUE,54.3125,0.663368174,124.5,0.857171528,141,0.985246003,,,0.835261901
9083,"An updated analysis of turning point, duration and attack rate of COVID-19 outbreaks in major Western countries with data of daily new cases.",Data Brief,32562479,6/21/20,pubmed,0,2,dataset,0.003214134,0.003214182,0.003214177,0.983929073,0.003214188,0.003214245,Epidemiology,0.23546213,FALSE,54.5,0.665161729,22,0.503746321,0,0.403234768,,,0.524047606
9084,"An improved mathematical prediction of the time evolution of the Covid-19 pandemic in Italy, with a Monte Carlo simulation and error analyses.",Eur Phys J Plus,32562477,6/21/20,pubmed,0,2,mathematical prediction,0.00131034,0.271274201,0.00131041,0.72348397,0.001310595,0.001310485,Epidemiology,0.19634536,FALSE,21.5,0.318263343,241,0.934372491,4,0.707574542,,,0.653403459
9085,A proposed role for the SARS-CoV-2 nucleocapsid protein in the formation and regulation of biomolecular condensates.,FASEB J,32562316,6/21/20,pubmed,0,2,bioinformatic,0.992173399,0.001565381,0.001565285,0.001565299,0.001565308,0.001565328,Drug discovery,0.4329682,FALSE,29,0.41993939,61,0.724578539,30,0.930057411,,,0.691525113
9086,A complex COVID-19 case with rheumatoid arthritis treated with tocilizumab.,Clin Rheumatol,32562070,6/21/20,pubmed,0,4,sequencing,0.376257867,0.057081861,0.001943495,0.228607419,0.001943531,0.334165828,Drug discovery,0.9863548,TRUE,86.75,0.832580865,27.5,0.549906342,10,0.828199272,,,0.736895493
9087,Community Mental Health Care Delivery During the COVID-19 Pandemic: Practical Strategies for Improving Care for People with Serious Mental Illness.,Community Ment Health J,32562033,6/21/20,pubmed,0,8,digital health,0.063015664,0.00190189,0.001901782,0.568776236,0.362502686,0.001901742,Epidemiology,0.9082475,TRUE,,,,,0,0.403234768,,,0.403234768
9088,Pharmacovigilance in patients with diabetes: A data-driven analysis identifying specific RAS antagonists with adverse pulmonary safety profiles that have implications for COVID-19 morbidity and mortality.,J Am Pharm Assoc (2003),32561317,6/21/20,pubmed,0,6,data mining,0.445033208,0.001187329,0.001187297,0.001187316,0.049317487,0.502087364,Clinics,0.9247633,TRUE,104.1666667,0.877852681,91.16666667,0.804187851,1,0.537564047,,,0.739868193
9089,Endoplasmic reticulum as a potential therapeutic target for covid-19 infection management?,Eur J Pharmacol,32561291,6/21/20,pubmed,0,10,"sequencing, deep sequencing",0.726350435,0.141791133,0.079838526,0.001511918,0.04899611,0.001511878,Drug discovery,0.31328994,FALSE,136.3,0.928319624,127.4,0.860784051,21,0.903944688,,,0.897682788
9090,In silico prediction of potential inhibitors for the main protease of SARS-CoV-2 using molecular docking and dynamics simulation based drug-repurposing.,J Infect Public Health,32561274,6/21/20,pubmed,0,3,"virtual screening, molecular dynamics simulation, in silico, genomes",0.868413593,0.10242015,0.026472037,0.000898092,0.00089807,0.000898058,Drug discovery,0.96326625,TRUE,19,0.285793803,2.666666667,0.185442869,47,0.95493549,,,0.47539072
9091,"AGILE-ACCORD: A Randomized, Multicentre, Seamless, Adaptive Phase I/II Platform Study to Determine the Optimal Dose, Safety and Efficacy of Multiple Candidate Agents for the Treatment of COVID-19: A structured summary of a study protocol for a randomised platform trial.",Trials,32560744,6/21/20,pubmed,0,17,bayes,0.278566181,0.000430558,0.000430546,0.22629458,0.085832771,0.408445364,Clinics,0.8816997,TRUE,19.70588235,0.292720638,11.29411765,0.374632058,7,0.785110192,,,0.484154296
9092,Prevention of thrombotic risk in hospitalized patients with COVID-19 and hemostasis monitoring.,Crit Care,32560658,6/21/20,pubmed,0,65,probabilistic,0.107810955,0.001141352,0.059590877,0.112000934,0.001141357,0.718314525,Clinics,0.8147478,TRUE,64.3559322,0.730842971,,,43,0.9507377,,,0.840790335
9093,Unreported Cases for Age Dependent COVID-19 Outbreak in Japan.,Biology (Basel),32560572,6/21/20,pubmed,0,3,"computational, mathematical model",0.002639146,0.002639041,0.002639069,0.915919061,0.002639006,0.073524677,Epidemiology,0.28197578,FALSE,22.66666667,0.335580432,17,0.451097137,2,0.618927094,,,0.468534888
9094,"Clinical Factors, Preventive Behaviours and Temporal Outcomes Associated with COVID-19 Infection in Health Professionals at a Spanish Hospital.",Int J Environ Res Public Health,32560180,6/21/20,pubmed,0,11,prediction model,0.001237062,0.001237149,0.001237111,0.192245801,0.661625814,0.142417063,Healthcare,0.9885099,TRUE,16.45454545,0.248190983,5.090909091,0.257358844,5,0.739490092,,,0.415013306
9095,"COVID-19 Diagnostics, Tools, and Prevention.",Diagnostics (Basel),32560091,6/21/20,pubmed,0,11,machine learning,0.208674903,0.001653092,0.157036662,0.59216024,0.001653137,0.038821965,Epidemiology,0.7682078,TRUE,8.181818182,0.119920836,4.181818182,0.233208456,16,0.881782826,,,0.411637373
9096,"Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study.",Lancet Infect Dis,32559451,6/20/20,pubmed,0,51,mathematical model,0.00062521,0.000625225,0.000625216,0.76238914,0.23510998,0.000625229,Epidemiology,0.3154155,FALSE,67.875,0.752984105,110.5,0.839309607,184,0.990122847,,,0.86080552
9097,Measurement of airborne particle exposure during simulated tracheal intubation using various proposed aerosol containment devices during the COVID-19 pandemic.,Anaesthesia,32559315,6/20/20,pubmed,0,6,simulation model,0.001987107,0.001987103,0.19964612,0.522221361,0.20193423,0.07222408,Epidemiology,0.31011987,FALSE,6.5,0.093512277,1.333333333,0.13252609,37,0.942712513,,,0.389583627
9098,"COVID-19 infection alters kynurenine and fatty acid metabolism, correlating with IL-6 levels and renal status.",JCI Insight,32559180,6/20/20,pubmed,0,12,metabolom,0.228684522,0.232598216,0.001272658,0.001272703,0.001272811,0.534899089,Clinics,0.8086164,TRUE,101.4166667,0.8717917,60.5,0.722772277,56,0.961540836,,,0.852034938
9099,Artificial Intelligence and the Future of Psychiatry.,IEEE Pulse,32559160,6/20/20,pubmed,0,1,artificial intelligence,0.00535312,0.005352903,0.240109519,0.143709724,0.600121585,0.005353149,Healthcare,0.8141947,TRUE,22,0.326056033,9,0.337904736,0,0.403234768,,,0.355731846
9100,Developing a Fully Glycosylated Full-Length SARS-CoV-2 Spike Protein Model in a Viral Membrane.,J Phys Chem B,32559081,6/20/20,pubmed,0,12,molecular dynamics simulation,0.852775832,0.138701201,0.002130738,0.002130835,0.002130703,0.002130691,Drug discovery,0.5307795,TRUE,58,0.689714887,80.08333333,0.779100883,13,0.858880178,,,0.775898649
9101,SARS-CoV-2 coinfections: Could influenza and the common cold be beneficial?,J Med Virol,32557776,6/20/20,pubmed,0,2,mathematical model,0.003760683,0.437699671,0.003760512,0.417752765,0.003760689,0.133265681,Genomics,0.20706236,FALSE,25.5,0.37435834,8.5,0.329141022,11,0.840175319,,,0.514558227
9102,Vaccine development and therapeutic design for 2019-nCoV/SARS-CoV-2: Challenges and chances.,J Cell Physiol,32557648,6/20/20,pubmed,0,5,computational,0.775497979,0.001538152,0.001538144,0.218349347,0.001538199,0.001538179,Drug discovery,0.7608676,TRUE,45.4,0.590017936,10.2,0.357706717,20,0.900117291,,,0.615947314
9103,Recognition of Natural Products as Potential Inhibitors of COVID-19 Main Protease (Mpro): In-Silico Evidences.,Nat Prod Bioprospect,32557405,6/20/20,pubmed,0,4,"in silico, in-silico",0.987888436,0.002422342,0.002422429,0.002422309,0.002422251,0.002422233,Drug discovery,0.97123444,TRUE,5.5,0.077246583,0.25,0.065493712,27,0.92443978,,,0.355726692
9104,Psychological Functioning of Slovene Adults during the COVID-19 Pandemic: Does Resilience Matter?,Psychiatr Q,32556914,6/20/20,pubmed,0,3,logistic regression,0.001461889,0.001461879,0.001461944,0.001461959,0.992690443,0.001461887,Healthcare,0.8610693,TRUE,15,0.227596017,8.666666667,0.331415574,10,0.828199272,,,0.462403621
9105,"Reconstruction of Transmission Pairs for Novel Coronavirus Disease 2019 (COVID-19) in Mainland China: Estimation of Superspreading Events, Serial Interval, and Hazard of Infection.",Clin Infect Dis,32556265,6/20/20,pubmed,0,9,model fit,0.001310371,0.001310429,0.001310436,0.54132328,0.324911217,0.129834267,Epidemiology,0.5690322,TRUE,126.2222222,0.915579195,137.6666667,0.872491303,33,0.936045435,,,0.908038644
9106,"Greater risk of severe COVID-19 in Black, Asian and Minority Ethnic populations is not explained by cardiometabolic, socioeconomic or behavioural factors, or by 25(OH)-vitamin D status: study of 1326 cases from the UK Biobank.",J Public Health (Oxf),32556213,6/20/20,pubmed,0,9,logistic regression,0.001310528,0.122362973,0.001310369,0.001310424,0.508546783,0.365158922,Healthcare,0.43297896,FALSE,150.5555556,0.944709011,490.7777778,0.978191062,77,0.972097043,,,0.964999039
9107,Global evaluation of echocardiography in patients with COVID-19.,Eur Heart J Cardiovasc Imaging,32556199,6/20/20,pubmed,0,17,logistic regression,0.001987294,0.001987253,0.034654779,0.001987338,0.133486916,0.82589642,Clinics,0.97015357,TRUE,233.1176471,0.98156967,254.8823529,0.939724378,62,0.965553429,,,0.962282492
9108,Evaluation of health-related quality of life using EQ-5D in China during the COVID-19 pandemic.,PLoS One,32555642,6/20/20,pubmed,0,7,logistic regression,0.001565282,0.001565318,0.001565302,0.099266495,0.894472272,0.001565331,Healthcare,0.9481361,TRUE,66,0.741356918,17.14285714,0.451565427,16,0.881782826,,,0.69156839
9109,Convergent antibody responses to SARS-CoV-2 in convalescent individuals.,Nature,32555388,6/20/20,pubmed,0,46,sequencing,0.510994949,0.367414952,0.001461888,0.001461952,0.001461989,0.11720427,Drug discovery,0.10650694,FALSE,64.7173913,0.733131301,340.4782609,0.964075462,532,0.998148034,,,0.898451599
9110,A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection: A Retrospective Cohort Study.,J Thorac Imaging,32555006,6/20/20,pubmed,0,5,"machine learning, radiom",0.028419644,0.001112723,0.63740408,0.001112647,0.00111264,0.330838267,Imaging,0.9630091,TRUE,6.4,0.090667326,0,0.055525823,2,0.618927094,,,0.255040081
9111,Clinical Characteristics and Outcomes of Community- and Hospital-Acquired Acute Kidney Injury with COVID-19 in a US Inner City Hospital System.,Cardiorenal Med,32554965,6/20/20,pubmed,0,12,logistic regression,0.001486401,0.001486485,0.001486418,0.001486484,0.001486564,0.992567648,Clinics,0.8456589,TRUE,172.4166667,0.96010885,263.25,0.9434038,16,0.881782826,,,0.928431825
9112,Incidence and case fatality rate of COVID-19 in patients with active epilepsy.,Neurology,32554773,6/20/20,pubmed,0,6,logistic regression,0.001392943,0.001392857,0.001392838,0.219845783,0.00139285,0.77458273,Clinics,0.8541206,TRUE,30.16666667,0.433731214,4.166666667,0.233074659,14,0.866658436,,,0.51115477
9113,Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China.,Science,32554754,6/20/20,pubmed,0,7,model simulation,0.099630068,0.002898394,0.002898449,0.888776415,0.002898363,0.002898309,Epidemiology,0.774064,TRUE,273.1428571,0.988125425,,,94,0.976726958,,,0.982426192
9114,"Protection against COVID-19 injury by qingfei paidu decoction via anti-viral, anti-inflammatory activity and metabolic programming.",Biomed Pharmacother,32554251,6/20/20,pubmed,0,10,in silico,0.876306996,0.001098819,0.001098851,0.001098864,0.001098843,0.119297628,Drug discovery,0.95851386,TRUE,15.9,0.238913971,,,27,0.92443978,,,0.581676876
9115,"Potential inhibitors of the interaction between ACE2 and SARS-CoV-2 (RBD), to develop a drug.",Life Sci,32553928,6/20/20,pubmed,0,2,virtual screening,0.992032122,0.001593557,0.001593612,0.001593645,0.001593527,0.001593537,Drug discovery,0.5988867,TRUE,32.5,0.461685942,6,0.280037463,7,0.785110192,,,0.508944532
9116,Performance of pneumonia severity index and CURB-65 in predicting 30-day mortality in patients with COVID-19.,Int J Infect Dis,32553714,6/20/20,pubmed,0,11,logistic regression,0.001652988,0.00165299,0.027876401,0.001653028,0.001653019,0.965511574,Clinics,0.7453631,TRUE,28.63636364,0.413569176,3.636363636,0.215413433,33,0.936045435,,,0.521676015
9117,Estimation of the secondary attack rate of COVID-19 using proportional meta-analysis of nationwide contact tracing data in Taiwan.,J Microbiol Immunol Infect,32553448,6/20/20,pubmed,0,3,bayes,0.007673913,0.007674016,0.007674189,0.961629573,0.007674181,0.007674128,Epidemiology,0.17839253,FALSE,111,0.892015585,66.33333333,0.743510838,12,0.850299401,,,0.828608608
9118,"COVID-19 and tuberculosis: A mathematical model based forecasting in Delhi, India.",Indian J Tuberc,32553309,6/20/20,pubmed,0,5,mathematical model,0.001717166,0.001717211,0.001717221,0.666607056,0.001717261,0.326524085,Epidemiology,0.52882594,TRUE,19.2,0.287339972,5.4,0.263914905,10,0.828199272,,,0.459818049
9119,"SARS-CoV-2 infection in farmed minks, the Netherlands, April and May 2020.",Euro Surveill,32553059,6/20/20,pubmed,0,19,genomes,0.004310111,0.574898112,0.004310118,0.004310758,0.248881399,0.163289502,Genomics,0.3977069,FALSE,51.84210526,0.645247078,83.89473684,0.78819909,161,0.98851781,,,0.807321326
9120,Dynamic linkage of COVID-19 test results between Public Health England's Second Generation Surveillance System and UK Biobank.,Microb Genom,32553051,6/20/20,pubmed,0,7,genome-wide,0.001622869,0.399321682,0.001622755,0.23392093,0.123197808,0.240313956,Genomics,0.33974722,FALSE,97.28571429,0.862576535,309.8571429,0.957251806,59,0.963516266,,,0.927781536
9121,Predictive models for COVID-19-related deaths and infections.,Int J Tuberc Lung Dis,32552999,6/20/20,pubmed,0,4,predictive model,0.025060359,0.025060255,0.025062183,0.874692959,0.025060362,0.025063882,Epidemiology,0.7849177,TRUE,191.5,0.969385862,132.5,0.866336634,4,0.707574542,,,0.847765679
9122,Compassionate use of others' immunity - understanding gut microbiome in Covid-19.,Crit Care,32552848,6/20/20,pubmed,0,2,microbiom,0.025062207,0.874694882,0.025060275,0.025061664,0.025060675,0.025060297,Genomics,0.5179528,TRUE,30,0.432432432,3.5,0.213607172,4,0.707574542,,,0.451204715
9123,0,J Biomol Struct Dyn,32552595,6/20/20,pubmed,0,5,"molecular dynamics simulation, in silico",0.993982856,0.001203438,0.001203436,0.001203455,0.001203414,0.001203401,Drug discovery,0.9940605,TRUE,46.2,0.598181706,17,0.451097137,12,0.850299401,,,0.633192748
9124,"Identification of a novel dual-target scaffold for 3CLpro and RdRp proteins of SARS-CoV-2 using 3D-similarity search, molecular docking, molecular dynamics and ADMET evaluation.",J Biomol Struct Dyn,32552534,6/20/20,pubmed,0,10,"virtual screening, computational",0.994365934,0.001126808,0.001126853,0.001126834,0.001126776,0.001126794,Drug discovery,0.9516785,TRUE,20.2,0.300327788,1.2,0.126103827,8,0.799987654,,,0.408806423
9125,Epidemiology of COVID-19 in Brazil: using a mathematical model to estimate the outbreak peak and temporal evolution.,Emerg Microbes Infect,32552473,6/20/20,pubmed,0,6,mathematical model,0.011749634,0.011750759,0.011749651,0.941250499,0.011749699,0.011749758,Epidemiology,0.60202265,TRUE,46.66666667,0.602510978,37.33333333,0.618209794,8,0.799987654,,,0.673569475
9126,0,J Biomol Struct Dyn,32552462,6/20/20,pubmed,0,10,in-silico,0.970038967,0.00112681,0.001126847,0.025453712,0.001126879,0.001126785,Drug discovery,0.9709472,TRUE,37.4,0.512585812,8.2,0.322785657,23,0.91129082,,,0.582220763
9127,Combined drug repurposing and virtual screening strategies with molecular dynamics simulation identified potent inhibitors for SARS-CoV-2 main protease (3CLpro).,J Biomol Struct Dyn,32552361,6/20/20,pubmed,0,10,"virtual screening, molecular dynamics simulation",0.913560658,0.001653033,0.001653021,0.079827194,0.001653054,0.001653039,Drug discovery,0.9730076,TRUE,33.1,0.467190302,4.8,0.249331014,14,0.866658436,,,0.527726584
9128,Coronavirus disease 2019: A new severe acute respiratory syndrome from Wuhan in China.,Acta Virol,32551792,6/20/20,pubmed,0,3,sequencing,0.199627639,0.324838972,0.002080729,0.314258372,0.002080731,0.157113557,Genomics,0.7939547,TRUE,4,0.054734368,0.333333333,0.073187048,4,0.707574542,,,0.278498653
9129,The Discovery of a Putative Allosteric Site in the SARS-CoV-2 Spike Protein Using an Integrated Structural/Dynamic Approach.,J Proteome Res,32551648,6/20/20,pubmed,0,5,computational,0.864391488,0.02700529,0.001022703,0.105535278,0.001022634,0.001022608,Drug discovery,0.92325866,TRUE,51.6,0.643577216,8.2,0.322785657,15,0.874313229,,,0.613558701
9130,0,J Chem Inf Model,32551639,6/20/20,pubmed,0,11,virtual screening,0.993726906,0.001254597,0.001254627,0.001254626,0.00125461,0.001254633,Drug discovery,0.9950284,TRUE,93.63636364,0.853485064,31.09090909,0.578405138,14,0.866658436,,,0.766182879
9131,Discovery of Aptamers Targeting the Receptor-Binding Domain of the SARS-CoV-2 Spike Glycoprotein.,Anal Chem,32551560,6/20/20,pubmed,0,10,machine learning,0.75090612,0.002806471,0.237868152,0.002806514,0.002806406,0.002806337,Drug discovery,0.4947609,FALSE,72.1,0.773269837,23.5,0.516791544,31,0.931971109,,,0.740677497
9132,Coronavirus Disease 2019 (COVID-19) From the Point of View of Neurologists: Observation of Neurological Findings and Symptoms During the Combat Against a Pandemic.,Noro Psikiyatr Ars,32550783,6/20/20,pubmed,0,8,sequencing,0.10872923,0.448375478,0.00186179,0.001861841,0.001861852,0.437309808,Genomics,0.98908436,TRUE,44.125,0.578699981,32.5,0.589376505,12,0.850299401,,,0.672791963
9133,Innovative use of artificial intelligence and digital communication in acute stroke pathway in response to COVID-19.,Future Healthc J,32550287,6/19/20,pubmed,0,6,artificial intelligence,0.134650045,0.001943463,0.280944037,0.397005426,0.001943578,0.183513451,Epidemiology,0.46714988,FALSE,12.33333333,0.186467932,8.666666667,0.331415574,4,0.707574542,,,0.408486016
9134,COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature.,ArXiv,32550254,6/19/20,pubmed,0,6,"image analysis, classifier, deep-learning, dataset",0.017595309,0.000966771,0.950721516,0.028782857,0.000966777,0.00096677,Imaging,0.24681091,FALSE,156.8333333,0.949161977,242.8333333,0.934974579,8,0.799987654,,,0.89470807
9135,Quantification of Tomographic Patterns associated with COVID-19 from Chest CT.,ArXiv,32550252,6/19/20,pubmed,0,20,"deep learning, dataset",0.001330018,0.001330032,0.786211486,0.001330049,0.001330166,0.208468249,Imaging,0.05885935,FALSE,77.45,0.796153133,121.45,0.853358309,37,0.942712513,,,0.864074651
9136,"A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models.",ArXiv,32550248,6/19/20,pubmed,0,8,machine learning,0.001486479,0.001486421,0.14839021,0.845663997,0.001486476,0.001486417,Epidemiology,0.40220585,FALSE,25.75,0.377698064,71.75,0.758295424,40,0.947033768,,,0.694342419
9137,A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration.,ArXiv,32550247,6/19/20,pubmed,0,7,dataset,0.001786594,0.001786616,0.001786654,0.953948131,0.038905462,0.001786543,Epidemiology,0.61651707,TRUE,45,0.587049292,22.85714286,0.510436179,46,0.954009507,,,0.683831659
9138,HLA predictions from the bronchoalveolar lavage fluid samples of five patients at the early stage of the wuhan seafood market COVID-19 outbreak.,ArXiv,32550246,6/19/20,pubmed,0,2,"sequencing, transcriptom",0.301357096,0.484029457,0.00289836,0.002898449,0.002898376,0.205918261,Genomics,0.54450655,TRUE,74.5,0.784464098,232,0.930693069,14,0.866658436,,,0.860605201
9139,SARS-CoV-2 Entry Genes Are Most Highly Expressed in Nasal Goblet and Ciliated Cells within Human Airways.,ArXiv,32550242,6/19/20,pubmed,0,4,"sequencing, dataset",0.90964362,0.086014912,0.001085386,0.001085382,0.001085342,0.001085358,Drug discovery,0.9660386,TRUE,13.75,0.208299833,40.5,0.637342788,68,0.968393111,,,0.604678577
9140,Dynamic Modeling COVID-19 for Comparing Containment Strategies in a Pandemic Scenario.,ArXiv,32550241,6/19/20,pubmed,0,2,prediction model,0.0284191,0.001786577,0.001786548,0.964434485,0.001786519,0.001786771,Epidemiology,0.18810156,FALSE,75,0.787061661,333,0.96273749,1,0.537564047,,,0.762454399
9141,Chest lesion CT radiological features and quantitative analysis in RT-PCR turned negative and clinical symptoms resolved COVID-19 patients.,Quant Imaging Med Surg,32550139,6/19/20,pubmed,0,9,artificial intelligence,0.001126799,0.001126812,0.618518582,0.001126826,0.001126815,0.376974165,Imaging,0.89205265,TRUE,19.77777778,0.293462799,,,6,0.764429903,,,0.528946351
9142,Artificial Intelligence-Powered Search Tools and Resources in the Fight Against COVID-19.,EJIFCC,32549878,6/19/20,pubmed,0,8,"artificial intelligence, dataset",0.005697602,0.005697509,0.203018351,0.774191391,0.005697747,0.0056974,Epidemiology,0.41879758,FALSE,37.25,0.51035933,17.5,0.45611453,7,0.785110192,,,0.583861351
9143,"Demographic and Health Indicators in Correlation to Interstate Variability of Incidence, Confirmation, Hospitalization, and Lethality in Mexico: Preliminary Analysis from Imported and Community Acquired Cases during COVID-19 Outbreak.",Int J Environ Res Public Health,32549337,6/19/20,pubmed,0,3,logistic regression,0.001717177,0.00171731,0.001717206,0.255305493,0.001717245,0.737825569,Clinics,0.4393604,FALSE,27.66666667,0.403488156,1.333333333,0.13252609,2,0.618927094,,,0.384980447
9144,A Pharmacovigilance Study of Hydroxychloroquine Cardiac Safety Profile: Potential Implication in COVID-19 Mitigation.,J Clin Med,32549293,6/19/20,pubmed,0,4,dataset,0.175204278,0.001254643,0.001254665,0.077425628,0.156863309,0.587997478,Clinics,0.90850306,TRUE,47.5,0.610551054,20,0.481000803,7,0.785110192,,,0.625554016
9145,Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection.,PeerJ,32547891,6/18/20,pubmed,0,2,"machine learning, computational",0.767763487,0.183763034,0.045884368,0.000863048,0.000863029,0.000863033,Drug discovery,0.87356853,TRUE,21.5,0.318263343,12,0.386740701,11,0.840175319,,,0.515059788
9146,Contributions of Latin American researchers in the understanding of the novel coronavirus outbreak: a literature review.,PeerJ,32547890,6/18/20,pubmed,0,2,bioinformatic,0.001861818,0.042988425,0.001861724,0.94956459,0.001861743,0.0018617,Epidemiology,0.43929484,FALSE,3.5,0.044344115,0,0.055525823,13,0.858880178,,,0.319583372
9147,Factors associated with death outcome in patients with severe coronavirus disease-19 (COVID-19): a case-control study.,Int J Med Sci,32547323,6/18/20,pubmed,0,11,"model fit, logistic regression",0.000988409,0.000988359,0.000988348,0.000988409,0.000988401,0.995058073,Clinics,0.9166068,TRUE,71,0.769064259,105.8181818,0.832820444,38,0.944132354,,,0.848672352
9148,Optimization of primer sets and detection protocols for SARS-CoV-2 of coronavirus disease 2019 (COVID-19) using PCR and real-time PCR.,Exp Mol Med,32546849,6/18/20,pubmed,0,4,in silico,0.001046876,0.666344068,0.241594333,0.08892102,0.00104687,0.001046834,Genomics,0.3025161,FALSE,38,0.519327107,20.25,0.483074659,21,0.903944688,,,0.635448818
9149,Age-dependent effects in the transmission and control of COVID-19 epidemics.,Nat Med,32546824,6/18/20,pubmed,0,26,mathematical model,0.001511883,0.001511897,0.001511807,0.500302646,0.432760937,0.062400831,Epidemiology,0.27405012,FALSE,62.85185185,0.720329025,,,102,0.978640657,,,0.849484841
9150,"Could Respiratory Fluoroquinolones, Levofloxacin and Moxifloxacin, Prove to be Beneficial as an Adjunct Treatment in COVID-19?",Arch Med Res,32546446,6/18/20,pubmed,0,2,in silico,0.99088282,0.001823409,0.001823512,0.001823473,0.00182339,0.001823397,Drug discovery,0.7417154,TRUE,97,0.861586987,60.5,0.722772277,6,0.764429903,,,0.782929723
9151,Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions.,J Infect Public Health,32546438,6/18/20,pubmed,0,3,"model fit, machine learning, prediction model",0.002238436,0.002238432,0.089267058,0.901779179,0.00223847,0.002238425,Epidemiology,0.79620886,TRUE,5.666666667,0.079473066,1.666666667,0.145036125,37,0.942712513,,,0.389073901
9152,The broad-spectrum antiviral recommendations for drug discovery against COVID-19.,Drug Metab Rev,32546018,6/18/20,pubmed,0,11,whole genome,0.680039653,0.22862603,0.001203431,0.001203433,0.001203503,0.08772395,Drug discovery,0.9119701,TRUE,15.54545455,0.234399159,4.545454545,0.24290875,2,0.618927094,,,0.365411668
9153,American Heart Association COVID-19 CVD Registry Powered by Get With The Guidelines.,Circ Cardiovasc Qual Outcomes,32546000,6/18/20,pubmed,0,13,dataset,0.001254665,0.001254641,0.117207411,0.128862287,0.32143602,0.429984977,Clinics,0.8703313,TRUE,194.8461538,0.971117571,352.6923077,0.96601552,3,0.667819001,,,0.868317364
9154,Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States.,Int J Environ Res Public Health,32545581,6/18/20,pubmed,0,3,"neural network, network model, logistic regression",0.001653402,0.001653105,0.222391147,0.486297512,0.079499529,0.208505306,Epidemiology,0.825489,TRUE,7,0.10179974,4.333333333,0.237958255,19,0.89561084,,,0.411789612
9155,Relative Abundance of SARS-CoV-2 Entry Genes in the Enterocytes of the Lower Gastrointestinal Tract.,Genes (Basel),32545271,6/18/20,pubmed,0,6,"sequencing, dataset",0.661168869,0.199240909,0.004310177,0.004310086,0.126659817,0.004310141,Drug discovery,0.66896045,TRUE,173.3333333,0.960912858,328.6666667,0.961734011,1,0.537564047,,,0.820070305
9156,Potential of Flavonoid-Inspired Phytomedicines against COVID-19.,Molecules,32545268,6/18/20,pubmed,0,8,in silico,0.917816446,0.002898541,0.002898386,0.070589904,0.002898344,0.002898379,Drug discovery,0.9620346,TRUE,120.25,0.907353578,141,0.875702435,25,0.918019631,,,0.900358548
9157,0,Aging (Albany NY),32544884,6/17/20,pubmed,0,3,computational,0.97523113,0.001350408,0.001350375,0.001350374,0.001350315,0.019367398,Drug discovery,0.7207072,TRUE,58,0.689714887,325.6666667,0.960998127,19,0.89561084,,,0.848774618
9158,A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results.,J Clin Virol,32544861,6/17/20,pubmed,0,19,"logistic regression, prediction model",0.000926273,0.000926338,0.179269212,0.000926366,0.209078433,0.608873376,Clinics,0.55558056,TRUE,68.52631579,0.756633063,83.36842105,0.786526626,10,0.828199272,,,0.790452987
9159,Electrocardiographic Findings in Coronavirus Disease-19: Insights on Mortality and Underlying Myocardial Processes.,J Card Fail,32544622,6/17/20,pubmed,0,6,logistic regression,0.001943643,0.00194344,0.001943494,0.001943498,0.001943491,0.990282434,Clinics,0.7747587,TRUE,190,0.968643701,156,0.888747659,12,0.850299401,,,0.902563587
9160,"Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies: Structural genomics approach.",Biochim Biophys Acta Mol Basis Dis,32544429,6/17/20,pubmed,0,10,proteom,0.560332606,0.433914918,0.001438103,0.001438126,0.001438124,0.001438121,Drug discovery,0.83537287,TRUE,38.2,0.520564042,15.7,0.43256623,114,0.981418606,,,0.644849626
9161,Delay in IVF treatment up to 180 days does not affect pregnancy outcomes in women with diminished ovarian reserve.,Hum Reprod,32544225,6/17/20,pubmed,0,4,logistic regression,0.000531378,0.000531381,0.000531382,0.333665802,0.124870953,0.539869104,Clinics,0.9934341,TRUE,382.75,0.995237801,269.5,0.945812149,15,0.874313229,,,0.938454393
9162,IL-6 and CD8+ T cell counts combined are an early predictor of in-hospital mortality of patients with COVID-19.,JCI Insight,32544099,6/17/20,pubmed,0,6,logistic regression,0.114291295,0.000988342,0.000988373,0.000988387,0.000988355,0.881755248,Clinics,0.5656892,TRUE,28,0.408312202,25.16666667,0.53017126,2,0.618927094,,,0.519136852
9163,0,J Biomol Struct Dyn,32543978,6/17/20,pubmed,0,3,computational,0.948077316,0.001203452,0.001203433,0.047108917,0.001203414,0.001203467,Drug discovery,0.91700697,TRUE,63.33333333,0.723545055,6.333333333,0.285121755,12,0.850299401,,,0.619655404
9164,"Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3C-like protease (3CLpro) enzyme inhibitors against SARS-CoV diseases.",SAR QSAR Environ Res,32543892,6/17/20,pubmed,0,2,"in silico, dataset",0.886875202,0.001593499,0.106750822,0.001593502,0.00159349,0.001593486,Drug discovery,0.84538925,TRUE,6.5,0.093512277,0.5,0.087101953,10,0.828199272,,,0.336271167
9165,Immune-related factors associated with pneumonia in 127 children with coronavirus disease 2019 in Wuhan.,Pediatr Pulmonol,32543756,6/17/20,pubmed,0,9,logistic regression,0.001187394,0.001187276,0.001187272,0.001187256,0.112352841,0.882897961,Clinics,0.9391912,TRUE,100.1111111,0.86894675,32.77777778,0.590714477,6,0.764429903,,,0.74136371
9166,Evolutionary relationships and sequence-structure determinants in human SARS coronavirus-2 spike proteins for host receptor recognition.,Proteins,32543705,6/17/20,pubmed,0,1,"genomes, sequence alignment",0.360923695,0.633323842,0.001438144,0.001438115,0.001438104,0.0014381,Genomics,0.36665407,FALSE,3,0.037293586,0,0.055525823,6,0.764429903,,,0.285749771
9167,SARS-CoV-2 genomic surveillance in Taiwan revealed novel ORF8-deletion mutant and clade possibly associated with infections in Middle East.,Emerg Microbes Infect,32543353,6/17/20,pubmed,0,20,"sequencing, genomes",0.001461856,0.95005718,0.001461872,0.001461923,0.001461907,0.044095262,Genomics,0.530446,TRUE,53.45,0.656750572,41.15,0.640018732,50,0.957775171,,,0.751514825
9168,Virus strain from a mild COVID-19 patient in Hangzhou represents a new trend in SARS-CoV-2 evolution potentially related to Furin cleavage site.,Emerg Microbes Infect,32543348,6/17/20,pubmed,0,36,"sequencing, sequence alignment",0.182270221,0.540787087,0.001565319,0.001565338,0.00156534,0.272246695,Genomics,0.69315195,TRUE,85.61111111,0.826952811,57.52777778,0.713071983,17,0.887338725,,,0.809121173
9169,Repositioning of 8565 Existing Drugs for COVID-19.,J Phys Chem Lett,32543196,6/17/20,pubmed,0,5,machine learning,0.82842328,0.003101466,0.159170362,0.003101708,0.003101526,0.003101657,Drug discovery,0.7618832,TRUE,45.2,0.588719154,18,0.46180091,13,0.858880178,,,0.636466747
9170,A novel cohort analysis approach to determining the case fatality rate of COVID-19 and other infectious diseases.,PLoS One,32542041,6/17/20,pubmed,0,1,mathematical model,0.001717273,0.023067325,0.001717226,0.86418553,0.001717186,0.107595459,Epidemiology,0.076928765,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
9171,"[Risks factors for death among COVID-19 patients combined with hypertension, coronary heart disease or diabetes].",Beijing Da Xue Xue Bao Yi Xue Ban,32541972,6/17/20,pubmed,0,5,logistic regression,0.000822948,0.000822919,0.000822905,0.000822905,0.0008229,0.995885423,Clinics,0.92689204,TRUE,9.2,0.136990537,0.2,0.061145304,5,0.739490092,,,0.312541978
9172,Geographic risk assessment of COVID-19 transmission using recent data: An observational study.,Medicine (Baltimore),32541529,6/17/20,pubmed,0,6,bayes,0.001237081,0.001237105,0.103317662,0.891733967,0.001237101,0.001237084,Epidemiology,0.11869952,FALSE,27,0.3960047,6.333333333,0.285121755,6,0.764429903,,,0.48185212
9173,A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study.,J Med Internet Res,32540845,6/17/20,pubmed,0,10,logistic regression,0.001034587,0.001034582,0.391424879,0.437045438,0.001034632,0.168425881,Epidemiology,0.92756563,TRUE,116.8,0.902096605,32.1,0.586031576,0,0.403234768,,,0.630454316
9174,Use of Health Belief Model-Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study.,JMIR Public Health Surveill,32540840,6/17/20,pubmed,0,3,"deep learning, neural network, classifier, network model",0.000746549,0.000746558,0.30560196,0.282868747,0.409289622,0.000746565,Healthcare,0.8566996,TRUE,46,0.596882924,26,0.53819909,2,0.618927094,,,0.584669703
9175,Molecular conservation and differential mutation on ORF3a gene in Indian SARS-CoV2 genomes.,Genomics,32540495,6/17/20,pubmed,0,4,genomes,0.292862889,0.637698248,0.002183242,0.0021833,0.002183239,0.062889082,Genomics,0.6744678,TRUE,47.75,0.612220917,20,0.481000803,10,0.828199272,,,0.640473664
9176,"Epidemic characteristics of the COVID-19 outbreak in Tianjin, a well-developed city in China.",Am J Infect Control,32540369,6/17/20,pubmed,0,3,dataset,0.002296557,0.002296711,0.002296571,0.988516796,0.002296772,0.002296594,Epidemiology,0.47665474,FALSE,14.33333333,0.216772837,5.666666667,0.270203372,2,0.618927094,,,0.368634434
9177,"COVID-19 in patients with thoracic malignancies (TERAVOLT): first results of an international, registry-based, cohort study.",Lancet Oncol,32539942,6/17/20,pubmed,0,49,logistic regression,0.069964302,0.000722182,0.047526361,0.040602761,0.000722213,0.840462181,Clinics,0.56322664,TRUE,86.69387755,0.832271631,81.26530612,0.781442333,152,0.987097969,,,0.866937311
9178,[Recommendations for the treatment of severe coronavirus disease 2019 based on critical care ultrasound].,Zhonghua Nei Ke Za Zhi,32539257,6/17/20,pubmed,0,10,artificial intelligence,0.000779481,0.000779443,0.113274628,0.251126775,0.070213861,0.563825812,Clinics,0.96003556,TRUE,19.2,0.287339972,4.7,0.246855767,0,0.403234768,,,0.312476835
9179,Clinical and radiological features of novel coronavirus pneumonia.,J Xray Sci Technol,32538893,6/17/20,pubmed,0,5,sequencing,0.001171559,0.232409253,0.609340621,0.001171658,0.001171657,0.154735252,Imaging,0.667353,TRUE,14.6,0.220298101,8.4,0.326598876,13,0.858880178,,,0.468592385
9180,Molecular simulation of SARS-CoV-2 spike protein binding to pangolin ACE2 or human ACE2 natural variants reveals altered susceptibility to infection.,J Gen Virol,32538738,6/17/20,pubmed,0,8,molecular dynamics simulation,0.893002069,0.099550873,0.001861659,0.001861947,0.001861731,0.001861721,Drug discovery,0.53473085,TRUE,146.875,0.9411219,100.375,0.823053251,7,0.785110192,,,0.849761781
9181,0,J Biomol Struct Dyn,32538276,6/17/20,pubmed,0,4,in silico,0.868496911,0.00133005,0.00133006,0.126182875,0.001330058,0.001330047,Drug discovery,0.8863184,TRUE,12,0.183190055,2.5,0.180826866,7,0.785110192,,,0.383042371
9182,A novel IDEA: The impact of serial interval on a modified-Incidence Decay and Exponential Adjustment (m-IDEA) model for projections of daily COVID-19 cases.,Infect Dis Model,32537527,6/17/20,pubmed,0,1,model fit,0.001511844,0.001511838,0.04705458,0.946897956,0.001511853,0.001511928,Epidemiology,0.35694957,FALSE,62,0.715814212,24,0.521808938,5,0.739490092,,,0.659037747
9183,Screening and druggability analysis of some plant metabolites against SARS-CoV-2: An integrative computational approach.,Inform Med Unlocked,32537482,6/17/20,pubmed,0,6,"virtual screening, computational",0.99482696,0.001034604,0.001034585,0.001034611,0.001034601,0.001034639,Drug discovery,0.9910067,TRUE,7.833333333,0.113798009,1,0.122023013,23,0.91129082,,,0.382370614
9184,EML webinar overview: Simulation-assisted discovery of membrane targeting nanomedicine.,Extreme Mech Lett,32537481,6/17/20,pubmed,0,3,molecular dynamics simulation,0.913015722,0.000830657,0.000830669,0.06766543,0.00083065,0.016826872,Drug discovery,0.61713237,TRUE,955.3333333,0.99962892,314.3333333,0.958121488,1,0.537564047,,,0.831771485
9185,"Airways Expression of SARS-CoV-2 Receptor, ACE2, and TMPRSS2 Is Lower in Children Than Adults and Increases with Smoking and COPD.",Mol Ther Methods Clin Dev,32537478,6/17/20,pubmed,0,7,dataset,0.599990967,0.00168452,0.001684533,0.001684515,0.108688019,0.286267447,Drug discovery,0.9674085,TRUE,31.14285714,0.445605789,30.28571429,0.571849077,71,0.96950429,,,0.662319719
9186,"Coronavirus pandemic: A predictive analysis of the peak outbreak epidemic in South Africa, Turkey, and Brazil.",Chaos Solitons Fractals,32536762,6/17/20,pubmed,0,2,mathematical model,0.002130723,0.002130699,0.002130659,0.953455483,0.038021632,0.002130804,Epidemiology,0.8035214,TRUE,51.5,0.643267982,4,0.231469093,21,0.903944688,,,0.592893921
9187,Novel Corona virus disease infection in Tunisia: Mathematical model and the impact of the quarantine strategy.,Chaos Solitons Fractals,32536761,6/17/20,pubmed,0,2,mathematical model,0.098562381,0.001371284,0.001371283,0.895952347,0.001371283,0.001371421,Epidemiology,0.74284357,TRUE,1.5,0.015523533,0,0.055525823,8,0.799987654,,,0.29034567
9188,0,Chaos Solitons Fractals,32536760,6/17/20,pubmed,0,3,mathematical model,0.013549819,0.013549649,0.013548986,0.932253227,0.013549129,0.01354919,Epidemiology,0.75316185,TRUE,3,0.037293586,0.333333333,0.073187048,6,0.764429903,,,0.291636846
9189,Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.,Chaos Solitons Fractals,32536759,6/17/20,pubmed,0,5,"deep learning, neural network",0.002238551,0.002238491,0.882767302,0.108278615,0.002238527,0.002238514,Imaging,0.83918285,TRUE,49.8,0.630465706,7.8,0.313286058,61,0.964936107,,,0.63622929
9190,A multicriteria approach for risk assessment of Covid-19 in urban district lockdown.,Saf Sci,32536749,6/17/20,pubmed,0,2,neural network,0.001350462,0.0013506,0.143865614,0.850732551,0.001350413,0.00135036,Epidemiology,0.1893737,FALSE,27,0.3960047,111.5,0.84058068,11,0.840175319,,,0.692253567
9191,COVID-19: Development of a robust mathematical model and simulation package with consideration for ageing population and time delay for control action and resusceptibility.,Physica D,32536738,6/17/20,pubmed,0,2,"computational, mathematical model",0.172390635,0.001751267,0.001751221,0.794933292,0.027422335,0.001751249,Epidemiology,0.43110022,FALSE,24.5,0.361988991,21,0.492239765,27,0.92443978,,,0.592889512
9192,Role of RNA Guanine Quadruplexes in Favoring the Dimerization of SARS Unique Domain in Coronaviruses.,J Phys Chem Lett,32536162,6/17/20,pubmed,0,9,molecular dynamics simulation,0.889341779,0.104396869,0.001565331,0.001565414,0.001565324,0.001565283,Drug discovery,0.425202,FALSE,51.22222222,0.639990105,4.444444444,0.239496923,1,0.537564047,,,0.472350358
9193,High mortality in COVID-19 patients with mild respiratory disease.,Eur J Clin Invest,32535885,6/15/20,pubmed,0,13,logistic regression,0.001310307,0.001310326,0.00131033,0.001310338,0.001310392,0.993448307,Clinics,0.8117059,TRUE,54.23076923,0.6628734,50.23076923,0.682231737,8,0.799987654,,,0.71503093
9194,Studying the pathophysiology of coronavirus disease 2019: a protocol for the Berlin prospective COVID-19 patient cohort (Pa-COVID-19).,Infection,32535877,6/15/20,pubmed,0,43,"transcriptom, dataset",0.204046138,0.00099959,0.215888105,0.212438519,0.076306171,0.290321477,Clinics,0.93877333,TRUE,129.2790698,0.920836168,167.0930233,0.897109981,7,0.785110192,,,0.867685447
9195,Multiple assays in a real-time RT-PCR SARS-CoV-2 panel can mitigate the risk of loss of sensitivity by new genomic variants during the COVID-19 outbreak.,Int J Infect Dis,32535302,6/15/20,pubmed,0,8,genomes,0.001684526,0.959886855,0.001684596,0.00168453,0.033374975,0.001684518,Genomics,0.4896246,FALSE,28.625,0.413445482,11.125,0.371755419,32,0.933699611,,,0.572966837
9196,"Virtual screening, ADME/Tox predictions and the drug repurposing concept for future use of old drugs against the COVID-19.",Life Sci,32535080,6/15/20,pubmed,0,10,"virtual screening, in silico",0.993636721,0.00127265,0.001272677,0.001272677,0.001272639,0.001272637,Drug discovery,0.77553535,TRUE,7.7,0.111880759,1,0.122023013,21,0.903944688,,,0.37928282
9197,Digital healthcare: The only solution for better healthcare during COVID-19 pandemic?,Indian Heart J,32534691,6/15/20,pubmed,0,5,digital health,0.005697486,0.00569746,0.005697897,0.971511655,0.005697984,0.005697518,Epidemiology,0.9176173,TRUE,26.2,0.383820892,3,0.199424672,33,0.936045435,,,0.506430333
9198,"Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study.",Lancet,32534626,6/15/20,pubmed,0,25,"bayes, logistic regression",0.000956309,0.122973907,0.012790381,0.330177985,0.532145033,0.000956385,Healthcare,0.2963881,FALSE,94.6,0.8556497,169.88,0.899585229,320,0.995617013,,,0.916950647
9199,CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images.,Comput Methods Programs Biomed,32534344,6/14/20,pubmed,0,3,"deep learning, neural network, network model, dataset",0.000977437,0.00097747,0.995112714,0.000977509,0.000977442,0.000977428,Imaging,0.38395593,FALSE,16,0.243552477,2,0.164302917,143,0.985863325,,,0.464572906
9200,Spatial analysis and GIS in the study of COVID-19. A review.,Sci Total Environ,32534320,6/14/20,pubmed,0,4,data mining,0.001486473,0.025841658,0.00148645,0.968212491,0.00148646,0.001486469,Epidemiology,0.7510271,TRUE,14.5,0.219617787,12.25,0.388546963,55,0.960800049,,,0.522988266
9201,Impact of the COVID-19 pandemic on the workflow of an ambulatory endoscopy center: an assessment by discrete event simulation.,Gastrointest Endosc,32534053,6/14/20,pubmed,0,1,simulation model,0.001438205,0.001438293,0.186730503,0.459642686,0.349312073,0.00143824,Epidemiology,0.95096695,TRUE,17,0.257467994,1,0.122023013,3,0.667819001,,,0.349103336
9202,"Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing: Results From 11,672 Patients.",Chest,32533957,6/14/20,pubmed,0,8,"logistic regression, prediction model",0.080101768,0.001220063,0.217897458,0.001220101,0.132267279,0.567293331,Clinics,0.4030464,FALSE,258.625,0.985589709,457.5,0.976585496,40,0.947033768,,,0.969736324
9203,"Clinical characteristics of COVID-19 in patients with preexisting ILD: A retrospective study in a single center in Wuhan, China.",J Med Virol,32533777,6/14/20,pubmed,0,10,logistic regression,0.001486503,0.001486433,0.001486439,0.001486442,0.001486454,0.99256773,Clinics,0.957881,TRUE,60.1,0.703754097,20.3,0.483542949,15,0.874313229,,,0.687203425
9204,COVID 19 diagnostic multiplicity and its role in community surveillance and control.,Infez Med,32532934,6/14/20,pubmed,0,4,artificial intelligence,0.002183265,0.424850319,0.321185429,0.185309432,0.06428833,0.002183225,Genomics,0.79155004,TRUE,58,0.689714887,19.5,0.475983409,5,0.739490092,,,0.635062796
9205,"Fighting COVID-19: A quick review of diagnoses, therapies, and vaccines.",Biomed J,32532623,6/14/20,pubmed,0,4,sequencing,0.531880752,0.267788817,0.068798255,0.110815202,0.001126825,0.019590148,Drug discovery,0.80368143,TRUE,45.25,0.589337621,39.25,0.629114263,26,0.920859312,,,0.713103732
9206,Exercising in Times of Lockdown: An Analysis of the Impact of COVID-19 on Levels and Patterns of Exercise among Adults in Belgium.,Int J Environ Res Public Health,32532013,6/14/20,pubmed,0,6,logistic regression,0.002080608,0.002080608,0.00208061,0.223064497,0.768613133,0.002080544,Healthcare,0.7015083,TRUE,140,0.933823984,37.66666667,0.619949157,42,0.949503056,,,0.834425399
9207,Changes in Dietary Behaviours during the COVID-19 Outbreak Confinement in the Spanish COVIDiet Study.,Nutrients,32531892,6/14/20,pubmed,0,7,logistic regression,0.00165308,0.001653098,0.001653159,0.001653133,0.991734343,0.001653187,Healthcare,0.95560277,TRUE,57.28571429,0.683715752,65,0.739028633,50,0.957775171,,,0.793506519
9208,Potential role of artificial intelligence to address the COVID-19 outbreak-related mental health issues in India.,Psychiatry Res,32531628,6/13/20,pubmed,0,5,artificial intelligence,0.013548834,0.013549547,0.47544037,0.214054955,0.269857562,0.013548732,Healthcare,0.34816718,FALSE,30.4,0.436823551,4,0.231469093,5,0.739490092,,,0.469260912
9209,C1 esterase inhibitor and the contact system in COVID-19.,Br J Haematol,32531085,6/13/20,pubmed,0,4,interactom,0.820950098,0.007061932,0.007062118,0.007062235,0.007061885,0.150801733,Drug discovery,0.7769749,TRUE,38.75,0.526748717,74.25,0.764985282,4,0.707574542,,,0.66643618
9210,Association between renin-angiotensin system inhibitors and COVID-19 complications.,Eur Heart J Cardiovasc Pharmacother,32531040,6/13/20,pubmed,0,14,logistic regression,0.071707462,0.001126828,0.001126802,0.001126854,0.001126856,0.923785198,Clinics,0.90923244,TRUE,67.07142857,0.748283753,32.35714286,0.588373026,20,0.900117291,,,0.745591357
9211,Projecting the course of COVID-19 in Turkey: A probabilistic modeling approach,Turk J Med Sci,32530587,6/13/20,pubmed,0,7,"bayes, probabilistic",0.001684495,0.001684536,0.082469363,0.910792444,0.001684564,0.001684598,Epidemiology,0.7011402,TRUE,27.14285714,0.396932402,6.428571429,0.286259031,0,0.403234768,,,0.362142067
9212,A simple method for detection of a novel coronavirus (SARS-CoV-2) using one-step RT-PCR followed by restriction fragment length polymorphism.,J Med Virol,32530490,6/13/20,pubmed,0,16,sequencing,0.131602806,0.741738128,0.122344623,0.001438223,0.001438111,0.00143811,Genomics,0.81925845,TRUE,13.3125,0.20056899,2.5625,0.181228258,0,0.403234768,,,0.261677339
9213,Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease.,J Chem Inf Model,32530282,6/13/20,pubmed,0,5,computational,0.971168715,0.001187275,0.024082151,0.001187316,0.001187256,0.001187289,Drug discovery,0.8072331,TRUE,17.6,0.264456676,7.8,0.313286058,22,0.908142478,,,0.495295071
9214,Detection of viral SARS-CoV-2 genomes and histopathological changes in endomyocardial biopsies.,ESC Heart Fail,32529795,6/13/20,pubmed,0,13,genomes,0.001593641,0.467451093,0.001593596,0.001593578,0.001593524,0.526174568,Clinics,0.91549575,TRUE,70.61538462,0.766590389,67.23076923,0.745919187,37,0.942712513,,,0.818407363
9215,Natural Products as Potential Leads Against Coronaviruses: Could They be Encouraging Structural Models Against SARS-CoV-2?,Nat Prod Bioprospect,32529545,6/13/20,pubmed,0,2,structural model,0.698986324,0.294275583,0.001684529,0.001684558,0.001684532,0.001684473,Drug discovery,0.92343557,TRUE,103,0.875378811,89,0.799304255,18,0.891474782,,,0.855385949
9216,Data on corona-virus readiness strategies influencing customer satisfaction and customer behavioural intentions in South African retail stores.,Data Brief,32529015,6/13/20,pubmed,0,2,dataset,0.004310407,0.004310175,0.004310139,0.854321767,0.128437477,0.004310035,Epidemiology,0.97565925,TRUE,32.5,0.461685942,7,0.299973241,5,0.739490092,,,0.500383092
9217,Genome-Wide Identification and Characterization of Point Mutations in the SARS-CoV-2 Genome.,Osong Public Health Res Perspect,32528815,6/13/20,pubmed,0,6,"genome-wide, sequence alignment",0.266561929,0.729096554,0.001085407,0.001085372,0.001085396,0.001085343,Genomics,0.6726151,TRUE,11,0.167171748,6.5,0.288132192,35,0.939317242,,,0.464873727
9218,Ten recommendations for supporting open pathogen genomic analysis in public health.,Nat Med,32528156,6/13/20,pubmed,0,4,"sequencing, whole-genome",0.00182336,0.66964821,0.001823384,0.323058187,0.001823534,0.001823325,Genomics,0.5528437,TRUE,12,0.183190055,25.75,0.535255553,14,0.866658436,,,0.528368014
9219,In Silico computational screening of Kabasura Kudineer - Official Siddha Formulation and JACOM against SARS-CoV-2 spike protein.,J Ayurveda Integr Med,32527713,6/13/20,pubmed,0,7,"computational, in silico",0.914942849,0.001823378,0.001823434,0.041698183,0.037888667,0.001823489,Drug discovery,0.9352144,TRUE,13.28571429,0.200321603,1.714285714,0.14577201,11,0.840175319,,,0.395422977
9220,Tracking the Genomic Footprints of SARS-CoV-2 Transmission.,Trends Genet,32527617,6/13/20,pubmed,0,1,sequencing,0.006089618,0.591472206,0.006089659,0.38416935,0.006089605,0.006089561,Genomics,0.2445859,FALSE,65,0.734801163,505,0.979060744,2,0.618927094,,,0.777596334
9221,[Analysis of the clinical characteristics and early warning model construction of severe/critical coronavirus disease 2019 patients].,Zhonghua Wei Zhong Bing Ji Jiu Yi Xue,32527341,6/13/20,pubmed,0,5,logistic regression,0.000568961,0.000568959,0.064012544,0.017392617,0.000568948,0.91688797,Clinics,0.7020801,TRUE,36,0.498299215,27.2,0.547230399,4,0.707574542,,,0.584368052
9222,Immunoinformatics and Structural Analysis for Identification of Immunodominant Epitopes in SARS-CoV-2 as Potential Vaccine Targets.,Vaccines (Basel),32526960,6/13/20,pubmed,0,5,"computational, proteom",0.912430337,0.080957434,0.001653043,0.001653078,0.001653074,0.001653034,Drug discovery,0.59675294,TRUE,12.6,0.190302431,7,0.299973241,19,0.89561084,,,0.46196217
9223,Examining the effect of social distancing on the compound growth rate of COVID-19 at the county level (United States) using statistical analyses and a random forest machine learning model.,Public Health,32526559,6/12/20,pubmed,0,2,machine learning,0.091206368,0.001565301,0.261519591,0.610639652,0.001565325,0.033503762,Epidemiology,0.5824204,TRUE,94.5,0.85527862,4,0.231469093,13,0.858880178,,,0.64854263
9224,The psychological status of people affected by the COVID-19 outbreak in China.,J Psychiatr Res,32526513,6/12/20,pubmed,0,9,logistic regression,0.001237052,0.001237088,0.001237071,0.001237135,0.993814553,0.001237101,Healthcare,0.92329514,TRUE,188.8888889,0.968458161,55.11111111,0.702301311,8,0.799987654,,,0.823582375
9225,Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests.,Travel Med Infect Dis,32526372,6/12/20,pubmed,0,8,prediction model,0.001461884,0.00146186,0.080021841,0.001461876,0.001461854,0.914130685,Clinics,0.82631433,TRUE,72.375,0.774816006,96.25,0.815694407,10,0.828199272,,,0.806236562
9226,What is the potential function of microRNAs as biomarkers and therapeutic targets in COVID-19?,Infect Genet Evol,32526370,6/12/20,pubmed,0,4,genomes,0.750054043,0.183148671,0.001461992,0.001461899,0.001461881,0.062411515,Drug discovery,0.86209637,TRUE,22.75,0.336508133,9,0.337904736,9,0.814309525,,,0.496240798
9227,ACE2 Expression Is Increased in the Lungs of Patients With Comorbidities Associated With Severe COVID-19.,J Infect Dis,32526012,6/12/20,pubmed,0,9,transcriptom,0.480658899,0.002296657,0.00229661,0.002296546,0.002296542,0.510154746,Clinics,0.89712256,TRUE,30,0.432432432,55,0.702167514,27,0.92443978,,,0.686346576
9228,Genetic cluster analysis of SARS-CoV-2 and the identification of those responsible for the major outbreaks in various countries.,Emerg Microbes Infect,32525765,6/12/20,pubmed,0,4,"genome sequences, genomes",0.002296568,0.988516988,0.002296617,0.002296683,0.002296559,0.002296585,Genomics,0.58601224,TRUE,42.5,0.562743522,60.75,0.72357506,22,0.908142478,,,0.73148702
9229,"Genetic structure of SARS-CoV-2 reflects clonal superspreading and multiple independent introduction events, North-Rhine Westphalia, Germany, February and March 2020.",Euro Surveill,32524946,6/12/20,pubmed,0,17,whole-genome,0.003335338,0.836526216,0.00333528,0.15013268,0.003335258,0.003335228,Genomics,0.5808454,TRUE,35.41176471,0.490877605,30.70588235,0.574458121,16,0.881782826,,,0.649039518
9230,Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks.,Phys Eng Sci Med,32524445,6/12/20,pubmed,0,2,"deep learning, neural network, transfer learning, dataset",0.000999543,0.000999537,0.995002322,0.000999521,0.000999522,0.000999555,Imaging,0.75020105,TRUE,4,0.054734368,5.5,0.267259834,404,0.997098586,,,0.439697596
9231,Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China.,Eur Radiol,32524223,6/12/20,pubmed,0,12,deep learning,0.000786336,0.000786407,0.28904691,0.22480064,0.000786358,0.48379335,Clinics,0.9782067,TRUE,111.3333333,0.892386666,,,18,0.891474782,,,0.891930724
9232,What patients "see" doctors in online fever clinics during COVID-19 in Wuhan?,J Am Med Inform Assoc,32524147,6/12/20,pubmed,0,4,dataset,0.001010934,0.001010993,0.001011035,0.001010983,0.429437167,0.566518889,Clinics,0.91516995,TRUE,27,0.3960047,33.5,0.595999465,1,0.537564047,,,0.509856071
9233,Clustering analysis of countries using the COVID-19 cases dataset.,Data Brief,32523977,6/12/20,pubmed,0,4,dataset,0.00280646,0.002806492,0.002806632,0.985967626,0.002806429,0.002806361,Epidemiology,0.50302845,TRUE,45.5,0.591440411,27.75,0.551645705,15,0.874313229,,,0.672466448
9234,A quantitative and qualitative analysis of the COVID-19 pandemic model.,Chaos Solitons Fractals,32523257,6/12/20,pubmed,0,4,"computational, mathematical model",0.00131036,0.001310383,0.00131043,0.993448102,0.001310372,0.001310353,Epidemiology,0.41101342,FALSE,33.25,0.468860165,8.5,0.329141022,14,0.866658436,,,0.554886541
9235,Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses.,Proc Natl Acad Sci U S A,32522874,6/12/20,pubmed,0,6,machine learning,0.001901844,0.837232962,0.155159831,0.001901884,0.001901774,0.001901705,Genomics,0.48005295,FALSE,261.8333333,0.986455563,2112.166667,0.998795826,15,0.874313229,,,0.953188206
9236,Widely available lysosome targeting agents should be considered as potential therapy for COVID-19.,Int J Antimicrob Agents,32522674,6/12/20,pubmed,0,2,in-silico,0.736310231,0.001187308,0.001187328,0.190719612,0.001187298,0.069408224,Drug discovery,0.9579042,TRUE,15.5,0.234028078,1.5,0.138747659,13,0.858880178,,,0.410551972
9237,Compliance measurement and observed influencing factors of hand hygiene based on COVID-19 guidelines in China.,Am J Infect Control,32522610,6/12/20,pubmed,0,4,logistic regression,0.001392927,0.001392931,0.001392861,0.094197205,0.749112254,0.152511823,Healthcare,0.9388013,TRUE,225,0.979899808,136,0.870818839,2,0.618927094,,,0.823215247
9238,Cancer history is an independent risk factor for mortality in hospitalized COVID-19 patients: a propensity score-matched analysis.,J Hematol Oncol,32522278,6/12/20,pubmed,0,22,logistic regression,0.029829792,0.001046869,0.001046818,0.001046839,0.001046825,0.965982858,Clinics,0.8632224,TRUE,69.04545455,0.759478013,41.40909091,0.641423602,35,0.939317242,,,0.780072952
9239,COVID-19: viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection.,J Transl Med,32522207,6/12/20,pubmed,0,54,"transcriptom, proteom, interactom",0.844340905,0.150909844,0.001187357,0.001187367,0.001187264,0.001187263,Drug discovery,0.63150704,TRUE,119.4909091,0.906302183,,,12,0.850299401,,,0.878300792
9240,"Coronavirus disease 19 in minority populations of Newark, New Jersey.",Int J Equity Health,32522191,6/12/20,pubmed,0,15,logistic regression,0.001022616,0.001022657,0.001022625,0.001022684,0.051858858,0.94405056,Clinics,0.9165809,TRUE,39.66666667,0.536211268,27,0.546026224,18,0.891474782,,,0.657904092
9241,Non-Overt Coagulopathy in Non-ICU Patients with Mild to Moderate COVID-19 Pneumonia.,J Clin Med,32521707,6/12/20,pubmed,0,10,logistic regression,0.001330084,0.001330054,0.001330132,0.001330037,0.001330075,0.993349618,Clinics,0.95858294,TRUE,51.6,0.643577216,12.8,0.396574793,13,0.858880178,,,0.633010729
9242,Structural Basis of the Potential Binding Mechanism of Remdesivir to SARS-CoV-2 RNA-Dependent RNA Polymerase.,J Phys Chem B,32521159,6/11/20,pubmed,0,2,molecular dynamics simulation,0.994505842,0.001098857,0.001098835,0.001098834,0.001098821,0.001098812,Drug discovery,0.6516991,TRUE,159,0.950522605,116,0.846133262,29,0.928020248,,,0.908225372
9243,The association of lung ultrasound images with COVID-19 infection in an emergency room cohort.,Anaesthesia,32520406,6/11/20,pubmed,0,9,predictive model,0.18118698,0.001717282,0.407732123,0.001717184,0.001717206,0.405929225,Imaging,0.7005168,TRUE,3.222222222,0.038530521,0.111111111,0.056663099,17,0.887338725,,,0.327510782
9244,"Spatial Analysis of COVID-19 cases and intensive care beds in the State of Ceará, Brazil.",Cien Saude Colet,32520290,6/11/20,pubmed,0,2,bayes,0.001486415,0.001486519,0.001486462,0.992567668,0.001486427,0.001486509,Epidemiology,0.34700072,FALSE,14,0.213494959,0,0.055525823,5,0.739490092,,,0.336170291
9245,Factors associated with people's behavior in social isolation during the COVID-19 pandemic.,Cien Saude Colet,32520286,6/11/20,pubmed,0,4,probabilistic,0.001330094,0.001330097,0.001330076,0.136882461,0.857797245,0.001330026,Healthcare,0.98734987,TRUE,12.75,0.191786752,1.25,0.127776291,6,0.764429903,,,0.361330982
9246,In silico evaluation of marine fish proteins as nutritional supplements for COVID-19 patients.,Food Funct,32520031,6/11/20,pubmed,0,3,in silico,0.836942755,0.002562619,0.002562621,0.002562637,0.081995018,0.07337435,Drug discovery,0.9607005,TRUE,25.66666667,0.376461129,14.33333333,0.415975381,3,0.667819001,,,0.486751837
9247,Toward Nanotechnology-Enabled Approaches against the COVID-19 Pandemic.,ACS Nano,32519842,6/11/20,pubmed,0,17,computational,0.521042508,0.001098901,0.148114098,0.267910917,0.001098854,0.060734723,Drug discovery,0.88446033,TRUE,130.5882353,0.92232049,137.7058824,0.872558202,85,0.975121921,,,0.923333538
9248,0,ChemistrySelect,32518817,6/11/20,pubmed,0,2,in silico,0.981963141,0.00360728,0.003607352,0.003607673,0.003607279,0.003607275,Drug discovery,0.7455385,TRUE,28.5,0.412579628,6,0.280037463,0,0.403234768,,,0.365283953
9249,Pay attention to situation of SARS-CoV-2 and TCM advantages in treatment of novel coronavirus infection.,Chin Herb Med,32518555,6/11/20,pubmed,0,1,mathematical model,0.001565377,0.001565338,0.001565409,0.837841232,0.00156538,0.155897264,Epidemiology,0.7233913,TRUE,19,0.285793803,3,0.199424672,10,0.828199272,,,0.437805916
9250,Immunoinformatics-guided designing of epitope-based subunit vaccines against the SARS Coronavirus-2 (SARS-CoV-2).,Immunobiology,32517882,6/11/20,pubmed,0,5,"molecular dynamics simulation, computational, in silico",0.846405789,0.002130828,0.002130757,0.145071246,0.002130717,0.002130663,Drug discovery,0.78696847,TRUE,14.4,0.217576845,0,0.055525823,20,0.900117291,,,0.39107332
9251,Silibinin and SARS-CoV-2: Dual Targeting of Host Cytokine Storm and Virus Replication Machinery for Clinical Management of COVID-19 Patients.,J Clin Med,32517353,6/11/20,pubmed,0,6,computational,0.796399716,0.001415208,0.001415149,0.080495041,0.001415194,0.118859693,Drug discovery,0.8913375,TRUE,122.6666667,0.911002536,102.8333333,0.827267862,10,0.828199272,,,0.85548989
9252,Dietary Diversity among Chinese Residents during the COVID-19 Outbreak and Its Associated Factors.,Nutrients,32517210,6/11/20,pubmed,0,8,logistic regression,0.000916684,0.146354171,0.000916688,0.000916723,0.849979037,0.000916696,Healthcare,0.47604337,FALSE,53.625,0.658049354,23.625,0.517393631,17,0.887338725,,,0.687593903
9253,Clinical characteristics and prognostic factors in COVID-19 patients aged ≥80 years.,Geriatr Gerontol Int,32516861,6/10/20,pubmed,0,8,model fit,0.001438276,0.001438141,0.001438109,0.001438245,0.001438152,0.992809077,Clinics,0.8593313,TRUE,78.75,0.801533799,41.25,0.64102221,41,0.948144947,,,0.796900319
9254,Pathogenesis of SARS-CoV-2 in Transgenic Mice Expressing Human Angiotensin-Converting Enzyme 2.,Cell,32516571,6/10/20,pubmed,0,20,genome sequences,0.641940293,0.296538771,0.002639268,0.002639059,0.002639033,0.053603576,Drug discovery,0.5378672,TRUE,116.05,0.900797823,501.65,0.978860048,147,0.986418915,,,0.955358929
9255,Impact of COVID-19 stay-at-home orders on weight-related behaviours among patients with obesity.,Clin Obes,32515555,6/10/20,pubmed,0,8,logistic regression,0.001203415,0.001203418,0.001203522,0.001203482,0.699310886,0.295875278,Healthcare,0.96625185,TRUE,33.375,0.470097099,15.5,0.430157881,26,0.920859312,,,0.607038097
9256,Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: A stochastic simulation and machine learning study.,Am J Transplant,32515544,6/10/20,pubmed,0,11,machine learning,0.037429777,0.001823385,0.049509911,0.700550373,0.001823359,0.208863196,Epidemiology,0.73277164,TRUE,177.0909091,0.963139341,184.9090909,0.909352422,16,0.881782826,,,0.91809153
9257,Mutations in SARS-CoV-2 viral RNA identified in Eastern India: Possible implications for the ongoing outbreak in India and impact on viral structure and host susceptibility.,J Biosci,32515358,6/10/20,pubmed,0,18,sequencing,0.229750011,0.765231587,0.001254584,0.001254621,0.001254592,0.001254605,Genomics,0.81691617,TRUE,69.5,0.761271569,42.61111111,0.647979663,56,0.961540836,,,0.790264023
9258,COVID-19 and its effects on the digestive system and endoscopy practice.,JGH Open,32514432,6/10/20,pubmed,0,8,dataset,0.064766013,0.001823394,0.001823447,0.333282273,0.238513916,0.359790958,Clinics,0.9774468,TRUE,9.375,0.139464407,0.375,0.073789136,14,0.866658436,,,0.35997066
9259,A single-cell atlas of the peripheral immune response in patients with severe COVID-19.,Nat Med,32514174,6/10/20,pubmed,0,15,sequencing,0.528449248,0.029377657,0.001565325,0.001565323,0.001565323,0.437477124,Drug discovery,0.4030436,FALSE,22.93333333,0.338425382,38.06666667,0.622290607,288,0.99469103,,,0.65180234
9260,Genomic surveillance reveals multiple introductions of SARS-CoV-2 into Northern California.,Science,32513865,6/10/20,pubmed,0,50,genomic epidemiology,0.002562544,0.574766587,0.002562568,0.375332333,0.0025626,0.042213367,Genomics,0.4788554,FALSE,54.98,0.667573752,149.06,0.882726786,98,0.977652942,,,0.84265116
9261,Evaluation of modelling study shows limits of COVID-19 importing risk simulations in sub-Saharan Africa.,Epidemiol Infect,32513346,6/10/20,pubmed,0,3,mathematical model,0.002422451,0.076221631,0.031895448,0.759928338,0.002422454,0.127109677,Epidemiology,0.24166384,FALSE,12.66666667,0.191044592,0.333333333,0.073187048,3,0.667819001,,,0.310683547
9262,"The SARS-CoV-2 Ivermectin Navarra-ISGlobal Trial (SAINT) to Evaluate the Potential of Ivermectin to Reduce COVID-19 Transmission in low risk, non-severe COVID-19 patients in the first 48 hours after symptoms onset: A structured summary of a study protocol for a randomized control pilot trial.",Trials,32513289,6/10/20,pubmed,0,17,transcriptom,0.071085167,0.169139635,0.035206631,0.000312113,0.272848432,0.451408022,Clinics,0.9909328,TRUE,,,,,0,0.403234768,,,0.403234768
9263,Taste and Smell Impairment in COVID-19: An AAO-HNS Anosmia Reporting Tool-Based Comparative Study.,Otolaryngol Head Neck Surg,32513096,6/10/20,pubmed,0,3,logistic regression,0.001823391,0.001823478,0.001823503,0.001823432,0.102528221,0.890177975,Clinics,0.9972074,TRUE,44.66666667,0.583462181,14.66666667,0.420323789,24,0.914439163,,,0.639408378
9264,"From Wuhan to COVID-19 Pandemic: An Up-to-Date Review of Its Pathogenesis, Potential Therapeutics, and Recent Advances.",Microorganisms,32512950,6/10/20,pubmed,0,3,"virtual screening, sequencing, in silico",0.669558798,0.192970816,0.002080598,0.131228539,0.002080581,0.002080668,Drug discovery,0.57600296,TRUE,8.333333333,0.123693488,0,0.055525823,2,0.618927094,,,0.266048802
9265,The Prediction of miRNAs in SARS-CoV-2 Genomes: hsa-miR Databases Identify 7 Key miRs Linked to Host Responses and Virus Pathogenicity-Related KEGG Pathways Significant for Comorbidities.,Viruses,32512929,6/10/20,pubmed,0,7,"transcriptom, whole-genome, genomes",0.288822136,0.576346468,0.00107218,0.001072195,0.001072264,0.131614756,Genomics,0.89735425,TRUE,48.71428571,0.619580679,17,0.451097137,16,0.881782826,,,0.650820214
9266,Corona virus versus existence of human on the earth: A computational and biophysical approach.,Int J Biol Macromol,32512089,6/9/20,pubmed,0,6,computational,0.753561836,0.242250766,0.00104683,0.001046848,0.001046819,0.001046901,Drug discovery,0.8744369,TRUE,37.16666667,0.509493475,7.166666667,0.301311212,7,0.785110192,,,0.531971626
9267,Artificial intelligence in diagnosis and management of COVID-19 in dermatology.,Dermatol Ther,32511846,6/9/20,pubmed,0,7,artificial intelligence,0.019529417,0.019529428,0.902351364,0.019529957,0.01952964,0.019530194,Clinics,0.7691116,TRUE,42,0.558537943,4.428571429,0.239296227,1,0.537564047,,,0.445132739
9268,Disruption of the CCL5/RANTES-CCR5 Pathway Restores Immune Homeostasis and Reduces Plasma Viral Load in Critical COVID-19.,medRxiv,32511656,6/9/20,pubmed,0,24,"sequencing, transcriptom",0.523387859,0.046712424,0.001310323,0.001310388,0.00131042,0.425968586,Drug discovery,0.5135107,TRUE,25.70833333,0.376708516,27.95833333,0.552983677,44,0.952157541,,,0.627283245
9269,"Contextualizing covid-19 spread: a county level analysis, urban versus rural, and implications for preparing for the next wave.",medRxiv,32511653,6/9/20,pubmed,0,3,"machine learning, dataset",0.002806398,0.002806458,0.271392963,0.717381088,0.002806612,0.002806482,Epidemiology,0.36060107,FALSE,5.666666667,0.079473066,0,0.055525823,3,0.667819001,,,0.267605963
9270,Validating a Widely Implemented Deterioration Index Model Among Hospitalized COVID-19 Patients.,medRxiv,32511650,6/9/20,pubmed,0,15,"predictive model, prediction model",0.001203436,0.001203469,0.001203473,0.001203473,0.123434567,0.871751583,Clinics,0.14598233,FALSE,83.8,0.820582596,119.8666667,0.851217554,10,0.828199272,,,0.833333141
9271,Experience with Social Distancing Early in the COVID-19 Pandemic in the United States: Implications for Public Health Messaging.,medRxiv,32511643,6/9/20,pubmed,0,5,computational,0.000880286,0.000880236,0.000880228,0.309405181,0.687073824,0.000880244,Healthcare,0.6167263,TRUE,25.4,0.372626631,8,0.320511105,20,0.900117291,,,0.531085009
9272,Association of Digestive Symptoms and Hospitalization in Patients with SARS-CoV-2 Infection.,medRxiv,32511634,6/9/20,pubmed,0,9,logistic regression,0.00146193,0.001461973,0.001461939,0.001461982,0.04156635,0.952585826,Clinics,0.6371779,TRUE,22.55555556,0.33409611,11.44444444,0.376705914,9,0.814309525,,,0.508370516
9273,0,medRxiv,32511625,6/9/20,pubmed,0,16,transcriptom,0.54988645,0.002562624,0.002562639,0.002562585,0.002562648,0.439863054,Drug discovery,0.53254277,TRUE,152.375,0.946069639,288.1875,0.95096334,15,0.874313229,,,0.923782069
9274,"Fast spread of COVID-19 in Europe and the US suggests the necessity of early, strong and comprehensive interventions.",medRxiv,32511619,6/9/20,pubmed,0,4,mathematical model,0.002238428,0.002238474,0.002238459,0.98880773,0.002238447,0.002238461,Epidemiology,0.20531186,FALSE,49.5,0.628362917,74.5,0.765252877,17,0.887338725,,,0.760318173
9275,Trends and prediction in daily incidence and deaths of COVID-19 in the United States: a search-interest based model.,medRxiv,32511604,6/9/20,pubmed,0,6,dataset,0.001112618,0.001112611,0.00111276,0.820268885,0.001112668,0.175280457,Epidemiology,0.18557957,FALSE,65.66666667,0.738450121,56.33333333,0.708255285,6,0.764429903,,,0.737045103
9276,Symptom-Based Isolation Policies: Evidence from a Mathematical Model of Outbreaks of Influenza and COVID-19.,medRxiv,32511602,6/9/20,pubmed,0,2,"computational, mathematical model",0.000966812,0.022675542,0.000966789,0.560296764,0.414127269,0.000966825,Epidemiology,0.13773862,FALSE,30.5,0.438493413,16,0.437316029,2,0.618927094,,,0.498245512
9277,"Covid-19 Testing, Hospital Admission, and Intensive Care Among 2,026,227 United States Veterans Aged 54-75 Years.",medRxiv,32511595,6/9/20,pubmed,0,16,logistic regression,0.001220061,0.001220063,0.001220055,0.001220069,0.145077265,0.850042487,Clinics,0.5130937,TRUE,63.5625,0.725153071,75,0.766590848,79,0.972899562,,,0.821547827
9278,Household Secondary Attack Rate of COVID-19 and Associated Determinants.,medRxiv,32511590,6/9/20,pubmed,0,16,dataset,0.001156267,0.068488631,0.001156235,0.470983359,0.45705922,0.001156288,Epidemiology,0.16576877,FALSE,76.6875,0.792813408,137.0625,0.871822317,74,0.970924131,,,0.878519952
9279,"Sequencing identifies multiple, early introductions of SARS-CoV2 to New York City Region.",medRxiv,32511587,6/9/20,pubmed,0,45,sequencing,0.001786533,0.494626449,0.001786571,0.498227312,0.001786579,0.001786556,Epidemiology,0.44917703,FALSE,26.76315789,0.391922815,30.97368421,0.576665775,28,0.926168282,,,0.631585624
9280,Reliability of self-sampling for accurate assessment of respiratory virus viral and immunologic kinetics.,medRxiv,32511581,6/9/20,pubmed,0,16,mathematical model,0.08111968,0.638632512,0.003214388,0.069583141,0.204236088,0.00321419,Genomics,0.59785753,TRUE,16.25,0.246026347,16.3125,0.439456784,5,0.739490092,,,0.474991074
9281,A Genomic Survey of SARS-CoV-2 Reveals Multiple Introductions into Northern California without a Predominant Lineage.,medRxiv,32511579,6/9/20,pubmed,0,48,"genomic epidemiology, genomes",0.001538072,0.684190594,0.001538114,0.275464964,0.001538121,0.035730136,Genomics,0.14474201,FALSE,41.57446809,0.553713897,92.87234043,0.808067969,29,0.928020248,,,0.763267371
9282,Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing.,medRxiv,32511578,6/9/20,pubmed,0,2,forecasting model,0.001823457,0.001823379,0.001823371,0.990883105,0.001823339,0.001823349,Epidemiology,0.17204079,FALSE,3,0.037293586,0,0.055525823,15,0.874313229,,,0.322377546
9283,"Treatment of COVID-19 Patients with Convalescent Plasma in Houston, Texas.",medRxiv,32511574,6/9/20,pubmed,0,35,"sequencing, whole genome",0.001330087,0.168915326,0.001330057,0.087510302,0.001330125,0.739584104,Clinics,0.52128965,TRUE,32.91428571,0.465025666,78.88571429,0.775755954,17,0.887338725,,,0.709373448
9284,COVID-19 infection results in alterations of the kynurenine pathway and fatty acid metabolism that correlate with IL-6 levels and renal status.,medRxiv,32511571,6/9/20,pubmed,0,12,metabolom,0.329339546,0.273823779,0.001187256,0.001187289,0.001187365,0.393274765,Clinics,0.8824651,TRUE,74.91666667,0.786195807,49.16666667,0.677950227,12,0.850299401,,,0.771481812
9285,Automated assessment of COVID-19 pulmonary disease severity on chest radiographs using convolutional Siamese neural networks.,medRxiv,32511570,6/9/20,pubmed,0,13,"neural network, transfer learning",0.001022605,0.00102262,0.575274204,0.001022624,0.001022632,0.420635314,Imaging,0.9302616,TRUE,29.15384615,0.420496011,16.76923077,0.446146642,14,0.866658436,,,0.577767029
9286,How Efficacious Must a COVID-19 Coronavirus Vaccine be to Prevent or Stop an Epidemic by Itself.,medRxiv,32511569,6/9/20,pubmed,0,11,"simulation experiment, computational",0.001786575,0.001786538,0.001786518,0.991067248,0.001786617,0.001786504,Epidemiology,0.08300239,FALSE,25.81818182,0.378440225,18,0.46180091,4,0.707574542,,,0.515938559
9287,Acute Kidney Injury in Hospitalized Patients with COVID-19.,medRxiv,32511564,6/9/20,pubmed,0,63,"machine learning, predictive model",0.000822896,0.000822912,0.115102868,0.000822915,0.000822934,0.881605475,Clinics,0.56289643,TRUE,25.51020408,0.374482034,64.16326531,0.735817501,44,0.952157541,,,0.687485692
9288,Artificial intelligence-enabled rapid diagnosis of COVID-19 patients.,medRxiv,32511559,6/9/20,pubmed,0,28,"artificial intelligence, sequencing",0.001098798,0.059876881,0.788793312,0.001098814,0.001098852,0.148033342,Imaging,0.4718618,FALSE,48.67857143,0.619395139,73.25,0.762510035,24,0.914439163,,,0.765448112
9289,A Unique Clade of SARS-CoV-2 Viruses is Associated with Lower Viral Loads in Patient Upper Airways.,medRxiv,32511558,6/9/20,pubmed,0,11,genome sequences,0.002296535,0.921711989,0.002296554,0.002296622,0.002296517,0.069101783,Genomics,0.61877984,TRUE,42.72727273,0.56428969,65.63636364,0.740968691,44,0.952157541,,,0.752471974
9290,Interpretable Artificial Intelligence for COVID-19 Diagnosis from Chest CT Reveals Specificity of Ground-Glass Opacities.,medRxiv,32511545,6/9/20,pubmed,0,10,artificial intelligence,0.001046807,0.001046873,0.743284568,0.001046862,0.001046857,0.252528034,Imaging,0.27995226,FALSE,117,0.902900612,405.1,0.971635001,7,0.785110192,,,0.886548602
9291,The landscape of host genetic factors involved in infection to common viruses and SARS-CoV-2.,medRxiv,32511533,6/9/20,pubmed,0,9,"transcriptom, genome-wide",0.380157276,0.564234526,0.000907294,0.000907289,0.052886218,0.000907397,Genomics,0.3708498,FALSE,109.2222222,0.888614014,167.6666667,0.897578271,11,0.840175319,,,0.875455868
9292,Risk of COVID-19 among frontline healthcare workers and the general community: a prospective cohort study.,medRxiv,32511531,6/9/20,pubmed,0,30,classifier,0.001330073,0.001330259,0.034880501,0.001330147,0.780706468,0.180422552,Healthcare,0.46283364,FALSE,273.7,0.988249119,929.4,0.992841852,50,0.957775171,,,0.979622047
9293,Risk of Ischemic Stroke in Patients with Covid-19 versus Patients with Influenza.,medRxiv,32511527,6/9/20,pubmed,0,22,logistic regression,0.001034629,0.056730106,0.001034588,0.001034613,0.18111844,0.759047624,Clinics,0.77702177,TRUE,69.63636364,0.761766343,104.6818182,0.830545892,27,0.92443978,,,0.838917339
9294,Clinical predictors of COVID-19 mortality.,medRxiv,32511520,6/9/20,pubmed,0,6,machine learning,0.00146187,0.001461882,0.313061007,0.001461919,0.001461858,0.681091463,Clinics,0.6244866,TRUE,20.5,0.304966294,9.166666667,0.338774418,16,0.881782826,,,0.508507846
9295,Rapid and extraction-free detection of SARS-CoV-2 from saliva with colorimetric LAMP.,medRxiv,32511508,6/9/20,pubmed,0,13,genomes,0.001823376,0.561760355,0.366526785,0.001823393,0.066242744,0.001823346,Genomics,0.3951393,FALSE,24.33333333,0.359700662,209.5833333,0.921996254,36,0.941169208,,,0.740955374
9296,Psychological morbidities and fatigue in patients with confirmed COVID-19 during disease outbreak: prevalence and associated biopsychosocial risk factors.,medRxiv,32511502,6/9/20,pubmed,0,15,logistic regression,0.001310316,0.001310344,0.026834582,0.001310379,0.71264681,0.256587568,Healthcare,0.95220965,TRUE,150.8666667,0.945080092,290.1333333,0.951565427,15,0.874313229,,,0.923652916
9297,Identification of Immune complement function as a determinant of adverse SARS-CoV-2 infection outcome.,medRxiv,32511494,6/9/20,pubmed,0,11,"proteom, genome-wide",0.62655931,0.12693008,0.001203418,0.001203447,0.001203484,0.242900261,Drug discovery,0.8477385,TRUE,5.75,0.080957388,2,0.164302917,15,0.874313229,,,0.373191178
9298,"Clinical features, diagnostics, and outcomes of patients presenting with acute respiratory illness: a comparison of patients with and without COVID-19.",medRxiv,32511488,6/9/20,pubmed,0,46,"sequencing, metagenom",0.001046871,0.123838096,0.048856828,0.001046835,0.001046873,0.824164497,Clinics,0.6083366,TRUE,18.67391304,0.279794669,22.56521739,0.507760235,15,0.874313229,,,0.553956045
9299,Efficient prevalence estimation and infected sample identification with group testing for SARS-CoV-2.,medRxiv,32511487,6/9/20,pubmed,0,13,mathematical model,0.001126847,0.333402066,0.001126851,0.504703559,0.158513844,0.001126833,Epidemiology,0.1937212,FALSE,37.66666667,0.515430763,165.8333333,0.896440995,9,0.814309525,,,0.742060428
9300,A collaborative online AI engine for CT-based COVID-19 diagnosis.,medRxiv,32511484,6/9/20,pubmed,0,38,artificial intelligence,0.001098871,0.031903089,0.772654866,0.071778761,0.062713876,0.059850537,Imaging,0.1942684,FALSE,67.78947368,0.752180098,75.71052632,0.768196414,11,0.840175319,,,0.78685061
9301,Lockdown exit strategies and risk of a second epidemic peak: a stochastic agent-based model of SARS-CoV-2 epidemic in France.,medRxiv,32511469,6/9/20,pubmed,0,8,simulation model,0.001392881,0.024662594,0.00139282,0.969765908,0.001392891,0.001392906,Epidemiology,0.14103848,FALSE,110,0.89003649,266.375,0.944607974,8,0.799987654,,,0.878210706
9302,Facing the COVID-19 epidemic in NYC: a stochastic agent-based model of various intervention strategies.,medRxiv,32511467,6/9/20,pubmed,0,7,simulation model,0.001272753,0.026520037,0.031379214,0.816971086,0.00127271,0.1225842,Epidemiology,0.19346154,FALSE,125.4285714,0.914898881,304.4285714,0.955780037,25,0.918019631,,,0.929566183
9303,"SARS-CoV-2 receptor networks in diabetic kidney disease, BK-Virus nephropathy and COVID-19 associated acute kidney injury.",medRxiv,32511461,6/9/20,pubmed,0,32,"bayes, dataset",0.79669118,0.000999614,0.000999533,0.000999592,0.000999548,0.199310532,Drug discovery,0.6757208,TRUE,30.96153846,0.442513452,,,4,0.707574542,,,0.575043997
9304,Single-cell Transcriptome Analysis Indicates New Potential Regulation Mechanism of ACE2 and NPs signaling among heart failure patients infected with SARS-CoV-2.,medRxiv,32511460,6/9/20,pubmed,0,11,"sequencing, transcriptom",0.577565276,0.013854382,0.000898075,0.000898073,0.000898093,0.4058861,Drug discovery,0.78898335,TRUE,93.18181818,0.851877049,37.54545455,0.61934707,7,0.785110192,,,0.752111437
9305,"Quantifying bias of COVID-19 prevalence and severity estimates in Wuhan, China that depend on reported cases in international travelers.",medRxiv,32511442,6/9/20,pubmed,0,4,"bayes, mathematical model, bayesian model",0.001237067,0.257850353,0.001237141,0.737201132,0.001237184,0.001237123,Epidemiology,0.2410287,FALSE,139.5,0.93295813,298.25,0.95377308,66,0.967652324,,,0.951461178
9306,Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19).,medRxiv,32511439,6/9/20,pubmed,0,7,bayes,0.001511815,0.001511853,0.001511821,0.828745768,0.165206844,0.0015119,Epidemiology,0.46655774,FALSE,84.71428571,0.823860474,124.7142857,0.857439122,206,0.991727884,,,0.89100916
9307,Evolving epidemiology of novel coronavirus diseases 2019 and possible interruption of local transmission outside Hubei Province in China: a descriptive and modeling study.,medRxiv,32511424,6/9/20,pubmed,0,24,bayes,0.000956295,0.000956327,0.000956317,0.826460376,0.000956391,0.169714295,Epidemiology,0.26299423,FALSE,92.125,0.849093945,,,40,0.947033768,,,0.898063856
9308,Identification of Candidate COVID-19 Therapeutics using hPSC-derived Lung Organoids.,bioRxiv,32511403,6/9/20,pubmed,0,29,transcriptom,0.992309241,0.001538143,0.001538177,0.00153816,0.001538107,0.001538171,Drug discovery,0.49975443,FALSE,75.86206897,0.789906611,319.0689655,0.959258764,36,0.941169208,,,0.896778194
9309,Baseline pulmonary levels of CD8+ T cells and NK cells inversely correlate with expression of the SARS-CoV-2 entry receptor ACE2.,bioRxiv,32511391,6/9/20,pubmed,0,1,"in silico, dataset",0.660886382,0.001511851,0.001511885,0.0015119,0.001511888,0.333066094,Drug discovery,0.7108223,TRUE,13,0.197352959,2,0.164302917,8,0.799987654,,,0.38721451
9310,Modeling and Simulation of a Fully-glycosylated Full-length SARS-CoV-2 Spike Protein in a Viral Membrane.,bioRxiv,32511389,6/9/20,pubmed,0,12,molecular dynamics simulation,0.852775832,0.138701201,0.002130738,0.002130835,0.002130703,0.002130691,Drug discovery,0.51869595,TRUE,47.75,0.612220917,76.58333333,0.770136473,37,0.942712513,,,0.775023301
9311,Single-cell longitudinal analysis of SARS-CoV-2 infection in human bronchial epithelial cells.,bioRxiv,32511382,6/9/20,pubmed,0,21,sequencing,0.897977595,0.064003445,0.001486408,0.033559627,0.001486426,0.001486499,Drug discovery,0.47596413,FALSE,90.88888889,0.845321294,264.7777778,0.943938988,35,0.939317242,,,0.909525841
9312,A transcriptional regulatory atlas of coronavirus infection of human cells.,bioRxiv,32511379,6/9/20,pubmed,0,3,"transcriptom, dataset",0.990064015,0.001987202,0.001987295,0.001987252,0.001987112,0.001987124,Drug discovery,0.045437783,FALSE,55,0.668686994,155,0.887944876,3,0.667819001,,,0.741483623
9313,An insertion unique to SARS-CoV-2 exhibits superantigenic character strengthened by recent mutations.,bioRxiv,32511374,6/9/20,pubmed,0,5,computational,0.600654051,0.185176555,0.001330032,0.001330045,0.088537155,0.122972162,Drug discovery,0.13739333,FALSE,79,0.802585194,118,0.848876104,35,0.939317242,,,0.863592847
9314,RNA-GPS Predicts SARS-CoV-2 RNA Localization to Host Mitochondria and Nucleolus.,bioRxiv,32511373,6/9/20,pubmed,0,3,"machine learning, computational, transcriptom, genomes",0.547154912,0.381259008,0.067200445,0.001461925,0.001461858,0.001461852,Drug discovery,0.7337024,TRUE,167.6666667,0.956707279,942,0.993243243,14,0.866658436,,,0.938869653
9315,Targeting the SARS-CoV-2 Main Protease to Repurpose Drugs for COVID-19.,bioRxiv,32511370,6/9/20,pubmed,0,9,computational,0.989083635,0.002183238,0.00218332,0.002183363,0.002183218,0.002183225,Drug discovery,0.7419633,TRUE,77.22222222,0.795596512,40.44444444,0.63633931,8,0.799987654,,,0.743974492
9316,"A modular framework for multiscale multicellular spatial modeling of viral infection, immune response and drug therapy timing and efficacy in epithelial tissues: A multiscale model of viral infection in epithelial tissues.",bioRxiv,32511367,6/9/20,pubmed,0,11,dataset,0.587871463,0.076381869,0.000871584,0.333131956,0.000871552,0.000871577,Drug discovery,0.14955914,FALSE,36.33333333,0.500773084,47.88888889,0.672330747,2,0.618927094,,,0.597343642
9317,Age-related expression of SARS-CoV-2 priming protease TMPRSS2 in the developing lung.,bioRxiv,32511364,6/9/20,pubmed,0,18,sequencing,0.716315922,0.083155037,0.002639002,0.002639024,0.041751385,0.153499629,Drug discovery,0.46916515,FALSE,69.58823529,0.761457109,97.88235294,0.818571046,15,0.874313229,,,0.818113795
9318,"Shotgun Transcriptome and Isothermal Profiling of SARS-CoV-2 Infection Reveals Unique Host Responses, Viral Diversification, and Drug Interactions.",bioRxiv,32511352,6/9/20,pubmed,0,52,"transcriptom, metatranscriptom",0.360056603,0.566152968,0.001987272,0.001987246,0.001987136,0.067828774,Genomics,0.43977687,FALSE,54.65384615,0.665965737,134.1346154,0.868009098,60,0.964503982,,,0.832826272
9319,Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2.,bioRxiv,32511351,6/9/20,pubmed,0,7,"machine learning, computational, genomes",0.528843417,0.229515295,0.238573264,0.0010227,0.001022652,0.001022672,Drug discovery,0.045681536,FALSE,73.71428571,0.7806296,359.5714286,0.967152796,4,0.707574542,,,0.818452313
9320,Prediction of SARS-CoV-2 epitopes across 9360 HLA class I alleles.,bioRxiv,32511325,6/9/20,pubmed,0,5,"proteom, dataset",0.876541034,0.064228954,0.055051306,0.001392946,0.001392872,0.001392888,Drug discovery,0.22130793,FALSE,35,0.488032655,77.4,0.771808938,26,0.920859312,,,0.726900302
9321,Rapid community-driven development of a SARS-CoV-2 tissue simulator.,bioRxiv,32511322,6/9/20,pubmed,0,23,simulation model,0.553182993,0.001141381,0.076949799,0.194669369,0.072640908,0.10141555,Drug discovery,0.085104376,FALSE,69,0.759416167,,,24,0.914439163,,,0.836927665
9322,"Mutations, Recombination and Insertion in the Evolution of 2019-nCoV.",bioRxiv,32511312,6/9/20,pubmed,0,26,"genome sequences, genomes",0.212730223,0.75463571,0.001046832,0.001046821,0.001046854,0.029493561,Genomics,0.5303786,TRUE,20.19230769,0.29970932,16.03846154,0.437449826,26,0.920859312,,,0.55267282
9323,Analysis of the SARS-CoV-2 spike protein glycan shield: implications for immune recognition.,bioRxiv,32511307,6/9/20,pubmed,0,4,glycomics,0.990883012,0.00182342,0.001823373,0.001823393,0.001823437,0.001823365,Drug discovery,0.20164976,FALSE,27.5,0.401570907,11,0.371287129,30,0.930057411,,,0.567638482
9324,RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses.,bioRxiv,32511306,6/9/20,pubmed,0,3,"genome sequences, sequence alignment",0.210935303,0.784315631,0.001187262,0.001187286,0.001187261,0.001187257,Genomics,0.23783088,FALSE,7.666666667,0.111633373,0.333333333,0.073187048,30,0.930057411,,,0.371625944
9325,Reversal of Infected Host Gene Expression Identifies Repurposed Drug Candidates for COVID-19.,bioRxiv,32511305,6/9/20,pubmed,0,9,computational,0.990063961,0.001987197,0.001987321,0.001987243,0.00198713,0.001987148,Drug discovery,0.45379364,FALSE,45.88888889,0.595027522,34.66666667,0.603157613,17,0.887338725,,,0.69517462
9326,Coronavirus and PARP expression dysregulate the NAD Metabolome: a potentially actionable component of innate immunity.,bioRxiv,32511303,6/9/20,pubmed,0,10,metabolom,0.882856026,0.001987164,0.001987102,0.001987162,0.109195403,0.001987142,Drug discovery,0.34546703,FALSE,57.2,0.683097285,90.8,0.802782981,8,0.799987654,,,0.761955973
9327,Rapid metagenomic characterization of a case of imported COVID-19 in Cambodia.,bioRxiv,32511296,6/9/20,pubmed,0,18,"bioinformatic, sequencing, metagenom, genome sequences, deep sequencing",0.00392754,0.69056391,0.221319453,0.076334344,0.003927393,0.003927361,Genomics,0.41976047,FALSE,32.27777778,0.458346218,42.61111111,0.647979663,11,0.840175319,,,0.648833733
9328,"Computational target-based drug repurposing of elbasvir, an antiviral drug predicted to bind multiple SARS-CoV-2 proteins.",ChemRxiv,32511290,6/9/20,pubmed,0,2,computational,0.94162081,0.020834506,0.001272632,0.001272638,0.001272624,0.033726791,Drug discovery,0.90219796,TRUE,24.5,0.361988991,4,0.231469093,13,0.858880178,,,0.484112754
9329,Computational Models Identify Several FDA Approved or Experimental Drugs as Putative Agents Against SARS-CoV-2.,ChemRxiv,32511287,6/9/20,pubmed,0,8,"virtual screening, computational, dataset",0.989346422,0.00213083,0.002130751,0.002130709,0.002130649,0.002130639,Drug discovery,0.6334039,TRUE,53.125,0.654771476,34.75,0.603559005,8,0.799987654,,,0.686106045
9330,Quantify the role of superspreaders -opinion leaders- on COVID-19 information propagation in the Chinese Sina-microblog.,PLoS One,32511260,6/9/20,pubmed,0,5,mathematical model,0.001486463,0.001486442,0.001486471,0.992567699,0.001486505,0.00148642,Epidemiology,0.7446773,TRUE,109,0.888304781,104.4,0.830010704,2,0.618927094,,,0.77908086
9331,Effect of Cancer on Clinical Outcomes of Patients With COVID-19: A Meta-Analysis of Patient Data.,JCO Glob Oncol,32511066,6/9/20,pubmed,0,3,dataset,0.001141333,0.001141349,0.001141368,0.122020085,0.001141338,0.873414527,Clinics,0.6802666,TRUE,43.33333333,0.571154679,59.33333333,0.718290072,30,0.930057411,,,0.739834054
9332,COVID-19 Digital Health Innovation Policy: A Portal to Alternative Futures in the Making.,OMICS,32511054,6/9/20,pubmed,0,4,"artificial intelligence, digital health",0.0010346,0.020584388,0.038576625,0.888478716,0.050291086,0.001034585,Epidemiology,0.017973691,FALSE,53.25,0.655328097,32,0.585763982,11,0.840175319,,,0.693755799
9333,COVID-19 and the Futures of Machine Learning.,OMICS,32511048,6/9/20,pubmed,0,1,machine learning,0.034961874,0.034961874,0.825190394,0.034962111,0.034961874,0.034961874,Epidemiology,0.7511214,TRUE,14,0.213494959,2,0.164302917,3,0.667819001,,,0.348538959
9334,Fast Identification of Possible Drug Treatment of Coronavirus Disease -19 (COVID-19) Through Computational Drug Repurposing Study.,ChemRxiv,32510523,6/9/20,pubmed,0,1,"molecular dynamics simulation, computational",0.994827176,0.001034568,0.001034588,0.001034565,0.001034552,0.00103455,Drug discovery,0.7630428,TRUE,40,0.539860226,30,0.570176612,11,0.840175319,,,0.650070719
9335,CORD-19: The Covid-19 Open Research Dataset.,ArXiv,32510522,6/9/20,pubmed,0,24,"information retrieval, text mining, dataset",0.002357893,0.002357778,0.257391304,0.73317759,0.002357734,0.002357702,Epidemiology,0.019976556,FALSE,41.125,0.549384625,233.7916667,0.931562751,193,0.99104883,,,0.823998735
9336,Diagnostic methods and potential portable biosensors for coronavirus disease 2019.,Biosens Bioelectron,32510340,6/9/20,pubmed,0,2,artificial intelligence,0.095475627,0.226460191,0.635202754,0.040454434,0.00120347,0.001203525,Imaging,0.9066349,TRUE,6.5,0.093512277,0,0.055525823,46,0.954009507,,,0.367682535
9337,Homogeneous circle-to-circle amplification for real-time optomagnetic detection of SARS-CoV-2 RdRp coding sequence.,Biosens Bioelectron,32510339,6/9/20,pubmed,0,5,mathematical model,0.002032813,0.562584979,0.377123735,0.054193017,0.00203273,0.002032726,Genomics,0.678183,TRUE,209.4,0.975261302,45,0.658817233,19,0.89561084,,,0.843229792
9338,"A novel risk score to predict diagnosis with coronavirus disease 2019 (COVID-19) in suspected patients: A retrospective, multicenter, and observational study.",J Med Virol,32510164,6/9/20,pubmed,0,6,logistic regression,0.001987193,0.001987129,0.114934257,0.001987145,0.001987122,0.877117154,Clinics,0.92520183,TRUE,63,0.721998887,25.66666667,0.534854161,8,0.799987654,,,0.685613567
9339,Clinical Presentation of COVID19 in Dementia Patients.,J Nutr Health Aging,32510106,6/9/20,pubmed,0,8,logistic regression,0.00171728,0.001717201,0.00171734,0.001717185,0.001717278,0.991413715,Clinics,0.98979354,TRUE,70.625,0.766652236,32,0.585763982,2,0.618927094,,,0.657114437
9340,Dataset of Vietnamese teachers' perspectives and perceived support during the COVID-19 pandemic.,Data Brief,32509940,6/9/20,pubmed,0,9,dataset,0.001861757,0.001861704,0.001861769,0.54424024,0.448312742,0.001861787,Epidemiology,0.49820435,FALSE,8.333333333,0.123693488,0.888888889,0.10422799,5,0.739490092,,,0.322470523
9341,Insights on early mutational events in SARS-CoV-2 virus reveal founder effects across geographical regions.,PeerJ,32509472,6/9/20,pubmed,0,5,"sequencing, dataset",0.001717171,0.922124055,0.071007161,0.001717224,0.001717191,0.001717197,Genomics,0.504257,TRUE,28.8,0.415548271,44.6,0.657144769,3,0.667819001,,,0.58017068
9342,Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming.,Chaos Solitons Fractals,32508399,6/9/20,pubmed,0,3,prediction model,0.002357775,0.063971816,0.002357844,0.926597069,0.002357727,0.00235777,Epidemiology,0.6020033,TRUE,54,0.661574618,35.33333333,0.607439122,44,0.952157541,,,0.740390427
9343,Mathematical modeling of COVID-19 fatality trends: Death kinetics law versus infection-to-death delay rule.,Chaos Solitons Fractals,32508398,6/9/20,pubmed,0,3,mathematical model,0.001415141,0.001415143,0.001415153,0.992924145,0.001415158,0.00141526,Epidemiology,0.4659334,FALSE,90.33333333,0.843465892,44.66666667,0.65754616,6,0.764429903,,,0.755147318
9344,Significance of clinical phenomes of patients with COVID-19 infection: A learning from 3795 patients in 80 reports.,Clin Transl Med,32508041,6/9/20,pubmed,0,4,"machine learning, artificial intelligence, radiom",0.102517323,0.001511911,0.3614899,0.185950362,0.001511914,0.34701859,Imaging,0.8264072,TRUE,29,0.41993939,12,0.386740701,8,0.799987654,,,0.535555915
9345,Can routine laboratory tests discriminate SARS-CoV-2-infected pneumonia from other causes of community-acquired pneumonia?,Clin Transl Med,32508038,6/9/20,pubmed,0,14,logistic regression,0.001511933,0.001511823,0.324264038,0.001511895,0.001511858,0.669688453,Clinics,0.74380505,TRUE,38.85714286,0.527552724,74.14285714,0.764383195,7,0.785110192,,,0.692348704
9346,Collaborated effort against SARS-CoV-2 outbreak in China.,Clin Transl Med,32508021,6/9/20,pubmed,0,12,"sequencing, whole-genome",0.002422332,0.408404812,0.002422514,0.523124488,0.002422355,0.061203498,Epidemiology,0.27870473,FALSE,27.5,0.401570907,33.33333333,0.594995986,2,0.618927094,,,0.538497996
9347,COVID-19 Epidemic: Possibility of Artificial Intelligence in Infection Control and Prevention.,J Epidemiol,32507776,6/9/20,pubmed,0,5,artificial intelligence,0.015998921,0.015999051,0.45302027,0.482983685,0.015999558,0.015998515,Epidemiology,0.6688909,TRUE,11.4,0.17131548,1.6,0.140687717,0,0.403234768,,,0.238412655
9348,Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability.,Math Biosci,32507746,6/9/20,pubmed,0,5,mathematical model,0.031159092,0.181971496,0.00165315,0.457995227,0.287109844,0.040111191,Epidemiology,0.79057,TRUE,89,0.839384006,98.4,0.819708322,0,0.403234768,,,0.687442365
9349,Quantifying what could have been - The impact of the Australian and New Zealand governments' response to COVID-19.,Infect Dis Health,32507662,6/9/20,pubmed,0,2,bayes,0.003466141,0.003465971,0.00346586,0.982670193,0.003465985,0.00346585,Epidemiology,0.41010666,FALSE,9,0.135320675,0,0.055525823,6,0.764429903,,,0.318425467
9350,Self-reported anosmia and dysgeusia as key symptoms of coronavirus disease 2019.,CJEM,32507123,6/9/20,pubmed,0,6,logistic regression,0.001684488,0.001684624,0.001684585,0.107922634,0.611603005,0.275420665,Healthcare,0.95367455,TRUE,39,0.530521368,65.16666667,0.739496923,15,0.874313229,,,0.714777173
9351,The origin and underlying driving forces of the SARS-CoV-2 outbreak.,J Biomed Sci,32507105,6/9/20,pubmed,0,9,"genomes, network analysis",0.00111267,0.749304784,0.001112649,0.246244443,0.001112654,0.001112799,Genomics,0.44362438,FALSE,65.77777778,0.738883048,84.55555556,0.789336366,6,0.764429903,,,0.764216439
9352,Molecular analysis of several in-house rRT-PCR protocols for SARS-CoV-2 detection in the context of genetic variability of the virus in Colombia.,Infect Genet Evol,32505692,6/9/20,pubmed,0,13,"sequencing, whole-genome, genomes",0.000946102,0.869768279,0.102191856,0.025201501,0.000946172,0.000946091,Genomics,0.7308132,TRUE,9,0.135320675,1.692307692,0.145169922,4,0.707574542,,,0.329355046
9353,Covid-19 and lung cancer: A greater fatality rate?,Lung Cancer,32505076,6/7/20,pubmed,0,9,logistic regression,0.001072209,0.001072236,0.001072241,0.135149439,0.001072232,0.860561644,Clinics,0.6955983,TRUE,15.88888889,0.238790278,2,0.164302917,21,0.903944688,,,0.435679294
9354,Risk of a second wave of Covid-19 infections: using artificial intelligence to investigate stringency of physical distancing policies in North America.,Int Orthop,32504213,6/7/20,pubmed,0,5,"bayes, machine learning, artificial intelligence",0.001438108,0.001438123,0.001438239,0.992809307,0.001438128,0.001438094,Epidemiology,0.51515406,TRUE,207.2,0.974580988,192,0.913633931,16,0.881782826,,,0.923332582
9355,Human Gene Sequences in SARS-CoV-2 and Other Viruses.,In Vivo,32503822,6/7/20,pubmed,0,2,genomes,0.219235178,0.758532136,0.001330009,0.001330031,0.001330082,0.018242564,Genomics,0.51241434,TRUE,103.5,0.876306512,24.5,0.525086968,2,0.618927094,,,0.673440192
9356,Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis.,In Vivo,32503819,6/7/20,pubmed,0,2,machine learning,0.002357762,0.002357839,0.12402845,0.866540292,0.002357829,0.002357828,Epidemiology,0.27795434,FALSE,242.5,0.982992145,82,0.783114798,20,0.900117291,,,0.888741412
9357,Exploring the genomic and proteomic variations of SARS-CoV-2 spike glycoprotein: A computational biology approach.,Infect Genet Evol,32502733,6/6/20,pubmed,0,10,"computational, proteom, whole-genome, genome sequences, genomes, sequence alignment",0.401293279,0.593616155,0.001272638,0.001272656,0.001272623,0.00127265,Genomics,0.72954637,TRUE,4.6,0.062588905,0.8,0.101351351,13,0.858880178,,,0.340940145
9358,"Co-expression of SARS-CoV-2 entry genes in the superficial adult human conjunctival, limbal and corneal epithelium suggests an additional route of entry via the ocular surface.",Ocul Surf,32502616,6/6/20,pubmed,0,14,dataset,0.812668826,0.108868196,0.001861753,0.001861749,0.072877725,0.001861752,Drug discovery,0.63193387,TRUE,47,0.606407323,55.42857143,0.703706181,24,0.914439163,,,0.741517556
9359,Estimating COVID-19 outbreak risk through air travel.,J Travel Med,32502274,6/6/20,pubmed,0,3,probabilistic,0.001126828,0.001126834,0.00112681,0.99436591,0.001126827,0.001126791,Epidemiology,0.16421461,FALSE,45.66666667,0.592677346,18.33333333,0.464343056,2,0.618927094,,,0.558649165
9360,0,Aging (Albany NY),32501810,6/6/20,pubmed,0,4,exom,0.251678173,0.295626006,0.001823414,0.00182343,0.001823397,0.447225579,Clinics,0.7331253,TRUE,81.25,0.812109592,103.5,0.828137543,29,0.928020248,,,0.856089128
9361,Closing the Psychological Treatment Gap During the COVID-19 Pandemic With a Supportive Text Messaging Program: Protocol for Implementation and Evaluation.,JMIR Res Protoc,32501805,6/6/20,pubmed,0,14,"machine learning, data mining",0.000936072,0.000936072,0.068486852,0.238300057,0.690404771,0.000936176,Healthcare,0.93370485,TRUE,33.92857143,0.475230379,24.78571429,0.526625636,1,0.537564047,,,0.513140021
9362,"Digital Health Strategies to Fight COVID-19 Worldwide: Challenges, Recommendations, and a Call for Papers.",J Med Internet Res,32501804,6/6/20,pubmed,0,5,digital health,0.00137132,0.001371272,0.001371324,0.930519918,0.037030452,0.028335712,Epidemiology,0.77669525,TRUE,61,0.709196611,77,0.771139952,1,0.537564047,,,0.672633537
9363,Identification of nsp1 gene as the target of SARS-CoV-2 real-time RT-PCR using nanopore whole-genome sequencing.,J Med Virol,32501535,6/6/20,pubmed,0,20,"sequencing, whole-genome",0.001203456,0.880298559,0.001203515,0.00120342,0.001203437,0.114887613,Genomics,0.52255005,TRUE,49.05,0.624404725,242.7,0.934840781,5,0.739490092,,,0.766245199
9364,"Assessing risk factors for SARS-CoV-2 infection in patients presenting with symptoms in Shanghai, China: a multicentre, observational cohort study.",Lancet Digit Health,32501440,6/6/20,pubmed,0,37,logistic regression,0.001254681,0.001254624,0.169649954,0.001254653,0.073542432,0.753043656,Clinics,0.89749897,TRUE,61.78378378,0.713835117,29.86486486,0.568102756,17,0.887338725,,,0.723092199
9365,A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2.,Inform Med Unlocked,32501424,6/6/20,pubmed,0,2,"neural network, dataset",0.001486404,0.001486417,0.992567918,0.001486454,0.001486412,0.001486393,Imaging,0.22208947,FALSE,4.5,0.061784897,0,0.055525823,65,0.966911538,,,0.361407419
9366,Developing an ultra-efficient microsatellite discoverer to find structural differences between SARS-CoV-1 and Covid-19.,Inform Med Unlocked,32501423,6/6/20,pubmed,0,5,genome sequences,0.271689031,0.435111608,0.174836899,0.001538197,0.115286119,0.001538147,Genomics,0.88513315,TRUE,49,0.624281032,27.6,0.550441531,3,0.667819001,,,0.614180521
9367,"Quantifying the role of social distancing, personal protection and case detection in mitigating COVID-19 outbreak in Ontario, Canada.",J Math Ind,32501416,6/6/20,pubmed,0,5,mathematical model,0.001593464,0.00159354,0.001593608,0.992032407,0.00159351,0.001593471,Epidemiology,0.17518526,FALSE,47.8,0.612777537,14.2,0.414102221,20,0.900117291,,,0.64233235
9368,Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan.,Chaos Solitons Fractals,32501377,6/6/20,pubmed,0,5,forecasting model,0.003101477,0.003101435,0.003101516,0.984492611,0.003101458,0.003101503,Epidemiology,0.3978405,FALSE,113.2,0.896344857,41.2,0.640620819,41,0.948144947,,,0.828370208
9369,Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps.,Chaos Solitons Fractals,32501376,6/6/20,pubmed,0,4,"neural network, dataset",0.001751182,0.00175121,0.180741401,0.812253652,0.001751378,0.001751177,Epidemiology,0.37476724,FALSE,265.75,0.986950337,70.75,0.755485684,32,0.933699611,,,0.892045211
9370,Modelling the spread of COVID-19 with new fractal-fractional operators: Can the lockdown save mankind before vaccination?,Chaos Solitons Fractals,32501371,6/6/20,pubmed,0,1,mathematical model,0.001098847,0.001098854,0.001098887,0.967412409,0.028192164,0.001098839,Epidemiology,0.8143848,TRUE,38,0.519327107,4,0.231469093,120,0.98265325,,,0.577816483
9371,Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil.,Chaos Solitons Fractals,32501370,6/6/20,pubmed,0,4,"ensemble learning, forecasting model",0.00123706,0.001237058,0.363829291,0.631222447,0.001237069,0.001237075,Epidemiology,0.66351247,TRUE,147,0.94130744,118.25,0.849143698,100,0.978270264,,,0.922907134
9372,Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks.,Process Saf Environ Prot,32501368,6/6/20,pubmed,0,2,"artificial intelligence, neural network, forecasting model",0.002422283,0.002422307,0.203617709,0.74963392,0.039481494,0.002422286,Epidemiology,0.6234372,TRUE,6.5,0.093512277,2.5,0.180826866,32,0.933699611,,,0.402679585
9373,"Dysfunctional personality features, non-scientifically supported causal beliefs, and emotional problems during the first month of the COVID-19 pandemic in Italy.",Pers Individ Dif,32501318,6/6/20,pubmed,0,7,logistic regression,0.003101655,0.003101679,0.003101597,0.003101667,0.984491874,0.003101529,Healthcare,0.45797473,FALSE,95.71428571,0.858432804,397.5714286,0.970564624,16,0.881782826,,,0.903593418
9374,First detection and genome sequencing of SARS-CoV-2 in an infected cat in France.,Transbound Emerg Dis,32500944,6/6/20,pubmed,0,15,sequencing,0.002490529,0.856927128,0.002490452,0.002490536,0.00249047,0.133110885,Genomics,0.41834432,FALSE,45.53333333,0.591502257,41.93333333,0.643831951,58,0.963022409,,,0.732785539
9375,Epidemiology of the 2020 Pandemic of COVID-19 in the State of Texas: The First Month of Community Spread.,J Community Health,32500438,6/6/20,pubmed,0,3,dataset,0.001684489,0.001684546,0.001684506,0.625556118,0.367705755,0.001684586,Epidemiology,0.5282913,TRUE,25.66666667,0.376461129,12,0.386740701,14,0.866658436,,,0.543286755
9376,Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world.,Nat Hum Behav,32499576,6/6/20,pubmed,0,7,network model,0.001392875,0.001392877,0.001392867,0.906325288,0.088103251,0.001392841,Epidemiology,0.71970177,TRUE,32.28571429,0.458531758,51.71428571,0.68818571,134,0.984443484,,,0.710386984
9377,Covid-19 Pandemic and Current Medical Interventions.,Arch Med Res,32499154,6/6/20,pubmed,0,7,proteom,0.643675574,0.297040828,0.001156251,0.001156281,0.027888671,0.029082394,Drug discovery,0.714795,TRUE,30.42857143,0.436947245,13.14285714,0.401525288,7,0.785110192,,,0.541194241
9378,Risk estimation of the SARS-CoV-2 acute respiratory disease outbreak outside China.,Theor Biol Med Model,32498721,6/6/20,pubmed,0,5,mathematical model,0.001187285,0.001187305,0.001187343,0.951218967,0.001187301,0.044031799,Epidemiology,0.5928052,TRUE,72.4,0.774939699,28,0.554321648,4,0.707574542,,,0.678945296
9379,Related Health Factors of Psychological Distress During the COVID-19 Pandemic in Spain.,Int J Environ Res Public Health,32498401,6/6/20,pubmed,0,5,logistic regression,0.001565461,0.046920171,0.001565383,0.001565406,0.946818118,0.001565462,Healthcare,0.9100034,TRUE,44,0.578390748,8.4,0.326598876,43,0.9507377,,,0.618575775
9380,Impact of Rumors and Misinformation on COVID-19 in Social Media.,J Prev Med Public Health,32498140,6/6/20,pubmed,0,3,data mining,0.001901808,0.001901734,0.161734904,0.463531566,0.369028293,0.001901695,Epidemiology,0.92518425,TRUE,19.66666667,0.292596945,1.666666667,0.145036125,67,0.967960985,,,0.468531352
9381,"Ethnic disparities in hospitalisation for COVID-19 in England: The role of socioeconomic factors, mental health, and inflammatory and pro-inflammatory factors in a community-based cohort study.",Brain Behav Immun,32497776,6/5/20,pubmed,0,5,logistic regression,0.001861755,0.001862079,0.001861679,0.001861845,0.794635333,0.197917308,Healthcare,0.5155969,TRUE,175.8,0.962520873,288,0.950829542,54,0.959812334,,,0.957720917
9382,"A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy.",Math Biosci,32497623,6/5/20,pubmed,0,8,"model fit, predictive model, network model",0.002130669,0.002130722,0.00213084,0.989346321,0.002130751,0.002130698,Epidemiology,0.58658636,TRUE,31.875,0.453584019,31,0.578204442,26,0.920859312,,,0.650882591
9383,"Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis.",Lancet,32497510,6/5/20,pubmed,0,46,bayes,0.000807909,0.040315983,0.000807912,0.754159932,0.203100343,0.000807921,Epidemiology,0.671826,TRUE,72.70212766,0.776176634,,,948,0.99944441,,,0.887810522
9384,Alterations of the Gut Microbiota in Patients With Coronavirus Disease 2019 or H1N1 Influenza.,Clin Infect Dis,32497191,6/5/20,pubmed,0,21,"sequencing, microbiom",0.001751292,0.368733305,0.100128741,0.001751182,0.001751219,0.525884261,Clinics,0.94682515,TRUE,71.61904762,0.771228895,72.80952381,0.761238962,84,0.974689796,,,0.835719217
9385,In silico veritas? Potential limitations for SARS-CoV-2 vaccine development based on T-cell epitope prediction.,PLoS Pathog,32497149,6/5/20,pubmed,0,3,in silico,0.545661503,0.019529774,0.376218252,0.019530681,0.019530047,0.019529744,Drug discovery,0.64161927,TRUE,116.3333333,0.901168903,427.6666667,0.973307466,3,0.667819001,,,0.84743179
9386,A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images.,Curr Med Imaging,32496988,6/5/20,pubmed,0,1,"deep learning, neural network, network model",0.001350428,0.001350389,0.920253691,0.00135045,0.07434467,0.001350372,Imaging,0.68973565,TRUE,19,0.285793803,2,0.164302917,5,0.739490092,,,0.396528937
9387,"Distribution of ACE2, CD147, CD26, and other SARS-CoV-2 associated molecules in tissues and immune cells in health and in asthma, COPD, obesity, hypertension, and COVID-19 risk factors.",Allergy,32496587,6/5/20,pubmed,0,18,sequencing,0.656684925,0.02220219,0.000880206,0.000880202,0.062479605,0.256872873,Drug discovery,0.93483776,TRUE,92,0.848970252,123.0555556,0.855164571,113,0.981171677,,,0.895102166
9388,Impact of lockdown following COVID-19 on the gaming behavior of college students.,Indian J Public Health,32496250,6/5/20,pubmed,0,5,logistic regression,0.001415204,0.00141517,0.001415133,0.145417541,0.848921803,0.00141515,Healthcare,0.34586674,FALSE,24,0.35574247,3.4,0.208589778,20,0.900117291,,,0.488149846
9389,0,Indian J Public Health,32496248,6/5/20,pubmed,0,5,"neural network, mathematical model, prediction model",0.001203527,0.001203426,0.0214031,0.835597235,0.03625983,0.104332882,Epidemiology,0.8355462,TRUE,14.4,0.217576845,2.6,0.18256623,6,0.764429903,,,0.388190992
9390,Genome analysis of SARS-CoV-2 isolates occurring in India: Present scenario.,Indian J Public Health,32496247,6/5/20,pubmed,0,3,genome sequences,0.001371298,0.931408887,0.00137125,0.032091277,0.001371331,0.032385957,Genomics,0.6423462,TRUE,440.6666667,0.996351042,147,0.880853626,2,0.618927094,,,0.832043921
9391,The global experience of digital health interventions in COVID-19 management.,Indian J Public Health,32496240,6/5/20,pubmed,0,2,digital health,0.009284448,0.009284213,0.00928422,0.953578053,0.009284963,0.009284103,Epidemiology,0.8182469,TRUE,23,0.34225988,7.5,0.307867273,7,0.785110192,,,0.478412448
9392,The psychological impact of COVID-19 pandemic on physicians in Saudi Arabia: A cross-sectional study.,Saudi J Gastroenterol,32496223,6/5/20,pubmed,0,3,logistic regression,0.001126783,0.001126828,0.001126867,0.001126802,0.932544659,0.062948061,Healthcare,0.9879106,TRUE,33.33333333,0.469911559,3.333333333,0.206515922,11,0.840175319,,,0.505534267
9393,Influence of diabetes mellitus on the severity and fatality of SARS-CoV-2 (COVID-19) infection.,Diabetes Obes Metab,32496012,6/5/20,pubmed,0,8,logistic regression,0.001717146,0.001717165,0.001717179,0.001717208,0.001717184,0.991414119,Clinics,0.7977369,TRUE,43.625,0.574185169,8.625,0.330144501,5,0.739490092,,,0.547939921
9394,Viral kinetics and factors associated with rapid viral clearance during lopinavir/ritonavir-based combination therapy in non-severe COVID-19 patients.,Eur Rev Med Pharmacol Sci,32495917,6/5/20,pubmed,0,9,logistic regression,0.055182696,0.458396611,0.039258653,0.001461916,0.001461908,0.444238216,Genomics,0.9881223,TRUE,8.888888889,0.130558476,4,0.231469093,5,0.739490092,,,0.367172554
9395,[Pharmacovigilance study on drug-induced cardiac injury during treatment of COVID-19].,Zhongguo Zhong Yao Za Zhi,32495580,6/5/20,pubmed,0,5,bioinformatic,0.521639343,0.001034585,0.001034633,0.087949035,0.106080315,0.282262089,Drug discovery,0.99575293,TRUE,47.6,0.611169522,9.2,0.339577201,1,0.537564047,,,0.49610359
9396,[TCMATCOV--a bioinformatics platform to predict efficacy of TCM against COVID-19].,Zhongguo Zhong Yao Za Zhi,32495578,6/5/20,pubmed,0,9,"bioinformatic, network model",0.587507676,0.001622726,0.28527542,0.001622812,0.001622796,0.12234857,Drug discovery,0.98041517,TRUE,58.88888889,0.69534294,21.66666667,0.499732406,3,0.667819001,,,0.620964782
9397,[Mechanism of Xuanfei Baidu Tang in treatment of COVID-19 based on network pharmacology].,Zhongguo Zhong Yao Za Zhi,32495577,6/5/20,pubmed,0,11,network model,0.921258135,0.001653126,0.001653048,0.001653125,0.001653047,0.07212952,Drug discovery,0.9846351,TRUE,80.36363636,0.808336941,,,7,0.785110192,,,0.796723567
9398,Abnormal liver tests in patients hospitalized with Coronavirus disease 2019: Should we worry?,Liver Int,32495496,6/5/20,pubmed,0,10,logistic regression,0.002080547,0.002080612,0.002080533,0.002080565,0.002080581,0.989597161,Clinics,0.7538785,TRUE,96.7,0.860473746,69.6,0.752274552,8,0.799987654,,,0.804245317
9399,Development and Validation of a Clinical Risk Score for Intensive Care Resource Utilization After Colon Cancer Surgery: a Practical Guide to the Selection of Patients During COVID-19.,J Gastrointest Surg,32495141,6/5/20,pubmed,0,10,prediction model,0.021744068,0.001511852,0.151598331,0.001511902,0.001511864,0.822121983,Clinics,0.7399878,TRUE,23.8,0.351227658,16.9,0.447551512,0,0.403234768,,,0.400671312
9400,Interpreting COVID-19 Test Results: a Bayesian Approach.,J Gen Intern Med,32495086,6/5/20,pubmed,0,3,bayes,0.057799149,0.057800306,0.057799311,0.711002168,0.057799235,0.057799832,Epidemiology,0.7562374,TRUE,150.3333333,0.944276084,144,0.878244581,1,0.537564047,,,0.786694904
9401,The Efficacy of Lockdown Against COVID-19: A Cross-Country Panel Analysis.,Appl Health Econ Health Policy,32495067,6/5/20,pubmed,0,2,dataset,0.001291207,0.001291224,0.00129124,0.99354384,0.001291268,0.001291221,Epidemiology,0.083850294,FALSE,21.5,0.318263343,1.5,0.138747659,49,0.956787456,,,0.471266153
9402,Lactate dehydrogenase and susceptibility to deterioration of mild COVID-19 patients: a multicenter nested case-control study.,BMC Med,32493370,6/5/20,pubmed,0,14,logistic regression,0.001415201,0.0014151,0.001415102,0.001415105,0.001415133,0.992924358,Clinics,0.93093485,TRUE,62.85714286,0.720390871,38.28571429,0.623026492,16,0.881782826,,,0.741733396
9403,Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients.,J Clin Med,32492874,6/5/20,pubmed,0,10,"machine learning, prediction model",0.016504468,0.00103461,0.385141691,0.001034644,0.058780892,0.537503695,Clinics,0.8460075,TRUE,46.2,0.598181706,37.3,0.617607707,22,0.908142478,,,0.707977297
9404,The association between obesity and poor outcome after COVID-19 indicates a potential therapeutic role for montelukast.,Med Hypotheses,32492562,6/4/20,pubmed,0,2,in silico,0.637487397,0.000880257,0.000880207,0.00088027,0.000880229,0.35899164,Drug discovery,0.8707119,TRUE,45.5,0.591440411,17,0.451097137,16,0.881782826,,,0.641440125
9405,Proteomic and Metabolomic Characterization of COVID-19 Patient Sera.,Cell,32492406,6/4/20,pubmed,0,40,"machine learning, classifier, proteom, metabolom",0.255791028,0.002296765,0.335173913,0.002296601,0.002296588,0.402145106,Clinics,0.82388234,TRUE,31.45,0.449069207,27.925,0.55284988,47,0.95493549,,,0.652284859
9406,Rationale and Design of ORCHID: A Randomized Placebo-controlled Clinical Trial of Hydroxychloroquine for Adults Hospitalized with COVID-19.,Ann Am Thorac Soc,32492354,6/4/20,pubmed,0,33,bayes,0.199417441,0.00171721,0.001717378,0.54170343,0.001717288,0.253727253,Epidemiology,0.7476301,TRUE,123.2727273,0.91205393,218.7272727,0.926344661,8,0.799987654,,,0.879462082
9407,Understanding the possible origin and genotyping of the first Bangladeshi SARS-CoV-2 strain.,J Med Virol,32492206,6/4/20,pubmed,0,5,sequencing,0.001717199,0.775769289,0.001717391,0.21736165,0.001717269,0.001717203,Genomics,0.21925548,FALSE,2,0.022141134,0,0.055525823,8,0.799987654,,,0.292551537
9408,Structural variations and expression profiles of the SARS-CoV-2 host invasion genes in lung cancer.,J Med Virol,32492203,6/4/20,pubmed,0,2,"in silico, correlation analysis",0.502671458,0.281965839,0.001203464,0.001203466,0.00120343,0.211752342,Drug discovery,0.77613074,TRUE,6,0.086028821,0.5,0.087101953,4,0.707574542,,,0.293568439
9409,High-coverage SARS-CoV-2 genome sequences acquired by target capture sequencing.,J Med Virol,32492196,6/4/20,pubmed,0,19,"sequencing, genome sequences",0.096245736,0.820521405,0.03042498,0.001461903,0.001461946,0.049884029,Genomics,0.39538145,FALSE,75.52631579,0.788669677,55.26315789,0.702903398,4,0.707574542,,,0.733049206
9410,Evidence for mutations in SARS-CoV-2 Italian isolates potentially affecting virus transmission.,J Med Virol,32492183,6/4/20,pubmed,0,9,"bioinformatic, genome sequences",0.300412643,0.632423218,0.001823379,0.001823408,0.001823411,0.061693942,Genomics,0.65787315,TRUE,138.2222222,0.93085534,108.8888889,0.837436446,8,0.799987654,,,0.856093147
9411,"COVID-19 mortality rates in the European Union, Switzerland, and the UK: effect of timeliness, lockdown rigidity, and population density.",Minerva Med,32491297,6/4/20,pubmed,0,7,predictive model,0.00121999,0.001220036,0.01694899,0.928170555,0.001220059,0.051220371,Epidemiology,0.26517546,FALSE,315.4285714,0.991403303,314.2857143,0.958054589,13,0.858880178,,,0.93611269
9412,Coagulation modifiers targeting SARS-CoV-2 main protease Mpro for COVID-19 treatment: an in silico approach.,Mem Inst Oswaldo Cruz,32490889,6/4/20,pubmed,0,2,in silico,0.981197302,0.003760579,0.003760604,0.003760676,0.003760416,0.003760424,Drug discovery,0.5027905,TRUE,4,0.054734368,0,0.055525823,7,0.785110192,,,0.298456794
9413,"Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study.",JMIR Public Health Surveill,32490846,6/4/20,pubmed,0,9,machine learning,0.000740297,0.000740329,0.157674549,0.565951268,0.274153233,0.000740324,Epidemiology,0.1210545,FALSE,48.11111111,0.615375101,20.33333333,0.483810543,31,0.931971109,,,0.677052251
9414,0,Virulence,32490723,6/4/20,pubmed,0,7,genomic structure,0.001943589,0.779103032,0.11613256,0.098933765,0.001943565,0.001943489,Genomics,0.8934205,TRUE,57.71428571,0.686622549,26.57142857,0.542279904,0,0.403234768,,,0.54404574
9415,In vitro analysis of the renin-angiotensin system and inflammatory gene transcripts in human bronchial epithelial cells after infection with severe acute respiratory syndrome coronavirus.,J Renin Angiotensin Aldosterone Syst,32490715,6/4/20,pubmed,0,5,"bioinformatic, whole-genome, network analysis",0.60145288,0.171482566,0.001684533,0.001684521,0.222010872,0.001684628,Drug discovery,0.8049878,TRUE,90.4,0.843775125,48.6,0.67594327,7,0.785110192,,,0.768276196
9416,COVID-19 Virulence in Aged Patients Might Be Impacted by the Host Cellular MicroRNAs Abundance/Profile.,Aging Dis,32489698,6/4/20,pubmed,0,7,in silico,0.528551156,0.332108284,0.001254602,0.001254634,0.001254669,0.135576655,Drug discovery,0.96907824,TRUE,111.4285714,0.89288144,84.71428571,0.789938453,25,0.918019631,,,0.866946508
9417,Italian validation of CoViD-19 Peritraumatic Distress Index and preliminary data in a sample of general population.,Riv Psichiatr,32489191,6/4/20,pubmed,0,2,logistic regression,0.037981747,0.001254691,0.081663534,0.001254737,0.797891164,0.079954128,Healthcare,0.95307195,TRUE,39,0.530521368,47.5,0.670591383,20,0.900117291,,,0.700410014
9418,Associations Between State Public Health Agency Structure and Pace and Extent of Implementation of Social Distancing Control Measures.,J Public Health Manag Pract,32487927,6/4/20,pubmed,0,3,logistic regression,0.001943537,0.001943479,0.001943513,0.848898812,0.001943614,0.143327046,Epidemiology,0.90035987,TRUE,44,0.578390748,13.33333333,0.403866738,3,0.667819001,,,0.550025496
9419,Risk factors for mortality in patients with Coronavirus Disease 2019 (COVID-19) in Bolivia: An analysis of the first 107 confirmed cases.,Infez Med,32487789,6/4/20,pubmed,0,8,logistic regression,0.001987114,0.001987193,0.001987091,0.065031926,0.001987295,0.927019381,Clinics,0.7811791,TRUE,110.5,0.89059311,49.625,0.679957185,8,0.799987654,,,0.790179316
9420,0,Int J Mol Sci,32486229,6/4/20,pubmed,0,4,computational,0.927270706,0.001141376,0.001141324,0.068163922,0.001141325,0.001141347,Drug discovery,0.99120796,TRUE,20.75,0.307254623,0.25,0.065493712,21,0.903944688,,,0.425564341
9421,Weakly Labeled Data Augmentation for Deep Learning: A Study on COVID-19 Detection in Chest X-Rays.,Diagnostics (Basel),32486140,6/4/20,pubmed,0,2,"supervised learning, deep learning, artificial intelligence, neural network, dataset",0.000880232,0.078013147,0.898213215,0.000880254,0.021132896,0.000880258,Imaging,0.32043976,FALSE,171.5,0.959428536,135.5,0.87028365,18,0.891474782,,,0.907062323
9422,Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death.,Sensors (Basel),32486055,6/4/20,pubmed,0,3,"correlation analysis, lstm, dataset",0.001203435,0.180295485,0.132869365,0.628513487,0.001203453,0.055914776,Epidemiology,0.63746166,TRUE,8.666666667,0.12839384,0.333333333,0.073187048,15,0.874313229,,,0.358631373
9423,"Sociodemographic Predictors of Health Risk Perception, Attitude and Behavior Practices Associated with Health-Emergency Disaster Risk Management for Biological Hazards: The Case of COVID-19 Pandemic in Hong Kong, SAR China.",Int J Environ Res Public Health,32485979,6/4/20,pubmed,0,6,logistic regression,0.001461891,0.001461898,0.001461882,0.208936487,0.785215982,0.001461861,Healthcare,0.9159273,TRUE,74,0.782113922,53.5,0.695477656,26,0.920859312,,,0.79948363
9424,In Silico Evaluation of the Effectivity of Approved Protease Inhibitors against the Main Protease of the Novel SARS-CoV-2 Virus.,Molecules,32485894,6/4/20,pubmed,0,4,in silico,0.939399807,0.001272711,0.0012727,0.001272758,0.001272645,0.055509379,Drug discovery,0.9409065,TRUE,62.25,0.71661822,10,0.355632861,19,0.89561084,,,0.655953974
9425,COVID-19 Emergence and Social and Health Determinants in Colorado: A Rapid Spatial Analysis.,Int J Environ Res Public Health,32485854,6/4/20,pubmed,0,2,correlation analysis,0.002130638,0.002130707,0.002130639,0.829373108,0.002130845,0.162104064,Epidemiology,0.586784,TRUE,50,0.632073721,26.5,0.542012309,18,0.891474782,,,0.688520271
9426,Interaction of the prototypical α-ketoamide inhibitor with the SARS-CoV-2 main protease active site in silico: Molecular dynamic simulations highlight the stability of the ligand-protein complex.,Comput Biol Chem,32485652,6/3/20,pubmed,0,7,"molecular dynamics simulation, in silico",0.942904633,0.001126848,0.001126796,0.05258797,0.001126851,0.001126902,Drug discovery,0.81940246,TRUE,36.28571429,0.500463851,22.71428571,0.50896441,19,0.89561084,,,0.635013034
9427,SARS-CoV-2 Molecular and Phylogenetic analysis in COVID-19 patients: A preliminary report from Iran.,Infect Genet Evol,32485332,6/3/20,pubmed,0,17,"bioinformatic, sequencing",0.001751202,0.842038374,0.001751197,0.001751233,0.001751231,0.150956764,Genomics,0.9249021,TRUE,39.35294118,0.532686004,15.52941176,0.430224779,7,0.785110192,,,0.582673658
9428,"Digital Health Management During and Beyond the COVID-19 Pandemic: Opportunities, Barriers, and Recommendations.",JMIR Ment Health,32484783,6/3/20,pubmed,0,11,digital health,0.001901729,0.001901713,0.001901748,0.733191764,0.259201352,0.001901693,Epidemiology,0.45451337,FALSE,19.45454545,0.289937535,15,0.42594327,7,0.785110192,,,0.500330332
9429,The Oxford Royal College of General Practitioners Clinical Informatics Digital Hub: Protocol to Develop Extended COVID-19 Surveillance and Trial Platforms.,JMIR Public Health Surveill,32484782,6/3/20,pubmed,0,37,digital health,0.066182115,0.045036139,0.00063423,0.478688432,0.374773777,0.034685307,Epidemiology,0.5080345,TRUE,64.51351351,0.731894366,55.13513514,0.702435108,7,0.785110192,,,0.739813222
9430,Neck circumference as reliable predictor of mechanical ventilation support in adult inpatients with COVID-19: A multicentric prospective evaluation.,Diabetes Metab Res Rev,32484298,6/3/20,pubmed,0,21,logistic regression,0.001371243,0.001371245,0.001371294,0.001371287,0.001371296,0.993143635,Clinics,0.92646456,TRUE,57.14285714,0.682726204,35.95238095,0.609713674,2,0.618927094,,,0.637122324
9431,Discovery of G-quadruplex-forming sequences in SARS-CoV-2.,Brief Bioinform,32484220,6/3/20,pubmed,0,6,genomes,0.581946257,0.410606924,0.001861693,0.001861727,0.001861726,0.001861673,Drug discovery,0.6483721,TRUE,30.5,0.438493413,36,0.611118544,9,0.814309525,,,0.621307161
9432,Clinical Validation of a SARS-CoV-2 Real-Time Reverse Transcription PCR Assay Targeting the Nucleocapsid Gene.,J Appl Lab Med,32483586,6/3/20,pubmed,0,8,in silico,0.001171601,0.710662804,0.284650766,0.00117162,0.001171582,0.001171627,Genomics,0.87256753,TRUE,8.875,0.130249242,5.375,0.263446615,5,0.739490092,,,0.37772865
9433,No evidence of severe acute respiratory syndrome-coronavirus 2 in semen of males recovering from coronavirus disease 2019.,Fertil Steril,32482249,6/3/20,pubmed,0,12,"transcriptom, dataset",0.415086494,0.297718615,0.001254687,0.001254691,0.001254652,0.283430861,Drug discovery,0.89052993,TRUE,71.83333333,0.772218443,49.33333333,0.678953706,129,0.983887894,,,0.811686681
9434,Effect of Implementing Simulation Education on Health Care Worker Comfort With Nasopharyngeal Swabbing for COVID-19.,Otolaryngol Head Neck Surg,32482155,6/3/20,pubmed,0,6,simulation model,0.001112681,0.254194613,0.12672086,0.170427892,0.446431279,0.001112674,Healthcare,0.6271998,TRUE,35.16666667,0.488651123,6.5,0.288132192,6,0.764429903,,,0.513737739
9435,The computation of case fatality rate for novel coronavirus (COVID-19) based on Bayes theorem: An observational study.,Medicine (Baltimore),32481256,6/3/20,pubmed,0,6,bayes,0.001059373,0.001059391,0.001059364,0.788876886,0.00105938,0.206885607,Epidemiology,0.049483806,FALSE,14.83333333,0.223081205,4.333333333,0.237958255,5,0.739490092,,,0.400176517
9436,Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study.,JMIR Mhealth Uhealth,32480361,6/2/20,pubmed,0,4,"machine learning, neural network",0.000854788,0.000854765,0.424313607,0.000854753,0.441044292,0.132077794,Healthcare,0.9979499,TRUE,48.25,0.616364648,33,0.593256623,4,0.707574542,,,0.639065271
9437,"Clinical characteristics and risk factors associated with COVID-19 disease severity in patients with cancer in Wuhan, China: a multicentre, retrospective, cohort study.",Lancet Oncol,32479790,6/2/20,pubmed,0,33,logistic regression,0.000793438,0.000793401,0.000793394,0.000793411,0.0007934,0.996032956,Clinics,0.70777786,TRUE,71.42424242,0.770239347,,,126,0.983517501,,,0.876878424
9438,"Clinical characteristics, outcomes, and risk factors for mortality in patients with cancer and COVID-19 in Hubei, China: a multicentre, retrospective, cohort study.",Lancet Oncol,32479787,6/2/20,pubmed,0,24,logistic regression,0.001022622,0.001022609,0.001022618,0.001022633,0.001022635,0.994886883,Clinics,0.96378696,TRUE,71.66666667,0.771414435,60.16666667,0.721768799,142,0.985616396,,,0.826266543
9439,Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients.,Cell,32479746,6/2/20,pubmed,0,15,"computational, sequencing",0.299175727,0.573236447,0.057561941,0.001565373,0.001565311,0.0668952,Genomics,0.20071194,FALSE,39.53333333,0.534417713,361.3333333,0.967420391,119,0.982344589,,,0.828060898
9440,Using Information Technology to Manage the COVID-19 Pandemic: Development of a Technical Framework Based on Practical Experience in China.,JMIR Med Inform,32479411,6/2/20,pubmed,0,3,artificial intelligence,0.001330036,0.001330035,0.145891863,0.848787876,0.001330114,0.001330077,Epidemiology,0.529906,TRUE,162.3333333,0.952996475,75,0.766590848,0,0.403234768,,,0.707607364
9441,Comparing the Binding Interactions in the Receptor Binding Domains of SARS-CoV-2 and SARS-CoV.,J Phys Chem Lett,32478523,6/2/20,pubmed,0,3,molecular dynamics simulation,0.83993777,0.152455298,0.00190173,0.001901756,0.001901742,0.001901703,Drug discovery,0.5767689,TRUE,14,0.213494959,3.666666667,0.217621086,27,0.92443978,,,0.451851942
9442,The origin of SARS-CoV-2 in Istanbul: Sequencing findings from the epicenter of the pandemic in Turkey.,North Clin Istanb,32478289,6/2/20,pubmed,0,15,"sequencing, genome sequences, genomes",0.001310327,0.850830051,0.001310458,0.001310358,0.001310345,0.143928459,Genomics,0.738014,TRUE,16.73333333,0.25146886,1.6,0.140687717,6,0.764429903,,,0.385528827
9443,"Virtual screening, ADME/T, and binding free energy analysis of anti-viral, anti-protease, and anti-infectious compounds against NSP10/NSP16 methyltransferase and main protease of SARS CoV-2.",J Recept Signal Transduct Res,32476594,6/2/20,pubmed,0,4,virtual screening,0.925457561,0.001438097,0.001438163,0.001438112,0.068789907,0.00143816,Drug discovery,0.7950314,TRUE,33.25,0.468860165,6,0.280037463,8,0.799987654,,,0.516295094
9444,Identification of potential natural inhibitors of SARS-CoV2 main protease by molecular docking and simulation studies.,J Biomol Struct Dyn,32476576,6/2/20,pubmed,0,7,"molecular dynamics simulation, in silico",0.950909589,0.001010941,0.045046656,0.001010937,0.00101094,0.001010936,Drug discovery,0.8917241,TRUE,38.71428571,0.52650133,10.28571429,0.358643297,24,0.914439163,,,0.599861263
9445,"Virtual screening-driven drug discovery of SARS-CoV2 enzyme inhibitors targeting viral attachment, replication, post-translational modification and host immunity evasion infection mechanisms.",J Biomol Struct Dyn,32476574,6/2/20,pubmed,0,12,"virtual screening, molecular dynamics simulation, computational, bioinformatic, in silico",0.930552379,0.001141371,0.001141331,0.064882145,0.001141385,0.001141389,Drug discovery,0.90708476,TRUE,22.08333333,0.32630342,3.333333333,0.206515922,23,0.91129082,,,0.481370054
9446,The association between treatment with heparin and survival in patients with Covid-19.,J Thromb Thrombolysis,32476080,6/2/20,pubmed,0,3,logistic regression,0.030949477,0.001861686,0.001861699,0.001861725,0.001861754,0.961603658,Clinics,0.72521126,TRUE,69.33333333,0.760467561,107.6666667,0.835763982,15,0.874313229,,,0.823514924
9447,Prediction of the Severity of the Coronavirus Disease and Its Adverse Clinical Outcomes.,Jpn J Infect Dis,32475880,6/2/20,pubmed,0,7,logistic regression,0.001653029,0.00165302,0.001653146,0.001653059,0.001653012,0.991734733,Clinics,0.98437166,TRUE,31.28571429,0.447461191,18.71428571,0.467152796,7,0.785110192,,,0.566574727
9448,[The effects of quarantine for SARS-CoV-2 on sleep: An online survey].,Encephale,32475692,6/2/20,pubmed,0,8,logistic regression,0.001943524,0.00194356,0.001943486,0.202811876,0.761677744,0.029679809,Healthcare,0.9908278,TRUE,12.5,0.189436576,0.625,0.090446883,4,0.707574542,,,0.329152667
9449,Profiling serum cytokines in COVID-19 patients reveals IL-6 and IL-10 are disease severity predictors.,Emerg Microbes Infect,32475230,6/2/20,pubmed,0,14,logistic regression,0.001046868,0.001046874,0.00104686,0.001046903,0.001046826,0.99476567,Clinics,0.9924985,TRUE,126,0.915331808,65.42857143,0.740366604,157,0.987900488,,,0.881199633
9450,Antiviral effects of probiotic metabolites on COVID-19.,J Biomol Struct Dyn,32475223,6/2/20,pubmed,0,6,computational,0.930689637,0.001156271,0.027743542,0.022897867,0.016356468,0.001156215,Drug discovery,0.9434278,TRUE,117,0.902900612,34.66666667,0.603157613,17,0.887338725,,,0.797798983
9451,Binding affinities of 438 HLA proteins to complete proteomes of seven pandemic viruses and distributions of strongest and weakest HLA peptide binders in populations worldwide.,HLA,32475052,6/1/20,pubmed,0,8,proteom,0.515938072,0.44677459,0.001046849,0.001046891,0.034146687,0.00104691,Drug discovery,0.40168902,FALSE,66.25,0.742346465,63.25,0.73300776,12,0.850299401,,,0.775217876
9452,Relationship between clinical types and radiological subgroups defined by latent class analysis in 2019 novel coronavirus pneumonia caused by SARS-CoV-2.,Eur Radiol,32474631,6/1/20,pubmed,0,5,logistic regression,0.000907291,0.000907307,0.229802083,0.000907315,0.000907295,0.766568709,Clinics,0.33158875,FALSE,136.8,0.928999938,54.6,0.700093658,3,0.667819001,,,0.765637532
9453,An overview of mobile applications (apps) to support the coronavirus disease 2019 response in India.,Indian J Med Res,32474557,6/1/20,pubmed,0,4,digital health,0.001010951,0.001010952,0.001010965,0.786981687,0.208974494,0.001010951,Epidemiology,0.94232595,TRUE,55.75,0.672768879,23.75,0.518731603,14,0.866658436,,,0.686052972
9454,Analysis of RNA sequences of 3636 SARS-CoV-2 collected from 55 countries reveals selective sweep of one virus type.,Indian J Med Res,32474553,6/1/20,pubmed,0,2,"deep learning, sequencing, whole-genome, genome sequences, genomes",0.050355101,0.86505727,0.044690893,0.001046854,0.001046842,0.03780304,Genomics,0.58835804,TRUE,144,0.938524337,215,0.925073588,36,0.941169208,,,0.934922378
9455,Unfolding SARS-CoV-2 viral genome to understand its gene expression regulation.,Infect Genet Evol,32473977,6/1/20,pubmed,0,2,"computational, bioinformatic, in silico, genomes",0.452210078,0.467527298,0.001059389,0.001059379,0.021782514,0.056361342,Genomics,0.9763242,TRUE,10,0.15214299,29.5,0.566095799,0,0.403234768,,,0.373824519
9456,Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study.,Lancet,32473681,6/1/20,pubmed,0,236,logistic regression,0.00077946,0.000779458,0.013285065,0.000779468,0.000779456,0.983597093,Clinics,0.86190957,TRUE,89.05485232,0.8395077,,,400,0.99691339,,,0.918210545
9457,Mechanism of baricitinib supports artificial intelligence-predicted testing in COVID-19 patients.,EMBO Mol Med,32473600,5/31/20,pubmed,0,22,artificial intelligence,0.565703856,0.001861828,0.189115714,0.001861808,0.001861785,0.239595008,Drug discovery,0.6991618,TRUE,77.90909091,0.798008535,116.2727273,0.846267059,51,0.958392493,,,0.867556029
9458,Understanding the B and T cell epitopes of spike protein of severe acute respiratory syndrome coronavirus-2: A computational way to predict the immunogens.,Infect Genet Evol,32473352,5/31/20,pubmed,0,3,computational,0.822418449,0.16635579,0.002806461,0.002806492,0.002806357,0.002806451,Drug discovery,0.80860656,TRUE,39,0.530521368,15,0.42594327,7,0.785110192,,,0.580524943
9459,Single cell RNA sequencing analysis did not predict hepatocyte infection by SARS-CoV-2.,J Hepatol,32473193,5/31/20,pubmed,0,3,sequencing,0.329549346,0.592332855,0.019529472,0.019529356,0.019529352,0.019529619,Genomics,0.45837024,FALSE,40.33333333,0.542890717,77.33333333,0.771541343,6,0.764429903,,,0.692953988
9460,Impact of SARS-CoV2 (Covid-19) on dental practices: Economic analysis.,J Dent,32473182,5/31/20,pubmed,0,3,probabilistic,0.001141342,0.001141359,0.054470914,0.640358162,0.301746763,0.00114146,Epidemiology,0.9548324,TRUE,117.6666667,0.903766467,59.66666667,0.71936045,21,0.903944688,,,0.842357201
9461,Treatment of Coronavirus Disease 2019 (COVID-19) Patients with Convalescent Plasma.,Am J Pathol,32473109,5/31/20,pubmed,0,35,"sequencing, whole genome",0.001538178,0.24061867,0.001538151,0.001538219,0.001538219,0.753228563,Clinics,0.39709273,FALSE,50.74285714,0.636155606,96.97142857,0.816430292,56,0.961540836,,,0.804708911
9462,Age-dependent Gender Differences in COVID-19 in Mainland China: Comparative Study.,Clin Infect Dis,32473009,5/31/20,pubmed,0,5,logistic regression,0.00133007,0.001330093,0.001330049,0.160019078,0.261632819,0.574357891,Clinics,0.9146926,TRUE,59.2,0.697507576,18.2,0.462938186,25,0.918019631,,,0.692821798
9463,Predictors for Severe COVID-19 Infection.,Clin Infect Dis,32472676,5/31/20,pubmed,0,7,logistic regression,0.00133007,0.001330069,0.001330067,0.001330067,0.001330093,0.993349633,Clinics,0.90799296,TRUE,68.85714286,0.758241079,29.57142857,0.566363393,51,0.958392493,,,0.760998988
9464,Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study.,Diabetologia,32472191,5/31/20,pubmed,0,40,logistic regression,0.001310376,0.001310333,0.001310337,0.001310361,0.00131042,0.993448174,Clinics,0.95940745,TRUE,62.7,0.71927763,47.825,0.671929355,222,0.993024261,,,0.794743749
9465,Characteristics associated with hospitalisation for COVID-19 in people with rheumatic disease: data from the COVID-19 Global Rheumatology Alliance physician-reported registry.,Ann Rheum Dis,32471903,5/31/20,pubmed,0,28,logistic regression,0.10604412,0.002490492,0.002490489,0.041582039,0.002490602,0.844902258,Clinics,0.8894203,TRUE,95.67857143,0.858247263,96.53571429,0.816296495,287,0.994629298,,,0.889724352
9466,Introductions and early spread of SARS-CoV-2 in the New York City area.,Science,32471856,5/31/20,pubmed,0,35,genomes,0.003760497,0.883307342,0.00376046,0.003760812,0.003760819,0.101650069,Genomics,0.29402053,FALSE,61.37142857,0.710804626,,,51,0.958392493,,,0.83459856
9467,Novel Immunoglobulin Domain Proteins Provide Insights into Evolution and Pathogenesis of SARS-CoV-2-Related Viruses.,mBio,32471829,5/31/20,pubmed,0,5,"computational, genomes",0.43107466,0.565693659,0.000807913,0.000807934,0.000807919,0.000807915,Genomics,0.5928818,TRUE,100.2,0.869194137,843.8,0.991370083,22,0.908142478,,,0.922902233
9468,Practical approaches to pedagogically rich online tutorials in health professions education.,Rural Remote Health,32471311,5/31/20,pubmed,0,3,active learning,0.001486537,0.001486473,0.299984738,0.001486565,0.694069232,0.001486455,Healthcare,0.82797396,TRUE,22.66666667,0.335580432,1,0.122023013,4,0.707574542,,,0.388392662
9469,Spatial-Temporal Variations in Atmospheric Factors Contribute to SARS-CoV-2 Outbreak.,Viruses,32471302,5/31/20,pubmed,0,4,"neural network, classifier",0.001684514,0.001684621,0.158972849,0.691999538,0.001684618,0.14397386,Epidemiology,0.8149806,TRUE,33,0.466757375,31.25,0.579542414,1,0.537564047,,,0.527954612
9470,Prediction of Novel Inhibitors of the Main Protease (M-pro) of SARS-CoV-2 through Consensus Docking and Drug Reposition.,Int J Mol Sci,32471205,5/31/20,pubmed,0,8,virtual screening,0.866112718,0.001943632,0.126112969,0.001943591,0.001943601,0.001943489,Drug discovery,0.56172884,TRUE,20.5,0.304966294,41,0.639751137,33,0.936045435,,,0.626920955
9471,Early network properties of the COVID-19 pandemic - The Chinese scenario.,Int J Infect Dis,32470603,5/30/20,pubmed,0,7,logistic regression,0.055813108,0.00131037,0.001310407,0.762732005,0.001310344,0.177523767,Epidemiology,0.7707548,TRUE,49.14285714,0.625085039,39.57142857,0.631054322,2,0.618927094,,,0.625022152
9472,High throughput virtual screening reveals SARS-CoV-2 multi-target binding natural compounds to lead instant therapy for COVID-19 treatment.,Int J Biol Macromol,32470577,5/30/20,pubmed,0,6,"virtual screening, in silico",0.949422126,0.001330063,0.001330095,0.045257446,0.001330144,0.001330126,Drug discovery,0.9379982,TRUE,66,0.741356918,58.33333333,0.71507894,14,0.866658436,,,0.774364765
9473,The novel coronavirus SARS-CoV-2: From a zoonotic infection to coronavirus disease 2019.,J Med Virol,32470173,5/30/20,pubmed,0,8,genomes,0.070963969,0.923551004,0.001371236,0.001371258,0.001371272,0.001371261,Genomics,0.7491826,TRUE,78.25,0.799369163,29.25,0.564557131,2,0.618927094,,,0.66095113
9474,Early risk factors of the exacerbation of coronavirus disease 2019 pneumonia.,J Med Virol,32470167,5/30/20,pubmed,0,5,logistic regression,0.000966821,0.000966748,0.073930074,0.000966751,0.000966762,0.922202844,Clinics,0.79578435,TRUE,117,0.902900612,51.8,0.688587102,10,0.828199272,,,0.806562329
9475,Statistical analysis of the impact of environmental temperature on the exponential growth rate of cases infected by COVID-19.,PLoS One,32469989,5/30/20,pubmed,0,1,dataset,0.001943467,0.001943536,0.001943489,0.990282528,0.001943482,0.001943498,Epidemiology,0.5203678,TRUE,108,0.886078298,91,0.803987155,2,0.618927094,,,0.769664183
9476,[Model for a threshold of daily rate reduction of COVID-19 cases to avoid hospital collapse in Chile].,Medwave,32469855,5/30/20,pubmed,0,4,mathematical model,0.00272014,0.002720129,0.002720137,0.809444565,0.002720182,0.179674847,Epidemiology,0.72571135,TRUE,19,0.285793803,3,0.199424672,2,0.618927094,,,0.368048523
9477,Potential RNA-dependent RNA polymerase inhibitors as prospective therapeutics against SARS-CoV-2.,J Med Microbiol,32469301,5/30/20,pubmed,0,3,"molecular dynamics simulation, in silico",0.871581267,0.124328163,0.00102263,0.001022671,0.001022665,0.001022605,Drug discovery,0.7608652,TRUE,45,0.587049292,16,0.437316029,13,0.858880178,,,0.6277485
9478,Withanone and Withaferin-A are predicted to interact with transmembrane protease serine 2 (TMPRSS2) and block entry of SARS-CoV-2 into cells.,J Biomol Struct Dyn,32469279,5/30/20,pubmed,0,9,"molecular dynamics simulation, structural model",0.930917186,0.0011716,0.00117158,0.064396424,0.001171587,0.001171623,Drug discovery,0.8030878,TRUE,83.77777778,0.820335209,56.66666667,0.709660155,38,0.944132354,,,0.824709239
9479,0,J Biomol Struct Dyn,32469265,5/30/20,pubmed,0,2,"computational, in silico",0.830557093,0.023821803,0.001072202,0.00107224,0.070603858,0.072872804,Drug discovery,0.91905427,TRUE,9.5,0.143051518,1,0.122023013,10,0.828199272,,,0.364424601
9480,A novel simple scoring model for predicting severity of patients with SARS-CoV-2 infection.,Transbound Emerg Dis,32469137,5/30/20,pubmed,0,12,"logistic regression, prediction model",0.001461955,0.001461901,0.001461992,0.173389257,0.026070676,0.796154219,Clinics,0.985405,TRUE,115.8333333,0.900364896,72.66666667,0.760770672,18,0.891474782,,,0.850870117
9481,Which medical specialties should be prioritised to fill staffing gaps caused by COVID-19 in the United Kingdom? A network analysis.,Int J Health Plann Manage,32468623,5/30/20,pubmed,0,4,network analysis,0.025060814,0.02506253,0.478048107,0.025063023,0.421704386,0.025061139,Healthcare,0.50070137,TRUE,6.75,0.095862453,0,0.055525823,0,0.403234768,,,0.184874348
9482,Estimation of reproduction numbers of COVID-19 in typical countries and epidemic trends under different prevention and control scenarios.,Front Med,32468343,5/30/20,pubmed,0,17,"bayes, mathematical model",0.001565296,0.001565341,0.001565326,0.967864065,0.001565334,0.025874638,Epidemiology,0.32418892,FALSE,30.52941176,0.43855526,,,15,0.874313229,,,0.656434245
9483,Social Media Data Analytics on Telehealth During the COVID-19 Pandemic.,Cureus,32467813,5/30/20,pubmed,0,2,"supervised learning, unsupervised learning",0.001126812,0.001126829,0.001126883,0.842310233,0.136977155,0.017332089,Epidemiology,0.591081,TRUE,13,0.197352959,0.5,0.087101953,7,0.785110192,,,0.356521702
9484,Sequencing Treatments for Cancer During the COVID-19 Pandemic.,Am J Clin Oncol,32467526,5/30/20,pubmed,0,3,sequencing,0.034962712,0.430076011,0.034961875,0.034961874,0.034961874,0.430075655,Genomics,0.39620885,FALSE,317.6666667,0.991588843,351,0.965747926,2,0.618927094,,,0.858754621
9485,Coding-Complete Genome Sequences of Two SARS-CoV-2 Isolates from Egypt.,Microbiol Resour Announc,32467284,5/30/20,pubmed,0,18,genome sequences,0.005697428,0.971512779,0.005697513,0.005697412,0.005697439,0.005697429,Genomics,0.48165756,FALSE,21.83333333,0.322468922,17.88888889,0.459526358,6,0.764429903,,,0.515475061
9486,Pedagogical foundations to online lectures in health professions education.,Rural Remote Health,32466654,5/30/20,pubmed,0,3,active learning,0.00120344,0.001203408,0.228379949,0.384699288,0.38331052,0.001203395,Epidemiology,0.55725324,TRUE,22.66666667,0.335580432,1,0.122023013,5,0.739490092,,,0.399031179
9487,"Early Phylogenetic Diversification of SARS-CoV-2: Determination of Variants and the Effect on Epidemiology, Immunology, and Diagnostics.",J Clin Med,32466577,5/30/20,pubmed,0,1,"whole genome, genomes",0.1059952,0.764553784,0.001330106,0.125460845,0.001330034,0.00133003,Genomics,0.12693918,FALSE,34,0.477766096,8,0.320511105,2,0.618927094,,,0.472401432
9488,Depression and Anxiety in Hong Kong during COVID-19.,Int J Environ Res Public Health,32466251,5/30/20,pubmed,0,3,logistic regression,0.002639072,0.002639009,0.00263909,0.002639049,0.954661438,0.034782341,Healthcare,0.6550665,TRUE,20.33333333,0.302059497,8.666666667,0.331415574,76,0.971664918,,,0.535046663
9489,Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications.,Int J Environ Res Public Health,32466199,5/30/20,pubmed,0,8,"artificial intelligence, neural network, dataset",0.001415124,0.001415124,0.331139585,0.663199925,0.001415126,0.001415116,Epidemiology,0.54476434,TRUE,17.125,0.258148308,4.375,0.238426545,14,0.866658436,,,0.454411097
9490,Metatranscriptomic Characterization of COVID-19 Identified A Host Transcriptional Classifier Associated With Immune Signaling.,Clin Infect Dis,32463434,5/29/20,pubmed,0,15,"classifier, transcriptom, microbiom, metatranscriptom",0.389444537,0.264585258,0.197409184,0.001371267,0.001371251,0.145818503,Drug discovery,0.81499255,TRUE,46.2,0.598181706,23.2,0.514383195,14,0.866658436,,,0.659741112
9491,Influence of the SARS-CoV-2 Outbreak on the Uptake of a Popular Smoking Cessation App in UK Smokers: Interrupted Time Series Analysis.,JMIR Mhealth Uhealth,32463375,5/29/20,pubmed,0,4,bayes,0.001371281,0.001371292,0.001371294,0.693136105,0.301378706,0.001371323,Epidemiology,0.79390335,TRUE,133.5,0.925536521,89.25,0.799839443,4,0.707574542,,,0.810983502
9492,Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis.,J Med Internet Res,32463367,5/29/20,pubmed,0,4,network analysis,0.000846542,0.00084655,0.000846572,0.408019285,0.57889157,0.010549481,Healthcare,0.99450207,TRUE,299.25,0.990537448,187.75,0.910623495,1,0.537564047,,,0.81290833
9493,Knowledge synthesis of 100 million biomedical documents augments the deep expression profiling of coronavirus receptors.,Elife,32463365,5/29/20,pubmed,0,18,"neural network, sequencing, proteom,  omics",0.69645098,0.068186361,0.115832662,0.115726502,0.001901772,0.001901723,Drug discovery,0.7851281,TRUE,29.83333333,0.428907168,20.61111111,0.487088574,17,0.887338725,,,0.601111489
9494,Is the Rigidity of SARS-CoV-2 Spike Receptor-Binding Motif the Hallmark for Its Enhanced Infectivity? Insights from All-Atom Simulations.,J Phys Chem Lett,32463239,5/29/20,pubmed,0,3,molecular dynamics simulation,0.84174011,0.002130883,0.002130882,0.095268205,0.056599217,0.002130703,Drug discovery,0.6775591,TRUE,55,0.668686994,5.333333333,0.262911426,34,0.937156615,,,0.622918345
9495,0,J Biomol Struct Dyn,32462996,5/29/20,pubmed,0,5,molecular dynamics simulation,0.892001708,0.001371252,0.001371265,0.001371266,0.001371303,0.102513205,Drug discovery,0.9085138,TRUE,16.8,0.253138722,4.4,0.238961734,15,0.874313229,,,0.455471228
9496,0,J Biomol Struct Dyn,32462988,5/29/20,pubmed,0,2,"molecular dynamics simulation, computational",0.932285691,0.001461919,0.001461896,0.001461967,0.061866598,0.001461929,Drug discovery,0.81500345,TRUE,17,0.257467994,4.5,0.242708055,21,0.903944688,,,0.468040246
9497,Identification of potential inhibitors of SARS-COV-2 endoribonuclease (EndoU) from FDA approved drugs: a drug repurposing approach to find therapeutics for COVID-19.,J Biomol Struct Dyn,32462970,5/29/20,pubmed,0,4,"virtual screening, molecular dynamics simulation, in silico",0.957159158,0.001237052,0.001237052,0.022407573,0.001237134,0.016722031,Drug discovery,0.93201566,TRUE,80.25,0.807904014,72.75,0.760971367,19,0.89561084,,,0.821495407
9498,Shortlisting SARS-CoV-2 Peptides for Targeted Studies from Experimental Data-Dependent Acquisition Tandem Mass Spectrometry Data.,Proteomics,32462744,5/29/20,pubmed,0,7,"proteom, dataset",0.585373517,0.103342456,0.302864736,0.002806462,0.002806398,0.00280643,Drug discovery,0.7083092,TRUE,42.85714286,0.565959552,25.85714286,0.535991437,12,0.850299401,,,0.65075013
9499,"Clinical features, isolation, and complete genome sequence of severe acute respiratory syndrome coronavirus 2 from the first two patients in Vietnam.",J Med Virol,32462705,5/29/20,pubmed,0,21,genome sequences,0.002562621,0.680999698,0.002562641,0.002562631,0.002562682,0.308749728,Genomics,0.33258468,FALSE,13.0952381,0.197785887,9.904761905,0.351953439,9,0.814309525,,,0.45468295
9500,Any unique image biomarkers associated with COVID-19?,Eur Radiol,32462445,5/29/20,pubmed,0,14,"neural network, image analysis, classifier",0.001085387,0.001085391,0.875017231,0.001085341,0.001085361,0.12064129,Imaging,0.7097619,TRUE,68.78571429,0.757993692,126.2857143,0.859579877,13,0.858880178,,,0.825484582
9501,Deep learning COVID-19 detection bias: accuracy through artificial intelligence.,Int Orthop,32462314,5/29/20,pubmed,0,3,"deep learning, artificial intelligence, neural network, transfer learning, dataset",0.051768136,0.001171601,0.808824273,0.084754693,0.052309639,0.001171658,Imaging,0.4638388,FALSE,344.3333333,0.993939019,320,0.959793952,25,0.918019631,,,0.957250867
9502,Design of a Multiepitope-Based Peptide Vaccine against the E Protein of Human COVID-19: An Immunoinformatics Approach.,Biomed Res Int,32461973,5/29/20,pubmed,0,7,sequencing,0.6363943,0.179139958,0.00141516,0.096152926,0.085482516,0.00141514,Drug discovery,0.6845881,TRUE,14.71428571,0.221473189,0,0.055525823,55,0.960800049,,,0.412599687
9503,Predicting intervention effect for COVID-19 in Japan: state space modeling approach.,Biosci Trends,32461511,5/29/20,pubmed,0,4,bayes,0.001717173,0.001717179,0.001717188,0.991414097,0.001717177,0.001717186,Epidemiology,0.18828866,FALSE,20.75,0.307254623,14.75,0.421327268,11,0.840175319,,,0.52291907
9504,Anosmia and dysgeusia associated with SARS-CoV-2 infection: an age-matched case-control study.,CMAJ,32461325,5/29/20,pubmed,0,13,logistic regression,0.0020328,0.002032902,0.157541839,0.002032798,0.39294159,0.443418071,Clinics,0.74728125,TRUE,26.46153846,0.387222463,,,23,0.91129082,,,0.649256642
9505,Modelling SARS-COV2 Spread in London: Approaches to Lift the Lockdown.,J Infect,32461062,5/29/20,pubmed,0,5,mathematical model,0.00162271,0.001622715,0.001622736,0.964831683,0.028677276,0.001622881,Epidemiology,0.2741833,FALSE,1.6,0.015709073,0,0.055525823,18,0.891474782,,,0.320903226
9506,"Fighting COVID-19, a place for artificial intelligence.",Transbound Emerg Dis,32460383,5/28/20,pubmed,0,2,artificial intelligence,0.019529786,0.019530042,0.902344974,0.019533402,0.019532048,0.019529748,Epidemiology,0.47543225,FALSE,66.5,0.743830787,16,0.437316029,1,0.537564047,,,0.572903621
9507,Improvements in Patient Monitoring in the Intensive Care Unit: Survey Study.,J Med Internet Res,32459655,5/28/20,pubmed,0,7,artificial intelligence,0.000576366,0.000576367,0.368292013,0.000576395,0.342424221,0.287554639,Healthcare,0.9670373,TRUE,72,0.77302245,64.85714286,0.738158951,5,0.739490092,,,0.750223831
9508,Constructing co-occurrence network embeddings to assist association extraction for COVID-19 and other coronavirus infectious diseases.,J Am Med Inform Assoc,32458963,5/28/20,pubmed,0,4,"bayes, supervised learning, unsupervised learning, classifier, logistic regression, dataset",0.042208978,0.09543041,0.79521509,0.000988429,0.065168685,0.000988409,Genomics,0.5597335,TRUE,67.75,0.751932711,20.75,0.488827937,3,0.667819001,,,0.636193216
9509,Neutrophil to lymphocyte ratio as prognostic and predictive factor in patients with coronavirus disease 2019: A retrospective cross-sectional study.,J Med Virol,32458459,5/28/20,pubmed,0,25,logistic regression,0.001565275,0.001565285,0.001565269,0.001565283,0.001565296,0.992173592,Clinics,0.8412221,TRUE,78.52,0.800544251,49.32,0.678619213,40,0.947033768,,,0.808732411
9510,The lethal sex gap: COVID-19.,Immun Ageing,32457811,5/28/20,pubmed,0,5,bioinformatic,0.472281709,0.003927621,0.003927449,0.003927623,0.512007981,0.003927617,Healthcare,0.6509567,TRUE,14,0.213494959,13.4,0.404602622,28,0.926168282,,,0.514755288
9511,Cancer datasets and the SARS-CoV-2 pandemic: establishing principles for collaboration.,ESMO Open,32457054,5/28/20,pubmed,0,6,dataset,0.025061592,0.025061207,0.405238408,0.0250619,0.025060626,0.494516266,Clinics,0.5931015,TRUE,129,0.920588781,157,0.889884934,3,0.667819001,,,0.826097572
9512,Advanced Digital Health Technologies for COVID-19 and Future Emergencies.,Telemed J E Health,32456560,5/28/20,pubmed,0,8,digital health,0.019529898,0.019529664,0.019530937,0.902348078,0.019530558,0.019530866,Epidemiology,0.67256546,TRUE,19.375,0.288947987,17.5,0.45611453,11,0.840175319,,,0.528412612
9513,Smoking-Mediated Upregulation of the Androgen Pathway Leads to Increased SARS-CoV-2 Susceptibility.,Int J Mol Sci,32455539,5/28/20,pubmed,0,6,sequencing,0.543636191,0.137953969,0.001987167,0.001987198,0.181741476,0.132693998,Drug discovery,0.84639215,TRUE,70.33333333,0.765353454,29.66666667,0.567099277,22,0.908142478,,,0.74686507
9514,Potential Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 606 Million Compounds.,Int J Mol Sci,32455534,5/28/20,pubmed,0,5,"virtual screening, molecular dynamics simulation, computational",0.934729887,0.001112647,0.001112657,0.045799157,0.001112637,0.016133015,Drug discovery,0.96208864,TRUE,37.6,0.514626755,20.4,0.484613326,45,0.953206988,,,0.65081569
9515,Face coverings for the public: Laying straw men to rest.,J Eval Clin Pract,32455503,5/27/20,pubmed,0,1,mathematical model,0.001786566,0.001786541,0.001786568,0.92562228,0.067231428,0.001786617,Epidemiology,0.06328261,FALSE,189,0.968581854,389,0.970029435,23,0.91129082,,,0.94996737
9516,When predictions are used to allocate scarce health care resources: three considerations for models in the era of Covid-19.,Diagn Progn Res,32455168,5/27/20,pubmed,0,4,prediction model,0.001717209,0.001717235,0.001717274,0.740898038,0.001717258,0.252232986,Epidemiology,0.5991799,TRUE,168.5,0.957387594,214.75,0.924672197,2,0.618927094,,,0.833662295
9517,C-Reactive Protein Level May Predict the Risk of COVID-19 Aggravation.,Open Forum Infect Dis,32455147,5/27/20,pubmed,0,12,logistic regression,0.001622753,0.001622737,0.001622813,0.001622811,0.001622763,0.991886124,Clinics,0.99066925,TRUE,107.4166667,0.884408436,61.08333333,0.724712336,55,0.960800049,,,0.856640274
9518,Projecting the impact of the coronavirus disease-2019 pandemic on childhood obesity in the United States: A microsimulation model.,J Sport Health Sci,32454174,5/27/20,pubmed,0,1,simulation model,0.017606067,0.001098804,0.001098809,0.468334334,0.325547261,0.186314724,Epidemiology,0.95349014,TRUE,27,0.3960047,40,0.633395772,16,0.881782826,,,0.637061099
9519,Crosstalk between endoplasmic reticulum stress and anti-viral activities: A novel therapeutic target for COVID-19.,Life Sci,32454157,5/27/20,pubmed,0,4,computational,0.857642284,0.00162282,0.001622739,0.00162274,0.135866485,0.001622931,Drug discovery,0.9459624,TRUE,101.75,0.872781248,66,0.742574257,18,0.891474782,,,0.835610096
9520,Design of a Novel Multi Epitope-Based Vaccine for Pandemic Coronavirus Disease (COVID-19) by Vaccinomics and Probable Prevention Strategy against Avenging Zoonotics.,Eur J Pharm Sci,32454128,5/27/20,pubmed,0,8,vaccinom,0.81706053,0.177186702,0.001438146,0.001438224,0.001438229,0.001438169,Drug discovery,0.48994225,FALSE,21.375,0.315913167,6.25,0.283315494,8,0.799987654,,,0.466405438
9521,Oncologic patient with COVID-19 discovered incidentally by PET/CT examination: (A COVID–19-pandémia orvosszakmai kérdései),Orv Hetil,32453695,5/27/20,pubmed,0,7,radiom,0.001786556,0.001786574,0.583972632,0.001786686,0.175927701,0.234739851,Imaging,0.84191483,TRUE,31.42857143,0.448945513,21.28571429,0.494380519,0,0.403234768,,,0.4488536
9522,Digital Health Equity and COVID-19: The Innovation Curve Cannot Reinforce the Social Gradient of Health.,J Med Internet Res,32452816,5/27/20,pubmed,0,2,digital health,0.002296545,0.002296533,0.002296702,0.677004274,0.313809402,0.002296543,Epidemiology,0.84379864,TRUE,20,0.298163152,6,0.280037463,30,0.930057411,,,0.502752675
9523,Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study.,J Med Internet Res,32452804,5/27/20,pubmed,0,6,"machine learning, classifier, predictive model",0.000822957,0.000822941,0.152585639,0.811815374,0.033130102,0.000822987,Epidemiology,0.4233552,FALSE,40.66666667,0.546292288,32.83333333,0.591249666,9,0.814309525,,,0.650617159
9524,An up-to-date overview of computational polypharmacology in modern drug discovery.,Expert Opin Drug Discov,32452701,5/27/20,pubmed,0,5,"machine learning, computational, artificial intelligence, multi-omics",0.219698649,0.001330133,0.399611692,0.376699463,0.001330044,0.001330018,Epidemiology,0.53166515,TRUE,26.2,0.383820892,17.8,0.458790474,1,0.537564047,,,0.460058471
9525,New insights into the evolutionary features of viral overlapping genes by discriminant analysis.,Virology,32452417,5/27/20,pubmed,0,1,"computational, dataset",0.270432923,0.588624275,0.134550778,0.002130678,0.002130666,0.00213068,Genomics,0.5496924,TRUE,22,0.326056033,31,0.578204442,10,0.828199272,,,0.577486582
9526,Fragment tailoring strategy to design novel chemical entities as potential binders of novel corona virus main protease.,J Biomol Struct Dyn,32452282,5/27/20,pubmed,0,1,"molecular dynamics simulation, computational",0.995269394,0.000946122,0.000946134,0.000946143,0.000946117,0.00094609,Drug discovery,0.9241712,TRUE,6,0.086028821,0,0.055525823,24,0.914439163,,,0.351997935
9527,The G-Quadruplex/Helicase World as a Potential Antiviral Approach Against COVID-19.,Drugs,32451923,5/27/20,pubmed,0,3,genomes,0.581654887,0.406359112,0.002996449,0.002996588,0.002996423,0.002996541,Drug discovery,0.61399543,TRUE,164.6666667,0.954480797,168,0.898046561,6,0.764429903,,,0.872319087
9528,Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.,J Med Syst,32451808,5/27/20,pubmed,0,16,"machine learning, artificial intelligence, data mining, dataset",0.000779445,0.147458208,0.49888484,0.351318624,0.000779446,0.000779437,Epidemiology,0.72095394,TRUE,22.9375,0.338549075,,,39,0.945737391,,,0.642143233
9529,Molecular Detection of SARS-CoV-2 Infection in FFPE Samples and Histopathologic Findings in Fatal SARS-CoV-2 Cases.,Am J Clin Pathol,32451533,5/27/20,pubmed,0,9,sequencing,0.234115712,0.625594225,0.001593592,0.001593505,0.028056229,0.109046738,Genomics,0.85211205,TRUE,63.88888889,0.7274414,107.5555556,0.835563286,31,0.931971109,,,0.831658599
9530,The potential effects of widespread community transmission of SARS-CoV-2 infection in the World Health Organization African Region: a predictive model.,BMJ Glob Health,32451366,5/27/20,pubmed,0,15,predictive model,0.001350337,0.048214443,0.001350396,0.789778456,0.104230785,0.055075583,Epidemiology,0.5968817,TRUE,15.6,0.235450554,11.8,0.38252609,55,0.960800049,,,0.526258898
9531,Do underlying cardiovascular diseases have any impact on hospitalised patients with COVID-19?,Heart,32451362,5/27/20,pubmed,0,17,logistic regression,0.001538198,0.001538252,0.001538211,0.001538216,0.001538165,0.992308958,Clinics,0.98110485,TRUE,45.82352941,0.594037974,19.29411765,0.473441263,14,0.866658436,,,0.644712558
9532,A possible strategy to fight COVID-19: Interfering with spike glycoprotein trimerization.,Biochem Biophys Res Commun,32451080,5/27/20,pubmed,0,5,computational,0.987888382,0.002422376,0.002422401,0.002422326,0.002422249,0.002422266,Drug discovery,0.50669754,TRUE,71.6,0.771105201,35,0.605298368,9,0.814309525,,,0.730237698
9533,0,Life Sci,32450166,5/26/20,pubmed,0,5,virtual screening,0.994765899,0.001046846,0.001046832,0.001046799,0.001046816,0.001046809,Drug discovery,0.92106307,TRUE,47.8,0.612777537,12.4,0.390286326,33,0.936045435,,,0.646369766
9534,SARS-CoV-2 spike glycoprotein-binding proteins expressed by upper respiratory tract bacteria may prevent severe viral infection.,FEBS Lett,32449939,5/26/20,pubmed,0,1,bioinformatic,0.772043535,0.003214346,0.003214235,0.003214416,0.084831809,0.13348166,Drug discovery,0.5161557,TRUE,7,0.10179974,0,0.055525823,2,0.618927094,,,0.258750886
9535,The impact of having inadequate safety equipment on mental health.,Occup Med (Lond),32449770,5/26/20,pubmed,0,3,logistic regression,0.001653045,0.001653027,0.001653082,0.00165312,0.991734695,0.00165303,Healthcare,0.7954575,TRUE,24.33333333,0.359700662,23,0.513513514,9,0.814309525,,,0.5625079
9536,An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.,J Am Med Inform Assoc,32449766,5/26/20,pubmed,0,7,"deep learning, artificial intelligence, neural network",0.001861765,0.001861727,0.827984368,0.001861768,0.164568542,0.001861829,Healthcare,0.9634721,TRUE,68.16666667,0.754530274,48.66666667,0.676143966,9,0.814309525,,,0.748327922
9537,COVID-19: Putting the General Data Protection Regulation to the Test.,JMIR Public Health Surveill,32449686,5/26/20,pubmed,0,3,digital health,0.002806554,0.002806455,0.002806449,0.985967634,0.002806508,0.0028064,Epidemiology,0.23218596,FALSE,145,0.939390191,167,0.897043083,0,0.403234768,,,0.746556014
9538,Willingness of Chinese nurses to practice in Hubei combating the coronavirus disease 2019 epidemic: A cross-sectional study.,J Adv Nurs,32449187,5/26/20,pubmed,0,5,logistic regression,0.001022626,0.001022633,0.001022751,0.202455748,0.79345361,0.001022632,Healthcare,0.850925,TRUE,19,0.285793803,7.2,0.302047097,3,0.667819001,,,0.4185533
9539,Marked Up-Regulation of ACE2 in Hearts of Patients With Obstructive Hypertrophic Cardiomyopathy: Implications for SARS-CoV-2-Mediated COVID-19.,Mayo Clin Proc,32448590,5/26/20,pubmed,0,17,transcriptom,0.59110086,0.138197085,0.00108537,0.001085358,0.001085351,0.267445976,Drug discovery,0.89515054,TRUE,277.0588235,0.988558352,352.7058824,0.966082419,14,0.866658436,,,0.940433069
9540,Structural and simulation analysis of hotspot residues interactions of SARS-CoV 2 with human ACE2 receptor.,J Biomol Struct Dyn,32448098,5/26/20,pubmed,0,4,"computational, sequence alignment",0.91524525,0.080359553,0.001098798,0.001098813,0.001098798,0.001098788,Drug discovery,0.9007684,TRUE,10,0.15214299,7.75,0.312215681,28,0.926168282,,,0.463508984
9541,Repurposing approved drugs as inhibitors of SARS-CoV-2 S-protein from molecular modeling and virtual screening.,J Biomol Struct Dyn,32448085,5/26/20,pubmed,0,4,virtual screening,0.99414211,0.001171622,0.001171561,0.001171586,0.001171557,0.001171564,Drug discovery,0.7478547,TRUE,39.5,0.534232173,8,0.320511105,33,0.936045435,,,0.596929571
9542,In-silico strategies for probing chloroquine based inhibitors against SARS-CoV-2.,J Biomol Struct Dyn,32448039,5/26/20,pubmed,0,2,"computational, in-silico",0.991734814,0.001653014,0.001653005,0.001653125,0.001653039,0.001653004,Drug discovery,0.893986,TRUE,11.5,0.17416043,0,0.055525823,11,0.840175319,,,0.356620524
9543,"Extrapolation of mortality in COVID-19: Exploring the role of age, sex, co-morbidities and health-care related occupation.",Monaldi Arch Chest Dis,32447949,5/26/20,pubmed,0,6,logistic regression,0.002130951,0.002130856,0.002130774,0.243414306,0.002130769,0.748062345,Clinics,0.5296716,TRUE,19,0.285793803,0.666666667,0.096200161,12,0.850299401,,,0.410764455
9544,The impact of COVID-19 pandemic on urological emergencies: a single-center experience.,World J Urol,32447443,5/25/20,pubmed,0,8,logistic regression,0.001538106,0.001538137,0.00153813,0.348285779,0.206280799,0.440819048,Clinics,0.8426059,TRUE,80,0.807532933,80.25,0.779636072,11,0.840175319,,,0.809114775
9545,Overdose and risk factors for coronavirus disease 2019.,Drug Alcohol Depend,32447172,5/25/20,pubmed,0,6,logistic regression,0.002238423,0.002238494,0.002238482,0.002238541,0.616994654,0.374051406,Healthcare,0.9291532,TRUE,83.5,0.819221968,61.5,0.726585496,5,0.739490092,,,0.761765852
9546,Stability of RNA sequences derived from the coronavirus genome in human cells.,Biochem Biophys Res Commun,32446559,5/25/20,pubmed,0,8,"sequencing, genomes",0.265649703,0.729030205,0.001330018,0.001330026,0.001330006,0.001330042,Genomics,0.21504062,FALSE,9.75,0.146267549,4.375,0.238426545,9,0.814309525,,,0.399667873
9547,COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios.,Comput Methods Programs Biomed,32446037,5/24/20,pubmed,0,5,classifier,0.000863064,0.018589119,0.946241791,0.018343562,0.015099422,0.000863041,Imaging,0.38537136,FALSE,26,0.382398417,18.8,0.468156275,94,0.976726958,,,0.609093883
9548,Overwhelming mutations or SNPs of SARS-CoV-2: A point of caution.,Gene,32445924,5/24/20,pubmed,0,1,"sequencing, deep sequencing",0.078297394,0.852726534,0.030784853,0.001684637,0.001684623,0.03482196,Genomics,0.68976194,TRUE,10,0.15214299,15,0.42594327,17,0.887338725,,,0.488474995
9549,Ion torrent-based nasopharyngeal swab metatranscriptomics in COVID-19.,J Virol Methods,32445875,5/24/20,pubmed,0,12,"bioinformatic, sequencing, transcriptom, metatranscriptom",0.004775173,0.976123975,0.004775352,0.004775266,0.004775111,0.004775122,Genomics,0.41266322,FALSE,11.16666667,0.168161296,15,0.42594327,2,0.618927094,,,0.404343887
9550,Individual quarantine versus active monitoring of contacts for the mitigation of COVID-19: a modelling study.,Lancet Infect Dis,32445710,5/24/20,pubmed,0,7,model fit,0.00065776,0.000657753,0.000657766,0.919504777,0.077864169,0.000657775,Epidemiology,0.30429244,FALSE,136.8571429,0.929061785,285.5714286,0.950294354,66,0.967652324,,,0.949002821
9551,Severe Obesity as an Independent Risk Factor for COVID-19 Mortality in Hospitalized Patients Younger than 50.,Obesity (Silver Spring),32445512,5/24/20,pubmed,0,6,logistic regression,0.001652991,0.001653003,0.001652999,0.001653035,0.001653076,0.991734896,Clinics,0.8231112,TRUE,92.33333333,0.849836106,72.83333333,0.76130586,83,0.974381135,,,0.861841034
9552,Current applications of artificial intelligence for COVID-19.,Dermatol Ther,32445213,5/24/20,pubmed,0,2,artificial intelligence,0.034961874,0.034961874,0.825190423,0.034962082,0.034961874,0.034961874,Epidemiology,0.4574478,FALSE,80.5,0.809202795,3,0.199424672,0,0.403234768,,,0.470620745
9553,BBMRI-ERIC's contributions to research and knowledge exchange on COVID-19.,Eur J Hum Genet,32444797,5/24/20,pubmed,0,9,artificial intelligence,0.127654584,0.288683096,0.184276321,0.213238993,0.184816865,0.001330141,Genomics,0.39242935,FALSE,48.44444444,0.61747789,78.33333333,0.774217287,3,0.667819001,,,0.686504726
9554,A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.,Eur Respir J,32444412,5/24/20,pubmed,0,16,deep learning,0.001156257,0.001156295,0.667236651,0.001156329,0.001156298,0.32813817,Imaging,0.5744753,TRUE,44.9375,0.584946503,20.625,0.487222371,21,0.903944688,,,0.65870452
9555,Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study.,BMJ,32444366,5/24/20,pubmed,0,10,logistic regression,0.000846523,0.000846516,0.000846513,0.000846525,0.000846533,0.995767391,Clinics,0.7971618,TRUE,86.4,0.830787309,110.5,0.839309607,621,0.998765356,,,0.889620757
9556,"An Efficient COVID-19 Prediction Model Validated with the Cases of China, Italy and Spain: Total or Partial Lockdowns?",J Clin Med,32443871,5/24/20,pubmed,0,4,prediction model,0.002638961,0.002639009,0.002639054,0.986804921,0.002639025,0.00263903,Epidemiology,0.3211886,FALSE,42.25,0.559898571,2.5,0.180826866,10,0.828199272,,,0.522974903
9557,SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence.,Int J Environ Res Public Health,32443640,5/24/20,pubmed,0,3,computational,0.00256255,0.002562668,0.002562603,0.987187031,0.002562595,0.002562554,Epidemiology,0.23188862,FALSE,83.66666667,0.819716742,16.33333333,0.440460262,34,0.937156615,,,0.73244454
9558,"Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea.",Int J Environ Res Public Health,32443476,5/24/20,pubmed,0,5,dataset,0.001438098,0.001438117,0.237010617,0.757236848,0.001438167,0.001438153,Epidemiology,0.7490808,TRUE,73.8,0.781124374,46.2,0.664704308,18,0.891474782,,,0.779101155
9559,Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort.,J Clin Med,32443442,5/24/20,pubmed,0,16,machine learning,0.001310402,0.00131035,0.424379668,0.070232491,0.001310329,0.501456759,Clinics,0.6002259,TRUE,117.4375,0.903457233,99.625,0.821782178,3,0.667819001,,,0.797686137
9560,Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19.,J Clin Virol,32442756,5/23/20,pubmed,0,12,dataset,0.001565478,0.00156533,0.375742787,0.00156538,0.001565364,0.617995662,Clinics,0.83059525,TRUE,56.08333333,0.675428289,19,0.471367407,0,0.403234768,,,0.516676821
9561,Alterations in Gut Microbiota of Patients With COVID-19 During Time of Hospitalization.,Gastroenterology,32442562,5/23/20,pubmed,0,21,"sequencing, metagenom, microbiom",0.065117046,0.246041662,0.001220011,0.00122002,0.00122002,0.685181241,Clinics,0.97159034,TRUE,67.42857143,0.749768075,85.9047619,0.792480599,171,0.989196864,,,0.843815179
9562,Estimation of the number of blood donors during the COVID-19 incubation period across China and analysis of prevention and control measures for blood transfusion transmission.,Transfusion,32442333,5/23/20,pubmed,0,4,predictive model,0.001987162,0.084221534,0.001987149,0.552699415,0.001987157,0.357117583,Epidemiology,0.9438013,TRUE,18,0.271569052,3.25,0.203572384,2,0.618927094,,,0.36468951
9563,Alternative splicing of ACE2 possibly generates variants that may limit the entry of SARS-CoV-2: a potential therapeutic approach using SSOs.,Clin Sci (Lond),32442315,5/23/20,pubmed,0,2,bioinformatic,0.770019807,0.001751225,0.00175121,0.222975302,0.001751204,0.001751252,Drug discovery,0.7432072,TRUE,46,0.596882924,8.5,0.329141022,9,0.814309525,,,0.580111157
9564,Predicting Infectious Severe Acute Respiratory Syndrome Coronavirus 2 From Diagnostic Samples.,Clin Infect Dis,32442256,5/23/20,pubmed,0,15,logistic regression,0.061970789,0.328382512,0.160792844,0.056557976,0.000889082,0.391406798,Clinics,0.39075202,FALSE,29.86666667,0.429030862,55.33333333,0.703371689,359,0.996296068,,,0.709566206
9565,Agile Health Care Analytics: Enabling Real-Time Disease Surveillance With a Computational Health Platform.,J Med Internet Res,32442130,5/23/20,pubmed,0,5,computational,0.00256281,0.126002104,0.195649455,0.6706601,0.002562627,0.002562904,Epidemiology,0.2533219,FALSE,441.8,0.996474736,488.8,0.978057265,7,0.785110192,,,0.919880731
9566,Systematic analysis of ACE2 and TMPRSS2 expression in salivary glands reveals underlying transmission mechanism caused by SARS-CoV-2.,J Med Virol,32441816,5/23/20,pubmed,0,8,"sequencing, transcriptom, dataset",0.685016052,0.18075961,0.001653072,0.001653127,0.129265024,0.001653114,Drug discovery,0.48263365,FALSE,296,0.990413755,205,0.919521006,18,0.891474782,,,0.933803181
9567,Glecaprevir and Maraviroc are high-affinity inhibitors of SARS-CoV-2 main protease: possible implication in COVID-19 therapy.,Biosci Rep,32441299,5/23/20,pubmed,0,8,"virtual screening, computational",0.992032375,0.001593495,0.001593554,0.00159354,0.001593507,0.001593529,Drug discovery,0.87229156,TRUE,58,0.689714887,7.625,0.309071448,37,0.942712513,,,0.647166282
9568,A simple algorithm helps early identification of SARS-CoV-2 infection patients with severe progression tendency.,Infection,32440918,5/23/20,pubmed,0,7,prediction model,0.001943472,0.001943497,0.253960265,0.001943478,0.001943453,0.738265835,Clinics,0.5632429,TRUE,64.28571429,0.72991527,86,0.793015788,12,0.850299401,,,0.79107682
9569,"Reply to Sánchez-Pacheco et al., Chookajorn, and Mavian et al.: Explaining phylogenetic network analysis of SARS-CoV-2 genomes.",Proc Natl Acad Sci U S A,32439706,5/23/20,pubmed,0,4,"genomes, network analysis",0.019530806,0.698781658,0.223096866,0.01953112,0.019529974,0.019529576,Genomics,0.65270025,TRUE,75.5,0.78860783,822.75,0.990701097,13,0.858880178,,,0.879396368
9570,Management and Treatment of COVID-19: The Chinese Experience.,Can J Cardiol,32439306,5/23/20,pubmed,0,11,sequencing,0.052698333,0.027902143,0.001330095,0.742977916,0.00133013,0.173761384,Epidemiology,0.77889186,TRUE,33.81818182,0.474240831,29.27272727,0.56462403,59,0.963516266,,,0.667460376
9571,Characteristics and prognostic factors of disease severity in patients with COVID-19: The Beijing experience.,J Autoimmun,32439209,5/23/20,pubmed,0,7,logistic regression,0.001141369,0.001141332,0.021151968,0.00114137,0.001141333,0.974282627,Clinics,0.785769,TRUE,21.57142857,0.318696271,5.285714286,0.26130586,67,0.967960985,,,0.515987705
9572,Global Access to Handwashing: Implications for COVID-19 Control in Low-Income Countries.,Environ Health Perspect,32438824,5/23/20,pubmed,0,4,forecasting model,0.004109775,0.004109808,0.004109949,0.497754819,0.485805853,0.004109796,Epidemiology,0.24346805,FALSE,41.75,0.555383759,93.75,0.810409419,6,0.764429903,,,0.71007436
9573,The COVID-19 Pandemic during the Time of the Diabetes Pandemic: Likely Fraternal Twins?,Pathogens,32438687,5/23/20,pubmed,0,5,immunome,0.357959486,0.001565391,0.001565367,0.133405822,0.001565339,0.503938594,Clinics,0.85193664,TRUE,69.2,0.759663554,49.4,0.679154402,11,0.840175319,,,0.759664425
9574,Diabetes and metabolic syndrome as risk factors for COVID-19.,Diabetes Metab Syndr,32438331,5/22/20,pubmed,0,4,"data mining, network analysis, knowledge graph",0.118133964,0.001565453,0.050583136,0.244825285,0.001565413,0.58332675,Clinics,0.51519096,TRUE,43.25,0.569855897,19.5,0.475983409,26,0.920859312,,,0.655566206
9575,Chest X-ray severity index as a predictor of in-hospital mortality in coronavirus disease 2019: A study of 302 patients from Italy.,Int J Infect Dis,32437939,5/22/20,pubmed,0,7,logistic regression,0.001461897,0.001461852,0.182242438,0.001461891,0.00146186,0.811910062,Clinics,0.8261632,TRUE,9.142857143,0.13637207,2.285714286,0.171193471,34,0.937156615,,,0.414907385
9576,Visualizing COVID-19 pandemic risk through network connectedness.,Int J Infect Dis,32437929,5/22/20,pubmed,0,5,network analysis,0.004775237,0.004775242,0.00477553,0.976123688,0.004775184,0.004775119,Epidemiology,0.30930173,FALSE,29.4,0.423650195,31.6,0.582218357,12,0.850299401,,,0.618722651
9577,Pediatrician Attitudes Toward and Experiences With Telehealth Use: Results From a National Survey.,Acad Pediatr,32437881,5/22/20,pubmed,0,7,logistic regression,0.001717282,0.0017172,0.001717437,0.04834519,0.8992586,0.047244291,Healthcare,0.98647153,TRUE,56.42857143,0.678025852,79.28571429,0.776960128,11,0.840175319,,,0.765053767
9578,What Is the Role for Algorithmics and Computational Biology in Responding to the COVID-19 Pandemic?,Cell Syst,32437681,5/22/20,pubmed,0,1,computational,0.801587467,0.010373184,0.010372695,0.156920609,0.010373468,0.010372577,Drug discovery,0.22552589,FALSE,28,0.408312202,60,0.720899117,1,0.537564047,,,0.555591789
9579,Combat COVID-19 with artificial intelligence and big data.,J Travel Med,32437541,5/22/20,pubmed,0,2,artificial intelligence,0.019530615,0.019529392,0.902350649,0.019530492,0.019529568,0.019529284,Drug discovery,0.43308255,FALSE,35.5,0.492547467,11.5,0.378378378,16,0.881782826,,,0.584236224
9580,"Knowledge, perceptions and preventive practices towards COVID-19 early in the outbreak among Jimma university medical center visitors, Southwest Ethiopia.",PLoS One,32437432,5/22/20,pubmed,0,5,logistic regression,0.001112634,0.001112708,0.001112681,0.001112702,0.994436619,0.001112654,Healthcare,0.9726931,TRUE,43.8,0.575916878,28.6,0.559071448,58,0.963022409,,,0.699336911
9581,Virus-induced genetics revealed by multidimensional precision medicine transcriptional workflow applicable to COVID-19.,Physiol Genomics,32437232,5/22/20,pubmed,0,35,transcriptom,0.454500104,0.205046916,0.087190657,0.00118739,0.001187272,0.250887661,Drug discovery,0.9332143,TRUE,18.34285714,0.274970623,9.428571429,0.343591116,10,0.828199272,,,0.48225367
9582,Disparities In Outcomes Among COVID-19 Patients In A Large Health Care System In California.,Health Aff (Millwood),32437224,5/22/20,pubmed,0,8,logistic regression,0.00198719,0.00198716,0.001987251,0.001987318,0.356678539,0.635372542,Clinics,0.64291906,TRUE,20.25,0.301317336,22,0.503746321,148,0.986727576,,,0.597263744
9583,Obesity predisposes to the risk of higher mortality in young COVID-19 patients.,J Med Virol,32437016,5/22/20,pubmed,0,10,logistic regression,0.080953634,0.001461872,0.001461889,0.001461885,0.001461952,0.913198769,Clinics,0.83572185,TRUE,170.7,0.958810069,280.4,0.948956382,38,0.944132354,,,0.950632935
9584,The value of clinical parameters in predicting the severity of COVID-19.,J Med Virol,32436996,5/22/20,pubmed,0,7,logistic regression,0.00182337,0.001823314,0.001823358,0.00182333,0.001823337,0.990883292,Clinics,0.9693349,TRUE,89.28571429,0.840126167,29.71428571,0.567366872,38,0.944132354,,,0.783875131
9585,A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019.,Diagn Interv Radiol,32436845,5/22/20,pubmed,0,3,"artificial intelligence, dataset",0.002080546,0.002080544,0.989597056,0.002080646,0.002080559,0.002080649,Imaging,0.17891777,FALSE,174.6666667,0.961778712,102.3333333,0.82639818,4,0.707574542,,,0.831917145
9586,Metabolic associated fatty liver disease increases coronavirus disease 2019 disease severity in nondiabetic patients.,J Gastroenterol Hepatol,32436622,5/22/20,pubmed,0,10,logistic regression,0.002720089,0.002720131,0.002720164,0.002720112,0.061757284,0.927362219,Clinics,0.97787285,TRUE,106.2,0.881439792,133.8,0.867340112,31,0.931971109,,,0.893583671
9587,Associations of Early COVID-19 Cases in San Francisco With Domestic and International Travel.,Clin Infect Dis,32436571,5/22/20,pubmed,0,10,genomes,0.006539615,0.747523338,0.006539559,0.00654015,0.006539741,0.226317597,Genomics,0.14289713,FALSE,45.8,0.593852434,81.7,0.782312015,3,0.667819001,,,0.681327817
9588,The impact of imposed delay in elective pediatric neurosurgery: an informed hierarchy of need in the time of mass casualty crisis.,Childs Nerv Syst,32435890,5/22/20,pubmed,0,4,neural network,0.002898345,0.002898354,0.122797363,0.583006965,0.285500583,0.002898391,Epidemiology,0.886166,TRUE,65.5,0.737646113,16.75,0.446012845,1,0.537564047,,,0.573741002
9589,Racial demographics and COVID-19 confirmed cases and deaths: a correlational analysis of 2886 US counties.,J Public Health (Oxf),32435809,5/22/20,pubmed,0,2,dataset,0.001717149,0.001717184,0.00171718,0.479668988,0.201170325,0.314009174,Epidemiology,0.8289952,TRUE,4.5,0.061784897,1,0.122023013,47,0.95493549,,,0.379581133
9590,Predictive molecular pathology in the time of COVID-19.,J Clin Pathol,32434770,5/22/20,pubmed,0,12,sequencing,0.001511939,0.323790113,0.197953677,0.138705756,0.062968735,0.275069781,Genomics,0.65417176,TRUE,137.4166667,0.929803946,90.58333333,0.802113995,7,0.785110192,,,0.839009378
9591,Viral and host factors related to the clinical outcome of COVID-19.,Nature,32434211,5/21/20,pubmed,0,25,dataset,0.126410051,0.461758843,0.029514943,0.001486482,0.001486468,0.379343213,Genomics,0.5218782,TRUE,84.32,0.822870926,182.8,0.908215146,278,0.994567566,,,0.908551213
9592,No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic.,JMIR Public Health Surveill,32434145,5/21/20,pubmed,0,3,machine learning,0.001046808,0.022126651,0.001046855,0.973686037,0.001046821,0.001046828,Epidemiology,0.48930988,FALSE,69.66666667,0.762075577,149.3333333,0.883061279,18,0.891474782,,,0.845537213
9593,Extended ORF8 Gene Region Is Valuable in the Epidemiological Investigation of Severe Acute Respiratory Syndrome-Similar Coronavirus.,J Infect Dis,32433742,5/21/20,pubmed,0,10,genome sequences,0.002296604,0.893112843,0.002296543,0.09770088,0.002296582,0.002296548,Genomics,0.29524323,FALSE,32.4,0.45983054,14.2,0.414102221,9,0.814309525,,,0.562747429
9594,Immunogenicity of a DNA vaccine candidate for COVID-19.,Nat Commun,32433465,5/21/20,pubmed,0,59,dataset,0.880764595,0.111941582,0.001823456,0.001823398,0.001823605,0.001823365,Drug discovery,0.4131817,FALSE,42.50847458,0.562805368,59.18644068,0.717888681,193,0.99104883,,,0.757247626
9595,Impact of the COVID-19 Epidemic on Stroke Care and Potential Solutions.,Stroke,32432997,5/21/20,pubmed,0,6,dataset,0.001565307,0.001565349,0.001565355,0.353351688,0.289665625,0.352286676,Epidemiology,0.9467875,TRUE,69.83333333,0.762941431,29.33333333,0.565226117,91,0.976047904,,,0.768071818
9596,0,Am J Respir Crit Care Med,32432483,5/21/20,pubmed,0,7,"sequencing, transcriptom",0.800889007,0.126748849,0.001861752,0.001861982,0.0018618,0.06677661,Drug discovery,0.9858219,TRUE,245.4285714,0.983486919,400,0.971166711,70,0.969072165,,,0.974575265
9597,0,J Biomol Struct Dyn,32431217,5/21/20,pubmed,0,5,"molecular dynamics simulation, bioinformatic",0.96443367,0.001237096,0.001237103,0.001237111,0.001237062,0.030617959,Drug discovery,0.72044563,TRUE,140,0.933823984,118.6,0.849812684,40,0.947033768,,,0.910223479
9598,Artificial Intelligence during a pandemic: The COVID-19 example.,Int J Health Plann Manage,32430976,5/21/20,pubmed,0,2,artificial intelligence,0.025060588,0.02506071,0.874696285,0.025061958,0.02506024,0.02506022,Epidemiology,0.38048,FALSE,11,0.167171748,0,0.055525823,5,0.739490092,,,0.320729221
9599,Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries.,Eur J Epidemiol,32430840,5/21/20,pubmed,0,14,prediction model,0.0007529,0.000752911,0.000752889,0.865500047,0.000752927,0.131488326,Epidemiology,0.56684196,TRUE,47.71428571,0.611911683,60.85714286,0.723775756,79,0.972899562,,,0.769529
9600,"Exploring Diseases/Traits and Blood Proteins Causally Related to Expression of ACE2, the Putative Receptor of SARS-CoV-2: A Mendelian Randomization Analysis Highlights Tentative Relevance of Diabetes-Related Traits.",Diabetes Care,32430459,5/21/20,pubmed,0,3,"proteom, genome-wide",0.669996539,0.069831652,0.001126916,0.001126845,0.001126887,0.25679116,Drug discovery,0.73263747,TRUE,21.66666667,0.320737213,21.33333333,0.495450896,73,0.970677202,,,0.595621771
9601,Factors associated with prolonged viral shedding and impact of lopinavir/ritonavir treatment in hospitalised non-critically ill patients with SARS-CoV-2 infection.,Eur Respir J,32430428,5/21/20,pubmed,0,7,logistic regression,0.00135047,0.293103905,0.001350368,0.001350431,0.001350408,0.701494418,Clinics,0.9879229,TRUE,36.85714286,0.506710372,11.85714286,0.383328873,62,0.965553429,,,0.618530891
9602,Antenatal corticosteroids for pregnant women with COVID-19 infection and preterm prelabor rupture of membranes: a decision analysis.,J Matern Fetal Neonatal Med,32429722,5/21/20,pubmed,0,4,probabilistic,0.042625591,0.001203471,0.00120345,0.090311275,0.194039981,0.670616232,Clinics,0.68895745,TRUE,48.75,0.62032284,7,0.299973241,4,0.707574542,,,0.542623541
9603,"De-Escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study.",Biology (Basel),32429450,5/21/20,pubmed,0,10,model fit,0.001684506,0.001684639,0.001684587,0.991577223,0.001684538,0.001684509,Epidemiology,0.46597627,FALSE,75,0.787061661,77.6,0.772411025,22,0.908142478,,,0.822538388
9604,Use of Saliva for Diagnosis and Monitoring the SARS-CoV-2: A General Perspective.,J Clin Med,32429101,5/21/20,pubmed,0,7,proteom,0.172109846,0.396412221,0.292497561,0.002639194,0.002639271,0.133701907,Genomics,0.84659916,TRUE,120.2857143,0.907539118,83.85714286,0.788065293,26,0.920859312,,,0.872154574
9605,Increased ACE2 Expression in Bronchial Epithelium of COPD Patients who are Overweight.,Obesity (Silver Spring),32428380,5/20/20,pubmed,0,2,sequencing,0.493175896,0.061472993,0.003465928,0.003465919,0.003465953,0.43495331,Drug discovery,0.9622749,TRUE,63,0.721998887,24,0.521808938,28,0.926168282,,,0.723325369
9606,Vertical social distancing policy is ineffective to contain the COVID-19 pandemic.,Cad Saude Publica,32428075,5/20/20,pubmed,0,8,mathematical model,0.001438116,0.001438097,0.001438173,0.94437411,0.00143819,0.049873314,Epidemiology,0.21781862,FALSE,27.375,0.399839198,13.375,0.404000535,10,0.828199272,,,0.544013002
9607,Artificial intelligence-enabled rapid diagnosis of patients with COVID-19.,Nat Med,32427924,5/20/20,pubmed,0,28,"artificial intelligence, sequencing",0.001112614,0.061953524,0.806258112,0.001112627,0.001112673,0.12845045,Imaging,0.4986798,FALSE,58.64285714,0.693796772,79.5,0.77782981,187,0.990308044,,,0.820644875
9608,Generalizability of Deep Learning Tuberculosis Classifier to COVID-19 Chest Radiographs: New Tricks for an Old Algorithm?,J Thorac Imaging,32427650,5/20/20,pubmed,0,3,"deep learning, classifier",0.013548715,0.013548713,0.779114362,0.013548755,0.01355044,0.166689015,Clinics,0.59090334,TRUE,86.66666667,0.832209784,26.66666667,0.543216484,8,0.799987654,,,0.725137974
9609,COVID-19 Pandemic: Hopes from Proteomics and Multiomics Research.,OMICS,32427517,5/20/20,pubmed,0,2,"sequencing, proteom,  omics, multiom",0.190621013,0.489310023,0.00608999,0.301799566,0.006089856,0.006089552,Genomics,0.62851304,TRUE,58.5,0.693240151,25.5,0.533382392,7,0.785110192,,,0.670577578
9610,Influence of wind and relative humidity on the social distancing effectiveness to prevent COVID-19 airborne transmission: A numerical study.,J Aerosol Sci,32427227,5/20/20,pubmed,0,4,computational,0.126476916,0.00093613,0.000936117,0.793134947,0.000936112,0.077579778,Epidemiology,0.46589994,FALSE,232.5,0.981445977,104.25,0.829475515,62,0.965553429,,,0.92549164
9611,An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image.,Chemometr Intell Lab Syst,32427226,5/20/20,pubmed,0,3,"image processing, classifier",0.001272637,0.068738575,0.926170791,0.001272689,0.001272675,0.001272633,Imaging,0.57041633,TRUE,26.66666667,0.390995114,1.666666667,0.145036125,15,0.874313229,,,0.470114823
9612,Psychometric Evaluation of the Arabic Version of the Fear of COVID-19 Scale.,Int J Ment Health Addict,32427217,5/20/20,pubmed,0,4,dataset,0.067365702,0.003214186,0.003214658,0.003214335,0.919776746,0.003214372,Healthcare,0.9773456,TRUE,49.75,0.630032779,23.25,0.514583891,56,0.961540836,,,0.702052502
9613,Rare and extreme events: the case of COVID-19 pandemic.,Nonlinear Dyn,32427206,5/20/20,pubmed,0,2,"computational, mathematical model",0.002490592,0.002490685,0.002490552,0.987547249,0.002490466,0.002490456,Epidemiology,0.6232949,TRUE,110.5,0.89059311,14,0.412898047,17,0.887338725,,,0.730276627
9614,Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak.,Chaos Solitons Fractals,32427205,5/20/20,pubmed,0,3,mathematical model,0.00182333,0.054779695,0.001823511,0.937926808,0.001823353,0.001823303,Epidemiology,0.3901698,FALSE,54.33333333,0.663677407,35.33333333,0.607439122,20,0.900117291,,,0.723744607
9615,Natural experiment concept to accelerate the Re-purposing of existing therapeutics for Covid-19.,Glob Epidemiol,32427166,5/20/20,pubmed,0,1,computational,0.346669325,0.001237147,0.099594374,0.381730168,0.001237115,0.16953187,Epidemiology,0.3648614,FALSE,67,0.748160059,272,0.946280439,3,0.667819001,,,0.787419833
9616,Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study.,JMIR Public Health Surveill,32427104,5/20/20,pubmed,0,8,predictive model,0.000734153,0.000734194,0.014546974,0.950895584,0.032354916,0.000734179,Epidemiology,0.16991594,FALSE,35.25,0.489331437,23.375,0.515520471,1,0.537564047,,,0.514138652
9617,Nursing homes and the elderly regarding the COVID-19 pandemic: situation report from Hungary.,Geroscience,32426693,5/20/20,pubmed,0,10,network analysis,0.054041538,0.182199815,0.001943538,0.232150892,0.448317178,0.081347039,Healthcare,0.9267799,TRUE,39.6,0.53503618,19.6,0.476786192,2,0.618927094,,,0.543583156
9618,Artificial intelligence approach fighting COVID-19 with repurposing drugs.,Biomed J,32426387,5/20/20,pubmed,0,15,artificial intelligence,0.584897559,0.001098857,0.261325933,0.00109887,0.09946811,0.052110672,Drug discovery,0.8775166,TRUE,39.93333333,0.538314058,29.46666667,0.565627509,47,0.95493549,,,0.686292352
9619,Cigarette Smoke Exposure and Inflammatory Signaling Increase the Expression of the SARS-CoV-2 Receptor ACE2 in the Respiratory Tract.,Dev Cell,32425701,5/20/20,pubmed,0,7,sequencing,0.883147116,0.047671025,0.002357726,0.00235781,0.0023579,0.062108423,Drug discovery,0.45988217,FALSE,11.28571429,0.169460078,10.71428571,0.365400054,33,0.936045435,,,0.490301855
9620,Changes in implementation of personal protective measures by ordinary Japanese citizens: A longitudinal study from the early phase to the community transmission phase of the COVID-19 outbreak.,Int J Infect Dis,32425637,5/20/20,pubmed,0,13,logistic regression,0.001415084,0.001415128,0.001415117,0.001415196,0.992924356,0.001415118,Healthcare,0.74054563,TRUE,58.84615385,0.695033706,22.15384615,0.504348408,6,0.764429903,,,0.654604006
9621,"Factors associated with the duration of viral shedding in adults with COVID-19 outside of Wuhan, China: a retrospective cohort study.",Int J Infect Dis,32425636,5/20/20,pubmed,0,7,logistic regression,0.001861713,0.236546744,0.001861728,0.001861799,0.001861742,0.756006274,Clinics,0.9177461,TRUE,34,0.477766096,7.714285714,0.311479797,53,0.959195012,,,0.582813635
9622,"[Elderly people, dependency and vulnerability in the coronavirus pandemic: an emergency for a social and health integration].",Enferm Clin,32425485,5/20/20,pubmed,0,4,digital health,0.001684541,0.001684558,0.001684639,0.764051702,0.001684613,0.229209947,Epidemiology,0.82685816,TRUE,11.75,0.177562001,0.75,0.099411292,2,0.618927094,,,0.298633463
9623,Royal Flying Doctor Service Coronavirus Disease 2019 Activity and Surge Modeling in Australia.,Air Med J,32425475,5/20/20,pubmed,0,4,"simulation model, in silico",0.024997647,0.001371264,0.07099042,0.603251265,0.132478496,0.166910908,Epidemiology,0.8920826,TRUE,133.25,0.92535098,91,0.803987155,5,0.739490092,,,0.822942743
9624,Sequence mismatch in PCR probes may mask the COVID-19 detection in Nepal.,Mol Cell Probes,32425334,5/20/20,pubmed,0,2,"whole genome, genomes",0.076916287,0.825124625,0.003927507,0.003927506,0.003927576,0.0861765,Genomics,0.64047766,TRUE,2,0.022141134,0,0.055525823,7,0.785110192,,,0.287592383
9625,Cardiovascular Implications of the COVID-19 Pandemic: A Global Perspective.,Can J Cardiol,32425328,5/20/20,pubmed,0,12,artificial intelligence,0.122811394,0.001415172,0.113033398,0.323911884,0.001415178,0.437412973,Clinics,0.9936706,TRUE,89.33333333,0.840435401,45.5,0.660957988,44,0.952157541,,,0.81785031
9626,Potent Neutralizing Antibodies against SARS-CoV-2 Identified by High-Throughput Single-Cell Sequencing of Convalescent Patients' B Cells.,Cell,32425270,5/20/20,pubmed,0,33,sequencing,0.656776541,0.305494161,0.001943508,0.001943508,0.001943463,0.031898819,Drug discovery,0.34158176,FALSE,52.48484848,0.650194817,,,393,0.996728193,,,0.823461505
9627,"Corticosteroids, But Not TNF Antagonists, Are Associated With Adverse COVID-19 Outcomes in Patients With Inflammatory Bowel Diseases: Results From an International Registry.",Gastroenterology,32425234,5/20/20,pubmed,0,15,logistic regression,0.001565292,0.001565458,0.001565308,0.00156556,0.001565415,0.992172967,Clinics,0.809441,TRUE,177.6,0.963324881,301.9333333,0.954843457,179,0.989875918,,,0.969348086
9628,"Hydroxychloroquine versus Azithromycin for Hospitalized Patients with Suspected or Confirmed COVID-19 (HAHPS). Protocol for a Pragmatic, Open-Label, Active Comparator Trial.",Ann Am Thorac Soc,32425051,5/20/20,pubmed,0,13,bayes,0.001593585,0.001593526,0.001593597,0.623793588,0.001593551,0.369832153,Epidemiology,0.38892686,FALSE,76.15384615,0.790772466,57.07692308,0.711399518,12,0.850299401,,,0.784157128
9629,Distribution of HLA allele frequencies in 82 Chinese individuals with coronavirus disease-2019 (COVID-19).,HLA,32424945,5/20/20,pubmed,0,5,sequencing,0.005047965,0.768966368,0.005047644,0.005047704,0.005047683,0.210842636,Genomics,0.40161645,FALSE,492.6,0.99734059,56.8,0.70992775,34,0.937156615,,,0.881474985
9630,Factors Associated With Intubation and Prolonged Intubation in Hospitalized Patients With COVID-19.,Otolaryngol Head Neck Surg,32423368,5/20/20,pubmed,0,8,logistic regression,0.001717147,0.00171715,0.001717166,0.001717172,0.001717261,0.991414105,Clinics,0.9995724,TRUE,14.625,0.220421795,2,0.164302917,57,0.962281622,,,0.449002111
9631,Aerosolized Particle Reduction: A Novel Cadaveric Model and a Negative Airway Pressure Respirator (NAPR) System to Protect Health Care Workers From COVID-19.,Otolaryngol Head Neck Surg,32423338,5/20/20,pubmed,0,7,image processing,0.001486584,0.001486477,0.34699632,0.561578682,0.086965382,0.001486555,Epidemiology,0.5929274,TRUE,71,0.769064259,20.28571429,0.48347605,3,0.667819001,,,0.64011977
9632,Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve.,Int J Environ Res Public Health,32423150,5/20/20,pubmed,0,2,artificial intelligence,0.001653073,0.001653065,0.263899189,0.729488483,0.001653145,0.001653044,Epidemiology,0.10592508,FALSE,115,0.899066114,42.5,0.647578271,10,0.828199272,,,0.791614552
9633,The Rationale for Potential Pharmacotherapy of COVID-19.,Pharmaceuticals (Basel),32423024,5/20/20,pubmed,0,3,computational,0.654122622,0.001461972,0.001461939,0.286580834,0.001462066,0.054910567,Drug discovery,0.88156796,TRUE,13.33333333,0.201558538,6.666666667,0.290607439,16,0.881782826,,,0.457982934
9634,Highly Conserved Homotrimer Cavity Formed by the SARS-CoV-2 Spike Glycoprotein: A Novel Binding Site.,J Clin Med,32422996,5/20/20,pubmed,0,14,"virtual screening, genomes",0.896406459,0.099726483,0.000966783,0.000966769,0.000966757,0.000966749,Drug discovery,0.5761513,TRUE,32.14285714,0.456861896,37.28571429,0.61747391,24,0.914439163,,,0.66292499
9635,Somatic symptoms and concern regarding COVID-19 among Chinese college and primary school students: A cross-sectional survey.,Psychiatry Res,32422501,5/19/20,pubmed,0,3,logistic regression,0.001310351,0.001310357,0.001310324,0.001310378,0.993448201,0.001310388,Healthcare,0.8579292,TRUE,45,0.587049292,33,0.593256623,18,0.891474782,,,0.690593566
9636,Risk factors for SARS-CoV-2 among patients in the Oxford Royal College of General Practitioners Research and Surveillance Centre primary care network: a cross-sectional study.,Lancet Infect Dis,32422204,5/19/20,pubmed,0,22,logistic regression,0.001112648,0.001112654,0.038679232,0.001112681,0.550353455,0.407629329,Healthcare,0.7531752,TRUE,80.86363636,0.81043973,67.36363636,0.746454375,191,0.990554973,,,0.849149693
9637,Investigation of a COVID-19 outbreak in Germany resulting from a single travel-associated primary case: a case series.,Lancet Infect Dis,32422201,5/19/20,pubmed,0,41,"sequencing, whole genome",0.000580132,0.147746922,0.000580152,0.353623573,0.287272569,0.210196652,Epidemiology,0.6155361,TRUE,45.70731707,0.593110273,106.2195122,0.83348943,174,0.989567257,,,0.805388987
9638,Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction-Based SARS-CoV-2 Tests by Time Since Exposure.,Ann Intern Med,32422057,5/19/20,pubmed,0,5,bayes,0.000907277,0.000907322,0.126219808,0.249792611,0.162099403,0.460073579,Clinics,0.85946906,TRUE,118.4,0.904879708,238.8,0.933636607,520,0.998086302,,,0.945534206
9639,"Development and validation a nomogram for predicting the risk of severe COVID-19: A multi-center study in Sichuan, China.",PLoS One,32421703,5/19/20,pubmed,0,8,"logistic regression, prediction model",0.001237066,0.001237084,0.234231526,0.001237142,0.00123708,0.760820102,Clinics,0.82068354,TRUE,304.125,0.990908529,205.125,0.9197886,30,0.930057411,,,0.94691818
9640,Mining Physicians' Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis.,JMIR Public Health Surveill,32421686,5/19/20,pubmed,0,4,machine learning,0.001237061,0.001237056,0.001237173,0.681974582,0.313077068,0.001237059,Epidemiology,0.865652,TRUE,34.5,0.482404601,25.25,0.530773348,0,0.403234768,,,0.472137572
9641,Correlation analysis of the severity and clinical prognosis of 32 cases of patients with COVID-19.,Respir Med,32421546,5/19/20,pubmed,0,5,correlation analysis,0.019529348,0.019529385,0.019529614,0.019529621,0.019529543,0.902352489,Clinics,0.53259313,TRUE,95.8,0.858556497,25,0.529435376,16,0.881782826,,,0.756591566
9642,Analysis of the impact of COVID-19 on the correlations between crude oil and agricultural futures.,Chaos Solitons Fractals,32421108,5/19/20,pubmed,0,3,correlation analysis,0.003760809,0.003760846,0.547091592,0.437865519,0.003760696,0.003760539,Epidemiology,0.36994317,FALSE,59.66666667,0.70066176,12.66666667,0.39530372,7,0.785110192,,,0.627025224
9643,The impact of MicroRNAs (miRNAs) on the genotype of coronaviruses.,Acta Biomed,32420944,5/19/20,pubmed,0,2,genomic structure,0.449128337,0.506330331,0.00213063,0.002130686,0.00213071,0.038149306,Genomics,0.4878173,FALSE,203,0.97322036,70.5,0.755351887,6,0.764429903,,,0.831000717
9644,Repurposing current therapeutics for treating COVID-19: A vital role of prescription records data mining.,Drug Dev Res,32420637,5/19/20,pubmed,0,1,data mining,0.490535275,0.001786561,0.001786603,0.252176052,0.001786604,0.251928906,Drug discovery,0.64604366,TRUE,170,0.958191601,196,0.91557399,5,0.739490092,,,0.871085228
9645,"[Home Care and COVID-19. Before, in and after the state of alarm].",Enferm Clin,32419772,5/19/20,pubmed,0,2,digital health,0.001684518,0.001684524,0.001684626,0.517547306,0.386549506,0.090849521,Epidemiology,0.63364345,TRUE,2.5,0.027459954,0,0.055525823,1,0.537564047,,,0.206849941
9646,0,J Biomol Struct Dyn,32419646,5/19/20,pubmed,0,5,"computational, in silico",0.993899877,0.001220056,0.001220017,0.001220045,0.001219996,0.001220009,Drug discovery,0.96186364,TRUE,2.4,0.025419012,0,0.055525823,25,0.918019631,,,0.332988155
9647,Emergence of New Disease: How Can Artificial Intelligence Help?,Trends Mol Med,32418724,5/19/20,pubmed,0,5,artificial intelligence,0.164931523,0.004775443,0.338271847,0.482470879,0.00477511,0.004775198,Epidemiology,0.5843576,TRUE,25.6,0.375162348,37.8,0.620818839,5,0.739490092,,,0.578490426
9648,Digital health and the COVID-19 epidemic: an assessment framework for apps from an epidemiological and legal perspective.,Swiss Med Wkly,32418194,5/18/20,pubmed,0,5,digital health,0.001538326,0.001538167,0.001538225,0.958125094,0.035722105,0.001538082,Epidemiology,0.92945933,TRUE,119.4,0.906116643,107.4,0.835228793,19,0.89561084,,,0.878985425
9649,High throughput and comprehensive approach to develop multiepitope vaccine against minacious COVID-19.,Eur J Pharm Sci,32417398,5/18/20,pubmed,0,7,"molecular dynamics simulation, simulation experiment, in silico, in-silico",0.804634514,0.046865678,0.001085343,0.145243692,0.001085388,0.001085384,Drug discovery,0.6412234,TRUE,58.28571429,0.691508442,50.71428571,0.683904201,13,0.858880178,,,0.744764274
9650,"Characterization of eight novel full-length genomes of SARS-CoV-2 among imported COVID-19 cases from abroad in Yunnan, China.",J Infect,32417310,5/18/20,pubmed,0,11,genomes,0.019529572,0.902352091,0.01952947,0.019529615,0.019529435,0.019529818,Genomics,0.62385136,TRUE,24.36363636,0.359824355,29.90909091,0.568303452,3,0.667819001,,,0.531982269
9651,COVID-19: CADD to the rescue.,Virus Res,32417181,5/18/20,pubmed,0,5,virtual screening,0.9890835,0.002183293,0.002183314,0.002183449,0.00218322,0.002183224,Drug discovery,0.86305773,TRUE,13,0.197352959,1.2,0.126103827,5,0.739490092,,,0.354315626
9652,Digital Orthopaedics: A Glimpse Into the Future in the Midst of a Pandemic.,J Arthroplasty,32416956,5/18/20,pubmed,0,10,digital health,0.002130839,0.002130699,0.044302226,0.800234826,0.002130854,0.149070556,Epidemiology,0.97833407,TRUE,24.1,0.356237244,13.7,0.407613059,13,0.858880178,,,0.54091016
9653,Use of renin-angiotensin-aldosterone system inhibitors and risk of COVID-19 requiring admission to hospital: a case-population study.,Lancet,32416785,5/18/20,pubmed,0,31,logistic regression,0.167661342,0.00090732,0.000907296,0.07739536,0.000907317,0.752221364,Clinics,0.46893987,FALSE,20.78125,0.30731647,,,197,0.991295759,,,0.649306114
9654,"Glycosylated hemoglobin is associated with systemic inflammation, hypercoagulability, and prognosis of COVID-19 patients.",Diabetes Res Clin Pract,32416121,5/18/20,pubmed,0,3,correlation analysis,0.001538124,0.001538098,0.001538114,0.001538088,0.001538112,0.992309463,Clinics,0.9730566,TRUE,29.33333333,0.423093574,10.66666667,0.365333155,38,0.944132354,,,0.577519694
9655,A Novel Bat Coronavirus Closely Related to SARS-CoV-2 Contains Natural Insertions at the S1/S2 Cleavage Site of the Spike Protein.,Curr Biol,32416074,5/18/20,pubmed,0,13,metagenom,0.340071107,0.652154783,0.001943521,0.001943525,0.001943559,0.001943504,Genomics,0.37300408,FALSE,248.2307692,0.984414621,387.3846154,0.969962537,205,0.99160442,,,0.981993859
9656,COVID-19 knowledge prevents biologics discontinuation: Data from an Italian multicenter survey during RED-ZONE declaration.,Dermatol Ther,32415727,5/18/20,pubmed,0,7,logistic regression,0.044367567,0.001943683,0.001943488,0.001943562,0.490236367,0.459565333,Healthcare,0.91472197,TRUE,97.42857143,0.863071309,17.28571429,0.453371689,16,0.881782826,,,0.732741941
9657,"Connecting data, tools and people across Europe: ELIXIR's response to the COVID-19 pandemic.",Eur J Hum Genet,32415272,5/18/20,pubmed,0,2,computational,0.001684542,0.091346717,0.001684604,0.901914908,0.00168464,0.001684589,Epidemiology,0.33508995,FALSE,51,0.63875317,86.5,0.794153064,11,0.840175319,,,0.757693851
9658,Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions.,Science,32414780,5/18/20,pubmed,0,7,bayes,0.00208054,0.002080621,0.002080598,0.989597096,0.002080565,0.002080579,Epidemiology,0.15870914,FALSE,32.71428571,0.463479498,47,0.668718223,262,0.994011976,,,0.708736566
9659,A review of modern technologies for tackling COVID-19 pandemic.,Diabetes Metab Syndr,32413821,5/16/20,pubmed,0,3,"machine learning, computational, artificial intelligence, image processing",0.001786619,0.001786586,0.421106068,0.571747677,0.00178654,0.001786509,Epidemiology,0.68036425,TRUE,50.66666667,0.635908219,11,0.371287129,59,0.963516266,,,0.656903872
9660,Vitamin D concentrations and COVID-19 infection in UK Biobank.,Diabetes Metab Syndr,32413819,5/16/20,pubmed,0,14,logistic regression,0.002032766,0.002032807,0.002032719,0.002032798,0.227880801,0.76398811,Clinics,0.27806973,FALSE,126.4285714,0.916012122,153.9285714,0.886673803,165,0.988703006,,,0.930462977
9661,SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues.,Cell,32413319,5/16/20,pubmed,0,121,"sequencing, dataset",0.91955536,0.031864382,0.001653056,0.001653035,0.022725583,0.022548585,Drug discovery,0.6848104,TRUE,96.1147541,0.859113118,,,786,0.999259214,,,0.929186166
9662,"Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil.",Chaos Solitons Fractals,32412556,5/16/20,pubmed,0,7,mathematical model,0.001046816,0.001046839,0.00104683,0.994765837,0.001046853,0.001046824,Epidemiology,0.388342,FALSE,26,0.382398417,4.857142857,0.249933101,39,0.945737391,,,0.52602297
9663,Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases.,J Med Biol Eng,32412551,5/16/20,pubmed,0,3,"deep learning, neural network, transfer learning, dataset",0.000977433,0.000977425,0.970137361,0.000977435,0.025952873,0.000977473,Imaging,0.4686118,FALSE,3,0.037293586,3,0.199424672,75,0.971417989,,,0.402712082
9664,Complex Reporting of the COVID-19 Epidemic in the Czech Republic: Use of an Interactive Web-Based App in Practice.,J Med Internet Res,32412422,5/16/20,pubmed,0,15,data mining,0.000863078,0.000863054,0.068212772,0.814418688,0.114779256,0.000863152,Epidemiology,0.7586447,TRUE,199.4666667,0.972416352,60.2,0.721835697,4,0.707574542,,,0.800608864
9665,A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis.,JMIR Public Health Surveill,32412415,5/16/20,pubmed,0,4,"genome sequences, genomes, sequence alignment",0.001098818,0.626437943,0.001098832,0.348923516,0.021342078,0.001098813,Genomics,0.2855372,FALSE,59.25,0.697878657,103,0.827468558,25,0.918019631,,,0.814455615
9666,Challenges in the Practice of Sexual Medicine in the Time of COVID-19 in the United Kingdom.,J Sex Med,32411271,5/16/20,pubmed,0,9,logistic regression,0.000716359,0.000716368,0.000716321,0.13244042,0.864694149,0.000716384,Healthcare,0.8974354,TRUE,75,0.787061661,49.33333333,0.678953706,17,0.887338725,,,0.784451364
9667,Prevalence and Influencing Factors of Anxiety and Depression Symptoms in the First-Line Medical Staff Fighting Against COVID-19 in Gansu.,Front Psychiatry,32411034,5/16/20,pubmed,0,8,correlation analysis,0.000966743,0.000966749,0.014287941,0.00096675,0.981845007,0.00096681,Healthcare,0.9631769,TRUE,77.125,0.795163585,20.375,0.484078138,70,0.969072165,,,0.749437962
9668,0,J Biomol Struct Dyn,32410502,5/16/20,pubmed,0,3,in silico,0.929063809,0.063789814,0.001786532,0.001786587,0.001786581,0.001786677,Drug discovery,0.84421957,TRUE,33.33333333,0.469911559,16.33333333,0.440460262,33,0.936045435,,,0.615472419
9669,Evidence of increasing diversification of emerging Severe Acute Respiratory Syndrome Coronavirus 2 strains.,J Med Virol,32410229,5/16/20,pubmed,0,4,"bayes, genome sequences",0.001717206,0.916423677,0.001717236,0.076707378,0.001717249,0.001717254,Genomics,0.70662403,TRUE,58.75,0.694538933,57.25,0.711867808,10,0.828199272,,,0.744868671
9670,Machine learning can help get COVID-19 aid to those who need it most.,Nature,32409767,5/16/20,pubmed,0,1,machine learning,0.019529656,0.019529463,0.90235144,0.019529809,0.01953032,0.019529312,Healthcare,0.55994415,TRUE,66,0.741356918,86,0.793015788,5,0.739490092,,,0.757954266
9671,Complete Genome Sequences of SARS-CoV-2 Strains Detected in Malaysia.,Microbiol Resour Announc,32409547,5/16/20,pubmed,0,12,"sequencing, genome sequences, genomes",0.004775052,0.976124437,0.004775051,0.004775322,0.004775089,0.004775049,Genomics,0.557738,TRUE,51.91666667,0.645556311,51.16666667,0.685978057,7,0.785110192,,,0.705548187
9672,Clinical Characteristics and Outcomes of Patients With Diabetes and COVID-19 in Association With Glucose-Lowering Medication.,Diabetes Care,32409498,5/16/20,pubmed,0,12,logistic regression,0.044350411,0.001187286,0.001187389,0.001187255,0.001187299,0.95090036,Clinics,0.9812875,TRUE,104.1666667,0.877852681,41.41666667,0.6414905,110,0.980739552,,,0.833360911
9673,"Virtual Screening of Natural Products against Type II Transmembrane Serine Protease (TMPRSS2), the Priming Agent of Coronavirus 2 (SARS-CoV-2).",Molecules,32408547,5/16/20,pubmed,0,6,"virtual screening, in silico",0.994703103,0.001059372,0.0010594,0.001059391,0.001059361,0.001059373,Drug discovery,0.91168964,TRUE,150.5,0.944523471,70.83333333,0.755619481,38,0.944132354,,,0.881425102
9674,Infection of dogs with SARS-CoV-2.,Nature,32408337,5/15/20,pubmed,0,13,sequencing,0.001987124,0.810323726,0.001987088,0.001987197,0.086186477,0.097528388,Genomics,0.3593682,FALSE,22.30769231,0.32877729,70.30769231,0.754281509,172,0.989443793,,,0.690834197
9675,Proteomics of SARS-CoV-2-infected host cells reveals therapy targets.,Nature,32408336,5/15/20,pubmed,0,8,proteom,0.909971646,0.083981153,0.001511809,0.00151181,0.001511789,0.001511794,Drug discovery,0.85669446,TRUE,63.25,0.722926588,62.5,0.730867006,219,0.992530403,,,0.815441332
9676,Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study.,Eur J Radiol,32408222,5/15/20,pubmed,0,11,"deep learning, dataset",0.001237049,0.001237043,0.993814675,0.001237067,0.001237048,0.001237118,Imaging,0.6866399,TRUE,86,0.829117447,25,0.529435376,42,0.949503056,,,0.76935196
9677,How can we evaluate an interrelation of symptoms?,Arch Gerontol Geriatr,32408043,5/15/20,pubmed,0,3,dataset,0.003101478,0.003101474,0.003101795,0.143231219,0.003101726,0.844362307,Clinics,0.63191545,TRUE,12,0.183190055,0,0.055525823,2,0.618927094,,,0.285880991
9678,The dark cloud with a silver lining: Assessing the impact of the SARS COVID-19 pandemic on the global environment.,Sci Total Environ,32408041,5/15/20,pubmed,0,8,dataset,0.126900204,0.075723739,0.001823373,0.6400301,0.001823425,0.153699159,Epidemiology,0.8735183,TRUE,19.375,0.288947987,5.25,0.260971367,54,0.959812334,,,0.503243896
9679,Heightened Innate Immune Responses in the Respiratory Tract of COVID-19 Patients.,Cell Host Microbe,32407669,5/15/20,pubmed,0,26,"sequencing, transcriptom, metatranscriptom",0.804667238,0.059188697,0.002130712,0.002130824,0.00213073,0.129751798,Drug discovery,0.6546779,TRUE,60.30769231,0.704681799,55.5,0.704575863,260,0.993888512,,,0.801048724
9680,"Genomic Epidemiology, Evolution, and Transmission Dynamics of Porcine Deltacoronavirus.",Mol Biol Evol,32407507,5/15/20,pubmed,0,18,genomic epidemiology,0.187457566,0.807728748,0.001203408,0.001203457,0.001203418,0.001203403,Genomics,0.8113035,TRUE,156.3333333,0.948852743,559.0555556,0.982138079,8,0.799987654,,,0.910326158
9681,iBioProVis: interactive visualization and analysis of compound bioactivity space.,Bioinformatics,32407491,5/15/20,pubmed,0,6,"bioinformatic, dataset",0.794444823,0.000977459,0.000977492,0.201645348,0.000977437,0.00097744,Drug discovery,0.59015155,TRUE,30,0.432432432,12.83333333,0.396775488,1,0.537564047,,,0.455590656
9682,"COVID-19 Crisis in Jordan: Response, Scenarios, Strategies, and Recommendations.",JMIR Public Health Surveill,32407289,5/15/20,pubmed,0,8,digital health,0.00117159,0.001171602,0.001171647,0.905993675,0.063459803,0.027031682,Epidemiology,0.415414,FALSE,47.625,0.611293215,50.875,0.684640086,10,0.828199272,,,0.708044191
9683,Clinical and Chest Radiography Features Determine Patient Outcomes in Young and Middle-aged Adults with COVID-19.,Radiology,32407255,5/15/20,pubmed,0,13,logistic regression,0.000977439,0.000977442,0.180089558,0.00097746,0.000977518,0.816000583,Clinics,0.86030555,TRUE,39.76923077,0.536891583,46.15384615,0.664302917,83,0.974381135,,,0.725191878
9684,A Web- and App-Based Connected Care Solution for COVID-19 In- and Outpatient Care: Qualitative Study and Application Development.,JMIR Public Health Surveill,32406855,5/15/20,pubmed,0,7,digital health,0.001684501,0.001684478,0.001684626,0.463146931,0.322177012,0.209622452,Epidemiology,0.8694948,TRUE,69.85714286,0.763003278,46.57142857,0.666443671,0,0.403234768,,,0.610893906
9685,Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound.,IEEE Trans Med Imaging,32406829,5/15/20,pubmed,0,22,"deep learning, deep model, dataset",0.001415102,0.001415129,0.950752345,0.043587131,0.001415123,0.00141517,Imaging,0.0700607,FALSE,26.72727273,0.391428041,22.13636364,0.504281509,69,0.968763504,,,0.621491018
9686,0,J Phys Chem Lett,32406687,5/15/20,pubmed,0,3,computational,0.990282359,0.001943472,0.001943547,0.001943479,0.001943566,0.001943576,Drug discovery,0.7897764,TRUE,58.66666667,0.694106005,72,0.7594327,41,0.948144947,,,0.800561218
9687,Positive detection of SARS-CoV-2 combined HSV1 and HHV6B virus nucleic acid in tear and conjunctival secretions of a non-conjunctivitis COVID-19 patient with obstruction of common lacrimal duct.,Acta Ophthalmol,32406606,5/15/20,pubmed,0,10,sequencing,0.064512397,0.650581959,0.001187297,0.001187298,0.001187288,0.28134376,Genomics,0.96520585,TRUE,92.6,0.850701961,,,15,0.874313229,,,0.862507595
9688,A model based study on the dynamics of COVID-19: Prediction and control.,Chaos Solitons Fractals,32406395,5/15/20,pubmed,0,6,mathematical model,0.001593514,0.001593497,0.001593568,0.992032425,0.001593517,0.001593479,Epidemiology,0.9175271,TRUE,16,0.243552477,3.333333333,0.206515922,62,0.965553429,,,0.471873943
9689,0,J Biomol Struct Dyn,32406317,5/15/20,pubmed,0,10,molecular dynamics simulation,0.957540279,0.001415141,0.001415136,0.036799071,0.001415171,0.001415202,Drug discovery,0.9422497,TRUE,26.9,0.39315975,6.2,0.282512711,33,0.936045435,,,0.537239299
9690,The MERS-CoV Receptor DPP4 as a Candidate Binding Target of the SARS-CoV-2 Spike.,iScience,32405622,5/15/20,pubmed,0,9,bioinformatic,0.892730564,0.098946933,0.002080682,0.002080644,0.00208055,0.002080627,Drug discovery,0.47505876,FALSE,106.1111111,0.881254252,93,0.808670056,64,0.966479412,,,0.885467907
9691,Dataset for country profile and mobility analysis in the assessment of COVID-19 pandemic.,Data Brief,32405515,5/15/20,pubmed,0,6,dataset,0.002357736,0.002357795,0.11391315,0.791388585,0.087624893,0.002357841,Epidemiology,0.61882734,TRUE,42.16666667,0.55915641,6.166666667,0.281977522,3,0.667819001,,,0.502984311
9692,0,Open Forum Infect Dis,32405509,5/15/20,pubmed,0,3,microbiom,0.185614743,0.377999032,0.002490534,0.205806713,0.113328463,0.114760515,Genomics,0.8971489,TRUE,259.3333333,0.985775249,545.6666667,0.98160289,6,0.764429903,,,0.910602681
9693,Precautions and recommendations for orthodontic settings during the COVID-19 outbreak: A review.,Am J Orthod Dentofacial Orthop,32405152,5/15/20,pubmed,0,1,dataset,0.001371272,0.001371296,0.001371268,0.885356838,0.090500698,0.020028628,Epidemiology,0.9442264,TRUE,11,0.167171748,4,0.231469093,23,0.91129082,,,0.436643887
9694,Mental health consequences during the initial stage of the 2020 Coronavirus pandemic (COVID-19) in Spain.,Brain Behav Immun,32405150,5/15/20,pubmed,0,7,predictive model,0.001486511,0.001486442,0.001486486,0.001486545,0.992567532,0.001486484,Healthcare,0.8972454,TRUE,41.42857143,0.552229575,31.14285714,0.578806529,188,0.990431508,,,0.707155871
9695,Development of multi-specific humanized llama antibodies blocking SARS-CoV-2/ACE2 interaction with high affinity and avidity.,Emerg Microbes Infect,32403995,5/15/20,pubmed,0,7,in-silico,0.852073105,0.14078072,0.001786597,0.001786543,0.001786526,0.00178651,Drug discovery,0.59963727,TRUE,45.71428571,0.593233966,44.57142857,0.656944073,22,0.908142478,,,0.719440172
9696,Neurotropic mechanisms in COVID-19 and their potential influence on neuropsychological outcomes in children.,Child Neuropsychol,32403983,5/15/20,pubmed,0,1,transcriptom,0.001901793,0.068231577,0.063620452,0.130571572,0.641765769,0.093908836,Healthcare,0.76931185,TRUE,7,0.10179974,0,0.055525823,5,0.739490092,,,0.298938552
9697,"COVID-19, MERS and SARS with Concomitant Liver Injury-Systematic Review of the Existing Literature.",J Clin Med,32403255,5/15/20,pubmed,0,11,microbiom,0.251351191,0.084876698,0.00139285,0.001392959,0.070065098,0.590921205,Clinics,0.85893154,TRUE,105.7272727,0.880512091,30.09090909,0.570377308,24,0.914439163,,,0.788442854
9698,A RT-PCR assay for the detection of coronaviruses from four genera.,J Clin Virol,32403008,5/14/20,pubmed,0,11,"sequencing, genome sequences",0.001350361,0.9258938,0.034691165,0.00135037,0.00135034,0.035363964,Genomics,0.8098183,TRUE,32.09090909,0.456243429,64.27272727,0.736085095,6,0.764429903,,,0.652252809
9699,"Importation and early local transmission of COVID-19 in Brazil, 2020.",Rev Inst Med Trop Sao Paulo,32401959,5/14/20,pubmed,0,24,sequencing,0.005697518,0.971511353,0.005697703,0.005698019,0.005697601,0.005697806,Genomics,0.265706,FALSE,74.45833333,0.783969324,658.2916667,0.986085095,35,0.939317242,,,0.903123887
9700,Genetic variants and source of introduction of SARS-CoV-2 in South America.,J Med Virol,32401345,5/14/20,pubmed,0,2,genomes,0.00190174,0.990491409,0.001901711,0.001901743,0.001901714,0.001901683,Genomics,0.31199744,FALSE,14,0.213494959,1,0.122023013,19,0.89561084,,,0.410376271
9701,Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts.,J Med Internet Res,32401210,5/14/20,pubmed,0,9,data mining,0.000688471,0.096699411,0.186150737,0.107871661,0.292507885,0.316081835,Clinics,0.9359946,TRUE,28.66666667,0.414125796,6,0.280037463,23,0.91129082,,,0.53515136
9702,State-of-the-art tools to identify druggable protein ligand of SARS-CoV-2.,Arch Med Sci,32399095,5/14/20,pubmed,0,7,in-silico,0.793815233,0.001330112,0.055070705,0.001330177,0.00133013,0.147123644,Drug discovery,0.7841742,TRUE,17.14285714,0.258519389,6.714285714,0.291142628,5,0.739490092,,,0.42971737
9703,0,Arch Med Sci,32399094,5/14/20,pubmed,0,9,computational,0.99173455,0.001653157,0.001653034,0.001653051,0.001653126,0.001653081,Drug discovery,0.9717786,TRUE,217.3333333,0.977673325,158.6666667,0.89102221,77,0.972097043,,,0.94693086
9704,Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19.,Nat Med,32398875,5/14/20,pubmed,0,14,sequencing,0.575121519,0.080600037,0.003335254,0.003335283,0.003335256,0.334272652,Drug discovery,0.75699675,TRUE,81.57142857,0.812975447,248.5,0.93771742,539,0.99833323,,,0.916342033
9705,RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look.,RNA,32398273,5/14/20,pubmed,0,7,"genome sequences, sequence alignment",0.187897654,0.807222315,0.001220006,0.001220029,0.00122,0.001219995,Genomics,0.37511256,FALSE,46.66666667,0.602510978,93,0.808670056,64,0.966479412,,,0.792553482
9706,Using X-ray images and deep learning for automated detection of coronavirus disease.,J Biomol Struct Dyn,32397844,5/14/20,pubmed,0,2,"deep learning, dataset",0.100314108,0.00218322,0.890952536,0.002183364,0.002183249,0.002183523,Imaging,0.44986445,FALSE,4.5,0.061784897,2.5,0.180826866,55,0.960800049,,,0.401137271
9707,"SARS-CoV-2/COVID-19: Viral Genomics, Epidemiology, Vaccines, and Therapeutic Interventions.",Viruses,32397688,5/14/20,pubmed,0,13,"transcriptom,  omics",0.421505741,0.399656627,0.001751268,0.001751309,0.173583789,0.001751266,Drug discovery,0.61595434,TRUE,64.76923077,0.733502381,43.76923077,0.652930158,75,0.971417989,,,0.785950176
9708,Potential Drugs Targeting Early Innate Immune Evasion of SARS-Coronavirus 2 via 2'-O-Methylation of Viral RNA.,Viruses,32397643,5/14/20,pubmed,0,2,"virtual screening, computational, interactom",0.966337547,0.001684555,0.00168456,0.026924312,0.001684544,0.001684483,Drug discovery,0.63714635,TRUE,217,0.977487785,216,0.925408081,23,0.91129082,,,0.938062229
9709,Using Early Data to Estimate the Actual Infection Fatality Ratio from COVID-19 in France.,Biology (Basel),32397286,5/14/20,pubmed,0,5,probabilistic,0.054529799,0.002422327,0.002422437,0.715590171,0.002422444,0.222612821,Epidemiology,0.33473784,FALSE,45,0.587049292,70,0.753344929,24,0.914439163,,,0.751611128
9710,"Epidemiological, clinical, and virological characteristics of 465 hospitalized cases of coronavirus disease 2019 (COVID-19) from Zhejiang province in China.",Influenza Other Respir Viruses,32397011,5/13/20,pubmed,0,21,"bioinformatic, logistic regression",0.001330041,0.465795771,0.001330046,0.001330159,0.001330109,0.528883873,Clinics,0.9583616,TRUE,65.0952381,0.734924856,55.28571429,0.703037196,27,0.92443978,,,0.787467277
9711,Truncated human angiotensin converting enzyme 2; a potential inhibitor of SARS-CoV-2 spike glycoprotein and potent COVID-19 therapeutic agent.,J Biomol Struct Dyn,32396773,5/13/20,pubmed,0,3,in-silico,0.993636827,0.001272662,0.00127262,0.001272636,0.001272637,0.001272618,Drug discovery,0.8836154,TRUE,108.6666667,0.887129693,48.66666667,0.676143966,24,0.914439163,,,0.825904274
9712,Identification of potential molecules against COVID-19 main protease through structure-guided virtual screening approach.,J Biomol Struct Dyn,32396769,5/13/20,pubmed,0,5,"virtual screening, in-silico",0.958853458,0.001187265,0.021821136,0.001187314,0.001187252,0.015763576,Drug discovery,0.8052662,TRUE,27.6,0.402374915,5.2,0.259700294,64,0.966479412,,,0.542851541
9713,Elucidating biophysical basis of binding of inhibitors to SARS-CoV-2 main protease by using molecular dynamics simulations and free energy calculations.,J Biomol Struct Dyn,32396767,5/13/20,pubmed,0,5,molecular dynamics simulation,0.992032533,0.001593491,0.001593465,0.001593513,0.001593508,0.00159349,Drug discovery,0.953784,TRUE,13.8,0.208980147,3.8,0.221501204,5,0.739490092,,,0.389990481
9714,"Measures Undertaken in China to Avoid COVID-19 Infection: Internet-Based, Cross-Sectional Survey Study.",J Med Internet Res,32396516,5/13/20,pubmed,0,10,logistic regression,0.001072224,0.00107218,0.001072187,0.391826372,0.587703548,0.01725349,Healthcare,0.86239606,TRUE,39.1,0.530645062,10.8,0.366871822,11,0.840175319,,,0.579230734
9715,"The prevalence, characteristics, and related factors of pressure injury in medical staff wearing personal protective equipment against COVID-19 in China: A multicentre cross-sectional survey.",Int Wound J,32396265,5/13/20,pubmed,0,15,logistic regression,0.002032748,0.002032764,0.002032853,0.00203283,0.92365557,0.068213235,Healthcare,0.98633486,TRUE,68.2,0.754653967,17.73333333,0.457720096,3,0.667819001,,,0.626731022
9716,Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19.,JAMA Intern Med,32396163,5/13/20,pubmed,0,20,logistic regression,0.000734157,0.000734168,0.111599528,0.000734178,0.00073423,0.885463738,Clinics,0.82674384,TRUE,87.85,0.836539056,146.2,0.880385336,358,0.996234335,,,0.904386243
9717,ENDOCRINOLOGY IN THE TIME OF COVID-19: Management of calcium metabolic disorders and osteoporosis.,Eur J Endocrinol,32396134,5/13/20,pubmed,0,7,digital health,0.00162282,0.00162287,0.001622766,0.423849293,0.311464877,0.259817374,Epidemiology,0.9474466,TRUE,158,0.950027831,169.8571429,0.89951833,6,0.764429903,,,0.871325355
9718,Mathematical Modeling of COVID-19 Control and Prevention Based on Immigration Population Data in China: Model Development and Validation.,JMIR Public Health Surveill,32396132,5/13/20,pubmed,0,2,mathematical model,0.001511902,0.001511871,0.073946481,0.920005969,0.001511907,0.00151187,Epidemiology,0.65381074,TRUE,5,0.070752675,0.5,0.087101953,1,0.537564047,,,0.231806225
9719,Predicting COVID-19 in China Using Hybrid AI Model.,IEEE Trans Cybern,32396126,5/13/20,pubmed,0,16,lstm,0.001438143,0.001438123,0.272993836,0.721253663,0.001438117,0.001438117,Epidemiology,0.89207196,TRUE,42.25,0.559898571,20.3125,0.483676746,54,0.959812334,,,0.667795884
9720,Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets.,IEEE Trans Med Imaging,32396075,5/13/20,pubmed,0,3,"deep learning, artificial intelligence, neural network",0.002080539,0.002080525,0.989597184,0.002080599,0.002080522,0.002080631,Imaging,0.52014285,TRUE,125,0.914280413,147.3333333,0.881321916,143,0.985863325,,,0.927155218
9721,Elective surgery cancellations due to the COVID-19 pandemic: global predictive modelling to inform surgical recovery plans.,Br J Surg,32395848,5/13/20,pubmed,0,5441,"bayes, predictive model",0.001593489,0.00159348,0.001593542,0.678596844,0.087600338,0.229022307,Epidemiology,0.9932827,TRUE,31,0.445111015,12.5,0.392761573,300,0.995184888,,,0.611019159
9722,Molecular docking and dynamic simulations for antiviral compounds against SARS-CoV-2: A computational study.,Inform Med Unlocked,32395606,5/13/20,pubmed,0,9,"virtual screening, computational, in silico",0.992309454,0.001538082,0.00153811,0.001538112,0.001538153,0.001538088,Drug discovery,0.5961716,TRUE,8,0.118683901,0.222222222,0.061412898,38,0.944132354,,,0.374743051
9723,Dataset of Vietnamese student's learning habits during COVID-19.,Data Brief,32395572,5/13/20,pubmed,0,6,dataset,0.001511831,0.001511827,0.230298171,0.001511902,0.763654418,0.001511852,Healthcare,0.8626461,TRUE,17.66666667,0.266373925,1.666666667,0.145036125,14,0.866658436,,,0.426022829
9724,A profiling analysis on the receptor ACE2 expression reveals the potential risk of different type of cancers vulnerable to SARS-CoV-2 infection.,Ann Transl Med,32395525,5/13/20,pubmed,0,6,dataset,0.532600594,0.025888849,0.001461911,0.001461911,0.001461908,0.437124826,Drug discovery,0.6725199,TRUE,79.66666667,0.805924918,10.5,0.363459995,27,0.92443978,,,0.697941564
9725,Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study.,Ann Transl Med,32395494,5/13/20,pubmed,0,10,deep learning,0.001112638,0.001112627,0.789814545,0.001112658,0.001112689,0.205734843,Imaging,0.8928298,TRUE,27.1,0.396375781,14.4,0.416644367,25,0.918019631,,,0.57701326
9726,Clinical characteristics of Coronavirus Disease 2019 and development of a prediction model for prolonged hospital length of stay.,Ann Transl Med,32395487,5/13/20,pubmed,0,6,prediction model,0.001112629,0.001112653,0.001112662,0.115272324,0.001112666,0.880277066,Clinics,0.914802,TRUE,20.5,0.304966294,1,0.122023013,16,0.881782826,,,0.436257378
9727,Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19.,Chaos Solitons Fractals,32395038,5/13/20,pubmed,0,4,"forecasting model, dataset",0.001486429,0.001486447,0.197556732,0.796497519,0.001486439,0.001486433,Epidemiology,0.26008463,FALSE,22.75,0.336508133,2.5,0.180826866,27,0.92443978,,,0.480591593
9728,Comparison of chest CT findings between COVID-19 pneumonia and other types of viral pneumonia: a two-center retrospective study.,Eur Radiol,32394279,5/13/20,pubmed,0,4,logistic regression,0.001171569,0.043764224,0.475445068,0.001171603,0.001171539,0.477275997,Clinics,0.5931961,TRUE,20.5,0.304966294,4.25,0.235750602,23,0.91129082,,,0.484002572
9729,Therapeutic targets and signaling mechanisms of vitamin C activity against sepsis: a bioinformatics study.,Brief Bioinform,32393985,5/13/20,pubmed,0,5,"bioinformatic, genomes",0.906820659,0.028125268,0.00141523,0.001415176,0.019025457,0.043198211,Drug discovery,0.8994538,TRUE,277.4,0.988620199,144.4,0.878512176,11,0.840175319,,,0.902435898
9730,Hydroxychloroquine in Patients with Rheumatic Disease Complicated by COVID-19: Clarifying Target Exposures and the Need for Clinical Trials.,J Rheumatol,32393664,5/13/20,pubmed,0,11,model simulation,0.366781962,0.001022646,0.001022689,0.080699155,0.050898482,0.499575065,Clinics,0.41436285,FALSE,109.3636364,0.889046942,69.18181818,0.750668986,10,0.828199272,,,0.8226384
9731,Intelligent classification of platelet aggregates by agonist type.,Elife,32393438,5/13/20,pubmed,0,10,"deep learning, neural network, classifier, dataset",0.31605299,0.000759399,0.447594329,0.000759389,0.000759352,0.234074541,Imaging,0.7832881,TRUE,113.4,0.896592244,58,0.714476853,7,0.785110192,,,0.79872643
9732,The Challenges of COVID-19 for People Living With Diabetes: Considerations for Digital Health.,JMIR Diabetes,32392473,5/12/20,pubmed,0,4,digital health,0.001622754,0.001622734,0.001622805,0.307003757,0.377396499,0.310731451,Healthcare,0.20787111,FALSE,9,0.135320675,,,7,0.785110192,,,0.460215434
9733,SARS-CoV-2: Combating Coronavirus Emergence.,Immunity,32392464,5/12/20,pubmed,0,2,sequencing,0.284059213,0.693149954,0.005697836,0.005697694,0.005697569,0.005697733,Genomics,0.5735766,TRUE,269.5,0.987568805,1089,0.994447418,12,0.850299401,,,0.944105208
9734,Diabetes and COVID-19: psychosocial consequences of the COVID-19 pandemic in people with diabetes in Denmark-what characterizes people with high levels of COVID-19-related worries?,Diabet Med,32392380,5/12/20,pubmed,0,8,logistic regression,0.001254648,0.00125472,0.001254663,0.066875535,0.459634766,0.469725668,Clinics,0.8848511,TRUE,46.75,0.603314985,19.75,0.477990367,24,0.914439163,,,0.665248172
9735,Association of Treatment With Hydroxychloroquine or Azithromycin With In-Hospital Mortality in Patients With COVID-19 in New York State.,JAMA,32392282,5/12/20,pubmed,0,14,logistic regression,0.061110723,0.000854741,0.024923634,0.000854727,0.00085474,0.911401434,Clinics,0.97187185,TRUE,58.35714286,0.692003216,59.64285714,0.718892159,557,0.998456695,,,0.803117357
9736,Screening of healthcare workers for SARS-CoV-2 highlights the role of asymptomatic carriage in COVID-19 transmission.,Elife,32392129,5/12/20,pubmed,0,137,sequencing,0.000648124,0.207517369,0.00064814,0.000648163,0.664836079,0.125702125,Healthcare,0.37694836,FALSE,72.15384615,0.773640918,242.9230769,0.935041477,197,0.991295759,,,0.899992718
9737,Novel Coronavirus Polymerase and Nucleotidyl-Transferase Structures: Potential to Target New Outbreaks.,J Phys Chem Lett,32392072,5/12/20,pubmed,0,5,"sequence alignment, structural model",0.74016794,0.252538646,0.001823378,0.001823391,0.00182333,0.001823315,Drug discovery,0.6755328,TRUE,20.4,0.302863504,20,0.481000803,20,0.900117291,,,0.561327199
9738,Characteristics and clinical significance of myocardial injury in patients with severe coronavirus disease 2019.,Eur Heart J,32391877,5/12/20,pubmed,0,13,logistic regression,0.0014619,0.001461861,0.001461864,0.001461856,0.001461864,0.992690655,Clinics,0.9872582,TRUE,90.46153846,0.843960665,117,0.847002944,137,0.984690413,,,0.891884674
9739,Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.,JMIR Public Health Surveill,32391801,5/12/20,pubmed,0,12,mathematical model,0.000786364,0.000786361,0.000786371,0.996068188,0.000786343,0.000786374,Epidemiology,0.50074613,TRUE,96,0.858927577,29.25,0.564557131,5,0.739490092,,,0.7209916
9740,Abdominal Imaging Findings in COVID-19: Preliminary Observations.,Radiology,32391742,5/12/20,pubmed,0,23,logistic regression,0.147870673,0.00122006,0.215614865,0.00122003,0.00122004,0.632854332,Clinics,0.8505849,TRUE,126.2608696,0.915641041,133.8695652,0.867540808,96,0.977282548,,,0.920154799
9741,[Analysis of medication characteristics of traditional Chinese medicine in treating COVID-19 based on data mining].,Zhejiang Da Xue Xue Bao Yi Xue Ban,32391675,5/12/20,pubmed,0,8,data mining,0.313989008,0.00148645,0.001486439,0.52567615,0.155875465,0.001486487,Epidemiology,0.9844326,TRUE,30.25,0.434782609,3.375,0.207051111,2,0.618927094,,,0.420253604
9742,[Clinical analysis of suspected COVID-19 patients with anxiety and depression].,Zhejiang Da Xue Xue Bao Yi Xue Ban,32391665,5/12/20,pubmed,0,16,logistic regression,0.02016398,0.001511814,0.001511921,0.071936742,0.570918489,0.333957054,Healthcare,0.9500465,TRUE,211.875,0.97606531,76.6875,0.770470966,10,0.828199272,,,0.858245183
9743,Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis.,Glob Health Res Policy,32391439,5/12/20,pubmed,0,7,"machine learning, neural network, mathematical model, network model",0.001022613,0.001022637,0.062043643,0.885221682,0.001022696,0.049666729,Epidemiology,0.66988134,TRUE,44.42857143,0.581050158,20.28571429,0.48347605,11,0.840175319,,,0.634900509
9744,"Survey data of COVID-19-related Knowledge, Risk Perceptions and Precautionary Behavior among Nigerians.",Data Brief,32391411,5/12/20,pubmed,0,7,dataset,0.001861771,0.001861714,0.001861836,0.325774647,0.666778339,0.001861693,Healthcare,0.92239666,TRUE,10,0.15214299,2,0.164302917,21,0.903944688,,,0.406796865
9745,Dataset on dynamics of Coronavirus on Twitter.,Data Brief,32391410,5/12/20,pubmed,0,5,"network analysis, dataset",0.001751221,0.037077888,0.001751257,0.9559172,0.001751228,0.001751206,Epidemiology,0.5606612,TRUE,51.8,0.645061538,210.4,0.922598341,8,0.799987654,,,0.789215844
9746,ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA.,Data Brief,32391409,5/12/20,pubmed,0,3,"artificial intelligence, mathematical model",0.001823354,0.001823343,0.125649523,0.867057033,0.001823397,0.00182335,Epidemiology,0.2047984,FALSE,49,0.624281032,14,0.412898047,16,0.881782826,,,0.639653968
9747,A Precision Medicine Approach to SARS-CoV-2 Pandemic Management.,Curr Treat Options Allergy,32391242,5/12/20,pubmed,0,4,sequencing,0.549057109,0.141797594,0.098589176,0.00097753,0.03899749,0.1705811,Drug discovery,0.8694918,TRUE,5.25,0.072855464,6,0.280037463,16,0.881782826,,,0.411558584
9748,A multi-region discrete time mathematical modeling of the dynamics of Covid-19 virus propagation using optimal control.,J Appl Math Comput,32390786,5/12/20,pubmed,0,4,mathematical model,0.002032819,0.134569406,0.002032815,0.857299345,0.002032839,0.002032776,Epidemiology,0.461613,FALSE,9.5,0.143051518,0,0.055525823,15,0.874313229,,,0.35763019
9749,On a comprehensive model of the novel coronavirus (COVID-19) under Mittag-Leffler derivative.,Chaos Solitons Fractals,32390692,5/12/20,pubmed,0,4,mathematical model,0.003760565,0.00376047,0.003760513,0.981197525,0.003760501,0.003760426,Epidemiology,0.41743746,FALSE,33.75,0.473622364,3,0.199424672,63,0.965985555,,,0.546344197
9750,Time series forecasting of COVID-19 transmission in Canada using LSTM networks.,Chaos Solitons Fractals,32390691,5/12/20,pubmed,0,2,lstm,0.019529791,0.019529723,0.01953068,0.902351033,0.019529372,0.019529402,Epidemiology,0.55402774,TRUE,191,0.969138475,17,0.451097137,116,0.981788999,,,0.80067487
9751,Ascertainment rate of novel coronavirus disease (COVID-19) in Japan.,Int J Infect Dis,32389846,5/12/20,pubmed,0,3,dataset,0.004530646,0.004530706,0.004530791,0.502618875,0.004530756,0.479258226,Epidemiology,0.18721166,FALSE,146.3333333,0.940998206,224.3333333,0.927682633,2,0.618927094,,,0.829202645
9752,Computational screening of antagonists against the SARS-CoV-2 (COVID-19) coronavirus by molecular docking.,Int J Antimicrob Agents,32389723,5/12/20,pubmed,0,5,"molecular dynamics simulation, computational",0.990491467,0.001901718,0.001901728,0.00190171,0.001901699,0.001901678,Drug discovery,0.93713117,TRUE,67,0.748160059,21.8,0.501204174,56,0.961540836,,,0.736968357
9753,Infection prevention and control compliance in Tanzanian outpatient facilities: a cross-sectional study with implications for the control of COVID-19.,Lancet Glob Health,32389195,5/12/20,pubmed,0,7,logistic regression,0.000800599,0.000800597,0.040650568,0.000800605,0.882132388,0.074815243,Healthcare,0.9048959,TRUE,34.57142857,0.482837529,29,0.562884667,21,0.903944688,,,0.649888961
9754,Psychological distress among health professional students during the COVID-19 outbreak.,Psychol Med,32389148,5/12/20,pubmed,0,9,logistic regression,0.001254642,0.001254642,0.00125469,0.001254681,0.961860145,0.0331212,Healthcare,0.8985075,TRUE,62.88888889,0.720514565,,,33,0.936045435,,,0.82828
9755,BRCA testing in a genomic diagnostics referral center during the COVID-19 pandemic.,Mol Biol Rep,32388698,5/11/20,pubmed,0,5,sequencing,0.001861807,0.252557551,0.001861767,0.51884172,0.001861804,0.223015352,Epidemiology,0.7955598,TRUE,276.2,0.988496506,146.4,0.880452234,7,0.785110192,,,0.884686311
9756,"Lockdown, one, two, none, or smart. Modeling containing covid-19 infection. A conceptual model.",Sci Total Environ,32387821,5/11/20,pubmed,0,1,mathematical model,0.001622733,0.001622727,0.001622714,0.991886206,0.001622748,0.001622872,Epidemiology,0.5345736,TRUE,10,0.15214299,1,0.122023013,25,0.918019631,,,0.397395211
9757,Emergence of genomic diversity and recurrent mutations in SARS-CoV-2.,Infect Genet Evol,32387564,5/11/20,pubmed,0,12,"whole genome, genomes, dataset",0.028391698,0.967319451,0.00107221,0.001072269,0.001072194,0.001072178,Genomics,0.25400552,FALSE,45.58333333,0.591811491,145.9166667,0.880117742,306,0.995431817,,,0.822453683
9758,Coding potential and sequence conservation of SARS-CoV-2 and related animal viruses.,Infect Genet Evol,32387562,5/11/20,pubmed,0,4,"genome-wide, genomes",0.119437448,0.876111869,0.001112681,0.001112714,0.001112629,0.001112658,Genomics,0.35512406,FALSE,173.25,0.960789164,171,0.900120417,36,0.941169208,,,0.934026263
9759,Study of combining virtual screening and antiviral treatments of the Sars-CoV-2 (Covid-19).,Microb Pathog,32387389,5/11/20,pubmed,0,9,virtual screening,0.558136757,0.193731252,0.195270733,0.049837588,0.001511855,0.001511816,Drug discovery,0.88992584,TRUE,34.88888889,0.485682479,9.333333333,0.342253144,18,0.891474782,,,0.573136802
9760,Coast-to-Coast Spread of SARS-CoV-2 during the Early Epidemic in the United States.,Cell,32386545,5/11/20,pubmed,0,36,genomes,0.002357719,0.732699002,0.002357757,0.2264933,0.002357745,0.033734476,Genomics,0.47878537,FALSE,67.86111111,0.752860412,131.1388889,0.864664169,145,0.986048522,,,0.867857701
9761,A Primer on COVID-19 Mathematical Models.,Obesity (Silver Spring),32386464,5/10/20,pubmed,0,5,mathematical model,0.034961941,0.034961874,0.034962477,0.82518967,0.034961874,0.034962164,Epidemiology,0.66354394,TRUE,62,0.715814212,61.2,0.725180626,7,0.785110192,,,0.74203501
9762,Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning.,IEEE Trans Med Imaging,32386147,5/10/20,pubmed,0,11,"machine learning, neural network",0.001350377,0.001350345,0.993248158,0.001350402,0.001350344,0.001350374,Imaging,0.33358383,FALSE,35.45454545,0.491248686,36.09090909,0.611586834,58,0.963022409,,,0.68861931
9763,"Value of CT application in the screening,diagnosis,and treatment of COVID-19.",Zhong Nan Da Xue Xue Bao Yi Xue Ban,32386018,5/10/20,pubmed,0,9,artificial intelligence,0.00321417,0.003214165,0.983928759,0.003214333,0.003214417,0.003214156,Imaging,0.69999707,TRUE,295.4444444,0.990351908,233.2222222,0.931161359,0,0.403234768,,,0.774916012
9764,Rapid Detection of SARS-CoV-2 by Low Volume Real-Time Single Tube Reverse Transcription Recombinase Polymerase Amplification Using an Exo Probe with an Internally Linked Quencher (Exo-IQ).,Clin Chem,32384153,5/10/20,pubmed,0,8,computational,0.071191214,0.923643699,0.001291304,0.001291283,0.001291222,0.001291278,Genomics,0.42666468,FALSE,48.375,0.617106809,53.75,0.69621354,21,0.903944688,,,0.739088346
9765,COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System.,Radiology,32384019,5/10/20,pubmed,0,13,artificial intelligence,0.001237048,0.001237077,0.800539012,0.00123706,0.001237085,0.194512718,Clinics,0.90402687,TRUE,45.69230769,0.592924733,57,0.711198823,39,0.945737391,,,0.749953649
9766,"Risk Factors for Mortality in 244 Older Adults With COVID-19 in Wuhan, China: A Retrospective Study.",J Am Geriatr Soc,32383809,5/10/20,pubmed,0,9,logistic regression,0.001171548,0.001171532,0.001171536,0.001171566,0.001171604,0.994142214,Clinics,0.8888348,TRUE,43,0.567629414,,,69,0.968763504,,,0.768196459
9767,In silico studies on the comparative characterization of the interactions of SARS-CoV-2 spike glycoprotein with ACE-2 receptor homologs and human TLRs.,J Med Virol,32383269,5/10/20,pubmed,0,2,in silico,0.774870935,0.153590455,0.001171583,0.001171624,0.048450526,0.020744877,Drug discovery,0.97221583,TRUE,30,0.432432432,8.5,0.329141022,62,0.965553429,,,0.575708961
9768,Median-joining network analysis of SARS-CoV-2 genomes is neither phylogenetic nor evolutionary.,Proc Natl Acad Sci U S A,32381733,5/10/20,pubmed,0,5,"genomes, network analysis",0.015999108,0.579515482,0.175747946,0.015999467,0.015998685,0.196739312,Genomics,0.57973456,TRUE,82,0.814769002,179,0.905739898,26,0.920859312,,,0.880456071
9769,Chaos theory applied to the outbreak of COVID-19: an ancillary approach to decision making in pandemic context.,Epidemiol Infect,32381148,5/10/20,pubmed,0,6,predictive model,0.002639088,0.002639099,0.002639066,0.986804678,0.002639028,0.002639041,Epidemiology,0.23103717,FALSE,13.5,0.205393036,9.333333333,0.342253144,2,0.618927094,,,0.388857758
9770,Delivering healthcare remotely to cardiovascular patients during COVID-19 : A rapid review of the evidence.,Eur J Cardiovasc Nurs,32380858,5/10/20,pubmed,0,5,digital health,0.0012546,0.001254581,0.001254614,0.531289672,0.375667059,0.089279473,Epidemiology,0.84834564,TRUE,194.6,0.970993877,362,0.967621086,20,0.900117291,,,0.946244085
9771,In silico design of antiviral peptides targeting the spike protein of SARS-CoV-2.,Peptides,32380200,5/8/20,pubmed,0,7,"molecular dynamics simulation, computational, in silico, sequence alignment",0.902791546,0.001254716,0.001254627,0.042353572,0.001254613,0.051090926,Drug discovery,0.51679146,TRUE,9.285714286,0.138165626,1.857142857,0.151525288,15,0.874313229,,,0.388001381
9772,Knowledge-based structural models of SARS-CoV-2 proteins and their complexes with potential drugs.,FEBS Lett,32379896,5/8/20,pubmed,0,7,structural model,0.981963809,0.003607186,0.003607319,0.003607288,0.003607261,0.003607137,Drug discovery,0.87878346,TRUE,58.57142857,0.693301998,35.57142857,0.608375702,9,0.814309525,,,0.705329075
9773,Metagenomic Analysis Reveals Clinical SARS-CoV-2 Infection and Bacterial or Viral Superinfection and Colonization.,Clin Chem,32379863,5/8/20,pubmed,0,14,"sequencing, metagenom",0.066889022,0.877885786,0.001330123,0.001330116,0.001330172,0.051234781,Genomics,0.36980367,FALSE,66.64285714,0.744696642,62.64285714,0.731268397,22,0.908142478,,,0.794702506
9774,Herbal medicine for treatment of children diagnosed with COVID-19: A review of guidelines.,Complement Ther Clin Pract,32379639,5/8/20,pubmed,0,6,network analysis,0.003101611,0.257807638,0.003101646,0.729785984,0.003101602,0.003101519,Epidemiology,0.9700221,TRUE,112.3333333,0.894736842,49.5,0.679622692,17,0.887338725,,,0.820566086
9775,3CL hydrolase-based multiepitope peptide vaccine against SARS-CoV-2 using immunoinformatics.,J Med Virol,32379348,5/8/20,pubmed,0,3,in silico,0.991734207,0.001653181,0.001653206,0.001653135,0.00165327,0.001653,Drug discovery,0.67180175,TRUE,16.66666667,0.251159626,3.333333333,0.206515922,6,0.764429903,,,0.407368484
9776,"A virus that has gone viral: amino acid mutation in S protein of Indian isolate of Coronavirus COVID-19 might impact receptor binding, and thus, infectivity.",Biosci Rep,32378705,5/8/20,pubmed,0,5,"sequencing, genome sequences",0.373086541,0.621252988,0.001415114,0.001415127,0.0014151,0.00141513,Genomics,0.622171,TRUE,72.2,0.773950152,23.2,0.514383195,10,0.828199272,,,0.705510873
9777,"Towards effective COVID‑19 vaccines: Updates, perspectives and challenges (Review).",Int J Mol Med,32377694,5/8/20,pubmed,0,12,sequencing,0.240143625,0.258209097,0.002996687,0.492657272,0.002996662,0.002996657,Epidemiology,0.5976474,TRUE,184.0833333,0.965984291,102.4166667,0.826531978,64,0.966479412,,,0.919665227
9778,Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing.,Cell Discov,32377375,5/8/20,pubmed,0,18,sequencing,0.678095928,0.001171661,0.001171584,0.001171595,0.001171593,0.317217639,Drug discovery,0.5763083,TRUE,63.44444444,0.724287216,46.33333333,0.665908483,103,0.978949318,,,0.789715006
9779,Estimating and projecting air passenger traffic during the COVID-19 coronavirus outbreak and its socio-economic impact.,Saf Sci,32377034,5/8/20,pubmed,0,5,forecasting model,0.00127265,0.001272666,0.001272691,0.993636643,0.001272707,0.001272643,Epidemiology,0.29670978,FALSE,54.4,0.664048488,304.4,0.95564624,48,0.955614544,,,0.858436424
9780,An Extensive Meta-Metagenomic Search Identifies SARS-CoV-2-Homologous Sequences in Pangolin Lung Viromes.,mSphere,32376697,5/8/20,pubmed,0,7,"sequencing, metagenom, virom, deep sequencing",0.001291289,0.839515612,0.092448323,0.064162289,0.00129126,0.001291227,Genomics,0.45711595,FALSE,22,0.326056033,67.28571429,0.746119882,20,0.900117291,,,0.657431069
9781,[An increased neutrophil/lymphocyte ratio is an early warning signal of severe COVID-19].,Nan Fang Yi Ke Da Xue Xue Bao,32376581,5/8/20,pubmed,0,5,logistic regression,0.001684814,0.001684658,0.001684703,0.001684545,0.001684502,0.991576779,Clinics,0.7252652,TRUE,82.6,0.816624405,29.4,0.565426813,22,0.908142478,,,0.763397899
9782,[Psychological status and sleep quality of nursing interns during the outbreak of COVID-19].,Nan Fang Yi Ke Da Xue Xue Bao,32376580,5/8/20,pubmed,0,4,correlation analysis,0.001415095,0.001415143,0.001415106,0.001415148,0.992924348,0.00141516,Healthcare,0.97300637,TRUE,137,0.929371019,65.5,0.740767996,2,0.618927094,,,0.763022036
9783,[Effect of heat inactivation of blood samples on the efficacy of three detection methods of SARS-CoV-2 antibodies].,Nan Fang Yi Ke Da Xue Xue Bao,32376571,5/8/20,pubmed,0,6,correlation analysis,0.001126775,0.669545771,0.001126859,0.001126798,0.001126794,0.325947003,Genomics,0.9104198,TRUE,14.66666667,0.221163956,3.333333333,0.206515922,7,0.785110192,,,0.404263357
9784,[Laboratory testing techniques for SARS-CoV-2].,Nan Fang Yi Ke Da Xue Xue Bao,32376546,5/8/20,pubmed,0,7,sequencing,0.002183303,0.372939788,0.157864074,0.316504738,0.002183401,0.148324697,Genomics,0.73349035,TRUE,25,0.369286907,17.57142857,0.456515922,6,0.764429903,,,0.530077577
9785,[Analysis of variation and evolution of SARS-CoV-2 genome].,Nan Fang Yi Ke Da Xue Xue Bao,32376535,5/8/20,pubmed,0,6,bioinformatic,0.001565318,0.813950654,0.027124377,0.121636887,0.001565404,0.034157361,Genomics,0.513147,TRUE,81.33333333,0.812480673,34.16666667,0.599879583,8,0.799987654,,,0.737449303
9786,[Mental health status and its influencing factors among college students during the epidemic of COVID-19].,Nan Fang Yi Ke Da Xue Xue Bao,32376528,5/8/20,pubmed,0,3,logistic regression,0.001187242,0.001187339,0.001187377,0.001187308,0.946151402,0.049099332,Healthcare,0.8342469,TRUE,66.33333333,0.742903086,18,0.46180091,33,0.936045435,,,0.713583144
9787,"Transcriptional landscape of SARS-CoV-2 infection dismantles pathogenic pathways activated by the virus, proposes unique sex-specific differences and predicts tailored therapeutic strategies.",Autoimmun Rev,32376402,5/8/20,pubmed,0,9,transcriptom,0.806484937,0.055596213,0.002296595,0.002296609,0.002296628,0.131029017,Drug discovery,0.60011643,TRUE,110.6666667,0.891026037,96.22222222,0.815426813,29,0.928020248,,,0.8781577
9788,Identifying potential treatments of COVID-19 from Traditional Chinese Medicine (TCM) by using a data-driven approach.,J Ethnopharmacol,32376368,5/8/20,pubmed,0,18,"virtual screening, data mining",0.806566564,0.001237142,0.00123714,0.00123714,0.188484903,0.001237112,Drug discovery,0.91805077,TRUE,24.61111111,0.362545612,10.11111111,0.356301846,22,0.908142478,,,0.542329979
9789,Design of a peptide-based subunit vaccine against novel coronavirus SARS-CoV-2.,Microb Pathog,32376359,5/8/20,pubmed,0,4,"molecular dynamics simulation, computational",0.888585954,0.001717234,0.001717194,0.10454514,0.001717249,0.00171723,Drug discovery,0.34367847,FALSE,9.25,0.137918239,7.25,0.302983677,65,0.966911538,,,0.469271151
9790,The Surge After the Surge: Cardiac Surgery Post-COVID-19.,Ann Thorac Surg,32376350,5/8/20,pubmed,0,10,mathematical model,0.001538076,0.001538104,0.001538121,0.75685502,0.001538146,0.236992532,Epidemiology,0.38371682,FALSE,136.4,0.928567011,136.2,0.871019534,23,0.91129082,,,0.903625789
9791,COVID19 pandemic impacts on anxiety of French urologist in training: Outcomes from a national survey.,Prog Urol,32376208,5/8/20,pubmed,0,22,logistic regression,0.001861662,0.001861686,0.001861857,0.001861721,0.901728772,0.090824302,Healthcare,0.95662177,TRUE,61,0.709196611,25.09090909,0.529769869,26,0.920859312,,,0.719941931
9792,Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes in Korea.,Epidemiol Health,32375455,5/8/20,pubmed,0,3,mathematical model,0.001901669,0.001901732,0.001901699,0.962473631,0.001901728,0.029919541,Epidemiology,0.13307133,FALSE,36,0.498299215,4,0.231469093,14,0.866658436,,,0.532142248
9793,COVID-2019-associated overexpressed Prevotella proteins mediated host-pathogen interactions and their role in coronavirus outbreak.,Bioinformatics,32374823,5/7/20,pubmed,0,2,"bioinformatic, proteom, metagenom, whole genome",0.606790746,0.292065139,0.002238441,0.002238515,0.002238446,0.094428713,Drug discovery,0.45911732,FALSE,47,0.606407323,20,0.481000803,9,0.814309525,,,0.633905884
9794,The anti-HIV drug nelfinavir mesylate (Viracept) is a potent inhibitor of cell fusion caused by the SARSCoV-2 spike (S) glycoprotein warranting further evaluation as an antiviral against COVID-19 infections.,J Med Virol,32374457,5/7/20,pubmed,0,7,in-silico,0.935078657,0.018524983,0.001085383,0.001085341,0.001085422,0.043140214,Drug discovery,0.3717814,FALSE,40.28571429,0.542024862,48.42857143,0.675408081,41,0.948144947,,,0.721859297
9795,"SARS-CoV-2: Structural diversity, phylogeny, and potential animal host identification of spike glycoprotein.",J Med Virol,32374452,5/7/20,pubmed,0,2,"genomes, structural model",0.002357862,0.988211119,0.002357777,0.00235779,0.002357707,0.002357744,Genomics,0.873891,TRUE,3.5,0.044344115,0,0.055525823,1,0.537564047,,,0.212477995
9796,Expression of the COVID-19 receptor ACE2 in the human conjunctiva.,J Med Virol,32374427,5/7/20,pubmed,0,11,sequencing,0.646080269,0.236428413,0.002183226,0.002183261,0.002183206,0.110941626,Drug discovery,0.92432624,TRUE,58.18181818,0.690271507,29.09090909,0.56321916,49,0.956787456,,,0.736759374
9797,Potential anti-SARS-CoV-2 drug candidates identified through virtual screening of the ChEMBL database for compounds that target the main coronavirus protease.,FEBS Open Bio,32374074,5/7/20,pubmed,0,1,virtual screening,0.959449952,0.001901741,0.001901712,0.032943068,0.00190179,0.001901737,Drug discovery,0.8486309,TRUE,9,0.135320675,2,0.164302917,21,0.903944688,,,0.401189427
9798,Bioinformatic analysis indicates that SARS-CoV-2 is unrelated to known artificial coronaviruses.,Eur Rev Med Pharmacol Sci,32373995,5/7/20,pubmed,0,8,bioinformatic,0.00190177,0.667463481,0.251066367,0.001901797,0.075764829,0.001901756,Genomics,0.764606,TRUE,67.875,0.752984105,28.5,0.558402462,5,0.739490092,,,0.683625553
9799,In silico screening of natural compounds against COVID-19 by targeting Mpro and ACE2 using molecular docking.,Eur Rev Med Pharmacol Sci,32373991,5/7/20,pubmed,0,7,"virtual screening, in silico",0.824286704,0.001823505,0.001823479,0.118677742,0.001823381,0.051565189,Drug discovery,0.9831947,TRUE,2.571428571,0.027707341,0,0.055525823,37,0.942712513,,,0.341981892
9800,Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence.,Lancet Digit Health,32373786,5/7/20,pubmed,0,1,artificial intelligence,0.019530086,0.019529926,0.902350348,0.019530608,0.019529376,0.019529656,Epidemiology,0.64313734,TRUE,21,0.312016822,1,0.122023013,20,0.900117291,,,0.444719042
9801,Using the contact network model and Metropolis-Hastings sampling to reconstruct the COVID-19 spread on the "Diamond Princess".,Sci Bull (Beijing),32373394,5/7/20,pubmed,0,3,"bayes, network model",0.002238447,0.002238474,0.002238603,0.988807524,0.002238482,0.00223847,Epidemiology,0.43732518,FALSE,24.66666667,0.36334962,2,0.164302917,19,0.89561084,,,0.474421126
9802,Multicenter cohort study demonstrates more consolidation in upper lungs on initial CT increases the risk of adverse clinical outcome in COVID-19 patients.,Theranostics,32373237,5/7/20,pubmed,0,10,logistic regression,0.001098787,0.001098812,0.217719341,0.001098834,0.001098827,0.777885399,Clinics,0.9745147,TRUE,131.4,0.923248191,,,36,0.941169208,,,0.932208699
9803,CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients.,Theranostics,32373235,5/7/20,pubmed,0,19,"artificial intelligence, logistic regression",0.000926271,0.000926272,0.392088635,0.000926292,0.00092628,0.60420625,Clinics,0.9335242,TRUE,65.68421053,0.738511967,33.10526316,0.593323522,60,0.964503982,,,0.76544649
9804,Sleep disturbances among medical workers during the outbreak of COVID-2019.,Occup Med (Lond),32372077,5/7/20,pubmed,0,6,logistic regression,0.001350317,0.001350348,0.001350328,0.001350382,0.90291781,0.091680814,Healthcare,0.97242975,TRUE,4.5,0.061784897,6.166666667,0.281977522,40,0.947033768,,,0.430265396
9805,Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2.,J Hum Genet,32372051,5/7/20,pubmed,0,4,bioinformatic,0.818730957,0.176320675,0.001237092,0.001237107,0.001237105,0.001237065,Drug discovery,0.7190088,TRUE,26.25,0.384748593,16,0.437316029,43,0.9507377,,,0.590934107
9806,"Impact of corticosteroid therapy on outcomes of persons with SARS-CoV-2, SARS-CoV, or MERS-CoV infection: a systematic review and meta-analysis.",Leukemia,32372026,5/7/20,pubmed,0,7,dataset,0.090225969,0.00102268,0.001022639,0.215532116,0.001022673,0.691173923,Clinics,0.8365483,TRUE,248.8571429,0.984476467,236.5714286,0.932833824,101,0.978517192,,,0.965275828
9807,"COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.",Sci Data,32371892,5/7/20,pubmed,0,25,computational,0.762767265,0.011750635,0.19023227,0.011750475,0.011749667,0.011749689,Drug discovery,0.4986987,FALSE,124.24,0.913105325,317.56,0.959058068,44,0.952157541,,,0.941440311
9808,Family cluster of three recovered cases of pneumonia due to severe acute respiratory syndrome coronavirus 2 infection.,BMJ Case Rep,32371416,5/7/20,pubmed,0,4,sequencing,0.00213071,0.717562953,0.002130706,0.002130813,0.00213083,0.273913987,Genomics,0.8941409,TRUE,52.5,0.650875131,360,0.967219695,6,0.764429903,,,0.79417491
9809,"Computational analysis of SARS-CoV-2/COVID-19 surveillance by wastewater-based epidemiology locally and globally: Feasibility, economy, opportunities and challenges.",Sci Total Environ,32371231,5/7/20,pubmed,0,2,computational,0.001203441,0.001203444,0.116829739,0.878356476,0.001203466,0.001203434,Epidemiology,0.530312,TRUE,283.5,0.98936236,308,0.956850415,134,0.984443484,,,0.97688542
9810,Risk factors for disease progression in hospitalized patients with COVID-19: a retrospective cohort study.,Infect Dis (Lond),32370577,5/7/20,pubmed,0,6,logistic regression,0.001622741,0.001622761,0.001622735,0.001622709,0.001622696,0.991886359,Clinics,0.97167253,TRUE,59.66666667,0.70066176,59.16666667,0.717821782,60,0.964503982,,,0.794329175
9811,How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic.,Int J Environ Res Public Health,32370204,5/7/20,pubmed,0,6,"computational, artificial intelligence",0.059199887,0.002898427,0.185990949,0.746114141,0.00289833,0.002898265,Epidemiology,0.8512722,TRUE,94.33333333,0.855154926,28,0.554321648,39,0.945737391,,,0.785071322
9812,"Dengue Fever and Severe Dengue in Barbados, 2008-2016.",Trop Med Infect Dis,32370128,5/7/20,pubmed,0,6,sequencing,0.001291223,0.502457255,0.001291262,0.163492986,0.205743272,0.125724002,Genomics,0.93758255,TRUE,31.5,0.450058754,26.83333333,0.544286861,1,0.537564047,,,0.510636554
9813,A Nationwide Survey of Psychological Distress among Italian People during the COVID-19 Pandemic: Immediate Psychological Responses and Associated Factors.,Int J Environ Res Public Health,32370116,5/7/20,pubmed,0,7,logistic regression,0.001392866,0.027307108,0.001392878,0.063687463,0.904826831,0.001392854,Healthcare,0.82185835,TRUE,75.28571429,0.787989362,48.28571429,0.674872893,348,0.995987407,,,0.819616554
9814,Individualized prediction nomograms for disease progression in mild COVID-19.,J Med Virol,32369205,5/6/20,pubmed,0,5,logistic regression,0.001511829,0.001511878,0.001511899,0.001511872,0.001511853,0.992440669,Clinics,0.75420964,TRUE,39.2,0.53182015,18.2,0.462938186,16,0.881782826,,,0.62551372
9815,"Epidemiological characteristics of confirmed COVID-19 in Guizhou province, China.",Disaster Med Public Health Prep,32367793,5/6/20,pubmed,0,5,correlation analysis,0.001901693,0.001901748,0.001901692,0.324655169,0.391639213,0.278000485,Healthcare,0.564408,TRUE,254.6,0.985033088,139.6,0.873695478,2,0.618927094,,,0.82588522
9816,0,J Biomol Struct Dyn,32367767,5/6/20,pubmed,0,5,in silico,0.969807421,0.001371309,0.001371326,0.001371314,0.001371302,0.024707327,Drug discovery,0.9824195,TRUE,7.4,0.106190859,1,0.122023013,66,0.967652324,,,0.398622065
9817,"SARS-CoV-2 variants: Relevance for symptom granularity, epidemiology, immunity (herd, vaccines), virus origin and containment?",Environ Microbiol,32367648,5/6/20,pubmed,0,2,genome sequences,0.003927474,0.980362325,0.003927393,0.003927788,0.003927531,0.003927488,Genomics,0.3421511,FALSE,314.5,0.991279609,1079.5,0.994380519,7,0.785110192,,,0.923590107
9818,Trends and Innovations in Biosensors for COVID-19 Mass Testing.,Chembiochem,32367615,5/6/20,pubmed,0,1,sequencing,0.001622768,0.475333885,0.488444136,0.001622847,0.03135363,0.001622734,Genomics,0.89429605,TRUE,29,0.41993939,14,0.412898047,35,0.939317242,,,0.590718226
9819,Artificial intelligence to codify lung CT in Covid-19 patients.,Radiol Med,32367319,5/6/20,pubmed,0,7,artificial intelligence,0.033510853,0.000946119,0.796701067,0.166949699,0.000946129,0.000946133,Imaging,0.84490716,TRUE,101.5714286,0.872286474,26.85714286,0.544487557,26,0.920859312,,,0.779211114
9820,A first estimation of the impact of public health actions against COVID-19 in Veneto (Italy).,J Epidemiol Community Health,32366584,5/6/20,pubmed,0,7,bayes,0.002033093,0.002032827,0.0020328,0.989835661,0.002032794,0.002032826,Epidemiology,0.93011105,TRUE,127.7142857,0.918238605,43.42857143,0.651123896,16,0.881782826,,,0.817048442
9821,"Genetic alteration, RNA expression, and DNA methylation profiling of coronavirus disease 2019 (COVID-19) receptor ACE2 in malignancies: a pan-cancer analysis.",J Hematol Oncol,32366279,5/6/20,pubmed,0,5,bioinformatic,0.488494594,0.351957885,0.002490434,0.002490424,0.002490402,0.152076262,Drug discovery,0.7046485,TRUE,61.4,0.71111386,26.2,0.539001873,35,0.939317242,,,0.729810992
9822,The SARS-CoV-2 Exerts a Distinctive Strategy for Interacting with the ACE2 Human Receptor.,Viruses,32365751,5/6/20,pubmed,0,3,molecular dynamics simulation,0.791785875,0.048547475,0.001751225,0.154413069,0.001751185,0.001751172,Drug discovery,0.3119945,FALSE,89,0.839384006,120.6666667,0.852689323,13,0.858880178,,,0.850317836
9823,"The Human Coronavirus Disease COVID-19: Its Origin, Characteristics, and Insights into Potential Drugs and Its Mechanisms.",Pathogens,32365466,5/6/20,pubmed,0,3,genomic structure,0.28399463,0.615741165,0.002898381,0.00289848,0.091568795,0.002898549,Genomics,0.8551728,TRUE,19.33333333,0.288638753,14.33333333,0.415975381,57,0.962281622,,,0.555631919
9824,Rapid reconstruction of SARS-CoV-2 using a synthetic genomics platform.,Nature,32365353,5/5/20,pubmed,0,28,genomes,0.001350375,0.960122148,0.034476457,0.001350375,0.001350345,0.0013503,Genomics,0.44500852,FALSE,30.42857143,0.436947245,106.0357143,0.833221836,42,0.949503056,,,0.739890712
9825,TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19.,J Am Med Inform Assoc,32365190,5/5/20,pubmed,0,9,information retrieval,0.001987129,0.001987143,0.174035011,0.781758474,0.038245076,0.001987166,Epidemiology,0.47169554,FALSE,139.3333333,0.932587049,393.6666667,0.970497725,39,0.945737391,,,0.949607389
9826,Mathematical Modelling to Assess the Impact of Lockdown on COVID-19 Transmission in India: Model Development and Validation.,JMIR Public Health Surveill,32365045,5/5/20,pubmed,0,2,mathematical model,0.001272632,0.00127264,0.001272661,0.99363669,0.001272668,0.00127271,Epidemiology,0.3321513,FALSE,11.5,0.17416043,0.5,0.087101953,22,0.908142478,,,0.389801621
9827,"Mental Health, Risk Factors, and Social Media Use During the COVID-19 Epidemic and Cordon Sanitaire Among the Community and Health Professionals in Wuhan, China: Cross-Sectional Survey.",JMIR Ment Health,32365044,5/5/20,pubmed,0,9,logistic regression,0.000988354,0.000988363,0.000988383,0.095726756,0.890734488,0.010573656,Healthcare,0.9449109,TRUE,175,0.961902406,,,94,0.976726958,,,0.969314682
9828,Towards Contactless Patient Positioning.,IEEE Trans Med Imaging,32365022,5/5/20,pubmed,0,6,dataset,0.001254667,0.001254722,0.511489294,0.167384142,0.183474242,0.135142933,Healthcare,0.574415,TRUE,52.16666667,0.648153875,46,0.66416912,6,0.764429903,,,0.692250966
9829,COVID-19 and paediatric health services: A survey of paediatric physicians in Australia and New Zealand.,J Paediatr Child Health,32364301,5/5/20,pubmed,0,10,logistic regression,0.001622756,0.001622743,0.001622737,0.212381493,0.781127497,0.001622774,Healthcare,0.5697323,TRUE,65.5,0.737646113,46.7,0.667179556,5,0.739490092,,,0.71477192
9830,Using integrated computational approaches to identify safe and rapid treatment for SARS-CoV-2.,J Biomol Struct Dyn,32364041,5/5/20,pubmed,0,3,"molecular dynamics simulation, computational, in silico",0.991577332,0.001684558,0.001684514,0.001684568,0.001684518,0.001684511,Drug discovery,0.9728631,TRUE,3.666666667,0.045952131,0.666666667,0.096200161,51,0.958392493,,,0.366848262
9831,"FDA-approved thiol-reacting drugs that potentially bind into the SARS-CoV-2 main protease, essential for viral replication.",J Biomol Struct Dyn,32364011,5/5/20,pubmed,0,4,in silico,0.921612301,0.001461915,0.001461989,0.001461972,0.001461986,0.072539837,Drug discovery,0.91986704,TRUE,18.75,0.28072237,5,0.257024351,50,0.957775171,,,0.498507297
9832,Smell and taste symptom-based predictive model for COVID-19 diagnosis.,Int Forum Allergy Rhinol,32363809,5/5/20,pubmed,0,5,"classifier, predictive model, logistic regression",0.001203414,0.001203425,0.296770438,0.001203438,0.480342088,0.219277197,Healthcare,0.7852577,TRUE,51.2,0.639866411,38.4,0.623896173,36,0.941169208,,,0.734977264
9833,Prevalence and socio-demographic correlates of psychological health problems in Chinese adolescents during the outbreak of COVID-19.,Eur Child Adolesc Psychiatry,32363492,5/5/20,pubmed,0,9,logistic regression,0.001371259,0.001371318,0.001371254,0.001371308,0.975159248,0.019355612,Healthcare,0.9616667,TRUE,4.555555556,0.061970437,3.666666667,0.217621086,108,0.980060498,,,0.419884007
9834,Deducing the N- and O-glycosylation profile of the spike protein of novel coronavirus SARS-CoV-2.,Glycobiology,32363391,5/5/20,pubmed,0,4,bioinformatic,0.992309262,0.001538171,0.001538139,0.001538129,0.001538084,0.001538216,Drug discovery,0.30552274,FALSE,62.25,0.71661822,67,0.745718491,36,0.941169208,,,0.80116864
9835,Genomics functional analysis and drug screening of SARS-CoV-2.,Genes Dis,32363223,5/5/20,pubmed,0,2,"whole genome, sequence alignment",0.534510411,0.29859434,0.082095981,0.001565355,0.00156532,0.081668592,Drug discovery,0.7636973,TRUE,117,0.902900612,42.5,0.647578271,15,0.874313229,,,0.808264038
9836,Binding site analysis of potential protease inhibitors of COVID-19 using AutoDock.,Virusdisease,32363219,5/5/20,pubmed,0,2,"in silico, sequence alignment",0.993035675,0.001392915,0.00139286,0.001392836,0.001392867,0.001392845,Drug discovery,0.93475413,TRUE,12.5,0.189436576,5.5,0.267259834,30,0.930057411,,,0.462251274
9837,Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction.,Appl Soft Comput,32362799,5/5/20,pubmed,0,5,"machine learning, deep learning",0.001156243,0.001156281,0.230500699,0.764874216,0.001156282,0.001156279,Epidemiology,0.25232267,FALSE,266.6,0.987135877,261.8,0.942333423,82,0.973578616,,,0.967682639
9838,Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach.,Indian J Med Res,32362645,5/5/20,pubmed,0,8,mathematical model,0.039519229,0.001059385,0.001059464,0.895206427,0.001059428,0.062096066,Epidemiology,0.3260495,FALSE,35.5,0.492547467,20.25,0.483074659,0,0.403234768,,,0.459618965
9839,Identification of new anti-nCoV drug chemical compounds from Indian spices exploiting SARS-CoV-2 main protease as target.,J Biomol Struct Dyn,32362243,5/5/20,pubmed,0,5,"virtual screening, bioinformatic",0.994945313,0.00101093,0.001010937,0.001010976,0.001010922,0.001010922,Drug discovery,0.8096225,TRUE,85.6,0.826890964,21.2,0.493644635,65,0.966911538,,,0.762482379
9840,Understanding the binding affinity of noscapines with protease of SARS-CoV-2 for COVID-19 using MD simulations at different temperatures.,J Biomol Struct Dyn,32362235,5/5/20,pubmed,0,8,molecular dynamics simulation,0.86780199,0.002130745,0.00213076,0.082149762,0.002130692,0.043656051,Drug discovery,0.9130227,TRUE,36.125,0.498670295,8.875,0.333623227,44,0.952157541,,,0.594817021
9841,0,J Biomol Struct Dyn,32362217,5/5/20,pubmed,0,4,"molecular dynamics simulation, in silico",0.975791591,0.001392816,0.001392832,0.001392821,0.0013929,0.01863704,Drug discovery,0.9600518,TRUE,19,0.285793803,4,0.231469093,56,0.961540836,,,0.492934577
9842,Correspondence: Importance of the validated serum biochemistry and hemogram parameters for rapid diagnosis and to prevent false negative results during COVID-19 pandemic.,Biotechnol Appl Biochem,32362005,5/4/20,pubmed,0,2,"artificial intelligence, bioinformatic",0.073545102,0.003466227,0.444478534,0.003466302,0.003466237,0.471577598,Clinics,0.5892642,TRUE,35.5,0.492547467,2.5,0.180826866,2,0.618927094,,,0.430767143
9843,Single-cell transcriptome analysis of the novel coronavirus (SARS-CoV-2) associated gene ACE2 expression in normal and non-obstructive azoospermia (NOA) human male testes.,Sci China Life Sci,32361911,5/4/20,pubmed,0,11,transcriptom,0.764981606,0.001415121,0.00141511,0.001415185,0.001415176,0.229357802,Drug discovery,0.8717145,TRUE,171.5454545,0.959490383,133.4545455,0.867072518,32,0.933699611,,,0.920087504
9844,"Risk Factors Associated With Clinical Outcomes in 323 Coronavirus Disease 2019 (COVID-19) Hospitalized Patients in Wuhan, China.",Clin Infect Dis,32361738,5/4/20,pubmed,0,19,logistic regression,0.001901762,0.001901759,0.001901749,0.094081477,0.001901752,0.8983115,Clinics,0.84121895,TRUE,106.8,0.882738574,88.95,0.798969762,134,0.984443484,,,0.888717273
9845,"Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China.",Clin Infect Dis,32361723,5/4/20,pubmed,0,9,"predictive model, prediction model",0.001237065,0.001237059,0.125459095,0.001237065,0.001237055,0.869592661,Clinics,0.7924559,TRUE,134,0.926031294,48.33333333,0.675073588,77,0.972097043,,,0.857733975
9846,"Predictive factors for disease progression in hospitalized patients with coronavirus disease 2019 in Wuhan, China.",J Clin Virol,32361327,5/4/20,pubmed,0,5,logistic regression,0.001622809,0.001622819,0.001622758,0.001622756,0.001622728,0.99188613,Clinics,0.98074484,TRUE,57.4,0.684828994,8.2,0.322785657,68,0.968393111,,,0.658669254
9847,Interpret with caution: An evaluation of the commercial AusDiagnostics versus in-house developed assays for the detection of SARS-CoV-2 virus.,J Clin Virol,32361322,5/4/20,pubmed,0,17,sequencing,0.001126817,0.883318872,0.084285904,0.001126814,0.00112685,0.029014744,Genomics,0.49568394,FALSE,49.05882353,0.624528419,38.29411765,0.62309339,30,0.930057411,,,0.725893073
9848,"Bioinformatic analysis and identification of single-stranded RNA sequences recognized by TLR7/8 in the SARS-CoV-2, SARS-CoV, and MERS-CoV genomes.",Microbes Infect,32361001,5/4/20,pubmed,0,3,"bioinformatic, whole genome, genomes",0.636461755,0.319687977,0.002720265,0.002720158,0.002720099,0.035689746,Drug discovery,0.47134554,FALSE,52.33333333,0.649452656,32.66666667,0.59011239,35,0.939317242,,,0.726294096
9849,Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel Coronavirus.,Math Biosci,32360770,5/4/20,pubmed,0,7,mathematical model,0.011342699,0.000722165,0.000722162,0.9857686,0.000722203,0.000722171,Epidemiology,0.33539957,FALSE,92.14285714,0.849217639,82.42857143,0.783850682,39,0.945737391,,,0.859601904
9850,Forecasting the timeframe of 2019-nCoV and human cells interaction with reverse engineering.,Prog Biophys Mol Biol,32360608,5/4/20,pubmed,0,2,"computational, mathematical model",0.556633449,0.000765974,0.040325703,0.40074301,0.00076593,0.000765934,Drug discovery,0.29999554,FALSE,45,0.587049292,1,0.122023013,4,0.707574542,,,0.472215616
9851,Chemical composition and pharmacological mechanism of Qingfei Paidu Decoction and Ma Xing Shi Gan Decoction against Coronavirus Disease 2019 (COVID-19): In silico and experimental study.,Pharmacol Res,32360484,5/4/20,pubmed,0,13,"in silico, transcriptom, network model",0.907036482,0.001350411,0.087561836,0.001350388,0.001350335,0.00135055,Drug discovery,0.96027976,TRUE,89.38461538,0.840620941,,,38,0.944132354,,,0.892376648
9852,"Genomic Epidemiology of SARS-CoV-2 in Guangdong Province, China.",Cell,32359424,5/4/20,pubmed,0,41,"sequencing, metagenom, genomic epidemiology, genomes",0.00198715,0.678533154,0.001987113,0.313518216,0.001987137,0.00198723,Genomics,0.47640365,FALSE,65.17073171,0.735172243,339.0731707,0.963874766,90,0.975800975,,,0.891615995
9853,A Dynamic Immune Response Shapes COVID-19 Progression.,Cell Host Microbe,32359396,5/4/20,pubmed,0,11,transcriptom,0.759993862,0.055830102,0.002183211,0.002183381,0.002183247,0.177626196,Drug discovery,0.8631674,TRUE,49.36363636,0.626569361,71.36363636,0.757225047,138,0.984813877,,,0.789536095
9854,Repurposing Antiviral Protease Inhibitors Using Extracellular Vesicles for Potential Therapy of COVID-19.,Viruses,32357553,5/3/20,pubmed,0,4,"whole-genome, genome sequences",0.730466043,0.162905798,0.001187291,0.031275564,0.031853327,0.042311977,Drug discovery,0.9182149,TRUE,24.75,0.364710248,12.75,0.395972705,47,0.95493549,,,0.571872814
9855,Feasibility of Known RNA Polymerase Inhibitors as Anti-SARS-CoV-2 Drugs.,Pathogens,32357471,5/3/20,pubmed,0,9,"computational, bioinformatic",0.596284274,0.305571802,0.00171721,0.001717261,0.001717237,0.092992217,Drug discovery,0.53009874,TRUE,76.77777778,0.793122642,69.66666667,0.752475248,15,0.874313229,,,0.80663704
9856,Mathematical modeling of interaction between innate and adaptive immune responses in COVID-19 and implications for viral pathogenesis.,J Med Virol,32356908,5/2/20,pubmed,0,2,mathematical model,0.546450357,0.081226087,0.001461879,0.169455553,0.001461917,0.199944207,Drug discovery,0.47250676,FALSE,4,0.054734368,0,0.055525823,33,0.936045435,,,0.348768542
9857,The Role of Imaging in the Detection and Management of COVID-19: A Review.,IEEE Rev Biomed Eng,32356760,5/2/20,pubmed,0,19,"artificial intelligence, image analysis",0.00242236,0.002422333,0.987887815,0.002422531,0.002422356,0.002422605,Imaging,0.8971381,TRUE,33.94736842,0.475292226,13.10526316,0.4009232,64,0.966479412,,,0.614231613
9858,Considering the Effects of Microbiome and Diet on SARS-CoV-2 Infection: Nanotechnology Roles.,ACS Nano,32356654,5/2/20,pubmed,0,4,microbiom,0.298568281,0.670733773,0.007674264,0.007674569,0.007674176,0.007674937,Genomics,0.87240136,TRUE,348.25,0.994248253,573.75,0.98253947,50,0.957775171,,,0.978187631
9859,Renin-Angiotensin-Aldosterone System Inhibitors and Risk of Covid-19.,N Engl J Med,32356628,5/2/20,pubmed,0,16,bayes,0.165957286,0.001392883,0.001392866,0.001392911,0.00139291,0.828471145,Clinics,0.97517216,TRUE,185.9375,0.966850145,324.0625,0.960663634,535,0.998209766,,,0.975241182
9860,Renin-Angiotensin-Aldosterone System Blockers and the Risk of Covid-19.,N Engl J Med,32356627,5/2/20,pubmed,0,5,logistic regression,0.352492257,0.001415142,0.001415102,0.041862561,0.00141521,0.601399728,Clinics,0.7228733,TRUE,144.8,0.939204651,245.2,0.936379449,551,0.998394963,,,0.957993021
9861,Delayed Radical Prostatectomy is Not Associated with Adverse Oncologic Outcomes: Implications for Men Experiencing Surgical Delay Due to the COVID-19 Pandemic.,J Urol,32356508,5/2/20,pubmed,0,5,logistic regression,0.001511869,0.001511837,0.001511875,0.205242285,0.001511884,0.78871025,Clinics,0.9734776,TRUE,94,0.854598305,54.4,0.699090179,15,0.874313229,,,0.809333905
9862,To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic.,Infect Dis Model,32355904,5/2/20,pubmed,0,8,model simulation,0.000800587,0.000800571,0.000800596,0.995996959,0.000800625,0.000800661,Epidemiology,0.41854432,FALSE,56.25,0.676788917,120.5,0.852421729,296,0.994814495,,,0.841341713
9863,Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China.,Commun Nonlinear Sci Numer Simul,32355435,5/2/20,pubmed,0,4,mathematical model,0.001486428,0.001486472,0.001486465,0.961229202,0.001486445,0.032824988,Epidemiology,0.15604314,FALSE,5.75,0.080957388,2.5,0.180826866,151,0.987036237,,,0.416273497
9864,Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis.,Chaos Solitons Fractals,32355424,5/2/20,pubmed,0,2,forecasting model,0.00162271,0.001622748,0.1045458,0.888963293,0.001622724,0.001622726,Epidemiology,0.47281486,FALSE,43.5,0.573381161,9.5,0.345531175,32,0.933699611,,,0.617537316
9865,Home Surgical Skill Training Resources for Obstetrics and Gynecology Trainees During a Pandemic.,Obstet Gynecol,32355132,5/2/20,pubmed,0,4,simulation model,0.003101612,0.00310152,0.170566935,0.559746335,0.260382143,0.003101454,Epidemiology,0.90574646,TRUE,18.25,0.273609994,4,0.231469093,11,0.840175319,,,0.448418136
9866,Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria.,Int J Environ Res Public Health,32353991,5/2/20,pubmed,0,3,bayes,0.001684465,0.00168453,0.00168449,0.836467437,0.001684591,0.156794487,Epidemiology,0.49731326,FALSE,42.33333333,0.560888119,6,0.280037463,24,0.914439163,,,0.585121582
9867,Structural and Evolutionary Analysis Indicate That the SARS-CoV-2 Mpro Is a Challenging Target for Small-Molecule Inhibitor Design.,Int J Mol Sci,32353978,5/2/20,pubmed,0,6,molecular dynamics simulation,0.867319722,0.126067942,0.001653095,0.001653166,0.001653065,0.001653009,Drug discovery,0.8743136,TRUE,112.8333333,0.895788237,44.5,0.656743377,49,0.956787456,,,0.83643969
9868,Epidemiological characteristics of coronavirus disease 2019 (COVID-19) patients in IRAN: A single center study.,J Clin Virol,32353762,5/1/20,pubmed,0,20,logistic regression,0.001237046,0.001237092,0.001237074,0.089181525,0.001237093,0.90587017,Clinics,0.7191004,TRUE,25.35,0.371946317,11.2,0.373026492,113,0.981171677,,,0.575381495
9869,Genotyping coronavirus SARS-CoV-2: methods and implications.,Genomics,32353474,5/1/20,pubmed,0,1,"genomes, sequence alignment",0.001565606,0.906370974,0.001565392,0.087367394,0.001565317,0.001565317,Genomics,0.72662354,TRUE,40,0.539860226,29,0.562884667,142,0.985616396,,,0.69612043
9870,COVID-19 pandemic and personal protective equipment shortage: protective efficacy comparing masks and scientific methods for respirator reuse.,Gastrointest Endosc,32353457,5/1/20,pubmed,0,4,"deep learning, artificial intelligence",0.075069373,0.001684574,0.248614815,0.468393396,0.204553252,0.00168459,Epidemiology,0.9054692,TRUE,222.25,0.979219494,77,0.771139952,37,0.942712513,,,0.897690653
9871,Development of CRISPR as an Antiviral Strategy to Combat SARS-CoV-2 and Influenza.,Cell,32353252,5/1/20,pubmed,0,15,bioinformatic,0.749599447,0.136214992,0.002357698,0.002357706,0.107112331,0.002357827,Drug discovery,0.5125745,TRUE,26.33333333,0.386047375,74.86666667,0.765988761,90,0.975800975,,,0.709279037
9872,Severe psychological distress among patients with epilepsy during the COVID-19 outbreak in southwest China.,Epilepsia,32353184,5/1/20,pubmed,0,10,logistic regression,0.05356181,0.001022633,0.001022685,0.001022679,0.496733559,0.446636634,Healthcare,0.95064706,TRUE,21.6,0.319438432,9.3,0.340848274,42,0.949503056,,,0.536596587
9873,Thousands Of Lives Could Be Saved In The US During The COVID-19 Pandemic If States Exchanged Ventilators.,Health Aff (Millwood),32352846,5/1/20,pubmed,0,1,forecasting model,0.002562556,0.002562575,0.00256268,0.924271979,0.002562655,0.065477555,Epidemiology,0.59506625,TRUE,35,0.488032655,179,0.905739898,12,0.850299401,,,0.748023985
9874,Association of Obesity with Disease Severity Among Patients with Coronavirus Disease 2019.,Obesity (Silver Spring),32352637,5/1/20,pubmed,0,7,logistic regression,0.001538058,0.001538068,0.001538061,0.001538069,0.001538162,0.992309583,Clinics,0.87050533,TRUE,104,0.877605294,126.8571429,0.860449558,108,0.980060498,,,0.90603845
9875,COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data.,J Med Internet Res,32352383,5/1/20,pubmed,0,4,network analysis,0.000752936,0.093219833,0.00075293,0.862390201,0.042131168,0.000752933,Epidemiology,0.72643745,TRUE,20.25,0.301317336,4,0.231469093,10,0.828199272,,,0.4536619
9876,Infection Control for CT Equipment and Radiographers' Personal Protection During the Coronavirus Disease (COVID-19) Outbreak in China.,AJR Am J Roentgenol,32352309,5/1/20,pubmed,0,5,artificial intelligence,0.003607235,0.003607221,0.690410354,0.003607516,0.295160307,0.003607368,Imaging,0.47283396,FALSE,42,0.558537943,33,0.593256623,10,0.828199272,,,0.659997946
9877,Clinical and epidemiological characteristics of 1420 European patients with mild-to-moderate coronavirus disease 2019.,J Intern Med,32352202,5/1/20,pubmed,0,56,bayes,0.001371232,0.0013713,0.001371252,0.001371306,0.200849047,0.793665863,Clinics,0.9877982,TRUE,52.33333333,0.649452656,,,141,0.985246003,,,0.81734933
9878,Decoding the evolution and transmissions of the novel pneumonia coronavirus (SARS-CoV-2 / HCoV-19) using whole genomic data.,Zool Res,32351056,5/1/20,pubmed,0,4,"phylogenom, genomes",0.001786586,0.795207747,0.001786552,0.197645995,0.001786542,0.001786577,Genomics,0.5262103,TRUE,121.75,0.909147133,163.75,0.895103024,83,0.974381135,,,0.926210431
9879,Rethinking the role of hydroxychloroquine in the treatment of COVID-19.,FASEB J,32350928,5/1/20,pubmed,0,9,dataset,0.615184673,0.001237136,0.001237102,0.249167259,0.001237089,0.131936741,Drug discovery,0.9230869,TRUE,65.11111111,0.734986703,65.33333333,0.740032111,63,0.965985555,,,0.813668123
9880,Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.,Radiol Med,32350794,5/1/20,pubmed,0,4,artificial intelligence,0.00117169,0.044206562,0.664391073,0.129507756,0.001171597,0.159551321,Imaging,0.8343959,TRUE,69.5,0.761271569,17,0.451097137,32,0.933699611,,,0.715356106
9881,Massively multiplexed nucleic acid detection with Cas13.,Nature,32349121,4/30/20,pubmed,0,18,genome sequences,0.028800813,0.793791191,0.174111294,0.001098852,0.001098931,0.00109892,Genomics,0.15406746,FALSE,39.11111111,0.530892449,147.5555556,0.881388815,66,0.967652324,,,0.793311196
9882,Laboratory information system requirements to manage the COVID-19 pandemic: A report from the Belgian national reference testing center.,J Am Med Inform Assoc,32348469,4/30/20,pubmed,0,20,data mining,0.001291333,0.001291283,0.258344352,0.582951285,0.001291299,0.154830449,Epidemiology,0.8613512,TRUE,40.95,0.547529223,25.3,0.530907145,6,0.764429903,,,0.614288757
9883,Trends and Prediction in Daily New Cases and Deaths of COVID-19 in the United States: An Internet Search-Interest Based Model.,Explor Res Hypothesis Med,32348380,4/30/20,pubmed,0,6,dataset,0.001112616,0.001112623,0.00111265,0.730024615,0.001112668,0.265524828,Epidemiology,0.3788459,FALSE,82,0.814769002,49.16666667,0.677950227,22,0.908142478,,,0.800287236
9884,Agile Requirements Engineering and Software Planning for a Digital Health Platform to Engage the Effects of Isolation Caused by Social Distancing: Case Study.,JMIR Public Health Surveill,32348293,4/30/20,pubmed,0,4,"machine learning, digital health",0.018731138,0.000779423,0.041794187,0.673362436,0.26455327,0.000779546,Epidemiology,0.659372,TRUE,74.25,0.782856083,40.5,0.637342788,9,0.814309525,,,0.744836132
9885,"Strong associations and moderate predictive value of early symptoms for SARS-CoV-2 test positivity among healthcare workers, the Netherlands, March 2020.",Euro Surveill,32347200,4/30/20,pubmed,0,12,predictive model,0.06289668,0.004109987,0.150761911,0.00410978,0.639875284,0.138246357,Healthcare,0.8853164,TRUE,76.91666667,0.793555569,89.25,0.799839443,87,0.975492314,,,0.856295776
9886,"Epidemiological characteristics of the first 53 laboratory-confirmed cases of COVID-19 epidemic in Hong Kong, 13 February 2020.",Euro Surveill,32347198,4/30/20,pubmed,0,5,bayes,0.001187274,0.001187276,0.001187296,0.994063531,0.001187341,0.001187283,Epidemiology,0.4954003,FALSE,97,0.861586987,100.2,0.822718758,31,0.931971109,,,0.872092285
9887,Quantitative computed tomography of the coronavirus disease 2019 (COVID-19) pneumonia.,Radiol Infect Dis,32346594,4/30/20,pubmed,0,9,neural network,0.001098791,0.0010988,0.373987194,0.001098832,0.00109882,0.621617563,Clinics,0.9238124,TRUE,38.77777778,0.526934257,26.77777778,0.54388547,14,0.866658436,,,0.645826054
9888,COVID-19 in Italy: Dataset of the Italian Civil Protection Department.,Data Brief,32346568,4/30/20,pubmed,0,5,dataset,0.001392911,0.001392896,0.001392896,0.993035432,0.001392932,0.001392933,Epidemiology,0.7509245,TRUE,32.6,0.462242563,3,0.199424672,9,0.814309525,,,0.491992253
9889,"Structural elucidation of SARS-CoV-2 vital proteins: Computational methods reveal potential drug candidates against main protease, Nsp12 polymerase and Nsp13 helicase.",J Pharm Anal,32346490,4/30/20,pubmed,0,2,"virtual screening, molecular dynamics simulation, computational",0.975294124,0.001237099,0.001237085,0.001237091,0.019757547,0.001237053,Drug discovery,0.95309854,TRUE,28.5,0.412579628,6,0.280037463,75,0.971417989,,,0.55467836
9890,Survey of Insomnia and Related Social Psychological Factors Among Medical Staff Involved in the 2019 Novel Coronavirus Disease Outbreak.,Front Psychiatry,32346373,4/30/20,pubmed,0,13,logistic regression,0.001098795,0.001098792,0.001098815,0.001098838,0.994505882,0.001098879,Healthcare,0.90129155,TRUE,19.07692308,0.285917496,,,159,0.988394345,,,0.637155921
9891,"Artificial intelligence vs COVID-19: limitations, constraints and pitfalls.",AI Soc,32346223,4/30/20,pubmed,0,1,artificial intelligence,0.152761508,0.003335359,0.634159748,0.20307279,0.003335334,0.00333526,Epidemiology,0.7600998,TRUE,7,0.10179974,7,0.299973241,63,0.965985555,,,0.455919512
9892,Renal Involvement and Early Prognosis in Patients with COVID-19 Pneumonia.,J Am Soc Nephrol,32345702,4/30/20,pubmed,0,12,logistic regression,0.001291207,0.001291237,0.001291224,0.001291223,0.001291276,0.993543833,Clinics,0.9516664,TRUE,73.75,0.780753293,,,252,0.993518118,,,0.887135706
9893,Stilbene-based natural compounds as promising drug candidates against COVID-19.,J Biomol Struct Dyn,32345140,4/30/20,pubmed,0,3,molecular dynamics simulation,0.99516605,0.000966769,0.000966848,0.000966795,0.000966789,0.000966751,Drug discovery,0.9523622,TRUE,27.33333333,0.399529965,9.333333333,0.342253144,85,0.975121921,,,0.572301677
9894,0,J Biomol Struct Dyn,32345124,4/30/20,pubmed,0,7,computational,0.894977116,0.001538159,0.001538121,0.098870173,0.001538216,0.001538215,Drug discovery,0.865358,TRUE,22,0.326056033,6.714285714,0.291142628,71,0.96950429,,,0.528900984
9895,3-D Printed Protective Equipment during COVID-19 Pandemic.,Materials (Basel),32344688,4/30/20,pubmed,0,8,dataset,0.001684519,0.049303758,0.001684649,0.654794925,0.290847523,0.001684626,Epidemiology,0.5253797,TRUE,85.375,0.825963263,41.125,0.639951833,16,0.881782826,,,0.782565974
9896,COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images.,Med Hypotheses,32344309,4/29/20,pubmed,0,2,"bayes, deep learning, dataset",0.053800761,0.043614467,0.794606322,0.105102094,0.001438212,0.001438143,Imaging,0.6325051,TRUE,17.5,0.263776362,2.5,0.180826866,112,0.980986481,,,0.47519657
9897,A data-driven hypothesis on the epigenetic dysregulation of host metabolism by SARS coronaviral infection: Potential implications for the SARS-CoV-2 modus operandi.,Med Hypotheses,32344305,4/29/20,pubmed,0,1,transcriptom,0.775746928,0.038211002,0.00162275,0.001622729,0.001622697,0.181173894,Drug discovery,0.5760356,TRUE,39,0.530521368,3,0.199424672,10,0.828199272,,,0.519381771
9898,"Sex-specific clinical characteristics and prognosis of coronavirus disease-19 infection in Wuhan, China: A retrospective study of 168 severe patients.",PLoS Pathog,32343745,4/29/20,pubmed,0,13,logistic regression,0.001330072,0.001330175,0.001330019,0.001330086,0.00133007,0.993349578,Clinics,0.8080002,TRUE,81.46153846,0.81272806,61.23076923,0.725314423,106,0.979628372,,,0.839223618
9899,Strengths and limitations of mathematical models in pandemicsthe case of COVID-19 in Chile.,Medwave,32343682,4/29/20,pubmed,0,1,mathematical model,0.0036073,0.003607275,0.003607302,0.981963472,0.003607384,0.003607268,Epidemiology,0.7138634,TRUE,9,0.135320675,1,0.122023013,1,0.537564047,,,0.264969245
9900,Evaluating the Effectiveness of Social Distancing Interventions to Delay or Flatten the Epidemic Curve of Coronavirus Disease.,Emerg Infect Dis,32343222,4/29/20,pubmed,0,2,mathematical model,0.001751139,0.001751172,0.001751163,0.770149962,0.170412784,0.05418378,Epidemiology,0.5041938,TRUE,4.5,0.061784897,0,0.055525823,104,0.979134514,,,0.365481745
9901,The importance of naturally attenuated SARS-CoV-2in the fight against COVID-19.,Environ Microbiol,32342578,4/29/20,pubmed,0,27,sequencing,0.002490666,0.48874969,0.002490692,0.38448052,0.11929795,0.002490483,Genomics,0.41130403,FALSE,101.4814815,0.871853547,313.1111111,0.957920792,30,0.930057411,,,0.919943917
9902,Outbreak dynamics of COVID-19 in China and the United States.,Biomech Model Mechanobiol,32342242,4/29/20,pubmed,0,4,"mathematical model, network model",0.001126781,0.001126815,0.001126794,0.994365938,0.001126831,0.001126841,Epidemiology,0.4558582,FALSE,6.5,0.093512277,0.25,0.065493712,19,0.89561084,,,0.351538943
9903,Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization.,Am J Obstet Gynecol MFM,32342041,4/29/20,pubmed,0,5,forecasting model,0.001330193,0.001330049,0.001330022,0.621539492,0.135228554,0.23924169,Epidemiology,0.5910939,TRUE,19.8,0.293957573,3.6,0.21507894,5,0.739490092,,,0.416175535
9904,Chaos game representation dataset of SARS-CoV-2 genome.,Data Brief,32341946,4/29/20,pubmed,0,2,"machine learning, bioinformatic, dataset",0.002898354,0.466622113,0.397062449,0.127620499,0.002898265,0.00289832,Genomics,0.64307964,TRUE,43.5,0.573381161,3,0.199424672,6,0.764429903,,,0.512411912
9905,Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan.,Chaos Solitons Fractals,32341628,4/29/20,pubmed,0,4,mathematical model,0.003760558,0.003760567,0.003760791,0.981197168,0.003760433,0.003760482,Epidemiology,0.793469,TRUE,122.75,0.911188076,41,0.639751137,158,0.988270881,,,0.846403365
9906,SARS-CoV-2: Camazotz's Curse.,Med J Armed Forces India,32341622,4/29/20,pubmed,0,4,"whole genome, genomes",0.001653146,0.863931111,0.001653037,0.129456299,0.00165308,0.001653327,Genomics,0.21900007,FALSE,54.5,0.665161729,29.25,0.564557131,12,0.850299401,,,0.693339421
9907,A molecular modeling approach to identify effective antiviral phytochemicals against the main protease of SARS-CoV-2.,J Biomol Struct Dyn,32340562,4/29/20,pubmed,0,9,computational,0.9215269,0.001237095,0.073524673,0.001237143,0.001237091,0.001237098,Drug discovery,0.9059478,TRUE,18.33333333,0.274908776,4.444444444,0.239496923,91,0.976047904,,,0.496817868
9908,Natural products may interfere with SARS-CoV-2 attachment to the host cell.,J Biomol Struct Dyn,32340551,4/29/20,pubmed,0,1,molecular dynamics simulation,0.921055688,0.001438136,0.001438175,0.00143812,0.001438183,0.073191698,Drug discovery,0.84274924,TRUE,35,0.488032655,32,0.585763982,75,0.971417989,,,0.681738209
9909,Putative Inhibitors of SARS-CoV-2 Main Protease from A Library of Marine Natural Products: A Virtual Screening and Molecular Modeling Study.,Mar Drugs,32340389,4/29/20,pubmed,0,6,"virtual screening, computational",0.991576968,0.001684635,0.001684597,0.001684608,0.001684656,0.001684537,Drug discovery,0.9867271,TRUE,88.66666667,0.838394459,15.66666667,0.432365534,81,0.973331687,,,0.74803056
9910,The Use of Digital Health in the Detection and Management of COVID-19.,Int J Environ Res Public Health,32340107,4/29/20,pubmed,0,1,digital health,0.00431006,0.004310277,0.323866339,0.658892903,0.004310378,0.004310044,Epidemiology,0.8054443,TRUE,11,0.167171748,4,0.231469093,28,0.926168282,,,0.441603041
9911,Turning the Crisis Into an Opportunity: Digital Health Strategies Deployed During the COVID-19 Outbreak.,JMIR Public Health Surveill,32339998,4/28/20,pubmed,0,12,digital health,0.002130697,0.002130664,0.002130772,0.989346332,0.002130754,0.002130781,Epidemiology,0.9712762,TRUE,2.166666667,0.022450368,0.333333333,0.073187048,25,0.918019631,,,0.337885682
9912,"Statistical and network analysis of 1212 COVID-19 patients in Henan, China.",Int J Infect Dis,32339715,4/28/20,pubmed,0,6,network analysis,0.001565337,0.001565333,0.001565346,0.733279981,0.086103257,0.175920747,Epidemiology,0.45098558,FALSE,34,0.477766096,10.16666667,0.357171528,25,0.918019631,,,0.584319085
9913,Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.,Radiology,32339081,4/28/20,pubmed,0,22,"artificial intelligence, neural network",0.00080056,0.000800567,0.798373285,0.000800573,0.000800586,0.198424428,Imaging,0.92964584,TRUE,26.27272727,0.38499598,15.95454545,0.434773883,66,0.967652324,,,0.595807396
9914,Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.,Eur J Clin Microbiol Infect Dis,32337662,4/28/20,pubmed,0,4,"machine learning, neural network",0.001237074,0.001237192,0.946035789,0.049015803,0.001237061,0.001237082,Imaging,0.71413565,TRUE,177,0.963077494,75.25,0.76699224,107,0.979813569,,,0.903294434
9915,Combination of Biodata Mining and Computational Modelling in Identification and Characterization of ORF1ab Polyprotein of SARS-CoV-2 Isolated from Oronasopharynx of an Iranian Patient.,Biol Proced Online,32336957,4/28/20,pubmed,0,3,"computational, bioinformatic, data mining",0.371098591,0.38824533,0.001943507,0.130552515,0.001943525,0.106216531,Genomics,0.76419866,TRUE,29.66666667,0.42773208,2.666666667,0.185442869,10,0.828199272,,,0.480458073
9916,"Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): Emergence, history, basic and clinical aspects.",Saudi J Biol Sci,32336927,4/28/20,pubmed,0,1,genomic structure,0.001371285,0.568760953,0.001371276,0.425753866,0.001371306,0.001371314,Genomics,0.41088033,FALSE,85,0.825035562,74,0.764182499,23,0.91129082,,,0.833502961
9917,Extracorporeal membrane oxygenation for critically ill patients with coronavirus-associated disease 2019: an updated perspective of the European experience.,Minerva Cardioangiol,32336080,4/28/20,pubmed,0,11,logistic regression,0.002032831,0.002032797,0.002032809,0.179639919,0.002032892,0.812228752,Clinics,0.15595242,FALSE,163.1818182,0.953676789,101.3636364,0.824658817,20,0.900117291,,,0.892817632
9918,In the search of potential epitopes for Wuhan seafood market pneumonia virus using high order nullomers.,J Immunol Methods,32335161,4/27/20,pubmed,0,2,bioinformatic,0.464901739,0.001861849,0.044470723,0.485042041,0.001861874,0.001861775,Epidemiology,0.69727814,TRUE,58.5,0.693240151,22.5,0.507492641,4,0.707574542,,,0.636102445
9919,Can we predict the occurrence of COVID-19 cases? Considerations using a simple model of growth.,Sci Total Environ,32334161,4/26/20,pubmed,0,2,prediction model,0.003760584,0.003760643,0.003760737,0.98119692,0.003760599,0.003760518,Epidemiology,0.48393142,FALSE,59,0.696579875,16.5,0.44293551,25,0.918019631,,,0.685845005
9920,Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020.,Sci Total Environ,32334160,4/26/20,pubmed,0,3,prediction model,0.001987211,0.05196017,0.001987168,0.897032333,0.001987187,0.04504593,Epidemiology,0.3350575,FALSE,23.33333333,0.345723298,10.33333333,0.36038266,82,0.973578616,,,0.559894858
9921,Prediction for the spread of COVID-19 in India and effectiveness of preventive measures.,Sci Total Environ,32334157,4/26/20,pubmed,0,2,lstm,0.002422307,0.002422403,0.00242237,0.883340304,0.106970321,0.002422294,Epidemiology,0.69297945,TRUE,29,0.41993939,5,0.257024351,133,0.984196555,,,0.553720099
9922,When past is not a prologue: Adapting informatics practice during a pandemic.,J Am Med Inform Assoc,32333757,4/26/20,pubmed,0,5,predictive model,0.002898441,0.002898378,0.223806677,0.470678767,0.296819348,0.002898389,Epidemiology,0.14339945,FALSE,95,0.856824788,83.8,0.787931496,6,0.764429903,,,0.803062062
9923,Expression of SARS-CoV-2 Entry Molecules ACE2 and TMPRSS2 in the Gut of Patients With IBD.,Inflamm Bowel Dis,32333601,4/26/20,pubmed,0,17,sequencing,0.685907409,0.088455445,0.00127273,0.001272722,0.019559669,0.203532025,Drug discovery,0.9708873,TRUE,52,0.647349867,17.70588235,0.457653198,55,0.960800049,,,0.688601038
9924,"The genetic sequence, origin, and diagnosis of SARS-CoV-2.",Eur J Clin Microbiol Infect Dis,32333222,4/26/20,pubmed,0,7,genome sequences,0.255713566,0.407342569,0.103997361,0.1805009,0.001653099,0.050792505,Genomics,0.6686714,TRUE,80.28571429,0.808027707,39.42857143,0.630117742,115,0.981665535,,,0.806603661
9925,"Comparative Global Epidemiological Investigation of SARS-CoV-2 and SARS-CoV Diseases Using Meta-MUMS Tool Through Incidence, Mortality, and Recovery Rates.",Arch Med Res,32331787,4/26/20,pubmed,0,7,dataset,0.001861807,0.00186203,0.103402434,0.333635826,0.001861712,0.55737619,Clinics,0.35465303,FALSE,74,0.782113922,20,0.481000803,8,0.799987654,,,0.687700793
9926,Can mathematical modelling solve the current Covid-19 crisis?,BMC Public Health,32331516,4/26/20,pubmed,0,1,mathematical model,0.004775141,0.004775252,0.004775127,0.976124268,0.004775124,0.004775088,Epidemiology,0.5164514,TRUE,30,0.432432432,4,0.231469093,34,0.937156615,,,0.533686047
9927,COVID-19 Factors and Psychological Factors Associated with Elevated Psychological Distress among Dentists and Dental Hygienists in Israel.,Int J Environ Res Public Health,32331401,4/26/20,pubmed,0,6,logistic regression,0.002490451,0.002490458,0.002490436,0.002490532,0.928347077,0.061691046,Healthcare,0.9589956,TRUE,64.33333333,0.730781124,37,0.616671127,62,0.965553429,,,0.771001893
9928,"Hand Hygiene, Mask-Wearing Behaviors and Its Associated Factors during the COVID-19 Epidemic: A Cross-Sectional Study among Primary School Students in Wuhan, China.",Int J Environ Res Public Health,32331344,4/26/20,pubmed,0,6,logistic regression,0.001717158,0.001717159,0.001717161,0.001717222,0.991414124,0.001717176,Healthcare,0.8653314,TRUE,53.16666667,0.654957017,35.16666667,0.606234948,47,0.95493549,,,0.738709151
9929,Human Interaction Smart Subsystem-Extending Speech-Based Human-Robot Interaction Systems with an Implementation of External Smart Sensors.,Sensors (Basel),32331291,4/26/20,pubmed,0,6,artificial intelligence,0.175302351,0.00165308,0.341817682,0.477920806,0.001653064,0.001653016,Epidemiology,0.64505935,TRUE,32.33333333,0.459273919,12.5,0.392761573,4,0.707574542,,,0.519870011
9930,The Architecture of SARS-CoV-2 Transcriptome.,Cell,32330414,4/25/20,pubmed,0,6,"sequencing, transcriptom",0.275741746,0.716127017,0.002032936,0.0020328,0.002032772,0.002032729,Genomics,0.2163586,FALSE,42.83333333,0.565650319,332.1666667,0.962469896,112,0.980986481,,,0.836368898
9931,Estimating the effects of asymptomatic and imported patients on COVID-19 epidemic using mathematical modeling.,J Med Virol,32330299,4/25/20,pubmed,0,2,"model fit, mathematical model",0.00235776,0.002357798,0.002358016,0.856124347,0.002357852,0.134444227,Epidemiology,0.3364966,FALSE,18.5,0.278001113,13,0.400521809,15,0.874313229,,,0.51761205
9932,Optimization of group size in pool testing strategy for SARS-CoV-2: A simple mathematical model.,J Med Virol,32330297,4/25/20,pubmed,0,3,mathematical model,0.001330039,0.154295096,0.113944099,0.354662826,0.374437834,0.001330106,Healthcare,0.19742492,FALSE,51.33333333,0.641288886,4,0.231469093,26,0.920859312,,,0.59787243
9933,A precision medicine approach to managing 2019 novel coronavirus pneumonia.,Precis Clin Med,32330209,4/25/20,pubmed,0,12,sequencing,0.001684555,0.413221023,0.111841988,0.197887844,0.001684574,0.273680016,Genomics,0.7779017,TRUE,59.91666667,0.702269775,31.41666667,0.580278298,23,0.91129082,,,0.731279631
9934,Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.,PLoS One,32330208,4/25/20,pubmed,0,6,"machine learning, whole-genome, genome sequences, genomes, dataset",0.001098843,0.532451878,0.463152784,0.001098877,0.001098813,0.001098805,Genomics,0.55202264,TRUE,59.83333333,0.701651308,39.16666667,0.628512176,71,0.96950429,,,0.766555925
9935,Presymptomatic SARS-CoV-2 Infections and Transmission in a Skilled Nursing Facility.,N Engl J Med,32329971,4/25/20,pubmed,0,84,sequencing,0.000999507,0.287662701,0.00099953,0.000999568,0.531982519,0.177356175,Healthcare,0.6972181,TRUE,60.63888889,0.706042427,171.4722222,0.900588708,949,0.999506142,,,0.868712426
9936,Andrographolide as a potential inhibitor of SARS-CoV-2 main protease: an in silico approach.,J Biomol Struct Dyn,32329419,4/25/20,pubmed,0,4,"computational, in silico",0.695361279,0.001622742,0.04441981,0.126351678,0.00162275,0.130621741,Drug discovery,0.93396306,TRUE,29.25,0.421671099,90.75,0.802582285,107,0.979813569,,,0.734688984
9937,Discovery of potential multi-target-directed ligands by targeting host-specific SARS-CoV-2 structurally conserved main protease.,J Biomol Struct Dyn,32329408,4/25/20,pubmed,0,9,network analysis,0.865705243,0.129414684,0.001220004,0.001220009,0.001219991,0.001220069,Drug discovery,0.8883519,TRUE,38.33333333,0.522233904,33.66666667,0.596802248,121,0.982714982,,,0.700583712
9938,Self-reported olfactory loss associates with outpatient clinical course in COVID-19.,Int Forum Allergy Rhinol,32329222,4/25/20,pubmed,0,5,logistic regression,0.001901774,0.001901733,0.139127489,0.001901834,0.116255387,0.738911784,Clinics,0.9826258,TRUE,47.6,0.611169522,37,0.616671127,148,0.986727576,,,0.738189408
9939,"[E-health tools to overcome the gap in epilepsy care before, during and after COVID-19 pandemics].",Rev Neurol,32329045,4/25/20,pubmed,0,8,digital health,0.001486422,0.001486436,0.001486499,0.863302287,0.072588662,0.059649694,Epidemiology,0.9815696,TRUE,9.5,0.143051518,,,5,0.739490092,,,0.441270805
9940,SBDiEM: A new mathematical model of infectious disease dynamics.,Chaos Solitons Fractals,32327901,4/25/20,pubmed,0,2,"artificial intelligence, mathematical model",0.001786622,0.001786537,0.001786702,0.991067031,0.001786568,0.001786541,Epidemiology,0.5293878,TRUE,57,0.68204589,53,0.693738293,21,0.903944688,,,0.759909624
9941,SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes.,Nat Med,32327758,4/25/20,pubmed,0,80,sequencing,0.634216492,0.288305966,0.002357862,0.002358009,0.03777695,0.034984722,Drug discovery,0.59036565,TRUE,114.7407407,0.898633187,,,914,0.999382678,,,0.949007932
9942,Clinical and virologic characteristics of the first 12 patients with coronavirus disease 2019 (COVID-19) in the United States.,Nat Med,32327757,4/25/20,pubmed,0,179,"sequencing, whole genome",0.001141333,0.454976219,0.001141344,0.001141363,0.001141344,0.540458397,Clinics,0.625489,TRUE,34.08888889,0.477951636,,,145,0.986048522,,,0.732000079
9943,Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development.,Int J Environ Res Public Health,32325763,4/25/20,pubmed,0,5,prediction model,0.002080978,0.002080545,0.002080701,0.95559712,0.002080666,0.03607999,Epidemiology,0.6651176,TRUE,21.4,0.316469788,9,0.337904736,24,0.914439163,,,0.522937896
9944,Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea.,J Med Internet Res,32325426,4/24/20,pubmed,0,3,network analysis,0.000728138,0.037058527,0.000728156,0.960028871,0.000728185,0.000728124,Epidemiology,0.95398575,TRUE,9.666666667,0.144968767,1.666666667,0.145036125,1,0.537564047,,,0.275856313
9945,A Guide to Chatbots for COVID-19 Screening at Pediatric Health Care Facilities.,JMIR Public Health Surveill,32325425,4/24/20,pubmed,0,3,digital health,0.003927503,0.00392742,0.200952677,0.477579849,0.309685138,0.003927412,Epidemiology,0.54098797,TRUE,8,0.118683901,1,0.122023013,13,0.858880178,,,0.366529031
9946,"Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey.",Psychiatry Res,32325383,4/24/20,pubmed,0,2,logistic regression,0.001717158,0.001717193,0.001717171,0.001717208,0.991414056,0.001717214,Healthcare,0.8128366,TRUE,26.5,0.389325252,40.5,0.637342788,558,0.998518427,,,0.675062156
9947,Bayesian phylodynamic inference on the temporal evolution and global transmission of SARS-CoV-2.,J Infect,32325130,4/24/20,pubmed,0,4,bayes,0.015998345,0.015999961,0.015998456,0.920006453,0.01599839,0.015998395,Epidemiology,0.17449424,FALSE,58,0.689714887,8.25,0.323789136,11,0.840175319,,,0.617893114
9948,Incidence of Adverse Drug Reactions in COVID-19 Patients in China: An Active Monitoring Study by Hospital Pharmacovigilance System.,Clin Pharmacol Ther,32324898,4/24/20,pubmed,0,9,logistic regression,0.156670438,0.001593597,0.001593574,0.001593635,0.001593598,0.836955159,Clinics,0.7605181,TRUE,11.88888889,0.17892263,12.55555556,0.393162965,27,0.92443978,,,0.498841792
9949,Estimating the Size of a COVID-19 Epidemic from Surveillance Systems.,Epidemiology,32324625,4/24/20,pubmed,0,3,bayes,0.003101436,0.003102014,0.098642011,0.888951294,0.0031017,0.003101545,Epidemiology,0.35168207,FALSE,85,0.825035562,88.66666667,0.798501472,13,0.858880178,,,0.827472404
9950,Do Not Forget Afghanistan in Times of COVID-19: Telemedicine and the Internet of Things to Strengthen Planetary Health Systems.,OMICS,32324451,4/24/20,pubmed,0,3,digital health,0.033140267,0.001684574,0.055412793,0.550375737,0.357702094,0.001684535,Epidemiology,0.95916796,TRUE,31.33333333,0.448388892,16.33333333,0.440460262,10,0.828199272,,,0.572349475
9951,The Potential Health Care Costs And Resource Use Associated With COVID-19 In The United States.,Health Aff (Millwood),32324428,4/24/20,pubmed,0,7,simulation model,0.001486439,0.00148645,0.001486473,0.53264572,0.001486504,0.461408414,Epidemiology,0.7788241,TRUE,34.57142857,0.482837529,16,0.437316029,73,0.970677202,,,0.63027692
9952,0,Antioxid Redox Signal,32323565,4/24/20,pubmed,0,4,sequencing,0.783518608,0.001171627,0.00117157,0.001171545,0.158941126,0.054025525,Drug discovery,0.9158912,TRUE,142.5,0.936854475,227.25,0.928552315,24,0.914439163,,,0.926615317
9953,Emerging SARS-CoV-2 mutation hot spots include a novel RNA-dependent-RNA polymerase variant.,J Transl Med,32321524,4/24/20,pubmed,0,12,genomes,0.140245722,0.799565267,0.000889047,0.00088907,0.000889053,0.057521842,Genomics,0.79112625,TRUE,156,0.948481662,177.0833333,0.90426813,394,0.996789925,,,0.949846572
9954,Covid-19: Simulation models for epidemics.,Tidsskr Nor Laegeforen,32321234,4/24/20,pubmed,0,3,simulation model,0.034965429,0.034962249,0.034961885,0.825186484,0.034962079,0.034961874,Epidemiology,0.607154,TRUE,86.33333333,0.830354382,36.66666667,0.614998662,1,0.537564047,,,0.660972364
9955,Déjà vu: Stimulating open drug discovery for SARS-CoV-2.,Drug Discov Today,32320852,4/23/20,pubmed,0,12,computational,0.869098382,0.003335504,0.003335381,0.117560121,0.003335345,0.003335266,Drug discovery,0.7054632,TRUE,46.75,0.603314985,36.08333333,0.611386139,37,0.942712513,,,0.719137879
9956,Letter to the Editor: Low-density lipoprotein is a potential predictor of poor prognosis in patients with coronavirus disease 2019.,Metabolism,32320740,4/23/20,pubmed,0,7,logistic regression,0.001786588,0.073046175,0.001786594,0.001786617,0.001786575,0.919807451,Clinics,0.9712186,TRUE,35.57142857,0.492918548,22,0.503746321,37,0.942712513,,,0.646459127
9957,Phylogenetic Analysis and Structural Modeling of SARS-CoV-2 Spike Protein Reveals an Evolutionary Distinct and Proteolytically Sensitive Activation Loop.,J Mol Biol,32320687,4/23/20,pubmed,0,5,structural model,0.594596407,0.398110085,0.001823338,0.001823457,0.00182337,0.001823342,Drug discovery,0.56647885,TRUE,58.4,0.69231245,164,0.895437517,162,0.988579542,,,0.858776503
9958,"The Prevalence, Characteristics, and Prevention Status of Skin Injury Caused by Personal Protective Equipment Among Medical Staff in Fighting COVID-19: A Multicenter, Cross-Sectional Study.",Adv Wound Care (New Rochelle),32320359,4/23/20,pubmed,0,15,logistic regression,0.00135047,0.001350354,0.001350362,0.001350376,0.667910881,0.326687556,Healthcare,0.98687285,TRUE,47.2,0.607520564,15.73333333,0.432900723,12,0.850299401,,,0.630240229
9959,Special Issue: Digital Health in Times of COVID-19.,OMICS,32319847,4/23/20,pubmed,0,1,digital health,0.025060584,0.025060628,0.025061213,0.874695469,0.025061545,0.025060562,Epidemiology,0.56777596,TRUE,104,0.877605294,29,0.562884667,1,0.537564047,,,0.659351336
9960,Transmission Potential of Asymptomatic and Paucisymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infections: A 3-Family Cluster Study in China.,J Infect Dis,32319519,4/23/20,pubmed,0,19,genomes,0.002996431,0.623365588,0.002996437,0.002996581,0.19996247,0.167682493,Genomics,0.36038783,FALSE,38.36842105,0.522419445,21,0.492239765,31,0.931971109,,,0.648876773
9961,The Effect of COVID-19 on Youth Mental Health.,Psychiatr Q,32319041,4/23/20,pubmed,0,7,logistic regression,0.00198709,0.001987206,0.00198711,0.001987255,0.990064094,0.001987244,Healthcare,0.9346831,TRUE,86,0.829117447,23.14285714,0.513981804,127,0.983640965,,,0.775580072
9962,D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19.,Acta Pharm Sin B,32318328,4/23/20,pubmed,0,9,virtual screening,0.875573269,0.00148649,0.118480818,0.001486499,0.00148644,0.001486484,Drug discovery,0.7826919,TRUE,35.66666667,0.493908096,11.22222222,0.373227188,18,0.891474782,,,0.586203355
9963,Potential therapeutic effects of dipyridamole in the severely ill patients with COVID-19.,Acta Pharm Sin B,32318327,4/23/20,pubmed,0,25,"virtual screening, in silico",0.375573318,0.00203276,0.002033051,0.002032849,0.002032791,0.616295231,Clinics,0.81908596,TRUE,52.28,0.648834189,48.2,0.674672197,83,0.974381135,,,0.765962507
9964,"Understanding SARS-CoV-2: Genetic Diversity, Transmission and Cure in Human.",Indian J Microbiol,32317810,4/23/20,pubmed,0,2,microbiom,0.077853206,0.58369031,0.001751192,0.2547269,0.08022713,0.001751263,Genomics,0.23561296,FALSE,136.5,0.928690704,100,0.822584961,16,0.881782826,,,0.877686164
9965,0,Indian J Med Res,32317409,4/23/20,pubmed,0,22,"sequencing, whole genome, genomes",0.000734206,0.97215817,0.000734163,0.000734182,0.024905123,0.000734156,Genomics,0.9865643,TRUE,27.77272727,0.404415858,16.40909091,0.441263045,7,0.785110192,,,0.543596365
9966,Outbreak Trends of Coronavirus Disease-2019 in India: A Prediction.,Disaster Med Public Health Prep,32317044,4/23/20,pubmed,0,3,"machine learning, prediction model",0.001786496,0.001786526,0.001786642,0.991067137,0.001786555,0.001786643,Epidemiology,0.39009917,FALSE,22.33333333,0.330261612,4.333333333,0.237958255,33,0.936045435,,,0.501421767
9967,"Suppressed T cell-mediated immunity in patients with COVID-19: A clinical retrospective study in Wuhan, China.",J Infect,32315725,4/22/20,pubmed,0,9,logistic regression,0.000765986,0.000765935,0.000765947,0.000765958,0.000765978,0.996170196,Clinics,0.9535274,TRUE,26,0.382398417,9,0.337904736,115,0.981665535,,,0.567322896
9968,Understanding evolution of SARS-CoV-2: A perspective from analysis of genetic diversity of RdRp gene.,J Med Virol,32314811,4/22/20,pubmed,0,5,bayes,0.001371309,0.954121774,0.040393146,0.001371287,0.001371238,0.001371245,Genomics,0.2863595,FALSE,15.6,0.235450554,4,0.231469093,11,0.840175319,,,0.435698322
9969,What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization.,EClinicalMedicine,32313879,4/22/20,pubmed,0,7,computational,0.000565322,0.000565328,0.000565308,0.661457584,0.336281128,0.000565331,Epidemiology,0.2659378,FALSE,3.285714286,0.039210836,,,41,0.948144947,,,0.493677891
9970,A "One-Health" approach for diagnosis and molecular characterization of SARS-CoV-2 in Italy.,One Health,32313828,4/22/20,pubmed,0,21,"sequencing, whole genome",0.001943481,0.554092161,0.001943613,0.398163681,0.001943582,0.041913481,Genomics,0.9134585,TRUE,54.42857143,0.664172181,52.61904762,0.691329944,22,0.908142478,,,0.754548201
9971,Tracing New Clinical Manifestations in Patients with COVID-19 in Chile and Its Potential Relationship with the SARS-CoV-2 Divergence.,Curr Trop Med Rep,32313804,4/22/20,pubmed,0,4,genomic epidemiology,0.002898411,0.663736415,0.002898342,0.002898514,0.002898466,0.324669852,Genomics,0.8388436,TRUE,190.75,0.968891088,54.5,0.699892962,13,0.858880178,,,0.842554743
9972,Chest CT findings of early and progressive phase COVID-19 infection from a US patient.,Radiol Case Rep,32313588,4/22/20,pubmed,0,2,artificial intelligence,0.002296547,0.002296604,0.590754336,0.269749939,0.132605852,0.002296722,Imaging,0.82151675,TRUE,5.5,0.077246583,1,0.122023013,9,0.814309525,,,0.337859707
9973,0,Chem Phys Lett,32313296,4/22/20,pubmed,0,3,"virtual screening, computational",0.838268412,0.002806412,0.150506016,0.002806449,0.002806369,0.002806342,Drug discovery,0.81727266,TRUE,57.66666667,0.686375162,11.33333333,0.375501739,50,0.957775171,,,0.673217358
9974,0,F1000Res,32194944,4/22/20,pubmed,0,3,virtual screening,0.988210897,0.002358009,0.002357897,0.002357761,0.002357723,0.002357714,Drug discovery,0.962888,TRUE,57,0.68204589,25.33333333,0.531576131,128,0.983826162,,,0.732482728
9975,Specific ACE2 expression in small intestinal enterocytes may cause gastrointestinal symptoms and injury after 2019-nCoV infection.,Int J Infect Dis,32311451,4/21/20,pubmed,0,8,dataset,0.431685149,0.001220115,0.001220062,0.001220141,0.09819739,0.466457142,Clinics,0.8530772,TRUE,26.75,0.391799122,5,0.257024351,63,0.965985555,,,0.538269676
9976,Detection and analysis of nucleic acid in various biological samples of COVID-19 patients.,Travel Med Infect Dis,32311437,4/21/20,pubmed,0,8,sequencing,0.000572633,0.718738814,0.054962171,0.000572663,0.00057265,0.22458107,Genomics,0.7394747,TRUE,23.25,0.34454821,12.5,0.392761573,72,0.970121612,,,0.569143798
9977,"Etiology and genetic evolution of canine coronavirus circulating in five provinces of China, during 2018-2019.",Microb Pathog,32311431,4/21/20,pubmed,0,11,genomes,0.001751208,0.991243984,0.001751188,0.001751202,0.001751218,0.0017512,Genomics,0.6431591,TRUE,17.72727273,0.266806853,9.363636364,0.342386941,5,0.739490092,,,0.449561295
9978,COVID-19 (Coronavirus Disease 2019): Opportunities and Challenges for Digital Health and the Internet of Medical Things in China.,OMICS,32311300,4/21/20,pubmed,0,2,digital health,0.013548901,0.013549011,0.013550346,0.932251992,0.01355043,0.013549321,Epidemiology,0.87942755,TRUE,21.5,0.318263343,10,0.355632861,10,0.828199272,,,0.500698492
9979,Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study.,Lancet Digit Health,32309796,4/21/20,pubmed,0,3,dataset,0.000704911,0.000704924,0.013839479,0.677440913,0.096033354,0.211276419,Epidemiology,0.74933976,TRUE,130.3333333,0.92213495,398,0.970966016,239,0.993332922,,,0.962144629
9980,Video for Active and Remote Learning.,Trends Chem,32309794,4/21/20,pubmed,0,1,active learning,0.006089746,0.006089601,0.123794781,0.329611312,0.528325045,0.006089515,Healthcare,0.9362093,TRUE,102,0.873523409,36,0.611118544,2,0.618927094,,,0.701189683
9981,Mathematical prediction of the time evolution of the COVID-19 pandemic in Italy by a Gauss error function and Monte Carlo simulations.,Eur Phys J Plus,32309108,4/21/20,pubmed,0,2,mathematical prediction,0.001254606,0.071058966,0.001254807,0.923922201,0.001254633,0.001254788,Epidemiology,0.3094259,FALSE,21.5,0.318263343,243,0.935242173,34,0.937156615,,,0.73022071
9982,Coronaviruses and people with intellectual disability: an exploratory data analysis.,J Intellect Disabil Res,32307762,4/21/20,pubmed,0,5,"text mining, dataset",0.001751226,0.099696721,0.300523381,0.556701567,0.039575934,0.001751171,Epidemiology,0.5351707,TRUE,16.8,0.253138722,1,0.122023013,14,0.866658436,,,0.413940057
9983,0,J Integr Med,32307268,4/21/20,pubmed,0,16,"computational, bioinformatic",0.964389054,0.03252034,0.000772661,0.000772662,0.000772642,0.00077264,Drug discovery,0.9975399,TRUE,62.8125,0.720019791,50.6875,0.683703505,11,0.840175319,,,0.747966205
9984,0,J Biomol Struct Dyn,32306862,4/21/20,pubmed,0,4,virtual screening,0.958854685,0.001684516,0.001684484,0.034407343,0.001684495,0.001684476,Drug discovery,0.9401938,TRUE,12.5,0.189436576,2.75,0.187583623,153,0.987344898,,,0.454788366
9985,Moroccan Medicinal plants as inhibitors against SARS-CoV-2 main protease: Computational investigations.,J Biomol Struct Dyn,32306860,4/21/20,pubmed,0,6,computational,0.872077267,0.001272714,0.001272722,0.001272718,0.001272685,0.122831894,Drug discovery,0.9645457,TRUE,6.166666667,0.087080215,1.333333333,0.13252609,110,0.980739552,,,0.400115286
9986,"Novel 2019 coronavirus structure, mechanism of action, antiviral drug promises and rule out against its treatment.",J Biomol Struct Dyn,32306836,4/21/20,pubmed,0,3,"virtual screening, computational",0.625558589,0.00089815,0.000898116,0.356797191,0.000898124,0.014949829,Drug discovery,0.9313481,TRUE,34.33333333,0.480425506,12.33333333,0.389550442,174,0.989567257,,,0.619847735
9987,Peptide-like and small-molecule inhibitors against Covid-19.,J Biomol Struct Dyn,32306822,4/21/20,pubmed,0,5,in-silico,0.994639057,0.001072214,0.001072213,0.001072177,0.001072174,0.001072165,Drug discovery,0.9576964,TRUE,35.6,0.493042241,8.4,0.326598876,128,0.983826162,,,0.60115576
9988,Supporting pandemic response using genomics and bioinformatics: A case study on the emergent SARS-CoV-2 outbreak.,Transbound Emerg Dis,32306500,4/20/20,pubmed,0,13,"bioinformatic, genome-wide",0.001461993,0.809247609,0.001461934,0.184904717,0.001461898,0.001461849,Genomics,0.29250354,FALSE,28.15384615,0.408992517,36.07692308,0.611252341,9,0.814309525,,,0.611518128
9989,"Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19.",IEEE Rev Biomed Eng,32305937,4/20/20,pubmed,0,9,artificial intelligence,0.001237146,0.001237098,0.928185965,0.06686556,0.001237123,0.001237108,Imaging,0.85906404,TRUE,58.66666667,0.694106005,39.11111111,0.628244581,274,0.994382369,,,0.772244319
9990,0,Int J Infect Dis,32305520,4/20/20,pubmed,0,6,correlation analysis,0.001098811,0.001098836,0.001098818,0.843568662,0.001098826,0.152036046,Epidemiology,0.42914346,FALSE,120.1666667,0.907168038,94.5,0.811613594,73,0.970677202,,,0.896486278
9991,Artificial Intelligence (AI) applications for COVID-19 pandemic.,Diabetes Metab Syndr,32305024,4/19/20,pubmed,0,4,"machine learning, artificial intelligence",0.001392896,0.001392878,0.517497617,0.476930775,0.001392934,0.0013929,Epidemiology,0.9268594,TRUE,109,0.888304781,12,0.386740701,155,0.987653559,,,0.754233014
9992,Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China.,Sci Total Environ,32304942,4/19/20,pubmed,0,18,dataset,0.001415091,0.001415181,0.001415112,0.641787899,0.001415174,0.352551542,Epidemiology,0.62710553,TRUE,56.11111111,0.675613829,30.72222222,0.574591919,176,0.989690722,,,0.746632156
9993,"Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China.",Clin Microbiol Infect,32304745,4/19/20,pubmed,0,17,logistic regression,0.001565281,0.00156529,0.001565286,0.001565303,0.001565321,0.992173518,Clinics,0.9915389,TRUE,41.70588235,0.554950832,26.88235294,0.544688253,220,0.992839064,,,0.697492716
9994,Boosting the arsenal against COVID-19 through computational drug repurposing.,Drug Discov Today,32304645,4/19/20,pubmed,0,2,computational,0.874698336,0.025060263,0.025060409,0.02506055,0.02506022,0.02506022,Drug discovery,0.47299054,FALSE,140,0.933823984,93,0.808670056,23,0.91129082,,,0.884594954
9995,Human Leukocyte Antigen Susceptibility Map for Severe Acute Respiratory Syndrome Coronavirus 2.,J Virol,32303592,4/19/20,pubmed,0,7,"in silico, proteom",0.379283308,0.615315064,0.001350373,0.001350386,0.001350453,0.001350415,Genomics,0.3580053,FALSE,16.28571429,0.246211887,7,0.299973241,4,0.707574542,,,0.41791989
9996,Impact of Human Disasters and COVID-19 Pandemic on Mental Health: Potential of Digital Psychiatry.,Psychiatr Danub,32303026,4/18/20,pubmed,0,4,artificial intelligence,0.001059409,0.001059411,0.124040964,0.343902636,0.528878237,0.001059344,Healthcare,0.98083293,TRUE,12.5,0.189436576,1,0.122023013,50,0.957775171,,,0.423078254
9997,Comparative computational analysis of SARS-CoV-2 nucleocapsid protein epitopes in taxonomically related coronaviruses.,Microbes Infect,32302675,4/18/20,pubmed,0,8,"computational, in-silico, sequence alignment",0.464848368,0.429080508,0.001653083,0.001653146,0.101111848,0.001653047,Drug discovery,0.7058482,TRUE,100.375,0.869565217,52.625,0.69153064,55,0.960800049,,,0.840631969
9998,Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia.,Radiology,32301647,4/18/20,pubmed,0,9,logistic regression,0.000772608,0.000772617,0.401498513,0.000772639,0.000772636,0.595410987,Clinics,0.97486067,TRUE,53.77777778,0.659595522,35.22222222,0.606435644,171,0.989196864,,,0.751742677
9999,A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study.,Eur Radiol,32300971,4/18/20,pubmed,0,15,logistic regression,0.000999518,0.000999537,0.582258726,0.000999709,0.033518602,0.381223908,Imaging,0.72798467,TRUE,28.6,0.413321789,22.86666667,0.510703773,44,0.952157541,,,0.625394367
10000,Genomic characterization of a novel SARS-CoV-2.,Gene Rep,32300673,4/18/20,pubmed,0,3,"bioinformatic, genome sequences, genomes, genomic structure",0.001438176,0.881172048,0.001438176,0.001438165,0.001438167,0.113075268,Genomics,0.72601944,TRUE,15,0.227596017,5.666666667,0.270203372,259,0.993826779,,,0.497208723
10001,Mental health problems and social media exposure during COVID-19 outbreak.,PLoS One,32298385,4/17/20,pubmed,0,9,logistic regression,0.001237081,0.001237069,0.00123706,0.001237105,0.993814614,0.001237072,Healthcare,0.8830841,TRUE,30.11111111,0.432803513,,,482,0.997777641,,,0.715290577
10002,The SARS-CoV-2 receptor ACE2 expression of maternal-fetal interface and fetal organs by single-cell transcriptome study.,PLoS One,32298273,4/17/20,pubmed,0,5,"sequencing, transcriptom",0.599753539,0.092153827,0.001538145,0.111418449,0.061200901,0.133935139,Drug discovery,0.678517,TRUE,73.8,0.781124374,38.2,0.622758898,26,0.920859312,,,0.774914195
10003,Predictive Mathematical Models of the COVID-19 Pandemic: Underlying Principles and Value of Projections.,JAMA,32297897,4/17/20,pubmed,0,3,mathematical model,0.019531358,0.01952974,0.019530362,0.902345375,0.01953006,0.019533104,Epidemiology,0.6559847,TRUE,106.3333333,0.881687179,127.3333333,0.860583356,126,0.983517501,,,0.908596012
10004,Global Preparedness Against COVID-19: We Must Leverage the Power of Digital Health.,JMIR Public Health Surveill,32297868,4/17/20,pubmed,0,4,digital health,0.003760564,0.003760648,0.217146449,0.617030344,0.154541477,0.003760518,Epidemiology,0.787962,TRUE,68.25,0.755086895,65.5,0.740767996,52,0.958701154,,,0.818185348
10005,"Relationships among lymphocyte subsets, cytokines, and the pulmonary inflammation index in coronavirus (COVID-19) infected patients.",Br J Haematol,32297671,4/17/20,pubmed,0,15,correlation analysis,0.001511842,0.089676323,0.001511905,0.001511853,0.001511808,0.904276268,Clinics,0.4684098,FALSE,38.4,0.523285299,34.06666667,0.599411292,135,0.984505216,,,0.702400603
10006,Computational Identification of Small Interfering RNA Targets in SARS-CoV-2.,Virol Sin,32297156,4/17/20,pubmed,0,5,computational,0.920005615,0.015999707,0.015999052,0.015998712,0.015998444,0.015998469,Drug discovery,0.44794372,FALSE,856.6,0.999072299,633.4,0.985416109,17,0.887338725,,,0.957275711
10007,An emergent clade of SARS-CoV-2 linked to returned travellers from Iran.,Virus Evol,32296544,4/17/20,pubmed,0,21,"sequencing, whole-genome",0.00376046,0.779956505,0.003760533,0.2050015,0.003760492,0.00376051,Genomics,0.2798769,FALSE,82.47619048,0.81575855,253.952381,0.939590581,16,0.881782826,,,0.879043986
10008,0,J Biomol Struct Dyn,32295479,4/17/20,pubmed,0,9,"computational, bioinformatic, in silico",0.925908738,0.001272718,0.001272662,0.069000555,0.001272694,0.001272633,Drug discovery,0.9023566,TRUE,18,0.271569052,6.333333333,0.285121755,98,0.977652942,,,0.511447916
10009,A search for medications to treat COVID-19 via in silico molecular docking models of the SARS-CoV-2 spike glycoprotein and 3CL protease.,Travel Med Infect Dis,32294562,4/16/20,pubmed,0,2,in silico,0.983929033,0.003214307,0.003214103,0.003214164,0.003214176,0.003214217,Drug discovery,0.87280804,TRUE,55.5,0.671346404,32,0.585763982,83,0.974381135,,,0.743830507
10010,Why Are Lopinavir and Ritonavir Effective against the Newly Emerged Coronavirus 2019? Atomistic Insights into the Inhibitory Mechanisms.,Biochemistry,32293875,4/16/20,pubmed,0,8,molecular dynamics simulation,0.991243836,0.001751285,0.001751214,0.001751305,0.001751191,0.001751168,Drug discovery,0.8801017,TRUE,30.5,0.438493413,4,0.231469093,65,0.966911538,,,0.545624681
10011,Emerging and reemerging respiratory viral infections up to Covid-19,Turk J Med Sci,32293833,4/16/20,pubmed,0,3,genomes,0.002639076,0.652357009,0.002639029,0.241393798,0.002638982,0.098332106,Genomics,0.51486844,TRUE,18,0.271569052,3,0.199424672,7,0.785110192,,,0.418701305
10012,Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study.,JMIR Public Health Surveill,32293582,4/16/20,pubmed,0,5,data mining,0.00078637,0.000786409,0.044137122,0.874777217,0.078726501,0.000786381,Epidemiology,0.8747971,TRUE,44.6,0.582658173,21.8,0.501204174,43,0.9507377,,,0.678200016
10013,Facemask shortage and the novel coronavirus disease (COVID-19) outbreak: Reflections on public health measures.,EClinicalMedicine,32292898,4/16/20,pubmed,0,5,mathematical model,0.000740285,0.000740305,0.000740301,0.826437276,0.170601538,0.000740295,Epidemiology,0.4508512,FALSE,20.8,0.30768755,10.2,0.357706717,45,0.953206988,,,0.539533752
10014,Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods.,Acta Pharm Sin B,32292689,4/16/20,pubmed,0,13,computational,0.963986423,0.00122003,0.001220008,0.031133509,0.001220009,0.001220023,Drug discovery,0.9491956,TRUE,59.84615385,0.701713155,,,715,0.999135749,,,0.850424452
10015,0,New Microbes New Infect,32292587,4/16/20,pubmed,0,1,"whole genome, genome sequences",0.1380654,0.67635956,0.002357793,0.002357884,0.178501578,0.002357785,Genomics,0.9049456,TRUE,5,0.070752675,0,0.055525823,42,0.949503056,,,0.358593851
10016,The missing pieces in the jigsaw and need for cohesive research amidst coronavirus infectious disease 2019 global response.,Med J Armed Forces India,32292235,4/16/20,pubmed,0,6,sequencing,0.181142556,0.221090397,0.001511893,0.306554178,0.087133706,0.20256727,Epidemiology,0.7237065,TRUE,23,0.34225988,8,0.320511105,3,0.667819001,,,0.443529995
10017,Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model.,Med J Armed Forces India,32292232,4/16/20,pubmed,0,4,mathematical model,0.00139282,0.001392821,0.001392833,0.721013875,0.001392899,0.273414752,Epidemiology,0.5771702,TRUE,46.25,0.598800173,23.25,0.514583891,126,0.983517501,,,0.698967188
10018,Diagnosing COVID-19: Did We Miss Anything?,Acta Med Indones,32291365,4/16/20,pubmed,0,2,sequencing,0.201253518,0.606523945,0.003335417,0.003335444,0.003335551,0.182216125,Genomics,0.49103415,FALSE,7.5,0.108355495,0.5,0.087101953,0,0.403234768,,,0.199564072
10019,Repurposing Didanosine as a Potential Treatment for COVID-19 Using Single-Cell RNA Sequencing Data.,mSystems,32291351,4/16/20,pubmed,0,1,"bioinformatic, sequencing",0.910269225,0.037292829,0.002238507,0.002238434,0.002238443,0.045722563,Drug discovery,0.27295542,FALSE,32,0.455810502,9,0.337904736,15,0.874313229,,,0.556009489
10020,Prediction models for diagnosis and prognosis in Covid-19.,BMJ,32291266,4/16/20,pubmed,0,3,prediction model,0.025060382,0.025060303,0.874692424,0.02506063,0.025060246,0.025066015,Clinics,0.4001906,FALSE,65.66666667,0.738450121,27.33333333,0.54856837,31,0.931971109,,,0.7396632
10021,COVID-19 pneumonia: A review of typical CT findings and differential diagnosis.,Diagn Interv Imaging,32291197,4/16/20,pubmed,0,7,sequencing,0.003335317,0.074113442,0.828350935,0.087529693,0.003335305,0.003335309,Imaging,0.55694735,TRUE,23,0.34225988,9,0.337904736,123,0.983085376,,,0.554416664
10022,Extreme Genomic CpG Deficiency in SARS-CoV-2 and Evasion of Host Antiviral Defense.,Mol Biol Evol,32289821,4/15/20,pubmed,0,1,genomes,0.446958669,0.499904771,0.001219993,0.001220023,0.049476539,0.001220004,Genomics,0.8751086,TRUE,179,0.963695961,1340,0.996454375,73,0.970677202,,,0.976942513
10023,COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread.,Lancet Digit Health,32289116,4/15/20,pubmed,0,1,artificial intelligence,0.015998618,0.015998424,0.436205921,0.01599962,0.499798297,0.01599912,Healthcare,0.81683993,TRUE,46,0.596882924,2,0.164302917,103,0.978949318,,,0.580045053
10024,Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case.,Transp Res E Logist Transp Rev,32288597,4/15/20,pubmed,0,1,simulation experiment,0.001187282,0.001187286,0.0545248,0.94072611,0.001187262,0.00118726,Epidemiology,0.22696966,FALSE,42,0.558537943,17,0.451097137,219,0.992530403,,,0.667388494
10025,0,J Shanghai Jiaotong Univ Sci,32288418,4/15/20,pubmed,0,5,mathematical model,0.001717168,0.00171718,0.001717203,0.991414125,0.001717165,0.001717159,Epidemiology,0.13623339,FALSE,67.8,0.752303791,48.8,0.676545357,1,0.537564047,,,0.655471065
10026,Computational Design of ACE2-Based Peptide Inhibitors of SARS-CoV-2.,ACS Nano,32286790,4/15/20,pubmed,0,2,"molecular dynamics simulation, computational",0.901049226,0.002490481,0.002490645,0.057956893,0.033522232,0.002490523,Drug discovery,0.83791494,TRUE,41.5,0.553219123,1.5,0.138747659,98,0.977652942,,,0.556539908
10027,From the handling of an outbreak by an unknown pathogen in Wuhan to the preparedness and response in the face of the emergence of Covid-19 in Mexico.,Gac Med Mex,32285862,4/15/20,pubmed,0,5,"sequencing, genomes",0.043897077,0.483509191,0.001350485,0.335335464,0.134557401,0.001350382,Genomics,0.661999,TRUE,18.4,0.27558909,3.6,0.21507894,4,0.707574542,,,0.399414191
10028,"scRNA-seq Profiling of Human Testes Reveals the Presence of the ACE2 Receptor, A Target for SARS-CoV-2 Infection in Spermatogonia, Leydig and Sertoli Cells.",Cells,32283711,4/15/20,pubmed,0,2,"bioinformatic, transcriptom",0.65273658,0.00168468,0.001684491,0.237483452,0.00168459,0.104726207,Drug discovery,0.74502265,TRUE,42,0.558537943,34.5,0.602221033,196,0.991110562,,,0.717289846
10029,Graft Cryopreservation Does Not Impact Overall Survival after Allogeneic Hematopoietic Cell Transplantation Using Post-Transplantation Cyclophosphamide for Graft-versus-Host Disease Prophylaxis.,Biol Blood Marrow Transplant,32283185,4/14/20,pubmed,0,14,logistic regression,0.078271547,0.000830671,0.024561666,0.060489868,0.000830688,0.835015561,Clinics,0.7778259,TRUE,254.1428571,0.984909395,316.5,0.958723575,12,0.850299401,,,0.93131079
10030,Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19.,J Infect,32283162,4/14/20,pubmed,0,9,logistic regression,0.019084124,0.001310318,0.001310329,0.00131034,0.001310359,0.97567453,Clinics,0.7786324,TRUE,274,0.988434659,140.8888889,0.875234145,247,0.993456386,,,0.952375063
10031,C-reactive protein correlates with computed tomographic findings and predicts severe COVID-19 early.,J Med Virol,32281668,4/14/20,pubmed,0,8,correlation analysis,0.001059355,0.001059346,0.139039259,0.00105938,0.001059354,0.856723306,Clinics,0.6833233,TRUE,35.625,0.493227782,11.25,0.374364463,124,0.983147108,,,0.616913118
10032,[Study on medication regularity of traditional Chinese medicine in treatment of COVID-19 based on data mining].,Zhongguo Zhong Yao Za Zhi,32281332,4/14/20,pubmed,0,3,data mining,0.58245797,0.00165314,0.001653113,0.182089066,0.230493633,0.001653077,Drug discovery,0.99048537,TRUE,49,0.624281032,6,0.280037463,4,0.707574542,,,0.537297679
10033,[Study on screening potential traditional Chinese medicines against 2019-nCoV based on Mpro and PLP].,Zhongguo Zhong Yao Za Zhi,32281328,4/14/20,pubmed,0,7,virtual screening,0.768153903,0.000988395,0.093690349,0.135190503,0.000988399,0.000988451,Drug discovery,0.9910588,TRUE,152.7142857,0.94644072,289,0.951030238,10,0.828199272,,,0.908556743
10034,[Rapid establishment of traditional Chinese medicine prevention and treatment of 2019-nCoV based on clinical experience and molecular docking].,Zhongguo Zhong Yao Za Zhi,32281327,4/14/20,pubmed,0,14,optimization model,0.475786302,0.000907349,0.00090733,0.357066938,0.16442473,0.000907351,Drug discovery,0.9372314,TRUE,125.5,0.914960727,96.07142857,0.815159219,7,0.785110192,,,0.838410046
10035,Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model.,Comput Struct Biotechnol J,32280433,4/14/20,pubmed,0,5,deep learning,0.885624171,0.001943508,0.062741062,0.045804187,0.001943509,0.001943564,Drug discovery,0.95984393,TRUE,26,0.382398417,21,0.492239765,179,0.989875918,,,0.6215047
10036,Association of chemosensory dysfunction and COVID-19 in patients presenting with influenza-like symptoms.,Int Forum Allergy Rhinol,32279441,4/13/20,pubmed,0,5,logistic regression,0.098625651,0.001350443,0.001350405,0.001350405,0.553226434,0.344096662,Healthcare,0.98917866,TRUE,49.4,0.627002288,38.6,0.625100348,357,0.996172603,,,0.74942508
10037,In silico studies on therapeutic agents for COVID-19: Drug repurposing approach.,Life Sci,32278693,4/13/20,pubmed,0,3,in silico,0.835104112,0.001371314,0.0013713,0.07561941,0.064262104,0.022271759,Drug discovery,0.9480226,TRUE,6.666666667,0.094996598,3.666666667,0.217621086,112,0.980986481,,,0.431201389
10038,"Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS.",Infect Genet Evol,32278147,4/12/20,pubmed,0,1,mathematical model,0.266572253,0.002996655,0.002996674,0.648872679,0.00299643,0.075565308,Epidemiology,0.8605002,TRUE,2,0.022141134,1,0.122023013,55,0.960800049,,,0.368321399
10039,"Development of a Novel, Genome Subtraction-Derived, SARS-CoV-2-Specific COVID-19-nsp2 Real-Time RT-PCR Assay and Its Evaluation Using Clinical Specimens.",Int J Mol Sci,32276333,4/12/20,pubmed,0,18,genomes,0.001987194,0.906433157,0.001987289,0.085618044,0.001987126,0.001987189,Genomics,0.5600898,TRUE,49.16666667,0.62545612,240.2222222,0.934171796,43,0.9507377,,,0.836788538
10040,"Symptom Cluster of ICU Nurses Treating COVID-19 Pneumonia Patients in Wuhan, China.",J Pain Symptom Manage,32276095,4/11/20,pubmed,0,8,logistic regression,0.001171552,0.189393317,0.098937318,0.00117162,0.363037816,0.346288376,Healthcare,0.94612265,TRUE,40.375,0.543138104,3.625,0.215346535,23,0.91129082,,,0.55659182
10041,Genomic Epidemiology and its importance in the study of the COVID-19 pandemic.,Infez Med,32275255,4/11/20,pubmed,0,8,genomic epidemiology,0,0,0,0,0,0,0,0.5776361,TRUE,231.875,0.981260437,100.25,0.822852556,11,0.840175319,,,0.881429437
10042,Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions.,J Thorac Dis,32274081,4/11/20,pubmed,0,26,"machine learning, artificial intelligence",0.001112625,0.001112656,0.058411435,0.883976243,0.054274359,0.001112683,Epidemiology,0.5319543,TRUE,23.23076923,0.343991589,,,447,0.99746898,,,0.670730284
10043,High Prevalence of Obesity in Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Requiring Invasive Mechanical Ventilation.,Obesity (Silver Spring),32271993,4/10/20,pubmed,0,35,logistic regression,0.001653009,0.001653066,0.001653012,0.001653091,0.001653179,0.991734643,Clinics,0.9051217,TRUE,91.6,0.847733317,99.8,0.822116671,697,0.999012285,,,0.889620758
10044,Generalizability of Coronavirus Disease 2019 (COVID-19) Clinical Prediction Models.,Clin Infect Dis,32271865,4/10/20,pubmed,0,2,prediction model,0.057800659,0.057799149,0.710996772,0.057802975,0.057799148,0.057801298,Epidemiology,0.4920158,FALSE,58,0.689714887,10.5,0.363459995,0,0.403234768,,,0.485469883
10045,The Exponentially Increasing Rate of Patients Infected with COVID-19 in Iran.,Arch Iran Med,32271595,4/10/20,pubmed,0,2,dataset,0.002490459,0.002490529,0.002490586,0.747510881,0.002490467,0.242527078,Epidemiology,0.6285058,TRUE,12,0.183190055,5,0.257024351,14,0.866658436,,,0.435624281
10046,A classifier prediction model to predict the status of Coronavirus COVID-19 patients in South Korea.,Eur Rev Med Pharmacol Sci,32271458,4/10/20,pubmed,0,2,"neural network, classifier, network model, prediction model",0.001861736,0.001861751,0.363415237,0.288228954,0.001861796,0.342770528,Clinics,0.9188612,TRUE,11,0.167171748,1,0.122023013,12,0.850299401,,,0.379831388
10047,Prediction for Progression Risk in Patients With COVID-19 Pneumonia: The CALL Score.,Clin Infect Dis,32271369,4/10/20,pubmed,0,13,predictive model,0.00125468,0.001254621,0.171330905,0.00125467,0.001254634,0.82365049,Clinics,0.972381,TRUE,65.84615385,0.739377822,,,213,0.992221742,,,0.865799782
10048,"Ultrarapid diagnosis, microscope imaging, genome sequencing, and culture isolation of SARS-CoV-2.",Eur J Clin Microbiol Infect Dis,32270412,4/10/20,pubmed,0,6,sequencing,0.013548822,0.635971042,0.30983129,0.013549919,0.013549945,0.013548982,Genomics,0.4533696,FALSE,192.3333333,0.969756942,326.8333333,0.961198823,22,0.908142478,,,0.946366081
10049,Upheaval in cancer care during the COVID-19 outbreak.,Ecancermedicalscience,32269597,4/10/20,pubmed,0,8,digital health,0.001751411,0.001751172,0.001751175,0.231476888,0.237254538,0.526014817,Clinics,0.45176333,FALSE,19.25,0.287896592,5.125,0.257760235,25,0.918019631,,,0.487892153
10050,Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study.,Eur Respir J,32269088,4/10/20,pubmed,0,14,logistic regression,0.001901658,0.001901689,0.001901696,0.001901734,0.001901716,0.990491507,Clinics,0.684161,TRUE,90.57142857,0.844517286,93.14285714,0.808803853,536,0.998271498,,,0.883864213
10051,Phylogenetic network analysis of SARS-CoV-2 genomes.,Proc Natl Acad Sci U S A,32269081,4/10/20,pubmed,0,4,"genomes, network analysis",0.002357829,0.871983906,0.118584802,0.00235784,0.002357811,0.002357812,Genomics,0.2887008,FALSE,75.5,0.78860783,822.75,0.990701097,500,0.997839373,,,0.9257161
10052,Projecting demand for critical care beds during COVID-19 outbreaks in Canada.,CMAJ,32269020,4/10/20,pubmed,0,6,computational,0.001126775,0.001126783,0.001126789,0.549373338,0.218026935,0.229219379,Epidemiology,0.2090672,FALSE,145.8333333,0.940317892,302.8333333,0.955111052,54,0.959812334,,,0.951747093
10053,"Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada.",CMAJ,32269018,4/10/20,pubmed,0,3,mathematical model,0.00112679,0.001126782,0.001126783,0.86609689,0.050906802,0.079615953,Epidemiology,0.45082614,FALSE,109.3333333,0.888923248,145.6666667,0.879983944,147,0.986418915,,,0.918442036
10054,In Silico Discovery of Candidate Drugs against Covid-19.,Viruses,32268515,4/10/20,pubmed,0,3,"in silico, dataset",0.96781151,0.025450367,0.001684548,0.001684539,0.00168453,0.001684506,Drug discovery,0.7485019,TRUE,63.66666667,0.725957078,84.33333333,0.788934975,61,0.964936107,,,0.826609387
10055,Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review.,Cochrane Database Syst Rev,32267544,4/9/20,pubmed,0,11,mathematical model,0.000657799,0.083049483,0.00065778,0.829102076,0.041207459,0.045325402,Epidemiology,0.6209126,TRUE,58,0.689714887,45.18181818,0.659017929,301,0.99524662,,,0.781326478
10056,Effects of Social Grooming on Incivility in COVID-19.,Cyberpsychol Behav Soc Netw,32267163,4/9/20,pubmed,0,1,network analysis,0.002996629,0.002996461,0.088501747,0.835686766,0.066821788,0.00299661,Epidemiology,0.38655794,FALSE,14,0.213494959,1,0.122023013,17,0.887338725,,,0.407618899
10057,Targeting SARS-CoV-2: a systematic drug repurposing approach to identify promising inhibitors against 3C-like proteinase and 2'-O-ribose methyltransferase.,J Biomol Struct Dyn,32266873,4/9/20,pubmed,0,9,"virtual screening, computational",0.995002238,0.000999562,0.000999533,0.000999618,0.000999527,0.000999522,Drug discovery,0.942443,TRUE,53.11111111,0.65470963,31.44444444,0.580478994,142,0.985616396,,,0.74026834
10058,In-silico homology assisted identification of inhibitor of RNA binding against 2019-nCoV N-protein (N terminal domain).,J Biomol Struct Dyn,32266867,4/9/20,pubmed,0,11,"virtual screening, in-silico",0.994142228,0.001171586,0.001171552,0.001171531,0.001171549,0.001171553,Drug discovery,0.977388,TRUE,91.81818182,0.848228091,28.54545455,0.55846936,105,0.979381443,,,0.795359632
10059,Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal.,BMJ,32265220,4/9/20,pubmed,0,46,prediction model,0.000547851,0.000547835,0.354764613,0.256810116,0.000547845,0.38678174,Clinics,0.16586375,FALSE,212.25,0.97625085,308.5625,0.956917313,352,0.996049139,,,0.976405768
10060,Genotype and phenotype of COVID-19: Their roles in pathogenesis.,J Microbiol Immunol Infect,32265180,4/9/20,pubmed,0,2,genomic structure,0.243028458,0.746415209,0.002639186,0.002639116,0.002639005,0.002639027,Genomics,0.69808745,TRUE,3.5,0.044344115,2.5,0.180826866,264,0.994073708,,,0.406414897
10061,Whole genome and phylogenetic analysis of two SARS-CoV-2 strains isolated in Italy in January and February 2020: additional clues on multiple introductions and further circulation in Europe.,Euro Surveill,32265007,4/9/20,pubmed,0,19,"whole genome, genome sequences",0.002806347,0.843669991,0.002806369,0.002806425,0.002807122,0.145103746,Genomics,0.31719792,FALSE,120.2777778,0.907415425,115.4444444,0.844862189,77,0.972097043,,,0.908124886
10062,High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2.,Emerg Infect Dis,32255761,4/8/20,pubmed,0,6,mathematical model,0.002080664,0.002080694,0.002080534,0.989596959,0.002080552,0.002080597,Epidemiology,0.2836321,FALSE,27.83333333,0.405219865,50.33333333,0.682967621,621,0.998765356,,,0.695650947
10063,Informing emergency care for COVID-19 patients: The COVID-19 Emergency Department Quality Improvement Project protocol.,Emerg Med Australas,32255567,4/8/20,pubmed,0,11,dataset,0.003607176,0.003607288,0.171202148,0.373778065,0.003607438,0.444197886,Clinics,0.9328876,TRUE,85.63636364,0.827014658,62.09090909,0.728726251,13,0.858880178,,,0.804873695
10064,Evidence synthesis relevant to COVID-19: a protocol for multiple systematic reviews and overviews of systematic reviews.,Medwave,32255438,4/8/20,pubmed,0,57,artificial intelligence,0.001187297,0.001187309,0.096426299,0.845246867,0.054764876,0.001187352,Epidemiology,0.3691505,FALSE,42.875,0.566021399,8.25,0.323789136,21,0.903944688,,,0.597918408
10065,Structural and molecular modelling studies reveal a new mechanism of action of chloroquine and hydroxychloroquine against SARS-CoV-2 infection.,Int J Antimicrob Agents,32251731,4/7/20,pubmed,0,4,in-silico,0.95474531,0.001171624,0.00117158,0.00117166,0.001171561,0.040568265,Drug discovery,0.907639,TRUE,114.75,0.89875688,120.75,0.852756222,197,0.991295759,,,0.91426962
10066,Virtual screening and repurposing of FDA approved drugs against COVID-19 main protease.,Life Sci,32251634,4/7/20,pubmed,0,2,"virtual screening, dataset",0.814863661,0.179651012,0.001371286,0.001371353,0.001371351,0.001371337,Drug discovery,0.85420024,TRUE,22,0.326056033,5.5,0.267259834,117,0.981912464,,,0.52507611
10067,Internet Hospitals Help Prevent and Control the Epidemic of COVID-19 in China: Multicenter User Profiling Study.,J Med Internet Res,32250962,4/7/20,pubmed,0,5,logistic regression,0.000946094,0.000946114,0.000946128,0.225977656,0.669000694,0.102183314,Healthcare,0.9984263,TRUE,40.2,0.541159008,13.4,0.404602622,34,0.937156615,,,0.627639415
10068,"Computational studies of drug repurposing and synergism of lopinavir, oseltamivir and ritonavir binding with SARS-CoV-2 protease against COVID-19.",J Biomol Struct Dyn,32248766,4/7/20,pubmed,0,4,computational,0.902353297,0.019529301,0.019529384,0.019529463,0.019529277,0.019529277,Drug discovery,0.7843591,TRUE,88.75,0.838579999,104.25,0.829475515,180,0.98993765,,,0.885997721
10069,"Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study.",Lancet Infect Dis,32247326,4/6/20,pubmed,0,24,bayes,0.000772599,0.000772628,0.000772614,0.802468582,0.085305124,0.109908453,Epidemiology,0.4380673,FALSE,91.25,0.846867462,,,335,0.995863942,,,0.921365702
10070,Blended learning via distance in pre-registration nursing education: A scoping review.,Nurse Educ Pract,32247200,4/5/20,pubmed,0,4,active learning,0.001717227,0.001717218,0.220786551,0.300823743,0.473238052,0.001717209,Healthcare,0.82545257,TRUE,24.25,0.358092646,14.75,0.421327268,22,0.908142478,,,0.562520797
10071,"Using observational data to quantify bias of traveller-derived COVID-19 prevalence estimates in Wuhan, China.",Lancet Infect Dis,32246905,4/5/20,pubmed,0,4,"bayes, mathematical model, bayesian model",0.00088905,0.173536762,0.000889115,0.742849389,0.080946568,0.000889116,Epidemiology,0.20220166,FALSE,141.25,0.935432,315,0.958255285,35,0.939317242,,,0.944334842
10072,SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells.,EMBO J,32246845,4/5/20,pubmed,0,14,sequencing,0.989083652,0.002183371,0.002183261,0.0021833,0.002183207,0.002183211,Drug discovery,0.76709604,TRUE,115.5714286,0.899931969,172,0.901190795,367,0.996419532,,,0.932514098
10073,A highly conserved cryptic epitope in the receptor binding domains of SARS-CoV-2 and SARS-CoV.,Science,32245784,4/5/20,pubmed,0,8,structural model,0.953846434,0.002720438,0.002720174,0.002720164,0.002720219,0.03527257,Drug discovery,0.53323054,TRUE,192.5,0.970004329,520,0.980331817,220,0.992839064,,,0.981058403
10074,Blockchain and Artificial Intelligence Technology for Novel Coronavirus Disease-19 Self-Testing.,Diagnostics (Basel),32244841,4/5/20,pubmed,0,2,artificial intelligence,0.002490443,0.002490529,0.085372045,0.610213319,0.249422798,0.050010866,Epidemiology,0.6863352,TRUE,32,0.455810502,4.5,0.242708055,42,0.949503056,,,0.549340537
10075,Master Regulator Analysis of the SARS-CoV-2/Human Interactome.,J Clin Med,32244779,4/5/20,pubmed,0,4,interactom,0.566723418,0.335797424,0.002806639,0.089059728,0.00280643,0.002806361,Drug discovery,0.46926743,FALSE,87.75,0.836229822,43.75,0.652863259,22,0.908142478,,,0.79907852
10076,"Endonasal instrumentation and aerosolization risk in the era of COVID-19: simulation, literature review, and proposed mitigation strategies.",Int Forum Allergy Rhinol,32243678,4/4/20,pubmed,0,8,image processing,0.001622761,0.059286118,0.068430901,0.867414635,0.001622826,0.001622759,Epidemiology,0.53210276,TRUE,141.875,0.936236007,123,0.855097672,186,0.990246312,,,0.92719333
10077,The effectiveness of quarantine of Wuhan city against the Corona Virus Disease 2019 (COVID-19): A well-mixed SEIR model analysis.,J Med Virol,32243599,4/4/20,pubmed,0,13,model fit,0.001653016,0.001653031,0.022586795,0.970801031,0.001653027,0.001653101,Epidemiology,0.7042688,TRUE,89.46153846,0.840992022,58.84615385,0.716550709,108,0.980060498,,,0.845867743
10078,Full-genome sequences of the first two SARS-CoV-2 viruses from India.,Indian J Med Res,32242873,4/4/20,pubmed,0,11,"sequencing, genome sequences, genomes",0.224778897,0.771130531,0.001022674,0.001022654,0.001022613,0.001022631,Genomics,0.78029025,TRUE,37.63636364,0.514750448,38,0.622223709,84,0.974689796,,,0.703887984
10079,School Opening Delay Effect on Transmission Dynamics of Coronavirus Disease 2019 in Korea: Based on Mathematical Modeling and Simulation Study.,J Korean Med Sci,32242349,4/4/20,pubmed,0,4,mathematical model,0.001187254,0.001187339,0.001187289,0.636962791,0.358287814,0.001187512,Epidemiology,0.19861895,FALSE,151.5,0.945513019,129.75,0.863593792,29,0.928020248,,,0.912375686
10080,Peer-to-Peer Contact Tracing: Development of a Privacy-Preserving Smartphone App.,JMIR Mhealth Uhealth,32240973,4/3/20,pubmed,0,3,simulation model,0.001291263,0.00129121,0.001291281,0.993543709,0.001291335,0.001291202,Epidemiology,0.07086778,FALSE,34.66666667,0.483703383,4.333333333,0.237958255,75,0.971417989,,,0.564359876
10081,"Liver impairment in COVID-19 patients: A retrospective analysis of 115 cases from a single centre in Wuhan city, China.",Liver Int,32239796,4/3/20,pubmed,0,6,logistic regression,0.001538137,0.001538169,0.001538129,0.00153821,0.001538193,0.992309162,Clinics,0.902181,TRUE,128.3333333,0.91947554,58.16666667,0.714677549,154,0.987530094,,,0.873894394
10082,Clinical characteristics of non-ICU hospitalized patients with coronavirus disease 2019 and liver injury: A retrospective study.,Liver Int,32239591,4/3/20,pubmed,0,6,logistic regression,0.001156257,0.001156247,0.072791107,0.017734,0.001156273,0.906006116,Clinics,0.99798286,TRUE,13.33333333,0.201558538,5.666666667,0.270203372,133,0.984196555,,,0.485319488
10083,Potential covalent drugs targeting the main protease of the SARS-CoV-2 coronavirus.,Bioinformatics,32239142,4/3/20,pubmed,0,3,computational,0.885357222,0.002183295,0.002183397,0.062675034,0.002183271,0.045417782,Drug discovery,0.9841263,TRUE,47,0.606407323,18.33333333,0.464343056,39,0.945737391,,,0.67216259
10084,Covid-19: Pandemonium in our time.,Geospat Health,32238978,4/3/20,pubmed,0,2,dataset,0.003335333,0.00333534,0.003335556,0.923146093,0.063512395,0.003335284,Epidemiology,0.6396692,TRUE,66,0.741356918,50,0.681763447,5,0.739490092,,,0.720870152
10085,"Computational Inference of Selection Underlying the Evolution of the Novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2.",J Virol,32238584,4/3/20,pubmed,0,4,computational,0.13986834,0.856464774,0.000916702,0.000916724,0.000916735,0.000916725,Genomics,0.43081737,FALSE,173.25,0.960789164,171,0.900120417,50,0.957775171,,,0.939561584
10086,0,J Biomol Struct Dyn,32238094,4/3/20,pubmed,0,5,computational,0.995112642,0.00097746,0.000977455,0.000977508,0.000977476,0.000977458,Drug discovery,0.968099,TRUE,47,0.606407323,20.4,0.484613326,147,0.986418915,,,0.692479855
10087,0,J Biomol Struct Dyn,32238078,4/3/20,pubmed,0,6,computational,0.881264152,0.001684557,0.001684603,0.111997527,0.001684634,0.001684525,Drug discovery,0.77623224,TRUE,18.5,0.278001113,3.166666667,0.201097137,138,0.984813877,,,0.487970709
10088,Isolation and rapid sharing of the 2019 novel coronavirus (SARS-CoV-2) from the first patient diagnosed with COVID-19 in Australia.,Med J Aust,32237278,4/3/20,pubmed,0,16,"sequencing, whole genome, genomes",0.033136457,0.66878944,0.045038793,0.001072251,0.001072249,0.250890811,Genomics,0.7770913,TRUE,81.3125,0.812295133,107.25,0.834827402,122,0.982838447,,,0.87665366
10089,Identification of a potential mechanism of acute kidney injury during the COVID-19 outbreak: a study based on single-cell transcriptome analysis.,Intensive Care Med,32236644,4/3/20,pubmed,0,6,transcriptom,0.654126752,0.011750292,0.011750021,0.011749741,0.011749692,0.298873502,Drug discovery,0.75554705,TRUE,140.5,0.934504298,243.6666667,0.935643564,214,0.992283474,,,0.954143779
10090,Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study.,JMIR Public Health Surveill,32234709,4/3/20,pubmed,0,5,"deep learning, data mining, lstm",0.001901719,0.001901794,0.063996406,0.928396553,0.001901776,0.001901752,Epidemiology,0.9030467,TRUE,9.8,0.147071557,2.4,0.174872893,10,0.828199272,,,0.38338124
10091,Multiomics Evaluation of Gastrointestinal and Other Clinical Characteristics of COVID-19.,Gastroenterology,32234303,4/3/20,pubmed,0,6,multiom,0.057801506,0.057799576,0.710997177,0.057800852,0.057800061,0.057800828,Drug discovery,0.8773251,TRUE,224,0.979652421,346.1666667,0.964945143,26,0.920859312,,,0.955152292
10092,Network pharmacology-based analysis of the role of traditional Chinese herbal medicines in the treatment of COVID-19.,Ann Palliat Med,32233641,4/3/20,pubmed,0,3,genomes,0.473700354,0.271663894,0.002130771,0.24824352,0.002130783,0.002130678,Drug discovery,0.91369104,TRUE,26.66666667,0.390995114,18.33333333,0.464343056,9,0.814309525,,,0.556549232
10093,"A mathematical model for the novel coronavirus epidemic in Wuhan, China.",Math Biosci Eng,32233562,4/3/20,pubmed,0,2,mathematical model,0.053349457,0.003335333,0.00333531,0.933308985,0.003335558,0.003335357,Epidemiology,0.5177444,TRUE,17,0.257467994,4,0.231469093,118,0.982159393,,,0.490365493
10094,Effects of media reporting on mitigating spread of COVID-19 in the early phase of the outbreak.,Math Biosci Eng,32233561,4/3/20,pubmed,0,5,correlation analysis,0.0499248,0.001622734,0.001622785,0.891456977,0.053749852,0.001622852,Epidemiology,0.8400436,TRUE,11,0.167171748,4.6,0.244246722,43,0.9507377,,,0.454052057
10095,Quarantine Vehicle Scheduling for Transferring High-Risk Individuals in Epidemic Areas.,Int J Environ Res Public Health,32230995,4/2/20,pubmed,0,4,computational,0.001987105,0.001987225,0.533578772,0.458472669,0.001987149,0.001987079,Epidemiology,0.82342166,TRUE,30,0.432432432,12.25,0.388546963,5,0.739490092,,,0.520156496
10096,The psychological impact of the COVID-19 epidemic on college students in China.,Psychiatry Res,32229390,4/2/20,pubmed,0,7,correlation analysis,0.001461888,0.001461892,0.001461847,0.099482766,0.894669694,0.001461913,Healthcare,0.85242707,TRUE,8.285714286,0.121590698,26.85714286,0.544487557,771,0.999197481,,,0.555091912
10097,Practical Strategies Against the Novel Coronavirus and COVID-19-the Imminent Global Threat.,Arch Med Res,32229157,4/2/20,pubmed,0,2,"mathematical model, sequencing, whole-genome",0.08267567,0.179057526,0.001272731,0.379661819,0.105212456,0.252119798,Epidemiology,0.32220775,FALSE,43.5,0.573381161,27.5,0.549906342,13,0.858880178,,,0.66072256
10098,Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients.,Emerg Microbes Infect,32228226,4/2/20,pubmed,0,19,"sequencing, transcriptom, dataset",0.625092533,0.206069775,0.001717232,0.001717251,0.001717185,0.163686023,Drug discovery,0.7388539,TRUE,79.10526316,0.802832581,126.2631579,0.859512978,398,0.996851658,,,0.886399072
10099,"Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: a quick online cross-sectional survey.",Int J Biol Sci,32226294,4/1/20,pubmed,0,7,logistic regression,0.000966766,0.000966761,0.000966814,0.000966812,0.995166089,0.000966758,Healthcare,0.95792997,TRUE,34.57142857,0.482837529,91,0.803987155,602,0.998641891,,,0.761822192
10100,Diagnosing COVID-19: The Disease and Tools for Detection.,ACS Nano,32223179,4/1/20,pubmed,0,10,"sequencing, whole genome",0.0031016,0.329395304,0.399973794,0.261326298,0.003101528,0.003101476,Imaging,0.70593286,TRUE,24.9,0.36638011,25.5,0.533382392,460,0.997530712,,,0.632431071
10101,Phylogenetic analysis of the first four SARS-CoV-2 cases in Chile.,J Med Virol,32222995,3/31/20,pubmed,0,15,genome sequences,0.002130713,0.837063441,0.002130705,0.118615791,0.002130756,0.037928595,Genomics,0.4764574,FALSE,13.33333333,0.201558538,15.53333333,0.430358576,32,0.933699611,,,0.521872242
10102,Genomic characterization and phylogenetic analysis of SARS-COV-2 in Italy.,J Med Virol,32222993,3/31/20,pubmed,0,13,genomes,0.006540085,0.883097511,0.006539628,0.006540235,0.006539943,0.090742598,Genomics,0.56614,TRUE,126.6153846,0.916197662,86.30769231,0.793617875,73,0.970677202,,,0.89349758
10103,A Genomic Perspective on the Origin and Emergence of SARS-CoV-2.,Cell,32220310,3/30/20,pubmed,0,2,sequencing,0.005697723,0.971511836,0.005697571,0.005697784,0.005697617,0.00569747,Genomics,0.60449153,TRUE,364.5,0.994619333,1670.5,0.997390955,300,0.995184888,,,0.995731726
10104,Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins.,Nature,32218527,3/29/20,pubmed,0,29,"sequencing, metagenom",0.164549137,0.825488896,0.002490607,0.002490491,0.002490415,0.002490455,Genomics,0.6639261,TRUE,89.5862069,0.841424949,256.6896552,0.940326465,713,0.999074017,,,0.92694181
10105,Structural Genomics of SARS-CoV-2 Indicates Evolutionary Conserved Functional Regions of Viral Proteins.,Viruses,32218151,3/29/20,pubmed,0,9,"computational, interactom",0.486990176,0.186922645,0.002806497,0.317667663,0.002806614,0.002806404,Drug discovery,0.32135588,FALSE,117.2222222,0.903209846,70.22222222,0.754013915,101,0.978517192,,,0.878580318
10106,Artificial intelligence and machine learning to fight COVID-19.,Physiol Genomics,32216577,3/29/20,pubmed,0,6,"machine learning, artificial intelligence",0.015998552,0.015998353,0.920007687,0.015998621,0.015998442,0.015998346,Epidemiology,0.6104162,TRUE,30.83333333,0.441833137,13.83333333,0.409084827,111,0.980801284,,,0.610573083
10107,"Epidemiological, clinical and virological characteristics of 74 cases of coronavirus-infected disease 2019 (COVID-19) with gastrointestinal symptoms.",Gut,32213556,3/28/20,pubmed,0,46,bioinformatic,0.033408288,0.143120583,0.00131037,0.001310405,0.172502377,0.648347977,Clinics,0.8049015,TRUE,59.45652174,0.699053745,40.7173913,0.637877977,510,0.997962837,,,0.778298186
10108,Digital Mental Health and COVID-19: Using Technology Today to Accelerate the Curve on Access and Quality Tomorrow.,JMIR Ment Health,32213476,3/28/20,pubmed,0,4,digital health,0.002183271,0.002183291,0.002183432,0.738071637,0.253195115,0.002183254,Epidemiology,0.6570314,TRUE,132.5,0.924423279,93,0.808670056,148,0.986727576,,,0.90660697
10109,Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study.,Lancet Infect Dis,32213337,3/28/20,pubmed,0,25,"sequencing, whole-genome",0.070793678,0.544685287,0.013430318,0.000728158,0.069141177,0.301221382,Genomics,0.9242571,TRUE,43.52,0.573504855,256.96,0.940393364,1606,0.999753071,,,0.837883763
10110,Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study.,Lancet Infect Dis,32213332,3/28/20,pubmed,0,8,simulation model,0.000716326,0.000716361,0.000716343,0.859005881,0.138128741,0.000716348,Epidemiology,0.37369898,FALSE,44.875,0.584513575,51.625,0.687784319,346,0.995925674,,,0.756074523
10111,[Risk assessment and early warning of imported COVID-19 in Guangdong province].,Zhonghua Liu Xing Bing Xue Za Zhi,32213268,3/28/20,pubmed,0,19,correlation analysis,0.001486392,0.001486445,0.001486444,0.992567787,0.001486452,0.00148648,Epidemiology,0.55426353,TRUE,28.47368421,0.41159008,20.89473684,0.489764517,2,0.618927094,,,0.506760564
10112,Epidemiological and Clinical Predictors of COVID-19.,Clin Infect Dis,32211755,3/27/20,pubmed,0,38,"logistic regression, prediction model",0.001203425,0.215972787,0.317501506,0.066426933,0.001203477,0.397691872,Clinics,0.7444602,TRUE,67.61538462,0.750633929,90.53846154,0.801980198,92,0.976233101,,,0.842949076
10113,0,EXCLI J,32210742,3/27/20,pubmed,0,4,in silico,0.547696138,0.446256267,0.001511803,0.001511961,0.001511841,0.001511989,Drug discovery,0.46298626,FALSE,62,0.715814212,62.75,0.731669789,141,0.985246003,,,0.810910001
10114,0,EXCLI J,32210741,3/27/20,pubmed,0,4,in silico,0.881971814,0.06867546,0.04388249,0.001823463,0.001823357,0.001823416,Drug discovery,0.92127335,TRUE,62,0.715814212,62.75,0.731669789,43,0.9507377,,,0.799407234
10115,Evolutionary Trajectory for the Emergence of Novel Coronavirus SARS-CoV-2.,Pathogens,32210130,3/27/20,pubmed,0,4,whole-genome,0.096376533,0.761085078,0.001511826,0.138002643,0.001511939,0.001511982,Genomics,0.6691485,TRUE,45.75,0.593419506,14.25,0.415038801,86,0.975307118,,,0.661255142
10116,"Computers and viral diseases. Preliminary bioinformatics studies on the design of a synthetic vaccine and a preventative peptidomimetic antagonist against the SARS-CoV-2 (2019-nCoV, COVID-19) coronavirus.",Comput Biol Med,32209231,3/27/20,pubmed,0,1,bioinformatic,0.474563324,0.256745149,0.062069234,0.204076966,0.001272695,0.001272632,Drug discovery,0.53559184,TRUE,4,0.054734368,1,0.122023013,83,0.974381135,,,0.383712839
10117,Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus.,Infect Dis Poverty,32209118,3/27/20,pubmed,0,5,"predictive model, prediction model",0.053360669,0.603852531,0.340222584,0.000854764,0.000854721,0.000854731,Genomics,0.36518282,FALSE,26.8,0.392417589,53,0.693738293,28,0.926168282,,,0.670774721
10118,"Structural, glycosylation and antigenic variation between 2019 novel coronavirus (2019-nCoV) and SARS coronavirus (SARS-CoV).",Virusdisease,32206694,3/25/20,pubmed,0,5,sequence alignment,0.54292116,0.435246226,0.001141348,0.018408522,0.001141377,0.001141366,Drug discovery,0.30704188,FALSE,53.6,0.657987507,16.2,0.438252609,91,0.976047904,,,0.690762673
10119,Deep Learning Localization of Pneumonia: 2019 Coronavirus (COVID-19) Outbreak.,J Thorac Imaging,32205822,3/25/20,pubmed,0,3,deep learning,0.025060268,0.025060278,0.874698435,0.02506022,0.02506022,0.025060579,Clinics,0.46692377,FALSE,26.66666667,0.390995114,23.33333333,0.515386674,16,0.881782826,,,0.596054871
10120,The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users.,Int J Environ Res Public Health,32204411,3/25/20,pubmed,0,5,predictive model,0.001350409,0.001350354,0.093355785,0.325641617,0.576951408,0.001350426,Healthcare,0.9677874,TRUE,23.8,0.351227658,,,385,0.996604729,,,0.673916193
10121,Factors Associated With Mental Health Outcomes Among Health Care Workers Exposed to Coronavirus Disease 2019.,JAMA Netw Open,32202646,3/24/20,pubmed,0,18,logistic regression,0.000580123,0.00058014,0.000580134,0.000580143,0.773182964,0.224496495,Healthcare,0.997759,TRUE,190.2777778,0.968767394,,,1647,0.999814803,,,0.984291099
10122,Predicting the angiotensin converting enzyme 2 (ACE2) utilizing capability as the receptor of SARS-CoV-2.,Microbes Infect,32199943,3/23/20,pubmed,0,7,sequence alignment,0.445766869,0.544270836,0.002490692,0.002490629,0.002490484,0.00249049,Genomics,0.70106757,TRUE,22.42857143,0.331498547,33.14285714,0.593524217,107,0.979813569,,,0.634945444
10123,Single cell RNA sequencing of 13 human tissues identify cell types and receptors of human coronaviruses.,Biochem Biophys Res Commun,32199615,3/23/20,pubmed,0,4,sequencing,0.914541918,0.001538143,0.001538082,0.001538162,0.001538108,0.079305587,Drug discovery,0.55568963,TRUE,30.25,0.434782609,67.75,0.747524752,66,0.967652324,,,0.716653228
10124,Probable Pangolin Origin of SARS-CoV-2 Associated with the COVID-19 Outbreak.,Curr Biol,32197085,3/21/20,pubmed,0,3,whole-genome,0.35831585,0.631433729,0.002562608,0.002562669,0.002562559,0.002562585,Genomics,0.44357964,FALSE,21.33333333,0.31548024,30.66666667,0.574257426,670,0.99888882,,,0.629542162
10125,Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2.,Cell Discov,32194980,3/21/20,pubmed,0,6,"transcriptom, interactom, whole genome, genomes",0.79593176,0.199672826,0.001098857,0.001098866,0.001098825,0.001098865,Drug discovery,0.85141003,TRUE,29.5,0.426000371,30.66666667,0.574257426,577,0.998580159,,,0.666279319
10126,Social Capital and Sleep Quality in Individuals Who Self-Isolated for 14 Days During the Coronavirus Disease 2019 (COVID-19) Outbreak in January 2020 in China.,Med Sci Monit,32194290,3/21/20,pubmed,0,5,correlation analysis,0.001085375,0.0010854,0.001085329,0.144761904,0.850896635,0.001085357,Healthcare,0.98182243,TRUE,72.6,0.775867401,,,257,0.993765047,,,0.884816224
10127,Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.,Radiology,32191588,3/20/20,pubmed,0,18,"deep learning, artificial intelligence, neural network, dataset",0.001046791,0.001046815,0.821439476,0.001046826,0.001046897,0.174373195,Imaging,0.8772627,TRUE,41.22222222,0.550312326,40.38888889,0.635804121,423,0.997222051,,,0.727779499
10128,A doubt of multiple introduction of SARS-CoV-2 in Italy: A preliminary overview.,J Med Virol,32190908,3/20/20,pubmed,0,4,"genomic epidemiology, genomes",0.002720135,0.869963464,0.002720271,0.119155701,0.00272034,0.002720088,Genomics,0.6365121,TRUE,89.25,0.840064321,62.25,0.729595933,36,0.941169208,,,0.836943154
10129,"Transmission potential of the novel coronavirus (COVID-19) onboard the diamond Princess Cruises Ship, 2020.",Infect Dis Model,32190785,3/20/20,pubmed,0,2,mathematical model,0.002720121,0.002720188,0.002720081,0.986399177,0.002720257,0.002720176,Epidemiology,0.41659242,FALSE,53.5,0.657245346,72,0.7594327,136,0.984566949,,,0.800414998
10130,AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data.,J Med Syst,32189081,3/20/20,pubmed,0,1,"machine learning, artificial intelligence, active learning",0.002422351,0.002422315,0.695100113,0.295210523,0.002422378,0.002422321,Epidemiology,0.59108096,TRUE,156,0.948481662,23,0.513513514,105,0.979381443,,,0.813792206
10131,Supersensitive Multifluorophore RNA-FISH for Early Virus Detection and Flow-FISH by Using Click Chemistry.,Chembiochem,32187837,3/19/20,pubmed,0,11,transcriptom,0.325759103,0.383625444,0.284653955,0.001987222,0.001987167,0.001987109,Genomics,0.41247964,FALSE,58.63636364,0.693734925,51.36363636,0.686647043,0,0.403234768,,,0.594538912
10132,A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2.,Cell Host Microbe,32183941,3/19/20,pubmed,0,6,bioinformatic,0.700378873,0.291098127,0.002130791,0.002130792,0.002130674,0.002130743,Drug discovery,0.36970556,FALSE,438.1666667,0.996289195,778.3333333,0.989697618,404,0.997098586,,,0.9943618
10133,Reverse Logistics Network Design for Effective Management of Medical Waste in Epidemic Outbreaks: Insights from the Coronavirus Disease 2019 (COVID-19) Outbreak in Wuhan (China).,Int J Environ Res Public Health,32182811,3/19/20,pubmed,0,4,computational,0.001392888,0.001392871,0.321710066,0.628867952,0.045243352,0.00139287,Epidemiology,0.822963,TRUE,283.5,0.98936236,215,0.925073588,58,0.963022409,,,0.959152786
10134,"Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions.",Biology (Basel),32182724,3/19/20,pubmed,0,4,mathematical model,0.004530624,0.004530732,0.004530664,0.977346472,0.004530759,0.004530748,Epidemiology,0.27810133,FALSE,23.75,0.350547344,20,0.481000803,110,0.980739552,,,0.604095899
10135,Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2).,Science,32179701,3/18/20,pubmed,0,7,bayes,0.00299643,0.002996518,0.002996487,0.795102519,0.192911434,0.002996611,Epidemiology,0.47990865,FALSE,58.85714286,0.6951574,163.1428571,0.894701632,1875,0.999938268,,,0.863265767
10136,Anesthetic Management of Patients with COVID 19 Infections during Emergency Procedures.,J Cardiothorac Vasc Anesth,32178954,3/18/20,pubmed,0,8,dataset,0.001072196,0.078974043,0.227362744,0.001072215,0.059066755,0.632452047,Clinics,0.9853728,TRUE,170.125,0.958253448,101.25,0.824257426,23,0.91129082,,,0.897933898
10137,"Era of molecular diagnosis for pathogen identification of unexplained pneumonia, lessons to be learned.",Emerg Microbes Infect,32174267,3/17/20,pubmed,0,5,"sequencing, metagenom",0.002490482,0.775675617,0.214362158,0.002490505,0.002490454,0.002490784,Genomics,0.80119133,TRUE,64.8,0.733626075,30,0.570176612,36,0.941169208,,,0.748323965
10138,False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases.,Korean J Radiol,32174053,3/17/20,pubmed,0,7,"sequencing, deep-learning",0.003335295,0.540691001,0.44596751,0.003335533,0.003335324,0.003335338,Genomics,0.40477115,FALSE,15.14285714,0.228276331,35.14285714,0.605967353,182,0.990061115,,,0.6081016
10139,The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemics in the final phase of the current outbreak in China.,Int J Infect Dis,32171948,3/17/20,pubmed,0,9,dataset,0.001751171,0.001751187,0.199962584,0.793032648,0.001751195,0.001751215,Epidemiology,0.5579138,TRUE,61.22222222,0.709938772,42.55555556,0.647712069,95,0.976912155,,,0.778187665
10140,[Genomic analysis of a 2019-novel coronavirus (2019-nCoV) strain in the first COVID-19 patient found in Hangzhou].,Zhonghua Yu Fang Yi Xue Za Zhi,32171191,3/17/20,pubmed,0,10,"sequencing, genomes",0.001112623,0.961823362,0.001112718,0.001112724,0.001112614,0.03372596,Genomics,0.88934994,TRUE,6.2,0.087574989,2.9,0.190794755,3,0.667819001,,,0.315396249
10141,"Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.",Lancet,32171076,3/15/20,pubmed,0,19,logistic regression,0.02505794,0.102140634,0.00088024,0.108803806,0.00088024,0.762237139,Clinics,0.9659829,TRUE,97.52631579,0.86344239,,,9978,1,,,0.931721195
10142,Early dynamics of transmission and control of COVID-19: a mathematical modelling study.,Lancet Infect Dis,32171059,3/15/20,pubmed,0,22,"mathematical model, dataset",0.00073415,0.00073418,0.000734154,0.982697157,0.000734212,0.014366147,Epidemiology,0.33281088,FALSE,67.69565217,0.751437937,,,1242,0.999691339,,,0.875564638
10143,COVID-19 spike-host cell receptor GRP78 binding site prediction.,J Infect,32169481,3/15/20,pubmed,0,4,bioinformatic,0.993814589,0.001237185,0.001237055,0.001237091,0.001237043,0.001237038,Drug discovery,0.7986535,TRUE,10.75,0.160244913,11,0.371287129,177,0.989752454,,,0.507094832
10144,"Evidence of the COVID-19 Virus Targeting the CNS: Tissue Distribution, Host-Virus Interaction, and Proposed Neurotropic Mechanisms.",ACS Chem Neurosci,32167747,3/14/20,pubmed,0,4,transcriptom,0.785674768,0.068696383,0.002357759,0.002357892,0.002357807,0.138555392,Drug discovery,0.629698,TRUE,26.25,0.384748593,19,0.471367407,815,0.999320946,,,0.618478982
10145,The establishment of reference sequence for SARS-CoV-2 and variation analysis.,J Med Virol,32167180,3/14/20,pubmed,0,7,sequence alignment,0.001371262,0.993143476,0.001371297,0.001371368,0.001371302,0.001371294,Genomics,0.8150656,TRUE,81.85714286,0.814088688,89.85714286,0.801177415,216,0.992406939,,,0.869224347
10146,COVID-19 - the role of mass gatherings.,Travel Med Infect Dis,32165283,3/14/20,pubmed,0,2,mathematical model,0.001943532,0.147594785,0.001943496,0.844631137,0.001943546,0.001943504,Epidemiology,0.5990708,TRUE,326.5,0.99220731,788.5,0.989898314,46,0.954009507,,,0.978705044
10147,"COVID-19: An Update on the Epidemiological, Clinical, Preventive and Therapeutic Evidence and Guidelines of Integrative Chinese-Western Medicine for the Management of 2019 Novel Coronavirus Disease.",Am J Chin Med,32164424,3/14/20,pubmed,0,3,in silico,0.330843187,0.001511826,0.001511861,0.142143903,0.001511873,0.52247735,Clinics,0.74026763,TRUE,32,0.455810502,25.33333333,0.531576131,158,0.988270881,,,0.658552504
10148,Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds.,Mol Inform,32162456,3/13/20,pubmed,0,5,"virtual screening, deep learning",0.853483128,0.001861736,0.139069797,0.001861779,0.001861794,0.001861766,Drug discovery,0.7589287,TRUE,58.2,0.690518894,59.8,0.719962537,167,0.988826471,,,0.799769301
10149,"Emergence of a Novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2: Biology and Therapeutic Options.",J Clin Microbiol,32161092,3/13/20,pubmed,0,8,sequencing,0.153294673,0.837518866,0.002296602,0.002296759,0.002296568,0.002296533,Genomics,0.8816633,TRUE,22.375,0.330632692,40.125,0.633663366,64,0.966479412,,,0.643591824
10150,"Identification of coronavirus sequences in carp cDNA from Wuhan, China.",J Med Virol,32159234,3/12/20,pubmed,0,1,bioinformatic,0.125178536,0.822705588,0.002898323,0.002898398,0.002898371,0.043420785,Genomics,0.4691926,FALSE,43,0.567629414,30,0.570176612,7,0.785110192,,,0.640972073
10151,"Positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to Feb 2020.",Clin Chim Acta,32156607,3/12/20,pubmed,0,8,logistic regression,0.00182335,0.548559574,0.001823401,0.001823446,0.174212224,0.271758005,Genomics,0.68450433,TRUE,110.875,0.891397118,111.75,0.84104897,213,0.992221742,,,0.90822261
10152,[Clinical characteristics and influencing factors of patients with novel coronavirus pneumonia combined with liver injury in Shaanxi region].,Zhonghua Gan Zang Bing Za Zhi,32153170,3/11/20,pubmed,0,7,logistic regression,0.04186445,0.02338884,0.000786349,0.000786376,0.000786382,0.932387602,Clinics,0.9894601,TRUE,7.285714286,0.10445915,7.714285714,0.311479797,38,0.944132354,,,0.4533571
10153,Identification of Coronavirus Isolated from a Patient in Korea with COVID-19.,Osong Public Health Res Perspect,32149036,3/10/20,pubmed,0,10,sequencing,0.108497937,0.638943663,0.001751205,0.001751213,0.001751196,0.247304787,Genomics,0.6176449,TRUE,15.6,0.235450554,14.6,0.419253412,211,0.991913081,,,0.548872349
10154,Serial interval of novel coronavirus (COVID-19) infections.,Int J Infect Dis,32145466,3/8/20,pubmed,0,3,dataset,0.00198718,0.001987257,0.001987154,0.63038254,0.001987175,0.361668694,Epidemiology,0.19506714,FALSE,138.3333333,0.931102727,301.6666667,0.954776559,517,0.998024569,,,0.961301285
10155,"Machine Learning, COVID-19 (2019-nCoV), and multi-OMICS.",Cytometry A,32142596,3/7/20,pubmed,0,1,"machine learning, multi-omics",0.02506776,0.025077795,0.874673013,0.025060386,0.025060566,0.02506048,Genomics,0.46364626,FALSE,366,0.994804874,116,0.846133262,17,0.887338725,,,0.90942562
10156,COVID-19 (Novel Coronavirus 2019) - recent trends.,Eur Rev Med Pharmacol Sci,32141569,3/7/20,pubmed,0,4,genome sequences,0.242528533,0.718645114,0.001823359,0.001823509,0.033356018,0.001823467,Genomics,0.3436103,FALSE,4,0.054734368,6,0.280037463,170,0.988949935,,,0.441240589
10157,[Analysis of myocardial injury in patients with COVID-19 and association between concomitant cardiovascular diseases and severity of COVID-19].,Zhonghua Xin Xue Guan Bing Za Zhi,32141280,3/7/20,pubmed,0,6,logistic regression,0.001156331,0.001156323,0.001156226,0.001156235,0.001156261,0.994218624,Clinics,0.98098993,TRUE,7.6,0.109283196,17.4,0.454642762,164,0.988641274,,,0.517522411
10158,Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures.,J Med Virol,32134116,3/7/20,pubmed,0,4,prediction model,0.001392867,0.269400707,0.055850087,0.571832371,0.03098161,0.070542358,Epidemiology,0.6025655,TRUE,27,0.3960047,,,668,0.998827088,,,0.697415894
10159,The Effects of Social Support on Sleep Quality of Medical Staff Treating Patients with Coronavirus Disease 2019 (COVID-19) in January and February 2020 in China.,Med Sci Monit,32132521,3/7/20,pubmed,0,5,correlation analysis,0.001046903,0.001046879,0.001046815,0.001046876,0.883467587,0.112344941,Healthcare,0.9688937,TRUE,80.8,0.809944956,41.2,0.640620819,316,0.995555281,,,0.815373685
10160,Optimization Method for Forecasting Confirmed Cases of COVID-19 in China.,J Clin Med,32131537,3/7/20,pubmed,0,4,"forecasting model, dataset",0.001653035,0.001653071,0.214144724,0.77924306,0.001653071,0.001653039,Epidemiology,0.7983074,TRUE,74,0.782113922,52.5,0.690928552,123,0.983085376,,,0.818709283
10161,Genomic Diversity of Severe Acute Respiratory Syndrome-Coronavirus 2 in Patients With Coronavirus Disease 2019.,Clin Infect Dis,32129843,3/5/20,pubmed,0,19,"sequencing, transcriptom, metatranscriptom",0.070116957,0.738783627,0.001751244,0.001751247,0.001751236,0.185845688,Genomics,0.5385672,TRUE,74.89473684,0.78594842,165.3157895,0.896039604,147,0.986418915,,,0.88946898
10162,[Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak].,Zhonghua Liu Xing Bing Xue Za Zhi,32129581,3/5/20,pubmed,0,4,"mathematical model, prediction model",0.001622726,0.001622817,0.001622843,0.99188608,0.001622764,0.001622771,Epidemiology,0.5776383,TRUE,20.25,0.301317336,11.5,0.378378378,9,0.814309525,,,0.498001746
10163,"Early transmission patterns of coronavirus disease 2019 (COVID-19) in travellers from Wuhan to Thailand, January 2020.",Euro Surveill,32127124,3/5/20,pubmed,0,16,"sequencing, genomes",0.004309951,0.978449617,0.004309969,0.004310286,0.004310045,0.004310132,Genomics,0.28391573,FALSE,45.25,0.589337621,60.625,0.723039872,45,0.953206988,,,0.755194827
10164,"[Estimating the basic reproduction number of COVID-19 in Wuhan, China].",Zhonghua Liu Xing Bing Xue Za Zhi,32125128,3/4/20,pubmed,0,11,"bayes, model fit",0.001538082,0.001538128,0.001538204,0.928412782,0.001538114,0.06543469,Epidemiology,0.938299,TRUE,24.36363636,0.359824355,,,23,0.91129082,,,0.635557588
10165,Analyzing the epidemiological outbreak of COVID-19: A visual exploratory data analysis approach.,J Med Virol,32124990,3/4/20,pubmed,0,4,dataset,0.002238541,0.043321727,0.040561771,0.909400852,0.002238551,0.002238558,Epidemiology,0.42460254,FALSE,15.75,0.237862577,2.25,0.170925876,58,0.963022409,,,0.457270287
10166,Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine.,Infect Control Hosp Epidemiol,32122430,3/4/20,pubmed,0,2,"machine learning, artificial intelligence",0.009284333,0.009284298,0.486023668,0.29831474,0.187808819,0.009284142,Epidemiology,0.60528433,TRUE,57,0.68204589,171.5,0.900655606,99,0.977961603,,,0.853554366
10167,On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management.,Healthcare (Basel),32120822,3/4/20,pubmed,0,2,artificial intelligence,0.001684504,0.001684565,0.001684598,0.953116906,0.040144944,0.001684483,Epidemiology,0.84718764,TRUE,70.5,0.766033768,25,0.529435376,100,0.978270264,,,0.757913136
10168,"Anti-HCV, nucleotide inhibitors, repurposing against COVID-19.",Life Sci,32119961,3/3/20,pubmed,0,1,in silico,0.553283396,0.248170025,0.001751201,0.193292919,0.001751187,0.001751272,Drug discovery,0.92227036,TRUE,49,0.624281032,63,0.732606369,298,0.994999691,,,0.783962364
10169,Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts.,Lancet Glob Health,32119825,3/3/20,pubmed,0,22,mathematical model,0.000728101,0.000728125,0.000728108,0.872204702,0.124882834,0.00072813,Epidemiology,0.18040228,FALSE,74.34782609,0.78328901,,,1199,0.999629607,,,0.891459308
10170,Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease.,Chin Med J (Engl),32118640,3/3/20,pubmed,0,12,logistic regression,0.000620856,0.00062086,0.025631952,0.011632445,0.000620849,0.960873038,Clinics,0.92179924,TRUE,317.8333333,0.99165069,169.5833333,0.899116939,477,0.997715908,,,0.962827846
10171,[Fitting and forecasting the trend of COVID-19 by SEIR(+CAQ) dynamic model].,Zhonghua Liu Xing Bing Xue Za Zhi,32113198,3/3/20,pubmed,0,9,model fit,0.001126799,0.001126848,0.001126848,0.905692183,0.001126827,0.089800494,Epidemiology,0.7875114,TRUE,11.44444444,0.171748407,2.777777778,0.187985015,16,0.881782826,,,0.413838749
10172,[Dynamic basic reproduction number based evaluation for current prevention and control of COVID-19 outbreak in China].,Zhonghua Liu Xing Bing Xue Za Zhi,32113197,3/3/20,pubmed,0,4,predictive model,0.150481014,0.001272688,0.00127264,0.844428252,0.001272712,0.001272694,Epidemiology,0.53956515,TRUE,12.5,0.189436576,2.75,0.187583623,13,0.858880178,,,0.411966792
10173,A mathematical model for simulating the phase-based transmissibility of a novel coronavirus.,Infect Dis Poverty,32111262,3/1/20,pubmed,0,6,"mathematical model, network model",0.001538125,0.462087379,0.001538199,0.531760031,0.001538139,0.001538127,Epidemiology,0.40752652,FALSE,67.66666667,0.75137609,28.83333333,0.560944608,313,0.995493549,,,0.769271416
10174,Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes.,Bioinformatics,32108862,2/29/20,pubmed,0,8,"bioinformatic, sequencing, whole-genome, whole genome, genomes, dataset",0.035789255,0.858522771,0.001786673,0.100327989,0.001786593,0.00178672,Genomics,0.17972028,FALSE,35.5,0.492547467,47,0.668718223,56,0.961540836,,,0.707602175
10175,"Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study.",Lancet Infect Dis,32105637,2/28/20,pubmed,0,8,sequencing,0.000734149,0.071146889,0.326145998,0.000734175,0.000734189,0.6005046,Clinics,0.99038994,TRUE,54.875,0.667450059,57.125,0.711533315,1710,0.999876536,,,0.792953303
10176,Immune responses in COVID-19 and potential vaccines: Lessons learned from SARS and MERS epidemic.,Asian Pac J Allergy Immunol,32105090,2/28/20,pubmed,0,3,sequencing,0.424933306,0.2106304,0.002357795,0.357362862,0.00235789,0.002357747,Drug discovery,0.565892,TRUE,44.33333333,0.580679077,40,0.633395772,691,0.998950553,,,0.737675134
10177,"Evolutionary history, potential intermediate animal host, and cross-species analyses of SARS-CoV-2.",J Med Virol,32104911,2/28/20,pubmed,0,7,"bayes, genomes, virom",0.173060968,0.819792713,0.001786609,0.001786648,0.001786546,0.001786515,Genomics,0.58597076,TRUE,105.1428571,0.879275156,103.7142857,0.828605834,213,0.992221742,,,0.900034244
10178,Passengers' destinations from China: low risk of Novel Coronavirus (2019-nCoV) transmission into Africa and South America.,Epidemiol Infect,32100667,2/27/20,pubmed,0,7,dataset,0.001098865,0.196411357,0.051792226,0.708308301,0.041290394,0.001098858,Epidemiology,0.25928438,FALSE,173,0.960603624,275.7142857,0.94715012,74,0.970924131,,,0.959559292
10179,Systematic Comparison of Two Animal-to-Human Transmitted Human Coronaviruses: SARS-CoV-2 and SARS-CoV.,Viruses,32098422,2/27/20,pubmed,0,8,proteom,0.002806522,0.637691306,0.22763682,0.002806464,0.126252102,0.002806786,Genomics,0.9028035,TRUE,29.875,0.429154555,,,303,0.995370085,,,0.71226232
10180,Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19).,J Clin Med,32098019,2/27/20,pubmed,0,12,dataset,0.001684488,0.001684606,0.04338899,0.599363137,0.001684642,0.352194136,Epidemiology,0.2617584,FALSE,100.9166667,0.870554765,194.4166667,0.914704308,77,0.972097043,,,0.919118706
10181,Early phylogenetic estimate of the effective reproduction number of SARS-CoV-2.,J Med Virol,32096566,2/26/20,pubmed,0,5,genomes,0.002898325,0.291462781,0.00289831,0.696943877,0.002898332,0.002898375,Epidemiology,0.6077954,TRUE,95.2,0.857195869,88.4,0.797564892,102,0.978640657,,,0.877800472
10182,Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses.,Nat Microbiol,32094589,2/26/20,pubmed,0,3,virom,0.395681699,0.598565807,0.001438103,0.001438136,0.001438125,0.001438132,Genomics,0.4682965,FALSE,55,0.668686994,121.3333333,0.85329141,1092,0.999567875,,,0.840515426
10183,Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China.,J Clin Med,32093043,2/26/20,pubmed,0,6,computational,0.126822358,0.001823468,0.001823382,0.865883972,0.001823449,0.001823371,Epidemiology,0.12390876,FALSE,27.5,0.401570907,11.33333333,0.375501739,133,0.984196555,,,0.587089734
10184,Rigidity of the Outer Shell Predicted by a Protein Intrinsic Disorder Model Sheds Light on the COVID-19 (Wuhan-2019-nCoV) Infectivity.,Biomolecules,32092911,2/26/20,pubmed,0,4,computational,0.002639143,0.630776395,0.002639004,0.29237439,0.068931974,0.002639094,Genomics,0.629305,TRUE,343.5,0.993877172,875.25,0.991771474,35,0.939317242,,,0.97498863
10185,Genetic diversity and evolution of SARS-CoV-2.,Infect Genet Evol,32092483,2/25/20,pubmed,0,1,genomes,0.005353132,0.973232985,0.005353046,0.005353269,0.005353636,0.005353933,Genomics,0.58774775,TRUE,16,0.243552477,24,0.521808938,274,0.994382369,,,0.586581261
10186,Chest CT Findings in Patients With Coronavirus Disease 2019 and Its Relationship With Clinical Features.,Invest Radiol,32091414,2/25/20,pubmed,0,8,correlation analysis,0.001059384,0.001059362,0.466816889,0.001059359,0.001059363,0.528945643,Clinics,0.97359616,TRUE,220.125,0.978477333,168.5,0.898180359,303,0.995370085,,,0.957342592
10187,Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19.,Elife,32091395,2/25/20,pubmed,0,5,mathematical model,0.259936109,0.002238713,0.002238559,0.436062155,0.244687193,0.054837271,Epidemiology,0.5566232,TRUE,69.6,0.761518956,120.6,0.852488627,153,0.987344898,,,0.867117494
10188,[Risk assessment of exported risk of COVID-19 from Hubei Province].,Zhonghua Yu Fang Yi Xue Za Zhi,32083409,2/23/20,pubmed,0,13,correlation analysis,0.001254584,0.001254616,0.00125458,0.993726977,0.001254616,0.001254628,Epidemiology,0.7329101,TRUE,29.53846154,0.426062218,17.46153846,0.454910356,7,0.785110192,,,0.555360922
10189,Virus Isolation from the First Patient with SARS-CoV-2 in Korea.,J Korean Med Sci,32080990,2/23/20,pubmed,0,12,"whole genome, genome sequences",0.037746722,0.778205536,0.004309928,0.004310288,0.004309959,0.171117568,Genomics,0.13974357,FALSE,76.41666667,0.791885707,82.75,0.785255553,94,0.976726958,,,0.851289406
10190,2019_nCoV/SARS-CoV-2: rapid classification of betacoronaviruses and identification of Traditional Chinese Medicine as potential origin of zoonotic coronaviruses.,Lett Appl Microbiol,32060933,2/16/20,pubmed,0,2,"whole-genome, genomes",0.180133567,0.775230444,0.001237198,0.001237151,0.040924587,0.001237053,Genomics,0.80323935,TRUE,139.5,0.93295813,193,0.914236018,47,0.95493549,,,0.934043213
10191,Does SARS-CoV-2 has a longer incubation period than SARS and MERS?,J Med Virol,32056235,2/15/20,pubmed,0,3,dataset,0.003335475,0.305436387,0.16682183,0.517735254,0.003335542,0.003335513,Epidemiology,0.45513433,FALSE,63.33333333,0.723545055,57,0.711198823,123,0.983085376,,,0.805943085
10192,Immunoinformatics-aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019-nCoV.,J Med Virol,32022276,2/6/20,pubmed,0,2,molecular dynamics simulation,0.990282045,0.001943634,0.001943474,0.001943677,0.001943565,0.001943606,Drug discovery,0.75631386,TRUE,7.5,0.108355495,1,0.122023013,141,0.985246003,,,0.40520817
10193,"Probabilistic $K$-mean with local alignment for clustering and motif
  discovery in functional data",0,1808.04773,8/14/18,arxiv,0,2,"bioinformatic, probabilistic",0.002080756,0.381105001,0.206077081,0.406576014,0.002080545,0.002080604,Epidemiology,0.11399415,FALSE,68,0.753973653,282,0.949290875,3,0.667819001,,,0.790361176
10194,"RelDenClu:A Relative Density based Biclustering Method for identifying
  non-linear feature relations with an Application to identify factors
  effecting spread of COVID-19",0,1811.04661,11/12/18,arxiv,0,3,"supervised learning, unsupervised learning, dataset",0.001622741,0.044584628,0.664433414,0.28611372,0.001622767,0.001622731,Epidemiology,0.001666248,FALSE,63.33333333,0.723545055,71,0.756556061,0,0.403234768,,,0.627778628
10195,"Cross-Sensor Periocular Biometrics for Partial Face Recognition in a
  Global Pandemic: Comparative Benchmark and Novel Multialgorithmic Approach",0,1902.08123,2/21/19,arxiv,0,7,"bayes, computational, logistic regression, probabilistic",0.001272679,0.001272739,0.720080565,0.274828666,0.001272678,0.001272673,Imaging,0.009162694,FALSE,100.5714286,0.870245532,109.5714286,0.838172331,0,0.403234768,,,0.70388421
10196,State-domain Change Point Detection for Nonlinear Time Series Regression,0,1904.11075,4/24/19,arxiv,0,3,dataset,0.002238561,0.002238544,0.246483132,0.744562811,0.002238492,0.002238459,Epidemiology,0.004842699,FALSE,236.3333333,0.981940751,118.3333333,0.849344394,0,0.403234768,,,0.744839971
10197,Dynamic Graph Convolutional Networks Using the Tensor M-Product,0,1910.07643,10/16/19,arxiv,0,5,"neural network, dataset",0.001461937,0.001461892,0.582206625,0.411945836,0.001461856,0.001461855,Epidemiology,0.005874425,FALSE,57.8,0.68736471,132,0.866002141,0,0.403234768,,,0.65220054
10198,Quantifying the Trendiness of Trends,0,1912.11848,12/26/19,arxiv,0,2,"bayes, probabilistic",0.001486403,0.001486453,0.001486426,0.992567792,0.001486465,0.001486461,Epidemiology,0.06693897,FALSE,91,0.846310842,76,0.769132994,2,0.618927094,,,0.74479031
10199,Shapley value confidence intervals for attributing variance explained,0,2001.09593,1/27/20,arxiv,0,3,computational,0.002296632,0.002296571,0.064403973,0.926409552,0.002296648,0.002296624,Epidemiology,0.09148976,FALSE,38.33333333,0.522233904,14.66666667,0.420323789,2,0.618927094,,,0.520494929
10200,"Beyond $R_0$: Heterogeneity in secondary infections and probabilistic
  epidemic forecasting",0,2002.04004,2/10/20,arxiv,0,4,probabilistic,0.002032818,0.002033016,0.002032807,0.989835814,0.002032759,0.002032786,Epidemiology,0.23499388,FALSE,90.75,0.845073907,66.25,0.743042547,27,0.92443978,,,0.837518745
10201,"Abnormal respiratory patterns classifier may contribute to large-scale
  screening of people infected with COVID-19 in an accurate and unobtrusive
  manner",0,2002.05534,2/12/20,arxiv,0,6,"simulation model, deep learning, classifier, deep model",0.001203436,0.001203458,0.653406292,0.341779865,0.001203486,0.001203463,Epidemiology,0.04052207,FALSE,21.33333333,0.31548024,,,79,0.972899562,,,0.644189901
10202,"Insights from early mathematical models of 2019-nCoV acute respiratory
  disease (COVID-19) dynamics",Journal of Environmental Science and Management 23-1 (2020) 1-12,2002.05296,2/13/20,arxiv,Journal of Environmental Science and Management 23-1 (2020) 1-12,1,mathematical model,0.002238492,0.002238514,0.002238509,0.988807273,0.002238619,0.002238593,Epidemiology,0.33978575,FALSE,39,0.530521368,4,0.231469093,35,0.939317242,,,0.567102568
10203,Artificial Intelligence Forecasting of Covid-19 in China,0,2002.07112,2/17/20,arxiv,0,5,artificial intelligence,0.001171551,0.001171578,0.070569715,0.924744016,0.001171565,0.001171575,Epidemiology,0.8118432,TRUE,44.8,0.584142495,78.6,0.774752475,153,0.987344898,,,0.782079956
10204,Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia,"Engineering, Volume 6, Issue 10, October 2020, Pages 1122-1129",2002.09334,2/21/20,arxiv,"Engineering, Volume 6, Issue 10, October 2020, Pages 1122-1129",15,"bayes, deep learning, dataset",0.001046858,0.153787193,0.842025416,0.001046848,0.001046822,0.001046863,Imaging,0.359728,FALSE,50.28571429,0.633186963,75.57142857,0.76792882,255,0.993579851,,,0.798231878
10205,"Joint spatio-temporal analysis of multiple response types using the
  hierarchical generalized transformation model with application to coronavirus
  disease 2019 and social distancing",0,2002.09983,2/23/20,arxiv,0,1,"bayes, bayesian model, dataset",0.026601116,0.001371327,0.001371357,0.903338532,0.065946303,0.001371365,Epidemiology,0.20192945,FALSE,46,0.596882924,30,0.570176612,1,0.537564047,,,0.568207861
10206,"Prediction of potential commercially inhibitors against SARS-CoV-2 by
  multi-task deep model",0,2003.00728,3/2/20,arxiv,0,3,"deep model, dataset",0.707475549,0.002238542,0.162842711,0.002238575,0.002238542,0.12296608,Drug discovery,0.44577387,FALSE,15.33333333,0.230997588,4,0.231469093,27,0.92443978,,,0.462302154
10207,"Advertisers Jump on Coronavirus Bandwagon: Politics, News, and Business",0,2003.00923,3/2/20,arxiv,0,2,dataset,0.002806694,0.080636741,0.002806528,0.908137087,0.002806621,0.002806328,Epidemiology,0.09957045,FALSE,58,0.689714887,40.5,0.637342788,19,0.89561084,,,0.740889505
10208,"Unsupervised and Interpretable Domain Adaptation to Rapidly Filter
  Tweets for Emergency Services",0,2003.04991,3/4/20,arxiv,0,3,dataset,0.001085401,0.001085388,0.632525017,0.363133467,0.001085372,0.001085355,Epidemiology,0.057028532,FALSE,12.33333333,0.186467932,0.666666667,0.096200161,0,0.403234768,,,0.228634287
10209,"Old Drugs for Newly Emerging Viral Disease, COVID-19: Bioinformatic
  Prospective",0,2003.04524,3/10/20,arxiv,0,1,bioinformatic,0.885042768,0.001393024,0.001392907,0.001392947,0.109385441,0.001392913,Drug discovery,0.4666912,FALSE,34,0.477766096,2,0.164302917,14,0.866658436,,,0.502909149
10210,"Lung Infection Quantification of COVID-19 in CT Images with Deep
  Learning",0,2003.04655,3/10/20,arxiv,0,9,deep learning,0.001461892,0.001461923,0.878621455,0.11553087,0.001461892,0.001461968,Imaging,0.5143925,TRUE,60.66666667,0.706351661,64.11111111,0.735683704,225,0.993209457,,,0.811748274
10211,"Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic:
  Initial Results for Automated Detection & Patient Monitoring using Deep
  Learning CT Image Analysis",0,2003.05037,3/10/20,arxiv,0,8,"deep learning, image analysis, dataset",0.001237093,0.001237147,0.85572614,0.139325372,0.001237107,0.001237141,Imaging,0.33798257,FALSE,66,0.741356918,104.5,0.8302114,299,0.995061424,,,0.855543247
10212,Weather-inspired ensemble-based probabilistic prediction of COVID-19,0,2003.06418,3/11/20,arxiv,0,1,probabilistic,0.002638946,0.00263902,0.002638994,0.986805037,0.002638996,0.002639008,Epidemiology,0.088314354,FALSE,175,0.961902406,685,0.986887878,2,0.618927094,,,0.855905793
10213,Prediction and analysis of Coronavirus Disease 2019,0,2003.05447,3/11/20,arxiv,0,5,mathematical model,0.001653015,0.001653074,0.001653045,0.905467977,0.001653051,0.087919838,Epidemiology,0.5708913,TRUE,70.4,0.765724535,9.4,0.343256623,45,0.953206988,,,0.687396049
10214,COVID-19 Evolves in Human Hosts,0,2003.0558,3/12/20,arxiv,0,10,genome sequences,0.001751347,0.8938812,0.001751259,0.0991131,0.001751464,0.00175163,Genomics,0.25772923,FALSE,23,0.34225988,7.8,0.313286058,3,0.667819001,,,0.441121647
10215,"Rational evaluation of various epidemic models based on the COVID-19
  data of China",0,2003.05666,3/12/20,arxiv,0,5,bayes,0.001220042,0.001220012,0.001220107,0.993899781,0.001220047,0.00122001,Epidemiology,0.13727045,FALSE,26.8,0.392417589,13.8,0.408884132,23,0.91129082,,,0.57086418
10216,"SARS-CoV-2 Entry Genes Are Most Highly Expressed in Nasal Goblet and
  Ciliated Cells within Human Airways","Nature Medicine, 2020",2003.06122,3/13/20,arxiv,"Nature Medicine, 2020",5,"sequencing, dataset",0.90964362,0.086014912,0.001085386,0.001085382,0.001085342,0.001085358,Drug discovery,0.9586059,TRUE,13.75,0.208299833,40.5,0.637342788,68,0.968393111,,,0.604678577
10217,"Day Level Forecasting for Coronavirus Disease (COVID-19) Spread:
  Analysis, Modeling and Recommendations",0,2003.07778,3/15/20,arxiv,0,2,forecasting model,0.003101615,0.049078194,0.003101442,0.938515717,0.003101566,0.003101466,Epidemiology,0.10965583,FALSE,13.5,0.205393036,3.5,0.213607172,41,0.948144947,,,0.455715052
10218,"A Machine Learning Application for Raising WASH Awareness in the Times
  of COVID-19 Pandemic",0,2003.07074,3/16/20,arxiv,0,20,machine learning,0.001330045,0.001330037,0.259590063,0.607005266,0.129414527,0.001330062,Epidemiology,0.23542553,FALSE,15.25,0.229884347,3.75,0.21982874,16,0.881782826,,,0.443831971
10219,"Tracking Social Media Discourse About the COVID-19 Pandemic: Development
  of a Public Coronavirus Twitter Data Set",JMIR Public Health Surveill 2020;6(2):e19273,2003.07372,3/16/20,arxiv,JMIR Public Health Surveill 2020;6(2):e19273,3,dataset,0.001684583,0.029134217,0.001684551,0.904029671,0.061782507,0.001684469,Epidemiology,0.17443764,FALSE,168.3333333,0.9572639,347.3333333,0.96507894,149,0.986912772,,,0.969751871
10220,"The long-time behaviour of a stochastic SIR epidemic model with
  distributed delay and multidimensional Lévy jumps",0,2003.08219,3/16/20,arxiv,0,2,mathematical model,0.001565325,0.027294115,0.001565329,0.966444377,0.001565431,0.001565424,Epidemiology,0.087541014,FALSE,17,0.257467994,4,0.231469093,0,0.403234768,,,0.297390618
10221,"Machine Learning the Phenomenology of COVID-19 From Early Infection
  Dynamics",0,2003.07602,3/17/20,arxiv,0,1,machine learning,0.002996513,0.002996497,0.092268967,0.757988349,0.002996496,0.140753179,Epidemiology,0.28334755,FALSE,16,0.243552477,4,0.231469093,12,0.850299401,,,0.441773657
10222,"Neural Network aided quarantine control model estimation of COVID spread
  in Wuhan, China",0,2003.09403,3/18/20,arxiv,0,2,"machine learning, neural network",0.001330108,0.001330077,0.001330078,0.993349616,0.001330054,0.001330068,Epidemiology,0.26171243,FALSE,21,0.312016822,2.5,0.180826866,19,0.89561084,,,0.462818176
10223,"Potential Neutralizing Antibodies Discovered for Novel Corona Virus
  Using Machine Learning",0,2003.08447,3/18/20,arxiv,0,3,"machine learning, bioinformatic",0.507098671,0.305996203,0.182124516,0.001593503,0.00159351,0.001593598,Drug discovery,0.31427386,FALSE,23.33333333,0.345723298,10,0.355632861,29,0.928020248,,,0.543125469
10224,Masked Face Recognition Dataset and Application,0,2003.09093,3/20/20,arxiv,0,14,dataset,0.001291211,0.001291213,0.564459286,0.430375817,0.001291286,0.001291187,Epidemiology,0.008741379,FALSE,32.5,0.461685942,6.071428571,0.280238159,40,0.947033768,,,0.562985956
10225,"Coronavirus (COVID-19) Classification using CT Images by Machine
  Learning Methods",0,2003.09424,3/20/20,arxiv,0,3,"machine learning, dataset",0.001371266,0.001371319,0.993143366,0.001371331,0.001371457,0.00137126,Imaging,0.1603888,FALSE,9,0.135320675,3,0.199424672,87,0.975492314,,,0.436745887
10226,A simple mathematical model for the evolution of the corona virus,0,2003.09477,3/20/20,arxiv,0,1,mathematical model,0.005352942,0.005352895,0.005353356,0.973234717,0.005353177,0.005352913,Epidemiology,0.21544069,FALSE,68,0.753973653,43,0.649785925,1,0.537564047,,,0.647107875
10227,Forecasting and evaluating intervention of Covid-19 in the World,0,2003.098,3/22/20,arxiv,0,6,artificial intelligence,0.001717172,0.001717189,0.001717274,0.991413983,0.001717191,0.001717191,Epidemiology,0.2801653,FALSE,37.5,0.513946441,65.5,0.740767996,17,0.887338725,,,0.71401772
10228,"Large-Scale Screening of COVID-19 from Community Acquired Pneumonia
  using Infection Size-Aware Classification",Physics in Medicine & Biology (2021),2003.0986,3/22/20,arxiv,Physics in Medicine & Biology (2021),10,radiom,0.001786516,0.00178654,0.930116722,0.00178658,0.001786559,0.062737082,Imaging,0.31694826,FALSE,175.7,0.962335333,213,0.923534921,97,0.977467745,,,0.954446
10229,"COVID-Net: A Tailored Deep Convolutional Neural Network Design for
  Detection of COVID-19 Cases from Chest X-Ray Images",0,2003.09871,3/22/20,arxiv,0,2,"deep learning, neural network, dataset",0.000956334,0.000956324,0.995218332,0.000956353,0.000956333,0.000956324,Imaging,0.12474772,FALSE,24,0.35574247,48,0.673735617,462,0.997654176,,,0.675710754
10230,"Estimating Uncertainty and Interpretability in Deep Learning for
  Coronavirus (COVID-19) Detection",0,2003.10769,3/22/20,arxiv,0,2,"bayes, deep learning, artificial intelligence, neural network, dataset",0.00118726,0.001187269,0.926075394,0.069175507,0.0011873,0.00118727,Imaging,0.05625102,FALSE,19,0.285793803,4.5,0.242708055,114,0.981418606,,,0.503306821
10231,"Attention U-Net Based Adversarial Architectures for Chest X-ray Lung
  Segmentation",0,2003.10304,3/23/20,arxiv,0,3,"deep learning, neural network, dataset",0.002080632,0.002080576,0.958195936,0.00208061,0.002080557,0.03348169,Imaging,0.115214854,FALSE,2.666666667,0.029377203,3,0.199424672,31,0.931971109,,,0.386924328
10232,"Understanding the perception of COVID-19 policies by mining a
  multilanguage Twitter dataset",0,2003.10359,3/23/20,arxiv,0,3,"network analysis, text mining, dataset",0.002490516,0.002490522,0.002490607,0.987547403,0.002490517,0.002490434,Epidemiology,0.010925651,FALSE,18,0.271569052,7.333333333,0.304656141,49,0.956787456,,,0.511004216
10233,"Exploring the Effects of COVID-19 Containment Policies on Crime: An
  Empirical Analysis of the Short-term Aftermath in Los Angeles",Am J Crim Just (2020),2003.11021,3/23/20,arxiv,Am J Crim Just (2020),3,bayes,0.002898411,0.002898437,0.002898329,0.985507845,0.002898413,0.002898565,Epidemiology,0.9584285,TRUE,29,0.41993939,7.333333333,0.304656141,8,0.799987654,,,0.508194395
10234,"In Silico Investigations on the Potential Inhibitors for COVID-19
  Protease",0,2003.10642,3/24/20,arxiv,0,5,in silico,0.81520003,0.003101939,0.003101479,0.106674443,0.0031016,0.068820509,Drug discovery,0.88361573,TRUE,39.6,0.53503618,,,5,0.739490092,,,0.637263136
10235,"Personalized workflow to identify optimal T-cell epitopes for
  peptide-based vaccines against COVID-19",0,2003.1065,3/24/20,arxiv,0,5,"machine learning, immunopeptidom",0.47829622,0.414303306,0.103526693,0.001291295,0.001291289,0.001291198,Drug discovery,0.034344405,FALSE,22,0.326056033,71.6,0.757760235,7,0.785110192,,,0.622975487
10236,"Using early data to estimate the actual infection fatality ratio from
  COVID-19 in France (Running title: Infection fatality ratio from COVID-19)","MDPI Biology 2020, 9(5), 97",2003.1072,3/24/20,arxiv,"MDPI Biology 2020, 9(5), 97",5,probabilistic,0.091711944,0.002238687,0.002238561,0.714522088,0.002238516,0.187050204,Epidemiology,0.22966507,FALSE,45,0.587049292,70,0.753344929,24,0.914439163,,,0.751611128
10237,"Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images
  and Deep Convolutional Neural Networks",0,2003.10849,3/24/20,arxiv,0,3,"neural network, dataset",0.001861732,0.0018618,0.990690855,0.001861875,0.001861942,0.001861796,Imaging,0.02245009,FALSE,6.666666667,0.094996598,6.333333333,0.285121755,389,0.996666461,,,0.458928272
10238,"COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose
  COVID-19 in X-Ray Images",0,2003.11055,3/24/20,arxiv,0,3,"deep learning, neural network, classifier, network model, dataset",0.001022629,0.083235101,0.842509908,0.071186993,0.001022682,0.001022686,Imaging,0.06589267,FALSE,19.33333333,0.288638753,3.333333333,0.206515922,239,0.993332922,,,0.496162532
10239,"COVID-19 and Computer Audition: An Overview on What Speech & Sound
  Analysis Could Contribute in the SARS-CoV-2 Corona Crisis",0,2003.11117,3/24/20,arxiv,0,6,artificial intelligence,0.002080616,0.002080597,0.355761724,0.534753892,0.10324247,0.002080701,Epidemiology,0.60280967,TRUE,18.5,0.278001113,1.666666667,0.145036125,31,0.931971109,,,0.451669449
10240,"What is the people posting about symptoms related to Coronavirus in
  Bogota, Colombia?",0,2003.11159,3/25/20,arxiv,0,2,text mining,0.003607441,0.061700339,0.003607487,0.824857364,0.102620176,0.003607193,Epidemiology,0.06993464,FALSE,13.5,0.205393036,0,0.055525823,13,0.858880178,,,0.373266346
10241,"Mapping the Landscape of Artificial Intelligence Applications against
  COVID-19",Journal of Artificial Intelligence Research 69 (2020) 807-845,2003.11336,3/25/20,arxiv,Journal of Artificial Intelligence Research 69 (2020) 807-845,5,"machine learning, artificial intelligence, dataset",0.074690886,0.002296592,0.553892912,0.364526468,0.002296563,0.002296579,Epidemiology,0.87835574,TRUE,19.8,0.293957573,11.6,0.379515654,89,0.975615779,,,0.549696335
10242,"Mathematical Modeling of Epidemic Diseases; A Case Study of the COVID-19
  Coronavirus",0,2003.11371,3/25/20,arxiv,0,1,mathematical model,0.001987177,0.001987193,0.001987283,0.990063966,0.001987222,0.001987159,Epidemiology,0.0957267,FALSE,49,0.624281032,114,0.843791812,53,0.959195012,,,0.809089285
10243,"Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors
  using deep neural networks",0,2004.00979,3/25/20,arxiv,0,11,"virtual screening, neural network",0.805100634,0.00208057,0.186577077,0.002080597,0.002080571,0.002080551,Drug discovery,0.35662025,FALSE,10.90909091,0.162038469,32.09090909,0.58583088,16,0.881782826,,,0.543217392
10244,"Covid-19: Automatic detection from X-Ray images utilizing Transfer
  Learning with Convolutional Neural Networks",Physical and Engineering Sciences in Medicine 43:635-40;2020,2003.11617,3/25/20,arxiv,Physical and Engineering Sciences in Medicine 43:635-40;2020,2,"neural network, transfer learning, dataset",0.001486462,0.00148641,0.992567881,0.001486415,0.001486412,0.001486419,Imaging,0.8464726,TRUE,4,0.054734368,5.5,0.267259834,404,0.997098586,,,0.439697596
10245,Spatial-Temporal Dataset of COVID-19 Outbreak in China,0,2003.11716,3/26/20,arxiv,0,3,dataset,0.001943454,0.001943494,0.001943501,0.990282592,0.001943489,0.00194347,Epidemiology,0.11572173,FALSE,36,0.498299215,7.666666667,0.310877709,0,0.403234768,,,0.404137231
10246,"COVID-19 on Social Media: Analyzing Misinformation in Twitter
  Conversations",0,2003.12309,3/26/20,arxiv,0,5,dataset,0.001684583,0.001684507,0.001684558,0.991577259,0.001684627,0.001684466,Epidemiology,0.026951402,FALSE,48.4,0.61729235,47.6,0.670792079,30,0.930057411,,,0.739380613
10247,"Epidemics, the Ising-model and percolation theory: a comprehensive
  review focussed on Covid-19",0,2003.1186,3/26/20,arxiv,0,5,mathematical model,0.001438246,0.001438135,0.001438164,0.99280922,0.001438121,0.001438114,Epidemiology,0.13480967,FALSE,28.75,0.415115344,2.5,0.180826866,5,0.739490092,,,0.445144101
10248,"Classification of COVID-19 in chest X-ray images using DeTraC deep
  convolutional neural network",0,2003.13815,3/26/20,arxiv,0,3,"neural network, transfer learning, dataset",0.001171558,0.001171535,0.994142282,0.001171541,0.001171539,0.001171545,Imaging,0.44968507,FALSE,87.33333333,0.834621807,55.66666667,0.705244849,141,0.985246003,,,0.84170422
10249,"Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using
  Quantitative Features from Chest CT Images",0,2003.11988,3/26/20,arxiv,0,7,machine learning,0.000946077,0.000946103,0.827673664,0.000946105,0.000946093,0.168541958,Imaging,0.48883057,FALSE,53,0.654400396,51.14285714,0.685911159,60,0.964503982,,,0.768271845
10250,"Age-structured impact of social distancing on the COVID-19 epidemic in
  India",0,2003.12055,3/26/20,arxiv,0,2,bayes,0.001653134,0.001653042,0.001653024,0.95494866,0.038439076,0.001653064,Epidemiology,0.14662215,FALSE,69.5,0.761271569,18,0.46180091,203,0.991542688,,,0.738205056
10251,"Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak
  Supervision",0,2003.12218,3/27/20,arxiv,0,5,dataset,0.238501939,0.285932748,0.356081565,0.115222338,0.002130715,0.002130695,Genomics,0.113514215,FALSE,35.4,0.490815759,29.6,0.566630987,23,0.91129082,,,0.656245855
10252,"$α$-Satellite: An AI-driven System and Benchmark Datasets for
  Hierarchical Community-level Risk Assessment to Help Combat COVID-19",0,2003.12232,3/27/20,arxiv,0,8,"artificial intelligence, dataset",0.001187284,0.001187296,0.13821188,0.857038973,0.001187294,0.001187274,Epidemiology,0.3140323,FALSE,33.375,0.470097099,18.625,0.465948622,16,0.881782826,,,0.605942849
10253,"Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware
  Anomaly Detection",0,2003.12338,3/27/20,arxiv,0,11,dataset,0.000956336,0.221630151,0.743897657,0.031603198,0.000956333,0.000956325,Imaging,0.004712731,FALSE,33.72727273,0.473374977,26.81818182,0.544153064,55,0.960800049,,,0.659442697
10254,"Knowledge synthesis from 100 million biomedical documents augments the
  deep expression profiling of coronavirus receptors",0,2003.12773,3/28/20,arxiv,0,18,"neural network, sequencing",0.569909033,0.001565466,0.186954477,0.238440148,0.001565402,0.001565473,Drug discovery,0.7506106,TRUE,15.44444444,0.231987136,17.05555556,0.451364731,43,0.9507377,,,0.544696522
10255,Mining Coronavirus (COVID-19) Posts in Social Media,0,2004.06778,3/28/20,arxiv,0,2,machine learning,0.002562567,0.002562686,0.193635196,0.796114377,0.002562611,0.002562563,Epidemiology,0.088668525,FALSE,6,0.086028821,3.5,0.213607172,8,0.799987654,,,0.366541215
10256,Can AI help in screening Viral and COVID-19 pneumonia?,IEEE Access 2020,2003.13145,3/29/20,arxiv,IEEE Access 2020,12,"artificial intelligence, neural network, deep-learning, transfer learning",0.00097744,0.000977466,0.995112816,0.000977437,0.000977422,0.000977418,Imaging,0.52487314,TRUE,41.63636364,0.554208671,35.90909091,0.609579877,168,0.988888203,,,0.717558917
10257,Contact network models matching the dynamics of the COVID-19 spreading,0,2003.1316,3/29/20,arxiv,0,1,network model,0.002422254,0.002422325,0.002422326,0.987888541,0.002422288,0.002422265,Epidemiology,0.41269514,FALSE,6,0.086028821,1,0.122023013,3,0.667819001,,,0.291956945
10258,Planning as Inference in Epidemiological Models,0,2003.13221,3/30/20,arxiv,0,9,simulation model,0.00321417,0.003214222,0.057667742,0.929475589,0.003214141,0.003214136,Epidemiology,0.7608353,TRUE,13.22222222,0.199332055,5.666666667,0.270203372,6,0.764429903,,,0.411321777
10259,"Sex Differences in Severity and Mortality Among Patients With COVID-19:
  Evidence from Pooled Literature Analysis and Insights from Integrated
  Bioinformatic Analysis",0,2003.13547,3/30/20,arxiv,0,15,"bioinformatic, sequencing",0.32390435,0.096329918,0.001156256,0.001156275,0.001156309,0.576296892,Clinics,0.56755906,TRUE,34.26666667,0.479621498,9.733333333,0.348742307,43,0.9507377,,,0.593033835
10260,"Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on
  the COVID-19 propagation model",0,2003.13633,3/30/20,arxiv,0,9,deep learning,0.001622726,0.030329524,0.286533246,0.632606583,0.001622736,0.047285186,Epidemiology,0.37261003,FALSE,53.55555556,0.657492733,26.66666667,0.543216484,14,0.866658436,,,0.689122551
10261,"Structural analysis of SARS-CoV-2 and prediction of the human
  interactome",0,2003.13655,3/30/20,arxiv,0,5,"interactom, genomes",0.721122375,0.273786939,0.001272695,0.001272674,0.001272669,0.001272649,Drug discovery,0.56728745,TRUE,11.33333333,0.170635166,5.777777778,0.272009633,7,0.785110192,,,0.409251664
10262,COVID-CT-Dataset: A CT Scan Dataset about COVID-19,0,2003.13865,3/30/20,arxiv,0,6,"supervised learning, dataset",0.001861704,0.001861677,0.990691429,0.001861706,0.001861727,0.001861757,Imaging,0.009860635,FALSE,52.4,0.649823737,71.8,0.75849612,193,0.99104883,,,0.799789562
10263,"Robust predictive model for Carriers, Infections and Recoveries (CIR):
  predicting death rates for CoVid-19 in Spain",0,2003.1389,3/31/20,arxiv,0,1,predictive model,0.002562612,0.002562724,0.00256281,0.771643471,0.002562612,0.218105771,Epidemiology,0.16519126,FALSE,18,0.271569052,8,0.320511105,2,0.618927094,,,0.403669084
10264,"Automated Methods for Detection and Classification Pneumonia based on
  X-Ray Images Using Deep Learning",0,2003.14363,3/31/20,arxiv,0,3,"deep learning, neural network, image processing, dataset",0.001486523,0.022854931,0.971199245,0.001486474,0.001486431,0.001486396,Imaging,0.046313077,FALSE,72.33333333,0.774630466,27,0.546026224,37,0.942712513,,,0.754456401
10265,"COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from
  Radiographs",0,2003.14395,3/31/20,arxiv,0,2,"deep learning, computational, neural network, dataset",0.001085335,0.00108538,0.944455809,0.051202787,0.001085366,0.001085324,Imaging,0.001828402,FALSE,42.5,0.562743522,5.5,0.267259834,127,0.983640965,,,0.604548107
10266,"Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep
  Learning and Transfer Learning Algorithms",0,2004.00038,3/31/20,arxiv,0,5,"deep learning, neural network, transfer learning, dataset",0.001392829,0.001392851,0.993035768,0.001392885,0.001392846,0.001392819,Imaging,0.110306144,FALSE,96.2,0.859484198,102,0.82592989,84,0.974689796,,,0.886701295
10267,Neural network based country wise risk prediction of COVID-19,"Applied Sciences, 2020",2004.00959,3/31/20,arxiv,"Applied Sciences, 2020",4,"bayes, artificial intelligence, neural network, lstm",0.001943474,0.00194347,0.511258195,0.480967912,0.001943496,0.001943452,Epidemiology,0.62723297,TRUE,69.75,0.762446657,55.5,0.704575863,31,0.931971109,,,0.799664543
10268,"Molecular docking studies on Jensenone from eucalyptus essential oil as
  a potential inhibitor of COVID 19 corona virus infection",0,2004.00217,4/1/20,arxiv,0,2,in silico,0.955776298,0.001593496,0.001593508,0.037849699,0.001593515,0.001593485,Drug discovery,0.8518341,TRUE,57.5,0.685447461,55.5,0.704575863,13,0.858880178,,,0.749634501
10269,"A County-level Dataset for Informing the United States' Response to
  COVID-19",0,2004.00756,4/1/20,arxiv,0,11,dataset,0.00151182,0.001511923,0.043603356,0.821782228,0.130078768,0.001511906,Epidemiology,0.034318745,FALSE,17.45454545,0.262044653,9.545454545,0.345664972,37,0.942712513,,,0.516807379
10270,"Leveraging Data Preparation, HBase NoSQL Storage, and HiveQL Querying
  for COVID-19 Big Data Analytics Projects",0,2004.00253,4/1/20,arxiv,0,1,"machine learning, predictive model",0.00333547,0.003335382,0.173211299,0.722688333,0.094094194,0.003335322,Epidemiology,0.26203865,FALSE,11,0.167171748,0,0.055525823,1,0.537564047,,,0.253420539
10271,"Extracting possibly representative COVID-19 Biomarkers from X-Ray images
  with Deep Learning approach and image data related to Pulmonary Diseases",Journal of Medical and Biological Engineering; 40:462-69;2020,2004.00338,4/1/20,arxiv,Journal of Medical and Biological Engineering; 40:462-69;2020,3,"deep learning, neural network, dataset",0.001310321,0.001310321,0.99344821,0.001310358,0.001310435,0.001310355,Imaging,0.5761939,TRUE,3,0.037293586,3,0.199424672,75,0.971417989,,,0.402712082
10272,"Hate multiverse spreads malicious COVID-19 content online beyond
  individual platform control",0,2004.00673,4/1/20,arxiv,0,9,machine learning,0.003335412,0.064994899,0.003335757,0.843515685,0.081483035,0.003335212,Epidemiology,0.060026288,FALSE,27.66666667,0.403488156,16.77777778,0.446280439,17,0.887338725,,,0.579035773
10273,"Detection of Coronavirus (COVID-19) Associated Pneumonia based on
  Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model
  using Chest X-ray Dataset",0,2004.01184,4/2/20,arxiv,0,4,"adversarial network, transfer learning, dataset",0.000999535,0.016485843,0.979516045,0.000999563,0.000999505,0.000999509,Imaging,0.004779875,FALSE,236.75,0.982064444,83.25,0.786259031,49,0.956787456,,,0.908370311
10274,Detecting Suspected Epidemic Cases Using Trajectory Big Data,CSIAM Transactions on Applied Mathematics. 1(2020).186-206,2004.00908,4/2/20,arxiv,CSIAM Transactions on Applied Mathematics. 1(2020).186-206,8,machine learning,0.00115626,0.001156298,0.207588944,0.742251896,0.001156299,0.046690302,Epidemiology,0.11344412,FALSE,63.5,0.72496753,33.625,0.596400856,6,0.764429903,,,0.695266097
10275,"Regression Approach for Modeling COVID-19 Spread and its Impact On Stock
  Market",0,2004.01489,4/2/20,arxiv,0,1,bayes,0.003760496,0.003760567,0.003760568,0.981197173,0.003760618,0.003760578,Epidemiology,0.123655915,FALSE,27,0.3960047,2,0.164302917,5,0.739490092,,,0.433265903
10276,A mathematical model for the coronavirus COVID-19 outbreak,0,2004.01487,4/2/20,arxiv,0,2,mathematical model,0.007061669,0.00706164,0.007062212,0.964691209,0.007061696,0.007061574,Epidemiology,0.10771167,FALSE,55.5,0.671346404,5.5,0.267259834,2,0.618927094,,,0.519177777
10277,"Inferring change points in the COVID-19 spreading reveals the
  effectiveness of interventions",0,2004.01105,4/2/20,arxiv,0,7,bayes,0.002130637,0.002130722,0.002130695,0.989346605,0.002130663,0.002130678,Epidemiology,0.14847732,FALSE,32.71428571,0.463479498,47,0.668718223,262,0.994011976,,,0.708736566
10278,"CogMol: Target-Specific and Selective Drug Design for COVID-19 Using
  Deep Generative Models",0,2004.01215,4/2/20,arxiv,0,11,classifier,0.795453424,0.000863075,0.18257279,0.000863056,0.000863096,0.019384559,Drug discovery,0.056007028,FALSE,33.36363636,0.469973406,30.18181818,0.570845598,13,0.858880178,,,0.633233061
10279,"Neural Network aided quarantine control model estimation of global
  Covid-19 spread",0,2004.02752,4/2/20,arxiv,0,2,neural network,0.001538088,0.001538142,0.090625597,0.90322194,0.001538106,0.001538127,Epidemiology,0.2760127,FALSE,21,0.312016822,2.5,0.180826866,19,0.89561084,,,0.462818176
10280,"AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough
  Samples via an App","Informatics in Medicine Unlocked, vol. 20, p. 100378, 2020",2004.01275,4/2/20,arxiv,"Informatics in Medicine Unlocked, vol. 20, p. 100378, 2020",9,transfer learning,0.001392884,0.001392847,0.880371589,0.001392904,0.114056921,0.001392855,Healthcare,0.4950059,FALSE,12.375,0.186839013,1.25,0.127776291,73,0.970677202,,,0.428430835
10281,"Automated Quantification of CT Patterns Associated with COVID-19 from
  Chest CT","Radiology: Artificial Intelligence, Vol. 2, No. 4, 2020",2004.01279,4/2/20,arxiv,"Radiology: Artificial Intelligence, Vol. 2, No. 4, 2020",20,"deep learning, dataset",0.001330018,0.001330032,0.786211486,0.001330049,0.001330166,0.208468249,Imaging,0.6768713,TRUE,56.1,0.675551982,53.8,0.696414236,8,0.799987654,,,0.723984624
10282,"Different scenarios in the Dynamics of SARS-Cov-2 Infection: an adapted
  ODE model",0,2004.01295,4/2/20,arxiv,0,1,mathematical model,0.002357731,0.002357762,0.002357732,0.988211336,0.002357716,0.002357722,Epidemiology,0.11735287,FALSE,9,0.135320675,5,0.257024351,4,0.707574542,,,0.366639856
10283,"A Model of Supply-Chain Decisions for Resource Sharing with an
  Application to Ventilator Allocation to Combat COVID-19",0,2004.01318,4/3/20,arxiv,0,5,optimization model,0.001823331,0.001823326,0.001823446,0.781846766,0.001823362,0.210859769,Epidemiology,0.08318657,FALSE,11.2,0.168594224,3.2,0.202100615,20,0.900117291,,,0.423604043
10284,"Generating Similarity Map for COVID-19 Transmission Dynamics with
  Topological Autoencoder","Informatics in Medicine Unlocked, Vol. 20, 100386, 2020",2004.01481,4/3/20,arxiv,"Informatics in Medicine Unlocked, Vol. 20, 100386, 2020",1,neural network,0.001861816,0.040215483,0.132595842,0.821603164,0.001861697,0.001861998,Epidemiology,0.6330251,TRUE,17,0.257467994,3,0.199424672,5,0.739490092,,,0.398794253
10285,"Analysis of the COVID-19 pandemic by SIR model and machine learning
  technics for forecasting",0,2004.01574,4/3/20,arxiv,0,3,machine learning,0.003760593,0.003760791,0.130283747,0.8546739,0.003760461,0.003760508,Epidemiology,0.29177904,FALSE,31,0.445111015,0.666666667,0.096200161,27,0.92443978,,,0.488583652
10286,"In Silico Screening of Some Naturally Occurring Bioactive Compounds
  Predicts Potential Inhibitors against SARS-COV-2 (COVID-19) Protease",0,2004.01634,4/3/20,arxiv,0,2,in silico,0.700022094,0.001220066,0.046458117,0.227553097,0.001220083,0.023526543,Drug discovery,0.664943,TRUE,139,0.932092275,36.5,0.614329676,3,0.667819001,,,0.738080318
10287,COVID-19: Strategies for Allocation of Test Kits,0,2004.0174,4/3/20,arxiv,0,4,machine learning,0.002996653,0.00299669,0.440595628,0.401092708,0.149321712,0.002996608,Epidemiology,0.089448094,FALSE,21.25,0.314614386,72.25,0.759767193,1,0.537564047,,,0.537315209
10288,"Open access institutional and news media tweet dataset for COVID-19
  social science research",0,2004.01791,4/3/20,arxiv,0,1,dataset,0.002898302,0.002898334,0.002898356,0.985508274,0.002898449,0.002898285,Epidemiology,0.58390003,TRUE,49,0.624281032,16,0.437316029,4,0.707574542,,,0.589723867
10289,"Accounting for Symptomatic and Asymptomatic in a SEIR-type model of
  COVID-19",0,2004.01805,4/3/20,arxiv,0,4,mathematical model,0.002032757,0.002032776,0.002032746,0.937023255,0.002032773,0.054845694,Epidemiology,0.26780882,FALSE,18.25,0.273609994,0.5,0.087101953,14,0.866658436,,,0.409123461
10290,"Identifying Radiological Findings Related to COVID-19 from Medical
  Literature",0,2004.01862,4/4/20,arxiv,0,2,dataset,0.004530782,0.004530726,0.752473526,0.004530946,0.004530751,0.229403268,Clinics,0.19079095,FALSE,43.5,0.573381161,100,0.822584961,6,0.764429903,,,0.720132009
10291,"COVID-19: Development of a Robust Mathematical Model and Simulation
  Package with Consideration for Ageing Population and Time Delay for Control
  Action and Resusceptibility",Physica D: Nonlinear Phenomena (2020),2004.01974,4/4/20,arxiv,Physica D: Nonlinear Phenomena (2020),2,"computational, mathematical model",0.16762923,0.001717281,0.001717243,0.800503851,0.026715129,0.001717266,Epidemiology,0.53859395,TRUE,24.5,0.361988991,21,0.492239765,27,0.92443978,,,0.592889512
10292,"Generic probabilistic modelling and non-homogeneity issues for the UK
  epidemic of COVID-19",0,2004.01991,4/4/20,arxiv,0,13,"computational, probabilistic",0.001392867,0.001392903,0.001392937,0.993035544,0.001392886,0.001392863,Epidemiology,0.15819913,FALSE,69.5,0.761271569,31,0.578204442,9,0.814309525,,,0.717928512
10293,"Finding Covid-19 from Chest X-rays using Deep Learning on a Small
  Dataset",0,2004.0206,4/5/20,arxiv,0,4,"deep learning, neural network, dataset",0.001653025,0.001653066,0.991734779,0.001653091,0.001653031,0.001653007,Imaging,0.21082011,FALSE,100,0.868884903,496.5,0.978525555,75,0.971417989,,,0.939609482
10294,"Information Mining for COVID-19 Research From a Large Volume of
  Scientific Literature",0,2004.02085,4/5/20,arxiv,0,2,information mining,0.364822083,0.24306398,0.00146195,0.387728258,0.001461869,0.001461859,Epidemiology,0.4764006,FALSE,14.5,0.219617787,3,0.199424672,14,0.866658436,,,0.428566965
10295,"One-shot screening of potential peptide ligands on HR1 domain in
  COVID-19 glycosylated spike (S) protein with deep siamese network",0,2004.02136,4/5/20,arxiv,0,1,"neural network, dataset",0.611677521,0.112152944,0.249584131,0.024040127,0.001272658,0.001272619,Drug discovery,0.03798476,FALSE,9,0.135320675,0,0.055525823,4,0.707574542,,,0.29947368
10296,"On the Inhibition of COVID-19 Protease by Indian Herbal Plants: An In
  Silico Investigation",0,2004.03411,4/5/20,arxiv,0,3,in silico,0.696540644,0.002183328,0.002183309,0.294725837,0.00218354,0.002183343,Drug discovery,0.30617386,FALSE,82.66666667,0.816871792,2.666666667,0.185442869,9,0.814309525,,,0.605541395
10297,"Comparing the binding interactions in the receptor binding domains of
  SARS-CoV-2 and SARS-CoV",0,2004.02158,4/5/20,arxiv,0,3,molecular dynamics simulation,0.795508539,0.194802302,0.002422269,0.002422341,0.002422298,0.002422251,Drug discovery,0.19286287,FALSE,14,0.213494959,3.666666667,0.217621086,27,0.92443978,,,0.451851942
10298,"The Framework for the Prediction of the Critical Turning Period for
  Outbreak of COVID-19 Spread in China based on the iSEIR Model",0,2004.02278,4/5/20,arxiv,0,5,mathematical model,0.001141319,0.001141342,0.001141373,0.994293242,0.001141353,0.001141371,Epidemiology,0.1287345,FALSE,21,0.312016822,6,0.280037463,6,0.764429903,,,0.452161396
10299,"Analyzing initial stage of COVID-19 transmission through Bayesian
  time-varying model",0,2004.02281,4/5/20,arxiv,0,2,bayes,0.002720182,0.002720203,0.002720287,0.780373978,0.208745101,0.002720249,Epidemiology,0.17054316,FALSE,7.5,0.108355495,0.5,0.087101953,3,0.667819001,,,0.287758817
10300,"Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia
  Cases at the Era of COVID-19",0,2004.03399,4/5/20,arxiv,0,7,"deep learning, dataset",0.001046839,0.001046894,0.916257682,0.079554817,0.001046892,0.001046877,Imaging,0.06815112,FALSE,88.28571429,0.837528604,33.85714286,0.597805727,22,0.908142478,,,0.781158936
10301,Modelling death rates due to COVID-19: A Bayesian approach,0,2004.02386,4/6/20,arxiv,0,3,bayes,0.002562544,0.002562559,0.002562552,0.987187099,0.002562584,0.002562663,Epidemiology,0.2652046,FALSE,19,0.285793803,8,0.320511105,7,0.785110192,,,0.463805033
10302,Discovering associations in COVID-19 related research papers,0,2004.03397,4/6/20,arxiv,0,3,text mining,0.002898486,0.002898371,0.00289847,0.76172385,0.226682289,0.002898534,Epidemiology,0.09057614,FALSE,17,0.257467994,3.333333333,0.206515922,4,0.707574542,,,0.390519486
10303,"Pandemic Populism: Facebook Pages of Alternative News Media and the
  Corona Crisis -- A Computational Content Analysis",0,2004.02566,4/6/20,arxiv,0,4,computational,0.001622779,0.001622744,0.001622719,0.991886144,0.001622909,0.001622704,Epidemiology,0.031854242,FALSE,60.5,0.705547653,44.5,0.656743377,20,0.900117291,,,0.754136107
10304,"COVID-19 forecasting based on an improved interior search algorithm and
  multi-layer feed forward neural network",0,2004.0596,4/6/20,arxiv,0,2,"neural network, forecasting model",0.001220104,0.001220009,0.478445767,0.516674087,0.001220001,0.001220032,Epidemiology,0.08918381,FALSE,449.5,0.996722123,142,0.87643832,11,0.840175319,,,0.904445254
10305,"Estimating the number of SARS-CoV-2 infections and the impact of social
  distancing in the United States",0,2004.02605,4/6/20,arxiv,0,4,"bayes, bayesian model",0.00289827,0.002898286,0.002898275,0.985508533,0.002898315,0.002898322,Epidemiology,0.06054905,FALSE,32.75,0.463788732,18.75,0.467621086,7,0.785110192,,,0.572173337
10306,Coronavirus Detection and Analysis on Chest CT with Deep Learning,0,2004.0264,4/6/20,arxiv,0,6,"deep learning, image processing, dataset",0.002357736,0.002357784,0.776309856,0.127890065,0.002357782,0.088726776,Imaging,0.19938803,FALSE,40.16666667,0.540602387,108.1666667,0.836499866,45,0.953206988,,,0.776769747
10307,"COVID-CAPS: A Capsule Network-based Framework for Identification of
  COVID-19 cases from X-ray Images",0,2004.02696,4/6/20,arxiv,0,6,dataset,0.001010945,0.001010962,0.873891151,0.122065056,0.001010954,0.001010933,Imaging,0.429008,FALSE,15.33333333,0.230997588,5.5,0.267259834,110,0.980739552,,,0.492998991
10308,"Review of Artificial Intelligence Techniques in Imaging Data
  Acquisition, Segmentation and Diagnosis for COVID-19",IEEE Reviews in Biomedical Engineering (2020),2004.02731,4/6/20,arxiv,IEEE Reviews in Biomedical Engineering (2020),9,artificial intelligence,0.001187345,0.0011873,0.927606664,0.067644059,0.001187322,0.00118731,Imaging,0.8683464,TRUE,58.66666667,0.694106005,39.11111111,0.628244581,274,0.994382369,,,0.772244319
10309,COVID-19: Analytics Of Contagion On Inhomogeneous Random Social Networks,0,2004.02779,4/6/20,arxiv,0,1,network model,0.001220038,0.001220023,0.001220075,0.993899785,0.001220053,0.001220027,Epidemiology,0.1027011,FALSE,108,0.886078298,213,0.923534921,2,0.618927094,,,0.809513438
10310,"A decision support system for optimizing the cost of social distancing
  in order to stop the spread of COVID-19",0,2004.02807,4/6/20,arxiv,0,1,computational,0.001684538,0.001684502,0.185407712,0.807854142,0.00168457,0.001684536,Epidemiology,0.018001556,FALSE,92,0.848970252,29,0.562884667,3,0.667819001,,,0.69322464
10311,"COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using
  Chest X-rays",0,2004.03042,4/6/20,arxiv,0,3,neural network,0.001187318,0.00118733,0.932726586,0.001187367,0.062524099,0.001187299,Imaging,0.5746752,TRUE,0,0.006432061,,,2,0.618927094,,,0.312679578
10312,"Prediction of COVID-19 Disease Progression in India : Under the Effect
  of National Lockdown",0,2004.03147,4/7/20,arxiv,0,1,machine learning,0.002490438,0.002490458,0.002490736,0.987547382,0.002490489,0.002490497,Epidemiology,0.14306542,FALSE,44,0.578390748,17,0.451097137,18,0.891474782,,,0.640320889
10313,"To mask or not to mask: Modeling the potential for face mask use by the
  general public to curtail the COVID-19 pandemic",Infectious Disease Modelling. 5 (2020) 248-255,2004.03251,4/7/20,arxiv,Infectious Disease Modelling. 5 (2020) 248-255,8,model simulation,0.000977459,0.000977435,0.000977473,0.995112582,0.000977513,0.000977538,Epidemiology,0.3974304,FALSE,56.25,0.676788917,120.5,0.852421729,296,0.994814495,,,0.841341713
10314,"A SARS-CoV-2 Microscopic Image Dataset with Ground Truth Images and
  Visual Features",0,2004.03416,4/7/20,arxiv,0,5,"deep learning, dataset",0.003927507,0.003927732,0.872851271,0.111438415,0.003927497,0.003927577,Imaging,0.14447215,FALSE,20.2,0.300327788,6.4,0.286058336,0,0.403234768,,,0.32987363
10315,"Assessment of Lockdown Effect in Some States and Overall India: A
  Predictive Mathematical Study on COVID-19 Outbreak",0,2004.03487,4/7/20,arxiv,0,4,mathematical model,0.001565456,0.026632798,0.001565363,0.967105763,0.001565323,0.001565297,Epidemiology,0.3312649,FALSE,70.25,0.76504422,26.25,0.539670859,55,0.960800049,,,0.75517171
10316,"A Mathematical Description of the Dynamics of Coronavirus Disease
  (COVID-19): A Case Study of Brazil",0,2004.03495,4/7/20,arxiv,0,3,mathematical model,0.003607222,0.003607189,0.003607185,0.981964062,0.003607162,0.00360718,Epidemiology,0.6005381,TRUE,16.66666667,0.251159626,6,0.280037463,10,0.828199272,,,0.45313212
10317,"A large-scale COVID-19 Twitter chatter dataset for open scientific
  research -- an international collaboration",0,2004.03688,4/7/20,arxiv,0,9,dataset,0.001786594,0.001786616,0.001786654,0.953948131,0.038905462,0.001786543,Epidemiology,0.64019746,TRUE,45,0.587049292,22.85714286,0.510436179,46,0.954009507,,,0.683831659
10318,"Coronavirus (COVID-19) Classification using Deep Features Fusion and
  Ranking Technique",0,2004.03698,4/7/20,arxiv,0,3,"neural network, transfer learning, dataset",0.001511834,0.00151187,0.939257377,0.054695151,0.001511928,0.00151184,Imaging,0.35249296,FALSE,9,0.135320675,3,0.199424672,37,0.942712513,,,0.425819287
10319,COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches,0,2004.03747,4/7/20,arxiv,0,5,"deep learning, neural network, transfer learning",0.001511826,0.001511876,0.992440741,0.001511834,0.001511849,0.001511875,Imaging,0.025875837,FALSE,116.4,0.901292597,81.6,0.782111319,36,0.941169208,,,0.874857708
10320,"Screening of Therapeutic Agents for COVID-19 using Machine Learning and
  Ensemble Docking Simulations",0,2004.03766,4/8/20,arxiv,0,6,"machine learning, computational, dataset",0.839215973,0.001310401,0.155542607,0.001310365,0.001310333,0.001310322,Drug discovery,0.074322134,FALSE,24.66666667,0.36334962,3.833333333,0.222103291,9,0.814309525,,,0.466587479
10321,"A machine learning methodology for real-time forecasting of the
  2019-2020 COVID-19 outbreak using Internet searches, news alerts, and
  estimates from mechanistic models",0,2004.04019,4/8/20,arxiv,0,8,machine learning,0.001486479,0.001486421,0.14839021,0.845663997,0.001486476,0.001486417,Epidemiology,0.43996847,FALSE,25.75,0.377698064,71.75,0.758295424,40,0.947033768,,,0.694342419
10322,"""Go eat a bat, Chang!"": On the Emergence of Sinophobic Behavior on Web
  Communities in the Face of COVID-19",0,2004.04046,4/8/20,arxiv,0,7,dataset,0.001371285,0.001371316,0.001371264,0.993143544,0.001371341,0.00137125,Epidemiology,0.00113827,FALSE,96.71428571,0.860535593,88.42857143,0.797698689,0,0.403234768,,,0.68715635
10323,"Parametrization Model Motivated from Physical Processes for Studying the
  Spread of COVID-19 Epidemic",0,2004.05992,4/8/20,arxiv,0,1,mathematical model,0.00223864,0.002238595,0.002238541,0.988807264,0.002238509,0.002238451,Epidemiology,0.044323772,FALSE,673,0.998701218,1187,0.995317099,2,0.618927094,,,0.870981804
10324,Measuring Emotions in the COVID-19 Real World Worry Dataset,0,2004.04225,4/8/20,arxiv,0,3,"predictive model, dataset",0.0018618,0.001861707,0.147823549,0.686686677,0.159904576,0.001861691,Epidemiology,0.068029344,FALSE,14.66666667,0.221163956,30.66666667,0.574257426,53,0.959195012,,,0.584872131
10325,A comparative analysis for SARS-CoV-2,0,2004.04281,4/8/20,arxiv,0,1,"computational, bioinformatic, genomes",0.637908931,0.354142397,0.001987233,0.001987203,0.001987127,0.001987109,Drug discovery,0.2223562,FALSE,44,0.578390748,20,0.481000803,0,0.403234768,,,0.487542106
10326,Large Arabic Twitter Dataset on COVID-19,0,2004.04315,4/9/20,arxiv,0,3,dataset,0.002562881,0.002562793,0.002562712,0.987186344,0.002562712,0.002562558,Epidemiology,0.0177041,FALSE,25.66666667,0.376461129,8.333333333,0.325662296,18,0.891474782,,,0.531199403
10327,"GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the
  Diagnosis of COVID-19 Disease based on Gravitational Search Optimization
  Algorithm","Applied Soft Computing, Volume 98, January 2021, 106742",2004.05084,4/9/20,arxiv,"Applied Soft Computing, Volume 98, January 2021, 106742",3,"deep learning, neural network, image analysis",0.001622727,0.001622724,0.949943256,0.043565833,0.001622771,0.001622689,Imaging,0.7619114,TRUE,332,0.992825778,107.3333333,0.835094996,4,0.707574542,,,0.845165105
10328,"DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray
  Images",0,2004.04582,4/9/20,arxiv,0,6,neural network,0.001350343,0.001350328,0.993248156,0.00135036,0.001350425,0.001350388,Imaging,0.021197677,FALSE,39,0.530521368,22.66666667,0.508629917,41,0.948144947,,,0.662432077
10329,"Modeling Epidemic Spreading through Public Transit using Time-Varying
  Encounter Network",0,2004.04602,4/9/20,arxiv,0,7,computational,0.001901755,0.001901789,0.001901783,0.990491268,0.001901723,0.001901683,Epidemiology,0.592361,TRUE,128,0.919104459,79.28571429,0.776960128,7,0.785110192,,,0.82705826
10330,"Estimating required 'lockdown' cycles before immunity to SARS-CoV-2:
  Model-based analyses of susceptible population sizes, 'S0', in seven European
  countries including the UK and Ireland",0,2004.0506,4/9/20,arxiv,0,9,"bayes, bayesian model",0.017940628,0.001291246,0.001291207,0.976894486,0.00129121,0.001291223,Epidemiology,0.18936089,FALSE,401.1111111,0.995732575,,,14,0.866658436,,,0.931195505
10331,"Real-time forecasts and risk assessment of novel coronavirus (COVID-19)
  cases: A data-driven analysis",0,2004.09996,4/9/20,arxiv,0,2,forecasting model,0.00162271,0.001622748,0.1045458,0.888963293,0.001622724,0.001622726,Epidemiology,0.48046052,FALSE,43.5,0.573381161,9.5,0.345531175,32,0.933699611,,,0.617537316
10332,"Implications of the virus-encoded miRNA and host miRNA in the
  pathogenicity of SARS-CoV-2",0,2004.04874,4/10/20,arxiv,0,11,computational,0.765043662,0.123866123,0.034595807,0.002183274,0.002183342,0.072127792,Drug discovery,0.71214926,TRUE,23.90909091,0.352464593,52.45454545,0.690594059,14,0.866658436,,,0.636572363
10333,"CoroNet: A deep neural network for detection and diagnosis of COVID-19
  from chest x-ray images",Computer Methods and Programs in Biomedicine 196C (2020) 105581,2004.04931,4/10/20,arxiv,Computer Methods and Programs in Biomedicine 196C (2020) 105581,3,"neural network, network model, dataset",0.001059346,0.001059388,0.994703141,0.001059431,0.001059355,0.001059339,Imaging,0.6332793,TRUE,16,0.243552477,2,0.164302917,143,0.985863325,,,0.464572906
10334,"A single-cell RNA expression map of coronavirus receptors and associated
  factors in developing human embryos",0,2004.04935,4/10/20,arxiv,0,11,dataset,0.922973382,0.039737195,0.001565324,0.001565319,0.001565332,0.032593448,Drug discovery,0.8686213,TRUE,18.72727273,0.280165749,8,0.320511105,2,0.618927094,,,0.40653465
10335,"Impact of intervention on the spread of COVID-19 in India: A model based
  study",0,2004.0495,4/10/20,arxiv,0,4,mathematical model,0.00135039,0.001350343,0.001350343,0.993248255,0.001350354,0.001350316,Epidemiology,0.14933422,FALSE,64.25,0.729791576,24,0.521808938,13,0.858880178,,,0.703493564
10336,"Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research
  Dataset: Preliminary Thoughts and Lessons Learned",0,2004.05125,4/10/20,arxiv,0,5,dataset,0.002422333,0.002422363,0.535195076,0.455115621,0.002422358,0.00242225,Epidemiology,0.030809373,FALSE,87.8,0.836415363,1602.6,0.99719026,26,0.920859312,,,0.918154978
10337,"Hi Sigma, do I have the Coronavirus?: Call for a New Artificial
  Intelligence Approach to Support Health Care Professionals Dealing With The
  COVID-19 Pandemic",0,2004.0651,4/10/20,arxiv,0,13,"artificial intelligence, transfer learning, dataset",0.001350365,0.001350342,0.267800434,0.59745048,0.001350397,0.130697981,Epidemiology,0.07877833,FALSE,41.53846154,0.553342817,53.15384615,0.69387209,11,0.840175319,,,0.695796742
10338,CONTAIN: Privacy-oriented Contact Tracing Protocols for Epidemics,0,2004.05251,4/10/20,arxiv,0,3,dataset,0.00156532,0.001565351,0.001565349,0.992173352,0.001565343,0.001565285,Epidemiology,0.014562875,FALSE,172.3333333,0.959985157,542.6666667,0.981469093,17,0.887338725,,,0.942930991
10339,"Deciphering the Protein Motion of S1 Subunit in SARS-CoV-2 Spike
  Glycoprotein Through Integrated Computational Methods",0,2004.05256,4/10/20,arxiv,0,2,computational,0.537257742,0.001987309,0.049497407,0.407283212,0.001987204,0.001987126,Drug discovery,0.38799226,FALSE,11.5,0.17416043,0.5,0.087101953,4,0.707574542,,,0.322945642
10340,"Modeling the Heterogeneity in COVID-19's Reproductive Number and its
  Impact on Predictive Scenarios",0,2004.05272,4/11/20,arxiv,0,2,"bayes, bayesian model",0.001254619,0.001254649,0.001254654,0.993726862,0.001254607,0.001254609,Epidemiology,0.039492995,FALSE,66,0.741356918,159.5,0.8914905,11,0.840175319,,,0.824340912
10341,A critique of the Covid-19 analysis for India by Singh and Adhikari,0,2004.05373,4/11/20,arxiv,0,1,mathematical model,0.003927393,0.003927434,0.00392752,0.980362878,0.003927379,0.003927396,Epidemiology,0.12822688,FALSE,43,0.567629414,0,0.055525823,7,0.785110192,,,0.46942181
10342,"Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race
  with small data","Int. J. Environ. Res. Public Health 2020, 17(18), 6933",2004.05405,4/11/20,arxiv,"Int. J. Environ. Res. Public Health 2020, 17(18), 6933",5,"deep learning, deep-learning, dataset",0.001538121,0.036505515,0.728678978,0.230201089,0.001538113,0.001538183,Imaging,0.2380833,FALSE,49,0.624281032,37.4,0.618678084,45,0.953206988,,,0.732055368
10343,"Detection of Covid-19 From Chest X-ray Images Using Artificial
  Intelligence: An Early Review",0,2004.05436,4/11/20,arxiv,0,3,"deep learning, artificial intelligence",0.002032759,0.002032789,0.679951723,0.311917086,0.002032844,0.0020328,Imaging,0.06321022,FALSE,41.33333333,0.551549261,4.333333333,0.237958255,23,0.91129082,,,0.566932779
10344,"The effect of anti-COVID-19 policies to the evolution of the disease: A
  complex network analysis to the successful case of Greece","Physics 2020, 2(2), 325-339",2004.06536,4/12/20,arxiv,"Physics 2020, 2(2), 325-339",2,network analysis,0.001220062,0.029232314,0.038375952,0.928731599,0.001220028,0.001220046,Epidemiology,0.5037338,TRUE,45,0.587049292,5,0.257024351,17,0.887338725,,,0.577137456
10345,"Robust predictive model for Carriers, Infections and Recoveries (CIR):
  first update for CoVid-19 in Spain",0,2004.05639,4/12/20,arxiv,0,1,predictive model,0.003927527,0.003927636,0.003927886,0.713376908,0.003927451,0.270912592,Epidemiology,0.13007832,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
10346,"Residual Attention U-Net for Automated Multi-Class Segmentation of
  COVID-19 Chest CT Images",0,2004.05645,4/12/20,arxiv,0,3,"deep learning, dataset",0.00156533,0.001565301,0.992173377,0.001565355,0.001565336,0.001565302,Imaging,0.16566941,FALSE,114,0.897767333,77.33333333,0.771541343,46,0.954009507,,,0.874439394
10347,"Modeling the transmission of new coronavirus in São Paulo State,
  Brazil -- Assessing epidemiological impacts of isolating young and elder
  persons",0,2004.05715,4/12/20,arxiv,0,3,mathematical model,0.002238478,0.002238455,0.002238441,0.90695723,0.084088805,0.002238591,Epidemiology,0.4427164,FALSE,84,0.821881378,111.6666667,0.840915173,6,0.764429903,,,0.809075485
10348,"Towards an Effective and Efficient Deep Learning Model for COVID-19
  Patterns Detection in X-ray Images",0,2004.05717,4/12/20,arxiv,0,6,"deep learning, computational, classifier, dataset",0.000880232,0.020771762,0.975707258,0.000880259,0.000880245,0.000880245,Imaging,0.013136625,FALSE,46.66666667,0.602510978,15,0.42594327,35,0.939317242,,,0.65592383
10349,"Low-Cost and High-Throughput Testing of COVID-19 Viruses and Antibodies
  via Compressed Sensing: System Concepts and Computational Experiments",0,2004.05759,4/13/20,arxiv,0,3,computational,0.034884582,0.346054272,0.130924273,0.485306549,0.001415203,0.00141512,Epidemiology,0.2119613,FALSE,64,0.729173109,63.66666667,0.734145036,10,0.828199272,,,0.763839139
10350,"COVID-19 identification in chest X-ray images on flat and hierarchical
  classification scenarios",0,2004.05835,4/13/20,arxiv,0,5,classifier,0.000999544,0.000999634,0.961225764,0.03477603,0.000999516,0.000999512,Imaging,0.29509497,FALSE,26,0.382398417,18.8,0.468156275,94,0.976726958,,,0.609093883
10351,"Prospects and limits of SIR-type Mathematical Models to Capture the
  COVID-19 Pandemic",0,2004.06522,4/13/20,arxiv,0,1,mathematical model,0.001653051,0.001653022,0.001653033,0.991734778,0.001653047,0.001653069,Epidemiology,0.07331538,FALSE,6,0.086028821,0,0.055525823,3,0.667819001,,,0.269791215
10352,"ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation
  Networks",0,2004.05861,4/13/20,arxiv,0,4,dataset,0.002238596,0.002238509,0.176375967,0.8146699,0.002238477,0.002238552,Epidemiology,0.009638339,FALSE,36.25,0.500154617,30.25,0.57171528,12,0.850299401,,,0.640723099
10353,"Using Reports of Own and Others' Symptoms and Diagnosis on Social Media
  to Predict COVID-19 Case Counts: Observational Infoveillance Study in
  Mainland China",J Med Internet Res 2020;22(5):e19421,2004.06169,4/13/20,arxiv,J Med Internet Res 2020;22(5):e19421,6,"machine learning, classifier",0.001438132,0.001438122,0.117773912,0.804142937,0.073768702,0.001438195,Epidemiology,0.57362074,TRUE,40.66666667,0.546292288,32.83333333,0.591249666,9,0.814309525,,,0.650617159
10354,"Using altmetrics for detecting impactful research in quasi-zero-day
  time-windows: the case of COVID-19",0,2004.06179,4/13/20,arxiv,0,8,knowledge graph,0.001684523,0.125449566,0.001684742,0.84356611,0.001684607,0.025930452,Epidemiology,0.14629886,FALSE,114.375,0.898076566,130,0.863928285,1,0.537564047,,,0.766522966
10355,"Modeling, state estimation, and optimal control for the US COVID-19
  outbreak",Scientific Reports (2020) 10:10711,2004.06291,4/14/20,arxiv,Scientific Reports (2020) 10:10711,4,"computational, sequencing",0.001901737,0.031680159,0.001901749,0.960712897,0.001901748,0.001901711,Epidemiology,0.3418818,FALSE,52,0.647349867,30,0.570176612,26,0.920859312,,,0.712795264
10356,"Simulation of Covid-19 epidemic evolution: are compartmental models
  really predictive?",0,2004.08207,4/14/20,arxiv,0,1,"machine learning, computational, dataset",0.001622826,0.001622781,0.180390634,0.813118331,0.001622731,0.001622697,Epidemiology,0.058024287,FALSE,282,0.988929433,55,0.702167514,2,0.618927094,,,0.770008014
10357,"An automatic COVID-19 CT segmentation network using spatial and channel
  attention mechanism","International journal of imaging systems and technology, 2020",2004.06673,4/14/20,arxiv,"International journal of imaging systems and technology, 2020",3,dataset,0.001511842,0.001511816,0.992440878,0.001511833,0.00151181,0.001511821,Imaging,0.65346116,TRUE,66.66666667,0.745191416,39,0.62784319,1,0.537564047,,,0.636866218
10358,"Weakly Supervised Deep Learning for COVID-19 Infection Detection and
  Classification from CT Images",0,2004.06689,4/14/20,arxiv,0,12,deep learning,0.001461892,0.001461952,0.731422661,0.001461986,0.001461922,0.262729586,Imaging,0.39014196,FALSE,53.41666667,0.656565032,18.08333333,0.462001606,65,0.966911538,,,0.695159392
10359,"The socio-economic determinants of the coronavirus disease (COVID-19)
  pandemic",0,2004.07947,4/14/20,arxiv,0,5,"bayes, bayesian model",0.001823413,0.001823409,0.001823372,0.990883007,0.001823434,0.001823365,Epidemiology,0.32265663,FALSE,28.6,0.413321789,9.4,0.343256623,31,0.931971109,,,0.56284984
10360,"Firefly-Algorithm Supported Scheme to Detect COVID-19 Lesion in Lung CT
  Scan Images using Shannon Entropy and Markov-Random-Field",0,2004.09239,4/14/20,arxiv,0,5,dataset,0.00175117,0.001751166,0.991244104,0.001751215,0.001751179,0.001751166,Imaging,0.021480232,FALSE,73,0.778464964,11.6,0.379515654,4,0.707574542,,,0.62185172
10361,"The Tajima heterochronous n-coalescent: inference from heterochronously
  sampled molecular data",0,2004.06826,4/14/20,arxiv,0,3,"bayes, computational",0.001254639,0.473497912,0.001254698,0.521483547,0.001254608,0.001254597,Epidemiology,0.0826233,FALSE,17,0.257467994,17,0.451097137,1,0.537564047,,,0.415376393
10362,Computer Vision For COVID-19 Control: A Survey,0,2004.0942,4/15/20,arxiv,0,4,artificial intelligence,0.002183407,0.002183289,0.513104855,0.432133079,0.048212158,0.002183211,Epidemiology,0.684794,TRUE,20.5,0.304966294,1.75,0.148381054,28,0.926168282,,,0.459838543
10363,"Combining Visible Light and Infrared Imaging for Efficient Detection of
  Respiratory Infections such as COVID-19 on Portable Device",0,2004.06912,4/15/20,arxiv,0,7,"deep learning, neural network, dataset",0.001254669,0.001254662,0.519234578,0.327151389,0.149850022,0.001254681,Epidemiology,0.041894794,FALSE,38.14285714,0.520069268,4.142857143,0.232472572,8,0.799987654,,,0.517509831
10364,"Social network-based distancing strategies to flatten the COVID 19 curve
  in a post-lockdown world",0,2004.07052,4/15/20,arxiv,0,7,network model,0.001310419,0.001310384,0.001310362,0.879228574,0.115529904,0.001310358,Epidemiology,0.6260561,TRUE,32.28571429,0.458531758,51.71428571,0.68818571,134,0.984443484,,,0.710386984
10365,"JCS: An Explainable COVID-19 Diagnosis System by Joint Classification
  and Segmentation",0,2004.07054,4/15/20,arxiv,0,7,dataset,0.001203413,0.00120344,0.99398269,0.001203477,0.001203417,0.001203563,Imaging,0.5046592,TRUE,42.71428571,0.564165997,296.7142857,0.953237891,73,0.970677202,,,0.829360363
10366,"Prediction of potential inhibitors for RNA-dependent RNA polymerase of
  SARS-CoV-2 using comprehensive drug repurposing and molecular docking
  approach","International Journal of Biological Macromolecules 163 (2020)
  1787-1797",2004.07086,4/15/20,arxiv,"International Journal of Biological Macromolecules 163 (2020)
  1787-1797",8,"virtual screening, computational",0.972186488,0.001511864,0.00151186,0.00151183,0.021766139,0.001511819,Drug discovery,0.98266876,TRUE,23.375,0.346032531,16,0.437316029,9,0.814309525,,,0.532552695
10367,"HLA predictions from the bronchoalveolar lavage fluid samples of five
  patients at the early stage of the Wuhan seafood market COVID-19 outbreak","Bioinformatics, btaa756 (2020)",2004.07108,4/15/20,arxiv,"Bioinformatics, btaa756 (2020)",2,"sequencing, transcriptom",0.301357096,0.484029457,0.00289836,0.002898449,0.002898376,0.205918261,Genomics,0.54764986,TRUE,74.5,0.784464098,232,0.930693069,14,0.866658436,,,0.860605201
10368,"Quantifying the Effects of Contact Tracing, Testing, and Containment
  Measures in the Presence of Infection Hotspots",0,2004.07641,4/15/20,arxiv,0,7,bayes,0.001486494,0.116653857,0.001486473,0.821819983,0.057066751,0.001486443,Epidemiology,0.029024214,FALSE,14.57142857,0.220112561,28,0.554321648,8,0.799987654,,,0.524807288
10369,"Mathematical model of SARS-Cov-2 propagation versus ACE2 fits COVID-19
  lethality across age and sex and predicts that of SARS, supporting possible
  therapy",0,2004.07224,4/15/20,arxiv,0,1,"model fit, mathematical model",0.4301812,0.001310446,0.00131058,0.300500721,0.029391955,0.237305098,Drug discovery,0.09804115,FALSE,24,0.35574247,20,0.481000803,1,0.537564047,,,0.45810244
10370,"Network Medicine Framework for Identifying Drug Repurposing
  Opportunities for COVID-19",0,2004.07229,4/15/20,arxiv,0,11,"artificial intelligence, dataset",0.51539674,0.001156268,0.479978205,0.001156295,0.001156252,0.00115624,Drug discovery,0.09712526,FALSE,161.8181818,0.952501701,616.9090909,0.984613326,66,0.967652324,,,0.968255784
10371,"Mathematical assessment of the impact of non-pharmaceutical
  interventions on curtailing the 2019 novel Coronavirus",0,2004.07391,4/15/20,arxiv,0,7,mathematical model,0.016389681,0.001112616,0.001112611,0.979159764,0.001112692,0.001112637,Epidemiology,0.33928648,FALSE,87.71428571,0.835796895,60.14285714,0.721434306,148,0.986727576,,,0.847986259
10372,"Determination of an Optimal Control Strategy for Vaccine Administration
  in COVID-19 Pandemic Treatment",0,2004.07397,4/15/20,arxiv,0,4,mathematical model,0.00139285,0.001392836,0.001392923,0.99303568,0.001392869,0.001392842,Epidemiology,0.7508421,TRUE,98.25,0.865483332,7,0.299973241,15,0.874313229,,,0.679923267
10373,"Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in
  Chest X-rays",0,2004.08379,4/16/20,arxiv,0,6,"deep learning, neural network, ensemble learning",0.001565325,0.001565382,0.992173361,0.001565331,0.001565292,0.001565309,Imaging,0.5974116,TRUE,101.6666667,0.872595708,60.16666667,0.721768799,51,0.958392493,,,0.850919
10374,"Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented
  Capsule Networks",0,2004.07407,4/16/20,arxiv,0,9,"adversarial network, dataset",0.05629568,0.001141337,0.939138855,0.001141405,0.001141407,0.001141317,Imaging,0.000824422,FALSE,21.44444444,0.316655328,19.11111111,0.4717019,20,0.900117291,,,0.56282484
10375,"Measuring Human and Economic Activity from Satellite Imagery to Support
  City-Scale Decision-Making during COVID-19 Pandemic",0,2004.07438,4/16/20,arxiv,0,4,"deep learning, neural network, dataset",0.001330153,0.001330126,0.430630797,0.564048801,0.001330081,0.001330042,Epidemiology,0.22660062,FALSE,85,0.825035562,161.25,0.893162965,4,0.707574542,,,0.808591023
10376,"Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation
  in CT Scans",0,2004.07443,4/16/20,arxiv,0,4,"neural network, transfer learning",0.046296593,0.001392875,0.915554723,0.001392901,0.001392931,0.033969976,Imaging,0.8402777,TRUE,70.75,0.767270703,81,0.781241638,18,0.891474782,,,0.813329041
10377,"A data driven analysis and forecast of an SEIARD epidemic model for
  COVID-19 in Mexico","Big Data and Information Analytics, Vol. 5, No. 1 (2020) 14-28",2004.08288,4/16/20,arxiv,"Big Data and Information Analytics, Vol. 5, No. 1 (2020) 14-28",3,mathematical model,0.002996433,0.002996554,0.002996505,0.934133819,0.05388014,0.002996548,Epidemiology,0.69803077,TRUE,12.33333333,0.186467932,0.666666667,0.096200161,12,0.850299401,,,0.377655831
10378,"Rapidly evaluating lockdown strategies using spectral analysis: the
  cycles behind new daily COVID-19 cases and what happens after lockdown",0,2004.07696,4/16/20,arxiv,0,1,bayes,0.001438202,0.001438139,0.135152261,0.859095164,0.001438132,0.001438102,Epidemiology,0.011845231,FALSE,46,0.596882924,83,0.785590045,2,0.618927094,,,0.667133355
10379,"Computational Drug Repositioning and Elucidation of Mechanism of Action
  of Compounds against SARS-CoV-2",0,2004.07697,4/16/20,arxiv,0,5,"computational, transcriptom",0.989835642,0.002032876,0.002032961,0.002032933,0.002032822,0.002032766,Drug discovery,0.66501987,TRUE,348.2,0.994186406,184.4,0.909151726,4,0.707574542,,,0.870304225
10380,"CO.ME.T.A. -- covid-19 media textual analysis. A dashboard for media
  monitoring",0,2004.07742,4/16/20,arxiv,0,6,text mining,0.002080611,0.002080603,0.002080632,0.98959697,0.002080605,0.002080578,Epidemiology,0.053275585,FALSE,15.83333333,0.238233657,4,0.231469093,2,0.618927094,,,0.362876615
10381,"BETS: The dangers of selection bias in early analyses of the coronavirus
  disease (COVID-19) pandemic",0,2004.07743,4/16/20,arxiv,0,4,bayes,0.002080545,0.096409502,0.002080575,0.643406068,0.253942669,0.002080642,Epidemiology,0.29395843,FALSE,20.25,0.301317336,30.25,0.57171528,9,0.814309525,,,0.56244738
10382,"Coronavirus (COVID-19): ARIMA based time-series analysis to forecast
  near future",0,2004.07859,4/16/20,arxiv,0,4,prediction model,0.003927459,0.00392744,0.003927745,0.980362262,0.003927637,0.003927456,Epidemiology,0.37273103,FALSE,15.75,0.237862577,1.5,0.138747659,22,0.908142478,,,0.428250904
10383,"A New Modified Deep Convolutional Neural Network for Detecting COVID-19
  from X-ray Images",0,2004.08052,4/17/20,arxiv,0,2,"artificial intelligence, neural network, dataset",0.00096675,0.000966765,0.995166099,0.000966797,0.000966819,0.000966771,Imaging,0.035952657,FALSE,7.5,0.108355495,2,0.164302917,36,0.941169208,,,0.404609207
10384,NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset,0,2004.08145,4/17/20,arxiv,0,4,"text-mining, dataset",0.001861751,0.001861703,0.001861776,0.990691315,0.001861771,0.001861685,Epidemiology,0.045840412,FALSE,58.25,0.691384749,27.25,0.547497993,10,0.828199272,,,0.689027338
10385,"Women worry about family, men about the economy: Gender differences in
  emotional responses to COVID-19",0,2004.08202,4/17/20,arxiv,0,2,dataset,0.001786551,0.001786575,0.001786603,0.648648184,0.344205542,0.001786547,Epidemiology,0.056382537,FALSE,14,0.213494959,4.5,0.242708055,14,0.866658436,,,0.440953817
10386,"Augmented Curation of Unstructured Clinical Notes from a Massive EHR
  System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis",0,2004.09338,4/17/20,arxiv,0,27,neural network,0.001141384,0.229743018,0.488641512,0.001141413,0.001141368,0.278191305,Clinics,0.50740844,TRUE,79.62962963,0.805244604,105.4814815,0.83208456,16,0.881782826,,,0.839703997
10387,"Estimating and Projecting Air Passenger Traffic during the COVID-19
  Coronavirus Outbreak and its Socio-Economic Impact",0,2004.0846,4/17/20,arxiv,0,5,forecasting model,0.001751183,0.001751213,0.001751253,0.991243897,0.001751277,0.001751178,Epidemiology,0.26794058,FALSE,54.4,0.664048488,304.4,0.95564624,48,0.955614544,,,0.858436424
10388,"A Study of Knowledge Sharing related to Covid-19 Pandemic in Stack
  Overflow",0,2004.09495,4/18/20,arxiv,0,4,text mining,0.00178655,0.001786551,0.001786562,0.991067249,0.001786581,0.001786507,Epidemiology,0.09115091,FALSE,76.5,0.792194941,78,0.773548301,0,0.403234768,,,0.656326003
10389,Future of COVID-19 in Italy: A mathematical perspective,"Mathematical Engineering. Springer, Singapore. (2021) 101--124",2004.08588,4/18/20,arxiv,"Mathematical Engineering. Springer, Singapore. (2021) 101--124",3,mathematical model,0.003101436,0.003101525,0.003101549,0.984492604,0.003101445,0.003101442,Epidemiology,0.5496939,TRUE,139,0.932092275,34.66666667,0.603157613,1,0.537564047,,,0.690937979
10390,"Automatically Characterizing Targeted Information Operations Through
  Biases Present in Discourse on Twitter",0,2004.08726,4/18/20,arxiv,0,5,artificial intelligence,0.00360735,0.00360731,0.479540402,0.399979487,0.109658105,0.003607346,Epidemiology,0.44161305,FALSE,18.8,0.281278991,,,0,0.403234768,,,0.342256879
10391,"Predictability: Can the turning point and end of an expanding epidemic
  be precisely forecast?",0,2004.08842,4/19/20,arxiv,0,4,bayes,0.031000417,0.001861743,0.001861725,0.961552746,0.001861677,0.001861692,Epidemiology,0.20127112,FALSE,168.25,0.957202053,188.75,0.911359379,27,0.92443978,,,0.931000404
10392,"Approximate Nearest Neighbour Search on Privacy-aware Encoding of User
  Locations to Identify Susceptible Infections in Simulated Epidemics",0,2004.08851,4/19/20,arxiv,0,3,"information retrieval, dataset",0.001461902,0.001461927,0.129501695,0.86465067,0.001461929,0.001461877,Epidemiology,0.000765026,FALSE,101,0.871296926,66.33333333,0.743510838,0,0.403234768,,,0.672680844
10393,"AI-Driven CT-based quantification, staging and short-term outcome
  prediction of COVID-19 pneumonia",0,2004.12852,4/20/20,arxiv,0,31,artificial intelligence,0.001653029,0.00165303,0.748910356,0.001653125,0.001653034,0.244477427,Imaging,0.6081014,TRUE,34.80645161,0.485064011,44.29032258,0.655472304,15,0.874313229,,,0.671616515
10394,"Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep
  Transfer Learning",0,2004.09363,4/20/20,arxiv,0,5,"deep learning, neural network, transfer learning, dataset",0.001156238,0.001156258,0.994218723,0.001156288,0.001156234,0.001156259,Imaging,0.16447905,FALSE,132.6,0.924608819,263.4,0.943537597,92,0.976233101,,,0.948126506
10395,"On the uncertainty of real-time predictions of epidemic growths: a
  COVID-19 case study for China and Italy",0,2004.1006,4/20/20,arxiv,0,2,dataset,0.001786505,0.001786545,0.00178654,0.991067352,0.001786526,0.001786531,Epidemiology,0.2592087,FALSE,79.5,0.80493537,18.5,0.46554723,10,0.828199272,,,0.699560624
10396,First-principles machine learning modelling of COVID-19,0,2004.09478,4/20/20,arxiv,0,2,"machine learning, mathematical model",0.003101495,0.003101519,0.083561511,0.904032478,0.003101467,0.003101529,Epidemiology,0.03538826,FALSE,57,0.68204589,38.5,0.624765855,11,0.840175319,,,0.715662355
10397,"MiniSeg: An Extremely Minimum Network for Efficient COVID-19
  Segmentation",0,2004.0975,4/21/20,arxiv,0,4,"deep learning, computational, deep-learning",0.001861723,0.001861678,0.990691291,0.001861806,0.00186169,0.001861812,Clinics,0.03775552,FALSE,81.66666667,0.813346527,16.66666667,0.444674873,26,0.920859312,,,0.726293571
10398,CovidAID: COVID-19 Detection Using Chest X-Ray,0,2004.09803,4/21/20,arxiv,0,7,dataset,0.001171537,0.024501961,0.860890761,0.001171608,0.001171576,0.111092556,Imaging,0.13469729,FALSE,8.857142857,0.129940009,3,0.199424672,30,0.930057411,,,0.419807364
10399,"ICT Intervention in the Containment of the Pandemic Spread of COVID-19:
  An Exploratory Study",0,2004.09888,4/21/20,arxiv,0,4,artificial intelligence,0.001622731,0.001622711,0.099514325,0.766691574,0.128925919,0.001622739,Epidemiology,0.669711,TRUE,24.5,0.361988991,5,0.257024351,4,0.707574542,,,0.442195961
10400,"Data-driven modeling reveals a universal dynamic underlying the COVID-19
  pandemic under social distancing",0,2004.10666,4/21/20,arxiv,0,2,forecasting model,0.001823457,0.001823379,0.001823371,0.990883105,0.001823339,0.001823349,Epidemiology,0.18915263,FALSE,3,0.037293586,0,0.055525823,15,0.874313229,,,0.322377546
10401,"Mathematical Modeling of the Spread of COVID-19 in Moscow and Russian
  Regions",0,2004.10118,4/21/20,arxiv,0,3,mathematical model,0.003607202,0.003607292,0.00360718,0.981964016,0.003607142,0.003607169,Epidemiology,0.5315697,TRUE,36,0.498299215,0.666666667,0.096200161,6,0.764429903,,,0.452976426
10402,"COVID-19 and Company Knowledge Graphs: Assessing Golden Powers and
  Economic Impact of Selective Lockdown via AI Reasoning",0,2004.10119,4/21/20,arxiv,0,7,"artificial intelligence, knowledge graph",0.002806598,0.002806625,0.316223475,0.672550342,0.002806613,0.002806348,Epidemiology,0.021403253,FALSE,20.71428571,0.306759849,9.857142857,0.35141825,6,0.764429903,,,0.474202667
10403,LinearDesign: Efficient Algorithms for Optimized mRNA Sequence Design,0,2004.10177,4/21/20,arxiv,0,7,computational,0.489461696,0.258233962,0.248593005,0.001237152,0.001237109,0.001237076,Drug discovery,0.055665284,FALSE,56,0.675304595,169.7142857,0.899384533,4,0.707574542,,,0.760754557
10404,"Recognition of potential Covid-19 drug treatments through the study of
  existing protein-drug and protein-protein structures: an analysis of
  kinetically active residues",0,2004.10233,4/21/20,arxiv,0,1,bioinformatic,0.93943076,0.003335343,0.003335559,0.003335454,0.003335448,0.047227436,Drug discovery,0.72518533,TRUE,2,0.022141134,0,0.055525823,3,0.667819001,,,0.248495319
10405,"Novel Corona virus Disease infection in Tunisia: Mathematical model and
  the impact of the quarantine strategy",0,2004.10321,4/21/20,arxiv,0,2,mathematical model,0.092284893,0.002357801,0.002357768,0.898283805,0.002357775,0.002357958,Epidemiology,0.676229,TRUE,1.5,0.015523533,0,0.055525823,8,0.799987654,,,0.29034567
10406,"SIRNet: Understanding Social Distancing Measures with Hybrid Neural
  Network Model for COVID-19 Infectious Spread",0,2004.10376,4/22/20,arxiv,0,8,"machine learning, network model",0.001786626,0.10374614,0.001786701,0.862186392,0.028707496,0.001786644,Epidemiology,0.14720652,FALSE,81.75,0.813655761,28.625,0.559339042,13,0.858880178,,,0.743958327
10407,"OUTBREAK: A user-friendly georeferencing online tool for disease
  surveillance",0,2004.1049,4/22/20,arxiv,0,6,computational,0.003607201,0.003607304,0.003607283,0.981963714,0.003607301,0.003607197,Epidemiology,0.23856154,FALSE,56.33333333,0.677407385,53.16666667,0.694005887,0,0.403234768,,,0.591549346
10408,"Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT
  Images: A Machine Learning-Based Approach",0,2004.10641,4/22/20,arxiv,0,5,"machine learning, neural network, classifier, dataset",0.063198533,0.001156254,0.837669551,0.001156242,0.001156326,0.095663095,Imaging,0.3463341,FALSE,76.2,0.790958006,47.8,0.671862457,35,0.939317242,,,0.800712568
10409,"Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy
  Measurements",0,2004.11841,4/22/20,arxiv,0,9,machine learning,0.069229201,0.004310122,0.179306341,0.738534306,0.004310026,0.004310005,Epidemiology,0.2022199,FALSE,61.66666667,0.713278496,153.2222222,0.886138614,11,0.840175319,,,0.813197476
10410,"Mathematical Modeling of COVID-19 Transmission Dynamics with a Case
  Study of Wuhan","Chaos Solitons Fractals 135 (2020), Art. 109846, 6 pp",2004.10885,4/22/20,arxiv,"Chaos Solitons Fractals 135 (2020), Art. 109846, 6 pp",4,mathematical model,0.003760558,0.003760567,0.003760791,0.981197168,0.003760433,0.003760482,Epidemiology,0.8195425,TRUE,122.75,0.911188076,41,0.639751137,158,0.988270881,,,0.846403365
10411,"What are We Depressed about When We Talk about COVID19: Mental Health
  Analysis on Tweets Using Natural Language Processing",0,2004.10899,4/22/20,arxiv,0,6,dataset,0.002238506,0.002238468,0.296239216,0.350097324,0.34694804,0.002238446,Epidemiology,0.035368055,FALSE,37.6,0.514626755,14.4,0.416644367,18,0.891474782,,,0.607581968
10412,"COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural
  Network Solution",0,2004.10987,4/23/20,arxiv,0,10,"neural network, dataset",0.001059361,0.001059366,0.994702939,0.001059403,0.001059454,0.001059476,Imaging,0.116285145,FALSE,32.2,0.45754221,47.3,0.669654803,35,0.939317242,,,0.688838085
10413,"Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray
  images using fine-tuned deep neural networks",Appl Intell (2020),2004.11676,4/23/20,arxiv,Appl Intell (2020),2,"deep learning, neural network, transfer learning, dataset",0.000988397,0.000988384,0.978752752,0.000988403,0.017293699,0.000988366,Imaging,0.70862806,TRUE,68.5,0.756571217,28.5,0.558402462,27,0.92443978,,,0.746471153
10414,Clinical concepts might be included in health-related mathematic models,0,2004.13555,4/23/20,arxiv,0,4,mathematical model,0.001861726,0.00186173,0.083381673,0.9091714,0.001861755,0.001861716,Epidemiology,0.034047037,FALSE,13.25,0.199950523,2.25,0.170925876,0,0.403234768,,,0.258037056
10415,"Inferring epidemic parameters for COVID-19 from fatality counts in
  Mumbai",0,2004.11677,4/23/20,arxiv,0,1,bayes,0.002720055,0.002720126,0.002720103,0.986399221,0.002720134,0.002720361,Epidemiology,0.104202986,FALSE,53,0.654400396,49,0.677481937,2,0.618927094,,,0.650269809
10416,"The TVBG-SEIR spline model for analysis of COVID-19 spread, and a Tool
  for prediction scenarios",0,2004.11338,4/23/20,arxiv,0,3,mathematical model,0.001187286,0.00118729,0.001187289,0.994063507,0.001187268,0.00118736,Epidemiology,0.15743601,FALSE,15.33333333,0.230997588,4.666666667,0.246721969,3,0.667819001,,,0.381846186
10417,Rapidly Bootstrapping a Question Answering Dataset for COVID-19,0,2004.11339,4/23/20,arxiv,0,7,"machine learning, dataset",0.002183235,0.002183241,0.608581372,0.382685605,0.002183336,0.002183211,Epidemiology,0.013669521,FALSE,61.14285714,0.709691385,949.5714286,0.993310142,30,0.930057411,,,0.877686313
10418,"Estimating the number of infections and the impact of non-pharmaceutical
  interventions on COVID-19 in European countries: technical description update",0,2004.11342,4/23/20,arxiv,0,23,"bayes, probabilistic",0.001171545,0.001171584,0.001171539,0.994142176,0.001171585,0.001171572,Epidemiology,0.15615165,FALSE,64.08695652,0.729234956,274.8695652,0.946949425,79,0.972899562,,,0.883027981
10419,"Mobile phone location data reveal the effect and geographic variation of
  social distancing on the spread of the COVID-19 epidemic",JAMA Network Open. 2020;3(9):e2020485,2004.1143,4/23/20,arxiv,JAMA Network Open. 2020;3(9):e2020485,10,prediction model,0.002032737,0.002032786,0.002032744,0.878077594,0.113791307,0.002032833,Epidemiology,0.55041796,TRUE,59.1,0.696765415,163.8,0.895236821,21,0.903944688,,,0.831982308
10420,"Target specific mining of COVID-19 scholarly articles using one-class
  approach","Chaos, Solitons and Fractals, 2020",2004.11706,4/24/20,arxiv,"Chaos, Solitons and Fractals, 2020",3,dataset,0.00175131,0.137422313,0.711254431,0.146069529,0.001751252,0.001751165,Epidemiology,0.7394619,TRUE,62,0.715814212,23.66666667,0.518062617,12,0.850299401,,,0.69472541
10421,"Development of a Machine-Learning System to Classify Lung CT Scan Images
  into Normal/COVID-19 Class",0,2004.13122,4/24/20,arxiv,0,6,"bayes, classifier",0.001291219,0.047725132,0.947109907,0.001291234,0.001291232,0.001291276,Imaging,0.02795434,FALSE,59.5,0.699857752,12.33333333,0.389550442,3,0.667819001,,,0.585742398
10422,"A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray
  Screening",0,2004.12786,4/24/20,arxiv,0,23,"neural network, dataset",0.001653027,0.001653038,0.991734828,0.001653052,0.001653031,0.001653024,Imaging,0.13430887,FALSE,33.86666667,0.474735605,34.93333333,0.604361788,13,0.858880178,,,0.645992523
10423,"Deep Sentiment Classification and Topic Discovery on Novel Coronavirus
  or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network
  Approach",0,2004.11695,4/24/20,arxiv,0,4,"computational, neural network, lstm",0.002130713,0.002130683,0.310627866,0.68084922,0.002130773,0.002130745,Epidemiology,0.20884028,FALSE,31,0.445111015,,,31,0.931971109,,,0.688541062
10424,"Evaluation of Pool-based Testing Approaches to Enable Population-wide
  Screening for COVID-19",0,2004.11851,4/24/20,arxiv,0,5,mathematical model,0.001392905,0.001392994,0.33681384,0.549048911,0.109958434,0.001392916,Epidemiology,0.4903509,FALSE,7.8,0.113550622,2.6,0.18256623,8,0.799987654,,,0.365368168
10425,"Comparative prediction of confirmed cases with COVID-19 pandemic by
  machine learning, deterministic and stochastic SIR models",0,2004.13489,4/24/20,arxiv,0,3,machine learning,0.004109842,0.004109772,0.075088622,0.908472227,0.004109771,0.004109767,Epidemiology,0.22984904,FALSE,31,0.445111015,0.666666667,0.096200161,13,0.858880178,,,0.466730451
10426,"In silico identification of clinically approved medicines against the
  main protease of SARS-CoV-2, causative agent of covid-19",0,2004.12055,4/25/20,arxiv,0,4,"in silico, in-silico",0.990064507,0.001987133,0.001987117,0.001987089,0.001987081,0.001987074,Drug discovery,0.82522464,TRUE,14.75,0.222586431,0.75,0.099411292,2,0.618927094,,,0.313641606
10427,"POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound
  Imaging Dataset (POCUS)",ISMB TransMed COSI 2020,2004.12084,4/25/20,arxiv,ISMB TransMed COSI 2020,7,"deep learning, neural network, predictive model, dataset",0.027166217,0.000916724,0.969166879,0.000916714,0.000916786,0.00091668,Imaging,0.085665494,FALSE,7.142857143,0.102541901,1.428571429,0.134265454,31,0.931971109,,,0.389592821
10428,A First Instagram Dataset on COVID-19,0,2004.12226,4/25/20,arxiv,0,4,dataset,0.002490453,0.00249044,0.002490533,0.987547513,0.002490661,0.0024904,Epidemiology,0.008733004,FALSE,60.25,0.704372565,36,0.611118544,17,0.887338725,,,0.734276611
10429,"A Smartphone enabled Approach to Manage COVID-19 Lockdown and Economic
  Crisis",0,2004.1224,4/25/20,arxiv,0,2,"machine learning, prediction model",0.001565288,0.001565321,0.001565419,0.99217331,0.001565319,0.001565343,Epidemiology,0.1804294,FALSE,44.5,0.581977859,19.5,0.475983409,6,0.764429903,,,0.607463724
10430,"Agent-Level Pandemic Simulation (ALPS) for Analyzing Effects of Lockdown
  Measures",0,2004.1225,4/25/20,arxiv,0,1,simulation model,0.003214391,0.003214173,0.003214172,0.983929005,0.003214162,0.003214097,Epidemiology,0.3011279,FALSE,67,0.748160059,39,0.62784319,0,0.403234768,,,0.593079339
10431,"Nota Técnica dos Modelos Implementados pelo Coletivo Covid19br para
  Projeções de Cenários Futuros da Pandemia COVID-19 no Brasil",0,2004.13488,4/26/20,arxiv,0,17,simulation model,0.002357756,0.002357755,0.002357748,0.98821114,0.002357786,0.002357815,Epidemiology,0.16011879,FALSE,14.70588235,0.221287649,1.764705882,0.148514851,0,0.403234768,,,0.257679089
10432,"A fractional-order SEIHDR model for COVID-19 with inter-city networked
  coupling effects",0,2004.12308,4/26/20,arxiv,0,7,mathematical model,0.001291233,0.001291228,0.001291274,0.932407496,0.001291219,0.06242755,Epidemiology,0.48782662,FALSE,7.857142857,0.113921702,2.142857143,0.165640888,20,0.900117291,,,0.393226627
10433,"Second waves, social distancing, and the spread of COVID-19 across
  America",0,2004.13017,4/26/20,arxiv,0,12,"bayes, bayesian model",0.001684538,0.046748269,0.001684494,0.929759099,0.018438995,0.001684606,Epidemiology,0.17269841,FALSE,305.5,0.991032222,1617.333333,0.997257158,26,0.920859312,,,0.969716231
10434,"Exploring the SARS-CoV-2 virus-host-drug interactome for drug
  repurposing","Nat Commun 11, 3518 (2020)",2004.1242,4/26/20,arxiv,"Nat Commun 11, 3518 (2020)",17,interactom,0.796995791,0.001203437,0.076239064,0.123154761,0.001203538,0.001203409,Drug discovery,0.7974588,TRUE,20.94117647,0.308800792,30.58823529,0.573454643,38,0.944132354,,,0.608795929
10435,"Power of Artificial Intelligence to Diagnose and Prevent Further
  COVID-19 Outbreak: A Short Communication",0,2004.12463,4/26/20,arxiv,0,2,artificial intelligence,0.002183304,0.413480302,0.170175237,0.409794633,0.002183259,0.002183264,Genomics,0.66166586,TRUE,4.5,0.061784897,0.5,0.087101953,5,0.739490092,,,0.296125647
10436,"Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and
  Infection Segmentation",0,2004.12537,4/27/20,arxiv,0,13,"deep learning, computational, dataset",0.001098908,0.001098821,0.994505785,0.001098844,0.001098835,0.001098807,Imaging,0.12163088,FALSE,23.23076923,0.343991589,,,7,0.785110192,,,0.56455089
10437,"How to Return to Normalcy: Fast and Comprehensive Contact Tracing of
  COVID-19 through Proximity Sensing Using Mobile Devices",0,2004.12576,4/27/20,arxiv,0,2,probabilistic,0.021732306,0.001461929,0.001461921,0.972420019,0.001461927,0.001461898,Epidemiology,0.049539983,FALSE,31,0.445111015,55.5,0.704575863,11,0.840175319,,,0.663287399
10438,"Robust Screening of COVID-19 from Chest X-ray via Discriminative
  Cost-Sensitive Learning",0,2004.12592,4/27/20,arxiv,0,6,"neural network, dataset",0.000946088,0.000946099,0.995269563,0.000946097,0.000946082,0.000946071,Imaging,0.020347714,FALSE,34,0.477766096,40.33333333,0.635469628,18,0.891474782,,,0.668236835
10439,"A Critic Evaluation of Methods for COVID-19 Automatic Detection from
  X-Ray Images",0,2004.12823,4/27/20,arxiv,0,2,"neural network, classifier, dataset",0.001653034,0.001653085,0.991734637,0.001653078,0.001653085,0.001653082,Imaging,0.006227016,FALSE,139,0.932092275,157,0.889884934,40,0.947033768,,,0.923003659
10440,"How to restart? An agent-based simulation model towards the definition
  of strategies for COVID-19 ""second phase"" in public buildings",0,2004.12927,4/27/20,arxiv,0,3,"simulation model, probabilistic",0.001141431,0.026799478,0.001141359,0.968635024,0.001141369,0.00114134,Epidemiology,0.04174772,FALSE,97,0.861586987,23.66666667,0.518062617,20,0.900117291,,,0.759922299
10441,"A feedback SIR (fSIR) model highlights advantages and limitations of
  infection-dependent mitigation strategies",0,2004.13216,4/28/20,arxiv,0,1,computational,0.061888742,0.03166768,0.001059369,0.903265444,0.001059399,0.001059366,Epidemiology,0.104525,FALSE,108,0.886078298,61,0.724578539,0,0.403234768,,,0.671297201
10442,Predicting Infection of COVID-19 in Japan: State Space Modeling Approach,0,2004.13483,4/28/20,arxiv,0,4,bayes,0.001593481,0.001593479,0.001593496,0.992032569,0.001593487,0.001593489,Epidemiology,0.2666704,FALSE,20.75,0.307254623,14.75,0.421327268,0,0.403234768,,,0.377272219
10443,"Antimalarial Artefenomel Inhibits Human SARS-CoV-2 Replication in Cells
  while Suppressing the Receptor ACE2",0,2004.13493,4/28/20,arxiv,0,13,"virtual screening, computational",0.994505712,0.001098928,0.00109883,0.00109886,0.001098845,0.001098826,Drug discovery,0.15584853,FALSE,35.07692308,0.488218195,36,0.611118544,1,0.537564047,,,0.545633596
10444,"COVID-19: Estimating spread in Spain solving an inverse problem with a
  probabilistic model",0,2004.13695,4/28/20,arxiv,0,4,probabilistic,0.001751141,0.001751176,0.001751174,0.991244154,0.001751166,0.001751188,Epidemiology,0.0566054,FALSE,18.75,0.28072237,18,0.46180091,5,0.739490092,,,0.494004457
10445,CoronaVis: A Real-time COVID-19 Tweets Data Analyzer and Data Repository,0,2004.13932,4/29/20,arxiv,0,2,dataset,0.001310377,0.001310365,0.001310376,0.993448137,0.001310416,0.001310328,Epidemiology,0.001838327,FALSE,13.5,0.205393036,7.5,0.307867273,7,0.785110192,,,0.432790167
10446,"Zero-Shot Learning and its Applications from Autonomous Vehicles to
  COVID-19 Diagnosis: A Review","Journal of Intelligence-Based Medicine, Volumes 4, 2020",2004.14143,4/29/20,arxiv,"Journal of Intelligence-Based Medicine, Volumes 4, 2020",2,"deep learning, dataset",0.000916715,0.000916698,0.968081357,0.028251827,0.000916712,0.00091669,Imaging,0.5629123,TRUE,32.5,0.461685942,7.5,0.307867273,1,0.537564047,,,0.435705754
10447,"Advancing computerized cognitive training for early Alzheimer's disease
  in a Covid-19 pandemic and post-pandemic world",0,2004.14344,4/29/20,arxiv,0,4,digital health,0.002806498,0.002806669,0.002806542,0.356391053,0.561176856,0.074012382,Healthcare,0.61899316,TRUE,170.5,0.958562682,623.5,0.985014718,0,0.403234768,,,0.782270722
10448,"Putting the Air Transportation System to sleep: a passenger perspective
  measured by passenger-generated data",0,2004.14372,4/29/20,arxiv,0,4,dataset,0.005047501,0.005047528,0.005047864,0.974762203,0.005047478,0.005047427,Epidemiology,0.061864763,FALSE,138.25,0.930979034,61.5,0.726585496,0,0.403234768,,,0.686933099
10449,COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19,0,2005.01577,4/30/20,arxiv,0,9,"deep learning, classifier, dataset",0.001098811,0.001098816,0.976793318,0.001098869,0.001098814,0.018811372,Imaging,0.004056633,FALSE,88.33333333,0.837714144,181,0.906944073,15,0.874313229,,,0.872990482
10450,Observed mobility behavior data reveal social distancing inertia,0,2004.14748,4/30/20,arxiv,0,6,dataset,0.001751186,0.001751201,0.001751188,0.991243993,0.001751266,0.001751167,Epidemiology,0.054724336,FALSE,48.5,0.618158204,30.5,0.573187048,22,0.908142478,,,0.699829243
10451,"A Deep Convolutional Neural Network for COVID-19 Detection Using Chest
  X-Rays",0,2005.01578,4/30/20,arxiv,0,2,"artificial intelligence, neural network, classifier, transfer learning, dataset",0.001156259,0.001156274,0.994218644,0.00115631,0.00115628,0.001156233,Imaging,0.43947175,FALSE,80.5,0.809202795,17.5,0.45611453,18,0.891474782,,,0.718930703
10452,"Dynamical Theory and Cellular Automata Simulations of Pandemic Spread:
  Understanding Different Temporal Patterns of Infections",0,2004.14787,4/30/20,arxiv,0,3,mathematical model,0.05271766,0.001237137,0.00123708,0.942333972,0.001237074,0.001237078,Epidemiology,0.025080025,FALSE,185,0.966479065,23,0.513513514,0,0.403234768,,,0.627742449
10453,"A computational insight of the improved nicotine binding with
  ACE2-SARS-CoV-2 complex with its clinical impact",0,2004.14943,4/30/20,arxiv,0,3,"computational, in-silico",0.90222089,0.001653022,0.001653025,0.001653023,0.001653111,0.09116693,Drug discovery,0.42897806,FALSE,22.66666667,0.335580432,1.666666667,0.145036125,3,0.667819001,,,0.382811853
10454,"SARS-CoV-2 mortality in blacks and temperature-sensitivity to an
  angiotensin-2 receptor blocker",0,2005.01579,4/30/20,arxiv,0,1,metabolom,0.572580784,0.001330129,0.00133007,0.185312159,0.001330107,0.23811675,Drug discovery,0.5293109,TRUE,216,0.977240398,77,0.771139952,0,0.403234768,,,0.717205039
10455,Fact or Fiction: Verifying Scientific Claims,0,2004.14974,4/30/20,arxiv,0,7,dataset,0.00223852,0.002238502,0.822275257,0.168770837,0.002238461,0.002238424,Epidemiology,0.02325815,FALSE,33.71428571,0.47331313,176.8571429,0.904134332,22,0.908142478,,,0.761863313
10456,"Fighting the COVID-19 Infodemic: Modeling the Perspective of
  Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the
  Society",0,2005.00033,4/30/20,arxiv,0,11,dataset,0.001901753,0.001901811,0.265532672,0.701871754,0.001901777,0.026890232,Epidemiology,0.001615703,FALSE,77.18181818,0.795287278,166.5454545,0.896775488,20,0.900117291,,,0.864060019
10457,"A Multi-Dimensional Big Data Storing System for Generated COVID-19
  Large-Scale Data using Apache Spark",0,2005.05036,4/30/20,arxiv,0,3,dataset,0.001786591,0.001786596,0.46206288,0.383767086,0.001786643,0.148810203,Epidemiology,0.009217918,FALSE,26.33333333,0.386047375,4,0.231469093,3,0.667819001,,,0.428445156
10458,"An Early Study on Intelligent Analysis of Speech under COVID-19:
  Severity, Sleep Quality, Fatigue, and Anxiety",0,2005.00096,4/30/20,arxiv,0,14,artificial intelligence,0.002296568,0.002296582,0.598567355,0.16660375,0.060773075,0.169462669,Clinics,0.27759895,FALSE,42,0.558537943,11.71428571,0.380987423,17,0.887338725,,,0.608954697
10459,"Intra-model Variability in COVID-19 Classification Using Chest X-ray
  Images",0,2005.02167,4/30/20,arxiv,0,4,"deep learning, dataset",0.001098813,0.001098844,0.994505833,0.001098861,0.001098852,0.001098797,Imaging,0.005505383,FALSE,5.5,0.077246583,0.25,0.065493712,7,0.785110192,,,0.309283495
10460,A cascade network for Detecting COVID-19 using chest x-rays,0,2005.01468,5/1/20,arxiv,0,5,deep learning,0.001098873,0.10116557,0.779371572,0.051715895,0.001098845,0.065549244,Imaging,0.009242743,FALSE,20.6,0.305832148,,,14,0.866658436,,,0.586245292
10461,"Assessing the impacts of mutations to the structure of COVID-19 spike
  protein via sequential Monte Carlo","Journal of Data Science, 2020, 18(3): 511-525",2005.0755,5/1/20,arxiv,"Journal of Data Science, 2020, 18(3): 511-525",1,computational,0.441375806,0.429045178,0.002130758,0.1231869,0.002130673,0.002130685,Drug discovery,0.7077216,TRUE,17,0.257467994,1,0.122023013,1,0.537564047,,,0.305685018
10462,"Integrated Time Series Summarization and Prediction Algorithm and its
  Application to COVID-19 Data Mining",0,2005.00592,5/1/20,arxiv,0,1,"data mining, dataset",0.001751178,0.078294224,0.261655511,0.654796752,0.001751151,0.001751185,Epidemiology,0.24227661,FALSE,25,0.369286907,2,0.164302917,1,0.537564047,,,0.35705129
10463,"Enabling and Emerging Technologies for Social Distancing: A
  Comprehensive Survey and Open Problems",0,2005.02816,5/1/20,arxiv,0,12,"machine learning, artificial intelligence",0.001059369,0.034909707,0.15949937,0.695173346,0.108298856,0.001059353,Epidemiology,0.6875183,TRUE,172.8333333,0.960294391,174.9166667,0.903063955,23,0.91129082,,,0.924883055
10464,"Estimation of COVID-19 spread curves integrating global data and
  borrowing information",PLOS ONE 15 (2020) 1- 17,2005.00662,5/2/20,arxiv,PLOS ONE 15 (2020) 1- 17,3,bayes,0.182062251,0.001987148,0.1823853,0.629590896,0.001987275,0.001987131,Epidemiology,0.24081016,FALSE,67,0.748160059,129.3333333,0.863125502,3,0.667819001,,,0.759701521
10465,"Data-Driven Modeling Reveals the Impact of Stay-at-Home Orders on Human
  Mobility during the COVID-19 Pandemic in the U.S",0,2005.00667,5/2/20,arxiv,0,7,dataset,0.001538123,0.001538127,0.001538237,0.992309311,0.00153812,0.001538082,Epidemiology,0.03395495,FALSE,27.28571429,0.398540417,7.142857143,0.300709125,8,0.799987654,,,0.499745732
10466,"Simultaneous estimation of the effective reproducing number and the
  detection rate of COVID-19",0,2005.02766,5/2/20,arxiv,0,1,"bayes, bayesian model",0.002898269,0.002898535,0.002898683,0.985507188,0.002898616,0.002898709,Epidemiology,0.08559808,FALSE,38,0.519327107,11,0.371287129,1,0.537564047,,,0.476059428
10467,Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19,0,2005.06012,5/2/20,arxiv,0,6,dataset,0.002806599,0.123173532,0.00280661,0.865600387,0.002806436,0.002806437,Epidemiology,0.00649038,FALSE,17.2,0.259323397,21.8,0.501204174,6,0.764429903,,,0.508319158
10468,"Deep Convolutional Neural Networks to Diagnose COVID-19 and other
  Pneumonia Diseases from Posteroanterior Chest X-Rays",0,2005.00845,5/2/20,arxiv,0,1,"machine learning, neural network",0.001126874,0.001126843,0.89876911,0.096723461,0.001126875,0.001126837,Imaging,0.043114394,FALSE,2,0.022141134,0,0.055525823,5,0.739490092,,,0.272385683
10469,Visualization of Diseases at Risk in the COVID-19 Literature,0,2005.00848,5/2/20,arxiv,0,1,dataset,0.003101579,0.003101484,0.564906365,0.422687653,0.003101465,0.003101455,Epidemiology,0.033643782,FALSE,20,0.298163152,8,0.320511105,5,0.739490092,,,0.45272145
10470,"Spatiotemporal dynamics in demography-sensitive disease transmission:
  COVID-19 spread in NY as a case study",0,2005.01001,5/3/20,arxiv,0,3,"computational, predictive model",0.031788299,0.001684508,0.048872955,0.864610919,0.001684549,0.05135877,Epidemiology,0.08204627,FALSE,12,0.183190055,0.666666667,0.096200161,2,0.618927094,,,0.299439103
10471,"Transcriptional landscape of SARS-CoV-2 infection dismantles pathogenic
  pathways activated by the virus, proposes unique sex-specific differences and
  predicts tailored therapeutic strategies",0,2005.01042,5/3/20,arxiv,0,9,transcriptom,0.806484937,0.055596213,0.002296595,0.002296609,0.002296628,0.131029017,Drug discovery,0.6897745,TRUE,110.6666667,0.891026037,96.22222222,0.815426813,29,0.928020248,,,0.8781577
10472,"Pandemic Programming: How COVID-19 affects software developers and how
  their organizations can help","Empirical Software Engineering, 2020",2005.01127,5/3/20,arxiv,"Empirical Software Engineering, 2020",17,structural model,0.002562746,0.002562682,0.002562758,0.332743122,0.657006125,0.002562567,Healthcare,0.9283837,TRUE,68.11764706,0.754282887,77.58823529,0.772277228,9,0.814309525,,,0.78028988
10473,"A combination of 'pooling' with a prediction model can reduce by 73% the
  number of COVID-19 (Corona-virus) tests",0,2005.03453,5/3/20,arxiv,0,7,"neural network, prediction model",0.01354938,0.013549751,0.68640834,0.259394379,0.013549136,0.013549013,Epidemiology,0.281502,FALSE,21.14285714,0.312758983,,,1,0.537564047,,,0.425161515
10474,"Crucial Inflammatory Mediators and Efficacy of Drug Interventions in
  Pneumonia Inflated COVID-19: An Invivo Mathematical Modelling Study",0,2005.02261,5/3/20,arxiv,0,8,mathematical model,0.369042073,0.003101507,0.181833094,0.43981995,0.003101693,0.003101682,Epidemiology,0.9000703,TRUE,13.125,0.198033274,2.875,0.190259566,0,0.403234768,,,0.263842536
10475,"Quantifying human mobility behavior changes in response to
  non-pharmaceutical interventions during the COVID-19 outbreak in the United
  States",0,2005.01224,5/4/20,arxiv,0,7,dataset,0.001220001,0.00122,0.001220016,0.993899871,0.001220115,0.001219997,Epidemiology,0.14123124,FALSE,42,0.558537943,12.14285714,0.387008295,7,0.785110192,,,0.576885477
10476,"Monitoring COVID-19 social distancing with person detection and tracking
  via fine-tuned YOLO v3 and Deepsort techniques",0,2005.01385,5/4/20,arxiv,0,3,neural network,0.036070515,0.001254661,0.388248035,0.57191756,0.001254619,0.00125461,Epidemiology,0.029297382,FALSE,50.33333333,0.63380543,19,0.471367407,39,0.945737391,,,0.683636743
10477,"CAiRE-COVID: A Question Answering and Query-focused Multi-Document
  Summarization System for COVID-19 Scholarly Information Management",0,2005.03975,5/4/20,arxiv,0,6,dataset,0.002183221,0.002183232,0.567867926,0.423399138,0.002183289,0.002183194,Epidemiology,0.24202281,FALSE,45.66666667,0.592677346,26.66666667,0.543216484,4,0.707574542,,,0.614489457
10478,"3D Tomographic Pattern Synthesis for Enhancing the Quantification of
  COVID-19",0,2005.01903,5/5/20,arxiv,0,13,"adversarial network, dataset",0.001059394,0.001059417,0.929332733,0.00105943,0.001059383,0.066429643,Imaging,0.019725919,FALSE,30.84615385,0.441956831,87.76923077,0.796360717,11,0.840175319,,,0.692830956
10479,Computational modeling of Human-nCoV protein-protein interaction network,0,2005.04108,5/5/20,arxiv,0,6,computational,0.933235665,0.001392912,0.001392905,0.061192786,0.001392818,0.001392913,Drug discovery,0.09942749,FALSE,12.16666667,0.184117756,1.166666667,0.124565159,0,0.403234768,,,0.237305894
10480,"An Improved Method for the Fitting and Prediction of the Number of
  COVID-19 Confirmed Cases Based on LSTM",0,2005.03446,5/5/20,arxiv,0,9,"neural network, prediction model, lstm",0.001622724,0.001622723,0.312341277,0.681167846,0.001622713,0.001622718,Epidemiology,0.3801523,FALSE,24,0.35574247,7,0.299973241,3,0.667819001,,,0.441178237
10481,"An Investigation of COVID-19 Spreading Factors with Explainable AI
  Techniques",0,2005.06612,5/5/20,arxiv,0,4,machine learning,0.002638985,0.00263902,0.146913644,0.842530338,0.002639037,0.002638976,Epidemiology,0.11390999,FALSE,74.75,0.785515493,34.5,0.602221033,2,0.618927094,,,0.668887873
10482,"Inferring UK COVID-19 fatal infection trajectories from daily mortality
  data: were infections already in decline before the UK lockdowns?",Biometrics 2021,2005.0209,5/5/20,arxiv,Biometrics 2021,1,bayes,0.001415125,0.022195096,0.001415138,0.97214424,0.001415192,0.001415209,Epidemiology,0.18504685,FALSE,123,0.911806543,1272,0.995718491,0,0.403234768,,,0.770253267
10483,"In Silico Trial to test COVID-19 candidate vaccines: a case study with
  UISS platform",0,2005.02289,5/5/20,arxiv,0,4,in silico,0.740151874,0.002296725,0.002296638,0.250661344,0.002296731,0.002296688,Drug discovery,0.6700049,TRUE,76.75,0.792998949,16.75,0.446012845,3,0.667819001,,,0.635610265
10484,"Using posterior predictive distributions to analyse epidemic models:
  COVID-19 in Mexico City","Physical Biology 17 (2020), 065001",2005.02294,5/5/20,arxiv,"Physical Biology 17 (2020), 065001",7,bayes,0.001438104,0.001438112,0.001438153,0.992809248,0.001438187,0.001438197,Epidemiology,0.25130445,FALSE,25.42857143,0.372997712,25.85714286,0.535991437,8,0.799987654,,,0.569658934
10485,"SLEDGE: A Simple Yet Effective Baseline for COVID-19 Scientific
  Knowledge Search",0,2005.02365,5/5/20,arxiv,0,3,dataset,0.001901812,0.001901749,0.41514278,0.577250166,0.001901726,0.001901766,Epidemiology,0.002074868,FALSE,71,0.769064259,133.3333333,0.866938721,8,0.799987654,,,0.811996878
10486,"CODA-19: Using a Non-Expert Crowd to Annotate Research Aspects on
  10,000+ Abstracts in the COVID-19 Open Research Dataset",0,2005.02367,5/5/20,arxiv,0,5,dataset,0.002032837,0.002032829,0.863387183,0.002032907,0.090248509,0.040265736,Healthcare,0.011039078,FALSE,133.6,0.925722061,354.8,0.966617608,3,0.667819001,,,0.853386223
10487,Adaptive Invariance for Molecule Property Prediction,0,2005.03004,5/5/20,arxiv,0,3,in-silico,0.344524732,0.001593577,0.613239571,0.001593592,0.001593611,0.037454917,Drug discovery,0.071511716,FALSE,184,0.965922444,1287.333333,0.995852288,5,0.739490092,,,0.900421608
10488,"Quest: Practical and Oblivious Mitigation Strategies for COVID-19 using
  WiFi Datasets",0,2005.0251,5/5/20,arxiv,0,6,"computational, dataset",0.045364866,0.00141514,0.075601673,0.874787781,0.001415176,0.001415363,Epidemiology,0.053639114,FALSE,150.8333333,0.944956398,257.6666667,0.940594059,5,0.739490092,,,0.875013516
10489,"Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor
  Candidates",LNCS 12270 (2020) 357-371,2005.02666,5/6/20,arxiv,LNCS 12270 (2020) 357-371,6,"computational, artificial intelligence",0.650216355,0.090463693,0.251848427,0.00249063,0.002490465,0.002490429,Drug discovery,0.6274049,TRUE,5.333333333,0.074339786,1,0.122023013,2,0.618927094,,,0.271763298
10490,"Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community
  Acquired Pneumonia",0,2005.0269,5/6/20,arxiv,0,18,dataset,0.001072175,0.001072193,0.994638922,0.001072228,0.00107225,0.001072232,Imaging,0.32026953,FALSE,98.11111111,0.864926712,56.88888889,0.710329141,61,0.964936107,,,0.846730653
10491,"Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent
  Multi-View Representation Learning",IEEE Transactions on Medical Imaging (2020),2005.03227,5/6/20,arxiv,IEEE Transactions on Medical Imaging (2020),11,machine learning,0.001371303,0.001371269,0.993143532,0.001371325,0.00137127,0.001371301,Imaging,0.3018664,FALSE,35.45454545,0.491248686,36.09090909,0.611586834,58,0.963022409,,,0.68861931
10492,"CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19
  Using CT Image",0,2005.03059,5/6/20,arxiv,0,22,deep learning,0.001187291,0.001187284,0.960703399,0.001187304,0.00118729,0.034547432,Imaging,0.1074633,FALSE,42.95454545,0.566454326,29.63636364,0.566764785,18,0.891474782,,,0.674897964
10493,"Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and
  DiGraphs",0,2005.03082,5/6/20,arxiv,0,3,machine learning,0.021469887,0.001254697,0.134184452,0.678416345,0.163419991,0.001254628,Epidemiology,0.011490852,FALSE,29,0.41993939,51,0.685509767,28,0.926168282,,,0.677205813
10494,"Adaptive Feature Selection Guided Deep Forest for COVID-19
  Classification with Chest CT",0,2005.03264,5/7/20,arxiv,0,17,"machine learning, dataset",0.001203394,0.001203408,0.99398297,0.001203415,0.001203398,0.001203415,Imaging,0.18174952,FALSE,33.41176471,0.470591873,25,0.529435376,24,0.914439163,,,0.638155471
10495,"Importance of suppression and mitigation measures in managing COVID-19
  outbreaks",0,2005.03323,5/7/20,arxiv,0,1,mathematical model,0.002357769,0.084764446,0.002357844,0.905804383,0.002357775,0.002357784,Epidemiology,0.13783443,FALSE,3,0.037293586,0,0.055525823,6,0.764429903,,,0.285749771
10496,"Estimation of the actual disease occurrence based on official case
  numbers during a COVID outbreak in Germany 2020",0,2005.03408,5/7/20,arxiv,0,2,mathematical model,0.0028064,0.07528472,0.002806596,0.913489472,0.002806444,0.002806367,Epidemiology,0.114993244,FALSE,40.5,0.544746119,11.5,0.378378378,0,0.403234768,,,0.442119755
10497,"Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins
  University",0,2005.0506,5/7/20,arxiv,0,4,"neural network, dataset",0.0028983,0.002898371,0.633477254,0.354929322,0.002898375,0.00289838,Epidemiology,0.02841118,FALSE,28.75,0.415115344,3,0.199424672,2,0.618927094,,,0.411155704
10498,Domain Adaptation in Highly Imbalanced and Overlapping Datasets,0,2005.03585,5/7/20,arxiv,0,2,"machine learning, dataset",0.002639182,0.002639244,0.537999601,0.246867059,0.207215792,0.002639122,Epidemiology,0.052817762,FALSE,9,0.135320675,1.5,0.138747659,0,0.403234768,,,0.2257677
10499,"Synergistic Learning of Lung Lobe Segmentation and Hierarchical
  Multi-Instance Classification for Automated Severity Assessment of COVID-19
  in CT Images",0,2005.03832,5/8/20,arxiv,0,11,dataset,0.001046809,0.001046839,0.947827504,0.00104684,0.001046817,0.047985192,Imaging,0.2822568,FALSE,55.18181818,0.669058074,34.63636364,0.602622424,9,0.814309525,,,0.695330008
10500,"Bluetooth Smartphone Apps: Are they the most private and effective
  solution for COVID-19 contact tracing?",0,2005.06621,5/8/20,arxiv,0,8,"bayes, bayesian model",0.00198713,0.001987147,0.001987248,0.845667697,0.146383593,0.001987185,Epidemiology,0.2009305,FALSE,69.625,0.76164265,177.875,0.904870217,5,0.739490092,,,0.802000986
10501,Detecting East Asian Prejudice on Social Media,0,2005.03909,5/8/20,arxiv,0,9,"classifier, dataset",0.001901729,0.107775534,0.369923085,0.414669795,0.103828147,0.001901709,Epidemiology,0.38308075,FALSE,52.66666667,0.651308059,69.22222222,0.751070377,9,0.814309525,,,0.738895987
10502,"Convolutional Sparse Support Estimator Based Covid-19 Recognition from
  X-ray Images",0,2005.04014,5/8/20,arxiv,0,6,"machine learning, deep learning, dataset",0.001098804,0.001098827,0.994505944,0.00109883,0.001098802,0.001098794,Imaging,0.003836453,FALSE,54.16666667,0.662254932,41.5,0.642092588,13,0.858880178,,,0.721075899
10503,"Bayesian dynamical estimation of the parameters of an SE(A)IR COVID-19
  spread model",0,2005.04365,5/9/20,arxiv,0,4,bayes,0.001254611,0.035962169,0.001254715,0.869233644,0.001254608,0.091040254,Epidemiology,0.0334993,FALSE,73,0.778464964,53.75,0.69621354,8,0.799987654,,,0.758222053
10504,"TREC-COVID: Constructing a Pandemic Information Retrieval Test
  Collection",0,2005.04474,5/9/20,arxiv,0,9,information retrieval,0.003760522,0.00376057,0.003760696,0.981197204,0.003760499,0.003760509,Epidemiology,0.023991764,FALSE,139.3333333,0.932587049,393.6666667,0.970497725,36,0.941169208,,,0.948084661
10505,"Hemogram Data as a Tool for Decision-making in COVID-19 Management:
  Applications to Resource Scarcity Scenarios",0,2005.10227,5/10/20,arxiv,0,4,"bayes, machine learning",0.001098833,0.001098839,0.433656122,0.561948498,0.00109884,0.001098868,Epidemiology,0.40176934,FALSE,45,0.587049292,7.5,0.307867273,5,0.739490092,,,0.544802219
10506,"Fast and accurate detection of Covid-19-related pneumonia from chest
  X-ray images with novel deep learning model",0,2005.04562,5/10/20,arxiv,0,13,"deep learning, dataset",0.001291215,0.001291348,0.993543434,0.001291332,0.001291345,0.001291325,Imaging,0.003905654,FALSE,16.23076923,0.245284186,0.769230769,0.099678887,2,0.618927094,,,0.321296722
10507,"Modeling the spread of infectious disease in urban areas with travel
  contagion",0,2005.04583,5/10/20,arxiv,0,2,mathematical model,0.00127269,0.001272641,0.001272643,0.993636721,0.001272681,0.001272623,Epidemiology,0.07926485,FALSE,144,0.938524337,117.5,0.847872625,1,0.537564047,,,0.77465367
10508,COVID-19 growth prediction using multivariate long short term memory,"IAENG International Journal of Computer Science, vol. 47, no. 4,
  pp829-837, 2020",2005.04809,5/10/20,arxiv,"IAENG International Journal of Computer Science, vol. 47, no. 4,
  pp829-837, 2020",1,"machine learning, deep learning, neural network, mathematical model, lstm",0.001565315,0.001565306,0.648960329,0.344778463,0.00156529,0.001565298,Epidemiology,0.048357427,FALSE,3,0.037293586,0,0.055525823,5,0.739490092,,,0.2774365
10509,"Using statistics and mathematical modelling to understand infectious
  disease outbreaks: COVID-19 as an example","Infectious Disease Modelling, Volume 5 (2020), 409-441",2005.04937,5/11/20,arxiv,"Infectious Disease Modelling, Volume 5 (2020), 409-441",19,mathematical model,0.003214189,0.003214254,0.003214145,0.922424146,0.064719056,0.003214211,Epidemiology,0.56915927,TRUE,24.78947368,0.365143175,21.36842105,0.495651592,13,0.858880178,,,0.573224982
10510,"CrisisBERT: a Robust Transformer for Crisis Classification and
  Contextual Crisis Embedding",0,2005.06627,5/11/20,arxiv,0,5,"machine learning, neural network, network model",0.00102265,0.001022638,0.598682083,0.397227341,0.001022661,0.001022627,Epidemiology,0.000230908,FALSE,142,0.936359701,179.4,0.905873695,7,0.785110192,,,0.875781196
10511,"Using Bayesian Optimization to Accelerate Virtual Screening for the
  Discovery of Therapeutics Appropriate for Repurposing for COVID-19",0,2005.07121,5/11/20,arxiv,0,1,"bayes, virtual screening",0.487284991,0.002562676,0.0025627,0.502464393,0.002562661,0.002562579,Epidemiology,0.49924305,FALSE,12,0.183190055,4,0.231469093,2,0.618927094,,,0.344528747
10512,"Decentralised, privacy-preserving Bayesian inference for mobile phone
  contact tracing",0,2005.05086,5/11/20,arxiv,0,1,bayes,0.001112668,0.012738292,0.082836726,0.901086984,0.001112714,0.001112616,Epidemiology,0.012173593,FALSE,86,0.829117447,25,0.529435376,0,0.403234768,,,0.58726253
10513,"Simulating the spread of COVID-19 via spatially-resolved
  susceptible-exposed-infected-recovered-deceased (SEIRD) model with
  heterogeneous diffusion",0,2005.0532,5/11/20,arxiv,0,9,mathematical model,0.001943512,0.001943542,0.025231659,0.966994321,0.001943482,0.001943484,Epidemiology,0.49768883,FALSE,136.2222222,0.928072237,101.3333333,0.824591919,19,0.89561084,,,0.882758332
10514,"Using Computer Vision to enhance Safety of Workforce in Manufacturing in
  a Post COVID World",0,2005.05287,5/11/20,arxiv,0,6,deep learning,0.112845761,0.001593526,0.213616262,0.611163382,0.059187566,0.001593502,Epidemiology,0.2075716,FALSE,5,0.070752675,0.166666667,0.058736955,9,0.814309525,,,0.314599718
10515,"Psychometric Analysis and Coupling of Emotions Between State Bulletins
  and Twitter in India during COVID-19 Infodemic",0,2005.05513,5/12/20,arxiv,0,6,dataset,0.001254603,0.001254622,0.024982178,0.937845929,0.033408082,0.001254586,Epidemiology,0.08445591,FALSE,51.33333333,0.641288886,80.66666667,0.780773348,1,0.537564047,,,0.65320876
10516,"Multi-Channel Transfer Learning of Chest X-ray Images for Screening of
  COVID-19",0,2005.05576,5/12/20,arxiv,0,5,"transfer learning, dataset",0.001330073,0.027261472,0.967418256,0.001330048,0.001330042,0.001330108,Imaging,0.36230594,FALSE,22.4,0.331065619,4.6,0.244246722,9,0.814309525,,,0.463207289
10517,"COVID-19Base: A knowledgebase to explore biomedical entities related to
  COVID-19",JMIR Med Inform 2020;8(11):e21648,2005.05954,5/12/20,arxiv,JMIR Med Inform 2020;8(11):e21648,7,"deep learning, literature mining",0.517446065,0.002238474,0.174337518,0.301501056,0.002238483,0.002238404,Drug discovery,0.8453131,TRUE,18,0.271569052,7.285714286,0.30331817,7,0.785110192,,,0.453332471
10518,"Observed and estimated prevalence of Covid-19 in Italy: Is it possible
  to estimate the total cases from medical swabs data?",0,2005.07268,5/12/20,arxiv,0,3,probabilistic,0.001461869,0.072215742,0.001461928,0.664047095,0.230067857,0.030745509,Epidemiology,0.04289505,FALSE,51.33333333,0.641288886,63.33333333,0.733409152,1,0.537564047,,,0.637420695
10519,"Metapopulation network models for understanding, predicting and managing
  the coronavirus disease COVID-19",0,2005.06137,5/13/20,arxiv,0,4,"bayes, computational, mathematical model, network model",0.043229113,0.001141427,0.001141361,0.935504128,0.001141412,0.017842559,Epidemiology,0.18449703,FALSE,66.5,0.743830787,49.25,0.678552315,9,0.814309525,,,0.745564209
10520,"In silico comparison of spike protein-ACE2 binding affinities across
  species; significance for the possible origin of the SARS-CoV-2 virus",0,2005.06199,5/13/20,arxiv,0,4,in silico,0.630909268,0.363689212,0.001350371,0.001350412,0.001350411,0.001350325,Drug discovery,0.41307178,FALSE,127.25,0.917496444,118.25,0.849143698,13,0.858880178,,,0.87517344
10521,MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset,0,2005.06465,5/13/20,arxiv,0,10,"machine learning, artificial intelligence, dataset",0.002898415,0.002898499,0.925671029,0.002898556,0.00289848,0.062735021,Imaging,0.69403124,TRUE,13.3,0.200507143,0.5,0.087101953,36,0.941169208,,,0.409592768
10522,Modeling and forecasting of the COVID-19 pandemic in India,0,2005.07071,5/13/20,arxiv,0,2,"model simulation, mathematical model",0.024794178,0.001141377,0.001141321,0.951303375,0.020478408,0.001141341,Epidemiology,0.37445253,FALSE,16,0.243552477,6,0.280037463,85,0.975121921,,,0.49957062
10523,Visualising COVID-19 Research,0,2005.0638,5/13/20,arxiv,0,6,dataset,0.091475302,0.016732933,0.199187085,0.638102315,0.053064257,0.001438109,Epidemiology,0.09535897,FALSE,21.66666667,0.320737213,5.333333333,0.262911426,15,0.874313229,,,0.48598729
10524,Crackovid: Optimizing Group Testing,0,2005.06413,5/13/20,arxiv,0,3,bayes,0.003101536,0.161093769,0.294259752,0.535341496,0.003101597,0.00310185,Epidemiology,0.026188672,FALSE,34.66666667,0.483703383,234.3333333,0.931629649,1,0.537564047,,,0.650965693
10525,"When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and
  Policy Assessment using Compartmental Gaussian Processes",0,2005.08837,5/13/20,arxiv,0,3,"bayes, machine learning, bayesian model",0.000946094,0.000946097,0.098523692,0.89769194,0.000946085,0.000946092,Epidemiology,0.04769641,FALSE,32,0.455810502,7,0.299973241,2,0.618927094,,,0.458236945
10526,"Triaging moderate COVID-19 and other viral pneumonias from routine blood
  tests",0,2005.06546,5/13/20,arxiv,0,12,"machine learning, classifier",0.001392892,0.001392955,0.865085175,0.001392918,0.068433505,0.062302556,Healthcare,0.07470098,FALSE,110.9166667,0.891644505,69.25,0.751204174,2,0.618927094,,,0.753925258
10527,"Regional analysis of COVID-19 in France from fit of hospital data with
  different evolutionary models",0,2005.06552,5/13/20,arxiv,0,1,bayes,0.00114132,0.043528793,0.001141332,0.95190577,0.00114135,0.001141434,Epidemiology,0.23073903,FALSE,263,0.986641103,535,0.981335296,2,0.618927094,,,0.862301164
10528,Simulation-Based Inference for Global Health Decisions,"ICML Workshop on Machine Learning for Global Health,
  Thirty-Seventh International Conference on Machine Learning (ICML 2020)",2005.07062,5/14/20,arxiv,"ICML Workshop on Machine Learning for Global Health,
  Thirty-Seventh International Conference on Machine Learning (ICML 2020)",11,"bayes, machine learning, in-silico, probabilistic",0.047269747,0.002357775,0.058393023,0.887264005,0.002357745,0.002357705,Epidemiology,0.5441885,TRUE,49.63636364,0.628981384,466.5454545,0.977321381,0,0.403234768,,,0.669845844
10529,"COVID-Twitter-BERT: A Natural Language Processing Model to Analyse
  COVID-19 Content on Twitter",0,2005.07503,5/15/20,arxiv,0,3,dataset,0.10242483,0.002898363,0.504680736,0.384199442,0.002898369,0.00289826,Imaging,0.004126757,FALSE,61.66666667,0.713278496,154,0.887008295,57,0.962281622,,,0.854189471
10530,"Accelerating drug repurposing for COVID-19 via modeling drug mechanism
  of action with large scale gene-expression profiles",0,2005.07567,5/15/20,arxiv,0,5,deep learning,0.447613008,0.001653096,0.545774629,0.00165313,0.001653118,0.001653019,Imaging,0.040305644,FALSE,13.2,0.199270208,2.6,0.18256623,1,0.537564047,,,0.306466828
10531,"Intermittent yet coordinated regional strategies can alleviate the
  COVID-19 epidemic: a network model of the Italian case",0,2005.07594,5/15/20,arxiv,0,13,network model,0.001203412,0.001203434,0.001203463,0.993982837,0.001203426,0.001203427,Epidemiology,0.0989126,FALSE,58.84615385,0.695033706,40.61538462,0.637476585,8,0.799987654,,,0.710832649
10532,"Keystroke Biometrics in Response to Fake News Propagation in a Global
  Pandemic","IEEE International Workshop on Secure Digital Identity Management
  (SDIM) 2020",2005.07688,5/15/20,arxiv,"IEEE International Workshop on Secure Digital Identity Management
  (SDIM) 2020",7,neural network,0.340747198,0.001486621,0.179325693,0.475467626,0.001486441,0.001486422,Epidemiology,0.6184157,TRUE,116.5714286,0.901601831,141.7142857,0.876103827,4,0.707574542,,,0.828426733
10533,Urban Scaling of COVID-19 epidemics,0,2005.07791,5/15/20,arxiv,0,2,mathematical model,0.001461858,0.023585498,0.001461943,0.970566968,0.001461877,0.001461856,Epidemiology,0.005873978,FALSE,3.5,0.044344115,0,0.055525823,4,0.707574542,,,0.26914816
10534,"Identification of Repurposable Drugs and Adverse Drug Reactions for
  Various Courses of COVID-19 Based on Single-Cell RNA Sequencing Data",0,2005.07856,5/16/20,arxiv,0,7,"sequencing, dataset",0.990882972,0.001823437,0.001823369,0.001823377,0.001823371,0.001823473,Drug discovery,0.4436193,FALSE,37,0.508936854,29,0.562884667,0,0.403234768,,,0.49168543
10535,"In silico ADMET and molecular docking study on searching potential
  inhibitors from limonoids and triterpenoids for COVID-19",0,2005.07955,5/16/20,arxiv,0,2,"virtual screening, computational, in silico",0.993143774,0.001371259,0.001371252,0.001371272,0.001371223,0.001371221,Drug discovery,0.98203146,TRUE,91,0.846310842,6.5,0.288132192,13,0.858880178,,,0.66444107
10536,"An epidemiological model for the spread of COVID-19: A South African
  case study",0,2005.08012,5/16/20,arxiv,0,2,model fit,0.001461853,0.02923665,0.001461898,0.964915772,0.001461907,0.00146192,Epidemiology,0.09922725,FALSE,19.5,0.29117447,8,0.320511105,3,0.667819001,,,0.426501525
10537,"Longitudinal high-throughput TCR repertoire profiling reveals the
  dynamics of T cell memory formation after mild COVID-19 infection",eLife 10 e63502 (2020),2005.0829,5/17/20,arxiv,eLife 10 e63502 (2020),13,"sequencing, dataset",0.684834767,0.246808444,0.001538165,0.001538139,0.00153813,0.063742356,Drug discovery,0.28664628,FALSE,66.53846154,0.744016328,84.69230769,0.789804656,0,0.403234768,,,0.64568525
10538,"Enhancing Covid-19 Decision-Making by Creating an Assurance Case for
  Simulation Models",0,2005.08381,5/17/20,arxiv,0,7,simulation model,0.002238513,0.002238453,0.002238491,0.988807516,0.002238533,0.002238494,Epidemiology,0.16255254,FALSE,45.14285714,0.587791453,27.71428571,0.551110516,0,0.403234768,,,0.514045579
10539,"Impact studies of nationwide measures COVID-19 anti-pandemic:
  compartmental model and machine learning",0,2005.08395,5/17/20,arxiv,0,3,machine learning,0.004775143,0.004775385,0.34217525,0.63872407,0.004775073,0.004775079,Epidemiology,0.04636699,FALSE,14,0.213494959,0.666666667,0.096200161,5,0.739490092,,,0.349728404
10540,"Bayesian adjustment for preferential testing in estimating the COVID-19
  infection fatality rate",0,2005.08459,5/18/20,arxiv,0,7,"bayes, bayesian model",0.002996422,0.059678876,0.002996533,0.928335022,0.002996685,0.002996462,Epidemiology,0.04564351,FALSE,18.14285714,0.272311213,17.57142857,0.456515922,5,0.739490092,,,0.489439076
10541,Epidemic parameters for COVID-19 in several regions of India,0,2005.08499,5/18/20,arxiv,0,1,bayes,0.002806707,0.002806614,0.002806471,0.985966975,0.002806553,0.00280668,Epidemiology,0.17605934,FALSE,12,0.183190055,0,0.055525823,5,0.739490092,,,0.326068657
10542,COVI White Paper,0,2005.08502,5/18/20,arxiv,0,24,machine learning,0.001171574,0.014924718,0.131641436,0.849919048,0.001171627,0.001171598,Epidemiology,0.35724878,FALSE,55.7826087,0.673016266,1120.956522,0.994848809,9,0.814309525,,,0.827391533
10543,"A note on 'Collider bias undermines our understanding of COVID-19
  disease risk and severity' and how causal Bayesian networks both expose and
  resolve the problem",0,2005.08608,5/18/20,arxiv,0,1,"bayes, network model, dataset",0.002080603,0.002080609,0.002080654,0.588824168,0.402853267,0.0020807,Epidemiology,0.038673133,FALSE,237,0.982311831,879,0.991838373,5,0.739490092,,,0.904546765
10544,"Public discourse and sentiment during the COVID-19 pandemic: using
  Latent Dirichlet Allocation for topic modeling on Twitter",0,2005.08817,5/18/20,arxiv,0,6,machine learning,0.002638967,0.002639168,0.108484778,0.711868133,0.171729842,0.002639112,Epidemiology,0.7575314,TRUE,75.16666667,0.787432742,17,0.451097137,11,0.840175319,,,0.692901733
10545,"A Semantically Enriched Dataset based on Biomedical NER for the COVID19
  Open Research Dataset Challenge",0,2005.08823,5/18/20,arxiv,0,4,dataset,0.218694426,0.003335684,0.767963931,0.003335442,0.003335305,0.003335213,Drug discovery,0.11338386,FALSE,53,0.654400396,37.5,0.619213273,4,0.707574542,,,0.66039607
10546,"Simulation-free estimation of an individual-based SEIR model for
  evaluating nonpharmaceutical interventions with an application to COVID-19 in
  Iowa",0,2005.08827,5/18/20,arxiv,0,2,computational,0.001786555,0.00178654,0.001786679,0.991067123,0.001786568,0.001786535,Epidemiology,0.13954255,FALSE,20,0.298163152,8,0.320511105,0,0.403234768,,,0.340636341
10547,"Mathematical model of COVID-19 intervention scenarios for Sao Paulo-
  Brazil",0,2005.09426,5/18/20,arxiv,0,11,mathematical model,0.002422273,0.031823382,0.002422355,0.958487317,0.002422419,0.002422254,Epidemiology,0.26726678,FALSE,15.54545455,0.234399159,6.909090909,0.294286861,2,0.618927094,,,0.382537705
10548,Inbetweening auto-animation via Fokker-Planck dynamics and thresholding,0,2005.08858,5/18/20,arxiv,0,3,dataset,0.002898558,0.002898432,0.400834213,0.587572023,0.002898403,0.002898372,Epidemiology,0.01361984,FALSE,83.33333333,0.818727194,35,0.605298368,0,0.403234768,,,0.609086776
10549,"DHP Framework: Digital Health Passports Using Blockchain -- Use case on
  international tourism during the COVID-19 pandemic",0,2005.08922,5/18/20,arxiv,0,3,digital health,0.001486465,0.001486448,0.026758686,0.948274988,0.001486451,0.020506962,Epidemiology,0.25834122,FALSE,55,0.668686994,10.33333333,0.36038266,3,0.667819001,,,0.565629552
10550,Weibo-COV: A Large-Scale COVID-19 Social Media Dataset from Weibo,0,2005.09174,5/19/20,arxiv,0,4,dataset,0.002032916,0.002032802,0.083666647,0.908202039,0.002032858,0.002032738,Epidemiology,0.1697402,FALSE,31,0.445111015,4.75,0.248260637,9,0.814309525,,,0.502560392
10551,"What country, university or research institute, performed the best on
  COVID-19? Bibliometric analysis of scientific literature",0,2005.10082,5/19/20,arxiv,0,7,data mining,0.003760678,0.003760636,0.003760579,0.981197116,0.003760562,0.003760429,Epidemiology,0.5072666,TRUE,51.14285714,0.639247944,21.42857143,0.496387477,4,0.707574542,,,0.614403321
10552,"Inference, prediction and optimization of non-pharmaceutical
  interventions using compartment models: the PyRoss library",0,2005.09625,5/19/20,arxiv,0,18,bayes,0.054660231,0.001203451,0.001203462,0.940526017,0.001203413,0.001203426,Epidemiology,0.038013816,FALSE,42.61111111,0.563238296,27.77777778,0.551779502,14,0.866658436,,,0.660558744
10553,"The challenges of deploying artificial intelligence models in a rapidly
  evolving pandemic",0,2005.12137,5/19/20,arxiv,0,6,"computational, artificial intelligence, neural network",0.079002641,0.001371281,0.323627469,0.593256008,0.001371321,0.00137128,Epidemiology,0.7177085,TRUE,95.33333333,0.857566949,63.83333333,0.734613326,11,0.840175319,,,0.810785198
10554,Lung Segmentation from Chest X-rays using Variational Data Imputation,0,2005.10052,5/20/20,arxiv,0,7,image analysis,0.050726073,0.002639011,0.93871776,0.002639014,0.002638954,0.002639189,Imaging,0.037422836,FALSE,64,0.729173109,42,0.644902328,12,0.850299401,,,0.741458279
10555,"Blocking of the CD80/86 axis as a therapeutic approach to prevent
  progression to more severe forms of COVID-19",0,2005.10055,5/20/20,arxiv,0,10,"in-silico, dataset",0.809105074,0.001350353,0.001350388,0.001350365,0.001350347,0.185493474,Drug discovery,0.45493236,FALSE,42.7,0.563980456,31.2,0.579274819,0,0.403234768,,,0.515496681
10556,"A Bayesian - Deep Learning model for estimating Covid-19 evolution in
  Spain",0,2005.10335,5/20/20,arxiv,0,1,"bayes, deep learning",0.002296523,0.180233033,0.141563154,0.671314124,0.002296526,0.00229664,Epidemiology,0.123610616,FALSE,134,0.926031294,75,0.766590848,3,0.667819001,,,0.786813715
10557,"Symptom extraction from the narratives of personal experiences with
  COVID-19 on Reddit",0,2005.10454,5/21/20,arxiv,0,4,dataset,0.001350361,0.001350382,0.001350463,0.537044401,0.304994272,0.153910123,Epidemiology,0.027668387,FALSE,56.5,0.678891706,79,0.776023548,4,0.707574542,,,0.720829932
10558,"An efficient explicit approach for predicting the Covid-19 spreading
  with undetected infectious: The case of Cameroon",0,2005.11279,5/21/20,arxiv,0,1,mathematical model,0.004310178,0.004310012,0.004310033,0.978449821,0.00431002,0.004309936,Epidemiology,0.20117337,FALSE,20,0.298163152,0,0.055525823,1,0.537564047,,,0.297084341
10559,"Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19
  Diagnosis",0,2005.10548,5/21/20,arxiv,0,8,"machine learning, dataset",0.001593516,0.001593579,0.675367603,0.273433081,0.046418669,0.001593552,Epidemiology,0.603082,TRUE,61.375,0.71092832,26.125,0.538600482,20,0.900117291,,,0.716548697
10560,"Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep
  Learning",0,2005.10831,5/21/20,arxiv,0,9,"deep learning, transcriptom, proteom, knowledge graph",0.749989802,0.001786554,0.242863778,0.00178667,0.001786596,0.0017866,Drug discovery,0.5732577,TRUE,86.77777778,0.832766405,569.4444444,0.982405673,17,0.887338725,,,0.900836934
10561,Markets for Efficient Public Good Allocation with Social Distancing,0,2005.10765,5/21/20,arxiv,0,4,computational,0.123223114,0.001653054,0.00165318,0.870164567,0.001653061,0.001653025,Epidemiology,0.30474693,FALSE,174.25,0.961469479,466.25,0.977187584,0,0.403234768,,,0.78063061
10562,"COVID-19 Public Sentiment Insights and Machine Learning for Tweets
  Classification",https://www.mdpi.com/2078-2489/11/6/314/htm,2005.10898,5/21/20,arxiv,https://www.mdpi.com/2078-2489/11/6/314/htm,5,"bayes, machine learning, logistic regression",0.001901734,0.00190174,0.290644314,0.599510482,0.104139988,0.001901741,Epidemiology,0.676055,TRUE,23.8,0.351227658,3.4,0.208589778,11,0.840175319,,,0.466664252
10563,CovidNet: To Bring Data Transparency in the Era of COVID-19,0,2005.10948,5/22/20,arxiv,0,13,dataset,0.001901732,0.001901758,0.280422751,0.682387368,0.03148463,0.00190176,Epidemiology,0.13507232,FALSE,40.84615385,0.547096295,6.615384615,0.289269467,10,0.828199272,,,0.554855011
10564,"SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set
  Domain Adaptation",0,2005.11003,5/22/20,arxiv,0,3,"deep learning, neural network, adversarial network, dataset",0.000977508,0.000977466,0.995112645,0.000977468,0.000977472,0.000977441,Imaging,0.18126318,FALSE,6.333333333,0.089863319,3.666666667,0.217621086,3,0.667819001,,,0.325101135
10565,"Vulnerability of deep neural networks for detecting COVID-19 cases from
  chest X-ray images to universal adversarial attacks","PLoS ONE 5(12), e0243963 (2020)",2005.11061,5/22/20,arxiv,"PLoS ONE 5(12), e0243963 (2020)",3,"neural network, dataset",0.087809506,0.001171579,0.832319576,0.076356082,0.001171644,0.001171614,Imaging,0.86277384,TRUE,39,0.530521368,21.33333333,0.495450896,6,0.764429903,,,0.596800723
10566,"Drug repurposing prediction for COVID-19 using probabilistic networks
  and crowdsourced curation",0,2005.11088,5/22/20,arxiv,0,15,"knowledge graph, probabilistic",0.584006465,0.00256267,0.06365811,0.344647551,0.00256265,0.002562554,Drug discovery,0.15815529,FALSE,33.73333333,0.473436824,92.2,0.806194809,0,0.403234768,,,0.560955467
10567,"GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19
  Tweets with Location Information",0,2005.11177,5/22/20,arxiv,0,3,"computational, dataset",0.001943553,0.001943517,0.001943511,0.990282389,0.001943564,0.001943466,Epidemiology,0.40465498,FALSE,51.33333333,0.641288886,7.666666667,0.310877709,27,0.92443978,,,0.625535459
10568,"Virtual Screening of Plant Metabolites against Main protease,
  RNA-dependent RNA polymerase and Spike protein of SARS-CoV-2: Therapeutics
  option of COVID-19",0,2005.11254,5/22/20,arxiv,0,9,virtual screening,0.979403632,0.001187309,0.001187294,0.001187306,0.015847175,0.001187284,Drug discovery,0.9383913,TRUE,15.88888889,0.238790278,13.88888889,0.409686915,1,0.537564047,,,0.39534708
10569,CoAID: COVID-19 Healthcare Misinformation Dataset,0,2006.00885,5/22/20,arxiv,0,2,dataset,0.002898311,0.002898336,0.178716572,0.809690088,0.002898448,0.002898245,Epidemiology,0.031548977,FALSE,50,0.632073721,54,0.697952903,25,0.918019631,,,0.749348752
10570,"COVID-19 Public Opinion and Emotion Monitoring System Based on Time
  Series Thermal New Word Mining",0,2005.11458,5/23/20,arxiv,0,9,deep learning,0.002806657,0.002806517,0.288200191,0.700573713,0.002806514,0.002806407,Epidemiology,0.47377276,FALSE,23.66666667,0.34986703,,,1,0.537564047,,,0.443715538
10571,Bayesian workflow for disease transmission modeling in Stan,0,2006.02985,5/23/20,arxiv,0,4,"bayes, computational, bayesian model, probabilistic",0.001653022,0.001653065,0.084668158,0.908719641,0.001653072,0.001653042,Epidemiology,0.02327934,FALSE,27.5,0.401570907,11.75,0.381857105,4,0.707574542,,,0.497000851
10572,"Coronavirus: Comparing COVID-19, SARS and MERS in the eyes of AI",0,2005.11524,5/23/20,arxiv,0,9,deep learning,0.001187306,0.289504573,0.705746282,0.001187304,0.001187268,0.001187268,Imaging,0.06830776,FALSE,47.375,0.608819346,42.375,0.646374097,8,0.799987654,,,0.685060365
10573,Emotion-robust EEG Classification for Motor Imagery,0,2005.13523,5/23/20,arxiv,0,1,machine learning,0.125236105,0.001987169,0.517429273,0.35137305,0.001987263,0.00198714,Epidemiology,0.056260437,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
10574,"Mathematical Modeling of Business Reopening when Facing SARS-CoV-2
  Pandemic: Protection, Cost and Risk",0,2006.06416,5/23/20,arxiv,0,14,mathematical model,0.001987135,0.044335523,0.001987262,0.947715666,0.001987299,0.001987115,Epidemiology,0.48164862,FALSE,78.57142857,0.800853485,46.64285714,0.666711266,1,0.537564047,,,0.668376266
10575,"Forecasting the Spread of Covid-19 Under Control Scenarios Using LSTM
  and Dynamic Behavioral Models",0,2005.1227,5/24/20,arxiv,0,3,"neural network, lstm",0.001751213,0.025191464,0.356826115,0.612728857,0.001751171,0.00175118,Epidemiology,0.23108393,FALSE,26.33333333,0.386047375,8.333333333,0.325662296,4,0.707574542,,,0.473094737
10576,"Protein-ligand interaction study to identify potential dietary compounds
  binding at the active site of therapeutic target proteins of SARS-CoV-2",0,2005.11767,5/24/20,arxiv,0,3,in silico,0.989346667,0.002130645,0.002130709,0.002130704,0.002130639,0.002130636,Drug discovery,0.9491197,TRUE,66,0.741356918,6,0.280037463,0,0.403234768,,,0.474876383
10577,"A Big Data Based Framework for Executing Complex Query Over COVID-19
  Datasets (COVID-QF)",0,2005.12271,5/25/20,arxiv,0,4,"machine learning, artificial intelligence, dataset",0.00120348,0.039415297,0.776090752,0.180883615,0.001203438,0.001203418,Epidemiology,0.05012113,FALSE,19,0.285793803,1.75,0.148381054,1,0.537564047,,,0.323912968
10578,Estimates of the proportion of SARS-CoV-2 infected individuals in Sweden,0,2005.13519,5/25/20,arxiv,0,2,bayes,0.001565313,0.001565339,0.001565304,0.992173356,0.001565343,0.001565345,Epidemiology,0.2215201,FALSE,34.5,0.482404601,83.5,0.787128713,2,0.618927094,,,0.629486803
10579,Mixing properties of Skellam-GARCH processes,0,2005.12093,5/25/20,arxiv,0,3,probabilistic,0.003760613,0.003760586,0.003760669,0.981196861,0.00376054,0.003760732,Epidemiology,0.011805445,FALSE,57.66666667,0.686375162,199.3333333,0.917380252,1,0.537564047,,,0.713773154
10580,"Modelling provincial Covid-19 epidemic data in Italy using an adjusted
  time-dependent SIRD model",0,2005.1217,5/25/20,arxiv,0,7,predictive model,0.003465888,0.003465902,0.076601138,0.909534932,0.003466051,0.003466089,Epidemiology,0.22137982,FALSE,29.71428571,0.427979467,7.714285714,0.311479797,6,0.764429903,,,0.501296389
10581,"Racism is a Virus: Anti-Asian Hate and Counterhate in Social Media
  during the COVID-19 Crisis",0,2005.12423,5/25/20,arxiv,0,4,"classifier, dataset",0.00153823,0.001538122,0.066233802,0.927613572,0.001538191,0.001538083,Epidemiology,0.0962801,FALSE,46,0.596882924,42,0.644902328,23,0.91129082,,,0.717692024
10582,COVID-19 and the Social Distancing Paradox: dangers and solutions,0,2005.12446,5/26/20,arxiv,0,1,dataset,0.001112623,0.001112615,0.001112631,0.994436811,0.001112711,0.001112609,Epidemiology,0.005171329,FALSE,23,0.34225988,96,0.81509232,9,0.814309525,,,0.657220575
10583,"Modeling the Dynamics of the COVID-19 Population in Australia: A
  Probabilistic Analysis",0,2005.12455,5/26/20,arxiv,0,4,probabilistic,0.002898306,0.002898284,0.002898748,0.985508099,0.002898284,0.002898278,Epidemiology,0.2514363,FALSE,29.5,0.426000371,20.25,0.483074659,0,0.403234768,,,0.437436599
10584,What Are People Asking About COVID-19? A Question Classification Dataset,0,2005.12522,5/26/20,arxiv,0,4,dataset,0.037188704,0.002238524,0.48309487,0.473000868,0.002238589,0.002238444,Epidemiology,0.011349887,FALSE,23.5,0.348506401,47.25,0.669454108,11,0.840175319,,,0.619378609
10585,"Sustainable and resilient strategies for touristic cities against
  COVID-19: an agent-based approach",0,2005.12547,5/26/20,arxiv,0,3,probabilistic,0.001098828,0.001098814,0.001098827,0.994505829,0.001098893,0.00109881,Epidemiology,0.10510057,FALSE,97,0.861586987,23.66666667,0.518062617,7,0.785110192,,,0.721586599
10586,"Modelling aerosol transport and virus exposure with numerical
  simulations in relation to SARS-CoV-2 transmission by inhalation indoors",0,2005.12612,5/26/20,arxiv,0,30,computational,0.13775959,0.001392917,0.001392905,0.839015213,0.001392872,0.019046504,Epidemiology,0.060025603,FALSE,60.8,0.707155668,35.36666667,0.607506021,62,0.965553429,,,0.760071706
10587,SEIRD Model for Qatar Covid-19 Outbreak: A Case Study,0,2005.12777,5/26/20,arxiv,0,3,bayes,0.003335265,0.003335245,0.003335314,0.983323356,0.003335462,0.003335357,Epidemiology,0.21080083,FALSE,53,0.654400396,20,0.481000803,5,0.739490092,,,0.624963764
10588,"Twitter discussions and emotions about COVID-19 pandemic: a machine
  learning approach",0,2005.1283,5/26/20,arxiv,0,7,machine learning,0.001415115,0.001415125,0.040272091,0.810052722,0.145429842,0.001415105,Epidemiology,0.30853432,FALSE,68.71428571,0.757684458,16.71428571,0.445143163,10,0.828199272,,,0.677008964
10589,"COVIDNet-S: Towards computer-aided severity assessment via training and
  validation of deep neural networks for geographic extent and opacity extent
  scoring of chest X-rays for SARS-CoV-2 lung disease severity",0,2005.12855,5/26/20,arxiv,0,8,"deep learning, neural network",0.000946076,0.000946105,0.970988204,0.000946135,0.025227302,0.000946177,Imaging,0.1557838,FALSE,57.75,0.68699363,,,8,0.799987654,,,0.743490642
10590,"Cross Hashing: Anonymizing encounters in Decentralised Contact Tracing
  Protocols",0,2005.12884,5/26/20,arxiv,0,2,computational,0.001861784,0.001861764,0.048694667,0.94385821,0.00186182,0.001861755,Epidemiology,0.12433913,FALSE,9.5,0.143051518,5,0.257024351,2,0.618927094,,,0.339667655
10591,"How does Working from Home Affect Developer Productivity? -- A Case
  Study of Baidu During COVID-19 Pandemic",0,2005.13167,5/27/20,arxiv,0,6,dataset,0.002238465,0.002238474,0.002238583,0.549981685,0.441064331,0.002238463,Epidemiology,0.007211596,FALSE,61.33333333,0.710680933,17.83333333,0.459058068,11,0.840175319,,,0.66997144
10592,"PaccMann$^{RL}$ on SARS-CoV-2: Designing antiviral candidates with
  conditional generative models",ICML Workshop on Computational Biology 2020,2005.13285,5/27/20,arxiv,ICML Workshop on Computational Biology 2020,7,deep learning,0.720734532,0.001330087,0.273945097,0.001330057,0.001330097,0.001330129,Drug discovery,0.07472357,FALSE,10,0.15214299,2.285714286,0.171193471,1,0.537564047,,,0.286966836
10593,"Pandemic News: Facebook Pages of Mainstream News Media and the
  Coronavirus Crisis -- A Computational Content Analysis",0,2005.1329,5/27/20,arxiv,0,4,computational,0.001085388,0.023630487,0.071204224,0.806759567,0.096234913,0.001085422,Epidemiology,0.087736994,FALSE,60.5,0.705547653,44.5,0.656743377,1,0.537564047,,,0.633285026
10594,"Synthetic Observational Health Data with GANs: from slow adoption to a
  boom in medical research and ultimately digital twins?",0,2005.1351,5/27/20,arxiv,0,2,adversarial network,0.00115629,0.001156247,0.481734635,0.513640162,0.001156327,0.00115634,Epidemiology,0.8063631,TRUE,14.5,0.219617787,22.5,0.507492641,0,0.403234768,,,0.376781732
10595,"Unveiling the molecular mechanism of SARS-CoV-2 main protease inhibition
  from 92 crystal structures",0,2005.13653,5/27/20,arxiv,0,5,"deep learning, dataset",0.80221011,0.001622709,0.191299028,0.001622743,0.001622702,0.001622708,Drug discovery,0.04041168,FALSE,27.6,0.402374915,13.8,0.408884132,4,0.707574542,,,0.506277863
10596,"Challenges in Combating COVID-19 Infodemic -- Data, Tools, and Ethics",0,2005.13691,5/27/20,arxiv,0,5,computational,0.002130738,0.002130717,0.002130785,0.989346206,0.00213091,0.002130643,Epidemiology,0.000926703,FALSE,25.4,0.372626631,42.8,0.648648649,3,0.667819001,,,0.563031427
10597,"COVID-19 and Global Economic Growth: Policy Simulations with a
  Pandemic-Enabled Neoclassical Growth Model",0,2005.13722,5/28/20,arxiv,0,9,mathematical model,0.001220043,0.001220026,0.001220024,0.99389988,0.001220029,0.001219998,Epidemiology,0.09855497,FALSE,16.66666667,0.251159626,1.555555556,0.138948354,0,0.403234768,,,0.264447583
10598,"Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First
  Data Release",0,2006.02431,5/28/20,arxiv,0,18,"machine learning, deep learning, dataset",0.488827828,0.001272671,0.506081551,0.00127268,0.001272644,0.001272626,Imaging,0.02774176,FALSE,66.5,0.743830787,324.5,0.960730533,5,0.739490092,,,0.814683804
10599,COVID-19 and Your Smartphone: BLE-based Smart Contact Tracing,0,2005.13754,5/28/20,arxiv,0,3,"machine learning, classifier",0.035885868,0.000916725,0.372449627,0.588914376,0.000916705,0.0009167,Epidemiology,0.023689061,FALSE,203.3333333,0.9734059,320,0.959793952,5,0.739490092,,,0.890896648
10600,"From Prediction to Prescription: Evolutionary Optimization of
  Non-Pharmaceutical Interventions in the COVID-19 Pandemic",0,2005.13766,5/28/20,arxiv,0,6,predictive model,0.027931068,0.086696179,0.077217112,0.803311066,0.002422333,0.002422241,Epidemiology,0.5784628,TRUE,87.16666667,0.834127033,245.6666667,0.936580145,5,0.739490092,,,0.836732423
10601,Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images,0,2005.13928,5/28/20,arxiv,0,4,"artificial intelligence, radiom, deep-learning",0.001310368,0.086539026,0.850856788,0.001310417,0.001310373,0.058673028,Imaging,0.07783261,FALSE,15.75,0.237862577,3.5,0.213607172,2,0.618927094,,,0.356798947
10602,"Clinical trials impacted by the COVID-19 pandemic: Adaptive designs to
  the rescue?",0,2005.13979,5/28/20,arxiv,0,9,bayes,0.002130803,0.057386889,0.095836427,0.840384336,0.002130737,0.002130808,Epidemiology,0.32907104,FALSE,121.3333333,0.908590513,185.1111111,0.90941932,13,0.858880178,,,0.89229667
10603,"First Principle Simulation of Coated Hydroxychloroquine on Ag, Au and Pt
  Nanoparticle as a Potential Candidate for Treatment of SARS-CoV-2 (COVID-19)",0,2006.02343,5/28/20,arxiv,0,6,computational,0.822958562,0.001751212,0.001751246,0.128631041,0.001751261,0.043156678,Drug discovery,0.37140656,FALSE,23,0.34225988,2.5,0.180826866,5,0.739490092,,,0.420858946
10604,"A Data-Driven Network Model for the Emerging COVID-19 Epidemics in
  Wuhan, Toronto and Italy",Mathematical Biosciences 2020,2005.14533,5/28/20,arxiv,Mathematical Biosciences 2020,8,"model fit, predictive model, network model",0.002130667,0.002130722,0.002130839,0.989346323,0.00213075,0.002130698,Epidemiology,0.57807744,TRUE,31.875,0.453584019,31,0.578204442,26,0.920859312,,,0.650882591
10605,"Analyzing COVID-19 on Online Social Media: Trends, Sentiments and
  Emotions",0,2005.14464,5/29/20,arxiv,0,6,computational,0.002130774,0.002130743,0.002130657,0.98934629,0.002130909,0.002130626,Epidemiology,0.011358798,FALSE,33.66666667,0.47312759,145.5,0.879783249,7,0.785110192,,,0.712673677
10606,Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware,0,2005.14619,5/29/20,arxiv,0,10,dataset,0.001565362,0.108702854,0.001565334,0.885035793,0.001565373,0.001565284,Epidemiology,0.038692445,FALSE,60.9,0.707588595,,,9,0.814309525,,,0.76094906
10607,"Internet search effort on Covid-19 and the underlying public
  interventions and epidemiological status",0,2006.00971,5/29/20,arxiv,0,1,machine learning,0.001046844,0.001046824,0.001046909,0.994765717,0.001046863,0.001046843,Epidemiology,0.3181699,FALSE,13,0.197352959,6,0.280037463,0,0.403234768,,,0.29354173
10608,Modelling oral adrenal cortisol support,0,2006.00043,5/29/20,arxiv,0,5,mathematical model,0.442525414,0.002639027,0.002639018,0.420232817,0.002639401,0.129324323,Drug discovery,0.21611688,FALSE,89.6,0.841734183,53,0.693738293,0,0.403234768,,,0.646235748
10609,"In Silico Investigation of Phytoconstituents from Indian Medicinal Herb
  'Tinospora cordifolia (Giloy)' against SARS-CoV-2 (COVID-19) by Molecular
  Dynamics Approach",0,2007.00621,5/29/20,arxiv,0,1,in silico,0.966633725,0.001751231,0.001751206,0.001751255,0.001751226,0.026361358,Drug discovery,0.97541606,TRUE,29,0.41993939,0,0.055525823,17,0.887338725,,,0.454267979
10610,"Tracking Public Opinion in China through Various Stages of the COVID-19
  Pandemic",0,2006.00163,5/30/20,arxiv,0,3,classifier,0.001823329,0.001823349,0.033909001,0.741081611,0.219539371,0.001823338,Epidemiology,0.002014726,FALSE,221.3333333,0.978972107,584,0.982807064,0,0.403234768,,,0.78833798
10611,"Ontology-based systematic classification and analysis of coronaviruses,
  hosts, and host-coronavirus interactions towards deep understanding of
  COVID-19",0,2006.00639,5/31/20,arxiv,0,20,"computational, literature mining",0.823487334,0.001565411,0.170251,0.001565419,0.001565339,0.001565497,Drug discovery,0.0826633,FALSE,60.6,0.705856887,,,2,0.618927094,,,0.66239199
10612,"In silico identification of potential natural product inhibitors of
  human proteases key to SARS-CoV-2 infection","Molecules 2020, 25(17), 3822",2006.00652,6/1/20,arxiv,"Molecules 2020, 25(17), 3822",5,"virtual screening, in silico",0.968670833,0.001254647,0.001254668,0.026310617,0.001254613,0.001254623,Drug discovery,0.8396746,TRUE,28.2,0.409734677,9.2,0.339577201,8,0.799987654,,,0.516433177
10613,"Automatic classification between COVID-19 pneumonia, non-COVID-19
  pneumonia, and the healthy on chest X-ray image: combination of data
  augmentation methods","Sci Rep 10, 17532 (2020)",2006.0073,6/1/20,arxiv,"Sci Rep 10, 17532 (2020)",4,dataset,0.001415107,0.001415124,0.992924429,0.001415106,0.001415089,0.001415145,Imaging,0.8661973,TRUE,28.5,0.412579628,14.25,0.415038801,4,0.707574542,,,0.51173099
10614,COVID-19: Social Media Sentiment Analysis on Reopening,0,2006.00804,6/1/20,arxiv,0,3,dataset,0.001861762,0.001861737,0.001861716,0.835970969,0.15658212,0.001861695,Epidemiology,0.013815492,FALSE,7,0.10179974,0.333333333,0.073187048,2,0.618927094,,,0.264637961
10615,"SEI1I2HRSVM model applied to the coronavirus pandemic (COVID-19) in
  Paraguay",0,2006.00926,6/1/20,arxiv,0,1,mathematical model,0.003335274,0.003335301,0.003335319,0.916265353,0.003335483,0.070393271,Epidemiology,0.31765473,FALSE,5,0.070752675,3,0.199424672,0,0.403234768,,,0.224470705
10616,"BIMCV COVID-19+: a large annotated dataset of RX and CT images from
  COVID-19 patients",0,2006.01174,6/1/20,arxiv,0,13,dataset,0.001593532,0.142990055,0.850635872,0.001593524,0.001593506,0.00159351,Imaging,0.07210493,FALSE,42.61538462,0.563361989,13.07692308,0.400655606,25,0.918019631,,,0.627345742
10617,"Independent Component Analysis for Trustworthy Cyberspace during High
  Impact Events: An Application to Covid-19",0,2006.01284,6/1/20,arxiv,0,8,"machine learning, deep learning, dataset",0.002357794,0.002357763,0.55210518,0.438463694,0.002357841,0.002357728,Epidemiology,0.009623379,FALSE,60.25,0.704372565,134.25,0.868276693,1,0.537564047,,,0.703404435
10618,"Comparing and Integrating US COVID-19 Data from Multiple Sources with
  Anomaly Detection and Repairing",0,2006.01333,6/2/20,arxiv,0,8,dataset,0.001987127,0.001987195,0.142646418,0.849405003,0.001987148,0.00198711,Epidemiology,0.015880167,FALSE,37.5,0.513946441,,,3,0.667819001,,,0.590882721
10619,"COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19
  based on Chest X-Ray images",0,2006.01409,6/2/20,arxiv,0,13,"deep learning, neural network, dataset",0.001220027,0.001220048,0.790359145,0.038317142,0.001220039,0.1676636,Imaging,0.20087749,FALSE,13.69230769,0.207495825,14.46153846,0.417179556,11,0.840175319,,,0.488283567
10620,"CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint
  Identification and Severity Quantification",0,2006.01441,6/2/20,arxiv,0,11,"deep learning, neural network, dataset",0.001112654,0.041003688,0.789129909,0.001112698,0.00111278,0.166528271,Imaging,0.18737796,FALSE,59.09090909,0.696703569,110.9090909,0.8396441,3,0.667819001,,,0.734722223
10621,Forecasting hospital demand during COVID-19 pandemic outbreaks,0,2006.01873,6/2/20,arxiv,0,3,"bayes, probabilistic",0.002638992,0.002639026,0.002639307,0.815103514,0.002639003,0.174340159,Epidemiology,0.43803814,FALSE,70.33333333,0.765353454,37.33333333,0.618209794,6,0.764429903,,,0.715997717
10622,"Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist
  Clinicians with COVID-19 ECMO Planning",0,2006.01898,6/2/20,arxiv,0,5,machine learning,0.099233244,0.001751304,0.197596261,0.001751258,0.040566295,0.659101638,Clinics,0.7712086,TRUE,38,0.519327107,88,0.796895906,0,0.403234768,,,0.573152594
10623,"Identifying Human Interactors of SARS-CoV-2 Proteins and Drug Targets
  for COVID-19 using Network-Based Label Propagation",0,2006.01968,6/2/20,arxiv,0,10,computational,0.902527359,0.03401929,0.058757238,0.00156538,0.001565419,0.001565314,Drug discovery,0.5672909,TRUE,55.3,0.669800235,203.1,0.918651325,5,0.739490092,,,0.775980551
10624,"Automatic Text Summarization of COVID-19 Medical Research Articles using
  BERT and GPT-2",0,2006.01997,6/3/20,arxiv,0,3,"machine learning, dataset",0.002639023,0.00263904,0.568395883,0.421048051,0.002639015,0.002638988,Epidemiology,0.005052716,FALSE,4.333333333,0.057950399,1.333333333,0.13252609,7,0.785110192,,,0.32519556
10625,"Modeling COVID-19 dynamics in Illinois under non-pharmaceutical
  interventions","Phys. Rev. X 10, 041033 (2020)",2006.02036,6/3/20,arxiv,"Phys. Rev. X 10, 041033 (2020)",6,bayes,0.001220007,0.001220037,0.001220026,0.921546615,0.001220035,0.073573281,Epidemiology,0.19849002,FALSE,69.33333333,0.760467561,113.1666667,0.842788333,5,0.739490092,,,0.780915329
10626,"Within host dynamics of SARS-CoV-2 in humans: Modeling immune responses
  and antiviral treatments",0,2006.02936,6/3/20,arxiv,0,1,mathematical model,0.575129379,0.001438216,0.001438189,0.419117968,0.001438135,0.001438113,Drug discovery,0.15946177,FALSE,55,0.668686994,12,0.386740701,2,0.618927094,,,0.558118263
10627,"Mathematical Models for Describing and Predicting the COVID-19 Pandemic
  Crisis",0,2006.02507,6/3/20,arxiv,0,3,mathematical model,0.002996597,0.002996462,0.002996622,0.985017401,0.0029965,0.002996418,Epidemiology,0.05814174,FALSE,10,0.15214299,2.666666667,0.185442869,5,0.739490092,,,0.359025317
10628,"Exploration of Interpretability Techniques for Deep COVID-19
  Classification using Chest X-ray Images",0,2006.0257,6/3/20,arxiv,0,10,"deep learning, artificial intelligence, network model",0.001901689,0.001901673,0.990491243,0.00190171,0.001901933,0.001901752,Imaging,0.045407385,FALSE,102.5,0.87445111,66.3,0.743109446,6,0.764429903,,,0.79399682
10629,"Modeling the molecular impact of SARS-CoV-2 infection on the
  renin-angiotensin system",0,2006.02772,6/4/20,arxiv,0,3,mathematical model,0.882359516,0.002898424,0.002898394,0.054596322,0.00289836,0.054348984,Drug discovery,0.58008677,TRUE,125.3333333,0.914651494,105,0.831482473,7,0.785110192,,,0.843748053
10630,COVID-19 diagnosis by routine blood tests using machine learning,0,2006.03476,6/4/20,arxiv,0,10,"machine learning, predictive model",0.001291325,0.001291323,0.551008702,0.001291279,0.001291293,0.443826077,Imaging,0.4151971,FALSE,39.1,0.530645062,10.2,0.357706717,7,0.785110192,,,0.557820657
10631,"SAveRUNNER: a network-based algorithm for drug repurposing and its
  application to COVID-19",0,2006.0311,6/4/20,arxiv,0,4,"in-silico, interactom",0.901515689,0.001171613,0.055195466,0.0011716,0.017918104,0.023027527,Drug discovery,0.5030452,TRUE,16.41666667,0.247758056,6.833333333,0.293484078,2,0.618927094,,,0.386723076
10632,COVID-19 Real-Time Tracker and Analytical Study,0,2006.03146,6/4/20,arxiv,0,1,logistic regression,0.001593541,0.001593546,0.044251018,0.949374817,0.001593561,0.001593517,Epidemiology,0.024323434,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
10633,Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment,0,2006.03202,6/5/20,arxiv,0,2,transfer learning,0.00249049,0.002490742,0.462292493,0.527745316,0.002490509,0.00249045,Epidemiology,0.005091041,FALSE,71.5,0.770734121,197,0.916242976,2,0.618927094,,,0.76863473
10634,"Classification Aware Neural Topic Model and its Application on a New
  COVID-19 Disinformation Corpus",PLOS ONE 2021,2006.03354,6/5/20,arxiv,PLOS ONE 2021,6,computational,0.00203287,0.00203292,0.319808601,0.672060055,0.00203281,0.002032744,Epidemiology,0.5734155,TRUE,119.3333333,0.905931103,245.3333333,0.936446347,4,0.707574542,,,0.849983997
10635,"Artificial Intelligence-based Clinical Decision Support for COVID-19 --
  Where Art Thou?",0,2006.03434,6/5/20,arxiv,0,5,artificial intelligence,0.004310006,0.004310062,0.682958329,0.299801421,0.004310172,0.004310009,Epidemiology,0.7363429,TRUE,142.6,0.937040015,256,0.940058871,2,0.618927094,,,0.83200866
10636,"Data Augmentation using Generative Adversarial Networks (GANs) for
  GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images",0,2006.03622,6/5/20,arxiv,0,3,"neural network, adversarial network, dataset",0.001717162,0.001717155,0.991414196,0.001717184,0.001717157,0.001717146,Imaging,0.044709593,FALSE,23.66666667,0.34986703,9,0.337904736,0,0.403234768,,,0.363668844
10637,"Time Series Analysis and Forecasting of COVID-19 Cases Using LSTM and
  ARIMA Models",0,2006.13852,6/5/20,arxiv,0,1,lstm,0.001310324,0.041536931,0.294265943,0.660266056,0.00131037,0.001310377,Epidemiology,0.14256406,FALSE,15,0.227596017,2,0.164302917,3,0.667819001,,,0.353239312
10638,Planning of School Teaching during COVID-19,0,2006.03672,6/5/20,arxiv,0,1,mathematical model,0.001415129,0.001415144,0.14079041,0.609819757,0.245144444,0.001415117,Epidemiology,0.021289408,FALSE,44,0.578390748,28,0.554321648,4,0.707574542,,,0.613428979
10639,"Causal Network Models of SARS-CoV-2 Expression and Aging to Identify
  Candidates for Drug Repurposing",0,2006.03735,6/5/20,arxiv,0,7,"transcriptom, proteom, network model",0.99069089,0.001861752,0.001861864,0.001861825,0.001861826,0.001861843,Drug discovery,0.5565862,TRUE,25.28571429,0.371018616,16.57142857,0.443336901,2,0.618927094,,,0.47776087
10640,"Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report
  Generation",0,2006.03744,6/6/20,arxiv,0,4,dataset,0.001072231,0.001072258,0.994638632,0.001072246,0.001072382,0.001072251,Imaging,0.000888288,FALSE,50.25,0.633001422,29.25,0.564557131,0,0.403234768,,,0.533597774
10641,"A new estimation method for COVID-19 time-varying reproduction number
  using active cases",0,2006.03766,6/6/20,arxiv,0,7,"bayes, bayesian model",0.002130701,0.002130661,0.002130811,0.989346505,0.002130661,0.00213066,Epidemiology,0.15837091,FALSE,26.42857143,0.387160616,3.285714286,0.203973776,8,0.799987654,,,0.463707348
10642,"Multi-Task Temporal Shift Attention Networks for On-Device Contactless
  Vitals Measurement",0,2006.0379,6/6/20,arxiv,0,4,dataset,0.001565297,0.001565323,0.583057224,0.27936205,0.089113345,0.04533676,Epidemiology,0.2008704,FALSE,122,0.909889294,248.75,0.937851218,6,0.764429903,,,0.870723472
10643,Learning Diagnosis of COVID-19 from a Single Radiological Image,0,2006.1222,6/6/20,arxiv,0,5,"deep model, dataset",0.001220009,0.001220053,0.993899901,0.001220025,0.001220005,0.001220007,Imaging,0.003822535,FALSE,4,0.054734368,,,3,0.667819001,,,0.361276685
10644,"The Zoltar forecast archive: a tool to facilitate standardization and
  storage of interdisciplinary prediction research",0,2006.03922,6/6/20,arxiv,0,5,probabilistic,0.002183227,0.002183256,0.002183307,0.989083748,0.002183258,0.002183204,Epidemiology,0.042106777,FALSE,11,0.167171748,4.8,0.249331014,0,0.403234768,,,0.273245843
10645,"UMLS-ChestNet: A deep convolutional neural network for radiological
  findings, differential diagnoses and localizations of COVID-19 in chest
  x-rays",0,2006.05274,6/6/20,arxiv,0,10,"neural network, dataset",0.001593521,0.00159357,0.992032284,0.001593589,0.001593509,0.001593528,Imaging,0.024149656,FALSE,61.7,0.713402189,21,0.492239765,3,0.667819001,,,0.624486985
10646,"Projecting and comparing non-pharmaceutical interventions to contain
  COVID-19 in major economies",0,2006.04018,6/7/20,arxiv,0,5,model fit,0.001415112,0.001415132,0.001415139,0.992924339,0.001415162,0.001415115,Epidemiology,0.10009372,FALSE,25.6,0.375162348,3.4,0.208589778,0,0.403234768,,,0.328995631
10647,Interactive Extractive Search over Biomedical Corpora,0,2006.04148,6/7/20,arxiv,0,7,dataset,0.002238475,0.111043076,0.154317437,0.727924122,0.002238457,0.002238433,Epidemiology,0.3659035,FALSE,10,0.15214299,34.42857143,0.601284453,3,0.667819001,,,0.473748815
10648,Advance Warning Methodologies for COVID-19 using Chest X-Ray Images,"in IEEE Access, vol. 9, pp. 41052-41065, 2021",2006.05332,6/7/20,arxiv,"in IEEE Access, vol. 9, pp. 41052-41065, 2021",9,"machine learning, deep learning, classifier, dataset",0.001330025,0.001330122,0.970967647,0.023711698,0.001330234,0.001330275,Imaging,0.4386375,FALSE,42.11111111,0.558723483,26.88888889,0.54482205,3,0.667819001,,,0.590454845
10649,"Disinformation and Misinformation on Twitter during the Novel
  Coronavirus Outbreak",0,2006.04278,6/7/20,arxiv,0,2,dataset,0.002130712,0.002130668,0.002130682,0.989346456,0.002130814,0.002130669,Epidemiology,0.06893194,FALSE,342,0.993691632,597.5,0.983810543,14,0.866658436,,,0.948053537
10650,"Cross-Domain Segmentation with Adversarial Loss and Covariate Shift for
  Biomedical Imaging",0,2006.0439,6/8/20,arxiv,0,6,"deep learning, dataset",0.001291298,0.113535654,0.841317895,0.001291302,0.001291269,0.041272583,Imaging,0.018646091,FALSE,43.83333333,0.576287958,25.16666667,0.53017126,0,0.403234768,,,0.503231329
10651,"A Public Website for the Automated Assessment and Validation of
  SARS-CoV-2 Diagnostic PCR Assays",0,2006.04566,6/8/20,arxiv,0,11,bioinformatic,0.001901722,0.732012722,0.260380268,0.001901805,0.00190173,0.001901753,Genomics,0.42859602,FALSE,52.18181818,0.648277568,135.4545455,0.870149853,1,0.537564047,,,0.685330489
10652,"BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray
  dataset",0,2006.04603,6/8/20,arxiv,0,11,"supervised learning, deep learning, dataset",0.000907355,0.029746001,0.884675416,0.082856521,0.000907352,0.000907354,Imaging,0.17343917,FALSE,54.18181818,0.662316779,29.54545455,0.566162697,16,0.881782826,,,0.703420767
10653,Misinformation Has High Perplexity,0,2006.04666,6/8/20,arxiv,0,4,dataset,0.002639113,0.002639111,0.558997341,0.430445814,0.00263919,0.002639431,Epidemiology,0.001869857,FALSE,32,0.455810502,42.25,0.645838908,4,0.707574542,,,0.60307465
10654,"Collective response to the media coverage of COVID-19 Pandemic on Reddit
  and Wikipedia",0,2006.06446,6/8/20,arxiv,0,7,dataset,0.001254636,0.00125465,0.001254615,0.71958184,0.275399635,0.001254623,Epidemiology,0.1695649,FALSE,23.42857143,0.346836539,21.14285714,0.492841852,3,0.667819001,,,0.502499131
10655,"Machine Learning Automatically Detects COVID-19 using Chest CTs in a
  Large Multicenter Cohort",0,2006.04998,6/9/20,arxiv,0,18,"machine learning, deep learning, classifier, logistic regression",0.001187271,0.001187336,0.901655255,0.001187277,0.001187266,0.093595596,Imaging,0.07784158,FALSE,58.38888889,0.69212691,99.05555556,0.820511105,4,0.707574542,,,0.740070852
10656,"Deep learning to estimate the physical proportion of infected region of
  lung for COVID-19 pneumonia with CT image set",0,2006.05018,6/9/20,arxiv,0,8,"deep learning, classifier",0.000977483,0.000977473,0.714748128,0.23001076,0.000977501,0.052308655,Imaging,0.17090043,FALSE,41,0.549013544,41.25,0.64102221,2,0.618927094,,,0.602987616
10657,"CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection
  Risk Estimation Based on Contact Data",0,2006.04942,6/9/20,arxiv,0,3,probabilistic,0.001371266,0.001371357,0.097114294,0.848285796,0.050486003,0.001371284,Epidemiology,0.019773513,FALSE,127,0.917125363,921.3333333,0.992708055,3,0.667819001,,,0.859217473
10658,"Endpoints for randomized controlled clinical trials for COVID-19
  treatments",0,2006.10533,6/9/20,arxiv,0,15,simulation model,0.001126824,0.001126843,0.001126853,0.653494164,0.001126837,0.341998479,Epidemiology,0.4098496,FALSE,71.46666667,0.770424887,,,11,0.840175319,,,0.805300103
10659,"Predicting inhibitors for SARS-CoV-2 RNA-dependent RNA polymerase using
  machine learning and virtual screening",0,2006.06523,6/9/20,arxiv,0,3,"virtual screening, machine learning, computational, dataset",0.823775699,0.001112638,0.171773791,0.001112651,0.001112613,0.001112608,Drug discovery,0.61070555,TRUE,35,0.488032655,10.33333333,0.36038266,2,0.618927094,,,0.489114136
10660,A Review of Automated Diagnosis of COVID-19 Based on Scanning Images,0,2006.05245,6/9/20,arxiv,0,5,"machine learning, deep learning, transfer learning",0.001786557,0.001786757,0.991066351,0.001786765,0.001786713,0.001786857,Imaging,0.12617499,FALSE,16.6,0.250046385,31.6,0.582218357,2,0.618927094,,,0.483730612
10661,Deep learning of contagion dynamics on complex networks,0,2006.0541,6/9/20,arxiv,0,3,"deep learning, computational, neural network",0.001751274,0.001751196,0.409417438,0.583577809,0.001751146,0.001751136,Epidemiology,0.005753696,FALSE,57.33333333,0.68433422,23.33333333,0.515386674,0,0.403234768,,,0.534318554
10662,ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research,0,2006.05557,6/9/20,arxiv,0,4,dataset,0.001987131,0.001987124,0.13073147,0.861320017,0.001987139,0.001987119,Epidemiology,0.052729547,FALSE,67.25,0.748964067,169.75,0.899451432,18,0.891474782,,,0.846630094
10663,"Semiparametric Bayesian Inference for the Transmission Dynamics of
  COVID-19 with a State-Space Model",0,2006.05581,6/10/20,arxiv,0,2,"bayes, probabilistic, dataset",0.001684494,0.001684718,0.079147523,0.914114098,0.001684575,0.001684591,Epidemiology,0.16780227,FALSE,23,0.34225988,4,0.231469093,5,0.739490092,,,0.437739688
10664,"On a coupled time-dependent SIR models fitting with New York and
  New-Jersey states COVID-19 data",0,2006.05665,6/10/20,arxiv,0,2,model fit,0.001901723,0.001901711,0.001901704,0.99049132,0.001901773,0.00190177,Epidemiology,0.16633856,FALSE,59.5,0.699857752,44.5,0.656743377,7,0.785110192,,,0.713903774
10665,"Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory
  Sound Data",0,2006.05919,6/10/20,arxiv,0,9,"machine learning, classifier, dataset",0.001371346,0.001371309,0.806784471,0.085434883,0.001371453,0.103666538,Clinics,0.35706913,FALSE,66.11111111,0.741480611,113.7777778,0.843524217,37,0.942712513,,,0.842572447
10666,A predictive model for Covid-19 spread applied to eight US states,0,2006.05955,6/10/20,arxiv,0,3,predictive model,0.001461845,0.001461917,0.001461896,0.992690558,0.001461908,0.001461876,Epidemiology,0.11126885,FALSE,25.66666667,0.376461129,5.333333333,0.262911426,0,0.403234768,,,0.347535774
10667,Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes,"Proceedings of the 14th ACM International Conference on Web Search
  and Data Mining, 2021",2006.06167,6/11/20,arxiv,"Proceedings of the 14th ACM International Conference on Web Search
  and Data Mining, 2021",3,dataset,0.001786581,0.00178657,0.001786615,0.753834077,0.23901967,0.001786487,Epidemiology,0.10979235,FALSE,27,0.3960047,18.66666667,0.466818303,0,0.403234768,,,0.422019257
10668,"An Unsupervised Machine Learning Approach to Assess the ZIP Code Level
  Impact of COVID-19 in NYC",0,2006.08361,6/11/20,arxiv,0,7,machine learning,0.003214185,0.003214205,0.276028928,0.71111406,0.003214404,0.003214217,Epidemiology,0.1287179,FALSE,18.71428571,0.280103903,5.142857143,0.25802783,3,0.667819001,,,0.401983578
10669,"COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and
  CT image collection on COVID-19 from biomedical literature",0,2006.06177,6/11/20,arxiv,0,6,"image analysis, classifier, deep-learning, dataset",0.017595309,0.000966771,0.950721516,0.028782857,0.000966777,0.00096677,Imaging,0.24713495,FALSE,156.8333333,0.949161977,242.8333333,0.934974579,8,0.799987654,,,0.89470807
10670,"COVID-19 Mobility Data Collection of Seoul, South Korea",0,2006.08365,6/11/20,arxiv,0,7,dataset,0.001751266,0.00175125,0.001751242,0.991243526,0.001751358,0.001751358,Epidemiology,0.03117147,FALSE,28.71428571,0.41462057,27.28571429,0.547832486,2,0.618927094,,,0.527126717
10671,"The Number of Confirmed Cases of Covid-19 by using Machine Learning:
  Methods and Challenges",0,2006.09184,6/11/20,arxiv,0,6,machine learning,0.002996419,0.002996471,0.721107363,0.266906559,0.002996588,0.002996599,Epidemiology,0.36874342,FALSE,47.66666667,0.611787989,27.66666667,0.550909821,6,0.764429903,,,0.642375904
10672,"Improving performance of CNN to predict likelihood of COVID-19 using
  chest X-ray images with preprocessing algorithms","International Journal of Medical Informatics, 104284. 23 Sep. 2020",2006.12229,6/11/20,arxiv,"International Journal of Medical Informatics, 104284. 23 Sep. 2020",6,"neural network, transfer learning, dataset",0.00107218,0.001072209,0.994638989,0.001072197,0.001072198,0.001072225,Imaging,0.32249898,FALSE,27.92307692,0.405652792,24,0.521808938,22,0.908142478,,,0.611868069
10673,The hidden side of COVID-19 spread in Italy,Math Meth Appl Sci. (2020) 1-14,2006.08356,6/11/20,arxiv,Math Meth Appl Sci. (2020) 1-14,3,mathematical model,0.001943604,0.001943532,0.026514706,0.965711093,0.001943568,0.001943498,Epidemiology,0.17101726,FALSE,58.33333333,0.691941369,38,0.622223709,0,0.403234768,,,0.572466615
10674,"Targeted Pandemic Containment Through Identifying Local Contact Network
  Bottlenecks",0,2006.06939,6/12/20,arxiv,0,5,mathematical model,0.001901914,0.001901775,0.143452072,0.848940798,0.001901755,0.001901686,Epidemiology,0.014785826,FALSE,67.6,0.750572082,69,0.750535189,3,0.667819001,,,0.722975424
10675,Data-driven Simulation and Optimization for Covid-19 Exit Strategies,0,2006.07087,6/12/20,arxiv,0,8,"deep learning, mathematical model",0.001220001,0.018571287,0.184907152,0.774868405,0.019213154,0.001220001,Epidemiology,0.39975625,FALSE,108.875,0.887624467,280.125,0.948755686,7,0.785110192,,,0.873830115
10676,"Carbon Monitor: a near-real-time daily dataset of global CO2 emission
  from fossil fuel and cement production",0,2006.0769,6/13/20,arxiv,0,18,dataset,0.001254606,0.001254646,0.001254622,0.993726933,0.001254602,0.001254593,Epidemiology,0.60489357,TRUE,86.61904762,0.831838704,,,7,0.785110192,,,0.808474448
10677,Geospatial Spread of the COVID-19 Pandemic in Mexico,0,2006.07784,6/14/20,arxiv,0,4,dataset,0.003101659,0.003101663,0.003101645,0.984492111,0.003101494,0.003101428,Epidemiology,0.14901224,FALSE,2.25,0.023439916,0,0.055525823,1,0.537564047,,,0.205509929
10678,Support Estimation with Sampling Artifacts and Errors,0,2006.07999,6/14/20,arxiv,0,3,"machine learning, computational",0.001438147,0.319785483,0.346846741,0.329053371,0.001438153,0.001438105,Epidemiology,0.092193425,FALSE,94,0.854598305,130.3333333,0.86412898,0,0.403234768,,,0.707320684
10679,"COVID-19 dynamic model: Balanced identification of general biological
  and country specific social features",0,2006.0737,6/14/20,arxiv,0,2,mathematical model,0.082211074,0.002130692,0.12372152,0.787675384,0.002130683,0.002130647,Epidemiology,0.07880053,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
10680,"EDGE COVID-19: A Web Platform to generate submission-ready genomes for
  SARS-CoV-2 sequencing efforts",0,2006.08058,6/15/20,arxiv,0,11,"bioinformatic, sequencing, genomes",0.001593581,0.665387624,0.001593564,0.328238209,0.001593531,0.001593492,Genomics,0.5743821,TRUE,50.45454545,0.634980518,108.4545455,0.836968156,2,0.618927094,,,0.69695859
10681,"Public Willingness to Get Vaccinated Against COVID-19: How AI-Developed
  Vaccines Can Affect Acceptance",0,2006.08164,6/15/20,arxiv,0,4,artificial intelligence,0.125533341,0.003466039,0.107107426,0.294190028,0.466237273,0.003465893,Healthcare,0.020668,FALSE,51.75,0.644566764,248,0.937583623,0,0.403234768,,,0.661795051
10682,"Is Working From Home The New Norm? An Observational Study Based on a
  Large Geo-tagged COVID-19 Twitter Dataset",0,2006.08581,6/15/20,arxiv,0,2,dataset,0.001717248,0.001717233,0.001717305,0.99141381,0.001717235,0.001717168,Epidemiology,0.05009955,FALSE,25.5,0.37435834,1.5,0.138747659,7,0.785110192,,,0.43273873
10683,Document Classification for COVID-19 Literature,0,2006.13816,6/15/20,arxiv,0,5,dataset,0.002490501,0.002490502,0.52249511,0.467542856,0.002490521,0.002490511,Epidemiology,0.53846395,TRUE,27.2,0.397674562,14.2,0.414102221,4,0.707574542,,,0.506450442
10684,"Using Mobility for Electrical Load Forecasting During the COVID-19
  Pandemic",0,2006.08826,6/15/20,arxiv,0,3,"transfer learning, dataset",0.001059374,0.044122284,0.237835857,0.714863763,0.001059379,0.001059342,Epidemiology,0.004660159,FALSE,127.3333333,0.917620137,49.66666667,0.680157881,4,0.707574542,,,0.768450853
10685,"Optimisation of non-pharmaceutical measures in COVID-19 growth via
  neural networks",0,2006.08867,6/16/20,arxiv,0,4,"neural network, network model",0.031823952,0.001371413,0.027763867,0.936298262,0.001371278,0.001371228,Epidemiology,0.16537023,FALSE,25.75,0.377698064,12.25,0.388546963,1,0.537564047,,,0.434603025
10686,"Lio -- A Personal Robot Assistant for Human-Robot Interaction and Care
  Applications",0,2006.09019,6/16/20,arxiv,0,11,artificial intelligence,0.00123715,0.001237093,0.419513542,0.29109155,0.185957112,0.100963552,Epidemiology,0.45248663,FALSE,39,0.530521368,22.90909091,0.511038266,6,0.764429903,,,0.601996512
10687,"Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest
  CT Images",0,2006.13276,6/16/20,arxiv,0,5,"deep learning, dataset",0.001438105,0.001438203,0.992809194,0.001438172,0.001438217,0.001438109,Imaging,0.55078244,TRUE,137.6,0.929989486,80.2,0.779502275,14,0.866658436,,,0.858716732
10688,A small molecule drug candidate targeting SARS-CoV-2 main protease,0,2006.09125,6/16/20,arxiv,0,1,computational,0.931025313,0.060451758,0.002130697,0.002130799,0.002130771,0.002130664,Drug discovery,0.7671152,TRUE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
10689,"Network model and analysis of the spread of Covid-19 with social
  distancing",0,2006.09189,6/16/20,arxiv,0,2,network model,0.170918637,0.001392875,0.001392857,0.823509917,0.001392886,0.001392828,Epidemiology,0.26900947,FALSE,4,0.054734368,0,0.055525823,1,0.537564047,,,0.215941413
10690,Digital Contact Tracing Using IP Colocation,0,2006.09326,6/16/20,arxiv,0,3,dataset,0.001486436,0.001486435,0.001486498,0.992567729,0.001486489,0.001486414,Epidemiology,0.008041561,FALSE,11.33333333,0.170635166,23,0.513513514,0,0.403234768,,,0.362461149
10691,Adaptive County Level COVID-19 Forecast Models: Analysis and Improvement,0,2006.12617,6/16/20,arxiv,0,5,"neural network, lstm",0.019205117,0.001291207,0.311999093,0.664922141,0.001291217,0.001291226,Epidemiology,0.027237803,FALSE,15.2,0.229142186,9.6,0.346735349,1,0.537564047,,,0.371147194
10692,"COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep
  Learning",0,2006.13807,6/16/20,arxiv,0,5,"neural network, transfer learning, dataset",0.001622739,0.001622786,0.991886231,0.001622761,0.001622729,0.001622755,Imaging,0.10043225,FALSE,11.66666667,0.176510607,0.333333333,0.073187048,13,0.858880178,,,0.369525944
10693,"CO-Search: COVID-19 Information Retrieval with Semantic Search, Question
  Answering, and Abstractive Summarization",0,2006.09595,6/17/20,arxiv,0,7,"information retrieval, dataset",0.002032833,0.002032771,0.433089355,0.504915196,0.055897049,0.002032796,Epidemiology,0.004151136,FALSE,45.85714286,0.594656441,1591.857143,0.997056462,20,0.900117291,,,0.830610065
10694,"Exact and computationally efficient Bayesian inference for generalized
  Markov modulated Poisson processes",0,2006.09949,6/17/20,arxiv,0,3,"bayes, computational",0.001593613,0.001593548,0.273177861,0.720447862,0.001593507,0.001593608,Epidemiology,0.047391087,FALSE,6.333333333,0.089863319,2,0.164302917,0,0.403234768,,,0.219133668
10695,PECAIQR: A Model for Infectious Disease Applied to the Covid-19 Epidemic,0,2006.13693,6/17/20,arxiv,0,4,forecasting model,0.001187254,0.001187262,0.089116084,0.831017744,0.00118729,0.076304366,Epidemiology,0.047856033,FALSE,3.5,0.044344115,1.25,0.127776291,0,0.403234768,,,0.191785058
10696,"Are you wearing a mask? Improving mask detection from speech using
  augmentation by cycle-consistent GANs",0,2006.10147,6/17/20,arxiv,0,2,"computational, neural network, classifier, adversarial network",0.001751295,0.001751206,0.590143727,0.402851124,0.001751486,0.001751163,Epidemiology,0.3455888,FALSE,52,0.647349867,67.5,0.746855767,4,0.707574542,,,0.700593392
10697,"Genetic Programming visitation scheduling solution can deliver a less
  austere COVID-19 pandemic population lockdown",0,2006.10748,6/17/20,arxiv,0,1,computational,0.001438147,0.049232304,0.132459994,0.626729866,0.133719841,0.056419847,Epidemiology,0.061318874,FALSE,107,0.883357041,30,0.570176612,0,0.403234768,,,0.618922807
10698,Small Area Estimation of Health Outcomes,0,2006.10266,6/18/20,arxiv,0,3,bayes,0.002996787,0.002996772,0.00299675,0.808006626,0.180006448,0.002996618,Epidemiology,0.059659123,FALSE,58.66666667,0.694106005,118.3333333,0.849344394,3,0.667819001,,,0.7370898
10699,"Mathematical modeling and prediction of COVID-19 in Moscow city and
  Novosibirsk region",0,2006.12619,6/18/20,arxiv,0,6,mathematical model,0.001461936,0.106792144,0.001461971,0.809708428,0.001461932,0.079113589,Epidemiology,0.43634462,FALSE,69.45454545,0.760653102,12.54545455,0.393029168,0,0.403234768,,,0.518972346
10700,"Role of Edge Device and Cloud Machine Learning in Point-of-Care
  Solutions Using Imaging Diagnostics for Population Screening",0,2006.13808,6/18/20,arxiv,0,11,"machine learning, artificial intelligence",0.001565285,0.001565284,0.992173398,0.001565366,0.00156533,0.001565337,Imaging,0.20910019,FALSE,15.54545455,0.234399159,9.818181818,0.35094996,0,0.403234768,,,0.329527962
10701,The Weather Impacts the Outbreak of COVID-19 in Mainland China,0,2006.10376,6/18/20,arxiv,0,6,"predictive model, correlation analysis",0.002422271,0.002422354,0.002422315,0.946493163,0.002422406,0.043817492,Epidemiology,0.22254056,FALSE,59.16666667,0.697322036,51.66666667,0.687918116,1,0.537564047,,,0.640934733
10702,"An effective approach to reduce the penetration potential of Sars-Cov-2
  and other viruses by spike protein: Through surface particle electrostatic
  charge negotiation",0,2006.10603,6/18/20,arxiv,0,4,mathematical model,0.424832459,0.242097968,0.05355917,0.276323212,0.001593508,0.001593683,Drug discovery,0.5891952,TRUE,6.25,0.088564537,1.5,0.138747659,1,0.537564047,,,0.254958748
10703,"Modeling indoor-level non-pharmaceutical interventions during the
  COVID-19 pandemic: a pedestrian dynamics-based microscopic simulation
  approach",0,2006.10666,6/18/20,arxiv,0,6,mathematical model,0.001220066,0.076531806,0.001220164,0.918587792,0.001220137,0.001220034,Epidemiology,0.011701584,FALSE,29.5,0.426000371,18.83333333,0.468624565,3,0.667819001,,,0.520814646
10704,"Uncertainty quantification for epidemiological forecasts of COVID-19
  through combinations of model predictions",0,2006.10714,6/18/20,arxiv,0,4,computational,0.059858762,0.001684513,0.103443943,0.72313397,0.001684499,0.110194312,Epidemiology,0.12358108,FALSE,33.75,0.473622364,40.25,0.634934439,2,0.618927094,,,0.575827966
10705,Pervasive Communications Technologies For Managing Pandemics,0,2006.10805,6/18/20,arxiv,0,2,artificial intelligence,0.001751164,0.001751218,0.382882214,0.515512039,0.09635221,0.001751154,Epidemiology,0.44757795,FALSE,33.5,0.471890655,13,0.400521809,0,0.403234768,,,0.425215744
10706,"COVIDLite: A depth-wise separable deep neural network with white balance
  and CLAHE for detection of COVID-19",0,2006.13873,6/19/20,arxiv,0,2,neural network,0.001098848,0.001098849,0.944849566,0.00109889,0.001098832,0.050755015,Imaging,0.18196201,FALSE,7,0.10179974,0,0.055525823,5,0.739490092,,,0.298938552
10707,"A Qualitative Evaluation of Language Models on Automatic
  Question-Answering for COVID-19",0,2006.10964,6/19/20,arxiv,0,2,"transfer learning, dataset",0.058128356,0.00107222,0.277656914,0.600310212,0.061760127,0.00107217,Epidemiology,0.049182177,FALSE,1.5,0.015523533,,,2,0.618927094,,,0.317225313
10708,"A Bayesian Updating Scheme for Pandemics: Estimating the Infection
  Dynamics of COVID-19",0,2006.12177,6/19/20,arxiv,0,8,bayes,0.001943455,0.001943483,0.0019435,0.990282653,0.00194346,0.00194345,Epidemiology,0.14548448,FALSE,57,0.68204589,33,0.593256623,3,0.667819001,,,0.647707171
10709,Counting Risk Increments to Make Decisions During an Epidemic,0,2006.11244,6/19/20,arxiv,0,1,probabilistic,0.001156266,0.001156278,0.001156264,0.873518543,0.121856394,0.001156255,Epidemiology,0.07686558,FALSE,90,0.843094811,172,0.901190795,1,0.537564047,,,0.760616551
10710,Proper scoring rules for evaluating asymmetry in density forecasting,0,2006.11265,6/19/20,arxiv,0,3,"probabilistic, dataset",0.002080558,0.002080568,0.273359522,0.71831803,0.002080644,0.002080677,Epidemiology,0.3047747,FALSE,64,0.729173109,65.33333333,0.740032111,0,0.403234768,,,0.624146663
10711,"Computational model on COVID-19 Pandemic using Probabilistic Cellular
  Automata",0,2006.1127,6/19/20,arxiv,0,2,"computational, probabilistic",0.071122651,0.001511887,0.001512138,0.897218067,0.027123351,0.001511905,Epidemiology,0.13491136,FALSE,26.5,0.389325252,4,0.231469093,1,0.537564047,,,0.386119464
10712,"FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for
  COVID-19",0,2006.11343,6/19/20,arxiv,0,2,"classifier, dataset",0.002183213,0.002183252,0.516222367,0.475044747,0.002183231,0.002183191,Epidemiology,0.14056966,FALSE,20.5,0.304966294,5.5,0.267259834,20,0.900117291,,,0.49078114
10713,"Study of lockdown/testing mitigation strategies on stochastic SIR model
  and its comparison with South Korea, Germany and New York data",0,2006.14373,6/20/20,arxiv,0,2,mathematical model,0.001684532,0.001684499,0.001684554,0.991577386,0.001684526,0.001684504,Epidemiology,0.055276513,FALSE,14,0.213494959,0,0.055525823,2,0.618927094,,,0.295982626
10714,"Finding Patient Zero: Learning Contagion Source with Graph Neural
  Networks",0,2006.11913,6/21/20,arxiv,0,7,neural network,0.001653044,0.001653074,0.265669219,0.727718471,0.001653046,0.001653147,Epidemiology,0.008641839,FALSE,80.42857143,0.808460635,287.5714286,0.950762644,5,0.739490092,,,0.832904457
10715,COVID-19 Image Data Collection: Prospective Predictions Are the Future,0,2006.11988,6/22/20,arxiv,0,6,"machine learning, dataset",0.001717192,0.001717202,0.648477214,0.001717357,0.001717248,0.344653787,Imaging,0.17021981,FALSE,47.16666667,0.607087637,64.5,0.736887878,106,0.979628372,,,0.774534629
10716,"Did the lockdown curb the spread of COVID-19 infection rate in India: A
  data-driven analysis",0,2006.12006,6/22/20,arxiv,0,2,bayes,0.00133002,0.00133002,0.001330031,0.993349862,0.001330031,0.001330036,Epidemiology,0.35457033,FALSE,18.5,0.278001113,6,0.280037463,0,0.403234768,,,0.320424448
10717,"Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease
  from X-ray Images",0,2006.13817,6/22/20,arxiv,0,2,"neural network, network model, logistic regression, dataset",0.001203405,0.001203417,0.993982978,0.001203402,0.001203387,0.001203412,Imaging,0.0787102,FALSE,10.5,0.157338116,0.5,0.087101953,7,0.785110192,,,0.343183421
10718,"Does Non-COVID19 Lung Lesion Help? Investigating Transferability in
  COVID-19 CT Image Segmentation",0,2006.13877,6/23/20,arxiv,0,8,"deep learning, transfer learning, dataset",0.000956315,0.000956306,0.995218442,0.000956314,0.000956318,0.000956305,Imaging,0.26030236,FALSE,41.25,0.5508071,,,3,0.667819001,,,0.609313051
10719,"An Efficient Index for Contact Tracing Query in a Large Spatio-Temporal
  Database",0,2006.12812,6/23/20,arxiv,0,3,dataset,0.027733016,0.001538193,0.001538228,0.966114257,0.001538142,0.001538164,Epidemiology,0.015861213,FALSE,34.66666667,0.483703383,11,0.371287129,1,0.537564047,,,0.464184853
10720,"A self-supervised neural-analytic method to predict the evolution of
  COVID-19 in Romania",0,2006.12926,6/23/20,arxiv,0,5,"machine learning, computational",0.001126797,0.019007081,0.250848495,0.726763947,0.001126823,0.001126857,Epidemiology,0.017368495,FALSE,74.25,0.782856083,128.5,0.86178753,2,0.618927094,,,0.754523569
10721,"Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using
  Self-supervised Edge Features and Graph Neural Networks",0,2006.12971,6/23/20,arxiv,0,3,"supervised learning, deep learning, neural network, sequencing, transcriptom,  omics, dataset",0.400493024,0.001291371,0.540461635,0.00129127,0.05517138,0.00129132,Drug discovery,0.09817156,FALSE,33,0.466757375,21.66666667,0.499732406,2,0.618927094,,,0.528472292
10722,"A Deep Learning Pipeline for Patient Diagnosis Prediction Using
  Electronic Health Records","BIOKDD 2020 at the ACM SIGKDD Conference on Knowledge Discovery
  and Data Mining (KDD) 2020",2006.16926,6/23/20,arxiv,"BIOKDD 2020 at the ACM SIGKDD Conference on Knowledge Discovery
  and Data Mining (KDD) 2020",3,"machine learning, deep learning, dataset",0.001171568,0.001171595,0.88772607,0.107587589,0.001171608,0.001171571,Epidemiology,0.13244,FALSE,18.33333333,0.274908776,8,0.320511105,2,0.618927094,,,0.404782325
10723,Differentiable Segmentation of Sequences,International Conference on Learning Representations (ICLR) 2021,2006.13105,6/23/20,arxiv,International Conference on Learning Representations (ICLR) 2021,3,deep learning,0.026131617,0.029869118,0.628924278,0.312454259,0.001310388,0.001310339,Epidemiology,0.003791064,FALSE,41,0.549013544,107.3333333,0.835094996,1,0.537564047,,,0.640557529
10724,Rapid Response Crop Maps in Data Sparse Regions,0,2006.16866,6/23/20,arxiv,0,8,"machine learning, predictive model",0.001593668,0.001593566,0.335958693,0.657666726,0.001593859,0.00159349,Epidemiology,0.27711117,FALSE,18.375,0.275279857,13.5,0.405539203,1,0.537564047,,,0.406127702
10725,"Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with
  Similar Indications",0,2006.13262,6/23/20,arxiv,0,8,"deep learning, image processing, dataset",0.00162272,0.001622738,0.880330113,0.00162277,0.001622795,0.113178865,Imaging,0.017814934,FALSE,27.125,0.396561321,8.25,0.323789136,2,0.618927094,,,0.44642585
10726,Self-supervised edge features for improved Graph Neural Network training,0,2007.04777,6/23/20,arxiv,0,3,"supervised learning, unsupervised learning, neural network, sequencing, dataset",0.236541444,0.038863027,0.720831507,0.001254662,0.001254645,0.001254714,Drug discovery,0.02532962,FALSE,28.66666667,0.414125796,16,0.437316029,0,0.403234768,,,0.418225531
10727,Inference in Stochastic Epidemic Models via Multinomial Approximations,0,2006.137,6/24/20,arxiv,0,2,"bayes, computational",0.002638993,0.002639018,0.113944248,0.875499671,0.00263902,0.00263905,Epidemiology,0.028460383,FALSE,32,0.455810502,25,0.529435376,1,0.537564047,,,0.507603308
10728,"A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19
  Infection from Chest CT-Scan",0,2006.14419,6/24/20,arxiv,0,3,"deep learning, neural network",0.001438105,0.001438135,0.871560876,0.08074426,0.043380534,0.00143809,Imaging,0.011088997,FALSE,7.333333333,0.105572392,5.666666667,0.270203372,6,0.764429903,,,0.380068555
10729,"Quantifying Policy Responses to a Global Emergency: Insights from the
  COVID-19 Pandemic",0,2006.13853,6/24/20,arxiv,0,4,dataset,0.001141416,0.001141364,0.001141369,0.994293187,0.001141342,0.001141322,Epidemiology,0.2251037,FALSE,10,0.15214299,3.25,0.203572384,1,0.537564047,,,0.297759807
10730,"Automated Chest CT Image Segmentation of COVID-19 Lung Infection based
  on 3D U-Net",0,2007.04774,6/24/20,arxiv,0,3,"computational, image analysis, dataset",0.001098843,0.03958433,0.956020267,0.001098877,0.001098868,0.001098814,Imaging,0.4222105,FALSE,25.66666667,0.376461129,10.33333333,0.36038266,7,0.785110192,,,0.507317994
10731,Mobile smartphone tracing can detect almost all SARS-CoV-2 infections,0,2006.14285,6/25/20,arxiv,0,2,bayes,0.002357688,0.00235793,0.002357931,0.865415639,0.125153047,0.002357766,Epidemiology,0.026398748,FALSE,176,0.962644567,327,0.961265721,1,0.537564047,,,0.820491445
10732,"TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about
  the COVID-19 Pandemic",0,2006.14492,6/25/20,arxiv,0,7,"computational, dataset",0.00186178,0.001861769,0.427372553,0.505590951,0.061451273,0.001861674,Epidemiology,0.14017299,FALSE,67,0.748160059,24.14285714,0.522411025,10,0.828199272,,,0.699590119
10733,"Forecasting the daily and cumulative number of cases for the COVID-19
  pandemic in India",0,2006.14575,6/25/20,arxiv,0,2,mathematical model,0.072494956,0.002032767,0.002032752,0.919374034,0.002032758,0.002032733,Epidemiology,0.6736831,TRUE,18,0.271569052,6,0.280037463,27,0.92443978,,,0.492015432
10734,The State of AI Ethics Report (June 2020),0,2006.14662,6/25/20,arxiv,0,9,artificial intelligence,0.001751294,0.001751206,0.396464006,0.570529573,0.027752783,0.001751139,Epidemiology,0.048613608,FALSE,22.55555556,0.33409611,16.66666667,0.444674873,1,0.537564047,,,0.438778343
10735,"Lest We Forget: A Dataset of Coronavirus-Related News Headlines in Swiss
  Media",0,2006.16967,6/25/20,arxiv,0,2,dataset,0.003760595,0.003760709,0.003760877,0.981196612,0.003760621,0.003760585,Epidemiology,0.17546374,FALSE,25,0.369286907,31.5,0.581883864,0,0.403234768,,,0.451468513
10736,Machine-Learning Driven Drug Repurposing for COVID-19,0,2006.14707,6/25/20,arxiv,0,4,"machine learning, bioinformatic, in-silico, network model",0.711581521,0.086029592,0.198337633,0.001350426,0.00135041,0.001350418,Drug discovery,0.12992075,FALSE,28.25,0.410105758,6.75,0.292079208,3,0.667819001,,,0.456667989
10737,"Prediction of the Number of COVID-19 Confirmed Cases Based on
  K-Means-LSTM",0,2006.14752,6/26/20,arxiv,0,4,"neural network, prediction model, lstm",0.001461883,0.001461956,0.257892449,0.661820164,0.001461969,0.075901579,Epidemiology,0.1241526,FALSE,14.75,0.222586431,4.25,0.235750602,5,0.739490092,,,0.399275708
10738,"A Case Study in Model Failure? COVID-19 Daily Deaths and ICU Bed
  Utilisation Predictions in New York State",0,2006.15997,6/26/20,arxiv,0,7,forecasting model,0.00133001,0.001330021,0.261711299,0.703706243,0.001330028,0.030592399,Epidemiology,0.10289496,FALSE,91.28571429,0.846991156,353.5714286,0.966283115,12,0.850299401,,,0.887857891
10739,Drug Repurposing to find Inhibitors of SARS-CoV-2 Main Protease,0,2006.1479,6/26/20,arxiv,0,5,virtual screening,0.914265613,0.00139297,0.060227374,0.001392869,0.021328267,0.001392907,Drug discovery,0.17394361,FALSE,21.8,0.322283382,0.6,0.09011239,0,0.403234768,,,0.271876846
10740,"An Interactive Data Visualization and Analytics Tool to Evaluate
  Mobility and Sociability Trends During COVID-19",0,2006.14882,6/26/20,arxiv,0,8,data mining,0.002490436,0.002490486,0.056805638,0.933232549,0.002490469,0.002490421,Epidemiology,0.09097707,FALSE,71.75,0.771909209,36.125,0.61178753,10,0.828199272,,,0.73729867
10741,"A Measurement of Transportation Ban inside Wuhan on the COVID-19
  Epidemic by Vehicle Detection in Remote Sensing Imagery",0,2006.16098,6/26/20,arxiv,0,6,"deep learning, dataset",0.001717198,0.001717276,0.177660383,0.815470726,0.00171722,0.001717198,Epidemiology,0.1286681,FALSE,123,0.911806543,251.6666667,0.938520203,1,0.537564047,,,0.795963598
10742,"4S-DT: Self Supervised Super Sample Decomposition for Transfer learning
  with application to COVID-19 detection",0,2007.1145,6/26/20,arxiv,0,3,"supervised learning, neural network, transfer learning, dataset",0.000916702,0.000916696,0.957267409,0.000916685,0.039065795,0.000916712,Imaging,0.13765532,FALSE,5.666666667,0.079473066,0.333333333,0.073187048,5,0.739490092,,,0.297383402
10743,"COVID-19 Screening Using Residual Attention Network an Artificial
  Intelligence Approach",0,2006.16106,6/26/20,arxiv,0,2,"artificial intelligence, neural network, dataset",0.001415148,0.001415163,0.872424537,0.12191488,0.001415143,0.001415129,Imaging,0.36242706,FALSE,108.5,0.886882306,117,0.847002944,0,0.403234768,,,0.712373339
10744,"Spatio-temporal predictive modeling framework for infectious disease
  spread",0,2006.15336,6/27/20,arxiv,0,2,predictive model,0.001861764,0.001861735,0.00186198,0.916399084,0.001861686,0.076153751,Epidemiology,0.25736052,FALSE,49.5,0.628362917,48,0.673735617,2,0.618927094,,,0.640341876
10745,Individual-level Modeling of COVID-19 Epidemic Risk,0,2006.16761,6/28/20,arxiv,0,4,probabilistic,0.002720335,0.002720231,0.002720171,0.808017077,0.002720334,0.181101852,Epidemiology,0.10385549,FALSE,74.25,0.782856083,438,0.973976452,0,0.403234768,,,0.720022434
10746,"Human Mobility during COVID-19 in the Context of Mild Social Distancing:
  Implications for Technological Interventions",0,2006.16965,6/28/20,arxiv,0,4,dataset,0.001330063,0.001330075,0.001330049,0.993349637,0.001330065,0.00133011,Epidemiology,0.11560485,FALSE,124.75,0.913847486,68.25,0.748461333,1,0.537564047,,,0.733290955
10747,Answering Questions on COVID-19 in Real-Time,0,2006.1583,6/29/20,arxiv,0,8,"information retrieval, text mining, dataset",0.002562629,0.002562605,0.419913905,0.569835583,0.002562754,0.002562525,Epidemiology,0.030031234,FALSE,29,0.41993939,76.625,0.77027027,6,0.764429903,,,0.651546521
10748,"Using mobility data in the design of optimal lockdown strategies for the
  COVID-19 pandemic",0,2006.16059,6/29/20,arxiv,0,4,"bayes, computational, mathematical model, dataset",0.002898414,0.002898304,0.002898634,0.985507929,0.002898297,0.002898423,Epidemiology,0.25407147,FALSE,12.75,0.191786752,1.75,0.148381054,1,0.537564047,,,0.292577285
10749,"Modeling and Computation of High Efficiency and Efficacy Multi-Step
  Batch Testing for Infectious Diseases",0,2006.16079,6/29/20,arxiv,0,3,mathematical model,0.001786542,0.00178653,0.316218993,0.676634842,0.001786557,0.001786535,Epidemiology,0.120857656,FALSE,47.33333333,0.608633805,26.66666667,0.543216484,0,0.403234768,,,0.518361686
10750,"Estimation of Covid-19 Prevalence from Serology Tests: A Partial
  Identification Approach",0,2006.16214,6/29/20,arxiv,0,1,dataset,0.001717178,0.215625564,0.073569136,0.602930872,0.104440046,0.001717204,Epidemiology,0.22218242,FALSE,47,0.606407323,49,0.677481937,6,0.764429903,,,0.682773054
10751,"On the derivation of the renewal equation from an age-dependent
  branching process: an epidemic modelling perspective",0,2006.16487,6/30/20,arxiv,0,9,bayes,0.002357737,0.002357756,0.00235777,0.988211328,0.002357706,0.002357704,Epidemiology,0.19885516,FALSE,59.55555556,0.700043293,216.4444444,0.925541879,6,0.764429903,,,0.796671691
10752,"Autoregressive Mixture Models for Serial Correlation Clustering of Time
  Series Data",0,2006.16539,6/30/20,arxiv,0,2,model fit,0.002238459,0.002238644,0.002238773,0.988806856,0.002238636,0.002238631,Epidemiology,0.01679203,FALSE,3,0.037293586,2.5,0.180826866,0,0.403234768,,,0.207118407
10753,"Evaluation of Contemporary Convolutional Neural Network Architectures
  for Detecting COVID-19 from Chest Radiographs",0,2007.01108,6/30/20,arxiv,0,1,"deep learning, neural network, deep model",0.001861684,0.027051456,0.965501676,0.001861716,0.001861746,0.001861722,Imaging,0.006371886,FALSE,1,0.012307502,0,0.055525823,1,0.537564047,,,0.201799124
10754,"Using A Partial Differential Equation with Google Mobility Data to
  Predict COVID-19 in Arizona","Mathematical Biosciences and Engineering, 2020, 17(5): 4891-4904",2006.16928,6/30/20,arxiv,"Mathematical Biosciences and Engineering, 2020, 17(5): 4891-4904",2,forecasting model,0.001622743,0.001622735,0.001622868,0.991886179,0.001622795,0.00162268,Epidemiology,0.7246314,TRUE,6.5,0.093512277,0,0.055525823,12,0.850299401,,,0.3331125
10755,"Do not forget interaction: Predicting fatality of COVID-19 patients
  using logistic regression",0,2006.16942,6/30/20,arxiv,0,3,"machine learning, logistic regression",0.031167464,0.001943498,0.264445141,0.14294385,0.001943492,0.557556555,Clinics,0.209746,FALSE,48,0.614942173,30,0.570176612,2,0.618927094,,,0.601348627
10756,"Disease Detectives: Using Mathematics to Forecast the Spread of
  Infectious Diseases",0,2007.05495,6/30/20,arxiv,0,4,mathematical model,0.003607267,0.00360733,0.003607184,0.847717655,0.137853438,0.003607126,Epidemiology,0.39682236,FALSE,10,0.15214299,0.25,0.065493712,0,0.403234768,,,0.206957156
10757,Monitoring Depression Trend on Twitter during the COVID-19 Pandemic,0,2007.00228,7/1/20,arxiv,0,6,"classifier, dataset",0.001392855,0.001392873,0.342164787,0.303256234,0.350400411,0.00139284,Healthcare,0.123679906,FALSE,141.5,0.936050467,,,5,0.739490092,,,0.837770279
10758,"On the Minimization of Sobolev Norms of Time-Varying Graph Signals:
  Estimation of New Coronavirus Disease 2019 Cases",0,2007.00336,7/1/20,arxiv,0,2,mathematical model,0.002422351,0.002422323,0.332124049,0.658186679,0.002422305,0.002422293,Epidemiology,0.13268325,FALSE,21,0.312016822,11,0.371287129,0,0.403234768,,,0.362179573
10759,"Supporting Real-Time COVID-19 Medical Management Decisions: The
  Transition Matrix Model Approach",0,2007.01201,7/1/20,arxiv,0,4,forecasting model,0.001461855,0.001461876,0.001461969,0.830405763,0.028594369,0.136614168,Epidemiology,0.07132855,FALSE,38.5,0.524089307,24,0.521808938,1,0.537564047,,,0.527820764
10760,"COVID-19 Literature Knowledge Graph Construction and Drug Repurposing
  Report Generation",0,2007.00576,7/1/20,arxiv,0,26,knowledge graph,0.352131227,0.002562696,0.186281835,0.453898843,0.002562819,0.002562579,Epidemiology,0.1861549,FALSE,68.36,0.755705362,199.6,0.91744715,20,0.900117291,,,0.857756601
10761,"A Semantic Web Framework for Automated Smart Assistants: COVID-19 Case
  Study",0,2007.00747,7/1/20,arxiv,0,2,deep learning,0.070918907,0.001330062,0.441139589,0.483951279,0.001330102,0.001330061,Epidemiology,0.11311695,FALSE,41.5,0.553219123,1.5,0.138747659,1,0.537564047,,,0.40984361
10762,"An Early Warning Approach to Monitor COVID-19 Activity with Multiple
  Digital Traces in Near Real-Time",0,2007.00756,7/1/20,arxiv,0,17,"bayes, bayesian model",0.001254622,0.001254634,0.001254656,0.970911013,0.001254648,0.024070427,Epidemiology,0.37761873,FALSE,66.41176471,0.743088626,184.0588235,0.90895103,18,0.891474782,,,0.847838146
10763,"Automatic Detection of COVID-19 Cases on X-ray images Using
  Convolutional Neural Networks",0,2007.05494,7/2/20,arxiv,0,2,"deep learning, neural network",0.00148646,0.00148649,0.835102335,0.158951805,0.00148647,0.001486441,Imaging,0.11605534,FALSE,6,0.086028821,0,0.055525823,5,0.739490092,,,0.293681578
10764,An encoder-decoder-based method for COVID-19 lung infection segmentation,0,2007.00861,7/2/20,arxiv,0,3,deep-learning,0.001171537,0.001171545,0.994142268,0.00117158,0.001171545,0.001171525,Imaging,0.001717329,FALSE,16.66666667,0.251159626,10.33333333,0.36038266,5,0.739490092,,,0.450344126
10765,"System inference for the spatio-temporal evolution of infectious
  diseases: Michigan in the time of COVID-19",0,2007.00865,7/2/20,arxiv,0,5,"bayes, machine learning",0.003927386,0.003927554,0.121275687,0.86301464,0.003927377,0.003927356,Epidemiology,0.26587808,FALSE,92.4,0.850083493,34.2,0.60001338,10,0.828199272,,,0.759432048
10766,Zooming Into Video Conferencing Privacy and Security Threats,0,2007.01059,7/2/20,arxiv,0,3,"image processing, network analysis, dataset",0.001203477,0.001203434,0.357314527,0.180248945,0.45882621,0.001203407,Healthcare,0.006041765,FALSE,24.33333333,0.359700662,26.33333333,0.540607439,1,0.537564047,,,0.479290716
10767,"Bridging the COVID-19 Data and the Epidemiological Model using Time
  Varying Parameter SIRD Model",0,2007.02726,7/3/20,arxiv,0,2,computational,0.00162277,0.069070058,0.104315283,0.821746307,0.001622799,0.001622783,Epidemiology,0.011629313,FALSE,21.5,0.318263343,5.5,0.267259834,4,0.707574542,,,0.431032573
10768,"Impact of COVID-19 on Forecasting Stock Prices: An Integration of
  Stationary Wavelet Transform and Bidirectional Long Short-Term Memory",0,2007.02673,7/3/20,arxiv,0,6,lstm,0.003214121,0.003214083,0.003214375,0.937616396,0.049526793,0.003214232,Epidemiology,0.70084417,TRUE,10.16666667,0.152946997,0.166666667,0.058736955,6,0.764429903,,,0.325371285
10769,"Exploration and Discovery of the COVID-19 Literature through Semantic
  Visualization",0,2007.018,7/3/20,arxiv,0,4,"knowledge graph, dataset",0.217399461,0.001786561,0.290119403,0.487121503,0.001786555,0.001786517,Epidemiology,0.003819436,FALSE,132.75,0.9249799,452.25,0.975715815,10,0.828199272,,,0.909631662
10770,"A computational approach to aid clinicians in selecting anti-viral drugs
  for COVID-19 trials",0,2007.01902,7/3/20,arxiv,0,4,"computational, genomic structure, dataset",0.667770163,0.0581096,0.240619534,0.001141407,0.001141439,0.031217857,Drug discovery,0.295816,FALSE,33.5,0.471890655,21.5,0.498260637,1,0.537564047,,,0.50257178
10771,Coronavirus Knowledge Graph: A Case Study,0,2007.10287,7/4/20,arxiv,0,4,"machine learning, deep learning, knowledge graph, dataset",0.228975782,0.074237085,0.503708347,0.189355313,0.001861809,0.001861664,Drug discovery,0.07448852,FALSE,26.5,0.389325252,9,0.337904736,8,0.799987654,,,0.509072547
10772,"Dynamic tracking with model-based forecasting for the spread of the
  COVID-19 pandemic",0,2007.02032,7/4/20,arxiv,0,3,mathematical model,0.001786621,0.046034534,0.00178655,0.946819182,0.00178653,0.001786582,Epidemiology,0.27311265,FALSE,107.3333333,0.884161049,44,0.654401927,3,0.667819001,,,0.735460659
10773,"A Weakly Supervised Consistency-based Learning Method for COVID-19
  Segmentation in CT Images",0,2007.0218,7/4/20,arxiv,0,9,dataset,0.001461947,0.001462004,0.992689758,0.001461993,0.001462221,0.001462077,Imaging,0.000623435,FALSE,76.55555556,0.792256788,42.22222222,0.645638212,6,0.764429903,,,0.734108301
10774,"A Survey on Applications of Artificial Intelligence in Fighting Against
  COVID-19",0,2007.02202,7/4/20,arxiv,0,5,artificial intelligence,0.097831105,0.001684523,0.55040912,0.268230044,0.080160736,0.001684473,Imaging,0.23539597,FALSE,408.6,0.995979962,1360.6,0.996521274,8,0.799987654,,,0.93082963
10775,Experiments of Federated Learning for COVID-19 Chest X-ray Images,0,2007.05592,7/5/20,arxiv,0,5,deep learning,0.001823347,0.001823405,0.990882977,0.001823454,0.001823431,0.001823386,Imaging,0.08104414,FALSE,17.6,0.264456676,7.8,0.313286058,10,0.828199272,,,0.468647335
10776,"Superposition of waves for modeling COVID-19 epidemic in the world and
  in the countries with the maximum number of infected people in the first half
  of 2020",0,2007.02283,7/5/20,arxiv,0,3,mathematical model,0.001291226,0.001291269,0.001291202,0.99354387,0.00129122,0.001291212,Epidemiology,0.065901846,FALSE,28.66666667,0.414125796,0.333333333,0.073187048,0,0.403234768,,,0.296849204
10777,"Predicting potential drug targets and repurposable drugs for COVID-19
  via a deep generative model for graphs",0,2007.02338,7/5/20,arxiv,0,5,"deep learning, artificial intelligence",0.679438723,0.001291244,0.315396278,0.001291272,0.001291212,0.00129127,Drug discovery,0.08154407,FALSE,30.4,0.436823551,13.2,0.402194273,7,0.785110192,,,0.541376006
10778,CORD19STS: COVID-19 Semantic Textual Similarity Dataset,0,2007.02461,7/5/20,arxiv,0,5,"information retrieval, dataset",0.001538194,0.001538209,0.940061535,0.001538197,0.032794659,0.022529206,Healthcare,0.009386778,FALSE,24.2,0.357350485,15.2,0.427214343,7,0.785110192,,,0.523225007
10779,"Attacking COVID-19 Progression using Multi-Drug Therapy for Synergetic
  Target Engagement",0,2007.02557,7/6/20,arxiv,0,6,"machine learning, computational",0.858197287,0.001310394,0.072803715,0.001310402,0.065067774,0.001310429,Drug discovery,0.43176523,FALSE,42,0.558537943,38.5,0.624765855,0,0.403234768,,,0.528846189
10780,"Prospective Prediction of Future SARS-CoV-2 Infections Using Empirical
  Data on a National Level to Gauge Response Effectiveness","Epidemiology & Infection, Volume 149, 2021, e80",2007.02712,7/6/20,arxiv,"Epidemiology & Infection, Volume 149, 2021, e80",6,mathematical model,0.001751158,0.001751204,0.001751246,0.991244017,0.001751191,0.001751183,Epidemiology,0.5410754,TRUE,87.8,0.836415363,160.36,0.892493979,1,0.537564047,,,0.75549113
10781,"A review of spatial causal inference methods for environmental and
  epidemiological applications",0,2007.02714,7/6/20,arxiv,0,6,"bayes, computational",0.001717265,0.001717235,0.001717236,0.991413813,0.001717192,0.001717259,Epidemiology,0.08489132,FALSE,95.33333333,0.857566949,98.33333333,0.819641424,3,0.667819001,,,0.781675791
10782,"GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer:
  Porting, Optimization, and Application to COVID-19 Research",0,2007.03678,7/6/20,arxiv,0,15,"computational, in silico",0.748578448,0.001751205,0.244416585,0.001751268,0.001751335,0.001751159,Drug discovery,0.5065035,TRUE,27.13333333,0.396685015,14.4,0.416644367,4,0.707574542,,,0.506967974
10783,"Statistical Physics of Epidemic on Network Predictions for SARS-CoV-2
  Parameters",0,2007.03101,7/6/20,arxiv,0,3,"bayes, bayesian model, network model",0.00133003,0.001330051,0.001330055,0.993349658,0.001330052,0.001330155,Epidemiology,0.05155602,FALSE,58.33333333,0.691941369,27.33333333,0.54856837,0,0.403234768,,,0.547914836
10784,"An Evaluation of Two Commercial Deep Learning-Based Information
  Retrieval Systems for COVID-19 Literature",0,2007.03106,7/6/20,arxiv,0,2,"deep learning, information retrieval, text mining",0.001901722,0.001901707,0.046044729,0.946348352,0.001901794,0.001901696,Epidemiology,0.63624084,TRUE,81,0.811552972,94,0.810944608,1,0.537564047,,,0.720020542
10785,"Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural
  Networks",0,2007.03113,7/6/20,arxiv,0,7,"deep learning, neural network, forecasting model, dataset",0.001717239,0.001717251,0.353014734,0.64011643,0.001717171,0.001717175,Epidemiology,0.0407764,FALSE,20.42857143,0.302987198,173.4285714,0.901792882,22,0.908142478,,,0.704307519
10786,"The benefits of peer transparency in safe workplace operation post
  pandemic lockdown",0,2007.03283,7/7/20,arxiv,0,5,mathematical model,0.001684669,0.070294519,0.001684516,0.846620824,0.078030959,0.001684513,Epidemiology,0.26383322,FALSE,67,0.748160059,94.6,0.811948087,1,0.537564047,,,0.699224064
10787,"A Vision-based Social Distancing and Critical Density Detection System
  for COVID-19",0,2007.03578,7/7/20,arxiv,0,5,"deep learning, artificial intelligence, dataset",0.001512015,0.001511908,0.345078799,0.648873626,0.001511847,0.001511805,Epidemiology,0.1521419,FALSE,63.4,0.723977983,30.4,0.572451164,17,0.887338725,,,0.727922624
10788,"A Weakly Supervised Region-Based Active Learning Method for COVID-19
  Segmentation in CT Images",0,2007.07012,7/7/20,arxiv,0,8,"active learning, dataset",0.001371331,0.001371347,0.936044381,0.058470439,0.001371258,0.001371244,Imaging,0.000443786,FALSE,86,0.829117447,48.5,0.675675676,5,0.739490092,,,0.748094405
10789,"Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19
  Patients",0,2007.03643,7/7/20,arxiv,0,7,"probabilistic, dataset",0.001461906,0.001461904,0.852837625,0.001461966,0.001461926,0.141314672,Imaging,0.03502521,FALSE,95.57142857,0.857999876,60.14285714,0.721434306,0,0.403234768,,,0.66088965
10790,Bayesian Modeling of COVID-19 Positivity Rate -- the Indiana experience,0,2007.06541,7/9/20,arxiv,0,2,"bayes, bayesian model",0.00430992,0.004310264,0.00431013,0.978449409,0.004310139,0.004310137,Epidemiology,0.14359245,FALSE,11,0.167171748,2.5,0.180826866,0,0.403234768,,,0.250411127
10791,"SARS-CoV-2 virus RNA sequence classification and geographical analysis
  with convolutional neural networks approach",0,2007.05055,7/9/20,arxiv,0,1,"neural network, image processing, network model",0.001717316,0.4217562,0.50601908,0.067072964,0.001717194,0.001717246,Imaging,0.2130914,FALSE,5,0.070752675,1,0.122023013,0,0.403234768,,,0.198670152
10792,"The COVID-19 Pandemic, Community Mobility and the Effectiveness of
  Non-pharmaceutical Interventions: The United States of America, February to
  May 2020",0,2007.12644,7/9/20,arxiv,0,3,bayes,0.001593479,0.001593481,0.001593493,0.926821119,0.066804882,0.001593545,Epidemiology,0.2556042,FALSE,34.33333333,0.480425506,21.66666667,0.499732406,2,0.618927094,,,0.533028335
10793,"Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$α$
  Model",0,2007.0518,7/10/20,arxiv,0,3,bayes,0.001786524,0.001786513,0.153966463,0.838887458,0.001786517,0.001786525,Epidemiology,0.043468535,FALSE,327.6666667,0.992392851,352.3333333,0.965948622,2,0.618927094,,,0.859089522
10794,"Blockchain-Federated-Learning and Deep Learning Models for COVID-19
  detection using CT Imaging",0,2007.06537,7/10/20,arxiv,0,10,deep learning,0.00127265,0.001272677,0.993636483,0.001272735,0.001272697,0.001272758,Imaging,0.047256976,FALSE,19.71428571,0.292782485,4.285714286,0.236018196,22,0.908142478,,,0.478981053
10795,Climate & BCG: Effects on COVID-19 Death Growth Rates,0,2007.05542,7/10/20,arxiv,0,2,"bayes, machine learning",0.004109804,0.004110007,0.090313409,0.713771356,0.080705125,0.106990298,Epidemiology,0.16993138,FALSE,64,0.729173109,26.5,0.542012309,0,0.403234768,,,0.558140062
10796,EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision,0,2007.05597,7/10/20,arxiv,0,6,"machine learning, dataset",0.001034582,0.001034619,0.994827052,0.001034598,0.001034594,0.001034555,Imaging,0.000755429,FALSE,71.16666667,0.769559033,186,0.90988761,0,0.403234768,,,0.694227137
10797,"SARS-CoV-2 orthologs of pathogenesis-involved small viral RNAs of
  SARS-CoV",0,2007.05859,7/11/20,arxiv,0,2,"computational, genomes",0.260478353,0.590538871,0.067079969,0.002080689,0.002080601,0.077741517,Genomics,0.27500916,FALSE,32.5,0.461685942,41.5,0.642092588,0,0.403234768,,,0.502337766
10798,"Dynamics of B-cell repertoires and emergence of cross-reactive responses
  in COVID-19 patients with different disease severity",0,2007.06762,7/14/20,arxiv,0,15,sequencing,0.388495699,0.405332745,0.002130724,0.002130763,0.002130666,0.199779404,Genomics,0.36043674,FALSE,147.9333333,0.942111448,426.1333333,0.973039872,2,0.618927094,,,0.844692805
10799,"A Recurrent Neural Network and Differential Equation Based
  Spatiotemporal Infectious Disease Model with Application to COVID-19",0,2007.10929,7/14/20,arxiv,0,4,neural network,0.00299639,0.002996995,0.199556452,0.788457299,0.002996435,0.002996429,Epidemiology,0.082665026,FALSE,65.5,0.737646113,60.5,0.722772277,3,0.667819001,,,0.709412464
10800,"Global Reactions to COVID-19 on Twitter: A Labelled Dataset with Latent
  Topic, Sentiment and Emotion Attributes",0,2007.06954,7/14/20,arxiv,0,3,"machine learning, dataset",0.040470673,0.071019621,0.223583919,0.622063644,0.041324019,0.001538124,Epidemiology,0.028222471,FALSE,40.66666667,0.546292288,31.33333333,0.580010704,0,0.403234768,,,0.50984592
10801,"Monitoring physical distancing for crowd management: real-time
  trajectory and group analysis","PLoS ONE 15(10): e0240963, 2020",2007.06962,7/14/20,arxiv,"PLoS ONE 15(10): e0240963, 2020",4,computational,0.001187314,0.001187339,0.166583802,0.694728029,0.13512624,0.001187277,Epidemiology,0.34782654,FALSE,21.25,0.314614386,3.5,0.213607172,8,0.799987654,,,0.442736404
10802,"Use of Machine Learning and Artificial Intelligence to predict
  SARS-CoV-2 infection from Full Blood Counts in a population","International Immunopharmacology, Volume 86,2020,106705,",2007.06971,7/14/20,arxiv,"International Immunopharmacology, Volume 86,2020,106705,",8,"machine learning, artificial intelligence, neural network, dataset",0.001156244,0.001156289,0.427427366,0.001156302,0.001156313,0.567947487,Clinics,0.57901317,TRUE,15,0.227596017,10.375,0.360650254,15,0.874313229,,,0.487519833
10803,"Do Online Courses Provide an Equal Educational Value Compared to
  In-Person Classroom Teaching? Evidence from US Survey Data using Quantile
  Regression",0,2007.06994,7/14/20,arxiv,0,2,bayes,0.002183243,0.00218326,0.069649941,0.227358865,0.696441377,0.002183313,Healthcare,0.11885986,FALSE,10,0.15214299,4,0.231469093,1,0.537564047,,,0.30705871
10804,"Covidex: Neural Ranking Models and Keyword Search Infrastructure for the
  COVID-19 Open Research Dataset",0,2007.07846,7/14/20,arxiv,0,11,dataset,0.002183298,0.002183281,0.593242339,0.32758647,0.072621388,0.002183225,Epidemiology,0.13369232,FALSE,81.09090909,0.811614818,739.3636364,0.988827937,16,0.881782826,,,0.894075194
10805,"The natural polyphenol fortunellin and its structural analogs are
  inhibitors of the SARS-CoV-2 main proteinase dimerization, as revealed by
  molecular simulation studies",0,2007.07736,7/15/20,arxiv,0,7,"molecular dynamics simulation, in silico",0.989597177,0.002080554,0.002080565,0.002080652,0.002080532,0.002080521,Drug discovery,0.7483616,TRUE,60,0.703444864,52.28571429,0.689991972,0,0.403234768,,,0.598890534
10806,"Inference of COVID-19 epidemiological distributions from Brazilian
  hospital data",0,2007.10317,7/15/20,arxiv,0,11,"bayes, mathematical model, dataset",0.001684499,0.001684547,0.001684521,0.742807742,0.001684585,0.250454105,Epidemiology,0.15506023,FALSE,12.2,0.18461253,13.1,0.400856302,2,0.618927094,,,0.401465309
10807,"A parsimonious model for spatial transmission and heterogeneity in the
  COVID-19 propagation",0,2007.08002,7/15/20,arxiv,0,5,probabilistic,0.020497432,0.001141335,0.001141333,0.974937212,0.001141342,0.001141346,Epidemiology,0.224302,FALSE,38,0.519327107,143.8,0.877976987,3,0.667819001,,,0.688374365
10808,"Predicting Mechanical Ventilation Requirement and Mortality in COVID-19
  using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional
  Study",0,2007.08028,7/15/20,arxiv,0,7,"machine learning, deep learning, computational, classifier, radiom",0.00129123,0.001291212,0.60135392,0.001291292,0.001291227,0.393481118,Imaging,0.03737089,FALSE,23.85714286,0.352031666,,,2,0.618927094,,,0.48547938
10809,Heterogeneity Learning for SIRS model: an Application to the COVID-19,0,2007.08047,7/16/20,arxiv,0,2,bayes,0.001943519,0.001943665,0.23914321,0.753082645,0.001943503,0.001943458,Epidemiology,0.19599637,FALSE,12.5,0.189436576,0.5,0.087101953,4,0.707574542,,,0.32803769
10810,"An Efficient Mixture of Deep and Machine Learning Models for COVID-19
  and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings",0,2007.08223,7/16/20,arxiv,0,4,"machine learning, computational, classifier, dataset",0.000898078,0.000898096,0.961201026,0.000898098,0.035206596,0.000898106,Imaging,0.02923426,FALSE,45.75,0.593419506,24.5,0.525086968,3,0.667819001,,,0.595441825
10811,"Automated Detection and Forecasting of COVID-19 using Deep Learning
  Techniques: A Review",0,2007.10785,7/16/20,arxiv,0,17,"machine learning, deep learning, artificial intelligence, genomes",0.001203429,0.07931437,0.868843759,0.017150318,0.032284723,0.001203401,Imaging,0.03398174,FALSE,47.52941176,0.610674748,55.64705882,0.704843457,25,0.918019631,,,0.744512612
10812,An Autoencoder Based Approach to Simulate Sports Games,0,2007.10257,7/16/20,arxiv,0,6,"machine learning, dataset",0.002898316,0.002898352,0.484893334,0.503513315,0.002898384,0.002898299,Epidemiology,0.3647927,FALSE,2,0.022141134,0.166666667,0.058736955,0,0.403234768,,,0.161370952
10813,"Modern Hopfield Networks and Attention for Immune Repertoire
  Classification","Advances in Neural Information Processing Systems 33 (NeurIPS
  2020)",2007.13505,7/16/20,arxiv,"Advances in Neural Information Processing Systems 33 (NeurIPS
  2020)",11,"machine learning, deep learning, computational, dataset",0.087197456,0.020254025,0.807733129,0.082341256,0.001237083,0.001237051,Drug discovery,0.17809746,FALSE,35.18181818,0.488712969,727.1818182,0.988092052,9,0.814309525,,,0.763704849
10814,"COV-ELM classifier: An Extreme Learning Machine based identification of
  COVID-19 using Chest X-Ray Images",0,2007.08637,7/16/20,arxiv,0,5,"machine learning, image analysis, classifier, dataset",0.001126819,0.055316694,0.940176055,0.00112682,0.001126826,0.001126785,Imaging,0.035519034,FALSE,24.33333333,0.359700662,1.333333333,0.13252609,3,0.667819001,,,0.386681918
10815,Learning to Match Distributions for Domain Adaptation,0,2007.10791,7/17/20,arxiv,0,8,dataset,0.001126875,0.001126866,0.796840185,0.198652375,0.001126833,0.001126867,Imaging,0.002119541,FALSE,91,0.846310842,262.5,0.943002408,0,0.403234768,,,0.730849339
10816,"CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic
  for Interpretable Prognosis",0,2007.08848,7/17/20,arxiv,0,10,"deep-learning, transfer learning, dataset",0.001171579,0.001171565,0.593031958,0.140559459,0.001171629,0.262893811,Clinics,0.07887682,FALSE,29.9,0.429340095,,,0,0.403234768,,,0.416287431
10817,Estimating COVID-19 cases and reproduction number in Mexico,0,2007.09117,7/17/20,arxiv,0,4,bayes,0.006539597,0.006539602,0.006539916,0.967301632,0.006539648,0.006539605,Epidemiology,0.123249084,FALSE,16,0.243552477,11.5,0.378378378,1,0.537564047,,,0.386498301
10818,AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature,0,2007.09186,7/17/20,arxiv,0,15,machine learning,0.002032872,0.002032789,0.253308699,0.738560115,0.002032792,0.002032734,Epidemiology,0.15912229,FALSE,11.3,0.169583771,8.3,0.324458121,0,0.403234768,,,0.29909222
10819,"The Rhythms of the Night: increase in online night activity and
  emotional resilience during the Spring 2020 Covid-19 lockdown",0,2007.09353,7/18/20,arxiv,0,4,dataset,0.045758576,0.00165311,0.001653068,0.887920549,0.061361601,0.001653095,Epidemiology,0.45934433,FALSE,47.25,0.608015338,56,0.706850415,0,0.403234768,,,0.572700173
10820,"Probabilistic Neighbourhood Component Analysis: Sample Efficient
  Uncertainty Estimation in Deep Learning",0,2007.108,7/18/20,arxiv,0,5,"bayes, deep learning, neural network, classifier, probabilistic",0.00135034,0.001350329,0.731111398,0.263487274,0.00135032,0.00135034,Imaging,0.002413362,FALSE,24.6,0.362483765,8,0.320511105,2,0.618927094,,,0.433973988
10821,"A Parametrized Nonlinear Predictive Control Strategy for Relaxing
  COVID-19 Social Distancing Measures in Brazil",0,2007.09686,7/19/20,arxiv,0,4,model fit,0.001392852,0.021155396,0.001392876,0.973273189,0.001392844,0.001392844,Epidemiology,0.23040655,FALSE,67,0.748160059,35.5,0.608241905,6,0.764429903,,,0.706943956
10822,"Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest
  X-Ray Images",0,2007.09695,7/19/20,arxiv,0,1,"deep learning, neural network, network model, dataset",0.002032818,0.002032763,0.870497914,0.121370852,0.00203288,0.002032772,Imaging,0.035102725,FALSE,506,0.99771167,574,0.982606369,4,0.707574542,,,0.895964194
10823,A curated collection of COVID-19 online datasets,0,2007.09703,7/19/20,arxiv,0,2,dataset,0.001486447,0.001486438,0.001486504,0.774364535,0.219689642,0.001486435,Epidemiology,0.34077966,FALSE,56.5,0.678891706,26.5,0.542012309,5,0.739490092,,,0.653464703
10824,COVID-19 Data Analysis and Forecasting: Algeria and the World,0,2007.09755,7/19/20,arxiv,0,1,dataset,0.001622763,0.00162271,0.115403232,0.878105749,0.001622779,0.001622768,Epidemiology,0.11549264,FALSE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
10825,"Inter-Mobile-Device Distance Estimation using Network Localization
  Algorithms for Digital Contact Logging Applications",0,2007.10162,7/20/20,arxiv,0,5,dataset,0.026365837,0.001538123,0.426685167,0.542334566,0.001538165,0.001538142,Epidemiology,0.05095768,FALSE,112,0.894180221,321.8,0.960061547,1,0.537564047,,,0.797268605
10826,"Daily Forecasting of New Cases for Regional Epidemics of Coronavirus
  Disease 2019 with Bayesian Uncertainty Quantification",0,2007.12523,7/20/20,arxiv,0,10,"bayes, mathematical model",0.00213066,0.002130708,0.002130787,0.989346297,0.002130783,0.002130764,Epidemiology,0.13481215,FALSE,28.7,0.414373183,31.5,0.581883864,3,0.667819001,,,0.554692016
10827,"Few-shot link prediction via graph neural networks for Covid-19
  drug-repurposing",0,2007.10261,7/20/20,arxiv,0,3,"neural network, knowledge graph",0.443427817,0.001593534,0.491703792,0.036097691,0.001593656,0.02558351,Drug discovery,0.017682195,FALSE,221,0.978848414,1601,0.997123361,6,0.764429903,,,0.913467226
10828,"Ribonucleocapsid assembly/packaging signals in the genomes of the
  coronaviruses SARS-CoV and SARS-CoV-2: Detection, comparison and implications
  for therapeutic targeting",0,2007.10274,7/20/20,arxiv,0,2,genomes,0.200351969,0.7275622,0.001902009,0.066380052,0.001901917,0.001901854,Genomics,0.6847352,TRUE,61.5,0.712350795,12.5,0.392761573,2,0.618927094,,,0.574679821
10829,Characterizing drug mentions in COVID-19 Twitter Chatter,0,2007.10276,7/20/20,arxiv,0,2,"machine learning, dataset",0.175806293,0.002996435,0.446897263,0.368306578,0.002996843,0.002996587,Epidemiology,0.3831364,FALSE,38.5,0.524089307,19,0.471367407,0,0.403234768,,,0.466230494
10830,"Landmark Guidance Independent Spatio-channel Attention and Complementary
  Context Information based Facial Expression Recognition",Pattern Recognition Letters 145 (2021),2007.10298,7/20/20,arxiv,Pattern Recognition Letters 145 (2021),2,"neural network, dataset",0.046824767,0.001156293,0.665373059,0.284333335,0.001156264,0.001156282,Epidemiology,0.5435215,TRUE,12,0.183190055,0,0.055525823,3,0.667819001,,,0.302178293
10831,"PanRep: Graph neural networks for extracting universal node embeddings
  in heterogeneous graphs",0,2007.10445,7/20/20,arxiv,0,3,neural network,0.267911414,0.002183319,0.723355287,0.00218345,0.002183272,0.002183259,Drug discovery,0.020672232,FALSE,188,0.96808708,1511,0.996922665,0,0.403234768,,,0.789414838
10832,"CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors
  and Efficient Neural Networks",0,2007.10497,7/20/20,arxiv,0,9,"neural network, dataset",0.001511814,0.001511882,0.877430753,0.001511926,0.116521655,0.00151197,Healthcare,0.002719432,FALSE,131.25,0.923124497,171.75,0.900722505,5,0.739490092,,,0.854445698
10833,"Clustering patterns connecting COVID-19 dynamics and Human mobility
  using optimal transport",Sankhya B (16th March 2021),2007.10677,7/21/20,arxiv,Sankhya B (16th March 2021),4,computational,0.002238531,0.002238651,0.002238495,0.98880732,0.002238512,0.002238491,Epidemiology,0.22150326,FALSE,118,0.904632321,109.25,0.837904736,3,0.667819001,,,0.80345202
10834,On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic,0,2007.10712,7/21/20,arxiv,0,4,dataset,0.002639148,0.002639033,0.235324214,0.553901114,0.202857511,0.00263898,Epidemiology,0.009801984,FALSE,26.5,0.389325252,13.25,0.402997056,2,0.618927094,,,0.470416468
10835,"Forecasting Brazilian and American COVID-19 cases based on artificial
  intelligence coupled with climatic exogenous variables","Chaos, Solitons & Fractals. 139 (2020) 110027",2007.10981,7/21/20,arxiv,"Chaos, Solitons & Fractals. 139 (2020) 110027",4,"bayes, artificial intelligence, neural network, forecasting model",0.001291224,0.001291215,0.235338077,0.75949685,0.001291281,0.001291354,Epidemiology,0.58280325,TRUE,146.25,0.94093636,117.5,0.847872625,22,0.908142478,,,0.898983821
10836,"Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives
  for Brazil","Chaos, Solitons & Fractals. 135 (2020) 109853",2007.12261,7/21/20,arxiv,"Chaos, Solitons & Fractals. 135 (2020) 109853",4,"ensemble learning, forecasting model",0.001310333,0.001310332,0.328152512,0.666606132,0.001310341,0.00131035,Epidemiology,0.5569492,TRUE,147,0.94130744,118.25,0.849143698,100,0.978270264,,,0.922907134
10837,"CVR-Net: A deep convolutional neural network for coronavirus recognition
  from chest radiography images",0,2007.11993,7/21/20,arxiv,0,5,"supervised learning, neural network, dataset",0.001072182,0.00107217,0.994639125,0.001072193,0.001072164,0.001072165,Imaging,0.002162308,FALSE,39.4,0.532995238,20.2,0.482204977,3,0.667819001,,,0.561006405
10838,"A Comparison of Aggregation Methods for Probabilistic Forecasts of
  COVID-19 Mortality in the United States",0,2007.11103,7/21/20,arxiv,0,2,"forecasting model, probabilistic",0.001203398,0.00120341,0.068668922,0.884670514,0.001203418,0.043050338,Epidemiology,0.020109922,FALSE,45.5,0.591440411,130,0.863928285,2,0.618927094,,,0.69143193
10839,"An agent-based model for interrelation between COVID-19 outbreak and
  economic activities",0,2007.11988,7/22/20,arxiv,0,5,computational,0.173655347,0.002562567,0.002562607,0.816094253,0.002562655,0.002562572,Epidemiology,0.4771836,FALSE,111.6,0.893190674,22.8,0.509967889,4,0.707574542,,,0.703577701
10840,COVID-19 Pandemic Prediction using Time Series Forecasting Models,0,2009.12176,7/22/20,arxiv,0,2,forecasting model,0.001653016,0.001653089,0.001653074,0.963799012,0.029588755,0.001653053,Epidemiology,0.16868263,FALSE,97.5,0.863318696,24,0.521808938,2,0.618927094,,,0.668018243
10841,Backtesting the predictability of COVID-19,0,2007.11411,7/22/20,arxiv,0,5,forecasting model,0.001237081,0.001237076,0.028385347,0.966666348,0.001237072,0.001237077,Epidemiology,0.03197965,FALSE,26.4,0.386913229,21.4,0.496052984,0,0.403234768,,,0.42873366
10842,"Impact of a small number of large bubbles on Covid-19 transmission
  within universities",0,2008.08147,7/22/20,arxiv,0,1,computational,0.00360733,0.00360727,0.003607292,0.74898886,0.236581991,0.003607258,Epidemiology,0.3554526,FALSE,323,0.991898077,362,0.967621086,0,0.403234768,,,0.787584644
10843,"Understanding the temporal evolution of COVID-19 research through
  machine learning and natural language processing",0,2007.11604,7/22/20,arxiv,0,6,machine learning,0.001751203,0.114184225,0.086740614,0.696951344,0.001751319,0.098621295,Epidemiology,0.18172601,FALSE,37.33333333,0.511596264,25.5,0.533382392,3,0.667819001,,,0.570932553
10844,"STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using
  Real World Evidence",0,2008.04215,7/23/20,arxiv,0,8,"deep learning, prediction model",0.000966749,0.000966763,0.455397081,0.54073577,0.000966812,0.000966825,Epidemiology,0.060937315,FALSE,59.875,0.701898695,195.25,0.915306396,6,0.764429903,,,0.793878331
10845,"A machine learning aided global diagnostic and comparative tool to
  assess effect of quarantine control in Covid-19 spread",0,2007.1354,7/23/20,arxiv,0,3,"machine learning, neural network",0.002357908,0.094149125,0.334873657,0.563903862,0.00235774,0.002357708,Epidemiology,0.73072296,TRUE,22.33333333,0.330261612,2.666666667,0.185442869,0,0.403234768,,,0.306313083
10846,"Understanding the dynamics emerging from infodemics: A call to action
  for interdisciplinary research",0,2007.12226,7/23/20,arxiv,0,5,computational,0.001511954,0.001511859,0.190389797,0.75548169,0.049592911,0.001511788,Epidemiology,0.8798448,TRUE,106.8,0.882738574,93.4,0.809405941,0,0.403234768,,,0.698459761
10847,"A Preliminary Investigation in the Molecular Basis of Host Shutoff
  Mechanism in SARS-CoV",0,2007.13469,7/23/20,arxiv,0,5,"computational, sequencing",0.656516671,0.282870555,0.055916716,0.001565366,0.001565352,0.001565341,Drug discovery,0.33884332,FALSE,8.6,0.127280599,2.6,0.18256623,0,0.403234768,,,0.237693865
10848,"COVID TV-UNet: Segmenting COVID-19 Chest CT Images Using Connectivity
  Imposed U-Net",0,2007.12303,7/24/20,arxiv,0,5,dataset,0.001272664,0.0012727,0.99363655,0.001272739,0.001272667,0.001272681,Imaging,0.009117842,FALSE,136.2,0.927948544,269.4,0.94574525,6,0.764429903,,,0.879374566
10849,COVID-19 and India: What Next?,0,2007.13523,7/24/20,arxiv,0,2,predictive model,0.003101493,0.00310148,0.003101501,0.984492604,0.003101446,0.003101477,Epidemiology,0.31595114,FALSE,24,0.35574247,1.5,0.138747659,0,0.403234768,,,0.299241632
10850,"A model for the outbreak of COVID-19: Vaccine effectiveness in a case
  study of Italy",0,2008.00828,7/24/20,arxiv,0,3,mathematical model,0.005353104,0.005352909,0.005353137,0.973234993,0.005352985,0.005352872,Epidemiology,0.22612992,FALSE,31,0.445111015,6.333333333,0.285121755,0,0.403234768,,,0.377822513
10851,"Study of Different Deep Learning Approach with Explainable AI for
  Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray
  Image Dataset",0,2007.12525,7/24/20,arxiv,0,6,"deep learning, dataset",0.001461862,0.001461885,0.992690316,0.001461971,0.001461958,0.001462007,Imaging,0.40740147,FALSE,11.16666667,0.168161296,1.666666667,0.145036125,4,0.707574542,,,0.340257321
10852,"COVID-19 Knowledge Graph: Accelerating Information Retrieval and
  Discovery for Scientific Literature",0,2007.12731,7/24/20,arxiv,0,9,"information retrieval, knowledge graph",0.064863691,0.001593566,0.001593619,0.928762101,0.001593537,0.001593487,Epidemiology,0.00624004,FALSE,72,0.77302245,507,0.979328338,9,0.814309525,,,0.855553438
10853,"An Uncertainty-aware Transfer Learning-based Framework for Covid-19
  Diagnosis",0,2007.14846,7/26/20,arxiv,0,8,"machine learning, neural network, transfer learning, dataset",0.001171565,0.001171554,0.917228229,0.078085562,0.001171543,0.001171548,Imaging,0.40981865,FALSE,169.5,0.957944214,102.5,0.826933369,5,0.739490092,,,0.841455892
10854,"CoV-ABM: A stochastic discrete-event agent-based framework to simulate
  spatiotemporal dynamics of COVID-19",0,2007.13231,7/26/20,arxiv,0,3,computational,0.000956386,0.097853452,0.000956368,0.898321098,0.000956356,0.00095634,Epidemiology,0.003155768,FALSE,39.66666667,0.536211268,36.33333333,0.613393096,3,0.667819001,,,0.605807789
10855,"The Causality Inference of Public Interest in Restaurants and Bars on
  COVID-19 Daily Cases in the US: A Google Trends Analysis",0,2007.13255,7/27/20,arxiv,0,3,data mining,0.001415108,0.001415106,0.001415255,0.97325042,0.001415183,0.021088928,Epidemiology,0.03620985,FALSE,358,0.994371946,262,0.942400321,0,0.403234768,,,0.780002345
10856,"From climate change to pandemics: decision science can help scientists
  have impact",0,2007.13261,7/27/20,arxiv,0,11,mathematical model,0.018267932,0.001156247,0.001156288,0.977106958,0.001156297,0.001156277,Epidemiology,0.06504631,FALSE,37.81818182,0.516729544,45.72727273,0.661894568,1,0.537564047,,,0.57206272
10857,"Overview of digital health surveillance system during COVID-19 pandemic:
  public health issues and misapprehensions",0,2007.13633,7/27/20,arxiv,0,5,"artificial intelligence, digital health",0.002183227,0.002183266,0.137341475,0.853925294,0.002183432,0.002183305,Epidemiology,0.19141325,FALSE,26.2,0.383820892,3.8,0.221501204,2,0.618927094,,,0.408083063
10858,"A mathematical model of the COVID-19 pandemic dynamics with dependent
  variable infection rate: Application to South Korea",0,2008.03248,7/27/20,arxiv,0,4,"model simulation, mathematical model",0.00171723,0.035865186,0.001717251,0.957265813,0.00171723,0.001717291,Epidemiology,0.06895444,FALSE,90.5,0.844331746,39.25,0.629114263,0,0.403234768,,,0.625560259
10859,"CPAS: the UK's National Machine Learning-based Hospital Capacity
  Planning System for COVID-19",0,2007.13825,7/27/20,arxiv,0,3,machine learning,0.001254616,0.00125461,0.413619591,0.514716314,0.001254645,0.067900223,Epidemiology,0.5444957,TRUE,244,0.983301379,191.3333333,0.912831148,3,0.667819001,,,0.854650509
10860,"A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources
  in Medical Diagnoses",0,2007.13847,7/27/20,arxiv,0,4,bayes,0.001622754,0.036044871,0.681793461,0.27729346,0.001622729,0.001622726,Imaging,0.003380299,FALSE,13,0.197352959,5.75,0.271742039,1,0.537564047,,,0.335553015
10861,"Intelligent Optimization of Diversified Community Prevention of COVID-19
  using Traditional Chinese Medicine",0,2007.13926,7/28/20,arxiv,0,7,computational,0.001943524,0.001943503,0.164287398,0.580191707,0.249690405,0.001943462,Epidemiology,0.8042395,TRUE,22.71428571,0.335889665,2.571428571,0.181495852,2,0.618927094,,,0.378770871
10862,A System for Worldwide COVID-19 Information Aggregation,0,2008.01523,7/28/20,arxiv,0,29,"classifier, dataset",0.002130661,0.002130669,0.326186577,0.665290424,0.002131056,0.002130613,Epidemiology,0.11361566,FALSE,78.86206897,0.801966726,65.20689655,0.739563821,0,0.403234768,,,0.648255105
10863,"Real-Time Neural Network Scheduling of Emergency Medical Mask Production
  during COVID-19",0,2007.14055,7/28/20,arxiv,0,5,"computational, neural network",0.001272687,0.01978406,0.620825625,0.35557226,0.001272702,0.001272667,Epidemiology,0.5052687,TRUE,13.8,0.208980147,10.2,0.357706717,2,0.618927094,,,0.395204653
10864,"Parameter estimation in dynamical systems via Statistical Learning: a
  reinterpretation of Approximate Bayesian Computation applied to COVID-19
  spread",0,2007.14229,7/28/20,arxiv,0,1,bayes,0.002296621,0.002296599,0.142230745,0.848582971,0.00229655,0.002296515,Epidemiology,0.008739084,FALSE,21,0.312016822,1,0.122023013,0,0.403234768,,,0.279091534
10865,Scalable Estimation of Epidemic Thresholds via Node Sampling,0,2007.1482,7/28/20,arxiv,0,2,computational,0.001371264,0.00137157,0.001371382,0.993143074,0.00137136,0.001371349,Epidemiology,0.006000876,FALSE,74.5,0.784464098,58,0.714476853,0,0.403234768,,,0.634058573
10866,"CovMUNET: A Multiple Loss Approach towards Detection of COVID-19 from
  Chest X-ray",0,2007.14318,7/28/20,arxiv,0,3,neural network,0.001622828,0.001622742,0.899450398,0.094058549,0.001622801,0.001622682,Imaging,0.13246158,FALSE,4,0.054734368,0,0.055525823,1,0.537564047,,,0.215941413
10867,"COVID-19 CT Image Synthesis with a Conditional Generative Adversarial
  Network",0,2007.14638,7/29/20,arxiv,0,4,"machine learning, adversarial network, deep-learning",0.001112656,0.015526759,0.98002263,0.001112642,0.001112703,0.001112611,Imaging,0.46686292,FALSE,90.5,0.844331746,12.5,0.392761573,3,0.667819001,,,0.634970774
10868,"Deep Learning Models for Early Detection and Prediction of the spread of
  Novel Coronavirus (COVID-19)",0,2008.0117,7/29/20,arxiv,0,7,"machine learning, deep learning, prediction model, dataset",0.002490515,0.002490446,0.987547405,0.002490679,0.00249053,0.002490424,Epidemiology,0.265499,FALSE,11.5,0.17416043,3.333333333,0.206515922,0,0.403234768,,,0.261303707
10869,"Comparative study of deep learning methods for the automatic
  segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients",0,2007.15546,7/29/20,arxiv,0,22,"deep learning, dataset",0.001171539,0.001171536,0.994142237,0.001171553,0.001171574,0.001171561,Imaging,0.057539433,FALSE,111.1363636,0.892139279,169.4090909,0.898849344,4,0.707574542,,,0.832854388
10870,"PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture
  for Detecting COVID-19 from Chest X-Ray Images","Health information science and systems, 2020",2007.14777,7/29/20,arxiv,"Health information science and systems, 2020",3,neural network,0.000988378,0.00098839,0.995058134,0.000988375,0.000988355,0.000988367,Imaging,0.7112225,TRUE,29.33333333,0.423093574,11,0.371287129,1,0.537564047,,,0.443981583
10871,"An early warning tool for predicting mortality risk of COVID-19 patients
  using machine learning",0,2007.15559,7/29/20,arxiv,0,8,machine learning,0.00137127,0.001371267,0.184572494,0.001371311,0.001371329,0.809942328,Clinics,0.8772432,TRUE,22.75,0.336508133,22,0.503746321,4,0.707574542,,,0.515942998
10872,"Antiviral Drug-Membrane Permeability: the Viral Envelope and Cellular
  Organelles",0,2007.14965,7/29/20,arxiv,0,3,computational,0.87494178,0.001823363,0.117764239,0.001823464,0.001823578,0.001823576,Drug discovery,0.4160258,FALSE,21.66666667,0.320737213,6.333333333,0.285121755,0,0.403234768,,,0.336364579
10873,"Detecting Change Signs with Differential MDL Change Statistics for
  COVID-19 Pandemic Analysis",0,2007.15179,7/30/20,arxiv,0,5,dataset,0.001291272,0.103406283,0.221478013,0.671241802,0.00129126,0.00129137,Epidemiology,0.007504791,FALSE,38,0.519327107,31.6,0.582218357,0,0.403234768,,,0.501593411
10874,"Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News
  Exposure during COVID-19 Outbreak",0,2007.15209,7/30/20,arxiv,0,3,transfer learning,0.137120804,0.001047006,0.158525589,0.550414841,0.151844946,0.001046814,Epidemiology,0.1965329,FALSE,83,0.818170573,62.66666667,0.731469093,2,0.618927094,,,0.722855587
10875,"Artificial Intelligence in the Battle against Coronavirus (COVID-19): A
  Survey and Future Research Directions",0,2008.07343,7/30/20,arxiv,0,1,"computational, artificial intelligence, image processing, text mining",0.001565502,0.001565413,0.77297434,0.163773575,0.058555845,0.001565324,Imaging,0.72956336,TRUE,11,0.167171748,0,0.055525823,42,0.949503056,,,0.390733542
10876,"IIT Kanpur Consulting Group: Using Machine Learning and Management
  Consulting for Social Good",0,2007.15628,7/30/20,arxiv,0,5,machine learning,0.002357768,0.002357776,0.175717862,0.633572035,0.125904016,0.060090543,Epidemiology,0.8670833,TRUE,6.4,0.090667326,0.2,0.061145304,0,0.403234768,,,0.185015799
10877,COVID-19 therapy target discovery with context-aware literature mining,0,2007.15681,7/30/20,arxiv,0,7,"literature mining, dataset",0.209950487,0.00159354,0.615223438,0.170045355,0.001593605,0.001593574,Drug discovery,0.4979592,FALSE,76.42857143,0.792133094,109.1428571,0.837704041,3,0.667819001,,,0.765885379
10878,Cumulated burden of Covid-19 in Spain from a Bayesian perspective,0,2007.15727,7/30/20,arxiv,0,6,"bayes, bayesian model",0.001943461,0.001943536,0.001943499,0.990282494,0.001943515,0.001943495,Epidemiology,0.12780502,FALSE,51.83333333,0.645185231,7.833333333,0.314088841,0,0.403234768,,,0.454169613
10879,Deep Direct Likelihood Knockoffs,0,2007.15835,7/31/20,arxiv,0,3,"machine learning, neural network, predictive model, dataset",0.001653149,0.001653074,0.90551932,0.087868288,0.001653119,0.001653051,Epidemiology,0.001192689,FALSE,16.33333333,0.247015895,7.666666667,0.310877709,1,0.537564047,,,0.36515255
10880,Dynamics of epidemic diseases without guaranteed immunity,0,2007.15971,7/31/20,arxiv,0,1,mathematical model,0.107948024,0.002033065,0.00203277,0.883920606,0.002032776,0.002032758,Epidemiology,0.07072517,FALSE,87,0.833694106,10,0.355632861,0,0.403234768,,,0.530853911
10881,"Screening and evaluation of potential clinically significant HIV drug
  combinations against SARS-CoV-2 virus",0,2007.16177,7/31/20,arxiv,0,13,in silico,0.873279844,0.001141329,0.021220959,0.063401236,0.001141349,0.039815282,Drug discovery,0.90108013,TRUE,25.89361702,0.378996846,31.95744681,0.584024619,0,0.403234768,,,0.455418744
10882,"A fractional model for the COVID-19 pandemic: Application to Italian
  data",0,2008.00033,7/31/20,arxiv,0,4,probabilistic,0.007061531,0.007061637,0.007061899,0.964691606,0.007061562,0.007061766,Epidemiology,0.24481922,FALSE,42.25,0.559898571,63.25,0.73300776,1,0.537564047,,,0.610156793
10883,"DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive
  Surveillance of COVID-19 Using Heterogeneous Features and their Interactions",0,2008.00115,7/31/20,arxiv,0,3,deep learning,0.001254675,0.001254609,0.343389289,0.615982252,0.001254651,0.036864524,Epidemiology,0.102279365,FALSE,24,0.35574247,3.666666667,0.217621086,15,0.874313229,,,0.482558929
10884,"Large-scale, Language-agnostic Discourse Classification of Tweets During
  COVID-19",0,2008.00461,8/2/20,arxiv,0,1,"machine learning, computational, classifier",0.003927361,0.003927511,0.482976856,0.501313332,0.003927552,0.003927388,Epidemiology,0.21461317,FALSE,17,0.257467994,8,0.320511105,2,0.618927094,,,0.398968731
10885,"Identification of images of COVID-19 from Chest X-rays using Deep
  Learning: Comparing COGNEX VisionPro Deep Learning 1.0 Software with Open
  Source Convolutional Neural Networks",0,2008.00597,8/3/20,arxiv,0,5,"deep learning, neural network, dataset",0.000916681,0.000916684,0.99541656,0.000916701,0.000916698,0.000916675,Imaging,0.31858253,FALSE,24.4,0.360442823,113.6,0.843457319,1,0.537564047,,,0.580488063
10886,Interpretable Sequence Learning for COVID-19 Forecasting,0,2008.00646,8/3/20,arxiv,0,15,machine learning,0.002490426,0.002490495,0.264265719,0.665972525,0.002490578,0.062290257,Epidemiology,0.013561755,FALSE,34.33333333,0.480425506,42.46666667,0.647043083,7,0.785110192,,,0.63752626
10887,"Characterizing COVID-19 Misinformation Communities Using a Novel Twitter
  Dataset",0,2008.00791,8/3/20,arxiv,0,2,dataset,0.002032969,0.002032894,0.107311598,0.884556964,0.00203282,0.002032755,Epidemiology,0.002705216,FALSE,337,0.993196858,586.5,0.98300776,19,0.89561084,,,0.95727182
10888,"Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray
  Images",0,2008.03205,8/3/20,arxiv,0,10,dataset,0.001653034,0.001653079,0.991734569,0.001653167,0.001653086,0.001653066,Imaging,0.006803721,FALSE,86.4,0.830787309,60.6,0.722972973,1,0.537564047,,,0.69710811
10889,"Public risk perception and emotion on Twitter during the Covid-19
  pandemic","Appl Netw Sci 5, 99 (2020)",2008.00854,8/3/20,arxiv,"Appl Netw Sci 5, 99 (2020)",2,"network analysis, dataset",0.001371433,0.001371437,0.047238127,0.80427565,0.144372012,0.001371341,Epidemiology,0.57241774,TRUE,2,0.022141134,0,0.055525823,2,0.618927094,,,0.232198017
10890,"A Mathematical Framework for Estimating Risk of Airborne Transmission of
  COVID-19 with Application to Face Mask Use and Social Distancing",0,2008.00973,8/3/20,arxiv,0,3,mathematical model,0.154076093,0.002296634,0.002296546,0.836737597,0.002296621,0.002296509,Epidemiology,0.53252643,TRUE,480.3333333,0.997093203,440.3333333,0.974578539,24,0.914439163,,,0.962036968
10891,A Survey on the Use of AI and ML for Fighting the COVID-19 Pandemic,0,2008.07449,8/3/20,arxiv,0,6,"machine learning, artificial intelligence, dataset",0.001565314,0.001565311,0.885513041,0.108225712,0.001565306,0.001565316,Epidemiology,0.020441145,FALSE,30.66666667,0.440225122,13.16666667,0.401792882,1,0.537564047,,,0.459860684
10892,"Global Impact of COVID-19 Restrictions on the Atmospheric Concentrations
  of Nitrogen Dioxide and Ozone",0,2008.01127,8/3/20,arxiv,0,13,machine learning,0.001272761,0.001272729,0.034050181,0.960858994,0.001272676,0.001272659,Epidemiology,0.18944967,FALSE,33.84615385,0.474550065,51.30769231,0.68631255,0,0.403234768,,,0.521365794
10893,"COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease
  Monitoring",IEEE J Biomed Health Inform. 2021 Mar 26;PP,2008.0215,8/4/20,arxiv,IEEE J Biomed Health Inform. 2021 Mar 26;PP,5,deep learning,0.001861745,0.001861724,0.725211508,0.001861833,0.00186172,0.267341469,Imaging,0.7174645,TRUE,50.4,0.634547591,114.6,0.844193203,8,0.799987654,,,0.759576149
10894,"Effective Transfer Learning for Identifying Similar Questions: Matching
  User Questions to COVID-19 FAQs",0,2008.13546,8/4/20,arxiv,0,5,"neural network, transfer learning",0.00107219,0.001072213,0.530687634,0.298491459,0.167604335,0.00107217,Epidemiology,0.009132177,FALSE,36.6,0.503865421,78.6,0.774752475,2,0.618927094,,,0.632514997
10895,"An artificial intelligence system for predicting the deterioration of
  COVID-19 patients in the emergency department",0,2008.01774,8/4/20,arxiv,0,20,"artificial intelligence, neural network",0.001272634,0.001272676,0.839342968,0.028395428,0.001272709,0.128443586,Imaging,0.20648274,FALSE,45.05263158,0.587172985,25.63157895,0.53398448,4,0.707574542,,,0.609577336
10896,COVID-19 Kaggle Literature Organization,0,2008.13542,8/4/20,arxiv,0,5,"machine learning, dataset",0.004109883,0.004109892,0.283169061,0.700391172,0.004109964,0.004110027,Epidemiology,0.35900137,FALSE,27.2,0.397674562,27.2,0.547230399,0,0.403234768,,,0.44937991
10897,Modelling collective decision-making during epidemics,0,2008.01971,8/5/20,arxiv,0,4,network model,0.002238501,0.040309672,0.0022385,0.907467123,0.045507757,0.002238448,Epidemiology,0.20924449,FALSE,29.25,0.421671099,5,0.257024351,0,0.403234768,,,0.360643406
10898,"Ontology-driven weak supervision for clinical entity classification in
  electronic health records",Nature Communications 12.1 (2021): 1-11,2008.01972,8/5/20,arxiv,Nature Communications 12.1 (2021): 1-11,7,machine learning,0.002183225,0.002183315,0.703074154,0.002183362,0.288192588,0.002183356,Healthcare,0.013653219,FALSE,21.85714286,0.322778156,,,0,0.403234768,,,0.363006462
10899,"MultiCheXNet: A Multi-Task Learning Deep Network For Pneumonia-like
  Diseases Diagnosis From X-ray Scans",0,2008.01973,8/5/20,arxiv,0,7,"classifier, transfer learning, dataset",0.001350361,0.001350349,0.993248319,0.001350353,0.001350314,0.001350304,Imaging,0.007235795,FALSE,9.428571429,0.140268415,,,0,0.403234768,,,0.271751591
10900,"Spatiotemporal dynamic of COVID-19 mortality in the city of Sao Paulo,
  Brazil: shifting the high risk from the best to the worst socio-economic
  conditions",0,2008.02322,8/5/20,arxiv,0,6,"bayes, bayesian model",0.001310357,0.001310354,0.070531256,0.762447214,0.0013104,0.163090418,Epidemiology,0.21361521,FALSE,31,0.445111015,7.166666667,0.301311212,4,0.707574542,,,0.48466559
10901,"Pneumonia after bacterial or viral infection preceded or followed by
  radiation exposure -- a reanalysis of older radiobiological data and
  implications for low dose radiotherapy for COVID-19 pneumonia",0,2008.02625,8/6/20,arxiv,0,4,dataset,0.00143814,0.167096739,0.100199195,0.261869995,0.00143817,0.467957762,Clinics,0.34713227,FALSE,215.75,0.977178552,348.25,0.965346535,3,0.667819001,,,0.870114696
10902,"Visualization and machine learning for forecasting of COVID-19 in
  Senegal",0,2008.03135,8/6/20,arxiv,0,3,machine learning,0.005697407,0.005697479,0.075321818,0.901888166,0.005697617,0.005697513,Epidemiology,0.119205534,FALSE,34.66666667,0.483703383,0.666666667,0.096200161,2,0.618927094,,,0.399610213
10903,"Improving Explainability of Image Classification in Scenarios with Class
  Overlap: Application to COVID-19 and Pneumonia",0,2008.02866,8/6/20,arxiv,0,4,machine learning,0.00213071,0.002130674,0.989346461,0.002130815,0.002130721,0.002130619,Imaging,0.28219366,FALSE,21.75,0.321603068,7.75,0.312215681,0,0.403234768,,,0.345684505
10904,Modeling the evolution of COVID-19,0,2008.03165,8/7/20,arxiv,0,5,simulation model,0.003760572,0.003760622,0.00376075,0.928573835,0.003760767,0.056383454,Epidemiology,0.012130022,FALSE,23.8,0.351227658,10,0.355632861,1,0.537564047,,,0.414808189
10905,"COVID-19 mortality analysis from soft-data multivariate curve regression
  and machine learning",0,2008.06344,8/7/20,arxiv,0,3,"bayes, machine learning, correlation analysis",0.001943484,0.0019435,0.198310424,0.652974181,0.001943515,0.142884896,Epidemiology,0.43851578,FALSE,56.33333333,0.677407385,10.66666667,0.365333155,0,0.403234768,,,0.481991769
10906,"Modeling and Simulation of the spread of coronavirus disease (COVID-19)
  in Lebanon",0,2008.03264,8/7/20,arxiv,0,2,"mathematical model, probabilistic",0.002183278,0.002183294,0.002183277,0.989083722,0.002183225,0.002183203,Epidemiology,0.008292913,FALSE,8.5,0.126662131,4,0.231469093,2,0.618927094,,,0.325686106
10907,"Neuroprotective Immunity by Essential Nutrient ""Choline"" for the
  Prevention of SARS CoV2 Infections: An in Silico Study by Molecular Dynamics
  Approach",Chemical Physics Letters 761 (2020) 138057,2008.0487,8/7/20,arxiv,Chemical Physics Letters 761 (2020) 138057,2,"virtual screening, in silico",0.976123459,0.004775203,0.004775138,0.0047752,0.004775497,0.004775503,Drug discovery,0.9632228,TRUE,15,0.227596017,0,0.055525823,2,0.618927094,,,0.300682978
10908,COVID-19 in differential diagnosis of online symptom assessments,0,2008.03323,8/7/20,arxiv,0,5,"machine learning, deep learning",0.001350333,0.001350335,0.543999992,0.352416655,0.099532347,0.001350338,Epidemiology,0.008761019,FALSE,49.8,0.630465706,93.2,0.808937651,0,0.403234768,,,0.614212708
10909,"Evaluating the Impact of COVID-19 on Cyberbullying through Bayesian
  Trend Analysis","The European Interdisciplinary Cybersecurity Conference (EICC)
  2020",2009.02126,8/8/20,arxiv,"The European Interdisciplinary Cybersecurity Conference (EICC)
  2020",2,bayes,0.002032771,0.002032805,0.002032794,0.989835994,0.002032838,0.002032798,Epidemiology,0.57752347,TRUE,14,0.213494959,0,0.055525823,2,0.618927094,,,0.295982626
10910,"Statistical Analytics and Regional Representation Learning for COVID-19
  Pandemic Understanding",0,2008.07342,8/8/20,arxiv,0,3,"predictive model, correlation analysis, lstm, dataset",0.001171561,0.001171601,0.356345097,0.638968491,0.001171617,0.001171633,Epidemiology,0.2699712,FALSE,23,0.34225988,8.666666667,0.331415574,0,0.403234768,,,0.358970074
10911,"Comparative study of variational quantum circuit and quantum
  backpropagation multilayer perceptron for COVID-19 outbreak predictions",0,2008.07617,8/8/20,arxiv,0,2,neural network,0.002638999,0.002639065,0.811708807,0.177735005,0.002639071,0.002639053,Imaging,0.031095803,FALSE,22.5,0.333539489,12,0.386740701,0,0.403234768,,,0.374504986
10912,"A Review on Deep Learning Techniques for the Diagnosis of Novel
  Coronavirus (COVID-19)",0,2008.04815,8/9/20,arxiv,0,4,deep learning,0.001461936,0.001461911,0.818947897,0.144908745,0.031757628,0.001461883,Imaging,0.5244652,TRUE,,,,,0,0.403234768,,,0.403234768
10913,A Deep Learning Approach for COVID-19 Trend Prediction,0,2008.05644,8/9/20,arxiv,0,4,deep learning,0.004775364,0.004775167,0.351871342,0.62902758,0.00477513,0.004775416,Epidemiology,0.18927673,FALSE,64,0.729173109,35,0.605298368,0,0.403234768,,,0.579235415
10914,"How Efficient is Contact Tracing in Mitigating the Spread of Covid-19? A
  Mathematical Modeling Approach",0,2008.03859,8/10/20,arxiv,0,3,"model simulation, mathematical model",0.001486406,0.00148643,0.043936283,0.950117994,0.001486436,0.001486452,Epidemiology,0.08704439,FALSE,52.33333333,0.649452656,10.66666667,0.365333155,1,0.537564047,,,0.517449953
10915,"Data-driven Inferences of Agency-level Risk and Response Communication
  on COVID-19 through Social Media based Interactions",0,2008.03866,8/10/20,arxiv,0,3,machine learning,0.001237132,0.023049045,0.001237175,0.865915306,0.107324247,0.001237094,Epidemiology,0.11097431,FALSE,43.33333333,0.571154679,14.33333333,0.415975381,1,0.537564047,,,0.508231369
10916,"A single-cell mathematical model of SARS-CoV-2 induced pyroptosis and
  the effects of anti-inflammatory intervention",0,2008.04172,8/10/20,arxiv,0,2,mathematical model,0.744043222,0.001861698,0.001861685,0.248509993,0.001861674,0.001861727,Drug discovery,0.48796114,FALSE,12,0.183190055,2.5,0.180826866,0,0.403234768,,,0.255750563
10917,"Time Fused Coefficient SIR Model with Application to COVID-19 Epidemic
  in the United States",0,2008.04284,8/10/20,arxiv,0,5,bayes,0.003465962,0.003466027,0.221799437,0.764336714,0.003465974,0.003465886,Epidemiology,0.102298796,FALSE,12.4,0.187148247,0.4,0.075796093,2,0.618927094,,,0.293957145
10918,"City-Scale Agent-Based Simulators for the Study of Non-Pharmaceutical
  Interventions in the Context of the COVID-19 Epidemic","Journal of the Indian Institute of Science, volume 100, pages
  809-847, 2020",2008.04849,8/11/20,arxiv,"Journal of the Indian Institute of Science, volume 100, pages
  809-847, 2020",17,digital health,0.006089746,0.006089706,0.006090034,0.969551126,0.006089745,0.006089644,Epidemiology,0.39198637,FALSE,28.11764706,0.40874513,19.70588235,0.477388279,3,0.667819001,,,0.517984137
10919,"So You Need Datasets for Your COVID-19 Detection Research Using Machine
  Learning?",0,2008.05906,8/11/20,arxiv,0,1,"machine learning, dataset",0.003465969,0.003466079,0.714960384,0.271175735,0.003465906,0.003465928,Epidemiology,0.021252632,FALSE,4,0.054734368,2,0.164302917,2,0.618927094,,,0.27932146
10920,"Is MOOC Learning Different for Dropouts? A Visually-Driven,
  Multi-granularity Explanatory ML Approach",0,2008.05209,8/12/20,arxiv,0,6,machine learning,0.001823408,0.001823413,0.35121918,0.494544942,0.001823489,0.148765569,Epidemiology,0.105284214,FALSE,58,0.689714887,,,2,0.618927094,,,0.65432099
10921,"Network reinforcement driven drug repurposing for COVID-19 by exploiting
  disease-gene-drug associations",0,2008.05377,8/12/20,arxiv,0,10,"supervised learning, computational, interactom",0.496294443,0.001126792,0.279754277,0.001126898,0.001126906,0.220570684,Drug discovery,0.31117988,FALSE,81,0.811552972,100.4,0.823187048,0,0.403234768,,,0.679324929
10922,"Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous
  Infection of COVID-19 in India",0,2008.06335,8/13/20,arxiv,0,6,"mathematical model, dataset",0.002296575,0.002296625,0.002296617,0.988517004,0.002296615,0.002296564,Epidemiology,0.011726558,FALSE,9.833333333,0.147628177,2.666666667,0.185442869,1,0.537564047,,,0.290211698
10923,Exploration of Gender Differences in COVID-19 Discourse on Reddit,0,2008.05713,8/13/20,arxiv,0,3,dataset,0.068653688,0.003927439,0.003927626,0.204035646,0.715528078,0.003927523,Healthcare,0.1259023,FALSE,66.33333333,0.742903086,103.6666667,0.828338239,2,0.618927094,,,0.73005614
10924,"An empirical model on the dynamics of Covid-19 spread in human
  population",0,2008.06346,8/13/20,arxiv,0,2,mathematical model,0.002720108,0.002720231,0.002720175,0.986399208,0.002720076,0.002720202,Epidemiology,0.035206795,FALSE,74,0.782113922,113.5,0.843122826,1,0.537564047,,,0.720933598
10925,"Considerations, Good Practices, Risks and Pitfalls in Developing AI
  Solutions Against COVID-19","Harvard CRCS Workshop on AI for Social Good, United States, 2020",2008.09043,8/13/20,arxiv,"Harvard CRCS Workshop on AI for Social Good, United States, 2020",5,artificial intelligence,0.072056316,0.00453081,0.797324908,0.117026099,0.004531079,0.004530787,Epidemiology,0.6390861,TRUE,19.2,0.287339972,9.2,0.339577201,2,0.618927094,,,0.415281422
10926,"A Spatial Stochastic SIR Model for Transmission Networks with
  Application to COVID-19 Epidemic in China",0,2008.06051,8/13/20,arxiv,0,3,bayes,0.002032774,0.002032856,0.002032773,0.957737381,0.034131133,0.002033084,Epidemiology,0.31900704,FALSE,29,0.41993939,11,0.371287129,0,0.403234768,,,0.398153762
10927,"Automated detection and quantification of COVID-19 airspace disease on
  chest radiographs: A novel approach achieving radiologist-level performance
  using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based
  ground-truth",0,2008.0633,8/13/20,arxiv,0,16,"neural network, dataset",0.001072183,0.001072187,0.931330482,0.001072239,0.001072191,0.064380719,Imaging,0.254179,FALSE,,,,,0,0.403234768,,,0.403234768
10928,"A Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1,1) and
  application in predicting total COVID-19 infected cases",0,2008.07581,8/13/20,arxiv,0,2,dataset,0.004109989,0.004109872,0.489167283,0.494393235,0.004109771,0.004109851,Epidemiology,0.03588906,FALSE,15,0.227596017,4.5,0.242708055,1,0.537564047,,,0.33595604
10929,"MIXCAPS: A Capsule Network-based Mixture of Experts for Lung Nodule
  Malignancy Prediction",0,2008.06072,8/13/20,arxiv,0,6,"deep learning, neural network, radiom, dataset",0.000988375,0.000988358,0.927591614,0.000988385,0.000988361,0.068454907,Clinics,0.04210019,FALSE,93.66666667,0.853794298,155.1666667,0.888145571,0,0.403234768,,,0.715058212
10930,Secure Data Hiding for Contact Tracing,0,2008.06297,8/14/20,arxiv,0,2,computational,0.001415152,0.00141522,0.207206787,0.78713252,0.00141521,0.001415111,Epidemiology,0.000964999,FALSE,73.5,0.780011132,146,0.88018464,0,0.403234768,,,0.68781018
10931,"Common pitfalls and recommendations for using machine learning to detect
  and prognosticate for COVID-19 using chest radiographs and CT scans",0,2008.06388,8/14/20,arxiv,0,16,machine learning,0.001751244,0.001751218,0.764209436,0.228785586,0.001751208,0.001751308,Imaging,0.52378863,TRUE,27.86666667,0.405405405,10.6,0.364262778,2,0.618927094,,,0.462865092
10932,"A New Dynamic Model to Predict the Effects of Governmental Decisions on
  the Progress of the CoViD-19 Epidemic",0,2008.11716,8/15/20,arxiv,0,2,mathematical model,0.001717319,0.001717221,0.001717287,0.962526685,0.001717213,0.030604275,Epidemiology,0.031703442,FALSE,24.42857143,0.360628363,3.142857143,0.200561948,1,0.537564047,,,0.366251453
10933,"New Normal: Cooperative Paradigm for Covid-19 Timely Detection and
  Containment using Internet of Things and Deep Learning",0,2008.12103,8/15/20,arxiv,0,3,"deep learning, neural network",0.002033092,0.039233399,0.166332259,0.696005751,0.002032913,0.094362586,Epidemiology,0.14046368,FALSE,206.6666667,0.974457295,59.33333333,0.718290072,0,0.403234768,,,0.698660712
10934,"Four-tier response system and spatial propagation of COVID-19 in China
  by a network model",0,2008.08152,8/16/20,arxiv,0,5,network model,0.00162274,0.001622704,0.001622707,0.991886419,0.001622732,0.001622698,Epidemiology,0.44955435,FALSE,83.2,0.818665347,46.8,0.667714744,5,0.739490092,,,0.741956728
10935,"OpenFraming: We brought the ML; you bring the data. Interact with your
  data and discover its frames",0,2008.06974,8/16/20,arxiv,0,10,deep learning,0.001330059,0.001330086,0.352296896,0.642382733,0.001330197,0.001330029,Epidemiology,0.000899226,FALSE,,,,,0,0.403234768,,,0.403234768
10936,"Population-Scale Study of Human Needs During the COVID-19 Pandemic:
  Analysis and Implications",0,2008.07045,8/17/20,arxiv,0,4,computational,0.067034672,0.00151188,0.001511866,0.89203139,0.036398364,0.001511829,Epidemiology,0.49270698,FALSE,214.25,0.976931165,663.25,0.986285791,2,0.618927094,,,0.860714683
10937,"Analysis of COVID-19 cases in India through Machine Learning: A Study of
  Intervention",0,2008.1045,8/17/20,arxiv,0,3,machine learning,0.069111099,0.001098845,0.001098866,0.905769453,0.021822827,0.00109891,Epidemiology,0.14483917,FALSE,26.33333333,0.386047375,8,0.320511105,0,0.403234768,,,0.369931082
10938,Dynamics and Control of Covid-19: Comments by Two Mathematicians,"EMS Newsletter Issue 117 (1 September 2020), pp. 29-37, available
  at
  https://www.ems-ph.org/journals/show_abstract.php?issn=1027-488X&vol=9&iss=117&rank=6",2008.07929,8/17/20,arxiv,"EMS Newsletter Issue 117 (1 September 2020), pp. 29-37, available
  at
  https://www.ems-ph.org/journals/show_abstract.php?issn=1027-488X&vol=9&iss=117&rank=6",2,mathematical model,0.002296594,0.086443716,0.002296666,0.904369587,0.002296634,0.002296803,Epidemiology,0.8181329,TRUE,82,0.814769002,86.5,0.794153064,0,0.403234768,,,0.670718945
10939,"Computational timeline reconstruction of the stories surrounding Trump:
  Story turbulence, narrative control, and collective chronopathy",0,2008.07301,8/17/20,arxiv,0,7,computational,0.001987147,0.136608371,0.001987154,0.681748949,0.175681167,0.001987212,Epidemiology,0.033168674,FALSE,34.14285714,0.478137176,52.28571429,0.689991972,3,0.667819001,,,0.611982716
10940,"Do face masks introduce bias in speech technologies? The case of
  automated scoring of speaking proficiency","Proceedings of Interspeech 2020, 1942-1946",2008.0752,8/17/20,arxiv,"Proceedings of Interspeech 2020, 1942-1946",5,dataset,0.002080662,0.002080683,0.234161393,0.521860345,0.23773628,0.002080637,Epidemiology,0.3194582,FALSE,55.4,0.670542396,41.8,0.643363661,1,0.537564047,,,0.617156701
10941,"Contact Area Detector using Cross View Projection Consistency for
  COVID-19 Projects",0,2008.07712,8/18/20,arxiv,0,7,"neural network, transfer learning",0.090075444,0.001098842,0.443532093,0.46309598,0.001098833,0.001098808,Epidemiology,0.000538409,FALSE,16.42857143,0.248067289,1.428571429,0.134265454,1,0.537564047,,,0.306632263
10942,"MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images
  in the Context of COVID-19",0,2008.08016,8/18/20,arxiv,0,4,"deep learning, dataset",0.000926267,0.000926281,0.382390678,0.397974081,0.216856433,0.000926261,Epidemiology,0.17483297,FALSE,37,0.508936854,9.75,0.349678887,3,0.667819001,,,0.508811581
10943,"Physics-informed machine learning for the COVID-19 pandemic: Adherence
  to social distancing and short-term predictions for eight countries",0,2008.08162,8/18/20,arxiv,0,2,machine learning,0.001350337,0.001350355,0.095880671,0.89871792,0.001350351,0.001350366,Epidemiology,0.006789744,FALSE,37.5,0.513946441,4,0.231469093,0,0.403234768,,,0.382883434
10944,"A Data-Efficient Deep Learning Based Smartphone Application For
  Detection Of Pulmonary Diseases Using Chest X-rays",0,2008.08912,8/19/20,arxiv,0,6,"deep learning, adversarial network, dataset",0.001141371,0.001141342,0.775350032,0.174757163,0.046468756,0.001141335,Imaging,0.005700916,FALSE,8,0.118683901,7.666666667,0.310877709,0,0.403234768,,,0.277598793
10945,"Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using
  X-ray Chest Images",0,2008.08496,8/19/20,arxiv,0,7,"deep learning, dataset",0.001310342,0.001310349,0.973511507,0.001310394,0.021246994,0.001310415,Imaging,0.008799791,FALSE,25.71428571,0.376894057,11.85714286,0.383328873,2,0.618927094,,,0.459716675
10946,"Are temporary value-added tax reductions passed on to consumers?
  Evidence from Germany's stimulus",0,2008.08511,8/19/20,arxiv,0,3,dataset,0.003101545,0.003101641,0.003101612,0.984491905,0.003101641,0.003101656,Epidemiology,0.046560913,FALSE,35,0.488032655,43.66666667,0.652261172,0,0.403234768,,,0.514509532
10947,"Image quality assessment for closed-loop computer-assisted lung
  ultrasound",0,2008.0884,8/20/20,arxiv,0,11,"machine learning, classifier, deep-learning",0.00111262,0.001112634,0.994436688,0.001112653,0.001112678,0.001112726,Imaging,0.03053829,FALSE,128.9090909,0.919970314,299.4545455,0.953973776,1,0.537564047,,,0.803836045
10948,"How Have We Reacted To The COVID-19 Pandemic? Analyzing Changing Indian
  Emotions Through The Lens of Twitter",0,2008.09035,8/20/20,arxiv,0,4,dataset,0.00146194,0.00146192,0.118040175,0.633275574,0.244298545,0.001461846,Epidemiology,0.0331088,FALSE,4,0.054734368,0,0.055525823,1,0.537564047,,,0.215941413
10949,"Modeling the effects of prosocial awareness on COVID-19 dynamics: A case
  study on Colombia",0,2008.09109,8/20/20,arxiv,0,2,mathematical model,0.002238556,0.002238483,0.002238613,0.988807372,0.00223851,0.002238467,Epidemiology,0.090767145,FALSE,84.5,0.823489393,55.5,0.704575863,0,0.403234768,,,0.643766675
10950,"Dynamic change of gene-to-gene regulatory networks in response to
  SARS-CoV-2 infection",0,2008.09261,8/21/20,arxiv,0,6,"bayes, network analysis",0.75888159,0.001538158,0.001538196,0.213212258,0.001538106,0.023291693,Drug discovery,0.53082955,TRUE,47.5,0.610551054,40.66666667,0.63774418,0,0.403234768,,,0.550510001
10951,"BlindSignedID: Mitigating Denial-of-Service Attacks on Digital Contact
  Tracing",0,2008.09351,8/21/20,arxiv,0,2,mathematical model,0.001098833,0.001098831,0.023647422,0.971957268,0.001098836,0.001098809,Epidemiology,0.2303049,FALSE,86.5,0.831653163,585.5,0.982873963,2,0.618927094,,,0.811151407
10952,"Machine Learning and Meta-Analysis Approach to Identify Patient
  Comorbidities and Symptoms that Increased Risk of Mortality in COVID-19",0,2008.12683,8/21/20,arxiv,0,12,"machine learning, dataset",0.038183256,0.001254587,0.177903221,0.001254657,0.001254636,0.780149643,Clinics,0.29851925,FALSE,12.5,0.189436576,5.333333333,0.262911426,3,0.667819001,,,0.373389001
10953,"Comparative performance analysis of the ResNet backbones of Mask RCNN to
  segment the signs of COVID-19 in chest CT scans",0,2008.09713,8/21/20,arxiv,0,3,neural network,0.001171567,0.00117158,0.776328921,0.21898461,0.001171633,0.001171689,Imaging,0.039576918,FALSE,69,0.759416167,17.33333333,0.45424137,4,0.707574542,,,0.640410693
10954,COVID-19 Pandemic Outbreak in the Subcontinent: A data-driven analysis,0,2008.09803,8/22/20,arxiv,0,5,bayes,0.001717224,0.001717248,0.00171733,0.948597193,0.001717212,0.044533793,Epidemiology,0.17631704,FALSE,13.6,0.206011503,3.8,0.221501204,0,0.403234768,,,0.276915825
10955,"Symbolic Semantic Segmentation and Interpretation of COVID-19 Lung
  Infections in Chest CT volumes based on Emergent Languages",0,2008.09866,8/22/20,arxiv,0,5,"deep learning, adversarial network",0.056450511,0.0011873,0.810737308,0.129250273,0.001187337,0.001187271,Imaging,0.030736864,FALSE,9.4,0.140021028,0.4,0.075796093,0,0.403234768,,,0.20635063
10956,"Epidemics with asymptomatic transmission: Sub-critical phase from
  recursive contact tracing",0,2008.09896,8/22/20,arxiv,0,2,network model,0.000966765,0.000966805,0.000966804,0.995166083,0.000966788,0.000966755,Epidemiology,0.012416571,FALSE,66,0.741356918,188,0.910891089,1,0.537564047,,,0.729937351
10957,"Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis
  and Deep Learning -- a Case Study on COVID-19",0,2008.10031,8/23/20,arxiv,0,4,"deep learning, lstm, dataset",0.001254613,0.001254655,0.251003326,0.718856226,0.026376583,0.001254596,Epidemiology,0.07850978,FALSE,21.5,0.318263343,3,0.199424672,1,0.537564047,,,0.351750688
10958,"A New Mathematical Model for Controlled Pandemics Like COVID-19 : AI
  Implemented Predictions",0,2008.1053,8/24/20,arxiv,0,4,"machine learning, mathematical model",0.001330082,0.001330045,0.176013687,0.797932444,0.022063693,0.00133005,Epidemiology,0.042438895,FALSE,175.75,0.96239718,258.25,0.940861654,0,0.403234768,,,0.7688312
10959,"Machine Reasoning to Assess Pandemics Risks: Case of USS Theodore
  Roosevelt",0,2008.1104,8/24/20,arxiv,0,2,bayes,0.003465921,0.003465911,0.103551295,0.882585065,0.003465916,0.003465891,Epidemiology,0.14454985,FALSE,141.5,0.936050467,39,0.62784319,0,0.403234768,,,0.655709475
10960,"Optimal Scheduling of Anticipated COVID-19 Vaccination: A Case Study of
  New York State",0,2008.10702,8/24/20,arxiv,0,5,dataset,0.002238522,0.002238456,0.002238568,0.988807313,0.002238605,0.002238536,Epidemiology,0.09084126,FALSE,113.8,0.897272559,127.4,0.860784051,0,0.403234768,,,0.720430459
10961,"Quantifying the impact of COVID-19 on the US stock market: An analysis
  from multi-source information",0,2008.10885,8/25/20,arxiv,0,4,"machine learning, forecasting model",0.002422366,0.002422293,0.136974806,0.853335892,0.00242231,0.002422333,Epidemiology,0.055689335,FALSE,15.75,0.237862577,5,0.257024351,1,0.537564047,,,0.344150325
10962,"A comparison of deep machine learning algorithms in COVID-19 disease
  diagnosis",0,2008.11639,8/25/20,arxiv,0,5,"machine learning, neural network, network model",0.001717189,0.001717202,0.991413671,0.001717362,0.001717227,0.001717349,Imaging,0.043826908,FALSE,10.8,0.161110768,0.8,0.101351351,0,0.403234768,,,0.221898962
10963,Masked Face Recognition for Secure Authentication,0,2008.11104,8/25/20,arxiv,0,2,dataset,0.001392903,0.001392859,0.631531496,0.362897019,0.001392917,0.001392807,Epidemiology,0.004090011,FALSE,23,0.34225988,1,0.122023013,2,0.618927094,,,0.361069996
10964,At Your Service: Coffee Beans Recommendation From a Robot Assistant,0,2008.13585,8/26/20,arxiv,0,6,"machine learning, supervised learning, unsupervised learning, computational, dataset",0.001622794,0.001622746,0.483548278,0.509960687,0.001622776,0.00162272,Epidemiology,0.1780672,FALSE,56.33333333,0.677407385,13.66666667,0.407412363,0,0.403234768,,,0.496018172
10965,"On the Composition and Limitations of Publicly Available COVID-19 X-Ray
  Imaging Datasets",0,2008.11572,8/26/20,arxiv,0,4,"machine learning, deep learning, dataset",0.053167977,0.001461939,0.782657172,0.159789049,0.001461956,0.001461906,Imaging,0.006944358,FALSE,17.75,0.267672707,59,0.717554188,5,0.739490092,,,0.574905662
10966,"DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment
  in COVID-19 Pandemic","Applied Sciences. 2020, 10, 7514",2008.11672,8/26/20,arxiv,"Applied Sciences. 2020, 10, 7514",2,"neural network, network model, dataset",0.001112635,0.001112647,0.413103554,0.582445862,0.00111268,0.001112621,Epidemiology,0.47790405,FALSE,8.5,0.126662131,0.5,0.087101953,0,0.403234768,,,0.205666284
10967,"Share Price Prediction of Aerospace Relevant Companies with Recurrent
  Neural Networks based on PCA",0,2008.11788,8/26/20,arxiv,0,2,"neural network, prediction model",0.001511924,0.001511855,0.484331729,0.509620833,0.001511833,0.001511826,Epidemiology,0.011506081,FALSE,41,0.549013544,14.5,0.418450629,0,0.403234768,,,0.456899647
10968,"A Data-driven Understanding of COVID-19 Dynamics Using Sequential
  Genetic Algorithm Based Probabilistic Cellular Automata",0,2008.1202,8/27/20,arxiv,0,2,probabilistic,0.095876883,0.048887029,0.001330142,0.851245859,0.00133006,0.001330026,Epidemiology,0.32180834,FALSE,25,0.369286907,3.5,0.213607172,7,0.785110192,,,0.456001424
10969,"Cross-language sentiment analysis of European Twitter messages duringthe
  COVID-19 pandemic",0,2008.12172,8/27/20,arxiv,0,4,neural network,0.002490459,0.002490693,0.049108642,0.893686123,0.049733589,0.002490493,Epidemiology,0.009109288,FALSE,80.5,0.809202795,43.75,0.652863259,3,0.667819001,,,0.709961685
10970,"Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the
  COVID-19 Epidemic",Scientific Data; 2020,2008.12238,8/27/20,arxiv,Scientific Data; 2020,6,dataset,0.001310419,0.042760251,0.00131036,0.95199818,0.001310432,0.001310358,Epidemiology,0.30036163,FALSE,21.33333333,0.31548024,6.666666667,0.290607439,7,0.785110192,,,0.463732624
10971,"Twitter Interaction to Analyze Covid-19 Impact in Ghana, Africa from
  March to July",0,2008.12277,8/27/20,arxiv,0,2,text mining,0.002422283,0.002422352,0.002422329,0.890401105,0.099909663,0.002422269,Epidemiology,0.094135135,FALSE,11.5,0.17416043,0,0.055525823,3,0.667819001,,,0.299168418
10972,"Repurposing TREC-COVID Annotations to Answer the Key Questions of
  CORD-19",0,2008.12353,8/27/20,arxiv,0,2,dataset,0.150734749,0.001653122,0.218265915,0.626039843,0.001653182,0.001653189,Epidemiology,0.003828883,FALSE,37,0.508936854,36.5,0.614329676,0,0.403234768,,,0.508833766
10973,"A free web service for fast COVID-19 classification of chest X-Ray
  images",0,2009.01657,8/27/20,arxiv,0,9,"deep learning, computational, artificial intelligence",0.001272664,0.001272692,0.993636549,0.001272724,0.00127273,0.001272641,Imaging,0.15102494,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
10974,"HOPES -- An Integrative Digital Phenotyping Platform for Data
  Collection, Monitoring and Machine Learning",0,2008.12431,8/28/20,arxiv,0,7,machine learning,0.003101678,0.003101588,0.172593138,0.648001397,0.170100634,0.003101565,Epidemiology,0.4635628,FALSE,17.14285714,0.258519389,,,0,0.403234768,,,0.330877078
10975,"Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism
  Jobs Crisis in Australia from 2012 to 2020",0,2008.12459,8/28/20,arxiv,0,4,machine learning,0.001901711,0.001901777,0.036899027,0.463013251,0.494382428,0.001901805,Healthcare,0.15007433,FALSE,20.5,0.304966294,11.75,0.381857105,0,0.403234768,,,0.363352722
10976,"Pre-training of Graph Neural Network for Modeling Effects of Mutations
  on Protein-Protein Binding Affinity",0,2008.12473,8/28/20,arxiv,0,4,"deep learning, computational, neural network, dataset",0.389996783,0.210821857,0.394566937,0.001538231,0.001538097,0.001538095,Drug discovery,0.013152927,FALSE,27.75,0.404292164,,,0,0.403234768,,,0.403763466
10977,Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs,0,2008.12706,8/28/20,arxiv,0,5,bayes,0.004530683,0.194364984,0.281121139,0.510921646,0.00453085,0.004530697,Epidemiology,0.6402576,TRUE,82.8,0.817242872,205.6,0.920056195,2,0.618927094,,,0.78540872
10978,"Impact of COVID-19 on City-Scale Transportation and Safety: An Early
  Experience from Detroit",0,2012.1208,8/28/20,arxiv,0,3,deep learning,0.001371271,0.001371269,0.302606101,0.691908716,0.001371308,0.001371335,Epidemiology,0.036566973,FALSE,18.66666667,0.279670975,14.66666667,0.420323789,0,0.403234768,,,0.367743177
10979,Temporal Mental Health Dynamics on Social Media,0,2008.13121,8/30/20,arxiv,0,2,data mining,0.003465986,0.003465924,0.231228536,0.493225293,0.265148351,0.00346591,Epidemiology,0.46417063,FALSE,90,0.843094811,76.5,0.769935777,0,0.403234768,,,0.672088452
10980,"High-Throughput Virtual Screening of 4487 flavonoids: New insights on
  the structural inhibition of SARS-CoV-2 Main Protease",0,2008.13264,8/30/20,arxiv,0,7,virtual screening,0.993726697,0.001254648,0.001254659,0.00125468,0.001254655,0.001254661,Drug discovery,0.51226676,TRUE,3,0.037293586,0.142857143,0.057398983,1,0.537564047,,,0.210752206
10981,"Real-time Prediction of COVID-19 related Mortality using Electronic
  Health Records",0,2008.13412,8/31/20,arxiv,0,8,machine learning,0.001350363,0.001350382,0.244238073,0.199823438,0.001350369,0.551887376,Clinics,0.66096723,TRUE,52.75,0.651802833,71.5,0.757626438,0,0.403234768,,,0.604221346
10982,Perceiving Humans: from Monocular 3D Localization to Social Distancing,0,2009.00984,9/1/20,arxiv,0,3,neural network,0.087451076,0.001330083,0.419143274,0.410171275,0.067550578,0.014353714,Imaging,0.005128205,FALSE,112,0.894180221,764.6666667,0.989363126,0,0.403234768,,,0.762259371
10983,"Optimal control of the COVID-19 pandemic: controlled sanitary
  deconfinement in Portugal","Scientific Reports 11 (2021), Art. 3451, 15 pp",2009.0066,9/1/20,arxiv,"Scientific Reports 11 (2021), Art. 3451, 15 pp",12,mathematical model,0.001272667,0.001272668,0.001272671,0.993636601,0.001272696,0.001272697,Epidemiology,0.39584774,FALSE,63.16666667,0.722431814,20.5,0.486218892,3,0.667819001,,,0.625489902
10984,Checking individuals and sampling populations with imperfect tests,0,2009.04843,9/1/20,arxiv,0,2,"bayes, probabilistic",0.001350362,0.001350403,0.045731294,0.883035144,0.067182435,0.001350362,Epidemiology,0.005913466,FALSE,193.5,0.97037541,131.5,0.865199358,3,0.667819001,,,0.83446459
10985,"Computational screening of repurposed drugs and natural products against
  SARS-Cov-2 main protease (Mpro) as potential COVID-19 therapies",0,2009.00744,9/1/20,arxiv,0,4,"computational, in silico",0.905072484,0.001171596,0.001171593,0.001171611,0.031629812,0.059782905,Drug discovery,0.18926251,FALSE,6,0.086028821,,,1,0.537564047,,,0.311796434
10986,"Computational evidence on repurposing the anti-influenza drugs baloxavir
  acid and baloxavir marboxil against COVID-19",0,2009.01094,9/2/20,arxiv,0,2,computational,0.968442322,0.025710041,0.001461855,0.001462023,0.001461901,0.001461858,Drug discovery,0.7624952,TRUE,0,0.006432061,,,1,0.537564047,,,0.271998054
10987,CODO: An Ontology for Collection and Analysis of Covid-19 Data,0,2009.0121,9/2/20,arxiv,0,2,dataset,0.003927392,0.003927488,0.432180807,0.55210872,0.00392771,0.003927882,Epidemiology,0.6149269,TRUE,25,0.369286907,12,0.386740701,2,0.618927094,,,0.458318234
10988,"Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process
  Description (version 1.0)",0,2009.01265,9/2/20,arxiv,0,30,dataset,0.004109873,0.004109771,0.004109959,0.750693965,0.232866584,0.004109848,Epidemiology,0.38003302,FALSE,37.36666667,0.511843651,,,4,0.707574542,,,0.609709097
10989,"WNTRAC: AI Assisted Tracking of Non-pharmaceutical Interventions
  Implemented Worldwide for COVID-19",0,2009.07057,9/2/20,arxiv,0,26,dataset,0.001861749,0.001861718,0.082073915,0.910479138,0.001861808,0.001861671,Epidemiology,0.21633649,FALSE,1.2,0.012740429,,,2,0.618927094,,,0.315833762
10990,"orgFAQ: A New Dataset and Analysis on Organizational FAQs and User
  Questions",0,2009.0146,9/3/20,arxiv,0,4,dataset,0.002183352,0.002183244,0.32919473,0.662072214,0.002183283,0.002183178,Epidemiology,0.001769185,FALSE,,,,,0,0.403234768,,,0.403234768
10991,"PolSIRD: Modeling Epidemic Spread under Intervention Policies and an
  Application to the Spread of COVID-19",0,2009.01894,9/3/20,arxiv,0,5,mathematical model,0.002422347,0.002422307,0.063815472,0.926495349,0.002422279,0.002422246,Epidemiology,0.012733251,FALSE,21,0.312016822,9,0.337904736,1,0.537564047,,,0.395828535
10992,"A New Screening Method for COVID-19 based on Ocular Feature Recognition
  by Machine Learning Tools",0,2009.03184,9/4/20,arxiv,0,7,machine learning,0.00137128,0.001371295,0.884225791,0.110288958,0.001371325,0.001371352,Imaging,0.28135532,FALSE,46.57142857,0.60152143,12.57142857,0.393497458,0,0.403234768,,,0.466084552
10993,"Proximity Sensing: Modeling and Understanding Noisy RSSI-BLE Signals and
  Other Mobile Sensor Data for Digital Contact Tracing",0,2009.04991,9/4/20,arxiv,0,9,deep learning,0.073112981,0.00249049,0.352702932,0.566712663,0.002490478,0.002490456,Epidemiology,0.26965907,FALSE,18.55555556,0.278310347,6.666666667,0.290607439,0,0.403234768,,,0.324050851
10994,"Evaluating the effect of city lock-down on controlling COVID-19
  propagation through deep learning and network science models","[J]. Cities, 2020: 102869",2009.02152,9/4/20,arxiv,"[J]. Cities, 2020: 102869",5,"supervised learning, deep learning",0.002080558,0.002080584,0.183029672,0.808647867,0.002080693,0.002080626,Epidemiology,0.21567973,FALSE,94.6,0.8556497,51.8,0.688587102,8,0.799987654,,,0.781408152
10995,"Assess the impacts of human mobility change on COVID-19 dynamics in
  Arizona, U.S.: a modeling study incorporating Google Community Mobility
  Reports",0,2009.02419,9/4/20,arxiv,0,2,dataset,0.00203284,0.002032805,0.002032778,0.989835993,0.002032828,0.002032755,Epidemiology,0.33045432,FALSE,11.5,0.17416043,2.5,0.180826866,0,0.403234768,,,0.252740688
10996,Clustering COVID-19 Lung Scans,0,2009.09899,9/5/20,arxiv,0,5,"machine learning, dataset",0.001565474,0.001565423,0.713424094,0.213279776,0.001565383,0.068599851,Epidemiology,0.09069127,FALSE,15,0.227596017,9.8,0.350682366,0,0.403234768,,,0.32717105
10997,"Using multiple data streams to estimate and forecast SARS-CoV-2
  transmission dynamics, with application to the virus spread in Orange County,
  California",0,2009.02654,9/6/20,arxiv,0,16,bayes,0.00131033,0.001310366,0.001310358,0.993448203,0.001310342,0.0013104,Epidemiology,0.11764723,FALSE,30.125,0.433174593,32.75,0.590647578,4,0.707574542,,,0.577132238
10998,"BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter
  by Combining Deep Learning and Transfer Learning Models",0,2009.02671,9/6/20,arxiv,0,3,"deep learning, transfer learning, dataset",0.004310114,0.004310058,0.808308334,0.174451678,0.004309908,0.004309908,Epidemiology,0.026073009,FALSE,24,0.35574247,6,0.280037463,0,0.403234768,,,0.346338234
10999,Optimization of High-dimensional Simulation Models Using Synthetic Data,0,2009.02781,9/6/20,arxiv,0,4,"simulation model, machine learning, artificial intelligence",0.001486476,0.001486419,0.213472589,0.704013179,0.043444383,0.036096952,Epidemiology,0.12700593,FALSE,63.75,0.726390006,52.5,0.690928552,1,0.537564047,,,0.651627535
11000,COVID-19 Literature Topic-Based Search via Hierarchical NMF,0,2009.09074,9/7/20,arxiv,0,10,dataset,0.002183428,0.077508718,0.142556714,0.773384305,0.002183307,0.002183527,Epidemiology,0.19326636,FALSE,41.7,0.554888985,86,0.793015788,0,0.403234768,,,0.58371318
11001,"Projections for COVID-19 spread in India and its worst affected five
  states using the Modified SEIRD and LSTM models",0,2009.06457,9/7/20,arxiv,0,5,"deep learning, lstm",0.001310335,0.001310336,0.074656447,0.920102204,0.001310354,0.001310324,Epidemiology,0.09020528,FALSE,101,0.871296926,30.8,0.575729195,1,0.537564047,,,0.661530056
11002,"Uncovering the Corona Virus Map Using Deep Entities and Relationship
  Models",0,2009.03068,9/7/20,arxiv,0,5,neural network,0.110468287,0.003214348,0.501803347,0.378085595,0.003214256,0.003214167,Epidemiology,0.046934515,FALSE,54,0.661574618,12.6,0.394300241,0,0.403234768,,,0.486369876
11003,"The Role of Asymptomatic Individuals in the COVID-19 Pandemic via
  Complex Networks",0,2009.03649,9/8/20,arxiv,0,4,predictive model,0.068396255,0.001751194,0.001751207,0.883349512,0.043000627,0.001751206,Epidemiology,0.34629217,FALSE,23.5,0.348506401,16.75,0.446012845,3,0.667819001,,,0.487446082
11004,"Spatial Bayesian Hierarchical Modelling with Integrated Nested Laplace
  Approximation",0,2009.03712,9/8/20,arxiv,0,3,bayes,0.002996489,0.109288222,0.002996681,0.878725728,0.00299645,0.00299643,Epidemiology,0.2919858,FALSE,41,0.549013544,8.666666667,0.331415574,1,0.537564047,,,0.472664389
11005,"The impact of COVID-19 on relative changes in aggregated mobility using
  mobile-phone data",0,2009.03798,9/8/20,arxiv,0,3,dataset,0.004530663,0.004530728,0.004530719,0.70935209,0.004531008,0.272524793,Epidemiology,0.108122736,FALSE,213.6666667,0.976683778,334.6666667,0.963138881,0,0.403234768,,,0.781019142
11006,"COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for
  Detection of COVID-19 Cases from Chest CT Images",0,2009.05383,9/8/20,arxiv,0,3,"neural network, dataset",0.000936084,0.000936081,0.971768264,0.000936133,0.024487285,0.000936154,Imaging,0.4205972,FALSE,26,0.382398417,52,0.689523682,13,0.858880178,,,0.643600759
11007,"LynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for
  Identification of Informative COVID-19 English Tweets",0,2009.03849,9/8/20,arxiv,0,3,"machine learning, supervised learning, classifier",0.003607198,0.003607185,0.770219798,0.215351499,0.003607155,0.003607165,Epidemiology,0.14233974,FALSE,10,0.15214299,0,0.055525823,1,0.537564047,,,0.248410953
11008,"Referenced Thermodynamic Integration for Bayesian Model Selection:
  Application to COVID-19 Model Selection",0,2009.03851,9/8/20,arxiv,0,5,"bayes, bayesian model",0.001901733,0.001901824,0.285130659,0.707262271,0.001901803,0.00190171,Epidemiology,0.021966547,FALSE,51,0.63875317,175.8,0.903532245,0,0.403234768,,,0.648506727
11009,"Covid-Transformer: Detecting COVID-19 Trending Topics on Twitter Using
  Universal Sentence Encoder",0,2009.03947,9/8/20,arxiv,0,3,deep learning,0.001565338,0.001565419,0.259859971,0.733878612,0.001565334,0.001565326,Epidemiology,0.000951588,FALSE,24.66666667,0.36334962,14.66666667,0.420323789,1,0.537564047,,,0.440412485
11010,"Fuzzy Unique Image Transformation: Defense Against Adversarial Attacks
  On Deep COVID-19 Models",0,2009.04004,9/8/20,arxiv,0,2,"deep model, dataset",0.001272716,0.001272641,0.99363664,0.001272689,0.001272665,0.001272649,Imaging,0.029388487,FALSE,25,0.369286907,17.5,0.45611453,0,0.403234768,,,0.409545402
11011,"Semi-Supervised Active Learning for COVID-19 Lung Ultrasound
  Multi-symptom Classification",0,2009.05436,9/9/20,arxiv,0,6,"artificial intelligence, active learning, dataset",0.059332493,0.001291253,0.935502449,0.001291256,0.001291312,0.001291237,Imaging,0.72508895,TRUE,86.66666667,0.832209784,66.5,0.744246722,0,0.403234768,,,0.659897091
11012,"Forecasting financial markets with semantic network analysis in the
  COVID-19 crisis",0,2009.04975,9/9/20,arxiv,0,4,network analysis,0.002720128,0.087164542,0.002720185,0.901954618,0.002720279,0.002720248,Epidemiology,0.010547668,FALSE,21.25,0.314614386,4,0.231469093,1,0.537564047,,,0.361215842
11013,"Surveying the side-chain network approach to protein structure and
  dynamics: The SARS-CoV-2 spike protein as an illustrative case",0,2009.04438,9/9/20,arxiv,0,6,network analysis,0.569093376,0.001371338,0.001371326,0.425421421,0.001371284,0.001371256,Drug discovery,0.17224917,FALSE,58.33333333,0.691941369,21,0.492239765,1,0.537564047,,,0.57391506
11014,"Multi-Objective Reinforcement Learning for Infectious Disease Control
  with Application to COVID-19 Spread",0,2009.04607,9/9/20,arxiv,0,3,bayes,0.001901697,0.001901695,0.096255639,0.896137531,0.001901746,0.001901692,Epidemiology,0.06841785,FALSE,64.33333333,0.730781124,31.66666667,0.582686647,0,0.403234768,,,0.57223418
11015,"Comprehensive Comparison of Deep Learning Models for Lung and COVID-19
  Lesion Segmentation in CT scans",0,2009.06412,9/10/20,arxiv,0,3,"deep learning, dataset",0.001219999,0.001220019,0.993899966,0.001220026,0.001219999,0.001219991,Imaging,0.037568808,FALSE,144,0.938524337,64.66666667,0.737556864,0,0.403234768,,,0.693105323
11016,"Predicting COVID-19 cases using Bidirectional LSTM on multivariate time
  series",0,2009.12325,9/10/20,arxiv,0,4,lstm,0.001593527,0.001593518,0.322403971,0.647159482,0.001593591,0.025655911,Epidemiology,0.35709625,FALSE,45.25,0.589337621,28.5,0.558402462,0,0.403234768,,,0.516991617
11017,Transfer Graph Neural Networks for Pandemic Forecasting,0,2009.08388,9/10/20,arxiv,0,3,"neural network, transfer learning",0.001392864,0.001392876,0.426388031,0.568040535,0.001392851,0.001392844,Epidemiology,0.00584513,FALSE,126.3333333,0.915888428,246.3333333,0.936914637,1,0.537564047,,,0.796789038
11018,"COVID CT-Net: Predicting Covid-19 From Chest CT Images Using Attentional
  Convolutional Network",0,2009.05096,9/10/20,arxiv,0,5,"machine learning, deep learning, dataset",0.001622705,0.001622741,0.99188628,0.001622825,0.001622687,0.001622761,Imaging,0.008913517,FALSE,135,0.926773455,268.4,0.945343859,5,0.739490092,,,0.870535802
11019,Infection Kinetics of Covid-19: Is Lockdown a Potent Containment Tool?,0,2009.0528,9/11/20,arxiv,0,5,machine learning,0.001393009,0.001392883,0.14789924,0.598406837,0.001392859,0.249515172,Epidemiology,0.35614017,FALSE,12.8,0.192343373,0.8,0.101351351,0,0.403234768,,,0.232309831
11020,"Probabilistic and mean-field model of COVID-19 epidemics with user
  mobility and contact tracing",0,2009.05304,9/11/20,arxiv,0,4,probabilistic,0.003607167,0.00360718,0.003607265,0.981963876,0.003607173,0.003607339,Epidemiology,0.038186163,FALSE,123.25,0.911992084,301.25,0.954575863,2,0.618927094,,,0.828498347
11021,Bayesian Beta-Binomial Prevalence Estimation Using an Imperfect Test,0,2009.05446,9/11/20,arxiv,0,1,bayes,0.002898296,0.002898393,0.066131964,0.725456993,0.199715909,0.002898444,Epidemiology,0.0893231,FALSE,24,0.35574247,141,0.875702435,0,0.403234768,,,0.544893224
11022,"A deep-learning model for evaluating and predicting the impact of
  lockdown policies on COVID-19 cases",0,2009.05481,9/11/20,arxiv,0,4,"deep-learning, prediction model",0.002562529,0.002562602,0.312534699,0.677215016,0.002562636,0.002562517,Epidemiology,0.068389684,FALSE,45.25,0.589337621,28.5,0.558402462,0,0.403234768,,,0.516991617
11023,"Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-ray
  Segmentation",0,2009.10608,9/11/20,arxiv,0,4,"deep learning, neural network, dataset",0.000898084,0.012946542,0.983461121,0.000898105,0.000898066,0.000898082,Imaging,0.006324172,FALSE,36.5,0.503123261,33.5,0.595999465,0,0.403234768,,,0.500785831
11024,"Characterizing Twitter Interaction during COVID-19 pandemic using
  Complex Networks and Text Mining",0,2009.05619,9/11/20,arxiv,0,1,text mining,0.001486554,0.001486534,0.001486498,0.992567475,0.00148648,0.001486459,Epidemiology,0.016417831,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
11025,"Data mining and analysis of scientific research data records on Covid 19
  mortality, immunity, and vaccine development in the first wave of the Covid
  19 pandemic","Volume 14, Issue 5, September October 2020,",2009.05793,9/12/20,arxiv,"Volume 14, Issue 5, September October 2020,",3,data mining,0.002806624,0.002806544,0.002806477,0.901577376,0.00280646,0.087196518,Epidemiology,0.36202854,FALSE,24.33333333,0.359700662,7.666666667,0.310877709,6,0.764429903,,,0.478336091
11026,Country Image in COVID-19 Pandemic: A Case Study of China,0,2009.05817,9/12/20,arxiv,0,7,dataset,0.001438141,0.001438163,0.240809365,0.576230152,0.178646063,0.001438115,Epidemiology,0.15357688,FALSE,22.14285714,0.326736347,8.857142857,0.333422531,1,0.537564047,,,0.399240975
11027,Tracking disease outbreaks from sparse data with Bayesian inference,0,2009.05863,9/12/20,arxiv,0,3,bayes,0.001291253,0.112067037,0.065317784,0.818741429,0.001291265,0.001291232,Epidemiology,0.022385359,FALSE,302.6666667,0.990846682,507,0.979328338,1,0.537564047,,,0.835913022
11028,"Network analysis and disease subnets for the SARS-CoV-2/Human
  interactome",0,2009.06035,9/13/20,arxiv,0,3,"interactom, network analysis",0.85798883,0.001392902,0.001392897,0.080604361,0.001392993,0.057228017,Drug discovery,0.18848908,FALSE,64.66666667,0.732822067,291.6666667,0.951833021,0,0.403234768,,,0.695963285
11029,Deep Transparent Prediction through Latent Representation Analysis,0,2009.07044,9/13/20,arxiv,0,9,"supervised learning, unsupervised learning, deep learning, neural network",0.001415139,0.00141512,0.925361736,0.00141519,0.068977675,0.00141514,Imaging,0.008414358,FALSE,9.444444444,0.140392108,9.555555556,0.345999465,0,0.403234768,,,0.296542114
11030,Time-varying auto-regressive models for count time-series,0,2009.07634,9/13/20,arxiv,0,2,"bayes, bayesian model",0.002996504,0.002997045,0.002996661,0.894552018,0.002996679,0.093461093,Epidemiology,0.113609165,FALSE,18.5,0.278001113,1,0.122023013,1,0.537564047,,,0.312529391
11031,"Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound
  Image Analysis","Applied Sciences 2021 (special issue on: ""Fighting COVID-19:
  Emerging Techniques and Aid Systems for Prevention, Forecasting and
  Diagnosis"")",2009.06116,9/13/20,arxiv,"Applied Sciences 2021 (special issue on: ""Fighting COVID-19:
  Emerging Techniques and Aid Systems for Prevention, Forecasting and
  Diagnosis"")",6,"deep learning, neural network, image analysis, dataset",0.001272701,0.001272684,0.920977541,0.073931731,0.001272653,0.00127269,Imaging,0.41413298,FALSE,39,0.530521368,338.3333333,0.963740969,4,0.707574542,,,0.733945626
11032,"Short-Term Forecasting COVID-19 Cases In Turkey Using Long Short-Term
  Memory Network",0,2009.06343,9/14/20,arxiv,0,4,lstm,0.002720091,0.002720094,0.002720258,0.986399318,0.002720087,0.002720152,Epidemiology,0.2524884,FALSE,13.5,0.205393036,4.5,0.242708055,0,0.403234768,,,0.283778619
11033,"Adaptive Methods for Short-Term Electricity Load Forecasting During
  COVID-19 Lockdown in France",0,2009.06527,9/14/20,arxiv,0,3,forecasting model,0.001823351,0.001823396,0.222130254,0.770576081,0.0018235,0.001823418,Epidemiology,0.007332176,FALSE,16.66666667,0.251159626,15.66666667,0.432365534,0,0.403234768,,,0.362253309
11034,"Not-NUTs at W-NUT 2020 Task 2: A BERT-based System in Identifying
  Informative COVID-19 English Tweets",0,2009.06372,9/14/20,arxiv,0,2,dataset,0.003760648,0.003760532,0.251591098,0.507090027,0.230037162,0.003760533,Epidemiology,0.05362639,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,,,0.165351392
11035,"Fused Deep Convolutional Neural Network for Precision Diagnosis of
  COVID-19 Using Chest X-Ray Images",0,2009.08831,9/15/20,arxiv,0,3,"machine learning, neural network, classifier, dataset",0.000815357,0.000815335,0.995923123,0.000815372,0.000815389,0.000815423,Imaging,0.03981456,FALSE,10,0.15214299,0.666666667,0.096200161,0,0.403234768,,,0.217192639
11036,"Understanding Global Reaction to the Recent Outbreaks of COVID-19:
  Insights from Instagram Data Analysis",0,2009.06862,9/15/20,arxiv,0,5,"neural network, lstm, dataset",0.002080646,0.002080616,0.310739855,0.680937652,0.002080662,0.002080569,Epidemiology,0.78629184,TRUE,97.4,0.862947616,78,0.773548301,1,0.537564047,,,0.724686655
11037,COVID-19 Impact on Global Maritime Mobility,0,2009.0696,9/15/20,arxiv,0,7,dataset,0.001438127,0.001438143,0.037501297,0.956746029,0.001438174,0.00143823,Epidemiology,0.2402798,FALSE,97.28571429,0.862576535,67.42857143,0.746655071,3,0.667819001,,,0.759016869
11038,"Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT
  Classification",0,2009.07652,9/15/20,arxiv,0,3,"machine learning, dataset",0.001171549,0.001171554,0.9643064,0.031007433,0.001171539,0.001171526,Imaging,0.31643128,FALSE,14.66666667,0.221163956,35,0.605298368,11,0.840175319,,,0.555545881
11039,"What factors have caused Japanese prefectures to attract a larger
  population influx?",0,2009.07144,9/15/20,arxiv,0,1,correlation analysis,0.004109844,0.004109842,0.004109826,0.979450491,0.00411001,0.004109988,Epidemiology,0.27828103,FALSE,20,0.298163152,2,0.164302917,0,0.403234768,,,0.288566945
11040,"High-Performance Mining of COVID-19 Open Research Datasets for Text
  Classification and Insights in Cloud Computing Environments",0,2009.07399,9/16/20,arxiv,0,3,"machine learning, computational, dataset",0.001684529,0.001684523,0.548524769,0.444737139,0.001684557,0.001684484,Epidemiology,0.03684029,FALSE,29,0.41993939,31.33333333,0.580010704,0,0.403234768,,,0.467728287
11041,"Classification and Region Analysis of COVID-19 Infection using Lung CT
  Images and Deep Convolutional Neural Networks",0,2009.08864,9/16/20,arxiv,0,4,"neural network, dataset",0.001126797,0.001126815,0.994365929,0.001126866,0.001126804,0.001126789,Imaging,0.047891974,FALSE,37.75,0.516296617,17.25,0.453104094,2,0.618927094,,,0.529442602
11042,Multi-Stage CNN Architecture for Face Mask Detection,0,2009.07627,9/16/20,arxiv,0,4,"deep learning, neural network",0.001751269,0.001751389,0.280648858,0.642702544,0.071394803,0.001751136,Epidemiology,0.3960097,FALSE,6.25,0.088564537,1.75,0.148381054,2,0.618927094,,,0.285290895
11043,"Geometrical observational bounds on a fractal horizon holographic dark
  energy","Phys. Rev. D 102, 064047 (2020)",2009.08306,9/16/20,arxiv,"Phys. Rev. D 102, 064047 (2020)",2,bayes,0.386424223,0.004775413,0.004775243,0.594473988,0.00477548,0.004775653,Epidemiology,0.6697336,TRUE,94,0.854598305,20.5,0.486218892,0,0.403234768,,,0.581350655
11044,"Cross-Modal Alignment with Mixture Experts Neural Network for
  Intral-City Retail Recommendation",0,2009.09926,9/17/20,arxiv,0,5,"neural network, dataset",0.001156483,0.001156334,0.892706096,0.001156359,0.102668476,0.001156252,Imaging,0.000922203,FALSE,20.2,0.300327788,7.2,0.302047097,0,0.403234768,,,0.335203217
11045,"An early prediction of covid-19 associated hospitalization surge using
  deep learning approach",0,2009.08093,9/17/20,arxiv,0,3,"deep learning, neural network",0.002422264,0.085033698,0.318571443,0.161658125,0.133473852,0.298840618,Clinics,0.06670722,FALSE,14.66666667,0.221163956,38.33333333,0.62356168,0,0.403234768,,,0.415986801
11046,Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds,0,2009.0879,9/17/20,arxiv,0,9,dataset,0.002422297,0.002422429,0.509772445,0.174365521,0.308594824,0.002422485,Healthcare,0.07615647,FALSE,18.55555556,0.278310347,54,0.697952903,8,0.799987654,,,0.592083635
11047,"Functional data analysis: An application to COVID-19 data in the United
  States",0,2009.08363,9/17/20,arxiv,0,3,model fit,0.124274745,0.003101691,0.003101948,0.863318581,0.003101518,0.003101517,Epidemiology,0.089632004,FALSE,13.66666667,0.207310285,2.333333333,0.173401124,6,0.764429903,,,0.381713771
11048,Face Mask Detection using Transfer Learning of InceptionV3,0,2009.08369,9/17/20,arxiv,0,4,"transfer learning, dataset",0.001901681,0.001901696,0.632250779,0.36014234,0.00190184,0.001901664,Imaging,0.5766341,TRUE,11,0.167171748,1.25,0.127776291,6,0.764429903,,,0.353125981
11049,Dynamic causal modelling of immune heterogeneity,0,2009.08411,9/17/20,arxiv,0,7,bayes,0.328675581,0.00153825,0.040790526,0.625919228,0.001538179,0.001538236,Epidemiology,0.09239051,FALSE,57.14285714,0.682726204,97.85714286,0.818437249,0,0.403234768,,,0.634799407
11050,"Detection of Change Points in Piecewise Polynomial Signals Using Trend
  Filtering",0,2009.08573,9/18/20,arxiv,0,2,dataset,0.001415186,0.00141518,0.430601452,0.563737841,0.001415124,0.001415217,Epidemiology,0.056037188,FALSE,5.5,0.077246583,0.5,0.087101953,0,0.403234768,,,0.189194435
11051,"NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is
  Not Necessarily Informative",0,2009.0859,9/18/20,arxiv,0,1,"machine learning, classifier",0.002238483,0.002238491,0.461324974,0.529721118,0.002238484,0.002238449,Epidemiology,0.100599915,FALSE,1,0.012307502,0,0.055525823,2,0.618927094,,,0.22892014
11052,Detecting Malicious URLs of COVID-19 Pandemic using ML technologies,0,2009.09224,9/19/20,arxiv,0,2,machine learning,0.001987261,0.00198717,0.744065535,0.247985767,0.001987155,0.001987112,Epidemiology,0.010590315,FALSE,51.5,0.643267982,9,0.337904736,1,0.537564047,,,0.506245589
11053,Bias Field Poses a Threat to DNN-based X-Ray Recognition,0,2009.09247,9/19/20,arxiv,0,8,"neural network, dataset",0.000988366,0.000988346,0.995058185,0.000988393,0.000988373,0.000988336,Imaging,0.002345085,FALSE,33.875,0.474921145,27.375,0.548635269,9,0.814309525,,,0.61262198
11054,"Can questions summarize a corpus? Using question generation for
  characterizing COVID-19 research",0,2009.0929,9/19/20,arxiv,0,3,dataset,0.002080712,0.148620235,0.002080727,0.843057139,0.002080643,0.002080545,Epidemiology,0.02384609,FALSE,90,0.843094811,101.3333333,0.824591919,0,0.403234768,,,0.690307166
11055,Epidemic mitigation by statistical inference from contact tracing data,0,2009.09422,9/20/20,arxiv,0,13,"bayes, probabilistic",0.001330024,0.001330039,0.104907882,0.889771997,0.001330046,0.001330012,Epidemiology,0.030715257,FALSE,46.15384615,0.597563238,78.38461538,0.774284185,1,0.537564047,,,0.63647049
11056,"Early Indicators of COVID-19 Spread Risk Using Digital Trace Data of
  Population Activities",0,2009.09514,9/20/20,arxiv,0,7,correlation analysis,0.001126871,0.001126826,0.001126785,0.994365877,0.001126841,0.001126801,Epidemiology,0.22175673,FALSE,44.85714286,0.584451729,10,0.355632861,2,0.618927094,,,0.519670561
11057,"Improving Automated COVID-19 Grading with Convolutional Neural Networks
  in Computed Tomography Scans: An Ablation Study",0,2009.09725,9/21/20,arxiv,0,8,"neural network, transfer learning",0.001310352,0.001310347,0.993448263,0.001310372,0.001310328,0.001310337,Imaging,0.09314585,FALSE,55.875,0.673820273,214.875,0.924805994,2,0.618927094,,,0.739184454
11058,"Optimal Drug Regimen and Combined Drug Therapy and its Efficacy in the
  Treatment of COVID-19 : An Within-Host Modeling Study",0,2009.10049,9/21/20,arxiv,0,8,mathematical model,0.392546769,0.000926326,0.000926375,0.603747781,0.000926395,0.000926353,Epidemiology,0.44251814,FALSE,32.75,0.463788732,29.625,0.566697886,0,0.403234768,,,0.477907128
11059,"CCBlock: An Effective Use of Deep Learning for Automatic Diagnosis of
  COVID-19 Using X-Ray Images",0,2009.10141,9/21/20,arxiv,0,4,"deep learning, neural network, dataset",0.001438103,0.001438124,0.992809227,0.001438203,0.001438145,0.001438198,Imaging,0.39540195,FALSE,1.75,0.017007855,0,0.055525823,1,0.537564047,,,0.203365908
11060,"DeepVir -- Graphical Deep Matrix Factorization for ""In Silico"" Antiviral
  Repositioning: Application to COVID-19",0,2009.10333,9/22/20,arxiv,0,4,"in silico, genomic structure, dataset",0.502329414,0.110605884,0.380822376,0.002080686,0.002080852,0.002080788,Drug discovery,0.12457672,FALSE,91.25,0.846867462,66.5,0.744246722,0,0.403234768,,,0.664782984
11061,Dynamic Fusion based Federated Learning for COVID-19 Detection,0,2009.10401,9/22/20,arxiv,0,9,"machine learning, image analysis, dataset",0.001022628,0.001022627,0.851197607,0.144711859,0.001022663,0.001022616,Imaging,0.0391469,FALSE,49.11111111,0.624899499,38.66666667,0.625501739,2,0.618927094,,,0.623109444
11062,"Classification of COVID-19 in CT Scans using Multi-Source Transfer
  Learning",0,2009.10474,9/22/20,arxiv,0,1,"deep learning, neural network, transfer learning, dataset",0.001156281,0.001156243,0.994218641,0.001156302,0.001156247,0.001156286,Imaging,0.001158357,FALSE,12,0.183190055,0,0.055525823,2,0.618927094,,,0.285880991
11063,"The Use of AI for Thermal Emotion Recognition: A Review of Problems and
  Limitations in Standard Design and Data",0,2009.10589,9/22/20,arxiv,0,3,"machine learning, dataset",0.002183318,0.002183338,0.989083517,0.002183374,0.00218325,0.002183203,Imaging,0.042272806,FALSE,32.33333333,0.459273919,61.66666667,0.727187584,0,0.403234768,,,0.529898757
11064,"Using Machine Learning to Develop a Novel COVID-19 Vulnerability Index
  (C19VI)",0,2009.10808,9/22/20,arxiv,0,4,machine learning,0.001187287,0.001187281,0.250537368,0.564231012,0.181669687,0.001187365,Epidemiology,0.38658297,FALSE,14,0.213494959,1,0.122023013,2,0.618927094,,,0.318148356
11065,"Drug Repurposing for COVID-19 using Graph Neural Network with Genetic,
  Mechanistic, and Epidemiological Validation",0,2009.10931,9/23/20,arxiv,0,8,"neural network, knowledge graph",0.722109654,0.001371426,0.232459615,0.001371262,0.00137129,0.041316753,Drug discovery,0.34148467,FALSE,65.375,0.736471025,,,3,0.667819001,,,0.702145013
11066,"Agent-based Simulation Model and Deep Learning Techniques to Evaluate
  and Predict Transportation Trends around COVID-19",0,2010.09648,9/23/20,arxiv,0,14,"simulation model, deep learning",0.004530703,0.004530642,0.004530841,0.977344911,0.004531383,0.00453152,Epidemiology,0.034000695,FALSE,46.35714286,0.599480487,27.14285714,0.546561413,3,0.667819001,,,0.6046203
11067,"An Attention Mechanism with Multiple Knowledge Sources for COVID-19
  Detection from CT Images",0,2009.11008,9/23/20,arxiv,0,6,transfer learning,0.001415129,0.00141517,0.992924231,0.001415212,0.001415106,0.001415152,Imaging,0.02841255,FALSE,40.66666667,0.546292288,9.333333333,0.342253144,0,0.403234768,,,0.4305934
11068,"Steering a Historical Disease Forecasting Model Under a Pandemic: Case
  of Flu and COVID-19",0,2009.11407,9/23/20,arxiv,0,6,forecasting model,0.001310381,0.00131041,0.618199427,0.376558909,0.001310442,0.001310432,Epidemiology,0.061659127,FALSE,122,0.909889294,98.83333333,0.820176612,0,0.403234768,,,0.711100225
11069,"An elementary approach for minimax estimation of Bernoulli proportion in
  the restricted parameter space",0,2009.11413,9/23/20,arxiv,0,2,machine learning,0.003927475,0.003927763,0.272404471,0.599494834,0.116317974,0.003927483,Epidemiology,0.102532625,FALSE,39.5,0.534232173,27.5,0.549906342,0,0.403234768,,,0.495791094
11070,"The COUGHVID crowdsourcing dataset: A corpus for the study of
  large-scale cough analysis algorithms",0,2009.11644,9/24/20,arxiv,0,3,"machine learning, dataset",0.001486446,0.00148655,0.909623204,0.001486552,0.08443076,0.001486489,Healthcare,0.013977051,FALSE,127,0.917125363,112.6666667,0.84198555,8,0.799987654,,,0.853032856
11071,"ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on
  EfficientNet to Detect COVID-19 From Chest X-rays",0,2009.1185,9/24/20,arxiv,0,4,neural network,0.001943446,0.001943451,0.990282774,0.001943453,0.001943446,0.001943431,Imaging,0.016724885,FALSE,22.5,0.333539489,8.25,0.323789136,3,0.667819001,,,0.441715875
11072,"Characterization of Covid-19 Dataset using Complex Networks and Image
  Processing",0,2009.13302,9/24/20,arxiv,0,2,dataset,0.002490649,0.002490771,0.987546934,0.002490551,0.002490597,0.002490498,Imaging,0.004360318,FALSE,3,0.037293586,0.5,0.087101953,0,0.403234768,,,0.175876769
11073,"Assessing the Interplay between travel patterns and SARS-CoV-2 outbreak
  in realistic urban setting",0,2009.12076,9/25/20,arxiv,0,6,dataset,0.001330047,0.001330053,0.00133011,0.97364532,0.021034443,0.001330027,Epidemiology,0.14512673,FALSE,22.5,0.333539489,9.666666667,0.348274017,0,0.403234768,,,0.361682758
11074,Identification and control of SARS-CoV-2 epidemic model parameters,0,2009.1347,9/25/20,arxiv,0,1,mathematical model,0.001565385,0.001565388,0.001565411,0.992173098,0.001565353,0.001565365,Epidemiology,0.05313143,FALSE,91,0.846310842,12,0.386740701,0,0.403234768,,,0.54542877
11075,"Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer
  Learning GAN",0,2009.12478,9/26/20,arxiv,0,10,"classifier, adversarial network, transfer learning, dataset",0.00104686,0.001046849,0.994765703,0.001046903,0.001046835,0.00104685,Imaging,0.48807582,FALSE,91,0.846310842,81.7,0.782312015,2,0.618927094,,,0.749183317
11076,"Infection Risk Score: Identifying the risk of infection propagation
  based on human contact",0,2009.12588,9/26/20,arxiv,0,2,dataset,0.001786569,0.001786558,0.156215955,0.83663764,0.001786638,0.001786639,Epidemiology,0.2600142,FALSE,15,0.227596017,0.5,0.087101953,1,0.537564047,,,0.284087339
11077,Potential Features of ICU Admission in X-ray Images of COVID-19 Patients,0,2009.12597,9/26/20,arxiv,0,8,neural network,0.001254617,0.001254634,0.896310566,0.001254662,0.001254672,0.098670848,Imaging,0.055749655,FALSE,45,0.587049292,9.875,0.351819641,3,0.667819001,,,0.535562645
11078,"Deep Learning-based Four-region Lung Segmentation in Chest Radiography
  for COVID-19 Diagnosis",0,2009.1261,9/26/20,arxiv,0,13,"deep learning, image analysis",0.001310323,0.001310383,0.993448179,0.00131041,0.001310327,0.001310378,Imaging,0.017710626,FALSE,89.30769231,0.840188014,42.30769231,0.645972705,0,0.403234768,,,0.629798496
11079,"The impact of modelling choices on modelling outcomes: a spatio-temporal
  study of the association between COVID-19 spread and environmental conditions
  in Catalonia (Spain)",0,2009.12625,9/26/20,arxiv,0,1,dataset,0.002130671,0.002130734,0.002130678,0.930853387,0.060623723,0.002130806,Epidemiology,0.53874695,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
11080,COVID-19 Infection Map Generation and Detection from Chest X-Ray Images,0,2009.12698,9/26/20,arxiv,0,9,"deep learning, dataset",0.001203432,0.001203437,0.993982861,0.00120345,0.00120341,0.001203411,Imaging,0.2129476,FALSE,132,0.923990352,92.55555556,0.807131389,5,0.739490092,,,0.823537278
11081,"Viroinformatics-based investigation of SARS-CoV-2 core proteins for
  potential therapeutic targets",0,2009.12817,9/27/20,arxiv,0,5,"bioinformatic, in silico",0.918871215,0.054819159,0.001593546,0.001593522,0.021528904,0.001593654,Drug discovery,0.31812137,FALSE,11,0.167171748,6.6,0.28913567,0,0.403234768,,,0.286514062
11082,A model-based approach to assess epidemic risk,0,2009.12964,9/27/20,arxiv,0,2,dataset,0.002130691,0.032577526,0.002130813,0.958899689,0.00213064,0.00213064,Epidemiology,0.1660468,FALSE,9.5,0.143051518,3,0.199424672,0,0.403234768,,,0.248570319
11083,"A hierarchical spatio-temporal model to analyze relative risk variations
  of COVID-19: a focus on Spain, Italy and Germany",0,2009.13577,9/28/20,arxiv,0,2,bayes,0.00190167,0.00190173,0.001901685,0.990491534,0.00190169,0.001901692,Epidemiology,0.49605078,FALSE,182.5,0.965365823,98,0.819106235,0,0.403234768,,,0.729235609
11084,"COVID-CT-MD: COVID-19 Computed Tomography (CT) Scan Dataset Applicable
  in Machine Learning and Deep Learning",0,2009.14623,9/28/20,arxiv,0,10,"machine learning, deep learning, neural network, dataset",0.053481777,0.001593603,0.940144045,0.001593566,0.001593499,0.001593511,Imaging,0.2072353,FALSE,11.5,0.17416043,3.4,0.208589778,6,0.764429903,,,0.38239337
11085,"Machine Learning Research Towards Combating COVID-19: Virus Detection,
  Spread Prevention, and Medical Assistance",0,2010.07036,9/29/20,arxiv,0,9,machine learning,0.002357875,0.002357793,0.539524957,0.308257719,0.090361374,0.057140281,Epidemiology,0.64025474,TRUE,82.55555556,0.816315171,108.5555556,0.837101953,0,0.403234768,,,0.685550631
11086,Filling a theatre in times of corona,0,2010.01981,9/30/20,arxiv,0,3,computational,0.004530782,0.004530782,0.366806244,0.615069815,0.004530816,0.004531561,Epidemiology,0.06461197,FALSE,5.666666667,0.079473066,0.666666667,0.096200161,0,0.403234768,,,0.192969331
11087,A Deep Learning Framework for COVID Outbreak Prediction,0,2010.00382,9/30/20,arxiv,0,4,"deep learning, neural network, lstm",0.001072189,0.00107219,0.493947999,0.501763201,0.001072218,0.001072201,Epidemiology,0.001491547,FALSE,29.75,0.428226854,1,0.122023013,1,0.537564047,,,0.362604638
11088,"GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for
  Identifying COVID-19 on Chest X-rays",0,2010.00378,9/30/20,arxiv,0,4,"supervised learning, artificial intelligence, dataset",0.00141515,0.001415129,0.831754523,0.136880895,0.0271192,0.001415103,Imaging,0.001032382,FALSE,63,0.721998887,50.75,0.684238694,1,0.537564047,,,0.647933876
11089,"Interpretable Machine Learning for COVID-19: An Empirical Study on
  Severity Prediction Task",0,2010.02006,9/30/20,arxiv,0,11,"machine learning, neural network, dataset",0.024992574,0.001538158,0.524480799,0.095695375,0.001538145,0.351754949,Clinics,0.07584241,FALSE,68.77777778,0.757931845,65,0.739028633,1,0.537564047,,,0.678174842
11090,"Who Are the 'Silent Spreaders'?: Contact Tracing in Spatio-Temporal
  Memory Models",0,2010.00187,10/1/20,arxiv,0,4,"simulation model, computational, neural network, network model",0.00175128,0.001751211,0.344510771,0.648484418,0.00175117,0.00175115,Epidemiology,0.004646063,FALSE,39,0.530521368,16,0.437316029,0,0.403234768,,,0.457024055
11091,"Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis:
  A Review",0,2010.02154,10/1/20,arxiv,0,7,"machine learning, computational, image analysis",0.001330216,0.001330044,0.746727302,0.114431051,0.001330116,0.134851271,Imaging,0.18690884,FALSE,69.14285714,0.75953986,24.14285714,0.522411025,0,0.403234768,,,0.561728551
11092,"Identification of images of COVID-19 from Chest Computed Tomography (CT)
  images using Deep learning: Comparing COGNEX VisionPro Deep Learning 1.0
  Software with Open Source Convolutional Neural Networks",0,2010.00958,10/1/20,arxiv,0,5,"deep learning, neural network, dataset",0.000846515,0.000846527,0.995767318,0.000846554,0.000846551,0.000846536,Imaging,0.004236877,FALSE,24.4,0.360442823,113.6,0.843457319,1,0.537564047,,,0.580488063
11093,"Phonemer at WNUT-2020 Task 2: Sequence Classification Using COVID
  Twitter BERT and Bagging Ensemble Technique based on Plurality Voting",0,2010.00294,10/1/20,arxiv,0,1,"deep learning, dataset",0.002562586,0.002562555,0.879551064,0.110198626,0.002562562,0.002562607,Epidemiology,0.1805988,FALSE,3,0.037293586,0,0.055525823,1,0.537564047,,,0.210127819
11094,"Model-based Bayesian inference of disease outbreak dynamics with
  invertible neural networks",0,2010.003,10/1/20,arxiv,0,7,"bayes, neural network, mathematical model",0.002130727,0.002130667,0.250681702,0.740795674,0.00213061,0.00213062,Epidemiology,0.07603037,FALSE,23.71428571,0.35023811,10,0.355632861,0,0.403234768,,,0.369701913
11095,"TrueImage: A Machine Learning Algorithm to Improve the Quality of
  Telehealth Photos",0,2010.02086,10/1/20,arxiv,0,6,machine learning,0.001622711,0.001622725,0.916006154,0.001622778,0.077502768,0.001622864,Imaging,0.3289795,FALSE,33.5,0.471890655,64.5,0.736887878,1,0.537564047,,,0.582114193
11096,"Automatic Deep Learning System for COVID-19 Infection Quantification in
  chest CT",0,2010.01982,10/1/20,arxiv,0,1,"deep learning, dataset",0.001112671,0.032712194,0.962837134,0.001112684,0.001112628,0.001112689,Imaging,0.06881213,FALSE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
11097,"Global analysis of more than 50,000 SARS-Cov-2 genomes reveals epistasis
  between 8 viral genes",0,2010.0126,10/3/20,arxiv,0,5,"genome-wide, genomes",0.327622011,0.597009449,0.001943529,0.069538109,0.001943465,0.001943437,Genomics,0.3396342,FALSE,16.4,0.247572515,12.8,0.396574793,4,0.707574542,,,0.45057395
11098,Predicting traffic overflows on private peering,0,2010.0138,10/3/20,arxiv,0,5,deep learning,0.001046799,0.001046819,0.130378528,0.621996177,0.244484861,0.001046815,Epidemiology,0.007747889,FALSE,38.6,0.525078855,25.6,0.533716885,0,0.403234768,,,0.487343502
11099,"Assessing Automated Machine Learning service to detect COVID-19 from
  X-Ray and CT images: A Real-time Smartphone Application case study",IJCSI-2020-17-6-12569,2010.02715,10/3/20,arxiv,IJCSI-2020-17-6-12569,2,"machine learning, artificial intelligence",0.000946131,0.000946149,0.836926443,0.000946138,0.111236447,0.048998691,Imaging,0.71035373,TRUE,3.5,0.044344115,0,0.055525823,0,0.403234768,,,0.167701569
11100,Ensemble Machine Learning Methods for Modeling COVID19 Deaths,0,2010.04052,10/4/20,arxiv,0,4,"machine learning, dataset",0.002296534,0.002296597,0.002296781,0.988516888,0.002296607,0.002296593,Epidemiology,0.010277748,FALSE,4.25,0.056527924,1.75,0.148381054,0,0.403234768,,,0.202714582
11101,Data-driven Operation of the Resilient Electric Grid: A Case of COVID-19,0,2010.01746,10/5/20,arxiv,0,4,"machine learning, artificial intelligence",0.123977876,0.001098864,0.301253387,0.571471996,0.00109893,0.001098947,Epidemiology,0.7197826,TRUE,10.25,0.154245779,2.5,0.180826866,0,0.403234768,,,0.246102471
11102,"Multifractal scaling analyses of the spatial diffusion pattern of
  COVID-19 pandemic in Chinese mainland",0,2010.02747,10/5/20,arxiv,0,3,dataset,0.001415182,0.001415184,0.001415168,0.99292416,0.001415162,0.001415144,Epidemiology,0.05066693,FALSE,42.66666667,0.563856763,25.66666667,0.534854161,1,0.537564047,,,0.54542499
11103,"Temporal Positive-unlabeled Learning for Biomedical Hypothesis
  Generation via Risk Estimation",0,2010.01916,10/5/20,arxiv,0,5,"machine learning, dataset",0.038575832,0.034874473,0.364659402,0.558866492,0.001511947,0.001511854,Epidemiology,0.003191263,FALSE,79.8,0.806234152,95.8,0.814222638,0,0.403234768,,,0.674563853
11104,"Complex network model for COVID-19: human behavior, pseudo-periodic
  solutions and multiple epidemic waves",0,2010.02368,10/5/20,arxiv,0,7,"mathematical model, network model",0.001538194,0.001538171,0.001538137,0.992309213,0.001538177,0.001538108,Epidemiology,0.24099693,FALSE,88,0.837095677,29.28571429,0.564824726,1,0.537564047,,,0.646494817
11105,"Pay Attention to the cough: Early Diagnosis of COVID-19 using
  Interpretable Symptoms Embeddings with Cough Sound Signal Processing",0,2010.02417,10/6/20,arxiv,0,2,dataset,0.050479371,0.001901834,0.903944542,0.039870508,0.001901902,0.001901842,Drug discovery,0.37635893,FALSE,5.5,0.077246583,0.5,0.087101953,7,0.785110192,,,0.316486243
11106,"COVIDomaly: A Deep Convolutional Autoencoder Approach for Detecting
  Early Cases of COVID-19",0,2010.02814,10/6/20,arxiv,0,3,dataset,0.001112635,0.001112648,0.721278072,0.150454839,0.089826899,0.036214906,Imaging,0.016370595,FALSE,31,0.445111015,51,0.685509767,1,0.537564047,,,0.55606161
11107,"RANDGAN: Randomized Generative Adversarial Network for Detection of
  COVID-19 in Chest X-ray",0,2010.06418,10/6/20,arxiv,0,3,"deep learning, adversarial network, transfer learning, dataset",0.000977444,0.036427537,0.959662709,0.00097744,0.000977427,0.000977444,Imaging,0.037050486,FALSE,22.66666667,0.335580432,9,0.337904736,3,0.667819001,,,0.44710139
11108,"No increase in COVID-19 mortality after the 2020 primary elections in
  the USA",0,2010.02896,10/6/20,arxiv,0,4,bayes,0.060514575,0.002996658,0.002996467,0.927498992,0.002996675,0.002996633,Epidemiology,0.4554722,FALSE,86.5,0.831653163,316,0.958455981,2,0.618927094,,,0.803012079
11109,"EDNA-Covid: A Large-Scale Covid-19 Tweets Dataset Collected with the
  EDNA Streaming Toolkit",0,2010.04084,10/6/20,arxiv,0,2,dataset,0.002183229,0.002183301,0.002183449,0.989083476,0.002183297,0.002183248,Epidemiology,0.01223594,FALSE,270.5,0.987816191,296.5,0.952903398,1,0.537564047,,,0.826094546
11110,Time-dependent SI model for epidemiology and applications to Covid-19,0,2010.03097,10/7/20,arxiv,0,2,bayes,0.073532093,0.002080621,0.002080583,0.91814559,0.002080545,0.002080568,Epidemiology,0.010049343,FALSE,77,0.794668811,27.5,0.549906342,0,0.403234768,,,0.582603307
11111,"M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia
  Screening from CT Imaging",0,2010.03201,10/7/20,arxiv,0,7,"deep learning, dataset",0.001237067,0.001237101,0.993814382,0.001237185,0.001237088,0.001237177,Imaging,0.30165353,FALSE,39.85714286,0.537881131,65.71428571,0.74150388,2,0.618927094,,,0.632770702
11112,"A Seamless Phase I/II Platform Design with a Time-To-Event Efficacy
  Endpoint for Potential COVID-19 Therapies",0,2010.06518,10/7/20,arxiv,0,4,bayes,0.344536891,0.071120693,0.00346601,0.573943913,0.003466236,0.003466255,Epidemiology,0.31513876,FALSE,22.5,0.333539489,45.25,0.659218625,0,0.403234768,,,0.46533096
11113,"A Self-supervised Approach for Semantic Indexing in the Context of
  COVID-19 Pandemic",0,2010.03544,10/7/20,arxiv,0,2,dataset,0.001565323,0.001565327,0.642247169,0.351491414,0.001565417,0.001565349,Epidemiology,0.002637863,FALSE,7.5,0.108355495,0,0.055525823,0,0.403234768,,,0.189038695
11114,Singularity and Coordination Problems: Pandemic Lessons from 2020,0,2010.07018,10/7/20,arxiv,0,2,artificial intelligence,0.080188324,0.003214262,0.40057381,0.509595239,0.003214238,0.003214126,Epidemiology,0.5436941,TRUE,43.5,0.573381161,0.5,0.087101953,0,0.403234768,,,0.354572627
11115,"Spatial-temporal Analysis of COVID-19's Impact on Human Mobility: the
  Case of the United States",0,2010.03707,10/8/20,arxiv,0,4,"network analysis, dataset",0.001371277,0.001371301,0.001371254,0.993143629,0.001371274,0.001371265,Epidemiology,0.013461411,FALSE,27,0.3960047,9.75,0.349678887,2,0.618927094,,,0.454870227
11116,"AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting
  with Population Covariates",0,2010.03757,10/8/20,arxiv,0,4,"deep learning, lstm",0.046045997,0.002562737,0.251065214,0.695200745,0.002562675,0.002562633,Epidemiology,0.061736345,FALSE,328.5,0.992454697,321,0.95992775,1,0.537564047,,,0.829982165
11117,Extracting a Knowledge Base of Mechanisms from COVID-19 Papers,0,2010.03824,10/8/20,arxiv,0,9,dataset,0.262456442,0.002130673,0.394700996,0.336450529,0.002130703,0.002130656,Epidemiology,0.036491662,FALSE,108,0.886078298,399.4285714,0.971099813,0,0.403234768,,,0.753470959
11118,Modeling the spread of COVID-19 pandemic in Morocco,0,2010.04115,10/8/20,arxiv,0,8,mathematical model,0.051255581,0.00310145,0.003101625,0.936338313,0.003101576,0.003101456,Epidemiology,0.6999857,TRUE,78,0.798812543,25.625,0.533850682,1,0.537564047,,,0.623409091
11119,"Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data
  through Randomized Decision Trees","Proceedings of the 5th International Workshop on Knowledge
  Discovery in Healthcare Data co-located with 24th European Conference on
  Artificial Intelligence (ECAI 2020)",2010.0442,10/9/20,arxiv,"Proceedings of the 5th International Workshop on Knowledge
  Discovery in Healthcare Data co-located with 24th European Conference on
  Artificial Intelligence (ECAI 2020)",5,machine learning,0.002638987,0.002638968,0.986804562,0.002639144,0.002638977,0.002639362,Imaging,0.28607142,FALSE,44.2,0.579503989,105.8,0.832753546,1,0.537564047,,,0.649940527
11120,"Face Mask Assistant: Detection of Face Mask Service Stage Based on
  Mobile Phone",0,2010.06421,10/9/20,arxiv,0,7,dataset,0.001786538,0.06784877,0.414051279,0.512740285,0.0017866,0.001786527,Epidemiology,0.7267392,TRUE,107.4285714,0.884470283,46.28571429,0.665440193,3,0.667819001,,,0.739243159
11121,"EpidemiOptim: A Toolbox for the Optimization of Control Policies in
  Epidemiological Models",0,2010.04452,10/9/20,arxiv,0,7,"machine learning, neural network",0.001126815,0.017321247,0.428726478,0.550571887,0.001126798,0.001126774,Epidemiology,0.081703514,FALSE,60.71428571,0.706537201,,,1,0.537564047,,,0.622050624
11122,"Flattening the COVID-19 Curve: The ""Greek"" case in the Global Pandemic",0,2010.1204,10/9/20,arxiv,0,3,network model,0.002720169,0.002720148,0.002720331,0.986399079,0.00272016,0.002720112,Epidemiology,0.3643552,FALSE,47.33333333,0.608633805,6.666666667,0.290607439,0,0.403234768,,,0.434158671
11123,"The risk for a new COVID-19 wave -- and how it depends on $R_0$, the
  current immunity level and current restrictions",0,2010.04574,10/9/20,arxiv,0,3,mathematical model,0.001538126,0.001538169,0.00153809,0.992309349,0.001538181,0.001538085,Epidemiology,0.098653525,FALSE,217.6666667,0.977920712,252.6666667,0.938921595,0,0.403234768,,,0.773359025
11124,"Distributed Computing in a Pandemic: A Review of Technologies Available
  for Tackling COVID-19",0,2010.047,10/9/20,arxiv,0,1,"molecular dynamics simulation, dataset",0.396123979,0.096490985,0.386452753,0.117801579,0.001565405,0.001565299,Drug discovery,0.10346624,FALSE,10,0.15214299,2,0.164302917,0,0.403234768,,,0.239893558
11125,"Analytical parameter estimation of the SIR epidemic model. Applications
  to the COVID-19 pandemic",Entropy (Basel). 2020 Dec 31;23(1):E59. PMID: 33396355,2010.07,10/9/20,arxiv,Entropy (Basel). 2020 Dec 31;23(1):E59. PMID: 33396355,1,mathematical model,0.001861696,0.001861732,0.001861812,0.990691289,0.00186174,0.001861731,Epidemiology,0.4916371,FALSE,34,0.477766096,4,0.231469093,0,0.403234768,,,0.370823319
11126,"Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic
  with Natural Language Processing (NLP)",0,2010.16413,10/9/20,arxiv,0,7,"artificial intelligence, information retrieval, dataset",0.002238598,0.002238486,0.418033035,0.529116136,0.046135332,0.002238413,Epidemiology,0.007177502,FALSE,61.71428571,0.713464036,159.7142857,0.891624298,0,0.403234768,,,0.669441034
11127,"Lessons from Pandemics: Computational agent-based model approach for
  estimation of downstream and upstream measures to achieve requisite societal
  behavioural changes",0,2010.04833,10/9/20,arxiv,0,2,computational,0.001156295,0.001156254,0.001156299,0.931204216,0.001156325,0.064170612,Epidemiology,0.71342766,TRUE,38.5,0.524089307,3.5,0.213607172,0,0.403234768,,,0.380310415
11128,Rough-Fuzzy CPD: A Gradual Change Point Detection Algorithm,0,2010.0637,10/10/20,arxiv,0,3,dataset,0.041082677,0.001486473,0.30810246,0.646355493,0.001486474,0.001486422,Epidemiology,0.033512324,FALSE,188.6666667,0.968396314,299,0.953906877,0,0.403234768,,,0.77517932
11129,"An Empirical Study on Detecting COVID-19 in Chest X-ray Images Using
  Deep Learning Based Methods",0,2010.04936,10/10/20,arxiv,0,3,"deep learning, neural network",0.002638988,0.002639026,0.986804989,0.002639076,0.00263899,0.002638932,Imaging,0.011852086,FALSE,31.33333333,0.448388892,12.33333333,0.389550442,0,0.403234768,,,0.4137247
11130,"Semi-supervised Neural Networks solve an inverse problem for modeling
  Covid-19 spread",0,2010.05074,10/10/20,arxiv,0,5,neural network,0.002562595,0.002562694,0.160796264,0.82895332,0.002562567,0.002562561,Epidemiology,0.05527571,FALSE,44.4,0.580864617,55.8,0.705512443,2,0.618927094,,,0.635101385
11131,"Nowcasting of COVID-19 confirmed cases: Foundations, trends, and
  challenges",0,2010.05079,10/10/20,arxiv,0,4,"machine learning, forecasting model",0.001438105,0.001438105,0.155609104,0.810972442,0.00143816,0.029104083,Epidemiology,0.13605016,FALSE,9.25,0.137918239,2,0.164302917,0,0.403234768,,,0.235151974
11132,"Application and Extension of Mean-Field Theory such as SIR to Discuss
  the Non-Mean Field Problem of COVID-19",0,2010.05116,10/10/20,arxiv,0,2,mathematical model,0.001438135,0.001438154,0.001438143,0.936864355,0.001438127,0.057383086,Epidemiology,0.007941306,FALSE,54.5,0.665161729,45,0.658817233,0,0.403234768,,,0.57573791
11133,"ComStreamClust: A communicative text clustering approach to topic
  detection in streaming data",0,2010.05349,10/11/20,arxiv,0,5,dataset,0.002996424,0.002996651,0.27983121,0.708182695,0.002996587,0.002996432,Epidemiology,0.016251504,FALSE,9,0.135320675,4.2,0.234211935,0,0.403234768,,,0.257589126
11134,"The spread of COVID-19 increases with individual mobility and depends on
  political leaning",0,2103.14463,10/11/20,arxiv,0,3,dataset,0.002238478,0.002238514,0.002238443,0.896741994,0.094303972,0.0022386,Epidemiology,0.42568618,FALSE,57,0.68204589,45.66666667,0.661760771,0,0.403234768,,,0.582347143
11135,Predicting Clinical Trial Results by Implicit Evidence Integration,0,2010.05639,10/12/20,arxiv,0,5,dataset,0.085470196,0.002296562,0.702473315,0.096233552,0.00229664,0.111229735,Clinics,0.07396042,FALSE,83.8,0.820582596,42.8,0.648648649,1,0.537564047,,,0.668931764
11136,A Neurochaos Learning Architecture for Genome Classification,0,2010.10995,10/12/20,arxiv,0,3,"machine learning, neural network, genome sequences, dataset",0.110339251,0.150294899,0.73543479,0.001310401,0.001310326,0.001310333,Genomics,0.013480186,FALSE,10,0.15214299,3.666666667,0.217621086,0,0.403234768,,,0.257666281
11137,"COVID-19 Imaging Data Privacy by Federated Learning Design: A
  Theoretical Framework",0,2010.06177,10/13/20,arxiv,0,2,"machine learning, deep learning",0.001987132,0.001987213,0.830288454,0.16176284,0.001987225,0.001987136,Imaging,0.13761207,FALSE,59,0.696579875,10,0.355632861,1,0.537564047,,,0.529925594
11138,"Prediction and mitigation of mutation threats to COVID-19 vaccines and
  antibody therapies",0,2010.06357,10/13/20,arxiv,0,4,deep learning,0.474980638,0.471308919,0.050778054,0.000977459,0.000977502,0.000977428,Drug discovery,0.12354669,FALSE,117.25,0.903333539,32,0.585763982,3,0.667819001,,,0.718972174
11139,"Characterizing and Comparing COVID-19 Misinformation Across Languages,
  Countries and Platforms",0,2010.06455,10/13/20,arxiv,0,5,dataset,0.001350361,0.067277901,0.00135036,0.927320538,0.001350515,0.001350325,Epidemiology,0.09010646,FALSE,55.2,0.669181768,24.2,0.522946214,1,0.537564047,,,0.576564009
11140,"IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing
  Better LEads",0,2010.06574,10/13/20,arxiv,0,36,"computational, artificial intelligence, in silico",0.382088082,0.001653103,0.35825719,0.219665008,0.001653089,0.036683529,Drug discovery,0.058009595,FALSE,113.3333333,0.896530398,208.0555556,0.921394166,3,0.667819001,,,0.828581188
11141,"No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet
  Detection",0,2010.06906,10/14/20,arxiv,0,4,dataset,0.001350326,0.001350329,0.462614299,0.440446521,0.092888182,0.001350342,Epidemiology,0.000159025,FALSE,26,0.382398417,9.5,0.345531175,5,0.739490092,,,0.489139894
11142,"Spread of Covid-19 in urban neighbourhoods and slums of the developing
  world",0,2010.06958,10/14/20,arxiv,0,2,network model,0.001653053,0.001653066,0.001653036,0.991734662,0.001653137,0.001653046,Epidemiology,0.17383817,FALSE,177.5,0.963263034,120.5,0.852421729,3,0.667819001,,,0.827834588
11143,"Combining detection and reconstruction of correlational and periodic
  motifs in viral genomic sequences with transitional genome mapping",0,2010.07006,10/14/20,arxiv,0,2,"data mining, genomes",0.205355688,0.729913518,0.001593528,0.059950231,0.00159351,0.001593526,Genomics,0.8145728,TRUE,6.5,0.093512277,1.5,0.138747659,0,0.403234768,,,0.211831568
11144,Kids Today: Remote Education in the time of COVID-19,0,2010.07295,10/14/20,arxiv,0,3,dataset,0.002032762,0.002032825,0.227662533,0.190238734,0.576000356,0.00203279,Healthcare,0.040570766,FALSE,4,0.054734368,1,0.122023013,0,0.403234768,,,0.193330716
11145,"Tracking Results and Utilization of Artificial Intelligence (tru-AI) in
  Radiology: Early-Stage COVID-19 Pandemic Observations",0,2010.07437,10/14/20,arxiv,0,2,artificial intelligence,0.001987126,0.001987199,0.438360683,0.222343014,0.239139683,0.096182294,Imaging,0.19208947,FALSE,86,0.829117447,19,0.471367407,0,0.403234768,,,0.567906541
11146,"MedDG: A Large-scale Medical Consultation Dataset for Building Medical
  Dialogue System",0,2010.07497,10/15/20,arxiv,0,6,dataset,0.105739582,0.001272718,0.775116231,0.001272729,0.115326075,0.001272666,Healthcare,0.008031309,FALSE,55.83333333,0.673572886,21.5,0.498260637,1,0.537564047,,,0.56979919
11147,"Understanding the Hoarding Behaviors during the COVID-19 Pandemic using
  Large Scale Social Media Data",0,2010.07845,10/15/20,arxiv,0,3,computational,0.001511842,0.001511817,0.001511863,0.591647523,0.402305095,0.00151186,Epidemiology,0.024964422,FALSE,216.6666667,0.977364092,555.3333333,0.981937383,0,0.403234768,,,0.787512081
11148,"Quantum Simulations of SARS-CoV-2 Main Protease Mpro Enable Accurate
  Scoring of Diverse Ligands",0,2010.07883,10/15/20,arxiv,0,3,computational,0.798409733,0.026004346,0.001034643,0.144936092,0.001034649,0.028580538,Drug discovery,0.057665586,FALSE,29.66666667,0.42773208,,,0,0.403234768,,,0.415483424
11149,"QReLU and m-QReLU: Two novel quantum activation functions to aid medical
  diagnostics",0,2010.08031,10/15/20,arxiv,0,4,"computational, neural network, dataset",0.001141407,0.001141379,0.882260841,0.001141383,0.001141352,0.113173637,Imaging,0.10854101,FALSE,15,0.227596017,1.5,0.138747659,2,0.618927094,,,0.32842359
11150,"Early-stage COVID-19 diagnosis in presence of limited posteroanterior
  chest X-ray images via novel Pinball-OCSVM",0,2010.08115,10/16/20,arxiv,0,3,deep learning,0.000916732,0.000916765,0.995416307,0.000916751,0.000916703,0.000916742,Imaging,0.011272341,FALSE,22.33333333,0.330261612,4.333333333,0.237958255,0,0.403234768,,,0.323818212
11151,"Users Perceptions about Teleconferencing Applications Collected through
  Twitter",0,2010.09488,10/16/20,arxiv,0,3,classifier,0.002357719,0.002357725,0.318183773,0.611429105,0.063313938,0.002357739,Epidemiology,0.025256366,FALSE,12,0.183190055,1.333333333,0.13252609,0,0.403234768,,,0.239650304
11152,"CT Image Segmentation for Inflamed and Fibrotic Lungs Using a
  Multi-Resolution Convolutional Neural Network","Sci Rep 11, 1455 (2021)",2010.08582,10/16/20,arxiv,"Sci Rep 11, 1455 (2021)",12,neural network,0.00123718,0.033323256,0.893585832,0.001237125,0.001237052,0.069379555,Imaging,0.36942893,FALSE,141.0833333,0.935122766,125.25,0.858108108,1,0.537564047,,,0.77693164
11153,"Minimax Quasi-Bayesian estimation in sparse canonical correlation
  analysis via a Rayleigh quotient function",0,2010.08627,10/16/20,arxiv,0,2,"bayes, proteom, correlation analysis, dataset",0.001717331,0.034919035,0.31874014,0.641189003,0.001717257,0.001717233,Epidemiology,0.06842181,FALSE,27.5,0.401570907,56,0.706850415,0,0.403234768,,,0.503885363
11154,"Drink bleach or do what now? Covid-HeRA: A dataset for risk-informed
  health decision making in the presence of COVID19 misinformation",0,2010.08743,10/17/20,arxiv,0,4,dataset,0.002639113,0.002639107,0.483322495,0.506121121,0.002639091,0.002639074,Epidemiology,0.00349772,FALSE,8.25,0.121343311,3,0.199424672,7,0.785110192,,,0.368626058
11155,"A Calculation Model for Estimating Effect of COVID-19 Contact-Confirming
  Application (COCOA) on Decreasing Infectors",0,2010.12067,10/17/20,arxiv,0,5,mathematical model,0.019529597,0.019529734,0.019530564,0.90235089,0.019529903,0.019529312,Epidemiology,0.4433185,FALSE,69.2,0.759663554,32.2,0.586834359,1,0.537564047,,,0.628020653
11156,"ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation
  Detection",0,2010.08768,10/17/20,arxiv,0,4,dataset,0.001684573,0.00168453,0.298477727,0.694784198,0.001684491,0.001684482,Epidemiology,0.002381235,FALSE,22,0.326056033,12.5,0.392761573,0,0.403234768,,,0.374017458
11157,"Studying the Similarity of COVID-19 Sounds based on Correlation Analysis
  of MFCC",0,2010.0877,10/17/20,arxiv,0,3,"deep learning, artificial intelligence, correlation analysis",0.002130686,0.002130886,0.989346034,0.00213075,0.002130804,0.00213084,Genomics,0.2837671,FALSE,30.33333333,0.436266931,319,0.959124967,2,0.618927094,,,0.671439664
11158,"GOAT: GPU Outsourcing of Deep Learning Training With Asynchronous
  Probabilistic Integrity Verification Inside Trusted Execution Environment",0,2010.08855,10/17/20,arxiv,0,3,"machine learning, deep learning, computational, neural network, probabilistic",0.001538172,0.001538116,0.693159225,0.300688158,0.001538199,0.001538129,Epidemiology,0.021594018,FALSE,70.33333333,0.765353454,86,0.793015788,2,0.618927094,,,0.725765445
11159,CHECKED: Chinese COVID-19 Fake News Dataset,0,2010.09029,10/18/20,arxiv,0,3,dataset,0.002183449,0.002183261,0.002183393,0.98908337,0.002183343,0.002183184,Epidemiology,0.029694885,FALSE,48.33333333,0.616921269,59.66666667,0.71936045,0,0.403234768,,,0.579838829
11160,"A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with
  Application to COVID-19",0,2010.09077,10/18/20,arxiv,0,4,machine learning,0.001371277,0.001371275,0.386912805,0.607602165,0.001371234,0.001371243,Epidemiology,0.010499835,FALSE,65.5,0.737646113,60.5,0.722772277,0,0.403234768,,,0.621217719
11161,"Disinformation in the Online Information Ecosystem: Detection,
  Mitigation and Challenges",0,2010.09113,10/18/20,arxiv,0,4,computational,0.001653071,0.001653086,0.059654622,0.667071569,0.26831465,0.001653001,Epidemiology,0.038092732,FALSE,34.75,0.484507391,56,0.706850415,4,0.707574542,,,0.632977449
11162,GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation,0,2010.09456,10/19/20,arxiv,0,8,deep learning,0.001565316,0.001565327,0.992173458,0.001565296,0.001565289,0.001565313,Imaging,0.004392862,FALSE,119.375,0.906054796,119.625,0.850883061,2,0.618927094,,,0.791954984
11163,"Effects of demographic and weather parameters on COVID-19 basic
  reproduction number",Frontiers in Ecology and Evolution 8 (2021) 524,2010.09682,10/19/20,arxiv,Frontiers in Ecology and Evolution 8 (2021) 524,6,bioinformatic,0.00129127,0.092169827,0.00129125,0.855486714,0.001291239,0.048469701,Epidemiology,0.5975529,TRUE,40.33333333,0.542890717,16.16666667,0.438051913,1,0.537564047,,,0.506168892
11164,"Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image
  Translation",0,2010.10266,10/20/20,arxiv,0,2,"deep learning, dataset",0.001392873,0.001392852,0.972145899,0.00139289,0.022282655,0.001392831,Imaging,0.32945848,FALSE,75,0.787061661,61,0.724578539,1,0.537564047,,,0.683068082
11165,"Text Classification of Manifestos and COVID-19 Press Briefings using
  BERT and Convolutional Neural Networks",0,2010.10267,10/20/20,arxiv,0,1,"neural network, classifier",0.002898344,0.00289834,0.881526141,0.106880659,0.002898255,0.002898261,Epidemiology,0.004834026,FALSE,9,0.135320675,0,0.055525823,0,0.403234768,,,0.198027089
11166,"""Healthy surveillance"": Designing a concept for privacy-preserving mask
  recognition AI in the age of pandemics",0,2010.12026,10/20/20,arxiv,0,4,"artificial intelligence, deep-learning",0.001622817,0.027560103,0.356692882,0.6108787,0.001622814,0.001622683,Epidemiology,0.713353,TRUE,103.25,0.875688045,26.25,0.539670859,1,0.537564047,,,0.650974317
11167,Scalable HPC and AI Infrastructure for COVID-19 Therapeutics,0,2010.10517,10/20/20,arxiv,0,18,computational,0.418130223,0.0023578,0.300850067,0.273946264,0.002357795,0.002357851,Drug discovery,0.6498696,TRUE,145.6111111,0.939946812,215.3888889,0.925207386,2,0.618927094,,,0.828027097
11168,"A computational tool for trend analysis and forecast of the COVID-19
  pandemic",0,2010.10332,10/20/20,arxiv,0,4,computational,0.001203419,0.001203419,0.001203436,0.993982896,0.001203418,0.001203413,Epidemiology,0.12658706,FALSE,21.75,0.321603068,5,0.257024351,0,0.403234768,,,0.327287395
11169,Detection of COVID-19 through the analysis of vocal fold oscillations,0,2010.10707,10/21/20,arxiv,0,3,"classifier, logistic regression, dataset",0.002130834,0.414543873,0.408029841,0.002130818,0.002130723,0.17103391,Genomics,0.02217558,FALSE,67.66666667,0.75137609,59.66666667,0.71936045,3,0.667819001,,,0.712851847
11170,ReSCo-CC: Unsupervised Identification of Key Disinformation Sentences,0,2010.10836,10/21/20,arxiv,0,3,dataset,0.002032818,0.073736986,0.606440797,0.313723867,0.002032767,0.002032765,Epidemiology,0.05410722,FALSE,11.33333333,0.170635166,1,0.122023013,0,0.403234768,,,0.231964316
11171,"Efficient Bayesian inference of fully stochastic epidemiological models
  with applications to COVID-19",0,2010.11783,10/21/20,arxiv,0,14,bayes,0.002639232,0.002639212,0.002639166,0.986804299,0.002639052,0.002639038,Epidemiology,0.19503799,FALSE,53.57142857,0.657678273,36.85714286,0.615868344,2,0.618927094,,,0.63082457
11172,Detection of COVID-19 informative tweets using RoBERTa,0,2010.11238,10/21/20,arxiv,0,3,dataset,0.005047639,0.005047675,0.265323193,0.714486025,0.005047782,0.005047686,Epidemiology,0.03385827,FALSE,19,0.285793803,25,0.529435376,0,0.403234768,,,0.406154649
11173,"TeX-Graph: Coupled tensor-matrix knowledge-graph embedding for COVID-19
  drug repurposing",0,2010.11367,10/22/20,arxiv,0,2,knowledge graph,0.28043328,0.002080582,0.605039425,0.108285437,0.002080725,0.002080551,Drug discovery,0.022570819,FALSE,19,0.285793803,22.5,0.507492641,0,0.403234768,,,0.398840404
11174,"Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second
  wave of COVID-19 hospitalizations in Italy",0,2010.11617,10/22/20,arxiv,0,1,neural network,0.001220001,0.001220025,0.053672791,0.698087216,0.087492459,0.15830751,Epidemiology,0.19449008,FALSE,16,0.243552477,2,0.164302917,1,0.537564047,,,0.315139814
11175,"""It is just a flu"": Assessing the Effect of Watch History on YouTube's
  Pseudoscientific Video Recommendations",0,2010.11638,10/22/20,arxiv,0,6,"deep learning, classifier",0.001901718,0.00190179,0.24668446,0.745708425,0.001901835,0.001901772,Epidemiology,0.0208219,FALSE,74.33333333,0.783227163,160.5,0.892694675,2,0.618927094,,,0.764949644
11176,"Adaptive Mesh Refinement and Coarsening for Diffusion-Reaction
  Epidemiological Models",0,2010.11861,10/22/20,arxiv,0,2,mathematical model,0.001565339,0.001565397,0.001565421,0.992173235,0.001565299,0.001565309,Epidemiology,0.07213029,FALSE,127.5,0.917867524,28,0.554321648,1,0.537564047,,,0.66991774
11177,"A Visual Analytics Based Decision Making Environment for COVID-19
  Modeling and Visualization",0,2010.11897,10/22/20,arxiv,0,6,"simulation model, model simulation",0.001861853,0.001861755,0.001861802,0.990691042,0.001861719,0.001861829,Epidemiology,0.0640679,FALSE,154.3333333,0.947430268,138.5,0.87309339,0,0.403234768,,,0.741252809
11178,Scientific Claim Verification with VERT5ERINI,0,2010.1193,10/22/20,arxiv,0,4,dataset,0.002996623,0.178033098,0.809980599,0.002996715,0.00299647,0.002996496,Genomics,0.001565367,FALSE,34.25,0.479559651,58.5,0.715814825,2,0.618927094,,,0.60476719
11179,"Impact of (SARS-CoV-2) COVID 19 on the indigenous language-speaking
  population in Mexico",0,2010.15588,10/23/20,arxiv,0,2,data mining,0.002490484,0.002490499,0.180068743,0.471685549,0.002490548,0.340774177,Epidemiology,0.4667978,FALSE,14.5,0.219617787,0,0.055525823,0,0.403234768,,,0.226126126
11180,"When the Open Source Community Meets COVID-19: Characterizing COVID-19
  themed GitHub Repositories",0,2010.12218,10/23/20,arxiv,0,5,"deep learning, dataset",0.001622757,0.001622766,0.508668185,0.484840795,0.001622801,0.001622696,Epidemiology,0.004004985,FALSE,59.6,0.70029068,11.2,0.373026492,0,0.403234768,,,0.49218398
11181,Predicting Infectiousness for Proactive Contact Tracing,0,2010.12536,10/23/20,arxiv,0,23,deep-learning,0.001415101,0.001415113,0.079000503,0.776797714,0.13995642,0.001415149,Epidemiology,0.008101374,FALSE,46.82608696,0.603933453,49.82608696,0.680759968,0,0.403234768,,,0.562642729
11182,"Nowcasting COVID-19 incidence indicators during the Italian first
  outbreak",0,2010.12679,10/23/20,arxiv,0,7,computational,0.002806375,0.002806381,0.00280651,0.985967918,0.002806391,0.002806425,Epidemiology,0.21125424,FALSE,37,0.508936854,15.42857143,0.428886808,1,0.537564047,,,0.491795903
11183,"Diverse R-PPG: Camera-Based Heart Rate Estimation for Diverse Subject
  Skin-Tones and Scenes",0,2010.12769,10/24/20,arxiv,0,10,dataset,0.002422368,0.00242237,0.769699244,0.102508277,0.002422407,0.120525334,Clinics,0.0515714,FALSE,42.3,0.560269652,29,0.562884667,0,0.403234768,,,0.508796362
11184,COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval,0,2010.128,10/24/20,arxiv,0,6,dataset,0.002639113,0.002639107,0.906865126,0.002639183,0.082578487,0.002638983,Healthcare,0.016536355,FALSE,24.5,0.361988991,10.83333333,0.367273214,1,0.537564047,,,0.422275417
11185,"Automated triage of COVID-19 from various lung abnormalities using chest
  CT features",0,2010.12967,10/24/20,arxiv,0,6,"machine learning, classifier, dataset",0.001861677,0.00186171,0.961459593,0.001861748,0.031093523,0.00186175,Imaging,0.06359354,FALSE,5,0.070752675,5,0.257024351,0,0.403234768,,,0.243670598
11186,Inter-Series Attention Model for COVID-19 Forecasting,0,2010.13006,10/25/20,arxiv,0,3,forecasting model,0.001511864,0.031703988,0.062461145,0.901299292,0.00151185,0.001511861,Epidemiology,0.018007666,FALSE,3,0.037293586,,,2,0.618927094,,,0.32811034
11187,Global to local impacts on atmospheric CO2 caused by COVID-19 lockdown,0,2010.13025,10/25/20,arxiv,0,13,dataset,0.001310382,0.001310408,0.081279804,0.91347869,0.001310362,0.001310354,Epidemiology,0.06181693,FALSE,72.92307692,0.777166182,84.15384615,0.78866738,2,0.618927094,,,0.728253552
11188,"Early Warning of COVID-19 Hotspots using Mobility of High Risk Users
  from Web Search Queries",0,2010.13254,10/26/20,arxiv,0,3,dataset,0.00135033,0.00135034,0.001350368,0.993248138,0.001350431,0.001350393,Epidemiology,0.011660129,FALSE,120.6666667,0.908157586,103.6666667,0.828338239,0,0.403234768,,,0.713243531
11189,Interpreting Uncertainty in Model Predictions For COVID-19 Diagnosis,0,2010.13271,10/26/20,arxiv,0,2,"bayes, neural network, dataset",0.001203413,0.001203682,0.856496648,0.138689388,0.001203418,0.001203452,Imaging,0.014914781,FALSE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
11190,ActiveNet: A computer-vision based approach to determine lethargy,0,2010.13714,10/26/20,arxiv,0,2,machine learning,0.174934011,0.003214321,0.476604276,0.241566326,0.100466893,0.003214173,Imaging,0.35569036,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
11191,"Epidemic Dynamics via Wavelet Theory and Machine Learning, with
  Applications to Covid-19","Biology 2020, 9(12), 477",2010.14004,10/27/20,arxiv,"Biology 2020, 9(12), 477",11,"machine learning, dataset",0.002720228,0.002720174,0.089780257,0.899339035,0.002720143,0.002720163,Epidemiology,0.44867054,FALSE,19.18181818,0.286659657,16.45454545,0.441865132,3,0.667819001,,,0.46544793
11192,"Triple-view Convolutional Neural Networks for COVID-19 Diagnosis with
  Chest X-ray",0,2010.14091,10/27/20,arxiv,0,1,"deep learning, neural network",0.000898108,0.000898137,0.947289436,0.018461639,0.019108457,0.013344222,Imaging,0.002313554,FALSE,28,0.408312202,27,0.546026224,0,0.403234768,,,0.452524398
11193,Global Sentiment Analysis Of COVID-19 Tweets Over Time,0,2010.14234,10/27/20,arxiv,0,4,"lstm, dataset",0.082933321,0.001861729,0.230252425,0.681229045,0.001861773,0.001861707,Epidemiology,0.016643643,FALSE,2.5,0.027459954,0,0.055525823,0,0.403234768,,,0.162073515
11194,"Examining Deep Learning Models with Multiple Data Sources for COVID-19
  Forecasting",0,2010.14491,10/27/20,arxiv,0,6,"deep learning, neural network",0.001254597,0.001254615,0.400071894,0.594909665,0.001254613,0.001254615,Epidemiology,0.27157918,FALSE,82.16666667,0.81520193,113.5,0.843122826,2,0.618927094,,,0.75908395
11195,"Analyzing Societal Impact of COVID-19: A Study During the Early Days of
  the Pandemic",0,2010.15674,10/27/20,arxiv,0,3,lstm,0.001392841,0.001392893,0.001392887,0.808984702,0.185443819,0.001392857,Epidemiology,0.1842627,FALSE,24.33333333,0.359700662,10,0.355632861,0,0.403234768,,,0.372856097
11196,"Impact of Pre-symptomatic Transmission on Epidemic Spreading in Contact
  Networks",0,2010.14598,10/27/20,arxiv,0,2,"computational, probabilistic",0.001330027,0.024699339,0.001330053,0.969980506,0.001330053,0.001330022,Epidemiology,0.027964652,FALSE,169,0.957634981,149.5,0.883128178,0,0.403234768,,,0.747999309
11197,Point of Care Image Analysis for COVID-19,0,2011.01789,10/28/20,arxiv,0,25,"neural network, image analysis, dataset",0.00198709,0.001987137,0.990064218,0.001987127,0.001987108,0.00198732,Imaging,0.14681917,FALSE,62.36,0.717298534,114.68,0.844260102,0,0.403234768,,,0.654931134
11198,"A stochastic time-delayed model for the effectiveness of Moroccan
  COVID-19 deconfinement strategy","Math. Model. Nat. Phenom. 15 (2020), Art. 50, 14 pp",2010.16265,10/28/20,arxiv,"Math. Model. Nat. Phenom. 15 (2020), Art. 50, 14 pp",6,mathematical model,0.001717213,0.001717208,0.001717217,0.964251961,0.028879232,0.00171717,Epidemiology,0.8161386,TRUE,86.66666667,0.832209784,32.16666667,0.586633663,2,0.618927094,,,0.679256847
11199,Interpreting glottal flow dynamics for detecting COVID-19 from voice,0,2010.16318,10/29/20,arxiv,0,3,dataset,0.069925859,0.001987233,0.445222505,0.35799774,0.12287943,0.001987233,Epidemiology,0.021267563,FALSE,67.66666667,0.75137609,59.66666667,0.71936045,2,0.618927094,,,0.696554545
11200,"A statistical model to assess risk for supporting SARS-CoV-2 quarantine
  decisions",0,2010.15677,10/29/20,arxiv,0,6,bayes,0.001350343,0.076861049,0.120520205,0.729571686,0.070346337,0.001350379,Epidemiology,0.055738628,FALSE,20.83333333,0.307996784,8.666666667,0.331415574,0,0.403234768,,,0.347549042
11201,"Explainable Automated Coding of Clinical Notes using Hierarchical
  Label-wise Attention Networks and Label Embedding Initialisation",0,2010.15728,10/29/20,arxiv,0,4,"deep learning, neural network",0.001171567,0.001171555,0.965264443,0.001171606,0.001171584,0.030049245,Clinics,0.37634563,FALSE,9.75,0.146267549,0.5,0.087101953,1,0.537564047,,,0.25697785
11202,"Spatiotemporal effects of the causal factors on COVID-19 incidences in
  the contiguous United States",0,2010.15754,10/29/20,arxiv,0,6,machine learning,0.001823339,0.001823347,0.001823472,0.901692259,0.091014166,0.001823415,Epidemiology,0.36309803,FALSE,25.83333333,0.378625765,4.166666667,0.233074659,0,0.403234768,,,0.338311731
11203,"COVID-FACT: A Fully-Automated Capsule Network-based Framework for
  Identification of COVID-19 Cases from Chest CT scans",0,2010.16041,10/30/20,arxiv,0,11,"deep learning, neural network, dataset",0.000916704,0.000916729,0.99541644,0.000916738,0.000916672,0.000916716,Imaging,0.013606221,FALSE,27.54545455,0.401632754,8.181818182,0.321982874,2,0.618927094,,,0.447514241
11204,"CT-CAPS: Feature Extraction-based Automated Framework for COVID-19
  Disease Identification from Chest CT Scans using Capsule Networks",0,2010.16043,10/30/20,arxiv,0,7,"deep learning, neural network, dataset",0.001187271,0.001187276,0.960217463,0.035033439,0.001187276,0.001187274,Imaging,0.017572254,FALSE,27.14285714,0.396932402,11,0.371287129,1,0.537564047,,,0.435261192
11205,"Identifying the optimal parameters for sprayed and inhaled drug
  particulates for intranasal targeting of SARS-CoV-2 infection sites",0,2010.16325,10/30/20,arxiv,0,7,"computational, in silico",0.458986923,0.001717258,0.177551986,0.358309226,0.001717278,0.001717329,Drug discovery,0.33204645,FALSE,12.14285714,0.183808522,7.857142857,0.314222638,1,0.537564047,,,0.345198403
11206,(Un)Masked COVID-19 Trends from Social Media,0,2011.00052,10/30/20,arxiv,0,6,dataset,0.000926278,0.000926276,0.198505386,0.639565318,0.159150431,0.000926311,Epidemiology,0.2533803,FALSE,52.5,0.650875131,80.5,0.780572652,0,0.403234768,,,0.61156085
11207,"Multi-stage transfer learning for lung segmentation using portable X-ray
  devices for patients with COVID-19",Expert Systems with Applications 173 (2021) 114677,2011.00133,10/30/20,arxiv,Expert Systems with Applications 173 (2021) 114677,4,transfer learning,0.001220061,0.001220058,0.945504403,0.001220036,0.049615355,0.001220087,Imaging,0.2686668,FALSE,86.5,0.831653163,23.5,0.516791544,0,0.403234768,,,0.583893158
11208,"Estimating County-Level COVID-19 Exponential Growth Rates Using
  Generalized Random Forests",0,2011.01219,10/31/20,arxiv,0,5,machine learning,0.002080547,0.002080573,0.468377009,0.523300746,0.002080577,0.002080547,Epidemiology,0.043311685,FALSE,62.8,0.719957944,36.4,0.613593792,0,0.403234768,,,0.578928835
11209,"A Quantitative Framework for Establishing Low-risk Interdistrict Travel
  Corridors during COVID-19",0,2011.00267,10/31/20,arxiv,0,4,network analysis,0.001486433,0.001486521,0.001486447,0.992567657,0.001486486,0.001486456,Epidemiology,0.47132304,FALSE,39.25,0.532253077,16.5,0.44293551,0,0.403234768,,,0.459474451
11210,"Leveraging Natural Language Processing to Mine Issues on Twitter During
  the COVID-19 Pandemic",0,2011.00377,10/31/20,arxiv,0,5,machine learning,0.001823328,0.001823377,0.001823464,0.990883082,0.001823434,0.001823315,Epidemiology,0.6545939,TRUE,25.2,0.370214608,5.6,0.267995718,0,0.403234768,,,0.347148365
11211,"Comparing Machine Learning Algorithms with or without Feature Extraction
  for DNA Classification",0,2011.00485,11/1/20,arxiv,0,4,"machine learning, bioinformatic, neural network, probabilistic, dataset",0.001415126,0.256055371,0.738284129,0.001415164,0.001415112,0.001415099,Genomics,0.0158211,FALSE,54.75,0.666584204,58.75,0.716283115,0,0.403234768,,,0.595367362
11212,"Triage of Potential COVID-19 Patients from Chest X-ray Images using
  Hierarchical Convolutional Networks",0,2011.00618,11/1/20,arxiv,0,5,machine learning,0.001141319,0.001141409,0.994293278,0.001141347,0.001141323,0.001141325,Imaging,0.21782061,FALSE,26.5,0.389325252,16,0.437316029,5,0.739490092,,,0.522043791
11213,"Bifurcated Autoencoder for Segmentation of COVID-19 Infected Regions in
  CT Images",0,2011.00631,11/1/20,arxiv,0,7,"deep learning, neural network, image analysis, deep-learning",0.001371279,0.001371317,0.975350649,0.001371289,0.001371252,0.019164215,Imaging,0.415442,FALSE,54,0.661574618,,,0,0.403234768,,,0.532404693
11214,Screening for an Infectious Disease as a Problem in Stochastic Control,0,2011.00635,11/1/20,arxiv,0,1,bayes,0.005697993,0.005697925,0.34932603,0.627882474,0.005698059,0.005697519,Epidemiology,0.3489575,FALSE,13,0.197352959,0,0.055525823,0,0.403234768,,,0.218704517
11215,"Accessible Data Curation and Analytics for International-Scale Citizen
  Science Datasets",0,2011.00867,11/2/20,arxiv,0,17,dataset,0.00242228,0.002422385,0.266831574,0.37120255,0.354698865,0.002422347,Epidemiology,0.051207483,FALSE,327,0.992331004,351.6875,0.965881723,2,0.618927094,,,0.859046607
11216,"Bayesian inference of heterogeneous epidemic models: Application to
  COVID-19 spread accounting for long-term care facilities",0,2011.01058,11/2/20,arxiv,0,3,bayes,0.001622722,0.02818417,0.001622851,0.839659556,0.001622753,0.127287947,Epidemiology,0.03743115,FALSE,47,0.606407323,22.66666667,0.508629917,3,0.667819001,,,0.594285414
11217,"Assessing racial inequality in COVID-19 testing with Bayesian threshold
  tests",0,2011.01179,11/2/20,arxiv,0,1,"bayes, dataset",0.004775116,0.004775252,0.004775464,0.502926882,0.477972008,0.004775278,Epidemiology,0.19647887,FALSE,44,0.578390748,155,0.887944876,0,0.403234768,,,0.62319013
11218,"Tinker-HP : Accelerating Molecular Dynamics Simulations of Large Complex
  Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and
  Multi-GPUs systems","Journal of Chemical Theory and Computation, 2021",2011.01207,11/2/20,arxiv,"Journal of Chemical Theory and Computation, 2021",12,molecular dynamics simulation,0.132578043,0.001187332,0.500423644,0.363436372,0.001187292,0.001187317,Epidemiology,0.053765148,FALSE,31.58333333,0.450677222,,,0,0.403234768,,,0.426955995
11219,Participation in TREC 2020 COVID Track Using Continuous Active Learning,0,2011.01453,11/3/20,arxiv,0,3,active learning,0.003760621,0.003760603,0.125435424,0.781003357,0.082279531,0.003760465,Epidemiology,0.037519872,FALSE,36,0.498299215,28.33333333,0.556729997,0,0.403234768,,,0.486087993
11220,"CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web
  to Special Domain Search",0,2011.0158,11/3/20,arxiv,0,10,information retrieval,0.119902803,0.002080604,0.871774717,0.002080733,0.002080598,0.002080546,Drug discovery,0.004948109,FALSE,107.3,0.883975509,,,2,0.618927094,,,0.751451301
11221,Optimizing Molecules using Efficient Queries from Property Evaluations,0,2011.01921,11/3/20,arxiv,0,5,machine learning,0.515572843,0.001684479,0.477689212,0.001684524,0.001684469,0.001684474,Drug discovery,0.027333826,FALSE,39.6,0.53503618,20.4,0.484613326,0,0.403234768,,,0.474294758
11222,"Effectiveness of isolation measures with app support to contain COVID-19
  epidemics: a parametric approach",0,2011.02349,11/4/20,arxiv,0,3,mathematical model,0.002080752,0.002080726,0.061943469,0.89880602,0.033008402,0.002080631,Epidemiology,0.03457299,FALSE,62,0.715814212,117.3333333,0.847337436,0,0.403234768,,,0.655462139
11223,SD-Measure: A Social Distancing Detector,"12th CICN, 2020, pp. 306-311",2011.02365,11/4/20,arxiv,"12th CICN, 2020, pp. 306-311",5,"neural network, dataset",0.001751215,0.001751175,0.401847875,0.591147253,0.001751325,0.001751157,Epidemiology,0.27363762,FALSE,5.6,0.077803204,0.2,0.061145304,0,0.403234768,,,0.180727758
11224,Deep Learning Framework to Detect Face Masks from Video Footage,"12th CICN, 2020, pp. 435-440",2011.02371,11/4/20,arxiv,"12th CICN, 2020, pp. 435-440",5,"deep learning, classifier, dataset",0.001717184,0.001717219,0.600205744,0.392925453,0.001717246,0.001717154,Imaging,0.20682243,FALSE,6.2,0.087574989,0.2,0.061145304,1,0.537564047,,,0.228761447
11225,Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest,0,2011.02719,11/5/20,arxiv,0,6,dataset,0.002032775,0.002032779,0.964748178,0.002032898,0.002032812,0.027120559,Imaging,0.6274531,TRUE,75.66666667,0.789349991,94.83333333,0.812483275,0,0.403234768,,,0.668356011
11226,"Collaborative City Digital Twin For Covid-19 Pandemic: A Federated
  Learning Solution",0,2011.02883,11/5/20,arxiv,0,5,dataset,0.001901736,0.001901719,0.165835155,0.826557854,0.001901811,0.001901726,Epidemiology,0.09950659,FALSE,79.2,0.803265508,45.8,0.662697351,0,0.403234768,,,0.623065875
11227,"Drug design principles from electric field calculations: understanding
  SARS-CoV-2 main protease interaction with X77 non-covalent inhibitor",0,2011.02971,11/5/20,arxiv,0,1,molecular dynamics simulation,0.820094347,0.001156291,0.082303436,0.09413345,0.001156249,0.001156227,Drug discovery,0.061413974,FALSE,11,0.167171748,0,0.055525823,0,0.403234768,,,0.208644113
11228,"PubSqueezer: A Text-Mining Web Tool to Transform Unstructured Documents
  into Structured Data",0,2011.03123,11/5/20,arxiv,0,1,"machine learning, computational, text mining, text-mining",0.001823402,0.001823454,0.30687055,0.65553014,0.032129143,0.001823312,Epidemiology,0.002391368,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
11229,"Predicting special care during the COVID-19 pandemic: A machine learning
  approach",0,2011.03143,11/6/20,arxiv,0,2,"bayes, machine learning",0.072920482,0.002032787,0.305486751,0.385909878,0.002032793,0.231617309,Epidemiology,0.21677378,FALSE,7.5,0.108355495,0.5,0.087101953,0,0.403234768,,,0.199564072
11230,Fighting an Infodemic: COVID-19 Fake News Dataset,0,2011.03327,11/6/20,arxiv,0,9,"machine learning, logistic regression, dataset",0.002996458,0.002996441,0.583391562,0.404622519,0.002996592,0.002996427,Epidemiology,0.06786433,FALSE,32.11111111,0.456367122,13.66666667,0.407412363,22,0.908142478,,,0.590640654
11231,"Fluid dynamics simulations show that facial masks can suppress the
  spread of COVID-19 in indoor environments",0,2011.03394,11/6/20,arxiv,0,7,computational,0.001350416,0.001350393,0.001350409,0.931093371,0.001350377,0.063505033,Epidemiology,0.39735007,FALSE,76.42857143,0.792133094,75.28571429,0.767126037,3,0.667819001,,,0.742359377
11232,"Deep Transfer Learning for Automated Diagnosis of Skin Lesions from
  Photographs",0,2011.04475,11/6/20,arxiv,0,2,transfer learning,0.001943475,0.00194345,0.959893495,0.001943578,0.00194363,0.032332373,Clinics,0.3474418,FALSE,38.5,0.524089307,17,0.451097137,0,0.403234768,,,0.459473737
11233,"Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19
  Using Convolutional Neural Network",0,2011.03585,11/6/20,arxiv,0,4,"computational, neural network, dataset",0.000956312,0.000956323,0.995218436,0.00095633,0.000956297,0.000956301,Imaging,0.33886886,FALSE,64.75,0.733440534,34,0.5990768,0,0.403234768,,,0.578584034
11234,"Curse of Small Sample Size in Forecasting of the Active Cases in
  COVID-19 Outbreak",0,2011.03628,11/6/20,arxiv,0,3,"machine learning, forecasting model",0.001034607,0.001034619,0.580264011,0.415597562,0.00103459,0.001034612,Epidemiology,0.002274841,FALSE,23.66666667,0.34986703,19.33333333,0.473842655,0,0.403234768,,,0.408981484
11235,GP-LVM of categorical data from test-positive COVID-19 pregnant women,0,2011.03715,11/7/20,arxiv,0,7,bayes,0.004530689,0.004530713,0.004530759,0.656650467,0.325226423,0.004530949,Epidemiology,0.55812067,TRUE,24.71428571,0.3637207,0.571428571,0.088038534,0,0.403234768,,,0.284998
11236,"Reducing latency and bandwidth for video streaming using keypoint
  extraction and digital puppetry",0,2011.038,11/7/20,arxiv,0,7,computational,0.001823395,0.001823386,0.655564327,0.33714207,0.001823434,0.001823387,Epidemiology,0.006360292,FALSE,39.85714286,0.537881131,34.42857143,0.601284453,0,0.403234768,,,0.51413345
11237,"AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures based on
  Artificial Intelligence algorithms and multi-sources Data Processing",0,2011.05808,11/7/20,arxiv,0,6,"artificial intelligence, neural network",0.029719748,0.001330071,0.242487225,0.723802852,0.001330066,0.001330039,Epidemiology,0.08757934,FALSE,48,0.614942173,16.83333333,0.446815628,0,0.403234768,,,0.488330856
11238,Google Trends Analysis of COVID-19,0,2011.03847,11/7/20,arxiv,0,5,"deep learning, neural network, correlation analysis",0.001751158,0.001751141,0.249134773,0.743860637,0.001751162,0.00175113,Epidemiology,0.061226428,FALSE,47.2,0.607520564,27.2,0.547230399,3,0.667819001,,,0.607523321
11239,"Repurposing the Combination Drug of Favipiravir, Hydroxychloroquine and
  Oseltamivir as a Potential Inhibitor against SARS-CoV-2: A Computational
  Study",0,2012.00652,11/8/20,arxiv,0,2,"computational, in-silico",0.992173054,0.001565384,0.001565356,0.001565455,0.001565375,0.001565376,Drug discovery,0.48970127,FALSE,68,0.753973653,2.5,0.180826866,0,0.403234768,,,0.446011762
11240,"Ivermectin and Doxycycline Combination as a Promising Drug Candidate
  Against SARS-CoV-2 Infection: A Computational Study",0,2012.00653,11/8/20,arxiv,0,3,"computational, in silico",0.988806444,0.002238437,0.002239007,0.002238562,0.002238846,0.002238704,Drug discovery,0.75971603,TRUE,62,0.715814212,4.666666667,0.246721969,1,0.537564047,,,0.50003341
11241,"Inference under Superspreading: Determinants of SARS-CoV-2 Transmission
  in Germany",0,2011.04002,11/8/20,arxiv,0,1,bayes,0.001653016,0.001653087,0.020582784,0.744771362,0.229686627,0.001653124,Epidemiology,0.25522643,FALSE,63,0.721998887,51,0.685509767,0,0.403234768,,,0.603581141
11242,Elastocapillary network model of inhalation,Physical Review Research (2020),2011.04036,11/8/20,arxiv,Physical Review Research (2020),3,"computational, network model",0.238946221,0.001371307,0.180172089,0.458520033,0.001371329,0.11961902,Epidemiology,0.3063977,FALSE,23.33333333,0.345723298,9.333333333,0.342253144,0,0.403234768,,,0.36373707
11243,"MM-COVID: A Multilingual and Multimodal Data Repository for Combating
  COVID-19 Disinformation",0,2011.04088,11/8/20,arxiv,0,4,dataset,0.002422269,0.002422457,0.002422498,0.881645615,0.108664927,0.002422234,Epidemiology,0.2327008,FALSE,23.25,0.34454821,7.75,0.312215681,2,0.618927094,,,0.425230328
11244,COVID-19 Patient Detection from Telephone Quality Speech Data,0,2011.04299,11/9/20,arxiv,0,3,"classifier, dataset",0.002996541,0.002996592,0.985017362,0.002996579,0.002996448,0.002996477,Genomics,0.003534675,FALSE,14.66666667,0.221163956,7.666666667,0.310877709,2,0.618927094,,,0.383656253
11245,"Application and Comparison of Deep Learning Methods in the Prediction of
  RNA Sequence Degradation and Stability",0,2011.05136,11/9/20,arxiv,0,1,deep learning,0.23510734,0.031247837,0.621528693,0.002562732,0.002562698,0.106990699,Drug discovery,0.26414967,FALSE,29,0.41993939,2,0.164302917,0,0.403234768,,,0.329159025
11246,Artificial Intelligence Decision Support for Medical Triage,0,2011.04548,11/9/20,arxiv,0,9,"machine learning, artificial intelligence",0.001751271,0.001751204,0.488689161,0.449692854,0.05636421,0.001751301,Epidemiology,0.72380495,TRUE,21.77777778,0.321726761,5.222222222,0.259834092,0,0.403234768,,,0.328265207
11247,"Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: the
  Case of Northern Italy",Published in: 2020 AEIT International Annual Conference (AEIT),2103.15654,11/9/20,arxiv,Published in: 2020 AEIT International Annual Conference (AEIT),4,"neural network, forecasting model",0.031086413,0.001684524,0.040302969,0.923557054,0.001684538,0.001684503,Epidemiology,0.5220143,TRUE,108.75,0.887315233,37.75,0.620350549,0,0.403234768,,,0.63696685
11248,"Pristine annotations-based multi-modal trained artificial intelligence
  solution to triage chest X-ray for COVID-19",0,2011.05186,11/10/20,arxiv,0,16,"artificial intelligence, deep-learning",0.018384487,0.001254665,0.976596941,0.001254634,0.001254637,0.001254636,Imaging,0.25498286,FALSE,25.0625,0.369348754,11.3125,0.374698956,0,0.403234768,,,0.382427493
11249,AC-DC: Amplification Curve Diagnostics for Covid-19 Group Testing,0,2011.05223,11/10/20,arxiv,0,7,probabilistic,0.001684519,0.119383047,0.224159068,0.651404104,0.001684614,0.001684647,Epidemiology,0.11338177,FALSE,120.1428571,0.907044344,190,0.912095264,2,0.618927094,,,0.812688901
11250,"NIT COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for
  Identify Informative COVID-19 English Tweets",0,2011.05551,11/11/20,arxiv,0,2,deep learning,0.003335246,0.003335361,0.469992723,0.516665844,0.003335379,0.003335446,Epidemiology,0.27305573,FALSE,2.5,0.027459954,0.5,0.087101953,0,0.403234768,,,0.172598892
11251,"UTLDR: an agent-based framework for modeling infectious diseases and
  public interventions",0,2011.05606,11/11/20,arxiv,0,4,computational,0.001684589,0.001684521,0.001684572,0.961137109,0.032124636,0.001684574,Epidemiology,0.019297242,FALSE,21.25,0.314614386,14.25,0.415038801,0,0.403234768,,,0.377629318
11252,"Disentangling Community-level Changes in Crime Trends During the
  COVID-19 Pandemic in Chicago","Crime Sci 9, 21 (2020)",2011.05658,11/11/20,arxiv,"Crime Sci 9, 21 (2020)",4,bayes,0.001593563,0.001593561,0.001593521,0.992032289,0.00159355,0.001593515,Epidemiology,0.5123817,TRUE,165.75,0.955408498,429,0.973441263,3,0.667819001,,,0.865556254
11253,"Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest
  X-ray Images",0,2012.02278,11/11/20,arxiv,0,7,"deep learning, dataset",0.001272637,0.025121918,0.969787517,0.001272676,0.001272635,0.001272618,Imaging,0.013142943,FALSE,73.42857143,0.779392665,,,0,0.403234768,,,0.591313716
11254,"Classification of COVID-19 in Chest CT Images using Convolutional
  Support Vector Machines",0,2011.05746,11/11/20,arxiv,0,5,"deep learning, artificial intelligence, neural network, dataset",0.001717183,0.001717246,0.991413948,0.001717226,0.00171719,0.001717207,Imaging,0.037950724,FALSE,78.8,0.801657493,178.8,0.905405405,0,0.403234768,,,0.703432555
11255,Cryo-RALib -- a modular library for accelerating alignment in cryo-EM,0,2011.05755,11/11/20,arxiv,0,6,"bayes, image analysis",0.322424106,0.138576992,0.290834982,0.244002797,0.002080565,0.002080558,Drug discovery,0.00697428,FALSE,24.16666667,0.356670171,15.66666667,0.432365534,0,0.403234768,,,0.397423491
11256,"The Role of the Crowd in Countering Misinformation: A Case Study of the
  COVID-19 Infodemic",0,2011.05773,11/11/20,arxiv,0,5,"classifier, dataset",0.00129126,0.001291241,0.193123514,0.801711467,0.001291318,0.0012912,Epidemiology,0.23625249,FALSE,140.8,0.934937226,326.4,0.961065025,1,0.537564047,,,0.811188766
11257,"An epidemiological compartmental model with automated parameter
  estimation and forecasting of the spread of COVID-19 with analysis of data
  from Germany and Brazil",0,2011.06515,11/11/20,arxiv,0,2,probabilistic,0.001538186,0.001538138,0.026139585,0.967707887,0.001538104,0.0015381,Epidemiology,0.049715728,FALSE,13.5,0.205393036,1.5,0.138747659,0,0.403234768,,,0.249125154
11258,"Analysis of COVID-19 evolution in Senegal: impact of health care
  capacity",0,2011.06278,11/12/20,arxiv,0,4,machine learning,0.003465918,0.003465911,0.003466371,0.853658734,0.08687475,0.049068315,Epidemiology,0.37321138,FALSE,44.5,0.581977859,16,0.437316029,0,0.403234768,,,0.474176218
11259,"Optimal governance and implementation of vaccination programs to contain
  the COVID-19 pandemic",0,2011.06455,11/12/20,arxiv,0,14,network model,0.002238535,0.002238518,0.002238504,0.98880745,0.002238573,0.002238419,Epidemiology,0.2649898,FALSE,127.3571429,0.917681984,100.7142857,0.823722237,4,0.707574542,,,0.816326254
11260,"Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons
  Learned from Patients with ARDS",0,2011.1047,11/12/20,arxiv,0,6,"deep learning, neural network, network model",0.001350328,0.001350346,0.534944356,0.001350532,0.001350504,0.459653934,Clinics,0.5967081,TRUE,148.6666667,0.942915456,126.3333333,0.85991437,1,0.537564047,,,0.780131291
11261,"An exploratory assessment of a multidimensional healthcare and economic
  data on COVID-19 in Nigeria","Volume 33, December 2020, 106424",2011.06689,11/12/20,arxiv,"Volume 33, December 2020, 106424",10,dataset,0.003465889,0.003466003,0.003466114,0.932582613,0.003466068,0.053553313,Epidemiology,0.31196493,FALSE,6.9,0.097470468,0.3,0.06649719,1,0.537564047,,,0.233843902
11262,Group design in group testing for COVID-19 : A French case-study,0,2011.06927,11/13/20,arxiv,0,8,computational,0.001987209,0.001987146,0.551971985,0.234647863,0.001987223,0.207418574,Epidemiology,0.015505433,FALSE,50.625,0.635722679,111.875,0.841115868,0,0.403234768,,,0.626691105
11263,COVID-19 and the stock market: evidence from Twitter,0,2011.08717,11/13/20,arxiv,0,4,dataset,0.003607183,0.003607381,0.003607539,0.981963262,0.003607317,0.003607318,Epidemiology,0.06440088,FALSE,24,0.35574247,23.75,0.518731603,0,0.403234768,,,0.425902947
11264,"Classification based on invisible features and thereby finding the
  effect of tuberculosis vaccine on COVID-19",0,2011.07332,11/14/20,arxiv,0,2,neural network,0.001987242,0.142317799,0.652017385,0.199703142,0.001987235,0.001987196,Epidemiology,0.06642431,FALSE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
11265,"An Autonomous Approach to Measure Social Distances and Hygienic
  Practices during COVID-19 Pandemic in Public Open Spaces",0,2011.07375,11/14/20,arxiv,0,3,deep learning,0.031024582,0.001438208,0.308777908,0.655882974,0.001438217,0.001438112,Epidemiology,0.06263384,FALSE,16.66666667,0.251159626,7.333333333,0.304656141,0,0.403234768,,,0.319683512
11266,"Good proctor or ""Big Brother""? AI Ethics and Online Exam Supervision
  Technologies",0,2011.07647,11/15/20,arxiv,0,3,artificial intelligence,0.002032779,0.002032791,0.250276408,0.44965605,0.293969185,0.002032787,Epidemiology,0.5618806,TRUE,33.66666667,0.47312759,2.666666667,0.185442869,0,0.403234768,,,0.353935075
11267,"Critical data analysis of COVID-19 spreading in Indonesia to measure the
  readiness of new-normal policy",0,2011.07679,11/16/20,arxiv,0,5,forecasting model,0.028928393,0.001861689,0.001861762,0.963624688,0.001861765,0.001861703,Epidemiology,0.4517417,FALSE,9.2,0.136990537,0,0.055525823,0,0.403234768,,,0.198583709
11268,"Phenomenological description of spread of Covid-19 in Italy: people
  mobility as main factor controlling propagation of infection cases",0,2011.08111,11/16/20,arxiv,0,2,model fit,0.001901679,0.001901723,0.001901716,0.865843214,0.001901737,0.12654993,Epidemiology,0.0595631,FALSE,33.5,0.471890655,3.5,0.213607172,1,0.537564047,,,0.407687291
11269,The Role of Edge Robotics As-a-Service in Monitoring COVID-19 Infection,0,2011.08482,11/17/20,arxiv,0,5,"deep learning, dataset",0.00129128,0.001291261,0.993543731,0.001291274,0.001291212,0.001291241,Imaging,0.020730555,FALSE,73.8,0.781124374,27.8,0.552047097,0,0.403234768,,,0.578802079
11270,"Political Partisanship and Anti-Science Attitudes in Online Discussions
  about Covid-19",0,2011.08498,11/17/20,arxiv,0,7,dataset,0.002238464,0.037781068,0.002238563,0.637028387,0.318474986,0.002238532,Epidemiology,0.17223075,FALSE,98.57142857,0.866039953,178.5714286,0.905137811,0,0.403234768,,,0.724804177
11271,"Decision and Feature Level Fusion of Deep Features Extracted from Public
  COVID-19 Data-sets",0,2011.08528,11/17/20,arxiv,0,3,"neural network, classifier",0.001022662,0.02100803,0.951244722,0.001022641,0.024679308,0.001022637,Imaging,0.010135025,FALSE,23.33333333,0.345723298,6.333333333,0.285121755,0,0.403234768,,,0.344693274
11272,"A New Compartmental Epidemiological Model for COVID-19 with a Case Study
  of Portugal","Ecological Complexity 44 (2020) Art. 100885, 8 pp",2011.08741,11/17/20,arxiv,"Ecological Complexity 44 (2020) Art. 100885, 8 pp",3,mathematical model,0.001987135,0.001987113,0.001987145,0.961021453,0.00198714,0.031030015,Epidemiology,0.71408534,TRUE,173.3333333,0.960912858,67.66666667,0.74719026,1,0.537564047,,,0.748555722
11273,"Probing Fairness of Mobile Ocular Biometrics Methods Across Gender on
  VISOB 2.0 Dataset","25th International Conference on Pattern Recognition (ICPR) 2020|
  Milan, Italy",2011.08898,11/17/20,arxiv,"25th International Conference on Pattern Recognition (ICPR) 2020|
  Milan, Italy",3,"deep learning, dataset",0.030011592,0.001823363,0.475978608,0.001823393,0.488539594,0.001823451,Healthcare,0.9295044,TRUE,26.33333333,0.386047375,23,0.513513514,0,0.403234768,,,0.434265219
11274,"Comparative transcriptome analysis reveals key epigenetic targets in
  SARS-CoV-2 infection",0,2011.08902,11/17/20,arxiv,0,7,transcriptom,0.926445149,0.0407108,0.00186177,0.001861727,0.001861728,0.027258826,Drug discovery,0.5937663,TRUE,14.42857143,0.217886078,3,0.199424672,0,0.403234768,,,0.273515173
11275,"Estimating Seroprevalence of SARS-CoV-2 in Ohio: A Bayesian Multilevel
  Poststratification Approach with Multiple Diagnostic Tests",0,2011.09033,11/18/20,arxiv,0,7,bayes,0.001350425,0.300168036,0.001350444,0.224285184,0.424378108,0.048467803,Healthcare,0.040643185,FALSE,82.85714286,0.817366566,70.14285714,0.753813219,2,0.618927094,,,0.730035626
11276,"The Development and Deployment of a Model for Hospital-level COVID-19
  Associated Patient Demand Intervals from Consistent Estimators (DICE)",0,2011.09377,11/18/20,arxiv,0,4,"computational, probabilistic",0.001486411,0.001486429,0.035606871,0.816343018,0.001486455,0.143590816,Epidemiology,0.11565,FALSE,31.5,0.450058754,5.75,0.271742039,0,0.403234768,,,0.375011854
11277,"A Generalized Epidemiological Model for COVID-19 with Dynamic and
  Asymptomatic Population",0,2011.09686,11/19/20,arxiv,0,4,probabilistic,0.002806393,0.002806449,0.002806463,0.940041682,0.002806378,0.048732635,Epidemiology,0.12748456,FALSE,12,0.183190055,0.25,0.065493712,0,0.403234768,,,0.217306178
11278,"Variational Bayes method for ODE parameter estimation with application
  to time-varying SIR model for COVID-19 epidemic",0,2011.09718,11/19/20,arxiv,0,2,"bayes, bioinformatic, mathematical model",0.043336952,0.001371358,0.20873587,0.743813299,0.001371268,0.001371252,Epidemiology,0.03993669,FALSE,21,0.312016822,1.5,0.138747659,1,0.537564047,,,0.329442843
11279,"Predicting Patient COVID-19 Disease Severity by means of Statistical and
  Machine Learning Analysis of Blood Cell Transcriptome Data",0,2011.10657,11/19/20,arxiv,0,12,"machine learning, deep learning, transcriptom, dataset",0.001254626,0.029917862,0.408671255,0.001254625,0.00125465,0.557646981,Clinics,0.49929228,FALSE,23.25,0.34454821,21.41666667,0.496320578,2,0.618927094,,,0.486598627
11280,"Interpretable and Transferable Models to Understand the Impact of
  Lockdown Measures on Local Air Quality",0,2011.10144,11/19/20,arxiv,0,4,prediction model,0.001371248,0.001371254,0.178376831,0.816138148,0.001371275,0.001371244,Epidemiology,0.023471802,FALSE,66.25,0.742346465,73,0.761974846,0,0.403234768,,,0.635852026
11281,"Targeted Self Supervision for Classification on a Small COVID-19 CT Scan
  Dataset",0,2011.10188,11/20/20,arxiv,0,2,"neural network, dataset",0.001684643,0.001684541,0.782496388,0.001684515,0.210765458,0.001684455,Imaging,0.012833983,FALSE,5.5,0.077246583,0,0.055525823,0,0.403234768,,,0.178669058
11282,"Elementary Effects Analysis of factors controlling COVID-19 infections
  in computational simulation reveals the importance of Social Distancing and
  Mask Usage",0,2011.11381,11/20/20,arxiv,0,3,"simulation model, computational",0.002296597,0.002296549,0.002296545,0.825503215,0.16531052,0.002296574,Epidemiology,0.28579184,FALSE,112.3333333,0.894736842,147.3333333,0.881321916,1,0.537564047,,,0.771207602
11283,"A Deep Language-independent Network to analyze the impact of COVID-19 on
  the World via Sentiment Analysis",0,2011.10358,11/20/20,arxiv,0,2,dataset,0.001717265,0.032177014,0.60630992,0.356361318,0.001717231,0.001717252,Epidemiology,0.015434533,FALSE,63.5,0.72496753,7.5,0.307867273,0,0.403234768,,,0.478689857
11284,"Bridging Physics-based and Data-driven modeling for Learning Dynamical
  Systems",0,2011.10616,11/20/20,arxiv,0,5,deep learning,0.001943476,0.001943447,0.473094513,0.519131595,0.001943459,0.00194351,Epidemiology,0.009345442,FALSE,37.2,0.509926402,,,2,0.618927094,,,0.564426748
11285,"Investigation of superspreading COVID-19 outbreaks events in meat and
  poultry processing plants in Germany: A cross-sectional study",0,2011.11153,11/23/20,arxiv,0,14,logistic regression,0.140918713,0.001901747,0.001901693,0.183283662,0.399336894,0.272657291,Healthcare,0.5987135,TRUE,19.85714286,0.294514194,16.71428571,0.445143163,1,0.537564047,,,0.425740468
11286,Domain Adaptation based COVID-19 CT Lung Infections Segmentation Network,0,2011.11242,11/23/20,arxiv,0,3,deep learning,0.001126791,0.001126802,0.972654234,0.022838615,0.00112679,0.001126768,Imaging,0.002244771,FALSE,40.66666667,0.546292288,4.666666667,0.246721969,0,0.403234768,,,0.398749675
11287,Automated Quality Assessment of Hand Washing Using Deep Learning,0,2011.11383,11/23/20,arxiv,0,5,"deep learning, neural network, network model, dataset",0.001861725,0.001861711,0.694148532,0.24290886,0.057357469,0.001861703,Epidemiology,0.002757281,FALSE,18.4,0.27558909,6.6,0.28913567,0,0.403234768,,,0.322653176
11288,"A realistic agent-based simulation model for COVID-19 based on a traffic
  simulation and mobile phone data",0,2011.11453,11/23/20,arxiv,0,8,simulation model,0.001237095,0.001237073,0.001237078,0.976939821,0.001237088,0.018111845,Epidemiology,0.02325058,FALSE,52.25,0.648648649,26.25,0.539670859,2,0.618927094,,,0.602415534
11289,"condLSTM-Q: A novel deep learning model for predicting Covid-19
  mortality in fine geographical Scale",0,2011.11507,11/23/20,arxiv,0,4,"deep learning, neural network, predictive model, network model, lstm",0.001823341,0.001823344,0.165038742,0.82766774,0.001823346,0.001823485,Epidemiology,0.042534173,FALSE,14.5,0.219617787,8.25,0.323789136,0,0.403234768,,,0.31554723
11290,Decision Support for the Quickest Detection of Critical COVID-19 Phases,0,2011.1154,11/23/20,arxiv,0,6,mathematical model,0.00125461,0.001254644,0.159592265,0.835389255,0.001254608,0.001254617,Epidemiology,0.056010872,FALSE,286.1666667,0.989609747,148.5,0.882325395,0,0.403234768,,,0.75838997
11291,Conjecturing-Based Computational Discovery of Patterns in Data,0,2011.11576,11/23/20,arxiv,0,4,"machine learning, computational",0.152727617,0.002183258,0.769690123,0.002183399,0.00218334,0.071032262,Drug discovery,0.1703417,FALSE,142.25,0.936668934,110.75,0.839577201,0,0.403234768,,,0.726493634
11292,"Explainable-by-design Semi-Supervised Representation Learning for
  COVID-19 Diagnosis from CT Imaging",0,2011.11719,11/23/20,arxiv,0,40,"deep learning, neural network",0.002130691,0.002130675,0.989346734,0.00213066,0.002130635,0.002130605,Imaging,0.016401768,FALSE,80.45,0.808522481,102.675,0.827067166,2,0.618927094,,,0.751505581
11293,"Accurate and Rapid Diagnosis of COVID-19 Pneumonia with Batch Effect
  Removal of Chest CT-Scans and Interpretable Artificial Intelligence",0,2011.11736,11/23/20,arxiv,0,25,"artificial intelligence, neural network, dataset",0.00122,0.0012201,0.972662281,0.022457547,0.001220014,0.001220057,Imaging,0.022571981,FALSE,28.24,0.409858371,11.12,0.371621622,0,0.403234768,,,0.39490492
11294,"Federated Semi-Supervised Learning for COVID Region Segmentation in
  Chest CT using Multi-National Data from China, Italy, Japan",0,2011.1175,11/23/20,arxiv,0,20,"supervised learning, dataset",0.001486522,0.001486485,0.780262161,0.213791957,0.001486441,0.001486434,Imaging,0.31565738,FALSE,98.8,0.866596574,63.95,0.734747123,4,0.707574542,,,0.769639413
11295,Job Transitions in a Time of Automation and Labor Market Crises,0,2011.11801,11/23/20,arxiv,0,3,artificial intelligence,0.025758211,0.001622745,0.088707548,0.735559393,0.146729395,0.001622707,Epidemiology,0.14561981,FALSE,97.33333333,0.862762076,69,0.750535189,0,0.403234768,,,0.672177344
11296,"A dataset to assess mobility changes in Chile following local
  quarantines",0,2011.12162,11/24/20,arxiv,0,5,dataset,0.001717209,0.00171723,0.001717207,0.991413974,0.001717214,0.001717166,Epidemiology,0.01987806,FALSE,42.6,0.563176449,33,0.593256623,3,0.667819001,,,0.608084024
11297,"Classification supporting COVID-19 diagnostics based on patient survey
  data",0,2011.12247,11/24/20,arxiv,0,14,"classifier, logistic regression",0.002296589,0.002296607,0.577977733,0.151125786,0.122275119,0.144028167,Epidemiology,0.091564745,FALSE,36.42857143,0.501762632,6.785714286,0.292346802,0,0.403234768,,,0.399114734
11298,"Artificial Intelligence for COVID-19 Detection -- A state-of-the-art
  review",0,2012.0631,11/25/20,arxiv,0,4,"deep learning, artificial intelligence, transfer learning",0.001272701,0.001272736,0.856039851,0.113367261,0.001272695,0.026774756,Imaging,0.18274969,FALSE,25.25,0.370709382,12.5,0.392761573,0,0.403234768,,,0.388901908
11299,"Denmark's Participation in the Search Engine TREC COVID-19 Challenge:
  Lessons Learned about Searching for Precise Biomedical Scientific Information
  on COVID-19",0,2011.12684,11/25/20,arxiv,0,8,dataset,0.001272712,0.024748051,0.176545401,0.794888365,0.001272744,0.001272728,Epidemiology,0.044171542,FALSE,68.5,0.756571217,156,0.888747659,0,0.403234768,,,0.682851214
11300,"Exploring the Effect of Image Enhancement Techniques on COVID-19
  Detection using Chest X-rays Images",0,2012.02238,11/25/20,arxiv,0,11,dataset,0.000863029,0.000863027,0.995684841,0.000863031,0.000863037,0.000863036,Imaging,0.7597769,TRUE,48.45454545,0.61766343,40.18181818,0.634399251,1,0.537564047,,,0.596542243
11301,"Deep Metric Learning-based Image Retrieval System for Chest Radiograph
  and its Clinical Applications in COVID-19",Medical Image Analysis. 70 (2021) 101993,2012.03663,11/26/20,arxiv,Medical Image Analysis. 70 (2021) 101993,20,"deep learning, image analysis, dataset",0.001098822,0.001098817,0.994505894,0.001098824,0.001098811,0.001098831,Imaging,0.4064942,FALSE,86.25,0.829921455,51.55,0.687650522,1,0.537564047,,,0.685045341
11302,"Neural Networks for Pulmonary Disease Diagnosis using Auditory and
  Demographic Information",0,2011.13194,11/26/20,arxiv,0,6,"machine learning, neural network, dataset",0.00162273,0.001622725,0.947178737,0.046330202,0.00162284,0.001622766,Epidemiology,0.003943026,FALSE,35.16666667,0.488651123,22.66666667,0.508629917,7,0.785110192,,,0.594130411
11303,"Two Stage Transformer Model for COVID-19 Fake News Detection and Fact
  Checking",0,2011.13253,11/26/20,arxiv,0,4,"machine learning, dataset",0.001350346,0.001350351,0.551844661,0.442753917,0.001350371,0.001350355,Epidemiology,0.001459658,FALSE,21,0.312016822,22,0.503746321,6,0.764429903,,,0.526731015
11304,"Virufy: Global Applicability of Crowdsourced and Clinical Datasets for
  AI Detection of COVID-19 from Cough",0,2011.1332,11/26/20,arxiv,0,7,"machine learning, dataset",0.002720086,0.210574284,0.7785448,0.002720225,0.00272034,0.002720264,Genomics,0.20478222,FALSE,6.571428571,0.093883357,2.571428571,0.181495852,3,0.667819001,,,0.314399404
11305,"Unsupervised learning for economic risk evaluation in the context of
  Covid-19 pandemic",0,2011.1335,11/26/20,arxiv,0,2,"machine learning, supervised learning, unsupervised learning, computational",0.003760527,0.003760484,0.227757208,0.757200832,0.0037605,0.003760448,Epidemiology,0.163129,FALSE,17.5,0.263776362,2.5,0.180826866,0,0.403234768,,,0.282612665
11306,"Uncertainty-driven ensembles of deep architectures for multiclass
  classification. Application to COVID-19 diagnosis in chest X-ray images",0,2011.14894,11/27/20,arxiv,0,6,"bayes, deep learning, neural network",0.001310338,0.001310362,0.954821337,0.039937316,0.001310316,0.00131033,Imaging,0.00666216,FALSE,73,0.778464964,35.33333333,0.607439122,0,0.403234768,,,0.596379618
11307,"Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from
  CORD-19",0,2012.04682,11/28/20,arxiv,0,3,dataset,0.445826492,0.001538126,0.437114256,0.112444883,0.001538131,0.001538112,Drug discovery,0.023288608,FALSE,29,0.41993939,13.66666667,0.407412363,0,0.403234768,,,0.410195507
11308,Towards Combating Pandemic-related Misinformation in Social Media,0,2011.14146,11/28/20,arxiv,0,1,dataset,0.002296568,0.002296558,0.002296757,0.988516807,0.002296779,0.002296531,Epidemiology,0.14362302,FALSE,17,0.257467994,3,0.199424672,1,0.537564047,,,0.331485571
11309,"Clinical prediction system of complications among COVID-19 patients: a
  development and validation retrospective multicentre study",0,2012.01138,11/28/20,arxiv,0,13,"machine learning, logistic regression",0.001371268,0.001371398,0.301369939,0.001371309,0.001371286,0.693144799,Clinics,0.74491906,TRUE,11.53846154,0.174284124,5.230769231,0.25990099,0,0.403234768,,,0.279139961
11310,"The Vaccine Supply Chain: A Call for Resilience Analytics to Support
  COVID-19 Vaccine Production and Distribution",0,2011.14231,11/28/20,arxiv,0,4,network analysis,0.001272672,0.001272704,0.001272726,0.99363657,0.001272678,0.00127265,Epidemiology,0.144986,FALSE,50,0.632073721,16.75,0.446012845,0,0.403234768,,,0.493773778
11311,"Computationally repurposed drugs and natural products against RNA
  dependent RNA polymerase as potential COVID-19 therapies",0,2011.14241,11/29/20,arxiv,0,4,computational,0.958566243,0.001171597,0.001171609,0.001171593,0.001171596,0.036747362,Drug discovery,0.8067397,TRUE,23,0.34225988,5,0.257024351,1,0.537564047,,,0.378949426
11312,Plasmonic metamaterial based virus detection system: a review,0,2012.00551,11/29/20,arxiv,0,4,"machine learning, artificial intelligence",0.002422423,0.357676897,0.632633727,0.002422405,0.002422304,0.002422245,Genomics,0.14744693,FALSE,11.25,0.169274538,0.5,0.087101953,0,0.403234768,,,0.21987042
11313,"Artificial Intelligence applied to chest X-Ray images for the automatic
  detection of COVID-19. A thoughtful evaluation approach",0,2011.14259,11/29/20,arxiv,0,4,"artificial intelligence, neural network, dataset",0.022092796,0.001220058,0.973027029,0.00122004,0.001220025,0.001220052,Imaging,0.56126237,TRUE,54.5,0.665161729,46.25,0.665038801,0,0.403234768,,,0.577811766
11314,"Dank or Not? -- Analyzing and Predicting the Popularity of Memes on
  Reddit",0,2011.14326,11/29/20,arxiv,0,6,machine learning,0.00263914,0.0026393,0.440174109,0.549269286,0.00263915,0.002639014,Epidemiology,0.38281098,FALSE,6.833333333,0.096852001,0,0.055525823,0,0.403234768,,,0.185204197
11315,"Audio, Speech, Language, & Signal Processing for COVID-19: A
  Comprehensive Overview",0,2011.14445,11/29/20,arxiv,0,2,artificial intelligence,0.002296713,0.262957519,0.579193135,0.002296677,0.150959254,0.002296703,Genomics,0.030821592,FALSE,14,0.213494959,2,0.164302917,1,0.537564047,,,0.305120641
11316,"CovidExplorer: A Multi-faceted AI-based Search and Visualization Engine
  for COVID-19 Information",0,2011.14618,11/30/20,arxiv,0,5,dataset,0.002639097,0.002639017,0.153013199,0.836430607,0.002639108,0.002638972,Epidemiology,0.16441393,FALSE,9.8,0.147071557,0.6,0.09011239,0,0.403234768,,,0.213472905
11317,"Modified Dorfman procedure for pool tests with dilution -- COVID-19 case
  study",0,2012.00673,11/30/20,arxiv,0,1,computational,0.001565315,0.00156539,0.662349486,0.254570975,0.078383475,0.00156536,Epidemiology,0.03217879,FALSE,74,0.782113922,208,0.921327268,0,0.403234768,,,0.702225319
11318,"ViDi: Descriptive Visual Data Clustering as Radiologist Assistant in
  COVID-19 Streamline Diagnostic",0,2011.14871,11/30/20,arxiv,0,3,deep learning,0.023553105,0.159875721,0.758224107,0.001272697,0.055801689,0.001272681,Imaging,0.011004746,FALSE,16.66666667,0.251159626,48,0.673735617,0,0.403234768,,,0.442710004
11319,"Fake News Detection in Social Media using Graph Neural Networks and NLP
  Techniques: A COVID-19 Use-case",0,2012.07517,11/30/20,arxiv,0,7,neural network,0.002130745,0.002130636,0.825359827,0.166117441,0.002130677,0.002130674,Epidemiology,0.006012142,FALSE,27.42857143,0.400643206,47.42857143,0.670189992,1,0.537564047,,,0.536132415
11320,"The Unintended Consequences of Stay-at-Home Policies on Work Outcomes:
  The Impacts of Lockdown Orders on Content Creation",0,2011.15068,11/30/20,arxiv,0,3,"machine learning, dataset",0.000936104,0.000936107,0.000936139,0.906756006,0.089499569,0.000936076,Epidemiology,0.1852352,FALSE,37.66666667,0.515430763,18.33333333,0.464343056,0,0.403234768,,,0.461002862
11321,"Machine learning spatio-temporal epidemiological model to evaluate
  Germany-county-level COVID-19 risk",0,2012.00082,11/30/20,arxiv,0,6,"machine learning, prediction model",0.029644457,0.001987142,0.084749505,0.855161724,0.026470027,0.001987144,Epidemiology,0.069707274,FALSE,117,0.902900612,145.5,0.879783249,0,0.403234768,,,0.728639543
11322,Ultrasound Diagnosis of COVID-19: Robustness and Explainability,0,2012.01145,11/30/20,arxiv,0,2,dataset,0.002996658,0.002996584,0.819618249,0.115029356,0.056362597,0.002996555,Epidemiology,0.10126269,FALSE,32,0.455810502,30,0.570176612,0,0.403234768,,,0.476407294
11323,Semi-Mechanistic Bayesian Modeling of COVID-19 with Renewal Processes,0,2012.00394,12/1/20,arxiv,0,6,"bayes, bayesian model",0.001538077,0.001538105,0.001538103,0.912333388,0.023666746,0.05938558,Epidemiology,0.07592729,FALSE,84.66666667,0.823674934,333.6666667,0.963071983,1,0.537564047,,,0.774770321
11324,"The bat coronavirus RmYN02 is characterized by a 6-nucleotide deletion
  at the S1/S2 junction, and its claimed PAA insertion is highly doubtful",0,2012.00627,12/1/20,arxiv,0,2,sequencing,0.002238605,0.874828044,0.035550909,0.082905483,0.002238492,0.002238468,Genomics,0.17554563,FALSE,18.42857143,0.275836477,13.14285714,0.401525288,2,0.618927094,,,0.432096286
11325,Biomedical Knowledge Graph Refinement with Embedding and Logic Rules,0,2012.01031,12/2/20,arxiv,0,4,knowledge graph,0.002490517,0.002490487,0.670015984,0.002490667,0.320021909,0.002490436,Healthcare,0.01736492,FALSE,113.5,0.896715938,66,0.742574257,1,0.537564047,,,0.725618081
11326,"COVID-19 Cough Classification using Machine Learning and Global
  Smartphone Recordings",0,2012.01926,12/2/20,arxiv,0,4,"machine learning, neural network, classifier, logistic regression, lstm, dataset",0.001085337,0.001085373,0.946404906,0.027254782,0.001085395,0.023084206,Epidemiology,0.09558451,FALSE,110.25,0.89022203,229.5,0.929421996,4,0.707574542,,,0.842406189
11327,"Fighting together against the pandemic: learning multiple models on
  tomography images for COVID-19 diagnosis",0,2012.01251,12/2/20,arxiv,0,2,"neural network, image analysis, dataset",0.001098917,0.001098859,0.942714275,0.052890285,0.001098871,0.001098792,Imaging,0.003691018,FALSE,20.5,0.304966294,6.5,0.288132192,0,0.403234768,,,0.332111084
11328,End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training,0,2012.01414,12/2/20,arxiv,0,8,"information retrieval, dataset",0.052349885,0.002032882,0.710234637,0.231317002,0.002032843,0.002032751,Epidemiology,0.008580357,FALSE,64.25,0.729791576,522.375,0.980532513,1,0.537564047,,,0.749296045
11329,"ArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus
  (COVID-19) Pandemic",0,2012.01462,12/2/20,arxiv,0,2,dataset,0.002720131,0.048009964,0.223705248,0.720124394,0.00272015,0.002720113,Epidemiology,0.12457162,FALSE,36.5,0.503123261,72.5,0.760436179,1,0.537564047,,,0.600374495
11330,"CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion
  Segmentation of COVID-19 Chest CT Scans",0,2012.01473,12/2/20,arxiv,0,4,"computational, neural network, dataset",0.000977452,0.016208234,0.979881964,0.000977492,0.000977431,0.000977427,Imaging,0.018787175,FALSE,61.5,0.712350795,21.5,0.498260637,0,0.403234768,,,0.537948733
11331,Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning,0,2012.01736,12/3/20,arxiv,0,9,molecular dynamics simulation,0.619105178,0.035035974,0.203060697,0.138911024,0.001943624,0.001943503,Drug discovery,0.29911923,FALSE,9.777777778,0.146576783,8.333333333,0.325662296,0,0.403234768,,,0.291824615
11332,Digital Landscape of COVID-19 Testing: Challenges and Opportunities,0,2012.01772,12/3/20,arxiv,0,26,digital health,0.00101094,0.001010976,0.0250577,0.860309726,0.111599728,0.001010928,Epidemiology,0.011894643,FALSE,8.461538462,0.124930422,2.576923077,0.181562751,1,0.537564047,,,0.281352407
11333,Assaying Large-scale Testing Models to Interpret COVID-19 Case Numbers,0,2012.01912,12/3/20,arxiv,0,3,mathematical model,0.002296565,0.002296592,0.002296582,0.828794656,0.162018858,0.002296746,Epidemiology,0.033941716,FALSE,34.33333333,0.480425506,67.66666667,0.74719026,0,0.403234768,,,0.543616844
11334,"Drugs4Covid: Drug-driven Knowledge Exploitation based on Scientific
  Publications",0,2012.01953,12/3/20,arxiv,0,16,knowledge graph,0.370150317,0.002032824,0.44232279,0.181428249,0.002032828,0.002032992,Drug discovery,0.3278257,FALSE,56.5,0.678891706,54.6875,0.700428151,0,0.403234768,,,0.594184875
11335,"Reallocating and Sharing Health Equipments in Sanitary Emergency
  Situations: The COVID-19 Case in Spain",0,2012.02062,12/3/20,arxiv,0,3,computational,0.001823396,0.001823382,0.001823434,0.928462007,0.001823416,0.064244365,Epidemiology,0.071857244,FALSE,7,0.10179974,3.333333333,0.206515922,1,0.537564047,,,0.281959903
11336,Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing,0,2012.02151,12/3/20,arxiv,0,2,"computational, neural network",0.721628016,0.001310337,0.273130445,0.001310418,0.001310399,0.001310386,Drug discovery,0.105593234,FALSE,42.5,0.562743522,46,0.66416912,0,0.403234768,,,0.54338247
11337,"Predicting Misinformation and Engagement in COVID-19 Twitter Discourse
  in the First Months of the Outbreak",0,2012.02164,12/3/20,arxiv,0,9,classifier,0.055370094,0.001565353,0.289512585,0.650421259,0.001565376,0.001565333,Epidemiology,0.001559764,FALSE,27.11111111,0.396499474,3.888888889,0.223240567,1,0.537564047,,,0.38576803
11338,"Social Media Study of Public Opinions on Potential COVID-19 Vaccines:
  Informing Dissent, Disparities, and Dissemination",0,2012.02165,12/3/20,arxiv,0,7,"machine learning, logistic regression",0.001371304,0.001371321,0.00137143,0.472626279,0.521888348,0.001371317,Healthcare,0.010350436,FALSE,128,0.919104459,297,0.953438587,2,0.618927094,,,0.830490047
11339,"Addressing machine learning concept drift reveals declining vaccine
  sentiment during the COVID-19 pandemic",0,2012.02197,12/3/20,arxiv,0,2,machine learning,0.001538175,0.001538111,0.201826594,0.792020852,0.00153817,0.001538097,Epidemiology,0.009390831,FALSE,6,0.086028821,0,0.055525823,0,0.403234768,,,0.18159647
11340,"COVID-CLNet: COVID-19 Detection with Compressive Deep Learning
  Approaches",0,2012.02234,12/3/20,arxiv,0,2,"deep learning, neural network",0.001538114,0.001538114,0.992309425,0.001538143,0.001538129,0.001538075,Imaging,0.003293634,FALSE,18.5,0.278001113,1,0.122023013,0,0.403234768,,,0.267752965
11341,Birdspotter: A Tool for Analyzing and Labeling Twitter Users,0,2012.0237,12/4/20,arxiv,0,3,dataset,0.001786529,0.001786536,0.305473478,0.68738028,0.001786687,0.00178649,Epidemiology,0.020040363,FALSE,27.66666667,0.403488156,18.66666667,0.466818303,1,0.537564047,,,0.469290169
11342,"Efficient Social Distancing for COVID-19: An Integration of Economic
  Health and Public Health",0,2012.02397,12/4/20,arxiv,0,3,deep-learning,0.001219997,0.00122,0.053273512,0.941846472,0.001220016,0.001220002,Epidemiology,0.056898624,FALSE,57,0.68204589,31.66666667,0.582686647,0,0.403234768,,,0.555989102
11343,"Spread Mechanism and Influence Measurement of Online Rumors in China
  During the COVID-19 Pandemic",0,2012.02446,12/4/20,arxiv,0,3,deep learning,0.001461908,0.001461933,0.400050181,0.508712695,0.086851407,0.001461876,Epidemiology,0.20110473,FALSE,37,0.508936854,15,0.42594327,0,0.403234768,,,0.446038297
11344,"Utilizing Concept Drift for Measuring the Effectiveness of Policy
  Interventions: The Case of the COVID-19 Pandemic",European Journal of Information Systems (2020),2012.03728,12/4/20,arxiv,European Journal of Information Systems (2020),4,machine learning,0.00223843,0.002238505,0.064589436,0.92645669,0.002238498,0.00223844,Epidemiology,0.026510835,FALSE,54,0.661574618,8.5,0.329141022,0,0.403234768,,,0.464650136
11345,"TrollHunter [Evader]: Automated Detection [Evasion] of Twitter Trolls
  During the COVID-19 Pandemic",New Security Paradigms Workshop (NSPW) 2020,2012.02586,12/4/20,arxiv,New Security Paradigms Workshop (NSPW) 2020,3,"machine learning, dataset",0.001901845,0.001901735,0.457716714,0.53467626,0.001901752,0.001901694,Epidemiology,0.6536958,TRUE,14.33333333,0.216772837,1,0.122023013,1,0.537564047,,,0.292119966
11346,"Investigation of the Impacts of COVID-19 on the Electricity Consumption
  of a University Dormitory Using Weather Normalization",0,2012.07748,12/4/20,arxiv,0,3,"neural network, prediction model",0.002490502,0.002490569,0.227654452,0.762383362,0.002490575,0.002490539,Epidemiology,0.10335657,FALSE,30,0.432432432,13.33333333,0.403866738,0,0.403234768,,,0.413177979
11347,"Quantum-Enhanced Machine Learning for Covid-19 and Anderson Insulator
  Predictions",0,2012.03472,12/7/20,arxiv,0,2,machine learning,0.004310354,0.004310082,0.978449007,0.004310339,0.004310225,0.004309993,Drug discovery,0.0779331,FALSE,66,0.741356918,20.5,0.486218892,0,0.403234768,,,0.543603526
11348,"Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet
  Classification Using BERT",0,2012.04539,12/7/20,arxiv,0,2,machine learning,0.003101498,0.003101459,0.854423832,0.133170375,0.003101414,0.003101421,Epidemiology,0.28421152,FALSE,34.5,0.482404601,60.5,0.722772277,0,0.403234768,,,0.536137215
11349,STSIR: Spatial Temporal Pandemic Model with Mobility Data,0,2012.03509,12/7/20,arxiv,0,5,dataset,0.001901706,0.001901691,0.001901833,0.990491239,0.001901813,0.001901718,Epidemiology,0.008921862,FALSE,92.6,0.850701961,181.4,0.90707787,0,0.403234768,,,0.720338199
11350,Using Differentiable Programming for Flexible Statistical Modeling,0,2012.05722,12/7/20,arxiv,0,8,"deep learning, prediction model",0.001622781,0.001622757,0.444971607,0.54853725,0.001622791,0.001622814,Epidemiology,0.19106784,FALSE,25,0.369286907,19.375,0.473976452,0,0.403234768,,,0.415499375
11351,"The Role of Regularization in Shaping Weight and Node Pruning Dependency
  and Dynamics",0,2012.03827,12/7/20,arxiv,0,3,neural network,0.002130689,0.002130662,0.835914472,0.155562858,0.002130678,0.002130641,Imaging,0.000598609,FALSE,78.66666667,0.801224566,284.3333333,0.950026759,0,0.403234768,,,0.718162031
11352,Inferring the effect of interventions on COVID-19 transmission networks,0,2012.03846,12/7/20,arxiv,0,6,bayes,0.002490522,0.002490508,0.002490603,0.987547508,0.002490429,0.002490429,Epidemiology,0.11881694,FALSE,32.16666667,0.45717113,23.16666667,0.514182499,0,0.403234768,,,0.458196132
11353,"Improving Clinical Document Understanding on COVID-19 Research with
  Spark NLP",0,2012.04005,12/7/20,arxiv,0,2,"text mining, dataset",0.001511921,0.001511945,0.630545761,0.202120429,0.162798031,0.001511912,Epidemiology,0.021566778,FALSE,13,0.197352959,15.5,0.430157881,1,0.537564047,,,0.388358296
11354,"Adaptive Sampling for Estimating Distributions: A Bayesian Upper
  Confidence Bound Approach",0,2012.04137,12/8/20,arxiv,0,4,bayes,0.002183235,0.157658503,0.167106219,0.49701446,0.173854251,0.002183333,Epidemiology,0.15711719,FALSE,185.5,0.966664605,153.5,0.886272411,0,0.403234768,,,0.752057261
11355,COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN,0,2012.05073,12/8/20,arxiv,0,3,"neural network, dataset",0.001237079,0.001237112,0.993814575,0.00123711,0.001237076,0.001237048,Imaging,0.01832965,FALSE,19,0.285793803,10.33333333,0.36038266,1,0.537564047,,,0.39458017
11356,"Forecasting the Olympic medal distribution during a pandemic: a
  socio-economic machine learning model",0,2012.04378,12/8/20,arxiv,0,4,machine learning,0.002639003,0.00263899,0.413170278,0.514699677,0.064212871,0.002639181,Epidemiology,0.3974716,FALSE,35,0.488032655,10.75,0.366269735,0,0.403234768,,,0.419179053
11357,"Social Media Unrest Prediction during the {COVID}-19 Pandemic: Neural
  Implicit Motive Pattern Recognition as Psychometric Signs of Severe Crises","Proceedings of the Third Workshop on Computational Modeling of
  People's Opinions, Personality, and Emotion's in Social Media. Barcelona,
  Spain (Online). 2020",2012.04586,12/8/20,arxiv,"Proceedings of the Third Workshop on Computational Modeling of
  People's Opinions, Personality, and Emotion's in Social Media. Barcelona,
  Spain (Online). 2020",2,dataset,0.002357778,0.002357862,0.278524408,0.364402616,0.349999539,0.002357798,Epidemiology,0.5968501,TRUE,136,0.92763931,207.5,0.920992775,0,0.403234768,,,0.750622284
11358,"An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning
  System",0,2012.07574,12/8/20,arxiv,0,7,dataset,0.001622838,0.042217658,0.111039778,0.841874262,0.001622749,0.001622715,Epidemiology,0.12196782,FALSE,32.57142857,0.462057023,45.42857143,0.660021408,0,0.403234768,,,0.508437733
11359,"Recent Advances in Computer Audition for Diagnosing COVID-19: An
  Overview",0,2012.0465,12/8/20,arxiv,0,3,artificial intelligence,0.003214299,0.003214208,0.821745256,0.003214272,0.165397371,0.003214595,Healthcare,0.7863128,TRUE,41.33333333,0.551549261,6,0.280037463,2,0.618927094,,,0.483504606
11360,"Ensemble-CVDNet: A Deep Learning based End-to-End Classification
  Framework for COVID-19 Detection using Ensembles of Networks",0,2012.09132,12/9/20,arxiv,0,3,deep learning,0.000999541,0.000999567,0.995002122,0.000999601,0.000999603,0.000999566,Imaging,0.006627917,FALSE,8.666666667,0.12839384,2,0.164302917,1,0.537564047,,,0.276753601
11361,"COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for
  Automated Diagnosis and Severity Assessment of COVID-19",0,2012.05509,12/10/20,arxiv,0,7,transfer learning,0.001171664,0.001171768,0.911324006,0.001171557,0.001171569,0.083989436,Imaging,0.027827352,FALSE,37.28571429,0.510606717,13.85714286,0.40941932,0,0.403234768,,,0.441086935
11362,"Detection of Covid-19 Patients with Convolutional Neural Network Based
  Features on Multi-class X-ray Chest Images",0,2012.05525,12/10/20,arxiv,0,1,"neural network, network model",0.0018617,0.001861753,0.903464476,0.089088643,0.001861743,0.001861685,Imaging,0.56709546,TRUE,20,0.298163152,23,0.513513514,0,0.403234768,,,0.404970478
11363,"Mathematical model optimized for prediction and health care planning for
  COVID-19",0,2012.05804,12/10/20,arxiv,0,8,mathematical model,0.001751154,0.00175119,0.00175123,0.408346849,0.001751227,0.58464835,Clinics,0.41524684,FALSE,22.375,0.330632692,4.125,0.232204977,0,0.403234768,,,0.322024146
11364,"Deep Neural Networks for COVID-19 Detection and Diagnosis using Images
  and Acoustic-based Techniques: A Recent Review",0,2012.07655,12/10/20,arxiv,0,2,"deep learning, neural network",0.001291252,0.042905519,0.911270612,0.041950158,0.001291213,0.001291247,Imaging,0.029794812,FALSE,15.5,0.234028078,12.5,0.392761573,0,0.403234768,,,0.343341473
11365,"Calotropin from milk of Calotropis gigantean a potent inhibitor of COVID
  19 corona virus infection by Molecular docking studies","Research & Reviews in Biotechnology & Biosciences Website:
  www.biotechjournal.in Volume: 7, Issue: 2, Year: 2020 PP: 52-57",2012.06139,12/11/20,arxiv,"Research & Reviews in Biotechnology & Biosciences Website:
  www.biotechjournal.in Volume: 7, Issue: 2, Year: 2020 PP: 52-57",2,in-silico,0.90070631,0.001861708,0.001861722,0.001861746,0.001861804,0.09184671,Drug discovery,0.9357131,TRUE,61,0.709196611,32,0.585763982,0,0.403234768,,,0.56606512
11366,"ALReLU: A different approach on Leaky ReLU activation function to
  improve Neural Networks Performance",0,2012.07564,12/11/20,arxiv,0,1,"supervised learning, neural network, dataset",0.001823358,0.00182341,0.940323923,0.001823421,0.001823337,0.05238255,Imaging,0.006411701,FALSE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
11367,"Power-law multi-wave model for COVID-19 propagation in countries with
  nonuniform population density",0,2012.06383,12/11/20,arxiv,0,2,"model simulation, mathematical model",0.002996479,0.002996459,0.002996861,0.985017357,0.002996414,0.002996429,Epidemiology,0.010233521,FALSE,56.5,0.678891706,6,0.280037463,2,0.618927094,,,0.525952088
11368,"Context Matters: Graph-based Self-supervised Representation Learning for
  Medical Images",0,2012.06457,12/11/20,arxiv,0,3,"supervised learning, neural network, dataset",0.001310388,0.001310385,0.917594946,0.077163432,0.001310434,0.001310415,Imaging,0.0349631,FALSE,29.66666667,0.42773208,2,0.164302917,0,0.403234768,,,0.331756588
11369,"AIforCOVID: predicting the clinical outcomes in patients with COVID-19
  applying AI to chest-X-rays. An Italian multicentre study",0,2012.06531,12/11/20,arxiv,0,28,"artificial intelligence, dataset",0.00141513,0.001415147,0.747462213,0.001415195,0.001415135,0.24687718,Imaging,0.088679105,FALSE,38.07142857,0.519512648,18.17857143,0.462670591,1,0.537564047,,,0.506582429
11370,"Characterizing Twitter users behaviour during the Spanish Covid-19 first
  wave",0,2012.0655,12/11/20,arxiv,0,4,bayes,0.002639101,0.002639323,0.002639136,0.914016375,0.075427059,0.002639006,Epidemiology,0.056304812,FALSE,28,0.408312202,104.75,0.83067969,0,0.403234768,,,0.547408887
11371,"Do not repeat these mistakes -- a critical appraisal of applications of
  explainable artificial intelligence for image based COVID-19 detection",0,2012.08333,12/11/20,arxiv,0,6,"deep learning, artificial intelligence, neural network, network model",0.002357833,0.002357823,0.812759165,0.118875971,0.06129146,0.002357748,Imaging,0.022797197,FALSE,27,0.3960047,3.8,0.221501204,1,0.537564047,,,0.385023317
11372,"Optimal Control Studies on Age Structural Modeling of COVID-19 in
  Presence of Saturated Medical Treatment of Holling Type III",0,2012.06719,12/12/20,arxiv,0,3,structural model,0.002357875,0.002357781,0.002357862,0.778386198,0.002357886,0.212182398,Epidemiology,0.3211158,FALSE,58,0.689714887,56.33333333,0.708255285,1,0.537564047,,,0.645178073
11373,"Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep
  Learning Algorithm",0,2012.06745,12/12/20,arxiv,0,5,computational,0.001565314,0.001565323,0.128400458,0.865338304,0.001565316,0.001565286,Epidemiology,0.16236463,FALSE,23,0.34225988,39.8,0.632124699,0,0.403234768,,,0.459206449
11374,"Estimating Spread of Contact-Based Contagions in a Population Through
  Sub-Sampling",0,2012.06987,12/13/20,arxiv,0,3,dataset,0.001438138,0.040782383,0.001438166,0.860520585,0.094382613,0.001438114,Epidemiology,0.000762671,FALSE,264,0.986764797,610.6666667,0.98441263,0,0.403234768,,,0.791470732
11375,"CHS-Net: A Deep learning approach for hierarchical segmentation of
  COVID-19 infected CT images",0,2012.07079,12/13/20,arxiv,0,2,deep learning,0.000977498,0.022323945,0.973766154,0.000977464,0.000977437,0.000977501,Imaging,0.007310391,FALSE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
11376,"From #Jobsearch to #Mask: Improving COVID-19 Cascade Prediction with
  Spillover Effects",0,2012.07088,12/13/20,arxiv,0,3,"prediction model, dataset",0.003101592,0.003101624,0.509462307,0.478130991,0.003101875,0.00310161,Epidemiology,0.021626532,FALSE,9.666666667,0.144968767,0,0.055525823,0,0.403234768,,,0.201243119
11377,"Hospital Capacity Planning Using Discrete Event Simulation Under Special
  Consideration of the COVID-19 Pandemic",0,2012.07188,12/14/20,arxiv,0,4,simulation model,0.001511836,0.001511844,0.001511969,0.92796558,0.065986785,0.001511986,Epidemiology,0.35727262,FALSE,60,0.703444864,54,0.697952903,0,0.403234768,,,0.601544178
11378,"""Thought I'd Share First"": An Analysis of COVID-19 Conspiracy Theories
  and Misinformation Spread on Twitter",0,2012.07729,12/14/20,arxiv,0,8,"machine learning, supervised learning",0.037101299,0.076763679,0.213476175,0.670113375,0.001272731,0.00127274,Epidemiology,0.20696387,FALSE,11.5,0.17416043,3.375,0.207051111,0,0.403234768,,,0.261482103
11379,"Discovering Airline-Specific Business Intelligence from Online Passenger
  Reviews: An Unsupervised Text Analytics Approach",0,2012.08,12/14/20,arxiv,0,2,probabilistic,0.001511895,0.072837945,0.239108481,0.639406658,0.045623207,0.001511815,Epidemiology,0.04472509,FALSE,15.5,0.234028078,2.5,0.180826866,0,0.403234768,,,0.272696571
11380,"Spatiotemporal Characteristics and Factor Analysis of SARS-CoV-2
  Infections among Healthcare Workers in Wuhan, China","Journal of Hospital Infection,2021",2012.08819,12/16/20,arxiv,"Journal of Hospital Infection,2021",6,dataset,0.001254655,0.027749329,0.001254691,0.767158144,0.055913476,0.146669705,Epidemiology,0.8303292,TRUE,10.83333333,0.161481848,0.833333333,0.102488627,0,0.403234768,,,0.222401748
11381,"Assessing COVID-19 Impacts on College Students via Automated Processing
  of Free-form Text",0,2012.09369,12/17/20,arxiv,0,6,dataset,0.106952637,0.001987126,0.001987234,0.466800235,0.420285644,0.001987125,Epidemiology,0.2224428,FALSE,6.666666667,0.094996598,4.333333333,0.237958255,0,0.403234768,,,0.24539654
11382,"COVID-19 Emotion Monitoring as a Tool to Increase Preparedness for
  Disease Outbreaks in Developing Regions",0,2012.12184,12/17/20,arxiv,0,4,dataset,0.002357754,0.002357749,0.282828721,0.523997591,0.186100368,0.002357817,Epidemiology,0.37836465,FALSE,66.25,0.742346465,32.25,0.587570244,0,0.403234768,,,0.577717159
11383,"Leveraging Event Specific and Chunk Span features to Extract COVID
  Events from tweets",0,2012.10052,12/18/20,arxiv,0,2,dataset,0.06787469,0.002357816,0.630649939,0.294401785,0.002357924,0.002357847,Epidemiology,0.1081298,FALSE,5,0.070752675,0,0.055525823,0,0.403234768,,,0.176504422
11384,Attention-Based LSTM Network for COVID-19 Clinical Trial Parsing,2020 IEEE International Conference on Big Data (IEEE BigData 2020),2012.10063,12/18/20,arxiv,2020 IEEE International Conference on Big Data (IEEE BigData 2020),4,"lstm, dataset",0.002422408,0.002422324,0.643071272,0.002422459,0.00242238,0.347239158,Clinics,0.50569654,TRUE,20.5,0.304966294,2,0.164302917,0,0.403234768,,,0.290834659
11385,"Single-Virion SARS-CoV-2 Mass Spectrometry in Air by On-Chip Focusing
  NEMS",0,2012.10675,12/19/20,arxiv,0,13,sequencing,0.066553959,0.503076566,0.283474198,0.144152617,0.001371394,0.001371265,Genomics,0.22786671,FALSE,25.53846154,0.374605727,15.53846154,0.430492374,0,0.403234768,,,0.402777623
11386,"Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis
  from Chest X-rays",0,2012.10787,12/19/20,arxiv,0,5,"neural network, predictive model",0.044756005,0.001272631,0.950153381,0.001272671,0.001272659,0.001272653,Imaging,0.004324466,FALSE,52.5,0.650875131,57.16666667,0.711667113,0,0.403234768,,,0.588592337
11387,"Using Data Science to monitor the pandemic with a single number: the
  Synthetic COVID Index",0,2101.02013,12/20/20,arxiv,0,1,machine learning,0.00280638,0.002806398,0.269722969,0.719051328,0.002806457,0.002806468,Epidemiology,0.18905115,FALSE,10,0.15214299,3,0.199424672,0,0.403234768,,,0.25160081
11388,"Fake news agenda in the era of COVID-19: Identifying trends through
  fact-checking content","Online Social Networks and Media, 2020",2012.11004,12/20/20,arxiv,"Online Social Networks and Media, 2020",3,computational,0.001622795,0.001622829,0.001622888,0.991886076,0.00162273,0.001622681,Epidemiology,0.19709212,FALSE,24.66666667,0.36334962,4,0.231469093,1,0.537564047,,,0.37746092
11389,COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms,0,2101.10266,12/21/20,arxiv,0,12,dataset,0.001786567,0.001786548,0.001786668,0.673380071,0.31947358,0.001786565,Epidemiology,0.05330068,FALSE,11.5,0.17416043,4.333333333,0.237958255,0,0.403234768,,,0.271784484
11390,An Efficient K-means Clustering Algorithm for Analysing COVID-19,0,2101.0314,12/21/20,arxiv,0,6,"machine learning, dataset",0.04403716,0.161199684,0.362509162,0.428188421,0.002032806,0.002032766,Epidemiology,0.11907822,FALSE,27.16666667,0.397303482,3.5,0.213607172,0,0.403234768,,,0.338048474
11391,"The Impact of Temperature and Isolation on COVID-19 in India: A
  Mathematical Modelling approach",0,2012.11239,12/21/20,arxiv,0,5,mathematical model,0.002898361,0.002898315,0.002898391,0.985508245,0.002898303,0.002898386,Epidemiology,0.22791249,FALSE,43.2,0.569051889,38.6,0.625100348,0,0.403234768,,,0.532462335
11392,The COVID-19 pandemic: socioeconomic and health disparities,0,2012.11399,12/21/20,arxiv,0,1,correlation analysis,0.003466179,0.053656332,0.003465983,0.205914893,0.364603791,0.368892822,Clinics,0.24879932,FALSE,14,0.213494959,4,0.231469093,0,0.403234768,,,0.28273294
11393,"Deep Learning in Detection and Diagnosis of Covid-19 using Radiology
  Modalities: A Systematic Review",0,2012.11577,12/21/20,arxiv,0,2,"deep learning, image processing",0.00108533,0.001085346,0.82562922,0.170029389,0.001085363,0.001085353,Imaging,0.6100875,TRUE,13,0.197352959,0.5,0.087101953,1,0.537564047,,,0.27400632
11394,"Global Trends and Predictors of Face Mask Usage During the COVID-19
  Pandemic",0,2012.11678,12/21/20,arxiv,0,13,logistic regression,0.001098789,0.001098815,0.001098805,0.403350717,0.59225406,0.001098813,Healthcare,0.8714091,TRUE,145.3076923,0.939513885,160.3076923,0.892025689,2,0.618927094,,,0.816822223
11395,"Objective Evaluation of Deep Uncertainty Predictions for COVID-19
  Detection",0,2012.1184,12/22/20,arxiv,0,7,"neural network, transfer learning, dataset",0.001310326,0.001310329,0.779786712,0.214971972,0.001310337,0.001310323,Imaging,0.004433721,FALSE,205.7142857,0.974086214,137.2857143,0.872023013,2,0.618927094,,,0.821678774
11396,"Efficient and Visualizable Convolutional Neural Networks for COVID-19
  Classification Using Chest CT",0,2012.1186,12/22/20,arxiv,0,5,"deep learning, artificial intelligence, neural network",0.002080822,0.002080865,0.864768102,0.002080783,0.126908775,0.002080653,Healthcare,0.045492172,FALSE,12.2,0.18461253,1.6,0.140687717,1,0.537564047,,,0.287621432
11397,"Revealing the Transmission Dynamics of COVID-19: A Bayesian Framework
  for $R_t$ Estimation",0,2101.01532,12/22/20,arxiv,0,7,bayes,0.002130698,0.002130687,0.091773492,0.899703823,0.002130669,0.00213063,Epidemiology,0.103054196,FALSE,432,0.996165502,2138.285714,0.998862724,0,0.403234768,,,0.799420998
11398,"Establishment of a diagnostic model to distinguish coronavirus disease
  2019 from influenza A based on laboratory findings",0,2012.11889,12/22/20,arxiv,0,7,"network model, logistic regression, dataset",0.002032807,0.140642892,0.344616058,0.002032874,0.002032815,0.508642553,Clinics,0.57653904,TRUE,15.14285714,0.228276331,11.85714286,0.383328873,0,0.403234768,,,0.338279991
11399,Data Assimilation in the Latent Space of a Neural Network,0,2012.12056,12/22/20,arxiv,0,8,"machine learning, neural network",0.001786549,0.001786607,0.435893487,0.556960327,0.001786518,0.001786512,Epidemiology,0.03555131,FALSE,77.625,0.797080834,80.125,0.77923468,0,0.403234768,,,0.659850094
11400,"Modelling a novel Coronavirus (COVID-19): A stochastic SEIR-HCD
  approach, with real-time parameter estimation & forecasting for Scotland",0,2012.1239,12/22/20,arxiv,0,5,bayes,0.001565292,0.001565303,0.001565324,0.99217341,0.001565331,0.00156534,Epidemiology,0.06077513,FALSE,104,0.877605294,91.8,0.805458924,0,0.403234768,,,0.695432995
11401,Scalable Optical Learning Operator,0,2012.12404,12/22/20,arxiv,0,5,"machine learning, dataset",0.096930424,0.001901682,0.895462682,0.001901811,0.001901724,0.001901677,Imaging,0.013974011,FALSE,237,0.982311831,97.2,0.817032379,0,0.403234768,,,0.734192993
11402,"Hardware-accelerated Simulation-based Inference of Stochastic
  Epidemiology Models for COVID-19",0,2012.14332,12/23/20,arxiv,0,4,"bayes, computational",0.00190172,0.001901783,0.235199576,0.757193489,0.001901755,0.001901677,Epidemiology,0.038606614,FALSE,41.75,0.555383759,19.25,0.47310677,0,0.403234768,,,0.477241766
11403,"Simple mathematical models for controlling COVID-19 transmission through
  social distancing and community awareness",0,2012.13361,12/24/20,arxiv,0,1,mathematical model,0.04234028,0.002296549,0.002296556,0.948473468,0.002296651,0.002296495,Epidemiology,0.2884887,FALSE,36,0.498299215,6,0.280037463,1,0.537564047,,,0.438633575
11404,Whom to Test? Active Sampling Strategies for Managing COVID-19,0,2012.13483,12/25/20,arxiv,0,3,active learning,0.002080624,0.130043363,0.242240388,0.621474428,0.002080634,0.002080563,Epidemiology,0.08065423,FALSE,49,0.624281032,,,0,0.403234768,,,0.5137579
11405,"COVIDX: Computer-aided diagnosis of Covid-19 and its severity prediction
  with raw digital chest X-ray images",0,2012.13605,12/25/20,arxiv,0,3,dataset,0.047342556,0.025513703,0.922529345,0.001538133,0.001538123,0.00153814,Imaging,0.008576483,FALSE,40.66666667,0.546292288,8,0.320511105,0,0.403234768,,,0.423346053
11406,Detecting the patient's need for help with machine learning,0,2012.13626,12/25/20,arxiv,0,1,"machine learning, neural network",0.138615914,0.001112653,0.449996441,0.106636289,0.302525975,0.001112729,Healthcare,0.044924974,FALSE,32,0.455810502,1,0.122023013,0,0.403234768,,,0.327022761
11407,Spatial-Temporal Convolutional Network for Spread Prediction of COVID-19,0,2101.05304,12/27/20,arxiv,0,3,neural network,0.001901766,0.001901801,0.405933279,0.253687069,0.262006606,0.074569479,Healthcare,0.084254205,FALSE,11.66666667,0.176510607,,,0,0.403234768,,,0.289872687
11408,"Modeling of Network Based Digital Contact Tracing and Testing Strategies
  for the COVID-19 Pandemic",0,2012.14077,12/28/20,arxiv,0,1,mathematical model,0.001291224,0.001291241,0.001291259,0.901546388,0.093288683,0.001291205,Epidemiology,0.009098768,FALSE,28,0.408312202,10,0.355632861,0,0.403234768,,,0.389059944
11409,"Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and
  Applications",0,2012.14106,12/28/20,arxiv,0,10,"deep learning, radiom, predictive model",0.001203456,0.001203466,0.74496125,0.226521782,0.001203462,0.024906584,Imaging,0.014416724,FALSE,37,0.508936854,,,0,0.403234768,,,0.456085811
11410,"Screening COVID-19 Based on CT/CXR Images & Building a Publicly
  Available CT-scan Dataset of COVID-19",0,2012.14204,12/28/20,arxiv,0,5,"deep learning, transfer learning, dataset",0.001237078,0.001237088,0.99381459,0.00123709,0.001237056,0.001237098,Imaging,0.011271656,FALSE,28.2,0.409734677,25.4,0.532044421,0,0.403234768,,,0.448337955
11411,"Advanced Machine Learning Techniques for Fake News (Online
  Disinformation) Detection: A Systematic Mapping Study",0,2101.01142,12/28/20,arxiv,0,8,"machine learning, dataset",0.001565321,0.001565317,0.485999883,0.507738813,0.001565353,0.001565313,Epidemiology,0.012846351,FALSE,25.625,0.375286041,6.125,0.280907145,0,0.403234768,,,0.353142651
11412,"Incentivizing Routing Choices for Safe and Efficient Transportation in
  the Face of the COVID-19 Pandemic",0,2012.15749,12/28/20,arxiv,0,6,simulation experiment,0.001717242,0.00171719,0.149415517,0.84371568,0.001717207,0.001717164,Epidemiology,0.106043935,FALSE,35.66666667,0.493908096,43.83333333,0.653063955,0,0.403234768,,,0.516735606
11413,"Forecasting COVID-19 Chile's second outbreak by a generalized SIR model
  with constant time delays and a fitted positivity rate",0,2012.14319,12/28/20,arxiv,0,3,mathematical model,0.002296572,0.002296588,0.00229661,0.988517037,0.002296635,0.002296558,Epidemiology,0.096615314,FALSE,36.66666667,0.505102356,78.33333333,0.774217287,0,0.403234768,,,0.56085147
11414,"Docking study for Protein Nsp-12 of SARS-CoV with Betalains and
  Alfa-Bisabolol",0,2012.14504,12/28/20,arxiv,0,6,"computational, in silico",0.930114272,0.001786563,0.001786541,0.062739453,0.001786602,0.00178657,Drug discovery,0.63997495,TRUE,17.66666667,0.266373925,5.666666667,0.270203372,0,0.403234768,,,0.313270688
11415,"Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural
  Networks",0,2012.14553,12/29/20,arxiv,0,3,"bayes, neural network",0.001653016,0.001653094,0.732171986,0.261215724,0.001653113,0.001653067,Epidemiology,0.06285268,FALSE,10,0.15214299,1.333333333,0.13252609,0,0.403234768,,,0.229301283
11416,"Development and evaluation of a 3D annotation software for interactive
  COVID-19 lesion segmentation in chest CT",0,2012.14752,12/29/20,arxiv,0,10,dataset,0.001593527,0.001593654,0.869217542,0.124408006,0.001593607,0.001593664,Imaging,0.016715795,FALSE,63.2,0.722679201,41.2,0.640620819,0,0.403234768,,,0.588844929
11417,"Non-pharmaceutical interventions during the COVID-19 pandemic: a rapid
  review",0,2012.1523,12/30/20,arxiv,0,1,dataset,0.001171644,0.038183043,0.001171626,0.79738594,0.16091617,0.001171578,Epidemiology,0.006541103,FALSE,22,0.326056033,59,0.717554188,2,0.618927094,,,0.554179105
11418,"UMAP-assisted $K$-means clustering of large-scale SARS-CoV-2 mutation
  datasets",0,2012.15268,12/30/20,arxiv,0,4,"genome sequences, dataset",0.001786618,0.358619121,0.350915418,0.285105775,0.001786547,0.001786521,Genomics,0.25409678,FALSE,56.25,0.676788917,13.75,0.408348943,1,0.537564047,,,0.540900636
11419,"Bayesian state space modelling for COVID-19: with Tennessee and New York
  case studies",0,2012.15306,12/30/20,arxiv,0,4,"bayes, bayesian model",0.001823345,0.001823398,0.001823389,0.990883163,0.001823356,0.001823349,Epidemiology,0.046033353,FALSE,16,0.243552477,4.25,0.235750602,0,0.403234768,,,0.294179282
11420,"New Bag of Deep Visual Words based features to classify chest x-ray
  images for COVID-19 diagnosis",0,2012.15413,12/31/20,arxiv,0,2,dataset,0.001059359,0.001059349,0.994703248,0.00105936,0.001059341,0.001059343,Imaging,0.001956165,FALSE,7,0.10179974,1,0.122023013,0,0.403234768,,,0.209019174
11421,"Survey of the Detection and Classification of Pulmonary Lesions via CT
  and X-Ray",0,2012.15442,12/31/20,arxiv,0,5,"deep learning, dataset",0.001943517,0.00194358,0.965235827,0.001943531,0.02699,0.001943545,Imaging,0.042413354,FALSE,43.4,0.571958686,111.4,0.840446883,0,0.403234768,,,0.605213445
11422,"Exploiting Shared Knowledge from Non-COVID Lesions for
  Annotation-Efficient COVID-19 CT Lung Infection Segmentation",0,2012.15564,12/31/20,arxiv,0,6,dataset,0.00109885,0.001098837,0.994505807,0.001098861,0.001098841,0.001098804,Imaging,0.03872755,FALSE,15.5,0.234028078,1.833333333,0.151056998,0,0.403234768,,,0.262773281
11423,Dynamical Characterization of Antiviral Effects in COVID-19,0,2012.15585,12/31/20,arxiv,0,5,mathematical model,0.308162377,0.002639054,0.062093537,0.513198449,0.002639017,0.111267565,Epidemiology,0.6099741,TRUE,31.2,0.446471643,9.2,0.339577201,0,0.403234768,,,0.396427871
11424,"iGOS++: Integrated Gradient Optimized Saliency by Bilateral
  Perturbations",0,2012.15783,12/31/20,arxiv,0,3,classifier,0.001438138,0.001438186,0.824960569,0.169286752,0.001438211,0.001438144,Imaging,0.5770036,TRUE,49.33333333,0.626507514,140.3333333,0.874632058,0,0.403234768,,,0.634791447
11425,"COVID-19 spreading in financial networks: A semiparametric matrix
  regression model",0,2101.00422,1/2/21,arxiv,0,4,"bayes, bayesian model, network model",0.002996662,0.002996501,0.002996765,0.985017171,0.002996464,0.002996437,Epidemiology,0.049522072,FALSE,1.75,0.017007855,0,0.055525823,0,0.403234768,,,0.158589482
11426,"COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information
  Fusion Framework for Stock Price Movement Prediction",0,2101.02287,1/2/21,arxiv,0,4,"artificial intelligence, neural network, prediction model, lstm, dataset",0.001072212,0.001072188,0.674649889,0.321061229,0.001072229,0.001072253,Epidemiology,0.013392836,FALSE,14.5,0.219617787,2.25,0.170925876,0,0.403234768,,,0.26459281
11427,"Advanced and comprehensive research on the dynamics of COVID-19 under
  mass communication outlets intervention and quarantine strategy: a
  deterministic and probabilistic approach",0,2101.00517,1/2/21,arxiv,0,3,"mathematical model, probabilistic",0.001272752,0.001272676,0.001272674,0.993636455,0.001272733,0.001272709,Epidemiology,0.1243141,FALSE,12,0.183190055,2.666666667,0.185442869,0,0.403234768,,,0.25728923
11428,"Combining Graph Neural Networks and Spatio-temporal Disease Models to
  Predict COVID-19 Cases in Germany",0,2101.00661,1/3/21,arxiv,0,3,"machine learning, neural network",0.001653076,0.00165306,0.335914739,0.657472862,0.001653147,0.001653115,Epidemiology,0.037818044,FALSE,6,0.086028821,,,2,0.618927094,,,0.352477957
11429,"CovTANet: A Hybrid Tri-level Attention Based Network for Lesion
  Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans",0,2101.00691,1/3/21,arxiv,0,7,"neural network, dataset",0.000977437,0.000977436,0.9951128,0.000977469,0.000977426,0.000977433,Imaging,0.16065261,FALSE,38.28571429,0.52099697,8.571428571,0.329810008,1,0.537564047,,,0.462790342
11430,"WearMask: Fast In-browser Face Mask Detection with Serverless Edge
  Computing for COVID-19",0,2101.00784,1/4/21,arxiv,0,5,deep learning,0.034001943,0.001330034,0.594663508,0.367344339,0.001330113,0.001330062,Epidemiology,0.2276322,FALSE,29.2,0.420805245,5.8,0.272678619,1,0.537564047,,,0.410349304
11431,"Stochastic Optimization for Vaccine and Testing Kit Allocation for the
  COVID-19 Pandemic",0,2101.01204,1/4/21,arxiv,0,2,active learning,0.00178656,0.001786512,0.22291755,0.769936236,0.001786632,0.00178651,Epidemiology,0.003544867,FALSE,210.5,0.97569423,447.5,0.975247525,0,0.403234768,,,0.784725507
11432,A Research Ecosystem for Secure Computing,0,2101.01264,1/4/21,arxiv,0,5,"machine learning, artificial intelligence",0.001310373,0.001310352,0.28454646,0.557575739,0.15394675,0.001310326,Epidemiology,0.24169934,FALSE,133.6,0.925722061,376.4,0.968691464,0,0.403234768,,,0.765882764
11433,"An Automatic System to Monitor the Physical Distance and Face Mask
  Wearing of Construction Workers in COVID-19 Pandemic",0,2101.01373,1/5/21,arxiv,0,5,"transfer learning, dataset",0.000999504,0.000999517,0.407323013,0.365270523,0.224407936,0.000999506,Imaging,0.015409827,FALSE,5.6,0.077803204,0,0.055525823,1,0.537564047,,,0.223631025
11434,"What social media told about us in the time of COVID-19: a scoping
  review","The Lancet Digital Health, Review, Vol 3, Iss 3, E175-E194, March
  01, 2021",2101.01688,1/5/21,arxiv,"The Lancet Digital Health, Review, Vol 3, Iss 3, E175-E194, March
  01, 2021",6,machine learning,0.001987145,0.001987244,0.001987371,0.652251884,0.339799262,0.001987094,Epidemiology,0.5810088,TRUE,47,0.606407323,71,0.756556061,1,0.537564047,,,0.633509144
11435,"Exploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2
  Spike Protein Through Molecular Dynamics Simulation",0,2101.01884,1/6/21,arxiv,0,8,molecular dynamics simulation,0.945497068,0.002720258,0.002720171,0.043622178,0.00272025,0.002720075,Drug discovery,0.5062247,TRUE,20.25,0.301317336,,,0,0.403234768,,,0.352276052
11436,"Transmission of droplet-conveyed infectious agents such as SARS-CoV-2 by
  speech and vocal exercises during speech therapy: preliminary experiment
  concerning airflow velocity",0,2101.03907,1/6/21,arxiv,0,7,computational,0.126456106,0.001717276,0.001717235,0.866674915,0.001717201,0.001717268,Epidemiology,0.39657974,FALSE,30.28571429,0.434968149,5.714285714,0.270537865,5,0.739490092,,,0.481665369
11437,"Context, input and process as critical elements for successful Emergency
  Remote Learning",0,2101.06112,1/6/21,arxiv,0,5,correlation analysis,0.001861748,0.001861733,0.213082805,0.527263238,0.254068781,0.001861696,Epidemiology,0.057336062,FALSE,28.8,0.415548271,2.8,0.188787798,0,0.403234768,,,0.335856946
11438,"The Interplay of Demographic Variables and Social Distancing Scores in
  Deep Prediction of U.S. COVID-19 Cases",0,2101.02113,1/6/21,arxiv,0,4,lstm,0.001943659,0.001943531,0.114742081,0.877483499,0.001943606,0.001943624,Epidemiology,0.09744316,FALSE,53.5,0.657245346,1012.5,0.993778432,0,0.403234768,,,0.684752849
11439,"Tractable Bayes of Skew-Elliptical Link Models for Correlated Binary
  Data",0,2101.02233,1/6/21,arxiv,0,4,"bayes, dataset",0.002296631,0.002296582,0.002296681,0.663807503,0.327005897,0.002296706,Epidemiology,0.050198436,FALSE,43.25,0.569855897,97.25,0.817099277,0,0.403234768,,,0.596729981
11440,"Statistical challenges in the analysis of sequence and structure data
  for the COVID-19 spike protein",0,2101.02304,1/6/21,arxiv,0,2,bayes,0.002562714,0.673241684,0.002562655,0.316507637,0.002562632,0.002562679,Genomics,0.42576835,FALSE,12.5,0.189436576,1,0.122023013,1,0.537564047,,,0.283007879
11441,End-2-End COVID-19 Detection from Breath & Cough Audio,0,2102.08359,1/7/21,arxiv,0,6,"deep learning, neural network, dataset",0.002490497,0.002490531,0.987547297,0.002490748,0.002490463,0.002490464,Epidemiology,0.010129809,FALSE,27.33333333,0.399529965,18.16666667,0.462603693,0,0.403234768,,,0.421789475
11442,DICE: Deep Significance Clustering for Outcome-Aware Stratification,0,2101.02344,1/7/21,arxiv,0,5,dataset,0.001438118,0.103184933,0.542266674,0.001438161,0.001438115,0.350233999,Clinics,0.1327889,FALSE,270.8,0.987878038,252.2,0.938720899,0,0.403234768,,,0.776611235
11443,Adaptive Group Testing on Networks with Community Structure,0,2101.02405,1/7/21,arxiv,0,3,probabilistic,0.10472526,0.034964623,0.306630317,0.444923927,0.106722977,0.002032896,Epidemiology,0.022464514,FALSE,52.66666667,0.651308059,88.66666667,0.798501472,1,0.537564047,,,0.662457859
11444,"Lessons Learned from the Bayesian Design and Analysis for the BNT162b2
  COVID-19 Vaccine Phase 3 Trial",0,2103.05499,1/8/21,arxiv,0,2,bayes,0.107733663,0.002806397,0.002806384,0.777840824,0.002806545,0.106006187,Epidemiology,0.2006197,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
11445,"Studying Leaders During Times of Crisis Using Online Social Media -- A
  COVID Case Study",0,2101.03002,1/8/21,arxiv,0,2,network analysis,0.00151188,0.001511899,0.188329846,0.672191214,0.134943289,0.001511871,Epidemiology,0.008262932,FALSE,29,0.41993939,32.5,0.589376505,0,0.403234768,,,0.470850221
11446,"Deep Learning Models May Spuriously Classify Covid-19 from X-ray Images
  Based on Confounders",0,2102.043,1/8/21,arxiv,0,5,"deep learning, neural network, network model",0.002032757,0.002032755,0.98983614,0.002032787,0.002032728,0.002032833,Imaging,0.060293883,FALSE,154.6,0.947615808,523.6,0.980599411,0,0.403234768,,,0.777149996
11447,"Combating Hostility: Covid-19 Fake News and Hostile Post Detection in
  Social Media",0,2101.03291,1/9/21,arxiv,0,3,lstm,0.001943534,0.052978907,0.814983165,0.001943654,0.126207195,0.001943545,Healthcare,0.022687644,FALSE,43.33333333,0.571154679,18.66666667,0.466818303,3,0.667819001,,,0.568597328
11448,"Ribonucleic acid (RNA) virus and coronavirus in Google Dataset Search:
  their scope and epidemiological correlation",0,2101.03339,1/9/21,arxiv,0,2,dataset,0.001786584,0.08658209,0.001786629,0.87667443,0.001786684,0.031383584,Epidemiology,0.36645478,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
11449,"SUIHTER: A new mathematical model for COVID-19. Application to the
  analysis of the second epidemic outbreak in Italy",0,2101.03369,1/9/21,arxiv,0,9,mathematical model,0.001751254,0.001751309,0.001751458,0.919368281,0.001751198,0.073626499,Epidemiology,0.03512603,FALSE,141.1111111,0.935246459,164.2222222,0.895504415,1,0.537564047,,,0.789438307
11450,"Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on
  the Arabic Content of Twitter",0,2101.05626,1/9/21,arxiv,0,5,"machine learning, dataset",0.002562599,0.002562768,0.557198834,0.432550517,0.002562715,0.002562567,Epidemiology,0.011758775,FALSE,17.2,0.259323397,5,0.257024351,0,0.403234768,,,0.306527505
11451,"Unraveling the Dynamic Importance of County-level Features in Trajectory
  of COVID-19",0,2101.03458,1/10/21,arxiv,0,9,machine learning,0.000889064,0.000889063,0.2770207,0.719423023,0.00088908,0.000889069,Epidemiology,0.083910495,FALSE,34.66666667,0.483703383,6.222222222,0.282646508,0,0.403234768,,,0.389861553
11452,"TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on
  Corona Virus and 5G Conspiracy",0,2101.03529,1/10/21,arxiv,0,3,neural network,0.002720168,0.002720189,0.24131779,0.747801643,0.002720135,0.002720076,Epidemiology,0.003162921,FALSE,39.66666667,0.536211268,14,0.412898047,1,0.537564047,,,0.495557787
11453,A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection,0,2101.03545,1/10/21,arxiv,0,3,dataset,0.002032805,0.002032821,0.4348709,0.556997831,0.002032842,0.0020328,Epidemiology,0.005727142,FALSE,13,0.197352959,3,0.199424672,0,0.403234768,,,0.2666708
11454,"Constraint 2021: Machine Learning Models for COVID-19 Fake News
  Detection Shared Task",0,2101.03717,1/11/21,arxiv,0,1,machine learning,0.002806416,0.002806457,0.45694447,0.531829689,0.00280657,0.002806398,Epidemiology,0.03314069,FALSE,3,0.037293586,1,0.122023013,0,0.403234768,,,0.187517122
11455,"Impact of Interventional Policies Including Vaccine on Covid-19
  Propagation and Socio-Economic Factors",0,2101.03944,1/11/21,arxiv,0,5,machine learning,0.001330142,0.001330095,0.286952169,0.707727456,0.001330115,0.001330024,Epidemiology,0.043603837,FALSE,4.6,0.062588905,12.2,0.387476585,0,0.403234768,,,0.284433419
11456,Evaluating Deep Learning Approaches for Covid19 Fake News Detection,0,2101.04012,1/11/21,arxiv,0,5,"supervised learning, unsupervised learning, deep learning, neural network, data mining, lstm, dataset",0.001511842,0.001511835,0.447993323,0.545959314,0.00151185,0.001511836,Epidemiology,0.006638765,FALSE,4.4,0.058321479,0.4,0.075796093,2,0.618927094,,,0.251014889
11457,"Contrastive Learning Improves Critical Event Prediction in COVID-19
  Patients",0,2101.04013,1/11/21,arxiv,0,16,machine learning,0.020655729,0.001291286,0.735559383,0.001291272,0.086303146,0.154899183,Clinics,0.035317898,FALSE,57.8125,0.687488404,,,1,0.537564047,,,0.612526225
11458,"VIDA: A simulation model of domestic VIolence in times of social
  DistAncing",0,2101.04057,1/11/21,arxiv,0,3,simulation model,0.001220055,0.001220042,0.001220052,0.675698186,0.319421646,0.001220017,Epidemiology,0.25274143,FALSE,36.66666667,0.505102356,2,0.164302917,0,0.403234768,,,0.35754668
11459,Remote Pulse Estimation in the Presence of Face Masks,0,2101.04096,1/11/21,arxiv,0,5,"neural network, dataset",0.001203433,0.001203461,0.322772029,0.615298115,0.001203527,0.058319435,Epidemiology,0.015093654,FALSE,170.8,0.958871915,1047.6,0.993912229,0,0.403234768,,,0.785339637
11460,"Challenges and approaches to time-series forecasting in data center
  telemetry: A Survey",0,2101.04224,1/11/21,arxiv,0,3,deep learning,0.001823406,0.001823353,0.30073588,0.691970651,0.001823339,0.001823372,Epidemiology,0.079310864,FALSE,7.333333333,0.105572392,0.333333333,0.073187048,0,0.403234768,,,0.193998069
11461,FaceX-Zoo: A PyTorch Toolbox for Face Recognition,0,2101.04407,1/12/21,arxiv,0,5,deep learning,0.028920321,0.001046841,0.966892273,0.001046887,0.001046857,0.00104682,Drug discovery,0.018359482,FALSE,67.4,0.749706228,61.8,0.727856569,0,0.403234768,,,0.626932522
11462,"A patient-specific approach for quantitative and automatic analysis of
  computed tomography images in lung disease: application to COVID-19 patients",0,2101.0443,1/12/21,arxiv,0,10,radiom,0.001141346,0.00114136,0.562468058,0.242537523,0.001141338,0.191570375,Imaging,0.23156509,FALSE,61.6,0.713031109,62.3,0.729930425,0,0.403234768,,,0.615398767
11463,"Model-based cellular kinetic analysis of SARS-CoV-2 infection: different
  immune response modes and treatment strategies",0,2101.04477,1/12/21,arxiv,0,13,mathematical model,0.637582562,0.001593514,0.035064225,0.24023884,0.00159351,0.08392735,Drug discovery,0.13941613,FALSE,66.76923077,0.745562496,89.30769231,0.800040139,0,0.403234768,,,0.649612468
11464,"Capturing social media expressions during the COVID-19 pandemic in
  Argentina and forecasting mental health and emotions",0,2101.0454,1/12/21,arxiv,0,4,neural network,0.017727937,0.001059394,0.025710709,0.61087738,0.290175882,0.054448698,Epidemiology,0.18133986,FALSE,54.75,0.666584204,32,0.585763982,0,0.403234768,,,0.551860985
11465,"AI- and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes
  to Extract Druglike Molecules Contained in Natural Language Text",0,2101.04617,1/12/21,arxiv,0,6,"artificial intelligence, dataset",0.485261649,0.001901726,0.507131418,0.001901795,0.001901741,0.00190167,Drug discovery,0.03460008,FALSE,184.5,0.966293525,766.1666667,0.989430024,0,0.403234768,,,0.786319439
11466,"Amicus Plato, sed magis amica veritas: There is a reproducibility crisis
  in COVID-19 Computational Fluid Dynamics studies",0,2101.04874,1/13/21,arxiv,0,1,computational,0.001511898,0.031561217,0.001511912,0.962391292,0.001511826,0.001511855,Epidemiology,0.2838506,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
11467,"COVID-19 Prognosis via Self-Supervised Representation Learning and
  Multi-Image Prediction",0,2101.04909,1/13/21,arxiv,0,10,"machine learning, supervised learning",0.000999548,0.033273936,0.576083451,0.000999536,0.000999554,0.387643975,Imaging,0.09579143,FALSE,42.4,0.561382893,,,3,0.667819001,,,0.614600947
11468,"Protecting Privacy and Transforming COVID-19 Case Surveillance Datasets
  for Public Use",0,2101.05093,1/13/21,arxiv,0,12,dataset,0.001622798,0.001622802,0.183882207,0.809626634,0.001622831,0.001622729,Epidemiology,0.104908526,FALSE,31.25,0.447213804,36.08333333,0.611386139,0,0.403234768,,,0.487278237
11469,"Leveraging Structured Biological Knowledge for Counterfactual Inference:
  a Case Study of Viral Pathogenesis",0,2101.05136,1/13/21,arxiv,0,11,"knowledge graph, probabilistic",0.368183614,0.001653073,0.148754973,0.323140619,0.116333194,0.041934526,Drug discovery,0.21407276,FALSE,26.81818182,0.392479436,44.81818182,0.658081349,1,0.537564047,,,0.529374944
11470,"Estimating functional parameters for understanding the impact of weather
  and government interventions on COVID-19 outbreak",0,2101.0535,1/13/21,arxiv,0,1,bayes,0.118445183,0.002898349,0.002898431,0.869961454,0.002898298,0.002898285,Epidemiology,0.46997488,FALSE,21,0.312016822,1,0.122023013,0,0.403234768,,,0.279091534
11471,"A Multi-Stage Attentive Transfer Learning Framework for Improving
  COVID-19 Diagnosis",0,2101.0541,1/14/21,arxiv,0,2,"machine learning, supervised learning, transfer learning",0.001098825,0.001098823,0.994505825,0.001098831,0.001098882,0.001098814,Imaging,0.006617069,FALSE,144.5,0.939080957,412.5,0.97217019,1,0.537564047,,,0.816271732
11472,"Automated Model Design and Benchmarking of 3D Deep Learning Models for
  COVID-19 Detection with Chest CT Scans",0,2101.05442,1/14/21,arxiv,0,9,"deep learning, dataset",0.001350381,0.056219737,0.93837883,0.001350408,0.001350328,0.001350316,Imaging,0.000363499,FALSE,37.33333333,0.511596264,16.88888889,0.447283918,0,0.403234768,,,0.454038317
11473,"Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake
  News Detection",0,2101.05509,1/14/21,arxiv,0,8,dataset,0.00137127,0.001371275,0.696935732,0.249700534,0.049249955,0.001371235,Imaging,0.001279593,FALSE,32.125,0.456552663,,,1,0.537564047,,,0.497058355
11474,TUDublin team at Constraint@AAAI2021 -- COVID19 Fake News Detection,0,2101.05701,1/14/21,arxiv,0,2,"bayes, logistic regression",0.002296552,0.002296544,0.525253482,0.465560277,0.002296612,0.002296534,Epidemiology,0.028229177,FALSE,34.5,0.482404601,7,0.299973241,0,0.403234768,,,0.395204203
11475,"Challenges in the application of a mortality prediction model for
  COVID-19 patients on an Indian cohort",0,2101.07215,1/15/21,arxiv,0,12,"machine learning, classifier, prediction model, dataset",0.001156289,0.001156396,0.433778634,0.197022761,0.00115626,0.36572966,Clinics,0.24008325,FALSE,8.294117647,0.121652545,,,0,0.403234768,,,0.262443656
11476,"Comparison of Machine Learning for Sentiment Analysis in Detecting
  Anxiety Based on Social Media Data","Jurnal Informatika,15(1), 2021, 45-55",2101.06353,1/16/21,arxiv,"Jurnal Informatika,15(1), 2021, 45-55",3,"machine learning, classifier",0.001254601,0.001254606,0.610627981,0.069614454,0.315993636,0.001254723,Healthcare,0.60528886,TRUE,29,0.41993939,2,0.164302917,0,0.403234768,,,0.329159025
11477,"Visual Analytics approach for finding spatiotemporal patterns from
  COVID19",0,2101.06476,1/16/21,arxiv,0,1,"logistic regression, dataset",0.001861744,0.001861767,0.001861833,0.948451032,0.001861721,0.044101903,Epidemiology,0.39243668,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
11478,"Artificial Intelligence for Emotion-Semantic Trending and People Emotion
  Detection During COVID-19 Social Isolation",0,2101.06484,1/16/21,arxiv,0,6,artificial intelligence,0.001272695,0.001272661,0.36322616,0.631682932,0.001272783,0.001272768,Epidemiology,0.2595231,FALSE,98.5,0.86591626,,,0,0.403234768,,,0.634575514
11479,Transformer-Based Models for Question Answering on COVID19,0,2101.11432,1/16/21,arxiv,0,4,dataset,0.002639031,0.002639019,0.43204999,0.557393831,0.00263912,0.002639009,Epidemiology,0.021320134,FALSE,20,0.298163152,,,0,0.403234768,,,0.35069896
11480,"Accurate Evaluation on the Interactions of SARS-CoV-2 with Its Receptor
  ACE2 and Antibodies CR3022/CB6","CHIN.PHYS.LETT. Vol.38, No.1(2021)018701 Express Letter",2102.03305,1/17/21,arxiv,"CHIN.PHYS.LETT. Vol.38, No.1(2021)018701 Express Letter",5,computational,0.953640775,0.001350395,0.040957794,0.001350413,0.001350312,0.001350311,Drug discovery,0.33431667,FALSE,17,0.257467994,1.6,0.140687717,1,0.537564047,,,0.311906586
11481,Covid-19 classification with deep neural network and belief functions,0,2101.06958,1/18/21,arxiv,0,3,"deep learning, neural network",0.001987107,0.001987088,0.990064502,0.00198712,0.001987094,0.001987089,Imaging,0.010681748,FALSE,119.3333333,0.905931103,213.3333333,0.923802515,0,0.403234768,,,0.744322795
11482,"Peptides of H. sapiens and P. falciparum that are predicted to bind
  strongly to HLA-A*24:02 and homologous to a SARS-CoV-2 peptide",0,2101.07356,1/18/21,arxiv,0,1,proteom,0.858748528,0.135590994,0.001415131,0.001415158,0.001415105,0.001415084,Drug discovery,0.326948,FALSE,25,0.369286907,1,0.122023013,1,0.537564047,,,0.342957989
11483,Fidelity and Privacy of Synthetic Medical Data,0,2101.08658,1/18/21,arxiv,0,2,dataset,0.089987314,0.001220035,0.614557841,0.291794743,0.00122004,0.001220027,Epidemiology,0.20386544,FALSE,107,0.883357041,111,0.839844795,0,0.403234768,,,0.708812201
11484,"Work Online, Welfare Calls, and Wine Night: Effects of the COVID-19
  Pandemic on Individuals' Technology Use",0,2101.07388,1/19/21,arxiv,0,2,computational,0.002238638,0.002238533,0.086680663,0.190100865,0.716502871,0.002238429,Healthcare,0.3945788,FALSE,39,0.530521368,87,0.795223441,0,0.403234768,,,0.576326526
11485,"Inferring COVID-19 Biological Pathways from Clinical Phenotypes via
  Topological Analysis",0,2101.07417,1/19/21,arxiv,0,4,dataset,0.229126461,0.001653071,0.623981371,0.141932851,0.001653084,0.001653162,Drug discovery,0.06549838,FALSE,60,0.703444864,88.75,0.798769066,0,0.403234768,,,0.635149566
11486,"COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19
  from Chest CT Images Through Bigger, More Diverse Learning",0,2101.07433,1/19/21,arxiv,0,4,"neural network, dataset",0.000838514,0.033319789,0.83879436,0.095796063,0.000838571,0.030412703,Imaging,0.07899445,FALSE,26.5,0.389325252,31.5,0.581883864,3,0.667819001,,,0.546342706
11487,"SIR Simulation of COVID-19 Pandemic in Malaysia: Will the Vaccination
  Program be Effective?",0,2101.07494,1/19/21,arxiv,0,3,simulation model,0.044712173,0.001415099,0.001415111,0.94962732,0.001415187,0.00141511,Epidemiology,0.12540352,FALSE,41,0.549013544,29,0.562884667,1,0.537564047,,,0.549820753
11488,"Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19
  Diagnosis at the Edge",0,2101.07511,1/19/21,arxiv,0,5,"machine learning, dataset",0.001126802,0.001126828,0.835010066,0.093097251,0.068512238,0.001126815,Imaging,0.1941916,FALSE,48.2,0.615808028,72.4,0.759967889,1,0.537564047,,,0.637779988
11489,Continual Deterioration Prediction for Hospitalized COVID-19 Patients,0,2101.07581,1/19/21,arxiv,0,6,"predictive model, dataset",0.001823339,0.034254426,0.357887206,0.412624882,0.001823358,0.19158679,Epidemiology,0.038517445,FALSE,85,0.825035562,162.5,0.894233342,0,0.403234768,,,0.707501224
11490,"CoVaxxy: A global collection of English-language Twitter posts about
  COVID-19 vaccines",0,2101.07694,1/19/21,arxiv,0,10,dataset,0.00280641,0.002806435,0.002806575,0.760096311,0.22867787,0.002806399,Epidemiology,0.011260599,FALSE,70.1,0.764549446,101.4,0.824792614,0,0.403234768,,,0.664192276
11491,Machine learning applications for COVID-19: A state-of-the-art review,0,2101.07824,1/19/21,arxiv,0,5,machine learning,0.044747517,0.002490464,0.561698847,0.350836379,0.037736336,0.002490457,Epidemiology,0.20305759,FALSE,19.8,0.293957573,4.8,0.249331014,0,0.403234768,,,0.315507785
11492,"Classification of COVID-19 X-ray Images Using a Combination of Deep and
  Handcrafted Features",0,2101.07866,1/19/21,arxiv,0,4,"machine learning, neural network, classifier",0.001237064,0.001237099,0.993814687,0.001237051,0.001237045,0.001237055,Imaging,0.17226696,FALSE,9.5,0.143051518,,,0,0.403234768,,,0.273143143
11493,"Proceedings of the 3rd Annual International Applied Category Theory
  Conference 2020","EPTCS 333, 2021",2101.07888,1/19/21,arxiv,"EPTCS 333, 2021",2,probabilistic,0.002358016,0.002357927,0.103307791,0.887260656,0.002357837,0.002357773,Epidemiology,0.07966006,FALSE,81,0.811552972,57,0.711198823,0,0.403234768,,,0.641995521
11494,"Deep Epidemiological Modeling by Black-box Knowledge Distillation: An
  Accurate Deep Learning Model for COVID-19",0,2101.1028,1/20/21,arxiv,0,3,"deep learning, neural network",0.001565339,0.135907898,0.428434847,0.389794233,0.04273238,0.001565304,Epidemiology,0.015674204,FALSE,18.33333333,0.274908776,1.333333333,0.13252609,0,0.403234768,,,0.270223211
11495,Chest X-ray lung and heart segmentation based on minimal training sets,0,2101.08309,1/20/21,arxiv,0,1,"neural network, dataset",0.001511843,0.00151184,0.992440662,0.001511917,0.001511842,0.001511897,Imaging,0.004632354,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,,,0.171164986
11496,"TDA-Net: Fusion of Persistent Homology and Deep Learning Features for
  COVID-19 Detection in Chest X-Ray Images",0,2101.08398,1/21/21,arxiv,0,3,"deep learning, neural network, dataset",0.001901767,0.001901746,0.990491286,0.001901754,0.00190175,0.001901698,Imaging,0.019483984,FALSE,21.66666667,0.320737213,0.666666667,0.096200161,1,0.537564047,,,0.31816714
11497,"Investors Embrace Gender Diversity, Not Female CEOs: The Role of Gender
  in Startup Fundraising",0,2101.12008,1/21/21,arxiv,0,4,machine learning,0.001861724,0.048561564,0.001861929,0.527257057,0.418595929,0.001861798,Epidemiology,0.08181113,FALSE,4.75,0.064506154,0,0.055525823,0,0.403234768,,,0.174422248
11498,"Projected Statistical Methods for Distributional Data on the Real Line
  with the Wasserstein Metric",0,2101.09039,1/22/21,arxiv,0,2,computational,0.001717294,0.001717209,0.264415476,0.728715599,0.001717184,0.001717238,Epidemiology,0.023848563,FALSE,65.5,0.737646113,83,0.785590045,0,0.403234768,,,0.642156975
11499,"SUTRA: An Approach to Modelling Pandemics with Asymptomatic Patients,
  and Applications to COVID-19",0,2101.09158,1/22/21,arxiv,0,3,mathematical model,0.001538132,0.00153817,0.001538203,0.76522152,0.001538097,0.228625879,Epidemiology,0.010575712,FALSE,119.6666667,0.906796957,231.3333333,0.929957185,0,0.403234768,,,0.74666297
11500,Field-effect at electrical contacts to two-dimensional materials,0,2101.09487,1/23/21,arxiv,0,16,"neural network, prediction model",0.002130703,0.002130679,0.234192025,0.671128151,0.03029754,0.060120903,Epidemiology,0.27344522,FALSE,49.0625,0.624590265,37.9375,0.621153332,0,0.403234768,,,0.549659455
11501,"Implementation of Correlation and Regression Models for Health Insurance
  Fraud in Covid-19 Environment using Actuarial and Data Science Techniques",Int. J. Recent Technol. Eng. vol. 9 no. 3 pp. 699-706 Sep. 2020,2102.0421,1/24/21,arxiv,Int. J. Recent Technol. Eng. vol. 9 no. 3 pp. 699-706 Sep. 2020,4,dataset,0.002080623,0.002080599,0.193867577,0.797810021,0.002080625,0.002080556,Epidemiology,0.7148684,TRUE,22,0.326056033,2.5,0.180826866,0,0.403234768,,,0.303372556
11502,Maximum n-times Coverage for COVID-19 Vaccine Design,0,2101.10902,1/24/21,arxiv,0,3,machine learning,0.39104271,0.002183364,0.084518553,0.517888874,0.002183269,0.00218323,Epidemiology,0.020441145,FALSE,13.66666667,0.207310285,,,0,0.403234768,,,0.305272526
11503,"E-cheating Prevention Measures: Detection of Cheating at Online
  Examinations Using Deep Learning Approach -- A Case Study",0,2101.09841,1/25/21,arxiv,0,2,"deep learning, neural network, lstm",0.0727054,0.001751246,0.464028726,0.001751286,0.458012155,0.001751188,Healthcare,0.20645252,FALSE,14,0.213494959,5,0.257024351,0,0.403234768,,,0.291251359
11504,"3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates
  using transfer learning: State-of-the-art results on affordable hardware",0,2101.09976,1/25/21,arxiv,0,6,"neural network, transfer learning, dataset",0.001461864,0.001461865,0.937523524,0.001461901,0.001461877,0.056628968,Imaging,0.046586424,FALSE,78.66666667,0.801224566,32.5,0.589376505,0,0.403234768,,,0.597945279
11505,"Low incidence rate of COVID-19 undermines confidence in estimation of
  the vaccine efficacy",0,2101.10005,1/25/21,arxiv,0,1,probabilistic,0.002238565,0.002238654,0.058315634,0.79890277,0.13606585,0.002238525,Epidemiology,0.08184442,FALSE,7,0.10179974,2,0.164302917,0,0.403234768,,,0.223112475
11506,The field theoretical ABC of epidemic dynamics,0,2101.11399,1/25/21,arxiv,0,6,mathematical model,0.002806538,0.002806527,0.215974772,0.772799184,0.002806589,0.002806391,Epidemiology,0.017557174,FALSE,69.5,0.761271569,35.66666667,0.608576398,0,0.403234768,,,0.591027578
11507,"A two-step explainable approach for COVID-19 computer-aided diagnosis
  from chest x-ray images",0,2101.10223,1/25/21,arxiv,0,5,"machine learning, deep learning",0.001653173,0.075724646,0.865704955,0.001653086,0.053611013,0.001653127,Imaging,0.28752452,FALSE,51.4,0.642092894,39.2,0.628712871,0,0.403234768,,,0.558013511
11508,"Contrastive analysis for scatter plot-based representations of
  dimensionality reduction",0,2101.12044,1/26/21,arxiv,0,3,dataset,0.001823535,0.196361711,0.313587458,0.428647084,0.00182349,0.057756722,Epidemiology,0.004464984,FALSE,31.33333333,0.448388892,3,0.199424672,0,0.403234768,,,0.350349444
11509,"The Probabilistic Final Standing Calculator: a fair stochastic tool to
  handle abruptly stopped football seasons",0,2101.10597,1/26/21,arxiv,0,3,"prediction model, probabilistic",0.002238481,0.002238472,0.0022386,0.943475311,0.04757061,0.002238526,Epidemiology,0.06683174,FALSE,0,0.006432061,,,0,0.403234768,,,0.204833414
11510,"Understanding the uneven spread of COVID-19 in the context of the global
  interconnected economy",0,2101.11036,1/26/21,arxiv,0,2,network analysis,0.002422327,0.002422342,0.002422416,0.987888356,0.002422276,0.002422283,Epidemiology,0.11943194,FALSE,30,0.432432432,21.5,0.498260637,0,0.403234768,,,0.444642612
11511,"Deep learning via LSTM models for COVID-19 infection forecasting in
  India",0,2101.11881,1/28/21,arxiv,0,3,"deep learning, computational, neural network, mathematical model, forecasting model, lstm",0.00146191,0.028131324,0.186149258,0.781333779,0.00146188,0.001461849,Epidemiology,0.040469408,FALSE,14.66666667,0.221163956,8.333333333,0.325662296,1,0.537564047,,,0.361463433
11512,"Automatic design of novel potential 3CL$^{\text{pro}}$ and
  PL$^{\text{pro}}$ inhibitors",0,2101.1189,1/28/21,arxiv,0,4,"neural network, dataset",0.558931522,0.002183307,0.432334789,0.002183401,0.002183535,0.002183447,Drug discovery,0.059640884,FALSE,26.25,0.384748593,137.25,0.871956115,0,0.403234768,,,0.553313158
11513,"An Explainable AI System for Automated COVID-19 Assessment and Lesion
  Categorization from CT-scans",0,2101.11943,1/28/21,arxiv,0,15,"deep-learning, dataset",0.001220017,0.001220106,0.993899709,0.0012201,0.001219998,0.001220071,Imaging,0.018949717,FALSE,45,0.587049292,34.66666667,0.603157613,0,0.403234768,,,0.531147224
11514,Seroprevalence of SARS-CoV-2 antibodies in South Korea,0,2101.11991,1/28/21,arxiv,0,3,"bayes, bayesian model",0.003101615,0.052430411,0.153632238,0.684693522,0.103040712,0.003101503,Epidemiology,0.15866074,FALSE,15.66666667,0.236563795,1,0.122023013,0,0.403234768,,,0.253940525
11515,A transformer based approach for fighting COVID-19 fake news,0,2101.12027,1/28/21,arxiv,0,3,"classifier, dataset",0.002639013,0.002639059,0.654081643,0.335362022,0.002639157,0.002639105,Epidemiology,0.02620995,FALSE,8.333333333,0.123693488,0.666666667,0.096200161,0,0.403234768,,,0.207709472
11516,"On Mutual Information Analysis of Infectious Disease Transmission via
  Particle Propagation",0,2101.12121,1/28/21,arxiv,0,4,probabilistic,0.002357991,0.146372177,0.14131735,0.705236982,0.002357773,0.002357727,Epidemiology,0.069022596,FALSE,77,0.794668811,145.5,0.879783249,0,0.403234768,,,0.692562276
11517,"CML-COVID: A Large-Scale COVID-19 Twitter Dataset with Latent Topics,
  Sentiment and Location Information",0,2101.12202,1/28/21,arxiv,0,2,dataset,0.002080732,0.002080697,0.002080563,0.849154627,0.142522843,0.002080538,Epidemiology,0.017933458,FALSE,25,0.369286907,51,0.685509767,0,0.403234768,,,0.486010481
11518,Reliable COVID-19 Detection Using Chest X-ray Images,0,2101.12254,1/28/21,arxiv,0,5,"machine learning, dataset",0.001717163,0.001717172,0.991414169,0.001717166,0.001717159,0.001717171,Imaging,0.004476041,FALSE,61,0.709196611,46.6,0.666577469,0,0.403234768,,,0.593002949
11519,"Impacts of export restrictions on the global personal protective
  equipment trade network during COVID-19",0,2101.12444,1/29/21,arxiv,0,7,network model,0.002357736,0.002357738,0.002357882,0.988211045,0.002357899,0.002357699,Epidemiology,0.031202197,FALSE,116.5714286,0.901601831,128.4285714,0.861586834,0,0.403234768,,,0.722141144
11520,"BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein
  Interactions",0,2101.12547,1/29/21,arxiv,0,6,"supervised learning, deep learning, computational, neural network, dataset",0.436113775,0.001126925,0.559378818,0.001126829,0.001126845,0.001126808,Drug discovery,0.010641694,FALSE,85.5,0.82658173,15.83333333,0.434238694,0,0.403234768,,,0.554685064
11521,"TruthBot: An Automated Conversational Tool for Intent Learning, Curated
  Information Presenting, and Fake News Alerting",0,2102.00509,1/31/21,arxiv,0,8,neural network,0.002562639,0.002562585,0.543251701,0.332381801,0.116678726,0.002562547,Epidemiology,0.000413448,FALSE,39.5,0.534232173,14.375,0.416109178,0,0.403234768,,,0.45119204
11522,Few-shot Learning for CT Scan based COVID-19 Diagnosis,0,2102.00596,2/1/21,arxiv,0,4,deep learning,0.001291293,0.001291274,0.993543512,0.001291289,0.001291324,0.001291307,Imaging,0.002533615,FALSE,62.25,0.71661822,16.5,0.44293551,0,0.403234768,,,0.520929499
11523,"Methodology-centered review of molecular modeling, simulation, and
  prediction of SARS-CoV-2",0,2102.00971,2/1/21,arxiv,0,9,"machine learning, deep learning, computational, bioinformatic",0.50394921,0.000966806,0.1076568,0.38549356,0.000966842,0.000966782,Drug discovery,0.5879447,TRUE,49,0.624281032,4.555555556,0.243109446,1,0.537564047,,,0.468318175
11524,"Can Predominant Credible Information Suppress Misinformation in Crises?
  Empirical Studies of Tweets Related to Prevention Measures during COVID-19",0,2102.00976,2/1/21,arxiv,0,3,classifier,0.001943494,0.001943479,0.149613745,0.842612171,0.001943609,0.001943502,Epidemiology,0.054368377,FALSE,3.666666667,0.045952131,,,0,0.403234768,,,0.224593449
11525,"Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through
  analysis of Viral Genomics and Structure",0,2102.01521,2/1/21,arxiv,0,32,proteom,0.564091927,0.429646519,0.001565324,0.001565413,0.00156546,0.001565356,Drug discovery,0.37882054,FALSE,32.25,0.458098831,30.78125,0.575260904,3,0.667819001,,,0.567059579
11526,"Real-time Prediction for Mechanical Ventilation in COVID-19 Patients
  using A Multi-task Gaussian Process Multi-objective Self-attention Network",0,2102.01147,2/1/21,arxiv,0,5,predictive model,0.000926307,0.000926305,0.508894057,0.207812174,0.000926306,0.280514851,Clinics,0.016409725,FALSE,58.8,0.694848166,38.4,0.623896173,0,0.403234768,,,0.573993036
11527,"Interpretable COVID-19 Chest X-Ray Classification via Orthogonality
  Constraint",0,2102.0836,2/2/21,arxiv,0,5,"deep learning, neural network",0.001392839,0.00139285,0.993035699,0.001392902,0.001392876,0.001392835,Imaging,0.006815642,FALSE,44.2,0.579503989,63.6,0.733743645,0,0.403234768,,,0.5721608
11528,A Compartment Model of Human Mobility and Early Covid-19 Dynamics in NYC,0,2102.01821,2/3/21,arxiv,0,2,"bayes, probabilistic",0.002032827,0.002032791,0.002032823,0.956787233,0.002032855,0.03508147,Epidemiology,0.05802557,FALSE,66.5,0.743830787,71.5,0.757626438,0,0.403234768,,,0.634897331
11529,"HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis
  and Emotion Recognition",0,2102.01909,2/3/21,arxiv,0,2,"active learning, dataset",0.001987334,0.001987172,0.990063941,0.001987219,0.001987173,0.001987159,Drug discovery,0.000640243,FALSE,26.5,0.389325252,10,0.355632861,0,0.403234768,,,0.38273096
11530,"Revealing Critical Characteristics of Mobility Patterns in New York City
  during the Onset of COVID-19 Pandemic",0,2102.01918,2/3/21,arxiv,0,4,dataset,0.001415128,0.001415125,0.001415096,0.992924411,0.001415143,0.001415097,Epidemiology,0.043963134,FALSE,26.25,0.384748593,4.25,0.235750602,0,0.403234768,,,0.341244654
11531,Digital twins based on bidirectional LSTM and GAN for modelling COVID-19,0,2102.02664,2/3/21,arxiv,0,6,"machine learning, computational, neural network, adversarial network, lstm",0.001684529,0.001684505,0.186275252,0.806986752,0.001684483,0.001684478,Epidemiology,0.06922963,FALSE,72.83333333,0.776733255,33.66666667,0.596802248,0,0.403234768,,,0.592256757
11532,Mainstreaming of conspiracy theories and misinformation,0,2102.02382,2/4/21,arxiv,0,8,mathematical model,0.001823408,0.027518999,0.066453609,0.46530194,0.437078663,0.001823382,Epidemiology,0.030984908,FALSE,36.75,0.505968211,24.375,0.524150388,0,0.403234768,,,0.477784455
11533,An Analysis of International Use of Robots for COVID-19,0,2102.02509,2/4/21,arxiv,0,5,dataset,0.002422567,0.002422445,0.002422415,0.987887883,0.002422386,0.002422304,Epidemiology,0.02524808,FALSE,77.2,0.795472818,108.8,0.837369548,0,0.403234768,,,0.678692378
11534,"Bipartisan politics and poverty as a risk factor for contagion and
  mortality from SARS-CoV-2 virus in the United States of America",0,2102.04335,2/4/21,arxiv,0,1,machine learning,0.001823435,0.00182337,0.001823554,0.639502477,0.001823445,0.35320372,Epidemiology,0.34141272,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
11535,"Simulation of the impact of people mobility, vaccination rate, and virus
  variants on the evolution of Covid-19 outbreak",0,2102.02759,2/4/21,arxiv,0,2,model fit,0.002130636,0.12748167,0.00213067,0.79075861,0.002130852,0.075367562,Epidemiology,0.048222244,FALSE,34,0.477766096,3.5,0.213607172,0,0.403234768,,,0.364869345
11536,"Analyzing Host-Viral Interactome of SARS-CoV-2 for Identifying
  Vulnerable Host Proteins during COVID-19 Pathogenesis",0,2102.03253,2/5/21,arxiv,0,3,interactom,0.942846606,0.050540997,0.001653075,0.001653044,0.001653025,0.001653253,Drug discovery,0.64292896,TRUE,123.6666667,0.912363164,24,0.521808938,0,0.403234768,,,0.612468956
11537,"BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and
  Positioning Predictor on Edge Devices",0,2102.03456,2/6/21,arxiv,0,5,"neural network, classifier, dataset",0.018639371,0.000907307,0.569722619,0.408916096,0.000907324,0.000907284,Epidemiology,0.003063142,FALSE,45.2,0.588719154,13,0.400521809,1,0.537564047,,,0.508935003
11538,A Vector Autoregression Prediction Model for COVID-19 Outbreak,0,2102.04843,2/6/21,arxiv,0,3,prediction model,0.004775161,0.004775103,0.004775532,0.976123495,0.004775336,0.004775373,Epidemiology,0.16075096,FALSE,18,0.271569052,28.66666667,0.559673535,0,0.403234768,,,0.411492451
11539,"COVIDHunter: An Accurate, Flexible, and Environment-Aware Open-Source
  COVID-19 Outbreak Simulation Model",0,2102.03667,2/6/21,arxiv,0,5,simulation model,0.001272624,0.001272651,0.033192648,0.914777067,0.001272652,0.048212358,Epidemiology,0.11739659,FALSE,113.2,0.896344857,439.4,0.974310945,0,0.403234768,,,0.757963523
11540,"A novel multiple instance learning framework for COVID-19 severity
  assessment via data augmentation and self-supervised learning",0,2102.03837,2/7/21,arxiv,0,12,supervised learning,0.039286787,0.002032773,0.952581881,0.002032815,0.002032928,0.002032815,Imaging,0.42131206,FALSE,148,0.942606222,174.8333333,0.902930158,1,0.537564047,,,0.794366809
11541,"Measuring Global Multi-Scale Place Connectivity using Geotagged Social
  Media Data",0,2102.03991,2/8/21,arxiv,0,8,dataset,0.001415235,0.00141513,0.001415141,0.992924146,0.001415196,0.001415151,Epidemiology,0.03136742,FALSE,47.25,0.608015338,45.5,0.660957988,0,0.403234768,,,0.557402698
11542,"Diagnosis of COVID-19 and Non-COVID-19 Patients by Classifying Only a
  Single Cough Sound",0,2102.0488,2/8/21,arxiv,0,1,"machine learning, classifier",0.002032738,0.002032869,0.98983595,0.002032762,0.002032792,0.002032889,Clinics,0.4410654,FALSE,85,0.825035562,22,0.503746321,0,0.403234768,,,0.577338883
11543,"Hybrid quantum convolutional neural networks model for COVID-19
  prediction using chest X-Ray images",0,2102.06535,2/8/21,arxiv,0,4,"neural network, dataset",0.001392823,0.021647965,0.972780697,0.001392849,0.001392821,0.001392844,Imaging,0.02259487,FALSE,79.5,0.80493537,20.25,0.483074659,1,0.537564047,,,0.608524692
11544,"Analysis of the Effectiveness of Face-Coverings on the Death Rate of
  COVID-19 Using Machine Learning",0,2102.04419,2/8/21,arxiv,0,4,"bayes, machine learning",0.001371307,0.001371368,0.302194521,0.647070713,0.04662079,0.001371302,Epidemiology,0.24203938,FALSE,3.75,0.046817985,0,0.055525823,0,0.403234768,,,0.168526192
11545,"A Bayesian spatio-temporal nowcasting model for public health
  decision-making and surveillance",0,2102.04544,2/8/21,arxiv,0,6,bayes,0.001901689,0.001901704,0.107442739,0.884950363,0.001901793,0.001901712,Epidemiology,0.016774297,FALSE,29.33333333,0.423093574,15.33333333,0.428351619,1,0.537564047,,,0.46300308
11546,"Real-time tracking of COVID-19 and coronavirus research updates through
  text mining",0,2102.0764,2/9/21,arxiv,0,8,text mining,0.003214294,0.00321423,0.464960362,0.522182757,0.003214164,0.003214193,Epidemiology,0.003841698,FALSE,17.25,0.260189251,,,0,0.403234768,,,0.331712009
11547,"B.1.258$Δ$, a SARS-CoV-2 variant with $Δ$H69/$Δ$V70 in
  the Spike protein circulating in the Czech Republic and Slovakia",0,2102.04689,2/9/21,arxiv,0,10,sequencing,0.109873827,0.884078797,0.001511878,0.001511875,0.001511819,0.001511805,Genomics,0.14382899,FALSE,24.6,0.362483765,29.1,0.563286058,1,0.537564047,,,0.487777957
11548,"D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices
  with Dilated Convolution and Dual Attention Mechanism",0,2102.0521,2/10/21,arxiv,0,9,dataset,0.001171606,0.001171643,0.994141936,0.001171598,0.001171631,0.001171587,Imaging,0.04276949,FALSE,11.77777778,0.177623848,,,0,0.403234768,,,0.290429308
11549,FLOP: Federated Learning on Medical Datasets using Partial Networks,0,2102.05218,2/10/21,arxiv,0,5,"deep learning, dataset",0.001511835,0.001511866,0.903899458,0.090053106,0.001511868,0.001511866,Epidemiology,0.023061544,FALSE,193.4,0.970251716,426.2,0.97310677,0,0.403234768,,,0.782197751
11550,"A Tale of Two Countries: A Longitudinal Cross-Country Study of Mobile
  Users' Reactions to the COVID-19 Pandemic Through the Lens of App Popularity",0,2102.06528,2/10/21,arxiv,0,5,dataset,0.001943616,0.001943518,0.001943496,0.990282291,0.001943577,0.001943501,Epidemiology,0.013187438,FALSE,52,0.647349867,,,0,0.403234768,,,0.525292317
11551,Application of Yolo on Mask Detection Task,0,2102.05402,2/10/21,arxiv,0,2,"deep learning, computational, dataset",0.001371237,0.001371283,0.444266434,0.550248496,0.001371287,0.001371263,Epidemiology,0.13893849,FALSE,133.5,0.925536521,103,0.827468558,0,0.403234768,,,0.718746615
11552,Protein corona critically affects the bio-behaviors of SARS-CoV-2,0,2102.0544,2/10/21,arxiv,0,6,molecular dynamics simulation,0.895042357,0.00186176,0.001861714,0.00186174,0.00186178,0.097510649,Drug discovery,0.6977892,TRUE,32.33333333,0.459273919,9.166666667,0.338774418,0,0.403234768,,,0.400427702
11553,"Accelerating COVID-19 research with graph mining and transformer-based
  learning",0,2102.07631,2/10/21,arxiv,0,6,"computational, artificial intelligence, text mining, dataset",0.001392918,0.001392896,0.524097207,0.47033116,0.001392915,0.001392904,Epidemiology,0.16688973,FALSE,20,0.298163152,49,0.677481937,0,0.403234768,,,0.459626619
11554,Signal Propagation in a Gradient-Based and Evolutionary Learning System,0,2102.08929,2/10/21,arxiv,0,2,"computational, adversarial network, dataset",0.188042965,0.055194204,0.581714093,0.17161431,0.00171726,0.001717169,Imaging,0.122092515,FALSE,151,0.945203785,206,0.920457586,0,0.403234768,,,0.756298713
11555,"EventScore: An Automated Real-time Early Warning Score for Clinical
  Events",0,2102.05958,2/11/21,arxiv,0,6,"logistic regression, dataset",0.001511798,0.001511828,0.689850617,0.001511846,0.001511874,0.304102037,Clinics,0.009503782,FALSE,152,0.945822252,118.6666667,0.849946481,0,0.403234768,,,0.733001167
11556,"Comparative Analysis of Machine Learning Approaches to Analyze and
  Predict the Covid-19 Outbreak",0,2102.0596,2/11/21,arxiv,0,6,"machine learning, neural network, dataset",0.001059351,0.001059355,0.346878495,0.648883992,0.00105938,0.001059428,Epidemiology,0.093737066,FALSE,35.5,0.492547467,5,0.257024351,0,0.403234768,,,0.384268862
11557,"COVID-19 identification from volumetric chest CT scans using a
  progressively resized 3D-CNN incorporating segmentation, augmentation, and
  class-rebalancing",0,2102.06169,2/11/21,arxiv,0,5,"supervised learning, neural network, classifier, dataset",0.001126799,0.001126812,0.994365928,0.001126814,0.001126809,0.001126837,Imaging,0.02136901,FALSE,37.4,0.512585812,22.2,0.504816698,0,0.403234768,,,0.473545759
11558,"Selecting Treatment Effects Models for Domain Adaptation Using Causal
  Knowledge",0,2102.06271,2/11/21,arxiv,0,4,dataset,0.171728388,0.001291317,0.585113686,0.152275929,0.001291292,0.088299389,Drug discovery,0.009361386,FALSE,192.25,0.969571402,150.5,0.883864062,0,0.403234768,,,0.752223411
11559,"COVID-19 detection from scarce chest x-ray image data using few-shot
  deep learning approach",0,2102.06285,2/11/21,arxiv,0,1,"machine learning, supervised learning, unsupervised learning, deep learning, transfer learning",0.001310321,0.001310333,0.993448309,0.00131036,0.001310341,0.001310338,Imaging,0.16293848,FALSE,17,0.257467994,1,0.122023013,0,0.403234768,,,0.260908592
11560,"Uncertainty-Aware Semi-supervised Method using Large Unlabelled and
  Limited Labeled COVID-19 Data",0,2102.06388,2/12/21,arxiv,0,14,"adversarial network, dataset",0.001272689,0.001272641,0.944863768,0.001272685,0.001272824,0.050045393,Imaging,0.005392909,FALSE,154.9285714,0.947863195,77.21428571,0.77120685,1,0.537564047,,,0.752211364
11561,Control of COVID-19 dynamics through a fractional-order model,"Alexandria Engineering Journal 60 (2021), no. 4, 3587--3592",2102.06421,2/12/21,arxiv,"Alexandria Engineering Journal 60 (2021), no. 4, 3587--3592",4,mathematical model,0.00310146,0.003101487,0.003101491,0.87950233,0.108091632,0.0031016,Epidemiology,0.8169426,TRUE,149.5,0.943719463,50,0.681763447,1,0.537564047,,,0.721015652
11562,"When no news is bad news -- Detection of negative events from news media
  content",0,2102.06505,2/12/21,arxiv,0,6,bayes,0.001786518,0.089327944,0.001786555,0.903525856,0.001786606,0.001786521,Epidemiology,0.005119562,FALSE,41.33333333,0.551549261,69.33333333,0.75140487,0,0.403234768,,,0.568729633
11563,"Model-based Prediction and Optimal Control of Pandemics by
  Nonpharmaceutical Interventions",0,2102.06609,2/12/21,arxiv,0,1,"machine learning, computational",0.001310411,0.001310358,0.321983389,0.672775098,0.001310372,0.001310372,Epidemiology,0.036488682,FALSE,74,0.782113922,115,0.844594595,0,0.403234768,,,0.676647761
11564,Do-calculus enables causal reasoning with latent variable models,0,2102.06626,2/12/21,arxiv,0,4,probabilistic,0.118839486,0.001786552,0.160928485,0.714872299,0.001786591,0.001786587,Epidemiology,0.03970009,FALSE,48.5,0.618158204,77.25,0.771340648,0,0.403234768,,,0.597577873
11565,"Leveraging Artificial Intelligence to Analyze the COVID-19 Distribution
  Pattern based on Socio-economic Determinants",0,2102.06656,2/12/21,arxiv,0,2,"artificial intelligence, neural network",0.002357756,0.002357914,0.135240743,0.855327772,0.002357882,0.002357933,Epidemiology,0.06556696,FALSE,48,0.614942173,15.5,0.430157881,0,0.403234768,,,0.482778274
11566,"DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19
  forecasting",0,2102.06684,2/12/21,arxiv,0,7,"simulation model, deep learning",0.004530692,0.004530588,0.568190443,0.413686459,0.004530555,0.004531263,Epidemiology,0.05645159,FALSE,72.14285714,0.773517224,284.8571429,0.950093658,0,0.403234768,,,0.70894855
11567,"Fusion of convolution neural network, support vector machine and Sobel
  filter for accurate detection of COVID-19 patients using X-ray images",0,2102.06883,2/13/21,arxiv,0,16,"artificial intelligence, neural network, classifier, dataset",0.00125464,0.001254683,0.975239696,0.001254668,0.001254679,0.019741634,Imaging,0.63607657,TRUE,117.5,0.903642773,57.3125,0.712001606,0,0.403234768,,,0.672959715
11568,Adaptive Network Modeling of Social Distancing Interventions,0,2102.0699,2/13/21,arxiv,0,2,"mathematical model, network model",0.083885369,0.001751199,0.001751271,0.909109644,0.001751217,0.001751301,Epidemiology,0.014140964,FALSE,9,0.135320675,3,0.199424672,0,0.403234768,,,0.245993372
11569,"How Misuse of Statistics Can Spread Misinformation: A Study of
  Misrepresentation of COVID-19 Data",0,2102.07198,2/14/21,arxiv,0,2,mathematical model,0.003760505,0.003760594,0.003760845,0.981197016,0.003760532,0.003760508,Epidemiology,0.3383754,FALSE,26.5,0.389325252,20.5,0.486218892,0,0.403234768,,,0.426259637
11570,"Assessing the spatio-temporal spread of COVID-19 via compartmental
  models with diffusion in Italy, USA, and Brazil",0,2102.07208,2/14/21,arxiv,0,5,mathematical model,0.002032809,0.002032781,0.002032911,0.989835979,0.002032756,0.002032764,Epidemiology,0.01876381,FALSE,52.4,0.649823737,17.6,0.456984212,0,0.403234768,,,0.503347572
11571,"A Tale of Three Datasets: Towards Characterizing Mobile Broadband Access
  in the United States",0,2102.07288,2/15/21,arxiv,0,7,dataset,0.060717375,0.001717268,0.001717333,0.932413276,0.001717394,0.001717354,Epidemiology,0.23200631,FALSE,66.57142857,0.744511101,685.2857143,0.986954777,0,0.403234768,,,0.711566882
11572,Where to locate COVID-19 mass vaccination facilities?,0,2102.07309,2/15/21,arxiv,0,5,"predictive model, optimization model",0.001126937,0.001126886,0.001126886,0.936735227,0.058757212,0.001126851,Epidemiology,0.022962362,FALSE,101.2,0.871358773,609.2,0.984345732,0,0.403234768,,,0.752979757
11573,"Detection and severity classification of COVID-19 in CT images using
  deep learning",0,2102.07726,2/15/21,arxiv,0,10,"deep learning, neural network, dataset",0.001254618,0.02262287,0.853545285,0.00125462,0.001254609,0.120067998,Imaging,0.042532444,FALSE,50.8,0.636588534,29,0.562884667,0,0.403234768,,,0.534235989
11574,"What is the probability that a vaccinated person is shielded from
  Covid-19? A Bayesian MCMC based reanalysis of published data with emphasis on
  what should be reported as `efficacy'",0,2102.11022,2/15/21,arxiv,0,2,"bayes, bayesian model",0.132520853,0.002080585,0.002080625,0.859156596,0.002080747,0.002080595,Epidemiology,0.00160417,FALSE,200,0.972478199,134.5,0.86887878,0,0.403234768,,,0.748197249
11575,"Twin Augmented Architectures for Robust Classification of COVID-19 Chest
  X-Ray Images",0,2102.07975,2/16/21,arxiv,0,8,"deep learning, neural network, classifier, dataset",0.001237053,0.001237091,0.993814381,0.001237159,0.001237181,0.001237133,Imaging,0.06133634,FALSE,46.625,0.601830664,24.5,0.525086968,0,0.403234768,,,0.5100508
11576,Boosting Deep Transfer Learning for COVID-19 Classification,0,2102.08085,2/16/21,arxiv,0,4,"deep learning, transfer learning, dataset",0.002032924,0.002032883,0.989835708,0.002032859,0.002032843,0.002032783,Imaging,0.015992045,FALSE,24.75,0.364710248,42,0.644902328,0,0.403234768,,,0.470949115
11577,Mobility-based prediction of SARS-CoV-2 spreading,0,2102.08253,2/16/21,arxiv,0,4,deep learning,0.002238551,0.002238547,0.077825681,0.913220325,0.00223845,0.002238446,Epidemiology,0.17853165,FALSE,61.5,0.712350795,17,0.451097137,0,0.403234768,,,0.522227566
11578,Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies,0,2102.08366,2/16/21,arxiv,0,5,"transfer learning, dataset",0.001653101,0.001653118,0.8161868,0.177200864,0.001653097,0.001653019,Epidemiology,0.020298779,FALSE,86.4,0.830787309,161.4,0.893296762,0,0.403234768,,,0.70910628
11579,Social Bots and Social Media Manipulation in 2020: The Year in Review,0,2102.08436,2/16/21,arxiv,0,5,computational,0.00198725,0.001987183,0.00198735,0.990063715,0.001987237,0.001987265,Epidemiology,0.019953728,FALSE,47.6,0.611169522,102.4,0.826465079,0,0.403234768,,,0.613623123
11580,"Selfie Periocular Verification using an Efficient Super-Resolution
  Approach",0,2102.08449,2/16/21,arxiv,0,5,neural network,0.001220016,0.001220018,0.783640874,0.211479062,0.001220036,0.001219995,Imaging,0.000302166,FALSE,127.2,0.91737275,86.4,0.794019267,0,0.403234768,,,0.704875595
11581,Impact of asymptomatic COVID-19 carriers on pandemic policy outcomes,0,2102.10042,2/17/21,arxiv,0,3,"mathematical model, dataset",0.002130661,0.002130788,0.002130728,0.989346357,0.002130672,0.002130794,Epidemiology,0.08177686,FALSE,38.33333333,0.522233904,71.33333333,0.75709125,0,0.403234768,,,0.560853307
11582,"Knowledge discovery from emergency ambulance dispatch during COVID-19: A
  case study of Nagoya City, Japan","Journal of Biomedical Informatics, 2021",2102.08628,2/17/21,arxiv,"Journal of Biomedical Informatics, 2021",6,"deep learning, neural network",0.001392879,0.00139286,0.319770593,0.604766127,0.071284669,0.001392871,Epidemiology,0.4943035,FALSE,86.16666667,0.829797761,30.66666667,0.574257426,0,0.403234768,,,0.602429985
11583,"Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for
  COVID-19 Fake News Detection",0,2102.08924,2/17/21,arxiv,0,5,dataset,0.001684503,0.001684517,0.462749548,0.530512354,0.001684592,0.001684487,Epidemiology,0.004915476,FALSE,50.4,0.634547591,21.8,0.501204174,0,0.403234768,,,0.512995511
11584,"Bayesian optimal investment and reinsurance with dependent financial and
  insurance risks",0,2103.05777,2/18/21,arxiv,0,2,bayes,0.002806424,0.002806431,0.002806604,0.985967787,0.002806374,0.002806381,Epidemiology,0.01938346,FALSE,7.5,0.108355495,0.5,0.087101953,0,0.403234768,,,0.199564072
11585,Robust PDF Document Conversion Using Recurrent Neural Networks,0,2102.09395,2/18/21,arxiv,0,10,"computational, neural network, information retrieval",0.050295904,0.040269142,0.508654161,0.39827158,0.00125461,0.001254603,Epidemiology,0.000702292,FALSE,52.8,0.652173913,36.6,0.614664169,0,0.403234768,,,0.55669095
11586,Regular Expressions for Fast-response COVID-19 Text Classification,0,2102.09507,2/18/21,arxiv,0,3,classifier,0.156376812,0.002357826,0.630398479,0.206151413,0.002357751,0.002357718,Epidemiology,0.005265653,FALSE,4.333333333,0.057950399,0.333333333,0.073187048,0,0.403234768,,,0.178124072
11587,"Deep learning-based COVID-19 pneumonia classification using chest CT
  images: model generalizability",0,2102.09616,2/18/21,arxiv,0,8,"deep learning, artificial intelligence, dataset",0.001171547,0.001171578,0.963236943,0.001171614,0.001171567,0.032076751,Imaging,0.07678011,FALSE,101.625,0.872410168,118.125,0.848943002,0,0.403234768,,,0.708195979
11588,"Optimal Vaccination and Treatment Strategies in Reduction of COVID-19
  Burden",0,2102.09802,2/19/21,arxiv,0,4,mathematical model,0.001486509,0.001486427,0.00148644,0.992567567,0.001486521,0.001486535,Epidemiology,0.5009259,TRUE,49.5,0.628362917,46.25,0.665038801,0,0.403234768,,,0.565545495
11589,"Estimating the impact of non-pharmaceutical interventions and
  vaccination on the progress of the COVID-19 epidemic in Mexico: a
  mathematical approach",0,2102.11071,2/19/21,arxiv,0,3,bayes,0.001861778,0.001861735,0.001861692,0.990691282,0.001861756,0.001861757,Epidemiology,0.1347132,FALSE,8.333333333,0.123693488,0.333333333,0.073187048,0,0.403234768,,,0.200038435
11590,"Monitoring the pandemic: A fractional filter for the COVID-19 contact
  rate",0,2102.10067,2/19/21,arxiv,0,1,computational,0.027466282,0.001486784,0.001486513,0.966587405,0.001486555,0.001486461,Epidemiology,0.08197394,FALSE,15,0.227596017,2,0.164302917,0,0.403234768,,,0.265044567
11591,"The Association of Opening K-12 Schools and Colleges with the Spread of
  COVID-19 in the United States: County-Level Panel Data Analysis",0,2102.10453,2/20/21,arxiv,0,3,structural model,0.001717239,0.001717206,0.001717341,0.622659776,0.370471239,0.001717198,Epidemiology,0.16142642,FALSE,47.33333333,0.608633805,117.6666667,0.848140219,0,0.403234768,,,0.620002931
11592,"Delhi air quality prediction using LSTM deep learning models with a
  focus on COVID-19 lockdown",0,2102.10551,2/21/21,arxiv,0,3,"deep learning, network model, lstm",0.00159349,0.001593512,0.152591719,0.7110025,0.131625198,0.001593581,Epidemiology,0.110990554,FALSE,7.333333333,0.105572392,0.666666667,0.096200161,0,0.403234768,,,0.201669107
11593,Classification of COVID-19 via Homology of CT-SCAN,0,2102.10593,2/21/21,arxiv,0,5,dataset,0.001291263,0.001291259,0.83612235,0.128293379,0.031710515,0.001291233,Imaging,0.23809406,FALSE,56.8,0.680314181,76.4,0.769868879,0,0.403234768,,,0.617805943
11594,"Tchebichef Transform Domain-based Deep Learning Architecture for Image
  Super-resolution",0,2102.1064,2/21/21,arxiv,0,2,"deep learning, artificial intelligence, transfer learning",0.000966794,0.000966796,0.995166106,0.000966777,0.000966772,0.000966756,Imaging,0.00396508,FALSE,34,0.477766096,0,0.055525823,0,0.403234768,,,0.312175562
11595,"Estimating SARS-CoV-2 Infections from Deaths, Confirmed Cases, Tests,
  and Random Surveys",0,2102.10741,2/22/21,arxiv,0,2,bayes,0.001511845,0.030739311,0.001511848,0.753155849,0.211569286,0.001511861,Epidemiology,0.044073075,FALSE,188,0.96808708,1941,0.998260637,0,0.403234768,,,0.789860828
11596,Is Covid-19 severity associated with ACE2 degradation?,0,2102.1321,2/22/21,arxiv,0,5,mathematical model,0.457195953,0.001171591,0.001171603,0.173912051,0.131902143,0.23464666,Drug discovery,0.27654475,FALSE,113.6,0.896839631,106,0.833154937,0,0.403234768,,,0.711076445
11597,Agent-Based Campus Novel Coronavirus Infection and Control Simulation,0,2102.10971,2/22/21,arxiv,0,8,"simulation model, simulation experiment",0.001684533,0.001684553,0.001684547,0.749608468,0.24365342,0.001684479,Epidemiology,0.051640213,FALSE,29.75,0.428226854,8.5,0.329141022,0,0.403234768,,,0.386867548
11598,"RCoNet: Deformable Mutual Information Maximization and High-order
  Uncertainty-aware Learning for Robust COVID-19 Detection",0,2102.11099,2/22/21,arxiv,0,5,dataset,0.00114133,0.001141343,0.953063461,0.042371186,0.001141354,0.001141326,Imaging,0.003142864,FALSE,21,0.312016822,2.6,0.18256623,0,0.403234768,,,0.299272606
11599,WLAN-Log-Based Superspreader Detection in the COVID-19 Pandemic,0,2102.11171,2/22/21,arxiv,0,8,dataset,0.091049037,0.001237089,0.001237148,0.904002494,0.001237124,0.001237108,Epidemiology,0.19007313,FALSE,131.5,0.923495578,186.875,0.910422799,0,0.403234768,,,0.745717715
11600,Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting,0,2102.11249,2/22/21,arxiv,0,10,dataset,0.001511888,0.001511905,0.212090802,0.781861672,0.001511824,0.001511909,Epidemiology,0.05122173,FALSE,51.8,0.645061538,176.1,0.903732941,1,0.537564047,,,0.695452842
11601,"Combination anti-coronavirus therapies based on nonlinear mathematical
  models","CHAOS, 31, 023136 (2021)",2102.13208,2/23/21,arxiv,"CHAOS, 31, 023136 (2021)",8,mathematical model,0.457261202,0.015998982,0.015999463,0.23326521,0.015998411,0.261476731,Drug discovery,0.7575604,TRUE,14.375,0.217205764,3.75,0.21982874,0,0.403234768,,,0.280089757
11602,"Multi-Feature Multi-Scale CNN-Derived COVID-19 Classification from Lung
  Ultrasound Data",0,2102.11942,2/23/21,arxiv,0,6,"deep learning, neural network, dataset",0.001220045,0.001220098,0.950419159,0.044700583,0.001220081,0.001220034,Imaging,0.04168287,FALSE,94.83333333,0.856330014,182,0.907613059,0,0.403234768,,,0.722392613
11603,"Hospital management in the COVID-19 emergency: Abelian Sandpile paradigm
  and beyond",0,2102.11974,2/23/21,arxiv,0,4,mathematical model,0.031049781,0.002238472,0.00223862,0.741095636,0.002238676,0.221138815,Epidemiology,0.2546167,FALSE,18.75,0.28072237,14.25,0.415038801,0,0.403234768,,,0.36633198
11604,"Non-integer (or fractional) power model of a viral spreading:
  application to the COVID-19",0,2102.13471,2/24/21,arxiv,0,4,mathematical model,0.00139293,0.090653037,0.122002484,0.783165692,0.001392925,0.001392931,Epidemiology,0.005345106,FALSE,131.25,0.923124497,76.75,0.770604763,0,0.403234768,,,0.698988009
11605,Malicious and Low Credibility URLs on Twitter during COVID-19,0,2102.12223,2/24/21,arxiv,0,6,dataset,0.002996671,0.149378843,0.002996713,0.838634556,0.002996642,0.002996575,Epidemiology,0.021194428,FALSE,32.83333333,0.464592739,72.5,0.760436179,0,0.403234768,,,0.542754562
11606,"The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19
  Cough, COVID-19 Speech, Escalation & Primates",0,2102.13468,2/24/21,arxiv,0,24,"computational, classifier",0.00272024,0.052836804,0.936282237,0.002720383,0.002720149,0.002720187,Genomics,0.16792175,FALSE,46.54166667,0.601150349,80.375,0.779836767,2,0.618927094,,,0.66663807
11607,"Estimating the effective reproduction number for heterogeneous models
  using incidence data",0,2102.12637,2/25/21,arxiv,0,5,mathematical model,0.072361139,0.037344689,0.001622733,0.885425989,0.001622729,0.001622721,Epidemiology,0.04041511,FALSE,21.8,0.322283382,8.4,0.326598876,0,0.403234768,,,0.350705675
11608,"Highly Efficient Representation and Active Learning Framework for
  Imbalanced Data and its Application to COVID-19 X-Ray Classification",0,2103.05109,2/25/21,arxiv,0,5,"bayes, neural network, active learning, dataset",0.001085357,0.030505089,0.790785556,0.175453317,0.00108536,0.001085321,Imaging,0.005136788,FALSE,16.2,0.244974952,5,0.257024351,1,0.537564047,,,0.346521117
11609,"Evaluation of Peppermint Leaf Flavonoids as SARS-CoV-2 Spike
  Receptor-Binding Domain Attachment Inhibitors to the Human ACE2 Receptor: A
  Molecular Docking Study",0,2102.12651,2/25/21,arxiv,0,2,virtual screening,0.990691478,0.00186171,0.00186173,0.00186171,0.001861716,0.001861655,Drug discovery,0.30445534,FALSE,5.5,0.077246583,0,0.055525823,0,0.403234768,,,0.178669058
11610,"Design and Control of a Highly Redundant Rigid-Flexible Coupling Robot
  to Assist the COVID-19 Oropharyngeal-Swab Sampling",0,2102.12726,2/25/21,arxiv,0,12,neural network,0.002357802,0.301448429,0.23139712,0.274204607,0.188233984,0.002358058,Genomics,0.6615804,TRUE,54.09090909,0.661822005,75.54545455,0.767728124,0,0.403234768,,,0.610928299
11611,"Data-Driven Characterization and Detection of COVID-19 Themed Malicious
  Websites","2020 IEEE International Conference on Intelligence and Security
  Informatics (ISI), 2020, pp. 1-6",2102.13226,2/25/21,arxiv,"2020 IEEE International Conference on Intelligence and Security
  Informatics (ISI), 2020, pp. 1-6",5,classifier,0.002130911,0.00213079,0.625401102,0.366075768,0.002130789,0.002130639,Epidemiology,0.17032579,FALSE,66,0.741356918,62,0.728659352,0,0.403234768,,,0.624417013
11612,"Exploring the space-time pattern of log-transformed infectious count of
  COVID-19: a clustering-segmented autoregressive sigmoid model",0,2102.13287,2/26/21,arxiv,0,3,bayes,0.001565326,0.001565363,0.091732897,0.902005625,0.001565357,0.001565433,Epidemiology,0.042376637,FALSE,73,0.778464964,65,0.739028633,0,0.403234768,,,0.640242788
11613,"Contact Tracing: Computational Bounds, Limitations and Implications",0,2102.13349,2/26/21,arxiv,0,4,computational,0.002490462,0.002490457,0.188397174,0.801640865,0.002490576,0.002490466,Epidemiology,0.14689642,FALSE,55.25,0.669491001,70.5,0.755351887,0,0.403234768,,,0.609359218
11614,Reverse-Bayes methods: a review of recent technical advances,0,2102.13443,2/26/21,arxiv,0,4,bayes,0.037494473,0.001511924,0.137637971,0.726050085,0.001511875,0.095793671,Epidemiology,0.14507568,FALSE,41,0.549013544,140.75,0.875033449,0,0.403234768,,,0.60909392
11615,Variational Full Bayes Lasso: Knots Selection in Regression Splines,0,2102.13548,2/26/21,arxiv,0,3,bayes,0.002562669,0.00256264,0.530499586,0.45924995,0.002562606,0.00256255,Epidemiology,0.075209945,FALSE,39.66666667,0.536211268,26.33333333,0.540607439,0,0.403234768,,,0.493351158
11616,"CXR-Net: An Artificial Intelligence Pipeline for Quick Covid-19
  Screening of Chest X-Rays",0,2103.00087,2/26/21,arxiv,0,7,"artificial intelligence, dataset",0.00111262,0.001112638,0.994436811,0.001112626,0.001112614,0.001112692,Imaging,0.03976044,FALSE,103.5714286,0.876492053,27.57142857,0.550173936,0,0.403234768,,,0.609966919
11617,Semi-supervised Learning for COVID-19 Image Classification via ResNet,0,2103.0614,2/27/21,arxiv,0,5,"supervised learning, deep learning, neural network, dataset",0.001438115,0.001438247,0.992809043,0.001438246,0.001438175,0.001438174,Imaging,0.021242201,FALSE,22,0.326056033,3.4,0.208589778,0,0.403234768,,,0.31262686
11618,"Analysis, Prediction, and Control of Epidemics: A Survey from Scalar to
  Dynamic Network Models",0,2103.00181,2/27/21,arxiv,0,2,"mathematical model, network model",0.001987189,0.00198715,0.001987235,0.965288059,0.026763266,0.001987101,Epidemiology,0.078523934,FALSE,203,0.97322036,152,0.885001338,0,0.403234768,,,0.753818822
11619,COVID-19 Tweets Analysis through Transformer Language Models,0,2103.00199,2/27/21,arxiv,0,2,predictive model,0.00198711,0.001987096,0.431602006,0.438111035,0.124325613,0.00198714,Epidemiology,0.00886327,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,,,0.160300575
11620,Estimating and increasing the structural robustness of a network,0,2103.00247,2/27/21,arxiv,0,2,computational,0.004110214,0.004109924,0.004110151,0.979449944,0.004110007,0.00410976,Epidemiology,0.018865615,FALSE,196.5,0.971736038,135.5,0.87028365,0,0.403234768,,,0.748418152
11621,"Did Chatbots Miss Their 'Apollo Moment'? A Survey of the Potential, Gaps
  and Lessons from Using Collaboration Assistants During COVID-19",0,2103.05561,2/27/21,arxiv,0,1,artificial intelligence,0.002898339,0.002898405,0.462913947,0.458818471,0.069572535,0.002898302,Epidemiology,0.3748627,FALSE,29,0.41993939,48,0.673735617,0,0.403234768,,,0.498969925
11622,"MirrorME: Implementation of an IoT based Smart Mirror through Facial
  Recognition and Personalized Information Recommendation Algorithm",0,2103.05562,2/28/21,arxiv,0,5,artificial intelligence,0.001653085,0.001653041,0.319571621,0.603584906,0.071884289,0.001653058,Epidemiology,0.03492087,FALSE,13.4,0.202548086,0.4,0.075796093,0,0.403234768,,,0.227192982
11623,"Combat COVID-19 Infodemic Using Explainable Natural Language Processing
  Models",0,2103.00747,3/1/21,arxiv,0,3,"machine learning, deep learning, dataset",0.001141447,0.001141366,0.67535619,0.185795127,0.135424505,0.001141364,Epidemiology,0.27952296,FALSE,33.66666667,0.47312759,28.66666667,0.559673535,0,0.403234768,,,0.478678631
11624,"Towards Unbiased COVID-19 Lesion Localisation and Segmentation via
  Weakly Supervised Learning",0,2103.0078,3/1/21,arxiv,0,8,"supervised learning, adversarial network, dataset",0.002720106,0.002720113,0.986399447,0.002720191,0.00272008,0.002720064,Imaging,0.008212686,FALSE,67,0.748160059,122.375,0.854094193,0,0.403234768,,,0.66849634
11625,A 3D model-based approach for fitting masks to faces in the wild,0,2103.00803,3/1/21,arxiv,0,7,dataset,0.001486485,0.001486436,0.564420267,0.429633951,0.001486467,0.001486393,Imaging,0.000980556,FALSE,35.14285714,0.488465582,24.71428571,0.526224244,0,0.403234768,,,0.472641531
11626,Asymmetric Differential Privacy,0,2103.00996,3/1/21,arxiv,0,3,dataset,0.00168464,0.040156782,0.492543779,0.462245757,0.001684532,0.001684511,Epidemiology,0.005793363,FALSE,143.6666667,0.937967716,95.66666667,0.814021943,0,0.403234768,,,0.718408142
11627,"Delay differential equations for the spatially-resolved simulation of
  epidemics with specific application to COVID-19",0,2103.01102,3/1/21,arxiv,0,3,"computational, mathematical model",0.001291233,0.001291219,0.001291257,0.993543866,0.001291224,0.0012912,Epidemiology,0.017098606,FALSE,7.666666667,0.111633373,1,0.122023013,0,0.403234768,,,0.212297051
11628,"Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps
  with a Benchmark Dataset",0,2103.01196,3/1/21,arxiv,0,11,dataset,0.001350362,0.001350364,0.403504541,0.591094008,0.001350368,0.001350357,Epidemiology,0.001241505,FALSE,50.90909091,0.637268848,48,0.673735617,0,0.403234768,,,0.571413077
11629,Can Machine Learning Catch the COVID-19 Recession?,0,2103.01201,3/1/21,arxiv,0,3,"machine learning, dataset",0.085701427,0.0032143,0.580916256,0.323739606,0.003214228,0.003214183,Epidemiology,0.21317586,FALSE,18,0.271569052,46,0.66416912,0,0.403234768,,,0.446324313
11630,COVID-19 vaccination strategies on dynamic networks,0,2103.01957,3/2/21,arxiv,0,5,dataset,0.001684514,0.001684534,0.168964047,0.687327001,0.138655396,0.001684508,Epidemiology,0.21164042,FALSE,74.2,0.782608696,61.8,0.727856569,0,0.403234768,,,0.637900011
11631,"A multi-layer network model to assess school opening policies during the
  COVID-19 vaccination campaign",0,2103.12519,3/2/21,arxiv,0,2,network model,0.001622765,0.001622737,0.060041061,0.51172388,0.42336686,0.001622697,Epidemiology,0.05079317,FALSE,29.5,0.426000371,2,0.164302917,0,0.403234768,,,0.331179352
11632,"Unmasking Face Embeddings by Self-restrained Triplet Loss for Accurate
  Masked Face Recognition",0,2103.01716,3/2/21,arxiv,0,4,dataset,0.001751181,0.00175115,0.60519303,0.387802219,0.001751286,0.001751134,Epidemiology,0.001020998,FALSE,144.75,0.939142804,50.75,0.684238694,0,0.403234768,,,0.675538755
11633,"Transportation Density Reduction Caused by City Lockdowns Across the
  World during the COVID-19 Epidemic: From the View of High-resolution Remote
  Sensing Imagery",0,2103.01717,3/2/21,arxiv,0,9,deep learning,0.001254874,0.001254665,0.188879022,0.806102189,0.001254622,0.001254628,Epidemiology,0.36675394,FALSE,42.88888889,0.566145092,37.66666667,0.619949157,0,0.403234768,,,0.529776339
11634,"Virufy: A Multi-Branch Deep Learning Network for Automated Detection of
  COVID-19",0,2103.01806,3/2/21,arxiv,0,6,"deep learning, artificial intelligence, dataset",0.001622698,0.001622782,0.901245107,0.001622782,0.092263902,0.001622728,Healthcare,0.013011873,FALSE,10.16666667,0.152946997,3.666666667,0.217621086,0,0.403234768,,,0.257934284
11635,"Improving Neural Networks for Time Series Forecasting using Data
  Augmentation and AutoML",0,2103.01992,3/2/21,arxiv,0,5,"machine learning, neural network, dataset",0.001438106,0.001438118,0.58770702,0.406540551,0.001438087,0.001438118,Epidemiology,0.00521484,FALSE,98.8,0.866596574,175.4,0.903264651,0,0.403234768,,,0.724365331
11636,"The combined role of distance and frequency travel restrictions on
  spread of disease",0,2103.02009,3/2/21,arxiv,0,5,dataset,0.001098872,0.106599158,0.001098829,0.889005473,0.001098857,0.00109881,Epidemiology,0.013501763,FALSE,148,0.942606222,248.6,0.937784319,0,0.403234768,,,0.761208436
11637,"EnD: Entangling and Disentangling deep representations for bias
  correction",0,2103.02023,3/2/21,arxiv,0,3,"neural network, deep model",0.002032748,0.002032806,0.989836021,0.002032907,0.002032767,0.002032752,Imaging,0.00024873,FALSE,71.33333333,0.769930113,63,0.732606369,0,0.403234768,,,0.635257083
11638,"A Flexible Rolling Regression Framework for Time-Varying SIRD models:
  Application to COVID-19",0,2103.02048,3/2/21,arxiv,0,2,optimization model,0.002898315,0.04615368,0.131057509,0.81409389,0.002898325,0.00289828,Epidemiology,0.021755934,FALSE,3.5,0.044344115,,,0,0.403234768,,,0.223789441
11639,"Analytical estimation of maximum fraction of infected individuals with
  one-shot non-pharmaceutical intervention in a hybrid epidemic model",0,2103.02175,3/3/21,arxiv,0,7,mathematical model,0.001511811,0.001511835,0.001511858,0.945367637,0.001511839,0.04858502,Epidemiology,0.05579734,FALSE,214.5714286,0.976993011,124.1428571,0.856636339,0,0.403234768,,,0.745621373
11640,"Estimating the Expected Influence Capacities of Nodes in Complex
  Networks under the Susceptible-Infectious-Recovered (SIR) Model",0,2103.02324,3/3/21,arxiv,0,1,dataset,0.001823382,0.001823341,0.272747495,0.719959073,0.001823395,0.001823313,Epidemiology,0.000765592,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,,,0.157022697
11641,Learning Invariant Representations across Domains and Tasks,0,2103.05114,3/3/21,arxiv,0,8,"transfer learning, dataset",0.001415163,0.001415129,0.99292423,0.001415172,0.001415119,0.001415187,Imaging,0.00464201,FALSE,111.75,0.893499907,345,0.964878245,0,0.403234768,,,0.753870973
11642,Global Daily CO$_2$ emissions for the year 2020,0,2103.02526,3/3/21,arxiv,0,28,dataset,0.024149163,0.00143815,0.001438114,0.97009834,0.001438127,0.001438105,Epidemiology,0.33470494,FALSE,110.5357143,0.890716804,,,0,0.403234768,,,0.646975786
11643,Effect of Vaccination to COVID-19 Disease Progression,0,2103.02787,3/4/21,arxiv,0,4,"model fit, mathematical model",0.002639024,0.050326483,0.002639108,0.939117331,0.002639082,0.002638971,Epidemiology,0.07394755,FALSE,12.5,0.189436576,0.25,0.065493712,0,0.403234768,,,0.219388352
11644,When Face Recognition Meets Occlusion: A New Benchmark,0,2103.02805,3/4/21,arxiv,0,8,dataset,0.001538147,0.03850148,0.682315443,0.274568689,0.001538163,0.001538078,Imaging,0.017614275,FALSE,43.125,0.568000495,29.75,0.567835162,0,0.403234768,,,0.513023475
11645,"Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery
  with Hybrid Machine Learning- and Physics-based Simulations on High
  Performance Computers",0,2103.02843,3/4/21,arxiv,0,29,"machine learning, in silico",0.740819617,0.002080615,0.250857761,0.002080654,0.002080756,0.002080597,Drug discovery,0.13175276,FALSE,156.3448276,0.94891459,242.3103448,0.934706984,0,0.403234768,,,0.762285447
11646,"A Two-Sample Robust Bayesian Mendelian Randomization Method Accounting
  for Linkage Disequilibrium and Idiosyncratic Pleiotropy with Applications to
  the COVID-19 Outcome",0,2103.02877,3/4/21,arxiv,0,2,"bayes, genome-wide, probabilistic",0.001622747,0.225752732,0.192616406,0.505409395,0.001622793,0.072975927,Epidemiology,0.099387616,FALSE,66.5,0.743830787,25,0.529435376,0,0.403234768,,,0.558833644
11647,"Probabilistic combination of eigenlungs-based classifiers for COVID-19
  diagnosis in chest CT images",0,2103.02961,3/4/21,arxiv,0,10,"deep learning, computational, classifier, probabilistic",0.001415106,0.001415121,0.86284086,0.131498691,0.001415109,0.001415113,Imaging,0.003797859,FALSE,25,0.369286907,4.7,0.246855767,0,0.403234768,,,0.33979248
11648,"Self-supervised deep convolutional neural network for chest X-ray
  classification",0,2103.03055,3/4/21,arxiv,0,4,"neural network, dataset",0.003214062,0.003214061,0.983929468,0.003214113,0.003214211,0.003214085,Imaging,0.14215031,FALSE,31.5,0.450058754,2.25,0.170925876,0,0.403234768,,,0.341406466
11649,"Routing algorithms as tools for integrating social distancing with
  emergency evacuation",0,2103.03413,3/5/21,arxiv,0,4,neural network,0.002080541,0.002080551,0.247119609,0.707257204,0.039381574,0.00208052,Epidemiology,0.14170271,FALSE,213.5,0.976560084,1152.75,0.995250201,0,0.403234768,,,0.791681684
11650,"DOPE: D-Optimal Pooling Experimental design with application for
  SARS-CoV-2 screening",0,2103.03706,3/5/21,arxiv,0,3,bayes,0.001987201,0.001987248,0.277613977,0.714437102,0.001987232,0.001987241,Epidemiology,0.049278647,FALSE,32.33333333,0.459273919,17,0.451097137,0,0.403234768,,,0.437868608
11651,SoK: Cryptojacking Malware,0,2103.03851,3/5/21,arxiv,0,5,computational,0.002562648,0.002562603,0.291886511,0.697862879,0.002562803,0.002562556,Epidemiology,0.15698972,FALSE,83,0.818170573,252.6,0.938787798,0,0.403234768,,,0.72006438
11652,"Interplay between COVID-19 vaccines and social measures for ending the
  SARS-CoV-2 pandemic",0,2103.0612,3/6/21,arxiv,0,4,mathematical model,0.001511863,0.001511829,0.001511821,0.858888613,0.085559503,0.051016371,Epidemiology,0.37978628,FALSE,40,0.539860226,42.25,0.645838908,0,0.403234768,,,0.529644634
11653,"Graph-based Pyramid Global Context Reasoning with a Saliency-aware
  Projection for COVID-19 Lung Infections Segmentation",0,2103.04235,3/7/21,arxiv,0,12,dataset,0.001511855,0.00151193,0.807898008,0.186054545,0.001511839,0.001511822,Imaging,0.00811553,FALSE,82.16666667,0.81520193,52.33333333,0.690326465,0,0.403234768,,,0.636254387
11654,"Resource Distribution Under Spatiotemporal Uncertainty of Disease
  Spread: Stochastic versus Robust Approaches",0,2103.04266,3/7/21,arxiv,0,3,optimization model,0.001203428,0.001203432,0.026971114,0.968215154,0.001203463,0.001203408,Epidemiology,0.06647414,FALSE,28.66666667,0.414125796,15.33333333,0.428351619,0,0.403234768,,,0.415237394
11655,Improving Bayesian estimation of Vaccine Efficacy,0,2103.04462,3/7/21,arxiv,0,1,bayes,0.190961628,0.008403108,0.008403024,0.775426452,0.008402959,0.008402829,Epidemiology,0.30645186,FALSE,80,0.807532933,23,0.513513514,0,0.403234768,,,0.574760405
11656,Causal Inference in the Time of Covid-19,0,2103.04472,3/7/21,arxiv,0,4,structural model,0.002898423,0.002898364,0.0028985,0.985507995,0.002898345,0.002898373,Epidemiology,0.16271845,FALSE,105,0.879213309,347.5,0.965212737,0,0.403234768,,,0.749220271
11657,"A Review of Spatiotemporal Models for Count Data in R Packages. A Case
  Study of COVID-19 Data",0,2103.04697,3/8/21,arxiv,0,3,bayes,0.002490463,0.002490565,0.064263752,0.925774185,0.002490508,0.002490526,Epidemiology,0.015788615,FALSE,3.666666667,0.045952131,0,0.055525823,0,0.403234768,,,0.168237574
11658,Automatically Selecting Striking Images for Social Cards,0,2103.04899,3/8/21,arxiv,0,4,dataset,0.001141343,0.001141392,0.296449058,0.698985498,0.001141381,0.001141328,Epidemiology,0.018453896,FALSE,53.75,0.659348135,41.25,0.64102221,0,0.403234768,,,0.567868371
11659,"Rapid antigen tests: their sensitivity, benefits for epidemic control,
  and use in Austrian schools",0,2103.04979,3/8/21,arxiv,0,7,genomes,0.002806489,0.666397901,0.002806531,0.167521255,0.157661381,0.002806443,Genomics,0.19149405,FALSE,63.14285714,0.72230812,,,0,0.403234768,,,0.562771444
11660,"CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved
  Covid-19 Detection","IEEE Access, vol. 8, pp. 91916-91923, 2020",2103.05094,3/8/21,arxiv,"IEEE Access, vol. 8, pp. 91916-91923, 2020",6,"deep learning, neural network, classifier, adversarial network",0.001330052,0.001330166,0.993349578,0.001330092,0.00133004,0.001330072,Imaging,0.39842576,FALSE,160,0.951697693,37.66666667,0.619949157,80,0.973023026,,,0.848223292
11661,"Urban Epidemic Hazard Index for Chinese Cities: Why Did Small Cities
  Become Epidemic Hotspots?",0,2103.05189,3/9/21,arxiv,0,3,network model,0.001438125,0.001438135,0.001438137,0.992809252,0.00143816,0.001438191,Epidemiology,0.39390463,FALSE,12,0.183190055,5.333333333,0.262911426,0,0.403234768,,,0.283112083
11662,Stabilized Medical Image Attacks,0,2103.05232,3/9/21,arxiv,0,5,"neural network, image analysis, dataset",0.00094612,0.000946108,0.87543421,0.120781351,0.000946125,0.000946085,Imaging,0.000268132,FALSE,67.6,0.750572082,119.8,0.851016858,1,0.537564047,,,0.713050996
11663,Generalizable Limited-Angle CT Reconstruction via Sinogram Extrapolation,0,2103.05255,3/9/21,arxiv,0,7,"deep learning, dataset",0.002296609,0.002296841,0.988516606,0.002296751,0.002296623,0.002296569,Imaging,0.007196665,FALSE,63.85714286,0.727194013,50.85714286,0.684573187,0,0.403234768,,,0.605000656
11664,"Software Development During COVID-19 Pandemic: an Analysis of Stack
  Overflow and GitHub",0,2103.05494,3/9/21,arxiv,0,6,machine learning,0.002032763,0.00203278,0.203878882,0.787989987,0.002032841,0.002032746,Epidemiology,0.21045628,FALSE,9.833333333,0.147628177,2.5,0.180826866,0,0.403234768,,,0.243896604
11665,"A Universal Basic Income For Brazil: Fiscal and Distributional Effects
  of Alternative Schemes",0,2103.0602,3/10/21,arxiv,0,2,simulation model,0.0053529,0.005353165,0.005353602,0.973234158,0.005353241,0.005352934,Epidemiology,0.26798558,FALSE,11,0.167171748,4.5,0.242708055,0,0.403234768,,,0.27103819
11666,"A data-driven epidemic model with social structure for understanding the
  COVID-19 infection on a heavily affected Italian Province",0,2103.06027,3/10/21,arxiv,0,8,dataset,0.001593497,0.001593527,0.037619859,0.931400733,0.001593518,0.026198866,Epidemiology,0.030672818,FALSE,18.875,0.281897458,5,0.257024351,0,0.403234768,,,0.314052192
11667,Bayesian sequential data assimilation for COVID-19 forecasting,0,2103.06152,3/10/21,arxiv,0,4,bayes,0.001751155,0.001751446,0.04263805,0.950356846,0.001751278,0.001751225,Epidemiology,0.021603107,FALSE,47.5,0.610551054,21.75,0.50046829,0,0.403234768,,,0.504751371
11668,"Do e-scooters fill mobility gaps and promote equity before and during
  COVID-19? A spatiotemporal analysis using open big data",0,2103.0906,3/11/21,arxiv,0,6,dataset,0.002296627,0.002296587,0.002296695,0.988516804,0.002296725,0.002296561,Epidemiology,0.017188877,FALSE,6.166666667,0.087080215,,,0,0.403234768,,,0.245157491
11669,"Quantitative Interpretations of Energetic Features and Key Residues at
  SARS Coronavirus Spike Receptor-Binding Domain and ACE2 Receptor Interface",0,2103.06578,3/11/21,arxiv,0,7,molecular dynamics simulation,0.897659944,0.073565086,0.001350392,0.001350344,0.024723801,0.001350433,Drug discovery,0.2527305,FALSE,52,0.647349867,20.71428571,0.488292748,0,0.403234768,,,0.512959128
11670,"Automatic Social Distance Estimation From Images: Performance
  Evaluation, Test Benchmark, and Algorithm",0,2103.06759,3/11/21,arxiv,0,4,dataset,0.001237084,0.001237115,0.383054377,0.611997289,0.001237078,0.001237057,Epidemiology,0.001779288,FALSE,70.5,0.766033768,20.75,0.488827937,0,0.403234768,,,0.552698824
11671,Characterizing Partisan Political Narratives about COVID-19 on Twitter,0,2103.0696,3/11/21,arxiv,0,2,computational,0.002033015,0.002032917,0.002032859,0.989835571,0.002032877,0.002032761,Epidemiology,0.04419732,FALSE,61.5,0.712350795,282,0.949290875,0,0.403234768,,,0.688292146
11672,"Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature
  Corpus",0,2103.07055,3/12/21,arxiv,0,9,dataset,0.00190172,0.00190172,0.990491372,0.001901779,0.00190173,0.001901678,Epidemiology,0.008568645,FALSE,88.22222222,0.837343064,91,0.803987155,0,0.403234768,,,0.681521662
11673,"Severity Quantification and Lesion Localization of COVID-19 on CXR using
  Vision Transformer",0,2103.07062,3/12/21,arxiv,0,9,dataset,0.001462003,0.001461892,0.992690241,0.001461909,0.001461904,0.00146205,Imaging,0.010045052,FALSE,88.22222222,0.837343064,91,0.803987155,0,0.403234768,,,0.681521662
11674,"Longitudinal Quantitative Assessment of COVID-19 Infection Progression
  from Chest CTs",0,2103.0724,3/12/21,arxiv,0,10,"neural network, dataset",0.049104356,0.001593527,0.944521297,0.001593571,0.001593626,0.001593623,Imaging,0.20088863,FALSE,55.1,0.668872534,57.9,0.713941664,0,0.403234768,,,0.595349655
11675,"Quantifying efficiency gains of innovative designs of two-arm vaccine
  trials for COVID-19 using an epidemic simulation model",0,2104.00546,3/13/21,arxiv,0,5,simulation model,0.001901862,0.001901782,0.00190183,0.921143031,0.071249673,0.001901822,Epidemiology,0.045161456,FALSE,25,0.369286907,,,0,0.403234768,,,0.386260837
11676,"Impact of the COVID-19 outbreak on Italy's country reputation and stock
  market performance: a sentiment analysis approach",0,2103.13871,3/13/21,arxiv,0,3,"machine learning, classifier",0.00229663,0.002296623,0.252409168,0.738404203,0.002296707,0.00229667,Epidemiology,0.008783191,FALSE,43,0.567629414,3.333333333,0.206515922,0,0.403234768,,,0.392460035
11677,"Mechanism of inhibition of SARS-CoV-2 infection by the interaction of
  the spike glycoprotein with heparin",0,2103.07722,3/13/21,arxiv,0,4,molecular dynamics simulation,0.951429604,0.001203441,0.001203445,0.028463875,0.001203451,0.016496183,Drug discovery,0.45839378,FALSE,130,0.921763869,152.5,0.885469628,0,0.403234768,,,0.736822755
11678,"Model-based Task Analysis and Large-scale Video-based Remote Evaluation
  Methods for Extended Reality Research",0,2103.07757,3/13/21,arxiv,0,2,predictive model,0.179509224,0.002032785,0.534741973,0.002032936,0.279650295,0.002032788,Healthcare,0.15349126,FALSE,3.5,0.044344115,0,0.055525823,0,0.403234768,,,0.167701569
11679,"COVID-19 Infection Localization and Severity Grading from Chest X-ray
  Images",0,2103.07985,3/14/21,arxiv,0,14,dataset,0.001098837,0.001098829,0.994505906,0.001098828,0.0010988,0.001098801,Imaging,0.02968672,FALSE,32.64285714,0.462428103,20.14285714,0.481402194,0,0.403234768,,,0.449021688
11680,"Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep
  22, 2021, based on deep learning",0,2103.08178,3/15/21,arxiv,0,3,"deep learning, lstm, dataset",0.002032746,0.002032755,0.307413472,0.684455029,0.002033084,0.002032913,Epidemiology,0.12339121,FALSE,4.666666667,0.063392912,0,0.055525823,0,0.403234768,,,0.174051168
11681,"Fused Deep Features Based Classification Framework for COVID-19
  Classification with Optimized MLP",0,2103.09904,3/15/21,arxiv,0,3,"artificial intelligence, neural network, image analysis",0.001653053,0.001653026,0.966529824,0.001653041,0.00165313,0.026857927,Imaging,0.003813535,FALSE,36,0.498299215,10,0.355632861,0,0.403234768,,,0.419055614
11682,Is Medical Chest X-ray Data Anonymous?,0,2103.08562,3/15/21,arxiv,0,6,"deep learning, dataset",0.001141325,0.001141356,0.922806048,0.072628512,0.001141366,0.001141393,Imaging,0.09916377,FALSE,35.16666667,0.488651123,38.5,0.624765855,0,0.403234768,,,0.505550582
11683,"Estimation of parameters of the Gumbel type-II distribution under AT-II
  PHCS with an application of Covid-19 data",0,2103.08641,3/15/21,arxiv,0,2,bayes,0.108136731,0.037091128,0.00208067,0.84853022,0.002080614,0.002080637,Epidemiology,0.088377595,FALSE,31,0.445111015,6,0.280037463,0,0.403234768,,,0.376127749
11684,A Computer Vision System to Help Prevent the Transmission of COVID-19,0,2103.08773,3/16/21,arxiv,0,3,"deep learning, dataset",0.001011028,0.019551993,0.372952324,0.604462751,0.001010989,0.001010915,Epidemiology,0.014124811,FALSE,66,0.741356918,47.33333333,0.669855499,0,0.403234768,,,0.604815728
11685,A Multilayer Network Model implementation for Covid-19,0,2103.08843,3/16/21,arxiv,0,5,network model,0.003466093,0.00346597,0.003466202,0.935474585,0.050661219,0.003465931,Epidemiology,0.06925264,FALSE,13,0.197352959,10.6,0.364262778,0,0.403234768,,,0.321616835
11686,"A Large-Scale Dataset for Benchmarking Elevator Button Segmentation and
  Character Recognition",0,2103.0903,3/16/21,arxiv,0,6,dataset,0.002639219,0.231147961,0.62143552,0.002639198,0.139499122,0.002638979,Imaging,0.007924885,FALSE,67,0.748160059,,,0,0.403234768,,,0.575697413
11687,"DiCOVA Challenge: Dataset, task, and baseline system for COVID-19
  diagnosis using acoustics",0,2103.09148,3/16/21,arxiv,0,12,"machine learning, dataset",0.002720103,0.002720136,0.792289784,0.196829651,0.002720094,0.002720232,Epidemiology,0.018205255,FALSE,30.33333333,0.436266931,,,1,0.537564047,,,0.486915489
11688,A Multilingual African Embedding for FAQ Chatbots,0,2103.09185,3/16/21,arxiv,0,5,artificial intelligence,0.002806441,0.002806449,0.472042829,0.516731405,0.002806519,0.002806357,Epidemiology,0.03219688,FALSE,15,0.227596017,8.8,0.332753546,0,0.403234768,,,0.321194777
11689,Identify Hidden Spreaders of Pandemic over Contact Tracing Networks,0,2103.0939,3/17/21,arxiv,0,6,"machine learning, computational",0.001310345,0.001310368,0.349591066,0.645167538,0.001310349,0.001310334,Epidemiology,0.028543234,FALSE,214.8888889,0.977054858,,,0,0.403234768,,,0.690144813
11690,Towards Few-Shot Fact-Checking via Perplexity,0,2103.09535,3/17/21,arxiv,0,5,"transfer learning, dataset",0.001291231,0.001291243,0.890088682,0.104746347,0.001291261,0.001291236,Epidemiology,0.000214547,FALSE,39.2,0.53182015,50.8,0.684372491,0,0.403234768,,,0.539809136
11691,"Identification and prediction of time-varying parameters of COVID-19
  model: a data-driven deep learning approach",0,2103.09949,3/17/21,arxiv,0,3,"deep learning, neural network, lstm",0.001861695,0.028064172,0.245926162,0.720424627,0.001861668,0.001861676,Epidemiology,0.11746538,FALSE,63,0.721998887,,,0,0.403234768,,,0.562616827
11692,"COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data
  for AI-driven COVID-19 analytics",0,2103.10003,3/18/21,arxiv,0,6,"artificial intelligence, dataset",0.002130679,0.002130699,0.879705053,0.111772081,0.002130805,0.002130683,Imaging,0.23896998,FALSE,14.16666667,0.214175274,25.83333333,0.535790741,1,0.537564047,,,0.429176687
11693,"A Probabilistic State Space Model for Joint Inference from Differential
  Equations and Data",0,2103.10153,3/18/21,arxiv,0,3,"bayes, machine learning, computational, probabilistic",0.001901735,0.001901721,0.253287393,0.739105778,0.001901689,0.001901685,Epidemiology,0.002118588,FALSE,38.33333333,0.522233904,,,0,0.403234768,,,0.462734336
11694,Sensor Placement for Globally Optimal Coverage of 3D-Embedded Surfaces,0,2103.10521,3/18/21,arxiv,0,4,computational,0.265397461,0.002562661,0.002562747,0.63434674,0.09256785,0.002562542,Epidemiology,0.10577628,FALSE,0.75,0.008101923,,,0,0.403234768,,,0.205668346
11695,The evolving ecosystem of COVID-19 contact tracing applications,0,2103.10585,3/19/21,arxiv,0,2,dataset,0.001565375,0.027680126,0.001565349,0.931969121,0.035654738,0.001565291,Epidemiology,0.80375844,TRUE,6.5,0.093512277,7,0.299973241,0,0.403234768,,,0.265573428
11696,Predicting Covid-19 EMS Incidents from Daily Hospitalization Trends,0,2103.10885,3/19/21,arxiv,0,5,prediction model,0.00151181,0.001511843,0.060775642,0.747514431,0.00151188,0.187174395,Epidemiology,0.2863034,FALSE,13.2,0.199270208,,,0,0.403234768,,,0.301252488
11697,Modeling of crisis periods in stock markets,0,2103.13294,3/19/21,arxiv,0,4,computational,0.001861721,0.00186183,0.371953871,0.620599147,0.001861727,0.001861704,Epidemiology,0.014307648,FALSE,60.5,0.705547653,31.75,0.583088039,0,0.403234768,,,0.56395682
11698,"Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for
  Vaccine Development",0,2103.11214,3/20/21,arxiv,0,4,"bayes, deep learning",0.583671891,0.001684577,0.143244835,0.268029633,0.001684553,0.001684511,Drug discovery,0.24468428,FALSE,66.75,0.745438803,63,0.732606369,0,0.403234768,,,0.627093313
11699,"QUCoughScope: An Artificially Intelligent Mobile Application to Detect
  Asymptomatic COVID-19 Patients using Cough and Breathing Sounds",0,2103.12063,3/20/21,arxiv,0,9,machine learning,0.001310346,0.056725547,0.447490506,0.001310442,0.164377665,0.328785496,Clinics,0.07917079,FALSE,18.88888889,0.282021152,21.77777778,0.500668986,0,0.403234768,,,0.395308302
11700,"Development and Validation of a Deep Learning Model for Prediction of
  Severe Outcomes in Suspected COVID-19 Infection",0,2103.11269,3/21/21,arxiv,0,11,"deep learning, dataset",0.001022641,0.001022629,0.535417647,0.001022637,0.00102266,0.460491786,Imaging,0.5077698,TRUE,17.36363636,0.261302492,,,0,0.403234768,,,0.33226863
11701,Detection of fake news on CoViD-19 on Web Search Engines,0,2103.11804,3/22/21,arxiv,0,3,"machine learning, classifier, dataset",0.001511946,0.030372533,0.467109853,0.49798202,0.001511843,0.001511804,Epidemiology,0.007527322,FALSE,33,0.466757375,7.666666667,0.310877709,0,0.403234768,,,0.393623284
11702,Triage and diagnosis of COVID-19 from medical social media,0,2103.1185,3/22/21,arxiv,0,6,"machine learning, classifier, dataset",0.000966768,0.000966801,0.842007892,0.102756672,0.000966835,0.052335031,Epidemiology,0.017435133,FALSE,69.33333333,0.760467561,46,0.66416912,0,0.403234768,,,0.609290483
11703,Studying Moral-based Differences in the Framing of Political Tweets,0,2103.11853,3/22/21,arxiv,0,3,dataset,0.00272014,0.002720223,0.152064539,0.837054466,0.002720565,0.002720068,Epidemiology,0.007549554,FALSE,44.66666667,0.583462181,14.66666667,0.420323789,0,0.403234768,,,0.469006912
11704,Spatio-Temporal Neural Network for Fitting and Forecasting COVID-19,0,2103.1186,3/22/21,arxiv,0,5,"neural network, prediction model",0.002080636,0.087916917,0.436650548,0.469190199,0.002080942,0.002080758,Epidemiology,0.09676471,FALSE,37.8,0.516667697,,,0,0.403234768,,,0.459951233
11705,Extracting Causal Visual Features for Limited label Classification,0,2103.12322,3/23/21,arxiv,0,2,neural network,0.001823475,0.001823428,0.990882876,0.001823466,0.00182338,0.001823375,Imaging,0.001549453,FALSE,63,0.721998887,4,0.231469093,0,0.403234768,,,0.452234249
11706,Security of Healthcare Data Using Blockchains: A Survey,0,2103.12326,3/23/21,arxiv,0,4,machine learning,0.001786588,0.001786541,0.299535504,0.356728159,0.338376719,0.001786489,Epidemiology,0.0683147,FALSE,71,0.769064259,19.5,0.475983409,0,0.403234768,,,0.549427478
11707,"Bayesian imputation of COVID-19 positive test counts for nowcasting
  under reporting lag",0,2103.12661,3/23/21,arxiv,0,7,bayes,0.002639073,0.002639594,0.002639147,0.799148049,0.002639163,0.190294975,Epidemiology,0.04408261,FALSE,26,0.382398417,62.57142857,0.731067701,0,0.403234768,,,0.505566962
11708,Privacy-preserving Identity Broadcast for Contact Tracing Applications,0,2103.12865,3/23/21,arxiv,0,2,dataset,0.046651654,0.002720259,0.10588419,0.839303527,0.002720201,0.002720171,Epidemiology,0.012636393,FALSE,6.5,0.093512277,3,0.199424672,0,0.403234768,,,0.232057239
11709,"An Exploration of Geo-temporal Characteristics of Users' Reactions on
  Social Media During the Pandemic",0,2103.13032,3/24/21,arxiv,0,4,dataset,0.001330058,0.001330083,0.001330132,0.965878409,0.028801301,0.001330017,Epidemiology,0.021408439,FALSE,13,0.197352959,3,0.199424672,0,0.403234768,,,0.2666708
11710,"Pyfectious: An individual-level simulator to discover optimal
  containment polices for epidemic diseases",0,2103.15561,3/24/21,arxiv,0,5,probabilistic,0.001350458,0.001350371,0.001350442,0.993247995,0.001350348,0.001350385,Epidemiology,0.029166043,FALSE,217.4,0.977735172,2057.8,0.998528231,0,0.403234768,,,0.793166057
11711,"Building alternative consensus trees and supertrees using k-means and
  Robinson and Foulds distance",0,2103.13343,3/24/21,arxiv,0,3,dataset,0.001085376,0.785254306,0.21040423,0.001085389,0.001085352,0.001085347,Genomics,0.51637864,TRUE,36.66666667,0.505102356,40.66666667,0.63774418,0,0.403234768,,,0.515360435
11712,"Robust Stochastic Stability with Applications to Social Distancing in a
  Pandemic",0,2103.13475,3/24/21,arxiv,0,4,probabilistic,0.001593569,0.001593545,0.118690677,0.8175627,0.058965926,0.001593582,Epidemiology,0.002844214,FALSE,20.25,0.301317336,,,0,0.403234768,,,0.352276052
11713,"Deep Learning with robustness to missing data: A novel approach to the
  detection of COVID-19",0,2103.13833,3/25/21,arxiv,0,8,"deep learning, dataset",0.001187254,0.001187278,0.965558922,0.02969191,0.001187296,0.001187339,Imaging,0.042263865,FALSE,74.625,0.785082565,261.5,0.942132727,0,0.403234768,,,0.71015002
11714,"DCcov: Repositioning of Drugs and Drug Combinations for SARS-CoV-2
  Infected Lung through Constraint-Based Modelling",0,2103.13844,3/25/21,arxiv,0,3,in-silico,0.94923684,0.045814589,0.001237081,0.001237157,0.001237186,0.001237146,Drug discovery,0.45951498,FALSE,14,0.213494959,13.33333333,0.403866738,0,0.403234768,,,0.340198822
11715,"Intermittent non-pharmaceutical strategies to mitigate the COVID-19
  epidemic in a network model of Italy via constrained optimization",0,2103.16502,3/25/21,arxiv,0,5,network model,0.00235775,0.002357729,0.002357849,0.988211144,0.002357757,0.002357771,Epidemiology,0.03399843,FALSE,80.2,0.807718474,,,0,0.403234768,,,0.605476621
11716,SARS-CoV-2 spread and quarantine fatigue: a theoretical model,0,2103.14192,3/26/21,arxiv,0,2,"computational, mathematical model",0.001717347,0.001717394,0.001717209,0.967044078,0.026086771,0.001717201,Epidemiology,0.4476562,FALSE,17,0.257467994,0.5,0.087101953,0,0.403234768,,,0.249268238
11717,"Mixing-AdaSIN: Constructing a de-biased dataset using Adaptive
  Structural Instance Normalization and texture Mixing",0,2103.14255,3/26/21,arxiv,0,6,"deep learning, classifier, dataset",0.034234898,0.001254669,0.886067602,0.075933609,0.001254633,0.001254588,Imaging,0.043242097,FALSE,7.5,0.108355495,,,0,0.403234768,,,0.255795131
11718,"Superiority of mild interventions against COVID-19 on public health and
  economic measures",0,2103.14298,3/26/21,arxiv,0,7,dataset,0.00218323,0.002183251,0.002183287,0.93742232,0.053844513,0.002183399,Epidemiology,0.6991588,TRUE,21.57142857,0.318696271,2.714285714,0.186111854,0,0.403234768,,,0.302680964
11719,"YouTubing at Home: Media Sharing Behavior Change as Proxy for
  MobilityAround COVID-19 Lockdowns",0,2103.14601,3/26/21,arxiv,0,2,dataset,0.001823334,0.001823365,0.00182336,0.990883172,0.001823442,0.001823326,Epidemiology,0.035473406,FALSE,85,0.825035562,117,0.847002944,0,0.403234768,,,0.691757758
11720,"COVID-19 personal protective equipment detection using real-time deep
  learning methods",0,2103.14878,3/27/21,arxiv,0,3,"deep learning, artificial intelligence",0.00182331,0.001823331,0.644477977,0.348228629,0.001823462,0.001823291,Imaging,0.039957464,FALSE,15.66666667,0.236563795,3.666666667,0.217621086,0,0.403234768,,,0.28580655
11721,Acceptance of COVID-19 Vaccine and Its Determinants in Bangladesh,0,2103.15206,3/28/21,arxiv,0,5,logistic regression,0.001823372,0.00182337,0.001823379,0.193515896,0.799190597,0.001823386,Healthcare,0.2970315,FALSE,15,0.227596017,6,0.280037463,0,0.403234768,,,0.303622749
11722,"A quantitative assessment of epidemiological parameters to model COVID-
  19 burden",0,2103.1578,3/29/21,arxiv,0,13,mathematical model,0.001461884,0.001461959,0.001461904,0.321700297,0.00146194,0.672452018,Clinics,0.4891821,FALSE,48.38461538,0.617168656,75.76923077,0.768464009,0,0.403234768,,,0.596289144
11723,"Cognitive networks identify the content of English and Italian popular
  posts about COVID-19 vaccines: Anticipation, logistics, conspiracy and loss
  of trust",0,2103.15909,3/29/21,arxiv,0,3,"image analysis, knowledge graph",0.001085384,0.001085365,0.109585864,0.46718156,0.419976506,0.001085321,Epidemiology,0.017009437,FALSE,54,0.661574618,131,0.864597271,0,0.403234768,,,0.643135552
11724,"A database of travel-related behaviors and attitudes before, during, and
  after COVID-19 in the United States",0,2103.16012,3/30/21,arxiv,0,10,dataset,0.001684491,0.001684542,0.049575804,0.525955916,0.419414774,0.001684473,Epidemiology,0.05439648,FALSE,62.7,0.71927763,,,0,0.403234768,,,0.561256199
11725,"COVID-19 UK Social Media Dataset for Public Health Research: Methodology
  for Collection and Processing",0,2103.16446,3/30/21,arxiv,0,1,dataset,0.007061889,0.007061717,0.007061596,0.964691235,0.007061944,0.007061618,Epidemiology,0.16871136,FALSE,27,0.3960047,1,0.122023013,0,0.403234768,,,0.307087494
11726,"Solving Heterogeneous General Equilibrium Economic Models with Deep
  Reinforcement Learning",0,2103.16977,3/31/21,arxiv,0,3,computational,0.001823392,0.001823333,0.060986085,0.93172049,0.001823385,0.001823315,Epidemiology,0.026373088,FALSE,50,0.632073721,29,0.562884667,0,0.403234768,,,0.532731052
11727,Smartphone Camera Oximetry in an Induced Hypoxemia Study,0,2104.00038,3/31/21,arxiv,0,7,"deep learning, dataset",0.00165312,0.001653103,0.699976934,0.062226032,0.001653073,0.232837739,Clinics,0.17202786,FALSE,5,0.070752675,,,0,0.403234768,,,0.236993721
11728,Misinformation detection in Luganda-English code-mixed social media text,0,2104.00124,3/31/21,arxiv,0,5,"bayes, machine learning, dataset",0.001684524,0.001684575,0.78587502,0.2073868,0.001684574,0.001684508,Epidemiology,0.006136119,FALSE,5,0.070752675,0.4,0.075796093,0,0.403234768,,,0.183261179
11729,"Rapid quantification of COVID-19 pneumonia burden from computed
  tomography with convolutional LSTM networks",0,2104.00138,3/31/21,arxiv,0,25,"deep learning, lstm",0.000977434,0.000977432,0.995112719,0.000977456,0.000977462,0.000977496,Imaging,0.2848168,FALSE,94.64,0.855897087,,,0,0.403234768,,,0.629565927
11730,"Explaining COVID-19 and Thoracic Pathology Model Predictions by
  Identifying Informative Input Features",0,2104.00411,4/1/21,arxiv,0,8,"neural network, dataset",0.001220052,0.001220047,0.968263595,0.026856256,0.001220017,0.001220032,Imaging,0.005510658,FALSE,38.625,0.525202548,,,1,0.537564047,,,0.531383298
11731,"Weekly Bayesian modelling strategy to predict deaths by COVID-19: a
  model and case study for the state of Santa Catarina, Brazil",0,2104.01133,4/2/21,arxiv,0,4,"bayes, bayesian model",0.001141322,0.001141328,0.029174118,0.922555872,0.001141342,0.044846018,Epidemiology,0.028271317,FALSE,42.75,0.564598924,32.25,0.587570244,0,0.403234768,,,0.518467978
11732,"Simulating near-field enhancement in transmission of airborne viruses
  with a quadrature-based model",0,2104.01219,4/2/21,arxiv,0,3,"model simulation, computational",0.001254606,0.080084364,0.001254677,0.914897061,0.001254656,0.001254636,Epidemiology,0.012638301,FALSE,10.33333333,0.155049787,0.666666667,0.096200161,1,0.537564047,,,0.262937998
11733,"Machine Learning based COVID-19 Detection from Smartphone Recordings:
  Cough, Breath and Speech",0,2104.02477,4/2/21,arxiv,0,2,"machine learning, classifier, dataset",0.001786598,0.001786601,0.956686794,0.001786544,0.001786587,0.036166877,Clinics,0.023453534,FALSE,74.5,0.784464098,28.5,0.558402462,0,0.403234768,,,0.582033776
11734,Measuring Linguistic Diversity During COVID-19,"Proceedings of the 4th Workshop on NLP and Computational Social
  Science (2020)",2104.0129,4/3/21,arxiv,"Proceedings of the 4th Workshop on NLP and Computational Social
  Science (2020)",3,computational,0.002296579,0.154720048,0.002296596,0.83609362,0.002296607,0.002296551,Epidemiology,0.013943315,FALSE,53.33333333,0.656255798,61,0.724578539,0,0.403234768,,,0.594689702
11735,"Data-driven Contact Network Models of COVID-19 Reveal Trade-offs between
  Costs and Infections for Optimal Local Containment Policies",0,2104.01456,4/3/21,arxiv,0,4,network model,0.001565313,0.001565296,0.001565341,0.992173492,0.001565285,0.001565274,Epidemiology,0.24118263,FALSE,40.5,0.544746119,,,0,0.403234768,,,0.473990443
11736,"Detection of COVID-19 Disease using Deep Neural Networks with Ultrasound
  Imaging",0,2104.01509,4/4/21,arxiv,0,4,neural network,0.00392755,0.003927378,0.835896677,0.148393118,0.003927403,0.003927874,Imaging,0.30379033,FALSE,1.75,0.017007855,0,0.055525823,0,0.403234768,,,0.158589482
11737,"Finding Nano-Ötzi: Semi-Supervised Volume Visualization for
  Cryo-Electron Tomography",0,2104.01554,4/4/21,arxiv,0,9,deep-learning,0.082785656,0.001330053,0.911894124,0.001330097,0.001330042,0.001330028,Imaging,0.0155738,FALSE,61,0.709196611,,,0,0.403234768,,,0.556215689
11738,"STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework
  for Pandemic Monitoring and Management",0,2104.016,4/4/21,arxiv,0,4,"deep learning, knowledge graph, dataset",0.001059392,0.001059377,0.237458372,0.758304097,0.001059401,0.00105936,Epidemiology,0.021071196,FALSE,304.75,0.990970375,,,0,0.403234768,,,0.697102571
11739,"Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest
  X-ray Images",0,2104.01617,4/4/21,arxiv,0,4,"supervised learning, deep learning, neural network",0.001187253,0.00118731,0.966097034,0.001187299,0.029153838,0.001187266,Imaging,0.030395925,FALSE,90.75,0.845073907,,,0,0.403234768,,,0.624154337
11740,"Towards Semantic Interpretation of Thoracic Disease and COVID-19
  Diagnosis Models",0,2104.02481,4/4/21,arxiv,0,8,"neural network, dataset",0.001593522,0.001593515,0.992032261,0.001593567,0.001593623,0.001593513,Imaging,0.001797766,FALSE,36,0.498299215,,,0,0.403234768,,,0.450766991
11741,"A Heuristic-driven Uncertainty based Ensemble Framework for Fake News
  Detection in Tweets and News Articles",0,2104.01791,4/5/21,arxiv,0,3,dataset,0.001272645,0.001272639,0.442111396,0.514642945,0.039427732,0.001272643,Epidemiology,0.001546681,FALSE,7.666666667,0.111633373,,,0,0.403234768,,,0.25743407
11742,"Data-driven deep learning algorithms for time-varying infection rates of
  COVID-19 and mitigation measures",0,2104.02603,4/5/21,arxiv,0,3,"deep learning, neural network",0.001751172,0.001751224,0.15638987,0.836605407,0.001751165,0.001751161,Epidemiology,0.020607084,FALSE,4.333333333,0.057950399,0,0.055525823,0,0.403234768,,,0.172236996
11743,Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data,0,2104.02005,4/5/21,arxiv,0,5,"deep learning, dataset",0.001486411,0.001486453,0.855989515,0.13806465,0.001486523,0.001486448,Epidemiology,0.013088197,FALSE,73,0.778464964,,,0,0.403234768,,,0.590849866
11744,"Automated lung segmentation from CT images of normal and COVID-19
  pneumonia patients",0,2104.02042,4/5/21,arxiv,0,7,"deep learning, neural network, image analysis, dataset",0.000936115,0.000936112,0.995319423,0.000936123,0.000936102,0.000936124,Imaging,0.510652,TRUE,12,0.183190055,12,0.386740701,0,0.403234768,,,0.324388508
11745,"Insight about Detection, Prediction and Weather Impact of Coronavirus
  (Covid-19) using Neural Network","International Journal of Artificial Intelligence & Applications
  11(4):67-81, July. 2020",2104.02173,4/5/21,arxiv,"International Journal of Artificial Intelligence & Applications
  11(4):67-81, July. 2020",4,neural network,0.001653057,0.001653052,0.67823258,0.262186091,0.001653175,0.054622045,Imaging,0.66268194,TRUE,5.75,0.080957388,0,0.055525823,0,0.403234768,,,0.179905993
11746,A Machine Learning Model for Nowcasting Epidemic Incidence,0,2104.02174,4/5/21,arxiv,0,5,"bayes, machine learning, computational, bayesian model",0.002490466,0.002490418,0.101550855,0.888487056,0.002490544,0.00249066,Epidemiology,0.18510544,FALSE,107.6,0.885026903,143.6,0.877709393,0,0.403234768,,,0.721990354
11747,"In-Line Image Transformations for Imbalanced, Multiclass Computer Vision
  Classification of Lung Chest X-Rays",0,2104.02238,4/6/21,arxiv,0,1,"deep learning, artificial intelligence, neural network, dataset",0.001350339,0.00135033,0.974081436,0.001350389,0.020517187,0.001350319,Imaging,0.016402394,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,,,0.165351392
11748,C2CL: Contact to Contactless Fingerprint Matching,0,2104.02811,4/6/21,arxiv,0,3,dataset,0.022332209,0.001415135,0.552908584,0.420513709,0.001415187,0.001415177,Imaging,0.01029551,FALSE,8.333333333,0.123693488,,,0,0.403234768,,,0.263464128
11749,"Information Bottleneck Attribution for Visual Explanations of Diagnosis
  and Prognosis",0,2104.02869,4/7/21,arxiv,0,10,"neural network, classifier",0.001943511,0.001943478,0.829438298,0.162787594,0.001943495,0.001943624,Epidemiology,0.004174501,FALSE,131.2,0.922938957,,,0,0.403234768,,,0.663086862
11750,"Bootstrapping Your Own Positive Sample: Contrastive Learning With
  Electronic Health Record Data",0,2104.02932,4/7/21,arxiv,0,6,"machine learning, artificial intelligence",0.001126831,0.001126886,0.751147033,0.191373674,0.001126852,0.054098724,Imaging,0.009720475,FALSE,27.83333333,0.405219865,,,0,0.403234768,,,0.404227316
11751,"Dual-Consistency Semi-Supervised Learning with Uncertainty
  Quantification for COVID-19 Lesion Segmentation from CT Images",0,2104.03225,4/7/21,arxiv,0,5,supervised learning,0.00162276,0.001622792,0.904297158,0.089211789,0.001622755,0.001622747,Imaging,0.003270984,FALSE,118.6,0.905065248,,,0,0.403234768,,,0.654150008
11752,Synthetic repertoires derived from convalescent COVID-19 patients enable discovery of SARS-CoV-2 neutralizing antibodies and a novel quaternary binding modality,0,10.1101/2021.04.07.438849,4/8/21,biorxiv,0,39,proteom,0.478213755,0.493091855,0.00146187,0.001461928,0.001461874,0.024308718,Genomics,0.3217314,FALSE,,,,,0,0.403234768,0,0.00024079,0.201737779
11753,Genomic Sequencing of SARS-COV-2 in Rwanda: evolution and regional dynamics,0,10.1101/2021.04.02.21254839,4/7/21,medrxiv,0,36,"sequencing, whole genome, genome sequences",0.059731258,0.762520695,0.000946085,0.174909667,0.000946182,0.000946112,Genomics,0.22318467,FALSE,35.94444444,0.495825345,,,0,0.403234768,259,0.966771009,0.621943707
11754,Female-male differences in COVID vaccine adverse events have precedence in seasonal flu shots: a potential link to sex-associated baseline gene expression patterns,0,10.1101/2021.04.01.21254798,4/7/21,medrxiv,0,15,sequencing,0.243831841,0.092118968,0.000977444,0.000977479,0.131670076,0.530424192,Clinics,0.26977032,FALSE,87.46666667,0.835054734,,,0,0.403234768,469,0.983626294,0.740638599
11755,Covid-19 does not look like what you are looking for: clustering symptoms by nation and multi- morbidities reveal substantial differences to the classical symptom triad,0,10.1101/2021.04.02.21254818,4/7/21,medrxiv,0,5,machine learning,0.000822937,0.00082295,0.000823002,0.292597303,0.235663811,0.469269996,Clinics,0.83007973,TRUE,22,0.326056033,14,0.412898047,0,0.403234768,666,0.9882013,0.532597537
11756,Predicting the severity of disease progression in COVID-19 at the individual and population level: A mathematical model,0,10.1101/2021.04.01.21254804,4/7/21,medrxiv,0,6,mathematical model,0.43661994,0.001943624,0.001943522,0.26142784,0.001943495,0.296121578,Drug discovery,0.34604275,FALSE,5,0.070752675,6.666666667,0.290607439,0,0.403234768,424,0.981459186,0.436513517
11757,Mendelian randomisation identifies alternative splicing of the FAS death receptor as a mediator of severe COVID-19,0,10.1101/2021.04.01.21254789,4/7/21,medrxiv,0,69,"proteom, genome-wide",0.582831642,0.198507711,0.001987293,0.001987168,0.00198718,0.212699006,Drug discovery,0.5216479,TRUE,70.20930233,0.764920527,,,0,0.403234768,355,0.97736576,0.715173685
11758,"Sex and gender differences in COVID testing, hospital admission, presentation, and drivers of severe outcomes in the DC/Maryland region",0,10.1101/2021.04.05.21253827,4/7/21,medrxiv,0,14,logistic regression,0.083554308,0.001538267,0.001538185,0.001538256,0.001538212,0.910292771,Clinics,0.29591078,FALSE,52.21428571,0.648401262,,,0,0.403234768,199,0.950878883,0.667504971
11759,Higher Relative Viral Load Excretion Determined by Normalised Threshold Crossing Value in Acute Cases infected with the B.1.1.7 Lineage VOC 202012/01 (Using S gene target failure as a Proxy) When Compared to other Circulating Lineages in Wales,0,10.1101/2021.04.02.21254832,4/7/21,medrxiv,0,8,"sequencing, whole genome",0.001310387,0.939129281,0.055629254,0.001310372,0.001310358,0.001310347,Genomics,0.35819995,FALSE,2.625,0.028573196,,,0,0.403234768,717,0.989164459,0.473657474
11760,Industry and workplace characteristics associated with the use of a COVID-19 contact tracing app in Japan: a nation-wide employee survey,0,10.1101/2021.04.01.21254744,4/7/21,medrxiv,0,9,logistic regression,0.001438098,0.001438123,0.001438113,0.304761526,0.689486032,0.001438108,Healthcare,0.89336646,TRUE,11.55555556,0.174655204,1.777777778,0.148849344,0,0.403234768,191,0.948470985,0.418802575
11761,Rapid screening for variants of concern in routine SARS-CoV-2 PCR diagnostics,0,10.1101/2021.04.01.21254755,4/7/21,medrxiv,0,8,sequencing,0.004775336,0.976123184,0.00477547,0.004775409,0.004775312,0.004775288,Genomics,0.24755639,FALSE,12.125,0.183622982,5.125,0.257760235,0,0.403234768,202,0.951360462,0.448994612
11762,Phylogenetic estimates of SARS-CoV-2 introductions into Washington State,0,10.1101/2021.04.05.21254924,4/7/21,medrxiv,0,10,whole genome,0.001291197,0.593773476,0.001291234,0.401061574,0.001291243,0.001291276,Genomics,0.16436374,FALSE,47,0.606407323,,,0,0.403234768,295,0.971586805,0.660409632
11763,Molecular Profiling of COVID-19 Autopsies Uncovers Novel Disease Mechanisms,0,10.1101/2021.04.04.21253205,4/7/21,medrxiv,0,14,"sequencing, dataset",0.54791144,0.133656965,0.001046854,0.084963013,0.001046822,0.231374906,Drug discovery,0.33801377,FALSE,32.71428571,0.463479498,46.28571429,0.665440193,0,0.403234768,497,0.984589453,0.629185978
11764,Single Prime hAd5 Spike (S) + Nucleocapsid (N) Dual Antigen Vaccination of Healthy Volunteers Induces a Ten-Fold Increase in Mean S- and N- T-Cell Responses Equivalent to T-Cell Responses from Patients Previously Infected with SARS-CoV-2,0,10.1101/2021.04.05.21254940,4/7/21,medrxiv,0,29,in silico,0.650002294,0.226655597,0.000898093,0.000898099,0.037801834,0.083744083,Drug discovery,0.16677159,FALSE,14.34482759,0.216834684,,,0,0.403234768,1360,0.996388153,0.538819201
11765,COVID-19 Time-varying Reproduction Numbers Worldwide: An Empirical Analysis of Mandatory and Voluntary Social Distancing,0,10.1101/2021.04.06.21255033,4/7/21,medrxiv,0,3,network model,0.001156257,0.093893732,0.001156243,0.901481269,0.001156273,0.001156226,Epidemiology,0.14965248,FALSE,154.6666667,0.947739502,2723.666667,0.999397913,0,0.403234768,251,0.96508548,0.828864416
11766,"On the many advantages of using the VariantExperiment class to store, exchange and analyze SARS-CoV-2 genomic data and associated metadata",0,10.1101/2021.04.05.438328,4/6/21,biorxiv,0,5,sequencing,0.088576198,0.659772133,0.002898505,0.242956408,0.00289844,0.002898315,Genomics,0.8496218,TRUE,54.6,0.66553281,127.4,0.860784051,0,0.403234768,542,0.978569709,0.727030334
11767,Statins Are Associated with Improved 28-day Mortality in Patients Hospitalized with SARS-CoV-2 Infection,0,10.1101/2021.03.27.21254373,4/6/21,medrxiv,0,6,structural model,0.07254825,0.001291274,0.001291406,0.00129131,0.001291243,0.922286518,Clinics,0.84836185,TRUE,23.66666667,0.34986703,,,0,0.403234768,637,0.981699976,0.578267258
11768,Genomic epidemiology of SARS-CoV-2 in Russia reveals recurring cross-border transmission throughout 2020,0,10.1101/2021.03.31.21254115,4/6/21,medrxiv,0,21,"sequencing, whole-genome, genomic epidemiology",0.003214083,0.752121022,0.00321411,0.235022496,0.003214167,0.003214121,Genomics,0.28253877,FALSE,9.523809524,0.143113365,0.80952381,0.10141825,0,0.403234768,504,0.975680231,0.405861653
11769,"High SARS-CoV-2 attack rates following exposure during singing events in the Netherlands, September-October 2020",0,10.1101/2021.03.30.21253126,4/6/21,medrxiv,0,8,sequencing,0.001943497,0.44122737,0.001943506,0.550998466,0.00194359,0.001943572,Epidemiology,0.11841449,FALSE,11.625,0.175397365,,,0,0.403234768,594,0.980014447,0.51954886
11770,Quantifying Face Mask Comfort,0,10.1101/2021.03.31.21254723,4/6/21,medrxiv,0,9,machine learning,0.022890371,0.001291214,0.164626146,0.626787802,0.183113238,0.001291229,Epidemiology,0.63492477,TRUE,1.666666667,0.016451234,,,0,0.403234768,255,0.941728871,0.453804957
11771,Replication of LZTFL1 gene region as a susceptibility locus for COVID-19 in Latvian population.,0,10.1101/2021.03.31.21254708,4/6/21,medrxiv,0,13,genome-wide,0.002562661,0.546584906,0.002562691,0.002562651,0.002562678,0.443164412,Genomics,0.3123594,FALSE,19.07692308,0.285917496,3.230769231,0.202435108,0,0.403234768,196,0.922224898,0.453453068
11772,Tryptophan and arginine metabolism is significantly altered at the time of admission in hospital for severe COVID-19 patients: findings from longitudinal targeted metabolomics analysis,0,10.1101/2021.03.31.21254699,4/6/21,medrxiv,0,11,metabolom,0.606342964,0.001291301,0.001291262,0.001291282,0.001291259,0.388491932,Drug discovery,0.9324307,TRUE,22.18181818,0.327169275,3.727272727,0.218089377,0,0.403234768,484,0.973994703,0.48062203
11773,Unique protein features of SARS-CoV-2 relative to other Sarbecoviruses,0,10.1101/2021.04.06.438675,4/6/21,biorxiv,0,3,proteom,0.001987246,0.990064127,0.001987192,0.001987168,0.001987137,0.001987129,Genomics,0.11640361,FALSE,,,,,0,0.403234768,250,0.939561763,0.671398265
11774,Plasma microbiome in COVID-19 subjects: an indicator of gut barrier defects and dysbiosis,0,10.1101/2021.04.06.438634,4/6/21,biorxiv,0,12,"sequencing, transcriptom, microbiom, metatranscriptom",0.001943648,0.403073872,0.0019435,0.001943537,0.001943614,0.589151828,Clinics,0.91452193,TRUE,,,,,0,0.403234768,362,0.963159162,0.683196965
11775,ADNKA overcomes SARS-CoV2-mediated NK cell inhibition through non-spike antibodies,0,10.1101/2021.04.06.438630,4/6/21,biorxiv,0,19,proteom,0.940992199,0.051560761,0.00186169,0.001861827,0.001861782,0.001861742,Drug discovery,0.29236323,FALSE,,,,,0,0.403234768,614,0.980736817,0.691985792
11776,Ageing impairs the airway epithelium defence response to SARS-CoV-2,0,10.1101/2021.04.05.437453,4/6/21,biorxiv,0,20,"in silico, transcriptom, proteom",0.666631263,0.001046892,0.037014986,0.001046828,0.293213174,0.001046857,Drug discovery,0.4656742,FALSE,,,,,0,0.403234768,275,0.946303877,0.674769322
11777,Altered O-glycosylation Level of SARS-CoV-2 Spike Protein by Host O-glycosyltransferase Strengthens Its Trimeric Structure,0,10.1101/2021.04.06.438614,4/6/21,biorxiv,0,14,molecular dynamics simulation,0.958349556,0.00223853,0.032696548,0.002238477,0.002238474,0.002238414,Drug discovery,0.17469436,FALSE,,,,,0,0.403234768,210,0.928967012,0.66610089
11778,"Uneven growth of SARS-CoV-2 clones evidenced by more than 500,000 whole-genome sequences",0,10.1101/2021.04.06.437914,4/6/21,biorxiv,0,5,"whole-genome, genome sequences",0.002996541,0.747595664,0.002996748,0.240418054,0.002996543,0.00299645,Genomics,0.036135584,FALSE,,,,,0,0.403234768,485,0.974235492,0.68873513
11779,Antibody response to SARS-CoV-2 mRNA vaccines in pregnant women and their neonates,0,10.1101/2021.04.05.438524,4/6/21,biorxiv,0,9,correlation analysis,0.090184638,0.340330885,0.000515895,0.000515889,0.491640527,0.076812166,Healthcare,0.24177593,FALSE,,,,,0,0.403234768,973,0.98796051,0.695597639
11780,"Clinical Evidence for Improved Outcomes with Histamine Antagonists and Aspirin in 22,560 COVID-19 Patients",0,10.1101/2021.03.29.21253914,4/5/21,medrxiv,0,6,dataset,0.548582555,0.001220131,0.001220059,0.205587837,0.001220087,0.242169332,Drug discovery,0.075425506,FALSE,104,0.877605294,816.5,0.990299706,0,0.403234768,2985,0.996869733,0.817002375
11781,"SARS-CoV-2 infectivity by viral load, S gene variants and demographic factors and the utility of lateral flow devices to prevent transmission",0,10.1101/2021.03.31.21254687,4/5/21,medrxiv,0,17,logistic regression,0.000988363,0.404615172,0.047589316,0.401323933,0.144494834,0.000988382,Genomics,0.38216788,FALSE,19.47058824,0.290184922,,,0,0.403234768,6096,0.99975921,0.564392967
11782,Impact of COVID-19 in Individuals with Autism Spectrum Disorders: Analysis of a National Private Claims Insurance Database,0,10.1101/2021.03.31.21254434,4/5/21,medrxiv,0,4,logistic regression,0.003927617,0.003927571,0.003927467,0.251521904,0.198055849,0.538639592,Clinics,0.38246542,FALSE,11.25,0.169274538,3.25,0.203572384,0,0.403234768,526,0.967493378,0.435893767
11783,Is convalescent plasma futile in COVID-19? A Bayesian re-analysis of the RECOVERY randomised controlled trial,0,10.1101/2021.04.01.21254679,4/5/21,medrxiv,0,5,bayes,0.111879054,0.00131049,0.001310364,0.484511652,0.001310476,0.399677964,Epidemiology,0.075630516,FALSE,155.6,0.948296122,,,0,0.403234768,1072,0.985552613,0.779027834
11784,Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador,0,10.1101/2021.03.31.21254685,4/5/21,medrxiv,0,38,"whole genome, genomic epidemiology, genome sequences",0.002032809,0.515387889,0.002032756,0.476481022,0.002032782,0.002032741,Genomics,0.69474196,TRUE,32.44736842,0.460449007,,,0,0.403234768,568,0.969178907,0.610954227
11785,Increased household secondary attacks rates with Variant of Concern SARS-CoV-2 index cases,0,10.1101/2021.03.31.21254502,4/5/21,medrxiv,0,7,"sequencing, whole genome",0.001046831,0.153874104,0.001046815,0.235437525,0.306321815,0.302272911,Healthcare,0.09209758,FALSE,16.42857143,0.248067289,,,0,0.403234768,1513,0.990368408,0.547223488
11786,COVID-associated pediatric hospitalization and ICU admission trends across a multi-state health system and the broader US population,0,10.1101/2021.04.02.21254593,4/5/21,medrxiv,0,6,sequencing,0.001751207,0.315453617,0.001751168,0.401215721,0.001751256,0.278077031,Epidemiology,0.2924254,FALSE,47.33333333,0.608633805,42.66666667,0.648046561,0,0.403234768,495,0.964603901,0.656129759
11787,Signatures of COVID-19 severity and immune response in the respiratory tract microbiome,0,10.1101/2021.04.02.21254514,4/5/21,medrxiv,0,20,"machine learning, sequencing, microbiom, virom",0.001072255,0.440906331,0.104257348,0.001072207,0.001072194,0.451619665,Clinics,0.8360103,TRUE,46.8,0.603686066,202.95,0.918450629,0,0.403234768,765,0.979051288,0.726105688
11788,Rational Selection of PCR Primer/Probe Design Sites for SARS-CoV-2,0,10.1101/2021.04.04.438420,4/5/21,biorxiv,0,1,genomes,0.321618268,0.668691943,0.00242249,0.002422521,0.002422386,0.002422392,Genomics,0.42609823,FALSE,8.333333333,0.123693488,0.333333333,0.073187048,0,0.403234768,709,0.97712497,0.394310068
11789,Predictive modeling of COVID-19 New Confirmed Cases in Algeria using Artificial Neural Network,0,10.1101/2021.03.29.21254532,4/4/21,medrxiv,0,4,"neural network, predictive model",0.003335348,0.003335348,0.663438324,0.323220356,0.003335261,0.003335364,Epidemiology,0.835232,TRUE,3.25,0.039148989,0,0.055525823,0,0.403234768,308,0.915964363,0.353468486
11790,Genomic monitoring unveil the early detection of the SARS-CoV-2 B.1.351 lineage (20H/501Y.V2) in Brazil,0,10.1101/2021.03.30.21254591,4/4/21,medrxiv,0,25,genome sequences,0.001237108,0.935448473,0.001237138,0.001237156,0.059603045,0.00123708,Genomics,0.39719996,FALSE,63.8,0.726822933,,,0,0.403234768,1407,0.986274982,0.705444227
11791,Bioinformatics analysis of SARS-CoV-2 RBD mutant variants and insights into antibody and ACE2 receptor binding,0,10.1101/2021.04.03.438113,4/4/21,biorxiv,0,4,"bioinformatic, in silico",0.601176407,0.392211485,0.001652996,0.001653054,0.001653043,0.001653015,Drug discovery,0.0959,FALSE,1.5,0.015523533,,,0,0.403234768,1876,0.990127619,0.46962864
11792,A high-throughput fluorescence polarization assay to discover inhibitors of arenavirus and coronavirus exoribonucleases,0,10.1101/2021.04.02.437736,4/2/21,biorxiv,0,10,genomes,0.560591249,0.433561257,0.001461866,0.00146187,0.001461928,0.001461831,Drug discovery,0.9668548,TRUE,37.9,0.517595399,61.8,0.727856569,0,0.403234768,198,0.803274741,0.612990369
11793,COVIDOUTCOME - Estimating COVID Severity Based on Mutation Signatures in the SARS-CoV-2 Genome,0,10.1101/2021.04.01.438063,4/2/21,biorxiv,0,5,"machine learning, genomes, logistic regression",0.000786365,0.328825549,0.51086192,0.00078637,0.000786387,0.15795341,Genomics,0.2218031,FALSE,72.8,0.776547715,48,0.673735617,0,0.403234768,358,0.894774862,0.68707324
11794,Structural dynamics of the β-coronavirus Mpro protease ligand binding sites,0,10.1101/2021.03.31.437918,4/1/21,biorxiv,0,16,deep learning,0.75370556,0.107947464,0.133811535,0.001511829,0.001511818,0.001511794,Drug discovery,0.4160552,FALSE,20.9375,0.308677098,,,0,0.403234768,300,0.849747171,0.520553012
11795,Identification of lectin receptors for conserved SARS-CoV-2 glycosylation sites,0,10.1101/2021.04.01.438087,4/1/21,biorxiv,0,21,structural model,0.836133478,0.158775796,0.001272668,0.001272739,0.001272673,0.001272646,Drug discovery,0.686292,TRUE,99.28571429,0.867524275,,,0,0.403234768,342,0.872140621,0.714299888
11796,Fine-tuning the Spike: Role of the nature and topology of the glycan shield structure and dynamics of SARS-CoV-2 S,0,10.1101/2021.04.01.438036,4/1/21,biorxiv,0,6,molecular dynamics simulation,0.623182753,0.372678795,0.001034605,0.001034636,0.001034626,0.001034585,Drug discovery,0.081774026,FALSE,44.16666667,0.579132909,32,0.585763982,0,0.403234768,437,0.904647243,0.618194725
11797,Non-severe SARS-CoV-2 infection is characterised by very early T cell proliferation independent of type 1 interferon responses and distinct from other acute respiratory viruses.,0,10.1101/2021.03.30.21254540,3/31/21,medrxiv,0,27,"sequencing, transcriptom",0.478366003,0.334206408,0.002238466,0.002238693,0.093707298,0.089243131,Drug discovery,0.16432741,FALSE,52.07407407,0.647473561,,,0,0.403234768,2509,0.985793402,0.67883391
11798,Upregulated miR-200c may increase the risk of obese individuals to severe COVID-19,0,10.1101/2021.03.29.21254517,3/31/21,medrxiv,0,4,correlation analysis,0.451776696,0.237318468,0.002562686,0.00256263,0.002562691,0.303216829,Drug discovery,0.5828508,TRUE,21.25,0.314614386,7,0.299973241,0,0.403234768,671,0.933060438,0.487720708
11799,Assessing the impact of multiple comorbidities on fatal outcome in young COVID-19,0,10.1101/2021.03.29.21254599,3/31/21,medrxiv,0,4,bayes,0.00159353,0.001593545,0.124396554,0.233389392,0.001593617,0.637433362,Clinics,0.5199271,TRUE,18.5,0.278001113,7.5,0.307867273,0,0.403234768,654,0.93113412,0.480059318
11800,A time series forecasting of the proportion of SARS-CoV-2 N501Y lineage in North America,0,10.1101/2021.03.30.21254648,3/31/21,medrxiv,0,4,"computational, genomes, forecasting model",0.311568229,0.568680982,0.001046964,0.08951915,0.001046827,0.028137849,Genomics,0.12479302,FALSE,20.5,0.304966294,2.75,0.187583623,0,0.403234768,872,0.949434144,0.461304707
11801,Pediatric household transmission of SARS-CoV-2 infection,0,10.1101/2021.03.29.21254565,3/31/21,medrxiv,0,8,logistic regression,0.000936076,0.000936146,0.000936081,0.165402103,0.706817679,0.124971915,Healthcare,0.28198367,FALSE,8,0.118683901,,,0,0.403234768,3264,0.989405249,0.503774639
11802,Supramolecular cylinders target bulge structures in the 5-prime UTR of the RNA genome of SARS-CoV-2 and inhibit viral replication,0,10.1101/2021.03.30.437757,3/31/21,biorxiv,0,15,"molecular dynamics simulation, genomes",0.745364963,0.246503939,0.002032813,0.002032829,0.002032734,0.002032722,Drug discovery,0.106407225,FALSE,35.86666667,0.495392418,92.2,0.806194809,0,0.403234768,947,0.95376836,0.664647589
11803,"Migration of households from New York City and the Second Peak in Covid-19 cases in New Jersey, Connecticut and New York Counties.",0,10.1101/2021.03.29.21254583,3/31/21,medrxiv,0,2,model fit,0.001112623,0.001112635,0.047910915,0.871808762,0.076942354,0.001112711,Epidemiology,0.2703723,FALSE,116.5,0.901478137,,,0,0.403234768,771,0.94245124,0.749054715
11804,A RANDOMIZED TRIAL - INTENSIVE TREATMENT BASED IN IVERMECTIN AND IOTA-CARRAGEENAN AS PRE-EXPOSURE PROPHYLAXIS FOR COVID- 19 IN HEALTHCARE AGENTS,0,10.1101/2021.03.26.21254398,3/30/21,medrxiv,0,11,logistic regression,0.11377613,0.000710596,0.000710632,0.153950097,0.294305995,0.43654655,Clinics,0.9220469,TRUE,6.636363636,0.094130744,3.363636364,0.20658282,0,0.403234768,3065,0.987719721,0.422917013
11805,Impact of monoclonal antibody treatment on hospitalization and mortality among non-hospitalized adults with SARS-CoV-2 infection,0,10.1101/2021.03.25.21254322,3/30/21,medrxiv,0,22,logistic regression,0.001310419,0.013894642,0.001310324,0.001310392,0.065079561,0.917094661,Clinics,0.14238942,FALSE,83.77272727,0.820273363,,,1,0.537564047,1715,0.976161811,0.77799974
11806,Rapid genomic surveillance of SARS-CoV-2 in a dense urban community using environmental (sewage) samples,0,10.1101/2021.03.29.21254053,3/30/21,medrxiv,0,15,sequencing,0.001034608,0.762633898,0.00103462,0.23322766,0.001034641,0.001034573,Genomics,0.2653886,FALSE,11.8,0.177994929,,,0,0.403234768,1298,0.967011799,0.516080498
11807,"Six-month pulmonary impairment after severe COVID-19: a prospective, multicenter follow-up study",0,10.1101/2021.03.29.21254151,3/30/21,medrxiv,0,25,logistic regression,0.017843175,0.001126859,0.239731305,0.001126856,0.038292078,0.701879726,Clinics,0.5056614,TRUE,45.6,0.592058878,40.04,0.633529569,0,0.403234768,1055,0.95449073,0.645828486
11808,IVERMECTIN REPROPOSING FOR COVID-19 TREATMENT OUTPATIENTS IN MILD STAGE IN PRIMARY HEALTH CARE CENTERS,0,10.1101/2021.03.29.21254554,3/30/21,medrxiv,0,9,logistic regression,0.127325384,0.000531405,0.000531379,0.159903519,0.216283668,0.495424645,Clinics,0.7267873,TRUE,2.8,0.031047065,0.1,0.056328606,0,0.403234768,3909,0.990849988,0.370365107
11809,Development and validation of the RCOS prognostic index: a bedside multivariable logistic regression model to predict hypoxaemia or death in patients with of SARS-CoV-2 infection,0,10.1101/2021.03.29.21254393,3/30/21,medrxiv,0,7,logistic regression,0.001156241,0.00115625,0.001156265,0.043678128,0.001156255,0.951696861,Clinics,0.76320195,TRUE,16.57142857,0.249737151,,,0,0.403234768,503,0.892366964,0.515112961
11810,"Sequence analysis of SARS-CoV-2 in nasopharyngeal samples from patients with COVID-19 illustrates population variation and diverse phenotypes, placing the in vitro growth properties of B.1.1.7 and B.1.351 lineage viruses in context.",0,10.1101/2021.03.30.437704,3/30/21,biorxiv,0,20,genomes,0.069246131,0.775150201,0.000786359,0.089188228,0.01946185,0.046167231,Genomics,0.13954744,FALSE,80.68421053,0.809635723,,,0,0.403234768,1158,0.961714423,0.724861638
11811,"A rapid, cost efficient and simple method to identify current SARS-CoV-2 variants of concern by Sanger sequencing part of the spike protein gene",0,10.1101/2021.03.27.21252266,3/29/21,medrxiv,0,7,"sequencing, whole genome",0.001392853,0.76698114,0.001392954,0.199728535,0.029111558,0.00139296,Genomics,0.061136603,FALSE,21.57142857,0.318696271,65.28571429,0.739697618,0,0.403234768,1295,0.962196003,0.605956165
11812,"Prohibit, protect, or adapt? The changing role of volunteers in palliative and hospice care services during the COVID-19 pandemic. A multinational survey (CovPall).",0,10.1101/2021.03.28.21254486,3/29/21,medrxiv,0,14,logistic regression,0.001538135,0.001538155,0.001538125,0.178872485,0.731689927,0.084823173,Healthcare,0.72815627,TRUE,43.85714286,0.576411652,22.21428571,0.504883596,0,0.403234768,731,0.923910426,0.602110111
11813,Analyzing the Global Impact of COVID-19 Vaccination Progress: A Result-oriented Storytelling Approach,0,10.1101/2021.03.26.21254432,3/29/21,medrxiv,0,5,dataset,0.14786845,0.00146201,0.001461927,0.846283786,0.001461986,0.001461841,Epidemiology,0.5788602,TRUE,5.2,0.071927763,3.2,0.202100615,0,0.403234768,923,0.940524922,0.404447017
11814,A Novel Diagnostic Test to Screen SARS-CoV-2 Variants Containing E484K and N501Y Mutations,0,10.1101/2021.03.26.21253712,3/29/21,medrxiv,0,12,sequencing,0.001901842,0.939379442,0.001901945,0.001901833,0.001901724,0.053013212,Genomics,0.4038427,FALSE,45.41666667,0.590079782,,,0,0.403234768,1144,0.952564411,0.64862632
11815,Quantitative detection of SARS-CoV-2 B.1.1.7 variant in wastewater by allele-specific RT-qPCR,0,10.1101/2021.03.28.21254404,3/29/21,medrxiv,0,20,sequencing,0.001622726,0.905009156,0.001622804,0.088499721,0.001622745,0.001622847,Genomics,0.18643016,FALSE,19.1,0.28604119,,,0,0.403234768,1332,0.963399952,0.55089197
11816,Risk factors for developing COVID-19: a population-based longitudinal study (COVIDENCE UK),0,10.1101/2021.03.27.21254452,3/29/21,medrxiv,0,30,logistic regression,0.001438094,0.001438121,0.001438102,0.001438159,0.871538941,0.122708584,Healthcare,0.7595428,TRUE,83.43333333,0.818912734,,,0,0.403234768,1818,0.975198652,0.732448718
11817,LncRNAs NEAT1 and MALAT1 Differentiate Inflammation in Severe COVID-19 Patients,0,10.1101/2021.03.26.21254445,3/29/21,medrxiv,0,6,"transcriptom, dataset",0.669182858,0.002183363,0.113252908,0.002183216,0.002183483,0.211014173,Drug discovery,0.6572067,TRUE,65,0.734801163,,,0,0.403234768,524,0.88538406,0.67447333
11818,Targeting the Microbiome With KB109 in Outpatients with Mild to Moderate COVID-19 Reduced Medically Attended Acute Care Visits and Improved Symptom Duration in Patients With Comorbidities,0,10.1101/2021.03.26.21254422,3/29/21,medrxiv,0,6,microbiom,0.000677948,0.076362504,0.000677929,0.00067793,0.173372854,0.748230835,Clinics,0.9476975,TRUE,13.5,0.205393036,,,0,0.403234768,1308,0.962677582,0.523768462
11819,Modeling Substrate Coordination to Zn-Bound Angiotensin Converting Enzyme 2,0,10.1101/2021.03.27.437352,3/29/21,biorxiv,0,3,computational,0.913528617,0.082182546,0.001072189,0.001072252,0.0010722,0.001072196,Drug discovery,0.622102,TRUE,24.66666667,0.36334962,7.666666667,0.310877709,0,0.403234768,679,0.916205153,0.498416812
11820,Attitudes toward COVID-19 illness and COVID-19 vaccination among pregnant women: a cross-sectional multicenter study during August-December 2020,0,10.1101/2021.03.26.21254402,3/29/21,medrxiv,0,18,logistic regression,0.001310331,0.001310331,0.001310313,0.001310359,0.993448267,0.001310399,Healthcare,0.7266226,TRUE,25.94444444,0.379244233,,,0,0.403234768,926,0.940765712,0.574414904
11821,Enrichment of SARS-CoV-2 entry factors and interacting intracellular genes in peripheral immune cells,0,10.1101/2021.03.29.437515,3/29/21,biorxiv,0,2,"machine learning, dataset",0.788721202,0.001861922,0.127402254,0.001861775,0.001861708,0.078291139,Drug discovery,0.38897982,FALSE,34,0.477766096,3.5,0.213607172,0,0.403234768,993,0.945340718,0.509987188
11822,"Mental health in relation to changes in sleep, exercise, alcohol and diet during the COVID-19 pandemic: examination of five UK cohort studies",0,10.1101/2021.03.26.21254424,3/28/21,medrxiv,0,3,logistic regression,0.001438095,0.001438091,0.00143809,0.001438156,0.992809318,0.00143825,Healthcare,0.6583277,TRUE,16,0.243552477,0,0.055525823,0,0.403234768,769,0.918853841,0.405291727
11823,Detection of SARS-CoV-2 N501Y mutation by RT-PCR to identify the UK and the South African strains in the population of South Indian state of Telangana,0,10.1101/2021.03.27.21254107,3/28/21,medrxiv,0,4,sequencing,0.001461855,0.992690661,0.001461877,0.001461857,0.001461865,0.001461885,Genomics,0.31681806,FALSE,16.5,0.249366071,,,0,0.403234768,822,0.926799904,0.526466914
11824,"Estimating COVID-19 cases and deaths prevented by non-pharmaceutical interventions in 2020-2021, and the impact of individual actions: a retrospective model-based analysis",0,10.1101/2021.03.26.21254421,3/28/21,medrxiv,0,4,simulation model,0.002357727,0.002357753,0.002357727,0.775078144,0.215490764,0.002357885,Epidemiology,0.3537628,FALSE,61.75,0.713711423,81,0.781241638,0,0.403234768,1482,0.964363111,0.715637735
11825,Estimation of SARS-CoV-2 antibody prevalence through integration of serology and incidence data,0,10.1101/2021.03.27.21254471,3/28/21,medrxiv,0,5,"bayes, bayesian model",0.002638965,0.002639071,0.15979932,0.829644457,0.002639049,0.002639137,Epidemiology,0.15817377,FALSE,11.2,0.168594224,,,0,0.403234768,972,0.937394654,0.503074549
11826,Prevalence and associated factors with mental health outcomes among interns and residents physicians during COVID-19 epidemic in Panama: a cross-sectional study,0,10.1101/2021.03.26.21254435,3/28/21,medrxiv,0,4,logistic regression,0.001254654,0.001254603,0.001254662,0.001254697,0.993726621,0.001254762,Healthcare,0.9276686,TRUE,114.75,0.89875688,348.75,0.965413433,0,0.403234768,1577,0.967252588,0.808664417
11827,Emergence and spread of SARS-CoV-2 lineages B.1.1.7 and P.1 in Italy,0,10.1101/2021.03.24.21254277,3/28/21,medrxiv,0,9,sequencing,0.00223854,0.711672252,0.00223844,0.279373773,0.002238485,0.002238511,Genomics,0.08208868,FALSE,152.7777778,0.946502567,491.2222222,0.978257961,0,0.403234768,721,0.914278835,0.810568532
11828,On the association between SARS-COV-2 variants and COVID-19 mortality during the second wave of the pandemic in Europe,0,10.1101/2021.03.25.21254289,3/28/21,medrxiv,0,4,sequencing,0.000822898,0.351961758,0.000823013,0.428772566,0.000822927,0.216796838,Epidemiology,0.08301219,FALSE,239.75,0.982621065,43,0.649785925,0,0.403234768,1290,0.955935468,0.747894306
11829,The nucleotide addition cycle of the SARS-CoV-2 polymerase,0,10.1101/2021.03.27.437309,3/27/21,biorxiv,0,12,genomes,0.363170168,0.484665527,0.14592244,0.00208068,0.00208062,0.002080566,Genomics,0.5240303,TRUE,59.41666667,0.698806358,97.83333333,0.818303452,0,0.403234768,1332,0.951842042,0.718046655
11830,High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor,0,10.1101/2021.03.27.437323,3/27/21,biorxiv,0,30,"virtual screening, machine learning",0.815864619,0.020331037,0.161215119,0.000863094,0.000863084,0.000863045,Drug discovery,0.09707436,FALSE,54.2,0.662749706,,,0,0.403234768,1207,0.947507826,0.6711641
11831,Visible and real sizes of the COVID-19 pandemic in Ukraine,0,10.1101/2021.03.19.21253938,3/27/21,medrxiv,0,1,mathematical model,0.002562571,0.002562642,0.002562619,0.95347506,0.002562592,0.036274516,Epidemiology,0.4028424,FALSE,8,0.118683901,0,0.055525823,0,0.403234768,662,0.894293282,0.367934443
11832,"A small number of early introductions seeded widespread transmission of SARS-CoV-2 in Quebec, Canada",0,10.1101/2021.03.20.21253835,3/26/21,medrxiv,0,24,"genomic epidemiology, genomes",0.001187253,0.571119991,0.001187256,0.424130932,0.00118729,0.001187279,Genomics,0.13655567,FALSE,35.66666667,0.493908096,,,0,0.403234768,912,0.921984108,0.606375657
11833,Supporting COVID-19 policy response with large-scale mobility-based modeling,0,10.1101/2021.03.20.21254022,3/26/21,medrxiv,0,12,computational,0.001330078,0.001330034,0.00133007,0.993349618,0.001330163,0.001330037,Epidemiology,0.13676542,FALSE,44.58333333,0.582410786,,,0,0.403234768,751,0.902480135,0.629375229
11834,Localised community circulation of SARS-CoV-2 viruses with an increased accumulation of single nucleotide polymorphisms that adversely affect the sensitivity of real-time reverse transcription assays targeting Nucleocapsid protein.,0,10.1101/2021.03.22.21254006,3/26/21,medrxiv,0,14,"sequencing, genomes",0.000966818,0.939908384,0.056224413,0.000966803,0.000966802,0.000966779,Genomics,0.29806232,FALSE,15.57142857,0.23495578,,,0,0.403234768,600,0.87310378,0.503764776
11835,Quantitative SARS-CoV-2 anti-spike responses to Pfizer-BioNTech and Oxford-AstraZeneca vaccines by previous infection status,0,10.1101/2021.03.21.21254061,3/26/21,medrxiv,0,22,logistic regression,0.001272669,0.306463706,0.001272702,0.001272695,0.556010641,0.133707587,Healthcare,0.14327553,FALSE,87.91304348,0.836600903,,,0,0.403234768,1697,0.963640742,0.734492137
11836,A global database of COVID-19 vaccinations,0,10.1101/2021.03.22.21254100,3/26/21,medrxiv,0,7,dataset,0.002238652,0.00223846,0.002238563,0.721835354,0.269210415,0.002238556,Epidemiology,0.40544087,FALSE,7,0.10179974,14.42857143,0.41697886,0,0.403234768,637,0.880327474,0.45058521
11837,Epidemiology and transmission of COVID-19 in cases and close contacts in Georgia in the first four months of the epidemic,0,10.1101/2021.03.22.21254082,3/26/21,medrxiv,0,14,logistic regression,0.000551224,0.000551249,0.000551231,0.83988966,0.091861667,0.066594968,Epidemiology,0.22211367,FALSE,55.57142857,0.671655637,,,0,0.403234768,580,0.869010354,0.64796692
11838,COVID-19 reinfection: A Rapid Systematic Review of Case Reports and Case Series,0,10.1101/2021.03.22.21254081,3/26/21,medrxiv,0,4,sequencing,0.001486508,0.462091339,0.001486497,0.290064905,0.126299536,0.118571215,Genomics,0.2021175,FALSE,1.75,0.017007855,,,0,0.403234768,791,0.907295931,0.442512851
11839,SARS-CoV-2 N gene dropout and N gene Ct value shift as indicator for the presence of B.1.1.7 lineage in a widely used commercial multiplex PCR assay,0,10.1101/2021.03.23.21254171,3/26/21,medrxiv,0,13,"sequencing, whole-genome",0.00111264,0.794723237,0.200826195,0.001112613,0.001112616,0.0011127,Genomics,0.35458276,FALSE,45.15384615,0.587915146,46.69230769,0.667112657,0,0.403234768,573,0.867324825,0.631396849
11840,"Reconstructing the COVID-19 epidemic in Delhi, India: infection attack rate and reporting of deaths",0,10.1101/2021.03.23.21254092,3/26/21,medrxiv,0,7,model fit,0.002422252,0.04197878,0.002422231,0.948331907,0.00242245,0.002422379,Epidemiology,0.2331067,FALSE,42,0.558537943,83.14285714,0.785656944,0,0.403234768,553,0.861064291,0.652123486
11841,Point-of-care lung ultrasonography for early identification of mild COVID-19: a prospective cohort of outpatients in a Swiss screening center,0,10.1101/2021.03.23.21254150,3/26/21,medrxiv,0,11,logistic regression,0.001034614,0.053638426,0.476583726,0.001034607,0.033786346,0.433922281,Imaging,0.45505935,FALSE,40.72727273,0.546415981,17.63636364,0.457118009,0,0.403234768,285,0.728148327,0.533729271
11842,"Optimizing SARS-CoV-2 Variant of Concern Screening: Experience from British Columbia, Canada, Early 2021",0,10.1101/2021.03.23.21253520,3/26/21,medrxiv,0,11,"sequencing, whole genome",0.003214307,0.849398467,0.137744381,0.00321434,0.003214373,0.003214132,Genomics,0.4585197,FALSE,24.72727273,0.363844394,,,0,0.403234768,437,0.821574765,0.529551309
11843,Understanding the effectiveness of government interventions in Europe's second wave of COVID-19,0,10.1101/2021.03.25.21254330,3/26/21,medrxiv,0,22,"bayes, dataset",0.001291209,0.024092386,0.00129122,0.970742689,0.001291278,0.001291217,Epidemiology,0.1487175,FALSE,16.04545455,0.243614324,,,0,0.403234768,19826,0.999036841,0.548628644
11844,Why ODE models for COVID-19 fail: Heterogeneity shapes epidemic dynamics,0,10.1101/2021.03.25.21254292,3/26/21,medrxiv,0,3,"computational, mathematical model, probabilistic",0.000946156,0.067258643,0.000946094,0.928956871,0.000946102,0.000946134,Epidemiology,0.050926983,FALSE,9,0.135320675,0,0.055525823,0,0.403234768,408,0.808812906,0.350723543
11845,Comparison of cough particle exposure for indoor commercial and aircraft cabin spaces,0,10.1101/2021.03.24.21254275,3/26/21,medrxiv,0,5,computational,0.003101671,0.003101803,0.003101552,0.935367855,0.003101518,0.052225601,Epidemiology,0.4930776,FALSE,8,0.118683901,,,0,0.403234768,587,0.869973513,0.463964061
11846,Public Opinion about the UK Government during COVID-19 and Implications for Public Health: A Topic Modelling Analysis of Open-Ended Survey Response Data,0,10.1101/2021.03.24.21254094,3/26/21,medrxiv,0,5,text mining,0.001461922,0.001461896,0.0014619,0.679724575,0.314427825,0.001461882,Epidemiology,0.2809954,FALSE,66.2,0.741851692,77.4,0.771808938,0,0.403234768,675,0.887310378,0.701051444
11847,Association between the physical work environment and work functioning impairment while working from home under the COVID-19 pandemic in Japanese workers,0,10.1101/2021.03.23.21254207,3/26/21,medrxiv,0,9,logistic regression,0.001438192,0.001438147,0.001438141,0.001438211,0.992809131,0.001438178,Healthcare,0.67776304,TRUE,11.55555556,0.174655204,1,0.122023013,0,0.403234768,580,0.869010354,0.392230835
11848,Automatic identification of risk factors for SARS-CoV-2 positivity and severe clinical outcomes of COVID-19 using Data Mining and Natural Language Processing,0,10.1101/2021.03.25.21254314,3/26/21,medrxiv,0,4,data mining,0.001371374,0.001371287,0.371263663,0.001371344,0.001371288,0.623251045,Clinics,0.9457139,TRUE,29.75,0.428226854,15.5,0.430157881,0,0.403234768,704,0.892126174,0.538436419
11849,Predicting the Ophthalmic Surgical Backlog as a Result of the COVID-19 Pandemic: A population-based study and microsimulation model to inform surgical recovery plans,0,10.1101/2021.03.25.21254375,3/26/21,medrxiv,0,7,simulation model,0.001901699,0.001901724,0.001901745,0.88323279,0.001901766,0.109160276,Epidemiology,0.9654417,TRUE,54.14285714,0.662193086,17,0.451097137,0,0.403234768,655,0.883216952,0.599935485
11850,Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study,0,10.1101/2021.03.26.21254390,3/26/21,medrxiv,0,15,prediction model,0.001461855,0.001461848,0.249661562,0.001461953,0.001461893,0.744490889,Clinics,0.7681633,TRUE,26.46666667,0.387284309,10.46666667,0.361519936,0,0.403234768,412,0.812183963,0.491055744
11851,Tenofovir-DF versus Hydroxychloroquine in the Treatment of Hospitalized Patients with COVID-19: An Observational Study (THEDICOV),0,10.1101/2021.03.24.21252635,3/26/21,medrxiv,0,10,logistic regression,0.026757167,0.001072219,0.00107223,0.00107227,0.001072225,0.968953888,Clinics,0.6600915,TRUE,3.6,0.044838889,0.2,0.061145304,0,0.403234768,2691,0.979532868,0.372187957
11852,Longitudinal immune profiling of a SARS-CoV-2 reinfection in a solid organ transplant recipient,0,10.1101/2021.03.24.21253992,3/26/21,medrxiv,0,32,sequencing,0.205706356,0.527941602,0.001272653,0.001272703,0.001272744,0.262533942,Genomics,0.24501342,FALSE,47,0.606407323,,,0,0.403234768,6565,0.993017096,0.667553062
11853,"Hospital mortality in COVID-19 patients in Belgium treated with statins, ACE inhibitors and/or ARBs",0,10.1101/2021.03.24.21252687,3/26/21,medrxiv,0,12,immunome,0.338522117,0.002032801,0.002032762,0.056244947,0.229140581,0.372026793,Clinics,0.53638405,TRUE,34.16666667,0.47863195,11.41666667,0.376103827,0,0.403234768,952,0.926318324,0.546072217
11854,Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model,0,10.1101/2021.03.24.21254218,3/26/21,medrxiv,0,6,computational,0.000699347,0.000699349,0.000699375,0.921080532,0.000699359,0.076122038,Epidemiology,0.26596874,FALSE,28.33333333,0.410724225,22.16666667,0.504549104,0,0.403234768,557,0.86250903,0.545254282
11855,On discrete time epidemic models in Kermack-McKendrick form,0,10.1101/2021.03.26.21254385,3/26/21,medrxiv,0,4,computational,0.002296609,0.002296565,0.002296639,0.988516967,0.002296667,0.002296553,Epidemiology,0.13952816,FALSE,78.5,0.800482405,157,0.889884934,0,0.403234768,627,0.879123525,0.743181408
11856,Emergence of N antigen SARS-CoV-2 genetic variants escaping detection of antigenic tests,0,10.1101/2021.03.25.21253802,3/26/21,medrxiv,0,12,sequencing,0.156329921,0.839328634,0.001085388,0.001085359,0.001085348,0.00108535,Genomics,0.2707839,FALSE,38.33333333,0.522233904,35.75,0.609044688,0,0.403234768,2578,0.978810498,0.628330965
11857,COVID-19 RT-PCR diagnostic assay sensitivity and SARS-CoV-2 transmission: A missing link?,0,10.1101/2021.03.24.21254271,3/26/21,medrxiv,0,9,"sequencing, genomes",0.000822899,0.762704986,0.234003354,0.000822925,0.000822915,0.000822922,Genomics,0.14761308,FALSE,56.77777778,0.680128641,,,0,0.403234768,818,0.910907778,0.664757062
11858,"Increased angiotensin-converting enzyme 2, sRAGE and immune activation, but lowered calcium and magnesium in COVID-19: association with chest CT abnormalities and lowered peripheral oxygen saturation.",0,10.1101/2021.03.26.21254383,3/26/21,medrxiv,0,4,neural network,0.164925905,0.001350441,0.197739855,0.00135034,0.001350327,0.633283131,Clinics,0.98070216,TRUE,101.5,0.872100934,347,0.965012042,0,0.403234768,624,0.878882735,0.77980762
11859,Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units - a high risk community-hospital interface,0,10.1101/2021.03.24.21253587,3/26/21,medrxiv,0,43,"bayes, sequencing, whole-genome",0.002032807,0.373937706,0.002032775,0.333646798,0.002032806,0.286317108,Genomics,0.47785285,FALSE,20.14634146,0.299029006,,,1,0.537564047,769,0.905369612,0.580654222
11860,D155Y Substitution of SARS-CoV-2 ORF3a Weakens Binding with Caveolin-1: An in silico Study,0,10.1101/2021.03.26.437194,3/26/21,biorxiv,0,7,"computational, in silico, interactom",0.608233107,0.339683488,0.00139282,0.001392872,0.001392844,0.047904869,Drug discovery,0.3252056,FALSE,8.142857143,0.119426062,,,0,0.403234768,905,0.920298579,0.48098647
11861,Genetic screening for TLR7 variants in young and previously healthy men with severe COVID-19: a case series,0,10.1101/2021.03.14.21252289,3/25/21,medrxiv,0,18,"in silico, exom",0.110949711,0.312377893,0.025106414,0.025788416,0.233091032,0.292686535,Genomics,0.37485278,FALSE,105.5555556,0.880141011,205.5555556,0.919989296,0,0.403234768,1584,0.954731519,0.789524148
11862,System-wide hematopoietic and immune signaling aberrations in COVID-19 revealed by deep proteome and phosphoproteome analysis,0,10.1101/2021.03.19.21253675,3/25/21,medrxiv,0,11,"proteom, phosphoproteom",0.892768873,0.00208062,0.002080618,0.002080596,0.002080568,0.098908725,Drug discovery,0.459877,FALSE,22.36363636,0.330323458,,,0,0.403234768,1126,0.933542018,0.555700081
11863,Genomic surveillance of SARS-CoV-2 tracks early interstate transmission of P.1 lineage and diversification within P.2 clade in Brazil,0,10.1101/2021.03.21.21253418,3/25/21,medrxiv,0,22,"sequencing, whole-genome",0.001237055,0.879514008,0.001237051,0.115537768,0.001237058,0.00123706,Genomics,0.19421804,FALSE,22.31818182,0.328900983,,,0,0.403234768,1906,0.966289429,0.566141727
11864,Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance,0,10.1101/2021.03.24.436812,3/25/21,biorxiv,0,3,dataset,0.001059388,0.776432637,0.21932983,0.001059404,0.001059372,0.001059369,Genomics,0.49120972,FALSE,36.66666667,0.505102356,40.66666667,0.63774418,0,0.403234768,625,0.869732723,0.603953507
11865,A Sanger-based approach for scaling up screening of SARS-CoV-2 variants of interest and concern,0,10.1101/2021.03.20.21253956,3/25/21,medrxiv,0,8,"sequencing, whole-genome",0.033791641,0.724869369,0.217954783,0.001371302,0.020641577,0.001371329,Genomics,0.2428182,FALSE,24.125,0.356484631,24,0.521808938,0,0.403234768,809,0.903443294,0.546242908
11866,Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor,0,10.1101/2021.03.24.436885,3/25/21,biorxiv,0,12,"computational, neural network",0.549515075,0.219884634,0.165535651,0.063109652,0.000977526,0.000977463,Drug discovery,0.099950075,FALSE,36.75,0.505968211,,,0,0.403234768,803,0.902239345,0.603814108
11867,Genomic surveillance and phylodynamic analyses reveal emergence of novel mutation and co-mutation patterns within SARS-CoV2 variants prevalent in India,0,10.1101/2021.03.25.436930,3/25/21,biorxiv,0,10,"sequencing, whole-genome",0.001350329,0.968316409,0.001350313,0.001350421,0.00135035,0.026282178,Genomics,0.6655243,TRUE,20.8,0.30768755,,,0,0.403234768,1098,0.931856489,0.547592936
11868,Computational assessment of the spike protein antigenicity reveals diversity in B cell epitopes but stability in T cell epitopes across SARS-CoV-2 variants,0,10.1101/2021.03.25.437035,3/25/21,biorxiv,0,3,computational,0.470211474,0.524468128,0.001330028,0.001330089,0.001330104,0.001330176,Genomics,0.114263356,FALSE,6.666666667,0.094996598,1,0.122023013,0,0.403234768,738,0.889477486,0.377432966
11869,Target Capture Sequencing of SARS-CoV-2 Genomes Using the ONETest Coronaviruses Plus,0,10.1101/2021.03.25.437083,3/25/21,biorxiv,0,8,"sequencing, genome sequences, genomes",0.001187285,0.994063658,0.001187274,0.001187278,0.001187261,0.001187245,Genomics,0.5055716,TRUE,28.875,0.416290432,,,0,0.403234768,963,0.91909463,0.579539943
11870,SARS-CoV-2 genome sequencing from COVID-19 in Ecuadorian patients: a whole country analysis.,0,10.1101/2021.03.19.21253620,3/24/21,medrxiv,0,14,"sequencing, whole genome, genomes",0.001330043,0.945977645,0.001330102,0.001330137,0.001330062,0.048702011,Genomics,0.71521354,TRUE,21.125,0.312511596,13.3125,0.403465347,0,0.403234768,990,0.916445943,0.508914413
11871,"Characterising long COVID more than 6 months after acute infection in adults; prospective longitudinal cohort study, England",0,10.1101/2021.03.18.21253633,3/24/21,medrxiv,0,17,logistic regression,0.000537831,0.000537869,0.012019051,0.231891064,0.567893912,0.187120272,Healthcare,0.3788345,FALSE,85.82352941,0.827880512,,,0,0.403234768,1383,0.94293282,0.7246827
11872,SARS-CoV-2 Seroprevalence in 12 Cities of India from July-December 2020,0,10.1101/2021.03.19.21253429,3/24/21,medrxiv,0,9,bayes,0.001220003,0.106684244,0.001220007,0.496553826,0.229014635,0.165307285,Epidemiology,0.16231969,FALSE,67.33333333,0.749396994,,,0,0.403234768,600,0.849747171,0.667459644
11873,VIRAL AND ANTIBODY TESTING FOR CORONAVIRUS DISEASE 2019 (COVID-19): FACTORS ASSOCIATED WITH POSITIVITY IN ELECTRONIC HEALTH RECORDS FROM THE UNITED STATES,0,10.1101/2021.03.19.21253924,3/24/21,medrxiv,0,6,logistic regression,0.001461859,0.332075065,0.001461866,0.001461964,0.308185733,0.355353513,Clinics,0.38122907,FALSE,15.16666667,0.228709258,,,0,0.403234768,618,0.856730075,0.4962247
11874,Occupational risk of COVID-19 by country of birth. A register-based study.,0,10.1101/2021.03.17.21253349,3/24/21,medrxiv,0,2,logistic regression,0.002130641,0.00213072,0.00213066,0.114248207,0.877229094,0.002130679,Healthcare,0.58829075,TRUE,9,0.135320675,2.5,0.180826866,0,0.403234768,569,0.841319528,0.390175459
11875,Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study,0,10.1101/2021.03.19.21253921,3/24/21,medrxiv,0,15,"machine learning, prediction model, dataset",0.100758539,0.000765965,0.363393516,0.181502462,0.129787152,0.223792367,Clinics,0.7042319,TRUE,8.866666667,0.130001855,,,0,0.403234768,671,0.870455093,0.467897239
11876,Improved Prediction of COVID-19 Transmission and Mortality Using Google Search Trends for Symptoms in the United States,0,10.1101/2021.03.14.21253554,3/24/21,medrxiv,0,6,"computational, forecasting model, lstm, dataset",0.001220004,0.001219991,0.083434847,0.772390846,0.001220067,0.140514245,Epidemiology,0.17292792,FALSE,13.33333333,0.201558538,17.16666667,0.45189992,0,0.403234768,588,0.845653744,0.475586742
11877,Transcriptome analysis of PBMCs reveals distinct immune response in the asymptomatic and re-detectable positive COVID-19 patients,0,10.1101/2021.03.16.21251286,3/24/21,medrxiv,0,16,transcriptom,0.44229053,0.001823446,0.001823355,0.00182348,0.001823437,0.550415752,Clinics,0.7683878,TRUE,0.6875,0.00797823,,,0,0.403234768,849,0.901035396,0.437416131
11878,P1 variant and amino acid mutations at Spike gene identified using Sanger protocol,0,10.1101/2021.03.21.21253158,3/24/21,medrxiv,0,12,sequencing,0.001653088,0.991734421,0.001653128,0.001653192,0.001653136,0.001653036,Genomics,0.53479916,TRUE,15.58333333,0.235017626,,,0,0.403234768,807,0.892848543,0.510366979
11879,3D visualization of SARS-CoV-2 infection and receptor distribution in Syrian hamster lung lobes display distinct spatial arrangements,0,10.1101/2021.03.24.435771,3/24/21,biorxiv,0,11,transcriptom,0.789908598,0.159238005,0.045985208,0.001622735,0.001622726,0.001622728,Drug discovery,0.5066072,TRUE,70.18181818,0.764796833,86.72727273,0.794554455,0,0.403234768,591,0.846135324,0.702180345
11880,Untangling the cell immune response dynamic for severe and critical cases of SARS-CoV-2 infection,0,10.1101/2021.03.23.436686,3/24/21,biorxiv,0,3,mathematical model,0.320694241,0.002422299,0.002422359,0.289483451,0.002422296,0.382555355,Clinics,0.40664336,FALSE,36.66666667,0.505102356,,,0,0.403234768,613,0.854562967,0.587633363
11881,Factors Associated with Emerging and Re-emerging of SARS-CoV-2 Variants,0,10.1101/2021.03.24.436850,3/24/21,biorxiv,0,9,bioinformatic,0.073072046,0.918979311,0.001987114,0.001987206,0.001987149,0.001987173,Genomics,0.5450555,TRUE,50.44444444,0.634918672,,,0,0.403234768,1206,0.933782808,0.657312082
11882,Arginine Methylation Regulates SARS-CoV-2 Nucleocapsid Protein Function and Viral Replication,0,10.1101/2021.03.24.436822,3/24/21,biorxiv,0,5,interactom,0.736920083,0.257838475,0.001310339,0.001310348,0.001310383,0.001310372,Drug discovery,0.51248235,TRUE,35.4,0.490815759,,,0,0.403234768,942,0.911389357,0.601813294
11883,"Rapid, widespread, and preferential increase of SARS-CoV-2 B.1.1.7 variant in Houston, TX, revealed by 8,857 genome sequences",0,10.1101/2021.03.16.21253753,3/24/21,medrxiv,0,7,"genome sequences, genomes",0.002032857,0.770224279,0.002032822,0.002032873,0.002032788,0.22164438,Genomics,0.09673369,FALSE,120.2857143,0.907539118,,,0,0.403234768,2059,0.966530219,0.759101368
11884,"Physical, cognitive and mental health impacts of COVID-19 following hospitalisation: a multi-centre prospective cohort study",0,10.1101/2021.03.22.21254057,3/24/21,medrxiv,0,47,logistic regression,0.00109884,0.00109891,0.001098811,0.001098857,0.379367659,0.616236923,Clinics,0.7098644,TRUE,103.7555556,0.876986827,,,0,0.403234768,15011,0.998555261,0.759592285
11885,Economic Crisis and Mental Health during the COVID-19 Pandemic in Japan,0,10.1101/2021.03.20.21254038,3/24/21,medrxiv,0,0,logistic regression,0.001330085,0.001330055,0.001330075,0.001330125,0.993349613,0.001330046,Healthcare,0.80550104,TRUE,51,0.63875317,26,0.53819909,0,0.403234768,606,0.852395858,0.608145721
11886,What Explains the Socioeconomic Status-Health Gradient? Evidence from Workplace COVID-19 Infections,0,10.1101/2021.03.23.21254170,3/24/21,medrxiv,0,0,dataset,0.002996459,0.002996557,0.002996781,0.578649958,0.409363742,0.002996502,Epidemiology,0.58926594,TRUE,2,0.022141134,,,0,0.403234768,677,0.870936672,0.432104191
11887,A SIRD model applied to COVID-19 dynamics and intervention strategies during the first wave in Kenya,0,10.1101/2021.03.17.21253626,3/24/21,medrxiv,0,3,mathematical model,0.001653027,0.001653066,0.001653068,0.991734654,0.001653105,0.00165308,Epidemiology,0.24811155,FALSE,7.333333333,0.105572392,0.666666667,0.096200161,0,0.403234768,342,0.743558873,0.337141548
11888,Molecular Epidemiology of SARS-CoV-2 in Cyprus,0,10.1101/2021.03.16.21252974,3/24/21,medrxiv,0,14,"sequencing, whole genome",0.001112625,0.858367924,0.001112652,0.137181492,0.001112652,0.001112656,Genomics,0.4134277,FALSE,42,0.558537943,24.71428571,0.526224244,0,0.403234768,365,0.75824705,0.561561001
11889,Prediction of COVID-19 Mortality to Support Patient Prognosis and Triage and Limits of Current Open-Source Data,0,10.1101/2021.03.21.21253984,3/24/21,medrxiv,0,1,"machine learning, logistic regression, dataset",0.001156268,0.001156283,0.812549901,0.00115634,0.001156285,0.182824924,Clinics,0.4824782,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,771,0.887069588,0.350141137
11890,Impact of Clinical and Genomic Factors on SARS-CoV2 Disease Severity,0,10.1101/2021.03.15.21253549,3/24/21,medrxiv,0,10,"machine learning, dataset",0.00194359,0.326136036,0.502419067,0.001943583,0.001943598,0.165614126,Genomics,0.1933406,FALSE,17.4,0.26173542,,,0,0.403234768,1020,0.918613051,0.527861079
11891,First and second SARS-CoV-2 waves in inner London: A comparison of admission characteristics and the effects of the B.1.1.7 variant,0,10.1101/2021.03.16.21253377,3/24/21,medrxiv,0,11,sequencing,0.000752884,0.57628945,0.000752902,0.117126447,0.040339654,0.264738663,Genomics,0.15862963,FALSE,24.18181818,0.356793865,,,0,0.403234768,1230,0.934986757,0.56500513
11892,"Broad decline and subsequent differential re-emergence of respiratory viruses during COVID-19 pandemic response measures, Singapore 2020",0,10.1101/2021.03.23.21251968,3/24/21,medrxiv,0,7,dataset,0.001141353,0.460971435,0.001141326,0.419880952,0.11572355,0.001141383,Genomics,0.25778466,FALSE,20.57142857,0.305399221,,,0,0.403234768,517,0.82735372,0.511995903
11893,Detection of Mutations Associated with Variants of Concern Via High Throughput Sequencing of SARS-CoV-2 Isolated from NYC Wastewater,0,10.1101/2021.03.21.21253978,3/23/21,medrxiv,0,11,sequencing,0.045210278,0.897632958,0.000936103,0.054348456,0.000936113,0.000936091,Genomics,0.11375445,FALSE,29.45454545,0.424083122,16.09090909,0.437650522,0,0.403234768,1828,0.956417048,0.555346365
11894,Characterization and functional interrogation of SARS-CoV-2 RNA interactome,0,10.1101/2021.03.23.436611,3/23/21,biorxiv,0,9,interactom,0.772643159,0.219750043,0.001901693,0.00190172,0.001901705,0.001901681,Drug discovery,0.8397106,TRUE,34.33333333,0.480425506,65.33333333,0.740032111,0,0.403234768,1225,0.93065254,0.638586231
11895,Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers,0,10.1101/2021.03.23.436613,3/23/21,biorxiv,0,11,"computational, in silico",0.724111,0.268883628,0.001751301,0.001751329,0.001751414,0.001751327,Drug discovery,0.51644176,TRUE,11.72727273,0.176696147,,,0,0.403234768,1171,0.927522273,0.502484396
11896,The Impact of Early or Late Lockdowns on the Spread of COVID-19 in US Counties,0,10.1101/2021.03.19.21253997,3/22/21,medrxiv,0,6,"machine learning, dataset",0.000568963,0.000568954,0.036942898,0.850428928,0.110921277,0.00056898,Epidemiology,0.5867337,TRUE,3.833333333,0.047745686,,,0,0.403234768,1036,0.909222249,0.453400901
11897,T-cell receptor sequencing identifies prior SARS-CoV-2 infection and correlates with neutralizing antibody titers and disease severity,0,10.1101/2021.03.19.21251426,3/22/21,medrxiv,0,18,sequencing,0.164702331,0.435537017,0.093279411,0.000956365,0.050352447,0.255172429,Genomics,0.09551099,FALSE,127.7222222,0.918362298,186.7222222,0.9103559,0,0.403234768,1331,0.932819648,0.791193154
11898,Lives Saved from Age-Prioritised COVID-19 Vaccination,0,10.1101/2021.03.19.21253991,3/22/21,medrxiv,0,3,mathematical model,0.169247123,0.002183252,0.002183207,0.64706923,0.17713371,0.002183478,Epidemiology,0.0714069,FALSE,29.66666667,0.42773208,64.33333333,0.736218892,0,0.403234768,949,0.900313027,0.616874692
11899,Job stress among workers who telecommute during the coronavirus disease (COVID-19) pandemic in Japan: a cross-sectional study,0,10.1101/2021.03.19.21253958,3/22/21,medrxiv,0,9,logistic regression,0.001717259,0.052120269,0.001717277,0.001717299,0.941010615,0.00171728,Healthcare,0.88776374,TRUE,9.444444444,0.140392108,1.777777778,0.148849344,0,0.403234768,629,0.83915242,0.38290716
11900,Developing machine learning models for predicting intensive care unit resource use during the COVID-19 pandemic,0,10.1101/2021.03.19.21253947,3/22/21,medrxiv,0,8,"machine learning, forecasting model",0.000846519,0.01389094,0.249568063,0.167197795,0.000846523,0.567650161,Clinics,0.5027728,TRUE,133.25,0.92535098,179.625,0.906074391,0,0.403234768,503,0.801348423,0.75900214
11901,Symptoms of COVID-19 in a population-based cohort study,0,10.1101/2021.03.20.21254040,3/22/21,medrxiv,0,19,logistic regression,0.001823372,0.071961501,0.032483792,0.001823393,0.682360823,0.209547119,Healthcare,0.26729977,FALSE,35.36842105,0.490444678,,,0,0.403234768,941,0.899109078,0.597596174
11902,Predicting hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic in 2020,0,10.1101/2021.03.21.436312,3/22/21,biorxiv,0,12,deep learning,0.001565386,0.67857327,0.315165264,0.001565357,0.001565363,0.00156536,Genomics,0.45447645,FALSE,2.076923077,0.022264828,,,0,0.403234768,692,0.856007705,0.4271691
11903,Structural modeling of the SARS-CoV-2 Spike/human ACE2 complex interface can identify high-affinity variants associated with increased transmissibility,0,10.1101/2021.03.22.436454,3/22/21,biorxiv,0,4,structural model,0.568136707,0.416115556,0.000926279,0.000926304,0.000926319,0.012968835,Drug discovery,0.17994276,FALSE,32.5,0.461685942,188.25,0.911024886,0,0.403234768,739,0.867806405,0.660938
11904,TMPRSS2 inhibitor discovery facilitated through an in silico and biochemical screening platform,0,10.1101/2021.03.22.436465,3/22/21,biorxiv,0,6,"virtual screening, in silico",0.991413868,0.00171729,0.001717248,0.001717221,0.001717192,0.001717181,Drug discovery,0.60869217,TRUE,69.83333333,0.762941431,,,0,0.403234768,1025,0.9080183,0.691398166
11905,Critical interactions for SARS-CoV-2 spike protein binding to ACE2 identified by machine learning,0,10.1101/2021.03.19.436231,3/21/21,biorxiv,0,9,machine learning,0.791825427,0.108463425,0.094751955,0.001653063,0.001653034,0.001653096,Drug discovery,0.64396286,TRUE,56.55555556,0.6790154,,,0,0.403234768,944,0.890440645,0.657563604
11906,Structural and energetic profiling of SARS-CoV-2 antibody recognition and the impact of circulating variants,0,10.1101/2021.03.21.436311,3/21/21,biorxiv,0,7,"computational, dataset",0.550373807,0.443578836,0.001511895,0.001511839,0.001511822,0.001511802,Drug discovery,0.1777316,FALSE,32.14285714,0.456861896,51.42857143,0.686981536,0,0.403234768,760,0.863953768,0.602757992
11907,Sarcopenic obesity and the risk of hospitalisation or death from COVID-19: findings from UK Biobank,0,10.1101/2021.03.19.21253945,3/20/21,medrxiv,0,5,logistic regression,0.054495734,0.001511857,0.001511872,0.001511927,0.173136604,0.767832006,Clinics,0.4817896,FALSE,122.4,0.910569609,,,0,0.403234768,1024,0.895015651,0.736273342
11908,Effect of Increased Alcohol Consumption During COVID-19 Pandemic on Alcohol-related Liver Disease: A Modelling Study,0,10.1101/2021.03.18.21253887,3/20/21,medrxiv,0,6,simulation model,0.013983754,0.000956327,0.000956304,0.450122474,0.10051329,0.43346785,Epidemiology,0.3076929,FALSE,38.5,0.524089307,20.66666667,0.487958255,0,0.403234768,1198,0.913315675,0.582149501
11909,Smoking and Vaping Among a National Sample of U.S. Adults During the COVID-19 Pandemic,0,10.1101/2021.03.18.21253902,3/20/21,medrxiv,0,3,logistic regression,0.002130811,0.002130758,0.002130652,0.002130823,0.989346297,0.00213066,Healthcare,0.81863797,TRUE,159.3333333,0.951079226,302.6666667,0.955044153,0,0.403234768,1661,0.938839393,0.812049385
11910,Predicting clinical outcomes in the Machine Learning era: The Piacenza score a purely data driven approach for mortality prediction in COVID-19 Pneumonia,0,10.1101/2021.03.16.21253752,3/20/21,medrxiv,0,20,"bayes, machine learning, classifier, dataset",0.001203424,0.001203438,0.481908553,0.001203535,0.001203432,0.513277619,Clinics,0.25552726,FALSE,18.9,0.282144845,5.85,0.273280706,0,0.403234768,604,0.81651818,0.443794625
11911,"COVID-19 collateral damage: psychological distress and behavioral changes among older adults during the first outbreak in Stockholm, Sweden",0,10.1101/2021.03.16.21253750,3/20/21,medrxiv,0,5,logistic regression,0.000793442,0.000793434,0.000793426,0.068064256,0.928762,0.000793442,Healthcare,0.90261865,TRUE,27.8,0.404848785,19.8,0.478257961,0,0.403234768,933,0.882976162,0.542329419
11912,Exploring causal relationships between COVID-19 and cardiometabolic disorders: A bi-directional Mendelian randomization study,0,10.1101/2021.03.20.21254008,3/20/21,medrxiv,0,5,correlation analysis,0.001593636,0.253864136,0.001593592,0.001593538,0.118713272,0.622641826,Clinics,0.50730073,TRUE,22,0.326056033,,,0,0.403234768,712,0.842041897,0.523777566
11913,Targeted Hybridization Capture of SARS-CoV-2 and Metagenomics Enables Genetic Variant Discovery and Nasal Microbiome Insights,0,10.1101/2021.03.16.21252988,3/20/21,medrxiv,0,14,"sequencing, metagenom, microbiom",0.001486442,0.897017021,0.097037073,0.001486458,0.001486404,0.001486602,Genomics,0.38892823,FALSE,46.78571429,0.603500526,,,0,0.403234768,1199,0.913556465,0.640097253
11914,Prevalence and determinants of serum antibodies to SARS-CoV-2 in the general population of the Gardena Valley,0,10.1101/2021.03.19.21253883,3/20/21,medrxiv,0,17,logistic regression,0.001786576,0.204666665,0.08684255,0.001786658,0.343444077,0.361473473,Clinics,0.3763174,FALSE,39.35294118,0.532686004,131.8823529,0.865801445,0,0.403234768,719,0.843005057,0.661181818
11915,Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health Record,0,10.1101/2021.03.16.21253770,3/20/21,medrxiv,0,13,computational,0.000568959,0.130998933,0.371925035,0.000568973,0.027599766,0.468338335,Clinics,0.2669906,FALSE,213.3076923,0.976498237,,,0,0.403234768,1180,0.912111726,0.763948244
11916,Modelling the population-level protection conferred by COVID-19 vaccination,0,10.1101/2021.03.16.21253742,3/20/21,medrxiv,0,3,mathematical model,0.367104503,0.159961901,0.001987176,0.227656315,0.168408741,0.074881364,Drug discovery,0.07354498,FALSE,20.66666667,0.306512462,2,0.164302917,0,0.403234768,866,0.875030099,0.437270061
11917,Return to normal: COVID-19 vaccination under mitigation measures,0,10.1101/2021.03.19.21253893,3/20/21,medrxiv,0,14,mathematical model,0.001237094,0.088882041,0.001237057,0.837793773,0.069612871,0.001237163,Epidemiology,0.1175434,FALSE,186.6428571,0.967283073,52.92857143,0.692801713,0,0.403234768,1923,0.948952564,0.753068029
11918,Factors Associated with Intention to Vaccinate against COVID-19 in Puerto Rico,0,10.1101/2021.03.19.21253972,3/20/21,medrxiv,0,7,logistic regression,0.002238463,0.002238519,0.002238482,0.002238507,0.988807519,0.002238509,Healthcare,0.44880667,FALSE,23.71428571,0.35023811,,,0,0.403234768,557,0.800385264,0.517952714
11919,"Within-country age-based prioritisation, global allocation, and public health impact of a vaccine against SARS-CoV-2: a mathematical modelling analysis",0,10.1101/2021.03.19.21253960,3/20/21,medrxiv,0,20,mathematical model,0.093292467,0.001220024,0.00122001,0.671199555,0.231847853,0.001220091,Epidemiology,0.095493585,FALSE,51.15,0.63930979,161.9,0.893765052,0,0.403234768,673,0.833614255,0.692480966
11920,"Modelling the impact of rapid tests, tracing and distancing in lower-income countries suggest optimal policies varies with rural-urban settings",0,10.1101/2021.03.17.21253853,3/20/21,medrxiv,0,5,mathematical model,0.000793402,0.000793418,0.089141142,0.645325192,0.263153453,0.000793392,Epidemiology,0.1959872,FALSE,59,0.696579875,,,0,0.403234768,757,0.851914279,0.650576307
11921,"Importation, circulation, and emergence of variants of SARS-CoV-2 in the South Indian State of Karnataka",0,10.1101/2021.03.17.21253810,3/20/21,medrxiv,0,8,"whole genome, genomes",0.021082358,0.961844386,0.00092628,0.000926327,0.000926334,0.014294315,Genomics,0.37644374,FALSE,8.875,0.130249242,3.5,0.213607172,0,0.403234768,1183,0.912352516,0.414860924
11922,Homophily in risk and behavior complicate understanding the COVID-19 epidemic curve,0,10.1101/2021.03.16.21253708,3/20/21,medrxiv,0,4,predictive model,0.001943578,0.001943582,0.001943538,0.793241852,0.001943568,0.198983882,Epidemiology,0.1213007,FALSE,70,0.764178366,140,0.874096869,0,0.403234768,463,0.763062846,0.701143212
11923,Monitoring the propagation of SARS CoV2 variants by tracking identified mutation in wastewater using specific RT-qPCR,0,10.1101/2021.03.10.21253291,3/20/21,medrxiv,0,10,"sequencing, genomes",0.001786663,0.761958289,0.001786573,0.230895132,0.001786625,0.001786718,Genomics,0.134143,FALSE,16.7,0.25128332,23.6,0.517259834,2,0.618927094,690,0.837707681,0.556294482
11924,Intensity of COVID-19 in care homes following Hospital Discharge in the early stages of the UK epidemic,0,10.1101/2021.03.18.21253443,3/20/21,medrxiv,0,10,logistic regression,0.000926341,0.000926318,0.000926339,0.35191619,0.268422573,0.376882238,Clinics,0.20131612,FALSE,86.7,0.832333478,118.9,0.850147177,0,0.403234768,946,0.88490248,0.742654476
11925,Household transmission of SARS-CoV-2 R.1 lineage with spike E484K mutation in Japan,0,10.1101/2021.03.16.21253248,3/20/21,medrxiv,0,2,"sequencing, whole genome",0.068178509,0.723870237,0.001823328,0.00182335,0.001823415,0.202481161,Genomics,0.3900327,FALSE,917,0.999443379,971.5,0.993443939,0,0.403234768,1257,0.917409102,0.828382797
11926,Epitope-resolved serology test differentiates the clinical outcome of COVID-19 and identifies defects in antibody response in SARS-CoV-2 variants,0,10.1101/2021.03.16.21253716,3/20/21,medrxiv,0,26,proteom,0.490800269,0.373298888,0.001291264,0.00129123,0.001291238,0.132027111,Drug discovery,0.3248068,FALSE,51.34615385,0.641350733,,,0,0.403234768,951,0.88514327,0.643242923
11927,"Detecting SARS-CoV-2 lineages and mutational load in municipal wastewater; a use-case in the metropolitan area of Thessaloniki, Greece",0,10.1101/2021.03.17.21252673,3/20/21,medrxiv,0,18,sequencing,0.001901705,0.717986938,0.098025624,0.178281963,0.001901756,0.001902014,Genomics,0.173305,FALSE,16.5,0.249366071,3.166666667,0.201097137,0,0.403234768,1509,0.934023597,0.446930393
11928,Combined computational and cellular screening identifies synergistic inhibition of SARS-CoV-2 by lenvatinib and remdesivir,0,10.1101/2021.03.19.435806,3/19/21,biorxiv,0,10,computational,0.9964471,0.000710589,0.000710571,0.000710566,0.000710563,0.000710611,Drug discovery,0.58745944,TRUE,66.2,0.741851692,94.7,0.812081884,0,0.403234768,777,0.846616903,0.700946312
11929,"A novel SARS-CoV-2 related virus with complex recombination isolated from bats in Yunnan province, China",0,10.1101/2021.03.17.435823,3/18/21,biorxiv,0,7,sequence alignment,0.221961282,0.767788432,0.002562565,0.002562608,0.002562569,0.002562543,Genomics,0.46383664,FALSE,34.57142857,0.482837529,,,0,0.403234768,1990,0.945581507,0.610551268
11930,Genetic variability associated with OAS1 expression in myeloid cells increases the risk of Alzheimer's disease and severe COVID-19 outcomes,0,10.1101/2021.03.16.435702,3/18/21,biorxiv,0,20,"sequencing, genome-wide",0.581233613,0.372648449,0.001272662,0.001272676,0.001272662,0.042299939,Drug discovery,0.05757141,FALSE,89.05,0.839445853,151.65,0.884399251,0,0.403234768,1791,0.937635444,0.766178829
11931,Using Machine Learning along with Data Science algorithms to pre-process and forecast COVID-19 Cases and Deaths,0,10.1101/2021.03.15.21253571,3/17/21,medrxiv,0,1,"machine learning, neural network, lstm",0.001987286,0.001987111,0.460428114,0.53162326,0.001987125,0.001987105,Epidemiology,0.092413604,FALSE,6,0.086028821,1,0.122023013,0,0.403234768,630,0.795328678,0.35165382
11932,A proteome-wide genetic investigation identifies several SARS-CoV-2-exploited host targets of clinical relevance,0,10.1101/2021.03.15.21253625,3/17/21,medrxiv,0,18,"proteom, dataset",0.881579561,0.001901992,0.001901785,0.001901796,0.001901787,0.110813079,Drug discovery,0.16769549,FALSE,65.38888889,0.736532872,,,0,0.403234768,1536,0.924873585,0.688213741
11933,COVID-19 with early neurological and cardiac thromboembolic phenomena--timeline of incidence and clinical features,0,10.1101/2021.03.15.21253619,3/17/21,medrxiv,0,13,logistic regression,0.001461866,0.00146189,0.049684747,0.001461986,0.057680637,0.888248874,Clinics,0.53158486,TRUE,12.38461538,0.18690086,,,0,0.403234768,1200,0.898145919,0.496093849
11934,Associations of the BNT162b2 COVID-19 vaccine effectiveness with patient age and comorbidities,0,10.1101/2021.03.16.21253686,3/17/21,medrxiv,0,10,"logistic regression, dataset",0.001987288,0.001987137,0.001987168,0.562205296,0.00198729,0.42984582,Epidemiology,0.07916385,FALSE,72.3,0.774321232,85.7,0.791945411,2,0.618927094,12061,0.995184204,0.795094485
11935,Quantitative Comparison of SARS-CoV-2 Nucleic Acid Amplification Test and Antigen Testing Algorithms: A Decision Analysis Simulation Model,0,10.1101/2021.03.15.21253608,3/17/21,medrxiv,0,7,simulation model,0.001171561,0.143053607,0.30921401,0.248285052,0.29710418,0.001171591,Healthcare,0.07230461,FALSE,50.42857143,0.634733131,,,0,0.403234768,564,0.776065495,0.604677798
11936,"Patterns of compliance with COVID-19 preventive behaviours: a latent class analysis of 20,000 UK adults",0,10.1101/2021.03.16.21253717,3/17/21,medrxiv,0,3,logistic regression,0.00139283,0.001392837,0.001392865,0.198507177,0.795921472,0.001392818,Healthcare,0.8714049,TRUE,108,0.886078298,128.6666667,0.862055124,0,0.403234768,1077,0.883698531,0.75876668
11937,SARS-CoV-2 Epidemiology on a Public University Campus in Washington State,0,10.1101/2021.03.15.21253227,3/17/21,medrxiv,0,36,"sequencing, genomes",0.001272636,0.339397676,0.001272673,0.001272716,0.655511637,0.001272662,Healthcare,0.48856625,FALSE,58.77777778,0.694662626,,,0,0.403234768,1530,0.924151216,0.674016203
11938,Vaccine escape in a heterogeneous population: insights for SARS-CoV-2 from a simple model,0,10.1101/2021.03.14.21253544,3/17/21,medrxiv,0,4,mathematical model,0.209187192,0.237951662,0.001943485,0.325527018,0.22344711,0.001943533,Epidemiology,0.11930853,FALSE,46.75,0.603314985,,,2,0.618927094,1716,0.933301228,0.718514436
11939,"An integrated analysis of contact tracing and genomics to assess the efficacy of travel restrictions on SARS-CoV-2 introduction and transmission in England from June to September, 2020",0,10.1101/2021.03.15.21253590,3/17/21,medrxiv,0,21,"sequencing, genomic epidemiology, genomes, predictive model, dataset",0.000558171,0.158044739,0.000558174,0.826168379,0.014112301,0.000558235,Epidemiology,0.2931671,FALSE,52.0952381,0.647659101,,,0,0.403234768,3167,0.968456537,0.673116802
11940,Metagenomic sequencing of municipal wastewater provides a near-complete SARS-CoV-2 genome sequence identified as the B.1.1.7 variant of concern from a Canadian municipality concurrent with an outbreak,0,10.1101/2021.03.11.21253409,3/17/21,medrxiv,0,9,"sequencing, metagenom",0.001653077,0.97310761,0.00165308,0.001653051,0.00165313,0.020280052,Genomics,0.21563724,FALSE,4,0.054734368,,,0,0.403234768,1339,0.909944618,0.455971251
11941,High risk of patient self-inflicted lung injury in COVID-19 with frequently encountered spontaneous breathing patterns: a computational modelling study,0,10.1101/2021.03.17.21253788,3/17/21,medrxiv,0,10,computational,0.001254664,0.001254727,0.186415377,0.26509804,0.001254715,0.544722477,Clinics,0.83611584,TRUE,95.3,0.857381409,,,0,0.403234768,1141,0.890199856,0.716938677
11942,SARS-CoV-2 Spike receptor-binding domain with a G485R mutation in complex with human ACE2,0,10.1101/2021.03.16.434488,3/17/21,biorxiv,0,3,sequencing,0.614993412,0.378394594,0.001652991,0.001653006,0.001653003,0.001652993,Drug discovery,0.5526882,TRUE,66.66666667,0.745191416,48,0.673735617,0,0.403234768,1107,0.886588009,0.677187452
11943,Cluster Analysis of SARS-CoV-2 Gene using Deep Learning Autoencoder: Gene Profiling for Mutations and Transitions,0,10.1101/2021.03.16.435601,3/16/21,biorxiv,0,6,deep learning,0.001538182,0.836570843,0.001538192,0.001538223,0.001538169,0.157276392,Genomics,0.22329631,FALSE,20.66666667,0.306512462,,,0,0.403234768,555,0.762340477,0.490695902
11944,3D genomic capture of regulatory immuno-genetic profiles in COVID-19 patients for prognosis of severe COVID disease outcome,0,10.1101/2021.03.14.435295,3/16/21,biorxiv,0,18,"machine learning, whole genome",0.255578459,0.263072859,0.096106104,0.001237153,0.001237085,0.38276834,Clinics,0.21673378,FALSE,39.9,0.538066671,,,0,0.403234768,1838,0.934745967,0.625349135
11945,Immunoinformatic Approach for the identification of T Cell and B Cell Epitopes in the Surface Glycoprotein and Designing a Potent Multiepitope Vaccine Construct Against SARS-CoV-2 including the new UK variant,0,10.1101/2021.03.15.435391,3/16/21,biorxiv,0,8,"computational, bioinformatic",0.68438801,0.311702109,0.000977479,0.000977508,0.000977455,0.00097744,Drug discovery,0.6755146,TRUE,6.375,0.090172552,1.125,0.122959593,0,0.403234768,758,0.824223453,0.360147592
11946,Killed whole genome-reduced bacteria surface-expressed coronavirus fusion peptide vaccines protect against disease in a porcine model,0,10.1101/2021.03.15.435497,3/16/21,biorxiv,0,14,whole genome,0.78513685,0.211772569,0.000772642,0.000772693,0.000772642,0.000772603,Drug discovery,0.11140141,FALSE,17.71428571,0.266621312,,,0,0.403234768,1561,0.921502528,0.530452869
11947,Impact of School Reopening on Pandemic Spread: A Case Study using an Agent-Based Model for COVID-19,0,10.1101/2021.03.13.21253485,3/15/21,medrxiv,0,2,simulation model,0.001786593,0.047388601,0.001786523,0.829982992,0.117268792,0.0017865,Epidemiology,0.248003,FALSE,40.5,0.544746119,39.5,0.630920524,0,0.403234768,726,0.807608957,0.596627592
11948,The local and systemic response to SARS-CoV-2 infection in children and adults,0,10.1101/2021.03.09.21253012,3/15/21,medrxiv,0,54,multi-omics,0.462183305,0.176661612,0.000988373,0.000988444,0.170198916,0.18897935,Drug discovery,0.2841456,FALSE,23.56097561,0.348753788,36.14634146,0.611921327,0,0.403234768,2682,0.955694679,0.57990114
11949,Monitoring the rise of the SARS-CoV-2 lineage B.1.1.7 in Tenerife (Spain) since mid-December 2020,0,10.1101/2021.03.14.21253535,3/15/21,medrxiv,0,11,sequencing,0.002130666,0.989346457,0.002130629,0.002130779,0.002130717,0.002130752,Genomics,0.6008167,TRUE,14.54545455,0.219803327,10.27272727,0.358442601,0,0.403234768,1018,0.867084036,0.462141183
11950,HIV service interruptions during the COVID-19 pandemic in China: the role of HIV service challenges and institutional response from healthcare professional's perspective,0,10.1101/2021.03.12.21253463,3/15/21,medrxiv,0,7,logistic regression,0.000846577,0.000846549,0.089374289,0.000846569,0.90723948,0.000846536,Healthcare,0.34517953,FALSE,,,,,0,0.403234768,632,0.783529978,0.593382373
11951,"Structure, Mechanism and Crystallographic fragment screening of the SARS-CoV-2 NSP13 helicase",0,10.1101/2021.03.15.435326,3/15/21,biorxiv,0,13,"proteom, dataset",0.751015668,0.242246156,0.001684591,0.00168458,0.001684492,0.001684513,Drug discovery,0.79388404,TRUE,101,0.871296926,47.61538462,0.670925876,0,0.403234768,1331,0.901757765,0.711803834
11952,S-acylation controls SARS-Cov-2 membrane lipid organization and enhances infectivity,0,10.1101/2021.03.14.435299,3/15/21,biorxiv,0,11,"computational, lipidom",0.988807297,0.002238618,0.002238485,0.002238606,0.002238506,0.002238488,Drug discovery,0.43816325,FALSE,38.58333333,0.524769621,66.58333333,0.744380519,0,0.403234768,1461,0.911148567,0.645883369
11953,Effects of Mutations in the Receptor-Binding Domain of SARS-CoV-2 Spike on its Binding Affinity to ACE2 and Neutralizing Antibodies Revealed by Computational Analysis,0,10.1101/2021.03.14.435322,3/15/21,biorxiv,0,5,computational,0.642854798,0.351297664,0.001461862,0.001461921,0.001461887,0.001461867,Drug discovery,0.21077648,FALSE,64.6,0.73238914,11,0.371287129,0,0.403234768,1166,0.882735372,0.597411602
11954,"A randomized, double-blind, controlled trial of convalescent plasma in adults with severe COVID-19",0,10.1101/2021.03.12.21253373,3/13/21,medrxiv,0,41,sequencing,0.000889063,0.056110682,0.022593076,0.000889081,0.095643637,0.823874461,Clinics,0.5959397,TRUE,31.95121951,0.454016946,26.34146341,0.540674338,1,0.537564047,2617,0.949674934,0.620482566
11955,Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization,0,10.1101/2021.03.12.21253358,3/13/21,medrxiv,0,7,machine learning,0.00213066,0.002130709,0.269796311,0.378221679,0.067549024,0.280171618,Epidemiology,0.38835865,FALSE,64.85714286,0.733873462,76.28571429,0.769467487,0,0.403234768,926,0.837226102,0.685950455
11956,Rapid review of social contact patterns during the COVID-19 pandemic,0,10.1101/2021.03.12.21253410,3/13/21,medrxiv,0,11,mathematical model,0.000999509,0.00099952,0.000999512,0.930799495,0.065202445,0.000999519,Epidemiology,0.4468758,FALSE,59.72727273,0.701094687,150.4545455,0.883663366,0,0.403234768,579,0.744040453,0.683008318
11957,Seroprevalence of Antibodies to SARS-CoV-2 among Health Care Workers in Kenya,0,10.1101/2021.03.12.21253493,3/13/21,medrxiv,0,50,"bayes, bayesian model",0.002490542,0.423701816,0.103828886,0.172760784,0.294727423,0.002490549,Genomics,0.41581798,FALSE,14.8125,0.223019358,10.72916667,0.365466952,1,0.537564047,1620,0.913797255,0.509961903
11958,Is there a serum proteome signature to predict mortality in severe COVID-19 patients?,0,10.1101/2021.03.13.21253510,3/13/21,medrxiv,0,14,proteom,0.224873367,0.156178849,0.001438209,0.001438141,0.001438127,0.614633306,Clinics,0.23596749,FALSE,132.7142857,0.924856206,160.1428571,0.89195879,0,0.403234768,1099,0.866843246,0.771723253
11959,"Mutation hotspots, geographical and temporal distribution of SARS-CoV-2 lineages in Brazil, February 2020 to February 2021: insights and limitations from uneven sequencing efforts",0,10.1101/2021.03.08.21253152,3/12/21,medrxiv,0,5,"sequencing, genomes",0.048151511,0.622331725,0.001046835,0.326376238,0.00104685,0.001046842,Genomics,0.43680394,FALSE,17.8,0.268229328,8,0.320511105,0,0.403234768,954,0.836022153,0.456999338
11960,Forecasting the COVID-19 epidemic integrating symptom search behavior: an infodemiology study,0,10.1101/2021.03.09.21253186,3/12/21,medrxiv,0,7,neural network,0.000822925,0.000822901,0.045403038,0.91375442,0.038373765,0.000822952,Epidemiology,0.27842012,FALSE,185,0.966479065,80.71428571,0.780907145,0,0.403234768,908,0.82759451,0.744553872
11961,COVID-19 lockdowns may reduce resistance genes diversity in the human microbiome and the need for antibiotics,0,10.1101/2021.03.08.21253164,3/12/21,medrxiv,0,6,"computational, microbiom",0.499526635,0.005047877,0.005048036,0.480282276,0.005047601,0.005047575,Drug discovery,0.32236144,FALSE,14.66666667,0.221163956,2.166666667,0.166845063,0,0.403234768,459,0.660967975,0.36305294
11962,"SARS-CoV-2 antibody prevalence and determinants of six ethnic groups living in Amsterdam, the Netherlands: a population-based cross-sectional study, June-October 2020",0,10.1101/2021.03.08.21252788,3/12/21,medrxiv,0,16,logistic regression,0.001371261,0.001371323,0.001371268,0.001371293,0.993143536,0.001371318,Healthcare,0.1830565,FALSE,180.75,0.964623663,107.5,0.835429489,0,0.403234768,452,0.655911389,0.714799827
11963,Maximisation of open hospital capacity under shortage of SARS-CoV-2 vaccines,0,10.1101/2021.03.08.21253150,3/12/21,medrxiv,0,4,simulation model,0.002130702,0.002130756,0.002130699,0.609197704,0.382279267,0.002130873,Epidemiology,0.08094749,FALSE,85.75,0.827571278,47.75,0.671394166,0,0.403234768,1065,0.853118228,0.68882961
11964,Antimicrobial susceptibility patterns of respiratory Gram-negative bacterial isolates from COVID-19 patients in Switzerland,0,10.1101/2021.03.10.21253079,3/12/21,medrxiv,0,10,"sequencing, whole-genome",0.093503311,0.553307297,0.05708288,0.001593535,0.001593522,0.292919455,Genomics,0.8514776,TRUE,,,,,0,0.403234768,415,0.623163978,0.513199373
11965,A Recovery Algorithm and Pooling Designs for One-Stage Noisy Group Testing under the Probabilistic Framework,0,10.1101/2021.03.09.21253193,3/12/21,medrxiv,0,3,probabilistic,0.001786705,0.001786556,0.14249097,0.743328291,0.001786607,0.10882087,Epidemiology,0.14949045,FALSE,57.33333333,0.68433422,192.3333333,0.913767728,0,0.403234768,313,0.502046713,0.625845857
11966,"Detection of SARS-CoV-2 lineage P.1 in patients from a region with exponentially increasing hospitalization rates in February 2021, Rio Grande do Sul, Southern Brazil",0,10.1101/2021.03.09.21253204,3/12/21,medrxiv,0,9,"sequencing, whole-genome",0.003101436,0.486004969,0.003101436,0.309564799,0.003101519,0.195125841,Genomics,0.18921474,FALSE,36.11111111,0.498546602,21.33333333,0.495450896,0,0.403234768,1932,0.928003853,0.58130903
11967,Rapid screening for SARS-CoV-2 variants of concern in clinical and environmental samples using nested RT-PCR assays targeting key mutations of the Spike protein,0,10.1101/2021.03.09.21252450,3/12/21,medrxiv,0,14,sequencing,0.000786368,0.996068092,0.000786417,0.000786405,0.000786378,0.000786341,Genomics,0.43257943,FALSE,35.57142857,0.492918548,40.14285714,0.633864062,0,0.403234768,724,0.786901035,0.579229603
11968,Model Based Estimation of the SARS-CoV-2 Immunization Level in Austria and Consequences for Herd Immunity Effects,0,10.1101/2021.03.10.21253251,3/12/21,medrxiv,0,9,simulation model,0.001786586,0.103303552,0.00178655,0.889549913,0.001786779,0.001786621,Epidemiology,0.08047828,FALSE,21.66666667,0.320737213,6.888888889,0.294153064,0,0.403234768,369,0.575969179,0.398523556
11969,"Cumulative incidence of SARS-CoV-2 infection and associated risk factors among frontline health care workers in Paris, France: the SEROCOV prospective cohort study",0,10.1101/2021.03.09.21253200,3/12/21,medrxiv,0,20,logistic regression,0.001593572,0.001593758,0.001593506,0.14292985,0.452922376,0.399366938,Healthcare,0.57076454,TRUE,71.35,0.76999196,62.1,0.72879315,0,0.403234768,475,0.672044305,0.643516046
11970,"Genomic epidemiology of SARS-CoV-2 in the United Arab Emirates reveals novel virus mutation, patterns of co-infection and tissue specific host responses",0,10.1101/2021.03.09.21252822,3/12/21,medrxiv,0,37,"sequencing, transcriptom, genomic epidemiology",0.18159059,0.793945233,0.001461873,0.001462009,0.001461901,0.020078394,Genomics,0.43587536,FALSE,8.305555556,0.121899932,5.138888889,0.257827134,0,0.403234768,530,0.706236456,0.372299572
11971,Acceptability of contact management and care of simple cases of COVID-19 at home: a mixed-method study in Senegal,0,10.1101/2021.03.10.21253266,3/12/21,medrxiv,0,9,logistic regression,0.001511842,0.001511838,0.001511869,0.643538196,0.350414385,0.001511871,Epidemiology,0.7374214,TRUE,60.77777778,0.706908281,30.55555556,0.573320846,0,0.403234768,436,0.64314953,0.581653356
11972,An Extended COVID-19 Epidemiological Model with Vaccination and Multiple Interventions for Controlling COVID-19 Outbreaks in the UK,0,10.1101/2021.03.10.21252748,3/12/21,medrxiv,0,8,"mathematical model, dataset",0.016472968,0.114592557,0.000662734,0.866946308,0.00066271,0.000662724,Epidemiology,0.1324499,FALSE,4.5,0.061784897,1,0.122023013,0,0.403234768,478,0.675174573,0.315554313
11973,Development and validation of a clinical and genetic model for predicting risk of severe COVID-19,0,10.1101/2021.03.09.21253237,3/12/21,medrxiv,0,3,"logistic regression, dataset",0.002422278,0.186193233,0.331303544,0.21797826,0.259680187,0.002422498,Healthcare,0.10238439,FALSE,56,0.675304595,62.66666667,0.731469093,0,0.403234768,2427,0.943173609,0.688295516
11974,Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK,0,10.1101/2021.03.10.21253311,3/12/21,medrxiv,0,6,bayes,0.000344951,0.285149625,0.000344969,0.703863049,0.009952438,0.000344969,Epidemiology,0.028170168,FALSE,103.1666667,0.875564352,83,0.785590045,0,0.403234768,578,0.732723333,0.699278124
11975,Disulfiram associated with lower risk of Covid-19: a retrospective cohort study,0,10.1101/2021.03.10.21253331,3/12/21,medrxiv,0,13,dataset,0.325556407,0.000746586,0.000746578,0.077803687,0.050199195,0.544947546,Clinics,0.4907309,FALSE,35.38461538,0.490630218,15.46153846,0.429087503,0,0.403234768,4000,0.969660486,0.573153244
11976,Mapping the human genetic architecture of COVID-19 by worldwide meta-analysis,0,10.1101/2021.03.10.21252820,3/12/21,medrxiv,0,2,genome-wide,0.184949176,0.528031518,0.001461928,0.129003813,0.001461935,0.15509163,Genomics,0.5129664,TRUE,4.5,0.061784897,4.5,0.242708055,2,0.618927094,7923,0.986515772,0.477483954
11977,Specific allelic discrimination of N501Y and other SARS-CoV-2 mutations by ddPCR detects B.1.1.7 lineage in Washington State,0,10.1101/2021.03.10.21253321,3/12/21,medrxiv,0,14,sequencing,0.001187338,0.994063418,0.001187326,0.001187294,0.001187293,0.001187331,Genomics,0.22647297,FALSE,42.85714286,0.565959552,62,0.728659352,0,0.403234768,951,0.835540573,0.633348561
11978,Reducing the Cost of Rapid Antigen Tests through Swab Pooling and Extraction in a Device,0,10.1101/2021.03.11.21252969,3/12/21,medrxiv,0,5,image analysis,0.001622748,0.48237157,0.422449477,0.00162281,0.090310638,0.001622756,Genomics,0.5718529,TRUE,2.6,0.028263962,0,0.055525823,0,0.403234768,491,0.682398266,0.292355705
11979,Airborne Transmission of COVID-19 and Mitigation Using Box Fan Air Cleaners in a Poorly Ventilated Classroom,0,10.1101/2021.03.11.21253395,3/12/21,medrxiv,0,6,computational,0.001350361,0.00135036,0.083643852,0.782339202,0.001350379,0.129965846,Epidemiology,0.66530174,TRUE,22.33333333,0.330261612,13.66666667,0.407412363,0,0.403234768,981,0.83939321,0.495075488
11980,Acceptability of COVID-19 vaccination among health care workers in Ghana,0,10.1101/2021.03.11.21253374,3/12/21,medrxiv,0,4,logistic regression,0.001438144,0.001438107,0.059021571,0.001438167,0.93522588,0.001438131,Healthcare,0.62352157,TRUE,3.5,0.044344115,0,0.055525823,0,0.403234768,663,0.769082591,0.318046824
11981,Immunodominant B cell epitope in SARS-CoV-2 RBD comprises a B.1.351 and P.1 mutation hotspot: implications for viral spread and antibody escape,0,10.1101/2021.03.11.21253399,3/12/21,medrxiv,0,17,in silico,0.598426414,0.395627865,0.00148641,0.001486445,0.001486437,0.001486429,Drug discovery,0.16423637,FALSE,73.70588235,0.780505906,51.17647059,0.686044956,0,0.403234768,1423,0.894052492,0.69095953
11982,SARS-CoV-2 specific immune-signature in direct contacts of COVID-19 cases protect them from contracting disease: A Retrospective Study,0,10.1101/2021.03.11.21253367,3/12/21,medrxiv,0,15,sequencing,0.209113157,0.479129621,0.002562567,0.002562813,0.17412196,0.132509883,Genomics,0.42683867,FALSE,38.33333333,0.522233904,18.93333333,0.469226652,0,0.403234768,690,0.778473393,0.543292179
11983,An observational cohort study on the incidence of SARS-CoV-2 infection and B.1.1.7 variant infection in healthcare workers by antibody and vaccination status,0,10.1101/2021.03.09.21253218,3/12/21,medrxiv,0,36,"sequencing, whole genome",0.08605559,0.34386113,0.00115623,0.00115632,0.566614403,0.001156327,Healthcare,0.20876366,FALSE,78.39473684,0.799863937,106.2894737,0.833556329,2,0.618927094,5052,0.978328919,0.80766907
11984,Recovered not restored: Long-term health consequences after mild COVID-19 in non-hospitalized patients,0,10.1101/2021.03.11.21253207,3/12/21,medrxiv,0,26,logistic regression,0.000657756,0.000657826,0.000657807,0.275812731,0.275464723,0.446749157,Clinics,0.5664512,TRUE,73.26923077,0.778897891,54.57692308,0.699959861,0,0.403234768,4662,0.975439441,0.71438299
11985,"Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors",0,10.1101/2021.03.11.21253380,3/12/21,medrxiv,0,6,"machine learning, logistic regression, dataset",0.069156631,0.001486669,0.184993198,0.478421867,0.066009616,0.199932019,Epidemiology,0.22748637,FALSE,56,0.675304595,18.16666667,0.462603693,0,0.403234768,730,0.787864195,0.582251813
11986,Impact of Vaccine Prioritization Strategies on Mitigating COVID-19: An Agent-Based Simulation Study using an Urban Region in the United States,0,10.1101/2021.03.12.21253447,3/12/21,medrxiv,0,3,simulation model,0.001034606,0.058071218,0.001034577,0.92144617,0.001034663,0.017378766,Epidemiology,0.16409436,FALSE,28,0.408312202,26.33333333,0.540607439,1,0.537564047,598,0.743318083,0.557450443
11987,Proteome-wide Mendelian randomization identifies causal links between blood proteins and severe COVID-19,0,10.1101/2021.03.09.21253206,3/11/21,medrxiv,0,9,"proteom, genome-wide",0.570292246,0.083151912,0.001653065,0.00165307,0.001653072,0.341596635,Drug discovery,0.24570283,FALSE,106.4444444,0.881934566,117.2222222,0.847136741,0,0.403234768,836,0.806164219,0.734617573
11988,What level of neutralising antibody protects from COVID-19? .,0,10.1101/2021.03.09.21252641,3/11/21,medrxiv,0,10,predictive model,0.084560594,0.200967297,0.001486496,0.329253318,0.382245769,0.001486526,Healthcare,0.14174119,FALSE,143.1,0.937658482,180.7,0.906676478,3,0.667819001,20433,0.996628943,0.877195726
11989,Emerging variants of concern in SARS-CoV-2 membrane protein: a highly conserved target with potential pathological and therapeutic implications,0,10.1101/2021.03.11.434758,3/11/21,biorxiv,0,0,genomes,0.108599262,0.886835337,0.001141323,0.001141384,0.001141338,0.001141356,Genomics,0.38358557,FALSE,169.6666667,0.958129754,362.5,0.967687985,0,0.403234768,929,0.826872141,0.788981162
11990,Prediction of COVID-19 mortality among hospitalized patients in Sudan,0,10.1101/2021.03.09.21253179,3/11/21,medrxiv,0,3,prediction model,0.001622683,0.001622714,0.001622754,0.101686472,0.001622734,0.891822644,Clinics,0.7168722,TRUE,4.333333333,0.057950399,0,0.055525823,0,0.403234768,416,0.610161329,0.28171808
11991,Detection of autoimmune antibodies in severe but not in moderate or asymptomatic COVID-19 patients,0,10.1101/2021.03.02.21252438,3/10/21,medrxiv,0,14,sequencing,0.279745024,0.276933265,0.001565416,0.001565359,0.001565389,0.438625547,Clinics,0.5046009,TRUE,36.28571429,0.500463851,20.14285714,0.481402194,0,0.403234768,983,0.829039249,0.553535015
11992,Plasma ACE2 levels predict outcome of COVID-19 in hospitalized patients,0,10.1101/2021.03.08.21252819,3/10/21,medrxiv,0,10,proteom,0.158123459,0.000688492,0.000688498,0.000688494,0.045793425,0.794017631,Clinics,0.8174832,TRUE,43.4,0.571958686,54.6,0.700093658,0,0.403234768,853,0.8042379,0.619881253
11993,Increased mortality among individuals hospitalised with COVID-19 during the second wave in South Africa,0,10.1101/2021.03.09.21253184,3/10/21,medrxiv,0,14,"sequencing, logistic regression",0.000565309,0.188289039,0.000565301,0.439924995,0.000565327,0.37009003,Epidemiology,0.071837485,FALSE,49.71428571,0.629476158,56.14285714,0.707318705,0,0.403234768,1858,0.916686732,0.664179091
11994,Structure and dynamics of the SARS-CoV-2 envelope protein monomer,0,10.1101/2021.03.10.434722,3/10/21,biorxiv,0,5,molecular dynamics simulation,0.960178541,0.001901793,0.03221441,0.001901766,0.001901789,0.001901701,Drug discovery,0.5085845,TRUE,72,0.77302245,11.8,0.38252609,0,0.403234768,1440,0.886347219,0.611282632
11995,Longitudinal single-cell epitope and RNA-sequencing reveals the immunological impact of type 1 interferon autoantibodies in critical COVID-19,0,10.1101/2021.03.09.434529,3/10/21,biorxiv,0,46,"sequencing, transcriptom",0.548599797,0.001861857,0.001861713,0.00186171,0.001861799,0.443953125,Drug discovery,0.08057514,FALSE,96.28888889,0.859731585,135.4444444,0.870016056,0,0.403234768,2601,0.943414399,0.769099202
11996,Tocilizumab efficacy in COVID-19 patients is associated with respiratory severity-based stages,0,10.1101/2021.03.04.21252167,3/9/21,medrxiv,0,22,structural model,0.019742532,0.000966821,0.066214938,0.077401534,0.000966819,0.834707356,Clinics,0.8221861,TRUE,31.81818182,0.453089245,40.18181818,0.634399251,0,0.403234768,858,0.798218156,0.572235355
11997,Quantification of the spread of SARS-CoV-2 variant B.1.1.7 in Switzerland,0,10.1101/2021.03.05.21252520,3/9/21,medrxiv,0,53,"sequencing, whole genome",0.001350322,0.465609714,0.001350336,0.528988966,0.001350335,0.001350328,Epidemiology,0.02470401,FALSE,31.70212766,0.45203785,110.5957447,0.839443404,2,0.618927094,2626,0.941247291,0.71291391
11998,COVID-19 risk score as a public health tool to guide targeted testing: A demonstration study in Qatar,0,10.1101/2021.03.06.21252601,3/9/21,medrxiv,0,19,logistic regression,0.001330137,0.001330093,0.426550211,0.098683518,0.00133009,0.470775952,Clinics,0.6811833,TRUE,44.15789474,0.578761828,30.15789474,0.570644902,0,0.403234768,626,0.725018059,0.569414889
11999,A quantitative risk estimation platform for indoor aerosol transmission of COVID-19,0,10.1101/2021.03.05.21252990,3/9/21,medrxiv,0,4,"model fit, probabilistic",0.138176908,0.02383895,0.05165056,0.648079572,0.001059415,0.137194595,Epidemiology,0.19449946,FALSE,3.75,0.046817985,0.5,0.087101953,1,0.537564047,1772,0.908740669,0.395056164
12000,Predicting vaccine hesitancy from area-level indicators: A machine learning approach,0,10.1101/2021.03.08.21253109,3/9/21,medrxiv,0,3,machine learning,0.002296715,0.002296618,0.447741589,0.411760758,0.133607638,0.002296682,Epidemiology,0.74544847,TRUE,2.333333333,0.024800544,0.333333333,0.073187048,0,0.403234768,1317,0.871177462,0.343099956
12001,"A general computational framework for COVID-19 modelling, with applications to testing varied interventions in education environments",0,10.1101/2021.03.08.21253122,3/9/21,medrxiv,0,5,computational,0.002562613,0.002562571,0.002562611,0.546142436,0.407011625,0.039158143,Epidemiology,0.3396026,FALSE,19.6,0.291854784,4.4,0.238961734,0,0.403234768,1107,0.842764267,0.444203888
12002,Systemic Tissue and Cellular Disruption from SARS-CoV-2 Infection revealed in COVID-19 Autopsies and Spatial Omics Tissue Maps,0,10.1101/2021.03.08.434433,3/9/21,biorxiv,0,45, omics,0.611658803,0.075818548,0.001330095,0.054712403,0.00133007,0.255150081,Drug discovery,0.43104762,FALSE,74.36170213,0.783412703,168.5106383,0.898314156,0,0.403234768,2004,0.920539369,0.751375249
12003,Quantitative proteomics of hamster lung tissues infected with SARS-CoV-2 reveal host-factors having implication in the disease pathogenesis and severity,0,10.1101/2021.03.09.434371,3/9/21,biorxiv,0,15,proteom,0.657545799,0.142409133,0.001538147,0.00153823,0.001538161,0.195430529,Drug discovery,0.51836246,TRUE,42.4,0.561382893,33.66666667,0.596802248,0,0.403234768,1152,0.847339273,0.602189795
12004,Post discharge persistent symptoms after COVID-19 in rheumatic and musculoskeletal diseases,0,10.1101/2021.03.08.21253120,3/8/21,medrxiv,0,11,logistic regression,0.001943475,0.001943475,0.033474317,0.001943516,0.228595343,0.732099873,Clinics,0.9890745,TRUE,44.18181818,0.579256602,18.90909091,0.469159754,0,0.403234768,1066,0.831206357,0.57071437
12005,Pediatric nasal epithelial cells are less permissive to SARS-CoV-2 replication compared to adult cells,0,10.1101/2021.03.08.434300,3/8/21,biorxiv,0,17,transcriptom,0.689147712,0.000926315,0.000926285,0.000926331,0.307147046,0.000926311,Drug discovery,0.17364463,FALSE,115.1764706,0.899189808,99.94117647,0.822250468,0,0.403234768,6409,0.980255237,0.77623257
12006,A comparative recombination analysis of human coronaviruses and implications for the SARS-CoV-2 pandemic,0,10.1101/2021.03.07.434287,3/8/21,biorxiv,0,7,"bayes, genome sequences, dataset",0.002639186,0.98680388,0.002639294,0.002639177,0.002639203,0.00263926,Genomics,0.44038284,FALSE,33.14285714,0.46762323,34.85714286,0.604294889,0,0.403234768,1618,0.893089333,0.592060555
12007,Human nasal and lung tissues infected ex vivo with SARS-CoV-2 provide insights into differential tissue-specific and virus-specific innate immune responses in the upper and lower respiratory tract,0,10.1101/2021.03.08.434404,3/8/21,biorxiv,0,21,genome-wide,0.839667172,0.15757892,0.000688466,0.000688488,0.000688468,0.000688487,Drug discovery,0.49446717,FALSE,63.33333333,0.723545055,69,0.750535189,0,0.403234768,667,0.734890441,0.653051363
12008,Comparative host interactomes of the SARS-CoV-2 nonstructural protein 3 and human coronavirus homologs,0,10.1101/2021.03.08.434440,3/8/21,biorxiv,0,3,"interactom, dataset",0.650544747,0.344830223,0.001156251,0.001156252,0.001156275,0.001156252,Drug discovery,0.35324073,FALSE,39.66666667,0.536211268,18.33333333,0.464343056,0,0.403234768,733,0.759450999,0.540810023
12009,Structural Analysis of Spike Protein Mutations in an Emergent SARS-CoV-2 Variant from the Philippines,0,10.1101/2021.03.06.434059,3/8/21,biorxiv,0,3,"sequencing, whole genome",0.261086087,0.732045218,0.001717166,0.001717201,0.001717171,0.001717157,Genomics,0.717784,TRUE,8.333333333,0.123693488,1,0.122023013,0,0.403234768,10975,0.98844209,0.40934834
12010,Identification of novel bat coronaviruses sheds light on the evolutionary origins of SARS-CoV-2 and related viruses,0,10.1101/2021.03.08.434390,3/8/21,biorxiv,0,13,"sequencing, transcriptom, whole genome, genomes",0.001392864,0.993035727,0.001392824,0.001392924,0.001392838,0.001392822,Genomics,0.45558786,FALSE,141.9230769,0.936297854,,,0,0.403234768,13767,0.991572357,0.777034993
12011,Genome-Wide Covariation in SARS-CoV-2,0,10.1101/2021.03.08.434363,3/8/21,biorxiv,0,2,"genome-wide, whole genome, genome sequences",0.11234855,0.797637177,0.083298793,0.002238492,0.00223846,0.002238528,Genomics,0.73433226,TRUE,78.5,0.800482405,51,0.685509767,0,0.403234768,936,0.810257645,0.674871146
12012,AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations,0,10.1101/2021.03.07.434295,3/8/21,biorxiv,0,3,"molecular dynamics simulation, neural network",0.532311633,0.115131155,0.310958668,0.037121707,0.002238408,0.002238428,Drug discovery,0.44651255,FALSE,22.33333333,0.330261612,6,0.280037463,0,0.403234768,1018,0.825186612,0.459680114
12013,"Measuring the exposure of Black, Asian and other ethnic groups to Covid-infected neighbourhoods in English towns and cities",0,10.1101/2021.03.04.21252893,3/8/21,medrxiv,0,2,bayes,0.001861724,0.00186173,0.001861705,0.527787052,0.464765998,0.00186179,Epidemiology,0.20901576,FALSE,105,0.879213309,525.5,0.980800107,0,0.403234768,785,0.776547074,0.759948815
12014,Genome sequencing and detection of Philippine SARS-CoV-2 infections with viruses classified under the B.1.1.7 lineage,0,10.1101/2021.03.04.21252557,3/8/21,medrxiv,0,32,sequencing,0.007061465,0.964692512,0.007061574,0.0070615,0.007061453,0.007061496,Genomics,0.45425475,FALSE,21.34375,0.31566578,27.78125,0.551913299,0,0.403234768,669,0.73609439,0.501727059
12015,Comparison of COVID-19 vaccine prioritization strategies in the United States,0,10.1101/2021.03.04.21251264,3/8/21,medrxiv,0,9,simulation model,0.192947225,0.001010979,0.001010941,0.34081305,0.308947164,0.155270641,Epidemiology,0.081017196,FALSE,73,0.778464964,62.66666667,0.731469093,1,0.537564047,1228,0.855526126,0.725756057
12016,Serological reconstruction of COVID-19 epidemics through analysis of antibody kinetics to SARS-CoV-2 proteins,0,10.1101/2021.03.04.21252532,3/8/21,medrxiv,0,38,"machine learning, computational, mathematical model",0.101697707,0.445341497,0.194537943,0.076828732,0.119437699,0.062156422,Genomics,0.07783398,FALSE,65.78947368,0.738944895,84.13157895,0.788533583,0,0.403234768,544,0.673248254,0.650990375
12017,Diagnostic accuracy and predictive value of clinical symptoms for the diagnosis of mild COVID 19,0,10.1101/2021.03.05.21252963,3/8/21,medrxiv,0,5,correlation analysis,0.000999551,0.000999541,0.119527294,0.000999539,0.328590368,0.548883708,Clinics,0.88588893,TRUE,2.6,0.028263962,0,0.055525823,0,0.403234768,1414,0.877678786,0.341175835
12018,The COVIDome Explorer Researcher Portal,0,10.1101/2021.03.04.21252945,3/8/21,medrxiv,0,24,"transcriptom, proteom, metabolom, multi-omics, dataset",0.395265655,0.001901851,0.257795827,0.001901833,0.001901945,0.341232889,Drug discovery,0.3560987,FALSE,60.58333333,0.70579504,,,0,0.403234768,1488,0.882253792,0.6637612
12019,"High seroprevalence of anti-SARS-CoV-2 antibodies after the first wave of the COVID-19 pandemic in a vulnerable population in Perpignan, France",0,10.1101/2021.03.05.21252835,3/8/21,medrxiv,0,12,logistic regression,0.001593579,0.173531764,0.00159348,0.001593573,0.757917618,0.063769986,Healthcare,0.24024516,FALSE,24.75,0.364710248,34.5,0.602221033,0,0.403234768,454,0.605827113,0.49399829
12020,Adjusting COVID-19 seroprevalence survey results to account for test sensitivity and specificity,0,10.1101/2021.03.04.21252939,3/8/21,medrxiv,0,6,bayes,0.001943457,0.001943501,0.220278853,0.625166131,0.148724434,0.001943625,Epidemiology,0.04106912,FALSE,97,0.861586987,67.16666667,0.745852288,0,0.403234768,315,0.447628221,0.614575566
12021,Investigating the first wave of the COVID-19 pandemic in Ukraine using epidemiological and genomic sequencing data,0,10.1101/2021.03.05.21253014,3/8/21,medrxiv,0,7,sequencing,0.001187267,0.211285416,0.00118726,0.783965452,0.001187274,0.001187331,Epidemiology,0.3591447,FALSE,62,0.715814212,84.71428571,0.789938453,0,0.403234768,425,0.580062605,0.62226251
12022,A Simple RT-PCR Melting temperature Assay to Rapidly Screen for Widely Circulating SARS-CoV-2 Variants.,0,10.1101/2021.03.05.21252709,3/8/21,medrxiv,0,8,sequencing,0.001187296,0.978988248,0.001187341,0.001187309,0.001187275,0.01626253,Genomics,0.1815598,FALSE,37.125,0.509369782,131.75,0.865667648,0,0.403234768,1254,0.860341922,0.65965353
12023,The relationship between new PCR positive cases and going out in public during the COVID-19 epidemic in Japan,0,10.1101/2021.03.07.21252959,3/8/21,medrxiv,0,7,correlation analysis,0.001943504,0.186487922,0.001943508,0.756571343,0.051110197,0.001943527,Epidemiology,0.35008717,FALSE,57.71428571,0.686622549,10.42857143,0.361118544,0,0.403234768,855,0.792198411,0.560793568
12024,"Long-term clinical, virological and immunological outcomes in patients hospitalized for COVID-19: antibody response predicts long COVID.",0,10.1101/2021.03.08.21253124,3/8/21,medrxiv,0,10,sequencing,0.000926283,0.260346343,0.000926269,0.000926317,0.083126871,0.653747917,Clinics,0.5787694,TRUE,44.7,0.583524027,41,0.639751137,0,0.403234768,3376,0.95449073,0.645250165
12025,Long-read sequencing of SARS-CoV-2 reveals novel transcripts and a diverse complex transcriptome landscape.,0,10.1101/2021.03.05.434150,3/6/21,biorxiv,0,6,"sequencing, transcriptom, genomes",0.272611502,0.718655619,0.002183194,0.002183256,0.002183206,0.002183223,Genomics,0.15641448,FALSE,84.16666667,0.822376152,201.3333333,0.918049237,0,0.403234768,320,0.429328196,0.643247088
12026,Resolving the Dynamic Motions of SARS-CoV-2 nsp7 and nsp8 Proteins Using Structural Proteomics,0,10.1101/2021.03.06.434214,3/6/21,biorxiv,0,8,proteom,0.871039211,0.105002261,0.001254645,0.001254659,0.001254596,0.020194629,Drug discovery,0.46626154,FALSE,126.125,0.915455501,174.125,0.902261172,1,0.537564047,815,0.771249699,0.781632605
12027,Multi-Omic Profiling of Plasma Identify Biomarkers and Pathogenesis of COVID-19 in Children,0,10.1101/2021.03.04.21252876,3/6/21,medrxiv,0,17,"machine learning, proteom, metabolom",0.43642969,0.001717209,0.057256387,0.001717191,0.262096255,0.240783268,Drug discovery,0.62120867,TRUE,11.58823529,0.174778898,3.058823529,0.199625368,0,0.403234768,274,0.349145196,0.281696057
12028,"Clinical course and risk factors for in-hospital mortality of 205 patients with SARS-CoV-2 pneumonia in Como, Lombardy Region, Italy",0,10.1101/2021.02.25.20134866,3/5/21,medrxiv,0,16,logistic regression,0.155243796,0.00057263,0.000572646,0.137941106,0.000572646,0.705097176,Clinics,0.84815407,TRUE,29,0.41993939,10.75,0.366269735,0,0.403234768,202,0.178184445,0.341907084
12029,Sharing positive changes made during COVID-19 national lockdown: a multi-method co-production study,0,10.1101/2021.03.03.21252809,3/5/21,medrxiv,0,9,logistic regression,0.001237138,0.001237118,0.001237102,0.488093507,0.506958077,0.001237058,Healthcare,0.13580301,FALSE,31.88888889,0.453769559,20.55555556,0.486486486,0,0.403234768,220,0.217433181,0.390230998
12030,Schooling amidst a pandemic: parents' perceptions about reopening schools and anticipated challenges during COVID-19,0,10.1101/2021.03.02.21252777,3/5/21,medrxiv,0,6,logistic regression,0.000936078,0.000936097,0.084646084,0.131809383,0.780736275,0.000936082,Healthcare,0.8486825,TRUE,57.83333333,0.68755025,44.66666667,0.65754616,0,0.403234768,296,0.375150494,0.530870418
12031,An Explainable Artificial Intelligence based Prospective Framework for COVID-19 Risk Prediction,0,10.1101/2021.03.02.21252269,3/5/21,medrxiv,0,4,"deep learning, artificial intelligence",0.000926284,0.0009263,0.705934937,0.096453482,0.124148292,0.071610705,Imaging,0.29005274,FALSE,3.5,0.044344115,2.75,0.187583623,0,0.403234768,647,0.698049603,0.333303027
12032,HiSpike: A high-throughput cost effective sequencing method for the SARS-CoV-2 spike gene,0,10.1101/2021.03.02.21252290,3/5/21,medrxiv,0,15,"sequencing, whole genome",0.101618263,0.7015566,0.000815384,0.166243913,0.000815353,0.028950486,Genomics,0.3243252,FALSE,31.26666667,0.447275651,21.6,0.498795826,0,0.403234768,1079,0.819648447,0.542238673
12033,Genome sequencing and analysis of an emergent SARS-CoV-2 variant characterized by multiple spike protein mutations detected from the Central Visayas Region of the Philippines,0,10.1101/2021.03.03.21252812,3/5/21,medrxiv,0,32,sequencing,0.001486456,0.917908634,0.001486425,0.076145604,0.001486457,0.001486424,Genomics,0.27013248,FALSE,21.34375,0.31566578,27.78125,0.551913299,2,0.618927094,15467,0.992294727,0.619700225
12034,An upsurge of SARS CoV-2 B.1.1.7 Variant in Pakistan,0,10.1101/2021.02.26.21252562,3/5/21,medrxiv,0,6,"sequencing, whole-genome",0.001203456,0.993982678,0.001203473,0.0012035,0.001203441,0.001203451,Genomics,0.31884533,FALSE,63.5,0.72496753,14,0.412898047,0,0.403234768,255,0.293041175,0.45853538
12035,RT-RC-PCR: a novel and highly scalable next-generation sequencing method for simultaneous detection of SARS-COV-2 and typing variants of concern,0,10.1101/2021.03.02.21252704,3/5/21,medrxiv,0,3,sequencing,0.001653046,0.726396622,0.266990869,0.001653157,0.001653248,0.001653059,Genomics,0.34399122,FALSE,19.66666667,0.292596945,6,0.280037463,0,0.403234768,295,0.373224175,0.337273338
12036,SWIFT: A Deep Learning Approach to Prediction of Hypoxemic Events in Critically-Ill Patients Using SpO2 Waveform Prediction,0,10.1101/2021.02.25.21252234,3/5/21,medrxiv,0,6,deep learning,0.001943662,0.034326335,0.122795774,0.338369956,0.001943535,0.500620738,Clinics,0.2820269,FALSE,92.16666667,0.849341332,126.3333333,0.85991437,0,0.403234768,784,0.755116783,0.716901813
12037,Modeling transmission dynamics and effectiveness of worker screening programs for SARS-CoV-2 in pork processing plants,0,10.1101/2021.03.02.21249552,3/5/21,medrxiv,0,5,mathematical model,0.101681734,0.142556644,0.001022657,0.706830194,0.04688613,0.001022641,Epidemiology,0.43383208,FALSE,8.4,0.124435648,4.8,0.249331014,0,0.403234768,274,0.334938599,0.277985007
12038,A Two-Sample Robust Bayesian Mendelian Randomization Method Accounting for Linkage Disequilibrium and Idiosyncratic Pleiotropy With Applications to the COVID-19 Outcome,0,10.1101/2021.03.02.21252801,3/5/21,medrxiv,0,2,"bayes, genome-wide, probabilistic",0.001622747,0.225752732,0.192616406,0.505409395,0.001622793,0.072975927,Epidemiology,0.1345512,FALSE,66.5,0.743830787,25,0.529435376,0,0.403234768,363,0.467613773,0.536028676
12039,Analysis of SARS-CoV-2 Mutations Over Time Reveals Increasing Prevalence of Variants in the Spike Protein and RNA-Dependent RNA Polymerase,0,10.1101/2021.03.05.433666,3/5/21,biorxiv,0,4,genomes,0.310168012,0.685741483,0.001022611,0.001022636,0.001022638,0.00102262,Genomics,0.436416,FALSE,63.75,0.726390006,57,0.711198823,0,0.403234768,338,0.439200578,0.570006043
12040,SARS-CoV-2-host chimeric RNA-sequencing reads do not necessarily signify virus integration into the host DNA,0,10.1101/2021.03.05.434119,3/5/21,biorxiv,0,2,sequencing,0.109238332,0.884609382,0.001538085,0.001538076,0.001538068,0.001538059,Genomics,0.3741594,FALSE,47,0.606407323,140.5,0.874832754,0,0.403234768,1096,0.822537924,0.676753192
12041,Published Anti-SARS-CoV-2 In Vitro Hits Share Common Mechanisms of Action that Synergize with Antivirals,0,10.1101/2021.03.04.433931,3/4/21,biorxiv,0,9,bioinformatic,0.869792907,0.001392878,0.001392901,0.00139293,0.044467237,0.081561146,Drug discovery,0.72783625,TRUE,15.88888889,0.238790278,7.444444444,0.305592721,0,0.403234768,226,0.216710811,0.291082145
12042,Preliminary Efficacy of the NVX-CoV2373 Covid-19 Vaccine Against the B.1.351 Variant,0,10.1101/2021.02.25.21252477,3/3/21,medrxiv,0,49,"sequencing, whole genome",0.00111269,0.243243183,0.00111264,0.00111268,0.51543581,0.237982998,Healthcare,0.21640277,FALSE,21.60869565,0.319500278,23.43478261,0.515788065,6,0.764429903,9648,0.985071033,0.64619732
12043,"Lack of lockdown, open borders, and no vaccination in sight: is Bosnia and Herzegovina a control group?",0,10.1101/2021.03.01.21252700,3/3/21,medrxiv,0,8,logistic regression,0.001861828,0.001861735,0.001861782,0.176858135,0.784538576,0.033017945,Healthcare,0.54003733,TRUE,9.875,0.147999258,2,0.164302917,0,0.403234768,729,0.716349627,0.357971642
12044,Day by day symptoms following positive and negative PCR tests for SARS-CoV-2 in non-hospitalized health-care workers: a 90-day follow-up study,0,10.1101/2021.03.02.21252437,3/3/21,medrxiv,0,20,logistic regression,0.001126799,0.001126849,0.001126827,0.001126876,0.577297077,0.418195572,Healthcare,0.10488346,FALSE,58.25,0.691384749,62.5,0.730867006,0,0.403234768,1110,0.814832651,0.660079793
12045,redBERT: A Topic Discovery and Deep SentimentClassification Model on COVID-19 OnlineDiscussions Using BERT NLP Model,0,10.1101/2021.03.02.21252747,3/3/21,medrxiv,0,1,computational,0.003335476,0.003335382,0.218533342,0.697898685,0.073561856,0.00333526,Epidemiology,0.5464031,TRUE,2,0.022141134,0,0.055525823,0,0.403234768,812,0.749578618,0.307620086
12046,An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis,0,10.1101/2021.03.02.21252444,3/3/21,medrxiv,0,16,"machine learning, dataset",0.001438113,0.001438211,0.669293161,0.194669861,0.001438196,0.131722458,Imaging,0.61444736,TRUE,28.9375,0.416599666,28.1875,0.55565962,0,0.403234768,1137,0.819407657,0.548725428
12047,Atorvastatin effectively inhibits late replicative cycle steps of SARS-CoV-2 in vitro,0,10.1101/2021.03.01.433498,3/3/21,biorxiv,0,8,"bioinformatic, in silico",0.910173905,0.000838552,0.000838521,0.067649503,0.000838578,0.019660941,Drug discovery,0.9701235,TRUE,22.5,0.333539489,11.125,0.371755419,0,0.403234768,649,0.682639056,0.447792183
12048,"A novel, multiplexed RT-qPCR assay to distinguish lineage B.1.1.7 from the remaining SARS-CoV-2 lineages",0,10.1101/2021.02.09.21251168,3/3/21,medrxiv,0,24,"bioinformatic, sequencing",0.000988393,0.93053489,0.047637007,0.000988478,0.000988404,0.018862827,Genomics,0.15286568,FALSE,,,,,0,0.403234768,923,0.780399711,0.591817239
12049,A pharmacophore model for SARS-CoV-2 3CLpro small molecule inhibitors and in vitro experimental validation of computationally screened inhibitors,0,10.1101/2021.03.02.433618,3/3/21,biorxiv,0,3,"virtual screening, molecular dynamics simulation, machine learning, computational",0.8549825,0.017124378,0.125512861,0.000793401,0.000793405,0.000793455,Drug discovery,0.56963265,TRUE,39,0.530521368,42,0.644902328,0,0.403234768,647,0.681435107,0.565023393
12050,Altered Sub-Genomic RNA Expression in SARS-CoV-2 B.1.1.7 Infections,0,10.1101/2021.03.02.433156,3/3/21,biorxiv,0,32,sequencing,0.14614952,0.784687971,0.00123709,0.001237118,0.001237147,0.065451154,Genomics,0.23452562,FALSE,44.96666667,0.585070196,48.1,0.673936313,1,0.537564047,4304,0.961955213,0.689631442
12051,Identification and quantification of SARS-CoV-2 leader subgenomic mRNA gene junctions in nasopharyngeal samples shows phasic transcription in animal models of COVID-19 and aberrant pattens in humans,0,10.1101/2021.03.03.433753,3/3/21,biorxiv,0,19,"bioinformatic, sequencing, dataset",0.138807801,0.82945072,0.001943567,0.001943504,0.001943461,0.025910947,Genomics,0.24192074,FALSE,93.44444444,0.85280475,111.9444444,0.841316564,0,0.403234768,861,0.764507585,0.715465917
12052,"Comparative analysis of codon usage patterns in SARS-CoV-2, its mutants and other respiratory viruses",0,10.1101/2021.03.03.433699,3/3/21,biorxiv,0,3,"genomes, correlation analysis",0.087176407,0.868957373,0.00159358,0.001593589,0.001593567,0.039085486,Genomics,0.9799067,TRUE,115.6666667,0.900241202,92.66666667,0.80753278,0,0.403234768,825,0.754394414,0.716350791
12053,Impact of COVID-19 pre-test probability on positive predictive value of high cycle threshold SARS-CoV-2 real-time reverse transcription PCR test results,0,10.1101/2021.03.02.21252768,3/3/21,medrxiv,0,22,sequencing,0.001022643,0.675775919,0.001022679,0.001022664,0.198269402,0.122886692,Genomics,0.17861539,FALSE,52.09090909,0.647597254,59.72727273,0.719561145,0,0.403234768,2436,0.922947267,0.673335108
12054,Relation of severe COVID-19 in Scotland to transmission-related factors and risk conditions eligible for shielding support: REACT-SCOT case-control study,0,10.1101/2021.03.02.21252734,3/3/21,medrxiv,0,15,dataset,0.00135034,0.001350356,0.001350342,0.237192229,0.457006556,0.301750177,Healthcare,0.22470826,FALSE,97.8,0.864184551,91.26666667,0.804321648,0,0.403234768,7095,0.979292078,0.762758261
12055,Introductions and evolutions of SARS-CoV-2 strains in Japan,0,10.1101/2021.02.26.21252555,3/2/21,medrxiv,0,6,genomes,0.001098813,0.806106882,0.001098807,0.12304265,0.067553962,0.001098886,Genomics,0.48031753,FALSE,31.16666667,0.445976869,7,0.299973241,0,0.403234768,644,0.671803516,0.455247098
12056,"Sequence Analysis of 20,453 SARS-CoV-2 Genomes from the Houston Metropolitan Area Identifies the Emergence and Widespread Distribution of Multiple Isolates of All Major Variants of Concern",0,10.1101/2021.02.26.21252227,3/2/21,medrxiv,0,15,"sequencing, genomes",0.001254665,0.914113892,0.001254662,0.001254706,0.001254686,0.080867389,Genomics,0.25921983,FALSE,69.73333333,0.762137423,283.8666667,0.949892962,3,0.667819001,4064,0.955213099,0.833765621
12057,"The prevalence, incidence and risk factors of mental health problems and mental health services use before and 9 months after the COVID-19 outbreak among the general Dutch population. A 3-wave prospective study.",0,10.1101/2021.02.27.21251952,3/2/21,medrxiv,0,5,logistic regression,0.000759391,0.000759388,0.000759395,0.000759408,0.996203041,0.000759378,Healthcare,0.5541471,TRUE,24.2,0.357350485,22.6,0.508161627,0,0.403234768,735,0.711052251,0.494949783
12058,"Prevalence of SARS-CoV-2 antibodies among workers of the public higher education institutions of Porto, Portugal",0,10.1101/2021.02.28.21252628,3/2/21,medrxiv,0,9,bayes,0.001684565,0.493234752,0.001684712,0.001684653,0.46681705,0.034894268,Genomics,0.15349865,FALSE,28.66666667,0.414125796,11.88888889,0.383663366,0,0.403234768,332,0.393932097,0.398739007
12059,Vaccine hesitancy and reasons for refusing the COVID-19 vaccination among the U.S. public: A cross-sectional survey,0,10.1101/2021.02.28.21252610,3/2/21,medrxiv,0,5,logistic regression,0.001291262,0.001291247,0.001291256,0.001291239,0.919531469,0.075303527,Healthcare,0.15555513,FALSE,4.2,0.05584761,0.2,0.061145304,1,0.537564047,887,0.765952324,0.355127321
12060,The Risks and Benefits of Providing HIV Services during the COVID-19 Pandemic,0,10.1101/2021.03.01.21252663,3/2/21,medrxiv,0,10,simulation model,0.001034583,0.046491719,0.001034614,0.822709785,0.127694693,0.001034607,Epidemiology,0.09709287,FALSE,,,,,0,0.403234768,580,0.633036359,0.518135563
12061,"Novel clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health",0,10.1101/2021.02.28.21252645,3/2/21,medrxiv,0,7,machine learning,0.054708789,0.112753972,0.046943817,0.310351905,0.001371378,0.473870139,Clinics,0.16399768,FALSE,61,0.709196611,106.1428571,0.833355633,1,0.537564047,467,0.545388876,0.656376292
12062,Distinct systemic and mucosal immune responses to SARS-CoV-2,0,10.1101/2021.03.01.21251633,3/2/21,medrxiv,0,20,microbiom,0.294327388,0.472328221,0.00175116,0.001751203,0.001751231,0.228090797,Genomics,0.4091224,FALSE,79.85,0.806357845,147.25,0.881054322,0,0.403234768,1047,0.797014207,0.721915285
12063,Fragment-based computational design of antibodies targeting structured epitopes,0,10.1101/2021.03.02.433360,3/2/21,biorxiv,0,7,computational,0.757840351,0.112771461,0.123557763,0.001943524,0.001943457,0.001943443,Drug discovery,0.21887359,FALSE,128.1428571,0.919166306,259.7142857,0.941396842,0,0.403234768,771,0.726222008,0.747504981
12064,Perturbation of ACE2 structural ensembles by SARS-CoV-2 spike protein binding,0,10.1101/2021.03.02.433608,3/2/21,biorxiv,0,2,"molecular dynamics simulation, machine learning, dataset",0.792819544,0.001461905,0.201332828,0.001461947,0.001461878,0.001461898,Drug discovery,0.5998056,TRUE,13,0.197352959,2.5,0.180826866,0,0.403234768,566,0.624127137,0.351385433
12065,COVID-19 International Border Surveillance Cohort Study at Toronto's Pearson Airport,0,10.1101/2021.02.25.21252404,3/1/21,medrxiv,0,8,mathematical model,0.001141321,0.068458967,0.001141368,0.451057401,0.268835456,0.209365487,Epidemiology,0.32290024,FALSE,61.375,0.71092832,58.5,0.715814825,0,0.403234768,818,0.736816759,0.641698668
12066,Covid-19 lockdown: Ethnic differences in childrens self-reported physical activity and the importance of leaving the home environment. A longitudinal and cross-sectional,0,10.1101/2021.02.26.21252543,3/1/21,medrxiv,0,18,logistic regression,0.001085348,0.00108533,0.001085324,0.149011995,0.846646651,0.001085353,Healthcare,0.68406355,TRUE,58.66666667,0.694106005,84.61111111,0.789537062,0,0.403234768,732,0.702143029,0.647255216
12067,Opening schools and trends in SARS-CoV-2 transmission in European countries,0,10.1101/2021.02.26.21252504,3/1/21,medrxiv,0,10,bayes,0.001486423,0.001486522,0.001486444,0.805594349,0.188459769,0.001486492,Epidemiology,0.2587133,FALSE,60.2,0.704125178,25.2,0.530371956,0,0.403234768,376,0.438959788,0.519172922
12068,Impact of the COVID-19 pandemic on cognitive function in Japanese community-dwelling older adults in a class for preventing cognitive decline,0,10.1101/2021.02.26.21252497,3/1/21,medrxiv,0,4,logistic regression,0.001291277,0.001291231,0.001291287,0.001291312,0.927609826,0.067225066,Healthcare,0.95858,TRUE,4.25,0.056527924,0,0.055525823,0,0.403234768,849,0.747893089,0.315795401
12069,Estimating the burden of post-COVID-19 syndrome in a population-based cohort study of SARS-CoV-2 infected individuals: Implications for healthcare service planning,0,10.1101/2021.02.27.21252572,3/1/21,medrxiv,0,7,logistic regression,0.00114133,0.001141363,0.001141394,0.001141418,0.699102157,0.296332338,Healthcare,0.7410084,TRUE,110.5714286,0.890840497,117.8571429,0.848541611,1,0.537564047,2574,0.923669636,0.800153948
12070,Evaluating the trade-off between transmissibility and virulence of SARS-CoV-2 by mathematical modeling,0,10.1101/2021.02.27.21252592,3/1/21,medrxiv,0,3,mathematical model,0.002130652,0.182655021,0.002130628,0.808822332,0.00213068,0.002130687,Epidemiology,0.19939926,FALSE,11.66666667,0.176510607,0,0.055525823,0,0.403234768,246,0.220081869,0.213838266
12071,A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19).,0,10.1101/2021.02.09.21251427,3/1/21,medrxiv,0,8,"proteom, dataset",0.318872958,0.392587398,0.198303954,0.000889087,0.000889105,0.088457498,Genomics,0.5478559,TRUE,65.875,0.739625209,78.25,0.774016591,0,0.403234768,575,0.62099687,0.634468359
12072,Systematic analysis of SARS-CoV-2 infection of an ACE2-negative human airway cell,0,10.1101/2021.03.01.433431,3/1/21,biorxiv,0,24,"transcriptom, proteom",0.989836011,0.002032895,0.002032759,0.002032786,0.002032807,0.002032741,Drug discovery,0.4274596,FALSE,44.16666667,0.579132909,133.7083333,0.867273214,0,0.403234768,959,0.777269444,0.656727583
12073,A Comparison of Performance for Different SARS-Cov-2 Sequencing Protocols,0,10.1101/2021.03.01.433428,3/1/21,biorxiv,0,1,sequencing,0.003466496,0.776453373,0.209681304,0.003466275,0.003466199,0.003466353,Genomics,0.09080011,FALSE,8,0.118683901,4,0.231469093,0,0.403234768,283,0.290874067,0.261065457
12074,Day-night and seasonal variation of human gene expression across tissues,0,10.1101/2021.02.28.433266,3/1/21,biorxiv,0,5,transcriptom,0.728944769,0.002080681,0.002080536,0.26273276,0.002080609,0.002080645,Drug discovery,0.57123595,TRUE,98,0.864741171,821,0.990500401,0,0.403234768,604,0.641223212,0.724924888
12075,The impact of mutations on the structural and functional properties of SARS-CoV-2 proteins: A comprehensive bioinformatics analysis,0,10.1101/2021.03.01.433340,3/1/21,biorxiv,0,7,"bioinformatic, in silico, in-silico",0.508688206,0.485264474,0.001511808,0.001511843,0.001511859,0.001511811,Drug discovery,0.46505615,FALSE,21.42857143,0.316593481,10.85714286,0.367607707,0,0.403234768,417,0.48254274,0.392494674
12076,Nationwide rollout reveals efficacy of epidemic control through digital contact tracing,0,10.1101/2021.02.27.21252577,3/1/21,medrxiv,0,8,dataset,0.001901692,0.001901721,0.033188541,0.916818349,0.044287944,0.001901752,Epidemiology,0.1711328,FALSE,46.375,0.599604181,36.125,0.61178753,0,0.403234768,332,0.378280761,0.49822681
12077,"COVID-19 Vaccine hesitancy in Addis Ababa, Ethiopia: A mixed-methods study",0,10.1101/2021.02.25.21252443,3/1/21,medrxiv,0,9,logistic regression,0.001538109,0.001538136,0.001538159,0.001538185,0.992309317,0.001538094,Healthcare,0.7232528,TRUE,5.555555556,0.07749397,0.777777778,0.099946481,0,0.403234768,355,0.411991332,0.248166638
12078,Genetics of symptom remission in outpatients with COVID-19,0,10.1101/2021.02.24.21252396,3/1/21,medrxiv,0,33,genome-wide,0.272233149,0.15539379,0.002898259,0.002898314,0.210412894,0.356163595,Clinics,0.14278719,FALSE,72.78787879,0.776362175,56.06060606,0.706917313,0,0.403234768,386,0.450276908,0.584197791
12079,Impact of a new SARS-CoV-2 variant on the population: A mathematical modeling approach,0,10.1101/2021.02.24.21252406,3/1/21,medrxiv,0,3,mathematical model,0.001565327,0.286664423,0.001565305,0.674520461,0.001565364,0.034119119,Epidemiology,0.25134343,FALSE,4,0.054734368,1.333333333,0.13252609,0,0.403234768,338,0.387912353,0.244601895
12080,"GUIdeStaR (G-quadruplex, Uorf, IRES, Small RNA, Repeats), bioinformatics tool for gene characterization- case study: development of attribute selection and classification methods based on GuideStar for studying human transcription factor genes mediated by SARS-COV-2 small ncRNA",0,10.1101/2021.02.25.432957,2/27/21,biorxiv,0,1,"artificial intelligence, bioinformatic, neural network, sequencing, dataset",0.462651866,0.339683616,0.19395327,0.001237113,0.001237084,0.001237051,Drug discovery,0.53948575,TRUE,40,0.539860226,17,0.451097137,0,0.403234768,869,0.740910185,0.533775579
12081,A statewide analysis of SARS-CoV-2 transmission in New York,0,10.1101/2021.02.20.21251598,2/27/21,medrxiv,0,10,"sequencing, genomes",0.001392837,0.309159168,0.001392845,0.685269414,0.001392859,0.001392877,Epidemiology,0.35172457,FALSE,18.7,0.280042056,21.5,0.498260637,1,0.537564047,957,0.765711534,0.520394568
12082,Epidemiological and Genomic analysis of a Sydney Hospital COVID-19 Outbreak,0,10.1101/2021.02.17.21251943,2/26/21,medrxiv,0,16,"sequencing, genomic epidemiology",0.001022644,0.285408842,0.001022644,0.209361969,0.242126697,0.261057204,Genomics,0.36232263,FALSE,96.125,0.859236811,291.1875,0.951766123,0,0.403234768,555,0.576932338,0.69779251
12083,Scalable Epidemiological Workflows to Support COVID-19 Planning and Response,0,10.1101/2021.02.23.21252325,2/26/21,medrxiv,0,12,computational,0.001330077,0.058791384,0.098435767,0.83878259,0.001330094,0.001330087,Epidemiology,0.7498473,TRUE,31,0.445111015,32.25,0.587570244,5,0.739490092,847,0.7240549,0.624056563
12084,Risk factors for increased COVID-19 case-fatality in the United States: A county-level analysis during the first wave,0,10.1101/2021.02.24.21252135,2/26/21,medrxiv,0,10,dataset,0.002490548,0.002490481,0.00249049,0.443005878,0.373854886,0.175667717,Epidemiology,0.45173684,FALSE,20.1,0.298348692,8.9,0.333757024,0,0.403234768,819,0.71273778,0.437019566
12085,Heterogeneous Mental Health Development During the COVID-19 Pandemic in the United Kingdom,0,10.1101/2021.02.24.21251565,2/26/21,medrxiv,0,2,logistic regression,0.001461863,0.001461865,0.001461854,0.291590346,0.70256214,0.001461933,Healthcare,0.22026849,FALSE,21.5,0.318263343,13.5,0.405539203,0,0.403234768,584,0.598362629,0.431349986
12086,"Discovery of SARS-CoV-2 strain of P.1 lineage harboring K417T/ E484K / N501Y by whole genome sequencing in the city, Japan",0,10.1101/2021.02.24.21251892,2/26/21,medrxiv,0,2,"sequencing, whole genome",0.028704115,0.751134626,0.001622709,0.001622739,0.001622785,0.215293026,Genomics,0.43783498,FALSE,917,0.999443379,971.5,0.993443939,2,0.618927094,3338,0.936431495,0.887061477
12087,Prophylaxis for covid-19: living systematic review and network meta-analysis,0,10.1101/2021.02.24.21250469,2/26/21,medrxiv,0,40,bayes,0.060272298,0.000880229,0.000880251,0.452936212,0.061119919,0.423911092,Epidemiology,0.2845863,FALSE,121.575,0.908961593,,,0,0.403234768,3709,0.944377558,0.752191306
12088,"A Novel SARS-CoV-2 Variant of Concern, B.1.526, Identified in New York",0,10.1101/2021.02.23.21252259,2/25/21,medrxiv,0,8,"sequencing, whole genome",0.001203501,0.844516678,0.001203429,0.001203501,0.083755439,0.068117452,Genomics,0.10665217,FALSE,118.125,0.904756015,306.125,0.95611453,15,0.874313229,21470,0.994461835,0.932411402
12089,Visualization of SARS-CoV-2 Infection Scenes by 'Zero-Shot' Enhancements of Electron Microscopy Images,0,10.1101/2021.02.25.432265,2/25/21,biorxiv,0,3,"machine learning, probabilistic",0.129502556,0.106571176,0.467048023,0.294438214,0.001220012,0.00122002,Imaging,0.087966144,FALSE,30,0.432432432,16.33333333,0.440460262,0,0.403234768,733,0.674933783,0.487765311
12090,Modeling SARS-CoV-2 nsp1-5'-UTR complex via the extended ensemble simulations,0,10.1101/2021.02.24.432807,2/25/21,biorxiv,0,3,molecular dynamics simulation,0.990064153,0.001987306,0.001987173,0.001987144,0.001987147,0.001987076,Drug discovery,0.55981964,TRUE,29.2,0.420805245,11.8,0.38252609,0,0.403234768,759,0.684806164,0.472843067
12091,Pyroptosis of syncytia formed by fusion of SARS-CoV-2 Spike and ACE2 expressing cells,0,10.1101/2021.02.25.432853,2/25/21,biorxiv,0,10,sequencing,0.952401033,0.038167925,0.002357763,0.002357799,0.002357697,0.002357783,Drug discovery,0.14612022,FALSE,47.6,0.611169522,17.9,0.459727054,0,0.403234768,598,0.598603419,0.518183691
12092,Non-pharmaceutical interventions and inoculation rate shape SARS-COV-2 vaccination campaign success,0,10.1101/2021.02.22.21252240,2/25/21,medrxiv,0,9,bayes,0.002720158,0.002720097,0.002720077,0.909689732,0.079429675,0.002720261,Epidemiology,0.25249895,FALSE,44.11111111,0.578638135,47.88888889,0.672330747,0,0.403234768,660,0.635685047,0.572472174
12093,Genetic predisposition to psychiatric disorders and risk of COVID-19,0,10.1101/2021.02.23.21251866,2/25/21,medrxiv,0,14,"logistic regression, dataset",0.001059385,0.304229236,0.038863942,0.001059402,0.404760399,0.250027637,Healthcare,0.39769965,FALSE,18.78571429,0.280846063,20.5,0.486218892,0,0.403234768,623,0.613050807,0.445837632
12094,Impact of COVID-19 on Mental Health: A Longitudinal Study Using Penalized Logistic Regression,0,10.1101/2021.02.21.21252159,2/25/21,medrxiv,0,5,"logistic regression, dataset",0.002080665,0.002080756,0.201907823,0.362973532,0.428876552,0.002080673,Healthcare,0.30820584,FALSE,26.2,0.383820892,11.4,0.375970029,0,0.403234768,574,0.582470503,0.436374048
12095,Impact of the Tier system on SARS-CoV-2 transmission in the UK between the first and second national lockdowns,0,10.1101/2021.02.23.21252277,2/24/21,medrxiv,0,8,bayes,0.001203428,0.104287785,0.001203404,0.865571738,0.026530227,0.001203418,Epidemiology,0.18437937,FALSE,107.125,0.883480735,497.875,0.978592454,1,0.537564047,741,0.668914038,0.767137818
12096,Acceptance of COVID-19 Vaccine in Pakistan Among Health Care Workers,0,10.1101/2021.02.23.21252271,2/24/21,medrxiv,0,3,logistic regression,0.001751201,0.001751173,0.00175115,0.001751221,0.830982541,0.162012714,Healthcare,0.6372244,TRUE,6,0.086028821,0,0.055525823,0,0.403234768,1464,0.830724777,0.343878547
12097,Associations Between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States,0,10.1101/2021.02.22.21252254,2/24/21,medrxiv,0,4,predictive model,0.071526416,0.001330085,0.001330052,0.685820633,0.00133014,0.238662674,Epidemiology,0.6186849,TRUE,48,0.614942173,29.75,0.567835162,1,0.537564047,1077,0.775583915,0.623981324
12098,A genome-wide CRISPR screen identifies interactors of the autophagy pathway as conserved coronavirus targets,0,10.1101/2021.02.24.432634,2/24/21,biorxiv,0,11,genome-wide,0.888709587,0.104799466,0.001622694,0.001622735,0.001622743,0.001622775,Drug discovery,0.60071766,TRUE,68.58333333,0.756818604,123.6666667,0.856101151,0,0.403234768,1735,0.860823501,0.719244506
12099,A missense variant effect prediction and annotation resource for SARS-CoV-2,0,10.1101/2021.02.24.432721,2/24/21,biorxiv,0,5,"computational, structural model, dataset",0.391803394,0.472949063,0.130193876,0.001684607,0.001684546,0.001684514,Genomics,0.1205174,FALSE,40.2,0.541159008,112.4,0.841717956,0,0.403234768,1382,0.823019504,0.652282809
12100,Implications of central carbon metabolism in SARS-CoV-2 replication and disease severity,0,10.1101/2021.02.24.432759,2/24/21,biorxiv,0,22,multi-omics,0.731461732,0.002080749,0.002080597,0.045343966,0.002080577,0.21695238,Drug discovery,0.46764985,FALSE,34.40909091,0.481043973,21.13636364,0.492641156,0,0.403234768,1767,0.864676138,0.560399009
12101,SARS-CoV-2 detection and genomic sequencing from hospital surface samples collected at UC Davis,0,10.1101/2021.02.23.21252022,2/24/21,medrxiv,0,19,"sequencing, whole genome, genome sequences, genomes",0.032540792,0.727094264,0.001187289,0.001187309,0.056529536,0.18146081,Genomics,0.096704215,FALSE,72.42105263,0.775001546,249.3684211,0.937985015,1,0.537564047,2063,0.883939321,0.783622482
12102,Long-term air pollution and other risk factors associated with COVID-19 at the census-tract-level in Colorado,0,10.1101/2021.02.19.21252019,2/23/21,medrxiv,0,3,bayes,0.001310441,0.001310406,0.00131033,0.624919415,0.0013104,0.369839008,Epidemiology,0.7056952,TRUE,1.666666667,0.016451234,0,0.055525823,0,0.403234768,339,0.312304358,0.196879046
12103,The Association of Opening K-12 Schools and Colleges with the Spread of COVID-19 in the United States: County-Level Panel Data Analysis,0,10.1101/2021.02.20.21252131,2/23/21,medrxiv,0,3,structural model,0.001717239,0.001717206,0.001717341,0.622659776,0.370471239,0.001717198,Epidemiology,0.19504312,FALSE,47.33333333,0.608633805,117.6666667,0.848140219,0,0.403234768,1822,0.865639297,0.681412022
12104,Analysis of key factors of a SARS-CoV-2 vaccination program: A mathematical modeling approach,0,10.1101/2021.02.19.21252095,2/23/21,medrxiv,0,3,"computational, mathematical model",0.169926547,0.001786539,0.001786545,0.679613268,0.145100521,0.00178658,Epidemiology,0.22176653,FALSE,4,0.054734368,1.333333333,0.13252609,1,0.537564047,907,0.72381411,0.362159654
12105,Analyzing Socioeconomic Factors and Health Disparity of COVID-19 Spatiotemporal Spread Patterns at Neighborhood Levels in San Diego County,0,10.1101/2021.02.22.21251757,2/23/21,medrxiv,0,6,correlation analysis,0.001786521,0.001786578,0.001786575,0.588423427,0.404430302,0.001786597,Epidemiology,0.5977275,TRUE,26.5,0.389325252,18.16666667,0.462603693,0,0.403234768,923,0.731037804,0.496550379
12106,COVID-19 Spreading dynamics in an age-structured population with selective relaxation of restrictions for vaccinated individuals : a mathematical modeling study,0,10.1101/2021.02.22.21252241,2/23/21,medrxiv,0,3,mathematical model,0.00112689,0.050399202,0.017047115,0.733361192,0.196938791,0.00112681,Epidemiology,0.030539036,FALSE,33.33333333,0.469911559,1.333333333,0.13252609,0,0.403234768,426,0.432699254,0.359592918
12107,Group IIA Secreted Phospholipase A2 Plays a Central Role in the Pathobiology of COVID-19,0,10.1101/2021.02.22.21252237,2/23/21,medrxiv,0,18,"machine learning, lipidom",0.240308509,0.002357835,0.166711285,0.002357852,0.002357777,0.585906742,Clinics,0.42390352,FALSE,51.16666667,0.639557177,55,0.702167514,0,0.403234768,552,0.544184927,0.572286096
12108,Nosocomial outbreak of SARS-CoV-2 in a 'non-COVID-19' hospital ward: virus genome sequencing as a key tool to understand cryptic transmission,0,10.1101/2021.02.20.20248421,2/23/21,medrxiv,0,13,sequencing,0.001010967,0.404379306,0.001011001,0.406666836,0.068247818,0.118684072,Epidemiology,0.31617138,FALSE,26.84615385,0.39278867,15.76923077,0.433703505,0,0.403234768,522,0.516494101,0.436555261
12109,Evolving Infection Paradox of SARS-CoV-2: Fitness Costs Virulence?,0,10.1101/2021.02.21.21252137,2/23/21,medrxiv,0,9,"bioinformatic, genome-wide",0.173073987,0.757651207,0.000565331,0.000565344,0.012133465,0.056010666,Genomics,0.36745763,FALSE,59.33333333,0.698497124,1375,0.996588172,4,0.707574542,710,0.647483747,0.762535896
12110,The early dynamics of the SARS-CoV-2 epidemic in Portugal,0,10.1101/2021.02.22.21252216,2/23/21,medrxiv,0,12,"genomic epidemiology, genome sequences",0.001126787,0.561628081,0.001126797,0.433864662,0.001126842,0.00112683,Genomics,0.27870113,FALSE,30.33333333,0.436266931,11.83333333,0.382927482,1,0.537564047,786,0.681194317,0.509488194
12111,Real-time analysis of a mass vaccination effort via an Artificial Intelligence platform confirms the safety of FDA-authorized COVID-19 vaccines,0,10.1101/2021.02.20.21252134,2/23/21,medrxiv,0,26,"artificial intelligence, neural network",0.02031883,0.000889092,0.056131969,0.082834215,0.41658167,0.423244224,Clinics,0.08213219,FALSE,55.76923077,0.672892572,103.1153846,0.827602355,0,0.403234768,2562,0.90777751,0.702876801
12112,Wastewater Monitoring of SARS-CoV-2 from Acute Care Hospitals Identifies Nosocomial Transmission and Outbreaks,0,10.1101/2021.02.20.21251520,2/23/21,medrxiv,0,24,genomes,0.052807422,0.40950511,0.001141397,0.141730829,0.001141369,0.393673872,Genomics,0.27854782,FALSE,30.58333333,0.439050034,27.91666667,0.552782981,0,0.403234768,741,0.662653503,0.514430322
12113,Simple Scoring Tool to Estimate Risk of Hospitalization and Mortality in Ambulatory and Emergency Department Patients with COVID-19,0,10.1101/2021.02.22.21252171,2/23/21,medrxiv,0,9,"logistic regression, probabilistic",0.000889106,0.063841252,0.041701977,0.000889119,0.000889113,0.891789433,Clinics,0.50818723,TRUE,28.44444444,0.41140454,10.66666667,0.365333155,1,0.537564047,1239,0.796773417,0.52776879
12114,Modelling the Impact of Delaying Vaccination Against SARS-CoV-2 Assuming Unlimited Vaccines Supply,0,10.1101/2021.02.22.21252189,2/23/21,medrxiv,0,5,mathematical model,0.001350326,0.001350384,0.001350333,0.993248219,0.001350384,0.001350354,Epidemiology,0.13461721,FALSE,111.4,0.8926959,48.6,0.67594327,0,0.403234768,952,0.740187816,0.678015438
12115,Vaccination efforts in Brazil: scenarios and perspectives under a mathematical modeling approach,0,10.1101/2021.02.22.21252208,2/23/21,medrxiv,0,6,mathematical model,0.001943725,0.001943523,0.001943512,0.574839151,0.342418078,0.076912012,Epidemiology,0.14723969,FALSE,7,0.10179974,0.833333333,0.102488627,0,0.403234768,564,0.553334939,0.290214518
12116,"Factors Associated with Timely Test Seeking, Test Turnaround, and Public Reporting of COVID-19: a retrospective analysis in Ontario, Canada",0,10.1101/2021.02.22.21252219,2/23/21,medrxiv,0,5,logistic regression,0.000541127,0.055651175,0.000541122,0.362239334,0.5804861,0.000541142,Healthcare,0.23281452,FALSE,10.8,0.161110768,0,0.055525823,0,0.403234768,352,0.333975439,0.238461699
12117,A monocyte/dendritic cell molecular signature of SARS-CoV2-related multisystem inflammatory syndrome in children (MIS-C) with severe myocarditis,0,10.1101/2021.02.23.432486,2/23/21,biorxiv,0,49,transcriptom,0.63254021,0.00229662,0.002296509,0.002296553,0.190913655,0.169656452,Drug discovery,0.19163346,FALSE,77.02,0.794730657,108.64,0.837168852,0,0.403234768,1553,0.837948471,0.718270687
12118,High-resolution longitudinal serum proteome trajectories in COVID-19 reveal patients-specific seroconversion,0,10.1101/2021.02.22.21252236,2/23/21,medrxiv,0,14,"bioinformatic, proteom",0.282139406,0.298217091,0.001126853,0.001126884,0.001126825,0.41626294,Clinics,0.41556925,FALSE,42.42857143,0.561692127,44,0.654401927,2,0.618927094,2394,0.901516976,0.684134531
12119,Structural and functional characterization of SARS-CoV-2 RBD domains produced in mammalian cells,0,10.1101/2021.02.23.432424,2/23/21,biorxiv,0,9,sequencing,0.85588984,0.117291717,0.00125468,0.001254613,0.001254606,0.023054543,Drug discovery,0.5228465,TRUE,28.55555556,0.412827015,20.55555556,0.486486486,1,0.537564047,1125,0.78136287,0.554560105
12120,Comprehensive evaluation of ACE2 expression in female ovary by single-cell RNA-seq analysis,0,10.1101/2021.02.23.432460,2/23/21,biorxiv,0,11,"sequencing, dataset",0.622622654,0.00272016,0.088394236,0.002720148,0.002720309,0.280822494,Drug discovery,0.55722123,TRUE,157.7272727,0.949842291,102.5454545,0.827000268,0,0.403234768,622,0.596677101,0.694188607
12121,Metagenomic diagnosis and pathogenic network profile of SARS-CoV-2 in patients co-morbidly affected by type 2 diabetes,0,10.1101/2021.02.23.432535,2/23/21,biorxiv,0,8,"metagenom, microbiom, network analysis",0.212175158,0.254286371,0.001141403,0.001141408,0.001141368,0.530114292,Clinics,0.30656528,FALSE,25,0.369286907,12.25,0.388546963,0,0.403234768,503,0.501805923,0.41571864
12122,Using mixed-effects modeling to estimate decay kinetics of response to SARS-CoV-2 infection,0,10.1101/2021.02.22.432379,2/23/21,biorxiv,0,6,dataset,0.104065302,0.001987182,0.001987165,0.81492837,0.001987191,0.075044792,Epidemiology,0.12768957,FALSE,23.83333333,0.351598738,7.833333333,0.314088841,0,0.403234768,513,0.510474356,0.394849176
12123,Comparative Perturbation-Based Modeling of the SARS-CoV-2 Spike Protein Binding with Host Receptor and Neutralizing Antibodies : Structurally Adaptable Allosteric Communication Hotspots Define Spike Sites Targeted by Global Circulating Mutations,0,10.1101/2021.02.21.432165,2/22/21,biorxiv,0,4,"computational, network model",0.743984219,0.189007999,0.063711214,0.001098854,0.001098849,0.001098865,Drug discovery,0.8246362,TRUE,33,0.466757375,22.5,0.507492641,0,0.403234768,471,0.464001926,0.460371678
12124,What is the extent of COVID-19 vaccine hesitancy in Bangladesh? : A cross-sectional rapid national survey,0,10.1101/2021.02.17.21251917,2/22/21,medrxiv,0,2,logistic regression,0.00115624,0.001156288,0.001156337,0.001156324,0.994218543,0.001156267,Healthcare,0.38637978,FALSE,21.5,0.318263343,0,0.055525823,0,0.403234768,540,0.522032266,0.32476405
12125,"Nanoceutical Fabric Prevents COVID-19 Spread through Expelled Respiratory Droplets: A Combined Computational, Spectroscopic and Anti-microbial Study",0,10.1101/2021.02.20.432081,2/22/21,biorxiv,0,9,computational,0.651954282,0.001861754,0.001861785,0.340598747,0.001861764,0.001861668,Drug discovery,0.8703054,TRUE,178,0.963386728,23.66666667,0.518062617,0,0.403234768,386,0.370334698,0.563754703
12126,The interspecific fungal hybrid Verticillium longisporum displays sub-genome-specific gene expression,0,10.1101/341636,2/22/21,biorxiv,0,25,"transcriptom, genomes",0.244276827,0.75243148,0.000822933,0.000822945,0.000822905,0.000822911,Genomics,0.3525986,FALSE,24.3,0.358463727,52.6,0.691263045,0,0.403234768,1278,0.799903684,0.563216306
12127,Reanalysis of deep-sequencing data from Austria points towards a small SARS-COV-2 transmission bottleneck on the order of one to three virions,0,10.1101/2021.02.22.432096,2/22/21,biorxiv,0,2,"computational, sequencing",0.002183321,0.895375911,0.095890909,0.002183393,0.002183246,0.002183219,Genomics,0.18005395,FALSE,52,0.647349867,77.5,0.771942735,1,0.537564047,1791,0.858656393,0.70387826
12128,Targeting CoV-2 Spike RBD and ACE-2 Interaction with Flavonoids of Anatolian Propolis by in silico and in vitro Studies in terms of possible COVID-19 therapeutics,0,10.1101/2021.02.22.432207,2/22/21,biorxiv,0,8,in silico,0.954169747,0.036643894,0.002296597,0.002296556,0.002296658,0.002296548,Drug discovery,0.9447,TRUE,33.875,0.474921145,23.75,0.518731603,0,0.403234768,828,0.690825909,0.521928356
12129,"Predicting mortality, duration of treatment, pulmonary embolism and required ceiling of ventilatory support for COVID-19 inpatients: A Machine-Learning Approach",0,10.1101/2021.02.15.21251752,2/20/21,medrxiv,0,14,"bayes, predictive model",0.001098815,0.001098807,0.304051875,0.058703527,0.001098953,0.633948024,Clinics,0.5962479,TRUE,20.07142857,0.298224998,10.28571429,0.358643297,0,0.403234768,513,0.48230195,0.385601253
12130,KLF2 is a therapeutic target for COVID-19 induced endothelial dysfunction,0,10.1101/2021.02.20.432085,2/20/21,biorxiv,0,4,"sequencing, transcriptom",0.488626596,0.087565072,0.001438119,0.00143813,0.001438122,0.419493962,Drug discovery,0.9164151,TRUE,113.75,0.897210712,95.5,0.813821247,0,0.403234768,534,0.499157236,0.653355991
12131,Applicability of Neighborhood and Building Scale Wastewater-Based Genomic Epidemiology to Track the SARS-CoV-2 Pandemic and other Pathogens.,0,10.1101/2021.02.18.21251939,2/20/21,medrxiv,0,3,"sequencing, whole genome, genomic epidemiology",0.000846575,0.765122622,0.000846556,0.080502085,0.118009054,0.034673108,Genomics,0.43362114,FALSE,6,0.086028821,4,0.231469093,0,0.403234768,924,0.710570672,0.357825838
12132,Estimates of the COVID-19 pandemic dynamics in Ukraine based on two data sets,0,10.1101/2021.02.18.21252000,2/20/21,medrxiv,0,1,mathematical model,0.000977442,0.000977449,0.00097745,0.995112771,0.000977447,0.000977442,Epidemiology,0.18852529,FALSE,8,0.118683901,0,0.055525823,1,0.537564047,468,0.444738743,0.289128129
12133,Mortality after surgery with SARS-CoV-2 infection in England: A population-wide epidemiological study,0,10.1101/2021.02.17.21251928,2/20/21,medrxiv,0,9,logistic regression,0.001112692,0.001112637,0.001112641,0.054818829,0.060120951,0.88172225,Clinics,0.31746125,FALSE,116.7777778,0.901972911,52.77777778,0.692132727,0,0.403234768,2854,0.912593306,0.727483428
12134,A Multivariate Forecasting Model for the COVID-19 Hospital Census Based on Local Infection Incidence,0,10.1101/2021.02.18.21251243,2/20/21,medrxiv,0,3,forecasting model,0.00057262,0.000572635,0.057241687,0.777382499,0.000572647,0.163657912,Epidemiology,0.15797672,FALSE,2.666666667,0.029377203,1,0.122023013,0,0.403234768,829,0.678786419,0.308355351
12135,"Investigation and public health response to a COVID-19 outbreak in a rural resort community--Blaine County, Idaho, 2020",0,10.1101/2021.02.09.21251216,2/19/21,medrxiv,0,10,sequencing,0.001010928,0.146889213,0.001010934,0.348139143,0.266757722,0.236192061,Epidemiology,0.34210622,FALSE,20.5,0.304966294,5,0.257024351,0,0.403234768,315,0.230676619,0.298975508
12136,"Clustering of Countries for COVID-19 Cases based on Disease Prevalence, Health Systems and Environmental Indicators",0,10.1101/2021.02.15.21251762,2/19/21,medrxiv,0,3,correlation analysis,0.001310342,0.00131037,0.15434712,0.769509403,0.072212359,0.001310406,Epidemiology,0.7568867,TRUE,12.33333333,0.186467932,1.333333333,0.13252609,0,0.403234768,374,0.319528052,0.260439211
12137,Mortality in COVID-19 amongst women on Hormone Replacement Therapy or Combined Oral Contraception: A cohort study,0,10.1101/2021.02.16.21251853,2/19/21,medrxiv,0,5,logistic regression,0.001943483,0.001943537,0.001943469,0.001943525,0.31678634,0.675439646,Clinics,0.50686675,TRUE,38.2,0.520564042,3.6,0.21507894,1,0.537564047,1394,0.80471948,0.519481627
12138,Incidence and Outcomes of Pulmonary embolism among hospitalized COVID-19 patients,0,10.1101/2021.02.16.21251676,2/19/21,medrxiv,0,5,logistic regression,0.001272687,0.001272686,0.001272748,0.001272692,0.001272778,0.993636409,Clinics,0.90180135,TRUE,13.8,0.208980147,1.8,0.150120417,0,0.403234768,934,0.708162774,0.367624527
12139,One Shot Model For COVID-19 Classification and Lesions Segmentation In Chest CT Scans Using LSTM With Attention Mechanism,0,10.1101/2021.02.16.21251754,2/19/21,medrxiv,0,1,"lstm, dataset",0.001751198,0.057123363,0.935871491,0.001751258,0.001751332,0.001751359,Imaging,0.358768,FALSE,24,0.35574247,8,0.320511105,0,0.403234768,382,0.33301228,0.353125156
12140,Associations of Race/Ethnicity and Other Demographic and Socioeconomic Factors with Vaccination During the COVID-19 Pandemic in the United States,0,10.1101/2021.02.16.21251769,2/19/21,medrxiv,0,1,logistic regression,0.000807892,0.000807895,0.000807885,0.000807951,0.995960455,0.000807922,Healthcare,0.116930485,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,521,0.479894052,0.243987057
12141,Risk factors for long-term consequences of COVID-19 in hospitalised adults in Moscow using the ISARIC Global follow-up protocol: StopCOVID cohort study,0,10.1101/2021.02.17.21251895,2/19/21,medrxiv,0,32,logistic regression,0.000466888,0.000466889,0.000466895,0.026683866,0.537440013,0.434475448,Healthcare,0.7568805,TRUE,91.34375,0.847238543,102.71875,0.827134065,2,0.618927094,1876,0.854322177,0.78690547
12142,"Country differences in transmissibility, age distribution and case-fatality of SARS-CoV-2: a global ecological analysis",0,10.1101/2021.02.17.21251839,2/19/21,medrxiv,0,5,mathematical model,0.001220003,0.001220042,0.021783258,0.864112104,0.110444494,0.001220098,Epidemiology,0.35410413,FALSE,59.4,0.698682664,78.6,0.774752475,0,0.403234768,512,0.470262461,0.586733092
12143,COVID-19 associated autoimmunity is a feature of severe respiratory disease - a Bayesian analysis.,0,10.1101/2021.02.17.21251953,2/19/21,medrxiv,0,8,bayes,0.00151191,0.199984287,0.001511919,0.001511939,0.001511855,0.79396809,Clinics,0.09447163,FALSE,195.625,0.971364958,489.75,0.978124164,0,0.403234768,1034,0.738743077,0.772866742
12144,"The unexpected dynamics of COVID-19 in Manaus, Brazil: Herd immunity versus interventions",0,10.1101/2021.02.18.21251809,2/19/21,medrxiv,0,4,mathematical model,0.001593512,0.00159352,0.001593505,0.952687998,0.001593629,0.040937836,Epidemiology,0.18656695,FALSE,88.75,0.838579999,128.75,0.862322719,1,0.537564047,1801,0.845894534,0.771090325
12145,Public Covid-19 X-ray datasets and their impact on model bias - a systematic review of a significant problem,0,10.1101/2021.02.15.21251775,2/19/21,medrxiv,0,4,"prediction model, dataset",0.001861767,0.001861731,0.853858144,0.138694854,0.001861792,0.001861712,Imaging,0.3216889,FALSE,18.75,0.28072237,17.5,0.45611453,1,0.537564047,931,0.706958825,0.495339943
12146,"SARS-CoV-2 Shedding Dynamics Across the Respiratory Tract, Sex, and Disease Severity for Adult and Pediatric COVID-19",0,10.1101/2021.02.17.21251926,2/19/21,medrxiv,0,6,dataset,0.00117159,0.127133911,0.001171587,0.089505515,0.249487328,0.531530069,Clinics,0.55198854,TRUE,168.8333333,0.957573134,216,0.925408081,0,0.403234768,1762,0.843245846,0.782365457
12147,ESC - a comprehensive resource for SARS-CoV-2 immune escape variants,0,10.1101/2021.02.18.431922,2/19/21,biorxiv,0,6,sequencing,0.078156027,0.884743692,0.001901807,0.001901865,0.001901796,0.031394813,Genomics,0.2599085,FALSE,42.83333333,0.565650319,25,0.529435376,1,0.537564047,458,0.426197929,0.514711918
12148,Mobility and COVID-19 in Andorra: Country-scale analysis of high-resolution mobility patterns and infection spread,0,10.1101/2021.02.18.21251977,2/19/21,medrxiv,0,8,dataset,0.001415113,0.001415116,0.001415131,0.992924307,0.001415212,0.001415121,Epidemiology,0.27780068,FALSE,8.875,0.130249242,2,0.164302917,0,0.403234768,619,0.556465206,0.313563033
12149,SARS-CoV-2 variant evolution in the United States: High accumulation of viral mutations over time likely through serial Founder Events and mutational bursts.,0,10.1101/2021.02.19.431311,2/19/21,biorxiv,0,9,genomes,0.039305659,0.839773373,0.000734148,0.112216811,0.000734187,0.007235822,Genomics,0.16572097,FALSE,22.11111111,0.326427114,50.44444444,0.683168317,0,0.403234768,1659,0.835058993,0.561972298
12150,Viral genetic sequencing identifies staff transmission of COVID-19 is important in a community hospital outbreak,0,10.1101/2021.02.18.21250737,2/19/21,medrxiv,0,18,"sequencing, whole-genome",0.00156545,0.454330449,0.001565302,0.001565357,0.332221267,0.208752175,Genomics,0.23361912,FALSE,62.77777778,0.719772404,177.2222222,0.904535724,0,0.403234768,600,0.542740188,0.642570771
12151,Evaluation of a fully automated high-throughput SARS-CoV-2 multiplex qPCR assay with build-in screening functionality for DelHV69/70- and N501Y variants such as B.1.1.7,0,10.1101/2021.02.12.21251614,2/18/21,medrxiv,0,7,sequencing,0.001565308,0.817392445,0.097699058,0.001565318,0.001565418,0.080212452,Genomics,0.21608612,FALSE,48.42857143,0.617416043,87.57142857,0.796026224,0,0.403234768,710,0.606549482,0.605806629
12152,"Characterization of SARS-CoV-2 genetic structure and infection clusters in a large German city based on integrated genomic surveillance, outbreak analysis, and contact tracing",0,10.1101/2021.02.13.21251678,2/18/21,medrxiv,0,26,"sequencing, genomes",0.000815358,0.617835878,0.000815397,0.353503272,0.000815361,0.026214735,Genomics,0.3139484,FALSE,17.23076923,0.25944709,14.03846154,0.412964945,0,0.403234768,2168,0.875511678,0.48778962
12153,COVID-19 Associated Stroke--A Single Centre Experience,0,10.1101/2021.02.15.21249420,2/18/21,medrxiv,0,26,logistic regression,0.001717211,0.001717177,0.001717187,0.001717213,0.001717259,0.991413954,Clinics,0.80743706,TRUE,11.07692308,0.167295442,1.807692308,0.150187316,0,0.403234768,889,0.687454852,0.352043094
12154,Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review,0,10.1101/2021.02.15.21251727,2/18/21,medrxiv,0,7,"deep learning, artificial intelligence, predictive model, prediction model",0.001156278,0.001156271,0.561726003,0.152510996,0.07339628,0.210054172,Clinics,0.85469383,TRUE,14.42857143,0.217886078,6.142857143,0.281241638,0,0.403234768,980,0.714423308,0.404196448
12155,Meta-analysis of orthogonal OMICs data from COVID-19 patients unveils prognostic markers and antiviral factors.,0,10.1101/2021.02.18.431825,2/18/21,biorxiv,0,13,"transcriptom, proteom,  omics",0.361866177,0.318075758,0.001684534,0.074591333,0.001684581,0.242097617,Drug discovery,0.44964185,FALSE,26.23076923,0.384006432,25.15384615,0.529836767,0,0.403234768,377,0.313267517,0.407586371
12156,"Protocol for Safe, Affordable, and Reproducible Isolation and Quantitation of SARS-CoV-2 RNA from Wastewater",0,10.1101/2021.02.16.21251787,2/17/21,medrxiv,0,9,"sequencing, whole genome",0.001486484,0.721345849,0.001486507,0.272708213,0.001486491,0.001486455,Genomics,0.26338947,FALSE,27.33333333,0.399529965,7.777777778,0.312349478,1,0.537564047,1671,0.828798459,0.519560487
12157,COVID-19 European Regional Tracker,0,10.1101/2021.02.15.21251788,2/17/21,medrxiv,0,1,dataset,0.05205319,0.002996619,0.002996554,0.93596052,0.002996574,0.002996542,Epidemiology,0.2832101,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,981,0.710089092,0.301535817
12158,Association of CXCR6 with COVID-19 severity: Delineating the host genetic factors in transcriptomic regulation,0,10.1101/2021.02.17.431554,2/17/21,biorxiv,0,6,"deep learning, transcriptom, genome-wide, dataset",0.576591086,0.147255598,0.062014296,0.001022645,0.030812498,0.182303876,Drug discovery,0.22990915,FALSE,65.83333333,0.739192282,54.66666667,0.700361252,0,0.403234768,1200,0.767878642,0.652666736
12159,A selective sweep in the Spike gene has driven SARS-CoV-2 human adaptation,0,10.1101/2021.02.13.431090,2/17/21,biorxiv,0,7,genomes,0.389447437,0.58228663,0.023951574,0.001438133,0.001438131,0.001438096,Genomics,0.36382347,FALSE,54.42857143,0.664172181,40.14285714,0.633864062,1,0.537564047,3453,0.926077534,0.690419456
12160,Delayed induction of type I and III interferons and nasal epithelial cell permissiveness to SARS-CoV-2,0,10.1101/2021.02.17.431591,2/17/21,biorxiv,0,25,"sequencing, proteom",0.824722479,0.122897826,0.001622723,0.047511386,0.00162278,0.001622806,Drug discovery,0.5675225,TRUE,45.48,0.590327169,37.76,0.620417447,0,0.403234768,1234,0.772935228,0.596728653
12161,A rigorous framework for detecting SARS-CoV-2 spike protein mutational ensemble from genomic and structural features,0,10.1101/2021.02.17.431625,2/17/21,biorxiv,0,7,"molecular dynamics simulation, genome sequences",0.367368783,0.569915358,0.058664817,0.001350403,0.001350317,0.001350323,Genomics,0.057790905,FALSE,27,0.3960047,14.14285714,0.413433235,0,0.403234768,1065,0.73561281,0.487071378
12162,How the replication and transcription complex of SARS-CoV-2 functions in leader-to-body fusion,0,10.1101/2021.02.17.431652,2/17/21,biorxiv,0,10,genomes,0.881895868,0.080064363,0.001486455,0.00148654,0.033580357,0.001486417,Drug discovery,0.13564879,FALSE,49.8,0.630465706,43.9,0.653532245,0,0.403234768,661,0.568986275,0.564054748
12163,Traditional use of Cissampelos pareira L. for hormone disorder and fever provides molecular links of ESR1 modulation to viral inhibition,0,10.1101/2021.02.17.431579,2/17/21,biorxiv,0,20,"transcriptom, multi-omics",0.932113132,0.001653175,0.001653181,0.001653123,0.001653101,0.061274287,Drug discovery,0.6041767,TRUE,26.3,0.385305214,11.05,0.371354027,0,0.403234768,562,0.495304599,0.413799652
12164,Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction,0,10.1101/2021.02.16.431420,2/16/21,biorxiv,0,5,"machine learning, neural network, transfer learning, dataset",0.197217272,0.066932986,0.731671238,0.001392848,0.001392832,0.001392823,Drug discovery,0.059744596,FALSE,31.2,0.446471643,39.8,0.632124699,0,0.403234768,533,0.464724296,0.486638851
12165,Identification of common key genes and pathways between Covid-19 and lung cancer by using protein-protein interaction network analysis,0,10.1101/2021.02.16.431364,2/16/21,biorxiv,0,3,network analysis,0.700402848,0.002130654,0.002130668,0.002130625,0.002130624,0.29107458,Drug discovery,0.7569294,TRUE,30.33333333,0.436266931,8,0.320511105,0,0.403234768,1102,0.740428606,0.475110352
12166,"The impact of armed conflict on the epidemiological situation of Coronavirus disease (COVID-19) in Libya, Syria, and Yemen.",0,10.1101/2021.02.12.21251654,2/16/21,medrxiv,0,1,dataset,0.001593518,0.001593571,0.001593529,0.953235839,0.001593533,0.04039001,Epidemiology,0.21266752,FALSE,74,0.782113922,33,0.593256623,0,0.403234768,669,0.565133638,0.585934738
12167,Impact of January 2021 social distancing measures on SARS-CoV-2 B.1.1.7 circulation in France,0,10.1101/2021.02.14.21251708,2/16/21,medrxiv,0,5,mathematical model,0.00299641,0.226204604,0.002996409,0.761809198,0.002996555,0.002996825,Epidemiology,0.12893188,FALSE,109.8,0.889789103,123.8,0.856301846,2,0.618927094,3724,0.930411751,0.823857448
12168,Biological attributes of age and gender variations in Indian COVID-19 cases: A retrospective data analysis,0,10.1101/2021.02.13.21251681,2/16/21,medrxiv,0,4,logistic regression,0.001511881,0.001511953,0.001511838,0.152231048,0.237071241,0.60616204,Clinics,0.4906022,FALSE,19,0.285793803,13,0.400521809,0,0.403234768,517,0.452684806,0.385558796
12169,Learning Where to Look for COVID-19 Growth: Multivariate Analysis of COVID-19 Cases Over Time using Explainable Convolution-LSTM,0,10.1101/2021.02.13.21251683,2/16/21,medrxiv,0,4,lstm,0.00190177,0.164751607,0.001901746,0.685706669,0.143836336,0.001901871,Epidemiology,0.29763335,FALSE,9.25,0.137918239,2,0.164302917,0,0.403234768,1234,0.769323381,0.368694826
12170,SARS-CoV-2 lineage B.1.526 emerging in the New York region detected by software utility created to query the spike mutational landscape,0,10.1101/2021.02.14.431043,2/15/21,biorxiv,0,4,"sequencing, genomes",0.001987101,0.990064398,0.001987092,0.001987176,0.001987111,0.001987123,Genomics,0.112467706,FALSE,259.5,0.985837096,447,0.975180626,12,0.850299401,28601,0.995424994,0.951685529
12171,"HaVoC, a bioinformatic pipeline for reference-based consensus assembly and lineage assignment for SARS-CoV-2 sequences",0,10.1101/2021.02.12.431018,2/15/21,biorxiv,0,6,"bioinformatic, genomes",0.001461882,0.992690256,0.001461942,0.00146196,0.001462021,0.001461938,Genomics,0.24232718,FALSE,60.66666667,0.706351661,36.33333333,0.613393096,0,0.403234768,523,0.449072959,0.543013121
12172,Jumper Enables Discontinuous Transcript Assembly in Coronaviruses,0,10.1101/2021.02.12.431026,2/15/21,biorxiv,0,4,"sequencing, transcriptom",0.193451805,0.64478734,0.157515366,0.00141521,0.001415156,0.001415122,Genomics,0.050878912,FALSE,31.25,0.447213804,33.5,0.595999465,0,0.403234768,563,0.479412473,0.481465127
12173,Recent and forecast post-COVID trends in hospital activity in England amongst 0 to 24 year olds: analyses using routine hospital administrative data,0,10.1101/2021.02.11.21251584,2/15/21,medrxiv,0,7,bayes,0.000620848,0.022298465,0.000620839,0.464918077,0.369436652,0.142105119,Epidemiology,0.08030325,FALSE,71.57142857,0.770981508,87.42857143,0.795691731,0,0.403234768,1131,0.742595714,0.67812593
12174,Association of demographic and occupational factors with SARS-CoV-2 vaccine uptake in a multi-ethnic UK healthcare workforce: a rapid real-world analysis,0,10.1101/2021.02.11.21251548,2/15/21,medrxiv,0,11,logistic regression,0.001653002,0.001653095,0.001653003,0.001653034,0.991734695,0.001653171,Healthcare,0.2414012,FALSE,131.6363636,0.923557425,111.6363636,0.840714477,7,0.785110192,2422,0.880809054,0.857547787
12175,Impacts of school closures on physical and mental health of children and young people: a systematic review,0,10.1101/2021.02.10.21251526,2/15/21,medrxiv,0,14,machine learning,0.00076593,0.000765943,0.034582174,0.435505602,0.494553771,0.033826579,Healthcare,0.29237926,FALSE,112.5714286,0.895602697,132.7857143,0.866738025,3,0.667819001,4191,0.936190706,0.841587607
12176,Identification of high-risk COVID-19 patients using machine learning,0,10.1101/2021.02.10.21251510,2/15/21,medrxiv,0,5,machine learning,0.001717258,0.001717245,0.520297435,0.363556403,0.001717291,0.110994367,Epidemiology,0.74677587,TRUE,29.6,0.426495145,14,0.412898047,0,0.403234768,633,0.529978329,0.443151572
12177,Mutation N501Y in RBD of Spike Protein Strengthens the Interaction between COVID-19 and its Receptor ACE2,0,10.1101/2021.02.14.431117,2/15/21,biorxiv,0,8,computational,0.662563469,0.314135768,0.020335639,0.000988396,0.000988373,0.000988356,Drug discovery,0.29366177,FALSE,40.625,0.54548828,27.875,0.552448488,7,0.785110192,2969,0.906573561,0.69740513
12178,"CATALYST trial protocol: A multicentre, open-label, phase II, multi-arm trial for an early and accelerated evaluation of the potential treatments for COVID-19 in hospitalised adults",0,10.1101/2021.02.10.21251478,2/15/21,medrxiv,0,16,bayes,0.286426886,0.065722924,0.231433417,0.081346956,0.000977494,0.334092322,Clinics,0.5713968,TRUE,52.58823529,0.650936978,81.64705882,0.782178218,0,0.403234768,1270,0.771008909,0.651839718
12179,A single-cell atlas of lymphocyte adaptive immune repertoires and transcriptomes reveals age-related differences in convalescent COVID-19 patients,0,10.1101/2021.02.12.430907,2/12/21,biorxiv,0,12,"sequencing, transcriptom",0.64574439,0.041562375,0.001112633,0.001112668,0.001112666,0.309355267,Drug discovery,0.28858477,FALSE,19.16666667,0.28641227,14.91666667,0.422263848,0,0.403234768,1020,0.693715386,0.451406568
12180,Single-cell sequencing of plasma cells from COVID-19 patients reveals highly expanded clonal lineages produce specific and neutralizing antibodies to SARS-CoV-2,0,10.1101/2021.02.12.430940,2/12/21,biorxiv,0,18,sequencing,0.596419982,0.322483103,0.001085385,0.001085345,0.001085329,0.077840857,Drug discovery,0.31365344,FALSE,21.5,0.318263343,58.38888889,0.715145839,0,0.403234768,1065,0.707199615,0.535960891
12181,Accelerating COVID-19 research with graph mining and transformer-based learning,0,10.1101/2021.02.11.430789,2/11/21,biorxiv,0,6,"computational, artificial intelligence, bioinformatic, neural network, text mining, dataset",0.06530968,0.001085399,0.54779444,0.349317491,0.00108538,0.035407611,Epidemiology,0.21676597,FALSE,20,0.298163152,49,0.677481937,0,0.403234768,316,0.16084758,0.384931859
12182,Before the Surge: Molecular Evidence of SARS-CoV-2 in New York City Prior to the First Report,0,10.1101/2021.02.08.21251303,2/11/21,medrxiv,0,26,genome sequences,0.002806477,0.82462807,0.00280644,0.002806519,0.002806662,0.164145832,Genomics,0.21069667,FALSE,47.88461538,0.613334158,,,1,0.537564047,1070,0.702624609,0.617840938
12183,Implementation of an in-house real-time reverse transcription-PCR assay to detect the emerging SARS-CoV-2 N501Y variants,0,10.1101/2021.02.03.21250661,2/11/21,medrxiv,0,5,sequencing,0.029454259,0.817421558,0.147293286,0.00194362,0.001943531,0.001943745,Genomics,0.16513959,FALSE,905.4,0.999319686,1305.6,0.996119882,0,0.403234768,446,0.334216229,0.683222641
12184,Data-driven analysis of COVID-19 reveals specific severity patterns distinct from the temporal immune response,0,10.1101/2021.02.10.430668,2/11/21,biorxiv,0,33,"data mining, network analysis",0.323154239,0.001717307,0.044184876,0.127770603,0.001717182,0.501455794,Clinics,0.49175414,FALSE,48.75757576,0.620384687,70.06060606,0.753478726,0,0.403234768,556,0.444979533,0.555519428
12185,Novel SARS-CoV-2 spike variant identified through viral genome sequencing of the pediatric Washington D.C. COVID-19 outbreak,0,10.1101/2021.02.08.21251344,2/10/21,medrxiv,0,14,"sequencing, genomes",0.018314698,0.796207527,0.000759352,0.000759362,0.026720023,0.157239037,Genomics,0.43689203,FALSE,36.5,0.503123261,40.5,0.637342788,0,0.403234768,2794,0.886828798,0.607632404
12186,Genomic surveillance of SARS-CoV-2 in the Bronx enables clinical and epidemiological inference,0,10.1101/2021.02.08.21250641,2/10/21,medrxiv,0,24,genomes,0.002296592,0.719432983,0.002296615,0.271380692,0.002296583,0.002296536,Genomics,0.30458304,FALSE,39.91666667,0.538252211,34.79166667,0.603826599,0,0.403234768,742,0.562244161,0.526889435
12187,Associations of DMT therapies with COVID-19 severity in multiple sclerosis,0,10.1101/2021.02.08.21251316,2/10/21,medrxiv,0,47,logistic regression,0.00186172,0.001861755,0.107843004,0.065967505,0.00186177,0.820604246,Clinics,0.17154402,FALSE,47.65116279,0.611416909,40.95348837,0.638747659,2,0.618927094,1461,0.782566819,0.66291462
12188,Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank cohort study,0,10.1101/2021.02.08.21251343,2/10/21,medrxiv,0,11,"machine learning, prediction model",0.001943543,0.001943545,0.369411512,0.001943578,0.058563039,0.566194782,Clinics,0.38031343,FALSE,30.36363636,0.436452471,23.27272727,0.514717688,0,0.403234768,725,0.55213099,0.476633979
12189,Whole-genome sequencing of SARS-CoV-2 in the Republic of Ireland during waves 1 and 2 of the pandemic,0,10.1101/2021.02.09.21251402,2/10/21,medrxiv,0,15,"sequencing, whole-genome, genome sequences",0.0008802,0.822205694,0.000880202,0.058033074,0.000880242,0.117120587,Genomics,0.1936259,FALSE,50.66666667,0.635908219,25.33333333,0.531576131,2,0.618927094,1274,0.750300987,0.634178108
12190,Detection Of Genomic Variants Of SARS-CoV-2 Circulating In Wastewater By High-Throughput Sequencing,0,10.1101/2021.02.08.21251355,2/10/21,medrxiv,0,11,"sequencing, metagenom, genomes",0.004109843,0.979450978,0.004109822,0.004109839,0.004109767,0.004109751,Genomics,0.35142523,FALSE,43.54545455,0.573628548,48,0.673735617,0,0.403234768,1054,0.693474597,0.586018382
12191,SARS-CoV-2 infection models using in vivo and in vitro hACE2-lentivirus transduction,0,10.1101/2021.02.09.430547,2/10/21,biorxiv,0,7,transcriptom,0.959428959,0.035085799,0.00137135,0.001371356,0.001371251,0.001371284,Drug discovery,0.19531465,FALSE,51.28571429,0.640299338,74.57142857,0.765386674,0,0.403234768,529,0.41367686,0.55564941
12192,Dual RNA-Seq analysis of SARS-CoV-2 correlates specific human transcriptional response pathways directly to viral expression,0,10.1101/2021.02.09.430517,2/9/21,biorxiv,0,5,"sequencing, transcriptom",0.723835111,0.209630609,0.001486538,0.001486542,0.00148644,0.06207476,Drug discovery,0.44074345,FALSE,18.6,0.278990661,34.8,0.604094193,0,0.403234768,294,0.116060679,0.350595075
12193,Exosomes from COVID-19 patients carry tenascin-C and fibrinogen-β in triggering inflammatory signals in distant organ cells,0,10.1101/2021.02.08.430369,2/9/21,biorxiv,0,6,proteom,0.775691179,0.05173863,0.001098852,0.001098852,0.001098855,0.169273632,Drug discovery,0.7484469,TRUE,80.83333333,0.81025419,82.16666667,0.783315494,0,0.403234768,241,0.053214544,0.512504749
12194,L18F substrain of SARS-CoV-2 VOC-202012/01 is rapidly spreading in England,0,10.1101/2021.02.07.21251262,2/9/21,medrxiv,0,3,genomes,0.149656149,0.793210274,0.001653015,0.001653139,0.001653128,0.052174295,Genomics,0.28757644,FALSE,9.666666667,0.144968767,1.333333333,0.13252609,4,0.707574542,2813,0.885865639,0.46773376
12195,Current quantitative polymerase chain reaction to detect severe acute respiratory syndrome coronavirus 2 may give positive results for other described coronavirus,0,10.1101/2021.02.08.21251332,2/9/21,medrxiv,0,5,genomes,0.003760701,0.981196609,0.003760665,0.003760765,0.003760736,0.003760523,Genomics,0.13505456,FALSE,6.2,0.087574989,0.8,0.101351351,0,0.403234768,912,0.638815314,0.307744106
12196,High Throughput Nanopore Sequencing of SARS-CoV-2 Viral Genomes from Patient Samples,0,10.1101/2021.02.09.430478,2/9/21,biorxiv,0,14,"sequencing, genome sequences, genomes",0.001861933,0.819243377,0.001861791,0.173309079,0.001861993,0.001861827,Genomics,0.34290004,FALSE,6.714285714,0.095182139,4.714285714,0.24719026,0,0.403234768,981,0.665302191,0.352727339
12197,Implementation of an in-house real-time reverse transcription-PCR assay for the rapid detection of the SARS-CoV-2 Marseille-4 variant,0,10.1101/2021.02.03.21250823,2/8/21,medrxiv,0,17,sequencing,0.063538682,0.909348236,0.001310396,0.001310368,0.001310337,0.023181981,Genomics,0.39726907,FALSE,312.0588235,0.991217762,438.4705882,0.97404335,0,0.403234768,412,0.2641464,0.65816057
12198,Emergence of SARS-CoV-2 stains harbouring the signature mutations of both A2a and A3 clade,0,10.1101/2021.02.04.21251117,2/8/21,medrxiv,0,4,genomes,0.001237076,0.993814673,0.001237042,0.001237083,0.001237064,0.001237061,Genomics,0.5098827,TRUE,5.5,0.077246583,1.25,0.127776291,1,0.537564047,467,0.331567542,0.268538616
12199,Using Machine Learning to Predict Mortality for COVID-19 Patients on Day Zero in the ICU,0,10.1101/2021.02.04.21251131,2/8/21,medrxiv,0,12,"machine learning, logistic regression",0.001203529,0.001203459,0.303682952,0.001203507,0.001203461,0.691503092,Clinics,0.77653944,TRUE,22.41666667,0.331189313,3,0.199424672,0,0.403234768,514,0.382133398,0.328995538
12200,A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic,0,10.1101/2021.02.04.21251167,2/8/21,medrxiv,0,6,"bayes, dataset",0.001486458,0.00148653,0.001486438,0.992567642,0.001486429,0.001486502,Epidemiology,0.20091814,FALSE,60.5,0.705547653,65,0.739028633,0,0.403234768,404,0.255237178,0.525762058
12201,Development and validation of an algorithm to estimate the risk of severe complications of COVID-19 to prioritise vaccination,0,10.1101/2021.02.05.21251197,2/8/21,medrxiv,0,12,dataset,0.000693862,0.000693857,0.183668051,0.210148273,0.295268245,0.309527711,Clinics,0.22401533,FALSE,112.25,0.894365762,109.9166667,0.838439925,0,0.403234768,2693,0.879364315,0.753851192
12202,Impact of cobas PCR Media Freezing on SARS-CoV-2 Viral RNA Integrity and Whole Genome Sequencing Analyses,0,10.1101/2021.02.05.430022,2/8/21,biorxiv,0,5,"sequencing, whole genome",0.001059402,0.974591148,0.001059407,0.00105938,0.00105942,0.021171243,Genomics,0.2716878,FALSE,91,0.846310842,123.6666667,0.856101151,0,0.403234768,747,0.550204671,0.663962858
12203,Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity,0,10.1101/2021.02.05.21251173,2/8/21,medrxiv,0,10,"machine learning, metabolom, predictive model",0.002130741,0.002130957,0.248643146,0.002130801,0.002130784,0.742833571,Clinics,0.5714145,TRUE,33.1,0.467190302,89.2,0.799571849,0,0.403234768,883,0.619792921,0.57244746
12204,The antibody response to SARS-CoV-2 increases over 5 months in patients with anosmia/dysgeusia,0,10.1101/2021.02.05.21251219,2/8/21,medrxiv,0,10,"machine learning, logistic regression",0.002490502,0.289896999,0.10565838,0.002490571,0.384083009,0.215380538,Healthcare,0.31505978,FALSE,360.8,0.99449564,718.4,0.98775756,0,0.403234768,2640,0.876715627,0.815550899
12205,UK and other SARS-CoV-2-Covariants - Simulation Modeling 70% Increase,0,10.1101/2021.02.05.21251230,2/8/21,medrxiv,0,2,simulation model,0.250627719,0.182329151,0.001786548,0.561683465,0.001786555,0.001786563,Epidemiology,0.22143292,FALSE,7.5,0.108355495,0,0.055525823,1,0.537564047,500,0.365278112,0.266680869
12206,Design and Estimation for the Population Prevalence of Infectious Diseases,0,10.1101/2021.02.05.21251231,2/8/21,medrxiv,0,5,bayes,0.001622755,0.109795202,0.223977717,0.444890241,0.218091338,0.001622747,Epidemiology,0.05495906,FALSE,5.2,0.071927763,0.4,0.075796093,0,0.403234768,377,0.216951601,0.191977556
12207,Modeling the Effect of Population-Wide Vaccination on the Evolution of COVID-19 Epidemic in Canada,0,10.1101/2021.02.05.21250572,2/8/21,medrxiv,0,3,mathematical model,0.000977559,0.000977468,0.000977445,0.995112598,0.000977505,0.000977426,Epidemiology,0.092006445,FALSE,3.333333333,0.04044777,0,0.055525823,0,0.403234768,798,0.580544185,0.269938136
12208,Immune cell residency in the nasal mucosa and COVID-19 severity across the age range,0,10.1101/2021.02.05.21251067,2/8/21,medrxiv,0,15,transcriptom,0.682388787,0.043841878,0.001823337,0.001823431,0.17379077,0.096331797,Drug discovery,0.65450835,TRUE,27.06666667,0.396252087,28.93333333,0.561345999,0,0.403234768,982,0.660727185,0.50539001
12209,Emergence of an early SARS-CoV-2 epidemic in the United States,0,10.1101/2021.02.05.21251235,2/8/21,medrxiv,0,52,genomes,0.001943494,0.321731263,0.0019435,0.670494728,0.001943567,0.001943448,Epidemiology,0.4517727,FALSE,32.4047619,0.459954233,71.21428571,0.756890554,2,0.618927094,2703,0.879845895,0.678904444
12210,Risk of hospitalisation with COVID-19 among teachers compared to healthcare workers and other working-age adults. A nationwide case-control study,0,10.1101/2021.02.05.21251189,2/8/21,medrxiv,0,13,logistic regression,0.001126829,0.001126813,0.001126789,0.001126837,0.728184423,0.26730831,Healthcare,0.35099643,FALSE,74.53846154,0.784649638,106.8461538,0.834359112,2,0.618927094,7536,0.96532627,0.800815529
12211,Automated Detection of COVID-19 through Convolutional Neural Network using Chest x-ray images,0,10.1101/2021.02.06.21251271,2/8/21,medrxiv,0,5,"deep learning, neural network, transfer learning, dataset",0.001350348,0.00135034,0.971526754,0.023071788,0.001350368,0.001350402,Imaging,0.43405142,FALSE,6.8,0.09629538,0.6,0.09011239,0,0.403234768,781,0.571634963,0.290319375
12212,"COVIDHunter: An Accurate, Flexible, and Environment-Aware Open-Source COVID-19 Outbreak Simulation Model",0,10.1101/2021.02.06.21251265,2/8/21,medrxiv,0,5,"simulation model, bioinformatic",0.001272736,0.021088773,0.031289373,0.895773044,0.001272651,0.049303423,Epidemiology,0.14825189,FALSE,113.2,0.896344857,439.4,0.974310945,0,0.403234768,790,0.575728389,0.71240474
12213,A Quantitative Evaluation of COVID-19 Epidemiological Models,0,10.1101/2021.02.06.21251276,2/8/21,medrxiv,0,7,mathematical model,0.002032763,0.002032771,0.353433897,0.638434919,0.002032803,0.002032846,Epidemiology,0.13245183,FALSE,15.28571429,0.230131734,21,0.492239765,0,0.403234768,1229,0.730797014,0.46410082
12214,Social predictors of food insecurity during the stay-at-home order due to the COVID-19 pandemic in Peru. Results from a cross-sectional web-based survey,0,10.1101/2021.02.06.21251221,2/8/21,medrxiv,0,10,probabilistic,0.000863053,0.00086306,0.000863062,0.105896409,0.890651351,0.000863064,Healthcare,0.24075064,FALSE,8.4,0.124435648,1.1,0.1226251,0,0.403234768,958,0.651577173,0.325468172
12215,Genomic epidemiology identifies emergence and rapid transmission of SARS-CoV-2 B.1.1.7 in the United States,0,10.1101/2021.02.06.21251159,2/7/21,medrxiv,0,53,"bayes, genomic epidemiology, genomes",0.001371274,0.592636783,0.001371258,0.401878015,0.00137131,0.00137136,Genomics,0.040649503,FALSE,26.71111111,0.391304348,64,0.735549906,26,0.920859312,29754,0.994221045,0.760483653
12216,Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning,0,10.1101/2021.02.04.21251143,2/6/21,medrxiv,0,19,"machine learning, prediction model",0.000999557,0.000999537,0.384547777,0.000999586,0.220836551,0.391616991,Clinics,0.18690535,FALSE,5.842105263,0.081575855,1.052631579,0.12215681,0,0.403234768,696,0.503009872,0.277494326
12217,Assessing the performance of a serological point-of-care test in measuring detectable antibodies against SARS-CoV-2,0,10.1101/2021.02.04.21251126,2/6/21,medrxiv,0,24,logistic regression,0.002080601,0.754087918,0.191530865,0.002080656,0.048139286,0.002080674,Genomics,0.4227433,FALSE,31.70833333,0.452161544,16.875,0.44715012,0,0.403234768,372,0.194317361,0.374215948
12218,Subgenomic RNAs as molecular indicators of asymptomatic SARS-CoV-2 infection,0,10.1101/2021.02.06.430041,2/6/21,biorxiv,0,14,"sequencing, proteom, genomes",0.31198352,0.620491249,0.001272658,0.001272691,0.001272748,0.063707134,Genomics,0.26982182,FALSE,50.64285714,0.635784526,243.2142857,0.935309071,0,0.403234768,752,0.537683602,0.628002992
12219,"Could the new COVID-19 mutant strain undermine vaccination efforts? A mathematical modelling approach for estimating the spread of the UK mutant strain using Ontario, Canada, as a case study",0,10.1101/2021.02.02.21251039,2/5/21,medrxiv,0,5,mathematical model,0.00112685,0.458754686,0.001126799,0.536738079,0.001126798,0.001126788,Epidemiology,0.11165765,FALSE,26.8,0.392417589,17.6,0.456984212,0,0.403234768,1254,0.719961474,0.493149511
12220,Quantifying transmissibility of COVID-19 and impact of intervention within long-term health care facilities,0,10.1101/2021.02.01.21249903,2/5/21,medrxiv,0,10,"bayes, dataset",0.001684483,0.001684509,0.001684509,0.991577444,0.001684562,0.001684493,Epidemiology,0.13136145,FALSE,31.5,0.450058754,35.4,0.607639818,0,0.403234768,734,0.518901999,0.494958835
12221,Estimating vaccine confidence levels among future healthcare workers and their trainers: A quantitative study protocol,0,10.1101/2021.02.03.21251068,2/5/21,medrxiv,0,4,logistic regression,0.064021047,0.000966782,0.09849333,0.132010124,0.703541946,0.000966771,Healthcare,0.73290545,TRUE,80.25,0.807904014,49,0.677481937,0,0.403234768,786,0.55140862,0.610007335
12222,Increased SAR-CoV-2 shedding associated with reduced disease severity despite continually emerging genetic variants,0,10.1101/2021.02.03.21250928,2/5/21,medrxiv,0,17,genomes,0.001237103,0.740401556,0.001237083,0.001237165,0.001237172,0.254649921,Genomics,0.61890805,TRUE,10.76470588,0.16030676,,,0,0.403234768,1339,0.741632555,0.435058027
12223,How did COVID-19 measures impact sexual behaviour and access to STI&HIV services in Panama? Results from a national cross-sectional online survey,0,10.1101/2021.02.03.21251095,2/5/21,medrxiv,0,12,logistic regression,0.001310325,0.001310402,0.001310307,0.001310415,0.993448154,0.001310396,Healthcare,0.9884079,TRUE,50.58333333,0.635475292,28.41666667,0.557131389,0,0.403234768,1027,0.659282446,0.563780974
12224,Intention to receive a COVID-19 vaccine: Results from a population-based survey in Canada,0,10.1101/2021.02.03.21251007,2/5/21,medrxiv,0,13,logistic regression,0.001330064,0.00133004,0.001330074,0.093144941,0.901534815,0.001330066,Healthcare,0.22210282,FALSE,45.53846154,0.591564104,56.69230769,0.709727054,0,0.403234768,1022,0.658078497,0.590651106
12225,Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US,0,10.1101/2021.02.03.21250974,2/5/21,medrxiv,0,251,"forecasting model, probabilistic",0.00120341,0.001203416,0.154006053,0.841180119,0.001203477,0.001203525,Epidemiology,0.26940486,FALSE,10.69565217,0.159379059,,,2,0.618927094,5039,0.936913075,0.571739743
12226,Catching SARS-CoV-2 by sequence hybridization: a comparative analysis,0,10.1101/2021.02.05.429917,2/5/21,biorxiv,0,6,"sequencing, genomes",0.001717226,0.824557104,0.001717222,0.168574025,0.001717231,0.001717191,Genomics,0.19699427,FALSE,30.33333333,0.436266931,44.83333333,0.658148247,0,0.403234768,865,0.594509993,0.523039985
12227,Estimation of infection rate and the population size potentially exposed to SARS-CoV-2 in Japan during 2020,0,10.1101/2021.02.01.21250971,2/4/21,medrxiv,0,1,machine learning,0.002639014,0.002639045,0.059026269,0.836309003,0.096747577,0.002639092,Epidemiology,0.13524926,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,860,0.58656393,0.266866414
12228,The SARS-CoV-2 transcriptome and the dynamics of the S gene furin cleavage site in primary human airway epithelia,0,10.1101/2021.02.03.429670,2/4/21,biorxiv,0,9,transcriptom,0.583843526,0.363045277,0.001717229,0.047959372,0.00171726,0.001717337,Drug discovery,0.53426266,TRUE,67.22222222,0.748654833,72.33333333,0.75990099,0,0.403234768,1083,0.670599567,0.645597539
12229,"Targeted in situ cross-linking mass spectrometry and integrative modeling reveal the architectures of Nsp1, Nsp2, and Nucleocapsid proteins from SARS-CoV-2",0,10.1101/2021.02.04.429751,2/4/21,biorxiv,0,14,computational,0.713242203,0.281592736,0.001291263,0.001291325,0.001291241,0.001291231,Drug discovery,0.2255323,FALSE,44.07142857,0.578452594,85.64285714,0.791878512,0,0.403234768,2020,0.823982663,0.649387134
12230,Type-I interferon signatures in SARS-CoV-2 infected Huh7 cells,0,10.1101/2021.02.04.429738,2/4/21,biorxiv,0,11,proteom,0.991066956,0.001786571,0.001786566,0.001786775,0.00178654,0.001786591,Drug discovery,0.23921934,FALSE,36.63636364,0.504174655,25.45454545,0.532445812,1,0.537564047,1410,0.751264146,0.581362165
12231,Large-scale analysis of SARS-CoV-2 spike-glycoprotein mutants demonstrates the need for continuous screening of virus isolates,0,10.1101/2021.02.04.429765,2/4/21,biorxiv,0,6,dataset,0.208646877,0.768311859,0.00123709,0.001237099,0.001237132,0.019329942,Genomics,0.37330443,FALSE,96.33333333,0.859917125,78,0.773548301,1,0.537564047,3024,0.88466169,0.763922791
12232,Codon arrangement modulates MHC-I peptides presentation: implications for a SARS-CoV-2 peptide-based vaccine,0,10.1101/2021.02.04.429819,2/4/21,biorxiv,0,4,neural network,0.499764111,0.174162735,0.271641135,0.001392915,0.001392948,0.051646156,Drug discovery,0.16779736,FALSE,29.5,0.426000371,58,0.714476853,0,0.403234768,1089,0.673489044,0.554300259
12233,Bayesian Calibration of Using CO2 Sensors to Assess Ventilation Conditions and Associated COVID-19 Airborne Aerosol Transmission Risk in Schools,0,10.1101/2021.01.29.21250791,2/3/21,medrxiv,0,3,bayes,0.001098792,0.001098801,0.00109885,0.628759974,0.001098833,0.366844749,Epidemiology,0.21395934,FALSE,9.666666667,0.144968767,1.333333333,0.13252609,0,0.403234768,1143,0.683361425,0.341022763
12234,Towards a COVID-19 symptom triad: The importance of symptom constellations in the SARS-CoV-2 pandemic,0,10.1101/2021.02.01.21250537,2/3/21,medrxiv,0,9,dataset,0.00151187,0.001511847,0.256476005,0.461106247,0.277882056,0.001511974,Epidemiology,0.48218203,FALSE,40.33333333,0.542890717,25.44444444,0.532245116,0,0.403234768,698,0.484469059,0.490709915
12235,RAY: CRISPR diagnostic for rapid and accurate detection of SARS-CoV2 variants on a paper strip,0,10.1101/2021.02.01.21250900,2/3/21,medrxiv,0,13,"sequencing, deep sequencing",0.001684485,0.698889678,0.150188193,0.001684565,0.001684523,0.145868556,Genomics,0.24761793,FALSE,6.461538462,0.091100254,7.461538462,0.305793417,1,0.537564047,1993,0.818685288,0.438285751
12236,"ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores",0,10.1101/2021.02.01.21250306,2/3/21,medrxiv,0,120,"logistic regression, prediction model",0.000693875,0.000693866,0.025028092,0.040886505,0.053775593,0.87892207,Clinics,0.33085296,FALSE,7.764705882,0.112870307,2.382352941,0.173735617,1,0.537564047,1932,0.813869492,0.409509866
12237,Increased hazard of death in community-tested cases of SARS-CoV-2 Variant of Concern 202012/01,0,10.1101/2021.02.01.21250959,2/3/21,medrxiv,0,7,dataset,0.001622779,0.424824632,0.001622723,0.199635678,0.001622833,0.370671357,Genomics,0.1812543,FALSE,158.8333333,0.950398911,211.8333333,0.92313353,13,0.858880178,24633,0.990609198,0.930755454
12238,"Seroprevalence of SARS-CoV-2 during pregnancy and associated outcomes: results from an ongoing prospective cohort study, New York City",0,10.1101/2021.02.01.21250943,2/3/21,medrxiv,0,21,logistic regression,0.00063424,0.130696721,0.00063421,0.0006342,0.535701816,0.331698813,Healthcare,0.44588453,FALSE,28.71428571,0.41462057,28.47619048,0.55753278,0,0.403234768,2196,0.834577414,0.552491383
12239,Deleterious effects of SARS-CoV-2 infection on human pancreatic cells,0,10.1101/2021.02.01.21250846,2/3/21,medrxiv,0,10,transcriptom,0.807367825,0.059953212,0.001371281,0.001371331,0.001371342,0.128565009,Drug discovery,0.67198557,TRUE,7.2,0.103716989,,,0,0.403234768,1981,0.817481339,0.441477699
12240,Rapid vaccination and early reactive partial lockdown will minimize deaths from emerging highly contagious SARS-CoV-2 variants,0,10.1101/2021.02.02.21250985,2/3/21,medrxiv,0,6,mathematical model,0.001350369,0.113064967,0.00135031,0.881533592,0.00135039,0.001350373,Epidemiology,0.03919357,FALSE,9.166666667,0.136619457,3.166666667,0.201097137,0,0.403234768,3352,0.89646039,0.409352938
12241,Effect of co-infection with parasites on severity of COVID-19,0,10.1101/2021.02.02.21250995,2/3/21,medrxiv,0,12,logistic regression,0.001901786,0.001901726,0.001901716,0.001901714,0.001901825,0.990491233,Clinics,0.6611725,TRUE,12.5,0.189436576,12.33333333,0.389550442,0,0.403234768,3396,0.898627498,0.470212321
12242,Muscle strength is associated with COVID-19 hospitalization in adults 50 years of age and older,0,10.1101/2021.02.02.21250909,2/3/21,medrxiv,0,8,logistic regression,0.001461871,0.001461931,0.001461894,0.00146194,0.430592745,0.563559619,Clinics,0.7091792,TRUE,28.25,0.410105758,7.625,0.309071448,1,0.537564047,9087,0.968938117,0.556419842
12243,CovRadar: Continuously tracking and filtering SARS-CoV-2 mutations for molecular surveillance,0,10.1101/2021.02.03.429146,2/3/21,biorxiv,0,14,"bioinformatic, sequence alignment",0.23968682,0.645273449,0.00151193,0.110504145,0.00151186,0.001511796,Genomics,0.22825319,FALSE,46.83333333,0.604057146,34.83333333,0.60422799,0,0.403234768,1550,0.77028654,0.595451611
12244,Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection,0,10.1101/2021.02.03.429351,2/3/21,biorxiv,0,33,"sequencing, proteom",0.505352031,0.238001015,0.001126818,0.00112689,0.001126826,0.25326642,Drug discovery,0.15517384,FALSE,60.60606061,0.705918733,98.93939394,0.820310409,1,0.537564047,3393,0.898386708,0.740544975
12245,"In silico, in vitro and in cellulo models for monitoring SARS-CoV-2 spike/human ACE2 complex, viral entry and cell fusion",0,10.1101/2021.02.03.429555,2/3/21,biorxiv,0,9,"computational, in silico",0.787816375,0.184231076,0.024953853,0.000999606,0.000999543,0.000999546,Drug discovery,0.456114,FALSE,29.66666667,0.42773208,20.88888889,0.489697618,0,0.403234768,1188,0.694919335,0.50389595
12246,Characterization of the NiRAN domain from RNA-dependent RNA polymerase provides insights into a potential therapeutic target against SARS-CoV-2,0,10.1101/2021.02.03.429510,2/3/21,biorxiv,0,9,in silico,0.948595988,0.045356694,0.001511849,0.001511871,0.001511798,0.001511799,Drug discovery,0.9351982,TRUE,39.77777778,0.537015276,10.33333333,0.36038266,0,0.403234768,867,0.581988924,0.470655407
12247,Molecular dynamics simulation study of effects of key mutations in SARS-CoV-2 on protein structures,0,10.1101/2021.02.03.429495,2/3/21,biorxiv,0,9,"molecular dynamics simulation, genomes",0.591988747,0.3990573,0.002238484,0.002238525,0.0022385,0.002238444,Drug discovery,0.58775574,TRUE,11.33333333,0.170635166,,,0,0.403234768,1173,0.691789068,0.421886334
12248,COVID-19 vaccine acceptability and inequity in the United States: Results from a nationally representative survey,0,10.1101/2021.01.29.21250784,2/2/21,medrxiv,0,5,logistic regression,0.068580209,0.000759396,0.000759415,0.000759418,0.928382164,0.000759399,Healthcare,0.32459176,FALSE,12.2,0.18461253,0.4,0.075796093,0,0.403234768,1121,0.672525885,0.334042319
12249,COVID-19 risk perceptions of social interaction and essential activities and inequity in the United States: Results from a nationally representative survey,0,10.1101/2021.01.30.21250705,2/2/21,medrxiv,0,9,logistic regression,0.000871579,0.000871545,0.000871539,0.000871633,0.995642019,0.000871684,Healthcare,0.7088789,TRUE,36.44444444,0.501948172,21.66666667,0.499732406,0,0.403234768,741,0.503250662,0.477041502
12250,"Successful reboot of high-performance sporting activities by Japanese national women's handball team in Tokyo, 2020 during the COVID-19 pandemic: An initiative by Japan Sports-Cyber Physical System (JS-CPS) of Sports Research Innovation Project (SRIP)",0,10.1101/2021.01.29.21250745,2/2/21,medrxiv,0,17,artificial intelligence,0.000988366,0.000988387,0.400891199,0.303677953,0.292465659,0.000988436,Epidemiology,0.56759727,TRUE,6.470588235,0.0911621,0.529411765,0.087168852,0,0.403234768,1349,0.729352275,0.327729499
12251,Genetic determination of regional connectivity in modelling the spread of COVID-19 outbreak for improved mitigation strategies,0,10.1101/2021.01.30.21250785,2/2/21,medrxiv,0,5,"genomes, prediction model",0.001486439,0.541252253,0.001486615,0.452801821,0.001486418,0.001486454,Genomics,0.096989095,FALSE,18.6,0.278990661,40.8,0.638346267,0,0.403234768,713,0.487117746,0.45192236
12252,Molecular epidemiology of SARS-CoV-2 in Greece reveals low rates of onward virus transmission after lifting of travel restrictions based on risk assessment during summer 2020,0,10.1101/2021.01.31.21250868,2/2/21,medrxiv,0,32,dataset,0.001861736,0.485680063,0.001861736,0.50687301,0.001861744,0.00186171,Epidemiology,0.5798445,TRUE,40.125,0.540355,45.4375,0.660155205,0,0.403234768,2922,0.877919576,0.620416137
12253,Evolution of ACE2 and SARS-CoV-2 Interplay Across 247 Vertebrates,0,10.1101/2021.01.28.428568,2/2/21,biorxiv,0,6,computational,0.751066774,0.205942944,0.001392831,0.001392864,0.001392866,0.038811721,Drug discovery,0.32179493,FALSE,19,0.285793803,31.66666667,0.582686647,0,0.403234768,720,0.490970383,0.4406714
12254,Computational insights into differential interaction of mamalian ACE2 with the SARS-CoV-2 spike receptor binding domain,0,10.1101/2021.02.02.429327,2/2/21,biorxiv,0,4,computational,0.917357651,0.073211591,0.002357693,0.002357703,0.002357679,0.002357683,Drug discovery,0.6612641,TRUE,42,0.558537943,25.4,0.532044421,0,0.403234768,650,0.44859138,0.485602128
12255,Emergence of universality in the transmission dynamics of COVID-19,0,10.1101/2021.01.29.21250750,2/1/21,medrxiv,0,4,neural network,0.00182336,0.001823429,0.161044535,0.831661833,0.001823393,0.001823451,Epidemiology,0.042769432,FALSE,59.5,0.699857752,5.75,0.271742039,0,0.403234768,559,0.366722851,0.435389353
12256,Enhanced immunogenicity of a synthetic DNA vaccine expressing consensus SARS-CoV-2 Spike protein using needle-free immunization,0,10.1101/2021.02.01.429219,2/1/21,biorxiv,0,12,sequencing,0.714908735,0.281181327,0.00097745,0.000977551,0.000977488,0.000977448,Drug discovery,0.26896062,FALSE,19,0.285793803,14.83333333,0.421862457,0,0.403234768,1062,0.649410065,0.440075273
12257,Prediction Models for Severe Manifestations and Mortality due to COVID-19: A Rapid Systematic Review,0,10.1101/2021.01.28.21250718,2/1/21,medrxiv,0,5,"predictive model, prediction model",0.000977447,0.000977479,0.097239558,0.415268293,0.000977474,0.484559748,Clinics,0.4151975,FALSE,34.2,0.479188571,9.2,0.339577201,0,0.403234768,1190,0.686973272,0.477243453
12258,"Discrimination of SARS-Cov 2 and arboviruses (DENV, ZIKV and CHIKV) clinical features using machine learning techniques: a fast and inexpensive clinical screening for countries simultaneously affected by both diseases",0,10.1101/2021.01.28.21250714,2/1/21,medrxiv,0,1,"machine learning, dataset",0.003607176,0.003607218,0.845380044,0.140190849,0.003607229,0.003607484,Epidemiology,0.59325564,TRUE,1,0.012307502,0,0.055525823,0,0.403234768,489,0.293763544,0.191207909
12259,Viral sequencing reveals US healthcare personnel rarely become infected with SARS-CoV-2 through patient contact,0,10.1101/2021.01.28.21250421,2/1/21,medrxiv,0,12,sequencing,0.00056531,0.244121374,0.000565321,0.323276631,0.352461638,0.079009726,Healthcare,0.26728106,FALSE,15,0.227596017,6.833333333,0.293484078,0,0.403234768,511,0.318805683,0.310780136
12260,High variability in transmission of SARS-CoV-2 within households and implications for control,0,10.1101/2021.01.29.20248797,2/1/21,medrxiv,0,10,probabilistic,0.000966764,0.095491631,0.066313691,0.60067705,0.2355841,0.000966764,Epidemiology,0.07082361,FALSE,20.3,0.301564723,17.9,0.459727054,1,0.537564047,628,0.427161088,0.431504228
12261,Updated SARS-CoV-2 Single Nucleotide Variants and Mortality Association,0,10.1101/2021.01.29.21250757,2/1/21,medrxiv,0,10,"genomes, logistic regression",0.001538204,0.777835295,0.001538163,0.001538239,0.001538277,0.216011822,Genomics,0.2906244,FALSE,10.3,0.154493166,3.2,0.202100615,0,0.403234768,577,0.384300506,0.286032264
12262,A mathematical model to estimate percentage secondary infections from margin of error of diagnostic sensitivity: Useful tool for regulatory agencies to assess the risk of propagation due to false negative outcome of diagnostics,0,10.1101/2021.01.29.21250804,2/1/21,medrxiv,0,6,mathematical model,0.035109315,0.001220064,0.581925678,0.305219141,0.075305734,0.001220068,Epidemiology,0.38194644,FALSE,5.833333333,0.081514008,0.833333333,0.102488627,0,0.403234768,304,0.082350108,0.167396878
12263,Diagnostic accuracy of PanbioTM rapid antigen tests on oropharyngeal swabs for detection of SARS-CoV-2,0,10.1101/2021.01.30.21250314,2/1/21,medrxiv,0,17,genomes,0.001901716,0.427271639,0.468242556,0.001901729,0.001901748,0.098780612,Genomics,0.25611347,FALSE,9.647058824,0.143979219,5.588235294,0.267594327,0,0.403234768,494,0.299783289,0.278647901
12264,Under what circumstances could vaccination offset the harm from a more transmissible variant of SARS-COV-2 in NYC? Trade-offs regarding prioritization and speed of vaccination.,0,10.1101/2021.01.29.21250710,2/1/21,medrxiv,0,4,mathematical model,0.000591783,0.112813919,0.000591777,0.595705551,0.224458611,0.06583836,Epidemiology,0.052137464,FALSE,56.75,0.680004948,65.75,0.741637677,0,0.403234768,499,0.306525403,0.532850699
12265,Genetically predicted serum vitamin D and COVID-19: a Mendelian randomization study,0,10.1101/2021.01.29.21250759,2/1/21,medrxiv,0,5,genome-wide,0.001254632,0.369016785,0.001254631,0.001254668,0.001254711,0.625964573,Clinics,0.15206414,FALSE,22.6,0.334652731,21,0.492239765,2,0.618927094,852,0.558873104,0.501173173
12266,Identification of B.1.346 lineage of SARS-CoV-2 in Japan: Genomic evidence of re-entry of Clade 20C,0,10.1101/2021.01.29.21250798,2/1/21,medrxiv,0,29,"sequencing, whole genome",0.001171558,0.758200815,0.0011716,0.087297738,0.00117157,0.150986719,Genomics,0.20323965,FALSE,3.586206897,0.044653349,0.068965517,0.055793417,0,0.403234768,660,0.44883217,0.238128426
12267,Predicting Prognosis in COVID-19 Patients using Machine Learning and Readily Available Clinical Data,0,10.1101/2021.01.29.21250762,2/1/21,medrxiv,0,8,machine learning,0.001371253,0.001371246,0.28004787,0.001371341,0.001371271,0.714467019,Clinics,0.33776236,FALSE,38.125,0.519821881,19.375,0.473976452,0,0.403234768,1153,0.67854563,0.518894683
12268,Emergence of first strains of Sars-CoV-2 lineage B.1.1.7 in Romania,0,10.1101/2021.01.29.21250643,2/1/21,medrxiv,0,5,sequencing,0.002720216,0.881472868,0.002720364,0.069900352,0.002720339,0.040465862,Genomics,0.18396118,FALSE,28.4,0.411157153,4.2,0.234211935,0,0.403234768,471,0.275222731,0.330956646
12269,"Structural basis of fitness of emerging SARS-COV-2 variants and considerations for screening, testing and surveillance strategy to contain their threat.",0,10.1101/2021.01.28.21250666,1/31/21,medrxiv,0,3,"sequencing, in silico",0.270511641,0.529520706,0.001171577,0.162513634,0.035110709,0.001171733,Genomics,0.64637905,TRUE,3.333333333,0.04044777,0,0.055525823,0,0.403234768,1208,0.687214062,0.296605606
12270,High-dimensional profiling reveals phenotypic heterogeneity and disease-specific alterations of granulocytes in COVID-19,0,10.1101/2021.01.27.21250591,1/31/21,medrxiv,0,36,computational,0.391237945,0.001220104,0.001220074,0.001220071,0.001220014,0.603881792,Clinics,0.38649318,FALSE,29.80555556,0.428536088,35.16666667,0.606234948,1,0.537564047,2102,0.820130026,0.598116277
12271,Mitigation policies and vaccination in the COVID-19 pandemic: a modelling study,0,10.1101/2021.01.27.21250651,1/31/21,medrxiv,0,2,mathematical model,0.001943491,0.001943503,0.001943461,0.839047093,0.153178964,0.001943487,Epidemiology,0.4632841,FALSE,24,0.35574247,7,0.299973241,0,0.403234768,685,0.455333494,0.378570993
12272,SARS-CoV-2 Seroepidemiology in Children and Adolescents,0,10.1101/2021.01.28.21250466,1/31/21,medrxiv,0,14,logistic regression,0.062349746,0.286510336,0.001126816,0.001126886,0.621672948,0.027213268,Healthcare,0.24109483,FALSE,6.571428571,0.093883357,1.357142857,0.132592989,0,0.403234768,7412,0.957139417,0.396712633
12273,Awake prone positioning and oxygen therapy in patients with COVID-19: The APRONOX study,0,10.1101/2021.01.27.21250631,1/31/21,medrxiv,0,21,logistic regression,0.001987164,0.001987232,0.001987305,0.139946829,0.001987229,0.85210424,Clinics,0.67776203,TRUE,4.476190476,0.058630713,0.666666667,0.096200161,0,0.403234768,3662,0.903202504,0.365317036
12274,Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care,0,10.1101/2021.01.25.21249942,1/30/21,medrxiv,0,16,"logistic regression, dataset",0.038727771,0.000572632,0.26866262,0.000572646,0.000572637,0.690891694,Clinics,0.20474449,FALSE,28.8125,0.415610118,11.75,0.381857105,0,0.403234768,783,0.508066458,0.427192112
12275,Longitudinal analyses reveal age-specific immune correlates of COVID-19 severity,0,10.1101/2021.01.25.21250189,1/30/21,medrxiv,0,18,sequencing,0.470575091,0.047773472,0.001126802,0.001126827,0.001126838,0.478270969,Clinics,0.11064333,FALSE,6.222222222,0.087760529,1.611111111,0.140754616,1,0.537564047,1525,0.752227306,0.379576625
12276,Post-Mendelian genetic model in COVID-19,0,10.1101/2021.01.27.21250593,1/29/21,medrxiv,0,31,logistic regression,0.001291316,0.535757196,0.18294381,0.083102685,0.001291283,0.19561371,Genomics,0.1199812,FALSE,23.58064516,0.348815635,17.03225806,0.451164035,0,0.403234768,1599,0.75872863,0.490485767
12277,Covid-19 positive test cycle threshold trends predict covid-19 mortality in Rhode Island,0,10.1101/2021.01.26.21250557,1/29/21,medrxiv,0,3,dataset,0.001291225,0.121607454,0.26145016,0.272430972,0.001291264,0.341928926,Clinics,0.13674822,FALSE,211.6666667,0.976003463,282.6666667,0.949692267,0,0.403234768,4421,0.917890681,0.811705295
12278,First detection and report of SARS-CoV-2 Spike protein N501Y mutations in Oklahoma USA,0,10.1101/2021.01.26.21250584,1/29/21,medrxiv,0,6,"sequencing, whole genome, genomes",0.003927409,0.980363206,0.003927351,0.003927403,0.003927317,0.003927314,Genomics,0.34654245,FALSE,25.33333333,0.37188447,43.5,0.651725983,0,0.403234768,741,0.477486155,0.476082844
12279,"HLA-A*11:01:01:01, HLA*C*12:02:02:01-HLA-B*52:01:02:02, age and sex are associated with severity of Japanese COVID-19 with respiratory failure",0,10.1101/2021.01.26.21250349,1/29/21,medrxiv,0,13,sequencing,0.00162277,0.262778328,0.001622753,0.001622816,0.001622815,0.730730518,Clinics,0.2970396,FALSE,8.230769231,0.120724844,7.923076923,0.314958523,0,0.403234768,1055,0.628702143,0.366905069
12280,A Rapid and Low-Cost protocol for the detection of B.1.1.7 lineage of SARS-CoV-2 by using SYBR Green-Based RT-qPCR,0,10.1101/2021.01.27.21250048,1/29/21,medrxiv,0,5,sequencing,0.001565379,0.846842712,0.08829749,0.001565367,0.001565354,0.060163698,Genomics,0.22279435,FALSE,8,0.118683901,11,0.371287129,1,0.537564047,1394,0.720443053,0.436994533
12281,A prediction model for COVID-19 prevalence based on demographic and healthcare parameters in Iran,0,10.1101/2021.01.27.21250551,1/29/21,medrxiv,0,6,"neural network, prediction model",0.002238528,0.002238465,0.208279312,0.78276606,0.002238928,0.002238708,Epidemiology,0.4994601,FALSE,11.16666667,0.168161296,5.833333333,0.273213808,1,0.537564047,645,0.418251866,0.349297754
12282,COVID-19 mortality: positive correlation with cloudiness and sunlight but no correlation with latitude in Europe,0,10.1101/2021.01.27.21250658,1/29/21,medrxiv,0,3,model fit,0.001486455,0.001486484,0.001486438,0.594798248,0.001486485,0.399255891,Epidemiology,0.40076494,FALSE,7.666666667,0.111633373,0.666666667,0.096200161,0,0.403234768,1176,0.668432458,0.31987519
12283,The prevalence of olfactory dysfunction and its associated factors in patients with COVID-19 infection,0,10.1101/2021.01.27.21250153,1/29/21,medrxiv,0,4,logistic regression,0.001901812,0.001901695,0.001901856,0.001901683,0.001901852,0.990491101,Clinics,0.8649027,TRUE,4.25,0.056527924,0.25,0.065493712,1,0.537564047,1604,0.760173369,0.354939763
12284,Sequencing Data of North American SARS-CoV-2 Isolates Shows Widespread Complex Variants,0,10.1101/2021.01.27.21250648,1/29/21,medrxiv,0,4,"sequencing, sequence alignment, dataset",0.001156371,0.87355182,0.001156293,0.121823014,0.001156254,0.001156247,Genomics,0.04406461,FALSE,6,0.086028821,0.25,0.065493712,0,0.403234768,1623,0.763303636,0.329515234
12285,Recombination and low-diversity confound homoplasy-based methods to detect the effect of SARS-CoV-2 mutations on viral transmissibility,0,10.1101/2021.01.29.428535,1/29/21,biorxiv,0,6,dataset,0.002720303,0.915613278,0.002720353,0.073505556,0.002720238,0.002720272,Genomics,0.43089572,FALSE,64.66666667,0.732822067,282.6666667,0.949692267,1,0.537564047,1314,0.702383819,0.73061555
12286,Information retrieval in an infodemic: the case of COVID-19 publications,0,10.1101/2021.01.29.428847,1/29/21,biorxiv,0,8,information retrieval,0.002490552,0.002490447,0.312328494,0.677709298,0.002490793,0.002490416,Epidemiology,0.043081522,FALSE,18.5,0.278001113,3.125,0.200160557,0,0.403234768,844,0.533830966,0.353806851
12287,The evolutionary making of SARS-CoV-2,0,10.1101/2021.01.29.428808,1/29/21,biorxiv,0,6,in-silico,0.542048474,0.450344403,0.00190189,0.001901797,0.00190172,0.001901716,Drug discovery,0.09382564,FALSE,85.83333333,0.828127899,162.6666667,0.894300241,0,0.403234768,2456,0.839634,0.741324227
12288,"Individual factors underlie temperature variation in sickness and in health: influence of age, BMI and genetic factors in a multi-cohort study",0,10.1101/2021.01.26.21250480,1/28/21,medrxiv,0,19,logistic regression,0.00095636,0.00095637,0.000956435,0.180399197,0.210640457,0.606091181,Clinics,0.53293204,TRUE,23.89473684,0.352217206,12.21052632,0.387610383,0,0.403234768,3954,0.906332771,0.512348782
12289,Trajectories of Hypoxemia & Respiratory System Mechanics of COVID-19 ARDS in the NorthCARDS dataset,0,10.1101/2021.01.26.21250492,1/28/21,medrxiv,0,11,dataset,0.000765941,0.00076593,0.051771718,0.212375961,0.327502214,0.406818236,Clinics,0.5970106,TRUE,9.454545455,0.140515802,1.636363636,0.141356703,0,0.403234768,1174,0.663857452,0.337241181
12290,Genome-scale metabolic modeling reveals SARS-CoV-2-induced host metabolic reprogramming and identifies metabolic antiviral targets,0,10.1101/2021.01.27.428543,1/28/21,biorxiv,0,8,"sequencing, dataset",0.792074757,0.076511241,0.065266535,0.001072215,0.001072214,0.064003039,Drug discovery,0.12497413,FALSE,29.625,0.426680685,73.125,0.762175542,0,0.403234768,1980,0.800626053,0.598179262
12291,E484K as an innovative phylogenetic event for viral evolution: Genomic analysis of the E484K spike mutation in SARS-CoV-2 lineages from Brazil,0,10.1101/2021.01.27.426895,1/27/21,biorxiv,0,6,genomes,0.001987296,0.723049047,0.001987126,0.269002242,0.001987184,0.001987105,Genomics,0.507492,TRUE,11,0.167171748,6.333333333,0.285121755,4,0.707574542,7963,0.958584156,0.52961305
12292,Computational Analysis of Protein Stability and Allosteric Interaction Networks in Distinct Conformational Forms of the SARS-CoV-2 Spike D614G Mutant: Reconciling Functional Mechanisms through Allosteric Model of Spike Regulation,0,10.1101/2021.01.26.428331,1/27/21,biorxiv,0,4,computational,0.696079828,0.153874887,0.000800673,0.147643439,0.0008006,0.000800573,Drug discovery,0.24383575,FALSE,32.25,0.458098831,22.5,0.507492641,1,0.537564047,1234,0.676860101,0.545003905
12293,Genomic insights into early SARS-CoV-2 strains isolated in Reunion Island,0,10.1101/2021.01.21.21249623,1/26/21,medrxiv,0,9,sequencing,0.002080592,0.517820286,0.002080581,0.473857304,0.00208063,0.002080607,Genomics,0.15721315,FALSE,,,,,0,0.403234768,474,0.236937154,0.320085961
12294,A 3D CNN Classification Model for Accurate Diagnosis of Coronavirus Disease 2019 using Computed Tomography Images,0,10.1101/2021.01.21.21249999,1/26/21,medrxiv,0,3,dataset,0.001593515,0.001593548,0.887770187,0.105855574,0.001593598,0.001593577,Imaging,0.51323354,TRUE,2.333333333,0.024800544,,,0,0.403234768,587,0.348182037,0.258739116
12295,Rule of thumb in human intelligence for assessing the COVID-19 outbreak in Japan,0,10.1101/2021.01.20.21250204,1/26/21,medrxiv,0,3,mathematical model,0.001717254,0.001717321,0.122601519,0.787786232,0.001717243,0.084460431,Epidemiology,0.5221052,TRUE,0,0.006432061,,,0,0.403234768,594,0.354201782,0.25462287
12296,Role of FYVE and Coiled-Coil Domain Autophagy Adaptor 1 in severity of COVID-19 infection: from GWAS hit to therapeutic hypothesis,0,10.1101/2021.01.22.21250070,1/26/21,medrxiv,0,2,"sequencing, whole genome",0.461612093,0.447193862,0.001371244,0.001371289,0.001371337,0.087080175,Drug discovery,0.45078924,FALSE,0,0.006432061,,,0,0.403234768,1062,0.614495545,0.341387458
12297,The longest persistence of viable SARS-CoV-2 with recurrence of viremia and relapsing symptomatic COVID-19 in an immunocompromised patient - a case study,0,10.1101/2021.01.23.21249554,1/26/21,medrxiv,0,21,sequencing,0.182589638,0.560665031,0.001237069,0.00123708,0.001237087,0.253034095,Genomics,0.84714717,TRUE,,,,,0,0.403234768,8704,0.962918372,0.68307657
12298,Seroprevalence and attainment of herd immunity against SARS CoV-2: A modelling study,0,10.1101/2021.01.22.21250328,1/26/21,medrxiv,0,5,mathematical model,0.002130715,0.05009365,0.002130656,0.899530327,0.043983965,0.002130687,Epidemiology,0.05935976,FALSE,3,0.037293586,,,1,0.537564047,748,0.464242716,0.346366783
12299,ACoRE: Accurate SARS-CoV-2 genome reconstruction for the characterization of intra-host and inter-host viral diversity in clinical samples and for the evaluation of re-infections,0,10.1101/2021.01.22.21250285,1/26/21,medrxiv,0,21,sequencing,0.00280638,0.864459848,0.124314564,0.002806425,0.0028064,0.002806382,Genomics,0.26660436,FALSE,14.57142857,0.220112561,,,0,0.403234768,872,0.530219119,0.384522149
12300,Projected spread of COVID-19's second wave in South Africa under different levels of lockdown,0,10.1101/2021.01.22.21250308,1/26/21,medrxiv,0,2,bayes,0.002080519,0.002080566,0.002080557,0.98959716,0.00208066,0.002080538,Epidemiology,0.16897213,FALSE,0,0.006432061,,,0,0.403234768,1651,0.756079942,0.388582257
12301,"Genomic Epidemiology of SARS-CoV-2 in Esteio, Rio Grande do Sul, Brazil",0,10.1101/2021.01.21.21249906,1/26/21,medrxiv,0,16,"genomic epidemiology, genome sequences, genomes",0.052027817,0.943784825,0.00104682,0.001046878,0.001046848,0.001046812,Genomics,0.41578275,FALSE,7.1875,0.103098522,,,2,0.618927094,2460,0.833855045,0.518626887
12302,"Estimates of global SARS-CoV-2 infection exposure, infection morbidity, and infection mortality rates",0,10.1101/2021.01.24.21250396,1/26/21,medrxiv,0,6,mathematical model,0.001987101,0.001987214,0.001987095,0.404063808,0.001987191,0.58798759,Clinics,0.23082882,FALSE,2.833333333,0.031356299,,,0,0.403234768,1033,0.604623164,0.346404744
12303,"Trends, regional variation, and clinical characteristics of COVID-19 vaccine recipients: a retrospective cohort study in 23.4 million patients using OpenSAFELY.",0,10.1101/2021.01.25.21250356,1/26/21,medrxiv,0,35,dataset,0.000688498,0.000688496,0.000688528,0.330436715,0.51353216,0.153965603,Healthcare,0.052092254,FALSE,14.97142857,0.223575979,,,8,0.799987654,11087,0.972309174,0.665290935
12304,Patients with Asthma and Chronic Obstructive Pulmonary Disease (COPD) have increased levels of plasma inflammatory mediators upregulated in severe COVID-19,0,10.1101/2021.01.23.21250370,1/26/21,medrxiv,0,9,proteom,0.518834383,0.001310397,0.001310348,0.001310362,0.001310365,0.475924145,Drug discovery,0.9157871,TRUE,0,0.006432061,,,0,0.403234768,1689,0.762581267,0.390749365
12305,Novel COVID-19 phenotype definitions reveal phenotypically distinct patterns of genetic association and protective effects,0,10.1101/2021.01.24.21250324,1/26/21,medrxiv,0,44,genome-wide,0.001901851,0.25575668,0.033442477,0.001901726,0.061124489,0.645872776,Clinics,0.10162544,FALSE,2.727272727,0.02962459,,,1,0.537564047,2179,0.814351072,0.460513236
12306,Molecular epidemiology of SARS-CoV-2 - a regional to global perspective,0,10.1101/2021.01.25.21250447,1/26/21,medrxiv,0,10,"bioinformatic, sequencing, whole-genome, genomes",0.000946115,0.895555637,0.025819491,0.000946155,0.0757864,0.000946201,Genomics,0.25909805,FALSE,1.4,0.013915517,,,1,0.537564047,2254,0.819889237,0.457122934
12307,High infection attack rates of SARS-CoV-2 in Dutch households revealed by dense sampling,0,10.1101/2021.01.26.21250512,1/26/21,medrxiv,0,9,logistic regression,0.001272705,0.123856117,0.001272628,0.15840744,0.689012841,0.02617827,Healthcare,0.14737135,FALSE,0.555555556,0.007730843,,,0,0.403234768,2048,0.802552372,0.404505994
12308,Emergence and fast spread of B.1.1.7 lineage in Lebanon.,0,10.1101/2021.01.25.21249974,1/26/21,medrxiv,0,7,sequencing,0.001717229,0.949130197,0.001717211,0.001717315,0.001717231,0.044000818,Genomics,0.15294051,FALSE,0,0.006432061,,,3,0.667819001,1116,0.633999518,0.436083527
12309,A novel SARS-CoV-2 related coronavirus in bats from Cambodia,0,10.1101/2021.01.26.428212,1/26/21,biorxiv,0,16,"sequencing, metagenom",0.002296627,0.988517123,0.002296554,0.002296617,0.002296572,0.002296508,Genomics,0.52283907,TRUE,67.8125,0.752427485,115.75,0.845397378,6,0.764429903,17345,0.984107874,0.83659066
12310,Variant and mutation analysis of SARS-CoV-2 genomes isolated from the Kingdom of Bahrain,0,10.1101/2021.01.25.428191,1/26/21,biorxiv,0,3,genomes,0.002996493,0.875621687,0.002996615,0.11239235,0.002996473,0.002996382,Genomics,0.19434774,FALSE,20,0.298163152,10.66666667,0.365333155,0,0.403234768,1608,0.750782567,0.45437841
12311,Computational Investigation of Increased Virulence and Pathogenesis of SARS-CoV-2 Lineage B.1.1.7,0,10.1101/2021.01.25.428190,1/26/21,biorxiv,0,6,computational,0.768742357,0.228083997,0.000793445,0.000793419,0.000793394,0.000793388,Drug discovery,0.070286244,FALSE,23,0.34225988,1.833333333,0.151056998,0,0.403234768,2370,0.8278353,0.431096736
12312,40 minutes RT-qPCR Assay for Screening Spike N501Y and HV69-70del Mutations,0,10.1101/2021.01.26.428302,1/26/21,biorxiv,0,7,sequencing,0.00133005,0.993349641,0.001330122,0.001330075,0.001330066,0.001330046,Genomics,0.7422298,TRUE,23.85714286,0.352031666,10.85714286,0.367607707,1,0.537564047,2903,0.86226824,0.529867915
12313,Variability in codon usage in Coronaviruses is mainly driven by mutational bias and selective constraints on CpG dinucleotide,0,10.1101/2021.01.26.428296,1/26/21,biorxiv,0,2,genomes,0.001823412,0.990883175,0.001823339,0.001823369,0.001823376,0.001823329,Genomics,0.26511723,FALSE,11,0.167171748,26,0.53819909,0,0.403234768,1092,0.626053455,0.433664765
12314,Escape of SARS-CoV-2 501Y.V2 variants from neutralization by convalescent plasma,0,10.1101/2021.01.26.21250224,1/26/21,medrxiv,0,20,sequencing,0.180913503,0.679951576,0.001272629,0.001272737,0.00127273,0.135316824,Genomics,0.10093528,FALSE,15.8,0.238109964,,,48,0.955614544,20995,0.986997351,0.726907286
12315,Harnessing testing strategies and public health measures to avert COVID-19 outbreaks during ocean cruises,0,10.1101/2021.01.24.21250408,1/25/21,medrxiv,0,4,mathematical model,0.001717218,0.148023063,0.001717268,0.781298804,0.065526448,0.001717198,Epidemiology,0.33926034,FALSE,14.75,0.222586431,17,0.451097137,0,0.403234768,978,0.577173128,0.413522866
12316,"Distinct Autoimmune Antibody Signatures Between Hospitalized Acute COVID-19 Patients, SARS-CoV-2 Convalescent Individuals, and Unexposed Pre-Pandemic Controls",0,10.1101/2021.01.21.21249176,1/25/21,medrxiv,0,11,computational,0.105145202,0.196217058,0.018669512,0.000926308,0.103343408,0.575698512,Clinics,0.1262711,FALSE,20,0.298163152,29.72727273,0.567500669,5,0.739490092,11612,0.973272333,0.644606561
12317,An interactive COVID-19 virus Mutation Tracker (CovMT) with a particular focus on critical mutations in the Receptor Binding Domain (RBD) region of the Spike protein,0,10.1101/2021.01.22.21249716,1/25/21,medrxiv,10.1016/S1473-3099(21)00078-5,7,"sequencing, genomes",0.14486848,0.671848538,0.00153812,0.178668609,0.001538108,0.001538146,Genomics,0.21485412,FALSE,100.1428571,0.869070443,302.5714286,0.954977254,0,0.403234768,1120,0.631110041,0.714598127
12318,On mobility trends analysis of COVID-19 dissemination in Mexico City,0,10.1101/2021.01.24.21250406,1/25/21,medrxiv,0,3,"bayes, computational, mathematical model",0.00159351,0.001593603,0.001593498,0.970646158,0.022979741,0.001593489,Epidemiology,0.1501317,FALSE,7,0.10179974,1,0.122023013,1,0.537564047,701,0.433180833,0.298641908
12319,An external validation of the QCovid risk prediction algorithm for risk of mortality from COVID-19 in adults: national validation cohort study in England,0,10.1101/2021.01.22.21249968,1/25/21,medrxiv,0,12,"prediction model, dataset",0.000634186,0.000634195,0.160169911,0.300370202,0.251262347,0.286929159,Epidemiology,0.061009943,FALSE,24,0.35574247,36.33333333,0.613393096,0,0.403234768,1767,0.77052733,0.535724416
12320,Prediction of In-hospital Mortality among Adults with COVID-19 Infection,0,10.1101/2021.01.22.21249953,1/25/21,medrxiv,0,6,"machine learning, logistic regression",0.001717163,0.001717192,0.134687106,0.001717263,0.001717453,0.858443823,Clinics,0.18218347,FALSE,140.1666667,0.934009524,585.6666667,0.982940862,0,0.403234768,7712,0.952805201,0.818247589
12321,mRNA-1273 efficacy in a severe COVID-19 model: attenuated activation of pulmonary immune cells after challenge,0,10.1101/2021.01.25.428136,1/25/21,biorxiv,0,28,"sequencing, transcriptom",0.85359186,0.037154832,0.001565338,0.001565377,0.00156538,0.104557212,Drug discovery,0.15947115,FALSE,66.67857143,0.745315109,,,0,0.403234768,3857,0.897905129,0.682151669
12322,Profiling transcription factor sub-networks in type I interferon signaling and in response to SARS-CoV-2 infection,0,10.1101/2021.01.25.428122,1/25/21,biorxiv,0,1,"transcriptom, dataset",0.926241436,0.046278872,0.000710591,0.000710608,0.000710572,0.025347921,Drug discovery,0.44995934,FALSE,58,0.689714887,152,0.885001338,1,0.537564047,1270,0.677582471,0.697465686
12323,The CXCR6/CXCL16 axis links inflamm-aging to disease severity in COVID-19 patients,0,10.1101/2021.01.25.428125,1/25/21,biorxiv,0,15,genome-wide,0.466845098,0.068274422,0.002639084,0.002639029,0.002639036,0.456963332,Drug discovery,0.37196848,FALSE,87.5,0.835363968,121.8125,0.853625903,2,0.618927094,1144,0.639056104,0.736743267
12324,Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation,0,10.1101/2021.01.16.21249943,1/24/21,medrxiv,0,6,artificial intelligence,0.001220042,0.001220021,0.321424303,0.599555958,0.075359577,0.001220099,Epidemiology,0.36399856,FALSE,98.5,0.86591626,,,0,0.403234768,1252,0.66650614,0.645219056
12325,Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments,0,10.1101/2021.01.20.21249656,1/22/21,medrxiv,0,2,logistic regression,0.000734233,0.075708582,0.280612824,0.122681533,0.047195198,0.473067631,Clinics,0.37308773,FALSE,2.5,0.027459954,0,0.055525823,0,0.403234768,1672,0.74717072,0.308347816
12326,Using body temperature and variables commonly available in the EHR to predict acute infection: A proof-of-concept study showing improved pretest probability estimates for acute COVID-19 infection among discharged emergency department patients,0,10.1101/2021.01.21.21250261,1/22/21,medrxiv,0,7,logistic regression,0.022396637,0.001330053,0.037535817,0.152094782,0.001330057,0.785312654,Clinics,0.19772762,FALSE,26.85714286,0.392912363,11.71428571,0.380987423,0,0.403234768,1577,0.730556224,0.476922695
12327,Theoretical framework for retrospective studies of the effectiveness of SARS-CoV-2 vaccines,0,10.1101/2021.01.21.21250258,1/22/21,medrxiv,0,9,dataset,0.001593533,0.03914431,0.129876508,0.624686878,0.203105154,0.001593617,Epidemiology,0.18791097,FALSE,85.44444444,0.826272497,177.1111111,0.904468825,1,0.537564047,3066,0.862749819,0.782763797
12328,Insights from Genomes and Genetic Epidemiology of SARS-CoV-2 isolates from the state of Andhra Pradesh,0,10.1101/2021.01.22.427775,1/22/21,biorxiv,0,25,genomes,0.001823376,0.928390319,0.064316073,0.001823474,0.001823404,0.001823355,Genomics,0.26924193,FALSE,19.68,0.292658792,9.88,0.35188654,1,0.537564047,1234,0.652781122,0.458722625
12329,CCR1 regulatory variants linked to pulmonary macrophage recruitment in severe COVID-19,0,10.1101/2021.01.22.427813,1/22/21,biorxiv,0,4,genome-wide,0.356124217,0.368963314,0.004310018,0.004309987,0.004310167,0.261982297,Genomics,0.2994969,FALSE,0,0.006432061,,,0,0.403234768,1597,0.733686492,0.381117774
12330,"Association between COVID-19 Outcomes and Mask Mandates, Adherence, and Attitudes",0,10.1101/2021.01.19.21250132,1/21/21,medrxiv,0,9,dataset,0.001415101,0.001415134,0.001415116,0.83591876,0.081133539,0.07870235,Epidemiology,0.7786834,TRUE,78.22222222,0.799183623,57,0.711198823,0,0.403234768,3623,0.882013003,0.698907554
12331,Evidence of ongoing recombination in SARS-CoV-2 through genealogical reconstruction,0,10.1101/2021.01.21.427579,1/21/21,biorxiv,0,3,sequencing,0.00203285,0.779532731,0.002032915,0.212335931,0.002032813,0.002032759,Genomics,0.13627613,FALSE,43.66666667,0.57492733,77,0.771139952,0,0.403234768,2268,0.808090537,0.639348147
12332,Evolving Insights from SARS-CoV-2 Genome from 200K COVID-19 Patients,0,10.1101/2021.01.21.427574,1/21/21,biorxiv,0,4,"computational, genome sequences",0.001786637,0.810879873,0.181973771,0.001786625,0.001786583,0.001786512,Genomics,0.3294077,FALSE,7,0.10179974,,,2,0.618927094,2298,0.812665543,0.511130792
12333,"Design of Specific Primer Sets for the Detection of B.1.1.7, B.1.351 and P.1 SARS-CoV-2 Variants using Deep Learning",0,10.1101/2021.01.20.427043,1/21/21,biorxiv,0,7,"deep learning, in-silico",0.001237094,0.765701808,0.229349743,0.001237179,0.001237122,0.001237053,Genomics,0.221116,FALSE,0,0.006432061,,,1,0.537564047,4079,0.899349868,0.481115325
12334,Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave,0,10.1101/2021.01.15.21249884,1/20/21,medrxiv,0,12,bayes,0.002720124,0.00272026,0.002720077,0.901932761,0.087186546,0.002720231,Epidemiology,0.23128667,FALSE,24.41666667,0.360504669,14.5,0.418450629,0,0.403234768,1584,0.721887792,0.476019464
12335,Electronic Computer-Based Model of Combined Ventilation Using a New Medical Device,0,10.1101/2021.01.17.21249912,1/20/21,medrxiv,0,5,computational,0.001187304,0.00118737,0.001187361,0.585341432,0.001187318,0.409909215,Epidemiology,0.2128858,FALSE,9,0.135320675,1.2,0.126103827,0,0.403234768,1516,0.709607513,0.343566696
12336,Modelling the Long-Term Effects of Covid-19 Cancer Services Disruption on Patient Outcome in Scotland,0,10.1101/2021.01.17.21249993,1/20/21,medrxiv,0,9,dataset,0.000765929,0.000765942,0.08853487,0.537172886,0.000765957,0.371994416,Epidemiology,0.10637811,FALSE,50,0.632073721,91.88888889,0.805592721,0,0.403234768,557,0.276667469,0.52939217
12337,Population-scale patient safety data reveal inequalities in adverse events before and during COVID-19 pandemic,0,10.1101/2021.01.17.21249988,1/20/21,medrxiv,0,3,dataset,0.111195589,0.001072243,0.001072198,0.168060683,0.263777632,0.454821655,Clinics,0.27042872,FALSE,9.333333333,0.139278867,0,0.055525823,0,0.403234768,1981,0.78184445,0.344970977
12338,Optimizing SARS-CoV-2 vaccination strategies in France: Results from a stochastic agent-based model,0,10.1101/2021.01.17.21249970,1/20/21,medrxiv,0,9,simulation model,0.001511894,0.001511853,0.001511828,0.406167848,0.376646638,0.212649939,Epidemiology,0.2790267,FALSE,102.1111111,0.873708949,278.7777778,0.948421193,1,0.537564047,1114,0.603178425,0.740718154
12339,"Emergence of a novel SARS-CoV-2 strain in Southern California, USA",0,10.1101/2021.01.18.21249786,1/20/21,medrxiv,0,6,sequencing,0.00171719,0.810063743,0.001717185,0.18306739,0.001717221,0.001717273,Genomics,0.22766358,FALSE,7.666666667,0.111633373,3.833333333,0.222103291,12,0.850299401,22094,0.986034192,0.542517564
12340,NEWS2 and laboratory predictors correlated with clinical deterioration in hospitalised patients with COVID-19,0,10.1101/2021.01.17.21249878,1/20/21,medrxiv,0,16,logistic regression,0.001392846,0.001392855,0.001392873,0.001392887,0.001392835,0.993035704,Clinics,0.82117057,TRUE,7.5625,0.108788422,1.8125,0.150254215,0,0.403234768,589,0.307488562,0.242441492
12341,Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV 2 infection,0,10.1101/2021.01.18.21250044,1/20/21,medrxiv,0,22,"sequencing, transcriptom",0.162790105,0.327996958,0.107318471,0.001171647,0.080388221,0.320334598,Genomics,0.030180395,FALSE,24.22727273,0.357536026,30.13636364,0.570511105,1,0.537564047,1932,0.775343125,0.560238576
12342,The Impact of U.S. County-Level Factors on COVID-19 Morbidity and Mortality,0,10.1101/2021.01.19.21250092,1/20/21,medrxiv,0,4,classifier,0.00101094,0.00101094,0.105372779,0.359248143,0.19181926,0.341537938,Epidemiology,0.24960792,FALSE,3.75,0.046817985,0.25,0.065493712,0,0.403234768,694,0.397062365,0.228152207
12343,Forecasting the Spread of the COVID-19 Epidemic in Lombardy: A Dynamic Model Averaging Approach,0,10.1101/2021.01.18.21250053,1/20/21,medrxiv,0,2,machine learning,0.001538096,0.001538187,0.203843501,0.790003868,0.001538155,0.001538192,Epidemiology,0.20954964,FALSE,12,0.183190055,0.5,0.087101953,1,0.537564047,772,0.445461112,0.313329292
12344,Modeling and Simulation: A study on predicting the outbreak of COVID- 19 in Saudi Arabia,0,10.1101/2021.01.17.21249837,1/20/21,medrxiv,0,4,mathematical model,0.002130698,0.002130671,0.00213064,0.989346512,0.002130687,0.002130791,Epidemiology,0.5289237,TRUE,5.75,0.080957388,0,0.055525823,0,0.403234768,681,0.384782085,0.231125016
12345,Examining the effect of information channel on COVID-19 vaccine acceptance,0,10.1101/2021.01.18.21250049,1/20/21,medrxiv,0,8,logistic regression,0.002032875,0.002032842,0.002032772,0.485769068,0.506099654,0.00203279,Healthcare,0.13505065,FALSE,13.125,0.198033274,5.375,0.263446615,0,0.403234768,1116,0.604141584,0.36721406
12346,Improved screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test,0,10.1101/2021.01.18.21249821,1/20/21,medrxiv,0,15,"bayes, classifier",0.001203437,0.174934696,0.308165512,0.001203508,0.407235922,0.107256924,Healthcare,0.75239766,TRUE,15.4,0.231616055,26.53333333,0.542079208,0,0.403234768,849,0.484950638,0.415470167
12347,"Immunisation, asymptomatic infection, herd immunity and the new variants of COVID-19",0,10.1101/2021.01.16.21249946,1/20/21,medrxiv,0,2,mathematical model,0.177945738,0.163448022,0.000740316,0.422233441,0.234892161,0.000740321,Epidemiology,0.017971396,FALSE,4.5,0.061784897,0,0.055525823,1,0.537564047,10817,0.968215748,0.405772629
12348,"Factors associated with COVID-19 related hospitalisation, critical care admission and mortality using linked primary and secondary care data",0,10.1101/2021.01.19.20241844,1/20/21,medrxiv,0,6,logistic regression,0.001717161,0.001717201,0.043007739,0.001717253,0.001717256,0.950123389,Clinics,0.28713384,FALSE,9,0.135320675,1.333333333,0.13252609,1,0.537564047,1061,0.585600771,0.347752896
12349,Epidemic waves of COVID-19 in Scotland: a genomic perspective on the impact of the introduction and relaxation of lockdown on SARS-CoV-2,0,10.1101/2021.01.08.20248677,1/20/21,medrxiv,0,55,"sequencing, genomes",0.001171536,0.53144953,0.001171538,0.463864261,0.001171599,0.001171537,Genomics,0.28419822,FALSE,17.36363636,0.261302492,20.54545455,0.486352689,1,0.537564047,3313,0.870214303,0.538858383
12350,Temporal dynamics of SARS-CoV-2 mutation accumulation within and across infected hosts,0,10.1101/2021.01.19.427330,1/20/21,biorxiv,0,8,"sequencing, genomes",0.001203403,0.906111497,0.001203456,0.001203469,0.001203483,0.089074692,Genomics,0.2281071,FALSE,41.625,0.554146824,,,2,0.618927094,4058,0.895738021,0.68960398
12351,"Coevolutionary Analysis and Perturbation-Based Network Modeling of the SARS-CoV-2 Spike Protein Complexes with Antibodies: Binding-Induced Control of Dynamics, Allosteric Interactions and Signaling",0,10.1101/2021.01.19.427320,1/20/21,biorxiv,0,2,network model,0.812150805,0.142240249,0.000734179,0.043406442,0.000734184,0.000734141,Drug discovery,0.3273403,FALSE,0,0.006432061,,,0,0.403234768,991,0.553816518,0.321161116
12352,Estimating dates of origin and end of COVID-19 epidemics,0,10.1101/2021.01.19.21250080,1/20/21,medrxiv,0,4,mathematical model,0.001987137,0.001987283,0.001987139,0.990064097,0.001987165,0.00198718,Epidemiology,0.21547258,FALSE,7,0.10179974,2.75,0.187583623,0,0.403234768,1562,0.718035155,0.352663322
12353,Increased elastase sensitivity and decreased intramolecular interactions in the more transmissible SARS-CoV-2 variants' spike protein,0,10.1101/2021.01.19.427355,1/20/21,biorxiv,0,5,computational,0.27533722,0.570561582,0.003465952,0.143702961,0.003466046,0.003466239,Genomics,0.15609893,FALSE,0,0.006432061,,,0,0.403234768,1795,0.757043101,0.38890331
12354,Evaluation of the effects of SARS-CoV-2 genetic mutations on diagnostic RT-PCR assays,0,10.1101/2021.01.19.426622,1/20/21,biorxiv,0,5,in silico,0.108174694,0.843742714,0.002032922,0.002032857,0.04198398,0.002032834,Genomics,0.59040594,TRUE,0,0.006432061,,,1,0.537564047,1700,0.745003612,0.429666573
12355,Sleep in Frontline Healthcare Workers on Social Media During the COVID-19 Pandemic,0,10.1101/2021.01.19.21250128,1/20/21,medrxiv,0,6,logistic regression,0.000854717,0.000854761,0.000854763,0.056335016,0.920869494,0.020231249,Healthcare,0.9455597,TRUE,12.5,0.189436576,1.166666667,0.124565159,0,0.403234768,939,0.527329641,0.311141536
12356,Overcrowding and Exposure to Secondhand Smoke Increase Risk for COVID-19 Infection Among Latinx Families in Greater San Francisco Bay Area,0,10.1101/2021.01.19.21250139,1/20/21,medrxiv,0,4,logistic regression,0.001254648,0.00125464,0.001254596,0.16237322,0.683317347,0.15054555,Healthcare,0.45870662,FALSE,11.25,0.169274538,0,0.055525823,0,0.403234768,1073,0.588490248,0.304131344
12357,Comprehensive mapping of SARS-CoV-2 interactions in vivo reveals functional virus-host interactions,0,10.1101/2021.01.17.427000,1/19/21,biorxiv,0,17,"sequencing, genomes",0.611273666,0.381580216,0.001786514,0.001786564,0.001786529,0.001786512,Drug discovery,0.33463466,FALSE,40.29411765,0.542148556,,,1,0.537564047,2366,0.813387912,0.631033505
12358,SARS-CoV-2 RECoVERY: a multi-platform open-source bioinformatic pipeline for the automatic construction and analysis of SARS-CoV-2 genomes from NGS sequencing data,0,10.1101/2021.01.16.425365,1/18/21,biorxiv,0,6,"computational, bioinformatic, sequencing, genome sequences, genomes",0.001156275,0.738424735,0.081365498,0.176740953,0.001156265,0.001156274,Genomics,0.18716729,FALSE,0,0.006432061,,,0,0.403234768,2333,0.805923429,0.405196753
12359,SARS-CoV-2 infection reduces Krüppel-Like Factor 2 in human lung autopsy,0,10.1101/2021.01.15.426691,1/18/21,biorxiv,0,10,genome-wide,0.505655065,0.06753879,0.001717227,0.001717247,0.001717213,0.421654459,Drug discovery,0.51913154,TRUE,2.8,0.031047065,,,1,0.537564047,1217,0.627738984,0.398783365
12360,Large scale genomic and evolutionary study reveals SARS-CoV-2 virus isolates from Bangladesh strongly correlate with European origin and not with China.,0,10.1101/2021.01.17.425424,1/18/21,biorxiv,0,19,"sequencing, phylogenom, whole genome",0.000699384,0.80776539,0.041960037,0.000699365,0.037810628,0.111065197,Genomics,0.2451162,FALSE,0,0.006432061,,,0,0.403234768,1684,0.734168071,0.3812783
12361,Metabolic markers distinguish COVID-19 from other intensive care patients and show potential to stratify for disease risk,0,10.1101/2021.01.13.21249645,1/16/21,medrxiv,0,25,metabolom,0.077369103,0.201530445,0.001565375,0.095141075,0.001565393,0.62282861,Clinics,0.75549376,TRUE,42.44,0.561753974,37.12,0.616871822,0,0.403234768,3554,0.872622201,0.613620691
12362,Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics,0,10.1101/2021.01.15.21249881,1/15/21,medrxiv,0,5,model fit,0.002562712,0.00256259,0.002562584,0.845490547,0.144258865,0.002562703,Epidemiology,0.17811847,FALSE,110.8,0.891273424,292,0.95189992,0,0.403234768,847,0.455574284,0.675495599
12363,Importance of patient bed pathways and length of stay differences in predicting COVID-19 bed occupancy in England,0,10.1101/2021.01.14.21249791,1/15/21,medrxiv,0,11,dataset,0.135871671,0.000999679,0.00099961,0.648257007,0.000999525,0.212872508,Epidemiology,0.114806026,FALSE,47.81818182,0.612901231,,,0,0.403234768,1268,0.629424512,0.54852017
12364,"Do antibody positive healthcare workers have lower SARS-CoV-2 infection rates than antibody negative healthcare workers? Large multi-centre prospective cohort study (the SIREN study), England: June to November 2020",0,10.1101/2021.01.13.21249642,1/15/21,medrxiv,0,24,logistic regression,0.001126791,0.076909306,0.00112681,0.001126849,0.423655081,0.496055163,Clinics,0.38716972,FALSE,9.416666667,0.140206568,2.875,0.190259566,29,0.928020248,40952,0.993980255,0.56311666
12365,Direct Simulation of the CoVid-19 epidemic,0,10.1101/2021.01.14.21249829,1/15/21,medrxiv,0,3,"simulation model, computational",0.003927575,0.003927457,0.003927471,0.980362782,0.003927382,0.003927333,Epidemiology,0.16620064,FALSE,4,0.054734368,0.333333333,0.073187048,0,0.403234768,826,0.447387431,0.244635904
12366,Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature with Dynamic Word Embedding Networks and Machine Learning,0,10.1101/2021.01.14.21249855,1/15/21,medrxiv,0,8,machine learning,0.000779451,0.000779475,0.299289698,0.6590634,0.039308491,0.000779485,Epidemiology,0.17815602,FALSE,6,0.086028821,0.375,0.073789136,0,0.403234768,1245,0.623163978,0.296554175
12367,Antibiotic Prescribing Patterns at COVID-19 Dedicated Wards in Bangladesh: A Single Center Point-Prevalence Survey,0,10.1101/2021.01.15.21249868,1/15/21,medrxiv,10.1016/j.infpip.2021.100134,14,logistic regression,0.001653121,0.040567843,0.001653055,0.001653121,0.508400109,0.44607275,Healthcare,0.9279237,TRUE,5.642857143,0.078050591,0.357142857,0.073320846,0,0.403234768,829,0.449313749,0.250979988
12368,A Vital Sign-based Prediction Algorithm for Differentiating COVID-19 Versus Seasonal Influenza in Hospitalized Patients,0,10.1101/2021.01.13.21249540,1/15/21,medrxiv,0,9,"machine learning, classifier, predictive model, dataset",0.001653059,0.095001205,0.651594188,0.001653073,0.036176355,0.21392212,Clinics,0.4533915,FALSE,20.88888889,0.308367864,12.33333333,0.389550442,0,0.403234768,1974,0.767397062,0.467137534
12369,Persistently increased systemic ACE2 activity and Furin levels are associated with increased inflammatory response in smokers with SARS-CoV-2 COVID-19,0,10.1101/2021.01.14.21249836,1/15/21,medrxiv,0,4,lipidom,0.324869292,0.001022708,0.001022612,0.001022653,0.00102263,0.671040104,Clinics,0.4761571,FALSE,9.75,0.146267549,1.5,0.138747659,0,0.403234768,1369,0.659041657,0.336822908
12370,In vivo structure and dynamics of the RNA genome of SARS-Cov-2,0,10.1101/2021.01.15.426526,1/15/21,biorxiv,0,13,interactom,0.496490101,0.496897505,0.001653044,0.001653092,0.001653026,0.001653231,Genomics,0.24676669,FALSE,0,0.006432061,,,0,0.403234768,2467,0.811220804,0.406962544
12371,"SARS-CoV-2 reinfection in a cohort of 43,000 antibody-positive individuals followed for up to 35 weeks",0,10.1101/2021.01.15.21249731,1/15/21,medrxiv,0,23,sequencing,0.001141348,0.391469876,0.001141337,0.385787606,0.001141396,0.219318437,Genomics,0.16077164,FALSE,25.47826087,0.373059558,16.73913043,0.44527696,8,0.799987654,32978,0.991090778,0.652353737
12372,Distinct Patterns of Emergence of SARS-CoV-2 Spike Variants including N501Y in Clinical Samples in Columbus Ohio,0,10.1101/2021.01.12.426407,1/15/21,biorxiv,0,11,sequencing,0.060948846,0.934600596,0.001112618,0.001112664,0.001112641,0.001112635,Genomics,0.14350429,FALSE,0,0.006432061,,,7,0.785110192,9355,0.959306525,0.58361626
12373,Dynamic Prediction of SARS-CoV-2 RT-PCR status on Chest Radiographs using Deep Learning Enabled Radiogenomics,0,10.1101/2021.01.10.21249370,1/15/21,medrxiv,0,5,deep learning,0.003101495,0.003101666,0.984491422,0.003101618,0.00310197,0.00310183,Imaging,0.4130108,FALSE,27.6,0.402374915,25.4,0.532044421,0,0.403234768,857,0.462075608,0.449932428
12374,A Viral Fragmentation Signature for SARS-CoV-2 in Clinical Samples Correlating with Contagiousness,0,10.1101/2021.01.11.21249265,1/15/21,medrxiv,0,9,sequencing,0.001565343,0.88145627,0.001565378,0.001565346,0.00156536,0.112282303,Genomics,0.4214276,FALSE,28.88888889,0.416414126,21.33333333,0.495450896,0,0.403234768,1162,0.596917891,0.47800442
12375,Genomic surveillance at scale is required to detect newly emerging strains at an early timepoint,0,10.1101/2021.01.12.21249613,1/15/21,medrxiv,0,6,sequencing,0.001565312,0.958389387,0.035349281,0.001565376,0.00156536,0.001565285,Genomics,0.27106774,FALSE,,,,,0,0.403234768,3176,0.851673489,0.627454128
12376,Early immune pathology and persistent dysregulation characterise severe COVID-19,0,10.1101/2021.01.11.20248765,1/15/21,medrxiv,0,38,immunome,0.397664822,0.001823501,0.001823322,0.001823454,0.001823437,0.595041464,Clinics,0.4182947,FALSE,35.88235294,0.495577958,,,1,0.537564047,9055,0.956657838,0.663266614
12377,Impact of COVID-19 on Migrants' Access to Primary Care:A National Qualitative Study,0,10.1101/2021.01.12.21249692,1/15/21,medrxiv,0,7,digital health,0.025952233,0.000608045,0.000608059,0.102591691,0.859615702,0.010624271,Healthcare,0.82774854,TRUE,4.142857143,0.055476529,0.285714286,0.066296495,1,0.537564047,1730,0.731760173,0.347774311
12378,Understanding COVID-19 dynamics and the effects of interventions in the Philippines: A mathematical modelling study,0,10.1101/2021.01.14.21249848,1/15/21,medrxiv,0,12,mathematical model,0.001156266,0.001156286,0.001156295,0.994218576,0.001156339,0.001156239,Epidemiology,0.26130396,FALSE,19.91666667,0.294699734,8.583333333,0.329943805,0,0.403234768,1840,0.749097038,0.444243836
12379,The cellular immune response to COVID-19 deciphered by single cell multi-omics across three UK centres,0,10.1101/2021.01.13.21249725,1/15/21,medrxiv,0,68,"sequencing, data mining, transcriptom, multi-omics",0.59629244,0.03850191,0.001072209,0.001072239,0.00107222,0.361988982,Drug discovery,0.12860638,FALSE,16.76744186,0.252520255,22.1627907,0.504415306,4,0.707574542,4584,0.904406453,0.592229139
12380,Real-time optical analysis of a colorimetric LAMP assay for SARS-CoV-2 in saliva with a handheld instrument improves accuracy compared to endpoint assessment,0,10.1101/2021.01.13.21249412,1/15/21,medrxiv,0,3,genomes,0.052496185,0.704914193,0.239251403,0.001112725,0.001112732,0.001112761,Genomics,0.8122523,TRUE,21.66666667,0.320737213,14.33333333,0.415975381,0,0.403234768,758,0.408861064,0.387202107
12381,School and community reopening during the COVID-19 pandemic: a mathematical modeling study,0,10.1101/2021.01.13.21249753,1/15/21,medrxiv,0,11,mathematical model,0.000390624,0.040648878,0.000390637,0.481569029,0.462836564,0.014164269,Epidemiology,0.18831998,FALSE,7.545454545,0.108602882,3.454545455,0.209325662,0,0.403234768,996,0.526366482,0.311882448
12382,Early Analysis of a potential link between viral load and the N501Y mutation in the SARS-COV-2 spike protein,0,10.1101/2021.01.12.20249080,1/15/21,medrxiv,0,14,genomes,0.001072195,0.941414007,0.001072226,0.001072231,0.001072202,0.05429714,Genomics,0.14322472,FALSE,52.28571429,0.648957882,178.9285714,0.905472304,6,0.764429903,5597,0.924392006,0.810813024
12383,"Epidemiology of the early COVID-19 epidemic in Orange County, California: comparison of predictors of test positivity, mortality, and seropositivity",0,10.1101/2021.01.13.21249507,1/15/21,medrxiv,0,10,dataset,0.002357739,0.002357802,0.0023578,0.607337952,0.383230729,0.002357979,Epidemiology,0.10910851,FALSE,48,0.614942173,78.8,0.775287664,0,0.403234768,1081,0.565374428,0.589709758
12384,Evaluating the effects of re-opening plans on dynamics of COVID-19 in SP,0,10.1101/2021.01.14.21249809,1/15/21,medrxiv,0,2,mathematical model,0.002238474,0.002238461,0.002238476,0.988807673,0.002238481,0.002238435,Epidemiology,0.23391178,FALSE,34.5,0.482404601,0,0.055525823,0,0.403234768,601,0.284131953,0.306324286
12385,Occupational and environmental exposure to SARS-CoV-2 in and around infected mink farms,0,10.1101/2021.01.06.20248760,1/14/21,medrxiv,0,20,"whole genome, genome sequences",0.001786565,0.561519684,0.001786523,0.334481455,0.09863908,0.001786694,Genomics,0.25600487,FALSE,20.6,0.305832148,34.05,0.599210597,1,0.537564047,1837,0.746207561,0.547203588
12386,"The lethal triad: SARS-CoV-2 Spike, ACE2 and TMPRSS2. Mutations in host and pathogen may affect the course of pandemic.",0,10.1101/2021.01.12.426365,1/14/21,biorxiv,0,4,in silico,0.433291653,0.506237545,0.001415119,0.056225302,0.001415182,0.001415198,Genomics,0.25789165,FALSE,0,0.006432061,,,1,0.537564047,2936,0.838430051,0.46080872
12387,Surveillance of genetic diversity and evolution in locally transmitted SARS-CoV-2 in Pakistan during the first wave of the COVID-19 pandemic,0,10.1101/2021.01.13.426548,1/14/21,biorxiv,0,6,"sequencing, whole genome, genomes",0.001126783,0.99436602,0.001126809,0.001126825,0.00112678,0.001126782,Genomics,0.15362355,FALSE,0,0.006432061,,,0,0.403234768,1211,0.607031062,0.338899297
12388,The Relationship between Democracy embracement and COVID-19 reported casualties worldwide,0,10.1101/2021.01.11.21249549,1/13/21,medrxiv,0,6,dataset,0.001254625,0.001254612,0.00125463,0.796263767,0.001254632,0.198717735,Epidemiology,0.15724525,FALSE,10.66666667,0.159131672,1.833333333,0.151056998,1,0.537564047,1336,0.642186371,0.372484772
12389,Model-based cellular kinetic analysis of SARS-CoV-2 infection: different immune response modes and treatment strategies,0,10.1101/2021.01.11.21249562,1/13/21,medrxiv,0,13,mathematical model,0.637582562,0.001593514,0.035064225,0.24023884,0.00159351,0.08392735,Drug discovery,0.17892408,FALSE,66.76923077,0.745562496,89.30769231,0.800040139,0,0.403234768,483,0.165904166,0.528685392
12390,A national retrospective cohort study of mechanical ventilator availability and its association with mortality risk in intensive care patients with COVID-19,0,10.1101/2021.01.11.21249461,1/13/21,medrxiv,0,11,bayes,0.00063418,0.000634199,0.00063418,0.191797548,0.000634224,0.80566567,Clinics,0.3759601,FALSE,15.54545455,0.234399159,9.818181818,0.35094996,0,0.403234768,5064,0.911630147,0.475053508
12391,"Determining the optimal COVID-19 policy response using agent-based modelling linked to health and cost modelling: Case study for Victoria, Australia",0,10.1101/2021.01.11.21249630,1/13/21,medrxiv,0,12,simulation model,0.00078636,0.00078634,0.000786357,0.930346501,0.000786407,0.066508035,Epidemiology,0.8212632,TRUE,62.66666667,0.719030243,30.41666667,0.572518063,0,0.403234768,1038,0.534794125,0.557394299
12392,The 2020 SARS-CoV-2 epidemic in England: key epidemiological drivers and impact of interventions,0,10.1101/2021.01.11.21249564,1/13/21,medrxiv,0,31,mathematical model,0.002806345,0.002806437,0.00280636,0.917575094,0.002806583,0.07119918,Epidemiology,0.32784033,FALSE,52.35483871,0.649514503,,,7,0.785110192,2732,0.824945822,0.753190172
12393,Based Analysis Framework for identifying COVID-19 Incidence and Fatality Determinants at National Level Case study: Africa,0,10.1101/2021.01.12.21249661,1/13/21,medrxiv,0,4,correlation analysis,0.001461909,0.001461918,0.00146188,0.586196525,0.001461968,0.4079558,Epidemiology,0.15266258,FALSE,6,0.086028821,0,0.055525823,0,0.403234768,834,0.441849266,0.246659669
12394,Aerosol tracer testing in the cabin of wide-bodied Boeing 767 and 777 aircraft to simulate exposure potential of infectious particulate such as SARS-CoV-2,0,10.1101/2021.01.11.21249626,1/13/21,medrxiv,0,12,computational,0.001943618,0.082398434,0.001943611,0.909827308,0.001943548,0.001943482,Epidemiology,0.6355955,TRUE,8.083333333,0.119116828,5.083333333,0.257158148,0,0.403234768,1120,0.572116542,0.337906572
12395,Genomic and mobility data reveal mass population movement as a driver of SARS-CoV-2 dissemination and diversity in Bangladesh,0,10.1101/2021.01.05.21249196,1/13/21,medrxiv,0,22,"bayes, genomes, dataset",0.000531376,0.655553307,0.000531362,0.342321206,0.000531388,0.000531361,Genomics,0.15947825,FALSE,30.04545455,0.432494279,,,0,0.403234768,907,0.475078257,0.436935768
12396,An insight into neurotoxic and toxicity of spike fragments SARS-CoV-2 by exposure environment: A threat to aquatic health?,0,10.1101/2021.01.11.425914,1/13/21,biorxiv,0,18,in silico,0.738957382,0.123945795,0.001392857,0.001392901,0.047806225,0.086504841,Drug discovery,0.9170857,TRUE,0,0.006432061,,,0,0.403234768,2312,0.79316157,0.4009428
12397,A rapid phenomics workflow for the in vitro identification of antiviral drugs,0,10.1101/2021.01.13.423947,1/13/21,biorxiv,0,9,"image analysis, phenomics",0.792546567,0.001786619,0.200307275,0.001786549,0.001786494,0.001786495,Drug discovery,0.12721452,FALSE,29.33333333,0.423093574,12.88888889,0.397377576,0,0.403234768,1330,0.639537684,0.4658109
12398,Ineffectual AEC1 Differentiation from KRT8hi Transitional Cells without Fibrosis Associated with Fatal Acute Respiratory Failure in COVID-19 ARDS,0,10.1101/2021.01.12.426404,1/13/21,biorxiv,0,7,dataset,0.68247637,0.0021307,0.002130728,0.002130781,0.002130652,0.30900077,Drug discovery,0.3240987,FALSE,78.14285714,0.798998083,56.71428571,0.709860851,0,0.403234768,1598,0.699735131,0.652957208
12399,Distinct lung-homing receptor expression and activation profiles on NK cell and T cell subsets in COVID-19 and influenza,0,10.1101/2021.01.13.426553,1/13/21,biorxiv,0,13,dataset,0.783491309,0.000946132,0.000946116,0.000946122,0.000946185,0.212724136,Drug discovery,0.7345275,TRUE,0,0.006432061,,,0,0.403234768,1272,0.622200819,0.343955883
12400,Emergence and Evolution of a Prevalent New SARS-CoV-2 Variant in the United States,0,10.1101/2021.01.11.426287,1/13/21,biorxiv,0,15,genomes,0.002130705,0.96193785,0.002130662,0.002130746,0.00213077,0.029539268,Genomics,0.28695795,FALSE,0,0.006432061,,,5,0.739490092,14878,0.976402601,0.574108251
12401,Using image-based haplotype alignments to map global adaptation of SARS-CoV-2,0,10.1101/2021.01.13.426571,1/13/21,biorxiv,0,3,"deep learning, genomes",0.002806381,0.719943589,0.268830785,0.002806487,0.002806351,0.002806407,Genomics,0.19588706,FALSE,0,0.006432061,,,0,0.403234768,986,0.512641464,0.307436098
12402,Risk factors for bacterial infections in patients with moderate to severe COVID-19: A case control study,0,10.1101/2021.01.09.21249498,1/12/21,medrxiv,0,3,logistic regression,0.002490649,0.002491008,0.002490508,0.002490438,0.002490519,0.987546879,Clinics,0.39334255,FALSE,3.333333333,0.04044777,0,0.055525823,0,0.403234768,761,0.394413677,0.223405509
12403,"Role of pollution and weather indicators in the COVID-19 outbreak: A brief study on Delhi, India",0,10.1101/2021.01.04.21249249,1/12/21,medrxiv,0,2,dataset,0.002032806,0.002032817,0.002032769,0.989835993,0.002032797,0.002032817,Epidemiology,0.8378994,TRUE,3.5,0.044344115,0,0.055525823,0,0.403234768,556,0.227064772,0.18254237
12404,Epidemiological impact of prioritizing SARS-CoV-2 vaccination by antibody status: Mathematical modeling analyses,0,10.1101/2021.01.10.21249382,1/12/21,medrxiv,0,20,mathematical model,0.001415141,0.170151014,0.001415169,0.326670857,0.498932663,0.001415157,Healthcare,0.13228408,FALSE,27.15,0.396994248,15.35,0.428418518,4,0.707574542,1673,0.709848302,0.560708902
12405,"Male sex and age biases viral burden, viral shedding, and type 1 and 2 interferon responses during SARS-CoV-2 infection in ferrets",0,10.1101/2021.01.12.426381,1/12/21,biorxiv,0,14,transcriptom,0.282593197,0.001987432,0.001987092,0.001987452,0.13547274,0.575972088,Clinics,0.7357974,TRUE,33.46666667,0.470962954,51.53333333,0.687516725,0,0.403234768,1350,0.641464002,0.550794612
12406,Factors indicating intention to vaccinate with a COVID-19 vaccine among older U.S. Adults,0,10.1101/2021.01.10.20248831,1/11/21,medrxiv,0,16,logistic regression,0.001438202,0.001438177,0.114981064,0.001438221,0.87926613,0.001438205,Healthcare,0.25346574,FALSE,8.0625,0.118993135,7.25,0.302983677,0,0.403234768,1162,0.576691548,0.350475782
12407,"Viral mutation, contact rates and testing: a DCM study of fluctuations",0,10.1101/2021.01.10.21249520,1/11/21,medrxiv,0,4,"bayes, bayesian model",0.001786686,0.161439951,0.001786671,0.831413609,0.001786546,0.001786536,Epidemiology,0.023307294,FALSE,672,0.998639372,3837.25,0.999665507,0,0.403234768,854,0.442330845,0.710967623
12408,Proteomic and Metabolomic Investigation of COVID-19 Patients with Elevated Serum Lactate Dehydrogenase,0,10.1101/2021.01.10.21249333,1/11/21,medrxiv,0,23,"proteom, metabolom",0.23958137,0.001371272,0.026008731,0.00137129,0.001371267,0.730296071,Clinics,0.9363435,TRUE,10.04347826,0.152204836,,,0,0.403234768,1447,0.662171924,0.405870509
12409,The landscape of human brain immune response in patients with severe COVID-19,0,10.1101/2021.01.08.425999,1/11/21,biorxiv,0,18,transcriptom,0.8380633,0.002562682,0.002562596,0.058202524,0.002562619,0.096046279,Drug discovery,0.51993656,TRUE,97.72222222,0.863875317,190.6111111,0.912362858,0,0.403234768,2311,0.789308933,0.742195469
12410,Immunological and cardio-vascular pathologies associated with SARS-CoV-2 infection in golden syrian hamster,0,10.1101/2021.01.11.426080,1/11/21,biorxiv,0,14,"in silico, metabolom, lipidom",0.68023077,0.002357914,0.002357864,0.097200472,0.002357739,0.215495242,Drug discovery,0.38138595,FALSE,27.14285714,0.396932402,14.57142857,0.418918919,0,0.403234768,1812,0.731519384,0.487651368
12411,Molecular Dynamics Analysis of a Flexible Loop at the Binding Interface of the SARS-CoV-2 Spike Protein Receptor-Binding Domain,0,10.1101/2021.01.08.425965,1/11/21,biorxiv,0,6,molecular dynamics simulation,0.877953022,0.11629444,0.001438149,0.001438181,0.001438108,0.001438101,Drug discovery,0.15108311,FALSE,93.16666667,0.851753355,186.1666667,0.910155205,0,0.403234768,1099,0.55164941,0.679198184
12412,Impact of South African 501.V2 Variant on SARS-CoV-2 Spike Infectivity and Neutralization: A Structure-based Computational Assessment,0,10.1101/2021.01.10.426143,1/11/21,biorxiv,0,5,"computational, bioinformatic",0.642701999,0.35125067,0.001511836,0.001511859,0.001511819,0.001511816,Drug discovery,0.4696767,FALSE,88.6,0.838208918,150,0.883529569,11,0.840175319,8056,0.945822297,0.876934026
12413,Microsecond simulation unravel the structural dynamics of SARS-CoV-2 Spike-C-terminal cytoplasmic tail (residues 1242-1273),0,10.1101/2021.01.11.426227,1/11/21,biorxiv,0,4,molecular dynamics simulation,0.991413904,0.001717293,0.001717203,0.001717247,0.001717179,0.001717174,Drug discovery,0.5441158,TRUE,34,0.477766096,7,0.299973241,0,0.403234768,1259,0.608235011,0.447302279
12414,Cholinergic and lipid mediators crosstalk in Covid-19 and the impact of glucocorticoid therapy,0,10.1101/2021.01.07.20248970,1/9/21,medrxiv,0,48,"transcriptom, metabolom, lipidom",0.380958716,0.068116287,0.001622757,0.001622772,0.00162287,0.546056597,Clinics,0.21461841,FALSE,19.58333333,0.291607397,6.611111111,0.289202569,1,0.537564047,3967,0.876956417,0.498832607
12415,"MassMark: A Highly Scalable Multiplex NGS-based Method for High-Throughput, Accurate and Sensitive Detection of SARS-CoV-2 for Mass Testing",0,10.1101/2021.01.08.20249017,1/9/21,medrxiv,0,6,sequencing,0.001717201,0.531993882,0.461137316,0.001717224,0.001717201,0.001717177,Genomics,0.29097039,FALSE,36,0.498299215,30,0.570176612,0,0.403234768,774,0.383818926,0.46388238
12416,Levels of SARS-CoV-2 population exposure are considerably higher than suggested by seroprevalence surveys,0,10.1101/2021.01.08.21249432,1/9/21,medrxiv,0,4,dataset,0.002357741,0.044638018,0.002357904,0.840292856,0.107995606,0.002357876,Epidemiology,0.099666506,FALSE,19,0.285793803,21.25,0.494046026,2,0.618927094,3136,0.83891163,0.559419638
12417,Detection of SARS-CoV-2 variants in Switzerland by genomic analysis of wastewater samples,0,10.1101/2021.01.08.21249379,1/9/21,medrxiv,0,17,sequencing,0.002130652,0.959768553,0.002130666,0.002130736,0.002130673,0.03170872,Genomics,0.20578277,FALSE,29.88235294,0.429278249,70.05882353,0.753411828,9,0.814309525,7436,0.938598603,0.733899551
12418,The importance of non-pharmaceutical interventions during the COVID-19 vaccine rollout,0,10.1101/2021.01.09.21249480,1/9/21,medrxiv,0,3,in-silico,0.155555834,0.002130673,0.002130688,0.835921404,0.002130785,0.002130615,Epidemiology,0.29557475,FALSE,22.33333333,0.330261612,40.33333333,0.635469628,2,0.618927094,2378,0.790031303,0.593672409
12419,Deep learning-based detection of COVID-19 using wearables data,0,10.1101/2021.01.08.21249474,1/9/21,medrxiv,0,2,"machine learning, deep learning, neural network, deep-learning, prediction model, dataset",0.000412478,0.036043867,0.342263808,0.239897412,0.132359213,0.249023222,Clinics,0.09722215,FALSE,25,0.369286907,46,0.66416912,0,0.403234768,1978,0.750541777,0.546808143
12420,The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission,0,10.1101/2021.01.08.20249041,1/9/21,medrxiv,0,19,"machine learning, radiom",0.001187298,0.001187273,0.397136254,0.001187304,0.001187245,0.598114626,Clinics,0.5551875,TRUE,29.63157895,0.426866225,15.63157895,0.431562751,0,0.403234768,1169,0.570190224,0.457963492
12421,"Utilization of Whole Genome Sequencing to Understand SARS-CoV-2 Transmission Dynamics in Long-Term Care Facilities, Correctional Facilities and Meat Processing Plants in Minnesota, March - June 2020",0,10.1101/2020.12.30.20248277,1/8/21,medrxiv,0,21,"sequencing, whole genome",0.002639076,0.986804454,0.002639106,0.002639158,0.002639195,0.002639011,Genomics,0.2662897,FALSE,39.71428571,0.536334962,111.9047619,0.841182767,0,0.403234768,976,0.483987479,0.566184994
12422,"Host genome analysis of structural variations by Optical Genome Mapping provides clinically valuable insights into genes implicated in critical immune, viral infection, and viral replication pathways in patients with severe COVID-19.",0,10.1101/2021.01.05.21249190,1/8/21,medrxiv,0,22,"bioinformatic, sequencing, exom, genome-wide, whole-genome, genomes, dataset",0.259664318,0.51714322,0.000667723,0.000667709,0.012738664,0.209118366,Genomics,0.25447387,FALSE,57.36363636,0.684457913,218.5909091,0.926143966,0,0.403234768,7485,0.938357814,0.738048615
12423,"Integrated Vaccination and Non-Pharmaceutical Interventions based Strategies in Ontario, Canada, as a Case Study: a Mathematical Modeling Study",0,10.1101/2021.01.06.21249272,1/8/21,medrxiv,0,5,mathematical model,0.001538109,0.001538098,0.001538085,0.992309497,0.001538135,0.001538076,Epidemiology,0.17064625,FALSE,10,0.15214299,1.2,0.126103827,1,0.537564047,1304,0.60967975,0.356372653
12424,Identifying silent COVID-19 infections among children is critical for controlling the pandemic,0,10.1101/2021.01.06.21249349,1/8/21,medrxiv,0,5,simulation model,0.000815372,0.000815371,0.000815392,0.507940337,0.488798181,0.000815347,Epidemiology,0.07035652,FALSE,112.4,0.894860536,121,0.853157613,1,0.537564047,1522,0.667710089,0.738323071
12425,Clinical Validation of a Novel T-cell Receptor Sequencing Assay for Identification of Recent or Prior SARS-CoV-2 Infection,0,10.1101/2021.01.06.21249345,1/8/21,medrxiv,0,22,"sequencing, classifier",0.086281478,0.403634359,0.190187106,0.00108538,0.001085392,0.317726285,Genomics,0.09906396,FALSE,11.13636364,0.167666522,20.36363636,0.484011239,1,0.537564047,1549,0.673729834,0.465742911
12426,Inherent random fluctuations in COVID-19 outbreaks may explain rapid growth of new mutated virus variants,0,10.1101/2021.01.07.21249353,1/8/21,medrxiv,0,2,simulation model,0.002296578,0.457205141,0.002296563,0.533608542,0.002296625,0.002296552,Epidemiology,0.09559953,FALSE,9.5,0.143051518,9,0.337904736,0,0.403234768,2205,0.773416807,0.414401957
12427,Optimal design for phase 2 studies of SARS-CoV-2 antiviral drugs,0,10.1101/2021.01.06.21249368,1/8/21,medrxiv,0,5,model fit,0.17280164,0.4885681,0.001751249,0.186016285,0.001751242,0.149111484,Genomics,0.12152699,FALSE,221.8,0.979157647,514.6,0.979796628,0,0.403234768,2372,0.787382615,0.787392915
12428,"Epidemiological and Clinical Characteristics, and Virologic Features of COVID-19 Patients in Kazakhstan: a Nation-Wide, Retrospective, Cohort Study.",0,10.1101/2021.01.06.20249091,1/8/21,medrxiv,0,10,"whole-genome, logistic regression",0.001171591,0.206151832,0.00117155,0.060787054,0.001171596,0.729546378,Clinics,0.6592695,TRUE,7.9,0.114354629,0.8,0.101351351,0,0.403234768,1463,0.65470744,0.318412047
12429,Recurrent dissemination of SARS-CoV-2 through the Uruguayan-Brazilian border,0,10.1101/2021.01.06.20249026,1/8/21,medrxiv,0,37,genomes,0.000580139,0.752358028,0.000580145,0.245321385,0.000580141,0.000580162,Genomics,0.57210296,TRUE,6.027777778,0.086090667,1.777777778,0.148849344,4,0.707574542,1610,0.685769323,0.407070969
12430,A Novel Abnormality Annotation Database for COVID-19 Affected Frontal Lung X-rays,0,10.1101/2021.01.07.21249323,1/8/21,medrxiv,0,15,"deep learning, deep model",0.001203451,0.001203493,0.993982697,0.001203475,0.001203444,0.00120344,Imaging,0.30267876,FALSE,4,0.054734368,1,0.122023013,0,0.403234768,1278,0.601492897,0.295371261
12431,Predicting severity of Covid-19 using standard laboratory parameters,0,10.1101/2021.01.07.21249392,1/8/21,medrxiv,0,3,logistic regression,0.001565344,0.001565337,0.001565411,0.185796066,0.001565351,0.80794249,Clinics,0.46650717,FALSE,66,0.741356918,37.33333333,0.618209794,0,0.403234768,1405,0.638092945,0.600223606
12432,Genetic Characteristics and Phylogeny of 969-bp S Gene Sequence of SARS-CoV-2 from Hawaii Reveals the Worldwide Emerging P681H Mutation,0,10.1101/2021.01.06.425497,1/7/21,biorxiv,0,4,"sequencing, genomes",0.153899351,0.755716079,0.001415155,0.064278505,0.001415289,0.02327562,Genomics,0.56667817,TRUE,92.25,0.849650566,116,0.846133262,5,0.739490092,3738,0.866361666,0.825408897
12433,Comprehensive comparison of transcriptomes in SARS-CoV-2 infection: alternative entry routes and innate immune responses,0,10.1101/2021.01.07.425716,1/7/21,biorxiv,0,7,transcriptom,0.965203811,0.001786532,0.001786549,0.001786561,0.001786502,0.027650045,Drug discovery,0.18652296,FALSE,35.42857143,0.491001299,51.14285714,0.685911159,0,0.403234768,2044,0.753431255,0.58339462
12434,Neuropilin-1 Assists SARS-CoV-2 Infection by Stimulating the Separation of Spike Protein Domains S1 and S2,0,10.1101/2021.01.06.425627,1/7/21,biorxiv,0,2,computational,0.991243868,0.001751336,0.001751171,0.00175121,0.001751187,0.001751229,Drug discovery,0.17185202,FALSE,77,0.794668811,53.5,0.695477656,0,0.403234768,1634,0.686732483,0.645028429
12435,Stable Interaction Of The UK B.1.1.7 lineage SARS-CoV-2 S1 Spike N501Y Mutant With ACE2 Revealed By Molecular Dynamics Simulation,0,10.1101/2021.01.07.425307,1/7/21,biorxiv,0,3,molecular dynamics simulation,0.757167894,0.236984585,0.001461866,0.001461901,0.001461902,0.001461852,Drug discovery,0.4007757,FALSE,12,0.183190055,4.666666667,0.246721969,2,0.618927094,1724,0.701902239,0.43768534
12436,Immunoinformatic based analytics on T-cell epitope from spike protein of SARS-CoV-2 concerning Indian population.,0,10.1101/2021.01.07.425724,1/7/21,biorxiv,0,2,in silico,0.70762197,0.199540805,0.000946111,0.089998923,0.000946104,0.000946087,Drug discovery,0.37807822,FALSE,2.5,0.027459954,0,0.055525823,0,0.403234768,1253,0.589934987,0.269038883
12437,Interleukin-6 Receptor Antagonists in Critically Ill Patients with Covid-19 - Preliminary report,0,10.1101/2021.01.07.21249390,1/7/21,medrxiv,0,2,bayes,0.253982566,0.00151186,0.001511828,0.001511924,0.001511895,0.739969928,Clinics,0.623186,TRUE,4,0.054734368,1,0.122023013,30,0.930057411,156385,1,0.526703698
12438,Sequencing of SARS CoV2 in local transmission cases through oxford nanopore MinION platform from Karachi Pakistan,0,10.1101/2021.01.07.425705,1/7/21,biorxiv,0,7,sequencing,0.000772666,0.906497312,0.000772614,0.054383713,0.036801013,0.000772682,Genomics,0.25289494,FALSE,11.85714286,0.178551549,1.428571429,0.134265454,0,0.403234768,956,0.468095353,0.296036781
12439,A novel computational approach to reconstruct SARS-CoV-2 infection dynamics through the inference of unsampled sources of infection,0,10.1101/2021.01.04.21249233,1/6/21,medrxiv,0,7,"bayes, computational",0.002032798,0.247995403,0.002032839,0.743873403,0.002032807,0.002032751,Epidemiology,0.45628023,FALSE,9.142857143,0.13637207,7.714285714,0.311479797,0,0.403234768,776,0.366963641,0.304512569
12440,Stochastic model for COVID-19 in slums: interaction between biology and public policies,0,10.1101/2021.01.06.21249318,1/6/21,medrxiv,0,2,mathematical model,0.001203446,0.152404986,0.001203476,0.72889876,0.115085865,0.001203466,Epidemiology,0.08241984,FALSE,43.5,0.573381161,5,0.257024351,0,0.403234768,697,0.31085962,0.386124975
12441,Prevalence of RT-PCR-detected SARS-CoV-2 infection at schools: First results from the Austrian School-SARS-CoV-2 Study,0,10.1101/2021.01.05.20248952,1/6/21,medrxiv,10.1016/j.lanepe.2021.100086,18,logistic regression,0.001098807,0.128858893,0.001098872,0.087913536,0.779931029,0.001098863,Healthcare,0.40688768,FALSE,15.11111111,0.227967098,12.38888889,0.389818036,2,0.618927094,10360,0.959547315,0.549064886
12442,Machine Learning Forecast of Growth in COVID-19 Confirmed Infection Cases with Non-Pharmaceutical Interventions and Cultural Dimensions: Algorithm Development and Validation,0,10.1101/2021.01.04.21249235,1/6/21,medrxiv,0,4,"machine learning, dataset",0.001684478,0.001684496,0.287037125,0.706224878,0.001684517,0.001684505,Epidemiology,0.28379947,FALSE,53.75,0.659348135,38.5,0.624765855,0,0.403234768,815,0.395376836,0.520681398
12443,Genomic epidemiology of the SARS-CoV-2 epidemic in Zimbabwe: Role of international travel and regional migration in spread,0,10.1101/2021.01.04.20232520,1/6/21,medrxiv,0,35,"whole genome, genomic epidemiology, genomes",0.00118726,0.541901709,0.001187269,0.272956154,0.001187349,0.181580259,Genomics,0.27586764,FALSE,9.771428571,0.146514936,13.68571429,0.407479261,1,0.537564047,1815,0.71297857,0.451134204
12444,Prevalence and Factors associated with Mental health impact of COVID-19 Pandemic in Bangladesh: A survey-based cross-sectional study,0,10.1101/2021.01.05.21249216,1/6/21,medrxiv,0,13,logistic regression,0.0010226,0.001022601,0.001022609,0.00102262,0.994886911,0.001022659,Healthcare,0.97793305,TRUE,11.92307692,0.179046323,19.92307692,0.478726251,0,0.403234768,1865,0.722610161,0.445904376
12445,THE INTESTINAL AND ORAL MICROBIOMES ARE ROBUST PREDICTORS OF COVID-19 SEVERITY THE MAIN PREDICTOR OF COVID-19-RELATED FATALITY,0,10.1101/2021.01.05.20249061,1/6/21,medrxiv,0,19,microbiom,0.00159354,0.170942789,0.245598614,0.001593547,0.001593549,0.578677961,Clinics,0.3369596,FALSE,18.42105263,0.275650937,52.63157895,0.691597538,0,0.403234768,10142,0.957380207,0.581965862
12446,RNA-protein interaction analysis of SARS-CoV-2 5'- and 3'-untranslated regions identifies an antiviral role of lysosome-associated membrane protein-2,0,10.1101/2021.01.05.425516,1/6/21,biorxiv,0,6,bioinformatic,0.883045816,0.104145337,0.000765959,0.000765942,0.010511031,0.000765914,Drug discovery,0.060404032,FALSE,134.6666667,0.926587915,69,0.750535189,0,0.403234768,1047,0.503732242,0.646022528
12447,"SARS-CoV-2 RBD in vitro evolution follows contagious mutation spread, yet generates an able infection inhibitor",0,10.1101/2021.01.06.425392,1/6/21,biorxiv,0,10,in silico,0.601290872,0.391839978,0.0017172,0.001717384,0.00171725,0.001717316,Drug discovery,0.18865901,FALSE,51.88888889,0.645432618,79.88888889,0.778699492,19,0.89561084,22365,0.984348664,0.826022903
12448,Distinct mutations and lineages of SARS-CoV-2 virus in the early phase of COVID-19 global pandemic and subsequent global expansion,0,10.1101/2021.01.05.425339,1/6/21,biorxiv,0,5,"sequencing, genomes",0.000531378,0.75380343,0.043974395,0.20062802,0.000531367,0.00053141,Genomics,0.26396298,FALSE,59.8,0.701403921,85,0.790406743,2,0.618927094,4374,0.884420901,0.748789665
12449,No association between the SARS-CoV-2 variants and mortality rates in the Eastern Mediterranean Region,0,10.1101/2021.01.06.21249332,1/6/21,medrxiv,0,3,"sequencing, whole genome, genome sequences",0.001901682,0.990491285,0.001901706,0.001901823,0.001901727,0.001901776,Genomics,0.53884596,TRUE,41,0.549013544,31,0.578204442,1,0.537564047,1105,0.527088851,0.547967721
12450,State-level COVID-19 Trend Forecasting Using Mobility and Policy Data,0,10.1101/2021.01.04.21249218,1/6/21,medrxiv,0,5,machine learning,0.001901736,0.001901746,0.132874548,0.85951813,0.001901889,0.001901952,Epidemiology,0.24377298,FALSE,3.2,0.038468675,,,0,0.403234768,745,0.346255719,0.262653054
12451,Outbreak or pseudo-outbreak? Integrating SARS-CoV-2 sequencing to validate infection control practices in an end stage renal disease facility,0,10.1101/2020.12.30.20249062,1/6/21,medrxiv,0,11,"sequencing, genome sequences",0.001112647,0.428494024,0.001112664,0.062211841,0.363153007,0.143915817,Genomics,0.14120784,FALSE,32.09090909,0.456243429,,,0,0.403234768,823,0.400433422,0.419970539
12452,Modelling COVID -19 Transmission in a Hemodialysis Centre Using Simulation Generated Contacts Matrices,0,10.1101/2021.01.03.21249175,1/6/21,medrxiv,0,9,simulation model,0.001511896,0.001511853,0.001511852,0.694874389,0.124678018,0.175911991,Epidemiology,0.6261516,TRUE,3.111111111,0.037664667,0,0.055525823,1,0.537564047,881,0.433421623,0.26604404
12453,Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19,0,10.1101/2021.01.05.425384,1/5/21,biorxiv,0,7,"predictive model, prediction model, dataset",0.001112735,0.001112782,0.186013962,0.743423972,0.001112694,0.067223856,Epidemiology,0.11268309,FALSE,59.71428571,0.701032841,62.85714286,0.731803586,0,0.403234768,954,0.45966771,0.573934726
12454,Optimizing testing for COVID-19 in India,0,10.1101/2020.12.31.20249106,1/5/21,medrxiv,0,3,network model,0.001059347,0.139756699,0.305917036,0.551148158,0.001059368,0.001059393,Epidemiology,0.06708878,FALSE,7.666666667,0.111633373,0,0.055525823,0,0.403234768,1195,0.558632314,0.282256569
12455,Prior Bariatric Surgery in COVID-19 Positive Patients May Be Protective,0,10.1101/2020.12.29.20248991,1/5/21,medrxiv,0,5,logistic regression,0.001187263,0.001187272,0.001187274,0.001187285,0.001187322,0.994063584,Clinics,0.50213695,TRUE,25.6,0.375162348,17.2,0.452568906,0,0.403234768,700,0.306284614,0.384312659
12456,Optimizing vaccine allocation for COVID-19 vaccines: critical role of single-dose vaccination.,0,10.1101/2020.12.31.20249099,1/5/21,medrxiv,0,7,mathematical model,0.001156317,0.051210501,0.001156304,0.453442586,0.49187799,0.001156301,Healthcare,0.16300237,FALSE,4.5,0.061784897,0,0.055525823,7,0.785110192,3760,0.863953768,0.44159367
12457,Prediction of COVID-19 Pandemic of Top Ten Countries in the World Establishing a Hybrid AARNN LTM Model,0,10.1101/2020.12.31.20249105,1/5/21,medrxiv,0,4,forecasting model,0.002183225,0.066426638,0.002183327,0.924840291,0.002183277,0.002183242,Epidemiology,0.48170897,FALSE,11,0.167171748,1,0.122023013,1,0.537564047,744,0.339272815,0.291507906
12458,Value of radiomics features from adrenal gland and periadrenal fat CT images predicting COVID-19 progression,0,10.1101/2021.01.03.21249183,1/5/21,medrxiv,0,10,radiom,0.001187271,0.001187281,0.553229023,0.18503361,0.001187289,0.258175526,Imaging,0.2475881,FALSE,11.4,0.17131548,2.3,0.171394166,0,0.403234768,1154,0.544666506,0.32265273
12459,Molecular Mechanism of the N501Y Mutation for Enhanced Binding between SARS-CoV-2's Spike Protein and Human ACE2 Receptor,0,10.1101/2021.01.04.425316,1/5/21,biorxiv,10.1002/1873-3468.14076,3,molecular dynamics simulation,0.699394638,0.294757837,0.00146187,0.001461909,0.001461895,0.001461851,Drug discovery,0.37928647,FALSE,85.66666667,0.827509432,88.33333333,0.797364196,10,0.828199272,4163,0.878160366,0.832808316
12460,"Postlockdown Dynamics of COVID-19 in New York, Florida, Arizona, and Wisconsin",0,10.1101/2020.12.28.20248967,1/4/21,medrxiv,0,3,mathematical model,0.000699337,0.000699357,0.00069934,0.996503208,0.000699376,0.000699383,Epidemiology,0.071237475,FALSE,13,0.197352959,0.666666667,0.096200161,0,0.403234768,768,0.351071515,0.261964851
12461,Global surveillance of potential antiviral drug resistance in SARS-CoV-2: proof of concept focussing on the RNA-dependent RNA polymerase,0,10.1101/2020.12.28.20248663,1/4/21,medrxiv,0,25,"sequencing, in silico, genomes, structural model",0.484721178,0.422404228,0.001220081,0.001220107,0.001220255,0.089214151,Drug discovery,0.22414175,FALSE,59.96,0.702393469,131.36,0.864931763,0,0.403234768,678,0.283650373,0.563552593
12462,Autoimmunity to the Lung Protective Phospholipid-Binding Protein Annexin A2 Predicts Mortality Among Hospitalized COVID-19 Patients,0,10.1101/2020.12.28.20248807,1/4/21,medrxiv,0,8,logistic regression,0.183354318,0.184787692,0.001593494,0.001593522,0.001593539,0.627077435,Clinics,0.28060043,FALSE,14.25,0.215535902,3.875,0.222972973,4,0.707574542,15669,0.974957862,0.53026032
12463,SARS-CoV-2 Variant Under Investigation 202012/01 has more than twofold replicative advantage,0,10.1101/2020.12.28.20248906,1/4/21,medrxiv,10.3390/v13030392,5,genomes,0.137182731,0.798518713,0.001593485,0.001593632,0.001593583,0.059517857,Genomics,0.25802672,FALSE,6.75,0.095862453,1,0.122023013,4,0.707574542,4635,0.888273537,0.453433386
12464,Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research,0,10.1101/2020.12.29.20248969,1/4/21,medrxiv,0,1,dataset,0.00070492,0.000704967,0.000704932,0.930849498,0.066330737,0.000704947,Epidemiology,0.048998922,FALSE,11,0.167171748,0,0.055525823,1,0.537564047,614,0.234288466,0.248637521
12465,An Intrinsic and Extrinsic Evaluation of Learned COVID-19 Concepts using Open-Source Word Embedding Sources,0,10.1101/2020.12.29.20249005,1/4/21,medrxiv,0,6,"computational, prediction model",0.000871562,0.034993897,0.140318825,0.4904243,0.160227496,0.173163919,Epidemiology,0.29342997,FALSE,2.833333333,0.031356299,0,0.055525823,0,0.403234768,1008,0.479171683,0.242322143
12466,Efficiency of Artificial Intelligence in Detecting COVID-19 Pneumonia and Other Pneumonia Causes by Quantum Fourier Transform Method,0,10.1101/2020.12.29.20248900,1/4/21,medrxiv,0,6,"artificial intelligence, dataset",0.000830705,0.000830683,0.944394603,0.000830668,0.000830662,0.052282679,Imaging,0.17482036,FALSE,24,0.35574247,3.5,0.213607172,0,0.403234768,1222,0.564892849,0.384369314
12467,Transmission of SARS-CoV-2 Lineage B.1.1.7 in England: Insights from linking epidemiological and genetic data,0,10.1101/2020.12.30.20249034,1/4/21,medrxiv,0,35,whole genome,0.001034569,0.534530544,0.001034573,0.437198807,0.025166852,0.001034655,Genomics,0.08461848,FALSE,58.05714286,0.689776733,563.1714286,0.982271876,158,0.988270881,41657,0.992053937,0.913093357
12468,Is sickle cell disease a risk factor for severe COVID-19 : a multicenter national retrospective cohort,0,10.1101/2020.12.30.20249053,1/4/21,medrxiv,10.1002/jha2.170,8,logistic regression,0.001786611,0.001786631,0.001786573,0.001786689,0.001786585,0.991066911,Clinics,0.8056996,TRUE,5.125,0.071185602,,,0,0.403234768,704,0.303154346,0.259191572
12469,PREDICTORS OF UPTAKE OF A POTENTIAL COVID-19 VACCINE AMONG NIGERIAN ADULTS,0,10.1101/2020.12.28.20248965,1/4/21,medrxiv,0,15,logistic regression,0.001511913,0.001511827,0.001511817,0.001511857,0.992440722,0.001511864,Healthcare,0.38974065,FALSE,3.733333333,0.046199518,1.133333333,0.12309339,0,0.403234768,898,0.431013725,0.25088535
12470,Insights into the molecular mechanism of anticancer drug ruxolitinib repurposable in COVID-19 therapy,0,10.1101/2020.12.29.20248986,1/4/21,medrxiv,10.22270/jddt.v11i1.4472,2,dataset,0.936957807,0.002490449,0.002490508,0.053079875,0.002490667,0.002490694,Drug discovery,0.3063034,FALSE,5,0.070752675,0.5,0.087101953,0,0.403234768,636,0.251625331,0.203178682
12471,COVID Outcome Prediction in the Emergency Department (COPE),0,10.1101/2020.12.30.20249023,1/4/21,medrxiv,0,19,logistic regression,0.000956314,0.000956327,0.061601583,0.198975458,0.000956352,0.736553965,Clinics,0.36690533,FALSE,14.46153846,0.218009772,9,0.337904736,0,0.403234768,3573,0.85071033,0.452464901
12472,Lockdown Effects on Sars-CoV-2 Transmission - The evidence from Northern Jutland,0,10.1101/2020.12.28.20248936,1/4/21,medrxiv,0,2,dataset,0.002183229,0.072481056,0.002183246,0.918785889,0.002183326,0.002183255,Epidemiology,0.53441054,TRUE,63,0.721998887,25,0.529435376,2,0.618927094,23996,0.984830243,0.7137979
12473,Unsupervised Discovery of Risk Profiles on Negative and Positive COVID-19 Hospitalized Patients,0,10.1101/2020.12.30.20248908,1/4/21,medrxiv,0,2,"supervised learning, unsupervised learning, dataset",0.25746988,0.001291324,0.346657148,0.001291282,0.001291274,0.391999093,Clinics,0.22324106,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,591,0.212376595,0.177107693
12474,One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features,0,10.1101/2020.12.29.20248987,1/4/21,medrxiv,10.31763/sitech.v1i2.202,1,dataset,0.151299649,0.001943536,0.840926041,0.001943688,0.001943533,0.001943554,Imaging,0.033891678,FALSE,23,0.34225988,8,0.320511105,1,0.537564047,1043,0.493378281,0.423428328
12475,"THE INFLUENCE OF HLA GENOTYPE ON SUSCEPTIBILITY TO, AND SEVERITY OF, COVID-19 INFECTION",0,10.1101/2020.12.31.20249081,1/4/21,medrxiv,0,8,"sequencing, logistic regression",0.024440712,0.324987292,0.000815337,0.000815387,0.224542018,0.424399254,Clinics,0.33304754,FALSE,42.5,0.562743522,60.5,0.722772277,0,0.403234768,1581,0.665783771,0.588633584
12476,Can Catastrophe Theory explain expansion and contagious of Covid-19?,0,10.1101/2021.01.02.21249133,1/4/21,medrxiv,0,1,"mathematical model, probabilistic",0.001220029,0.001220039,0.001220069,0.993899856,0.00122001,0.001219997,Epidemiology,0.30915678,FALSE,34,0.477766096,10,0.355632861,0,0.403234768,1025,0.486876956,0.43087767
12477,Brazilian model estimation for SARS-CoV-2 peak contagion (BMESPC): first and second wave,0,10.1101/2021.01.02.20248940,1/4/21,medrxiv,0,3,forecasting model,0.002238503,0.002238479,0.002238495,0.98880764,0.002238447,0.002238436,Epidemiology,0.066461265,FALSE,2.333333333,0.024800544,0,0.055525823,0,0.403234768,941,0.450758488,0.233579906
12478,"Accuracy of the Veterans Health Administration COVID-19 (VACO) Index for predicting short-term mortality among 1,307 Yale New Haven Hospital inpatients and 427,224 Medicare patients",0,10.1101/2021.01.01.20249069,1/4/21,medrxiv,0,11,dataset,0.00115626,0.018054941,0.075367881,0.155486151,0.065463338,0.684471429,Clinics,0.20824584,FALSE,68.90909091,0.758364772,93.36363636,0.809339042,1,0.537564047,1375,0.612569227,0.679459272
12479,Structure-function investigation of a new VUI-202012/01 SARS-CoV-2 variant,0,10.1101/2021.01.01.425028,1/4/21,biorxiv,0,3,genome sequences,0.091184387,0.850836734,0.000693856,0.055897268,0.000693859,0.000693897,Genomics,0.15693012,FALSE,108.25,0.886387532,102,0.82592989,1,0.537564047,4640,0.888514327,0.784598949
12480,Spike protein disulfide disruption as a potential treatment for SARS-CoV-2,0,10.1101/2021.01.02.425099,1/4/21,biorxiv,0,7,molecular dynamics simulation,0.99381445,0.001237124,0.001237071,0.00123713,0.001237146,0.001237079,Drug discovery,0.41850245,FALSE,76.85714286,0.793431876,,,1,0.537564047,1709,0.690585119,0.673860347
12481,SARS-CoV-2 Fusion Peptide has a Greater Membrane Perturbating Effect than SARS-CoV with Highly Specific Dependence on Ca2+,0,10.1101/2021.01.04.425297,1/4/21,biorxiv,10.1016/j.jmb.2021.166946,2,sequence alignment,0.77467452,0.2195729,0.001438113,0.001438167,0.001438168,0.001438133,Drug discovery,0.4072647,FALSE,230,0.98101305,189.5,0.911760771,2,0.618927094,1075,0.506621719,0.754580658
12482,TMPRSS2 structure-phylogeny repositions Avoralstat for SARS-CoV-2 prophylaxis in mice,0,10.1101/2021.01.04.425289,1/4/21,biorxiv,0,9,computational,0.964653049,0.029299445,0.001511823,0.001511873,0.001511964,0.001511845,Drug discovery,0.35407138,FALSE,93.33333333,0.852433669,141.7777778,0.876170725,1,0.537564047,1243,0.573802071,0.709992628
12483,The impact of COVID-19 vaccination campaigns accounting for antibody-dependent enhancement,0,10.1101/2021.01.04.425198,1/4/21,biorxiv,0,13,predictive model,0.000936179,0.032209871,0.000936084,0.862747622,0.102234152,0.000936091,Epidemiology,0.16519085,FALSE,6.5,0.093512277,4.583333333,0.243510838,0,0.403234768,4361,0.881290633,0.405387129
12484,Comprehensive analysis of the host-virus interactome of SARS-CoV-2,0,10.1101/2020.12.31.424961,1/2/21,biorxiv,0,10,interactom,0.990282503,0.001943568,0.001943472,0.001943466,0.001943494,0.001943497,Drug discovery,0.32712066,FALSE,51,0.63875317,36.5,0.614329676,1,0.537564047,2052,0.739947026,0.63264848
12485,Unbuttoning the impact of N501Y mutant RBD on viral entry mechanism: A computational insight,0,10.1101/2020.12.30.424906,1/2/21,biorxiv,0,5,"computational, in silico",0.630615687,0.310412143,0.002032902,0.002032881,0.052873572,0.002032815,Drug discovery,0.20767227,FALSE,18.6,0.278990661,9.8,0.350682366,0,0.403234768,1299,0.584396822,0.404326154
12486,Determinants of in-hospital mortality in COVID-19; a prospective cohort study from Pakistan,0,10.1101/2020.12.28.20248920,1/2/21,medrxiv,0,7,logistic regression,0.001171522,0.001171535,0.001171524,0.001171544,0.001171551,0.994142323,Clinics,0.83209217,TRUE,5.428571429,0.074958253,0.142857143,0.057398983,1,0.537564047,653,0.254996388,0.231229418
12487,Vaccination and Non-Pharmaceutical Interventions: when can the UK relax about COVID-19?,0,10.1101/2020.12.27.20248896,1/2/21,medrxiv,0,5,mathematical model,0.150000891,0.000752907,0.000752896,0.606642549,0.204823115,0.037027642,Epidemiology,0.039001375,FALSE,14.2,0.214793741,1.2,0.126103827,2,0.618927094,11001,0.961232844,0.480264376
12488,Quantitative plasma proteomics of survivor and non-survivor COVID-19 patients admitted to hospital unravels potential prognostic biomarkers and therapeutic targets,0,10.1101/2020.12.26.20248855,1/2/21,medrxiv,0,13,proteom,0.190541222,0.000926347,0.000926315,0.000926337,0.000926301,0.805753479,Clinics,0.42639682,FALSE,15.46153846,0.232048983,3.692307692,0.217821782,0,0.403234768,2204,0.757283891,0.402597356
12489,"The new Coronavirus (SARS-CoV-2) in Central America: Demographic-spatial simulations, Analyses of Molecular Variance (AMOVA) and Neutrality Tests in complete genomes from Belize, Guatemala, Cuba, Jamaica and Puerto Rico",0,10.1101/2020.12.26.20248872,1/2/21,medrxiv,0,5,genomes,0.002357866,0.906792969,0.002357773,0.002357898,0.08377557,0.002357923,Genomics,0.711999,TRUE,6,0.086028821,0,0.055525823,0,0.403234768,382,0.043823742,0.147153288
12490,Number of tests required to flatten the curve of coronavirus disease-2019,0,10.1101/2020.12.26.20248818,1/2/21,medrxiv,0,4,mathematical model,0.00840254,0.008402634,0.008402697,0.957986719,0.008402765,0.008402644,Epidemiology,0.10370371,FALSE,9.75,0.146267549,11.5,0.378378378,0,0.403234768,423,0.072477727,0.250089605
12491,COVID-19 severity impacts on long-term neurological manifestation after hospitalisation,0,10.1101/2020.12.27.20248903,1/2/21,medrxiv,0,15,logistic regression,0.001717243,0.001717282,0.001717257,0.113174493,0.001717323,0.879956401,Clinics,0.6579525,TRUE,126.8666667,0.916568743,69.66666667,0.752475248,1,0.537564047,6858,0.929689381,0.784074355
12492,The high infectivity of SARS-CoV-2 B.1.1.7 is associated with increased interaction force between Spike-ACE2 caused by the viral N501Y mutation,0,10.1101/2020.12.29.424708,1/1/21,biorxiv,0,2,"in silico, dataset",0.558868757,0.436444977,0.001171564,0.001171574,0.001171565,0.001171563,Drug discovery,0.096590966,FALSE,32.5,0.461685942,8,0.320511105,20,0.900117291,12380,0.96580785,0.662030547
12493,Meta-analysis of virus-induced host gene expression reveals unique signatures of immune dysregulation induced by SARS-CoV-2,0,10.1101/2020.12.29.424739,12/30/20,biorxiv,0,5,"transcriptom, dataset",0.692672722,0.18120477,0.001034641,0.001034638,0.0010346,0.12301863,Drug discovery,0.094979286,FALSE,88.6,0.838208918,203.4,0.91885202,0,0.403234768,1441,0.611124488,0.692855049
12494,Characterization of cell-cell communication in COVID-19 patients,0,10.1101/2020.12.30.424641,12/30/20,biorxiv,0,7,dataset,0.688086764,0.067024174,0.001371291,0.04597412,0.001371316,0.196172335,Drug discovery,0.43075204,FALSE,23.14285714,0.343125734,14.71428571,0.420524485,0,0.403234768,1357,0.588008668,0.438723414
12495,S gene dropout patterns in SARS-CoV-2 tests suggest spread of the H69del/V70del mutation in the US.,0,10.1101/2020.12.24.20248814,12/30/20,medrxiv,0,6,sequencing,0.001538111,0.873739822,0.001538082,0.120107746,0.001538148,0.00153809,Genomics,0.059824258,FALSE,24.5,0.361988991,,,9,0.814309525,10565,0.954972309,0.710423609
12496,Smoking and SARS-CoV-2 Impair Dendritic Cells and Regulate DC-SIGN Expression in Tissues,0,10.1101/2020.12.23.20245316,12/30/20,medrxiv,0,19,"transcriptom, dataset",0.729870918,0.001565366,0.001565389,0.001565389,0.001565365,0.263867574,Drug discovery,0.2658543,FALSE,40.15789474,0.540478694,38.15789474,0.622491303,0,0.403234768,1046,0.470503251,0.509177004
12497,"Contact Tracing of COVID-19 in Karnataka, India: Superspreading and Determinants of Infectiousness and Symptomaticity",0,10.1101/2020.12.25.20248668,12/30/20,medrxiv,0,16,bayes,0.000572639,0.000572656,0.000572762,0.847885668,0.149823606,0.00057267,Epidemiology,0.21351275,FALSE,13.625,0.20607335,5.5,0.267259834,0,0.403234768,1055,0.475319046,0.33797175
12498,Genomic Diversity of the SARS-CoV-2 in Turkey and the Impact of Virus Genome Mutations on Clinical Outcomes,0,10.1101/2020.12.25.20248851,12/30/20,medrxiv,0,16,genomes,0.001823344,0.905041339,0.038663225,0.001823357,0.001823355,0.050825379,Genomics,0.34142613,FALSE,10.6875,0.159255365,1.75,0.148381054,0,0.403234768,921,0.417529497,0.282100171
12499,Paired SARS CoV-2 Spike Protein Mutations Observed During Ongoing SARS-CoV-2 Viral Transfer from Humans to Minks and Back to Humans,0,10.1101/2020.12.22.424003,12/29/20,biorxiv,0,11,genomes,0.117021689,0.811135672,0.06601219,0.001943506,0.001943463,0.00194348,Genomics,0.1559323,FALSE,59,0.696579875,96.45454545,0.815962002,0,0.403234768,2098,0.733927281,0.662425981
12500,Deep mining of early antibody response in COVID-19 patients yields potent neutralisers and reveals high level of convergence,0,10.1101/2020.12.29.424711,12/29/20,biorxiv,0,34,sequencing,0.402692738,0.503846574,0.040088291,0.001538152,0.001538107,0.050296138,Genomics,0.32250363,FALSE,32.17647059,0.457232977,50.20588235,0.682164838,0,0.403234768,1997,0.718275945,0.565227132
12501,Defective NETs Clearance contributes to sustained FXII Activation in COVID-19-associated Pulmonary Thrombo-Inflammation,0,10.1101/2020.12.29.424644,12/29/20,biorxiv,0,20,proteom,0.471962639,0.092869321,0.29693723,0.001653185,0.001653087,0.134924537,Drug discovery,0.4974394,FALSE,32.42105263,0.460139774,27.63157895,0.550508429,1,0.537564047,1816,0.690344329,0.559639145
12502,Host Specific SARS-CoV-2 Mutations: Insertion of the Phenylalanine in the NSP6 Linked to the United Kingdome and Premature Termination of the ORF-8 Associated with the European and the United States of America Derived Samples.,0,10.1101/2020.12.29.424530,12/29/20,biorxiv,0,4,genome sequences,0.027642778,0.920286552,0.001461891,0.001461899,0.001461859,0.04768502,Genomics,0.23210049,FALSE,35.5,0.492547467,39.5,0.630920524,0,0.403234768,1681,0.666024561,0.54818183
12503,Design of Specific Primer Set for Detection of B.1.1.7 SARS-CoV-2 Variant using Deep Learning,0,10.1101/2020.12.29.424715,12/29/20,biorxiv,10.1038/s41598-020-80363-5,7,"deep learning, in-silico",0.001486467,0.840722648,0.153331429,0.001486461,0.001486436,0.001486559,Genomics,0.23283839,FALSE,113.7142857,0.897148865,71.85714286,0.758696816,3,0.667819001,3906,0.857452444,0.795279281
12504,Landscapes and dynamic diversifications of B-cell receptor repertoires in COVID-19 patients,0,10.1101/2020.12.28.424622,12/29/20,biorxiv,0,37,sequencing,0.276087392,0.506205415,0.001653105,0.001653137,0.001653165,0.212747787,Genomics,0.3653499,FALSE,97.94594595,0.864308244,196.7027027,0.916109178,0,0.403234768,780,0.326751746,0.627600984
12505,ACE2 peptide fragment interacts with several sites on the SARS-CoV-2 spike protein S1,0,10.1101/2020.12.29.424682,12/29/20,biorxiv,0,7,computational,0.991577354,0.001684594,0.001684537,0.001684546,0.001684486,0.001684483,Drug discovery,0.47214824,FALSE,90.28571429,0.843404045,64.71428571,0.737623762,0,0.403234768,635,0.21791476,0.550544334
12506,Structural dynamics of the SARS-CoV-2 frameshift-stimulatory pseudoknot reveal topologically distinct conformers,0,10.1101/2020.12.28.424630,12/29/20,biorxiv,0,9,computational,0.729955104,0.001861836,0.044034918,0.17757607,0.001861739,0.044710334,Drug discovery,0.2936677,FALSE,22.55555556,0.33409611,16.66666667,0.444674873,1,0.537564047,831,0.357091259,0.418356572
12507,Maori and Pacific People in New Zealand have higher risk of hospitalisation for COVID-19,0,10.1101/2020.12.25.20248427,12/28/20,medrxiv,0,9,logistic regression,0.002562533,0.002562568,0.002562596,0.002562811,0.435127993,0.554621499,Clinics,0.15372899,FALSE,27.88888889,0.405529099,31.77777778,0.583154937,0,0.403234768,2296,0.755598363,0.536879292
12508,Emerging SARS-CoV-2 diversity revealed by rapid whole genome sequence typing.,0,10.1101/2020.12.28.424582,12/28/20,biorxiv,0,0,"machine learning, classifier, whole genome, genomes",0.001034591,0.651530444,0.180891022,0.16447478,0.001034598,0.001034565,Genomics,0.27560824,FALSE,16.5,0.249366071,2.5,0.180826866,0,0.403234768,1892,0.699253552,0.383170314
12509,SARS-CoV-2 escape in vitro from a highly neutralizing COVID-19 convalescent plasma,0,10.1101/2020.12.28.424451,12/28/20,biorxiv,0,18,computational,0.391744421,0.445723872,0.002183219,0.002183313,0.002183222,0.155981953,Genomics,0.25143397,FALSE,86.27777778,0.830045148,157.3333333,0.890152529,56,0.961540836,56782,0.995906574,0.919411272
12510,SPINT2 controls SARS-CoV-2 viral infection and is associated to disease severity,0,10.1101/2020.12.28.424029,12/28/20,biorxiv,0,6,dataset,0.727191899,0.001538175,0.001538134,0.001538131,0.001538158,0.266655503,Drug discovery,0.63407147,TRUE,28.5,0.412579628,57.66666667,0.713473374,0,0.403234768,1607,0.647002167,0.544072484
12511,SARS-CoV-2 mutations among minks show reduced lethality and infectivity to humans,0,10.1101/2020.12.23.424267,12/27/20,biorxiv,0,1,sequencing,0.003607371,0.807203757,0.003607315,0.178366946,0.003607449,0.003607162,Genomics,0.1795857,FALSE,56,0.675304595,20,0.481000803,0,0.403234768,4908,0.88562485,0.611291254
12512,The neutralization effect of Montelukast on SARS-CoV-2 is shown by multiscale in silico simulations and combined in vitro studies,0,10.1101/2020.12.26.424423,12/27/20,biorxiv,0,26,in silico,0.94098035,0.001538203,0.001538213,0.052866975,0.001538121,0.001538139,Drug discovery,0.58015704,TRUE,18.5,0.278001113,4.576923077,0.243443939,0,0.403234768,3766,0.845172165,0.442462996
12513,"Genomic characterization of a novel SARS-CoV-2 lineage from Rio de Janeiro, Brazil",0,10.1101/2020.12.23.20248598,12/26/20,medrxiv,10.1128/jvi.00119-21,20,"sequencing, genomes",0.039506944,0.952886071,0.00190173,0.001901844,0.001901708,0.001901702,Genomics,0.35035953,FALSE,11.35,0.17075886,11.95,0.383864062,44,0.952157541,10843,0.953045991,0.614956613
12514,Mechanical ventilation affects respiratory microbiome of COVID-19 patients and its interactions with the host,0,10.1101/2020.12.23.20248425,12/26/20,medrxiv,0,20,"sequencing, transcriptom, microbiom",0.105803569,0.476815816,0.001622724,0.001622725,0.001622731,0.412512435,Genomics,0.6658204,TRUE,27.7,0.403673697,60.75,0.72357506,0,0.403234768,1456,0.600529738,0.532753315
12515,"Seropositivity in blood donors and pregnant women during 9-months of SARS-CoV-2 transmission in Stockholm, Sweden",0,10.1101/2020.12.24.20248821,12/26/20,medrxiv,0,14,"bayes, probabilistic",0.191861581,0.216095794,0.00137128,0.315902878,0.273397067,0.0013714,Epidemiology,0.21824667,FALSE,23.84615385,0.351660585,112.5384615,0.841851753,0,0.403234768,4062,0.859137973,0.613971269
12516,Does COVID-19 Testing Create More Cases? An Empirical Evidence on the Importance of Mass Testing During a Pandemic,0,10.1101/2020.12.23.20248740,12/26/20,medrxiv,0,1,dataset,0.002032771,0.120375304,0.002032874,0.762750862,0.110775313,0.002032877,Epidemiology,0.3904149,FALSE,6,0.086028821,0,0.055525823,0,0.403234768,1984,0.708644353,0.313358441
12517,Genetic epidemiology of variants associated with immune escape from global SARS-CoV-2 genomes,0,10.1101/2020.12.24.424332,12/26/20,biorxiv,0,15,"genome sequences, genomes",0.00569815,0.9715107,0.005697879,0.005698244,0.005697494,0.005697534,Genomics,0.21508926,FALSE,26.13333333,0.382893191,15.53333333,0.430358576,4,0.707574542,5187,0.890681435,0.602876936
12518,Influence of HLA class II polymorphism on predicted cellular immunity against SARS-CoV-2 at the population and individual level,0,10.1101/2020.12.24.424326,12/26/20,biorxiv,0,4,"bioinformatic, proteom",0.581453323,0.213602673,0.001593528,0.001593582,0.200163317,0.001593577,Drug discovery,0.11946607,FALSE,61,0.709196611,117.5,0.847872625,0,0.403234768,1510,0.613773176,0.643519295
12519,Genomic diversity of SARS-CoV-2 can be accelerated by a mutation in the nsp14 gene,0,10.1101/2020.12.23.424231,12/26/20,biorxiv,0,4,genomes,0.115360815,0.878265121,0.001593506,0.001593539,0.00159347,0.001593549,Genomics,0.46501628,FALSE,62,0.715814212,119.5,0.850615467,1,0.537564047,2428,0.766193113,0.71754671
12520,Single point mutations can potentially enhance infectivity of SARS-CoV-2 revealed by in silico affinity maturation and SPR assay,0,10.1101/2020.12.24.424245,12/26/20,biorxiv,0,10,"in silico, in-silico",0.724642027,0.175110537,0.06682421,0.029449053,0.001987086,0.001987088,Drug discovery,0.20187694,FALSE,56.8,0.680314181,,,0,0.403234768,1597,0.636166627,0.573238525
12521,Extensive High-Order Complexes within SARS-CoV-2 Proteome Revealed by Compartmentalization-Aided Interaction Screening,0,10.1101/2020.12.26.424422,12/26/20,biorxiv,0,5,proteom,0.891561612,0.053315204,0.050163916,0.001653101,0.001653131,0.001653035,Drug discovery,0.18855137,FALSE,38.6,0.525078855,45.8,0.662697351,1,0.537564047,1500,0.611365278,0.584176383
12522,Short-term forecasting of COVID-19 in Germany and Poland during the second wave - a preregistered study,0,10.1101/2020.12.24.20248826,12/26/20,medrxiv,0,38,probabilistic,0.002183242,0.002183296,0.063617712,0.927649081,0.002183296,0.002183373,Epidemiology,0.27501014,FALSE,9.228571429,0.137052384,7.142857143,0.300709125,0,0.403234768,1646,0.648206116,0.372300598
12523,Financial Hardship and Social Assistance as Determinants of Mental Health and Food and Housing Insecurity During the COVID-19 Pandemic,0,10.1101/2020.12.24.20248835,12/26/20,medrxiv,0,1,logistic regression,0.001046845,0.001046842,0.00104685,0.17741254,0.818400062,0.001046861,Healthcare,0.13843268,FALSE,5,0.070752675,0,0.055525823,0,0.403234768,1599,0.637129786,0.291660763
12524,SARS-CoV-2 infecting the inner ear results in potential hearing damage at the early stage or prognosis of COVID-19 in rodents,0,10.1101/2020.12.23.423942,12/25/20,biorxiv,0,9,transcriptom,0.516942028,0.390153149,0.004310073,0.004310058,0.004310087,0.079974604,Drug discovery,0.4597612,FALSE,11.55555556,0.174655204,,,0,0.403234768,1162,0.496990128,0.358293367
12525,Patterns of within-host genetic diversity in SARS-CoV-2,0,10.1101/2020.12.23.424229,12/25/20,biorxiv,0,41,genome sequences,0.001392886,0.941874691,0.001392852,0.052553892,0.001392845,0.001392833,Genomics,0.23682106,FALSE,74.94871795,0.786257654,440.5384615,0.974645438,3,0.667819001,5706,0.904165663,0.833221939
12526,Detection and molecular characterisation of SARS-CoV-2 in farmed mink (Neovision vision) in Poland,0,10.1101/2020.12.24.422670,12/24/20,biorxiv,0,8,"sequencing, genomes",0.002806493,0.985967317,0.002806547,0.002806647,0.002806539,0.002806457,Genomics,0.3455961,FALSE,46.75,0.603314985,47,0.668718223,1,0.537564047,1517,0.60871659,0.604578462
12527,Real-time monitoring epidemic trends and key mutations in SARS-CoV-2 evolution by an automated tool,0,10.1101/2020.12.24.424271,12/24/20,biorxiv,0,12,genome sequences,0.035193941,0.714124716,0.087488609,0.160571974,0.001310383,0.001310377,Genomics,0.46735114,FALSE,43.83333333,0.576287958,18.75,0.467621086,1,0.537564047,1188,0.502769083,0.521060544
12528,Exploring epitope and functional diversity of anti-SARS-CoV2 antibodies using AI-based methods,0,10.1101/2020.12.23.424199,12/24/20,biorxiv,0,8,artificial intelligence,0.320549076,0.464546668,0.178100677,0.00115633,0.00115633,0.034490919,Genomics,0.18483579,FALSE,50,0.632073721,46,0.66416912,0,0.403234768,1864,0.683602215,0.595769956
12529,Automated processing of thermal imaging to detect COVID-19,0,10.1101/2020.12.22.20248691,12/24/20,medrxiv,0,26,"machine learning, image processing",0.001330026,0.001330055,0.874200745,0.001330073,0.032535817,0.089273284,Imaging,0.32051086,FALSE,54.19230769,0.662378626,37.26923077,0.617273214,0,0.403234768,2237,0.738983867,0.605467619
12530,THE SEARCH FOR AN ASSOCIATION OF HLA ALLELES AND COVID-19 RELATED MORTALITY IN THE RUSSIAN POPULATION,0,10.1101/2020.12.22.20248695,12/24/20,medrxiv,0,5,sequencing,0.308353276,0.365082817,0.002296609,0.002296536,0.002296548,0.319674213,Genomics,0.36069372,FALSE,10,0.15214299,0.2,0.061145304,0,0.403234768,780,0.297616181,0.228534811
12531,Predicting the Evolution of COVID-19 Mortality Risk: a Recurrent Neural Network Approach,0,10.1101/2020.12.22.20244061,12/24/20,medrxiv,0,11,"deep learning, neural network, classifier, dataset",0.000786354,0.042665329,0.527826861,0.260289645,0.000786365,0.167645445,Epidemiology,0.563492,TRUE,4.090909091,0.054796215,4.727272727,0.247390955,1,0.537564047,1313,0.548519143,0.34706759
12532,Identifying communities at risk for COVID-19-related burden across 500 U.S. Cities and within New York City,0,10.1101/2020.12.17.20248360,12/24/20,medrxiv,0,5,"supervised learning, unsupervised learning",0.001112732,0.001112679,0.0720999,0.398643848,0.00111267,0.52591817,Clinics,0.07158792,FALSE,10.6,0.158142124,22.8,0.509967889,0,0.403234768,1368,0.567300746,0.409661382
12533,Adenovirus and RNA-based COVID-19 vaccines: perceptions and acceptance among healthcare workers,0,10.1101/2020.12.22.20248657,12/24/20,medrxiv,0,18,logistic regression,0.209794564,0.001461941,0.001461988,0.001461954,0.784357665,0.001461887,Healthcare,0.73187053,TRUE,24.27777778,0.35821634,32.22222222,0.586968156,1,0.537564047,1130,0.480616422,0.490841241
12534,"How the COVID-19 pandemic affects transgender health care in upper-middle income and high income countries - A worldwide, cross-sectional survey",0,10.1101/2020.12.23.20248794,12/24/20,medrxiv,0,16,logistic regression,0.00087153,0.000871536,0.000871559,0.101808141,0.894705672,0.000871561,Healthcare,0.29978722,FALSE,31.125,0.445482095,11.25,0.374364463,0,0.403234768,985,0.421863713,0.41123626
12535,Acceptance and Attitudes Toward COVID-19 Vaccines: A Cross-Sectional Study from Jordan,0,10.1101/2020.12.22.20248676,12/24/20,medrxiv,0,5,logistic regression,0.001126888,0.001126811,0.001126814,0.001126866,0.994365824,0.001126797,Healthcare,0.25468886,FALSE,31.8,0.452965551,17.2,0.452568906,1,0.537564047,3097,0.810016855,0.56327884
12536,Introduction into the Marseille geographical area of a mild SARS-CoV-2 variant originating from sub-Saharan Africa,0,10.1101/2020.12.23.20248758,12/24/20,medrxiv,10.1016/j.tmaid.2021.101980,21,"genome sequences, genomes",0.00208057,0.835556451,0.002080625,0.002080644,0.002080567,0.156121144,Genomics,0.4819541,FALSE,88.85714286,0.838889232,127.8571429,0.861185443,1,0.537564047,3960,0.848302432,0.771485289
12537,Forecasting intensive care unit demand during the COVID-19 pandemic: A spatial age-structured microsimulation model,0,10.1101/2020.12.23.20248761,12/24/20,medrxiv,0,20,simulation model,0.000699431,0.013954568,0.000699355,0.868358104,0.01186772,0.104420823,Epidemiology,0.09744358,FALSE,40.45,0.543447337,19.9,0.478659352,1,0.537564047,1297,0.541777029,0.525361941
12538,HLA-C* 04:01 is a Genetic Risk Allele for Severe Course of COVID-19,0,10.1101/2020.12.21.20248121,12/24/20,medrxiv,0,50,"sequencing, exom",0.044558195,0.415923885,0.001653064,0.001653129,0.001653063,0.534558664,Clinics,0.15004766,FALSE,53.76744186,0.659471829,97.76744186,0.818035858,0,0.403234768,2932,0.797736576,0.669619757
12539,Inferring Toll-Like Receptor induced epitope subunit vaccine candidate against SARS-CoV-2: A Reverse Vaccinology approach,0,10.1101/2020.12.24.424322,12/24/20,biorxiv,0,0,in-silico,0.899990857,0.000793456,0.000793427,0.096835395,0.000793435,0.000793431,Drug discovery,0.1659466,FALSE,4.714285714,0.063640299,1.142857143,0.123628579,0,0.403234768,1994,0.704069347,0.323643248
12540,Combined RT-qPCR and Pyrosequencing of a SARS-CoV-2 Spike Glycoprotein Polybasic Cleavage Motif Uncovers Rare Pediatric COVID-19 Spectrum Diseases of Unusual Presentation,0,10.1101/2020.12.19.20243428,12/23/20,medrxiv,0,13,sequencing,0.001112632,0.521126205,0.444558407,0.001112673,0.001112689,0.030977394,Genomics,0.2493721,FALSE,8.230769231,0.120724844,8.461538462,0.327267862,0,0.403234768,1420,0.579340236,0.357641927
12541,Identification of COVID-19-relevant transcriptional regulatory networks and associated kinases as potential therapeutic targets,0,10.1101/2020.12.23.424177,12/23/20,biorxiv,0,3,computational,0.965694473,0.001272669,0.001272698,0.029214778,0.001272733,0.001272649,Drug discovery,0.48937744,FALSE,15,0.227596017,46.66666667,0.667045759,0,0.403234768,1216,0.508788827,0.451666343
12542,Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans,0,10.1101/2020.12.20.20248582,12/23/20,medrxiv,0,4,"machine learning, deep learning, artificial intelligence, neural network, ensemble learning",0.001751139,0.001751155,0.895021975,0.001751166,0.001751293,0.097973272,Imaging,0.7330351,TRUE,13.5,0.205393036,0.75,0.099411292,1,0.537564047,715,0.250662172,0.273257637
12543,"Knowledge, Attitudes, and Practices of People living with SCI towards COVID-19 and their Psychological State during In-patient Rehabilitation in Bangladesh",0,10.1101/2020.12.21.20248686,12/23/20,medrxiv,0,13,logistic regression,0.000966759,0.000966803,0.000966758,0.000966768,0.739449751,0.256683162,Healthcare,0.6941841,TRUE,12.23076923,0.184674377,7.769230769,0.31228258,0,0.403234768,660,0.208283169,0.277118723
12544,Dynamics of a dual SARS-CoV-2 strain co-infection on a prolonged viral shedding COVID-19 case: insights into clinical severity and disease duration,0,10.1101/2020.12.22.20248392,12/23/20,medrxiv,10.3390/microorganisms9020300,20,genome-wide,0.001330089,0.659794802,0.001330082,0.001330129,0.001330194,0.334884704,Genomics,0.3776135,FALSE,18.55,0.278124807,4.9,0.250334493,0,0.403234768,2028,0.706477245,0.409542828
12545,Direct RNA nanopore sequencing of SARS-CoV-2 extracted from critical material from swabs,0,10.1101/2020.12.21.20191346,12/23/20,medrxiv,0,17,"bioinformatic, sequencing",0.00156533,0.700401795,0.293336699,0.001565403,0.001565388,0.001565385,Genomics,0.32944247,FALSE,55.82352941,0.673449193,40.17647059,0.634198555,0,0.403234768,1090,0.460390079,0.542818149
12546,Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning,0,10.1101/2020.12.16.423122,12/22/20,biorxiv,0,10,"machine learning, bioinformatic, neural network, classifier, prediction model",0.395072103,0.001171665,0.169850159,0.001171621,0.001171583,0.431562869,Clinics,0.74123985,TRUE,26.3,0.385305214,34.4,0.601150656,1,0.537564047,19266,0.977847339,0.625466814
12547,A comparative analysis of COVID-19 mortality rate across the globe: An extensive analysis of the associated factors,0,10.1101/2020.12.22.20248696,12/22/20,medrxiv,0,7,"predictive model, dataset",0.001291215,0.001291253,0.001291249,0.443803388,0.001291328,0.551031568,Clinics,0.19130579,FALSE,11.125,0.167542829,1.625,0.141222906,0,0.403234768,736,0.259812184,0.242953172
12548,COVID-19 Network Model to Evaluate Vaccine Strategies towards Herd Immunity,0,10.1101/2020.12.22.20248693,12/22/20,medrxiv,0,3,"computational, network model",0.001593574,0.001593517,0.001593498,0.992032289,0.001593641,0.001593479,Epidemiology,0.19542703,FALSE,3.333333333,0.04044777,2,0.164302917,2,0.618927094,942,0.392246569,0.303981088
12549,"Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA)",0,10.1101/2020.12.20.423682,12/22/20,biorxiv,0,5,"sequencing, whole-genome, genomes, dataset",0.003607408,0.842889741,0.142681205,0.00360733,0.003607179,0.003607138,Genomics,0.43714976,FALSE,31.8,0.452965551,86.8,0.79482205,0,0.403234768,1467,0.588731038,0.559938352
12550,Cardiac SARS-CoV-2 infection is associated with distinct transcriptomic changes within the heart,0,10.1101/2020.12.19.20248542,12/22/20,medrxiv,0,23,"sequencing, transcriptom",0.343028857,0.276819788,0.001098825,0.001098833,0.001098794,0.376854902,Clinics,0.07116276,FALSE,33.82608696,0.474302678,75.04347826,0.766657747,0,0.403234768,1286,0.52925596,0.543362788
12551,Identifying Sequential Complication and Mortality Patterns in Diabetes Mellitus: Comparisons of Machine Learning Methodologies,0,10.1101/2020.12.21.20248646,12/22/20,medrxiv,0,10,"machine learning, logistic regression, prediction model",0.001237095,0.168579308,0.261810146,0.001237157,0.001237124,0.565899169,Clinics,0.69082844,TRUE,26.4,0.386913229,6.7,0.290808135,0,0.403234768,1448,0.584878401,0.416458633
12552,A New Extension of State-Space SIR Model to Account for Underreporting- An Application to the COVID-19 transmission in California and Florida,0,10.1101/2020.12.20.20248580,12/22/20,medrxiv,0,2,bayes,0.001392828,0.001392871,0.001392863,0.969125341,0.025303267,0.001392829,Epidemiology,0.10747337,FALSE,4,0.054734368,0.5,0.087101953,0,0.403234768,589,0.154587046,0.174914534
12553,"COVID-19 pandemic dynamics in Ukraine after September 1, 2020",0,10.1101/2020.12.21.20248627,12/22/20,medrxiv,0,1,mathematical model,0.000863039,0.000863068,0.000863057,0.995684748,0.000863046,0.000863042,Epidemiology,0.14542446,FALSE,6,0.086028821,0,0.055525823,2,0.618927094,436,0.051529015,0.203002688
12554,Mathematical Models for Assessing Vaccination Scenarios in Several Provinces in Indonesia,0,10.1101/2020.12.21.20248241,12/22/20,medrxiv,0,6,mathematical model,0.003607289,0.003607249,0.003607182,0.981963298,0.00360746,0.003607522,Epidemiology,0.2669075,FALSE,4.5,0.061784897,0.166666667,0.058736955,0,0.403234768,508,0.094389598,0.154536554
12555,Covid-19 Prediction in USA using modified SIR derived model,0,10.1101/2020.12.20.20248600,12/22/20,medrxiv,0,1,dataset,0.001861714,0.07290456,0.10345289,0.818057101,0.001861671,0.001862064,Epidemiology,0.31950316,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,558,0.131712015,0.153153435
12556,"Active learning tools improve the learning outcomes, scientific attitude and critical thinking in higher education: Experiences in an online course during the COVID-19 pandemic",0,10.1101/2020.12.22.423922,12/22/20,biorxiv,0,6,active learning,0.001291308,0.00129123,0.185450163,0.36115986,0.449516193,0.001291246,Healthcare,0.8190092,TRUE,10.66666667,0.159131672,31.16666667,0.579074124,0,0.403234768,1867,0.679267999,0.45517714
12557,LungAI: A Deep Learning Convolutional Neural Network for Automated Detection of COVID-19 from Posteroanterior Chest X-Rays,0,10.1101/2020.12.19.20248530,12/22/20,medrxiv,0,1,"deep learning, neural network, dataset",0.043436836,0.072434482,0.880417356,0.001237114,0.001237075,0.001237136,Imaging,0.312518,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,1709,0.647965326,0.282216763
12558,COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients,0,10.1101/2020.12.19.20248524,12/22/20,medrxiv,0,27,"machine learning, classifier, logistic regression, prediction model, dataset",0.001415109,0.046553316,0.269246781,0.059918093,0.001415133,0.621451567,Clinics,0.4838787,FALSE,10.22222222,0.153751005,3.666666667,0.217621086,0,0.403234768,1601,0.623404768,0.349502907
12559,COVID-19 in-hospital mortality in South Africa: the intersection of communicable and non-communicable chronic diseases in a high HIV prevalence setting,0,10.1101/2020.12.21.20248409,12/22/20,medrxiv,0,15,logistic regression,0.016821528,0.000450285,0.000450252,0.210701637,0.099454462,0.672121836,Clinics,0.2020998,FALSE,12.33333333,0.186467932,8.266666667,0.323856034,0,0.403234768,1023,0.432940043,0.336624694
12560,Clinical outcomes and risk factors for COVID-19 among migrant populations in high-income countries: a systematic review,0,10.1101/2020.12.21.20248475,12/22/20,medrxiv,0,23,dataset,0.024311844,0.001156262,0.001156284,0.772679546,0.199539758,0.001156307,Epidemiology,0.11967656,FALSE,19.95652174,0.294885274,15.73913043,0.432967621,2,0.618927094,1742,0.65542981,0.50055245
12561,The first wave of the Spanish COVID-19 epidemic was associated with early introductions and fast spread of a dominating genetic variant,0,10.1101/2020.12.21.20248328,12/22/20,medrxiv,0,71,"bayes, genome sequences",0.00111261,0.480401328,0.001112634,0.515148133,0.001112656,0.001112639,Epidemiology,0.3754173,FALSE,18.45714286,0.275898324,16.28571429,0.439256088,0,0.403234768,1475,0.591861305,0.427562621
12562,From SARS-CoV-2 infection to COVID-19 disease: a proposed mechanism for viral spread to the lower airway based on in silico estimation of virion flow rates,0,10.1101/2020.12.19.20248544,12/22/20,medrxiv,0,2,"computational, in silico",0.059074123,0.302950001,0.11252803,0.324436359,0.001310336,0.199701151,Epidemiology,0.15058747,FALSE,10.5,0.157338116,1.5,0.138747659,2,0.618927094,924,0.380688659,0.323925382
12563,A Systematic Review and Network Meta-Analysis for COVID-19 Treatments,0,10.1101/2020.12.21.20248621,12/22/20,medrxiv,0,4,bayes,0.152736113,0.001220067,0.038155618,0.311516654,0.001220034,0.495151516,Clinics,0.30591905,FALSE,8,0.118683901,2.25,0.170925876,1,0.537564047,1720,0.650614014,0.36944696
12564,Projecting the combined healthcare burden of seasonal influenza and COVID-19,0,10.1101/2020.12.20.20248599,12/22/20,medrxiv,0,5,mathematical model,0.007061664,0.007062066,0.007061681,0.526185307,0.255880633,0.196748649,Epidemiology,0.26814836,FALSE,70.8,0.767456243,194.8,0.914971903,0,0.403234768,570,0.138935709,0.556149656
12565,Clustering and longitudinal change in SARS-CoV-2 seroprevalence in school-children: prospective cohort study of 55 schools in Switzerland,0,10.1101/2020.12.19.20248513,12/22/20,medrxiv,10.1136/bmj.n616,12,logistic regression,0.000871552,0.155754876,0.000871586,0.000871598,0.840758817,0.000871571,Healthcare,0.3130554,FALSE,67.83333333,0.752674872,111.9166667,0.841249666,3,0.667819001,2456,0.757765471,0.754877252
12566,Several forms of SARS-CoV-2 RNA can be detected in wastewaters : implication for wastewater-based epidemiology and risk assessment.,0,10.1101/2020.12.19.20248508,12/22/20,medrxiv,0,10,genomes,0.0024906,0.666546405,0.002490621,0.323491362,0.002490566,0.002490447,Genomics,0.5453557,TRUE,8.4,0.124435648,10.4,0.360917849,2,0.618927094,1440,0.581507344,0.421446984
12567,Social disparities in the first wave of COVID-19 infections in Germany: A county-scale explainable machine learning approach,0,10.1101/2020.12.22.20248386,12/22/20,medrxiv,0,3,machine learning,0.000815348,0.000815351,0.101018132,0.706985566,0.189550226,0.000815375,Epidemiology,0.2229414,FALSE,18.33333333,0.274908776,7,0.299973241,0,0.403234768,802,0.304358295,0.32061877
12568,"Prioritizing Risk Groups for SARS-CoV-2 Vaccination ""By the Numbers""",0,10.1101/2020.12.18.20248504,12/22/20,medrxiv,0,4,"predictive model, logistic regression",0.001291269,0.001291232,0.001291241,0.215546447,0.420839064,0.359740746,Healthcare,0.016753137,FALSE,2.5,0.027459954,0.5,0.087101953,1,0.537564047,1411,0.572838912,0.306241217
12569,Development and external validation of a logistic regression derived formula based on repeated routine hematological measurements predicting survival of hospitalized Covid-19 patients,0,10.1101/2020.12.20.20248563,12/22/20,medrxiv,0,27,logistic regression,0.00096678,0.000966779,0.087479747,0.036928921,0.000966817,0.872690957,Clinics,0.28649682,FALSE,26.07407407,0.382583957,12.14814815,0.387075194,1,0.537564047,6593,0.915482784,0.555676495
12570,Impact of housing conditions on changes in youth's mental health following the initial national COVID-19 lockdown: A cohort study,0,10.1101/2020.12.16.20245191,12/22/20,medrxiv,0,6,logistic regression,0.000603883,0.000603896,0.000603892,0.110351332,0.887233098,0.000603899,Healthcare,0.09652799,FALSE,25.5,0.37435834,21.5,0.498260637,0,0.403234768,763,0.278112208,0.388491488
12571,The Ensembl COVID-19 resource: Ongoing integration of public SARS-CoV-2 data,0,10.1101/2020.12.18.422865,12/22/20,biorxiv,0,27,sequencing,0.002357946,0.781278739,0.002357859,0.183521637,0.002357935,0.028125884,Genomics,0.3958281,FALSE,0.62962963,0.007916383,,,0,0.403234768,1779,0.662894293,0.358015148
12572,Transcriptional and epi-transcriptional dynamics of SARS-CoV-2 during cellular infection,0,10.1101/2020.12.22.423893,12/22/20,biorxiv,0,16,"sequencing, transcriptom",0.524452985,0.420664503,0.002422306,0.047615358,0.002422543,0.002422305,Drug discovery,0.12218025,FALSE,0,0.006432061,,,0,0.403234768,1592,0.62051529,0.34339404
12573,Evolutionary tracking of SARS-CoV-2 genetic variants highlights intricate balance of stabilizing and destabilizing mutations,0,10.1101/2020.12.22.423920,12/22/20,biorxiv,0,7,genome sequences,0.182943063,0.692554462,0.001187436,0.120940484,0.001187284,0.001187271,Genomics,0.10942036,FALSE,0,0.006432061,,,3,0.667819001,1511,0.600288948,0.42484667
12574,SARS-CoV-2 Genomic Surveillance in Costa Rica: Evidence of a Divergent Population and an Increased Detection of a Spike T1117I Mutation,0,10.1101/2020.12.21.423850,12/22/20,biorxiv,0,12,"sequencing, in-silico, whole-genome, whole genome, genomes, structural model",0.054759615,0.941917765,0.000830643,0.000830686,0.00083065,0.000830641,Genomics,0.19046924,FALSE,28,0.408312202,53,0.693738293,0,0.403234768,4294,0.86202745,0.591828178
12575,"T cell activation, highly armed cytotoxic cells and a sharp shift in monocytes CD300 receptors expression is characteristic of patients with severe COVID-19",0,10.1101/2020.12.22.423917,12/22/20,biorxiv,10.3389/fimmu.2021.655934,11,correlation analysis,0.56257334,0.049872272,0.001684558,0.001684554,0.001684559,0.382500716,Drug discovery,0.77920294,TRUE,29.36363636,0.423279114,9.181818182,0.338908215,1,0.537564047,1611,0.625090296,0.481210418
12576,"Minimizing exposure to respiratory droplets, 'jet riders' and aerosols in air-conditioned hospital rooms by a 'Shield-and-Sink' strategy",0,10.1101/2020.12.08.20233056,12/21/20,medrxiv,0,1,computational,0.000946168,0.000946116,0.118277691,0.720862318,0.000946115,0.158021591,Epidemiology,0.39659208,FALSE,42,0.558537943,30,0.570176612,0,0.403234768,1136,0.468817722,0.500191761
12577,Evidence of a dysregulated Vitamin D pathway in SARS-CoV-2 infected patient's lung cells,0,10.1101/2020.12.21.423733,12/21/20,biorxiv,0,6,"computational, transcriptom, network analysis, dataset",0.839129807,0.001565315,0.001565362,0.001565297,0.001565315,0.154608904,Drug discovery,0.4276661,FALSE,0.4,0.007421609,,,0,0.403234768,4369,0.864194558,0.424950312
12578,From infection to immunity: understanding the response to SARS-CoV2 through in-silico modeling,0,10.1101/2020.12.20.423670,12/21/20,biorxiv,0,5,"computational, in-silico",0.271671224,0.000822978,0.000822939,0.623895099,0.000822922,0.101964838,Epidemiology,0.07396281,FALSE,0,0.006432061,,,0,0.403234768,1412,0.568504695,0.326057175
12579,Binding strength and hydrogen bond numbers between Covid-19 RBD and HVR of antibody,0,10.1101/2020.12.21.423787,12/21/20,biorxiv,0,6,sequencing,0.310746502,0.477713018,0.001310416,0.147849884,0.061069751,0.001310429,Genomics,0.4266272,FALSE,0,0.006432061,,,0,0.403234768,1182,0.488321695,0.299329508
12580,pH and Receptor Induced Confirmational Changes- Implications Towards S1 Dissociation of SARS-CoV2 Spike Glycoprotein,0,10.1101/2020.12.21.410357,12/21/20,biorxiv,0,4,in silico,0.916840553,0.000889119,0.00088906,0.079603103,0.000889075,0.00088909,Drug discovery,0.4090732,FALSE,4.5,0.061784897,0.25,0.065493712,0,0.403234768,967,0.401396581,0.232977489
12581,Mutation Landscape of SARS COV2 in Africa,0,10.1101/2020.12.20.423630,12/21/20,biorxiv,0,3,bayes,0.104603886,0.888391216,0.001751193,0.001751262,0.001751226,0.001751217,Genomics,0.5735746,TRUE,0.333333333,0.007174222,,,0,0.403234768,2131,0.713700939,0.37470331
12582,"The challenges of the coming mass vaccination and exit strategy in prevention and control of COVID-19, a modelling study",0,10.1101/2020.12.18.20248478,12/20/20,medrxiv,0,6,mathematical model,0.000926295,0.000926286,0.000926278,0.995368544,0.000926324,0.000926273,Epidemiology,0.10466683,FALSE,13.66666667,0.207310285,2.166666667,0.166845063,3,0.667819001,2233,0.726703588,0.442169484
12583,A comprehensive transcriptome analysis reveals broader but weaker host response of SARS-CoV-2 than SARS-CoV,0,10.1101/2020.12.19.423597,12/20/20,biorxiv,0,5,transcriptom,0.709950974,0.18727313,0.001684622,0.001684597,0.001684676,0.097722002,Drug discovery,0.37626562,FALSE,,,,,0,0.403234768,1111,0.457019022,0.430126895
12584,Failure to replicate the association of rare loss-of-function variants in type I IFN immunity genes with severe COVID-19,0,10.1101/2020.12.18.20248226,12/20/20,medrxiv,0,49,"sequencing, exom, whole-genome",0.332439061,0.559168756,0.003101654,0.003101529,0.003101537,0.099087464,Genomics,0.12925494,FALSE,9.688888889,0.145092461,28.84444444,0.561078405,1,0.537564047,2602,0.768841801,0.503144179
12585,Common variants at 21q22.3 locus influence MX1 gene expression and susceptibility to severe COVID-19,0,10.1101/2020.12.18.20248470,12/20/20,medrxiv,10.1016/j.isci.2021.102322,21,genome-wide,0.219115902,0.513562419,0.001786513,0.001786537,0.001786558,0.261962071,Genomics,0.6550615,TRUE,42.52380952,0.562867215,32.0952381,0.585897779,1,0.537564047,1407,0.562003371,0.562083103
12586,SARS-CoV-2 escapes CD8 T cell surveillance via mutations in MHC-I restricted epitopes,0,10.1101/2020.12.18.423507,12/20/20,biorxiv,0,26,"sequencing, deep sequencing",0.604255491,0.350951648,0.037902964,0.002296626,0.002296605,0.002296666,Drug discovery,0.07564995,FALSE,60,0.703444864,92.15384615,0.805994113,1,0.537564047,6086,0.905851192,0.738213554
12587,Longitudinal omics in Syrian hamsters integrated with human data unravel complexity of moderate immune responses to SARS-CoV-2,0,10.1101/2020.12.18.423524,12/19/20,biorxiv,0,25,"sequencing, proteom,  omics",0.676323425,0.051356624,0.001861703,0.001861694,0.030908653,0.237687901,Drug discovery,0.5851364,TRUE,,,,,0,0.403234768,2735,0.777991813,0.59061329
12588,Oxygen saturation instability in suspected covid-19 patients; contrasting effects of reduced VA/Q and shunt.,0,10.1101/2020.12.17.20248126,12/19/20,medrxiv,0,1,mathematical model,0.001861795,0.073275156,0.00186184,0.365902662,0.001861762,0.555236785,Clinics,0.14972982,FALSE,21,0.312016822,8,0.320511105,0,0.403234768,3112,0.802311582,0.459518569
12589,Error Rates in SARS-CoV-2 Testing Examined with Bayesian Inference,0,10.1101/2020.12.17.20248402,12/19/20,medrxiv,0,1,bayes,0.001943474,0.001943575,0.642555853,0.264534862,0.087078605,0.001943631,Epidemiology,0.5133196,TRUE,4,0.054734368,2,0.164302917,0,0.403234768,1568,0.603900795,0.306543212
12590,Risk Factors for SARS-CoV-2 Seropositivity in a Health Care Worker Population,0,10.1101/2020.12.17.20248430,12/19/20,medrxiv,0,28,logistic regression,0.001392869,0.137251324,0.001392926,0.001392967,0.632605949,0.225963964,Healthcare,0.38764146,FALSE,7.571428571,0.108973963,0.857142857,0.103224512,1,0.537564047,900,0.352516253,0.275569694
12591,Enhancing the estimation of compartmental model parameters for COVID-19 data with a high level of uncertainty,0,10.1101/2020.12.17.20248389,12/19/20,medrxiv,0,5,computational,0.001943504,0.001943471,0.001943535,0.990282557,0.001943483,0.00194345,Epidemiology,0.1565257,FALSE,6.4,0.090667326,0.2,0.061145304,0,0.403234768,642,0.179629184,0.183669145
12592,Fluoxetine pharmacokinetics and tissue distribution suggest a possible role in reducing SARS-CoV-2 titers,0,10.1101/2020.12.17.20248442,12/19/20,medrxiv,0,1,in silico,0.310414433,0.001392854,0.001392938,0.341421611,0.001392887,0.343985276,Clinics,0.44860405,FALSE,33,0.466757375,3,0.199424672,2,0.618927094,3522,0.824464243,0.527393346
12593,"Seroprevalence of anti-SARS-CoV-2 IgG antibodies, risk factors for infection and associated symptoms in Geneva, Switzerland: a population-based study",0,10.1101/2020.12.16.20248180,12/18/20,medrxiv,0,21,"bayes, bayesian model",0.001203476,0.151003403,0.028582837,0.001203504,0.792791807,0.025214973,Healthcare,0.2178728,FALSE,58.47619048,0.692559837,95.9047619,0.814423334,1,0.537564047,1453,0.569708644,0.653563966
12594,SNPnexus COVID: Facilitating the analysis of COVID-19 host genetics,0,10.1101/2020.12.18.423439,12/18/20,biorxiv,0,5,"computational, sequencing, genomes, dataset",0.001717264,0.383769564,0.001717287,0.502678309,0.001717317,0.108400258,Epidemiology,0.40306106,FALSE,0,0.006432061,,,0,0.403234768,1462,0.572598122,0.32742165
12595,assayM: a web application to monitor mutations in COVID-19 diagnostic assays.,0,10.1101/2020.12.18.423467,12/18/20,biorxiv,0,2,dataset,0.002183275,0.641781899,0.349485098,0.002183298,0.002183236,0.002183194,Genomics,0.3886208,FALSE,63.5,0.72496753,147,0.880853626,0,0.403234768,1241,0.497712497,0.626692105
12596,Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19,0,10.1101/2020.12.18.423363,12/18/20,biorxiv,0,21,"transcriptom, proteom",0.555491965,0.002183327,0.002183298,0.002183279,0.002183273,0.435774858,Drug discovery,0.2635891,FALSE,0.619047619,0.007854536,,,0,0.403234768,4520,0.865398507,0.425495937
12597,"Genomic and phylogenetic analyses of SARS-CoV-2 strains isolated in the city of Gwangju, South Korea",0,10.1101/2020.12.16.423178,12/18/20,biorxiv,0,12,"whole genome, genomes",0.001943546,0.990282548,0.001943491,0.001943485,0.001943448,0.001943481,Genomics,0.38117912,FALSE,33.75,0.473622364,19.83333333,0.478458657,0,0.403234768,996,0.403082109,0.439599474
12598,"Modelling of COVID-19 vaccination strategies and herd immunity, in scenarios of limited and full vaccine supply in NSW, Australia.",0,10.1101/2020.12.15.20248278,12/18/20,medrxiv,0,3,mathematical model,0.001350397,0.001350355,0.001350399,0.623427907,0.371170598,0.001350343,Epidemiology,0.27937326,FALSE,149.3333333,0.94359577,129.3333333,0.863125502,8,0.799987654,5459,0.888032747,0.873685418
12599,SARS-CoV-2 shifting transmission dynamics and hidden reservoirs limited the efficacy of public health interventions in Italy,0,10.1101/2020.12.16.20248355,12/18/20,medrxiv,0,28,mathematical model,0.002562705,0.371980352,0.002562532,0.617768788,0.002562915,0.002562708,Epidemiology,0.3358615,FALSE,60.10714286,0.703815944,56.25,0.707853893,0,0.403234768,1546,0.595954732,0.602714834
12600,"Association of working shifts, inside and outside of healthcare, with risk of severe COVID-19: An observational study",0,10.1101/2020.12.16.20248243,12/18/20,medrxiv,0,12,logistic regression,0.033386469,0.000677955,0.000677909,0.000677944,0.963901795,0.000677929,Healthcare,0.108128816,FALSE,25.33333333,0.37188447,11.41666667,0.376103827,0,0.403234768,751,0.25331086,0.351133481
12601,Interim evaluation of Google AI forecasting for COVID-19 compared with statistical forecasting by human intelligence in the first week,0,10.1101/2020.12.16.20248358,12/18/20,medrxiv,0,3,artificial intelligence,0.00153812,0.001538149,0.142959912,0.694504613,0.001538154,0.157921053,Epidemiology,0.13864088,FALSE,49,0.624281032,12,0.386740701,0,0.403234768,921,0.35685047,0.442776742
12602,Voxel-level forecast system for lesion development in patients with COVID-19,0,10.1101/2020.12.17.20248377,12/18/20,medrxiv,0,11,dataset,0.05431622,0.001987205,0.642085066,0.242080903,0.001987188,0.057543419,Imaging,0.45937204,FALSE,7.090909091,0.10198528,0.272727273,0.065828204,0,0.403234768,1514,0.587045509,0.28952344
12603,Effect of COVID-19 on Critical ICU Capacity in US Acute Care Hospitals,0,10.1101/2020.12.16.20248366,12/18/20,medrxiv,0,3,"logistic regression, dataset",0.000525069,0.000525085,0.000525063,0.414200057,0.123001438,0.461223287,Clinics,0.17160338,FALSE,225,0.979899808,371.6666667,0.968290072,0,0.403234768,1456,0.571394173,0.730704705
12604,Antibody responses to endemic coronaviruses modulate COVID-19 convalescent plasma functionality,0,10.1101/2020.12.16.20248294,12/18/20,medrxiv,0,29,genomes,0.642939428,0.236210664,0.00153828,0.001538115,0.00153812,0.116235394,Drug discovery,0.4307412,FALSE,122.3448276,0.910445915,205.0344828,0.919654803,3,0.667819001,1716,0.634721888,0.783160402
12605,Large-Scale Measurement of Aggregate Human Colocation Patterns for Epidemiological Modeling,0,10.1101/2020.12.16.20248272,12/18/20,medrxiv,0,9,dataset,0.001237142,0.001237141,0.04896063,0.946090942,0.001237089,0.001237056,Epidemiology,0.017017454,FALSE,6.222222222,0.087760529,15.55555556,0.430759968,0,0.403234768,952,0.377558392,0.324828414
12606,Determinants of SARS-CoV-2 transmission to guide vaccination strategy in a city,0,10.1101/2020.12.15.20248130,12/17/20,medrxiv,0,31,"sequencing, whole-genome",0.000432641,0.151298284,0.000432645,0.846971094,0.000432666,0.000432671,Epidemiology,0.1794683,FALSE,48.80645161,0.620817614,42.41935484,0.64670859,2,0.618927094,4690,0.869251144,0.68892611
12607,Utility of COVID-19 Decision Rules Related to Consecutive Decline in Positivity or Hospitalizations: A Data-driven Simulation Study,0,10.1101/2020.12.14.20248190,12/16/20,medrxiv,0,4,logistic regression,0.000916681,0.000916714,0.000916703,0.647359238,0.000916723,0.348973941,Epidemiology,0.13632345,FALSE,241.75,0.982868452,629.25,0.985215413,0,0.403234768,988,0.387912353,0.689807746
12608,The association of covid-19 infection with household food insecurity among Iranian population,0,10.1101/2020.12.15.20248221,12/16/20,medrxiv,0,3,logistic regression,0.002130643,0.002130643,0.002130674,0.002130829,0.989346497,0.002130714,Healthcare,0.77027833,TRUE,27.33333333,0.399529965,8.666666667,0.331415574,0,0.403234768,568,0.115819889,0.312500049
12609,Mathematical model for the mitigation of the economic effects of Covid-19 in the Democratic Republic of the Congo,0,10.1101/2020.12.14.20248182,12/16/20,medrxiv,0,1,mathematical model,0.006539726,0.006539626,0.006539829,0.967301574,0.006539656,0.00653959,Epidemiology,0.6743347,TRUE,2,0.022141134,0,0.055525823,0,0.403234768,575,0.122080424,0.150745537
12610,Social distancing and preventive practices of government employees in response to COVID-19 in Ethiopia,0,10.1101/2020.12.15.20248271,12/16/20,medrxiv,0,5,logistic regression,0.001046841,0.001046824,0.001046808,0.331133508,0.66467919,0.001046828,Healthcare,0.31349054,FALSE,5.6,0.077803204,0.6,0.09011239,0,0.403234768,735,0.233325307,0.201118917
12611,Uncertainty reduction in logistic regressions: a COVID-19 case-study using surrogate locations' asymptotic values,0,10.1101/2020.12.14.20248184,12/16/20,medrxiv,0,3,logistic regression,0.001538144,0.001538134,0.001538122,0.992309398,0.001538095,0.001538107,Epidemiology,0.038808048,FALSE,1.333333333,0.01366813,0,0.055525823,0,0.403234768,668,0.184685769,0.164278623
12612,Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images,0,10.1101/2020.12.14.20248158,12/16/20,medrxiv,0,5,"deep learning, neural network, transfer learning, dataset",0.001622746,0.001622778,0.991886299,0.001622749,0.001622741,0.001622687,Imaging,0.33224863,FALSE,18.8,0.281278991,4.2,0.234211935,1,0.537564047,1654,0.613291596,0.416586642
12613,Diagnostic accuracy of Loop mediated isothermal amplification coupled to Nanopore sequencing for the detection of SARS-CoV-2 infection at scale in symptomatic and asymptomatic populations,0,10.1101/2020.12.15.20247031,12/16/20,medrxiv,0,39,sequencing,0.001072175,0.593900408,0.111029727,0.001072252,0.185673195,0.107252243,Genomics,0.32841355,FALSE,8.736842105,0.128888614,5.184210526,0.258897511,1,0.537564047,5334,0.883457741,0.452201978
12614,Detection of long SARS-CoV-2 nucleocapsid sequences in peripheral blood monocytes collected soon after hospital admission,0,10.1101/2020.12.16.423113,12/16/20,biorxiv,0,7,"sequencing, genomes",0.371954312,0.346250675,0.001901747,0.001901768,0.001901716,0.276089782,Drug discovery,0.2841431,FALSE,53.66666667,0.658544128,58.83333333,0.716483811,0,0.403234768,3130,0.795810258,0.643518241
12615,Using Mobility Data to Understand and Forecast COVID19 Dynamics,0,10.1101/2020.12.13.20248129,12/15/20,medrxiv,0,15,neural network,0.00159359,0.001593527,0.121610587,0.872015364,0.001593468,0.001593464,Epidemiology,0.36677414,FALSE,57.26666667,0.683530212,64.6,0.737289269,0,0.403234768,905,0.336383337,0.540109397
12616,Rapid SARS-CoV-2 Detection and Classification Using Phase Imaging with Computational Specificity,0,10.1101/2020.12.14.422601,12/15/20,biorxiv,0,12,"deep learning, computational, neural network",0.001203455,0.105923689,0.889262305,0.001203471,0.00120353,0.001203551,Imaging,0.22729096,FALSE,30,0.432432432,7.833333333,0.314088841,0,0.403234768,1331,0.516012521,0.416442141
12617,Transfer Learning with MotifTransformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans,0,10.1101/2020.12.14.422772,12/15/20,biorxiv,0,4,"deep learning, in silico, transfer learning",0.679921804,0.002238519,0.311124291,0.002238543,0.002238419,0.002238425,Drug discovery,0.43612745,FALSE,19.75,0.293215412,27,0.546026224,1,0.537564047,1197,0.468336143,0.461285457
12618,A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics,0,10.1101/2020.12.14.422634,12/15/20,biorxiv,10.1021/acsptsci.0c00215,20,"virtual screening, computational",0.927640095,0.00127274,0.067269036,0.001272693,0.001272732,0.001272703,Drug discovery,0.66345656,TRUE,0.15,0.006555755,,,0,0.403234768,1909,0.665061401,0.358283975
12619,No detectable signal for ongoing genetic recombination in SARS-CoV-2,0,10.1101/2020.12.15.422866,12/15/20,biorxiv,0,4,"sequencing, whole genome, genomes",0.002080598,0.767306662,0.002080586,0.224371064,0.002080558,0.002080531,Genomics,0.18093014,FALSE,22,0.326056033,,,9,0.814309525,4760,0.868287985,0.669551181
12620,Genomic diversity analysis of SARS-CoV-2 genomes in Rwanda,0,10.1101/2020.12.14.422793,12/15/20,biorxiv,0,3,genomes,0.001622761,0.991886306,0.001622731,0.001622749,0.001622711,0.001622741,Genomics,0.32957667,FALSE,3.333333333,0.04044777,0,0.055525823,0,0.403234768,1161,0.456296653,0.238876253
12621,"Social Media Study of Public Opinions on Potential COVID-19 Vaccines: Informing Dissent, Disparities, and Dissemination",0,10.1101/2020.12.12.20248070,12/14/20,medrxiv,0,7,"machine learning, logistic regression",0.001371304,0.001371321,0.00137143,0.472626279,0.521888348,0.001371317,Healthcare,0.008388549,FALSE,128,0.919104459,297,0.953438587,2,0.618927094,847,0.294004334,0.696368619
12622,Current forecast of COVID-19: a Bayesian and Machine Learning approaches,0,10.1101/2020.12.11.20231829,12/14/20,medrxiv,0,1,"bayes, machine learning, classifier, dataset",0.001823347,0.001823352,0.312137675,0.397161414,0.001823351,0.285230859,Epidemiology,0.4057919,FALSE,7,0.10179974,0,0.055525823,1,0.537564047,817,0.2759451,0.242708678
12623,COVID-19 vaccines that reduce symptoms but do not block infection need higher coverage and faster rollout to achieve population impact,0,10.1101/2020.12.13.20248142,12/14/20,medrxiv,0,11,mathematical model,0.043608342,0.001126859,0.001126789,0.712572204,0.240438953,0.001126852,Epidemiology,0.053145945,FALSE,14.90909091,0.223514132,5.090909091,0.257358844,4,0.707574542,6286,0.903924874,0.523093098
12624,Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep Neural Network,0,10.1101/2020.12.13.20247254,12/14/20,medrxiv,0,5,"machine learning, deep learning, neural network",0.000772608,0.000772688,0.550725153,0.000772651,0.000772635,0.446184265,Clinics,0.60595065,TRUE,2.8,0.031047065,0,0.055525823,1,0.537564047,1292,0.500601974,0.281184727
12625,Deep Learning Fusion for COVID-19 Diagnosis,0,10.1101/2020.12.11.20246546,12/14/20,medrxiv,0,3,deep learning,0.001943482,0.001943445,0.990282585,0.001943461,0.001943502,0.001943524,Imaging,0.48183835,FALSE,21.66666667,0.320737213,5,0.257024351,0,0.403234768,810,0.272092463,0.313272199
12626,COVID-19 TARRACO Cohort Study: Development of a predictive prognostic rule for early assessment of COVID-19 patients in primary care settings.,0,10.1101/2020.12.11.20247932,12/14/20,medrxiv,0,7,"predictive model, logistic regression",0.001350328,0.001350326,0.052168161,0.001350378,0.001350397,0.942430409,Clinics,0.5495435,TRUE,26.57142857,0.389881873,12.85714286,0.39717688,0,0.403234768,622,0.145677823,0.333992836
12627,Simulation-Based Study on the COVID-19 Airborne Transmission in a Restaurant,0,10.1101/2020.12.10.20247403,12/14/20,medrxiv,0,4,computational,0.001415177,0.001415198,0.001415155,0.992924166,0.00141517,0.001415134,Epidemiology,0.4358686,FALSE,5.75,0.080957388,2.75,0.187583623,0,0.403234768,1655,0.607753431,0.319882302
12628,Mathematical assessment of the roles of vaccination and non-pharmaceutical interventions on COVID-19 dynamics: a multigroup modeling approach,0,10.1101/2020.12.11.20247916,12/14/20,medrxiv,0,4,mathematical model,0.235477661,0.001415114,0.001415105,0.566155661,0.194121281,0.001415177,Epidemiology,0.1675615,FALSE,26.25,0.384748593,30.25,0.57171528,3,0.667819001,1933,0.66626535,0.572637056
12629,Screening for high amounts of SARS-CoV-2 identifies pre-symptomatic subjects among healthy healthcare workers,0,10.1101/2020.12.13.20248122,12/14/20,medrxiv,0,27,logistic regression,0.001098831,0.195119788,0.107889047,0.001098844,0.621354166,0.073439324,Healthcare,0.12976298,FALSE,18,0.271569052,39.62962963,0.631455713,0,0.403234768,1326,0.511919095,0.454544657
12630,Vaccines that prevent SARS-CoV-2 transmission may prevent or dampen a spring wave of COVID-19 cases and deaths in 2021,0,10.1101/2020.12.13.20248120,12/14/20,medrxiv,0,15,mathematical model,0.001291289,0.127762534,0.001291253,0.668470813,0.199892861,0.001291251,Epidemiology,0.079550385,FALSE,30.26666667,0.434844455,35.13333333,0.605900455,3,0.667819001,6661,0.908981459,0.654386343
12631,Profiling of oral microbiota and cytokines in COVID-19 patients,0,10.1101/2020.12.13.422589,12/14/20,biorxiv,0,11,"sequencing, transcriptom",0.191477628,0.37148464,0.130781496,0.001371312,0.001371269,0.303513654,Genomics,0.7484666,TRUE,45.63636364,0.592120725,21.81818182,0.501271073,0,0.403234768,3563,0.818926078,0.578888161
12632,Computational Analysis of Dynamic Allostery and Control in the three SARS-CoV-2 non-structural proteins,0,10.1101/2020.12.12.422477,12/14/20,biorxiv,0,4,"computational, network model",0.927712582,0.037525627,0.030888091,0.001291259,0.001291231,0.00129121,Drug discovery,0.38631642,FALSE,4.75,0.064506154,0.25,0.065493712,0,0.403234768,1076,0.420900554,0.238533797
12633,Impact of mass testing during an epidemic rebound of SARS-CoV-2: A modelling study,0,10.1101/2020.12.08.20246009,12/13/20,medrxiv,10.2807/1560-7917.es.2020.26.1.2001978,0,mathematical model,0.004309986,0.004310242,0.004310085,0.978449266,0.004310225,0.004310196,Epidemiology,0.203123,FALSE,8.714285714,0.128764921,26.28571429,0.540272946,1,0.537564047,1367,0.520105948,0.431676965
12634,Millisecond-scale molecular dynamics simulation of spike RBD structure reveals evolutionary adaption of SARS-CoV-2 to stably bind ACE2,0,10.1101/2020.12.11.422055,12/12/20,biorxiv,0,7,"molecular dynamics simulation, in silico",0.899162048,0.095596255,0.001310567,0.001310448,0.001310342,0.00131034,Drug discovery,0.47670648,FALSE,27.28571429,0.398540417,15.14285714,0.426545357,1,0.537564047,4836,0.867565615,0.557553859
12635,"Social and racial inequalities in COVID-19 risk of hospitalisation and death across Sao Paulo state, Brazil",0,10.1101/2020.12.09.20246207,12/12/20,medrxiv,0,23,dataset,0.001141326,0.001141335,0.001141336,0.152716281,0.671380143,0.172479578,Healthcare,0.23811242,FALSE,13.82608696,0.209103841,31.08695652,0.578338239,2,0.618927094,3140,0.791235252,0.549401106
12636,Factors associated with clinical severity in Emergency Department patients presenting with symptomatic SARS-CoV-2 infection.,0,10.1101/2020.12.08.20246017,12/11/20,medrxiv,0,13,logistic regression,0.000916727,0.000916694,0.000916685,0.000916698,0.208904958,0.787428239,Clinics,0.41875315,FALSE,26.23076923,0.384006432,52.30769231,0.690125769,2,0.618927094,860,0.289910908,0.495742551
12637,Neutralising antibodies drive Spike mediated SARS-CoV-2 evasion,0,10.1101/2020.12.05.20241927,12/11/20,medrxiv,10.1038/s41586-021-03291-y,35,whole genome,0.279011489,0.587711582,0.000854711,0.000854735,0.000854712,0.13071277,Genomics,0.30358493,FALSE,14.73333333,0.221720576,12.6,0.394300241,39,0.945737391,57381,0.993739465,0.638874418
12638,Antibody landscape against SARS-CoV-2 proteome revealed significant differences between non-structural/ accessory proteins and structural proteins,0,10.1101/2020.12.08.20246314,12/11/20,medrxiv,0,25,proteom,0.389025902,0.474476294,0.001350314,0.001350403,0.001350398,0.132446689,Genomics,0.21888119,FALSE,4.12,0.055043602,0.4,0.075796093,0,0.403234768,1787,0.627498194,0.290393164
12639,Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic,0,10.1101/2020.12.08.20238154,12/11/20,medrxiv,0,2,bayes,0.002562643,0.00256259,0.002562657,0.987186736,0.002562702,0.002562672,Epidemiology,0.19547316,FALSE,78,0.798812543,71,0.756556061,0,0.403234768,590,0.113652781,0.518064038
12640,"Multimodal Data Acquisition at SARS-CoV-2 Drive Through Screening Centers: Setup Description and Experiences in Saarland, Germany",0,10.1101/2020.12.08.20240382,12/11/20,medrxiv,0,7,dataset,0.002898374,0.002898398,0.730207166,0.108407214,0.152690432,0.002898416,Healthcare,0.50549626,TRUE,29,0.41993939,3.714285714,0.218022478,0,0.403234768,781,0.241993739,0.320797594
12641,Impact of Convalescent Plasma Transfusion (CCP) In Patients With Previous Circulating Neutralizing Antibodies (nAb) to COVID-19,0,10.1101/2020.12.08.20246173,12/11/20,medrxiv,0,31,logistic regression,0.001156271,0.022603725,0.001156344,0.155557675,0.001156265,0.818369719,Clinics,0.21931458,FALSE,30.90322581,0.442266065,11.80645161,0.382592989,2,0.618927094,988,0.363351794,0.451784485
12642,Contacts and behaviours of university students during the COVID-19 pandemic at the start of the 2020/21 academic year,0,10.1101/2020.12.09.20246421,12/11/20,medrxiv,0,15,mathematical model,0.001392829,0.001392886,0.001392823,0.292053459,0.702375154,0.00139285,Healthcare,0.24062943,FALSE,7.733333333,0.112313687,6.266666667,0.283382392,2,0.618927094,1019,0.381651818,0.349068748
12643,Assessing the Performance of COVID-19 Forecasting Models in the U.S.,0,10.1101/2020.12.09.20246157,12/11/20,medrxiv,0,2,forecasting model,0.001330088,0.001330148,0.482367918,0.434442635,0.00133012,0.079199092,Epidemiology,0.14887306,FALSE,10,0.15214299,3,0.199424672,0,0.403234768,792,0.248735854,0.250884571
12644,Research on Recognition Method of COVID-19 Images Based on Deep Learning,0,10.1101/2020.12.09.20246371,12/11/20,medrxiv,0,4,"deep learning, transfer learning",0.002032821,0.002032822,0.989835646,0.002032766,0.002032961,0.002032984,Imaging,0.38980514,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,542,0.084035637,0.14123434
12645,Impact of Nasopharyngeal Specimen Quality on SARS-CoV-2 Test Performance,0,10.1101/2020.12.09.20246520,12/11/20,medrxiv,0,9,logistic regression,0.001112672,0.641267682,0.248774449,0.001112691,0.106619797,0.001112709,Genomics,0.5623911,TRUE,21.11111111,0.312326056,8.444444444,0.327134065,1,0.537564047,710,0.191668673,0.34217321
12646,COVID-19 vaccine confidence and hesitancy among healthcare workers: a cross-sectional survey from a MERS-CoV experienced nation,0,10.1101/2020.12.09.20246447,12/11/20,medrxiv,0,18,logistic regression,0.066770613,0.001717261,0.001717195,0.001717258,0.905772692,0.02230498,Healthcare,0.52355254,TRUE,8.222222222,0.12060115,2.666666667,0.185442869,4,0.707574542,1507,0.557428365,0.392761731
12647,"COVID-19 Spreading Dynamics with Vaccination - Allocation Strategy, Return to Normalcy and Vaccine Hesitancy",0,10.1101/2020.12.10.20247049,12/11/20,medrxiv,0,2,mathematical model,0.002996718,0.002996572,0.002996594,0.789006468,0.199007075,0.002996572,Epidemiology,0.20808715,FALSE,17,0.257467994,0.5,0.087101953,3,0.667819001,1416,0.52853359,0.385230635
12648,SARS-CoV-2 genome diversity at the binding sites of oligonucleotides used for COVID-19 diagnosis,0,10.1101/2020.12.10.20236943,12/11/20,medrxiv,0,18,"sequencing, genomes",0.077909602,0.789238814,0.130382712,0.000822955,0.000822941,0.000822977,Genomics,0.20249435,FALSE,20.11111111,0.298472385,24.83333333,0.526960128,0,0.403234768,1375,0.515530941,0.436049556
12649,Mathematical modelling projections versus the actual course of the COVID-19 epidemic following the nationwide lockdown in Kyrgyzstan,0,10.1101/2020.12.10.20247247,12/11/20,medrxiv,0,11,mathematical model,0.001653019,0.001653034,0.001653127,0.991734649,0.001653053,0.001653118,Epidemiology,0.11759007,FALSE,4,0.054734368,0.818181818,0.101752743,0,0.403234768,702,0.187093667,0.186703886
12650,Duplex formation between the template and the nascent strand in the transcription-regulating sequences determines the site of template switching in SARS - CoV-2,0,10.1101/2020.12.11.416818,12/11/20,biorxiv,0,7,transcriptom,0.454360223,0.539027494,0.001653058,0.001653087,0.001653031,0.001653106,Genomics,0.22371858,FALSE,19.22222222,0.287525512,27.33333333,0.54856837,0,0.403234768,1145,0.438237419,0.419391517
12651,Conformational Ensembles of Non-Coding Elements in the SARS-CoV-2 Genome from Molecular Dynamics Simulations,0,10.1101/2020.12.11.421784,12/11/20,biorxiv,0,3,molecular dynamics simulation,0.745867033,0.245810475,0.002080747,0.00208062,0.002080566,0.002080559,Drug discovery,0.6215523,TRUE,85,0.825035562,107.6666667,0.835763982,1,0.537564047,2159,0.692752227,0.722778955
12652,Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome.,0,10.1101/2020.12.09.20246389,12/11/20,medrxiv,0,10,"bayes, metabolom, predictive model, logistic regression",0.13127744,0.254346722,0.001220081,0.094842406,0.001220015,0.517093336,Clinics,0.76818943,TRUE,22.8,0.337064754,23.9,0.519868879,1,0.537564047,1435,0.534312545,0.482202556
12653,Oligonucleotide Capture Sequencing of the SARS-CoV-2 Genome and Subgenomic Fragments from COVID-19 Individuals,0,10.1101/2020.12.11.421057,12/11/20,biorxiv,0,21,"sequencing, transcriptom, genomes",0.071430697,0.881889931,0.001622754,0.001622769,0.001622716,0.041811134,Genomics,0.23464221,FALSE,92.38095238,0.850021646,421.4285714,0.97263848,0,0.403234768,1032,0.388393932,0.653572207
12654,Social Media Insights Into US Mental Health Amid the COVID-19 Pandemic. A Longitudinal Twitter Analysis (JANUARY-APRIL 2020),0,10.1101/2020.12.01.20241943,12/10/20,medrxiv,0,5,dataset,0.000677941,0.000677929,0.000677924,0.723875737,0.273412545,0.000677923,Epidemiology,0.51137406,TRUE,8.2,0.120477457,0.8,0.101351351,0,0.403234768,877,0.294245124,0.229827175
12655,An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2,0,10.1101/2020.12.09.417121,12/10/20,biorxiv,0,12,"computational, genomes, network analysis",0.001272686,0.638756677,0.205914092,0.151511066,0.001272742,0.001272736,Genomics,0.11572394,FALSE,49.41666667,0.627064135,92.5,0.80706449,1,0.537564047,925,0.324584638,0.574069328
12656,Intra-host variability in global SARS-CoV-2 genomes as signatures of RNA editing: implications in viral and host response outcomes,0,10.1101/2020.12.09.417519,12/9/20,biorxiv,0,16,"transcriptom, genomes",0.117129808,0.810262569,0.002183251,0.002183263,0.002183288,0.066057821,Genomics,0.31747496,FALSE,22.3125,0.328839137,8.6875,0.331482473,0,0.403234768,2344,0.711533831,0.443772552
12657,Characterization of protease activity of Nsp3 from SARS-CoV-2 and its in vitro inhibition by nanobodies,0,10.1101/2020.12.09.417741,12/9/20,biorxiv,0,18,interactom,0.990883126,0.001823474,0.001823387,0.001823367,0.001823323,0.001823323,Drug discovery,0.38565922,FALSE,84.11111111,0.822066918,266.1111111,0.944407279,1,0.537564047,1927,0.650373224,0.738602867
12658,"Smoking, distress and COVID-19 in England: cross-sectional population surveys from 2016 to 2020",0,10.1101/2020.12.07.20245514,12/9/20,medrxiv,0,6,logistic regression,0.001112648,0.001112639,0.001112653,0.040128283,0.955421097,0.00111268,Healthcare,0.14288935,FALSE,7.666666667,0.111633373,1.833333333,0.151056998,0,0.403234768,455,0.035877679,0.175450704
12659,"A novel multi-omics-based identification of symptoms, comorbid conditions, and possible long-term complications in COVID-19",0,10.1101/2020.12.08.20245753,12/9/20,medrxiv,10.1039/D0MO00189A,8,"bioinformatic, multi-omics",0.169238184,0.0387424,0.067537858,0.143861807,0.08055094,0.500068811,Clinics,0.32857198,FALSE,48.5,0.618158204,25.875,0.536058336,0,0.403234768,901,0.306043824,0.465873783
12660,A 6-mRNA host response whole-blood classifier trained using patients with non-COVID-19 viral infections accurately predicts severity of COVID-19,0,10.1101/2020.12.07.20230235,12/8/20,medrxiv,0,32,"classifier, transcriptom, logistic regression",0.229019803,0.086590895,0.26892219,0.00109889,0.026974991,0.38739323,Clinics,0.70583844,TRUE,24.46875,0.360937597,21,0.492239765,1,0.537564047,1896,0.642427161,0.508292142
12661,Association between RT-PCR Ct Values and COVID-19 New Daily Cases: A Multicenter Cross-Sectional Study,0,10.1101/2020.12.07.20245233,12/8/20,medrxiv,0,6,predictive model,0.001203437,0.10277885,0.273643382,0.316463547,0.0012035,0.304707285,Epidemiology,0.18364844,FALSE,17.66666667,0.266373925,2.5,0.180826866,0,0.403234768,2296,0.704310137,0.388686424
12662,Comparing Decision Tree-Based Ensemble Machine Learning Models for COVID-19 Death Probability Profiling,0,10.1101/2020.12.06.20244756,12/8/20,medrxiv,0,2,"machine learning, dataset",0.001717218,0.001717241,0.201536232,0.392135348,0.001717274,0.401176687,Clinics,0.18278319,FALSE,32,0.455810502,11,0.371287129,0,0.403234768,1284,0.473633518,0.425991479
12663,Single-cell RNA sequencing reveals in vivo signatures of SARS-CoV-2-reactive T cells through 'reverse phenotyping',0,10.1101/2020.12.07.20245274,12/8/20,medrxiv,0,30,sequencing,0.727701907,0.165985905,0.001392878,0.001392865,0.001392827,0.102133619,Drug discovery,0.3561114,FALSE,46.26666667,0.59886202,78.5,0.774484881,0,0.403234768,2267,0.699975921,0.619139397
12664,"Natural SARS-CoV-2 infections, including virus isolation, among serially tested cats and dogs in households with confirmed human COVID-19 cases in Texas, USA",0,10.1101/2020.12.08.416339,12/8/20,biorxiv,0,17,sequencing,0.003214329,0.799256429,0.00321425,0.003214265,0.187886436,0.003214291,Genomics,0.42300045,FALSE,35.35294118,0.490382831,45.82352941,0.662831148,11,0.840175319,4680,0.855285336,0.712168659
12665,Distant Residues Modulate the Conformational Opening in SARS-CoV-2 Spike Protein,0,10.1101/2020.12.07.415596,12/8/20,biorxiv,0,3,"molecular dynamics simulation, computational",0.744591794,0.224733653,0.028525505,0.000716366,0.000716329,0.000716353,Drug discovery,0.41911715,FALSE,46.66666667,0.602510978,21.66666667,0.499732406,4,0.707574542,2478,0.727425957,0.634310971
12666,Contrasting factors associated with COVID-19-related ICU and death outcomes: interpretable multivariable analyses of the UK CHESS dataset.,0,10.1101/2020.12.03.20242941,12/7/20,medrxiv,0,4,"logistic regression, dataset",0.001310338,0.001310381,0.129783738,0.001310434,0.00131044,0.864974669,Clinics,0.53181857,TRUE,36,0.498299215,62.25,0.729595933,2,0.618927094,875,0.281724055,0.532136574
12667,Lipid storm within the lungs of severe COVID-19 patients: Extensive levels of cyclooxygenase and lipoxygenase-derived inflammatory metabolites.,0,10.1101/2020.12.04.20242115,12/7/20,medrxiv,0,15,lipidom,0.612896439,0.001786626,0.001786574,0.001786667,0.001786554,0.37995714,Drug discovery,0.83768535,TRUE,32.66666667,0.462922877,36.06666667,0.611185443,3,0.667819001,3050,0.778954972,0.630220573
12668,Exploring drugs and vaccines associated with altered risks and severity of COVID-19: a UK Biobank cohort study of all ATC level-4 drug categories,0,10.1101/2020.12.05.20244426,12/7/20,medrxiv,0,3,logistic regression,0.260832521,0.000734192,0.000734174,0.000734186,0.348039329,0.388925597,Clinics,0.39354426,FALSE,4,0.054734368,0.333333333,0.073187048,1,0.537564047,3553,0.806405008,0.367972618
12669,"COVID-19 RELATED IMMUNIZATION DISRUPTIONS IN RAJASTHAN, INDIA: A RETROSPECTIVE OBSERVATIONAL STUDY",0,10.1101/2020.12.04.20244327,12/7/20,medrxiv,0,5,logistic regression,0.001291213,0.001291291,0.001291267,0.107012865,0.887822096,0.001291268,Healthcare,0.55973047,TRUE,16.2,0.244974952,67.6,0.746922665,0,0.403234768,634,0.130508066,0.381410113
12670,Shift work is associated with positive COVID-19 status in hospitalised patients,0,10.1101/2020.12.04.20244020,12/7/20,medrxiv,0,6,logistic regression,0.001653034,0.001653067,0.001653047,0.001653105,0.81413522,0.179252525,Healthcare,0.20472717,FALSE,53.83333333,0.659904756,43,0.649785925,1,0.537564047,1490,0.536238863,0.595873398
12671,Seroconversion stages COVID19 into distinct pathophysiological states,0,10.1101/2020.12.05.20244442,12/7/20,medrxiv,0,18,multi-omics,0.5106427,0.001653068,0.001653107,0.001653144,0.00165311,0.482744871,Drug discovery,0.4582523,FALSE,37.72222222,0.51567815,43.44444444,0.651190795,1,0.537564047,2611,0.741150975,0.611395992
12672,A two-pronged approach for rapid and high-throughput SARS-CoV-2 nucleic acid testing,0,10.1101/2020.12.04.20234450,12/7/20,medrxiv,0,9,sequencing,0.001220034,0.764461724,0.230657982,0.001220042,0.001220116,0.001220102,Genomics,0.16191006,FALSE,27.33333333,0.399529965,54.66666667,0.700361252,0,0.403234768,780,0.222489766,0.431403938
12673,Identification of potential coagulation pathway abnormalities in SARS-Cov-2 infection; insights from bioinformatics analysis,0,10.1101/2020.12.07.414631,12/7/20,biorxiv,0,2,bioinformatic,0.849467151,0.002296605,0.002296575,0.002296582,0.002296553,0.141346534,Drug discovery,0.63137686,TRUE,28.5,0.412579628,1,0.122023013,0,0.403234768,1120,0.412472911,0.33757758
12674,A framework for predicting potential host ranges of pathogenic viruses based on receptor ortholog analysis,0,10.1101/2020.12.07.414292,12/7/20,biorxiv,0,14,computational,0.519392002,0.233704038,0.240014082,0.002296842,0.002296514,0.002296522,Drug discovery,0.32782382,FALSE,186.5714286,0.967159379,161.5714286,0.893430559,0,0.403234768,957,0.331326752,0.648787864
12675,The aging whole blood transcriptome reveals a potential role of FASLG in COVID-19,0,10.1101/2020.12.04.412494,12/6/20,biorxiv,0,5,"sequencing, transcriptom",0.615798683,0.033050124,0.000977433,0.00097743,0.000977471,0.348218858,Drug discovery,0.63375354,TRUE,28.8,0.415548271,18.6,0.465881723,0,0.403234768,1511,0.539369131,0.456008473
12676,Machine learning analysis highlights the down-trending of the proportion of COVID-19 patients with a distinct laboratory result profile,0,10.1101/2020.11.28.20240150,12/6/20,medrxiv,0,15,machine learning,0.00139286,0.188041732,0.11369468,0.00139291,0.001392895,0.694084923,Clinics,0.74033844,TRUE,20.8,0.30768755,9.466666667,0.344126305,1,0.537564047,946,0.319287262,0.377166291
12677,Whole Genome Sequencing for Revealing the Point Mutations of SARS-CoV-2 Genome in Bangladeshi Isolates and their Structural Effects on Viral Proteins,0,10.1101/2020.12.05.413377,12/6/20,biorxiv,0,9,"sequencing, whole genome",0.3788681,0.615284365,0.001461867,0.001461893,0.001461888,0.001461887,Genomics,0.23530078,FALSE,8.888888889,0.130558476,1.555555556,0.138948354,0,0.403234768,1690,0.590657356,0.315849739
12678,Online biophysical predictions for SARS-CoV-2 proteins,0,10.1101/2020.12.04.411744,12/5/20,biorxiv,0,6,molecular dynamics simulation,0.690680656,0.046700421,0.001350416,0.25856773,0.001350452,0.001350326,Drug discovery,0.051037103,FALSE,38.33333333,0.522233904,67.83333333,0.747792347,0,0.403234768,1850,0.622441608,0.573925657
12679,Post-infectious inflammatory disease in MIS-C features elevated cytotoxicity signatures and autoreactivity that correlates with severity,0,10.1101/2020.12.01.20241364,12/4/20,medrxiv,0,32,"sequencing, proteom",0.393340541,0.138487922,0.001415134,0.001415121,0.122267792,0.343073489,Drug discovery,0.40919623,FALSE,39.78125,0.53713897,82.71875,0.785188654,3,0.667819001,6402,0.893811702,0.720989582
12680,THE THERAPEUTIC POTENTIAL OF IVERMECTIN FOR COVID-19: A REVIEW OF MECHANISMS AND EVIDENCE,0,10.1101/2020.11.30.20236570,12/4/20,medrxiv,0,4,in silico,0.504047958,0.001438219,0.001438131,0.318627522,0.00143813,0.17301004,Drug discovery,0.5098417,TRUE,7.25,0.104149917,1.5,0.138747659,2,0.618927094,21836,0.97664339,0.459617015
12681,Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications.,0,10.1101/2020.12.02.20235879,12/4/20,medrxiv,0,6,predictive model,0.001943653,0.001943575,0.277478947,0.085380317,0.001943552,0.631309955,Clinics,0.5907878,TRUE,67,0.748160059,28.33333333,0.556729997,1,0.537564047,1780,0.604382374,0.61170912
12682,McQ - An open-source multiplexed SARS-CoV-2 quantification platform,0,10.1101/2020.12.02.20242628,12/4/20,medrxiv,0,22,sequencing,0.073902424,0.680096612,0.242784291,0.001072238,0.00107226,0.001072174,Genomics,0.4737239,FALSE,8,0.118683901,7.909090909,0.314690929,0,0.403234768,1579,0.553094149,0.347425937
12683,Mapping each pre-existing conditions association to short-term and long-term COVID-19 complications,0,10.1101/2020.12.02.20242925,12/4/20,medrxiv,0,15,"neural network, network model",0.001593565,0.001593492,0.091459585,0.001593518,0.001593522,0.902166317,Clinics,0.44468018,FALSE,20.2,0.300327788,16.26666667,0.439055392,1,0.537564047,1055,0.368649169,0.411399099
12684,The application of Hybrid deep learning Approach to evaluate chest ray images for the diagnosis of pneumonia in children,0,10.1101/2020.12.03.20243550,12/4/20,medrxiv,0,4,"machine learning, deep learning, neural network, dataset",0.000652911,0.000652941,0.923185951,0.000652938,0.042204252,0.032651008,Imaging,0.19590634,FALSE,4.75,0.064506154,0,0.055525823,0,0.403234768,1098,0.391524199,0.228697736
12685,Detailed disease progression of 213 patients hospitalized with Covid-19 in the Czech Republic: An exploratory analysis,0,10.1101/2020.12.03.20239863,12/4/20,medrxiv,0,27,dataset,0.002032843,0.00203284,0.174246079,0.088302826,0.002032825,0.731352586,Clinics,0.6411574,TRUE,3.777777778,0.047003525,0.222222222,0.061412898,1,0.537564047,1959,0.642667951,0.322162105
12686,Elevated temperature inhibits SARS-CoV-2 replication in respiratory epithelium independently of the induction of IFN-mediated innate immune defences,0,10.1101/2020.12.04.411389,12/4/20,biorxiv,0,17,transcriptom,0.771696834,0.00127269,0.001272636,0.116920476,0.001272736,0.107564627,Drug discovery,0.20257995,FALSE,34.76470588,0.484569237,55.41176471,0.703505486,0,0.403234768,1572,0.55140862,0.535679528
12687,Application of respiratory metagenomics for COVID-19 patients on the intensive care unit to inform appropriate initial antimicrobial treatment and rapid detection of nosocomial transmission,0,10.1101/2020.11.26.20229989,12/3/20,medrxiv,0,20,"sequencing, metagenom",0.001438126,0.585621619,0.001438198,0.0014382,0.001438263,0.408625594,Genomics,0.2226251,FALSE,9.7,0.145216154,4,0.231469093,0,0.403234768,1811,0.607512641,0.346858164
12688,Clinical Characteristics and Risk Factors for Myocardial Injury and Arrhythmia in COVID-19 patients,0,10.1101/2020.11.30.20190926,12/3/20,medrxiv,0,13,logistic regression,0.002130738,0.002130703,0.0021306,0.002130603,0.002130653,0.989346703,Clinics,0.84928,TRUE,47.46153846,0.609437813,33.46153846,0.595196682,0,0.403234768,829,0.239104262,0.461743381
12689,Establishment of CORONET; COVID-19 Risk in Oncology Evaluation Tool to identify cancer patients at low versus high risk of severe complications of COVID-19 infection upon presentation to hospital,0,10.1101/2020.11.30.20239095,12/3/20,medrxiv,0,46,"logistic regression, dataset",0.000830655,0.00083064,0.324633384,0.000830661,0.00083068,0.672043979,Clinics,0.7400542,TRUE,10.84782609,0.161543695,9.456521739,0.343992507,1,0.537564047,1336,0.473392728,0.379123244
12690,Accuracy of automated computer aided-risk scoring systems to estimate the risk of COVID-19 and in-hospital mortality: a retrospective cohort study,0,10.1101/2020.12.01.20241828,12/3/20,medrxiv,0,5,dataset,0.000977427,0.000977441,0.340867576,0.022211899,0.000977467,0.633988191,Clinics,0.2720965,FALSE,10.2,0.153627312,0.6,0.09011239,0,0.403234768,717,0.169275223,0.204062423
12691,Single-Shot Lightweight Model For The Detection of Lesions And The Prediction of COVID-19 From Chest CT Scans,0,10.1101/2020.12.01.20241786,12/3/20,medrxiv,0,1,dataset,0.002638965,0.002639003,0.986804995,0.002639036,0.00263901,0.00263899,Imaging,0.3662073,FALSE,23,0.34225988,8,0.320511105,3,0.667819001,967,0.316879364,0.411867338
12692,"Is the end near? When the different countries will surmount COVID-19 pandemic: new approach applying physical, mathematical and game theory models.",0,10.1101/2020.12.01.20242099,12/3/20,medrxiv,0,3,"predictive model, probabilistic",0.001486438,0.001486458,0.001486566,0.992567618,0.001486479,0.00148644,Epidemiology,0.41500133,FALSE,66.66666667,0.745191416,46,0.66416912,0,0.403234768,813,0.228509511,0.510276204
12693,A Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered Model to Predict Demand for COVID-19 Inpatient Care in a Large Healthcare System,0,10.1101/2020.12.01.20241984,12/3/20,medrxiv,0,6,bayes,0.001684487,0.001684735,0.001684547,0.753739079,0.001684564,0.239522588,Epidemiology,0.110934675,FALSE,4.5,0.061784897,0.333333333,0.073187048,0,0.403234768,560,0.075607994,0.153453677
12694,Implications of delayed reopening in controlling the COVID-19 surge in Southern and West-Central USA,0,10.1101/2020.12.01.20242172,12/3/20,medrxiv,0,4,neural network,0.001717204,0.001717206,0.106037303,0.887093876,0.001717199,0.001717211,Epidemiology,0.13249442,FALSE,17.25,0.260189251,2.25,0.170925876,0,0.403234768,1637,0.566096797,0.350111673
12695,Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts,0,10.1101/2020.12.01.20241885,12/3/20,medrxiv,0,6,computational,0.001392848,0.00139283,0.001392903,0.993035667,0.001392909,0.001392844,Epidemiology,0.47513545,FALSE,5.333333333,0.074339786,0.166666667,0.058736955,0,0.403234768,974,0.32193595,0.214561865
12696,Variants in SARS-CoV-2 Associated with Mild or Severe Outcome,0,10.1101/2020.12.01.20242149,12/3/20,medrxiv,0,7,"genomes, logistic regression",0.001593502,0.593564705,0.023774495,0.001593544,0.001593558,0.377880195,Genomics,0.46409464,FALSE,6.428571429,0.090852867,3.142857143,0.200561948,2,0.618927094,1980,0.6438719,0.388553452
12697,"A Novel Model for Simulating COVID-19 Dynamics Through Layered Infection States that Integrate Concepts from Epidemiology, Biophysics and Medicine: SEI3R2S-Nrec",0,10.1101/2020.12.01.20242263,12/3/20,medrxiv,0,1,computational,0.062653663,0.08021902,0.000537853,0.670605399,0.051807951,0.134176114,Epidemiology,0.22493553,FALSE,12,0.183190055,2,0.164302917,0,0.403234768,989,0.332530701,0.27081461
12698,Identification of low micromolar SARS-CoV-2 Mpro inhibitors from hits identified by in silico screens,0,10.1101/2020.12.03.409441,12/3/20,biorxiv,0,10,in silico,0.830987541,0.163692204,0.001330084,0.001330084,0.001330039,0.001330048,Drug discovery,0.30979687,FALSE,32.8,0.464221659,107.4,0.835228793,0,0.403234768,1853,0.616181074,0.579716573
12699,Consistent and High-Frequency Identification of an Intra-Sample Genetic Variant of SARS-CoV-2 with Elevated Fusogenic Properties,0,10.1101/2020.12.03.409714,12/3/20,biorxiv,0,9,"sequencing, dataset",0.166858197,0.830051293,0.000772638,0.000772637,0.000772615,0.000772621,Genomics,0.13000852,FALSE,22.57142857,0.334343497,37,0.616671127,0,0.403234768,1282,0.456055863,0.452576314
12700,"Ethnicity, Household Composition and COVID-19 Mortality: A National Linked Data Study",0,10.1101/2020.11.27.20238147,12/2/20,medrxiv,10.1177/0141076821999973,14,dataset,0.001059345,0.001059368,0.001059373,0.001059411,0.754670111,0.241092392,Healthcare,0.5258743,TRUE,16.23076923,0.245284186,22.07692308,0.503880118,2,0.618927094,1979,0.639296894,0.501847073
12701,Effective post-exposure prophylaxis of Covid-19 is associated with use of hydroxychloroquine: Prospective re-analysis of a public dataset incorporating novel data.,0,10.1101/2020.11.29.20235218,12/2/20,medrxiv,0,4,dataset,0.099798223,0.000608054,0.000608049,0.357972022,0.352117837,0.188895815,Epidemiology,0.1100781,FALSE,17.75,0.267672707,4.25,0.235750602,1,0.537564047,8140,0.918131471,0.489779707
12702,Risk assessment for airborne disease transmission bypoly-pathogen aerosols,0,10.1101/2020.11.30.20241083,12/2/20,medrxiv,0,3,computational,0.221278962,0.242810451,0.001901718,0.530205184,0.001901807,0.001901878,Epidemiology,0.09602702,FALSE,5.333333333,0.074339786,0,0.055525823,0,0.403234768,841,0.242716109,0.193954121
12703,"Detection of the Novel SARS-CoV-2 European Lineage B.1.177 in Ontario, Canada",0,10.1101/2020.11.30.20241265,12/2/20,medrxiv,10.1016/j.jcvp.2021.100010,15,"sequencing, whole genome, genomes",0.001219994,0.892001725,0.001220017,0.06525595,0.039082273,0.001220041,Genomics,0.18584722,FALSE,7.2,0.103716989,4.333333333,0.237958255,1,0.537564047,1337,0.470021671,0.337315241
12704,Predictive accuracy of computer-aided versions of the on-admission National Early Warning Score in estimating the risk of COVID-19 for unplanned admission to hospital: a retrospective development and validation study,0,10.1101/2020.11.30.20241257,12/2/20,medrxiv,0,6,"logistic regression, dataset",0.001538078,0.001538085,0.258826831,0.001538117,0.001538144,0.735020745,Clinics,0.3517374,FALSE,229.3333333,0.980765663,452.8333333,0.975983409,0,0.403234768,537,0.059956658,0.604985124
12705,Development and validation of automated computer aided-risk score for predicting the risk of in-hospital mortality using first electronically recorded blood test results and vital signs for COVID-19 hospital admissions: a retrospective development and validation study,0,10.1101/2020.11.30.20241273,12/2/20,medrxiv,0,5,"logistic regression, dataset",0.000988342,0.000988344,0.269924835,0.000988373,0.000988387,0.726121719,Clinics,0.32778144,FALSE,10.2,0.153627312,0.6,0.09011239,0,0.403234768,613,0.100168553,0.186785755
12706,Bridging the gaps in test interpretation of SARS-CoV-2 through Bayesian network modelling,0,10.1101/2020.11.30.20241232,12/2/20,medrxiv,0,7,"bayes, network model",0.036790883,0.126167836,0.353572922,0.399052938,0.083274039,0.001141382,Epidemiology,0.1709817,FALSE,5,0.070752675,5.857142857,0.273414504,0,0.403234768,640,0.118709367,0.216527828
12707,Social network-based strategies for classroom size reduction can help limit outbreaks of SARS-CoV-2 in high schools. A simulation study in classrooms of four European countries.,0,10.1101/2020.11.30.20241166,12/2/20,medrxiv,0,3,dataset,0.002183276,0.002183378,0.194622859,0.307474822,0.329225273,0.164310392,Healthcare,0.3732953,FALSE,20.33333333,0.302059497,4,0.231469093,0,0.403234768,5135,0.864435348,0.450299676
12708,Smart Investment of Virus RNA Testing Resources to Enhance Covid-19 Mitigation,0,10.1101/2020.11.30.20239566,12/2/20,medrxiv,0,5,mathematical model,0.000822933,0.07109934,0.107907873,0.708133488,0.111213373,0.000822993,Epidemiology,0.14186904,FALSE,3,0.037293586,0.6,0.09011239,0,0.403234768,1199,0.426679509,0.239330063
12709,Lack of evidence of ACE2 expression and replicative infection by SARS-CoV-2 in human endothelial cells,0,10.1101/2020.12.02.391664,12/2/20,biorxiv,0,20,transcriptom,0.887095071,0.002032786,0.002032878,0.067509872,0.002032809,0.039296584,Drug discovery,0.33584544,FALSE,70.85,0.767579937,245.65,0.936513246,2,0.618927094,2271,0.686250903,0.752317795
12710,The N-glycosylation sites and Glycan-binding ability of S-protein in SARS-CoV-2 Coronavirus,0,10.1101/2020.12.01.406025,12/1/20,biorxiv,0,7,bioinformatic,0.831333842,0.160717609,0.001987117,0.00198717,0.00198716,0.0019871,Drug discovery,0.6775182,TRUE,25,0.369286907,11,0.371287129,0,0.403234768,997,0.327955695,0.367941125
12711,Guidelines for accurate genotyping of SARS-CoV-2 using amplicon-based sequencing of clinical samples,0,10.1101/2020.12.01.405738,12/1/20,biorxiv,0,29,"sequencing, genome sequences, genomes",0.001291228,0.795977986,0.00129128,0.176753667,0.001291285,0.023394554,Genomics,0.5406063,TRUE,54.86206897,0.667264519,109.6206897,0.838239229,2,0.618927094,2806,0.7476523,0.718020785
12712,Role of Long-range Allosteric Communication in Determining the Stability and Disassembly of SARS-COV-2 in Complex with ACE2,0,10.1101/2020.11.30.405340,12/1/20,biorxiv,0,4,"molecular dynamics simulation, network model",0.889080385,0.093319931,0.000988356,0.000988418,0.000988429,0.014634483,Drug discovery,0.39407164,FALSE,39,0.530521368,8.75,0.332218357,1,0.537564047,939,0.292559595,0.423215842
12713,Mucosal Associated Invariant T (MAIT) Cell Responses Differ by Sex in COVID-19,0,10.1101/2020.12.01.407148,12/1/20,biorxiv,0,20,"sequencing, dataset",0.53005491,0.074606812,0.00182338,0.001823502,0.258702922,0.132988474,Drug discovery,0.19940454,FALSE,62.05,0.715876059,69.55,0.752140755,1,0.537564047,2710,0.737057549,0.685659602
12714,Multimodal Single-Cell Omics Analysis of COVID-19 Sex Differences in Human Immune Systems,0,10.1101/2020.12.01.407007,12/1/20,biorxiv,0,11,"sequencing,  omics",0.293565238,0.001237052,0.001237052,0.001237066,0.001237075,0.701486518,Clinics,0.29566163,FALSE,157.8181818,0.949904138,417.5454545,0.972370886,0,0.403234768,1081,0.369371539,0.673720332
12715,The association of smoking status with hospitalisation for COVID-19 compared with other respiratory viruses a year previous: A case-control study at a single UK National Health Service trust,0,10.1101/2020.11.26.20238469,11/30/20,medrxiv,0,5,logistic regression,0.001622765,0.173304044,0.001622841,0.027361274,0.403425404,0.392663672,Healthcare,0.51758885,TRUE,18.6,0.278990661,11,0.371287129,1,0.537564047,1560,0.532386227,0.430057016
12716,Reduced access to care among older American adults during CoVID-19 pandemic: results from a prospective cohort study,0,10.1101/2020.11.29.20240317,11/30/20,medrxiv,0,3,logistic regression,0.001046828,0.001046803,0.001046836,0.001046868,0.945806255,0.050006411,Healthcare,0.75690746,TRUE,3,0.037293586,0.333333333,0.073187048,1,0.537564047,601,0.088851433,0.184224029
12717,"SARS-CoV-2 infection hospitalization, severity, criticality, and fatality rates",0,10.1101/2020.11.29.20240416,11/30/20,medrxiv,0,20,"bayes, mathematical model",0.001786554,0.001786539,0.026971646,0.484655523,0.001786536,0.483013202,Epidemiology,0.26407358,FALSE,17.5,0.263776362,14.4,0.416644367,8,0.799987654,887,0.261016133,0.435356129
12718,"Are psychiatric disorders risk factors for COVID-19 susceptibility and severity?a two-sample, bidirectional, univariable and multivariable Mendelian Randomization study",0,10.1101/2020.11.29.20240481,11/30/20,medrxiv,0,2,genome-wide,0.001565341,0.375323317,0.001565372,0.00156541,0.319596377,0.300384183,Genomics,0.14519787,FALSE,69,0.759416167,27.5,0.549906342,0,0.403234768,1114,0.382614977,0.523793063
12719,I'm alone but not lonely. U-shaped pattern of perceived loneliness during the COVID-19 pandemic in the UK and Greece,0,10.1101/2020.11.26.20239103,11/30/20,medrxiv,0,6,machine learning,0.001098803,0.001098807,0.001098947,0.62125543,0.374349168,0.001098845,Epidemiology,0.34176075,FALSE,20,0.298163152,3.166666667,0.201097137,0,0.403234768,971,0.308451722,0.302736694
12720,"Characteristics of COVID-19 patients admitted to a tertiary care hospital in Pune, India, and cost-effective predictors of intensive care treatment requirement",0,10.1101/2020.11.26.20239186,11/30/20,medrxiv,0,7,"machine learning, predictive model",0.000926262,0.000926284,0.274457175,0.02536731,0.000926322,0.697396648,Clinics,0.3072173,FALSE,21.85714286,0.322778156,17,0.451097137,0,0.403234768,728,0.164941007,0.335512767
12721,Predictors of QT Interval Prolongation in Critically-ill Patients with SARS-CoV-2 Infection Treated with Hydroxychloroquine,0,10.1101/2020.11.26.20239418,11/30/20,medrxiv,0,8,logistic regression,0.002080691,0.002080682,0.002080585,0.124370022,0.002080774,0.867307247,Clinics,0.72955954,TRUE,18.75,0.28072237,6,0.280037463,0,0.403234768,3479,0.792439201,0.43910845
12722,Quantifying superspreading for COVID-19 using Poisson mixture distributions,0,10.1101/2020.11.27.20239657,11/30/20,medrxiv,0,9,dataset,0.002032777,0.002032861,0.002032848,0.989835902,0.002032806,0.002032807,Epidemiology,0.024412125,FALSE,75.66666667,0.789349991,89.22222222,0.799638748,0,0.403234768,1037,0.344088611,0.584078029
12723,Comparative Genomic Study for Revealing the Complete Scenario of COVID-19 Pandemic in Bangladesh,0,10.1101/2020.11.27.20240002,11/30/20,medrxiv,0,9,genomes,0.036786093,0.539642609,0.018999426,0.402197236,0.001187314,0.001187322,Genomics,0.4205529,FALSE,4.333333333,0.057950399,1.333333333,0.13252609,1,0.537564047,788,0.198892367,0.231733226
12724,Predicting critical illness on initial diagnosis of COVID-19: Development and validation of the PRIORITY model for outpatient applicability.,0,10.1101/2020.11.27.20237966,11/30/20,medrxiv,0,24,"predictive model, logistic regression, prediction model",0.000977527,0.000977447,0.092429126,0.000977484,0.000977543,0.903660872,Clinics,0.52841896,TRUE,11.125,0.167542829,1.083333333,0.122491303,1,0.537564047,1242,0.436070311,0.315917122
12725,"Proteo-genomic analysis of SARS-CoV-2: A clinical landscape of SNPs, COVID-19 proteome and host responses",0,10.1101/2020.11.27.20237032,11/30/20,medrxiv,10.1021/acs.jproteome.0c00808,7,"sequencing, proteom",0.312496797,0.632391106,0.0015653,0.001565333,0.001565301,0.050416164,Genomics,0.29640925,FALSE,18.85714286,0.281773765,23,0.513513514,0,0.403234768,892,0.263664821,0.365546717
12726,Threatening second wave of COVID-19 is imminent: A deep learning perspective,0,10.1101/2020.11.28.20240259,11/30/20,medrxiv,0,1,"deep learning, neural network, lstm, dataset",0.001059369,0.001059349,0.500333793,0.477179558,0.001059412,0.019308519,Epidemiology,0.268863,FALSE,7,0.10179974,0,0.055525823,0,0.403234768,954,0.296893812,0.214363536
12727,"Rapid disappearance of influenza following the implementation of COVID-19 mitigation measures in Hamilton, Ontario",0,10.1101/2020.11.27.20240036,11/30/20,medrxiv,0,6,bayes,0.001438106,0.418517743,0.001438124,0.290882695,0.286285083,0.001438249,Genomics,0.26475275,FALSE,76.16666667,0.790834313,48.16666667,0.674404603,0,0.403234768,1449,0.501324344,0.592449507
12728,AI4CoV: Matching COVID-19 Patients to Treatment Options Using Artificial Intelligence,0,10.1101/2020.11.29.20240614,11/30/20,medrxiv,0,7,"machine learning, artificial intelligence",0.19337011,0.002080611,0.389544552,0.002080662,0.002080596,0.41084347,Clinics,0.8023342,TRUE,5.714285714,0.079782299,3.857142857,0.222705379,0,0.403234768,1485,0.511437515,0.30428999
12729,A Retrospective Longitudinal Study of COVID-19 as Seen by a Large Urban Hospital in Chicago,0,10.1101/2020.11.29.20240606,11/30/20,medrxiv,0,7,logistic regression,0.001171542,0.030788651,0.06657509,0.096644591,0.001171557,0.803648569,Clinics,0.2404269,FALSE,7,0.10179974,1.571428571,0.139416644,0,0.403234768,814,0.215747652,0.215049701
12730,CLINICAL TRIALS IN COVID-19 MANAGEMENT & PREVENTION: A META-EPIDEMIOLOGICAL STUDY EXAMINING METHODOLOGICAL QUALITY,0,10.1101/2020.11.29.20237875,11/30/20,medrxiv,0,19,logistic regression,0.19031466,0.000956366,0.00095634,0.176094083,0.185788315,0.445890235,Clinics,0.34371918,FALSE,89.84210526,0.84216711,78.63157895,0.774819374,0,0.403234768,959,0.300024079,0.580061333
12731,How effective are the COVID-19 vaccines? A Bayesian analysis,0,10.1101/2020.11.30.20240671,11/30/20,medrxiv,0,1,bayes,0.003335569,0.003335419,0.003335319,0.426214805,0.560443343,0.003335546,Healthcare,0.11187762,FALSE,11,0.167171748,0,0.055525823,0,0.403234768,2620,0.72357332,0.337376415
12732,Change of dominant strain during dual SARS-CoV-2 infection,0,10.1101/2020.11.29.20238402,11/30/20,medrxiv,0,10,"sequencing, genomes",0.001622693,0.799451954,0.029750544,0.00162275,0.001622725,0.165929334,Genomics,0.34864482,FALSE,12.1,0.183437442,1.3,0.12877977,3,0.667819001,2432,0.70117987,0.420304021
12733,First computational design of Covid-19 coronavirus vaccine using lambda superstrings,0,10.1101/2020.11.30.403824,11/30/20,biorxiv,0,9,computational,0.819862925,0.164426347,0.003927749,0.00392789,0.003927494,0.003927595,Drug discovery,0.06263265,FALSE,20.66666667,0.306512462,3.444444444,0.209191865,0,0.403234768,1222,0.429087407,0.337006625
12734,pH Effect on the Dynamics of SARS-CoV-2 Main Protease (Mpro),0,10.1101/2020.11.30.404384,11/30/20,biorxiv,0,2,molecular dynamics simulation,0.986398777,0.002720423,0.002720128,0.002720359,0.002720189,0.002720125,Drug discovery,0.6168079,TRUE,58,0.689714887,19,0.471367407,0,0.403234768,1121,0.385022875,0.487334984
12735,Clade GR and Clade GH Isolates in Asia Show Highest Amount of SNPs,0,10.1101/2020.11.30.402487,11/30/20,biorxiv,10.1016/j.meegid.2021.104724,3,"genome sequences, genomes",0.001786629,0.860133086,0.001786548,0.132720571,0.00178662,0.001786546,Genomics,0.48484465,FALSE,52.33333333,0.649452656,7,0.299973241,1,0.537564047,1257,0.439922947,0.481728223
12736,Clinical and immunological benefits of convalescent plasma therapy in severe COVID-19: insights from a single center open label randomised control trial,0,10.1101/2020.11.25.20237883,11/29/20,medrxiv,0,36,"sequencing, proteom",0.000999579,0.383719912,0.00099953,0.000999578,0.000999547,0.612281853,Clinics,0.7911304,TRUE,3.972222222,0.048364154,0.444444444,0.076933369,6,0.764429903,4622,0.840356369,0.432520949
12737,Symptom-based prediction model of SARS-1 CoV-2 infection developed from self-reported symptoms of SARS-CoV-2-infected individuals in an online survey,0,10.1101/2020.11.25.20236752,11/29/20,medrxiv,0,3,"bayes, prediction model",0.002183256,0.002183309,0.049800034,0.141667903,0.490624555,0.313540942,Healthcare,0.28457758,FALSE,5,0.070752675,0.666666667,0.096200161,0,0.403234768,916,0.272814833,0.210750609
12738,"The Impact of COVID-19 on Care Seeking Behavior of Patients at Tertiary Care Follow-up Clinics: A Cross-Sectional Telephone Survey. Addis Ababa, Ethiopia.",0,10.1101/2020.11.25.20236224,11/29/20,medrxiv,0,15,logistic regression,0.000728112,0.00072813,0.000728132,0.092375284,0.546876932,0.35856341,Healthcare,0.9281495,TRUE,3.066666667,0.037540973,0.2,0.061145304,0,0.403234768,1848,0.603660005,0.276395262
12739,Genome Scale-Differential Flux Analysis reveals deregulation of lung cell metabolism on SARS Cov2 infection,0,10.1101/2020.11.29.402404,11/29/20,biorxiv,0,2,"transcriptom, proteom, metabolom",0.788790914,0.204947606,0.001565379,0.001565413,0.00156537,0.001565319,Drug discovery,0.51356447,TRUE,14.5,0.219617787,4.5,0.242708055,0,0.403234768,1720,0.57500602,0.360141657
12740,Preterm birth rates in a large tertiary Australian maternity centre during COVID-19 mitigation measures,0,10.1101/2020.11.24.20237529,11/28/20,medrxiv,0,6,logistic regression,0.001717256,0.057663712,0.001717183,0.370353119,0.393172191,0.175376539,Healthcare,0.58614874,TRUE,68.33333333,0.755643515,43.66666667,0.652261172,1,0.537564047,1941,0.618588972,0.641014427
12741,How Timing of Stay-home Orders and Mobility Reductions Impacted First-Wave COVID-19 Deaths in US Counties,0,10.1101/2020.11.24.20238055,11/28/20,medrxiv,0,5,"bayes, model fit",0.000907272,0.000907292,0.00090727,0.977488222,0.018882588,0.000907356,Epidemiology,0.1974684,FALSE,13,0.197352959,9.8,0.350682366,0,0.403234768,738,0.163496268,0.27869159
12742,Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease,0,10.1101/2020.11.24.20235887,11/27/20,medrxiv,0,6,"artificial intelligence, neural network, deep-learning, transfer learning",0.001415125,0.054319604,0.748390632,0.001415211,0.001415152,0.193044276,Imaging,0.5364725,TRUE,7,0.10179974,1,0.122023013,0,0.403234768,1517,0.510715146,0.284443167
12743,Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males,0,10.1101/2020.11.19.20234237,11/27/20,medrxiv,0,24,logistic regression,0.169365971,0.342770285,0.054164088,0.000936237,0.069583814,0.363179605,Clinics,0.2447941,FALSE,26.83333333,0.392726823,24.16666667,0.522544822,1,0.537564047,1728,0.570671804,0.505876874
12744,Disentangling the roles of human mobility and deprivation on the transmission dynamics of COVID-19 using a spatially explicit simulation model.,0,10.1101/2020.11.25.20144139,11/27/20,medrxiv,0,14,"bayes, simulation model",0.000807879,0.000807894,0.00080789,0.995960503,0.000807913,0.00080792,Epidemiology,0.287306,FALSE,21.07142857,0.312078669,20.28571429,0.48347605,1,0.537564047,1213,0.41343607,0.436638709
12745,Peptide microarray based detection of antibody responses against SARS-CoV-2 species-specific epitopes in spike and nucleocapsid proteins with potential for diagnostic test development,0,10.1101/2020.11.24.20216663,11/27/20,medrxiv,0,16,proteom,0.330323904,0.536050388,0.001987145,0.001987125,0.001987223,0.127664215,Genomics,0.8146002,TRUE,33.5,0.471890655,38.125,0.622424405,0,0.403234768,1612,0.537202023,0.508687962
12746,A COVID-19 Model for Local Authorities of the United Kingdom,0,10.1101/2020.11.24.20236661,11/27/20,medrxiv,0,7,"bayes, model fit, bayesian model",0.002357707,0.002357715,0.002357784,0.988211291,0.002357761,0.002357742,Epidemiology,0.25985697,FALSE,67.14285714,0.748592987,265.7142857,0.944273481,5,0.739490092,2224,0.66722851,0.774896267
12747,Unheeded SARS-CoV-2 protein? Look deep into negative-sense RNA,0,10.1101/2020.11.27.400788,11/27/20,biorxiv,0,5,in silico,0.414973386,0.577078134,0.001987124,0.00198713,0.001987124,0.001987101,Genomics,0.1974945,FALSE,21.2,0.313686684,4.6,0.244246722,0,0.403234768,4369,0.829520828,0.447672251
12748,High-resolution mapping and characterization of epitopes in COVID-19 patients,0,10.1101/2020.11.23.20235002,11/26/20,medrxiv,0,30,proteom,0.419754097,0.538669708,0.001237171,0.001237099,0.001237086,0.037864839,Genomics,0.22371495,FALSE,18.7,0.280042056,26.26666667,0.539737758,2,0.618927094,3194,0.76980496,0.552127967
12749,Exploring Risks of Human Challenge Trials for COVID-19,0,10.1101/2020.11.19.20234658,11/26/20,medrxiv,0,4,"bayes, bayesian model",0.090551766,0.002130684,0.002130758,0.646478513,0.002130697,0.256577583,Epidemiology,0.04638341,FALSE,13.25,0.199950523,2.25,0.170925876,0,0.403234768,1752,0.573079701,0.336797717
12750,Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit,0,10.1101/241349,11/26/20,biorxiv,0,8,"machine learning, sequencing, transcriptom, proteom, dataset",0.447521896,0.261934935,0.230725913,0.001861752,0.001861836,0.056093667,Drug discovery,0.40013593,FALSE,59.28571429,0.69800235,94.57142857,0.81181429,0,0.403234768,3217,0.771731279,0.671195671
12751,Host-directed FDA-approved drugs with antiviral activity against SARS-CoV-2 identified by hierarchical in silico/in vitro screening methods,0,10.1101/2020.11.26.399436,11/26/20,biorxiv,0,11,"virtual screening, in silico",0.990282343,0.001943525,0.001943561,0.001943595,0.00194352,0.001943456,Drug discovery,0.65905154,TRUE,34.63636364,0.483270456,46.27272727,0.6651057,0,0.403234768,1279,0.435588731,0.496799914
12752,SARS-CoV-2 utilizes a multipronged strategy to suppress host protein synthesis,0,10.1101/2020.11.25.398578,11/25/20,biorxiv,0,15,sequencing,0.931104659,0.058933291,0.002490468,0.002490579,0.002490547,0.002490455,Drug discovery,0.29453826,FALSE,27.8,0.404848785,60.06666667,0.720966016,0,0.403234768,2861,0.740669396,0.567429741
12753,Global analysis of protein-RNA interactions in SARS-CoV-2 infected cells reveals key regulators of infection,0,10.1101/2020.11.25.398008,11/25/20,biorxiv,0,20,proteom,0.988807681,0.00223859,0.002238437,0.002238451,0.002238431,0.00223841,Drug discovery,0.14085892,FALSE,46.3,0.599171254,91.5,0.805124431,1,0.537564047,5020,0.847580063,0.697359949
12754,Use of alternative RNA storage and extraction reagents and development of a hybrid PCR-based method for SARS-CoV-2 detection,0,10.1101/2020.11.21.20236216,11/24/20,medrxiv,0,4,sequencing,0.000956324,0.694469899,0.301704702,0.000956389,0.000956373,0.000956313,Genomics,0.14029664,FALSE,15,0.227596017,20.5,0.486218892,0,0.403234768,666,0.108596196,0.306411468
12755,The variation of genome sites associated with severe COVID-19 across populations: the worldwide and national pattern,0,10.1101/2020.11.22.20236414,11/24/20,medrxiv,0,10,"correlation analysis, dataset",0.000999529,0.356647873,0.000999559,0.490527277,0.000999546,0.149826215,Epidemiology,0.03713432,FALSE,14.4,0.217576845,24,0.521808938,0,0.403234768,1179,0.388153142,0.382693423
12756,Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review,0,10.1101/2020.11.22.20232959,11/24/20,medrxiv,0,14,"deep learning, artificial intelligence",0.001486454,0.001486521,0.286976269,0.684011135,0.00148652,0.024553101,Epidemiology,0.59254134,TRUE,16.07142857,0.243799864,39,0.62784319,0,0.403234768,1401,0.466169034,0.435261714
12757,Effect of hot zone infection outbreaks on the dynamics of SARS-CoV-2 spread in the community at large,0,10.1101/2020.11.23.20237172,11/24/20,medrxiv,0,3,mathematical model,0.085630594,0.001511956,0.001511864,0.842609869,0.067223842,0.001511875,Epidemiology,0.2270442,FALSE,119,0.905683716,167.3333333,0.897243778,0,0.403234768,770,0.170719961,0.594220556
12758,COVID-19: Short term prediction model using daily incidence data,0,10.1101/2020.11.23.20237024,11/24/20,medrxiv,0,8,prediction model,0.001392846,0.001392869,0.00139286,0.993035711,0.001392851,0.001392863,Epidemiology,0.23058823,FALSE,63.5,0.72496753,74.875,0.76605566,1,0.537564047,649,0.099205394,0.531948158
12759,Severity of COVID-19 is inversely correlated with increased number counts of non-synonymous mutations in Tokyo,0,10.1101/2020.11.24.20235952,11/24/20,medrxiv,0,20,sequencing,0.298986866,0.518964405,0.001415104,0.001415115,0.001415149,0.177803362,Genomics,0.756444,TRUE,27.77777778,0.404601398,72.5,0.760436179,1,0.537564047,4101,0.81555502,0.629539161
12760,"How closely is COVID-19 related to HCoV, SARS, and MERS? : Clinical comparison of coronavirus infections and identification of risk factors influencing the COVID-19 severity using common data model (CDM)",0,10.1101/2020.11.23.20237487,11/24/20,medrxiv,0,4,logistic regression,0.000779453,0.133432988,0.000779454,0.000779462,0.027655321,0.836573323,Clinics,0.20015678,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,2018,0.623645557,0.284285129
12761,"Reducing travel-related SARS-CoV-2 transmission with layered mitigation measures: Symptom monitoring, quarantine, and testing",0,10.1101/2020.11.23.20237412,11/24/20,medrxiv,0,8,mathematical model,0.001112608,0.001112668,0.001112667,0.834023779,0.161525608,0.00111267,Epidemiology,0.1978668,FALSE,24.5,0.361988991,30.25,0.57171528,3,0.667819001,11491,0.941006501,0.635632443
12762,Phycobilins as potent food bioactive broad-spectrum inhibitor compounds against Mpro and PLpro of SARS-CoV-2 and other coronaviruses: A preliminary Study,0,10.1101/2020.11.21.392605,11/24/20,biorxiv,0,3,in silico,0.949089025,0.042779974,0.002032736,0.002032778,0.002032765,0.002032721,Drug discovery,0.9708171,TRUE,32,0.455810502,50.33333333,0.682967621,0,0.403234768,1663,0.541536239,0.520887282
12763,LinearTurboFold: Linear-Time RNA Structural Alignment and Conserved Structure Prediction with Applications to Coronaviruses,0,10.1101/2020.11.23.393488,11/24/20,biorxiv,0,7,"genomes, sequence alignment",0.107040619,0.677896605,0.210817097,0.00141525,0.001415151,0.001415279,Genomics,0.07592991,FALSE,42,0.558537943,188.1428571,0.910957988,0,0.403234768,1152,0.376354443,0.562271285
12764,Mutations in SARS-CoV-2 spike protein and RNA polymerase complex are associated with COVID-19 mortality risk,0,10.1101/2020.11.17.386714,11/24/20,biorxiv,0,15,"sequencing, genome-wide, whole-genome, genomes, logistic regression",0.13627633,0.656800664,0.001034677,0.001034596,0.001034601,0.203819132,Genomics,0.29041916,FALSE,102.8095238,0.874945884,134.1904762,0.868075997,4,0.707574542,5095,0.849024801,0.824905306
12765,Fragment binding to the Nsp3 macrodomain of SARS-CoV-2 identified through crystallographic screening and computational docking,0,10.1101/2020.11.24.393405,11/24/20,biorxiv,0,53,computational,0.820903263,0.055205407,0.118531671,0.001786613,0.001786524,0.001786522,Drug discovery,0.58397424,TRUE,49.55769231,0.628548457,,,2,0.618927094,4470,0.830002408,0.692492653
12766,Estimating the effectiveness of routine asymptomatic PCR testing at different frequencies for the detection of SARS-CoV-2 infections,0,10.1101/2020.11.24.20229948,11/24/20,medrxiv,0,10,"bayes, dataset",0.000956311,0.209221212,0.00095637,0.459167659,0.328742059,0.000956389,Epidemiology,0.0915474,FALSE,22.8,0.337064754,43.2,0.650321113,5,0.739490092,3029,0.753912834,0.620197198
12767,A Data Driven Change-point Epidemic Model for Assessing the Impact of Large Gathering and Subsequent Movement Control Order on COVID-19 Spread in Malaysia,0,10.1101/2020.11.20.20233890,11/23/20,medrxiv,0,6,"bayes, mathematical model",0.001438106,0.029693989,0.001438149,0.964553484,0.001438118,0.001438155,Epidemiology,0.20200223,FALSE,8.833333333,0.129816315,17.83333333,0.459058068,0,0.403234768,749,0.155309415,0.286854641
12768,Comparing Machine Learning Algorithms for Predicting ICU Admission and Mortality in COVID-19,0,10.1101/2020.11.20.20235598,11/23/20,medrxiv,0,10,machine learning,0.002720168,0.002720154,0.391522836,0.002720336,0.002720223,0.597596283,Clinics,0.70208865,TRUE,30.8,0.441523904,79.4,0.777160824,0,0.403234768,1506,0.49385986,0.528944839
12769,Long-Term Persistence of Spike Antibody and Predictive Modeling of Antibody Dynamics Following Infection with SARS-CoV-2,0,10.1101/2020.11.20.20235697,11/23/20,medrxiv,0,18,predictive model,0.141600591,0.367688735,0.013974766,0.149854152,0.232276481,0.094605276,Genomics,0.24927852,FALSE,5.222222222,0.07198961,1.5,0.138747659,2,0.618927094,2288,0.66674693,0.374102823
12770,"The risk of COVID hospital admission and COVID mortality during the first COVID 19 wave with a special emphasis on Ethnic Minorities: an observational study of a single, deprived, multi ethnic UK health economy",0,10.1101/2020.11.20.20224691,11/23/20,medrxiv,10.1136/bmjopen-2020-046556,7,dataset,0.000688457,0.034318602,0.000688474,0.253410149,0.10543554,0.605458777,Clinics,0.17232057,FALSE,7.285714286,0.10445915,2.714285714,0.186111854,0,0.403234768,976,0.282928004,0.244183444
12771,Predicting COVID19 Critical Care Beds - The London North-West University Healthcare Trust Experience,0,10.1101/2020.11.20.20235226,11/23/20,medrxiv,0,9,probabilistic,0.001310407,0.045403046,0.038746687,0.52505766,0.001310418,0.388171782,Epidemiology,0.09003723,FALSE,5.666666667,0.079473066,3.555555556,0.213874766,0,0.403234768,797,0.182759451,0.219835513
12772,BCG vaccine derived peptides induce SARS-CoV-2 T cell cross-reactivity,0,10.1101/2020.11.21.20236018,11/23/20,medrxiv,0,8,in silico,0.835000658,0.001653156,0.001653033,0.055799665,0.104240356,0.001653131,Drug discovery,0.12331462,FALSE,4.875,0.065619395,2.375,0.173668718,1,0.537564047,2862,0.7358536,0.37817644
12773,Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study,0,10.1101/2020.11.20.20235648,11/23/20,medrxiv,0,2,computational,0.002996447,0.002996479,0.002996555,0.839692223,0.002996652,0.148321644,Epidemiology,0.13051438,FALSE,41.5,0.553219123,6.5,0.288132192,1,0.537564047,947,0.268239827,0.411788797
12774,Role of asymptomatic COVID-19 cases in viral transmission: Findings from a hierarchical community contact network model,0,10.1101/2020.11.21.20236034,11/23/20,medrxiv,0,5,network model,0.001565339,0.00156539,0.001565332,0.961318004,0.001565394,0.03242054,Epidemiology,0.4596512,FALSE,86.8,0.833013792,,,0,0.403234768,905,0.244883217,0.493710592
12775,N439K variant in spike protein may alter the infection efficiency and antigenicity of SARS-CoV-2 based on molecular dynamics simulation,0,10.1101/2020.11.21.392407,11/23/20,biorxiv,0,12,molecular dynamics simulation,0.650586211,0.340227063,0.002296716,0.002296753,0.002296591,0.002296665,Drug discovery,0.6563601,TRUE,52.41666667,0.649885584,28.83333333,0.560944608,3,0.667819001,6387,0.882494582,0.690285944
12776,In-Silico analysis reveals lower transcription efficiency of C241T variant of SARS-CoV-2 with host replication factors MADP1 and hnRNP-1,0,10.1101/2020.11.22.393009,11/23/20,biorxiv,0,10,"molecular dynamics simulation, in-silico",0.437160406,0.446499749,0.002080586,0.002080687,0.002080587,0.110097984,Genomics,0.23127267,FALSE,42.8,0.565279238,16.8,0.446548033,0,0.403234768,1221,0.404045268,0.454776827
12777,Coagulation factors and COVID-19 severity: Mendelian randomization analyses and supporting evidence,0,10.1101/2020.11.20.20235440,11/23/20,medrxiv,0,10,"genome-wide, logistic regression",0.081110042,0.129791266,0.001203469,0.001203455,0.001203458,0.78548831,Clinics,0.36105263,FALSE,16.2,0.244974952,9.2,0.339577201,1,0.537564047,1197,0.394172887,0.379072272
12778,Actionable druggable genome-wide Mendelian randomization identifies repurposingopportunities for COVID-19,0,10.1101/2020.11.19.20234120,11/23/20,medrxiv,0,53,"transcriptom, proteom, genome-wide",0.692625348,0.04767015,0.033551808,0.002032816,0.002032848,0.22208703,Drug discovery,0.64354324,TRUE,52.11904762,0.647720947,184.1666667,0.909017929,1,0.537564047,1673,0.54057308,0.658719001
12779,Outbreaks of publications about emerging infectious diseases: the case of SARS-CoV-2 and Zika virus,0,10.1101/2020.11.20.20235242,11/23/20,medrxiv,10.1186/s12874-021-01244-7,17,mathematical model,0.001751153,0.001751308,0.096690155,0.837618703,0.00175129,0.06043739,Epidemiology,0.5552157,TRUE,6.352941176,0.089925165,1.647058824,0.141557399,0,0.403234768,1013,0.301468818,0.234046537
12780,A simple direct RT-LAMP SARS-CoV-2 saliva diagnostic,0,10.1101/2020.11.19.20234948,11/22/20,medrxiv,0,8,genomes,0.00203285,0.710531399,0.281337108,0.002032972,0.002032845,0.002032826,Genomics,0.30657896,FALSE,56.5,0.678891706,186.125,0.910088306,1,0.537564047,2080,0.628942933,0.688871748
12781,Association of HLA class I genotypes with age at death of COVID-19 patients,0,10.1101/2020.11.19.20234567,11/22/20,medrxiv,10.3389/fimmu.2021.641900,7,sequencing,0.311286304,0.100596395,0.050902333,0.001203437,0.001203457,0.534808074,Clinics,0.50173694,TRUE,4.285714286,0.056837158,0.142857143,0.057398983,0,0.403234768,2257,0.660486395,0.294489326
12782,"The Algerian chapter of SARS-CoV-2 pandemic: An evolutionary, genetic, and epidemiological prospect of the first wave.",0,10.1101/2020.11.19.20235135,11/22/20,medrxiv,0,7,"sequencing, genomes, dataset",0.023306046,0.657492983,0.001156255,0.315732181,0.001156268,0.001156267,Genomics,0.78896284,TRUE,18,0.271569052,8.285714286,0.324324324,0,0.403234768,900,0.238622682,0.309437707
12783,Community factors and excess mortality in first wave of the COVID-19 pandemic.,0,10.1101/2020.11.19.20234849,11/22/20,medrxiv,0,7,bayes,0.001786558,0.001786561,0.001786504,0.472784396,0.328079437,0.193776543,Epidemiology,0.39169133,FALSE,6.428571429,0.090852867,1.142857143,0.123628579,1,0.537564047,1788,0.568745485,0.330197745
12784,Cov-MS: a community-based template assay for clinical MS-based protein detection in Sars-Cov-2 patients,0,10.1101/2020.11.18.20231688,11/20/20,medrxiv,0,53,proteom,0.001751264,0.383437233,0.311966496,0.299342572,0.001751233,0.001751203,Genomics,0.24062398,FALSE,17.62790698,0.264642217,8.279069767,0.323989831,0,0.403234768,2434,0.680471948,0.418084691
12785,"Remdesivir induced viral RNA and subgenomic RNA suppression, and evolution of viral variants in SARS-CoV-2 infected patients.",0,10.1101/2020.11.18.20230599,11/20/20,medrxiv,0,24,"sequencing, deep sequencing",0.318630759,0.562571498,0.0197224,0.001022637,0.021133803,0.076918902,Genomics,0.084086865,FALSE,7.666666667,0.111633373,2.25,0.170925876,1,0.537564047,2004,0.60895738,0.357270169
12786,CLINICAL APPLICATIONS OF MACHINE LEARNING ON COVID-19: THE USE OF A DECISION TREE ALGORITHM FOR THE ASSESSMENT OF PERCEIVED STRESS IN MEXICAN HEALTHCARE PROFESSIONALS.,0,10.1101/2020.11.18.20233288,11/20/20,medrxiv,0,8,"machine learning, correlation analysis, prediction model, dataset",0.002490425,0.002490482,0.401458019,0.002490561,0.588579967,0.002490546,Healthcare,0.8744128,TRUE,6.25,0.088564537,1.75,0.148381054,0,0.403234768,1160,0.362147845,0.250582051
12787,A network modelling approach to assess non-pharmaceutical disease controls in a worker population: An application to SARS-CoV-2,0,10.1101/2020.11.18.20230649,11/20/20,medrxiv,0,5,network model,0.001059415,0.001059367,0.001059376,0.794215995,0.201546464,0.001059383,Epidemiology,0.3934638,FALSE,16.8,0.253138722,1.8,0.150120417,0,0.403234768,1596,0.510474356,0.329242066
12788,Anti-severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) potency of Mefloquine as an entry inhibitor in vitro,0,10.1101/2020.11.19.389726,11/20/20,biorxiv,0,17,mathematical model,0.832165139,0.001565335,0.001565303,0.134089388,0.001565324,0.029049511,Drug discovery,0.56885356,TRUE,122.2352941,0.910136681,162.4705882,0.894099545,1,0.537564047,2044,0.616662654,0.739615732
12789,Two independent introductions of SARS-CoV-2 into the Iranian outbreak,0,10.1101/2020.11.16.20229047,11/20/20,medrxiv,0,37,"whole-genome, genome sequences",0.00151193,0.682930012,0.001511814,0.311022542,0.001511851,0.001511851,Genomics,0.31356716,FALSE,32.59459459,0.462118869,41.83783784,0.643564356,0,0.403234768,1707,0.53985071,0.512192176
12790,Social patterning and stability of COVID-19 vaccination acceptance in Scotland: Will those most at risk accept a vaccine?,0,10.1101/2020.11.19.20234682,11/19/20,medrxiv,10.3390/vaccines9010017,6,logistic regression,0.001684584,0.001684604,0.001684508,0.079453236,0.913808542,0.001684526,Healthcare,0.61141443,TRUE,6.333333333,0.089863319,2,0.164302917,4,0.707574542,1309,0.421141344,0.34572053
12791,Forecasting the spread of COVID19 in Hungary,0,10.1101/2020.11.19.20234815,11/19/20,medrxiv,0,3,dataset,0.004775084,0.004775372,0.004775305,0.976123986,0.004775071,0.004775183,Epidemiology,0.10896066,FALSE,5.333333333,0.074339786,0,0.055525823,0,0.403234768,827,0.185408139,0.179627129
12792,Reprogrammed CRISPR-Cas13b suppresses SARS-CoV-2 replication and circumvents its mutational escape through mismatch tolerance,0,10.1101/2020.11.18.389312,11/19/20,biorxiv,0,11,"computational, genome-wide",0.656338688,0.337508758,0.001538235,0.001538147,0.001538079,0.001538093,Drug discovery,0.19406524,FALSE,93.81818182,0.854041685,177.0909091,0.904401927,0,0.403234768,1561,0.497230917,0.664727324
12793,Long-chain polyphosphates impair SARS-CoV-2 infection and replication: a route for therapy in man,0,10.1101/2020.11.18.388413,11/18/20,biorxiv,0,29,sequencing,0.655817363,0.337808696,0.001593472,0.001593489,0.001593495,0.001593484,Drug discovery,0.603611,TRUE,27.37931034,0.399901045,19.4137931,0.474177147,1,0.537564047,1145,0.349867566,0.440377451
12794,Accuracy of deep learning based computed tomography diagnostic system of COVID-19: a consecutive sampling external validation cohort study,0,10.1101/2020.11.15.20231621,11/18/20,medrxiv,0,13,"deep learning, artificial intelligence",0.000966742,0.000966765,0.549996577,0.000966802,0.000966786,0.446136329,Imaging,0.5055458,TRUE,17.84615385,0.268538561,3.615384615,0.215145839,0,0.403234768,1195,0.372261016,0.314795046
12795,Projections and fractional dynamics of COVID-19 with optimal control analysis,0,10.1101/2020.11.17.20233031,11/18/20,medrxiv,10.1016/j.chaos.2021.110689,1,mathematical model,0.021878813,0.00129131,0.001291294,0.956878541,0.001291235,0.017368808,Epidemiology,0.17801908,FALSE,7,0.10179974,0,0.055525823,1,0.537564047,594,0.059715868,0.18865137
12796,SARS-CoV-2 epidemic after social and economic reopening in three US states reveals shifts in age structure and clinical characteristics,0,10.1101/2020.11.17.20232918,11/18/20,medrxiv,0,16,bayes,0.001171617,0.019583793,0.001171597,0.759537151,0.217364217,0.001171626,Epidemiology,0.25924933,FALSE,33.0625,0.466819222,,,1,0.537564047,2438,0.67782326,0.56073551
12797,Delayed Stroke Treatment during COVID-19 Pandemic in China,0,10.1101/2020.11.17.20228122,11/18/20,medrxiv,0,2,logistic regression,0.002080549,0.002080576,0.002080572,0.356742169,0.002080728,0.634935406,Clinics,0.9667606,TRUE,12.5,0.189436576,3.5,0.213607172,0,0.403234768,593,0.059475078,0.216438398
12798,Optimizing Vaccine Allocation to Combat the COVID-19 Pandemic,0,10.1101/2020.11.17.20233213,11/18/20,medrxiv,0,7,"computational, predictive model, optimization model",0.000871575,0.000871547,0.07382389,0.906265461,0.000871602,0.017295926,Epidemiology,0.094116956,FALSE,5.428571429,0.074958253,1.428571429,0.134265454,5,0.739490092,2991,0.737779918,0.421623429
12799,Regular universal screening for SARS-CoV-2 infection may not allow reopening of society after controlling a pandemic wave,0,10.1101/2020.11.18.20233122,11/18/20,medrxiv,0,6,mathematical model,0.001291231,0.001291323,0.149588742,0.639497259,0.207040185,0.001291259,Epidemiology,0.17075303,FALSE,126.8333333,0.916445049,216.1666667,0.92547498,4,0.707574542,3939,0.798940525,0.837108774
12800,Detection of transmission change points during unlock-3 and unlock-4 measures controlling COVID-19 in India,0,10.1101/2020.11.17.20233221,11/18/20,medrxiv,10.22270/jddt.v11i2.4600,2,bayes,0.002032794,0.002032771,0.002032823,0.989835788,0.002032851,0.002032973,Epidemiology,0.34114885,FALSE,5,0.070752675,0.5,0.087101953,0,0.403234768,678,0.099446183,0.165133895
12801,Estimating COVID-19 Virus Prevalence from Records of Testing Rate and Test Positivity,0,10.1101/2020.11.17.20233643,11/18/20,medrxiv,0,1,dataset,0.001330019,0.001330064,0.001330088,0.665893244,0.328786519,0.001330066,Epidemiology,0.08204129,FALSE,6,0.086028821,1,0.122023013,0,0.403234768,872,0.20635685,0.204410863
12802,Assessing the impact of non-pharmaceutical interventions on the dynamics of COVID-19: A mathematical modelling study in the case of Ethiopia,0,10.1101/2020.11.16.20231746,11/18/20,medrxiv,0,6,mathematical model,0.001291203,0.001291208,0.001291211,0.949799889,0.001291256,0.045035234,Epidemiology,0.3125416,FALSE,5,0.070752675,0.666666667,0.096200161,0,0.403234768,1321,0.422826872,0.248253619
12803,The impact of early public health interventions on SARS-CoV-2 transmission and evolution,0,10.1101/2020.11.18.20233767,11/18/20,medrxiv,0,6,genomes,0.001987152,0.378000567,0.001987308,0.614050676,0.001987168,0.001987128,Epidemiology,0.18538111,FALSE,151.3333333,0.945451172,705.1666667,0.987289269,0,0.403234768,1660,0.520346737,0.714080487
12804,Global seroprevalence of SARS-CoV-2 antibodies: a systematic review and meta-analysis,0,10.1101/2020.11.17.20233460,11/18/20,medrxiv,0,25,bayes,0.00111263,0.0488279,0.035982528,0.556034246,0.250992736,0.10704996,Epidemiology,0.18237734,FALSE,13.28,0.200074216,7.52,0.307934172,5,0.739490092,3396,0.770768119,0.50456665
12805,Single-cell Transcriptome of Bronchoalveolar Lavage Fluid Reveals Dynamic Change of Macrophages During SARS-CoV-2 Infection in Ferrets,0,10.1101/2020.11.18.388280,11/18/20,biorxiv,0,11,"sequencing, transcriptom",0.691455251,0.169464781,0.001272715,0.001272754,0.001272687,0.135261813,Drug discovery,0.16369903,FALSE,41.45454545,0.552353269,19.45454545,0.474578539,0,0.403234768,1245,0.395617626,0.45644605
12806,Assessment of protein-protein interfaces in cryo-EM derived assemblies,0,10.1101/2020.11.17.387068,11/17/20,biorxiv,0,4,machine learning,0.432617192,0.001943524,0.559608703,0.001943594,0.001943474,0.001943513,Drug discovery,0.30360487,FALSE,47.75,0.612220917,70.25,0.754147712,0,0.403234768,1301,0.411750542,0.545338484
12807,Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease,0,10.1101/2020.11.15.383661,11/17/20,biorxiv,0,28,"computational, classifier, dataset",0.288762478,0.001717366,0.4820395,0.001717236,0.001717182,0.224046238,Drug discovery,0.08985144,FALSE,51.37037037,0.641536273,132.0740741,0.866069039,3,0.667819001,2504,0.682157477,0.714395448
12808,Assessment of the Impacts of Pharmaceutical and Non-pharmaceutical Intervention on COVID-19 in South Africa Using Mathematical Model,0,10.1101/2020.11.13.20231159,11/16/20,medrxiv,0,3,mathematical model,0.001538281,0.001538092,0.001538097,0.992309256,0.001538166,0.001538108,Epidemiology,0.12706372,FALSE,16.33333333,0.247015895,2,0.164302917,0,0.403234768,663,0.087647484,0.225550266
12809,Intra-host evolution during SARS-CoV-2 persistent infection,0,10.1101/2020.11.13.20231217,11/16/20,medrxiv,0,23,computational,0.408224792,0.463298174,0.001486457,0.066373589,0.001486453,0.059130534,Genomics,0.32446674,FALSE,24.26086957,0.358154493,28.39130435,0.556930693,7,0.785110192,1704,0.525162533,0.556339478
12810,Epigenetic clocks are not accelerated in COVID-19 patients,0,10.1101/2020.11.13.20229781,11/16/20,medrxiv,0,13,sequencing,0.002130822,0.556358066,0.002130756,0.00213083,0.002130684,0.435118842,Genomics,0.12527347,FALSE,11,0.167171748,8.307692308,0.324591919,0,0.403234768,1078,0.31037804,0.301344119
12811,High SARS-CoV-2 viral load is associated with a worse clinical outcome of COVID-19 disease,0,10.1101/2020.11.13.20229666,11/16/20,medrxiv,0,18,genomes,0.001565316,0.298983056,0.001565291,0.001565309,0.001565299,0.694755728,Clinics,0.43334234,FALSE,14.5,0.219617787,5.333333333,0.262911426,1,0.537564047,3542,0.777751023,0.449461071
12812,Specific immune-regulatory transcriptional signatures reveal sex and age differences in SARS-CoV-2 infected patients,0,10.1101/2020.11.12.20230417,11/16/20,medrxiv,0,22,"transcriptom, dataset",0.413339991,0.124564926,0.001272674,0.001272667,0.001272693,0.458277048,Clinics,0.34773475,FALSE,88.5,0.837961531,135.8636364,0.870484346,0,0.403234768,1981,0.595232362,0.676728252
12813,Prediction of Covid-19 Infections Through December 2020 for 10 US States Incorporating Outdoor Temperature and School Re-Opening Effects-October Update,0,10.1101/2020.11.14.20231902,11/16/20,medrxiv,0,1,prediction model,0.104881801,0.000988384,0.000988363,0.831792937,0.000988387,0.060360128,Epidemiology,0.19725814,FALSE,10,0.15214299,0,0.055525823,1,0.537564047,763,0.140862027,0.221523722
12814,COVATOR: A Software for Chimeric Coronavirus Identification,0,10.1101/2020.11.14.383075,11/16/20,biorxiv,0,1,"sequencing, genomes",0.003101579,0.694378439,0.003101592,0.293215383,0.003101524,0.003101483,Genomics,0.13488808,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,905,0.217192391,0.172065121
12815,"Novel gene-specific translation mechanism of dysregulated, chronic inflammation reveals promising, multifaceted COVID-19 therapeutics",0,10.1101/2020.11.14.382416,11/16/20,biorxiv,0,20,proteom,0.785169723,0.001438166,0.001438115,0.02305987,0.001438168,0.187455958,Drug discovery,0.2339609,FALSE,81.35,0.812604366,135.85,0.870417447,1,0.537564047,2042,0.605586323,0.706543046
12816,Effects of inactivation method on SARS-CoV-2 virion proteins and structure,0,10.1101/2020.11.14.383026,11/16/20,biorxiv,10.3390/v13040562,11,genomes,0.002562751,0.567342011,0.273797295,0.151172518,0.002562786,0.00256264,Genomics,0.44504887,FALSE,47.63636364,0.611355062,76,0.769132994,2,0.618927094,1544,0.483505899,0.620730263
12817,Transcriptome Profiling of different types of human respiratory tract cells infected by SARS-CoV-2 Highlight an unique Role for Inflammatory and Interferon Response,0,10.1101/2020.11.15.383927,11/16/20,biorxiv,10.1080/26895293.2021.1879280,3,transcriptom,0.993448249,0.001310388,0.00131034,0.001310374,0.001310321,0.001310328,Drug discovery,0.87451875,TRUE,43.72727273,0.575298411,,,0,0.403234768,1217,0.374668914,0.451067364
12818,Recurrent mutations in SARS-CoV-2 genomes isolated from mink point to rapid host-adaptation,0,10.1101/2020.11.16.384743,11/16/20,biorxiv,0,8,genomes,0.043770096,0.950658628,0.001392813,0.001392841,0.001392808,0.001392813,Genomics,0.31223464,FALSE,57.875,0.687859484,301.5,0.954642762,15,0.874313229,11664,0.937394654,0.863552532
12819,"An ancient coronavirus-like epidemic drove adaptation in East Asians from 25,000 to 5,000 years ago",0,10.1101/2020.11.16.385401,11/16/20,biorxiv,0,6,"genomes, dataset",0.092355282,0.829594885,0.029625333,0.001461967,0.04550065,0.001461882,Genomics,0.62840736,TRUE,17,0.257467994,24.66666667,0.526090447,2,0.618927094,22193,0.973031543,0.59387927
12820,Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data,0,10.1101/2020.11.12.20230326,11/15/20,medrxiv,0,17,"sequencing, dataset",0.001415104,0.330855862,0.001415233,0.331220449,0.001415177,0.333678176,Clinics,0.22881201,FALSE,22.82352941,0.337312141,20.35294118,0.483877442,0,0.403234768,1513,0.469299302,0.423430913
12821,Analysis of the Dynamics and Distribution of SARS-CoV-2 Mutations and its Possible Structural and Functional Implications,0,10.1101/2020.11.13.381228,11/14/20,biorxiv,0,8,"genomes, dataset",0.027172122,0.648911835,0.00123708,0.320204779,0.001237105,0.001237079,Genomics,0.56315327,TRUE,2.875,0.031603686,0,0.055525823,2,0.618927094,1486,0.4599085,0.291491276
12822,Using Convergent Sequential Design for Rapid Complex Case Study Descriptions: Example of Public Health Briefings During the Onset of the COVID-19 Pandemic,0,10.1101/2020.11.11.20229393,11/13/20,medrxiv,0,4,"text mining, dataset",0.002562808,0.002562768,0.115448859,0.874300386,0.002562621,0.002562558,Epidemiology,0.44044134,FALSE,14.25,0.215535902,5.25,0.260971367,0,0.403234768,632,0.067902721,0.236911189
12823,An evolutionary analysis of the SARS-CoV-2 genomes from the countries in the same meridian,0,10.1101/2020.11.12.380816,11/13/20,biorxiv,0,3,genomes,0.245841685,0.745204018,0.002238578,0.002238653,0.002238489,0.002238576,Genomics,0.7483875,TRUE,12.66666667,0.191044592,1,0.122023013,0,0.403234768,1381,0.428605827,0.28622705
12824,Does the timing of government COVID-19 policy interventions matter? Policy analysis of an original database.,0,10.1101/2020.11.13.20194761,11/13/20,medrxiv,0,4,dataset,0.000572626,0.000572633,0.000572626,0.898527649,0.000572643,0.099181823,Epidemiology,0.31241238,FALSE,19,0.285793803,4.75,0.248260637,0,0.403234768,1010,0.26462798,0.300479297
12825,A Whole Virion Vaccine for COVID-19 Produced Via a Novel Inactivation Method:  Results from Animal Challenge Model Studies,0,10.1101/2020.11.13.381335,11/13/20,biorxiv,0,16,sequencing,0.787714031,0.20680065,0.001371351,0.001371351,0.001371353,0.001371264,Drug discovery,0.0750303,FALSE,30.5625,0.438926341,29.125,0.563486754,0,0.403234768,1778,0.535757284,0.485351287
12826,A functional genomics approach to understand host genetic regulation ofCOVID-19 severity,0,10.1101/2020.11.10.20229203,11/13/20,medrxiv,0,13,"genome-wide, multi-omics",0.302370052,0.528646624,0.019480498,0.001371311,0.001371325,0.14676019,Genomics,0.30414993,FALSE,17.46153846,0.2621065,19.23076923,0.472437784,1,0.537564047,944,0.23019504,0.375575843
12827,SARS-CoV-2 antibody signatures for predicting the outcome of COVID-19,0,10.1101/2020.11.10.20228890,11/13/20,medrxiv,0,10,"computational, proteom",0.081992642,0.20358245,0.018792953,0.000946168,0.017858422,0.676827366,Clinics,0.3500197,FALSE,4.5,0.061784897,0,0.055525823,0,0.403234768,1269,0.385986034,0.22663288
12828,Non-occupational and occupational factors associated with specific SARS-CoV-2 antibodies among Hospital Workers - a multicentre cross-sectional study,0,10.1101/2020.11.10.20229005,11/13/20,medrxiv,0,25,logistic regression,0.001943498,0.001943659,0.001943514,0.001943647,0.813738929,0.178486753,Healthcare,0.12424064,FALSE,24.56,0.362112685,15.12,0.426277763,0,0.403234768,2890,0.715386468,0.476752921
12829,"Screening of the H69 and V70 deletions in the SARS-CoV-2 spike protein with a RT-PCR diagnosis assay reveals low prevalence in Lyon, France",0,10.1101/2020.11.10.20228528,11/13/20,medrxiv,10.2807/1560-7917.ES.2021.26.3.2100008,17,"sequencing, whole genome",0.177230364,0.817198198,0.001392872,0.001392892,0.001392843,0.001392832,Genomics,0.08355543,FALSE,18.07692308,0.271754592,13.76923077,0.408415842,2,0.618927094,8830,0.914038045,0.553283893
12830,"Evaluation of the disease outcome in Covid-19 infected patients by disease symptoms: a retrospective cross-sectional study in Ilam Province, Iran",0,10.1101/2020.11.10.20228908,11/13/20,medrxiv,0,9,logistic regression,0.001786516,0.001786595,0.171744051,0.001786582,0.224119416,0.59877684,Clinics,0.9415517,TRUE,23.77777778,0.350671037,3.777777778,0.220029435,0,0.403234768,679,0.088610643,0.265636471
12831,Changing probability of experiencing food insecurity by socioeconomic and demographic groups during the COVID-19 pandemic in the UK,0,10.1101/2020.11.10.20229278,11/13/20,medrxiv,0,4,logistic regression,0.000576359,0.000576372,0.008793016,0.165056066,0.824421818,0.00057637,Healthcare,0.24539065,FALSE,121.75,0.909147133,201,0.917982339,0,0.403234768,1092,0.305080665,0.633861226
12832,A computational analysis on Covid-19 transmission raises imuuno-epidemiology concerns.,0,10.1101/2020.11.11.20229641,11/13/20,medrxiv,0,2,computational,0.003465962,0.30288203,0.003466012,0.395547512,0.003466195,0.291172289,Epidemiology,0.27962995,FALSE,6,0.086028821,0.5,0.087101953,0,0.403234768,1343,0.41415844,0.247630995
12833,Detection of COVID-19 Disease from Chest X-Ray Images: A Deep Transfer Learning Framework,0,10.1101/2020.11.08.20227819,11/12/20,medrxiv,0,7,"neural network, transfer learning, dataset",0.001786513,0.001786561,0.991067362,0.001786539,0.001786509,0.001786516,Imaging,0.47173777,FALSE,6.857142857,0.097099388,0.285714286,0.066296495,0,0.403234768,857,0.180833133,0.186865946
12834,"State- and County-Level COVID-19 Public Health Orders in California: Constructing a Dataset and Describing Their Timing, Content, and Stricture",0,10.1101/2020.11.08.20224915,11/12/20,medrxiv,0,5,dataset,0.001371264,0.001371303,0.001371277,0.993143595,0.00137132,0.001371241,Epidemiology,0.2569018,FALSE,19,0.285793803,20.2,0.482204977,1,0.537564047,2002,0.587527089,0.473272479
12835,COVID-19 pandemic risk analytics: Data mining with reliability engineering methods for analyzing spreading behavior and comparison with infectious diseases,0,10.1101/2020.11.08.20227322,11/12/20,medrxiv,0,2,data mining,0.001565327,0.094031709,0.001565368,0.899706765,0.001565398,0.001565434,Epidemiology,0.2331526,FALSE,3.5,0.044344115,0,0.055525823,0,0.403234768,889,0.19528052,0.174596306
12836,Automatic COVID-19 Detection from chest radiographic images using Convolutional Neural Network,0,10.1101/2020.11.08.20228080,11/12/20,medrxiv,0,2,"deep learning, artificial intelligence, neural network",0.001823341,0.001823376,0.939663487,0.001823423,0.001823337,0.053043034,Imaging,0.38681984,FALSE,8,0.118683901,0,0.055525823,0,0.403234768,1186,0.346496509,0.23098525
12837,"Diagnosis and Tracking of Past SARS-CoV-2 Infection in a Large Study of Vo', Italy Through T-Cell Receptor Sequencing",0,10.1101/2020.11.09.20228023,11/12/20,medrxiv,0,28,sequencing,0.422811409,0.374136859,0.001112648,0.001112676,0.118037848,0.082788559,Drug discovery,0.17040578,FALSE,40.86363636,0.547219989,71.59090909,0.757693337,2,0.618927094,8920,0.914760414,0.709650209
12838,A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome,0,10.1101/2020.11.09.20228015,11/12/20,medrxiv,0,62,proteom,0.322275829,0.071938528,0.056878065,0.001022678,0.001022644,0.546862256,Clinics,0.12798348,FALSE,21.34146341,0.315542087,28.87804878,0.561212202,5,0.739490092,2910,0.715627257,0.58296791
12839,The repurposed drugs suramin and quinacrine inhibit cooperatively in vitro SARS-CoV-2 3CLpro,0,10.1101/2020.11.11.378018,11/12/20,biorxiv,0,6,molecular dynamics simulation,0.990064084,0.001987189,0.001987196,0.001987226,0.001987164,0.001987141,Drug discovery,0.76075184,TRUE,78.83333333,0.80190488,45.5,0.660957988,0,0.403234768,1067,0.289188538,0.538821543
12840,Molecular Mimicry Map (3M) of SARS-CoV-2: Prediction of potentially immunopathogenic SARS-CoV-2 epitopes via a novel immunoinformatic approach,0,10.1101/2020.11.12.344424,11/12/20,biorxiv,0,2,"computational, genomes",0.734560296,0.226678091,0.022824071,0.00099956,0.000999518,0.013938463,Drug discovery,0.37333244,FALSE,37.5,0.513946441,6,0.280037463,0,0.403234768,2280,0.637611365,0.458707509
12841,Combined in silico docking and in vitro antiviral testing for drug repurposing identified lurasidone and elbasvir as SARS-CoV-2 and HCoV-OC43 inhibitors,0,10.1101/2020.11.12.379958,11/12/20,biorxiv,10.1016/j.antiviral.2021.105055,9,in silico,0.865621065,0.125645705,0.002183238,0.002183304,0.00218336,0.002183328,Drug discovery,0.84868217,TRUE,54.11111111,0.661945699,54.11111111,0.698019802,1,0.537564047,1142,0.325788587,0.555829534
12842,Neuraminidase inhibitors rewire neutrophil function in murine sepsis and COVID-19 patient cells,0,10.1101/2020.11.12.379115,11/12/20,biorxiv,0,28,sequencing,0.665554742,0.071983409,0.001751288,0.001751197,0.00175117,0.257208195,Drug discovery,0.43666628,FALSE,22.10714286,0.326365267,27.14285714,0.546561413,0,0.403234768,3356,0.756561522,0.508180742
12843,Identifying and prioritizing potential human-infecting viruses from their genome sequences,0,10.1101/2020.11.12.379917,11/12/20,biorxiv,0,3,"machine learning, genome sequences, genomes",0.002080759,0.771212648,0.220464782,0.002080611,0.002080636,0.002080565,Genomics,0.2863778,FALSE,54,0.661574618,58.66666667,0.716149318,3,0.667819001,1143,0.326992536,0.593133868
12844,In silico analyses on the comparative sensing of SARS-CoV-2 mRNA by intracellular TLRs of human,0,10.1101/2020.11.11.377713,11/11/20,biorxiv,10.1002/jmv.26776,4,"in silico, in-silico",0.989083336,0.002183339,0.002183295,0.002183403,0.00218339,0.002183237,Drug discovery,0.8051237,TRUE,16.25,0.246026347,4.25,0.235750602,3,0.667819001,1504,0.455815073,0.401352756
12845,Characterization and structural basis of a lethal mouse-adapted SARS-CoV-2,0,10.1101/2020.11.10.377333,11/11/20,biorxiv,0,26,"sequencing, deep sequencing",0.474397202,0.291865687,0.03134742,0.001415141,0.001415195,0.199559355,Drug discovery,0.20881858,FALSE,98.28571429,0.865545179,93.67857143,0.810074926,5,0.739490092,2829,0.706718035,0.780457058
12846,Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation,0,10.1101/2020.11.07.20227462,11/10/20,medrxiv,0,9,simulation model,0.139918056,0.003607322,0.003607381,0.798578776,0.003607167,0.050681298,Epidemiology,0.18477085,FALSE,15.22222222,0.229389573,0.555555556,0.087302649,0,0.403234768,715,0.098483024,0.204602503
12847,Challenges of Deep Learning Methods for COVID-19 Detection Using Public Datasets,0,10.1101/2020.11.07.20227504,11/10/20,medrxiv,0,8,"deep learning, artificial intelligence, dataset",0.000956304,0.000956354,0.995218317,0.000956353,0.000956325,0.000956347,Imaging,0.15573019,FALSE,2.625,0.028573196,0,0.055525823,1,0.537564047,1184,0.337346497,0.239752391
12848,COVID-19 detection on IBM quantum computer with classical-quantum transfer learning,0,10.1101/2020.11.07.20227306,11/10/20,medrxiv,0,2,"machine learning, transfer learning",0.056737936,0.001438231,0.937509388,0.001438185,0.001438121,0.001438138,Imaging,0.25654745,FALSE,8.5,0.126662131,2.5,0.180826866,1,0.537564047,1180,0.335901758,0.295238701
12849,Human cardiac stromal cells exposed to SARS-CoV-2 evolve into hyper-inflammatory/pro-fibrotic phenotype and produce infective viral particles depending on the levels of ACE2 receptor expression,0,10.1101/2020.11.06.20226423,11/10/20,medrxiv,10.1093/cvr/cvab082,15,"transcriptom, proteom",0.578016197,0.126184716,0.029833113,0.001254662,0.001254611,0.263456701,Drug discovery,0.4599125,FALSE,70.53333333,0.766095615,41.66666667,0.643029168,0,0.403234768,892,0.190705514,0.500766266
12850,Balancing quarantine and self-distancing measures in adaptive epidemic networks,0,10.1101/2020.11.07.20227595,11/10/20,medrxiv,0,3,computational,0.001187291,0.001187286,0.001187315,0.994063519,0.00118732,0.001187269,Epidemiology,0.12020072,FALSE,38.66666667,0.525882862,24.66666667,0.526090447,1,0.537564047,676,0.080183,0.417430089
12851,Susceptibility of well-differentiated airway epithelial cell cultures from domestic and wildlife animals to SARS-CoV-2,0,10.1101/2020.11.10.374587,11/10/20,biorxiv,0,26,"sequencing, whole-genome",0.18821788,0.80235113,0.002357779,0.002357746,0.002357718,0.002357747,Genomics,0.40084073,FALSE,27.40740741,0.400457666,40.37037037,0.635603425,2,0.618927094,2515,0.667950879,0.580734766
12852,A benchmarking study of SARS-CoV-2 whole-genome sequencing protocols using COVID-19 patient samples,0,10.1101/2020.11.10.375022,11/10/20,biorxiv,0,12,"sequencing, whole-genome",0.001438147,0.748895618,0.19394003,0.001438182,0.001438138,0.052849884,Genomics,0.24839604,FALSE,48.75,0.62032284,42.75,0.648381054,2,0.618927094,1798,0.531663857,0.604823712
12853,SARS-CoV-2 infection causes transient olfactory dysfunction in mice,0,10.1101/2020.11.10.376673,11/10/20,biorxiv,0,18,"transcriptom, proteom",0.702979082,0.002720263,0.002720137,0.002720163,0.002720259,0.286140097,Drug discovery,0.37537235,FALSE,78.44444444,0.799987631,81.27777778,0.781509232,1,0.537564047,1572,0.47122562,0.647571632
12854,Symptomatic SARS-CoV-2 re-infection of a health care worker in a Belgian nosocomial outbreak despite primary neutralizing antibody response.,0,10.1101/2020.11.05.20225052,11/9/20,medrxiv,0,9,"sequencing, whole genome, genomes",0.001272718,0.745328128,0.001272643,0.0012727,0.13064288,0.12021093,Genomics,0.3670466,FALSE,13.33333333,0.201558538,4,0.231469093,4,0.707574542,3667,0.773657597,0.478564942
12855,Non applicability of validated predictive models for intensive care admission and death of COVID-19 patients in a secondary care hospital in Belgium,0,10.1101/2020.11.06.20205799,11/9/20,medrxiv,0,2,"predictive model, logistic regression, prediction model",0.000830639,0.000830651,0.280431342,0.000830673,0.000830665,0.716246029,Clinics,0.63276005,TRUE,2,0.022141134,0,0.055525823,0,0.403234768,806,0.143510715,0.15610311
12856,"Gout, rheumatoid arthritis and the risk of death from COVID-19: an analysis of the UK Biobank",0,10.1101/2020.11.06.20227405,11/9/20,medrxiv,0,7,"logistic regression, dataset",0.049718489,0.00218332,0.058086522,0.002183354,0.002183276,0.885645039,Clinics,0.41181827,FALSE,104.4285714,0.878285608,85.71428571,0.792146107,0,0.403234768,4523,0.814591861,0.722064586
12857,Human coronaviruses disassemble processing bodies,0,10.1101/2020.11.08.372995,11/9/20,biorxiv,0,6,genomes,0.878123254,0.115829397,0.001511824,0.001511832,0.001511868,0.001511826,Drug discovery,0.59060186,TRUE,6.833333333,0.096852001,7.333333333,0.304656141,0,0.403234768,1677,0.498675656,0.325854641
12858,Brd4-bound enhancers drive cell intrinsic sex differences in glioblastoma,0,10.1101/199059,11/9/20,biorxiv,0,6,transcriptom,0.519152752,0.045913967,0.000740291,0.000740306,0.000740334,0.432712351,Drug discovery,0.18962163,FALSE,63.92307692,0.727750634,137.9230769,0.8726251,1,0.537564047,3223,0.743077294,0.720254269
12859,Variability of Accessory Proteins Rules the SARS-CoV-2 Pathogenicity,0,10.1101/2020.11.06.372227,11/8/20,biorxiv,0,23,proteom,0.440287696,0.298199976,0.001943477,0.255681711,0.001943539,0.001943602,Drug discovery,0.49864775,FALSE,175.375,0.962026099,182.625,0.907880653,0,0.403234768,1330,0.394654467,0.666948997
12860,Team contact sports in times of the COVID-19 pandemic- a scientific concept for the Austrian football league,0,10.1101/2020.11.06.20226977,11/8/20,medrxiv,0,13,simulation model,0.001593582,0.00159358,0.001593647,0.697417219,0.296208457,0.001593515,Epidemiology,0.8204464,TRUE,14.53846154,0.219741481,1.384615385,0.133061279,1,0.537564047,830,0.155068625,0.261358858
12861,"Healthcare strain and intensive care during the COVID-19 outbreak in the Lombardy region: a retrospective observational study on 43,538 hospitalized patients",0,10.1101/2020.11.06.20149690,11/7/20,medrxiv,0,19,logistic regression,0.001254578,0.046628614,0.00125461,0.167423981,0.00125466,0.782183556,Clinics,0.17970029,FALSE,28.84210526,0.415919352,39.89473684,0.63252609,2,0.618927094,1740,0.509270407,0.544160736
12862,Risk mitigating behaviours in people with inflammatory joint and skin disease during the COVID-19 pandemic differ by treatment type: a cross-sectional patient survey,0,10.1101/2020.11.05.20226662,11/7/20,medrxiv,10.1111/bjd.19755,43,logistic regression,0.045682537,0.00077945,0.000779417,0.139477755,0.392550374,0.420730468,Clinics,0.44262248,FALSE,52.04651163,0.647411714,56.60465116,0.709459459,0,0.403234768,1160,0.315434626,0.518885142
12863,Predicting the impact of disruptions in lymphatic filariasis elimination programmes due to the outbreak of coronavirus disease (COVID-19) and possible mitigation strategies,0,10.1101/2020.11.06.20227017,11/7/20,medrxiv,0,11,mathematical model,0.03465905,0.002080553,0.002080614,0.957018616,0.002080598,0.002080571,Epidemiology,0.48208243,FALSE,18.54545455,0.27806296,15.09090909,0.426077067,0,0.403234768,840,0.156994943,0.316092435
12864,Longitudinal Physiological Data from a Wearable Device Identifies SARS-CoV-2 Infection and Symptoms and Predicts COVID-19 Diagnosis,0,10.1101/2020.11.06.20226803,11/7/20,medrxiv,0,21,digital health,0.001461925,0.001461976,0.224172084,0.156356661,0.313747066,0.302800286,Healthcare,0.59749836,TRUE,46,0.596882924,63.0952381,0.732740166,3,0.667819001,7482,0.888755117,0.721549302
12865,Characterising heterogeneity and sero-reversion in antibody responses to mild SARS-CoV-2 infection: a cohort study using time series analysis and mechanistic modelling,0,10.1101/2020.11.04.20225920,11/6/20,medrxiv,0,27,mathematical model,0.03571213,0.420086168,0.000490167,0.339870699,0.117497122,0.086343712,Genomics,0.11378646,FALSE,26.88461538,0.393036057,20.84615385,0.489430024,2,0.618927094,1911,0.549000722,0.512598474
12866,Biometric covariates and outcome in COVID-19 patients: Are we looking close enough?,0,10.1101/2020.11.04.20225961,11/6/20,medrxiv,0,5,computational,0.168605298,0.05957583,0.056199166,0.081223768,0.001034606,0.633361332,Clinics,0.04266432,FALSE,25.8,0.378316532,8.4,0.326598876,0,0.403234768,834,0.150734409,0.314721146
12867,Demographic and psychological correlates of SARS-CoV-2 vaccination intentions in a sample of Canadian families,0,10.1101/2020.11.04.20226050,11/6/20,medrxiv,10.1016/j.jvacx.2021.100091,2,logistic regression,0.001371284,0.001371301,0.001371277,0.274730063,0.719784811,0.001371263,Healthcare,0.41950452,FALSE,54.5,0.665161729,23,0.513513514,0,0.403234768,889,0.178425235,0.440083811
12868,Severity of Respiratory Infections due to SARS-CoV-2 in Working Population: Age and Body Mass Index Outweigh ABO Blood Group,0,10.1101/2020.11.05.20226100,11/6/20,medrxiv,0,18,logistic regression,0.001310409,0.120001543,0.001310409,0.001310402,0.404519025,0.471548211,Clinics,0.28376865,FALSE,40.27777778,0.541901169,32.33333333,0.588239229,1,0.537564047,3888,0.783048399,0.612688211
12869,Spatial Profiling of Lung SARS-CoV-2 and Influenza Virus Infection Dissects Virus-Specific Host Responses and Gene Signatures,0,10.1101/2020.11.04.20225557,11/6/20,medrxiv,0,20,transcriptom,0.456387719,0.250961346,0.001717325,0.0017174,0.001717194,0.287499016,Drug discovery,0.16543517,FALSE,24.95,0.36656565,52.85,0.692601017,0,0.403234768,3203,0.734408861,0.549202574
12870,"Identification, Isolation, Propagation And Inactivation Of SARS-CoV2 Isolated From Egypt",0,10.1101/2020.11.04.368431,11/5/20,biorxiv,0,5,sequencing,0.174069915,0.649414642,0.002898459,0.002898523,0.00289852,0.167819942,Genomics,0.42588276,FALSE,2.6,0.028263962,0,0.055525823,0,0.403234768,1062,0.263183241,0.187551948
12871,Shorter androgen receptor polyQ alleles protect against life-threatening COVID-19 disease in males.,0,10.1101/2020.11.04.20225680,11/5/20,medrxiv,10.1016/j.ebiom.2021.103246,41,"machine learning, sequencing, exom, logistic regression",0.126522132,0.360967217,0.106807557,0.035967269,0.047914846,0.32182098,Genomics,0.24091241,FALSE,37.46341463,0.51301874,41.68292683,0.643096066,2,0.618927094,1230,0.340476764,0.528879666
12872,Early survey with bibliometric analysis on machine learning approaches in controlling coronavirus,0,10.1101/2020.11.04.20225698,11/5/20,medrxiv,10.7717/peerj-cs.313,6,"machine learning, neural network, dataset",0.029086254,0.000746547,0.7962424,0.091782556,0.081395621,0.000746622,Imaging,0.66800517,TRUE,32.83333333,0.464592739,17.66666667,0.457586299,0,0.403234768,937,0.197929208,0.380835753
12873,Higher risk of mental health deterioration during the Covid-19 lockdown among students rather than non-students. The French Confins study,0,10.1101/2020.11.04.20225706,11/5/20,medrxiv,0,11,logistic regression,0.001392836,0.001392902,0.001392859,0.001392957,0.993035576,0.001392869,Healthcare,0.76411057,TRUE,13.72727273,0.207681366,4.636363636,0.244447418,0,0.403234768,2647,0.675415362,0.382694728
12874,COVID-19's U.S. Temperature Response Profile,0,10.1101/2020.11.03.20225581,11/5/20,medrxiv,0,6,forecasting model,0.001943549,0.001943612,0.00194346,0.886440559,0.001943535,0.105785284,Epidemiology,0.30150878,FALSE,44.33333333,0.580679077,253.8333333,0.939389885,2,0.618927094,2082,0.58608235,0.681269602
12875,COVID-19 surveillance - a descriptive study on data quality issues,0,10.1101/2020.11.03.20225565,11/5/20,medrxiv,0,12,dataset,0.001717356,0.001717234,0.210361623,0.443522475,0.340964097,0.001717215,Epidemiology,0.19181114,FALSE,22.25,0.328344363,18,0.46180091,1,0.537564047,2486,0.653503491,0.495303203
12876,"CovidSIMVL - Agent-Based Modeling of Localized Transmission within a Heterogeneous Array of Locations: Motivation, Configuration and Calibration",0,10.1101/2020.11.01.20217943,11/4/20,medrxiv,0,2,dataset,0.000838554,0.018977287,0.000838542,0.877912058,0.075525651,0.025907909,Epidemiology,0.19001591,FALSE,6,0.086028821,0,0.055525823,3,0.667819001,641,0.054900072,0.216068429
12877,Lightweight Model For The Prediction of COVID-19 Through The Detection And Segmentation of Lesions in Chest CT Scans,0,10.1101/2020.10.30.20223586,11/4/20,medrxiv,10.31763/sitech.v1i2.202,1,dataset,0.00186171,0.001861725,0.914413075,0.078140077,0.001861691,0.001861722,Imaging,0.5499825,TRUE,23,0.34225988,8,0.320511105,1,0.537564047,1086,0.271370094,0.367926282
12878,The signature features of COVID-19 pandemic in a hybrid mathematical model - implications for optimal work-school lockdown policy,0,10.1101/2020.11.02.20224584,11/4/20,medrxiv,0,2,"mathematical model, in silico",0.016203423,0.000515888,0.000515887,0.792873673,0.189375233,0.000515896,Epidemiology,0.14993837,FALSE,13,0.197352959,3,0.199424672,0,0.403234768,764,0.108836985,0.227212346
12879,"Simulation model for productivity, risk and GDP impact forecasting of the COVID-19 portfolio vaccines",0,10.1101/2020.11.01.20214122,11/4/20,medrxiv,0,1,simulation model,0.002720195,0.002720147,0.00272017,0.986399042,0.002720272,0.002720174,Epidemiology,0.13042375,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,760,0.106429087,0.146832703
12880,Effectiveness of quarantine and testing to prevent COVID-19 transmission from arriving travelers,0,10.1101/2020.11.02.20224568,11/4/20,medrxiv,0,2,simulation model,0.001861667,0.001861715,0.001861758,0.990691239,0.001861838,0.001861783,Epidemiology,0.08868942,FALSE,179.5,0.964005195,127,0.860516457,0,0.403234768,1241,0.342162292,0.642479678
12881,Characteristics of those most vulnerable to employment changes during the COVID-19 pandemic: a nationally representative cross-sectional study in Wales,0,10.1101/2020.11.03.20225144,11/4/20,medrxiv,0,4,logistic regression,0.0012371,0.001237093,0.001237061,0.001237158,0.993814443,0.001237145,Healthcare,0.57567734,TRUE,18.33333333,0.274908776,2,0.164302917,0,0.403234768,1122,0.28750301,0.282487368
12882,A prospective study of risk factors associated with seroprevalence of SARS-CoV-2 antibodies in healthcare workers at a large UK teaching hospital,0,10.1101/2020.11.03.20220699,11/4/20,medrxiv,0,40,logistic regression,0.000471867,0.000471896,0.000471871,0.097098526,0.761646653,0.139839187,Healthcare,0.3293996,FALSE,10.975,0.162100315,11.55,0.378512176,1,0.537564047,2282,0.617144233,0.423830193
12883,Covid-19 fatality prediction in people with diabetes and prediabetes using a simple score at hospital admission,0,10.1101/2020.11.02.20224311,11/4/20,medrxiv,10.1111/dom.14256,23,logistic regression,0.001392839,0.001392825,0.018800396,0.001392824,0.001392825,0.975628291,Clinics,0.44855994,FALSE,33.08695652,0.466942915,15.30434783,0.42781643,1,0.537564047,1474,0.430772935,0.465774082
12884,Space-Time Covid-19 Bayesian SIR modeling in South Carolina,0,10.1101/2020.11.03.20225227,11/4/20,medrxiv,10.1371/journal.pone.0242777,2,bayes,0.003607168,0.003607246,0.003607245,0.981963738,0.003607305,0.003607297,Epidemiology,0.32979375,FALSE,210,0.975632383,222,0.927080546,1,0.537564047,826,0.140139658,0.645104159
12885,Effect of control measure on the development of new COVID-19 cases through SIR model simulation,0,10.1101/2020.10.27.20220590,11/3/20,medrxiv,0,2,model simulation,0.001786555,0.001786547,0.001786557,0.991067174,0.001786571,0.001786596,Epidemiology,0.17058149,FALSE,19,0.285793803,25,0.529435376,0,0.403234768,485,0.010594751,0.307264674
12886,A Multi-Factor Risk Model for Severe Covid-19 for Vaccine Prioritization and Monitoring Based on a 15 Million Medicare Cohort,0,10.1101/2020.10.28.20219816,11/3/20,medrxiv,0,9,"predictive model, logistic regression",0.001350353,0.001350335,0.046183198,0.257993487,0.123167928,0.5699547,Clinics,0.039045423,FALSE,4.333333333,0.057950399,0.777777778,0.099946481,0,0.403234768,2292,0.616421864,0.294388378
12887,Public Mobility Data Enables COVID-19 Forecasting and Management at Local and Global Scales,0,10.1101/2020.10.29.20222547,11/3/20,medrxiv,0,6,machine learning,0.000629689,0.00062968,0.000629684,0.996851606,0.00062968,0.000629661,Epidemiology,0.04088813,FALSE,23,0.34225988,88.66666667,0.798501472,2,0.618927094,839,0.143992295,0.475920185
12888,The power and limitations of genomics to track COVID-19 outbreaks: a case study from New Zealand,0,10.1101/2020.10.28.20221853,11/3/20,medrxiv,0,21,"sequencing, genomes",0.00203276,0.989835874,0.002032783,0.002032892,0.002032896,0.002032796,Genomics,0.32519525,FALSE,124.3636364,0.913600099,861,0.99150388,0,0.403234768,2044,0.571875752,0.720053625
12889,A chest-CT and clinical chemistry based flowchart for rapid COVID-19 triage at emergency departments - a multicenter study,0,10.1101/2020.10.29.20218743,11/3/20,medrxiv,0,18,dataset,0.001291194,0.001291196,0.604593014,0.001291217,0.001291211,0.390242168,Imaging,0.76143634,TRUE,17.61111111,0.264518523,10.33333333,0.36038266,0,0.403234768,769,0.109077775,0.284303431
12890,A Time-dependent mathematical model for COVID-19 transmission dynamics and analysis of critical and hospitalized cases with bed requirements,0,10.1101/2020.10.28.20221721,11/3/20,medrxiv,0,3,mathematical model,0.001392836,0.001392831,0.001392871,0.844381216,0.00139283,0.150047416,Epidemiology,0.30370957,FALSE,10,0.15214299,0.333333333,0.073187048,1,0.537564047,675,0.068143511,0.207759399
12891,The prevalence of common mental disorders among health care professionals during the COVID-19 pandemic at a tertiary Hospital in East Africa,0,10.1101/2020.10.29.20222430,11/3/20,medrxiv,0,6,logistic regression,0.001786515,0.001786748,0.001786614,0.001786531,0.94774447,0.045109121,Healthcare,0.96955985,TRUE,4,0.054734368,1.333333333,0.13252609,0,0.403234768,741,0.096075126,0.171642588
12892,"SARSeq, a robust and highly multiplexed NGS assay for parallel detection of SARS-CoV2 and other respiratory infections",0,10.1101/2020.10.28.20217778,11/3/20,medrxiv,0,23,sequencing,0.025160896,0.735827045,0.235193786,0.001272756,0.001272754,0.001272763,Genomics,0.3959603,FALSE,12.13043478,0.183684829,8.869565217,0.33348943,2,0.618927094,4331,0.797254996,0.483339087
12893,Occupational risk of COVID-19 in the 1st vs 2nd wave of infection,0,10.1101/2020.10.29.20220426,11/3/20,medrxiv,0,5,logistic regression,0.001786505,0.001786574,0.03171903,0.372422132,0.473664512,0.118621247,Healthcare,0.29424477,FALSE,59.5,0.699857752,55,0.702167514,4,0.707574542,8085,0.8962196,0.751454852
12894,"The challenges of caring for people dying from COVID-19: a multinational,observational study of palliative and hospice services (CovPall)",0,10.1101/2020.10.30.20221465,11/3/20,medrxiv,0,13,logistic regression,0.000469365,0.000469364,0.000469365,0.253292716,0.583485,0.16181419,Healthcare,0.15448037,FALSE,89.84615385,0.842228957,105.6923077,0.83255285,0,0.403234768,3580,0.758006261,0.709005709
12895,"COVID-19, a social disease in Paris: a socio-economic wide association study on hospitalized patients highlights low-income neighbourhood as a key determinant of severe COVID-19 incidence during the first wave of the epidemic",0,10.1101/2020.10.30.20222901,11/3/20,medrxiv,0,5,predictive model,0.001751151,0.001751196,0.115497235,0.413329814,0.040804848,0.426865757,Clinics,0.34723282,FALSE,65.2,0.735543324,58.2,0.714811346,0,0.403234768,976,0.211895016,0.516371113
12896,Deep Learning Model for Improving the Characterization of Coronavirus on Chest X-ray Images Using CNN,0,10.1101/2020.10.30.20222786,11/3/20,medrxiv,0,2,"deep learning, computational, neural network, dataset",0.022279631,0.001203447,0.97290653,0.001203491,0.001203487,0.001203414,Imaging,0.675951,TRUE,31.5,0.450058754,3,0.199424672,0,0.403234768,801,0.12593306,0.294662814
12897,How well does societal mobility restriction help control the COVID-19 pandemic? Evidence from real-time evaluation,0,10.1101/2020.10.29.20222414,11/3/20,medrxiv,0,31,mathematical model,0.00051589,0.008646299,0.000515879,0.940608673,0.000515907,0.049197352,Epidemiology,0.12417966,FALSE,38.38709677,0.522790525,58.19354839,0.714744447,0,0.403234768,1363,0.389116301,0.50747151
12898,"Modeling COVID-19 Transmissions and Evaluation of Large Scale Social Restriction in Jakarta, Indonesia",0,10.1101/2020.10.30.20222984,11/3/20,medrxiv,0,9,mathematical model,0.002296646,0.002296613,0.002296743,0.988516579,0.002296695,0.002296724,Epidemiology,0.17515025,FALSE,4.111111111,0.054919908,0,0.055525823,0,0.403234768,736,0.092944859,0.15165634
12899,"Remdesivir-based therapy improved recovery of patients with COVID-19 in the SARSTer multicentre, real-world study",0,10.1101/2020.10.30.20215301,11/3/20,medrxiv,10.20452/pamw.15735,23,logistic regression,0.040666408,0.00111264,0.001112658,0.14265108,0.001112674,0.81334454,Clinics,0.95028514,TRUE,29.04347826,0.420001237,8.304347826,0.32452502,1,0.537564047,2053,0.573320491,0.463852699
12900,Availability of personal protective equipment and satisfaction of healthcare professionals during COVID-19 pandemic in Ethiopia,0,10.1101/2020.10.30.20223149,11/3/20,medrxiv,0,5,logistic regression,0.001126795,0.001126785,0.001126818,0.00112681,0.803830244,0.191662548,Healthcare,0.98681,TRUE,23.4,0.346527305,18,0.46180091,1,0.537564047,946,0.196725259,0.38565438
12901,Risk perceptions and preventive practices of COVID-19 among healthcare professionals in public hospitals in Ethiopia,0,10.1101/2020.10.30.20223180,11/3/20,medrxiv,0,5,logistic regression,0.001291242,0.001291242,0.001291232,0.001291298,0.958769341,0.036065645,Healthcare,0.79436505,TRUE,23.4,0.346527305,18,0.46180091,0,0.403234768,707,0.07994221,0.322876298
12902,Rapid classification and prediction of COVID-19 severity by MALDI-TOF mass spectrometry analysis of serum peptidome,0,10.1101/2020.10.30.20223057,11/3/20,medrxiv,0,7,machine learning,0.001538196,0.095932182,0.367951607,0.001538196,0.001538082,0.531501736,Clinics,0.52024263,TRUE,20.28571429,0.301502876,25.42857143,0.532178218,1,0.537564047,1081,0.266313508,0.409389662
12903,Role of ivermectin in the prevention of COVID-19 infection among healthcare workers in India: A matched case-control study,0,10.1101/2020.10.29.20222661,11/3/20,medrxiv,10.1371/journal.pone.0247163,12,logistic regression,0.093535139,0.001622906,0.001622806,0.001622765,0.709779276,0.191817107,Healthcare,0.6731957,TRUE,17.5,0.263776362,7.916666667,0.314891624,6,0.764429903,67948,0.991813147,0.583727759
12904,A New Approach to the Dynamic Modeling of an Infectious Disease,0,10.1101/2020.10.30.20223305,11/3/20,medrxiv,0,2,computational,0.124419368,0.001392868,0.079626475,0.791775457,0.001392903,0.001392929,Epidemiology,0.13554877,FALSE,17,0.257467994,0.5,0.087101953,4,0.707574542,1048,0.249939803,0.325521073
12905,Computational prediction of SARS-CoV-2 encoded miRNAs and their putative host targets,0,10.1101/2020.11.02.365049,11/3/20,biorxiv,0,4,"computational, transcriptom, dataset",0.741587934,0.198729944,0.001593547,0.001593594,0.00159357,0.054901411,Drug discovery,0.30766422,FALSE,42.25,0.559898571,9.5,0.345531175,0,0.403234768,1359,0.386949193,0.423903427
12906,Plasma proteomics reveals tissue-specific cell death and mediators of cell-cell interactions in severe COVID-19 patients,0,10.1101/2020.11.02.365536,11/3/20,biorxiv,0,42,"proteom, dataset",0.790253845,0.001254608,0.001254635,0.001254625,0.001254591,0.204727695,Drug discovery,0.13108504,FALSE,29.62790698,0.426804379,108.3023256,0.836700562,10,0.828199272,7371,0.884180111,0.743971081
12907,Looking for pathways related to COVID-19 phenotypes: Confirmation of pathogenic mechanisms by SARS-CoV-2 - Host interactome.,0,10.1101/2020.11.03.366666,11/3/20,biorxiv,0,13,"proteom, interactom, network analysis",0.8866983,0.001171652,0.048245067,0.001171622,0.001171579,0.06154178,Drug discovery,0.3316568,FALSE,317.1538462,0.991526996,403.2307692,0.971501204,4,0.707574542,1374,0.394895256,0.7663745
12908,Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning,0,10.1101/2020.11.02.350439,11/2/20,biorxiv,0,2,"machine learning, whole genome, genome sequences, genomes",0.001010969,0.812654735,0.183301467,0.001010955,0.00101094,0.001010934,Genomics,0.3757885,FALSE,15,0.227596017,5,0.257024351,1,0.537564047,1146,0.290633277,0.328204423
12909,The SARS-CoV-2 RNA interactome,0,10.1101/2020.11.02.364497,11/2/20,biorxiv,0,9,"transcriptom, interactom",0.845487164,0.147366517,0.001786592,0.001786642,0.001786583,0.001786501,Drug discovery,0.2705117,FALSE,52.77777778,0.651926526,239.4444444,0.933837303,2,0.618927094,2425,0.635444257,0.710033795
12910,Temporal patterns in the evolutionary genetic distance of SARS-CoV-2 during the COVID-19 pandemic,0,10.1101/2020.11.01.363739,11/2/20,biorxiv,0,9,computational,0.001684651,0.991577179,0.001684524,0.001684646,0.00168451,0.001684489,Genomics,0.3412377,FALSE,93.11111111,0.851629662,142.7777778,0.877040407,0,0.403234768,858,0.152660727,0.571141391
12911,D614G substitution enhances the stability of trimeric SARS-CoV-2 spike protein,0,10.1101/2020.11.02.364273,11/2/20,biorxiv,0,3,in-silico,0.594009321,0.4002382,0.001438099,0.001438141,0.001438116,0.001438123,Drug discovery,0.2178038,FALSE,66.66666667,0.745191416,51,0.685509767,1,0.537564047,1116,0.277871418,0.561534162
12912,Genetic diversity analysis of the D614G mutation in SARS-CoV-2,0,10.1101/2020.10.30.362954,11/2/20,biorxiv,0,4,genomes,0.108156908,0.64122922,0.001141355,0.247189773,0.001141373,0.001141371,Genomics,0.23569241,FALSE,1.25,0.013173356,0,0.055525823,0,0.403234768,1284,0.353960992,0.206473735
12913,Viral genome sequencing places White House COVID-19 outbreak into phylogenetic context,0,10.1101/2020.10.31.20223925,11/1/20,medrxiv,0,15,"sequencing, genomic epidemiology, genomes",0.002422735,0.661200466,0.002422304,0.002422361,0.329109746,0.002422389,Genomics,0.21026343,FALSE,42.42857143,0.561692127,165.6428571,0.896307198,0,0.403234768,6578,0.868047195,0.682320322
12914,"Excess mortality during the COVID-19 pandemic in Aden governorate, Yemen: a geospatial and statistical analysis",0,10.1101/2020.10.27.20216366,10/31/20,medrxiv,10.1136/bmjgh-2020-004564,9,mathematical model,0.00107222,0.001072198,0.001072255,0.934805465,0.001072225,0.060905637,Epidemiology,0.3479741,FALSE,32.66666667,0.462922877,193.7777778,0.914436714,3,0.667819001,2357,0.6202745,0.666363273
12915,"Pre-existing conditions are associated with long-COVID patients hospitalization, despite confirmed clearance of SARS-CoV-2 virus",0,10.1101/2020.10.28.20221655,10/30/20,medrxiv,10.1016/j.eclinm.2021.100793,12,"neural network, network model, dataset",0.000936093,0.150135404,0.074731494,0.079331943,0.029410501,0.665454566,Clinics,0.5690571,TRUE,26.08333333,0.38270765,21.5,0.498260637,2,0.618927094,3038,0.705032507,0.551231972
12916,"A Machine Learning Study of 534,023 Medicare Beneficiaries with COVID-19: Implications for Personalized Risk Prediction",0,10.1101/2020.10.27.20220970,10/30/20,medrxiv,0,10,"machine learning, logistic regression",0.048425092,0.001237071,0.044805796,0.001237147,0.152978284,0.751316609,Clinics,0.45070526,FALSE,13,0.197352959,5.1,0.257425743,2,0.618927094,13682,0.941488081,0.503798469
12917,Major new lineages of SARS-CoV-2 emerge and spread in South Africa during lockdown.,0,10.1101/2020.10.28.20221143,10/30/20,medrxiv,0,27,"whole genome, genomes",0.002422285,0.845051346,0.002422372,0.145259383,0.002422355,0.002422259,Genomics,0.23292774,FALSE,33.25925926,0.468922011,144.6666667,0.878645973,10,0.828199272,3027,0.703346978,0.719778558
12918,"The COVID-19 Healthcare Personnel Study (CHPS): Overview, Methods and Preliminary Report",0,10.1101/2020.10.29.20222372,10/30/20,medrxiv,0,13,logistic regression,0.001415118,0.001415118,0.001415122,0.141299721,0.853039775,0.001415146,Healthcare,0.4645191,FALSE,11.46153846,0.171872101,6.538461538,0.288332887,0,0.403234768,1154,0.284613532,0.287013322
12919,"COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning",0,10.1101/2020.10.29.339317,10/30/20,biorxiv,0,132,"machine learning, computational",0.806219614,0.001943637,0.086800021,0.101149803,0.001943465,0.001943459,Drug discovery,0.44264913,FALSE,40.03816794,0.539922073,52.41221374,0.690460262,6,0.764429903,8434,0.899590657,0.723600724
12920,Novel SARS-CoV-2 Whole-genome sequencing technique using Reverse Complement PCR enables fast and accurate outbreak analysis,0,10.1101/2020.10.29.360578,10/29/20,biorxiv,0,13,"sequencing, whole-genome, whole genome",0.000558183,0.710517191,0.09998873,0.000558205,0.144706731,0.04367096,Genomics,0.2344673,FALSE,51.38461538,0.64178366,71.38461538,0.757291945,1,0.537564047,2743,0.670840356,0.651870002
12921,Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients,0,10.1101/2020.10.29.360479,10/29/20,biorxiv,0,82,"sequencing, transcriptom",0.715196424,0.04412717,0.001291215,0.001291232,0.001291257,0.236802703,Drug discovery,0.30615458,FALSE,60.20224719,0.704187025,,,0,0.403234768,4453,0.796291837,0.63457121
12922,Epitope profiling reveals binding signatures of SARS-CoV-2 immune response and cross-reactivity with endemic HCoVs,0,10.1101/2020.10.29.360800,10/29/20,biorxiv,0,14,proteom,0.44795043,0.497723916,0.001717204,0.001717203,0.001717223,0.049174024,Genomics,0.14738807,FALSE,44,0.578390748,80.35714286,0.779769869,0,0.403234768,1942,0.531904647,0.573325008
12923,Modeling the Opening SARS-CoV-2 Spike: an Investigation of its Dynamic Electro-Geometric Properties,0,10.1101/2020.10.29.361261,10/29/20,biorxiv,0,4,"molecular dynamics simulation, computational",0.600936278,0.002130715,0.002130848,0.39054083,0.002130666,0.002130663,Drug discovery,0.34071755,FALSE,7.5,0.108355495,1.25,0.127776291,0,0.403234768,1199,0.302191187,0.235389435
12924,"COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms",0,10.1101/2020.10.26.356014,10/28/20,biorxiv,0,133,"computational, bioinformatic, text mining",0.315547426,0.002490528,0.205585095,0.471395972,0.002490529,0.00249045,Epidemiology,0.36149073,FALSE,42.05882353,0.55859979,124.2867647,0.856770136,4,0.707574542,10146,0.917168312,0.760028195
12925,A genome-wide CRISPR/Cas9 knock-out screen identifies the DEAD box RNA helicase DDX42 as a broad antiviral inhibitor,0,10.1101/2020.10.28.359356,10/28/20,biorxiv,0,16,genome-wide,0.655250655,0.338011204,0.001684523,0.001684527,0.001684586,0.001684505,Drug discovery,0.100265205,FALSE,1.5,0.015523533,0,0.055525823,0,0.403234768,3136,0.710329882,0.296153501
12926,SARS-CoV-2 desensitizes host cells to interferon through inhibition of the JAK-STAT pathway,0,10.1101/2020.10.27.358259,10/28/20,biorxiv,0,19,proteom,0.890531619,0.001622744,0.001622705,0.00162272,0.001622705,0.102977507,Drug discovery,0.7700533,TRUE,55.68421053,0.672459645,210.2631579,0.922464544,3,0.667819001,3447,0.739706236,0.750612357
12927,ACE2 Netlas: In-silico functional characterization and drug-gene interactions of ACE2 gene network to understand its potential involvement in COVID-19 susceptibility,0,10.1101/2020.10.27.20220665,10/28/20,medrxiv,0,6,"in-silico, transcriptom",0.624874028,0.247607125,0.001156308,0.001156299,0.001156302,0.124049938,Drug discovery,0.35396805,FALSE,33,0.466757375,10.33333333,0.36038266,0,0.403234768,1352,0.362388635,0.398190859
12928,Pandemic impacts on healthcare utilisation: a systematic review,0,10.1101/2020.10.26.20219352,10/28/20,medrxiv,0,14,dataset,0.000863144,0.035746112,0.000863102,0.4538352,0.303650157,0.205042285,Epidemiology,0.5964934,TRUE,14.28571429,0.215783289,7,0.299973241,3,0.667819001,2295,0.602696846,0.446568094
12929,Characteristics and outcome profile of Hospitalized African COVID-19 patients: The Ethiopian Context,0,10.1101/2020.10.27.20220640,10/28/20,medrxiv,0,13,logistic regression,0.001046811,0.001046857,0.050989825,0.001046908,0.062917518,0.882952081,Clinics,0.6496475,TRUE,9.769230769,0.146453089,3.923076923,0.22357506,3,0.667819001,1342,0.358054418,0.348975392
12930,Frailty and comorbidity in predicting community COVID-19 mortality in the UK Biobank: the effect of sampling,0,10.1101/2020.10.22.20217489,10/27/20,medrxiv,10.1111/jgs.17089,5,logistic regression,0.001538107,0.001538204,0.001538141,0.025629141,0.200886092,0.768870315,Clinics,0.33323404,FALSE,33.2,0.468056157,,,2,0.618927094,1249,0.317601734,0.468194995
12931,The importance of the human factor during the evolution of SARS-CoV-2 pandemic: the successful case of the Italian strategy,0,10.1101/2020.10.22.20215277,10/27/20,medrxiv,0,3,mathematical model,0.002490506,0.002490517,0.002490537,0.987547386,0.002490573,0.00249048,Epidemiology,0.04634297,FALSE,21.66666667,0.320737213,18.66666667,0.466818303,0,0.403234768,715,0.072236937,0.315756805
12932,Challenges for non-technical implementation of digital proximity tracing: early experiences from Switzerland,0,10.1101/2020.10.22.20218057,10/27/20,medrxiv,10.2196/25345,1,digital health,0.001010976,0.001010962,0.001010977,0.994945128,0.001011021,0.001010936,Epidemiology,0.85305893,TRUE,22,0.326056033,4,0.231469093,2,0.618927094,720,0.074404045,0.312714066
12933,Adaptive COVID-19 Forecasting via Bayesian Optimization,0,10.1101/2020.10.19.20215293,10/27/20,medrxiv,0,6,bayes,0.002238476,0.002238453,0.10374285,0.887303335,0.002238456,0.00223843,Epidemiology,0.14545739,FALSE,8.666666667,0.12839384,46.66666667,0.667045759,3,0.667819001,690,0.062123766,0.381345591
12934,The impact of the COVID-19 pandemic on the mental health and wellbeing of UK healthcare workers,0,10.1101/2020.10.23.20218396,10/27/20,medrxiv,0,5,logistic regression,0.001622737,0.001622756,0.001622935,0.001622864,0.991885936,0.001622772,Healthcare,0.6514093,TRUE,15.2,0.229142186,5.8,0.272678619,1,0.537564047,3062,0.70093908,0.435080983
12935,Prediction and control of COVID-19 infection based on a hybrid intelligent model,0,10.1101/2020.10.22.20218032,10/27/20,medrxiv,10.1371/journal.pone.0246360,2,"predictive model, lstm",0.002639022,0.04317755,0.087500061,0.861405362,0.002638997,0.002639009,Epidemiology,0.48116878,FALSE,4,0.054734368,0.5,0.087101953,0,0.403234768,757,0.088129063,0.158300038
12936,Quantification of a Viromed Klinik Akut V 500 disinfection device to reduce the indirect risk of SARS-CoV-2 infection by aerosol particles,0,10.1101/2020.10.23.20218099,10/27/20,medrxiv,0,3,virom,0.082475582,0.000898155,0.069365874,0.801386192,0.000898149,0.044976048,Epidemiology,0.25939566,FALSE,2.666666667,0.029377203,0.333333333,0.073187048,2,0.618927094,675,0.055622442,0.194278447
12937,Large-scale population analysis of SARS-CoV2 whole genome sequences reveals host-mediated viral evolution with emergence of mutations in the viral Spike protein associated with elevated mortality rates,0,10.1101/2020.10.23.20218511,10/27/20,medrxiv,0,3,"bioinformatic, sequencing, whole genome, genome sequences",0.216865485,0.74542739,0.000683154,0.000683166,0.000683144,0.035657662,Genomics,0.17203936,FALSE,38.33333333,0.522233904,60.66666667,0.723307466,3,0.667819001,3006,0.695400915,0.652190322
12938,Establishment & lineage dynamics of the SARS-CoV-2 epidemic in the UK,0,10.1101/2020.10.23.20218446,10/27/20,medrxiv,0,27,genomes,0.001823328,0.585031811,0.001823348,0.407674838,0.001823367,0.001823308,Genomics,0.31161746,FALSE,43.51851852,0.573443008,506.8148148,0.979194541,10,0.828199272,6543,0.862990609,0.810956858
12939,MEGA: Machine Learning-Enhanced Graph Analytics for COVID-19 Infodemic Control,0,10.1101/2020.10.24.20215061,10/27/20,medrxiv,0,4,"machine learning, computational, network analysis, dataset",0.001203453,0.001203452,0.73115168,0.264034532,0.001203436,0.001203448,Epidemiology,0.06110695,FALSE,2.25,0.023439916,0,0.055525823,0,0.403234768,1166,0.281001686,0.190800548
12940,"Clinical Outcome of Asymptomatic COVID-19 Infection Among a Large Nationwide Cohort of 5,621 Hospitalized Patients in Korea",0,10.1101/2020.10.25.20218982,10/27/20,medrxiv,0,7,prediction model,0.0226123,0.001593515,0.001593541,0.001593677,0.00159354,0.971013427,Clinics,0.12716621,FALSE,6,0.086028821,0.166666667,0.058736955,0,0.403234768,1249,0.317601734,0.216400569
12941,Serologic SARS-CoV-2 testing in healthcare workers with positive RT-PCR test or Covid-19 related symptoms,0,10.1101/2020.10.25.20219113,10/27/20,medrxiv,0,6,logistic regression,0.023379356,0.044577763,0.156177228,0.001861813,0.580395204,0.193608635,Healthcare,0.29515317,FALSE,140.6666667,0.934689839,181.5,0.907278566,0,0.403234768,822,0.120154105,0.591339319
12942,Tracking changes in reporting of epidemiological data during the COVID-19 pandemic in Southeast Asia: an observational study during the first wave,0,10.1101/2020.10.23.20217570,10/27/20,medrxiv,0,4,dataset,0.001171554,0.001171616,0.001171582,0.952319504,0.042994112,0.001171631,Epidemiology,0.1233691,FALSE,9,0.135320675,2,0.164302917,0,0.403234768,607,0.03443294,0.184322825
12943,COVIDTrach; a prospective cohort study of mechanically ventilated COVID-19 patients undergoing tracheostomy in the UK,0,10.1101/2020.10.20.20216085,10/27/20,medrxiv,0,2,"logistic regression, prediction model",0.001371331,0.001371272,0.001371268,0.100160079,0.184977335,0.710748714,Clinics,0.8338326,TRUE,3.5,0.044344115,0,0.055525823,2,0.618927094,3669,0.754875993,0.368418256
12944,Integrated Single-Cell Atlases Reveal an Oral SARS-CoV-2 Infection and Transmission Axis,0,10.1101/2020.10.26.20219089,10/27/20,medrxiv,0,50,sequencing,0.546066954,0.34373669,0.001987154,0.001987171,0.001987247,0.104234785,Drug discovery,0.30545995,FALSE,83.97916667,0.821015524,142.5,0.87690661,10,0.828199272,16033,0.950156513,0.86906948
12945,"Transmission of COVID-19 in 282 clusters in Catalonia, Spain: a cohort study",0,10.1101/2020.10.27.20220277,10/27/20,medrxiv,0,16,dataset,0.000441276,0.337612773,0.000441276,0.46155494,0.1206757,0.079274035,Epidemiology,0.21918043,FALSE,17.15384615,0.258581236,7.692307692,0.310944608,18,0.891474782,2622,0.652058753,0.528264845
12946,The Incidence and Severity of COVID-19 in Adult Professional Soccer Players,0,10.1101/2020.10.27.20220400,10/27/20,medrxiv,0,13,probabilistic,0.001593533,0.081892385,0.00159358,0.001593635,0.528855704,0.384471163,Healthcare,0.21634912,FALSE,9.538461538,0.143237059,0.461538462,0.077067166,0,0.403234768,2977,0.692993017,0.329133002
12947,Quantifying SARS-CoV-2 spread in Switzerland based on genomic sequencing data,0,10.1101/2020.10.14.20212621,10/27/20,medrxiv,0,41,"sequencing, genome sequences, genomes",0.002238428,0.717500572,0.002238458,0.273545615,0.002238493,0.002238434,Genomics,0.17042553,FALSE,14.97560976,0.223637825,28.41463415,0.55706449,6,0.764429903,3193,0.71249699,0.564407302
12948,Tuning Intrinsic Disorder Predictors for Virus Proteins,0,10.1101/2020.10.27.357954,10/27/20,biorxiv,10.1093/ve/veaa106,3,"computational, sequencing, classifier, genomes",0.36540689,0.173063344,0.312621037,0.001171597,0.146565529,0.001171603,Drug discovery,0.39530665,FALSE,41.33333333,0.551549261,75.33333333,0.767326733,0,0.403234768,953,0.181555502,0.475916566
12949,Genetic determinants of COVID-19 drug efficacy revealed by genome-wide CRISPR screens,0,10.1101/2020.10.26.356279,10/27/20,biorxiv,0,18,genome-wide,0.731010551,0.040752572,0.0020328,0.002032902,0.065430342,0.158740833,Drug discovery,0.8585749,TRUE,43.16666667,0.568433422,40.16666667,0.634131656,0,0.403234768,1810,0.495786179,0.525396506
12950,Binding Mode of SARS-CoV2 Fusion Peptide to Human Cellular Membrane,0,10.1101/2020.10.27.357350,10/27/20,biorxiv,10.1016/j.bpj.2021.02.041,4,molecular dynamics simulation,0.953277062,0.043947386,0.000693891,0.000693906,0.000693873,0.000693882,Drug discovery,0.08886936,FALSE,129.25,0.920774321,328,0.961466417,3,0.667819001,2017,0.545870455,0.773982549
12951,Is increased mortality by multiple exposures to COVID-19 an overseen factor when aiming for herd immunity?,0,10.1101/2020.10.22.20217638,10/26/20,medrxiv,0,10,simulation model,0.001203467,0.087101294,0.001203417,0.734360897,0.08892206,0.087208866,Epidemiology,0.17649841,FALSE,21,0.312016822,20.6,0.487021675,0,0.403234768,2288,0.59788105,0.450038579
12952,Extending the range of symptoms in a Bayesian Network for the Predictive Diagnosis of COVID-19,0,10.1101/2020.10.22.20217554,10/26/20,medrxiv,0,2,bayes,0.002183302,0.002183285,0.002183388,0.692863757,0.002183429,0.298402839,Epidemiology,0.47639886,FALSE,8,0.118683901,0,0.055525823,1,0.537564047,1039,0.21863713,0.232602725
12953,Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer,0,10.1101/2020.10.22.20217604,10/26/20,medrxiv,0,4,"machine learning, artificial intelligence, neural network, prediction model, dataset",0.00218323,0.002183223,0.587736786,0.403530199,0.002183321,0.002183241,Epidemiology,0.71344733,TRUE,7.75,0.112808461,1,0.122023013,1,0.537564047,802,0.104984349,0.219344967
12954,"Characteristics associated with household transmission of SARS-CoV-2 in Ontario, Canada",0,10.1101/2020.10.22.20217802,10/26/20,medrxiv,0,8,logistic regression,0.000490153,0.00049018,0.000490163,0.410776195,0.567215346,0.020537963,Healthcare,0.16240862,FALSE,37.625,0.514688602,23.5,0.516791544,3,0.667819001,1905,0.515049362,0.553587127
12955,A theoretical analysis of the putative ORF10 protein in SARS-CoV-2,0,10.1101/2020.10.26.355784,10/26/20,biorxiv,0,1,bioinformatic,0.469268099,0.524884278,0.00146197,0.001461882,0.001461884,0.001461888,Genomics,0.46496826,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,1921,0.518179629,0.249770338
12956,The genomic epidemiology of SARS-CoV-2 in Palestine,0,10.1101/2020.10.26.355677,10/26/20,biorxiv,0,9,"genomic epidemiology, genomes",0.001717181,0.724821368,0.001717196,0.241951539,0.001717253,0.028075464,Genomics,0.1529629,FALSE,22.72727273,0.336075206,173.6363636,0.902060476,0,0.403234768,3656,0.752708885,0.598519834
12957,Tracking cytosine depletion in SARS-CoV-2,0,10.1101/2020.10.26.354787,10/26/20,biorxiv,0,3,"bioinformatic, whole genome",0.003607202,0.665534006,0.003607233,0.320036559,0.003607243,0.003607757,Genomics,0.3566443,FALSE,26.66666667,0.390995114,336.6666667,0.963339577,0,0.403234768,1194,0.290151698,0.511930289
12958,Distinguishing non severe cases of dengue from COVID-19 in the context of co-epidemics: a cohort study in a SARS-CoV-2 testing center on Reunion island,0,10.1101/2020.10.20.20214718,10/25/20,medrxiv,0,10,logistic regression,0.000936091,0.215825364,0.000936149,0.172731002,0.255643962,0.353927432,Clinics,0.53719383,TRUE,9.2,0.136990537,2.9,0.190794755,0,0.403234768,1367,0.358776788,0.272449212
12959,"Cost and social distancing dynamics in a mathematical model of COVID-19 with application to Ontario, Canada",0,10.1101/2020.10.21.20217158,10/25/20,medrxiv,10.1098/rsos.201770,3,mathematical model,0.002080586,0.002080643,0.002080588,0.899995446,0.091682124,0.002080613,Epidemiology,0.44408298,FALSE,38.66666667,0.525882862,28.33333333,0.556729997,1,0.537564047,1309,0.336864917,0.489260456
12960,"Predictors of PTSD, depression and anxiety in UK frontline health and social care workers during COVID-19.",0,10.1101/2020.10.21.20216804,10/25/20,medrxiv,10.1080/20008198.2021.1882781,8,logistic regression,0.001310318,0.001310357,0.001310364,0.001310388,0.967436873,0.0273217,Healthcare,0.91651106,TRUE,64,0.729173109,256,0.940058871,2,0.618927094,3127,0.704791717,0.748237698
12961,Dysregulation of Pulmonary Responses in Severe COVID-19,0,10.1101/2020.10.23.353177,10/25/20,biorxiv,0,2,dataset,0.665705139,0.002490502,0.0024906,0.00249052,0.002490475,0.324332763,Drug discovery,0.31023294,FALSE,39,0.530521368,544.5,0.981535991,0,0.403234768,1804,0.488803275,0.60102385
12962,Single-cell analyses reveal SARS-CoV-2 interference with intrinsic immune response in the human gut,0,10.1101/2020.10.21.348854,10/24/20,biorxiv,0,13,transcriptom,0.894009119,0.000599815,0.000599798,0.000599844,0.028700452,0.075490972,Drug discovery,0.77793586,TRUE,38.15384615,0.520131115,131.5384615,0.865266256,6,0.764429903,4608,0.797495786,0.736830765
12963,"SARS-CoV-2 RBD mutations, ACE2 genetic polymorphism, and stability of the virus-receptor complex: The COVID-19 host-pathogen nexus.",0,10.1101/2020.10.23.352344,10/24/20,biorxiv,0,6,computational,0.577184561,0.395492399,0.022937329,0.001461959,0.001461887,0.001461865,Drug discovery,0.32295722,FALSE,3.8,0.047374606,0.6,0.09011239,0,0.403234768,2646,0.648687696,0.297352365
12964,Mathematical Analysis of COVID-19 Transmission Dynamics. A Case Study with Nigeria,0,10.1101/2020.10.20.20216473,10/23/20,medrxiv,0,4,mathematical model,0.002562672,0.002562677,0.002562616,0.98718693,0.002562575,0.00256253,Epidemiology,0.3355866,FALSE,3.5,0.044344115,0.25,0.065493712,0,0.403234768,765,0.083794847,0.14921686
12965,"Anti-vaccine attitudes and risk factors for not agreeing to vaccination against COVID-19 amongst 32,361 UK adults: Implications for public health communications",0,10.1101/2020.10.21.20216218,10/23/20,medrxiv,0,3,logistic regression,0.000518927,0.000518909,0.000518921,0.211741131,0.786183178,0.000518934,Healthcare,0.23576793,FALSE,95.33333333,0.857566949,128.6666667,0.862055124,7,0.785110192,2667,0.650132434,0.788716175
12966,Mining transcriptomics and clinical data reveals ACE2 expression modulators and identifies cardiomyopathy as a risk factor for mortality in COVID-19 patients,0,10.1101/2020.10.20.20216150,10/23/20,medrxiv,0,4,"transcriptom, dataset",0.623939549,0.089557974,0.048576785,0.001653142,0.001653044,0.234619506,Drug discovery,0.44384906,FALSE,77.5,0.79658606,113,0.842587637,0,0.403234768,1027,0.203708163,0.561529157
12967,Decrease in Hospitalizations for COVID-19 after Mask Mandates in 1083 U.S. Counties,0,10.1101/2020.10.21.20208728,10/23/20,medrxiv,0,6,machine learning,0.004310013,0.004310144,0.178375174,0.804384301,0.004310086,0.004310282,Epidemiology,0.63456804,TRUE,48.66666667,0.619333292,113,0.842587637,1,0.537564047,51034,0.987478931,0.746740977
12968,Patterns of Multimorbidity and Risk of Severe SARS-CoV-2 Infection: an observational study in the U.K.,0,10.1101/2020.10.21.20216721,10/23/20,medrxiv,0,12,logistic regression,0.001415278,0.071535754,0.001415121,0.001415177,0.001415187,0.922803482,Clinics,0.32322657,FALSE,100.5,0.869936298,115.5833333,0.84526358,2,0.618927094,1095,0.238381893,0.643127216
12969,Potential Achilles heels of SARS-CoV-2 displayed by the base order-dependent component of RNA folding energy,0,10.1101/2020.10.22.343673,10/23/20,biorxiv,0,1,genomes,0.640050864,0.35200046,0.001987177,0.001987178,0.001987145,0.001987176,Drug discovery,0.25044045,FALSE,143,0.937596636,93.5,0.809673535,0,0.403234768,1385,0.359739947,0.627561221
12970,SARS-CoV-2 Nucleocapsid protein attenuates stress granule formation and alters gene expression via direct interaction with host mRNAs,0,10.1101/2020.10.23.342113,10/23/20,biorxiv,0,15,"sequencing, proteom",0.795945312,0.078459821,0.001034592,0.001034576,0.122491103,0.001034595,Drug discovery,0.80922365,TRUE,81.26666667,0.812171439,319.2,0.959325662,8,0.799987654,3467,0.732000963,0.82587143
12971,Exposome changes in primary school children following the wide population non-pharmacological interventions implemented due to COVID-19 in Cyprus: a national survey,0,10.1101/2020.10.21.20216978,10/23/20,medrxiv,10.1016/j.eclinm.2021.100721,7,dataset,0.000471888,0.00047189,0.000471864,0.350211833,0.647900655,0.00047187,Healthcare,0.8379228,TRUE,41.42857143,0.552229575,65.42857143,0.740366604,2,0.618927094,902,0.146400193,0.514480867
12972,The effect of COVID-19 on critical care research: A prospective longitudinal multinational survey,0,10.1101/2020.10.21.20216945,10/23/20,medrxiv,0,5,logistic regression,0.001098815,0.001098836,0.001098824,0.233485261,0.270061208,0.493157055,Clinics,0.21752334,FALSE,241,0.982744758,617.2,0.984680225,0,0.403234768,1218,0.290392487,0.66526306
12973,Simulation and prediction of further spread of COVID-19 in The Republic of Serbia by SEIRDS model of disease transmission,0,10.1101/2020.10.21.20216986,10/23/20,medrxiv,10.1016/j.mran.2021.100161,5,mathematical model,0.001291288,0.001291227,0.001291221,0.993543752,0.001291261,0.001291252,Epidemiology,0.11661646,FALSE,12.2,0.18461253,1.8,0.150120417,0,0.403234768,1552,0.419215025,0.289295685
12974,Databiology Lab CORONAHACK: Collection of Public COVID-19 Data,0,10.1101/2020.10.22.328864,10/22/20,biorxiv,0,4,"bioinformatic, dataset",0.112667681,0.123637222,0.196631921,0.562218247,0.002422348,0.00242258,Epidemiology,0.30530328,FALSE,2.5,0.027459954,0.25,0.065493712,1,0.537564047,1265,0.31013725,0.235163741
12975,Immunogenicity of a new gorilla adenovirus vaccine candidate for COVID-19,0,10.1101/2020.10.22.349951,10/22/20,biorxiv,0,23,dataset,0.793525253,0.106318627,0.09376379,0.002130826,0.002130862,0.002130643,Drug discovery,0.44066364,FALSE,49.03571429,0.624342878,49.53571429,0.679689591,2,0.618927094,3027,0.68914038,0.653024986
12976,Distinct Phenotypes of SARS-CoV-2 Isolates Reveal Viral Traits Critical for Replication in Primary Human Respiratory Cells,0,10.1101/2020.10.22.350207,10/22/20,biorxiv,0,12,sequencing,0.372453452,0.584656722,0.001072211,0.001072198,0.001072215,0.039673203,Genomics,0.3711682,FALSE,22.16666667,0.327107428,42.5,0.647578271,1,0.537564047,3096,0.695160125,0.551852468
12977,Broad transcriptional dysregulation of brain and choroid plexus cell types with COVID-19,0,10.1101/2020.10.22.349415,10/22/20,biorxiv,0,19,"transcriptom, dataset",0.544661335,0.173160368,0.001538293,0.001538188,0.142713017,0.136388799,Drug discovery,0.69502854,TRUE,27,0.3960047,57.63157895,0.71320578,7,0.785110192,6597,0.858897183,0.688304464
12978,Deep learning segmentation model for automated detection of the opacity regions in the chest X-rays of the Covid-19 positive patients and the application for disease severity,0,10.1101/2020.10.19.20215483,10/21/20,medrxiv,0,3,"machine learning, deep learning, dataset",0.001022619,0.001022645,0.994886722,0.00102269,0.001022645,0.001022679,Imaging,0.4573938,FALSE,3.666666667,0.045952131,0,0.055525823,0,0.403234768,849,0.117986997,0.15567493
12979,On nonlinear incidence rate of Covid-19,0,10.1101/2020.10.19.20215665,10/21/20,medrxiv,0,3,lstm,0.001237136,0.001237107,0.194398273,0.800653351,0.00123706,0.001237073,Epidemiology,0.13114578,FALSE,17.66666667,0.266373925,4,0.231469093,0,0.403234768,893,0.137250181,0.259581992
12980,Modifiable lifestyle factors and severe COVID-19 risk: Evidence from Mendelian randomization analysis,0,10.1101/2020.10.19.20215525,10/21/20,medrxiv,10.1186/s12920-021-00887-1,1,genome-wide,0.001684554,0.194951373,0.001684531,0.001684557,0.436067946,0.36392704,Healthcare,0.66054547,TRUE,5,0.070752675,0,0.055525823,1,0.537564047,1114,0.240549001,0.226097886
12981,Analytical solution of equivalent SEIR and agent-based model of COVID-19; showing the bounds of contact tracing,0,10.1101/2020.10.20.20212522,10/21/20,medrxiv,0,6,mathematical model,0.001653067,0.001653029,0.00165304,0.991734821,0.001653032,0.001653011,Epidemiology,0.07955608,FALSE,12,0.183190055,2.333333333,0.173401124,1,0.537564047,1057,0.212135805,0.276572758
12982,Prevalence of SARS-CoV-2 antibodies in France: results from nationwide serological surveillance,0,10.1101/2020.10.20.20213116,10/21/20,medrxiv,0,20,bayes,0.001171562,0.378425485,0.001171543,0.435039932,0.183019857,0.001171621,Epidemiology,0.21950632,FALSE,37.85,0.516915084,54.8,0.700561948,14,0.866658436,3170,0.700457501,0.696148242
12983,"Cognitive deficits in people who have recovered from COVID-19 relative to controls: An N=84,285 online study",0,10.1101/2020.10.20.20215863,10/21/20,medrxiv,0,11,dataset,0.001392945,0.001392928,0.114054654,0.256668794,0.529321626,0.097169053,Healthcare,0.46370375,FALSE,68.63636364,0.756942297,79.63636364,0.778164303,15,0.874313229,150199,0.997832892,0.85181318
12984,Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays,0,10.1101/2020.10.20.20213793,10/21/20,medrxiv,0,22,"deep learning, artificial intelligence, dataset",0.016397823,0.001126803,0.795156013,0.146907834,0.039284681,0.001126845,Imaging,0.41861114,FALSE,5.952380952,0.082008782,1.238095238,0.12643832,0,0.403234768,1646,0.441126896,0.263202191
12985,Extending the range of COVID-19 risk factors ina Bayesian network model for personalised riskassessment,0,10.1101/2020.10.20.20215814,10/21/20,medrxiv,0,1,"bayes, network model, probabilistic",0.001350352,0.001350359,0.001350371,0.993247949,0.001350435,0.001350533,Epidemiology,0.15375209,FALSE,1,0.012307502,0,0.055525823,1,0.537564047,1205,0.279797737,0.221298777
12986,"Efficacy of stay-at-home policy and transmission of COVID-19 in Toronto, Canada: a mathematical modeling study",0,10.1101/2020.10.19.20181057,10/21/20,medrxiv,0,19,mathematical model,0.000424346,0.000424347,0.012768095,0.825821591,0.160137269,0.000424353,Epidemiology,0.0822486,FALSE,5.555555556,0.07749397,2.166666667,0.166845063,5,0.739490092,1401,0.359499157,0.335832071
12987,From multiplex serology to serolomics: A novel approach to the antibody response against the SARS-CoV-2 proteome,0,10.1101/2020.10.19.20214916,10/21/20,medrxiv,0,15,proteom,0.200569376,0.295460842,0.161777208,0.001171584,0.054301997,0.286718993,Genomics,0.26691535,FALSE,63.46666667,0.724349063,102.4666667,0.826665775,0,0.403234768,977,0.176498916,0.53268713
12988,Characteristics and evolution of COVID-19 cases in Brazil: mathematical modeling and simulation,0,10.1101/2020.10.14.20212829,10/20/20,medrxiv,0,4,mathematical model,0.052328715,0.001538143,0.001538116,0.941518818,0.00153811,0.001538097,Epidemiology,0.30193835,FALSE,9.5,0.143051518,0,0.055525823,0,0.403234768,844,0.111244883,0.178264248
12989,FebriDx point-of-care test in patients with suspected COVID-19: a pooled diagnostic accuracy study,0,10.1101/2020.10.15.20213108,10/20/20,medrxiv,0,19,logistic regression,0.000498481,0.139947009,0.310964149,0.00049847,0.144338125,0.403753766,Clinics,0.28115827,FALSE,57.36842105,0.68451976,87.47368421,0.795825528,0,0.403234768,2474,0.612328437,0.623977123
12990,A Comparative Analysis of System Features Used in the TREC-COVID Information Retrieval Challenge,0,10.1101/2020.10.15.20213645,10/20/20,medrxiv,0,2,"information retrieval, dataset",0.001330051,0.001330064,0.494863288,0.386294738,0.114851739,0.00133012,Epidemiology,0.04396236,FALSE,198,0.971983425,347.5,0.965212737,0,0.403234768,775,0.081386949,0.60545447
12991,Effective Deep Learning Approaches for Predicting COVID-19 Outcomes from Chest Computed Tomography Volumes,0,10.1101/2020.10.15.20213462,10/20/20,medrxiv,0,11,"deep learning, artificial intelligence",0.001371243,0.043457132,0.909775603,0.001371338,0.00137125,0.042653434,Imaging,0.13752973,FALSE,6.818181818,0.096419074,15.90909091,0.434640086,0,0.403234768,845,0.111967253,0.261565295
12992,Dataset of COVID-19 outbreak and potential predictive features in the USA,0,10.1101/2020.10.16.20214098,10/20/20,medrxiv,0,8,dataset,0.00410983,0.004109841,0.17080149,0.812759045,0.004109994,0.0041098,Epidemiology,0.094456196,FALSE,9.25,0.137918239,1.125,0.122959593,0,0.403234768,703,0.055140862,0.179813365
12993,Age-Stratified SARS-CoV-2 Infection Fatality Rates in New York City estimated from serological data,0,10.1101/2020.10.16.20214023,10/20/20,medrxiv,0,2,bayes,0.001350328,0.001350354,0.00135033,0.623941891,0.076852551,0.295154545,Epidemiology,0.08508417,FALSE,67,0.748160059,303,0.95517795,0,0.403234768,1408,0.358535998,0.616277194
12994,"Evaluation of Nowcasting for Real-Time COVID-19 Tracking - New York City, March-May 2020",0,10.1101/2020.10.18.20209189,10/20/20,medrxiv,10.2196/25538,7,bayes,0.001330004,0.019158405,0.050597675,0.66722273,0.054337811,0.207353375,Epidemiology,0.69516313,TRUE,107.5714286,0.884779516,227.2857143,0.928686112,0,0.403234768,1513,0.400192632,0.654223257
12995,"Zebrafish studies on the vaccine candidate to COVID-19, the Spike protein: Production of antibody and adverse reaction",0,10.1101/2020.10.20.346262,10/20/20,biorxiv,0,46,"bioinformatic, in silico",0.873181177,0.063669055,0.001565312,0.001565334,0.058453763,0.001565358,Drug discovery,0.4102219,FALSE,40.41304348,0.543323644,23.34782609,0.515453572,1,0.537564047,5714,0.832410306,0.607187892
12996,SARS-CoV-2 genome-wide mapping of CD8 T cell recognition reveals strong immunodominance and substantial CD8 T cell activation in COVID-19 patients,0,10.1101/2020.10.19.344911,10/19/20,biorxiv,0,8,genome-wide,0.775749366,0.105812037,0.001392834,0.001392853,0.00139288,0.114260029,Drug discovery,0.1775696,FALSE,28.75,0.415115344,33.875,0.598006422,0,0.403234768,2984,0.679749579,0.524026528
12997,"COVID-19: Variant screening, an important step towards precision epidemiology",0,10.1101/2020.10.19.345140,10/19/20,biorxiv,0,6,multi-omics,0.408685005,0.39049978,0.049336813,0.001751291,0.001751282,0.147975829,Drug discovery,0.43936124,FALSE,96.16666667,0.859360505,60.16666667,0.721768799,0,0.403234768,1635,0.434143992,0.604627016
12998,Diversity and genomic determinants of the microbiomes associated with COVID-19 and non-COVID respiratory diseases,0,10.1101/2020.10.19.345702,10/19/20,biorxiv,0,9,"metagenom, genome sequences, genomes, microbiom, virom",0.141444704,0.629321902,0.110787235,0.000830663,0.000830649,0.116784847,Genomics,0.5542227,TRUE,55.33333333,0.670171315,1375.777778,0.996655071,0,0.403234768,1841,0.48278353,0.638211171
12999,"Single cell resolution of SARS-CoV-2 tropism, antiviral responses, and susceptibility to therapies in primary human airway epithelium",0,10.1101/2020.10.19.343954,10/19/20,biorxiv,10.1371/journal.ppat.1009292,14,sequencing,0.940417218,0.001717306,0.001717177,0.052713916,0.001717177,0.001717207,Drug discovery,0.36276764,FALSE,32.78571429,0.463912425,59.85714286,0.720230131,2,0.618927094,3100,0.691307489,0.623594285
13000,Molecular mechanism of SARS-CoV-2 inactivation by temperature,0,10.1101/2020.10.16.343459,10/18/20,biorxiv,0,5,molecular dynamics simulation,0.790053129,0.002639164,0.002639037,0.199390594,0.002639036,0.00263904,Drug discovery,0.2883296,FALSE,142.4,0.936730781,12.4,0.390286326,0,0.403234768,1812,0.47074404,0.550248979
13001,"Structure, Dynamics, Receptor Binding, and Antibody Binding of Fully-glycosylated Full-length SARS-CoV-2 Spike Protein in a Viral Membrane",0,10.1101/2020.10.18.343715,10/18/20,biorxiv,10.1021/acs.jctc.0c01144,9,molecular dynamics simulation,0.881582061,0.112665302,0.001438135,0.001438193,0.001438162,0.001438146,Drug discovery,0.50421375,TRUE,59.22222222,0.697693116,93.66666667,0.810008028,2,0.618927094,3720,0.744762822,0.717847765
13002,Analysis of Collective Response Reveals that COVID-19-Related Activities Start From the End of 2019 in Mainland China,0,10.1101/2020.10.14.20202531,10/16/20,medrxiv,0,9,correlation analysis,0.00171727,0.001717292,0.001717258,0.991413643,0.00171723,0.001717307,Epidemiology,0.16003245,FALSE,3.777777778,0.047003525,0.444444444,0.076933369,2,0.618927094,946,0.14904888,0.222978217
13003,Understanding the value of clinical symptoms of COVID-19. A logistic regression model,0,10.1101/2020.10.07.20207019,10/16/20,medrxiv,0,7,logistic regression,0.049406042,0.00109888,0.158039327,0.001098839,0.117162217,0.673194694,Clinics,0.4447867,FALSE,25.14285714,0.369781681,16.57142857,0.443336901,1,0.537564047,2207,0.555983626,0.476666564
13004,On Modeling of COVID-19 for the Indian Subcontinent using Polynomial and Supervised Learning Regression,0,10.1101/2020.10.14.20212563,10/16/20,medrxiv,0,3,"bayes, supervised learning",0.033266337,0.001943533,0.262738549,0.435064361,0.00194357,0.26504365,Epidemiology,0.42195606,FALSE,12.33333333,0.186467932,1,0.122023013,0,0.403234768,1037,0.188538406,0.22506603
13005,Peptide vaccine candidate mimics the heterogeneity of natural SARS-CoV-2 immunity in convalescent humans and induces broad T cell responses in mice models,0,10.1101/2020.10.16.339937,10/16/20,biorxiv,0,12,in silico,0.640197477,0.001112721,0.033375678,0.001112677,0.181058937,0.143142511,Drug discovery,0.3202201,FALSE,10.25,0.154245779,0.75,0.099411292,0,0.403234768,2575,0.619552131,0.319110993
13006,Additional analyses exploring the hypothesized transdifferentiation of plasmablasts to developing neutrophils in severe COVID-19,0,10.1101/2020.10.15.339473,10/16/20,biorxiv,0,6,"sequencing, transcriptom, dataset",0.762116124,0.211723083,0.006540586,0.006540171,0.006539966,0.00654007,Drug discovery,0.21977308,FALSE,43.16666667,0.568433422,55,0.702167514,0,0.403234768,1523,0.39080183,0.516159383
13007,An attempt to optimize human resources allocation based on spatial diversity of COVID-19 cases in Poland,0,10.1101/2020.10.14.20090985,10/15/20,medrxiv,0,3,correlation analysis,0.002080557,0.002080573,0.002080664,0.878863912,0.112813632,0.002080663,Epidemiology,0.354327,FALSE,29,0.41993939,1.666666667,0.145036125,0,0.403234768,868,0.110763304,0.269743397
13008,Stay-at-home policy: is it a case of exception fallacy? An internet-based ecological study,0,10.1101/2020.10.13.20211284,10/15/20,medrxiv,10.1038/s41598-021-84092-1,4,mathematical model,0.001943602,0.043370571,0.001943498,0.948855023,0.001943626,0.00194368,Epidemiology,0.33631185,FALSE,43.5,0.573381161,8.5,0.329141022,0,0.403234768,2581,0.618107392,0.480966086
13009,Sociodemographic correlates of access to sanitary pads among college students in Lucknow during COVID 19 lockdown,0,10.1101/2020.10.14.20210815,10/15/20,medrxiv,0,4,logistic regression,0.001254626,0.00125469,0.001254634,0.098752476,0.896228958,0.001254616,Healthcare,0.46050996,FALSE,4.5,0.061784897,0.5,0.087101953,0,0.403234768,1104,0.216470022,0.19214791
13010,Development of a deep learning classifier to accurately distinguish COVID-19 from look-a-like pathology on lung ultrasound,0,10.1101/2020.10.13.20212258,10/15/20,medrxiv,0,8,"deep learning, neural network, classifier, dataset",0.001350386,0.060565227,0.79718578,0.001350351,0.075934348,0.063613907,Imaging,0.5310478,TRUE,3.5,0.044344115,0.25,0.065493712,0,0.403234768,1465,0.364555743,0.219407084
13011,Heterogeneity in transmissibility and shedding SARS-CoV-2 via droplets and aerosols,0,10.1101/2020.10.13.20212233,10/15/20,medrxiv,0,6,dataset,0.00156532,0.461563039,0.001565321,0.500084449,0.001565357,0.033656515,Epidemiology,0.11339742,FALSE,70,0.764178366,111.3333333,0.840313085,5,0.739490092,11459,0.921743318,0.816431215
13012,Multiplexed proteomics and imaging of resolving and lethal SARS-CoV-2 infection in the lung,0,10.1101/2020.10.14.339952,10/15/20,biorxiv,0,12,proteom,0.821771283,0.097508677,0.074621305,0.002032902,0.002032924,0.002032909,Drug discovery,0.27359882,FALSE,82.53333333,0.816191478,194.1333333,0.914570511,0,0.403234768,1653,0.428365037,0.640590448
13013,The Strand-biased Transcription of SARS-CoV-2 and Unbalanced Inhibition by Remdesivir,0,10.1101/2020.10.15.325050,10/15/20,biorxiv,0,8,simulation model,0.511222328,0.481772844,0.001751291,0.001751167,0.0017512,0.00175117,Drug discovery,0.08705819,FALSE,120.5,0.907848352,101.625,0.825060209,1,0.537564047,1492,0.376836022,0.661827157
13014,A Common Methodological Phylogenomics Framework for intra-patient heteroplasmies to infer SARS-CoV-2 sublineages and tumor clones,0,10.1101/2020.10.14.339986,10/15/20,biorxiv,0,4,"computational, phylogenom",0.001717458,0.538700503,0.026922779,0.216604659,0.001717169,0.214337432,Genomics,0.2136688,FALSE,29,0.41993939,22,0.503746321,0,0.403234768,1317,0.30989646,0.409204235
13015,Symptoms associated with SARS-CoV-2 infection in a community-based population: Results from an epidemiological study,0,10.1101/2020.10.11.20210922,10/14/20,medrxiv,10.1371/journal.pone.0241875,7,logistic regression,0.000643458,0.028059123,0.137539979,0.000643451,0.528201809,0.304912179,Healthcare,0.10149184,FALSE,72.42857143,0.77512524,53.71428571,0.696012845,5,0.739490092,1811,0.463520347,0.668537131
13016,COVID-CT-Mask-Net: Prediction of COVID-19 from CT Scans Using Regional Features,0,10.1101/2020.10.11.20211052,10/14/20,medrxiv,10.31763/sitech.v1i2.202,1,dataset,0.001511816,0.001511826,0.912826572,0.08112609,0.001511842,0.001511854,Imaging,0.27302533,FALSE,23,0.34225988,8,0.320511105,5,0.739490092,1480,0.368408379,0.442667364
13017,COVID-19 in Italy: targeted testing as a proxy of limited health care facilities and a key to reducing hospitalization rate and the death toll.,0,10.1101/2020.10.12.20211169,10/14/20,medrxiv,0,6,bayes,0.001059433,0.001059404,0.001059378,0.864685213,0.001059379,0.131077193,Epidemiology,0.0849818,FALSE,29.5,0.426000371,32.16666667,0.586633663,0,0.403234768,972,0.157476523,0.393336331
13018,SARS-CoV-2 sequencing reveals rapid transmission from college student clusters resulting in morbidity and deaths in vulnerable populations,0,10.1101/2020.10.12.20210294,10/14/20,medrxiv,0,5,"sequencing, genomes",0.002238414,0.330068378,0.002238457,0.32034151,0.342874764,0.002238477,Healthcare,0.21418914,FALSE,54.2,0.662749706,58,0.714476853,4,0.707574542,9358,0.900553817,0.746338729
13019,Using Automated-Machine Learning to Predict COVID-19 Patient Survival: Identify Influential Biomarkers,0,10.1101/2020.10.12.20211086,10/14/20,medrxiv,0,9,"machine learning, classifier",0.000793393,0.000793471,0.511918945,0.000793422,0.00079341,0.48490736,Clinics,0.20877555,FALSE,8.888888889,0.130558476,0.777777778,0.099946481,1,0.537564047,1177,0.247772694,0.253960425
13020,"How Policies on Restaurants, Bars, Nightclubs, Masks, Schools, and Travel Influenced Swiss COVID-19 Reproduction Ratios",0,10.1101/2020.10.11.20210641,10/14/20,medrxiv,0,5,machine learning,0.001461908,0.00146188,0.001461983,0.754335727,0.239816603,0.0014619,Epidemiology,0.23004201,FALSE,12.2,0.18461253,3.4,0.208589778,0,0.403234768,2067,0.519865158,0.329075558
13021,Automated chest radiograph diagnosis: A Twofer for tuberculosis and Covid-19,0,10.1101/2020.10.13.20178483,10/14/20,medrxiv,0,9,artificial intelligence,0.001901781,0.001901733,0.701473386,0.290919575,0.001901741,0.001901785,Imaging,0.4685684,FALSE,7,0.10179974,5.111111111,0.257492641,0,0.403234768,991,0.164700217,0.231806841
13022,Validation of a Derived International Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data,0,10.1101/2020.10.13.20201855,10/14/20,medrxiv,0,40,"machine learning, computational",0.049169205,0.001237142,0.506288678,0.001237105,0.001237078,0.440830793,Clinics,0.174182,FALSE,84.1,0.822005071,,,4,0.707574542,2005,0.507344089,0.678974567
13023,Preventing COVID-19 spread in closed facilities by regular testing of employees - an efficient intervention in long-term care facilities and prisons,0,10.1101/2020.10.12.20211573,10/14/20,medrxiv,0,7,simulation model,0.001022694,0.001022698,0.001022747,0.47297277,0.522936448,0.001022643,Healthcare,0.27266806,FALSE,4.428571429,0.05850702,6.428571429,0.286259031,2,0.618927094,1890,0.481338791,0.361257984
13024,"Dynamic dysregulation of IL-6 and genes functional in NETosis, complement and coagulation in severe COVID-19 illness",0,10.1101/2020.10.13.20211425,10/14/20,medrxiv,0,3,transcriptom,0.471812188,0.001943501,0.001943456,0.001943512,0.001943531,0.520413811,Clinics,0.84326786,TRUE,3,0.037293586,0,0.055525823,1,0.537564047,1050,0.189019986,0.204850861
13025,Virus detection and identification in minutes using single-particle imaging and deep learning,0,10.1101/2020.10.13.20212035,10/14/20,medrxiv,0,16,"deep learning, neural network",0.002032804,0.329193154,0.662675766,0.002032776,0.002032765,0.002032736,Imaging,0.33160657,FALSE,12.3125,0.185478385,12.6875,0.395370618,3,0.667819001,25510,0.970142066,0.554702517
13026,Methyltransferase-like 3 modulates severe acute respiratory syndrome coronavirus-2 RNA N6-methyladenosine modification and replication,0,10.1101/2020.10.14.338558,10/14/20,biorxiv,0,10,"sequencing, transcriptom",0.554683093,0.436130583,0.00229668,0.002296585,0.002296539,0.002296519,Drug discovery,0.6895008,TRUE,126.5,0.916073969,69.1,0.750602087,1,0.537564047,1522,0.383578136,0.64695456
13027,The emergence of inter-clade hybrid SARS-CoV-2 lineages revealed by 2D nucleotide variation mapping,0,10.1101/2020.10.13.338038,10/14/20,biorxiv,0,1,"sequencing, whole-genome, genome sequences",0.003607196,0.87221143,0.113359334,0.003607228,0.003607438,0.003607374,Genomics,0.39476472,FALSE,31,0.445111015,109,0.837637142,0,0.403234768,1252,0.278593788,0.491144178
13028,"ACIS, A Novel KepTide™, Binds to ACE-2 Receptor and Inhibits the Infection of SARS-CoV2 Virus in vitro in Primate Kidney Cells: Therapeutic Implications for COVID-19",0,10.1101/2020.10.13.337584,10/14/20,biorxiv,0,6,in-silico,0.860418332,0.001237105,0.042321772,0.093548578,0.001237107,0.001237106,Drug discovery,0.20229074,FALSE,23.66666667,0.34986703,22.83333333,0.510235483,1,0.537564047,2106,0.5282928,0.48148984
13029,COVID19: Exploring uncommon epitopes for a stable immune response through MHC1 binding,0,10.1101/2020.10.14.339689,10/14/20,biorxiv,0,11,sequencing,0.69639501,0.294650348,0.002238571,0.002238709,0.002238563,0.002238799,Drug discovery,0.29677272,FALSE,2.818181818,0.031108912,0.636363636,0.090647578,0,0.403234768,1625,0.416566338,0.235389399
13030,"Maternal health care services utilization in the amid of COVID-19 pandemic in West Shoa Zone, Central Ethiopia",0,10.1101/2020.10.09.20210054,10/13/20,medrxiv,0,11,logistic regression,0.001330029,0.001330065,0.001330047,0.103400958,0.891278849,0.001330052,Healthcare,0.2516412,FALSE,2.272727273,0.023563609,0.272727273,0.065828204,1,0.537564047,999,0.165663376,0.198154809
13031,Mathematical Modeling of COVID-19 pandemic in the African continent,0,10.1101/2020.10.10.20210427,10/13/20,medrxiv,0,2,mathematical model,0.002183224,0.002183343,0.002183258,0.989083649,0.002183282,0.002183243,Epidemiology,0.44327688,FALSE,8,0.118683901,1,0.122023013,2,0.618927094,798,0.078497472,0.23453287
13032,Predicting mortality of individual COVID-19 patients: A multicenter Dutch cohort,0,10.1101/2020.10.10.20210591,10/13/20,medrxiv,0,36,logistic regression,0.001034586,0.001034567,0.301045864,0.001034624,0.001034653,0.694815706,Clinics,0.32418025,FALSE,16.47222222,0.248252829,12.72222222,0.395437517,1,0.537564047,1983,0.500842764,0.420524289
13033,"Molecular characterization of SARS-CoV-2 from Bangladesh: Implications in genetic diversity, possible origin of the virus, and functional significance of the mutations",0,10.1101/2020.10.12.336099,10/13/20,biorxiv,0,3,"sequencing, genomes",0.023355289,0.939380144,0.00088905,0.034597399,0.000889058,0.00088906,Genomics,0.55877185,TRUE,7.666666667,0.111633373,2.666666667,0.185442869,0,0.403234768,2825,0.649891645,0.337550663
13034,"A 3D Structural Interactome to Explore the Impact of Evolutionary Divergence, Population Variation, and Small-molecule Drugs on SARS-CoV-2-Human Protein-Protein Interactions",0,10.1101/2020.10.13.308676,10/13/20,biorxiv,0,7,"in silico, interactom, structural model",0.798531754,0.092628167,0.00090731,0.093062449,0.013963045,0.000907275,Drug discovery,0.28808707,FALSE,56.42857143,0.678025852,141,0.875702435,0,0.403234768,1063,0.192150253,0.537278327
13035,Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities,0,10.1101/2020.10.08.20209163,10/13/20,medrxiv,0,9,logistic regression,0.002183199,0.002183229,0.002183221,0.707901645,0.002183278,0.283365428,Epidemiology,0.25461695,FALSE,22.55555556,0.33409611,3.888888889,0.223240567,0,0.403234768,687,0.040211895,0.250195835
13036,Development and validation of the 4C Deterioration model for adults hospitalised with COVID-19,0,10.1101/2020.10.09.20209957,10/13/20,medrxiv,0,40,logistic regression,0.030632317,0.002490592,0.14116126,0.218590479,0.002490586,0.604634766,Clinics,0.5575456,TRUE,35.25,0.489331437,41.65,0.642761573,5,0.739490092,2589,0.614254756,0.621459464
13037,Covid Pandemic Analysis using Regression,0,10.1101/2020.10.08.20208991,10/13/20,medrxiv,0,2,"machine learning, dataset",0.002639016,0.00263902,0.002639303,0.986804637,0.002639071,0.002638952,Epidemiology,0.28585,FALSE,22.5,0.333539489,2.5,0.180826866,0,0.403234768,849,0.098964604,0.254141432
13038,"The risk for a new COVID-19 wave -- and how it depends on $R_0$, the current immunity level and current restrictions",0,10.1101/2020.10.09.20209981,10/13/20,medrxiv,0,3,mathematical model,0.001538126,0.001538169,0.00153809,0.992309349,0.001538181,0.001538085,Epidemiology,0.06331295,FALSE,217.6666667,0.977920712,252.6666667,0.938921595,0,0.403234768,1201,0.255959547,0.644009156
13039,Determinants of Developing Symptomatic Disease in Ethiopian COVID-19 Patients,0,10.1101/2020.10.09.20209734,10/13/20,medrxiv,0,12,logistic regression,0.001392929,0.001392907,0.001392917,0.001392922,0.432489653,0.561938672,Clinics,0.50366783,TRUE,8.083333333,0.119116828,3.583333333,0.214276157,4,0.707574542,899,0.122321214,0.290822185
13040,COVID-19 Disease Severity and Determinants among Ethiopian Patients: A study of the Millennium COVID-19 Care Center,0,10.1101/2020.10.09.20209999,10/13/20,medrxiv,0,13,logistic regression,0.001254582,0.001254623,0.001254613,0.001254603,0.170602799,0.824378781,Clinics,0.65946,TRUE,7.076923077,0.101923434,1.230769231,0.126371421,4,0.707574542,848,0.098001445,0.25846771
13041,The SIR model estimates incorrectly the basic reproduction number for the covid-19 epidemic,0,10.1101/2020.10.11.20210831,10/13/20,medrxiv,0,3,mathematical model,0.001565297,0.001565362,0.00156531,0.897084545,0.03625379,0.061965696,Epidemiology,0.23799583,FALSE,9.666666667,0.144968767,0,0.055525823,0,0.403234768,1784,0.454129545,0.264464726
13042,Within-patient genetic diversity of SARS-CoV-2,0,10.1101/2020.10.12.335919,10/12/20,biorxiv,0,28,"bioinformatic, genomes",0.000871549,0.808015413,0.000871551,0.000871579,0.019032657,0.170337251,Genomics,0.5244452,TRUE,25.5,0.37435834,58.46428571,0.715346535,6,0.764429903,2452,0.593065254,0.611800008
13043,An Innovative Non-Pharmaceutical Intervention to Mitigate SARS-CoV02 Spread: Probability Sampling to Identify and Isolate Asymptomatic Cases,0,10.1101/2020.10.07.20208686,10/12/20,medrxiv,0,7,computational,0.000880229,0.085643569,0.000880238,0.607538752,0.304176992,0.000880219,Epidemiology,0.22174957,FALSE,10,0.15214299,7.714285714,0.311479797,0,0.403234768,752,0.060919817,0.231944343
13044,Forecasting COVID-19 cases in the Philippines using various mathematical models,0,10.1101/2020.10.07.20208421,10/12/20,medrxiv,0,6,mathematical model,0.002422249,0.002422238,0.045752549,0.944558408,0.002422312,0.002422243,Epidemiology,0.15417832,FALSE,3.333333333,0.04044777,0,0.055525823,0,0.403234768,977,0.154827835,0.163509049
13045,Using test positivity and reported case rates to estimate state-level COVID-19 prevalence in the United States,0,10.1101/2020.10.07.20208504,10/12/20,medrxiv,0,2,"bayes, computational, bayesian model",0.001622753,0.001622758,0.001622777,0.732453369,0.261055602,0.001622741,Epidemiology,0.06410542,FALSE,27,0.3960047,27.5,0.549906342,1,0.537564047,1577,0.399229473,0.470676141
13046,One size fits all?: Modeling face-mask fit on population-based faces,0,10.1101/2020.10.07.20208744,10/12/20,medrxiv,0,3,computational,0.068676914,0.001392941,0.001393052,0.693384676,0.201681579,0.033470838,Epidemiology,0.3679377,FALSE,19.33333333,0.288638753,9.333333333,0.342253144,0,0.403234768,1202,0.253792439,0.321979776
13047,"COVID-19 Susceptibility and Severity Risks in a Survey of Over 500,000 People",0,10.1101/2020.10.08.20209593,10/12/20,medrxiv,0,18,dataset,0.000966781,0.094921796,0.116006057,0.106328638,0.354804405,0.326972323,Healthcare,0.17196706,FALSE,10.38888889,0.155482714,6.111111111,0.280438855,0,0.403234768,2310,0.567059957,0.351554073
13048,Machine learning based prognostic model and mobile application software platform for predicting infection susceptibility of COVID-19 using health care data,0,10.1101/2020.10.09.20165431,10/12/20,medrxiv,0,5,"machine learning, classifier, correlation analysis, dataset",0.000710601,0.000710604,0.646938363,0.128885935,0.09480871,0.127945786,Epidemiology,0.19832766,FALSE,2.4,0.025419012,0,0.055525823,0,0.403234768,948,0.140621238,0.15620021
13049,SARS-CoV-2 infects carotid arteries: implications for vascular disease and organ injury in COVID-19,0,10.1101/2020.10.10.334458,10/12/20,biorxiv,0,22,transcriptom,0.263028079,0.117601193,0.005697702,0.171867142,0.005697891,0.436107992,Clinics,0.22235668,FALSE,75.13636364,0.787247201,155.1818182,0.88821247,0,0.403234768,2236,0.5518902,0.65764616
13050,Unsupervised explainable AI for simultaneous molecular evolutionary study of forty thousand SARS-CoV-2 genomes,0,10.1101/2020.10.11.335406,10/12/20,biorxiv,0,5,"artificial intelligence, genomes",0.001653077,0.565119097,0.428268658,0.001653076,0.001653079,0.001653013,Genomics,0.1905654,FALSE,40.6,0.54530274,48.6,0.67594327,0,0.403234768,708,0.04550927,0.417497512
13051,Alternate primers for whole-genome SARS-CoV-2 sequencing,0,10.1101/2020.10.12.335513,10/12/20,biorxiv,10.1093/ve/veab006,4,"sequencing, whole-genome, genomes",0.111583305,0.876823029,0.002898446,0.002898573,0.002898406,0.002898241,Genomics,0.7409306,TRUE,54.75,0.666584204,95,0.813085363,1,0.537564047,1913,0.482061161,0.624823694
13052,Genomic Similarity of Nucleotides in SARS CoronaVirus using K-Means Unsupervised Learning Algorithm,0,10.1101/2020.10.12.336339,10/12/20,biorxiv,0,1,"machine learning, supervised learning, unsupervised learning, bioinformatic, genomes",0.000926313,0.528853728,0.299164052,0.155544837,0.014584779,0.000926292,Genomics,0.1290831,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,965,0.148326511,0.165455367
13053,Sequences in the cytoplasmic tail of SARS-CoV-2 spike facilitate syncytia formation,0,10.1101/2020.10.12.335562,10/12/20,biorxiv,0,7,proteom,0.968828183,0.001717228,0.001717236,0.001717198,0.024302885,0.00171727,Drug discovery,0.4031708,FALSE,63,0.721998887,307.8571429,0.956783516,3,0.667819001,3536,0.717312786,0.765978548
13054,Integrated analysis of multimodal single-cell data,0,10.1101/2020.10.12.335331,10/12/20,biorxiv,0,25,"computational, transcriptom, dataset",0.447816356,0.219430591,0.285602264,0.001684618,0.001684634,0.043781537,Drug discovery,0.13247526,FALSE,37.64,0.514812295,191.76,0.913165641,32,0.933699611,42444,0.983867084,0.836386158
13055,SARS-CoV-2 infections in Italian schools: preliminary findings after one month of school opening during the second wave of the pandemic,0,10.1101/2020.10.10.20210328,10/11/20,medrxiv,10.3389/fped.2020.615894,4,dataset,0.001415111,0.001415198,0.00141525,0.384041221,0.61029809,0.001415131,Healthcare,0.18300569,FALSE,33,0.466757375,16.5,0.44293551,7,0.785110192,6073,0.834336624,0.632284925
13056,Extended laboratory panel testing in the Emergency Department for risk-stratification of patients with COVID-19: a single centre retrospective service evaluation,0,10.1101/2020.10.06.20205369,10/11/20,medrxiv,10.1136/jclinpath-2020-207157,14,logistic regression,0.032299777,0.001059397,0.038781926,0.196880929,0.001059396,0.729918575,Clinics,0.36476898,FALSE,25.28571429,0.371018616,20.21428571,0.482271876,0,0.403234768,1434,0.343125451,0.399912678
13057,Age-targeted dose allocation can halve COVID-19 vaccine requirements,0,10.1101/2020.10.08.20208108,10/11/20,medrxiv,0,7,mathematical model,0.087337843,0.002422354,0.002422342,0.584348564,0.002422486,0.321046411,Epidemiology,0.20526585,FALSE,55.71428571,0.672521492,19.85714286,0.478592454,6,0.764429903,3424,0.707440405,0.655746063
13058,"Enoxaparin is associated with lower rates of thrombosis, kidney injury, and mortality than Unfractionated Heparin in hospitalized COVID patients",0,10.1101/2020.10.06.20208025,10/11/20,medrxiv,10.1016/j.eclinm.2021.100774,8,neural network,0.045572617,0.001943541,0.137302002,0.001943672,0.155445176,0.657792991,Clinics,0.5041442,TRUE,,,,,0,0.403234768,1189,0.244642427,0.323938597
13059,The human brain vasculature shows a distinct expression pattern of SARS-CoV-2 entry factors,0,10.1101/2020.10.10.334664,10/11/20,biorxiv,0,8,"sequencing, dataset",0.760605567,0.074972791,0.001034593,0.001034604,0.001034643,0.161317802,Drug discovery,0.47256252,FALSE,85.5,0.82658173,449.25,0.975515119,1,0.537564047,5167,0.805682639,0.786335884
13060,Predictors of Death in Severe COVID-19 Patients at Millennium COVID-19 Care Center in Ethiopia: A Case-Control Study,0,10.1101/2020.10.07.20205575,10/9/20,medrxiv,0,12,logistic regression,0.001786502,0.001786524,0.001786541,0.001786594,0.067960046,0.924893793,Clinics,0.40168226,FALSE,2.615384615,0.028325809,0,0.055525823,2,0.618927094,967,0.141584397,0.211090781
13061,Psychiatric morbidity and protracted symptoms in recovered COVID-19 patients,0,10.1101/2020.10.07.20208249,10/9/20,medrxiv,0,8,logistic regression,0.001126776,0.001126797,0.001126797,0.00112681,0.584118026,0.411374794,Healthcare,0.87351763,TRUE,8.375,0.123940875,1.125,0.122959593,0,0.403234768,803,0.076089574,0.181556202
13062,Detecting and isolating false negatives ofSARS-CoV-2 primers and probe sets among the Japanese Population: A laboratory testing methodology and study,0,10.1101/2020.10.07.20208264,10/9/20,medrxiv,0,10,sequence alignment,0.045254506,0.875250298,0.072779574,0.002238531,0.002238563,0.002238528,Genomics,0.15542892,FALSE,23.90909091,0.352464593,107.6363636,0.835630185,0,0.403234768,1402,0.324343848,0.478918348
13063,High seroprevalence of SARS-CoV-2 antibodies among people living in precarious situations in Ile de France,0,10.1101/2020.10.07.20207795,10/9/20,medrxiv,10.1016/S2468-2667(21)00001-3,22,logistic regression,0.001085382,0.360081113,0.001085352,0.001085384,0.635577362,0.001085407,Healthcare,0.5637204,TRUE,17.72727273,0.266806853,30.22727273,0.571180091,5,0.739490092,1170,0.230676619,0.452038414
13064,AncestryDNA COVID-19 Host Genetic Study Identifies Three Novel Loci,0,10.1101/2020.10.06.20205864,10/9/20,medrxiv,0,16,genome-wide,0.001751309,0.618452693,0.001751164,0.001751192,0.144631366,0.231662276,Genomics,0.27502316,FALSE,11.1875,0.16828499,6.75,0.292079208,12,0.850299401,4138,0.757524681,0.51704707
13065,Modelling testing and response strategies for COVID-19 outbreaks in remote Australian Aboriginal communities,0,10.1101/2020.10.07.20208819,10/9/20,medrxiv,0,6,simulation model,0.001371254,0.001371311,0.001371308,0.67282747,0.321687341,0.001371316,Epidemiology,0.12975231,FALSE,62.16666667,0.716308986,39.33333333,0.629649451,0,0.403234768,594,0.015169757,0.441090741
13066,Role of high-dose exposure in transmission hot zones as a driver of SARS-CoV2 dynamics,0,10.1101/2020.10.07.20208231,10/9/20,medrxiv,0,3,mathematical model,0.001126887,0.109006579,0.001126811,0.606978773,0.280634088,0.001126863,Epidemiology,0.24999648,FALSE,117,0.902900612,167.6666667,0.897578271,1,0.537564047,720,0.04575006,0.595948248
13067,EFFECT OF CONVALESCENT PLASMA ON MORTALITY IN PATIENTS WITH COVID-19 PNEUMONIA,0,10.1101/2020.10.08.20202606,10/9/20,medrxiv,0,11,logistic regression,0.00094615,0.034517706,0.02257633,0.000946145,0.02709753,0.91391614,Clinics,0.30945998,FALSE,19.18181818,0.286659657,18,0.46180091,7,0.785110192,1945,0.481820371,0.503847782
13068,Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell Gene Expression Data,0,10.1101/2020.10.08.332080,10/9/20,biorxiv,0,4,dataset,0.168244672,0.002130843,0.477796919,0.34756599,0.002130734,0.002130842,Epidemiology,0.258268,FALSE,24.5,0.361988991,19.25,0.47310677,0,0.403234768,635,0.025523718,0.315963562
13069,Forecasting COVID-19 cases at the Amazon region: a comparison of classical and machine learning models,0,10.1101/2020.10.09.332908,10/9/20,biorxiv,0,3,"machine learning, computational, logistic regression",0.001187274,0.00118731,0.220665368,0.774585479,0.001187292,0.001187277,Epidemiology,0.17436251,FALSE,5.666666667,0.079473066,2.333333333,0.173401124,0,0.403234768,747,0.054418493,0.177631862
13070,"Real-World Effectiveness of hydroxychloroquine, azithromycin, and ivermectin among hospitalized COVID-19 patients: Results of a target trial emulation using observational data from a nationwide Healthcare System in Peru",0,10.1101/2020.10.06.20208066,10/8/20,medrxiv,0,4,machine learning,0.001751618,0.001751252,0.099372217,0.146524087,0.001751271,0.748849555,Clinics,0.30842066,FALSE,,,,,0,0.403234768,22949,0.964844691,0.684039729
13071,BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies,0,10.1101/2020.10.06.20208132,10/8/20,medrxiv,10.6339/21-jds1009,4,bayes,0.002720102,0.00272025,0.002720326,0.986398819,0.002720241,0.002720262,Epidemiology,0.26152962,FALSE,4.5,0.061784897,1.75,0.148381054,3,0.667819001,1091,0.191187094,0.267293012
13072,CD8+ T cell responses in convalescent COVID-19 individuals target epitopes from the entireSARS-CoV-2 proteome and show kinetics of early differentiation,0,10.1101/2020.10.08.330688,10/8/20,biorxiv,0,22,proteom,0.826081809,0.16754383,0.001593656,0.00159357,0.001593508,0.001593627,Drug discovery,0.29219472,FALSE,138.0454545,0.9306698,219.9545455,0.926478459,5,0.739490092,2762,0.629183723,0.806455518
13073,Tracking the introduction and spread of SARS-CoV-2 in coastal Kenya,0,10.1101/2020.10.05.20206730,10/7/20,medrxiv,0,26,genomes,0.001987217,0.990064116,0.001987128,0.001987255,0.001987149,0.001987134,Genomics,0.37797743,FALSE,12.30769231,0.185416538,6.346153846,0.285188654,3,0.667819001,1662,0.409583434,0.387001907
13074,Mental health symptoms in a cohort of hospital healthcare workers following the first peak of the Covid-19 pandemic in the United Kingdom.,0,10.1101/2020.10.02.20205674,10/7/20,medrxiv,10.1192/bjo.2020.150,10,logistic regression,0.00125457,0.001254588,0.00125458,0.001254637,0.930053428,0.064928196,Healthcare,0.674395,TRUE,14,0.213494959,7.9,0.31462403,1,0.537564047,967,0.13749097,0.300793502
13075,The potential impact of intervention strategies on COVID-19 transmission in Malawi: A mathematical modelling study,0,10.1101/2020.10.06.20207878,10/7/20,medrxiv,0,11,mathematical model,0.020741599,0.000547823,0.000547807,0.733479822,0.000547852,0.244135097,Epidemiology,0.12457222,FALSE,26.41666667,0.386975076,41,0.639751137,0,0.403234768,952,0.131952805,0.390478446
13076,Misinformation on COVID-19 origin and social distancing: A cross-sectional study,0,10.1101/2020.10.06.20207894,10/7/20,medrxiv,0,6,sequencing,0.001085354,0.131247673,0.001085379,0.099080939,0.743152883,0.024347773,Healthcare,0.5582248,TRUE,2.333333333,0.024800544,0.166666667,0.058736955,0,0.403234768,1354,0.297856971,0.196157309
13077,MAJORA: Continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance,0,10.1101/2020.10.06.328328,10/7/20,biorxiv,0,19,"sequencing, genomic epidemiology, genomes",0.001593524,0.557579504,0.001593563,0.436046369,0.001593553,0.001593488,Genomics,0.187576,FALSE,77.33333333,0.795967592,804.6111111,0.99009901,5,0.739490092,3764,0.730074645,0.813907835
13078,"SARS-CoV-2 has observably higher propensity to accept uracil as nucleotide substitution: Prevalence of amino acid substitutions and their predicted functional implications in circulating SARS-CoV-2 in India up to July, 2020",0,10.1101/2020.10.07.329771,10/7/20,biorxiv,0,4,"genome sequences, genomes",0.001141368,0.994293295,0.001141329,0.001141348,0.001141332,0.001141328,Genomics,0.14628914,FALSE,8.5,0.126662131,1.75,0.148381054,0,0.403234768,1055,0.174091019,0.213092243
13079,Mass spectrometric based detection of protein nucleotidylation in the RNA polymerase of SARS-CoV-2,0,10.1101/2020.10.07.330324,10/7/20,biorxiv,10.1038/s42004-021-00476-4,3,sequencing,0.590741854,0.383399667,0.001392902,0.001392886,0.001392838,0.021679853,Drug discovery,0.53009737,TRUE,80.66666667,0.809512029,363,0.967821782,0,0.403234768,1214,0.244401637,0.606242554
13080,NSP 11 of SARS-CoV-2 is an Intrinsically Disordered Protein,0,10.1101/2020.10.07.330068,10/7/20,biorxiv,0,6,proteom,0.81150979,0.002639178,0.00263903,0.002639104,0.177933769,0.002639129,Drug discovery,0.7319113,TRUE,32.5,0.461685942,4,0.231469093,3,0.667819001,2377,0.567782326,0.482189091
13081,"Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race",0,10.1101/2020.10.04.20206763,10/6/20,medrxiv,0,9,machine learning,0.001171554,0.001171565,0.001171634,0.941734825,0.053578838,0.001171585,Epidemiology,0.13536814,FALSE,12.77777778,0.191972293,21,0.492239765,1,0.537564047,951,0.129304118,0.337770055
13082,"A Data-Informed Approach for Analysis, Validation, and Identification of COVID-19 Models",0,10.1101/2020.10.03.20206250,10/6/20,medrxiv,0,7,"model fit, dataset",0.001717302,0.047235002,0.001717216,0.945896011,0.001717195,0.001717274,Epidemiology,0.098068476,FALSE,119,0.905683716,243.2857143,0.935442869,1,0.537564047,1152,0.213098965,0.647947399
13083,An in-depth investigation of the safety and immunogenicity of an inactivated SARS-CoV-2 vaccine,0,10.1101/2020.09.27.20189548,10/6/20,medrxiv,0,48,transcriptom,0.343356349,0.121115778,0.001171549,0.154316788,0.140463846,0.239575691,Drug discovery,0.42685705,FALSE,9.382978723,0.139526254,3.680851064,0.217687985,9,0.814309525,6841,0.846376114,0.504474969
13084,Multi-Clonal Live SARS-CoV-2 In Vitro Neutralization by Antibodies Isolated from Severe COVID-19 Convalescent Donors,0,10.1101/2020.10.06.323634,10/6/20,biorxiv,10.1371/journal.ppat.1009165,27,sequencing,0.734115219,0.204684767,0.001538088,0.001538096,0.001538162,0.056585668,Drug discovery,0.29297107,FALSE,23.37037037,0.345846991,32.14814815,0.586432968,4,0.707574542,3270,0.685046954,0.581225364
13085,"The strength of a NES motif in the nucleocapsid protein of human coronaviruses is related to genus, but not to pathogenic capacity",0,10.1101/2020.10.06.328138,10/6/20,biorxiv,0,3,"machine learning, in silico",0.340485222,0.493199593,0.160729921,0.001861788,0.001861771,0.001861705,Genomics,0.209932,FALSE,24,0.35574247,10.33333333,0.36038266,0,0.403234768,816,0.075848784,0.29880217
13086,Sub-second heat inactivation of coronavirus,0,10.1101/2020.10.05.327528,10/6/20,biorxiv,0,8,genomes,0.039117163,0.359726428,0.001203472,0.597546004,0.001203466,0.001203468,Epidemiology,0.21981594,FALSE,53.25,0.655328097,45.75,0.662229061,2,0.618927094,2998,0.657356128,0.648460095
13087,A genetic variant protective for COVID-19 is inherited from Neanderthals,0,10.1101/2020.10.05.327197,10/6/20,biorxiv,0,2,genome-wide,0.148047376,0.479338192,0.003760451,0.003760552,0.003760707,0.361332723,Genomics,0.2691511,FALSE,23.5,0.348506401,8,0.320511105,2,0.618927094,5135,0.799422105,0.521841676
13088,Proteolytic activation of the SARS-CoV-2 spike S1/S2 site: a re-evaluation of furin cleavage,0,10.1101/2020.10.04.325522,10/5/20,biorxiv,10.1021/acsinfecdis.0c00701,6,bioinformatic,0.939936313,0.054118017,0.001486407,0.001486444,0.001486404,0.001486415,Drug discovery,0.53834957,TRUE,39.66666667,0.536211268,99.16666667,0.820845598,0,0.403234768,1964,0.475800626,0.559023065
13089,Association of Pre-COVID-19 Lymphocytopenia with Fatality,0,10.1101/2020.10.02.20200931,10/5/20,medrxiv,0,4,logistic regression,0.002238428,0.002238571,0.002238496,0.002238682,0.002238572,0.988807251,Clinics,0.15848485,FALSE,17,0.257467994,10.5,0.363459995,0,0.403234768,972,0.135083072,0.289811457
13090,Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity irrespective of virus,0,10.1101/2020.10.02.20205880,10/5/20,medrxiv,10.1016/j.immuni.2021.03.002,15,transcriptom,0.597694839,0.063714755,0.050911765,0.001943689,0.001943649,0.283791302,Drug discovery,0.46450883,FALSE,34.6,0.483023069,53.4,0.695143163,3,0.667819001,2445,0.575487599,0.605368208
13091,Comparing biomarkers for COVID-19 disease with commonly associated preexisting conditions and complications.,0,10.1101/2020.10.02.20205609,10/5/20,medrxiv,0,1,"sequencing, network analysis",0.241396138,0.081820887,0.001310425,0.001310381,0.001310354,0.672851816,Clinics,0.8988968,TRUE,9,0.135320675,7,0.299973241,2,0.618927094,867,0.090777751,0.28624969
13092,COVID-19 Outbreaks in Refugee Camps. A simulation study.,0,10.1101/2020.10.02.20204818,10/5/20,medrxiv,0,4,mathematical model,0.002296532,0.002296564,0.002296584,0.796542551,0.002296719,0.194271051,Epidemiology,0.34695566,FALSE,31,0.445111015,11.5,0.378378378,1,0.537564047,742,0.048639538,0.352423245
13093,Metabolic syndrome increases COVID-19-related mortality in the UK Biobank sample,0,10.1101/2020.10.02.20205716,10/5/20,medrxiv,10.3389/fendo.2021.652765,2,logistic regression,0.002898353,0.049070786,0.00289833,0.002898437,0.002898567,0.939335527,Clinics,0.5448761,TRUE,15.5,0.234028078,0,0.055525823,0,0.403234768,1187,0.226342403,0.229782768
13094,Reference ontology and database annotation of the COVID-19 Open Research Dataset (CORD-19),0,10.1101/2020.10.04.325266,10/5/20,biorxiv,0,5,"machine learning, dataset",0.114518342,0.174897396,0.465038465,0.193096986,0.05046159,0.001987222,Epidemiology,0.17524585,FALSE,46,0.596882924,176.6,0.903933637,0,0.403234768,1219,0.240789791,0.53621028
13095,Combined metabolic cofactor supplementation accelerates recovery in mild-to-moderate COVID-19,0,10.1101/2020.10.02.20202614,10/5/20,medrxiv,0,18,"proteom, metabolom",0.248410727,0.001987175,0.001987289,0.001987253,0.112161569,0.633465987,Clinics,0.94643456,TRUE,109.3529412,0.888985095,188.9411765,0.911493176,4,0.707574542,15570,0.940284132,0.862084236
13096,COVID-19 Classification of X-ray Images Using Deep Neural Networks,0,10.1101/2020.10.01.20204073,10/4/20,medrxiv,0,27,"machine learning, deep learning, neural network, classifier, dataset",0.000956292,0.000956296,0.945351221,0.0009563,0.000956355,0.050823537,Imaging,0.16111094,FALSE,41.37037037,0.551796648,79.51851852,0.777963607,3,0.667819001,2323,0.549482302,0.63676539
13097,COVID-19 Pandemic in University Hospital: Impact on Medical Training of Medical Interns,0,10.1101/2020.10.01.20204255,10/4/20,medrxiv,0,4,logistic regression,0.001987124,0.001987141,0.207265416,0.066928159,0.719844906,0.001987254,Healthcare,0.73810077,TRUE,4,0.054734368,1,0.122023013,0,0.403234768,1770,0.432217674,0.253052456
13098,Risk factors associated with SARS-CoV-2 infection and outbreaks in Long Term Care Facilities in England: a national survey,0,10.1101/2020.10.02.20205591,10/4/20,medrxiv,10.1016/s2666-7568(20)30065-9,11,logistic regression,0.000595757,0.000595769,0.000595755,0.130970217,0.815555589,0.051686914,Healthcare,0.3555733,FALSE,7.454545455,0.106500093,0.818181818,0.101752743,3,0.667819001,2180,0.519383578,0.348863854
13099,Genetic and non-genetic factors affecting the expression of COVID-19 relevant genes in the large airway epithelium,0,10.1101/2020.10.01.20202820,10/4/20,medrxiv,0,45,sequencing,0.419336417,0.368422031,0.001751155,0.001751253,0.001751218,0.206987926,Drug discovery,0.27858114,FALSE,139.3,0.932401509,198.775,0.917179556,1,0.537564047,3020,0.656152179,0.760824323
13100,Prognostic accuracy of MALDI mass spectrometric analysis of plasma in COVID-19,0,10.1101/2020.10.01.20205310,10/4/20,medrxiv,0,14,proteom,0.181566893,0.091869175,0.316418166,0.000966795,0.000966771,0.408212199,Clinics,0.5849071,TRUE,54.35714286,0.663739254,47.85714286,0.672196949,1,0.537564047,1166,0.213580544,0.521770199
13101,An Integrated Framework with Machine Learning and Radiomics for Accurate and Rapid Early Diagnosis of COVID-19 from Chest X-ray,0,10.1101/2020.10.01.20205146,10/2/20,medrxiv,0,6,"machine learning, radiom",0.000629677,0.000629693,0.970126068,0.027355115,0.000629732,0.000629716,Imaging,0.4479976,FALSE,5.666666667,0.079473066,8.333333333,0.325662296,1,0.537564047,909,0.102094871,0.26119857
13102,COVID-19 Pandemic in University Hospital: Is There an Effect on The Medical Interns?,0,10.1101/2020.10.01.20205112,10/2/20,medrxiv,0,4,logistic regression,0.002238505,0.002238478,0.002238521,0.002238535,0.988807344,0.002238617,Healthcare,0.93090284,TRUE,4,0.054734368,1,0.122023013,0,0.403234768,942,0.116301469,0.174073404
13103,Biomathematical models for genetic diversity analyses in complete genomes of SARS-CoV-2,0,10.1101/2020.10.01.20205120,10/2/20,medrxiv,0,5,"mathematical model, genomes",0.00321417,0.868291602,0.118851501,0.003214363,0.00321418,0.003214184,Genomics,0.42857304,FALSE,3.4,0.040942544,0,0.055525823,0,0.403234768,889,0.094389598,0.148523183
13104,Theta autoregressive neural network model for COVID-19 outbreak predictions,0,10.1101/2020.10.01.20205021,10/2/20,medrxiv,0,3,"neural network, network model, forecasting model",0.001653033,0.001653091,0.269736386,0.723651365,0.001653031,0.001653095,Epidemiology,0.40842277,FALSE,11.66666667,0.176510607,2,0.164302917,1,0.537564047,1017,0.147604142,0.256495428
13105,SARS-CoV-2 infected cells present HLA-I peptides from canonical and out-of-frame ORFs,0,10.1101/2020.10.02.324145,10/2/20,biorxiv,0,21,"proteom, immunopeptidom",0.839956982,0.154471437,0.001392888,0.001392954,0.001392855,0.001392884,Drug discovery,0.06328714,FALSE,89.52380952,0.841301255,593.7619048,0.983676746,2,0.618927094,7194,0.853359018,0.824316028
13106,A small interfering RNA (siRNA) database for SARS-CoV-2,0,10.1101/2020.09.30.321596,10/1/20,biorxiv,0,6,dataset,0.483019687,0.510367949,0.001653173,0.00165313,0.001653039,0.001653022,Genomics,0.59142303,TRUE,28.83333333,0.415857505,8.666666667,0.331415574,1,0.537564047,1645,0.387189983,0.418006777
13107,Potential spreading dynamics of COVID-19 with temporary immunity - a mathematical modeling study,0,10.1101/2020.09.30.20204636,9/30/20,medrxiv,10.1016/j.ijid.2021.01.018,4,mathematical model,0.151591746,0.001220132,0.001220007,0.802497306,0.001220041,0.042250768,Epidemiology,0.108511716,FALSE,15,0.227596017,0.5,0.087101953,1,0.537564047,1169,0.204430532,0.264173137
13108,Large scale sequencing of SARS-CoV-2 genomes from one region allows detailed epidemiology and enables local outbreak management,0,10.1101/2020.09.28.20201475,9/30/20,medrxiv,0,42,"sequencing, whole genome, genome sequences, genomes",0.00067276,0.764152948,0.000672779,0.070025072,0.133570374,0.030906068,Genomics,0.27765334,FALSE,16.525,0.249427918,21.2,0.493644635,5,0.739490092,5785,0.817240549,0.574950798
13109,Artificial intelligence to predict the risk of mortality from COVID-19: Insights from a Canadian Application,0,10.1101/2020.09.29.20201632,9/30/20,medrxiv,0,6,"artificial intelligence, dataset",0.0024223,0.002422372,0.403084271,0.375128385,0.002422475,0.214520197,Epidemiology,0.28416836,FALSE,44.33333333,0.580679077,20.16666667,0.481803586,0,0.403234768,1035,0.151215988,0.404233355
13110,Passive Microwave Radiometry (MWR) for diagnostics of COVID-19 lung complications.,0,10.1101/2020.09.29.20202598,9/30/20,medrxiv,0,10,radiom,0.000515887,0.00051594,0.694223965,0.000515902,0.013771053,0.290457253,Imaging,0.42852718,FALSE,23.3,0.344857443,48.9,0.676812952,0,0.403234768,1134,0.189742355,0.403661879
13111,An integrated clinical and genetic model for predicting risk of severe COVID-19,0,10.1101/2020.09.30.20204453,9/30/20,medrxiv,10.1371/journal.pone.0247205,3,logistic regression,0.002080567,0.065535742,0.311006017,0.002080646,0.196057096,0.423239932,Clinics,0.15984368,FALSE,46.66666667,0.602510978,55.66666667,0.705244849,1,0.537564047,4579,0.771490489,0.654202591
13112,Dry loop mediated isothermal amplification assay for detection of SARS-CoV-2 from clinical specimens,0,10.1101/2020.09.29.20204297,9/30/20,medrxiv,0,11,genomes,0.000822973,0.584815593,0.411892621,0.000822949,0.000822928,0.000822936,Genomics,0.2632898,FALSE,14.36363636,0.216958377,26.63636364,0.542614397,0,0.403234768,1139,0.191427883,0.338558856
13113,Forecasting Covid-19 Infections and Deaths Horizon in Egypt,0,10.1101/2020.09.28.20202911,9/30/20,medrxiv,0,2,"prediction model, forecasting model",0.001156319,0.001156258,0.001156268,0.966193176,0.029181708,0.001156271,Epidemiology,0.114209384,FALSE,1.5,0.015523533,0,0.055525823,0,0.403234768,934,0.107633036,0.14547929
13114,SARS-CoV-2 viral load peaks prior to symptom onset: a systematic review and individual-pooled analysis of coronavirus viral load from 66 studies,0,10.1101/2020.09.28.20202028,9/30/20,medrxiv,0,6,mathematical model,0.000936091,0.493237956,0.000936077,0.320895768,0.000936133,0.183057974,Genomics,0.23937419,FALSE,33,0.466757375,26.5,0.542012309,14,0.866658436,10888,0.90849988,0.695982
13115,High-Quality Masks Can Reduce Infections and Deaths in the US,0,10.1101/2020.09.27.20199737,9/29/20,medrxiv,0,7,simulation model,0.00190176,0.069729793,0.00190169,0.830973437,0.093591465,0.001901855,Epidemiology,0.27529296,FALSE,27.83333333,0.405219865,25,0.529435376,3,0.667819001,3561,0.699012762,0.575371751
13116,Two Color Single Molecule Sequencing on GenoCare 1600 Platform to Facilitate Clinical Applications,0,10.1101/2020.09.28.20203455,9/29/20,medrxiv,0,45,sequencing,0.057840157,0.54520532,0.38903735,0.002639067,0.002639124,0.002638982,Genomics,0.17466459,FALSE,9.022727273,0.135382522,14.45454545,0.417045759,0,0.403234768,991,0.131230436,0.271723371
13117,Transfer transcriptomic signatures for infectious diseases,0,10.1101/2020.09.28.20203406,9/29/20,medrxiv,0,5,"machine learning, classifier, transcriptom, dataset",0.414277046,0.102059468,0.416097493,0.001392938,0.001392945,0.06478011,Drug discovery,0.098106116,FALSE,8.4,0.124435648,16.6,0.443537597,0,0.403234768,823,0.066939562,0.259536894
13118,DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting,0,10.1101/2020.09.28.20203109,9/29/20,medrxiv,0,8,deep learning,0.002130681,0.002130683,0.325043984,0.66643339,0.002130646,0.002130616,Epidemiology,0.09863135,FALSE,17.25,0.260189251,30.75,0.57499331,7,0.785110192,2690,0.600770527,0.55526582
13119,A global omics data sharing and analytics marketplace: Case study of a rapid data COVID-19 pandemic response platform.,0,10.1101/2020.09.28.20203257,9/29/20,medrxiv,0,4,"bioinformatic,  omics, genomes",0.049181263,0.393479153,0.440930678,0.001350438,0.113708052,0.001350416,Genomics,0.6023827,TRUE,4,0.054734368,0.5,0.087101953,0,0.403234768,2036,0.478690104,0.255940298
13120,Coding Complete Genome Sequences of Twenty-three SARS-CoV-2 Strains Isolated in the Philippines,0,10.1101/2020.09.29.20203695,9/29/20,medrxiv,0,16,"genome sequences, genomes",0.006089696,0.89756315,0.006089566,0.006089472,0.006089531,0.078078585,Genomics,0.50649816,TRUE,10.625,0.158265817,11,0.371287129,1,0.537564047,1434,0.308210932,0.343831981
13121,Comparison of infection control strategies to reduce COVID-19 outbreaks in homeless shelters in the United States: a simulation study,0,10.1101/2020.09.28.20203166,9/29/20,medrxiv,0,10,simulation model,0.001350344,0.094762097,0.001350389,0.475245124,0.425941633,0.001350413,Epidemiology,0.14534625,FALSE,34.3,0.479745191,30.7,0.574391223,0,0.403234768,3422,0.687936431,0.536326903
13122,Changes to the sebum lipidome upon COVID-19 infection observed via non-invasive and rapid sampling from the skin,0,10.1101/2020.09.29.20203745,9/29/20,medrxiv,10.1016/j.eclinm.2021.100786,14,lipidom,0.003101558,0.003101728,0.340832137,0.003101626,0.277812029,0.372050922,Clinics,0.86507034,TRUE,5.923076923,0.081946935,2.153846154,0.165707787,0,0.403234768,2430,0.557187575,0.302019266
13123,Predictive Modelling of COVID-19 New Cases in Algeria using An Extreme Learning Machines (ELM),0,10.1101/2020.09.28.20203299,9/29/20,medrxiv,0,4,predictive model,0.002996443,0.002996413,0.628657467,0.359356621,0.002996494,0.002996562,Epidemiology,0.4101127,FALSE,2.75,0.030304904,0,0.055525823,1,0.537564047,739,0.038767156,0.165540483
13124,An optimal control policy for COVID-19 pandemic until a vaccine deployment,0,10.1101/2020.09.26.20202325,9/28/20,medrxiv,0,1,dataset,0.002996507,0.002996419,0.086203245,0.901810918,0.002996495,0.002996415,Epidemiology,0.5136518,TRUE,20,0.298163152,10,0.355632861,1,0.537564047,1251,0.235251625,0.356652921
13125,Identification of TMEM106B as proviral host factor for SARS-CoV-2,0,10.1101/2020.09.28.316281,9/28/20,biorxiv,0,10,genome-wide,0.955444354,0.002130979,0.002130634,0.0021307,0.002130631,0.036032703,Drug discovery,0.43583322,FALSE,111.8,0.893561754,140.9,0.875301044,8,0.799987654,3550,0.696123284,0.816243434
13126,No evidence that plasmablasts transdifferentiate into developing neutrophils in severe COVID-19 disease,0,10.1101/2020.09.27.312538,9/28/20,biorxiv,0,3,transcriptom,0.530259822,0.121528105,0.002032823,0.002032942,0.002032885,0.342113422,Drug discovery,0.22235921,FALSE,81.66666667,0.813346527,218,0.926077067,2,0.618927094,3614,0.70166145,0.765003035
13127,Exploring dynamics and network analysis of spike glycoprotein of SARS-COV-2,0,10.1101/2020.09.28.317206,9/28/20,biorxiv,10.1016/j.bpj.2021.02.047,3,network analysis,0.992440625,0.001511935,0.001511866,0.001511902,0.001511876,0.001511797,Drug discovery,0.7517996,TRUE,170.6666667,0.958748222,322.3333333,0.960262242,4,0.707574542,2148,0.500120395,0.78167635
13128,Prioritising COVID-19 vaccination in changing social and epidemiological landscapes,0,10.1101/2020.09.25.20201889,9/27/20,medrxiv,0,3,mathematical model,0.000657782,0.014114857,0.000657752,0.792421921,0.1914899,0.000657788,Epidemiology,0.030836284,FALSE,130.6666667,0.922444183,125.3333333,0.858241905,11,0.840175319,7397,0.853840597,0.868675501
13129,Wide variabilities identified among spike proteins of SARS Cov2 globally-dominant variant identified,0,10.1101/2020.09.26.314385,9/27/20,biorxiv,0,2,"whole genome, genome sequences",0.190315481,0.800951515,0.002183206,0.002183278,0.002183309,0.00218321,Genomics,0.33864516,FALSE,25,0.369286907,3,0.199424672,0,0.403234768,1978,0.461112449,0.358264699
13130,SCOAT-Net: A Novel Network for Segmenting COVID-19 Lung Opacification from CT Images,0,10.1101/2020.09.23.20191726,9/25/20,medrxiv,0,8,deep learning,0.033409891,0.001203436,0.96177634,0.001203461,0.001203452,0.001203418,Imaging,0.17137498,FALSE,4,0.054734368,0.25,0.065493712,2,0.618927094,,,0.246385058
13131,Implications of Monsoon Season & UVB Radiation for COVID-19 in India,0,10.1101/2020.09.24.20200576,9/25/20,medrxiv,10.1038/s41598-021-82443-6,2,dataset,0.001126804,0.00112683,0.001126784,0.846104566,0.001126882,0.149388135,Epidemiology,0.5162865,TRUE,4,0.054734368,0,0.055525823,0,0.403234768,2650,0.587286299,0.275195314
13132,First phylogenetic analysis of Malian SARS-CoV-2 sequences provide molecular insights into the genomic diversity of the Sahel region,0,10.1101/2020.09.23.20165639,9/25/20,medrxiv,10.3390/v12111251,14,"sequencing, whole genome, genome sequences",0.002032824,0.911397897,0.002032902,0.051875989,0.002032925,0.030627462,Genomics,0.21117344,FALSE,20.57142857,0.305399221,25.35714286,0.531709928,1,0.537564047,969,0.112208042,0.37172031
13133,Quantifying the impact of quarantine duration on COVID-19 transmission,0,10.1101/2020.09.24.20201061,9/25/20,medrxiv,10.7554/eLife.63704,5,mathematical model,0.001415108,0.00141517,0.134763719,0.859575345,0.001415411,0.001415247,Epidemiology,0.14989766,FALSE,91.33333333,0.847176696,313,0.957853893,7,0.785110192,2670,0.590175777,0.79507914
13134,FeverIQ - A Privacy-Preserving COVID-19 SymptomTracker with 3.6 Million Reports,0,10.1101/2020.09.23.20200006,9/25/20,medrxiv,0,6,"classifier, digital health, dataset",0.00289835,0.002898332,0.326857617,0.409981334,0.254465997,0.002898371,Epidemiology,0.33799765,FALSE,3.666666667,0.045952131,1.166666667,0.124565159,0,0.403234768,4774,0.774620756,0.337093203
13135,Predictors of Incident Viral Symptoms Ascertained in the Era of Covid-19,0,10.1101/2020.09.24.20197632,9/25/20,medrxiv,0,12,logistic regression,0.000916734,0.243829687,0.000916698,0.148476952,0.517622565,0.088237363,Healthcare,0.3251645,FALSE,25,0.369286907,46.33333333,0.665908483,0,0.403234768,1304,0.248013484,0.42161091
13136,Sequence Analysis for SNP Detection and Phylogenetic Reconstruction of SARS-CoV-2 Isolated from Nigerian COVID-19 Cases,0,10.1101/2020.09.25.310078,9/25/20,biorxiv,0,6,"whole genome, sequence alignment",0.000977451,0.866830879,0.018317968,0.00097749,0.069580892,0.04331532,Genomics,0.46423194,FALSE,4.166666667,0.055662069,0.166666667,0.058736955,0,0.403234768,1396,0.282687214,0.200080251
13137,"Reanalysis of MERS, SARS and COVID-19 Infection Datasets using VirOmics Playground Reveals Common Patterns in Gene and Protein Expression",0,10.1101/2020.09.25.313510,9/25/20,biorxiv,0,3,"bioinformatic, transcriptom, proteom, genome-wide, virom, dataset",0.442307007,0.365547669,0.002562708,0.130917353,0.056102651,0.002562612,Drug discovery,0.44228524,FALSE,27,0.3960047,16.66666667,0.444674873,0,0.403234768,1871,0.435829521,0.419935965
13138,Risk factors for mortality among hospitalized patients with COVID-19,0,10.1101/2020.09.22.20196204,9/24/20,medrxiv,0,8,"logistic regression, dataset",0.001141339,0.001141323,0.123433871,0.001141365,0.001141371,0.872000731,Clinics,0.31130335,FALSE,9.75,0.146267549,0.875,0.103826599,2,0.618927094,1895,0.437755839,0.32669427
13139,Role of IgG against N-protein of SARS-CoV2 in COVID19 clinical outcomes,0,10.1101/2020.09.23.20197251,9/24/20,medrxiv,10.1038/s41598-021-83108-0,20,logistic regression,0.057045967,0.295928497,0.001371234,0.001371248,0.001371256,0.642911797,Clinics,0.5950373,TRUE,7.05,0.101861587,0.15,0.057465882,2,0.618927094,2447,0.549241512,0.331874019
13140,Functional genomic screens identify human host factors for SARS-CoV-2 and common cold coronaviruses,0,10.1101/2020.09.24.312298,9/24/20,biorxiv,0,12,genome-wide,0.51902907,0.472839633,0.00203275,0.002032775,0.002032914,0.00203286,Drug discovery,0.54164207,TRUE,46.91666667,0.60455192,268.25,0.94527696,15,0.874313229,7331,0.847820852,0.817990741
13141,In silico identification and validation of inhibitors of the interaction between neuropilin receptor 1 and SARS-CoV-2 Spike protein,0,10.1101/2020.09.22.308783,9/23/20,biorxiv,0,7,in silico,0.994218668,0.001156263,0.001156246,0.001156285,0.001156254,0.001156283,Drug discovery,0.30740404,FALSE,38.42857143,0.523470839,22.71428571,0.50896441,5,0.739490092,2574,0.571394173,0.585829879
13142,Respiratory disease in cats associated with human-to-cat transmission of SARS-CoV-2 in the UK,0,10.1101/2020.09.23.309948,9/23/20,biorxiv,0,20,sequencing,0.0793892,0.885660693,0.002130806,0.002130653,0.028557903,0.002130744,Genomics,0.3510056,FALSE,66.21052632,0.741913538,101.6315789,0.825127107,10,0.828199272,5830,0.812424753,0.801916168
13143,The effect of COVID-19 on the economy: evidence from an early adopter of localized lockdowns,0,10.1101/2020.09.21.20198887,9/23/20,medrxiv,10.7189/jogh.10.05002,4,dataset,0.001187268,0.001187268,0.00118727,0.994063634,0.001187304,0.001187255,Epidemiology,0.39825052,FALSE,29.5,0.426000371,87,0.795223441,5,0.739490092,880,0.077052733,0.509441659
13144,Delayed viral clearance and exacerbated airway hyperinflammation in hypertensive COVID-19 patients,0,10.1101/2020.09.22.20199471,9/23/20,medrxiv,10.1038/s41587-020-00796-1,36,sequencing,0.573222673,0.001272779,0.001272642,0.001272654,0.001272645,0.421686606,Drug discovery,0.92674595,TRUE,44.63888889,0.58290556,72.58333333,0.760703773,0,0.403234768,1414,0.283891163,0.507683816
13145,Efficacy and Safety of Guduchi Ghan Vati in the Management of Asymptomatic COVID-19 infection: An Open Label Feasibility Study,0,10.1101/2020.09.20.20198515,9/23/20,medrxiv,0,5,in silico,0.150035591,0.102006968,0.001486479,0.001486532,0.001486548,0.743497882,Clinics,0.41950828,FALSE,8.4,0.124435648,1,0.122023013,1,0.537564047,898,0.081868529,0.216472809
13146,COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior,0,10.1101/2020.09.20.20198432,9/23/20,medrxiv,0,7,"deep learning, neural network, probabilistic",0.001272666,0.001272671,0.228795925,0.766113465,0.001272644,0.001272628,Epidemiology,0.12012583,FALSE,17,0.257467994,11.85714286,0.383328873,4,0.707574542,1190,0.19552131,0.38597318
13147,"Knowledge, Attitude and Practice towards COVID-19 among people in Bangladesh during the pandemic: a cross-sectional study.",0,10.1101/2020.09.22.20198275,9/23/20,medrxiv,0,6,logistic regression,0.002296595,0.002296548,0.002296541,0.002296675,0.988517115,0.002296526,Healthcare,0.66069174,TRUE,9.333333333,0.139278867,3.166666667,0.201097137,1,0.537564047,1215,0.205875271,0.27095383
13148,Molecular Architecture of Early Disseminationand Massive Second Wave of the SARS-CoV-2 Virus in a Major Metropolitan Area,0,10.1101/2020.09.22.20199125,9/23/20,medrxiv,10.1128/mbio.02707-20,25,genomes,0.092952792,0.809063193,0.000863098,0.000863079,0.054902725,0.041355113,Genomics,0.22367492,FALSE,33.24,0.468365391,46.72,0.667313353,22,0.908142478,42245,0.981940766,0.756440497
13149,Self-applied ELCSA is valid for rapid tracking of household food insecurity among pregnant women during the COVID-19 pandemic,0,10.1101/2020.09.22.20199380,9/23/20,medrxiv,0,8,model fit,0.001486474,0.001486546,0.00148654,0.195926772,0.722483986,0.077129682,Healthcare,0.44488728,FALSE,28.125,0.408806976,10.625,0.364396575,0,0.403234768,967,0.107151457,0.320897444
13150,Potentials of constrained sliding mode control as an intervention guide to manage COVID19 spread,0,10.1101/2020.09.21.20166934,9/23/20,medrxiv,10.1016/j.bspc.2021.102557,5,model fit,0.003214141,0.003214163,0.003214326,0.983928882,0.003214201,0.003214286,Epidemiology,0.19407481,FALSE,33.6,0.472261735,16.4,0.441196147,0,0.403234768,517,0.002167108,0.329714939
13151,"College Openings, Mobility, and the Incidence of COVID-19 Cases",0,10.1101/2020.09.22.20196048,9/23/20,medrxiv,0,5,bayes,0.017525507,0.000988359,0.000988353,0.829384537,0.150124854,0.000988391,Epidemiology,0.17692798,FALSE,7.6,0.109283196,2.8,0.188787798,9,0.814309525,11538,0.910426198,0.505701679
13152,"Ruling In and Ruling Out COVID-19: Computing SARS-CoV-2 Infection Risk From Symptoms, Imaging and Test Data.",0,10.1101/2020.09.18.20197582,9/22/20,medrxiv,10.2196/24478,3,"bayes, machine learning",0.001034679,0.043875538,0.390239459,0.178544591,0.001034639,0.385271094,Imaging,0.618092,TRUE,13.66666667,0.207310285,10,0.355632861,0,0.403234768,981,0.110522514,0.269175107
13153,Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data,0,10.1101/2020.09.18.20197319,9/22/20,medrxiv,0,3,"machine learning, prediction model",0.141135718,0.000765957,0.343401379,0.000765985,0.000765948,0.513165014,Clinics,0.25648773,FALSE,3.5,0.044344115,0.5,0.087101953,1,0.537564047,1530,0.323380689,0.248097701
13154,An improved method to estimate the effective reproduction number of the COVID-19 pandemic: lessons from its application in Greece,0,10.1101/2020.09.19.20198028,9/22/20,medrxiv,0,4,bayes,0.00131036,0.035920455,0.001310365,0.918780223,0.001310484,0.041368113,Epidemiology,0.07385978,FALSE,84,0.821881378,89.25,0.799839443,1,0.537564047,1559,0.335179388,0.623616064
13155,Analysis of geo-temporal evolution and modeling of the COVID-19 epidemic in Libya,0,10.1101/2020.09.19.20197822,9/22/20,medrxiv,0,3,mathematical model,0.001254587,0.001254621,0.001254598,0.958262986,0.036718539,0.001254669,Epidemiology,0.3110454,FALSE,29.33333333,0.423093574,17,0.451097137,0,0.403234768,613,0.009872381,0.321824465
13156,A comparison of five epidemiological models for transmission of SARS-CoV-2 in India,0,10.1101/2020.09.19.20198010,9/22/20,medrxiv,0,8,bayes,0.001237052,0.001237045,0.001237223,0.99381452,0.001237057,0.001237104,Epidemiology,0.06819534,FALSE,9.25,0.137918239,0.75,0.099411292,1,0.537564047,1016,0.12617385,0.225266857
13157,An automatic pipeline for the design of irreversible derivatives identifies a potent SARS-CoV-2 Mpro inhibitor.,0,10.1101/2020.09.21.299776,9/22/20,biorxiv,0,16,computational,0.897494653,0.001622775,0.09601431,0.001622823,0.001622714,0.001622725,Drug discovery,0.22861871,FALSE,70.93333333,0.767827324,97.66666667,0.817835162,4,0.707574542,2716,0.591620515,0.721214386
13158,COVID-19 outbreak in Mauritius: Logistic growth and SEIR modelling with quarantine and an effective reproduction number,0,10.1101/2020.09.22.20199364,9/22/20,medrxiv,0,4,computational,0.000846527,0.000846565,0.000846549,0.995767278,0.000846543,0.000846539,Epidemiology,0.64357376,TRUE,10.5,0.157338116,0,0.055525823,0,0.403234768,1074,0.148808091,0.191226699
13159,Treatment of Moderate to Severe Respiratory COVID-19--A Cost-Utility Analysis,0,10.1101/2020.09.21.20199182,9/22/20,medrxiv,0,5,probabilistic,0.092365888,0.001684501,0.001684569,0.372607075,0.00168454,0.529973426,Clinics,0.38061526,FALSE,20.6,0.305832148,3,0.199424672,0,0.403234768,1227,0.207801589,0.279073294
13160,Monitoring SARS-CoV-2 circulation and diversity through community wastewater sequencing,0,10.1101/2020.09.21.20198838,9/22/20,medrxiv,0,13,sequencing,0.001538091,0.974203011,0.001538307,0.001538223,0.001538222,0.019644146,Genomics,0.28537744,FALSE,29.46153846,0.424206816,52.23076923,0.689791276,8,0.799987654,3091,0.641945582,0.638982832
13161,Protonation states in SARS-CoV-2 main protease mapped by neutron crystallography,0,10.1101/2020.09.22.308668,9/22/20,biorxiv,0,7,computational,0.79629673,0.001987167,0.001987181,0.195754613,0.001987126,0.001987183,Drug discovery,0.76703656,TRUE,120.625,0.908033892,73.75,0.763312818,1,0.537564047,1318,0.246568745,0.613869876
13162,Mass cytometry and artificial intelligence define CD169 as a specific marker of SARS-CoV2-induced acute respiratory distress syndrome,0,10.1101/2020.09.22.307975,9/22/20,biorxiv,0,19,artificial intelligence,0.243969618,0.001901853,0.105125274,0.001901823,0.001901855,0.645199578,Clinics,0.7457947,TRUE,28.94736842,0.416661513,27.21052632,0.547297297,2,0.618927094,2544,0.561521791,0.536101924
13163,"How super-spreader cities, highways, hospital bed availability, and dengue fever influenced the COVID-19 epidemic in Brazil",0,10.1101/2020.09.19.20197749,9/21/20,medrxiv,0,4,mathematical model,0.000854745,0.035192782,0.000854717,0.784958266,0.000854729,0.177284762,Epidemiology,0.2096993,FALSE,10.5,0.157338116,9.5,0.345531175,4,0.707574542,15687,0.935709126,0.53653824
13164,COVID-19 herd immunity in the Brazilian Amazon,0,10.1101/2020.09.16.20194787,9/21/20,medrxiv,10.1126/science.abe9728,34,mathematical model,0.001901992,0.156279512,0.001901743,0.83611328,0.001901752,0.00190172,Epidemiology,0.26457492,FALSE,42.61764706,0.563423836,119.6764706,0.85094996,27,0.92443978,74607,0.989646039,0.832114904
13165,Rapid detection of inter-clade recombination in SARS-CoV-2 with Bolotie,0,10.1101/2020.09.21.300913,9/21/20,biorxiv,0,4,"computational, sequencing, genomes",0.001751261,0.685265325,0.035142577,0.274338453,0.001751193,0.001751192,Genomics,0.15615743,FALSE,119.5,0.90654957,4332.25,0.999866203,11,0.840175319,2735,0.592583674,0.834793692
13166,Investigation of COVID-19 comorbidities reveals genes and pathways coincident with the SARS-CoV-2 viral disease,0,10.1101/2020.09.21.306720,9/21/20,biorxiv,10.1038/s41598-020-77632-8,6,"bioinformatic, dataset",0.666393872,0.002183407,0.002183362,0.002183298,0.002183253,0.324872808,Drug discovery,0.92036915,TRUE,80.5,0.809202795,602.8333333,0.984145036,2,0.618927094,2284,0.511196725,0.730867913
13167,An immunodominance hierarchy exists in CD8+ T cell responses to HLA-A*02:01-restricted epitopes identified from the non-structural polyprotein 1a of SARS-CoV-2.,0,10.1101/2020.09.18.304493,9/19/20,biorxiv,10.1128/jvi.01837-20,2,bioinformatic,0.878243841,0.118050956,0.000926283,0.000926308,0.000926302,0.000926311,Drug discovery,0.073744684,FALSE,40.5,0.544746119,29,0.562884667,0,0.403234768,1489,0.299542499,0.452602013
13168,Antisense oligonucleotides target a nearly invariant structural element from the SARS-CoV-2 genome and drive RNA degradation,0,10.1101/2020.09.18.304139,9/19/20,biorxiv,0,17,genomes,0.446469211,0.545756864,0.001943468,0.001943503,0.001943481,0.001943473,Genomics,0.2646582,FALSE,109.2,0.888490321,232.5333333,0.930893765,3,0.667819001,3733,0.697568023,0.796192778
13169,A comparative study and application of modified SIR and Logistic models at Municipal Corporation level database of CoViD-19 in India,0,10.1101/2020.09.12.20193375,9/18/20,medrxiv,0,5,machine learning,0.002032809,0.002032779,0.10107859,0.890790192,0.002032825,0.002032804,Epidemiology,0.11817664,FALSE,7.4,0.106190859,2,0.164302917,0,0.403234768,1058,0.134360703,0.202022312
13170,Dual Attention Multiple Instance Learning with Unsupervised Complementary Loss for COVID-19 Screening,0,10.1101/2020.09.14.20194654,9/18/20,medrxiv,0,6,dataset,0.00118729,0.001187321,0.994063424,0.001187364,0.001187265,0.001187337,Imaging,0.43406793,FALSE,5.666666667,0.079473066,0.333333333,0.073187048,0,0.403234768,633,0.010835541,0.141682606
13171,Disease control as an optimization problem,0,10.1101/2020.09.15.20194811,9/18/20,medrxiv,0,3,machine learning,0.001751174,0.001751187,0.001751329,0.991243934,0.001751235,0.001751141,Epidemiology,0.08007887,FALSE,29.33333333,0.423093574,23.33333333,0.515386674,1,0.537564047,659,0.014447387,0.372622921
13172,COVID-19 epidemic severity is associated with timing of non-pharmaceutical interventions,0,10.1101/2020.09.15.20194258,9/18/20,medrxiv,0,16,whole genome,0.000907273,0.134788973,0.000907283,0.800071313,0.000907303,0.062417854,Epidemiology,0.22850636,FALSE,45.375,0.589894242,148.125,0.881990902,1,0.537564047,2589,0.560077053,0.642381561
13173,Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves,0,10.1101/2020.09.14.20194589,9/18/20,medrxiv,0,1,bayes,0.001943496,0.001943477,0.001943563,0.990282546,0.001943453,0.001943466,Epidemiology,0.064833164,FALSE,52,0.647349867,89,0.799304255,4,0.707574542,1858,0.415121599,0.642337566
13174,A SARS-CoV-2 Reference Standard Quantified by Multi-digital PCR Platforms for Quality Assessment of Molecular Tests,0,10.1101/2020.09.14.20193904,9/18/20,medrxiv,10.1021/acs.analchem.0c03996,8,genome sequences,0.065690455,0.604603077,0.256167107,0.001085423,0.00108535,0.071368589,Genomics,0.79693896,TRUE,8.25,0.121343311,2.375,0.173668718,0,0.403234768,942,0.089814592,0.197015347
13175,Longitudinal Mediators of Early Pandemic Distress,0,10.1101/2020.09.15.20194829,9/18/20,medrxiv,0,15,bayes,0.001861729,0.001861736,0.00186174,0.352193692,0.640359356,0.001861748,Healthcare,0.519601,TRUE,77,0.794668811,106.6428571,0.834024619,0,0.403234768,1066,0.136768601,0.542174199
13176,"Longitudinal multi-omics analysis identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories",0,10.1101/2020.09.11.20187369,9/18/20,medrxiv,0,72,"transcriptom, multi-omics",0.428286679,0.1184269,0.00108537,0.0010854,0.001085366,0.450030285,Clinics,0.2019983,FALSE,16.89130435,0.25375719,41.17391304,0.640152529,3,0.667819001,3361,0.664339032,0.556516938
13177,COVID-19 Case-Age Distribution: Correction for Differential Testing by Age,0,10.1101/2020.09.15.20193862,9/18/20,medrxiv,0,6,dataset,0.000926276,0.000926342,0.0009263,0.387959865,0.395644321,0.213616895,Healthcare,0.16072467,FALSE,37.66666667,0.515430763,45.33333333,0.659820712,3,0.667819001,2677,0.576209969,0.604820111
13178,Superspreaders provide essential clues for mitigation of COVID-19,0,10.1101/2020.09.15.20195008,9/18/20,medrxiv,10.1103/PhysRevLett.126.118301,2,mathematical model,0.005047584,0.005047652,0.00504757,0.974761741,0.005047898,0.005047555,Epidemiology,0.35837683,FALSE,40.5,0.544746119,15,0.42594327,4,0.707574542,4113,0.725740429,0.60100109
13179,"Proteomics identifies a type I IFN, prothrombotic hyperinflammatory circulating COVID-19 neutrophil signature distinct from non-COVID-19 ARDS",0,10.1101/2020.09.15.20195305,9/18/20,medrxiv,0,34,proteom,0.442237632,0.001622835,0.001622766,0.001622763,0.029599143,0.523294861,Clinics,0.25416598,FALSE,19.84848485,0.2943905,24.51515152,0.525153867,3,0.667819001,2456,0.535998074,0.50584036
13180,Integrative Genomics Analysis Reveals a Novel 21q22.11 Locus Contributing to Susceptibility of COVID-19,0,10.1101/2020.09.16.20195685,9/18/20,medrxiv,0,8,"genome-wide, dataset",0.511750569,0.243549821,0.001823391,0.001823351,0.001823359,0.239229508,Drug discovery,0.3362112,FALSE,12.5,0.189436576,5.5,0.267259834,1,0.537564047,830,0.053696123,0.261989145
13181,Selecting pharmacies for COVID-19 testing to ensure access,0,10.1101/2020.09.17.20185090,9/18/20,medrxiv,10.1007/s10729-020-09538-w,4,optimization model,0.001653064,0.147905825,0.001653118,0.636441646,0.210693308,0.001653038,Epidemiology,0.20524853,FALSE,49.5,0.628362917,90.75,0.802582285,1,0.537564047,821,0.051769805,0.505069764
13182,On Topological Properties of COVID-19: Predicting and Controling Pandemic Risk with Network Statistics,0,10.1101/2020.09.17.20197020,9/18/20,medrxiv,10.1038/s41598-021-84094-z,4,network analysis,0.001486512,0.001486529,0.001486528,0.801799875,0.001486518,0.192254038,Epidemiology,0.6717527,TRUE,25.75,0.377698064,24.25,0.523414504,0,0.403234768,728,0.029857934,0.333551317
13183,A COVID-19 Nursing Home Transmission Study: sequence and metadata from weekly testing in an extensive nursing home outbreak,0,10.1101/2020.09.15.20195396,9/18/20,medrxiv,0,10,sequencing,0.001022614,0.17271346,0.04371944,0.00102268,0.780499136,0.00102267,Healthcare,0.21415237,FALSE,42,0.558537943,33.5,0.595999465,3,0.667819001,1099,0.150493619,0.493212507
13184,"SARS-CoV-2 genomes from Oklahoma, USA",0,10.1101/2020.09.15.20195420,9/18/20,medrxiv,0,9,"sequencing, genomes",0.002032784,0.948347954,0.002032761,0.043520859,0.002032755,0.002032887,Genomics,0.5128828,TRUE,13.11111111,0.19790958,11.77777778,0.3820578,2,0.618927094,1033,0.124969901,0.330966094
13185,Genomic epidemiology of the Los Angeles COVID-19 outbreak,0,10.1101/2020.09.15.20194712,9/18/20,medrxiv,0,17,"sequencing, genomic epidemiology, genomes",0.001717187,0.557630336,0.001717324,0.412408828,0.001717204,0.024809121,Genomics,0.31761074,FALSE,49.11764706,0.624961346,,,0,0.403234768,1461,0.2877438,0.438646638
13186,Identification of potential biomarkers and inhibitors for SARS-CoV-2 infection,0,10.1101/2020.09.15.20195487,9/18/20,medrxiv,0,2,"bioinformatic, genomes, dataset",0.921737424,0.068831395,0.002357773,0.002357749,0.002357721,0.002357937,Drug discovery,0.7789537,TRUE,2.5,0.027459954,0,0.055525823,4,0.707574542,863,0.064772454,0.213833193
13187,Model-based and model-free characterization of epidemic outbreaks,0,10.1101/2020.09.16.20187484,9/18/20,medrxiv,0,9,bayes,0.002238516,0.00223851,0.002238469,0.988807589,0.002238477,0.00223844,Epidemiology,0.118350685,FALSE,21.66666667,0.320737213,30.55555556,0.573320846,4,0.707574542,1063,0.135805442,0.434359511
13188,"Variation of SARS-CoV-2 viral loads by sample type, disease severity and time: a systematic review",0,10.1101/2020.09.16.20195982,9/18/20,medrxiv,0,8,genomes,0.001141337,0.428881495,0.216339782,0.001141392,0.00114139,0.351354604,Genomics,0.4094496,FALSE,14.5,0.219617787,4.375,0.238426545,2,0.618927094,1299,0.228027932,0.32624984
13189,Investigating the implications of COVID-19 for the rural and remote population of Northern Ontario using a mathematical model,0,10.1101/2020.09.17.20196949,9/18/20,medrxiv,0,7,mathematical model,0.001392824,0.001392823,0.001392831,0.458047886,0.100558052,0.437215584,Epidemiology,0.47360805,FALSE,3.857142857,0.04786938,0,0.055525823,0,0.403234768,1219,0.194798941,0.175357228
13190,CLINICALLY DISTINCT COVID-19 CASES SHARE STRIKINGLY SIMILAR IMMUNE RESPONSE PROGRESSION: A FOLLOW-UP ANALYSIS,0,10.1101/2020.09.16.20115972,9/18/20,medrxiv,10.1016/j.heliyon.2020.e05877,4,"proteom, dataset",0.402879204,0.001751271,0.001751246,0.001751274,0.186031537,0.405835467,Clinics,0.7484467,TRUE,78.75,0.801533799,43.25,0.65045491,0,0.403234768,1669,0.35636889,0.552898092
13191,Metabolic stress and disease-stage specific basigin expression of peripheral blood immune cell subsets in COVID-19 patients,0,10.1101/2020.09.18.20194175,9/18/20,medrxiv,0,34,"transcriptom, immunome",0.543841507,0.002720196,0.038846126,0.002720218,0.002720144,0.409151809,Drug discovery,0.62064147,TRUE,22.82352941,0.337312141,48.08823529,0.673802515,1,0.537564047,1617,0.342884662,0.472890841
13192,Distinct SARS-CoV-2 Antibody Reactivity Patterns in Coronavirus Convalescent Plasma Revealed by a Coronavirus Antigen Microarray,0,10.1101/2020.09.16.300871,9/17/20,biorxiv,0,12,"computational, prediction model",0.191070859,0.54110936,0.126099121,0.001156294,0.001156297,0.139408068,Genomics,0.16455775,FALSE,54.66666667,0.666151277,57.5,0.712938186,0,0.403234768,1926,0.430532145,0.553214094
13193,Mutational signatures in countries affected by SARS-CoV-2: Implications in host-pathogen interactome,0,10.1101/2020.09.17.301614,9/17/20,biorxiv,0,3,"interactom, whole-genome, genome sequences, genomes, dataset",0.158243477,0.83816391,0.000898095,0.000898145,0.00089809,0.000898282,Genomics,0.45864826,FALSE,67.75,0.751932711,68.5,0.749130318,1,0.537564047,1487,0.293281965,0.58297726
13194,Nowcasting CoVID-19 Deaths in England by Age and Region,0,10.1101/2020.09.15.20194209,9/16/20,medrxiv,0,4,"bayes, computational",0.001786518,0.001786525,0.00178652,0.991067174,0.001786611,0.001786652,Epidemiology,0.4757928,FALSE,30.25,0.434782609,36.75,0.615466952,4,0.707574542,1782,0.389597881,0.536855496
13195,Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Meta-Analysis Approach,0,10.1101/2020.09.14.20192609,9/15/20,medrxiv,0,4,"computational, dataset",0.607059182,0.00156547,0.001565361,0.001565352,0.001565401,0.386679233,Drug discovery,0.5155071,TRUE,5.25,0.072855464,1.5,0.138747659,0,0.403234768,1485,0.288947749,0.22594641
13196,A Comparative Study to Find a Suitable Model for an Improved Real-Time Monitoring of The Interventions to Contain COVID-19 Outbreak in The High Incidence States of India,0,10.1101/2020.09.14.20190447,9/15/20,medrxiv,0,3,dataset,0.001187289,0.001187296,0.001187321,0.994063451,0.001187312,0.00118733,Epidemiology,0.11595455,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,687,0.017577655,0.128407958
13197,Are we there yet? An adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States,0,10.1101/2020.09.13.20193896,9/15/20,medrxiv,10.2196/24389,4,computational,0.001291308,0.001291236,0.001291235,0.993543748,0.001291215,0.00129126,Epidemiology,0.15110746,FALSE,4.5,0.061784897,1,0.122023013,0,0.403234768,1633,0.339995184,0.231759465
13198,Mathematical Modelling of the Spread of the Coronavirus under Social Restrictions,0,10.1101/2020.09.14.20194068,9/15/20,medrxiv,0,4,mathematical model,0.000999547,0.000999512,0.000999504,0.995002375,0.000999521,0.000999541,Epidemiology,0.26632977,FALSE,5,0.070752675,,,0,0.403234768,1077,0.135323862,0.203103768
13199,"The Anti-histamine Azelastine, Identified by Computational Drug Repurposing, Inhibits SARS-CoV-2 Infection in Reconstituted Human Nasal Tissue In Vitro",0,10.1101/2020.09.15.296228,9/15/20,biorxiv,0,13,"computational, genomes",0.796699273,0.069123214,0.001187311,0.130615437,0.001187379,0.001187386,Drug discovery,0.67834026,TRUE,74.07692308,0.782237615,66.84615385,0.745317099,0,0.403234768,7735,0.85022875,0.695254558
13200,Emergence and expansion of highly infectious spike:D614G mutant SARS-CoV-2 in central India,0,10.1101/2020.09.15.297846,9/15/20,biorxiv,0,19,"whole genome, genome sequences",0.001034627,0.882252371,0.001034586,0.113609221,0.001034599,0.001034596,Genomics,0.572853,TRUE,36.15789474,0.498917682,32.94736842,0.591784854,1,0.537564047,2673,0.569949434,0.549554004
13201,Levels of genetic diversity of SARS-CoV-2 virus: reducing speculations about the genetic variability of the virus in South America,0,10.1101/2020.09.14.296491,9/15/20,biorxiv,0,5,genomes,0.153000773,0.834592686,0.003101543,0.0031018,0.003101714,0.003101483,Genomics,0.36023986,FALSE,5.4,0.074710866,0,0.055525823,4,0.707574542,1410,0.262460872,0.275068026
13202,CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations,0,10.1101/2020.09.14.296889,9/15/20,biorxiv,0,13,"deep learning, computational, multi-omics, deep-learning, knowledge graph",0.461231936,0.001371284,0.19667075,0.337983397,0.00137136,0.001371273,Drug discovery,0.29654557,FALSE,23.30769231,0.34491929,27.15384615,0.54669521,0,0.403234768,1755,0.37876234,0.418402902
13203,Estimates of outbreak-specific SARS-CoV-2 epidemiological parameters from genomic data,0,10.1101/2020.09.12.20193284,9/14/20,medrxiv,0,4,"bayes, genomes",0.001059352,0.246465908,0.001059356,0.749296592,0.001059377,0.001059415,Epidemiology,0.16528898,FALSE,58.25,0.691384749,409,0.972103291,0,0.403234768,1053,0.124729111,0.54786298
13204,Inequality in access to health and care services during lockdown - Findings from the COVID-19 survey in five UK national longitudinal studies,0,10.1101/2020.09.12.20191973,9/14/20,medrxiv,10.1136/bmjopen-2020-045813,9,logistic regression,0.00135032,0.001350356,0.001350319,0.001350383,0.888123225,0.106475397,Healthcare,0.67687756,TRUE,23.22222222,0.343620508,10.44444444,0.36131924,3,0.667819001,2034,0.447146641,0.454976348
13205,Clinical characteristics and outcomes of patients with COVID-19 and ARDS admitted to a third level health institution in Mexico City,0,10.1101/2020.09.12.20193409,9/14/20,medrxiv,0,8,logistic regression,0.001943434,0.001943455,0.001943454,0.001943466,0.001943452,0.99028274,Clinics,0.32318884,FALSE,2.333333333,0.024800544,0,0.055525823,0,0.403234768,835,0.051047436,0.133652143
13206,Role of Weather Factors in COVID-19 Deaths in Tropical Climate: A Data-Driven Study Focused on Brazil,0,10.1101/2020.09.13.20193532,9/14/20,medrxiv,0,1,dataset,0.000786342,0.000786359,0.000786345,0.855081751,0.000786372,0.141772831,Epidemiology,0.25528705,FALSE,4,0.054734368,1,0.122023013,0,0.403234768,1634,0.337587286,0.229394859
13207,"Rapid, accurate, nucleobase detection using FnCas9",0,10.1101/2020.09.13.20193581,9/14/20,medrxiv,0,26,sequencing,0.038002384,0.469661708,0.419525575,0.070189502,0.001310468,0.001310362,Genomics,0.7523404,TRUE,4.115384615,0.054981755,,,4,0.707574542,4309,0.734649651,0.499068649
13208,AI for radiographic COVID-19 detection selects shortcuts over signal,0,10.1101/2020.09.13.20193565,9/14/20,medrxiv,0,3,"deep learning, artificial intelligence",0.003607265,0.003607343,0.981963257,0.003607344,0.003607256,0.003607536,Imaging,0.713054,TRUE,7.666666667,0.111633373,9.333333333,0.342253144,17,0.887338725,5230,0.783289189,0.531128607
13209,Quantifying heterogeneity in SARS-CoV-2 transmission during the lockdown in India,0,10.1101/2020.09.10.20190017,9/14/20,medrxiv,0,5,mathematical model,0.001823388,0.00182335,0.001823323,0.990883288,0.001823328,0.001823324,Epidemiology,0.23344085,FALSE,5,0.070752675,3.8,0.221501204,1,0.537564047,1651,0.343847821,0.293416437
13210,Slight reduction in SARS-CoV-2 exposure viral load due to masking results in a significant reduction in transmission with widespread implementation,0,10.1101/2020.09.13.20193508,9/14/20,medrxiv,0,5,mathematical model,0.146466673,0.24091966,0.001565322,0.607917492,0.001565402,0.001565451,Epidemiology,0.16568935,FALSE,3.8,0.047374606,0.2,0.061145304,3,0.667819001,18405,0.944618348,0.430239315
13211,The Coronavirus Network Explorer: Mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function,0,10.1101/2020.09.14.296327,9/14/20,biorxiv,0,6,"machine learning, knowledge graph",0.863914471,0.002296668,0.126899027,0.00229666,0.002296618,0.002296556,Drug discovery,0.2956692,FALSE,85.66666667,0.827509432,158.8333333,0.891089109,0,0.403234768,1540,0.305562244,0.606848888
13212,Single-cell RNA Expression of SARS-CoV-2 Cell Entry Factors in Human Endometrium during Preconception,0,10.1101/2020.09.14.296806,9/14/20,biorxiv,0,5,sequencing,0.680676053,0.147251253,0.005047639,0.005047806,0.156929337,0.005047912,Drug discovery,0.6847793,TRUE,133.8,0.925845754,392.4,0.970363928,0,0.403234768,984,0.097760655,0.599301276
13213,Phylogenomic reveals multiple introductions and early spread of SARS-CoV-2 into Peru,0,10.1101/2020.09.14.296814,9/14/20,biorxiv,0,7,"phylogenom, genomes",0.004109794,0.91693872,0.004109792,0.004110012,0.004110047,0.066621634,Genomics,0.47127426,FALSE,11.42857143,0.17150102,10.42857143,0.361118544,5,0.739490092,3199,0.638333735,0.477610848
13214,Seroprevalence and Correlates of SARS-CoV-2 Antibodies in Healthcare Workers in Chicago,0,10.1101/2020.09.11.20192385,9/13/20,medrxiv,10.1093/ofid/ofaa582,14,logistic regression,0.001486444,0.051982046,0.001486497,0.001486456,0.659657307,0.28390125,Healthcare,0.83673984,TRUE,8.214285714,0.120539304,3.071428571,0.199692267,3,0.667819001,2654,0.560317843,0.387092103
13215,RAAS blockers and region-specific variations in COVID-19 outcomes: findings from a systematic review and meta-analysis,0,10.1101/2020.09.09.20191445,9/13/20,medrxiv,0,3,pharmacogenom,0.09957845,0.00092633,0.000926377,0.261225909,0.000926295,0.636416638,Clinics,0.22118077,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,706,0.020226342,0.12907013
13216,The Effect of Early Hydroxychloroquine-based Therapy in COVID-19 Patients in Ambulatory Care Settings: A Nationwide Prospective Cohort Study,0,10.1101/2020.09.09.20184143,9/13/20,medrxiv,0,22,logistic regression,0.000722233,0.000722183,0.000722177,0.000722203,0.022067185,0.975044019,Clinics,0.7379162,TRUE,5.090909091,0.070876368,0.818181818,0.101752743,7,0.785110192,31466,0.972068384,0.482451922
13217,On Machine Learning-Based Short-Term Adjustment of Epidemiological Projections of COVID-19 in US,0,10.1101/2020.09.11.20180521,9/13/20,medrxiv,0,12,machine learning,0.001565329,0.001565345,0.107642577,0.860807594,0.026853732,0.001565425,Epidemiology,0.28476363,FALSE,5.916666667,0.081885089,0.666666667,0.096200161,2,0.618927094,605,0.006019745,0.200758022
13218,Ignoring the elephant in the room: factors contributing to inadequate access to contraception and sources of contraception during novel coronavirus diseases 2019 in South Africa.,0,10.1101/2020.09.11.20192849,9/13/20,medrxiv,0,1,"logistic regression, dataset",0.00156531,0.001565348,0.001565325,0.391825283,0.601913402,0.001565333,Healthcare,0.36666086,FALSE,53,0.654400396,57,0.711198823,0,0.403234768,883,0.063809294,0.45816082
13219,Temporal Analysis of COVID-19 Peak Outbreak,0,10.1101/2020.09.11.20192229,9/13/20,medrxiv,0,1,mathematical model,0.002238482,0.002238459,0.11739485,0.873651195,0.002238512,0.002238502,Epidemiology,0.11967501,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,642,0.010353961,0.122813921
13220,SARS2 simplified scores to estimate risk of hospitalization and death among patients with COVID-19,0,10.1101/2020.09.11.20190520,9/13/20,medrxiv,10.1038/s41598-021-84603-0,5,prediction model,0.001392841,0.001392835,0.074948502,0.001392909,0.131516003,0.78935691,Clinics,0.1433605,FALSE,15.4,0.231616055,731.4,0.988359647,4,0.707574542,1032,0.11437515,0.510481349
13221,nanoDoc: RNA modification detection using Nanopore raw reads with Deep One-Class Classification,0,10.1101/2020.09.13.295089,9/13/20,biorxiv,0,1,"neural network, sequencing, dataset",0.057709259,0.261539421,0.675281211,0.001823367,0.00182336,0.001823382,Genomics,0.32138115,FALSE,6,0.086028821,0,0.055525823,1,0.537564047,2768,0.581025765,0.315036114
13222,Inferring MHC interacting SARS-CoV-2 epitopes recognized by TCRs towards designing T cell-based vaccines,0,10.1101/2020.09.12.294413,9/12/20,biorxiv,0,4,structural model,0.914497747,0.080337342,0.001291235,0.001291253,0.00129122,0.001291203,Drug discovery,0.4769244,FALSE,15.5,0.234028078,25.75,0.535255553,0,0.403234768,1836,0.396821575,0.392334993
13223,Novel Coronavirus (COVID-19) health awareness among the United Arab Emirates Population,0,10.1101/2020.09.10.20191890,9/11/20,medrxiv,0,3,logistic regression,0.001046814,0.001046802,0.001046809,0.001046826,0.994765923,0.001046826,Healthcare,0.22286099,FALSE,12.33333333,0.186467932,3.333333333,0.206515922,0,0.403234768,1602,0.319046472,0.278816274
13224,"Analysis of twenty-week time-series of confirmed cases of New Coronavirus COVID-19 and their simple short-term prediction for Georgia and Neighboring Countries (Armenia, Azerbaijan, Turkey, Russia) in amid of a global pandemic",0,10.1101/2020.09.09.20191494,9/11/20,medrxiv,0,3,correlation analysis,0.001786699,0.001786661,0.001786599,0.991066881,0.001786563,0.001786597,Epidemiology,0.21717837,FALSE,8.666666667,0.12839384,0.333333333,0.073187048,1,0.537564047,833,0.047435589,0.196645131
13225,Socio-economic disparities and COVID-19 in the USA,0,10.1101/2020.09.10.20192138,9/11/20,medrxiv,0,3,machine learning,0.003101458,0.003101532,0.06670296,0.808285968,0.115706454,0.003101627,Epidemiology,0.4095837,FALSE,8.333333333,0.123693488,0.333333333,0.073187048,3,0.667819001,1233,0.185889718,0.262647314
13226,The impact of asthma on mental health & wellbeing during COVID-19 lockdown,0,10.1101/2020.09.10.20190793,9/11/20,medrxiv,10.1183/13993003.04497-2020,5,logistic regression,0.001022606,0.052339789,0.001022611,0.001022647,0.943569693,0.001022653,Healthcare,0.461803,FALSE,12,0.183190055,0.4,0.075796093,1,0.537564047,1559,0.304117505,0.275166925
13227,"Mortality and risk factors among US Black, Hispanic, and White patients with COVID-19",0,10.1101/2020.09.08.20190686,9/11/20,medrxiv,0,4,logistic regression,0.08525618,0.00143814,0.001438102,0.001438119,0.051895738,0.858533719,Clinics,0.75583893,TRUE,5,0.070752675,1.5,0.138747659,0,0.403234768,708,0.018781604,0.157879176
13228,No association between circulating levels of testosterone and sex hormone-binding globulin and risk of COVID-19 mortality in UK biobank,0,10.1101/2020.09.11.20191783,9/11/20,medrxiv,0,10,logistic regression,0.032345395,0.001751298,0.001751147,0.001751341,0.236080811,0.726320009,Clinics,0.8370559,TRUE,4.5,0.061784897,0.2,0.061145304,0,0.403234768,959,0.084998796,0.152790941
13229,Saliva as a potential clinical specimen for diagnosis of SARS-CoV-2,0,10.1101/2020.09.11.20192591,9/11/20,medrxiv,0,14,sequencing,0.001272683,0.665008581,0.141895877,0.001272688,0.11837079,0.072179381,Genomics,0.577723,TRUE,7.928571429,0.114416476,1.071428571,0.122357506,5,0.739490092,1125,0.144955454,0.280304882
13230,Racial disparities in COVID-19 mortality are driven by unequal infection risks.,0,10.1101/2020.09.10.20192369,9/11/20,medrxiv,0,8,bayes,0.001171552,0.001171576,0.001171567,0.400752543,0.234867009,0.360865752,Epidemiology,0.44663438,FALSE,4.625,0.062774445,0.5,0.087101953,13,0.858880178,4859,0.762822056,0.442894658
13231,Socio-demographic correlate of knowledge and practice toward novel coronavirus among people living in Mosul-Iraq: A cross-sectional study,0,10.1101/2020.09.11.20192542,9/11/20,medrxiv,0,3,logistic regression,0.001371317,0.060818031,0.001371252,0.001371362,0.933696761,0.001371277,Healthcare,0.19557801,FALSE,19,0.285793803,19,0.471367407,0,0.403234768,747,0.028172405,0.297142096
13232,Identifying zoonotic origin of SARS-CoV-2 by modeling the binding affinity between Spike receptor-binding domain and host ACE2,0,10.1101/2020.09.11.293449,9/11/20,biorxiv,10.1021/acs.jproteome.0c00717,5,"computational, dataset",0.709237362,0.285191057,0.001392947,0.001392898,0.001392868,0.001392868,Drug discovery,0.5041883,TRUE,172.6,0.960232544,422,0.972772277,3,0.667819001,2218,0.476041416,0.76921631
13233,"Molecular Characterization, Phylogenetic and Variation Analyzes of SARS-CoV-2 strains in Turkey",0,10.1101/2020.09.11.293183,9/11/20,biorxiv,0,3,"sequencing, genome sequences, genomes",0.00131034,0.99344834,0.001310337,0.001310338,0.001310334,0.001310311,Genomics,0.56689197,TRUE,2,0.022141134,0,0.055525823,0,0.403234768,914,0.071273778,0.138043876
13234,A comparative survey of betacoronavirus strain molecular dynamics identifies key ACE2 binding sites,0,10.1101/2020.09.11.293258,9/11/20,biorxiv,0,5,molecular dynamics simulation,0.614389281,0.380592333,0.001254603,0.001254597,0.001254593,0.001254593,Drug discovery,0.17645082,FALSE,23.6,0.349124869,14,0.412898047,1,0.537564047,1952,0.422345293,0.430483064
13235,In silico prediction of COVID-19 test efficiency with DinoKnot,0,10.1101/2020.09.11.292730,9/11/20,biorxiv,0,3,"computational, in silico",0.363338379,0.633516143,0.000786401,0.00078638,0.000786355,0.000786342,Genomics,0.21730065,FALSE,16.66666667,0.251159626,6,0.280037463,0,0.403234768,1526,0.291596436,0.306507073
13236,Vitamin D and Covid-19 Susceptibility and Severity: a Mendelian Randomization Study,0,10.1101/2020.09.08.20190975,9/10/20,medrxiv,0,16,genome-wide,0.000898137,0.343885612,0.000898194,0.000898157,0.000898147,0.652521753,Clinics,0.116684616,FALSE,13.0625,0.1975385,18.25,0.463272679,3,0.667819001,7680,0.844209006,0.543209796
13237,Schools are not islands: Balancing COVID-19 risk and educational benefits using structural and temporal countermeasures,0,10.1101/2020.09.08.20190942,9/10/20,medrxiv,0,4,mathematical model,0.000377302,0.000377307,0.000377321,0.640437819,0.352987258,0.005442994,Epidemiology,0.039369732,FALSE,8.5,0.126662131,7.5,0.307867273,5,0.739490092,2707,0.564411269,0.434607691
13238,Robust SARS-COV-2 serological population screens via multi-antigen rules-based approach,0,10.1101/2020.09.09.20191122,9/10/20,medrxiv,0,16,computational,0.261814628,0.469139893,0.265334135,0.001237089,0.001237108,0.001237147,Genomics,0.23464364,FALSE,22.9375,0.338549075,12.375,0.389751137,0,0.403234768,1344,0.225138454,0.339168359
13239,Metagenomic sequencing to detect respiratory viruses in persons under investigation for COVID-19,0,10.1101/2020.09.09.20178764,9/10/20,medrxiv,10.1128/jcm.02142-20,7,"sequencing, metagenom",0.006089639,0.969551052,0.006089882,0.006089821,0.006090006,0.006089601,Genomics,0.77002263,TRUE,34.71428571,0.48413631,16.42857143,0.441731335,4,0.707574542,686,0.014688177,0.412032591
13240,A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools,0,10.1101/2020.09.03.20184226,9/10/20,medrxiv,0,7,machine learning,0.00137128,0.001371295,0.884225791,0.110288958,0.001371325,0.001371352,Imaging,0.35146755,FALSE,46.57142857,0.60152143,12.57142857,0.393497458,0,0.403234768,2462,0.519624368,0.479469506
13241,rSWeeP: A R/Bioconductor package deal with SWeeP sequences representation,0,10.1101/2020.09.09.290247,9/10/20,biorxiv,0,7,"bioinformatic, proteom, dataset",0.003335475,0.421302549,0.200380196,0.368311222,0.003335306,0.003335251,Genomics,0.39265478,FALSE,53.57142857,0.657678273,45.85714286,0.662964945,0,0.403234768,592,0.004334216,0.432053051
13242,Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease,0,10.1101/2020.09.07.286344,9/10/20,biorxiv,10.1039/D0SC04942E,18,molecular dynamics simulation,0.992690507,0.00146192,0.001461897,0.001461956,0.001461869,0.001461851,Drug discovery,0.91373855,TRUE,57.89473684,0.687921331,76.57894737,0.770002676,2,0.618927094,1383,0.24103058,0.57947042
13243,Conserved interactions required for in vitro inhibition of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2),0,10.1101/2020.09.10.288720,9/10/20,biorxiv,10.1038/s41598-020-77794-5,11,in silico,0.993543838,0.001291285,0.001291229,0.00129122,0.001291222,0.001291205,Drug discovery,0.8204546,TRUE,12.36363636,0.186653473,5.181818182,0.258830613,0,0.403234768,814,0.039730315,0.222112292
13244,Molecular basis for SARS-CoV-2 spike affinity for human ACE2 receptor,0,10.1101/2020.09.10.291757,9/10/20,biorxiv,0,6,molecular dynamics simulation,0.858282395,0.001415164,0.048533894,0.088938269,0.001415142,0.001415136,Drug discovery,0.19374189,FALSE,74.83333333,0.785824726,51.83333333,0.688787798,1,0.537564047,1022,0.104261979,0.529109638
13245,COVID-19 Biomarkers in research: Extension of the OncoMX cancer biomarker data model to capture biomarker data from other diseases.,0,10.1101/2020.09.09.196220,9/10/20,biorxiv,0,7,dataset,0.267702142,0.024792701,0.176217607,0.201594846,0.017153939,0.312538766,Clinics,0.48954943,FALSE,15.42857143,0.231801596,69.85714286,0.752943538,0,0.403234768,1694,0.347700458,0.43392009
13246,On the Role of Artificial Intelligence in Medical Imaging of COVID-19,0,10.1101/2020.09.02.20187096,9/9/20,medrxiv,0,15,"artificial intelligence, dataset",0.001098849,0.001098974,0.800721205,0.194883142,0.001098914,0.001098915,Imaging,0.32488468,FALSE,23.76923077,0.35060919,42.07692308,0.645103024,1,0.537564047,2734,0.566578377,0.52496366
13247,COVID-19 transmission in a university setting: a rapid review of modelling studies,0,10.1101/2020.09.07.20189688,9/9/20,medrxiv,0,10,mathematical model,0.000772637,0.000772624,0.000772655,0.703745052,0.293164418,0.000772614,Epidemiology,0.14554712,FALSE,5.7,0.079596759,1.8,0.150120417,10,0.828199272,3344,0.647242957,0.426289851
13248,Healthcare workers in elderly care: a source of silent SARS-CoV-2 transmission?,0,10.1101/2020.09.07.20178731,9/9/20,medrxiv,0,8,"sequencing, whole genome",0.001220049,0.245757332,0.001220037,0.001220054,0.635636137,0.114946391,Healthcare,0.29211098,FALSE,15.25,0.229884347,18.125,0.4622692,0,0.403234768,1239,0.1839634,0.319837929
13249,Network for subclinical prognostication of COVID 19 Patients from data of thoracic roentgenogram: A feasible alternative screening technology,0,10.1101/2020.09.07.20189852,9/9/20,medrxiv,0,4,"neural network, prediction model, dataset",0.011857728,0.021013371,0.908963046,0.001187338,0.001187306,0.05579121,Imaging,0.3845198,FALSE,4,0.054734368,0.25,0.065493712,0,0.403234768,671,0.012521069,0.133995979
13250,Ongoing natural selection drives the evolution of SARS-CoV-2 genomes,0,10.1101/2020.09.07.20189860,9/9/20,medrxiv,0,7,"genome sequences, genomes",0.001823436,0.990883101,0.001823333,0.00182348,0.001823313,0.001823337,Genomics,0.22372222,FALSE,3.714285714,0.046137671,0.571428571,0.088038534,4,0.707574542,3213,0.631350831,0.368275394
13251,Performance and Robustness of Machine Learning-based Radiomic COVID-19 Severity Prediction,0,10.1101/2020.09.07.20189977,9/9/20,medrxiv,0,7,"machine learning, radiom, logistic regression, dataset",0.001141312,0.001141325,0.740833662,0.001141345,0.001141347,0.254601008,Imaging,0.6606988,TRUE,68.71428571,0.757684458,36.42857143,0.613727589,2,0.618927094,934,0.075126415,0.516366389
13252,First report on the Latvian SARS-CoV-2 isolate genetic diversity,0,10.1101/2020.09.08.20190504,9/9/20,medrxiv,0,21,"sequencing, genome sequences",0.002296522,0.715776787,0.002296564,0.275036814,0.002296681,0.002296632,Genomics,0.22517738,FALSE,12.25,0.185107304,5.7,0.270404067,1,0.537564047,1803,0.377799181,0.34271865
13253,"Seroprevalence of the SARS-CoV-2 infection in health workers of the Sanitary Region VIII, at province of Buenos Aires",0,10.1101/2020.09.07.20189050,9/9/20,medrxiv,0,12,probabilistic,0.001565331,0.24310199,0.001565349,0.119783682,0.599441694,0.034541954,Healthcare,0.3630492,FALSE,7.75,0.112808461,6.166666667,0.281977522,2,0.618927094,875,0.056585601,0.267574669
13254,Demographic Differences in US Adult Intentions to Receive a Potential Coronavirus Vaccine and Implications for Ongoing Study,0,10.1101/2020.09.07.20190058,9/9/20,medrxiv,0,1,logistic regression,0.001010964,0.001010978,0.001010949,0.128568669,0.819547716,0.048850724,Healthcare,0.30935943,FALSE,79,0.802585194,241,0.934372491,1,0.537564047,1446,0.260052974,0.633643677
13255,Longitudinal immune profiling of mild and severe COVID-19 reveals innate and adaptive immune dysfunction and provides an early prediction tool for clinical progression,0,10.1101/2020.09.08.20189092,9/9/20,medrxiv,0,22,classifier,0.509273044,0.001486479,0.026933582,0.067334282,0.00148645,0.393486163,Drug discovery,0.34536526,FALSE,8.045454545,0.118931288,7.227272727,0.302247792,2,0.618927094,2383,0.503491452,0.385899407
13256,Effect of Screen time on Glycaemic control of Type 2 Diabetes patients during COVID-19 Outbreak: A Survey based Study,0,10.1101/2020.09.09.20188961,9/9/20,medrxiv,0,2,logistic regression,0.000807911,0.000807901,0.00080798,0.000807945,0.563463849,0.433304414,Healthcare,0.4307083,FALSE,3.5,0.044344115,0,0.055525823,0,0.403234768,949,0.078015892,0.145280149
13257,Candidate screening of host cell membrane proteins involved in SARS-CoV-2 entry,0,10.1101/2020.09.09.289488,9/9/20,biorxiv,0,2,proteom,0.952905261,0.002130762,0.038571825,0.002130831,0.002130664,0.002130657,Drug discovery,0.4950265,FALSE,68,0.753973653,25,0.529435376,1,0.537564047,1488,0.274500361,0.523868359
13258,Small molecules inhibit SARS-COV-2 induced aberrant inflammation and viral replication in mice by targeting S100A8/A9-TLR4 axis,0,10.1101/2020.09.09.288704,9/9/20,biorxiv,0,18,transcriptom,0.737813192,0.002562844,0.002562608,0.002562792,0.002562662,0.251935903,Drug discovery,0.40005395,FALSE,39.22222222,0.531943843,45.33333333,0.659820712,2,0.618927094,958,0.08090537,0.472899255
13259,Transcriptomic dysregulations associated with SARS-CoV-2 infection in human nasopharyngeal and peripheral blood mononuclear cells,0,10.1101/2020.09.09.289850,9/9/20,biorxiv,0,23,"sequencing, transcriptom, dataset",0.613940945,0.281344916,0.000966777,0.000966765,0.00096678,0.101813816,Drug discovery,0.3777948,FALSE,2.958333333,0.031851073,1.083333333,0.122491303,0,0.403234768,2389,0.504936191,0.265628334
13260,Interaction network of SARS-CoV-2 with host receptome through spike protein,0,10.1101/2020.09.09.287508,9/9/20,biorxiv,0,20,transcriptom,0.917858558,0.024697397,0.001046821,0.001046833,0.001046878,0.054303513,Drug discovery,0.6868361,TRUE,54.85,0.667202672,,,9,0.814309525,2848,0.58632314,0.689278446
13261,Tracking SARS-CoV-2 T cells with epitope T-cell receptor recognition models,0,10.1101/2020.09.09.289355,9/9/20,biorxiv,0,5,"machine learning, sequencing",0.513758062,0.202429986,0.155467816,0.002720249,0.002720266,0.122903621,Drug discovery,0.20013782,FALSE,32.6,0.462242563,12.8,0.396574793,2,0.618927094,1225,0.179147604,0.414223013
13262,Activin/Follistatin-axis deregulation is independently associated with COVID-19 in-hospital mortality,0,10.1101/2020.09.05.20184655,9/8/20,medrxiv,10.1093/infdis/jiab108,20,prediction model,0.135795237,0.149558531,0.001203496,0.001203497,0.001203492,0.711035748,Clinics,0.727738,TRUE,53.95,0.66039953,68.6,0.749331014,2,0.618927094,2679,0.554779677,0.645859329
13263,Covid-19 vs BCG Universal Immunization: Statistical Significance at Six Months of Exposure,0,10.1101/2020.09.06.20189423,9/8/20,medrxiv,0,1,dataset,0.003760704,0.003760572,0.003760686,0.981196553,0.003760719,0.003760766,Epidemiology,0.22785419,FALSE,13,0.197352959,2,0.164302917,0,0.403234768,1445,0.258126655,0.255754325
13264,Multiscale statistical physics of the Human-SARS-CoV-2 interactome,0,10.1101/2020.09.06.20189266,9/8/20,medrxiv,0,4,interactom,0.413449628,0.404386592,0.069485163,0.109176235,0.001751199,0.001751183,Drug discovery,0.16729638,FALSE,5.5,0.077246583,1,0.122023013,0,0.403234768,1265,0.189983145,0.198121877
13265,Analysis of SARS-CoV-2 genomes from across Africa reveals potentially clinically relevant mutations.,0,10.1101/2020.09.08.287201,9/8/20,biorxiv,0,9,"in silico, genome sequences, genomes, sequence alignment",0.001046904,0.964715049,0.031097453,0.001046881,0.001046854,0.00104686,Genomics,0.6992177,TRUE,6.444444444,0.091038407,0.444444444,0.076933369,1,0.537564047,2370,0.499638815,0.30129366
13266,The link between vitamin D deficiency and Covid-19 in a large population,0,10.1101/2020.09.04.20188268,9/7/20,medrxiv,0,8,logistic regression,0.001538115,0.001538243,0.001538102,0.001538194,0.314051455,0.679795891,Clinics,0.1112718,FALSE,13.5,0.205393036,6.125,0.280907145,9,0.814309525,63201,0.987238141,0.571961962
13267,Regression Models for Predictions of COVID-19 New Cases and New Deaths Based on May/June Data in Ethiopia,0,10.1101/2020.09.04.20188094,9/7/20,medrxiv,10.11648/j.ijtam.20200605.11,1,correlation analysis,0.001622701,0.001622716,0.001622717,0.824426796,0.001622749,0.169082321,Epidemiology,0.04620093,FALSE,4,0.054734368,0,0.055525823,1,0.537564047,2813,0.57524681,0.305767762
13268,Predicting illness trajectory and hospital resource utilization of COVID-19 hospitalized patients - a nationwide study,0,10.1101/2020.09.04.20185645,9/7/20,medrxiv,0,12,"machine learning, dataset",0.000436911,0.000436937,0.000436952,0.42964954,0.000436924,0.568602736,Clinics,0.117614895,FALSE,16.9,0.253880883,37.8,0.620818839,2,0.618927094,2348,0.493137491,0.496691077
13269,Trans-ethnic analysis reveals genetic and non-genetic associations with COVID-19 susceptibility and severity,0,10.1101/2020.09.04.20188318,9/7/20,medrxiv,0,11,genome-wide,0.002296612,0.201371443,0.002296585,0.002296627,0.002296749,0.789441984,Clinics,0.2762993,FALSE,13.63636364,0.206135197,32.81818182,0.59104897,33,0.936045435,30800,0.969901276,0.675782719
13270,COVID-19 Preprints and Their Publishing Rate: An Improved Method,0,10.1101/2020.09.04.20188771,9/7/20,medrxiv,0,1,dataset,0.00123708,0.056743191,0.001237156,0.938308327,0.001237156,0.00123709,Epidemiology,0.034940302,FALSE,8,0.118683901,0,0.055525823,3,0.667819001,3136,0.617866602,0.364973832
13271,CAN EDUCATIONAL INSTITUTIONS REOPEN FOR IN-PERSON CLASSES SAFELY AMID THE COVID-19 PANDEMIC?,0,10.1101/2020.09.04.20188680,9/7/20,medrxiv,0,8,bayes,0.001987109,0.001987144,0.001987168,0.990064134,0.001987286,0.001987158,Epidemiology,0.1361911,FALSE,8.625,0.127342445,2.625,0.182766925,0,0.403234768,2247,0.472911149,0.296563822
13272,Social capital and psychological distress during Colombian coronavirus disease lockdown,0,10.1101/2020.09.04.20187914,9/7/20,medrxiv,10.1002/jcop.22487,3,logistic regression,0.001717182,0.001717189,0.001717184,0.001717249,0.991413863,0.001717334,Healthcare,0.95855653,TRUE,97.66666667,0.863751623,29.33333333,0.565226117,1,0.537564047,854,0.049121117,0.503915726
13273,The association between COVID-19 and preterm delivery: A cohort study with a multivariate analysis,0,10.1101/2020.09.05.20188458,9/7/20,medrxiv,0,50,logistic regression,0.05386813,0.001786579,0.00178656,0.001786562,0.259716767,0.681055403,Clinics,0.3464921,FALSE,2.071428571,0.022202981,0,0.055525823,3,0.667819001,6140,0.805441849,0.387747414
13274,Mutational Analysis of SARS-CoV-2 Genome in African Population,0,10.1101/2020.09.07.286088,9/7/20,biorxiv,0,3,sequence alignment,0.327670549,0.60981416,0.019144676,0.001141405,0.041087857,0.001141353,Genomics,0.730186,TRUE,2.333333333,0.024800544,0,0.055525823,0,0.403234768,1847,0.385504455,0.217266397
13275,Genomic analysis reveals local transmission of SARS-CoV-2 in early pandemic phase in Peru,0,10.1101/2020.09.05.284604,9/6/20,biorxiv,0,10,genomes,0.001861699,0.990691077,0.001861854,0.001861778,0.001861862,0.00186173,Genomics,0.36031133,FALSE,2.9,0.031665533,0,0.055525823,3,0.667819001,2102,0.445701902,0.300178065
13276,"Computationally validated SARS-CoV-2 CTL and HTL Multi-Patch Vaccines designed by reverse epitomics approach, shows potential to cover large ethnically distributed human population worldwide",0,10.1101/2020.09.06.284992,9/6/20,biorxiv,10.1080/07391102.2020.1838329,13,"computational, proteom",0.651125483,0.197203388,0.000999564,0.14867248,0.000999542,0.000999543,Drug discovery,0.33434296,FALSE,32.46153846,0.460634548,13.53846154,0.405673,1,0.537564047,1671,0.330604382,0.433618994
13277,Cooperative efforts on developing vaccines and therapies for COVID-19,0,10.1101/2020.09.06.282145,9/6/20,biorxiv,0,4,"virom, network analysis",0.212093745,0.001415162,0.001415173,0.782245546,0.001415186,0.001415189,Epidemiology,0.15165702,FALSE,28,0.408312202,5,0.257024351,0,0.403234768,1719,0.34505177,0.353405773
13278,Public Preferences for Government Response Policies on Outbreak Control,0,10.1101/2020.09.04.20187906,9/5/20,medrxiv,0,5,logistic regression,0.001371259,0.00137127,0.001371306,0.577456462,0.417058376,0.001371328,Epidemiology,0.3405897,FALSE,2.8,0.031047065,0.4,0.075796093,0,0.403234768,555,0.001685529,0.127940864
13279,"SARS-CoV-2 phylogeny during the early outbreak in the Basel area, Switzerland: import and spread dominated by a single B.1 lineage variant (C15324T)",0,10.1101/2020.09.01.20186155,9/5/20,medrxiv,0,22,genomes,0.001059334,0.994703171,0.001059337,0.001059424,0.001059375,0.001059359,Genomics,0.28360358,FALSE,28.5,0.412579628,11.09090909,0.371554723,3,0.667819001,4402,0.728389116,0.545085617
13280,Estimating COVID-19 hospital demand using a non-parametric model: a case study in Galicia (Spain),0,10.1101/2020.09.04.20187963,9/5/20,medrxiv,0,4,prediction model,0.001237051,0.00123708,0.100403058,0.436306939,0.0012371,0.459578772,Clinics,0.2066563,FALSE,8.25,0.121343311,1.75,0.148381054,0,0.403234768,860,0.048398748,0.18033947
13281,Household Secondary Attack Rate in Gandhinagar district of Gujarat state from Western India,0,10.1101/2020.09.03.20187336,9/5/20,medrxiv,0,6,dataset,0.001823359,0.00182343,0.001823388,0.335538946,0.308132916,0.35085796,Clinics,0.12927377,FALSE,46.66666667,0.602510978,47.16666667,0.669253412,3,0.667819001,740,0.02263424,0.490554408
13282,Swab pooling for large-scale RT-qPCR screening of SARS-CoV-2,0,10.1101/2020.09.03.20187732,9/5/20,medrxiv,10.1371/journal.pone.0246544,12,"bayes, bayesian model",0.001156247,0.493475643,0.326590344,0.07504121,0.001156317,0.102580239,Genomics,0.09503496,FALSE,24.66666667,0.36334962,16.41666667,0.441329944,3,0.667819001,1965,0.411509752,0.471002079
13283,Genomic diversity and evolution of coronavirus (SARS-CoV-2) in France from 309 COVID-19-infected patients,0,10.1101/2020.09.04.282616,9/4/20,biorxiv,0,8,genomes,0.001254615,0.809383686,0.001254625,0.001254639,0.001254621,0.185597814,Genomics,0.323945,FALSE,138.625,0.931473808,251.125,0.938386406,3,0.667819001,3723,0.677341681,0.803755224
13284,A SARS-CoV-2 - host proximity interactome,0,10.1101/2020.09.03.282103,9/4/20,biorxiv,0,18,"proteom, interactom, dataset",0.940304016,0.055009649,0.001171611,0.001171598,0.00117155,0.001171576,Drug discovery,0.19063929,FALSE,57.66666667,0.686375162,274.5,0.94668183,12,0.850299401,5886,0.794847098,0.819550873
13285,Clustering analysis of single nucleotide polymorphism data reveals population structure of SARS-CoV-2 worldwide,0,10.1101/2020.09.04.283358,9/4/20,biorxiv,0,4,sequencing,0.001901781,0.882811717,0.109581128,0.001901827,0.001901757,0.001901791,Genomics,0.42654747,FALSE,186.3333333,0.967035686,126,0.859245384,0,0.403234768,1195,0.159162052,0.597169472
13286,"Face-masking, an acceptable protective measure against COVID-19: Findings of Ugandan high-risk groups",0,10.1101/2020.08.29.20184325,9/3/20,medrxiv,10.4269/ajtmh.20-1174,4,logistic regression,0.001486387,0.001486405,0.001486402,0.001486469,0.992567918,0.00148642,Healthcare,0.9283139,TRUE,14.75,0.222586431,5,0.257024351,0,0.403234768,892,0.053936913,0.234195616
13287,Biologic agents for rheumatic diseases in the break of COVID-19: friend or foe?,0,10.1101/2020.09.01.20184333,9/3/20,medrxiv,0,7,logistic regression,0.163675293,0.000608044,0.009111956,0.00060806,0.000608076,0.825388571,Clinics,0.4910302,FALSE,2.428571429,0.025480858,0,0.055525823,1,0.537564047,1432,0.242475319,0.215261512
13288,Pleotropic association between risk and prognosis of COVID-19 and gene expression in blood and lung: A Mendelian randomization analysis,0,10.1101/2020.09.02.20187179,9/3/20,medrxiv,10.1016/j.jinf.2020.11.031,6,genome-wide,0.4591507,0.191472671,0.001861709,0.001861746,0.001861836,0.343791338,Drug discovery,0.79146326,TRUE,24.33333333,0.359700662,10.5,0.363459995,5,0.739490092,2008,0.417770287,0.470105259
13289,Post-Anticoagulant D-dimer as a Highly Prognostic Biomarker of COVID-19 Mortality,0,10.1101/2020.09.02.20180984,9/3/20,medrxiv,10.1183/23120541.00018-2021,9,logistic regression,0.000677954,0.000677908,0.000677965,0.025379666,0.000677915,0.971908592,Clinics,0.6019173,TRUE,66.77777778,0.745624343,158,0.890487022,1,0.537564047,3983,0.694678546,0.717088489
13290,Racial/Ethnic Disparities in Hospital Admissions from COVID-19 and Determining the Impact of Neighborhood Deprivation and Primary Language.,0,10.1101/2020.09.02.20185983,9/3/20,medrxiv,0,10,logistic regression,0.001010932,0.001010944,0.001011085,0.001010999,0.400931664,0.595024376,Clinics,0.40150094,FALSE,46.1,0.597315851,29.4,0.565426813,4,0.707574542,1683,0.326510956,0.54920704
13291,The impact of high frequency rapid viral antigen screening on COVID-19 spread and outcomes: a validation and modeling study,0,10.1101/2020.09.01.20184713,9/3/20,medrxiv,0,18,computational,0.002080649,0.606701673,0.30506688,0.002080755,0.00208057,0.081989473,Genomics,0.2566331,FALSE,0.277777778,0.006803142,,,5,0.739490092,5139,0.768119432,0.504804222
13292,Maximizing and evaluating the impact of test-trace-isolate programs,0,10.1101/2020.09.02.20186916,9/3/20,medrxiv,0,7,mathematical model,0.001010947,0.149637115,0.083760814,0.763569203,0.001010982,0.001010938,Epidemiology,0.20759329,FALSE,0,0.006432061,,,6,0.764429903,1951,0.405971587,0.39227785
13293,Could seasonal influenza vaccination influence COVID-19 risk?,0,10.1101/2020.09.02.20186734,9/3/20,medrxiv,0,2,dataset,0.002238666,0.230940533,0.00223877,0.292429466,0.469913931,0.002238634,Healthcare,0.33409253,FALSE,57,0.68204589,88,0.796895906,1,0.537564047,3773,0.679508789,0.674003658
13294,An epidemic model for economical impact predicting and spatiotemporal spreading of COVID-19,0,10.1101/2020.09.02.20186551,9/3/20,medrxiv,0,3,"artificial intelligence, mathematical model",0.001861725,0.054468049,0.208416691,0.731529926,0.001861927,0.001861682,Epidemiology,0.14709231,FALSE,0,0.006432061,,,0,0.403234768,609,0.003852637,0.137839822
13295,Onset of effects of non-pharmaceutical interventions on COVID-19 worldwide,0,10.1101/2020.09.02.20185660,9/3/20,medrxiv,0,4,machine learning,0.002130682,0.002130636,0.002130785,0.989346548,0.002130704,0.002130645,Epidemiology,0.23835787,FALSE,0,0.006432061,,,0,0.403234768,1035,0.097279075,0.168981968
13296,COMPARISON OF ARTIFICIAL INTELLIGENCE ENABLED METHODS IN THE COMPUTED TOMOGRAPHIC ASSESSMENT OF COVID-19 DISEASE.,0,10.1101/2020.09.02.20186650,9/3/20,medrxiv,0,3,artificial intelligence,0.001072176,0.0010722,0.564712818,0.001072231,0.00107219,0.430998385,Imaging,0.71421707,TRUE,1,0.012307502,,,0,0.403234768,728,0.017818444,0.144453571
13297,Longitudinal single-cell immune profiling revealed distinct innate immune response in asymptomatic COVID-19 patients,0,10.1101/2020.09.02.276865,9/3/20,biorxiv,0,16,"sequencing, transcriptom",0.590734378,0.122829731,0.001156248,0.001156279,0.001156273,0.282967091,Drug discovery,0.12013972,FALSE,23.82352941,0.351413198,,,3,0.667819001,2212,0.459186131,0.49280611
13298,Boosting the analysis of protein interfaces with Multiple Interface String Alignment: illustration on the spikes of coronaviruses,0,10.1101/2020.09.03.281600,9/3/20,biorxiv,0,3,bioinformatic,0.69727474,0.29585621,0.001717317,0.001717276,0.001717241,0.001717216,Drug discovery,0.26329836,FALSE,48.66666667,0.619333292,33.33333333,0.594995986,0,0.403234768,915,0.060438237,0.419500571
13299,Phylo-geo-network and haplogroup analysis of 611 novel Coronavirus (nCov-2019) genomes from India,0,10.1101/2020.09.03.281774,9/3/20,biorxiv,10.26508/lsa.202000925,2,genomes,0.001786566,0.779187356,0.001786633,0.213666361,0.001786531,0.001786554,Genomics,0.32200617,FALSE,5.5,0.077246583,2,0.164302917,3,0.667819001,1864,0.380447869,0.322454092
13300,"The discovery of gene mutations making SARS-CoV-2 well adapted for humans: host-genome similarity analysis of 2594 genomes from China, the USA and Europe",0,10.1101/2020.09.03.280727,9/3/20,biorxiv,0,1,genomes,0.25832151,0.736017869,0.001415119,0.001415258,0.001415108,0.001415136,Genomics,0.47122508,FALSE,47,0.606407323,3,0.199424672,2,0.618927094,1018,0.0919817,0.379185197
13301,Early elevation of FIB-4 liver fibrosis score is associated with adverse outcomes among patients with COVID-19,0,10.1101/2020.09.01.20186080,9/3/20,medrxiv,10.1093/cid/ciaa1710,7,logistic regression,0.001171565,0.001171553,0.001171541,0.001171577,0.001171556,0.994142208,Clinics,0.73753774,TRUE,41.85714286,0.556064073,66.14285714,0.742774953,0,0.403234768,571,0.002648688,0.42618062
13302,The role of masks in reducing the risk of new waves of COVID-19 in low transmission settings: a modeling study,0,10.1101/2020.09.02.20186742,9/3/20,medrxiv,0,8,simulation model,0.001219999,0.00122003,0.051900758,0.94321909,0.001220044,0.001220079,Epidemiology,0.11252946,FALSE,14.5,0.219617787,,,2,0.618927094,1291,0.188056826,0.342200569
13303,Analyzing inherent biases in SARS-CoV-2 PCR and serological epidemiologic metrics,0,10.1101/2020.08.30.20184705,9/2/20,medrxiv,0,7,mathematical model,0.001126804,0.278594647,0.001126838,0.623064266,0.094960563,0.001126881,Epidemiology,0.09931949,FALSE,9.142857143,0.13637207,2.571428571,0.181495852,3,0.667819001,794,0.031543463,0.254307597
13304,Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the COVID-19 epidemic in Washington state,0,10.1101/2020.08.29.20184135,9/2/20,medrxiv,10.1038/s41746-021-00422-7,17,computational,0.001717234,0.001717176,0.001717216,0.864920289,0.128210917,0.001717167,Epidemiology,0.51361483,TRUE,19.47058824,0.290184922,14.11764706,0.413165641,23,0.91129082,15927,0.93137491,0.636504073
13305,One Study of COVID-19 Spreading at The United States - Brazil - Colombia,0,10.1101/2020.08.29.20184465,9/2/20,medrxiv,0,5,mathematical model,0.004310466,0.004310299,0.00431002,0.978449087,0.004310205,0.004309924,Epidemiology,0.51172984,TRUE,22,0.326056033,1,0.122023013,0,0.403234768,689,0.01155791,0.215717931
13306,Estimating Unreported Deaths Associated with COVID-19,0,10.1101/2020.08.29.20184176,9/2/20,medrxiv,0,6,dataset,0.081491174,0.001538147,0.001538122,0.91235627,0.001538109,0.001538178,Epidemiology,0.03899458,FALSE,29.83333333,0.428907168,13.66666667,0.407412363,0,0.403234768,1624,0.303395136,0.385737359
13307,To isolate or not to isolate: The impact of changing behavior on COVID-19 transmission,0,10.1101/2020.08.30.20184804,9/2/20,medrxiv,0,10,mathematical model,0.08204185,0.001538132,0.068218695,0.661638997,0.185024195,0.001538131,Epidemiology,0.07369262,FALSE,58.2,0.690518894,233.3,0.931228258,1,0.537564047,1284,0.185167349,0.586119637
13308,COVID-19 Detection From Chest Radiographs Using Machine Learning and Convolutional Neural Networks,0,10.1101/2020.08.31.20175828,9/2/20,medrxiv,0,5,"machine learning, neural network, classifier, dataset",0.001717191,0.027881943,0.892259176,0.001717266,0.074707198,0.001717226,Healthcare,0.37138206,FALSE,1.4,0.013915517,,,0,0.403234768,1034,0.096315916,0.1711554
13309,A neutrophil activation signature predicts critical illness and mortality in COVID-19,0,10.1101/2020.09.01.20183897,9/2/20,medrxiv,10.1182/bloodadvances.2020003568,30,"machine learning, proteom",0.215887065,0.001751184,0.083648176,0.001751158,0.001751149,0.695211268,Clinics,0.57572633,TRUE,11.5,0.17416043,,,14,0.866658436,7789,0.841560318,0.627459728
13310,"Covid-19 SEIDRD Modelling for Pakistan with implementation of seasonality, healthcare capacity and behavioral risk reduction",0,10.1101/2020.09.01.20182642,9/2/20,medrxiv,0,3,"simulation model, computational",0.000871555,0.000871559,0.000871552,0.916516974,0.000871564,0.079996797,Epidemiology,0.09894416,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,865,0.044786901,0.131422156
13311,Non-alcoholic fatty liver disease (NAFLD) and risk of hospitalization for Covid-19.,0,10.1101/2020.09.01.20185850,9/2/20,medrxiv,0,9,logistic regression,0.072558298,0.0011268,0.001126787,0.001126838,0.234712131,0.689349145,Clinics,0.3593962,FALSE,35.77777778,0.494650257,16.22222222,0.438453305,5,0.739490092,2502,0.511678305,0.54606799
13312,Development and calibration of a simple mortality risk score for hospitalized COVID-19 adults,0,10.1101/2020.08.31.20185363,9/2/20,medrxiv,0,11,machine learning,0.00056539,0.000565324,0.163343298,0.203704631,0.000565361,0.631255995,Clinics,0.25823689,FALSE,18,0.271569052,6.454545455,0.286459727,3,0.667819001,1068,0.106669877,0.333129414
13313,Immune transcriptomes of highly exposed SARS-CoV-2 asymptomatic seropositive versus seronegative individuals from the Ischgl community,0,10.1101/2020.09.01.20185884,9/2/20,medrxiv,10.1038/s41598-021-83110-6,16,transcriptom,0.311580142,0.231825901,0.001786532,0.19753713,0.001786655,0.25548364,Drug discovery,0.23660234,FALSE,23,0.34225988,32.8,0.590982071,1,0.537564047,1505,0.26486877,0.433918692
13314,Structural Variants in SARS-CoV-2 Occur at Template-Switching Hotspots,0,10.1101/2020.09.01.278952,9/2/20,biorxiv,0,10,transcriptom,0.001291312,0.993543833,0.0012912,0.001291228,0.001291208,0.001291219,Genomics,0.20231947,FALSE,15,0.227596017,4.2,0.234211935,0,0.403234768,1723,0.336142548,0.300296317
13315,Deep-learning convolutional neural networks with transfer learning accurately classify COVID19 lung infection on portable chest radiographs,0,10.1101/2020.09.02.20186759,9/2/20,medrxiv,10.7717/peerj.10309,5,"neural network, deep-learning, transfer learning",0.000966761,0.000966801,0.995166203,0.00096674,0.000966736,0.000966758,Imaging,0.851151,TRUE,1.2,0.012740429,,,0,0.403234768,884,0.051288225,0.155754474
13316,Jumping back and forth: anthropozoonotic and zoonotic transmission of SARS-CoV-2 on mink farms,0,10.1101/2020.09.01.277152,9/1/20,biorxiv,0,22,"sequencing, whole genome",0.002183292,0.989083485,0.002183362,0.002183411,0.002183245,0.002183204,Genomics,0.3996406,FALSE,46.22727273,0.598305399,88.5,0.797966283,20,0.900117291,31869,0.970382856,0.816692957
13317,Automated COVID-19 Detection from Chest X-Ray Images : A High Resolution Network (HRNet)Approach,0,10.1101/2020.08.26.20182311,9/1/20,medrxiv,0,6,"deep learning, classifier, dataset",0.00159351,0.001593565,0.992032136,0.001593565,0.001593674,0.00159355,Imaging,0.5083014,TRUE,5.666666667,0.079473066,0.666666667,0.096200161,0,0.403234768,866,0.043582952,0.155622736
13318,COVID-19 infection dynamics in care homes in the East of England: a retrospective genomic epidemiology study,0,10.1101/2020.08.26.20182279,9/1/20,medrxiv,10.7554/eLife.64618,38,"genomic epidemiology, genomes, dataset",0.002032819,0.404957594,0.002032937,0.002032922,0.451822809,0.137120919,Healthcare,0.40485483,FALSE,24.02564103,0.355804317,,,0,0.403234768,3036,0.595713942,0.451584342
13319,Predictors of healthcare worker burnout during the COVID-19 pandemic,0,10.1101/2020.08.26.20182378,9/1/20,medrxiv,0,4,logistic regression,0.001203428,0.001203408,0.001203421,0.00120342,0.899786057,0.095400267,Healthcare,0.8311261,TRUE,23.75,0.350547344,17.25,0.453104094,3,0.667819001,1861,0.375391283,0.461715431
13320,"Previous psychopathology predicted severe COVID-19 concern, anxiety and PTSD symptoms in pregnant women during lockdown in Italy",0,10.1101/2020.08.26.20182436,9/1/20,medrxiv,10.1007/s00737-020-01086-0,5,logistic regression,0.001371229,0.001371273,0.00137127,0.164268585,0.830246324,0.00137132,Healthcare,0.9182916,TRUE,56.4,0.677902158,,,3,0.667819001,870,0.045027691,0.46358295
13321,"A multipurpose machine learning approach to predict COVID-19 negative prognosis in Sao Paulo, Brazil",0,10.1101/2020.08.26.20182584,9/1/20,medrxiv,10.1038/s41598-021-82885-y,6,"machine learning, neural network",0.001156314,0.001156287,0.601800518,0.001156306,0.001156257,0.393574318,Clinics,0.6323519,TRUE,5.666666667,0.079473066,1.666666667,0.145036125,1,0.537564047,831,0.036840838,0.199728519
13322,Systematic review and meta-analysis identifies potential host therapeutic targets in COVID-19.,0,10.1101/2020.08.27.20182238,9/1/20,medrxiv,0,11,dataset,0.77367928,0.001310384,0.001310495,0.199397971,0.00131034,0.022991529,Drug discovery,0.33271086,FALSE,28.45454545,0.411528233,39.36363636,0.629783249,2,0.618927094,2853,0.569227065,0.55736641
13323,Empowering the crowd: Feasible strategies to minimize the spread of COVID-19 in high-density informal settlements,0,10.1101/2020.08.26.20181990,9/1/20,medrxiv,0,5,mathematical model,0.000688459,0.000688468,0.000688466,0.782959275,0.214286836,0.000688498,Epidemiology,0.06805661,FALSE,11,0.167171748,13,0.400521809,1,0.537564047,1342,0.202263424,0.326880257
13324,Data-Driven Inference of COVID-19 Clinical Prognosis,0,10.1101/2020.08.27.20183202,9/1/20,medrxiv,0,4,"machine learning, neural network, classifier",0.001593491,0.039316749,0.827173455,0.00159359,0.001593576,0.128729138,Clinics,0.4100214,FALSE,4.25,0.056527924,0.75,0.099411292,0,0.403234768,835,0.037322418,0.1491241
13325,DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic,0,10.1101/2020.08.27.20183277,9/1/20,medrxiv,10.3390/app10217514,2,"neural network, dataset",0.001486407,0.001486427,0.256717393,0.737336854,0.001486508,0.001486411,Epidemiology,0.5629399,TRUE,29.5,0.426000371,8,0.320511105,7,0.785110192,1361,0.209005538,0.435156802
13326,Predicting and interpreting COVID-19 transmission rates from the ensemble of government policies,0,10.1101/2020.08.27.20179853,9/1/20,medrxiv,0,5,"artificial intelligence, predictive model",0.001330019,0.001330034,0.100237714,0.894442177,0.001330042,0.001330014,Epidemiology,0.14590538,FALSE,10.6,0.158142124,3.4,0.208589778,1,0.537564047,1298,0.187334457,0.272907601
13327,"COVID-19 Seroprevalence in Baixada Santista Metropolitan Area, Sao Paulo, Brazil.",0,10.1101/2020.08.28.20184010,9/1/20,medrxiv,0,8,probabilistic,0.001085378,0.051983647,0.001085363,0.453907294,0.490852884,0.001085433,Healthcare,0.16928884,FALSE,9.75,0.146267549,2.75,0.187583623,2,0.618927094,1000,0.082831688,0.258902489
13328,The importance of saturating density dependence for predicting SARS-CoV-2 resurgence,0,10.1101/2020.08.28.20183921,9/1/20,medrxiv,0,4,mathematical model,0.002720173,0.002720194,0.002720427,0.747727538,0.080813148,0.163298521,Epidemiology,0.25083116,FALSE,49.66666667,0.629166924,32,0.585763982,1,0.537564047,1986,0.409101854,0.540399202
13329,Diagnosis of COVID-19 from X-rays Using Combined CNN-RNN Architecture with Transfer Learning,0,10.1101/2020.08.24.20181339,8/31/20,medrxiv,0,4,"computational, neural network, transfer learning",0.001187298,0.0011873,0.994063528,0.001187266,0.001187361,0.001187248,Imaging,0.6698529,TRUE,2.75,0.030304904,0,0.055525823,3,0.667819001,1149,0.132915964,0.221641423
13330,Phosphate levels and pulmonary damage in COVID-19 patients based on CO-RADS scheme: is there any link between parathyroid gland and COVID-19?,0,10.1101/2020.08.25.20181453,8/31/20,medrxiv,0,11,logistic regression,0.001593525,0.00159355,0.260725979,0.00159353,0.051151719,0.683341698,Clinics,0.4176235,FALSE,5.545454545,0.07730843,0.090909091,0.055994113,0,0.403234768,1134,0.128100169,0.16615937
13331,"Twitter Interaction to Analyze Covid-19 Impact in Ghana, Africa from March to July",0,10.1101/2020.08.25.20182048,8/31/20,medrxiv,0,2,text mining,0.002183241,0.068373248,0.002183287,0.922893431,0.002183575,0.002183218,Epidemiology,0.05650094,FALSE,11.5,0.17416043,0,0.055525823,3,0.667819001,659,0.007705273,0.226302632
13332,"Transfer learning to detect COVID-19 automatically from X-ray images, using convolutional neural networks",0,10.1101/2020.08.25.20182170,8/31/20,medrxiv,0,3,"deep learning, artificial intelligence, neural network, transfer learning",0.001046925,0.001046903,0.971926171,0.001046865,0.001046819,0.023886316,Imaging,0.41483557,FALSE,2.333333333,0.024800544,0,0.055525823,0,0.403234768,1748,0.339032025,0.20564829
13333,Clinical Features Associated with COVID-19 Outcome in MM: First Results from International Myeloma Society COVID-19 Dataset,0,10.1101/2020.08.24.20177576,8/31/20,medrxiv,0,25,"logistic regression, dataset",0.001350381,0.001350366,0.001350361,0.177562009,0.001350428,0.817036454,Clinics,0.70162296,TRUE,97.6,0.86362793,101.12,0.824190527,0,0.403234768,1107,0.119672526,0.552681438
13334,"Lymphocyte subset alterations with disease severity, imaging manifestation, and delayed hospitalization in COVID-19 patients",0,10.1101/2020.08.25.20181321,8/31/20,medrxiv,0,6,logistic regression,0.307557605,0.001034561,0.10366262,0.001034588,0.001034565,0.585676061,Clinics,0.6573584,TRUE,6.166666667,0.087080215,1.666666667,0.145036125,0,0.403234768,676,0.008668432,0.161004885
13335,"Risk of COVID-19 hospitalisation rises exponentially with age, inversely proportional to T-cell production",0,10.1101/2020.08.25.20181487,8/31/20,medrxiv,0,3,"bayes, model fit",0.126111798,0.00117165,0.00117156,0.492248215,0.077663447,0.30163333,Epidemiology,0.072121024,FALSE,3.666666667,0.045952131,0.333333333,0.073187048,0,0.403234768,5993,0.794606309,0.329245064
13336,SARS-CoV-2-RNA viremia is associated to hypercytokinemia and critical illness in COVID-19,0,10.1101/2020.08.25.20154252,8/31/20,medrxiv,10.1186/s13054-020-03398-0,66,logistic regression,0.08631572,0.305146589,0.000956361,0.000956341,0.000956337,0.605668653,Clinics,0.37522426,FALSE,19.42105263,0.289690148,9.657894737,0.347002944,0,0.403234768,6301,0.805201059,0.46128223
13337,Efficacy of Localized Lockdowns in the SARS-CoV-2 Pandemic,0,10.1101/2020.08.25.20182071,8/31/20,medrxiv,0,3,dataset,0.001786589,0.026589264,0.001786543,0.96626457,0.001786536,0.001786497,Epidemiology,0.21657497,FALSE,35.33333333,0.490259138,112,0.841450361,0,0.403234768,3099,0.601252107,0.584049093
13338,"Analysis and prediction of Covid-19 spreading through Bayesian modelling with a case study of Uttar Pradesh, India",0,10.1101/2020.08.25.20180265,8/31/20,medrxiv,0,4,"bayes, bayesian model",0.001943464,0.001943476,0.001943471,0.990282623,0.001943491,0.001943475,Epidemiology,0.5452998,TRUE,15,0.227596017,2.75,0.187583623,0,0.403234768,747,0.019022393,0.2093592
13339,"Post-infection depression, anxiety and PTSD: a retrospective cohort study with mild COVID-19 patients",0,10.1101/2020.08.25.20182113,8/31/20,medrxiv,0,10,logistic regression,0.001350308,0.001350461,0.001350446,0.001350391,0.70383622,0.290762173,Healthcare,0.89362586,TRUE,31.2,0.446471643,7.8,0.313286058,1,0.537564047,2327,0.473874308,0.442799014
13340,"The Role of Air Conditioning in the Diffusion of Sars-CoV-2 in Indoor Environments: a First Computational Fluid Dynamic Model, based on Investigations performed at the Vatican State Childrens Hospital.",0,10.1101/2020.08.25.20181420,8/31/20,medrxiv,10.1016/j.envres.2020.110343,6,computational,0.001272672,0.001272691,0.001272673,0.688628417,0.024053584,0.283499963,Epidemiology,0.29838347,FALSE,9.333333333,0.139278867,1,0.122023013,0,0.403234768,1576,0.283409583,0.236986558
13341,Optimising social mixing strategies to mitigate the impact of COVID-19 in six European countries: a mathematical modelling study,0,10.1101/2020.08.25.20182162,8/31/20,medrxiv,0,12,mathematical model,0.001112636,0.001112643,0.001112623,0.967166364,0.028383008,0.001112725,Epidemiology,0.14340311,FALSE,32.41666667,0.460077927,23.83333333,0.519467487,1,0.537564047,2171,0.446905851,0.491003828
13342,SARS-CoV-2 Cell Entry Factors ACE2 and TMPRSS2 are Expressed in the Pancreas but Not in Islet Endocrine Cells,0,10.1101/2020.08.31.275719,8/31/20,biorxiv,0,19,"sequencing, dataset",0.812539578,0.077317863,0.001901738,0.001901719,0.001901723,0.104437379,Drug discovery,0.26548272,FALSE,68.15,0.75440658,160.35,0.892427081,5,0.739490092,3258,0.62075608,0.751769958
13343,ACE2 and SARS-CoV-2 Expression in the Normal and COVID-19 Pancreas,0,10.1101/2020.08.31.270736,8/31/20,biorxiv,0,20,"in silico, dataset",0.681773489,0.002639037,0.075545801,0.002639013,0.002638979,0.234763681,Drug discovery,0.58063006,TRUE,71.75,0.771909209,103.25,0.827803051,6,0.764429903,3311,0.626535035,0.747669299
13344,Single-cell analysis of human trophoblast stem cell specification reveals activation of fetal cytotrophoblast expression programs including coronavirus associated host factors and human endogenous retroviruses,0,10.1101/2020.08.29.273425,8/29/20,biorxiv,0,19,transcriptom,0.676070949,0.188500628,0.001171572,0.097909285,0.035175975,0.001171591,Drug discovery,0.14902854,FALSE,86.78947368,0.832890098,390.6315789,0.970163233,0,0.403234768,1884,0.374428124,0.645179056
13345,Global BioID-based SARS-CoV-2 proteins proximal interactome unveils novel ties between viral polypeptides and host factors involved in multiple COVID19-associated mechanisms,0,10.1101/2020.08.28.272955,8/29/20,biorxiv,0,31,"proteom, interactom",0.885926774,0.089137525,0.001126833,0.001126842,0.021555196,0.00112683,Drug discovery,0.4517279,FALSE,64,0.729173109,184.2903226,0.909084827,10,0.828199272,6831,0.818203708,0.821165229
13346,A SARS-CoV-2 BioID-based virus-host membrane protein interactome and virus peptide compendium: new proteomics resources for COVID-19 research,0,10.1101/2020.08.28.269175,8/28/20,biorxiv,0,10,"proteom, interactom, dataset",0.750611434,0.047681916,0.108420563,0.090776863,0.001254628,0.001254596,Drug discovery,0.34495914,FALSE,67,0.748160059,344.1,0.964677549,8,0.799987654,3128,0.599566578,0.77809796
13347,Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19,0,10.1101/2020.08.27.270835,8/28/20,biorxiv,0,1,"transcriptom, proteom, genome-wide",0.780436421,0.19946542,0.016583403,0.001171564,0.001171575,0.001171616,Drug discovery,0.20674294,FALSE,62.33333333,0.717236687,153,0.88587102,0,0.403234768,2222,0.451721647,0.61451603
13348,A Transferable Deep Learning Approach to Fast Screen Potent Antiviral Drugs against SARS-CoV-2,0,10.1101/2020.08.28.271569,8/28/20,biorxiv,0,5,"deep learning, neural network, transfer learning, prediction model",0.794888527,0.001593485,0.198737521,0.001593515,0.001593486,0.001593466,Drug discovery,0.65503156,TRUE,21.4,0.316469788,10.6,0.364262778,0,0.403234768,1608,0.286539851,0.342626796
13349,Sequence analysis of Indian SARS-CoV-2 isolates shows a stronger interaction of mutated receptor binding domain with ACE2 receptor,0,10.1101/2020.08.28.271601,8/28/20,biorxiv,10.1016/j.ijid.2021.01.020,17,"sequencing, whole genome",0.416937478,0.567088518,0.001059353,0.001059353,0.00105933,0.012795967,Genomics,0.7241327,TRUE,18.88235294,0.281959305,13.82352941,0.40895103,0,0.403234768,1786,0.343607031,0.359438033
13350,SARS-CoV-2 Nucleocapsid protein is decorated with multiple N- and O-glycans.,0,10.1101/2020.08.26.269043,8/27/20,biorxiv,0,6,"proteom, glycomics, glycoproteom",0.569126166,0.425121056,0.00143815,0.001438179,0.00143822,0.001438228,Drug discovery,0.5523629,TRUE,55.66666667,0.672335952,61.16666667,0.72497993,4,0.707574542,4147,0.697086444,0.700494217
13351,Designing of Epitope-Based Vaccine from the Conserved Region of the Spike Glycoprotein of SARS-CoV-2,0,10.1101/2020.08.27.269456,8/27/20,biorxiv,0,3,in-silico,0.979236087,0.001034634,0.001034572,0.01662556,0.00103458,0.001034566,Drug discovery,0.6516527,TRUE,48.66666667,0.619333292,17,0.451097137,0,0.403234768,1982,0.398988683,0.46816347
13352,SARS-CoV-2 lineage B.6 is the major contributor to transmission in Malaysia,0,10.1101/2020.08.27.269738,8/27/20,biorxiv,10.1371/journal.pntd.0008744,11,"whole genome, genome sequences",0.000759349,0.95031271,0.014212947,0.00075938,0.026087916,0.0078677,Genomics,0.085605145,FALSE,49.63636364,0.628981384,53.81818182,0.696548033,1,0.537564047,1727,0.322899109,0.546498143
13353,Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN,0,10.1101/2020.08.20.20178913,8/26/20,medrxiv,0,1,"supervised learning, neural network, image analysis, dataset",0.001237087,0.001237066,0.993814603,0.001237112,0.001237053,0.00123708,Imaging,0.020579487,FALSE,3,0.037293586,0,0.055525823,1,0.537564047,4200,0.700216711,0.332650042
13354,Preventing disease after exposure to COVID-19 using hydroxychloroquine: A summary of a protocol for exploratory re-analysis of age and time-nuanced effects.,0,10.1101/2020.08.19.20178376,8/26/20,medrxiv,0,5,dataset,0.000880258,0.043949589,0.000880246,0.47241946,0.275560394,0.206310053,Epidemiology,0.44274735,FALSE,95.6,0.85812357,103.8,0.828672732,2,0.618927094,4963,0.750060197,0.763945898
13355,SARS-CoV-2 phylodynamics differentiates the effectiveness of non-pharmaceutical interventions,0,10.1101/2020.08.24.20180927,8/26/20,medrxiv,0,11,genomes,0.002296579,0.169246724,0.002296543,0.821566584,0.002296959,0.002296611,Epidemiology,0.43176728,FALSE,58.09090909,0.689900427,69.54545455,0.752073856,1,0.537564047,2803,0.548759933,0.632074566
13356,UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records,0,10.1101/2020.08.24.20179192,8/26/20,medrxiv,10.1186/s12889-021-10427-2,18,dataset,0.000591791,0.000591785,0.000591793,0.262451796,0.576934396,0.15883844,Healthcare,0.11398399,FALSE,192.3333333,0.969756942,166.2222222,0.896507894,1,0.537564047,2313,0.463761137,0.716897505
13357,Parameter Estimation of COVID-19 Pandemic Model with Self Protection Behavior Changes,0,10.1101/2020.08.24.20180695,8/26/20,medrxiv,10.1016/j.rinam.2020.100134,3,"bayes, mathematical model",0.001565355,0.001565331,0.001565367,0.992173214,0.001565432,0.0015653,Epidemiology,0.65901756,TRUE,3.666666667,0.045952131,5.333333333,0.262911426,0,0.403234768,1566,0.269925355,0.24550592
13358,MMGB/SA Consensus Estimate of the Binding Free Energy Between the Novel Coronavirus Spike Protein to the Human ACE2 Receptor,0,10.1101/2020.08.25.267625,8/26/20,biorxiv,0,2,computational,0.833831914,0.001187303,0.001187311,0.161418949,0.001187265,0.001187257,Drug discovery,0.3849727,FALSE,51.5,0.643267982,52,0.689523682,1,0.537564047,1520,0.254033229,0.531097235
13359,Mortality from COVID in Colombia and Peru: Analyses of Mortality Data and Statistical Forecasts,0,10.1101/2020.08.24.20181016,8/26/20,medrxiv,0,8,bayes,0.001330041,0.001330055,0.0013303,0.691912328,0.001330059,0.302767217,Epidemiology,0.21920002,FALSE,16.22222222,0.245160492,5.555555556,0.267393631,0,0.403234768,1317,0.183241031,0.27475748
13360,Serum lipid profile changes and their clinical diagnostic significance in COVID-19 Mexican Patients,0,10.1101/2020.08.24.20169789,8/26/20,medrxiv,0,17,"predictive model, logistic regression",0.001786632,0.238072424,0.001786624,0.001786594,0.001786587,0.754781139,Clinics,0.81503534,TRUE,8.411764706,0.124497495,7.352941176,0.30472304,1,0.537564047,1447,0.228750301,0.298883721
13361,Inhibiting coronavirus replication in cultured cells by chemical ER stress,0,10.1101/2020.08.26.266304,8/26/20,biorxiv,0,16,proteom,0.903233748,0.002422331,0.0024223,0.002422315,0.087077068,0.002422239,Drug discovery,0.5115723,TRUE,59.625,0.700414373,151.8125,0.884599946,2,0.618927094,1880,0.364314953,0.642064092
13362,AI aided design of epitope-based vaccine for the induction of cellular immune responses against SARS-CoV-2,0,10.1101/2020.08.26.267997,8/26/20,biorxiv,10.3389/fgene.2021.602196,12,"bioinformatic, in silico",0.608939037,0.098967358,0.041389841,0.129975855,0.098331427,0.022396482,Drug discovery,0.8238082,TRUE,15.91666667,0.239037665,24.58333333,0.52548836,0,0.403234768,2496,0.496026968,0.41594694
13363,Molecular dynamics reveals complex compensatory effects of ionic strength on the SARS-CoV-2 Spike/hACE-2 interaction,0,10.1101/2020.08.25.267351,8/26/20,biorxiv,10.1021/acs.jpclett.0c02602,10,computational,0.966252276,0.001717268,0.026878757,0.001717264,0.001717224,0.001717212,Drug discovery,0.26595134,FALSE,50.4,0.634547591,44.2,0.655137811,1,0.537564047,1795,0.341199133,0.542112146
13364,Monitoring COVID-19 transmission risks by RT-PCR tracing of droplets in hospital and living environments,0,10.1101/2020.08.22.20179754,8/25/20,medrxiv,10.1128/msphere.01070-20,19,sequencing,0.000916736,0.644164907,0.000916721,0.254472834,0.000916706,0.098612096,Genomics,0.49270064,FALSE,26.57894737,0.389943719,8.789473684,0.332352154,0,0.403234768,1336,0.186371298,0.327975485
13365,The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review.,0,10.1101/2020.08.23.20180158,8/25/20,medrxiv,10.2196/23811,7,"machine learning, computational",0.029676442,0.000916706,0.31507425,0.461837601,0.000916813,0.191578188,Epidemiology,0.8175338,TRUE,12.85714286,0.192714454,1.428571429,0.134265454,2,0.618927094,1671,0.298097761,0.311001191
13366,Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning,0,10.1101/2020.08.23.20179838,8/25/20,medrxiv,0,5,"machine learning, computational",0.034341448,0.001415135,0.258183174,0.001415182,0.001415123,0.703229938,Clinics,0.65881926,TRUE,20.4,0.302863504,28.6,0.559071448,0,0.403234768,815,0.030339514,0.323877308
13367,Declining SARS-CoV-2 PCR sensitivity with time and dependence on clinical features: consequences for control,0,10.1101/2020.08.23.20179408,8/25/20,medrxiv,0,14,bayes,0.002296548,0.391574043,0.136779793,0.168849107,0.00229659,0.298203919,Genomics,0.6634066,TRUE,35.07142857,0.488156349,58.92857143,0.716684506,0,0.403234768,1162,0.12665543,0.433682763
13368,ANALYSIS OF SARS-COV-2 GENOME SEQUENCES FROM THE PHILIPPINES: GENETIC SURVEILLANCE AND TRANSMISSION DYNAMICS,0,10.1101/2020.08.22.20180034,8/25/20,medrxiv,0,36,"phylogenom, whole genome, genome sequences, genomes",0.00115623,0.561839255,0.001156255,0.209727836,0.184037557,0.042082866,Genomics,0.30181974,FALSE,4.611111111,0.062650751,1.138888889,0.123160289,1,0.537564047,2109,0.421382133,0.286189305
13369,SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study,0,10.1101/2020.08.22.20179929,8/25/20,medrxiv,10.1098/rsos.201895,10,bayes,0.001203401,0.001203408,0.001203466,0.692217941,0.214802633,0.089369151,Epidemiology,0.3187812,FALSE,24.2,0.357350485,17,0.451097137,0,0.403234768,876,0.039971105,0.312913374
13370,Epidemiologically most successful SARS-CoV-2 variant: concurrent mutations in RNA-dependent RNA polymerase and spike protein,0,10.1101/2020.08.23.20180281,8/25/20,medrxiv,0,9,genome sequences,0.001203479,0.902711377,0.001203425,0.092474658,0.00120342,0.001203642,Genomics,0.17274639,FALSE,23.44444444,0.347083926,22.55555556,0.50755954,5,0.739490092,1892,0.365518902,0.489913115
13371,Suitability of Google Trends ™ for digital surveillance during ongoing COVID-19 epidemic: a case study from India,0,10.1101/2020.08.24.20176321,8/25/20,medrxiv,0,3,correlation analysis,0.000999575,0.020072379,0.000999517,0.975929425,0.000999564,0.000999541,Epidemiology,0.43507755,FALSE,6,0.086028821,0.333333333,0.073187048,0,0.403234768,900,0.047194799,0.152411359
13372,Hesitant or not? A global survey of potential acceptance of a COVID-19 vaccine,0,10.1101/2020.08.23.20180307,8/25/20,medrxiv,0,8,logistic regression,0.002996583,0.00299652,0.002996422,0.002996703,0.985017316,0.002996456,Healthcare,0.31921688,FALSE,39.125,0.531263529,47.375,0.670056195,7,0.785110192,3640,0.653262702,0.659923154
13373,Temporal increase in D614G mutation of SARS-CoV-2 in the Middle East and North Africa: Phylogenetic and mutation analysis study,0,10.1101/2020.08.24.20176792,8/25/20,medrxiv,10.1016/j.heliyon.2021.e06035,5,bayes,0.00143813,0.925403868,0.001438109,0.001438184,0.001438134,0.068843575,Genomics,0.69663733,TRUE,11,0.167171748,2.4,0.174872893,3,0.667819001,1280,0.168552853,0.294604124
13374,Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events,0,10.1101/2020.08.23.20178236,8/25/20,medrxiv,0,53,genomes,0.002032771,0.510385723,0.00203274,0.421381604,0.062134329,0.002032833,Genomics,0.6544477,TRUE,57.47169811,0.685076381,234.3584906,0.931763447,30,0.930057411,50465,0.982422345,0.882329896
13375,SARS-CoV-2 infection of human iPSC-derived cardiac cells predicts novel cytopathic features in hearts of COVID-19 patients,0,10.1101/2020.08.25.265561,8/25/20,biorxiv,10.1126/scitranslmed.abf7872,14,transcriptom,0.583831122,0.157131412,0.001901801,0.001901809,0.001901805,0.253332051,Drug discovery,0.5286169,TRUE,30,0.432432432,36.21428571,0.612456516,25,0.918019631,52951,0.983385504,0.736573521
13376,Identification of SARS-CoV-2 induced pathways reveal drug repurposing strategies,0,10.1101/2020.08.24.265496,8/25/20,biorxiv,0,15,"neural network, network analysis, dataset",0.880268195,0.002032828,0.1116007,0.002032766,0.002032737,0.002032773,Drug discovery,0.15922394,FALSE,46.2,0.598181706,261.3333333,0.942065828,1,0.537564047,3878,0.675656152,0.688366933
13377,SARS-CoV-2 3CLpro Whole Human Proteome Cleavage Prediction and Enrichment/Depletion Analysis,0,10.1101/2020.08.24.265645,8/25/20,biorxiv,0,1,"neural network, proteom",0.798061372,0.001141412,0.114897625,0.046758186,0.038000059,0.001141346,Drug discovery,0.2976089,FALSE,1,0.012307502,0,0.055525823,2,0.618927094,2101,0.420178184,0.276734651
13378,Unique mutational changes in SARS-CoV2 genome of different state of India,0,10.1101/2020.08.24.265827,8/25/20,biorxiv,0,7,"sequencing, genomes",0.002238451,0.785950879,0.002238649,0.002238635,0.002238519,0.205094867,Genomics,0.27980888,FALSE,43.35714286,0.571216525,25.57142857,0.533516189,0,0.403234768,2315,0.461594028,0.492390378
13379,The S1/S2 boundary of SARS-CoV-2 spike protein modulates cell entry pathways and transmission,0,10.1101/2020.08.25.266775,8/25/20,biorxiv,0,18,genome-wide,0.718066844,0.272979267,0.002238505,0.00223849,0.002238434,0.00223846,Drug discovery,0.3685826,FALSE,65.61111111,0.737955347,,,22,0.908142478,5228,0.761618107,0.802571977
13380,Discovery of drugs to treat cytokine storm-induced cardiac dysfunction using human cardiac organoids,0,10.1101/2020.08.23.258574,8/24/20,biorxiv,0,41,"sequencing, proteom, phosphoproteom",0.626843753,0.001461985,0.001461971,0.0014619,0.001461881,0.367308509,Drug discovery,0.3025983,FALSE,80,0.807532933,151.8913043,0.884666845,1,0.537564047,4571,0.721647002,0.737852707
13381,"Versatile, Multivalent Nanobody Cocktails for Highly Efficient SARS-CoV-2 Neutralization",0,10.1101/2020.08.24.264333,8/24/20,biorxiv,10.1126/science.abe4747,9,proteom,0.895638565,0.002130746,0.095838675,0.002130718,0.002130614,0.002130682,Drug discovery,0.2699744,FALSE,66.55555556,0.744140021,,,5,0.739490092,5955,0.788104984,0.757245032
13382,Dynamics of the N-terminal domain of SARS-CoV-2 nucleocapsid protein drives dsRNA melting in a counterintuitive tweezer-like mechanism,0,10.1101/2020.08.24.264465,8/24/20,biorxiv,0,5,molecular dynamics simulation,0.778144731,0.218817806,0.000759369,0.000759368,0.000759363,0.000759363,Drug discovery,0.15964043,FALSE,28.2,0.409734677,33.4,0.595129783,2,0.618927094,1813,0.341921503,0.491428264
13383,The influence of major S protein mutations of SARS-CoV-2 on the potential B cell epitopes,0,10.1101/2020.08.24.264895,8/24/20,biorxiv,0,2,bioinformatic,0.475824335,0.507334357,0.000854705,0.000854745,0.000854749,0.014277108,Genomics,0.29102963,FALSE,4.5,0.061784897,6,0.280037463,2,0.618927094,1378,0.197688418,0.289609468
13384,Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility,0,10.1101/2020.08.21.20177246,8/24/20,medrxiv,0,34,prediction model,0.002422292,0.547030854,0.002422533,0.203445843,0.24225605,0.002422428,Genomics,0.22977093,FALSE,28.79411765,0.415239038,80.17647059,0.779368477,1,0.537564047,2795,0.543703347,0.568968727
13385,Next generation sequencing of SARS-CoV-2 from patient specimens of Nevada reveals occurrence of specific nucleotide variants at high frequency,0,10.1101/2020.08.21.20178863,8/24/20,medrxiv,10.1016/j.jgg.2021.01.004,12,"sequencing, genomes, structural model",0.073252164,0.873484159,0.012999111,0.000688482,0.000688464,0.03888762,Genomics,0.67622006,TRUE,21.08333333,0.312140516,307.5833333,0.956716618,0,0.403234768,3531,0.640982422,0.578268581
13386,Quarantine and testing strategies in contact tracing for SARS-CoV-2,0,10.1101/2020.08.21.20177808,8/24/20,medrxiv,10.1016/S2468-2667(20)30308-X,6,simulation model,0.00104684,0.118306443,0.001046847,0.877506109,0.001046903,0.001046857,Epidemiology,0.18114892,FALSE,45.33333333,0.589646855,100.5,0.823454643,9,0.814309525,7788,0.836985312,0.766099084
13387,How do socio-demographic status and personal attributes influence adherence to COVID-19 preventive behaviours?,0,10.1101/2020.08.21.20179663,8/24/20,medrxiv,10.1016/j.paid.2021.110692,5,dataset,0.00139286,0.001392954,0.079071212,0.001392941,0.915357204,0.00139283,Healthcare,0.42614836,FALSE,2.8,0.031047065,0,0.055525823,0,0.403234768,1063,0.091018541,0.145206549
13388,Correlation of National and Healthcare Workers COVID-19 Infection Data; Implications for Large-scale Viral Testing Programs,0,10.1101/2020.08.21.20179283,8/24/20,medrxiv,0,3,computational,0.001085367,0.001085365,0.001085372,0.88443416,0.111224344,0.001085393,Epidemiology,0.43935037,FALSE,3.333333333,0.04044777,0.666666667,0.096200161,0,0.403234768,892,0.042619793,0.145625623
13389,The unintended consequences of inconsistent pandemic control policies,0,10.1101/2020.08.21.20179473,8/24/20,medrxiv,0,8,mathematical model,0.001203416,0.001203415,0.001203399,0.934232988,0.060953352,0.001203429,Epidemiology,0.042615294,FALSE,38.71428571,0.52650133,38.42857143,0.624163768,9,0.814309525,3531,0.640982422,0.651489261
13390,Control Strategies against COVID-19 in China: Significance of Effective Testing in the Long Run,0,10.1101/2020.08.22.20179697,8/24/20,medrxiv,0,2,"bayes, simulation model",0.000916681,0.000916706,0.055957812,0.909002403,0.032289663,0.000916735,Epidemiology,0.0848304,FALSE,4,0.054734368,0,0.055525823,1,0.537564047,1044,0.085239586,0.183265956
13391,Automatic analysis system of COVID-19 radiographic lung images (XrayCoviDetector),0,10.1101/2020.08.20.20178723,8/23/20,medrxiv,0,7,machine learning,0.003335268,0.003335402,0.983322652,0.003335643,0.003335491,0.003335545,Imaging,0.14046445,FALSE,6.285714286,0.088626384,0,0.055525823,0,0.403234768,899,0.043101373,0.147622087
13392,Associations of comorbidities and medications with COVID-19 outcome: A retrospective analysis of real-world evidence data,0,10.1101/2020.08.20.20174169,8/23/20,medrxiv,0,7,dataset,0.140420496,0.000822934,0.00082293,0.150567361,0.000822954,0.706543324,Clinics,0.42513308,FALSE,10.57142857,0.15764735,2,0.164302917,4,0.707574542,2378,0.467854563,0.374344843
13393,The active lung microbiota landscape of COVID-19 patients,0,10.1101/2020.08.20.20144014,8/23/20,medrxiv,0,5,"transcriptom, metatranscriptom, virom",0.516415432,0.123487616,0.002183287,0.002183299,0.002183245,0.35354712,Drug discovery,0.80829,TRUE,3.4,0.040942544,13.6,0.406208188,7,0.785110192,1516,0.246087166,0.369587023
13394,Dynamic causal modeling of the COVID-19 pandemic in northern Italy predicts possible scenarios for the second wave,0,10.1101/2020.08.20.20178798,8/23/20,medrxiv,0,7,"bayes, neural network, predictive model, bayesian model",0.001072213,0.001072222,0.019382808,0.976328341,0.00107221,0.001072207,Epidemiology,0.15283105,FALSE,140.7142857,0.934751685,129.5714286,0.863393096,1,0.537564047,1358,0.188779196,0.631122006
13395,Ferrets not infected by SARS-CoV-2 in a high-exposure domestic setting,0,10.1101/2020.08.21.254995,8/22/20,biorxiv,0,6,computational,0.325650343,0.603673883,0.001717224,0.00171731,0.065523998,0.001717241,Genomics,0.32889825,FALSE,6.333333333,0.089863319,4.833333333,0.249598609,8,0.799987654,4543,0.717071996,0.464130394
13396,Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.,0,10.1101/2020.08.19.20177493,8/22/20,medrxiv,0,40,probabilistic,0.001187268,0.001187269,0.174947174,0.820303751,0.001187266,0.001187272,Epidemiology,0.2474485,FALSE,23.025,0.342321727,,,36,0.941169208,26084,0.960992054,0.748160996
13397,"Genetically-predicted vitamin D status, ambient UVB during the pandemic and COVID-19 risk in UK Biobank: Mendelian Randomisation study",0,10.1101/2020.08.18.20177691,8/22/20,medrxiv,0,11,logistic regression,0.002898634,0.049551956,0.002898384,0.002898492,0.002898548,0.938853987,Clinics,0.3177518,FALSE,64.27272727,0.729853423,106.4545455,0.833623227,1,0.537564047,1491,0.235492415,0.584133278
13398,Machine learning based clinical decision supportsystem for early COVID-19 mortality prediction,0,10.1101/2020.08.19.20177477,8/22/20,medrxiv,0,4,machine learning,0.055590989,0.001901784,0.627577244,0.001901758,0.001901711,0.311126513,Clinics,0.3277886,FALSE,33,0.466757375,6.75,0.292079208,1,0.537564047,2324,0.457982182,0.438595703
13399,Evaluating aerosol and splatter during orthodontic debonding: implications for the COVID-19 pandemic,0,10.1101/2020.08.19.20178319,8/22/20,medrxiv,10.1038/s41415-020-2503-9,9,image analysis,0.001538129,0.109766434,0.134666876,0.663130279,0.001538191,0.089360092,Epidemiology,0.6430354,TRUE,11.88888889,0.17892263,16.55555556,0.443069307,3,0.667819001,2676,0.51769805,0.451877247
13400,Disparities in COVID-19 Hospitalizations and Mortality among Black and Hispanic Patients: Cross-Sectional Analysis from the Greater Houston Metropolitan Area,0,10.1101/2020.08.19.20177956,8/22/20,medrxiv,0,10,logistic regression,0.001310321,0.00131034,0.001310314,0.001310368,0.328704484,0.666054173,Clinics,0.60338384,TRUE,18.7,0.280042056,3.1,0.199826064,4,0.707574542,931,0.052010595,0.309863314
13401,Effects of (Un)lockdown on COVID-19 transmission: A mathematical study of different phases in India,0,10.1101/2020.08.19.20177840,8/22/20,medrxiv,0,3,mathematical model,0.00213066,0.002130714,0.08947188,0.902005196,0.00213071,0.002130841,Epidemiology,0.23853242,FALSE,4.666666667,0.063392912,1,0.122023013,2,0.618927094,1118,0.105225138,0.22739204
13402,Pandemic Control in ECON-EPI Networks,0,10.1101/2020.08.19.20178087,8/22/20,medrxiv,0,4,network model,0.002130804,0.002130712,0.002130722,0.989346387,0.00213071,0.002130665,Epidemiology,0.118430674,FALSE,23,0.34225988,45.75,0.662229061,8,0.799987654,837,0.032265832,0.459185607
13403,SARS-Cov-2 proliferation: an analytical aggregate-level model,0,10.1101/2020.08.20.20178301,8/22/20,medrxiv,0,1,mathematical model,0.002080676,0.002080693,0.002080578,0.989596615,0.002080706,0.002080732,Epidemiology,0.3726855,FALSE,6,0.086028821,1,0.122023013,0,0.403234768,1317,0.173368649,0.196163813
13404,Inhaled corticosteroids downregulate SARS-CoV-2-related gene expression in COPD: results from a RCT,0,10.1101/2020.08.19.20178368,8/22/20,medrxiv,0,11,"sequencing, transcriptom",0.731993492,0.107277233,0.0015653,0.001565363,0.0015654,0.156033213,Drug discovery,0.9063666,TRUE,90.45454545,0.843836972,156.4545455,0.889015253,2,0.618927094,4381,0.707921984,0.764925326
13405,High SARS-CoV-2 Seroprevalence in Children and Adults in the Austrian Ski Resort Ischgl,0,10.1101/2020.08.20.20178533,8/22/20,medrxiv,0,18,mathematical model,0.002130798,0.193897446,0.00213069,0.395448844,0.285012818,0.121379404,Epidemiology,0.2786826,FALSE,13,0.197352959,10.77777778,0.366537329,12,0.850299401,6107,0.790753672,0.551235841
13406,"Accurately Differentiating COVID-19, Other Viral Infection, and Healthy Individuals Using Multimodal Features via Late Fusion Learning",0,10.1101/2020.08.18.20176776,8/21/20,medrxiv,0,26,"machine learning, deep learning, neural network, dataset",0.000889081,0.000889131,0.919340106,0.000889068,0.05259309,0.025399525,Imaging,0.3231067,FALSE,7.6,0.109283196,6.6,0.28913567,1,0.537564047,977,0.062846135,0.249707262
13407,A DEEP LEARNING MODEL TO PREDICT THE NEED FOR MECHANICAL VENTILATION USING CHEST X-RAY IMAGES IN HOSPITALIZED COVID-19 PATIENTS,0,10.1101/2020.08.17.20176917,8/21/20,medrxiv,10.1136/bmjinnov-2020-000593,14,deep learning,0.001415122,0.001415115,0.633620018,0.001415196,0.001415364,0.360719184,Imaging,0.59457886,TRUE,7.153846154,0.102665595,2.538461538,0.180960664,0,0.403234768,1078,0.091259331,0.194530089
13408,The ImmuneRACE Study: A Prospective Multicohort Study of Immune Response Action to COVID-19 Events with the ImmuneCODE™ Open Access Database,0,10.1101/2020.08.17.20175158,8/21/20,medrxiv,0,8,"machine learning, artificial intelligence, classifier",0.123979475,0.251393453,0.126033143,0.000683177,0.282348009,0.215562744,Healthcare,0.3056141,FALSE,9.625,0.143917373,10.25,0.358375702,2,0.618927094,2424,0.472188779,0.398352237
13409,In Silico Modeling of Virus Particle Propagation and Infectivity along the Respiratory Tract: A Case Study for SARS-COV-2,0,10.1101/2020.08.20.259242,8/21/20,biorxiv,0,4,"computational, in silico",0.924100923,0.001717313,0.001717213,0.069030137,0.001717208,0.001717206,Drug discovery,0.45217213,FALSE,148.5,0.942853609,238.75,0.933569708,1,0.537564047,1162,0.120876475,0.63371596
13410,SARS-CoV-2 infects brain choroid plexus and disrupts the blood-CSF-barrier,0,10.1101/2020.08.20.259937,8/21/20,biorxiv,0,8,sequencing,0.758925116,0.037221736,0.055369315,0.001861687,0.083728584,0.062893561,Drug discovery,0.5636885,TRUE,62.375,0.717360381,198.25,0.91697886,7,0.785110192,8615,0.848061642,0.816877769
13411,Temporal landscape of mutation accumulation in SARS-CoV-2 genomes from Bangladesh: possible implications from the ongoing outbreak in Bangladesh,0,10.1101/2020.08.20.259721,8/21/20,biorxiv,0,6,genomes,0.05837499,0.903085024,0.000946079,0.000946123,0.00094613,0.035701654,Genomics,0.8126995,TRUE,9.166666667,0.136619457,0.666666667,0.096200161,1,0.537564047,1432,0.211172646,0.245389078
13412,Covidex: an ultrafast and accurate tool for virus subtyping,0,10.1101/2020.08.21.261347,8/21/20,biorxiv,0,4,"machine learning, genome sequences",0.00346598,0.314552443,0.480465027,0.194584582,0.003466103,0.003465865,Genomics,0.25182396,FALSE,29,0.41993939,11.25,0.374364463,2,0.618927094,1659,0.286780641,0.425002897
13413,COVID-19 and Cholinergic Anti-inflammatory Pathway: In silico Identification of an Interaction between alpha7 Nicotinic Acetylcholine Receptor and the Cryptic Epitopes of SARS-CoV and SARS-CoV-2 Spike Glycoproteins,0,10.1101/2020.08.20.259747,8/21/20,biorxiv,10.1016/j.fct.2021.112009,7,in silico,0.784204333,0.161622603,0.001684499,0.001684512,0.001684584,0.049119468,Drug discovery,0.51028264,TRUE,83.71428571,0.820087822,68,0.748193738,1,0.537564047,2133,0.418974235,0.631204961
13414,Look before diving into pooling of SARS-CoV-2 samples on high throughput analyzers,0,10.1101/2020.08.17.20176982,8/21/20,medrxiv,0,10,mathematical model,0.001751162,0.601010902,0.001751325,0.391984183,0.001751217,0.00175121,Genomics,0.06915343,FALSE,1.9,0.017317088,,,0,0.403234768,824,0.028894775,0.149815544
13415,Bayesian Spatio-Temporal Modeling of COVID-19: Inequalities on Case-Fatality Risk,0,10.1101/2020.08.18.20171074,8/21/20,medrxiv,0,14,bayes,0.001415128,0.001415132,0.001415149,0.992924182,0.001415207,0.001415202,Epidemiology,0.33648452,FALSE,7.714285714,0.11225184,1.214285714,0.126170725,1,0.537564047,1367,0.187575247,0.240890465
13416,Covid-19 transmission dynamics during the unlock phase and significance of testing,0,10.1101/2020.08.18.20176354,8/21/20,medrxiv,0,3,mathematical model,0.018018082,0.001022652,0.00102265,0.977891315,0.001022655,0.001022646,Epidemiology,0.28056067,FALSE,10.66666667,0.159131672,3,0.199424672,6,0.764429903,927,0.050325066,0.293327828
13417,An estimate of the COVID-19 infection fatality rate in Brazil based on a seroprevalence survey,0,10.1101/2020.08.18.20177626,8/21/20,medrxiv,0,2,"bayes, dataset",0.002720123,0.040393967,0.002720291,0.775823164,0.175622241,0.002720213,Epidemiology,0.15638912,FALSE,10,0.15214299,5.5,0.267259834,1,0.537564047,3621,0.645557428,0.400631075
13418,"Environmental risk factors of airborne viral transmission: Humidity, Influenza and SARS-CoV-2 in the Netherlands",0,10.1101/2020.08.18.20177444,8/21/20,medrxiv,0,2,bayes,0.001653037,0.28823236,0.001653051,0.705155301,0.001653124,0.001653128,Epidemiology,0.29964304,FALSE,3.5,0.044344115,0,0.055525823,1,0.537564047,8128,0.839874789,0.369327194
13419,Machine learning and AI aided tool to differentiate COVID 19 and non-COVID 19 lung CXR,0,10.1101/2020.08.18.20175521,8/21/20,medrxiv,0,1,machine learning,0.001438146,0.001438116,0.946148717,0.048098777,0.001438142,0.001438103,Imaging,0.2888939,FALSE,9,0.135320675,0,0.055525823,0,0.403234768,1051,0.083313268,0.169348633
13420,SARS-CoV-2 infection dynamics in lungs of African green monkeys,0,10.1101/2020.08.20.258087,8/20/20,biorxiv,0,15,"sequencing, dataset",0.775662806,0.113158423,0.001786581,0.001786606,0.001786605,0.105818979,Drug discovery,0.23248756,FALSE,76.26666667,0.791329087,206.4666667,0.920591383,4,0.707574542,2703,0.519142788,0.73465945
13421,SARS-CoV-2 Quasispecies provides insight into its genetic dynamics during infection,0,10.1101/2020.08.20.258376,8/20/20,biorxiv,0,16,sequencing,0.059114024,0.812520718,0.001254622,0.124601399,0.00125464,0.001254596,Genomics,0.2616707,FALSE,45.4375,0.590203476,71.8125,0.758563018,1,0.537564047,1581,0.259571394,0.536475484
13422,What if we perceive SARS-CoV-2 genomes as documents? Topic modelling using Latent Dirichlet Allocation to identify mutation signatures and classify SARS-CoV-2 genomes,0,10.1101/2020.08.20.258772,8/20/20,biorxiv,0,3,"machine learning, genomes, text-mining, probabilistic",0.001072212,0.532907834,0.109382271,0.354493349,0.001072167,0.001072167,Genomics,0.14218682,FALSE,40.33333333,0.542890717,38,0.622223709,0,0.403234768,1615,0.271129304,0.459869624
13423,Boosting SARS-CoV-2 qRT-PCR detection combining pool sample strategy and mathematical modeling,0,10.1101/2020.08.16.20167536,8/19/20,medrxiv,0,9,"mathematical model, in silico",0.048390119,0.238395995,0.15388173,0.55608663,0.001622803,0.001622721,Epidemiology,0.081599176,FALSE,7.888888889,0.114169089,9.444444444,0.343724913,0,0.403234768,1057,0.082109319,0.235809522
13424,The necessary cooperation between governments and public in the fight against COVID-19: why non-pharmaceutical interventions may be ineffective,0,10.1101/2020.08.17.20176347,8/19/20,medrxiv,0,4,bayes,0.001622701,0.001622715,0.001622719,0.991886446,0.001622728,0.001622691,Epidemiology,0.1444473,FALSE,114.5,0.8983858,326.75,0.961131924,0,0.403234768,1327,0.17192391,0.6086691
13425,Identification of potential key genes for SARS-CoV-2 infected human bronchial organoids based on bioinformatics analysis,0,10.1101/2020.08.18.256735,8/19/20,biorxiv,0,2,"bioinformatic, genomes, dataset",0.913800238,0.077245897,0.00223849,0.002238452,0.002238428,0.002238494,Drug discovery,0.5737524,TRUE,3,0.037293586,0,0.055525823,4,0.707574542,2713,0.518661209,0.32976379
13426,Data-Driven Development of a Small-Area COVID-19 Vulnerability Index for the United States,0,10.1101/2020.08.17.20176248,8/18/20,medrxiv,0,6,bayes,0.001461891,0.001461918,0.094829657,0.668340627,0.23244396,0.001461946,Epidemiology,0.19739664,FALSE,32.5,0.461685942,47.66666667,0.671126572,1,0.537564047,983,0.060679027,0.432763897
13427,Quantifying the efficiency of non-pharmaceutical interventions against SARS-COV-2 transmission in Europe,0,10.1101/2020.08.17.20174821,8/18/20,medrxiv,0,6,"bayes, mathematical model, dataset",0.001371357,0.04638515,0.001371273,0.948129659,0.001371283,0.001371278,Epidemiology,0.14631289,FALSE,20.75,0.307254623,1.75,0.148381054,2,0.618927094,1964,0.370575488,0.361284565
13428,Study of Coronavirus Impact on Parisian Population from April to June using Twitter and Text Mining Approach,0,10.1101/2020.08.15.20175810,8/18/20,medrxiv,0,2,text mining,0.002490496,0.00249049,0.002490521,0.987547623,0.002490469,0.002490401,Epidemiology,0.5604345,TRUE,3.5,0.044344115,0,0.055525823,2,0.618927094,1347,0.177221286,0.22400458
13429,The influence of climate factors on COVID-19 transmission in Malaysia: An autoregressive integrated moving average (ARIMA) model,0,10.1101/2020.08.14.20175372,8/17/20,medrxiv,0,5,dataset,0.001220013,0.001220041,0.001220026,0.993899635,0.001220143,0.001220142,Epidemiology,0.46607685,FALSE,3.2,0.038468675,2,0.164302917,0,0.403234768,775,0.016132916,0.155534819
13430,"SARS-Cov-2-, HIV-1-, Ebola-neutralizing and anti-PD1 clones are predisposed",0,10.1101/2020.08.13.249086,8/17/20,biorxiv,0,20,dataset,0.257244107,0.555852498,0.179830035,0.002357828,0.002357749,0.002357783,Genomics,0.19000855,FALSE,58.76,0.694600779,85.04,0.790473642,1,0.537564047,1992,0.376595232,0.599808425
13431,Transcriptomic profiling of human corona virus (HCoV)-229E -infected human cells and genomic mutational analysis of HCoV-229E and SARS-CoV-2,0,10.1101/2020.08.17.253682,8/17/20,biorxiv,10.1371/journal.pone.0247128,9,"sequencing, transcriptom",0.567752842,0.428654737,0.000898068,0.000898094,0.000898097,0.000898162,Drug discovery,0.52864563,TRUE,120.6666667,0.908157586,174.7777778,0.902796361,2,0.618927094,1343,0.17264628,0.65063183
13432,Assessment of physiological signs associated with COVID-19 measured using wearable devices,0,10.1101/2020.08.14.20175265,8/16/20,medrxiv,10.1038/s41746-020-00363-7,3,"neural network, classifier, logistic regression",0.001156248,0.001156267,0.341922793,0.001156289,0.110959434,0.543648968,Clinics,0.24832895,FALSE,5,0.070752675,4.333333333,0.237958255,16,0.881782826,9576,0.866120876,0.514153658
13433,"Vaccine optimization for COVID-19, who to vaccinate first?",0,10.1101/2020.08.14.20175257,8/16/20,medrxiv,10.1126/sciadv.abf1374,4,mathematical model,0.002296643,0.002296589,0.002296568,0.805068882,0.002296723,0.185744595,Epidemiology,0.12477681,FALSE,3.75,0.046817985,0.25,0.065493712,41,0.948144947,17232,0.931615699,0.498018086
13434,"Robust, reproducible clinical patterns in hospitalised patients with COVID-19",0,10.1101/2020.08.14.20168088,8/16/20,medrxiv,0,20,machine learning,0.064371973,0.098571405,0.149316579,0.00077946,0.150606138,0.536354445,Clinics,0.28203538,FALSE,45.25,0.589337621,53.65,0.69574525,4,0.707574542,3889,0.663135083,0.663948124
13435,The limits of estimating COVID-19 intervention effects using Bayesian models,0,10.1101/2020.08.14.20175240,8/16/20,medrxiv,0,2,"bayes, bayesian model",0.004530633,0.004530649,0.004530655,0.977346844,0.00453065,0.004530569,Epidemiology,0.18452951,FALSE,10,0.15214299,2,0.164302917,4,0.707574542,2822,0.531182278,0.388800681
13436,Sampling SARS-CoV-2 proteomes for predicted CD8 T-cell epitopes as a tool for understanding immunogenic breadth and rationale vaccine design,0,10.1101/2020.08.15.250647,8/16/20,biorxiv,10.3389/fbinf.2021.622992,3,"proteom, predictive model",0.477633152,0.240702102,0.00333541,0.271658642,0.003335287,0.003335405,Drug discovery,0.3283036,FALSE,33,0.466757375,33.33333333,0.594995986,1,0.537564047,1946,0.359980737,0.489824536
13437,Multifractal Analysis of SARS-CoV-2 Coronavirus genomes using the wavelet transform.,0,10.1101/2020.08.15.252411,8/16/20,biorxiv,0,1,genomes,0.003466028,0.559041973,0.003466032,0.427093487,0.003466584,0.003465896,Genomics,0.18205363,FALSE,86,0.829117447,2,0.164302917,2,0.618927094,2111,0.405730797,0.504519564
13438,Abusers indoors and coronavirus outside: an examination of public discourse about COVID-19 and family violence on Twitter using machine learning,0,10.1101/2020.08.13.20167452,8/15/20,medrxiv,10.2196/24361,5,machine learning,0.00139285,0.001392872,0.001393011,0.452847048,0.541581405,0.001392815,Healthcare,0.9068948,TRUE,3.4,0.040942544,0,0.055525823,1,0.537564047,1643,0.270166145,0.22604964
13439,Data Mining Approach to Analyze Covid19 Dataset of Brazilian Patients,0,10.1101/2020.08.13.20174508,8/15/20,medrxiv,0,1,"data mining, dataset",0.002639034,0.002639143,0.002639139,0.909378591,0.002639055,0.080065039,Epidemiology,0.21744722,FALSE,13,0.197352959,0,0.055525823,0,0.403234768,859,0.031302673,0.171854056
13440,Mortality Associated With Intubation and Mechanical Ventilation in Patients with COVID-19,0,10.1101/2020.08.13.20174524,8/15/20,medrxiv,0,9,logistic regression,0.001220087,0.001220111,0.020667178,0.001220091,0.001220116,0.974452417,Clinics,0.7711885,TRUE,4.555555556,0.061970437,0.444444444,0.076933369,1,0.537564047,3920,0.664098242,0.335141524
13441,Forecasting United States COVID-19 Cases and Deaths Through Machine Learning,0,10.1101/2020.08.13.20174631,8/15/20,medrxiv,0,1,machine learning,0.002238439,0.002238453,0.593967061,0.397079119,0.002238446,0.002238483,Epidemiology,0.16555083,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,1448,0.203467373,0.171092274
13442,Metaviromic identification of genetic hotspots of coronavirus pathogenicity using machine learning,0,10.1101/2020.08.13.248575,8/14/20,biorxiv,0,2,"machine learning, genomes, virom",0.253039846,0.597664334,0.14433675,0.001653065,0.001653004,0.001653001,Genomics,0.56167156,TRUE,28,0.408312202,70,0.753344929,0,0.403234768,1672,0.274741151,0.459908263
13443,Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19,0,10.1101/2020.08.11.20172809,8/14/20,medrxiv,0,23,"machine learning, predictive model, logistic regression, dataset",0.002639342,0.002639339,0.584667252,0.002639249,0.002639085,0.404775733,Clinics,0.25454247,FALSE,339.7826087,0.993506092,162.9130435,0.894567835,7,0.785110192,,,0.891061373
13444,Trends in Covid-19 risk-adjusted mortality rates in a single health system,0,10.1101/2020.08.11.20172775,8/14/20,medrxiv,0,7,logistic regression,0.000498469,0.04691481,0.000498451,0.290257432,0.000498477,0.661332361,Clinics,0.5255905,TRUE,105.5714286,0.880264704,120.4285714,0.851953439,6,0.764429903,18569,0.935227546,0.857968898
13445,Precise Prediction of COVID-19 in Chest X-Ray Images Using KE Sieve Algorithm,0,10.1101/2020.08.13.20174144,8/14/20,medrxiv,0,3,"machine learning, neural network",0.0015118,0.020695676,0.953226582,0.001511796,0.021542298,0.001511848,Imaging,0.47156066,FALSE,2.333333333,0.024800544,1,0.122023013,1,0.537564047,784,0.015892126,0.175069933
13446,Severity Assessment of COVID-19 based on Clinical and Imaging Data,0,10.1101/2020.08.12.20173872,8/14/20,medrxiv,0,14,"machine learning, logistic regression",0.001059331,0.001059348,0.803162799,0.00105938,0.001059363,0.192599779,Imaging,0.7890482,TRUE,41.92857143,0.55631146,54.5,0.699892962,1,0.537564047,771,0.014206598,0.451993767
13447,"Obesity, old age and frailty are the true risk factors for COVID-19 mortality and not chronic disease or ethnicity in Croydon.",0,10.1101/2020.08.12.20156257,8/14/20,medrxiv,0,5,logistic regression,0.026266352,0.000956311,0.000956305,0.000956312,0.077178123,0.893686597,Clinics,0.46440005,FALSE,3.6,0.044838889,0.6,0.09011239,5,0.739490092,3046,0.56272574,0.359291778
13448,Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics,0,10.1101/2020.08.12.20173252,8/14/20,medrxiv,0,13,bayes,0.00143816,0.040593826,0.001438149,0.872036121,0.08305557,0.001438174,Epidemiology,0.3284906,FALSE,16,0.243552477,9.230769231,0.339710998,0,0.403234768,1647,0.268962196,0.31386511
13449,Frontline healthcare workers' knowledge and perception of COVID-19 and willingness to work during the pandemic in Nepal: a nationwide cross-sectional web-based study,0,10.1101/2020.08.12.20173609,8/14/20,medrxiv,0,19,logistic regression,0.001034571,0.001034602,0.00103458,0.001034639,0.994826959,0.001034649,Healthcare,0.8553493,TRUE,13,0.197352959,6.210526316,0.282579609,0,0.403234768,987,0.058271129,0.235359616
13450,LAMP-BEAC: Detection of SARS-CoV-2 RNA Using RT-LAMP and Molecular Beacons,0,10.1101/2020.08.13.20173757,8/14/20,medrxiv,0,17,genomes,0.002130681,0.904386967,0.087090153,0.00213072,0.002130728,0.002130751,Genomics,0.57789165,TRUE,57.7,0.686498856,169.6,0.899183837,5,0.739490092,2807,0.523717794,0.712222645
13451,Validation of Saliva and Self-Administered Nasal Swabs for COVID-19 Testing,0,10.1101/2020.08.13.20173807,8/14/20,medrxiv,0,11,sequencing,0.001171539,0.64621906,0.072468875,0.001171593,0.277797283,0.00117165,Genomics,0.46195534,FALSE,26.27272727,0.38499598,20.36363636,0.484011239,7,0.785110192,3655,0.636407416,0.572631207
13452,COVID-19 Severity Index: predictive score for hospitalized patients,0,10.1101/2020.08.12.20166579,8/14/20,medrxiv,10.1016/j.medin.2020.12.001,10,predictive model,0.001415149,0.00141511,0.207613397,0.395990199,0.001415138,0.392151007,Epidemiology,0.44835874,FALSE,9.7,0.145216154,0.8,0.101351351,0,0.403234768,2566,0.486395377,0.284049413
13453,Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations,0,10.1101/2020.08.11.20173120,8/14/20,medrxiv,0,10,"supervised learning, computational, interactom",0.496294443,0.001126792,0.279754277,0.001126898,0.001126906,0.220570684,Drug discovery,0.33378893,FALSE,81,0.811552972,100.4,0.823187048,0,0.403234768,1347,0.170479172,0.55211349
13454,Superspreading k-cores at the center of COVID-19 pandemic persistence,0,10.1101/2020.08.12.20173476,8/14/20,medrxiv,0,9,network analysis,0.001237087,0.001237102,0.001237103,0.993814441,0.001237176,0.001237092,Epidemiology,0.32271653,FALSE,6.444444444,0.091038407,5.666666667,0.270203372,2,0.618927094,2415,0.45942692,0.359898948
13455,Clinical characteristics and Outcomes of 500 patients with COVID Pneumonia : Results from a Single center(Southend University Hospital),0,10.1101/2020.08.13.20163030,8/14/20,medrxiv,0,8,logistic regression,0.001438125,0.001438143,0.031464578,0.090012751,0.001438138,0.874208265,Clinics,0.72611755,TRUE,6.5,0.093512277,0.75,0.099411292,0,0.403234768,883,0.03491452,0.157768214
13456,Deep Learning for Automated Recognition of Covid-19 from Chest X-ray Images,0,10.1101/2020.08.13.20173997,8/14/20,medrxiv,0,4,"machine learning, deep learning, neural network, transfer learning, dataset",0.050058918,0.001330094,0.925643908,0.001330112,0.001330138,0.02030683,Imaging,0.65281594,TRUE,58,0.689714887,29.25,0.564557131,1,0.537564047,1756,0.300264869,0.523025233
13457,Identification on Admission of COVID-19 Patients at Risk of Subsequent Rapid Clinical Deterioration,0,10.1101/2020.08.13.20171751,8/14/20,medrxiv,0,9,predictive model,0.000838507,0.000838504,0.234350094,0.000838553,0.000838525,0.762295817,Clinics,0.40743676,FALSE,5.333333333,0.074339786,5.666666667,0.270203372,0,0.403234768,1984,0.366000482,0.278444602
13458,Clinical characteristics of COVID-19 and the model for predicting the occurrence of critically ill patients: a retrospective cohort study.,0,10.1101/2020.08.13.20173799,8/14/20,medrxiv,0,10,"logistic regression, prediction model",0.001593785,0.001593549,0.261599767,0.00159355,0.001593533,0.732025816,Clinics,0.6306545,TRUE,4.777777778,0.064753541,0,0.055525823,5,0.739490092,827,0.024801348,0.221142701
13459,"Knowledge, attitude and practice among Ophthalmic Health Care Personnel (HCP) towards COVID-19 pandemic in Nepal: A web-based cross-sectional study",0,10.1101/2020.08.13.20174052,8/14/20,medrxiv,0,6,logistic regression,0.001415089,0.001415091,0.001415095,0.001415145,0.99292444,0.001415139,Healthcare,0.77267885,TRUE,5.166666667,0.071371142,0.5,0.087101953,0,0.403234768,1294,0.153864676,0.178893135
13460,Hospital preparedness in epidemics by using simulation. The case of COVID-19,0,10.1101/2020.08.12.20173328,8/14/20,medrxiv,0,3,simulation model,0.001350335,0.001350317,0.001350379,0.73833841,0.00135033,0.256260229,Epidemiology,0.5541715,TRUE,4.666666667,0.063392912,0,0.055525823,1,0.537564047,864,0.032025042,0.172126956
13461,In Silico Design of siRNAs Targeting Existing and Future Respiratory Viruses with VirusSi,0,10.1101/2020.08.13.250076,8/14/20,biorxiv,0,2,"computational, in silico, genomes",0.547270067,0.355622687,0.091853671,0.001751214,0.001751192,0.001751169,Drug discovery,0.33145007,FALSE,25.5,0.37435834,46,0.66416912,2,0.618927094,1402,0.186612088,0.46101666
13462,Evaluating the impacts of release in Sao Paulo State (Brazil) on the epidemic of covid-19 based on mathematical model,0,10.1101/2020.08.03.20167221,8/14/20,medrxiv,0,4,mathematical model,0.001034579,0.001034597,0.001034578,0.994826991,0.001034606,0.00103465,Epidemiology,0.33224475,FALSE,9.75,0.146267549,0,0.055525823,0,0.403234768,851,0.029135565,0.158540926
13463,ApharSeq: An Extraction-free Early-Pooling Protocol for Massively Multiplexed SARS-CoV-2 Detection,0,10.1101/2020.08.08.20170746,8/13/20,medrxiv,0,17,sequencing,0.001171574,0.68216415,0.228634464,0.085686696,0.001171558,0.001171558,Genomics,0.19669244,FALSE,26.88235294,0.39297421,210,0.922330747,0,0.403234768,1997,0.36816759,0.521676828
13464,Predicting the future SARS-COV-2 reproductive rate.,0,10.1101/2020.08.09.20170845,8/13/20,medrxiv,0,4,predictive model,0.001098829,0.084431802,0.00109882,0.804987766,0.001098827,0.107283956,Epidemiology,0.27702925,FALSE,11.25,0.169274538,3.5,0.213607172,0,0.403234768,1524,0.225860824,0.252994325
13465,Association of 25 hydroxyvitamin D concentration with risk of COVID-19: a Mendelian randomization study,0,10.1101/2020.08.09.20171280,8/13/20,medrxiv,0,7,genome-wide,0.002996483,0.211254969,0.002996513,0.002996606,0.002996521,0.776758908,Clinics,0.48482466,FALSE,74.57142857,0.784897025,,,0,0.403234768,2935,0.544907296,0.577679696
13466,Characterisation of the SARS-CoV-2 ExoN (nsp14ExoN-nsp10) complex: implications for its role in viral genome stability and inhibitor identification,0,10.1101/2020.08.13.248211,8/13/20,biorxiv,0,11,genomes,0.869047733,0.122629937,0.002080636,0.002080633,0.002080548,0.002080513,Drug discovery,0.3304983,FALSE,120.7272727,0.908219432,366.2727273,0.968022478,5,0.739490092,2857,0.530941488,0.786668373
13467,"SARS-CoV-2 seroprevalence survey among 18,000 healthcare and administrative personnel at hospitals, pre-hospital services, and specialist practitioners in the Central Denmark Region",0,10.1101/2020.08.10.20171850,8/12/20,medrxiv,0,14,logistic regression,0.000926325,0.30455896,0.000926318,0.000926303,0.538890045,0.153772049,Healthcare,0.5300236,TRUE,23.42857143,0.346836539,10,0.355632861,9,0.814309525,2076,0.386467614,0.475811635
13468,On the track of the D839Y mutation in the SARS-CoV-2 Spike fusion peptide: emergence and geotemporal spread of a highly prevalent variant in Portugal,0,10.1101/2020.08.10.20171884,8/12/20,medrxiv,0,14,"sequencing, genomes",0.119577218,0.675575829,0.001010958,0.186019445,0.016805528,0.001011022,Genomics,0.07325661,FALSE,3.071428571,0.03760282,,,2,0.618927094,1837,0.320491211,0.325673709
13469,Love during lockdown: findings from an online survey examining the impact of COVID-19 on the sexual practices of people living in Australia,0,10.1101/2020.08.10.20171348,8/12/20,medrxiv,10.1136/sextrans-2020-054688,13,logistic regression,0.001059349,0.001059345,0.001059355,0.2209175,0.77484507,0.00105938,Healthcare,0.9542432,TRUE,49.76923077,0.630094626,35.53846154,0.608308804,2,0.618927094,2560,0.481098001,0.584607131
13470,Worldwide tracing of mutations and the evolutionary dynamics of SARS-CoV-2,0,10.1101/2020.08.07.242263,8/12/20,biorxiv,0,12,"genome sequences, dataset",0.188143261,0.805365726,0.001622824,0.001622795,0.001622693,0.001622701,Genomics,0.16736332,FALSE,69.23076923,0.759787247,87.84615385,0.796561413,2,0.618927094,1955,0.354683361,0.632489779
13471,Discovery of COVID-19 Inhibitors Targeting the SARS-CoV2 Nsp13 Helicase,0,10.1101/2020.08.09.243246,8/12/20,biorxiv,10.1021/acs.jpclett.0c02421,3,"virtual screening, structural model",0.958624353,0.002357748,0.002357745,0.002357722,0.002357725,0.031944707,Drug discovery,0.8068098,TRUE,291.6666667,0.990228214,397.6666667,0.97076532,1,0.537564047,2819,0.522273056,0.755207659
13472,Coronacept - a potent immunoadhesin against SARS-CoV-2,0,10.1101/2020.08.12.247940,8/12/20,biorxiv,0,10,computational,0.883683993,0.102974915,0.00333535,0.003335337,0.003335207,0.003335199,Drug discovery,0.18784216,FALSE,23,0.34225988,32.8,0.590982071,1,0.537564047,2055,0.381411028,0.463054257
13473,Quantifying threat from COVID-19 infection hazard in Primary Schools in England,0,10.1101/2020.08.07.20170035,8/11/20,medrxiv,0,4,probabilistic,0.000629665,0.000629684,0.000629678,0.506719414,0.490761872,0.000629689,Epidemiology,0.07121751,FALSE,22.25,0.328344363,83.5,0.787128713,0,0.403234768,1123,0.090536961,0.402311201
13474,Model to Describe Fast Shutoff of CoVID-19 Pandemic Spread,0,10.1101/2020.08.07.20169904,8/11/20,medrxiv,0,1,"model fit, dataset",0.454173328,0.001046846,0.001046823,0.541639257,0.001046869,0.001046877,Epidemiology,0.04537064,FALSE,8,0.118683901,0,0.055525823,0,0.403234768,616,0.001444739,0.144722308
13475,Comparative Clinical Outcomes and Mortality in Prisoner and Non-Prisoner Populations Hospitalized with COVID-19: A Cohort from Michigan,0,10.1101/2020.08.08.20170787,8/11/20,medrxiv,0,14,logistic regression,0.001593478,0.001593522,0.001593509,0.032058115,0.001593539,0.961567836,Clinics,0.7749798,TRUE,4.214285714,0.055909456,0.571428571,0.088038534,4,0.707574542,1493,0.210450277,0.265493202
13476,"LamPORE: rapid, accurate and highly scalable molecular screening for SARS-CoV-2 infection, based on nanopore sequencing",0,10.1101/2020.08.07.20161737,8/11/20,medrxiv,0,8,sequencing,0.004775513,0.733704821,0.247193706,0.004775326,0.004775346,0.004775288,Genomics,0.38111305,FALSE,17.5,0.263776362,89.625,0.800575328,20,0.900117291,9378,0.858415603,0.705721146
13477,Social Media Reveals Psychosocial Effects of the COVID-19 Pandemic,0,10.1101/2020.08.07.20170548,8/11/20,medrxiv,0,4,"classifier, transfer learning, dataset",0.126477817,0.000793402,0.045289875,0.231593277,0.595052242,0.000793387,Healthcare,0.022695392,FALSE,86.75,0.832580865,153,0.88587102,1,0.537564047,5887,0.775102336,0.757779567
13478,Outcomes of COVID-19 related hospitalisation among people with HIV in the ISARIC WHO Clinical Characterisation Protocol UK Protocol: prospective observational study,0,10.1101/2020.08.07.20170449,8/11/20,medrxiv,10.1093/cid/ciaa1605,14,dataset,0.000416364,0.000416372,0.000416366,0.057173258,0.208915762,0.732661878,Clinics,0.15302685,FALSE,85.21428571,0.825530336,83.21428571,0.78585764,7,0.785110192,5048,0.736335179,0.783208337
13479,A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China,0,10.1101/2020.08.07.20163402,8/11/20,medrxiv,0,34,"prediction model, dataset",0.013403331,0.00045491,0.530210819,0.042050203,0.179839631,0.234041107,Imaging,0.24734122,FALSE,18.55882353,0.278372194,,,0,0.403234768,1137,0.095111967,0.25890631
13480,Identification of key genes in SARS-CoV-2 patients on bioinformatics analysis,0,10.1101/2020.08.09.243444,8/11/20,biorxiv,0,2,"bioinformatic, genomes, dataset",0.931845027,0.057904562,0.002562634,0.00256261,0.002562572,0.002562595,Drug discovery,0.9422704,TRUE,2.5,0.027459954,0,0.055525823,5,0.739490092,2136,0.400674211,0.30578752
13481,"Pleiotropic effect of Lactoferrin in the prevention and treatment of COVID-19 infection: in vivo, in silico and in vitro preliminary evidences",0,10.1101/2020.08.11.244996,8/11/20,biorxiv,0,31,in silico,0.379351108,0.000956355,0.000956332,0.000956352,0.000956359,0.616823494,Clinics,0.6164951,TRUE,58.96296296,0.69552848,31.11111111,0.578472036,1,0.537564047,9682,0.864916928,0.669120373
13482,Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development,0,10.1101/2020.08.11.245993,8/11/20,biorxiv,0,6,proteom,0.581336354,0.401440132,0.000854727,0.000854757,0.00085473,0.0146593,Drug discovery,0.11633003,FALSE,31,0.445111015,25.83333333,0.535790741,0,0.403234768,1373,0.1721647,0.389075306
13483,Bioinformatic Analysis Reveals That Some Mutations May Affect On Both Spike Structure Damage and Ligand Binding Site,0,10.1101/2020.08.10.244632,8/10/20,biorxiv,0,1,bioinformatic,0.243946488,0.733186635,0.001237041,0.001237097,0.001237094,0.019155645,Genomics,0.21372452,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,1290,0.142788346,0.15346411
13484,Petabase-scale sequence alignment catalyses viral discovery,0,10.1101/2020.08.07.241729,8/10/20,biorxiv,0,15,sequence alignment,0.070245665,0.922749555,0.001751192,0.001751275,0.001751172,0.001751142,Genomics,0.2894073,FALSE,24.76923077,0.364957635,94.07692308,0.811011507,9,0.814309525,10771,0.878641946,0.717230153
13485,A Classification Approach for Predicting COVID-19 Patient Survival Outcome with Machine Learning Techniques,0,10.1101/2020.08.02.20129767,8/10/20,medrxiv,0,10,"machine learning, dataset",0.001717204,0.001717265,0.79077519,0.001717349,0.001717242,0.20235575,Clinics,0.9340402,TRUE,4,0.054734368,0.7,0.096601552,1,0.537564047,1200,0.113893571,0.200698385
13486,"Characterization of SARS-CoV-2 ORF6 deletion variants detected in a nosocomial cluster during routine genomic surveillance, Lyon, France",0,10.1101/2020.08.07.241653,8/10/20,biorxiv,10.1080/22221751.2021.1872351,12,"whole genome, genome sequences",0.10108954,0.752845297,0.000916699,0.000916696,0.01366321,0.130568558,Genomics,0.6773806,TRUE,25.75,0.377698064,10.25,0.358375702,3,0.667819001,1362,0.167108115,0.392750221
13487,SARS-CoV-2 and Stroke Characteristics: A Report from the Multinational COVID-19 Stroke Study Group,0,10.1101/2020.08.05.20169169,8/7/20,medrxiv,0,113,machine learning,0.001237091,0.001237137,0.117651816,0.215299267,0.001237188,0.663337501,Clinics,0.6591476,TRUE,12.30434783,0.185354691,3.217391304,0.202234413,0,0.403234768,2651,0.487840116,0.319665997
13488,Association of mental disorders with SARS-CoV-2 infection and severe health outcomes: a nationwide cohort study,0,10.1101/2020.08.05.20169201,8/7/20,medrxiv,10.1192/bjp.2020.251,4,logistic regression,0.001371233,0.001371246,0.001371229,0.001371256,0.466950122,0.527564914,Clinics,0.44112602,FALSE,15,0.227596017,17.75,0.458322184,1,0.537564047,1156,0.095352757,0.329708751
13489,Pre-pandemic psychiatric disorders and risk of COVID-19: a cohort analysis in the UK Biobank,0,10.1101/2020.08.07.20169847,8/7/20,medrxiv,10.1016/s2666-7568(20)30013-1,13,logistic regression,0.001126916,0.001126857,0.001126779,0.001126849,0.448115146,0.547377454,Clinics,0.40496808,FALSE,24.76923077,0.364957635,23.53846154,0.517126037,3,0.667819001,2044,0.366482061,0.479096184
13490,Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility,0,10.1101/2020.08.06.20169664,8/7/20,medrxiv,0,9,"bayes, bayesian model",0.001330033,0.001330063,0.001330034,0.971444332,0.02323546,0.001330078,Epidemiology,0.077565104,FALSE,24.33333333,0.359700662,226.4444444,0.928150923,2,0.618927094,1176,0.100890922,0.5019174
13491,Obtaining prevalence estimates of COVID-19: A model to inform decision-making,0,10.1101/2020.08.06.20169656,8/7/20,medrxiv,0,2,"bayes, bayesian model",0.001156256,0.001156264,0.001156262,0.572589209,0.422785751,0.001156258,Epidemiology,0.111889124,FALSE,22.5,0.333539489,12,0.386740701,0,0.403234768,1171,0.099927763,0.30586068
13492,Public perceptions and preventive behaviours during the early phase of the COVID-19 pandemic: a comparative study between Hong Kong and the United Kingdom,0,10.1101/2020.08.06.20169409,8/7/20,medrxiv,10.2196/23231,8,logistic regression,0.000854718,0.000854727,0.000854703,0.442393793,0.554187324,0.000854736,Healthcare,0.5092672,TRUE,22.875,0.338116148,14.5,0.418450629,2,0.618927094,1440,0.186130508,0.390406095
13493,"Expression of ACE2, TMPRSS2, and CTSL in human airway epithelial cells under physiological and pathological conditions: Implications for SARS-CoV2 infection",0,10.1101/2020.08.06.240796,8/7/20,biorxiv,0,4,"transcriptom, dataset",0.845816327,0.065448523,0.002183266,0.002183232,0.002183248,0.082185404,Drug discovery,0.6370032,TRUE,20.25,0.301317336,,,0,0.403234768,1932,0.337105707,0.34721927
13494,COVID-19: Beliefs in misinformation in the Australian community,0,10.1101/2020.08.04.20168583,8/6/20,medrxiv,0,13,digital health,0.001291233,0.001291222,0.001291225,0.20874412,0.786090959,0.001291241,Healthcare,0.5307425,TRUE,37.23076923,0.510050096,23.61538462,0.517326733,5,0.739490092,4350,0.685528534,0.613098864
13495,Safety of hot and cold site admissions within a high volume urology department in the United Kingdom at the peak of the COVID-19 pandemic,0,10.1101/2020.08.04.20154203,8/6/20,medrxiv,10.1002/bco2.56,60,logistic regression,0.001371338,0.118348193,0.001371292,0.001371319,0.172226345,0.705311512,Clinics,0.9678962,TRUE,11.12765957,0.167604676,3.574468085,0.214209259,2,0.618927094,3941,0.652540332,0.41332034
13496,Measurement lessons of a repeated cross-sectional household food insecurity survey during the COVID-19 pandemic in Mexico,0,10.1101/2020.08.04.20167650,8/6/20,medrxiv,0,4,"model fit, probabilistic",0.001786477,0.001786664,0.001786508,0.001786677,0.991067155,0.001786519,Healthcare,0.45294967,FALSE,34.75,0.484507391,12.5,0.392761573,0,0.403234768,1551,0.219118709,0.37490561
13497,Phylodynamics reveals the role of human travel and contact tracing in controlling COVID-19 in four island nations,0,10.1101/2020.08.04.20168518,8/6/20,medrxiv,0,14,"bayes, computational",0.000558185,0.232624601,0.000558196,0.765142669,0.000558178,0.000558171,Epidemiology,0.2261459,FALSE,15.21428571,0.229204032,102.4285714,0.826598876,1,0.537564047,2577,0.47098483,0.516087946
13498,The Trend of Neutralizing Antibody Response Against SARS-CoV-2 and the Cytokine/Chemokine Release in Patients with Differing Severities of COVID-19: All Individuals Infected with SARS-CoV-2 Obtained Neutralizing Antibody,0,10.1101/2020.08.05.20168682,8/6/20,medrxiv,0,9,genomes,0.196137202,0.168553993,0.001415089,0.001415157,0.001415119,0.63106344,Clinics,0.27000472,FALSE,10.77777778,0.1604923,9.555555556,0.345999465,1,0.537564047,1584,0.231880568,0.318984095
13499,Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and their Augmentation by Compact Joint Sets,0,10.1101/2020.08.04.200691,8/6/20,biorxiv,10.1016/j.cels.2020.11.010,3,machine learning,0.700111475,0.002130829,0.159321139,0.13417499,0.002130675,0.002130891,Drug discovery,0.06526184,FALSE,12.33333333,0.186467932,,,5,0.739490092,3013,0.542980978,0.489646334
13500,Development of mass spectrometry-based targeted assay for direct detection of novel SARS-CoV-2 coronavirus from clinical specimens,0,10.1101/2020.08.05.20168948,8/6/20,medrxiv,0,17,machine learning,0.001141381,0.669097507,0.305353625,0.02212481,0.001141317,0.001141361,Genomics,0.7090271,TRUE,29.88235294,0.429278249,35.17647059,0.606301846,3,0.667819001,4462,0.692511438,0.598977633
13501,Clinical Mortality Review in a Large COVID-19 Cohort,0,10.1101/2020.08.05.20168146,8/6/20,medrxiv,0,7,dataset,0.000580163,0.000580168,0.000580177,0.314191466,0.014014401,0.670053625,Clinics,0.5428784,TRUE,7.142857143,0.102541901,3.142857143,0.200561948,2,0.618927094,1560,0.222489766,0.286130177
13502,Estimation of Effective Reproduction Number for COVID-19 in Bangladesh and its districts,0,10.1101/2020.08.04.20168351,8/6/20,medrxiv,0,8,bayes,0.000793393,0.000793413,0.000793407,0.996032918,0.000793431,0.000793438,Epidemiology,0.08106652,FALSE,4.375,0.058135939,0.25,0.065493712,0,0.403234768,1240,0.121839634,0.162176013
13503,"Repeat COVID-19 Molecular Testing: Correlation with Recovery of Infectious Virus, Molecular Assay Cycle Thresholds, and Analytical Sensitivity",0,10.1101/2020.08.05.20168963,8/6/20,medrxiv,0,15,"sequencing, whole genome",0.001943476,0.536638158,0.143235104,0.093827045,0.001943544,0.222412672,Genomics,0.76292616,TRUE,24.06666667,0.356051704,20.8,0.489229328,8,0.799987654,4543,0.697327233,0.58564898
13504,An Examination of School Reopening Strategies during the SARS-CoV-2 Pandemic,0,10.1101/2020.08.05.20169086,8/6/20,medrxiv,0,7,mathematical model,0.001022639,0.018619715,0.072115232,0.58912442,0.318095297,0.001022697,Epidemiology,0.080871224,FALSE,43.71428571,0.575174717,80.14285714,0.779301579,2,0.618927094,5710,0.763785216,0.684297151
13505,Analysis of SIR-Network Model on COVID-19 with respect to its impact on West Bengal in India,0,10.1101/2020.08.05.20169037,8/6/20,medrxiv,0,5,"mathematical model, network model",0.001751188,0.001751173,0.001751224,0.957129282,0.035865969,0.001751164,Epidemiology,0.17697161,FALSE,3.2,0.038468675,0,0.055525823,1,0.537564047,937,0.038044787,0.167400833
13506,Persistent bacterial coinfection of a COVID-19 patient caused by a genetically adapted Pseudomonas aeruginosa chronic colonizer,0,10.1101/2020.08.05.238998,8/6/20,biorxiv,10.3389/fcimb.2021.641920,9,transcriptom,0.105083289,0.639525929,0.00477533,0.004775336,0.00477528,0.241064836,Genomics,0.4552521,FALSE,38.30769231,0.521120663,37.84615385,0.621019534,0,0.403234768,2422,0.44835059,0.498431389
13507,IFN signaling and neutrophil degranulation transcriptional signatures are induced during SARS-CoV-2 infection,0,10.1101/2020.08.06.239798,8/6/20,biorxiv,10.1038/s42003-021-01829-4,16,transcriptom,0.693740376,0.001350438,0.001350342,0.001350344,0.001350355,0.300858145,Drug discovery,0.6023688,TRUE,66.5625,0.744387408,108.3125,0.836834359,4,0.707574542,3522,0.60919817,0.72449862
13508,Evaluating the impact of non-pharmaceutical interventions for SARS-CoV-2 on a global scale,0,10.1101/2020.07.30.20164939,8/5/20,medrxiv,0,9,"bayes, bayesian model",0.001684475,0.00168449,0.001684515,0.772850519,0.220411443,0.001684559,Epidemiology,0.22398093,FALSE,18.55555556,0.278310347,15.22222222,0.42734814,2,0.618927094,2216,0.407175536,0.432940279
13509,Host metabolic reprogramming in response to SARS-Cov-2 infection,0,10.1101/2020.08.02.232645,8/5/20,biorxiv,0,4,"transcriptom, network model",0.626736012,0.325616441,0.00186176,0.001861749,0.001861749,0.042062289,Drug discovery,0.34037375,FALSE,3.75,0.046817985,2.5,0.180826866,6,0.764429903,2791,0.50614014,0.374553724
13510,Analysis of single nucleotide polymorphisms between 2019-nCoV genomes and its impact on codon usage,0,10.1101/2020.08.05.237404,8/5/20,biorxiv,0,3,"genome-wide, genomes",0.257974289,0.730799939,0.002806465,0.002806523,0.002806434,0.00280635,Genomics,0.7217744,TRUE,17,0.257467994,7.333333333,0.304656141,0,0.403234768,1210,0.109559355,0.268729564
13511,Alveolitis in severe SARS-CoV-2 pneumonia is driven by self-sustaining circuits between infected alveolar macrophages and T cells,0,10.1101/2020.08.05.238188,8/5/20,biorxiv,10.1038/s41586-020-03148-w,35,transcriptom,0.538456493,0.001371363,0.001371321,0.001371281,0.037806951,0.41962259,Drug discovery,0.1537801,FALSE,50.41176471,0.634671285,54.47058824,0.699357774,8,0.799987654,7201,0.809053696,0.735767602
13512,Identification of SARS-CoV-2 recombinant genomes,0,10.1101/2020.08.05.238386,8/5/20,biorxiv,0,4,genomes,0.001438126,0.963511261,0.001438111,0.001438175,0.030736105,0.001438221,Genomics,0.2562979,FALSE,47,0.606407323,108.75,0.837302649,9,0.814309525,6068,0.776787864,0.75870184
13513,Analysis of the potential impact of genomic variants in SARS-CoV-2 genomes from India on molecular diagnostic assays,0,10.1101/2020.08.05.238618,8/5/20,biorxiv,10.1016/j.ijid.2020.10.086,11,"sequencing, genomes",0.113732888,0.82457515,0.001203505,0.058081617,0.001203428,0.001203412,Genomics,0.39981145,FALSE,1.909090909,0.017378935,,,4,0.707574542,3056,0.547796773,0.424250083
13514,An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques,0,10.1101/2020.07.30.20165571,8/4/20,medrxiv,0,3,"text mining, probabilistic",0.034909802,0.000759401,0.000759484,0.817574258,0.145237689,0.000759366,Epidemiology,0.10960859,FALSE,2.333333333,0.024800544,0,0.055525823,1,0.537564047,1427,0.176980496,0.198717728
13515,SARS-CoV-2 Seroprevalence Across a Diverse Cohort of Healthcare Workers,0,10.1101/2020.07.31.20163055,8/4/20,medrxiv,0,39,bayes,0.000765986,0.151768668,0.00076597,0.000765994,0.808038355,0.037895028,Healthcare,0.09384903,FALSE,26.12820513,0.382831344,35.07692308,0.605499063,5,0.739490092,1265,0.125692271,0.463378192
13516,Genomic heterogeneity and clinical characterization of SARS-CoV-2 in Oregon,0,10.1101/2020.07.30.20160069,8/4/20,medrxiv,0,14,genomes,0.002562633,0.766269605,0.002562736,0.00256281,0.002562606,0.22347961,Genomics,0.14353946,FALSE,32.35714286,0.459335766,48,0.673735617,0,0.403234768,1322,0.143029136,0.419833821
13517,Analytical Model of COVID-19 for lifting non-pharmaceutical interventions,0,10.1101/2020.07.31.20166025,8/4/20,medrxiv,0,4,bayes,0.003465966,0.003465943,0.003466092,0.982669428,0.003465988,0.003466582,Epidemiology,0.23921928,FALSE,15.75,0.237862577,4,0.231469093,0,0.403234768,1023,0.056104021,0.232167615
13518,Estimating the reproductive number R0 of SARS-CoV-2 in the United States and eight European countries and implications for vaccination,0,10.1101/2020.07.31.20166298,8/4/20,medrxiv,10.1016/j.jtbi.2021.110621,4,mathematical model,0.001010948,0.001010943,0.001010917,0.9949453,0.001010942,0.00101095,Epidemiology,0.08566296,FALSE,14,0.213494959,21.5,0.498260637,6,0.764429903,4862,0.714664098,0.547712399
13519,The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic,0,10.1101/2020.07.31.20143016,8/4/20,medrxiv,10.1080/17538947.2021.1886358,7,dataset,0.000759364,0.000759377,0.000759375,0.934447038,0.062515483,0.000759362,Epidemiology,0.0714134,FALSE,7.142857143,0.102541901,1.714285714,0.14577201,17,0.887338725,1511,0.201300265,0.334238225
13520,Evaluating Data-Driven Forecasting Methods for Predicting SARS-CoV2 Cases: Evidence From 173 Countries,0,10.1101/2020.08.03.20167189,8/4/20,medrxiv,0,7,forecasting model,0.001823326,0.001823343,0.032889704,0.95981693,0.001823375,0.001823322,Epidemiology,0.13295999,FALSE,5.142857143,0.071247449,0.428571429,0.076665775,1,0.537564047,1273,0.128340958,0.203454557
13521,Severity Assessment and Progression Prediction of COVID-19 Patients based on the LesionEncoder Framework and Chest CT,0,10.1101/2020.08.03.20167007,8/4/20,medrxiv,0,14,neural network,0.000966755,0.014795908,0.82408628,0.033154546,0.000966753,0.126029759,Imaging,0.2670595,FALSE,41.78571429,0.555445606,53.92857143,0.696748729,4,0.707574542,1199,0.1037804,0.515887319
13522,"Physical activity, BMI and COVID-19: an observational and Mendelian randomisation study",0,10.1101/2020.08.01.20166405,8/4/20,medrxiv,10.7189/jogh.10.020514,9,logistic regression,0.002806479,0.002806467,0.002806733,0.267554773,0.341740188,0.38228536,Clinics,0.6072004,TRUE,56.88888889,0.680561568,97.33333333,0.817299973,2,0.618927094,1092,0.074644835,0.547858368
13523,Waves of COVID-19 pandemic. Detection and SIR simulations,0,10.1101/2020.08.03.20167098,8/4/20,medrxiv,0,1,mathematical model,0.013481353,0.000830653,0.000830669,0.956622507,0.000830642,0.027404176,Epidemiology,0.19997436,FALSE,3,0.037293586,0,0.055525823,3,0.667819001,969,0.042379003,0.200754353
13524,Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdown,0,10.1101/2020.08.03.20167254,8/4/20,medrxiv,0,4,"neural network, prediction model, forecasting model, lstm",0.00133003,0.020668088,0.096026905,0.879314787,0.001330085,0.001330105,Epidemiology,0.6347034,TRUE,6.5,0.093512277,0.75,0.099411292,3,0.667819001,1375,0.16132916,0.255517932
13525,Impacts of people's learning behavior in fighting the COVID-19 epidemic,0,10.1101/2020.08.02.20166967,8/4/20,medrxiv,0,2,mathematical model,0.001203402,0.001203455,0.001203491,0.993982735,0.001203483,0.001203434,Epidemiology,0.2019782,FALSE,0,0.006432061,,,2,0.618927094,736,0.007223694,0.21086095
13526,TClustVID: A Novel Machine Learning Classification Model to Investigate Topics and Sentiment inCOVID-19 Tweets,0,10.1101/2020.08.04.20167973,8/4/20,medrxiv,0,7,"machine learning, classifier, dataset",0.001330047,0.001330107,0.341797662,0.506387633,0.147824391,0.001330159,Epidemiology,0.26753783,FALSE,4.142857143,0.055476529,0,0.055525823,5,0.739490092,1426,0.176739706,0.256808038
13527,Fitting models to the COVID-19 outbreak and estimating R,0,10.1101/2020.08.04.20163782,8/4/20,medrxiv,0,8,"computational, mathematical model",0.001538129,0.001538125,0.001538195,0.992309175,0.001538171,0.001538206,Epidemiology,0.120910645,FALSE,68.28571429,0.755396128,161.8571429,0.893698154,8,0.799987654,3146,0.557909945,0.75174797
13528,Analysis of COVID-19 and comorbidity co-infection Model with Optimal Control,0,10.1101/2020.08.04.20168013,8/4/20,medrxiv,0,7,mathematical model,0.001187289,0.001187317,0.001187337,0.684691444,0.001187275,0.310559337,Epidemiology,0.33068824,FALSE,2,0.022141134,0,0.055525823,4,0.707574542,1495,0.196966049,0.245551887
13529,SCV-2000bp: a primer panel for SARS-CoV-2 full-genome sequencing,0,10.1101/2020.08.04.234880,8/4/20,biorxiv,0,0,"sequencing, genome sequences, genomes",0.003465908,0.838915737,0.147219988,0.003465874,0.003465886,0.003466607,Genomics,0.30454332,FALSE,2,0.022141134,0,0.055525823,1,0.537564047,1473,0.190223935,0.201363735
13530,"SARS-CoV-2 genome analysis of strains in Pakistan reveals GH, S and L clade strains at the start of the pandemic",0,10.1101/2020.08.04.234153,8/4/20,biorxiv,0,14,"sequencing, whole-genome",0.001511836,0.99244083,0.001511797,0.001511852,0.001511833,0.001511853,Genomics,0.52985257,TRUE,28.21428571,0.409796524,20.28571429,0.48347605,2,0.618927094,2042,0.358295208,0.467623719
13531,"SARS-CoV-2 genome sequences from late April in Stockholm, Sweden reveal a novel mutation in the spike protein",0,10.1101/2020.08.03.233866,8/3/20,biorxiv,10.1128/MRA.00934-20,7,"genome sequences, genomes",0.053956512,0.938269169,0.001943493,0.001943611,0.00194363,0.001943586,Genomics,0.3922145,FALSE,22.42857143,0.331498547,124.4285714,0.857037731,0,0.403234768,2916,0.521069107,0.528210038
13532,Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery,0,10.1101/2020.08.02.233064,8/3/20,biorxiv,0,32,"deep learning, phenomics, deep-learning",0.731933783,0.001565411,0.261804829,0.001565334,0.001565332,0.001565311,Drug discovery,0.19280294,FALSE,25.3125,0.371080463,55.03125,0.702234413,1,0.537564047,7926,0.825427402,0.609076581
13533,PAN-INDIA 1000 SARS-CoV-2 RNA Genome Sequencing Reveals Important Insights into the Outbreak,0,10.1101/2020.08.03.233718,8/3/20,biorxiv,0,23,"sequencing, genomes",0.00097744,0.84141441,0.00097749,0.154675791,0.000977438,0.000977431,Genomics,0.2470243,FALSE,53.72727273,0.658915208,66,0.742574257,2,0.618927094,3295,0.577895497,0.649578014
13534,Similarity between mutation spectra in hypermutated genomes of rubella virus and in SARS-CoV-2 genomes accumulated during the COVID-19 pandemic,0,10.1101/2020.08.03.234005,8/3/20,biorxiv,10.1371/journal.pone.0237689,5,"genomes, dataset",0.001511897,0.992440511,0.001511954,0.001511889,0.001511845,0.001511903,Genomics,0.50723726,TRUE,45.6,0.592058878,196.2,0.915707787,5,0.739490092,1809,0.291355647,0.634653101
13535,COVID-19 Recurrent Varies with Different Combinatorial Medical Treatments Determined by Machine Learning Approaches,0,10.1101/2020.07.29.20164699,8/1/20,medrxiv,0,14,machine learning,0.308411172,0.001220016,0.116833152,0.085902898,0.001220071,0.486412691,Clinics,0.3166294,FALSE,5.571428571,0.077555817,0.5,0.087101953,0,0.403234768,772,0.009390802,0.144320835
13536,Long-term patient-reported symptoms of COVID-19: an analysis of social media data,0,10.1101/2020.07.29.20164418,8/1/20,medrxiv,0,4,dataset,0.001823395,0.001823452,0.001823441,0.568227313,0.243817055,0.182485343,Epidemiology,0.752298,TRUE,56,0.675304595,17,0.451097137,4,0.707574542,4063,0.656874549,0.622712706
13537,Forecasting COVID-19: Using SEIR-D quantitative modelling for healthcare demand and capacity,0,10.1101/2020.07.29.20164566,8/1/20,medrxiv,0,14,"bayes, predictive model, forecasting model, dataset",0.000265335,0.022671953,0.016285727,0.885294677,0.000265316,0.075216993,Epidemiology,0.11970222,FALSE,8.692307692,0.128455687,5.692307692,0.27027027,1,0.537564047,1398,0.162292319,0.274645581
13538,Hybrid capture-based sequencing enables unbiased recovery of SAR-CoV-2 genomes from fecal samples and characterization of the dynamics of intra-host variants,0,10.1101/2020.07.30.230102,8/1/20,biorxiv,10.1016/j.jgg.2020.10.002,12,"sequencing, transcriptom, genomes, metatranscriptom",0.001098825,0.903847951,0.091756484,0.001098914,0.001098837,0.001098989,Genomics,0.61909986,TRUE,23.83333333,0.351598738,11.25,0.374364463,2,0.618927094,1115,0.076811943,0.35542556
13539,Five approaches to the suppression of SARS-CoV-2 without intensive social distancing,0,10.1101/2020.07.30.20165159,8/1/20,medrxiv,0,4,mathematical model,0.002080572,0.002080577,0.002080567,0.989597008,0.002080682,0.002080594,Epidemiology,0.38310036,FALSE,5.75,0.080957388,0,0.055525823,1,0.537564047,1336,0.142065976,0.204028309
13540,Mathematical modeling of the transmission of SARS-CoV-2 '' Evaluating the impact of isolation in Sao Paulo State (Brazil) and lockdown in Spain associated with protective measures on the epidemic of covid-19,0,10.1101/2020.07.30.20165191,8/1/20,medrxiv,0,4,mathematical model,0.001187254,0.001187263,0.001187259,0.889693136,0.073465678,0.033279411,Epidemiology,0.4440545,FALSE,17.5,0.263776362,4,0.231469093,4,0.707574542,819,0.014928967,0.304437241
13541,Global variation in the SARS-CoV-2 proteome reveals the mutational hotspots in the drug and vaccine candidates,0,10.1101/2020.07.31.230987,7/31/20,biorxiv,0,4,"proteom, whole genome, genome sequences",0.159913727,0.834514677,0.001392826,0.001392868,0.00139294,0.001392963,Genomics,0.46140268,FALSE,7.75,0.112808461,4,0.231469093,3,0.667819001,3355,0.581266554,0.398340777
13542,Phylogenomic analysis of SARS-CoV-2 genomes from western India reveals unique linked mutations,0,10.1101/2020.07.30.228460,7/31/20,biorxiv,0,17,"phylogenom, genomes",0.000746561,0.95072497,0.000746537,0.00074656,0.000746616,0.046288756,Genomics,0.27374065,FALSE,27.05882353,0.396066547,18.94117647,0.469293551,5,0.739490092,4378,0.679990368,0.57121014
13543,Biochemical and mathematical lessons from the evolution of the SARS-CoV-2 virus: paths for novel antiviral warfare,0,10.1101/2020.07.31.230607,7/31/20,biorxiv,0,5,probabilistic,0.35214949,0.466616952,0.001415121,0.176988207,0.001415124,0.001415106,Genomics,0.24522498,FALSE,106.8,0.882738574,262.2,0.942601017,0,0.403234768,1371,0.154105466,0.595669956
13544,Biomechanical Characterization of SARS-CoV-2 Spike RBD and Human ACE2 Protein-Protein Interaction,0,10.1101/2020.07.31.230730,7/31/20,biorxiv,10.1016/j.bpj.2021.02.007,8,molecular dynamics simulation,0.982173986,0.00092629,0.00092628,0.000926309,0.000926273,0.014120861,Drug discovery,0.5514453,TRUE,8.5,0.126662131,0.875,0.103826599,7,0.785110192,2739,0.489044065,0.376160747
13545,"Changes in the behavioural determinants of health during the coronavirus (COVID-19) pandemic: gender, socioeconomic and ethnic inequalities in 5 British cohort studies",0,10.1101/2020.07.29.20164244,7/31/20,medrxiv,0,7,logistic regression,0.00104681,0.001046863,0.001046813,0.138528091,0.857284528,0.001046894,Healthcare,0.56457615,TRUE,9.285714286,0.138165626,0.714285714,0.096936045,9,0.814309525,3434,0.590416566,0.409956941
13546,COVID-19 case-fatality rate and demographic and socioeconomic influencers: a worldwide spatial regression analysis based on country-level data,0,10.1101/2020.07.31.20165811,7/31/20,medrxiv,10.1136/bmjopen-2020-043560,3,dataset,0.001622709,0.02514053,0.001622729,0.642440879,0.327550375,0.001622778,Epidemiology,0.2823777,FALSE,3.333333333,0.04044777,0,0.055525823,5,0.739490092,919,0.032747412,0.217052774
13547,"Isolation Considered Epidemiological Model for the Prediction of COVID-19 Trend in Tokyo, Japan",0,10.1101/2020.07.31.20165829,7/31/20,medrxiv,0,3,mathematical model,0.001220001,0.00122007,0.001220065,0.891855684,0.001220033,0.103264147,Epidemiology,0.13528779,FALSE,9.333333333,0.139278867,0.666666667,0.096200161,2,0.618927094,1275,0.122802793,0.244302229
13548,"Job insecurity, financial threat and mental health in the COVID-19 context: The buffer role of perceived social support",0,10.1101/2020.07.31.20165910,7/31/20,medrxiv,0,5,probabilistic,0.001622731,0.001622731,0.00162278,0.234097893,0.759411155,0.00162271,Healthcare,0.67130595,TRUE,13.2,0.199270208,0,0.055525823,0,0.403234768,1383,0.156513364,0.203636041
13549,Model stability of COVID-19 mortality prediction with biomarkers,0,10.1101/2020.07.29.20161323,7/30/20,medrxiv,0,4,machine learning,0.002238524,0.002238514,0.345305892,0.205762272,0.002238438,0.442216359,Clinics,0.49433026,FALSE,2.25,0.023439916,0,0.055525823,1,0.537564047,1280,0.122562003,0.184772947
13550,The basic reproduction number of SARS-CoV-2: a scoping review of available evidence,0,10.1101/2020.07.28.20163535,7/30/20,medrxiv,0,13,mathematical model,0.000772623,0.000772636,0.00077262,0.99613689,0.000772611,0.000772621,Epidemiology,0.25168198,FALSE,39.15384615,0.531387222,40.38461538,0.635737222,3,0.667819001,1656,0.240067421,0.518752717
13551,SARS-CoV-2 Spike Protein Interacts with Multiple Innate Immune Receptors,0,10.1101/2020.07.29.227462,7/30/20,biorxiv,0,12,sequencing,0.942198143,0.04784002,0.002490426,0.002490533,0.002490444,0.002490434,Drug discovery,0.29609936,FALSE,130.9285714,0.922753417,296.6428571,0.953104094,20,0.900117291,5105,0.72453648,0.875127821
13552,Resveratrol and Copper for treatment of severe COVID-19: an observational study (RESCU 002),0,10.1101/2020.07.21.20151423,7/29/20,medrxiv,0,12,logistic regression,0.084263393,0.001371283,0.001371311,0.091211339,0.001371288,0.820411386,Clinics,0.34686726,FALSE,21.33333333,0.31548024,8.833333333,0.333154937,3,0.667819001,6658,0.788586564,0.526260186
13553,The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research,0,10.1101/2020.07.27.20162636,7/29/20,medrxiv,10.1186/s12911-020-01374-w,10,dataset,0.166538625,0.001371329,0.52682985,0.160808082,0.072141332,0.072310782,Imaging,0.34315038,FALSE,18.3,0.273795535,10.9,0.367808402,0,0.403234768,3178,0.552853359,0.399423016
13554,A machine learning explanation of the pathogen-immune relationship of SARS-CoV-2 and machine learning models of prognostic biomarkers to predict asymptomatic or symptomatic infections,0,10.1101/2020.07.27.20162867,7/29/20,medrxiv,0,1,machine learning,0.165035487,0.000746592,0.338745792,0.000746613,0.000746574,0.493978942,Clinics,0.6286636,TRUE,0,0.006432061,,,0,0.403234768,1825,0.28702143,0.23222942
13555,Mathematical Modelling the Impact Evaluation ofLockdown on Infection Dynamics of COVID-19 inItaly.,0,10.1101/2020.07.27.20162537,7/29/20,medrxiv,0,4,mathematical model,0.001461856,0.001461908,0.001461855,0.992690654,0.001461877,0.001461851,Epidemiology,0.5191913,TRUE,1.75,0.017007855,,,0,0.403234768,833,0.015651336,0.145297986
13556,"An Analysis of Territorial Patterns in COVID-19 Mortality in France, Spain, Italy and the UK",0,10.1101/2020.07.27.20162677,7/29/20,medrxiv,0,1,dataset,0.002296566,0.002296627,0.0022966,0.892609992,0.002296636,0.098203579,Epidemiology,0.28863448,FALSE,5,0.070752675,6,0.280037463,0,0.403234768,865,0.021189502,0.193803602
13557,"A novel predictive mathematical model for COVID-19 pandemic with quarantine, contagion dynamics, and environmentally mediated transmission",0,10.1101/2020.07.27.20163063,7/29/20,medrxiv,0,4,mathematical model,0.00608966,0.006089775,0.006089661,0.969551777,0.006089636,0.006089492,Epidemiology,0.38752472,FALSE,11,0.167171748,1.75,0.148381054,1,0.537564047,917,0.030580303,0.220924288
13558,Children with COVID-19 like symptoms in Italian Pediatric Surgeries: the dark side of the coin,0,10.1101/2020.07.27.20149757,7/29/20,medrxiv,0,11,logistic regression,0.001098874,0.001098977,0.18010252,0.129987972,0.686612788,0.00109887,Healthcare,0.76739013,TRUE,48.90909091,0.621312388,25.90909091,0.536259031,0,0.403234768,1630,0.22971346,0.447629912
13559,Close-range exposure to a COVID-19 carrier: transmission trends in the respiratory tract and estimation of infectious dose,0,10.1101/2020.07.27.20162362,7/29/20,medrxiv,0,1,computational,0.00333555,0.252284108,0.003335405,0.532913404,0.003335425,0.204796108,Epidemiology,0.35436338,FALSE,30,0.432432432,11,0.371287129,4,0.707574542,6965,0.795569468,0.576715893
13560,High-throughput SARS-CoV-2 and host genome sequencing from single nasopharyngeal swabs,0,10.1101/2020.07.27.20163147,7/29/20,medrxiv,0,43,"sequencing, transcriptom, whole genome, genomes",0.003466277,0.982669775,0.003466081,0.003465969,0.003465922,0.003465976,Genomics,0.21507818,FALSE,48.74418605,0.619828066,194.4883721,0.914838105,2,0.618927094,4803,0.705995666,0.714897233
13561,"Serial population based serosurvey of antibodies to SARS-CoV-2 in a low and high transmission area of Karachi, Pakistan",0,10.1101/2020.07.28.20163451,7/29/20,medrxiv,0,17,bayes,0.002996477,0.106159108,0.002996453,0.271838794,0.543265549,0.07274362,Healthcare,0.39093423,FALSE,31.4375,0.44900736,38.625,0.625167246,6,0.764429903,4472,0.683843005,0.630611879
13562,Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Kenyan blood donors,0,10.1101/2020.07.27.20162693,7/29/20,medrxiv,10.1126/science.abe1916,39,bayes,0.001461881,0.209623579,0.040350377,0.283239119,0.256629055,0.20869599,Epidemiology,0.42610317,FALSE,16.41025641,0.247634362,,,45,0.953206988,16474,0.921020949,0.707287433
13563,The Impact of COVID-19 on Mental Health outcomes among hospital fever clinic attendants across Nepal: A community-based cross-sectional study,0,10.1101/2020.07.28.20163295,7/29/20,medrxiv,10.1371/journal.pone.0248684,6,logistic regression,0.001126784,0.001126788,0.001126842,0.001126825,0.994365912,0.001126849,Healthcare,0.9650924,TRUE,6.833333333,0.096852001,0.333333333,0.073187048,0,0.403234768,1805,0.280760896,0.213508678
13564,"Cell-Free DNA in Blood Reveals Significant Cell, Tissue and Organ Specific injury and Predicts COVID-19 Severity",0,10.1101/2020.07.27.20163188,7/29/20,medrxiv,0,13,genome-wide,0.277297168,0.162264471,0.001751189,0.001751174,0.001751184,0.555184814,Clinics,0.6468425,TRUE,128.9230769,0.920155854,192.6923077,0.914102221,3,0.667819001,3459,0.589453407,0.772882621
13565,Eleven Routine Clinical Features Predict COVID-19 Severity,0,10.1101/2020.07.28.20163022,7/29/20,medrxiv,0,34,"machine learning, classifier",0.001486485,0.00148648,0.403699399,0.058001838,0.00148645,0.533839348,Clinics,0.59877133,TRUE,9.303030303,0.138351166,3.424242424,0.208723575,4,0.707574542,2684,0.476282206,0.382732872
13566,Genome-wide bioinformatic analyses predict key host and viral factors in SARS-CoV-2 pathogenesis,0,10.1101/2020.07.28.225581,7/29/20,biorxiv,0,13,"bioinformatic, sequencing, genome-wide",0.465680414,0.528058267,0.0015653,0.001565308,0.001565339,0.001565371,Genomics,0.3852551,FALSE,9.846153846,0.147690024,12,0.386740701,0,0.403234768,3660,0.610883699,0.387137298
13567,Impact Analysis of SARS-CoV2 on Signaling Pathways during COVID19 Pathogenesis using Codon Usage Assisted Host-Viral Protein Interactions,0,10.1101/2020.07.29.226217,7/29/20,biorxiv,0,3,"computational, interactom",0.973424626,0.023662865,0.000728123,0.00072816,0.000728127,0.000728099,Drug discovery,0.86193115,TRUE,25.33333333,0.37188447,10.33333333,0.36038266,3,0.667819001,1555,0.204671322,0.401189363
13568,The genetic variants analysis of circulating SARS-CoV-2 in Bangladesh.,0,10.1101/2020.07.29.226555,7/29/20,biorxiv,0,12,"whole-genome, genomes",0.032871713,0.964064458,0.000765925,0.000766057,0.000765927,0.00076592,Genomics,0.593421,TRUE,7.833333333,0.113798009,1.5,0.138747659,0,0.403234768,2210,0.39056104,0.261585369
13569,A Bayesian Framework for Estimating the Risk Ratio of Hospitalization for People with Comorbidity Infected by the SARS-CoV-2 Virus,0,10.1101/2020.07.25.20162131,7/28/20,medrxiv,10.1093/jamia/ocaa246,2,"bayes, dataset",0.001653117,0.017865682,0.001653116,0.272717274,0.001653109,0.704457701,Clinics,0.5855946,TRUE,4.5,0.061784897,0,0.055525823,2,0.618927094,1312,0.130267277,0.216626273
13570,AutoSEIR: Accurate Forecasting from Real-time Epidemic Data Using Machine Learning,0,10.1101/2020.07.25.20159715,7/28/20,medrxiv,0,4,machine learning,0.001786507,0.001786519,0.152196614,0.840657303,0.001786542,0.001786517,Epidemiology,0.26589853,FALSE,5.5,0.077246583,2,0.164302917,0,0.403234768,912,0.029376354,0.168540155
13571,"Magnitude, demographics and dynamics of the impact of the first phase of the Covid-19 pandemic on all-cause mortality in 17 industrialised countries",0,10.1101/2020.07.26.20161570,7/28/20,medrxiv,10.1038/s41591-020-1112-0,14,"bayes, probabilistic",0.00072221,0.000722186,0.000722179,0.646681958,0.10961912,0.241532348,Epidemiology,0.3433252,FALSE,32.57142857,0.462057023,246,0.93678084,0,0.403234768,2554,0.453647965,0.563930149
13572,Longitudinal COVID-19 Surveillance and Characterization in the Workplace with Public Health and Diagnostic Endpoints,0,10.1101/2020.07.25.20160812,7/28/20,medrxiv,0,12,"bayes, machine learning",0.00066772,0.292360229,0.00066775,0.274856213,0.296514485,0.134933604,Healthcare,0.46821904,FALSE,29.33333333,0.423093574,29.91666667,0.568370351,0,0.403234768,2013,0.337828076,0.433131692
13573,Association of olfactory dysfunction with hospitalization for COVID-19: a multicenter study in Kurdistan,0,10.1101/2020.07.26.20158550,7/28/20,medrxiv,0,10,logistic regression,0.001593469,0.0015935,0.001593514,0.001593499,0.001593572,0.992032446,Clinics,0.734364,TRUE,10.5,0.157338116,1.7,0.145236821,3,0.667819001,1134,0.077293523,0.261921865
13574,Prophylaxis with tetracyclines in ARDS: Potential therapy for COVID-19-induced ARDS?,0,10.1101/2020.07.22.20154542,7/28/20,medrxiv,0,7,logistic regression,0.181263445,0.001943483,0.001943535,0.001943534,0.001943513,0.81096249,Clinics,0.45472315,FALSE,12.42857143,0.187395634,17,0.451097137,3,0.667819001,4305,0.669395618,0.493926847
13575,Serial co-expression analysis of host factors from SARS-CoV viruses highly converges with former high-throughput screenings and proposes key regulators and co-option of cellular pathways,0,10.1101/2020.07.28.225078,7/28/20,biorxiv,10.1093/bib/bbaa419,7,"computational, transcriptom",0.846840539,0.000863108,0.055515288,0.056407894,0.039509996,0.000863176,Drug discovery,0.43332922,FALSE,21.28571429,0.314676232,8.285714286,0.324324324,0,0.403234768,2302,0.409342644,0.362894492
13576,Effects of non-pharmaceutical interventions on COVID-19: A Tale of Two Models,0,10.1101/2020.07.22.20160341,7/27/20,medrxiv,0,4,"bayes, bayesian model",0.001310346,0.001310379,0.001310371,0.993448179,0.001310389,0.001310335,Epidemiology,0.065170616,FALSE,46.75,0.603314985,140,0.874096869,4,0.707574542,68153,0.985311823,0.792574555
13577,An effective COVID-19 response in South America: the Uruguayan Conundrum,0,10.1101/2020.07.24.20161802,7/27/20,medrxiv,0,50,genomic epidemiology,0.001310417,0.276995529,0.001310419,0.717762819,0.001310374,0.001310442,Epidemiology,0.15472138,FALSE,12.57777778,0.18974581,15.4,0.42875301,5,0.739490092,6476,0.783770768,0.53543992
13578,COVID-19 Patients Form Memory CD8+ T Cells that Recognize a Small Set of Shared Immunodominant Epitopes in SARS-CoV-2,0,10.1101/2020.07.24.20161653,7/27/20,medrxiv,10.1016/j.immuni.2020.10.006,19,"sequencing, genome-wide",0.786220869,0.113813888,0.001010961,0.001010951,0.00101093,0.096932401,Drug discovery,0.46311527,FALSE,28.70588235,0.41443503,,,15,0.874313229,9033,0.840597159,0.709781806
13579,"Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients",0,10.1101/2020.07.25.20161661,7/27/20,medrxiv,0,16,artificial intelligence,0.002296529,0.002296559,0.250133563,0.443338754,0.002296594,0.299638001,Epidemiology,0.48105276,FALSE,3.625,0.045086276,0.5,0.087101953,0,0.403234768,1058,0.05706718,0.148122544
13580,Mathematical modelling based study and prediction of COVID-19 epidemic dissemination under the impact of lockdown in India,0,10.1101/2020.07.25.20161885,7/27/20,medrxiv,10.3389/fphy.2020.586899,3,mathematical model,0.001461854,0.001461878,0.00146196,0.992690566,0.001461869,0.001461872,Epidemiology,0.23907647,FALSE,2.666666667,0.029377203,0,0.055525823,0,0.403234768,1515,0.187816037,0.168988458
13581,Oligonucleotide capture sequencing of the SARS-CoV-2 genome and subgenomic fragments from COVID-19 individuals,0,10.1101/2020.07.27.223495,7/27/20,biorxiv,0,21,"sequencing, transcriptom, genomes",0.071430697,0.881889931,0.001622754,0.001622769,0.001622716,0.041811134,Genomics,0.28204638,FALSE,87.38095238,0.834745501,375.9047619,0.968624565,6,0.764429903,2540,0.452203227,0.755000799
13582,Molecular mechanisms of Cardiac Injury associated with myocardial SARS-CoV-2 infection,0,10.1101/2020.07.27.220954,7/27/20,biorxiv,0,3,"genomes, dataset",0.902697081,0.017640988,0.001112626,0.001112636,0.001112619,0.07632405,Drug discovery,0.5500164,TRUE,76,0.790586926,101.3333333,0.824591919,0,0.403234768,1593,0.212617385,0.557757749
13583,A-to-I RNA editing in SARS-COV-2: real or artifact?,0,10.1101/2020.07.27.223172,7/27/20,biorxiv,0,3,"transcriptom, genomes, metatranscriptom",0.469616191,0.484927146,0.002032792,0.002032928,0.002032851,0.039358091,Genomics,0.173276,FALSE,3,0.037293586,1.666666667,0.145036125,5,0.739490092,2148,0.370093908,0.322978428
13584,Structure and inhibition of the SARS-CoV-2 main protease reveals strategy for developing dual inhibitors against Mpro and cathepsin L,0,10.1101/2020.07.27.223727,7/27/20,biorxiv,10.1126/sciadv.abe0751,17,computational,0.95066205,0.001254622,0.044319527,0.001254615,0.001254596,0.001254591,Drug discovery,0.5310252,TRUE,39.52941176,0.534355866,27.05882353,0.546160021,18,0.891474782,2609,0.460630869,0.608155385
13585,Evolution And Genetic Diversity Of SARSCoV-2 In Africa Using Whole Genome Sequences,0,10.1101/2020.07.27.222901,7/27/20,biorxiv,10.1016/j.ijid.2020.11.190,6,"bayes, sequencing, whole genome, genome sequences",0.001511868,0.97182039,0.001511836,0.001511878,0.001511848,0.022132181,Genomics,0.5204529,TRUE,13.33333333,0.201558538,2,0.164302917,0,0.403234768,1467,0.173850229,0.235736613
13586,Predicting the Emergence of SARS-CoV-2 Clades,0,10.1101/2020.07.26.222117,7/27/20,biorxiv,0,4,"computational, classifier, sequence alignment",0.106391755,0.699718305,0.19042106,0.0011563,0.001156262,0.001156318,Genomics,0.2291609,FALSE,151.75,0.945574865,265,0.944139684,1,0.537564047,2505,0.445942692,0.718305322
13587,COVID-19 and Rheumatoid Arthritis share myeloid pathogenic and resolving pathways,0,10.1101/2020.07.26.221572,7/26/20,biorxiv,0,10,transcriptom,0.841889694,0.074142816,0.001237067,0.001237081,0.019533458,0.061959885,Drug discovery,0.51716185,TRUE,68.4,0.755890902,136.7,0.871554723,0,0.403234768,3333,0.569467855,0.650037062
13588,Optimal Testing Strategy for the Identification of COVID-19 Infections,0,10.1101/2020.07.20.20157818,7/26/20,medrxiv,0,8,bayes,0.002080615,0.002080666,0.002080718,0.989596871,0.002080566,0.002080564,Epidemiology,0.31874484,FALSE,49.5,0.628362917,20.25,0.483074659,1,0.537564047,1487,0.180110763,0.457278096
13589,Dysregulated transcriptional responses to SARS-CoV-2 in the periphery support novel diagnostic approaches,0,10.1101/2020.07.20.20155507,7/26/20,medrxiv,0,16,"sequencing, classifier, transcriptom",0.637648959,0.123402607,0.067562899,0.001717248,0.001717201,0.167951086,Drug discovery,0.520257,TRUE,50.875,0.636897767,54.9375,0.700896441,2,0.618927094,1949,0.315916205,0.568159377
13590,"Viral RNA level, serum antibody responses, and transmission risk in discharged COVID-19 patients with recurrent positive SARS-CoV-2 RNA test results: a population-based observational cohort study",0,10.1101/2020.07.21.20125138,7/26/20,medrxiv,10.1080/22221751.2020.1837018,35,"sequencing, whole genome",0.00079341,0.500722579,0.000793403,0.102204091,0.000793438,0.394693078,Genomics,0.34880614,FALSE,7.171428571,0.102912982,2.914285714,0.190995451,2,0.618927094,6810,0.790512882,0.425837102
13591,The role of mathematical model in curbing COVID-19 in Nigeria,0,10.1101/2020.07.22.20159210,7/25/20,medrxiv,0,3,mathematical model,0.00131037,0.022207187,0.001310363,0.795121864,0.178739851,0.001310366,Epidemiology,0.27848583,FALSE,4.666666667,0.063392912,0,0.055525823,0,0.403234768,1291,0.119190946,0.160336112
13592,Projecting contact matrices in 177 geographical regions: an update and comparison with empirical data for the COVID-19 era,0,10.1101/2020.07.22.20159772,7/25/20,medrxiv,0,8,mathematical model,0.000547836,0.040620901,0.000547861,0.810770146,0.14696544,0.000547817,Epidemiology,0.054610282,FALSE,32.375,0.459459459,,,14,0.866658436,3240,0.554298098,0.626805331
13593,Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data,0,10.1101/2020.07.24.20159947,7/25/20,medrxiv,0,5,dataset,0.001187322,0.001187294,0.001187287,0.994063502,0.001187293,0.001187303,Epidemiology,0.18644163,FALSE,2.2,0.022759602,0.4,0.075796093,11,0.840175319,4396,0.674452203,0.403295804
13594,Alternative Approaches for Modelling COVID-19:High-Accuracy Low-Data Predictions,0,10.1101/2020.07.22.20159731,7/25/20,medrxiv,0,7,neural network,0.001392834,0.00139285,0.348771105,0.645657462,0.001392887,0.001392863,Epidemiology,0.102073014,FALSE,2.285714286,0.023687303,0,0.055525823,0,0.403234768,1100,0.065254033,0.136925482
13595,Prevalence of mask wearing in northern Vermont in response to SARS-CoV-2,0,10.1101/2020.07.23.20158980,7/25/20,medrxiv,0,3,"bayes, logistic regression",0.001072166,0.001072197,0.001072175,0.05924189,0.936469377,0.001072195,Healthcare,0.78347087,TRUE,7.666666667,0.111633373,11,0.371287129,2,0.618927094,1746,0.257404286,0.33981297
13596,Prospective Observational Study of Screening Asymptomatic Healthcare Workers for SARS-CoV-2 at a Canadian Tertiary Care Center,0,10.1101/2020.07.21.20159053,7/25/20,medrxiv,0,27,sequencing,0.002639095,0.50851028,0.002639059,0.002639049,0.480933262,0.002639254,Genomics,0.34386075,FALSE,26.56521739,0.389758179,32.30434783,0.587770939,2,0.618927094,3251,0.555742837,0.538049762
13597,Reducing COVID-19 hospitalization risk through behavior change,0,10.1101/2020.07.21.20159350,7/24/20,medrxiv,0,4,logistic regression,0.00168451,0.001684504,0.001684515,0.45784978,0.13350752,0.403589171,Epidemiology,0.2538962,FALSE,84.75,0.824046014,131.75,0.865667648,0,0.403234768,3803,0.618348182,0.677824153
13598,We are at risk too: The disparate impacts of the pandemic on younger generations,0,10.1101/2020.07.21.20159236,7/24/20,medrxiv,10.1177/0706743721989162,2,dataset,0.001461969,0.001461973,0.03184628,0.160503327,0.803264356,0.001462095,Healthcare,0.8576344,TRUE,4.5,0.061784897,0,0.055525823,0,0.403234768,1447,0.163255478,0.170950241
13599,Cell type-specific immune dysregulation in severely ill COVID-19 patients,0,10.1101/2020.07.23.20161182,7/24/20,medrxiv,10.1016/j.celrep.2020.108590,21,"sequencing, transcriptom, immunome",0.52714126,0.03993875,0.001310337,0.00131039,0.001310373,0.428988889,Drug discovery,0.73967004,TRUE,34.38095238,0.480734739,38,0.622223709,6,0.764429903,2793,0.486636167,0.588506129
13600,"An in-silico study on SARS-CoV-2: Its compatibility with human tRNA pool, and the polymorphism arising in a single lineage over a month",0,10.1101/2020.07.23.217083,7/24/20,biorxiv,0,3,in-silico,0.468718089,0.526656719,0.001156247,0.00115631,0.001156327,0.001156308,Genomics,0.29554912,FALSE,60.66666667,0.706351661,23.66666667,0.518062617,0,0.403234768,1401,0.14928967,0.444234679
13601,Single-cell landscape of immunological responses in COVID-19 patients,0,10.1101/2020.07.23.217703,7/24/20,biorxiv,10.1038/s41590-020-0762-x,21,sequencing,0.731141111,0.035844741,0.001438126,0.001438111,0.001438122,0.228699789,Drug discovery,0.8580992,TRUE,105.8095238,0.880573938,162.047619,0.893965748,3,0.667819001,2791,0.486154587,0.732128318
13602,Mathematical modelling of dynamics and containment of COVID-19 in Ukraine,0,10.1101/2020.07.24.20161497,7/24/20,medrxiv,10.1038/s41598-020-76710-1,3,mathematical model,0.001392844,0.001392845,0.034099944,0.96032869,0.001392851,0.001392826,Epidemiology,0.23506114,FALSE,48,0.614942173,23.33333333,0.515386674,5,0.739490092,1459,0.167589694,0.509352158
13603,Lifting mobility restrictions and the induced short-term dynamics of COVID-19,0,10.1101/2020.07.23.20161026,7/24/20,medrxiv,0,3,mathematical model,0.001272631,0.001272653,0.001272659,0.993636755,0.001272652,0.00127265,Epidemiology,0.116663575,FALSE,12.66666667,0.191044592,13.66666667,0.407412363,1,0.537564047,1782,0.265591139,0.350403035
13604,Strategies to reduce the risk of SARS-CoV-2 re-introduction from international travellers,0,10.1101/2020.07.24.20161281,7/24/20,medrxiv,0,11,simulation model,0.001022611,0.001022679,0.001022629,0.85433845,0.141570947,0.001022684,Epidemiology,0.13012943,FALSE,44,0.578390748,82.45454545,0.783917581,18,0.891474782,11923,0.881772213,0.783888831
13605,Adherence to protective measures among health care workers in the UK; a cross-sectional study,0,10.1101/2020.07.24.20161422,7/24/20,medrxiv,0,5,logistic regression,0.001538089,0.001538097,0.001538151,0.001538202,0.992309368,0.001538093,Healthcare,0.740349,TRUE,38.6,0.525078855,81.8,0.782579609,0,0.403234768,1142,0.075367204,0.446565109
13606,The impact of COVID-19 on patients with asthma: A Big Data analysis,0,10.1101/2020.07.24.20161596,7/24/20,medrxiv,10.1183/13993003.03142-2020,7,artificial intelligence,0.001310363,0.034907217,0.062506406,0.001310405,0.001310419,0.89865519,Clinics,0.9247054,TRUE,101.5714286,0.872286474,149.1428571,0.882860583,1,0.537564047,8129,0.822778714,0.778872455
13607,Employing a Systematic Approach to Biobanking and Analyzing Genetic and Clinical Data for Advancing COVID-19 Research,0,10.1101/2020.07.24.20161307,7/24/20,medrxiv,10.1038/s41431-020-00793-7,27,"sequencing, exom, genomes",0.001350476,0.42620251,0.001350538,0.001350443,0.001350338,0.568395695,Clinics,0.3497654,FALSE,22.25925926,0.328406209,18.55555556,0.465614129,4,0.707574542,1997,0.324825427,0.456605077
13608,Complement C3 identified as a unique Risk Factor for Disease Severity among Young COVID-19 Patients in Wuhan,0,10.1101/2020.07.24.20161414,7/24/20,medrxiv,0,4,logistic regression,0.001010986,0.00101097,0.042402211,0.001010998,0.126978793,0.827586042,Clinics,0.18121356,FALSE,8.5,0.126662131,1.25,0.127776291,0,0.403234768,1001,0.040693475,0.174591666
13609,Host transcriptional responses and SARS-CoV-2 isolates from the nasopharyngeal samples of Bangladeshi COVID-19 patients,0,10.1101/2020.07.23.218198,7/24/20,biorxiv,0,7,"transcriptom, network analysis",0.551987111,0.37055574,0.00087158,0.000871571,0.026792583,0.048921415,Drug discovery,0.60505605,TRUE,9.142857143,0.13637207,4.285714286,0.236018196,0,0.403234768,2817,0.489525644,0.31628767
13610,Mathematical Modeling and Optimal Control Analysis of COVID-19 in Ethiopia,0,10.1101/2020.07.23.20160473,7/24/20,medrxiv,0,2,mathematical model,0.00223851,0.002238483,0.002238512,0.988807532,0.002238439,0.002238524,Epidemiology,0.7312273,TRUE,2.5,0.027459954,0,0.055525823,2,0.618927094,1118,0.06934746,0.192815083
13611,Modelling Palliative and End of Life resource requirements during COVID-19: implications for quality care,0,10.1101/2020.07.23.20160564,7/24/20,medrxiv,0,7,simulation model,0.000595764,0.000595766,0.000595776,0.60705221,0.313940931,0.077219553,Epidemiology,0.18958813,FALSE,50.28571429,0.633186963,46.28571429,0.665440193,0,0.403234768,2774,0.48326511,0.546281758
13612,Co-expression of Mitochondrial Genes and ACE2 in Cornea Involved in COVID-19 Infection,0,10.1101/2020.07.23.216770,7/24/20,biorxiv,10.1167/iovs.61.12.13,5,"interactom, network analysis",0.96112358,0.001901806,0.001901788,0.001901782,0.001901712,0.031269332,Drug discovery,0.65272915,TRUE,35,0.488032655,16,0.437316029,0,0.403234768,1520,0.185648928,0.378558095
13613,"Forecasting hospitalizations due to COVID-19 in South Dakota, USA.",0,10.1101/2020.07.22.20160184,7/24/20,medrxiv,0,5,bayes,0.001538098,0.001538134,0.001538102,0.865863534,0.001538122,0.12798401,Epidemiology,0.04956898,FALSE,4.6,0.062588905,0,0.055525823,0,0.403234768,997,0.039007946,0.14008936
13614,Geographic reconstruction of the SARS-CoV-2 outbreak in Lombardy (Italy) during the early phase,0,10.1101/2020.07.23.20159871,7/24/20,medrxiv,10.1002/jmv.26447,12,genomes,0.001943477,0.921922563,0.001943591,0.001943634,0.001943511,0.070303223,Genomics,0.66244066,TRUE,32.08333333,0.455996042,15.25,0.427682633,0,0.403234768,1189,0.086443535,0.343339244
13615,Mathematical model study of a pandemic: Graded lockdown approach,0,10.1101/2020.07.22.20159962,7/24/20,medrxiv,0,3,mathematical model,0.146985625,0.000854763,0.068808845,0.601038342,0.042108376,0.14020405,Epidemiology,0.015815467,FALSE,11.33333333,0.170635166,1,0.122023013,0,0.403234768,725,0.004093426,0.174996593
13616,Sentiment Informed Timeseries Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston,0,10.1101/2020.07.22.20159863,7/24/20,medrxiv,0,1,dataset,0.001203439,0.001203445,0.112433927,0.785797643,0.098158033,0.001203513,Epidemiology,0.2963416,FALSE,7,0.10179974,1,0.122023013,0,0.403234768,1613,0.213339754,0.210099319
13617,Evaluating Scenarios for School Reopening under COVID19,0,10.1101/2020.07.22.20160036,7/24/20,medrxiv,0,4,simulation model,0.000956317,0.000956313,0.000956311,0.526834852,0.469339869,0.000956338,Epidemiology,0.066007674,FALSE,8.25,0.121343311,2.75,0.187583623,4,0.707574542,1767,0.260534553,0.319259007
13618,Homebound by COVID19: The Benefits and Consequences of Non-Pharmaceutical Intervention Strategies,0,10.1101/2020.07.22.20160085,7/24/20,medrxiv,0,5,simulation model,0.001371281,0.001371245,0.001371243,0.891648088,0.102866859,0.001371282,Epidemiology,0.18435097,FALSE,7.4,0.106190859,4.2,0.234211935,3,0.667819001,940,0.031784252,0.260001512
13619,Phylogeny of the COVID-19 Virus SARS-CoV-2 by Compression,0,10.1101/2020.07.22.216242,7/23/20,biorxiv,0,2,whole genome,0.154191829,0.832466418,0.003335452,0.003335448,0.003335489,0.003335363,Genomics,0.5599514,TRUE,137.5,0.929865793,664.5,0.986352689,0,0.403234768,3961,0.63231399,0.73794181
13620,From people to Panthera: Natural SARS-CoV-2 infection in tigers and lions at the Bronx Zoo,0,10.1101/2020.07.22.213959,7/23/20,biorxiv,10.1128/mBio.02220-20,37,whole genome,0.078318338,0.911992245,0.002422414,0.002422324,0.002422262,0.002422418,Genomics,0.48734954,FALSE,45.91891892,0.595213062,67,0.745718491,49,0.956787456,7989,0.819166867,0.779221469
13621,SARS-CoV2 genome analysis of Indian isolates and molecular modelling of D614G mutated spike protein with TMPRSS2 depicted its enhanced interaction and virus infectivity,0,10.1101/2020.07.23.217430,7/23/20,biorxiv,10.3389/fmicb.2020.594928,26,"sequencing, whole genome, genomes",0.09624317,0.885554194,0.015062155,0.001046842,0.001046823,0.001046815,Genomics,0.7692275,TRUE,22.14814815,0.326798194,22.74074074,0.509165106,6,0.764429903,3400,0.573561281,0.543488621
13622,A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19,0,10.1101/2020.07.20.20158568,7/22/20,medrxiv,0,4,neural network,0.003101413,0.003102055,0.199115872,0.788477743,0.003101462,0.003101454,Epidemiology,0.12341261,FALSE,65.5,0.737646113,60.5,0.722772277,3,0.667819001,1067,0.053455333,0.545423181
13623,Temporal dynamics of human respiratory and gut microbiomes during the course of COVID-19 in adults,0,10.1101/2020.07.21.20158758,7/22/20,medrxiv,0,10,"sequencing, microbiom",0.063471948,0.496543728,0.001823391,0.105364274,0.001823478,0.330973181,Genomics,0.5388245,TRUE,6.8,0.09629538,1,0.122023013,2,0.618927094,2106,0.350108355,0.296838461
13624,In-silico modeling of COVID-19 ARDS: pathophysiological insights and potential management implications,0,10.1101/2020.07.21.20158659,7/22/20,medrxiv,0,9,"computational, in-silico",0.111292373,0.001046873,0.258963944,0.233327097,0.001046896,0.394322818,Clinics,0.3264029,FALSE,35.55555556,0.492733008,7.222222222,0.302113995,0,0.403234768,1165,0.077534313,0.318904021
13625,Assessing the relative contributions of healthcare protocols for epidemic control: an example with network transmission model for COVID-19,0,10.1101/2020.07.20.20158576,7/22/20,medrxiv,0,2,network model,0.001187406,0.001187308,0.001187295,0.994063215,0.001187339,0.001187436,Epidemiology,0.25775027,FALSE,26,0.382398417,4.5,0.242708055,0,0.403234768,2114,0.351553094,0.344973583
13626,"Importation of SARS-CoV-2 following the ""semaine de relache"" and Quebec's (Canada) COVID-19 burden - a mathematical modeling study",0,10.1101/2020.07.20.20158451,7/22/20,medrxiv,0,11,mathematical model,0.002490496,0.0024907,0.002490435,0.806026546,0.002490656,0.184011168,Epidemiology,0.21034524,FALSE,38.36363636,0.522357598,25.09090909,0.529769869,2,0.618927094,1949,0.307970142,0.494756176
13627,The Trans-omics Landscape of COVID-19,0,10.1101/2020.07.17.20155150,7/22/20,medrxiv,0,68,multi-omics,0.522147913,0.00182344,0.068048514,0.001823341,0.001823339,0.404333453,Drug discovery,0.5784988,TRUE,19.7704918,0.293400952,8.475409836,0.327401659,2,0.618927094,2355,0.408620275,0.412087495
13628,Elucidation of Genome Polymorphisms in Emerging SARS-CoV-2,0,10.1101/2020.07.22.215731,7/22/20,biorxiv,0,4,"in silico, genome-wide, whole-genome, genome sequences, sequence alignment",0.107279757,0.810852946,0.001511848,0.077331538,0.001512007,0.001511905,Genomics,0.6396152,TRUE,16.75,0.252458408,2.75,0.187583623,1,0.537564047,1858,0.282205634,0.314952928
13629,"Inadequate level of knowledge, mixed outlook and poor adherence to COVID-19 prevention guideline among Ethiopians",0,10.1101/2020.07.22.215590,7/22/20,biorxiv,0,3,logistic regression,0.00102264,0.001022634,0.001022655,0.083503998,0.912405438,0.001022636,Healthcare,0.33133355,FALSE,7.666666667,0.111633373,25,0.529435376,4,0.707574542,2146,0.35660968,0.426313242
13630,Statistical analysis of national & municipal corporation level database of COVID-19 cases In India,0,10.1101/2020.07.18.20156794,7/21/20,medrxiv,0,5,"machine learning, dataset",0.00178661,0.001786657,0.26777616,0.725077537,0.001786526,0.00178651,Epidemiology,0.08573818,FALSE,6.8,0.09629538,2,0.164302917,1,0.537564047,2030,0.329400433,0.281890694
13631,Explainable-Machine-Learning to discover drivers and to predict mental illness during COVID-19,0,10.1101/2020.07.19.20157164,7/21/20,medrxiv,0,5,"bayes, machine learning, network model",0.000946102,0.000946092,0.108380421,0.19380011,0.694981166,0.00094611,Healthcare,0.36303627,FALSE,4.6,0.062588905,0.2,0.061145304,2,0.618927094,3187,0.538165182,0.320206621
13632,"High Community SARS-CoV-2 Antibody Seroprevalence in a Ski Resort Community, Blaine County, Idaho, US. Preliminary Results",0,10.1101/2020.07.19.20157198,7/21/20,medrxiv,0,13,bayes,0.001171552,0.200762169,0.001171622,0.184390259,0.575540512,0.036963885,Healthcare,0.2667867,FALSE,43.30769231,0.570350671,,,12,0.850299401,4121,0.646520588,0.689056887
13633,A preliminary model to describe the transmission dynamics of Covid-19 between two neighboring cities or countries,0,10.1101/2020.07.18.20156695,7/21/20,medrxiv,0,1,mathematical model,0.005047532,0.005047553,0.005047599,0.974761922,0.005047497,0.005047897,Epidemiology,0.120107025,FALSE,2,0.022141134,0,0.055525823,1,0.537564047,931,0.027690826,0.160730458
13634,Observational Study of the Efficiency of Treatments in Patients Hospitalized with Covid-19 in Madrid,0,10.1101/2020.07.17.20155960,7/21/20,medrxiv,0,7,dataset,0.001823568,0.001823334,0.001823422,0.116107763,0.001823419,0.876598494,Clinics,0.82552505,TRUE,40.57142857,0.545055353,31.14285714,0.578806529,11,0.840175319,10097,0.856489285,0.705131622
13635,Humoral Response Dynamics Following Infection with SARS-CoV-2,0,10.1101/2020.07.16.20155663,7/21/20,medrxiv,0,18,"bayes, mathematical model",0.279717574,0.197146998,0.000772626,0.258716618,0.230809958,0.032836226,Drug discovery,0.22939458,FALSE,11.88888889,0.17892263,11.94444444,0.383797164,25,0.918019631,7876,0.815314231,0.574013414
13636,Discovery of potential imaging and therapeutic targets for severe inflammation in COVID-19 patients,0,10.1101/2020.07.20.213082,7/21/20,biorxiv,0,4,sequencing,0.814179719,0.052335688,0.126769046,0.002238492,0.002238477,0.002238578,Drug discovery,0.9276156,TRUE,38,0.519327107,14,0.412898047,0,0.403234768,1472,0.166144955,0.375401219
13637,Multi-site co-mutations and 5'UTR CpG immunity escape drive the evolution of SARS-CoV-2,0,10.1101/2020.07.21.213405,7/21/20,biorxiv,0,8,"whole genome, genome sequences",0.104744652,0.870294045,0.001538136,0.001538171,0.001538092,0.020346904,Genomics,0.27917773,FALSE,57.25,0.683344672,39.125,0.628378378,4,0.707574542,4650,0.686491693,0.676447321
13638,Single cell RNA sequencing of blood antigen-presenting cells in severe Covid-19 reveals multi-process defects in antiviral immunity,0,10.1101/2020.07.20.212837,7/21/20,biorxiv,0,13,"sequencing, dataset",0.694459757,0.051881957,0.001461942,0.001461926,0.001461943,0.249272475,Drug discovery,0.082965255,FALSE,36.61538462,0.503989115,78.76923077,0.775086968,0,0.403234768,3478,0.579581026,0.565472969
13639,THE ORIGIN OF A NEW HUMAN VIRUS: PHYLOGENETIC ANALYSIS OF THE EVOLUTION OF SARS-COV-2,0,10.1101/2020.07.21.212860,7/21/20,biorxiv,0,6,bayes,0.001371305,0.993143439,0.00137135,0.001371363,0.001371271,0.001371272,Genomics,0.3081243,FALSE,0,0.006432061,,,0,0.403234768,2847,0.488562485,0.299409771
13640,"COVID-19, Race, and Redlining",0,10.1101/2020.07.11.20148486,7/20/20,medrxiv,0,2,dataset,0.001538174,0.001538197,0.001538156,0.596384707,0.253661542,0.145339224,Epidemiology,0.34651494,FALSE,52.5,0.650875131,74,0.764182499,6,0.764429903,2267,0.384541295,0.641007207
13641,"Genetic validation of the use of tocilizumab, statins and dexamethasone in COVID-19",0,10.1101/2020.07.09.20149450,7/20/20,medrxiv,0,4,genome-wide,0.1561774,0.254241146,0.002422276,0.002422321,0.002422339,0.582314518,Clinics,0.20429587,FALSE,98.25,0.865483332,46.75,0.66744715,0,0.403234768,1722,0.240308211,0.544118365
13642,An Optimization Framework to Study the Balance Between Expected Fatalities due to COVID-19 and the Reopening of U.S. Communities,0,10.1101/2020.07.16.20152033,7/20/20,medrxiv,0,10,computational,0.001171552,0.001171544,0.001171601,0.780564335,0.2147494,0.001171568,Epidemiology,0.28306493,FALSE,21.2,0.313686684,8.4,0.326598876,1,0.537564047,1237,0.093908018,0.317939406
13643,Haplotype Explorer: an infection cluster visualization tool for spatiotemporal dissection of the COVID-19 pandemic,0,10.1101/2020.07.19.179101,7/20/20,biorxiv,0,7,"genome sequences, genomes",0.001415151,0.443971061,0.051254266,0.500529324,0.001415103,0.001415095,Epidemiology,0.3354178,FALSE,53.28571429,0.655575484,35.71428571,0.608710195,0,0.403234768,1810,0.265831929,0.483338094
13644,Probing the dynamic structure-function and structure-free energy relationships of the corona virus main protease with Biodynamics theory,0,10.1101/2020.07.19.211185,7/20/20,biorxiv,10.1021/acsptsci.0c00089,3,virtual screening,0.834377204,0.001171605,0.001171573,0.110856029,0.051252012,0.001171578,Drug discovery,0.7131643,TRUE,24,0.35574247,11.33333333,0.375501739,1,0.537564047,1706,0.236214784,0.37625576
13645,SARS-CoV2 spike protein displays biologically significant similarities with paramyxovirus surface proteins; a bioinformatics study,0,10.1101/2020.07.20.210534,7/20/20,biorxiv,0,4,"bioinformatic, proteom",0.664199,0.330480715,0.001330054,0.001330092,0.001330107,0.001330032,Drug discovery,0.1277661,FALSE,23,0.34225988,22,0.503746321,1,0.537564047,1756,0.250180592,0.40843771
13646,Population genetic analysis of Indian SARS-CoV-2 isolates reveals a unique phylogenetic cluster,0,10.1101/2020.07.19.197129,7/20/20,biorxiv,0,2,sequencing,0.037011196,0.924110367,0.00235807,0.002357859,0.002357736,0.031804772,Genomics,0.2873237,FALSE,1.5,0.015523533,0,0.055525823,0,0.403234768,1601,0.200577895,0.168715505
13647,Computational optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2,0,10.1101/2020.07.20.212068,7/20/20,biorxiv,0,2,computational,0.729231088,0.267757319,0.0007529,0.000752914,0.000752897,0.000752881,Drug discovery,0.491742,FALSE,35.5,0.492547467,132.5,0.866336634,0,0.403234768,1672,0.223693715,0.496453146
13648,Preparing For the Next Pandemic: Learning Wild Mutational Patterns At Scale For For Analyzing Sequence Divergence In Novel Pathogens,0,10.1101/2020.07.17.20156364,7/19/20,medrxiv,0,3,machine learning,0.0010722,0.683644781,0.114903918,0.176428214,0.022878636,0.001072251,Genomics,0.16217193,FALSE,6.666666667,0.094996598,1,0.122023013,0,0.403234768,1776,0.254514809,0.218692297
13649,Large-scale Multi-omic Analysis of COVID-19 Severity,0,10.1101/2020.07.17.20156513,7/19/20,medrxiv,10.1016/j.cels.2020.10.003,29,machine learning,0.459780048,0.001786665,0.229927741,0.001786614,0.001786569,0.304932363,Drug discovery,0.42153504,FALSE,44.93103448,0.584884656,74.13793103,0.764316296,44,0.952157541,5883,0.754153624,0.763878029
13650,Quantifying SARS-CoV-2 infection risk within the Apple/Google exposure notification framework to inform quarantine recommendations,0,10.1101/2020.07.17.20156539,7/19/20,medrxiv,0,13,bayes,0.000629693,0.000629685,0.042941444,0.708963759,0.246205716,0.000629703,Epidemiology,0.02769795,FALSE,25.33333333,0.37188447,18.83333333,0.468624565,7,0.785110192,4819,0.694437756,0.580014246
13651,Characterizing the Qatar advanced-phase SARS-CoV-2 epidemic,0,10.1101/2020.07.16.20155317,7/19/20,medrxiv,10.1038/s41598-021-85428-7,23,mathematical model,0.00095633,0.146716274,0.010680751,0.303254908,0.350634151,0.187757587,Healthcare,0.35117826,FALSE,6.571428571,0.093883357,5.619047619,0.268062617,26,0.920859312,5167,0.713941729,0.499186754
13652,Modeling the progression of SARS-CoV-2 infection in patients with COVID-19 risk factors through predictive analysis,0,10.1101/2020.07.14.20154021,7/19/20,medrxiv,0,2,"logistic regression, dataset",0.000772645,0.000772641,0.000772648,0.494469146,0.00077265,0.50244027,Clinics,0.15551764,FALSE,14,0.213494959,0,0.055525823,0,0.403234768,1768,0.251866121,0.231030418
13653,A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing,0,10.1101/2020.07.15.20154286,7/19/20,medrxiv,0,8,"bayes, network model, probabilistic",0.002357804,0.002357879,0.002357798,0.871291532,0.119277115,0.002357871,Epidemiology,0.36999536,FALSE,8.5,0.126662131,0.375,0.073789136,4,0.707574542,2081,0.336624127,0.311162484
13654,Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model,0,10.1101/2020.07.16.20155739,7/18/20,medrxiv,0,9,"machine learning, logistic regression, prediction model",0.001126816,0.001126806,0.279641658,0.001126868,0.001126864,0.715850988,Clinics,0.37294954,FALSE,17.88888889,0.268785948,4,0.231469093,1,0.537564047,1618,0.202985793,0.31020122
13655,Is it safe to lift COVID-19 travel bans? The Newfoundland story.,0,10.1101/2020.07.16.20155614,7/18/20,medrxiv,10.1007/s00466-020-01899-x,4,machine learning,0.002032793,0.002032817,0.002032958,0.989835843,0.002032816,0.002032772,Epidemiology,0.21155983,FALSE,163.5,0.953862329,134.25,0.868276693,10,0.828199272,7789,0.811702384,0.865510169
13656,A Contact-Explicit Covid-19 Epidemic and Response Assessment Model,0,10.1101/2020.07.16.20155812,7/18/20,medrxiv,0,3,bayes,0.000863204,0.000863093,0.000863063,0.885482966,0.000863103,0.111064571,Epidemiology,0.15589595,FALSE,91,0.846310842,328.6666667,0.961734011,3,0.667819001,1428,0.148085721,0.655987394
13657,DETERMINANTS OF BURNOUT AND OTHER ASPECTS OF PSYCHOLOGICAL WELL-BEING IN HEALTHCARE WORKERS DURING THE COVID-19 PANDEMIC: A MULTINATIONAL CROSS-SECTIONAL STUDY,0,10.1101/2020.07.16.20155622,7/18/20,medrxiv,0,31,logistic regression,0.001098855,0.001098825,0.001098831,0.001098828,0.977188784,0.018415876,Healthcare,0.8185186,TRUE,11.4516129,0.171810254,7.322580645,0.303451967,5,0.739490092,3370,0.559113894,0.443466552
13658,The landscape of SARS-CoV-2 RNA modifications,0,10.1101/2020.07.18.204362,7/18/20,biorxiv,0,13,sequencing,0.489615509,0.426797082,0.001220032,0.060958714,0.001220046,0.020188617,Drug discovery,0.20669043,FALSE,45.84615385,0.594347208,155.1538462,0.888078673,1,0.537564047,3510,0.578136287,0.649531554
13659,Mutational dynamics and transmission properties of SARS-CoV-2 superspreading events in Austria,0,10.1101/2020.07.15.204339,7/17/20,biorxiv,10.1126/scitranslmed.abe2555,38,"sequencing, whole-genome",0.002296558,0.835496771,0.113694786,0.002296743,0.043918165,0.002296976,Genomics,0.35650665,FALSE,95.13157895,0.857134022,227.8684211,0.929087503,5,0.739490092,5409,0.727666747,0.813344591
13660,COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model,0,10.1101/2020.07.15.205567,7/17/20,biorxiv,0,4,"deep learning, neural network",0.001034584,0.001034588,0.99482709,0.001034591,0.001034578,0.001034569,Imaging,0.63178474,TRUE,46.5,0.601026656,8.25,0.323789136,6,0.764429903,4009,0.628702143,0.579486959
13661,Progressive worsening of the respiratory and gut microbiome in children during the first two months of COVID-19,0,10.1101/2020.07.13.20152181,7/17/20,medrxiv,0,10,microbiom,0.003335483,0.2705033,0.003335792,0.070491042,0.249841982,0.402492402,Clinics,0.5076947,TRUE,19.3,0.288082132,6,0.280037463,1,0.537564047,1680,0.220322658,0.331501575
13662,Genomic epidemiology of the early stages of SARS-CoV-2 outbreak in Russia,0,10.1101/2020.07.14.20150979,7/17/20,medrxiv,0,14,genomic epidemiology,0.00256254,0.649292339,0.002562597,0.340456687,0.002562652,0.002563185,Genomics,0.18314025,FALSE,0,0.006432061,,,2,0.618927094,9640,0.843727426,0.489695527
13663,Computer-aided covid-19 patient screening using chest images (X-Ray and CT scans),0,10.1101/2020.07.16.20155093,7/17/20,medrxiv,0,1,"artificial intelligence, classifier",0.001046796,0.001046836,0.945426691,0.001046818,0.001046826,0.050386033,Imaging,0.7322892,TRUE,41,0.549013544,220,0.926545357,2,0.618927094,1744,0.24127137,0.583939342
13664,"SARS-CoV-2 infection induces robust, neutralizing antibody responses that are stable for at least three months",0,10.1101/2020.07.14.20151126,7/17/20,medrxiv,0,17,dataset,0.378215876,0.231191052,0.002238496,0.255595437,0.13052046,0.00223868,Drug discovery,0.09871489,FALSE,43.58823529,0.573937782,54,0.697952903,79,0.972899562,57146,0.982181556,0.806742951
13665,Single-Cell Omics Reveals Dyssynchrony of the Innate and Adaptive Immune System in Progressive COVID-19,0,10.1101/2020.07.16.20153437,7/17/20,medrxiv,0,44," omics, multiom",0.841450534,0.060288923,0.001461857,0.00146193,0.001461867,0.093874889,Drug discovery,0.43696094,FALSE,22.95348837,0.338610922,,,17,0.887338725,7806,0.810739225,0.67889629
13666,Systematic modeling of SARS-CoV-2 protein structures,0,10.1101/2020.07.16.207308,7/17/20,biorxiv,0,11,"proteom, structural model",0.823566551,0.000999561,0.000999574,0.17243522,0.000999586,0.000999507,Drug discovery,0.019241452,FALSE,86.75,0.832580865,171.25,0.900388012,3,0.667819001,5025,0.705273296,0.776515293
13667,"A deterministic, age-stratified, extended SEIRD model for investigating the effect of non-pharmaceutical interventions on SARS-CoV-2 spread in Belgium",0,10.1101/2020.07.17.20156034,7/17/20,medrxiv,0,7,dataset,0.002080572,0.00208057,0.023436707,0.925386811,0.002080628,0.044934712,Epidemiology,0.36555874,FALSE,100.6666667,0.870369225,59,0.717554188,1,0.537564047,1908,0.285335902,0.60270584
13668,Disease-associated antibody phenotypes and probabilistic seroprevalence estimates during the emergence of SARS-CoV-2,0,10.1101/2020.07.17.20155937,7/17/20,medrxiv,0,27,"machine learning, probabilistic",0.054568593,0.307003421,0.058993342,0.195597571,0.088461578,0.295375495,Genomics,0.25199032,FALSE,43.9,0.576720886,136.3666667,0.87122023,1,0.537564047,5267,0.718998314,0.676125869
13669,"Associations between Demographic Characteristics, Perceived Threat, Perceived Stress, Coping Responses and Adherence to COVID-19 Prevention Measures among Healthcare Students in China: A Cross-Sectional Survey with Implications for the Control of COVID-19",0,10.1101/2020.07.15.20154997,7/16/20,medrxiv,0,4,predictive model,0.000988352,0.000988355,0.000988365,0.000988415,0.99505814,0.000988373,Healthcare,0.572977,TRUE,5.5,0.077246583,0.75,0.099411292,1,0.537564047,1096,0.054659282,0.192220301
13670,Protocol for a multicentre randomized controlled trial of normobaric versus hyperbaric oxygen therapy for hypoxemic COVID-19 patients,0,10.1101/2020.07.15.20154609,7/16/20,medrxiv,0,22,bayes,0.000603935,0.000603896,0.000603901,0.175371813,0.03558006,0.787236395,Clinics,0.5145489,TRUE,31.5,0.450058754,11.86363636,0.383395772,2,0.618927094,1502,0.166867325,0.404812236
13671,Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning,0,10.1101/2020.07.15.20154773,7/16/20,medrxiv,0,4,machine learning,0.003214184,0.172387142,0.476868161,0.341102275,0.003214144,0.003214094,Epidemiology,0.13016748,FALSE,13.5,0.205393036,29,0.562884667,2,0.618927094,1751,0.24151216,0.407179239
13672,Time-dependent dynamic transmission potential and instantaneous reproduction number of COVID-19 pandemic in India.,0,10.1101/2020.07.15.20154971,7/16/20,medrxiv,0,4,bayes,0.001350315,0.001350337,0.001350381,0.993248294,0.001350331,0.001350341,Epidemiology,0.4231247,FALSE,6.5,0.093512277,0,0.055525823,0,0.403234768,897,0.018300024,0.142643223
13673,Utrametric diffusion model for spread of covid-19 in socially clustered population: Can herd immunity be approached in Sweden?,0,10.1101/2020.07.15.20154419,7/16/20,medrxiv,0,1,mathematical model,0.047347859,0.08528256,0.001141347,0.8216441,0.001141392,0.043442742,Epidemiology,0.17594275,FALSE,9,0.135320675,0,0.055525823,0,0.403234768,1236,0.089333012,0.17085357
13674,A parsimonious model for spatial transmission and heterogeneity in the COVID-19 propagation,0,10.1101/2020.07.15.20154740,7/16/20,medrxiv,10.1098/rsos.201382,5,probabilistic,0.020497432,0.001141335,0.001141333,0.974937212,0.001141342,0.001141346,Epidemiology,0.29543507,FALSE,38,0.519327107,143.8,0.877976987,3,0.667819001,1451,0.153383097,0.554626548
13675,Application of ARIMA and Holt-Winters forecasting model to predict the spreading of COVID-19 for India and its states,0,10.1101/2020.07.14.20153908,7/16/20,medrxiv,0,1,"predictive model, forecasting model, dataset",0.001786541,0.001786543,0.001786707,0.991067186,0.001786513,0.001786509,Epidemiology,0.33060986,FALSE,4,0.054734368,1,0.122023013,3,0.667819001,1445,0.14977125,0.248586908
13676,Interpreting Deep Ensemble Learning through Radiologist Annotations for COVID-19 Detection in Chest Radiographs,0,10.1101/2020.07.15.20154385,7/16/20,medrxiv,0,5,"deep learning, neural network, ensemble learning",0.000956322,0.000956338,0.995218287,0.000956373,0.000956374,0.000956307,Imaging,0.25174814,FALSE,76.6,0.792380481,57.4,0.712469896,1,0.537564047,1344,0.121117265,0.540882922
13677,Obesity has an impact on COVID-19 susceptibility and severity: a two-sample Mendelian randomization study,0,10.1101/2020.07.14.20153825,7/16/20,medrxiv,0,3,genome-wide,0.001330116,0.29285744,0.001330229,0.001330148,0.001330144,0.701821923,Clinics,0.33414102,FALSE,62.33333333,0.717236687,178.6666667,0.90520471,2,0.618927094,2156,0.350830725,0.648049804
13678,"Community-level SARS-CoV-2 Seroprevalence Survey in urban slum dwellers of Buenos Aires City, Argentina: a participatory research.",0,10.1101/2020.07.14.20153858,7/16/20,medrxiv,0,9,probabilistic,0.001823354,0.374148446,0.00182344,0.204794965,0.41558638,0.001823415,Healthcare,0.16474625,FALSE,9.111111111,0.135939143,2.222222222,0.168651325,15,0.874313229,7796,0.809776065,0.497169941
13679,"COVID-19 effective reproductive ratio determination: An application, and analysis of issues and influential factors",0,10.1101/2020.07.15.20154039,7/16/20,medrxiv,0,4,dataset,0.001438097,0.001438148,0.052797704,0.941449789,0.001438135,0.001438126,Epidemiology,0.54255444,TRUE,1.75,0.017007855,0,0.055525823,1,0.537564047,1605,0.1957621,0.201464956
13680,"The utility of established prognostic scores in COVID-19 hospital admissions: a multicentre prospective evaluation of CURB-65, NEWS2, and qSOFA",0,10.1101/2020.07.15.20154815,7/16/20,medrxiv,0,5,logistic regression,0.001059358,0.001059404,0.157827358,0.001059398,0.001059405,0.837935077,Clinics,0.2951905,FALSE,7.8,0.113550622,,,6,0.764429903,1989,0.307006983,0.394995836
13681,"Seasonality of non-SARS, non-MERS Corona viruses and the impact of meteorological factors",0,10.1101/2020.07.15.20154146,7/16/20,medrxiv,10.3390/pathogens10020187,8,logistic regression,0.001987162,0.301713773,0.244878847,0.237458336,0.001987196,0.211974687,Genomics,0.57570297,TRUE,95,0.856824788,136.375,0.871287129,0,0.403234768,1533,0.175776547,0.576780808
13682,A Quantitative Lung Computed Tomography Image Feature for Multi-Center Severity Assessment of COVID-19,0,10.1101/2020.07.13.20152231,7/15/20,medrxiv,0,7,machine learning,0.001022738,0.001022656,0.850719565,0.00102263,0.001022635,0.145189776,Imaging,0.63487244,TRUE,7.166666667,0.102851135,2.166666667,0.166845063,4,0.707574542,1394,0.133156754,0.277606873
13683,"RT-PCR testing to detect a COVID-19 outbreak in Austria: rapid, accurate and early diagnosis in primary care (The REAP study)",0,10.1101/2020.07.13.20152439,7/15/20,medrxiv,0,11,dataset,0.000572625,0.337079559,0.130519075,0.000572667,0.155290619,0.375965454,Clinics,0.2179895,FALSE,25.72727273,0.376955903,17.54545455,0.456248328,1,0.537564047,2610,0.444016374,0.453696163
13684,Analysis of COVID-19 cases and associated ventilator requirement in Indian States,0,10.1101/2020.07.13.20153056,7/15/20,medrxiv,0,5,dataset,0.003101439,0.003101438,0.003101616,0.932897529,0.003101475,0.054696504,Epidemiology,0.059729517,FALSE,12.6,0.190302431,0.8,0.101351351,0,0.403234768,916,0.022152661,0.179260303
13685,Smart Pooling: AI-powered COVID-19 testing,0,10.1101/2020.07.13.20152983,7/15/20,medrxiv,0,18,"machine learning, artificial intelligence, dataset",0.052143552,0.052399024,0.596403671,0.135200926,0.163397918,0.00045491,Healthcare,0.10608062,FALSE,23.05555556,0.342383574,63.77777778,0.734278833,3,0.667819001,3309,0.547555984,0.573009348
13686,Climate & BCG: Effects on COVID-19 DeathGrowth Rates,0,10.1101/2020.07.13.20152991,7/15/20,medrxiv,0,2,"bayes, machine learning",0.004309981,0.004310209,0.094552418,0.690442933,0.086886073,0.119498386,Epidemiology,0.15877932,FALSE,64,0.729173109,26.5,0.542012309,0,0.403234768,1650,0.20707922,0.470374851
13687,Bioinformatic analysis of shared B and T cell epitopes amongst relevant coronaviruses to human health: Is there cross-protection?,0,10.1101/2020.07.14.202887,7/15/20,biorxiv,0,8,bioinformatic,0.633887293,0.250160643,0.001141343,0.112528009,0.001141355,0.001141357,Drug discovery,0.2389063,FALSE,0,0.006432061,,,0,0.403234768,1982,0.301709608,0.237125479
13688,Alignment-free machine learning approaches for the lethality prediction of potential novel human-adapted coronavirus using genomic nucleotide,0,10.1101/2020.07.15.176933,7/15/20,biorxiv,0,3,"machine learning, dataset",0.048146625,0.416850341,0.450381397,0.08211241,0.001254607,0.00125462,Genomics,0.16243654,FALSE,0,0.006432061,,,0,0.403234768,1661,0.210931856,0.206866228
13689,The Effect Of Famotidine On SARS-CoV-2 Proteases And Virus Replication,0,10.1101/2020.07.15.203059,7/15/20,biorxiv,10.1038/s41598-021-84782-w,11,in silico,0.777245285,0.00135042,0.001350396,0.001350367,0.001350407,0.217353125,Drug discovery,0.83281845,TRUE,19.66666667,0.292596945,14.25,0.415038801,5,0.739490092,6518,0.774379966,0.555376451
13690,A direct RNA-protein interaction atlas of the SARS-CoV-2 RNA in infected human cells,0,10.1101/2020.07.15.204404,7/15/20,biorxiv,10.1038/s41564-020-00846-z,14,"proteom, interactom",0.9139746,0.066569051,0.016352591,0.00103462,0.001034582,0.001034556,Drug discovery,0.13605577,FALSE,105.7142857,0.880450244,1861.357143,0.998059941,15,0.874313229,7979,0.814110282,0.891733424
13691,Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients,0,10.1101/2020.07.13.20150177,7/14/20,medrxiv,0,30,machine learning,0.001126807,0.001126808,0.299255198,0.001126863,0.001126797,0.696237527,Clinics,0.19650304,FALSE,13.06666667,0.197662193,3.233333333,0.202502007,8,0.799987654,2545,0.431736094,0.407971987
13692,"COVID-19 incidence and R decreased on the Isle of Wight after the launch of the Test, Trace, Isolate programme",0,10.1101/2020.07.12.20151753,7/14/20,medrxiv,10.1016/S2589-7500(20)30241-7,10,bayes,0.001901708,0.001901796,0.001901693,0.990491319,0.001901763,0.00190172,Epidemiology,0.2180048,FALSE,8.8,0.129630775,8.4,0.326598876,6,0.764429903,3445,0.56344811,0.446026916
13693,Identification of Vulnerable Populations and Areas at Higher Risk of COVID-19 Related Mortality in the U.S.,0,10.1101/2020.07.11.20151563,7/14/20,medrxiv,0,6,bayes,0.000815354,0.000815361,0.000815327,0.609896123,0.22797147,0.159686366,Epidemiology,0.6137938,TRUE,72.5,0.77549632,50.33333333,0.682967621,4,0.707574542,2334,0.388634722,0.638668301
13694,Modelling suggests blood group incompatibility may substantially reduce SARS-CoV-2 transmission,0,10.1101/2020.07.13.20152637,7/14/20,medrxiv,10.1016/j.epidem.2021.100446,1,dataset,0.002296811,0.145837381,0.00229661,0.462034948,0.137045067,0.250489182,Epidemiology,0.23860246,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,5572,0.732964122,0.31161477
13695,Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers,0,10.1101/2020.07.13.20152330,7/14/20,medrxiv,0,7,simulation model,0.001901704,0.00190173,0.001901803,0.990491085,0.001901802,0.001901875,Epidemiology,0.36322027,FALSE,6.857142857,0.097099388,5.285714286,0.26130586,0,0.403234768,888,0.016373706,0.19450343
13696,The effect of international travel restrictions on internal spread of COVID-19,0,10.1101/2020.07.12.20152298,7/14/20,medrxiv,0,6,dataset,0.000551214,0.000551237,0.000551218,0.947258767,0.050536336,0.000551227,Epidemiology,0.05916071,FALSE,20.66666667,0.306512462,36.83333333,0.615667648,6,0.764429903,3155,0.521550686,0.552040175
13697,Single-cell analysis reveals the function of lung progenitor cells in COVID-19 patients,0,10.1101/2020.07.13.200188,7/13/20,biorxiv,0,6,"sequencing, transcriptom",0.853586243,0.001415149,0.001415146,0.001415146,0.001415167,0.140753148,Drug discovery,0.29280478,FALSE,183.8333333,0.965736904,,,0,0.403234768,1706,0.220804238,0.529925303
13698,Comparisons of the genome of SARS-CoV-2 and those of other betacoronaviruses,0,10.1101/2020.07.12.199521,7/13/20,biorxiv,0,2,"metagenom, genomes",0.107645475,0.884405766,0.001987123,0.001987292,0.001987189,0.001987154,Genomics,0.30164343,FALSE,89.5,0.841115715,191.5,0.913031844,0,0.403234768,2256,0.3679268,0.631327282
13699,Worldwide Geographical and Temporal Analysis of SARS-CoV-2 Haplotypes shows Differential Distribution Patterns,0,10.1101/2020.07.12.199414,7/13/20,biorxiv,10.3389/fmicb.2021.612432,8,genomes,0.001371262,0.869188038,0.001371342,0.001371339,0.052234926,0.074463093,Genomics,0.28606075,FALSE,1.5,0.015523533,0,0.055525823,3,0.667819001,5823,0.745485191,0.371088387
13700,What can the ideal gas say about global pandemics? Reinterpreting the basic reproduction number,0,10.1101/2020.07.09.20150128,7/11/20,medrxiv,0,1,probabilistic,0.001415108,0.001415145,0.001415137,0.968102083,0.026237303,0.001415224,Epidemiology,0.0993453,FALSE,7,0.10179974,3,0.199424672,0,0.403234768,976,0.030098724,0.183639476
13701,Societal heterogeneity contributes to complex dynamic patterns of the COVID-19 pandemics: insights from a novel Stochastic Heterogeneous Epidemic Model (SHEM),0,10.1101/2020.07.10.20150813,7/11/20,medrxiv,10.3389/fphy.2020.609224,2,forecasting model,0.000772646,0.055337105,0.000772618,0.941572312,0.000772658,0.000772661,Epidemiology,0.15198004,FALSE,11.5,0.17416043,2,0.164302917,3,0.667819001,1726,0.222730556,0.307253226
13702,Genetic associations for two biological age measures point to distinct aging phenotypes,0,10.1101/2020.07.10.20150797,7/11/20,medrxiv,0,5,genome-wide,0.442951599,0.280619191,0.035142152,0.002080684,0.153974614,0.085231761,Drug discovery,0.49181,FALSE,7.8,0.113550622,5.2,0.259700294,4,0.707574542,2085,0.321213581,0.35050976
13703,Wastewater SARS-CoV-2 Concentration and Loading Variability from Grab and 24-Hour Composite Samples,0,10.1101/2020.07.10.20150607,7/11/20,medrxiv,0,5,dataset,0.001059383,0.438830891,0.053301694,0.321066646,0.18468196,0.001059427,Genomics,0.21283269,FALSE,49.4,0.627002288,41,0.639751137,12,0.850299401,2480,0.413195281,0.632562027
13704,The emergence of COVID-19 in Indonesia: analysis of predictors of infection and mortality using independent and clustered data approaches,0,10.1101/2020.07.10.20147942,7/11/20,medrxiv,0,17,logistic regression,0.00109882,0.039603178,0.069523684,0.177781117,0.001098868,0.710894334,Clinics,0.30818003,FALSE,16.52941176,0.249489764,7.647058824,0.309272143,0,0.403234768,1668,0.205152902,0.291787394
13705,Undocumented infectives in the Covid-19 pandemic,0,10.1101/2020.07.09.20149682,7/11/20,medrxiv,10.1016/j.ijid.2021.01.010,2,mathematical model,0.00198718,0.03929141,0.001987173,0.952759835,0.001987193,0.001987209,Epidemiology,0.3382985,FALSE,35.5,0.492547467,15,0.42594327,0,0.403234768,1418,0.134601493,0.364081749
13706,Bibliometric Analysis of COVID-19 in the Context of Migration Health: A Study Protocol,0,10.1101/2020.07.09.20149401,7/11/20,medrxiv,0,5,network analysis,0.001310413,0.001310415,0.042416284,0.923998369,0.029654174,0.001310345,Epidemiology,0.83439255,TRUE,4,0.054734368,13.6,0.406208188,1,0.537564047,1058,0.041175054,0.259920414
13707,ReCoNet: Multi-level Preprocessing of Chest X-rays for COVID-19 Detection Using Convolutional Neural Networks,0,10.1101/2020.07.11.20149112,7/11/20,medrxiv,0,4,"neural network, radiom, dataset",0.001126808,0.00112681,0.964799431,0.001126843,0.030693322,0.001126787,Imaging,0.7342099,TRUE,30.25,0.434782609,21,0.492239765,5,0.739490092,1533,0.168071274,0.458645935
13708,Antibody dynamics to SARS-CoV-2 in Asymptomatic and Mild COVID-19 patients,0,10.1101/2020.07.09.20149633,7/11/20,medrxiv,10.1111/all.14622,25,proteom,0.00082296,0.620535135,0.000822927,0.00082296,0.179281774,0.197714245,Genomics,0.33404827,FALSE,6.36,0.089987012,4.04,0.23160289,8,0.799987654,7657,0.802793162,0.481092679
13709,A Highly Immunogenic Measles Virus-based Th1-biased COVID-19 Vaccine,0,10.1101/2020.07.11.198291,7/11/20,biorxiv,0,8,bioinformatic,0.601783461,0.182898259,0.000988393,0.212353128,0.000988392,0.000988368,Drug discovery,0.5406406,TRUE,17.125,0.258148308,16.25,0.438988493,8,0.799987654,3612,0.579821816,0.519236568
13710,ACE2-expressing endothelial cells in aging mouse brain,0,10.1101/2020.07.11.198770,7/11/20,biorxiv,0,4,transcriptom,0.814138731,0.002357803,0.002357742,0.00235776,0.176430086,0.002357876,Drug discovery,0.8668028,TRUE,246.75,0.984167234,1139.25,0.995183302,0,0.403234768,2375,0.393209728,0.693948758
13711,COVIDPEN: A Novel COVID-19 Detection Model using Chest X-Rays and CT Scans,0,10.1101/2020.07.08.20149161,7/10/20,medrxiv,0,6,"machine learning, transfer learning, dataset",0.001861678,0.001861721,0.990691325,0.001861784,0.001861753,0.001861739,Imaging,0.57771933,TRUE,58.5,0.693240151,13.5,0.405539203,7,0.785110192,1534,0.166385745,0.512568823
13712,Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (ai-LAMP) for Rapid and Reliable Detection of SARS-CoV-2,0,10.1101/2020.07.08.20148999,7/10/20,medrxiv,10.3390/v12090972,24,artificial intelligence,0.001291346,0.305007161,0.617545224,0.073573778,0.001291258,0.001291233,Imaging,0.862372,TRUE,19.58333333,0.291607397,5.125,0.257760235,1,0.537564047,2275,0.365278112,0.363052448
13713,Disentangling Increased Testing From Covid-19 Epidemic Spread,0,10.1101/2020.07.09.20141762,7/10/20,medrxiv,0,4,probabilistic,0.002296521,0.002296618,0.00229671,0.988516953,0.002296609,0.002296588,Epidemiology,0.24610806,FALSE,176.5,0.96276826,779.5,0.989764517,0,0.403234768,1744,0.226823983,0.645647882
13714,Predictors of Anxiety Regarding The COVID-19 Pandemic Among Health-care Workers in a Hospital Not Assigned to Manage COVID-19 Patients in Nepal.,0,10.1101/2020.07.08.20148866,7/10/20,medrxiv,0,11,logistic regression,0.001511792,0.001511821,0.001511816,0.001511866,0.992440801,0.001511904,Healthcare,0.5399186,TRUE,4.181818182,0.055723916,0.272727273,0.065828204,3,0.667819001,1224,0.078738261,0.217027346
13715,Severe SAR-CoV-2 infection in humans is defined by a shift in the serum lipidome resulting indysregulation of eicosanoid immune mediators,0,10.1101/2020.07.09.20149849,7/10/20,medrxiv,0,12,lipidom,0.564344566,0.002996701,0.002996652,0.00299662,0.002996577,0.423668884,Drug discovery,0.46902755,FALSE,63.90909091,0.727688787,98.18181818,0.819373829,13,0.858880178,2847,0.469058512,0.718750327
13716,Evolution and impact of COVID-19 outbreaks in care homes: population analysis in 189 care homes in one geographic region,0,10.1101/2020.07.09.20149583,7/10/20,medrxiv,10.1016/s2666-7568(20)30012-x,14,"sequencing, whole genome",0.000509924,0.022183113,0.00050995,0.539725364,0.353847974,0.083223676,Epidemiology,0.2108975,FALSE,15.85714286,0.238419197,11.5,0.378378378,12,0.850299401,6265,0.760654948,0.556937981
13717,Recurrent Neural Reinforcement Learning for Counterfactual Evaluation of Public Health Interventions on the Spread of Covid-19 in the world,0,10.1101/2020.07.08.20149146,7/10/20,medrxiv,0,7,neural network,0.001461867,0.001461898,0.14605821,0.84809421,0.001461858,0.001461958,Epidemiology,0.45398566,FALSE,3.857142857,0.04786938,0.571428571,0.088038534,4,0.707574542,1529,0.165181796,0.252166063
13718,Discovery of clinically approved drugs capable of inhibiting SARS-CoV-2 in vitro infection using a phenotypic screening strategy and network-analysis to predict their potential to treat covid-19,0,10.1101/2020.07.09.196337,7/10/20,biorxiv,0,10,"data mining, network analysis",0.846872066,0.001310359,0.001310377,0.147886521,0.001310339,0.001310339,Drug discovery,0.5462227,TRUE,50.5,0.635351599,39.6,0.631255017,2,0.618927094,7110,0.787141825,0.668168884
13719,"SARS-CoV-2 infection in the lungs of human ACE2 transgenic mice causes severe inflammation, immune cell infiltration, and compromised respiratory function",0,10.1101/2020.07.09.196188,7/10/20,biorxiv,0,17,sequencing,0.776381048,0.001462015,0.001461903,0.001461979,0.001461865,0.21777119,Drug discovery,0.17851686,FALSE,81,0.811552972,244.1176471,0.93584426,10,0.828199272,4980,0.694196966,0.817448367
13720,Severely ill COVID-19 patients display augmented functional properties in SARS-CoV-2-reactive CD8+ T cells,0,10.1101/2020.07.09.194027,7/10/20,biorxiv,0,11,transcriptom,0.620766304,0.064962346,0.001310399,0.001310341,0.001310366,0.310340243,Drug discovery,0.26185408,FALSE,30.54545455,0.4387408,63.81818182,0.734546428,5,0.739490092,4253,0.64339032,0.63904191
13721,Robust three-dimensional expansion of human adult alveolar stem cells and SARS-CoV-2 infection,0,10.1101/2020.07.10.194498,7/10/20,biorxiv,10.1016/j.stem.2020.10.004,19,transcriptom,0.765284121,0.151343107,0.078592136,0.001593669,0.001593486,0.001593481,Drug discovery,0.3100347,FALSE,53.5,0.657245346,72.15,0.759633396,2,0.618927094,2377,0.39104262,0.606712114
13722,Comprehensive in-vivo secondary structure of SARS-CoV-2 genome reveals novel regulatory motifs and mechanisms,0,10.1101/2020.07.10.197079,7/10/20,biorxiv,10.1016/j.molcel.2020.12.041,5,genomic structure,0.398387749,0.597521653,0.0010227,0.001022653,0.00102264,0.001022604,Genomics,0.3677764,FALSE,65.2,0.735543324,154,0.887008295,33,0.936045435,5063,0.698771972,0.814342257
13723,Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases,0,10.1101/2020.07.08.20148767,7/9/20,medrxiv,10.1371/journal.pone.0246056,2,mathematical model,0.066024285,0.001291285,0.001291291,0.928810661,0.001291239,0.001291239,Epidemiology,0.04984671,FALSE,62.5,0.718164389,28,0.554321648,0,0.403234768,1886,0.266072719,0.485448381
13724,Predictive Analysis for COVID-19 Spread in India by Adaptive Compartmental Model,0,10.1101/2020.07.08.20148619,7/9/20,medrxiv,0,2,"computational, mathematical model",0.001486441,0.001486437,0.001486442,0.992567823,0.001486423,0.001486435,Epidemiology,0.52308136,TRUE,3.5,0.044344115,0,0.055525823,1,0.537564047,1257,0.085961955,0.180848985
13725,"Comprehensive variant and haplotype landscapes of 50,500 global SARS-CoV-2 isolates and accelerating accumulation of country-private variant profiles",0,10.1101/2020.07.09.193722,7/9/20,biorxiv,0,0,genomes,0.000846538,0.742776596,0.000846521,0.185179905,0.036888252,0.033462187,Genomics,0.12862429,FALSE,106.8,0.882738574,229,0.929355098,1,0.537564047,1671,0.201781844,0.637859891
13726,Risk of Transmission of infection to Healthcare Workers delivering Supportive Care for Coronavirus Pneumonia;A Rapid GRADE Review,0,10.1101/2020.07.06.20146712,7/8/20,medrxiv,0,11,computational,0.080205519,0.000565343,0.06085786,0.517942149,0.112315434,0.228113696,Epidemiology,0.29362422,FALSE,9.181818182,0.136681304,1.909090909,0.152060476,0,0.403234768,1889,0.26438719,0.239090934
13727,Diagnostics and spread of SARS-CoV-2 in Western Africa: An observational laboratory-based study from Benin,0,10.1101/2020.06.29.20140749,7/8/20,medrxiv,10.3201/eid2701.203281,17,"bayes, genomes",0.001415137,0.769842707,0.068887178,0.15702448,0.001415128,0.001415369,Genomics,0.56003773,TRUE,18.23529412,0.273238914,38.35294118,0.623628579,1,0.537564047,2129,0.328678064,0.440777401
13728,Ultrametric model for covid-19 dynamics: an attempt to explain slow approaching herd immunity in Sweden,0,10.1101/2020.07.04.20146209,7/8/20,medrxiv,0,1,mathematical model,0.074617986,0.106975524,0.001010998,0.815373569,0.001010977,0.001010946,Epidemiology,0.07893905,FALSE,558,0.998206444,194,0.914503613,4,0.707574542,1863,0.257645076,0.719482419
13729,Predicting Health Disparities in Regions at Risk of Severe Illness to inform Healthcare Resource Allocations during Pandemics,0,10.1101/2020.07.06.20147181,7/8/20,medrxiv,10.2196/22470,2,dataset,0.003101447,0.003101483,0.003101647,0.984491954,0.003101687,0.003101783,Epidemiology,0.124747366,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,978,0.027931616,0.130996448
13730,Artificial intelligence driven assessment of routinely collected healthcare data is an effective screening test for COVID-19 in patients presenting to hospital,0,10.1101/2020.07.07.20148361,7/8/20,medrxiv,10.1016/S2589-7500(20)30274-0,10,"artificial intelligence, logistic regression",0.000430538,0.000430548,0.389513362,0.022580291,0.00043055,0.58661471,Clinics,0.41197973,FALSE,18.5,0.278001113,17.3,0.453572384,10,0.828199272,7049,0.785937876,0.586427661
13731,Reconstructing the global dynamics of under-ascertained COVID-19 cases and infections,0,10.1101/2020.07.07.20148460,7/8/20,medrxiv,10.1186/s12916-020-01790-9,13,bayes,0.000880202,0.000880228,0.00088023,0.923411273,0.000880243,0.073067824,Epidemiology,0.17977175,FALSE,34.83333333,0.485496939,93.25,0.809004549,18,0.891474782,3935,0.608475801,0.698613018
13732,Genes associated with liver damage signalling pathways may impact the severity of COVID-19 symptoms in Spanish and Italian populations,0,10.1101/2020.07.03.179028,7/8/20,biorxiv,0,5,"genome-wide, whole genome, genomes",0.287582424,0.430066636,0.000999523,0.096123641,0.000999538,0.184228238,Genomics,0.80207634,TRUE,36.6,0.503865421,18.4,0.464677549,0,0.403234768,1827,0.247531905,0.404827411
13733,Towards the design of multiepitope-based peptide vaccine candidate against SARS-CoV-2,0,10.1101/2020.07.07.186122,7/8/20,biorxiv,0,3,in silico,0.849396428,0.001350353,0.001350332,0.092695438,0.001350441,0.053857009,Drug discovery,0.6110057,TRUE,5.666666667,0.079473066,0,0.055525823,0,0.403234768,1704,0.210691067,0.187231181
13734,In vivo structural characterization of the whole SARS-CoV-2 RNA genome identifies host cell target proteins vulnerable to re-purposed drugs,0,10.1101/2020.07.07.192732,7/8/20,biorxiv,10.1016/j.cell.2021.02.008,13,"genomes, deep-learning, structural model",0.705731349,0.204148721,0.08690335,0.001072213,0.001072181,0.001072186,Drug discovery,0.5607808,TRUE,23.23076923,0.343991589,123.1538462,0.855499063,13,0.858880178,3200,0.517938839,0.644077417
13735,Enhanced COVID-19 data for improved prediction of survival,0,10.1101/2020.07.08.193144,7/8/20,biorxiv,0,3,dataset,0.001861779,0.001861824,0.433671552,0.354150317,0.001861764,0.206592763,Epidemiology,0.35811466,FALSE,9.666666667,0.144968767,0,0.055525823,0,0.403234768,1784,0.236455574,0.210046233
13736,Multi-Omics integration analysis of respiratory specimen characterizes baseline molecular determinants associated with COVID-19 diagnosis.,0,10.1101/2020.07.06.20147082,7/7/20,medrxiv,0,6,"proteom, metabolom, multiom, multi-omics",0.260513237,0.248443287,0.0024225,0.002422441,0.002422371,0.483776163,Clinics,0.4584812,FALSE,145.5,0.939884965,140.3333333,0.874632058,5,0.739490092,1693,0.205393691,0.689850202
13737,"Near Term Predictions of Covid-19 Cases in West Bengal, Maharashtra, Delhi and Tamil Nadu in India Based on Basu Model",0,10.1101/2020.07.05.20146910,7/7/20,medrxiv,0,1,mathematical model,0.006539621,0.00653981,0.006539745,0.967301538,0.006539688,0.006539597,Epidemiology,0.12391499,FALSE,7,0.10179974,1,0.122023013,0,0.403234768,943,0.021911871,0.162242348
13738,Early Diagnosis and Clinical Significance of Acute Cardiac Injury - Under the Iceberg: A Retrospective Cohort Study of 619 Non-critically Ill Hospitalized COVID-19 Pneumonia Patients,0,10.1101/2020.07.06.20147256,7/7/20,medrxiv,0,10,logistic regression,0.00156548,0.001565518,0.001565398,0.001565407,0.039545171,0.954193027,Clinics,0.76218945,TRUE,15.7,0.236687488,2.9,0.190794755,4,0.707574542,1627,0.186852877,0.330477416
13739,Does sub-Saharan Africa truly defy the forecasts of the COVID-19 pandemic? Response from population data,0,10.1101/2020.07.06.20147124,7/7/20,medrxiv,0,6,mathematical model,0.001392847,0.001392884,0.001392841,0.861018843,0.001392907,0.133409677,Epidemiology,0.07860899,FALSE,2.833333333,0.031356299,0.166666667,0.058736955,0,0.403234768,1350,0.107873826,0.150300462
13740,A Small interfering RNA lead targeting RNA-dependent RNA-polymerase effectively inhibit the SARS-CoV-2 infection in Golden Syrian hamster and Rhesus macaque,0,10.1101/2020.07.07.190967,7/7/20,biorxiv,0,10,bioinformatic,0.98501623,0.002996693,0.002996556,0.002996743,0.002996678,0.002997099,Drug discovery,0.4993739,FALSE,70.4,0.765724535,53.3,0.694875569,1,0.537564047,3332,0.535034915,0.633299766
13741,Favipiravir and severe acute respiratory syndrome coronavirus 2 in hamster model,0,10.1101/2020.07.07.191775,7/7/20,biorxiv,0,13,genomes,0.843161054,0.147651897,0.002296857,0.002296731,0.002296783,0.002296679,Drug discovery,0.23145846,FALSE,50.30769231,0.633248809,65.46153846,0.740500401,7,0.785110192,4153,0.628220563,0.696769992
13742,Single source of pangolin CoVs with a near identical Spike RBD to SARS-CoV-2,0,10.1101/2020.07.07.184374,7/7/20,biorxiv,0,2,"metagenom, genome sequences, genomes, dataset",0.034833237,0.670995558,0.065521451,0.225343683,0.001653056,0.001653015,Genomics,0.41096824,FALSE,22,0.326056033,36.5,0.614329676,6,0.764429903,14477,0.899831447,0.651161765
13743,"Genes Encoding ACE2, TMPRSS2 and Related Proteins Mediating SARS-CoV-2 Viral Entry are Upregulated with Age in Human Cardiomyocytes",0,10.1101/2020.07.07.191429,7/7/20,biorxiv,10.1016/j.yjmcc.2020.08.009,6,sequencing,0.610117343,0.06782594,0.001415138,0.00141517,0.067310011,0.251916398,Drug discovery,0.70630753,TRUE,58,0.689714887,198.8333333,0.917246454,4,0.707574542,2643,0.43679268,0.687832141
13744,Angiotensin-converting enzyme 2 (ACE2) expression increases with age in patients requiringmechanical ventilation.,0,10.1101/2020.07.05.20140467,7/7/20,medrxiv,10.1371/journal.pone.0247060,4,sequencing,0.387436722,0.09802958,0.000889062,0.000889059,0.000889068,0.511866509,Clinics,0.7571963,TRUE,143.75,0.938029563,439.75,0.974377843,0,0.403234768,2294,0.364074163,0.669929084
13745,Characteristics and outcomes of Acute Respiratory Distress Syndrome related to COVID-19 in Belgian and French Intensive Care Units according to antiviral strategies. The COVADIS multicenter observational study.,0,10.1101/2020.06.28.20141911,7/7/20,medrxiv,10.1186/s13613-020-00751-y,21,logistic regression,0.150411137,0.001046816,0.001046806,0.001046835,0.001046839,0.845401567,Clinics,0.8851782,TRUE,32.5,0.461685942,22.86363636,0.510503077,2,0.618927094,1920,0.270888514,0.465501157
13746,Partial Prediction of the Virus COVID-19 Spread in Russia  Based on SIR and SEIR Models,0,10.1101/2020.07.05.20146969,7/7/20,medrxiv,0,2,dataset,0.00203285,0.002032789,0.002032841,0.989836015,0.002032755,0.002032749,Epidemiology,0.3348228,FALSE,243.5,0.983115839,96,0.81509232,0,0.403234768,888,0.013725018,0.553791986
13747,"Computational fluid dynamic (CFD), air flow-droplet dispersion, and indoor CO2 analysis for healthy public space configuration to complywith COVID 19 protocol",0,10.1101/2020.07.02.20145219,7/6/20,medrxiv,0,1,computational,0.002562578,0.002562594,0.002562571,0.987187114,0.002562589,0.002562554,Epidemiology,0.1832754,FALSE,8,0.118683901,0,0.055525823,1,0.537564047,2393,0.386226824,0.274500149
13748,Explainable death toll motion modeling: COVID-19 narratives and counterfactuals,0,10.1101/2020.07.04.20146423,7/6/20,medrxiv,0,2,machine learning,0.002562591,0.002562554,0.071912815,0.917836863,0.002562604,0.002562573,Epidemiology,0.22403863,FALSE,4.5,0.061784897,0,0.055525823,0,0.403234768,1656,0.192872622,0.178354527
13749,Ultraviolet A Radiation and COVID-19 Deaths: A Multi Country Study,0,10.1101/2020.07.03.20145912,7/6/20,medrxiv,0,7,dataset,0.028183105,0.001461899,0.001461896,0.627556779,0.001462015,0.339874305,Epidemiology,0.28639746,FALSE,3.142857143,0.037912054,0,0.055525823,2,0.618927094,4645,0.668191669,0.34513916
13750,SARS-CoV-2 contributes to altering the post-transcriptional regulatory networks across human tissues by sponging RNA binding proteins and micro-RNAs,0,10.1101/2020.07.06.190348,7/6/20,biorxiv,10.3390/ijms21197090,4,"computational, dataset",0.791958647,0.203292246,0.00118728,0.001187289,0.001187268,0.001187271,Drug discovery,0.61359364,TRUE,42,0.558537943,92.5,0.80706449,2,0.618927094,3412,0.546592824,0.632780588
13751,Prediction of COVID-19 Active and Total Cases After a Fall and Rise of Cases,0,10.1101/2020.07.02.20145045,7/5/20,medrxiv,0,1,mathematical model,0.002898417,0.002898359,0.002898381,0.985508141,0.002898336,0.002898366,Epidemiology,0.029358238,FALSE,9,0.135320675,1,0.122023013,2,0.618927094,1298,0.09174091,0.242002923
13752,An Automatic Computer-Based Method for Fast and Accurate Covid-19 Diagnosis,0,10.1101/2020.07.02.20136721,7/5/20,medrxiv,0,10,neural network,0.044657455,0.137671593,0.748318601,0.001717335,0.065917743,0.001717273,Imaging,0.58383644,TRUE,3.2,0.038468675,0,0.055525823,1,0.537564047,1464,0.13821334,0.192442971
13753,Characterization of Microbial Co-infections in the Respiratory Tract of hospitalized COVID-19 patients,0,10.1101/2020.07.02.20143032,7/5/20,medrxiv,0,33,"sequencing, transcriptom, metatranscriptom",0.000988427,0.419104038,0.000988378,0.090309646,0.000988376,0.487621135,Clinics,0.40150592,FALSE,18.12903226,0.272125673,,,3,0.667819001,3907,0.601974476,0.51397305
13754,Age Matters: COVID-19 Prevalence in a Vaping Adolescent Population - An Observational Study,0,10.1101/2020.07.03.20146035,7/4/20,medrxiv,0,10,correlation analysis,0.001901677,0.00190171,0.001901763,0.106188214,0.796992253,0.091114383,Healthcare,0.08834806,FALSE,11.4,0.17131548,0.4,0.075796093,0,0.403234768,2570,0.418011076,0.267089354
13755,Optimal sample pooling: an efficient tool against SARS-CoV-2,0,10.1101/2020.07.03.20145953,7/4/20,medrxiv,0,3,mathematical model,0.001901733,0.140526388,0.139907877,0.7138605,0.001901763,0.001901739,Epidemiology,0.16754872,FALSE,2.333333333,0.024800544,0,0.055525823,1,0.537564047,1674,0.194317361,0.203051944
13756,The potential role of miR-21-3p in coronavirus-host interplay,0,10.1101/2020.07.03.184846,7/4/20,biorxiv,10.7717/peerj.9994,6,"computational, bioinformatic, sequencing",0.752273464,0.183894548,0.001823388,0.001823427,0.001823396,0.058361777,Drug discovery,0.88159657,TRUE,29.16666667,0.420619704,28.83333333,0.560944608,2,0.618927094,2101,0.312063569,0.478138744
13757,"Sub-epidemic model forecasts for COVID-19 pandemic spread in the USA and European hotspots, February-May 2020",0,10.1101/2020.07.03.20146159,7/4/20,medrxiv,0,6,mathematical model,0.001254617,0.00125467,0.040650053,0.954331467,0.001254597,0.001254596,Epidemiology,0.14818001,FALSE,31.66666667,0.451976003,64,0.735549906,1,0.537564047,993,0.028413195,0.438375788
13758,How was the Mental Health of Colombian people on March during Pandemics Covid19?,0,10.1101/2020.07.02.20145425,7/4/20,medrxiv,0,1,data mining,0.002183245,0.00218326,0.002183395,0.652671789,0.338595073,0.002183238,Epidemiology,0.7788222,TRUE,9,0.135320675,0,0.055525823,3,0.667819001,1220,0.073200096,0.232966399
13759,Forecasting COVID-19 cases using Machine Learning models,0,10.1101/2020.07.02.20145474,7/4/20,medrxiv,0,3,"bayes, machine learning, predictive model, lstm",0.00242226,0.002422287,0.291334583,0.698976269,0.002422293,0.002422308,Epidemiology,0.6080558,TRUE,4,0.054734368,0,0.055525823,6,0.764429903,1957,0.274259571,0.287237416
13760,Battle with COVID-19 Under Partial to Zero Lockdowns in India,0,10.1101/2020.07.03.20145664,7/4/20,medrxiv,0,1,machine learning,0.001034584,0.001034589,0.115770507,0.880091149,0.001034594,0.001034577,Epidemiology,0.102431566,FALSE,4,0.054734368,2,0.164302917,0,0.403234768,1032,0.03419215,0.164116051
13761,Tissue-specific tolerance in fatal Covid-19,0,10.1101/2020.07.02.20145003,7/4/20,medrxiv,10.1164/rccm.202008-3265OC,28,sequencing,0.480646217,0.149612821,0.000830696,0.000830655,0.00083064,0.367248972,Drug discovery,0.31876707,FALSE,33.10714286,0.467252149,34.96428571,0.604428686,28,0.926168282,31862,0.961473634,0.739830688
13762,Methodological Rigor in COVID-19 Clinical Research: A Systematic Review and Case-Control Analysis,0,10.1101/2020.07.02.20145102,7/3/20,medrxiv,0,11,logistic regression,0.001203443,0.001203471,0.064187332,0.637439816,0.165053321,0.130912616,Epidemiology,0.2286722,FALSE,18,0.271569052,4.545454545,0.24290875,2,0.618927094,6415,0.759932579,0.473334369
13763,"Blood biomarker score identifies individuals at high risk for severe COVID-19 a decade prior to diagnosis: metabolic profiling of 105,000 adults in the UK Biobank",0,10.1101/2020.07.02.20143685,7/3/20,medrxiv,0,5,metabolom,0.000846582,0.180849304,0.064840036,0.000846655,0.000846559,0.751770863,Clinics,0.27115017,FALSE,42.2,0.559341951,40.2,0.634533048,3,0.667819001,10075,0.843486636,0.676295159
13764,Comparison of Multimorbidity in COVID-19 infected and general population in Portugal,0,10.1101/2020.07.02.20144378,7/3/20,medrxiv,0,4,dataset,0.003607229,0.003607281,0.003607535,0.367545203,0.003607611,0.618025142,Clinics,0.64840853,TRUE,2.5,0.027459954,0,0.055525823,2,0.618927094,2004,0.284372743,0.246571403
13765,Orthogonal Functions for Evaluating Social Distancing Impact on CoVID-19 Spread,0,10.1101/2020.06.30.20143149,7/3/20,medrxiv,0,1,dataset,0.494583096,0.001717292,0.00171726,0.498547749,0.001717263,0.001717339,Epidemiology,0.12805665,FALSE,8,0.118683901,0,0.055525823,1,0.537564047,905,0.013965808,0.181434895
13766,Map of SARS-CoV-2 spike epitopes not shielded by glycans.,0,10.1101/2020.07.03.186825,7/3/20,biorxiv,0,6,molecular dynamics simulation,0.761165657,0.231829413,0.001751201,0.001751287,0.001751174,0.001751267,Drug discovery,0.14980808,FALSE,76,0.790586926,93.83333333,0.810476318,16,0.881782826,4390,0.645798218,0.782161072
13767,A CRISPR-based SARS-CoV-2 diagnostic assay that is robust against viral evolution and RNA editing,0,10.1101/2020.07.03.185850,7/3/20,biorxiv,0,8,transcriptom,0.149626985,0.758163531,0.001901806,0.086504287,0.001901711,0.00190168,Genomics,0.39755017,FALSE,14.375,0.217205764,33,0.593256623,2,0.618927094,3279,0.520828317,0.48755445
13768,Evolution of COVID-19 Pandemic in India,0,10.1101/2020.07.01.20143925,7/2/20,medrxiv,10.1007/s41403-020-00166-y,3,predictive model,0.001751173,0.001751189,0.001751186,0.991244073,0.001751155,0.001751225,Epidemiology,0.33411613,FALSE,4,0.054734368,0.666666667,0.096200161,5,0.739490092,1483,0.138694919,0.257279885
13769,Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong,0,10.1101/2020.06.30.20143651,7/2/20,medrxiv,0,10,"machine learning, logistic regression",0.165392863,0.001112627,0.149785406,0.001112642,0.001112645,0.681483818,Clinics,0.3590948,FALSE,21.1,0.312202363,6.6,0.28913567,4,0.707574542,1192,0.063568505,0.34312027
13770,Does Lockdown Decrease the Protective Role of ultraviolet-B (UVB) Radiation in Reducing COVID-19 Deaths?,0,10.1101/2020.06.30.20143586,7/2/20,medrxiv,0,2,dataset,0.000889046,0.000889063,0.000889046,0.802264496,0.00088912,0.19417923,Epidemiology,0.20053566,FALSE,4,0.054734368,0,0.055525823,5,0.739490092,5668,0.72429569,0.393511493
13771,Exploring Epidemiological Behavior of Novel Coronavirus Outbreak through the Development and Analysis of COVID-19 Daily Dataset in Bangladesh,0,10.1101/2020.06.30.20143909,7/2/20,medrxiv,0,4,dataset,0.002183288,0.071491711,0.002183237,0.919775105,0.002183381,0.002183278,Epidemiology,0.6791819,TRUE,5.75,0.080957388,0.75,0.099411292,0,0.403234768,931,0.016855285,0.150114683
13772,ESTIMATING UNDERDIAGNOSIS OF COVID-19 WITH NOWCASTING AND MACHINE LEARNING: EXPERIENCE FROM BRAZIL,0,10.1101/2020.07.01.20144402,7/2/20,medrxiv,0,9,machine learning,0.001751146,0.001751186,0.366672392,0.515670976,0.001751273,0.112403027,Epidemiology,0.2676742,FALSE,4.222222222,0.05603315,0.111111111,0.056663099,3,0.667819001,1113,0.04623164,0.206686722
13773,Temporary Immunity and Multiple Waves of COVID-19,0,10.1101/2020.07.01.20144394,7/2/20,medrxiv,0,3,mathematical model,0.000880219,0.060263047,0.000880213,0.920913323,0.000880217,0.01618298,Epidemiology,0.03444138,FALSE,9.333333333,0.139278867,0.333333333,0.073187048,1,0.537564047,1710,0.200096316,0.23753157
13774,Transmission Dynamics of the COVID-19 Epidemics in England,0,10.1101/2020.06.30.20143743,7/2/20,medrxiv,10.1016/j.ijid.2020.12.055,3,bayes,0.001717166,0.001717228,0.001717164,0.991414053,0.001717211,0.001717178,Epidemiology,0.40602344,FALSE,29,0.41993939,238.3333333,0.933435911,2,0.618927094,1476,0.137009391,0.527327947
13775,Attenuated Subcomponent Vaccine Design Targeting the SARS-CoV-2 Nucleocapsid Phosphoprotein RNA Binding Domain: In silico analysis,0,10.1101/2020.06.30.176537,7/1/20,biorxiv,10.1155/2020/2837670,10,"computational, in silico",0.931681343,0.001112664,0.029793704,0.035186992,0.001112654,0.001112643,Drug discovery,0.62313455,TRUE,2.4,0.025419012,0.1,0.056328606,3,0.667819001,1229,0.071755358,0.205330494
13776,Handyfuge-LAMP: low-cost and electricity-free centrifugation forisothermal SARS-CoV-2 detection in saliva.,0,10.1101/2020.06.30.20143255,7/1/20,medrxiv,0,4,genomes,0.060081114,0.515579366,0.420824513,0.001171625,0.00117167,0.001171712,Genomics,0.76995504,TRUE,4,0.054734368,0.75,0.099411292,6,0.764429903,19088,0.925836745,0.461103077
13777,Cluster analysis of epidemiological characteristic features of confirmed cases with the novel coronavirus (COVID-19) outside China: a descriptive study,0,10.1101/2020.06.28.20142000,7/1/20,medrxiv,0,9,logistic regression,0.001538084,0.001538168,0.001538082,0.992309399,0.00153816,0.001538106,Epidemiology,0.16788048,FALSE,2.222222222,0.022945142,0.333333333,0.073187048,0,0.403234768,1403,0.11509752,0.153616119
13778,periscope: sub-genomic RNA identification in SARS-CoV-2 ARTIC Network Nanopore Sequencing Data,0,10.1101/2020.07.01.181867,7/1/20,biorxiv,0,33,"sequencing, genomes, dataset",0.103655245,0.890192179,0.001538229,0.001538183,0.001538081,0.001538083,Genomics,0.11072251,FALSE,49.75,0.630032779,64.71875,0.737690661,2,0.618927094,,,0.662216845
13779,"Unsupervised cluster analysis of SARS-CoV-2 genomes indicates that recent (June 2020) cases in Beijing are from a genetic subgroup that consists of mostly European and South(east) Asian samples, of which the latter are the most recent",0,10.1101/2020.06.22.165936,6/30/20,biorxiv,0,2,genomes,0.001653062,0.963923347,0.029464392,0.001653101,0.001653031,0.001653067,Genomics,0.13498756,FALSE,246,0.983796153,285.4,0.950227455,4,0.707574542,2293,0.352275464,0.748468403
13780,Single-cell transcriptional atlas of the Chinese horseshoe bat (Rhinolophus sinicus) provides insight into the cellular mechanisms which enable bats to be viral reservoirs,0,10.1101/2020.06.30.175778,6/30/20,biorxiv,0,21,transcriptom,0.736905171,0.211041855,0.000936093,0.000936113,0.049244666,0.000936101,Drug discovery,0.73932064,TRUE,106.9047619,0.882800421,177.952381,0.904937115,3,0.667819001,2514,0.40260053,0.714539267
13781,Genetic variants in TMPRSS2 and Structure of SARS-CoV-2 spike glycoprotein and TMPRSS2 complex,0,10.1101/2020.06.30.179663,6/30/20,biorxiv,0,8,"sequencing, exom",0.824649813,0.165388357,0.002490479,0.002490512,0.002490446,0.002490394,Drug discovery,0.40932053,FALSE,30,0.432432432,22.125,0.504214611,8,0.799987654,2069,0.294726704,0.50784035
13782,Validation and Comparison of a Modified CDC Assay with two Commercially Available Assays for the Detection of SARS-CoV-2 in Respiratory Specimen,0,10.1101/2020.06.29.179192,6/30/20,biorxiv,0,4,in silico,0.001438167,0.657709213,0.257643514,0.001438208,0.001438253,0.080332645,Genomics,0.52406025,TRUE,19,0.285793803,21.5,0.498260637,0,0.403234768,1558,0.158921262,0.336552617
13783,Structure of the full SARS-CoV-2 RNA genome in infected cells,0,10.1101/2020.06.29.178343,6/30/20,biorxiv,0,10,"sequencing, transcriptom, genomic structure",0.542155078,0.396213038,0.058251502,0.001126818,0.001126785,0.001126779,Drug discovery,0.16880196,FALSE,46.375,0.599604181,23.125,0.513848006,36,0.941169208,7670,0.794365519,0.712246729
13784,Forecasting the Number of Coronavirus (COVID-19) Cases in Ethiopia Using Exponential Smoothing Times Series Model,0,10.1101/2020.06.29.20142489,6/30/20,medrxiv,0,1,forecasting model,0.003607212,0.00360725,0.060570549,0.925000609,0.003607179,0.003607201,Epidemiology,0.41587472,FALSE,8,0.118683901,5,0.257024351,0,0.403234768,1704,0.196243679,0.243796675
13785,Epidemic Trend Analysis of SARS-CoV-2 in SAARC Countries Using Modified SIR (M-SIR) Predictive Model,0,10.1101/2020.06.29.20142513,6/30/20,medrxiv,0,5,"predictive model, prediction model",0.001565296,0.001565321,0.001565309,0.992173346,0.001565338,0.00156539,Epidemiology,0.43301302,FALSE,4.6,0.062588905,0.6,0.09011239,1,0.537564047,1221,0.06910667,0.189843003
13786,"Knowledge, attitudes, and practices towards COVID-19 among primary and middle school students during the COVID-19 outbreak period in Beijing: An online cross-sectional survey",0,10.1101/2020.06.29.20138628,6/30/20,medrxiv,0,8,logistic regression,0.001156269,0.001156259,0.015987895,0.001156332,0.940071181,0.040472065,Healthcare,0.8890666,TRUE,6.125,0.086461748,1.5,0.138747659,0,0.403234768,1703,0.196002889,0.206111766
13787,Blood parameters measured on admission as predictors of outcome for COVID-19; a prospective UK cohort study,0,10.1101/2020.06.25.20137935,6/29/20,medrxiv,0,12,"logistic regression, dataset",0.000946146,0.000946129,0.085555938,0.054310883,0.000946107,0.857294798,Clinics,0.35371017,FALSE,13.5,0.205393036,8.25,0.323789136,4,0.707574542,2145,0.314230677,0.387746847
13788,Differential occupational risks to healthcare workers from SARS-CoV-2: A prospective observational study,0,10.1101/2020.06.24.20135038,6/29/20,medrxiv,0,50,logistic regression,0.001565306,0.093992505,0.001565314,0.001565401,0.74621065,0.155100823,Healthcare,0.68671125,TRUE,22.64444444,0.334714577,22.57777778,0.507894033,15,0.874313229,14593,0.89597881,0.653225162
13789,Estimation of COVID-19 dynamics in the different states of the United States using Time-Series Clustering,0,10.1101/2020.06.29.20142364,6/29/20,medrxiv,0,3,mathematical model,0.00146193,0.001461981,0.001461922,0.992690424,0.001461884,0.001461859,Epidemiology,0.33641487,FALSE,2,0.022141134,0,0.055525823,4,0.707574542,1457,0.127859379,0.228275219
13790,Macropahge expression and prognostic significance of the long pentraxin PTX3 in COVID-19,0,10.1101/2020.06.26.20139923,6/29/20,medrxiv,0,18,"bioinformatic, logistic regression",0.372953498,0.000988428,0.000988395,0.000988383,0.000988357,0.623092939,Clinics,0.5915244,TRUE,51.38888889,0.641845507,94.55555556,0.811747391,1,0.537564047,1527,0.147122562,0.534569877
13791,Genomic epidemiology of SARS-CoV-2 in Colombia,0,10.1101/2020.06.26.20135715,6/29/20,medrxiv,0,17,"sequencing, genomic epidemiology, genomes",0.002422252,0.987888422,0.002422284,0.002422455,0.002422265,0.002422321,Genomics,0.4101956,FALSE,22.52941176,0.333601336,23,0.513513514,10,0.828199272,4337,0.633277149,0.577147818
13792,From predictions to prescriptions: A data-drivenresponse to COVID-19,0,10.1101/2020.06.26.20141127,6/29/20,medrxiv,10.1007/s10729-020-09542-0,21,optimization model,0.000807888,0.000807946,0.000807938,0.787785973,0.000807933,0.208982322,Epidemiology,0.16342297,FALSE,7.095238095,0.102108974,2.666666667,0.185442869,1,0.537564047,2665,0.427642668,0.313189639
13793,"Estimating the infection fatality risk of COVID-19 in New York City, March 1-May 16, 2020",0,10.1101/2020.06.27.20141689,6/29/20,medrxiv,10.1016/S1473-3099(20)30769-6,12,network model,0.00249048,0.002490517,0.002490491,0.712217124,0.086808297,0.193503091,Epidemiology,0.19073203,FALSE,13.41666667,0.202671779,10.66666667,0.365333155,9,0.814309525,18337,0.92005779,0.575593062
13794,Outcomes and Cardiovascular Comorbidities in a Predominantly African-American Population with COVID-19,0,10.1101/2020.06.28.20141929,6/29/20,medrxiv,0,33,logistic regression,0.000683138,0.000683174,0.000683154,0.000683176,0.035369144,0.961898215,Clinics,0.42300355,FALSE,28.72727273,0.414744264,30.24242424,0.57124699,10,0.828199272,1596,0.166626535,0.495204265
13795,Chest CT Images for COVID-19: Radiologists and Computer-based detection,0,10.1101/2020.06.27.20141531,6/29/20,medrxiv,0,9,image analysis,0.000916694,0.066860374,0.718005815,0.000916735,0.000916712,0.21238367,Imaging,0.3664639,FALSE,11.22222222,0.168717917,4.111111111,0.232004282,0,0.403234768,963,0.019985553,0.20598563
13796,Predictive model of COVID-19 incidence and socioeconomic description of municipalities in Brazil,0,10.1101/2020.06.28.20141952,6/29/20,medrxiv,0,6,predictive model,0.001987169,0.07969156,0.001987213,0.634885911,0.102605519,0.178842628,Epidemiology,0.39314294,FALSE,5.666666667,0.079473066,0.333333333,0.073187048,0,0.403234768,1555,0.156031784,0.177981667
13797,A new estimation method for COVID-19 time-varying reproduction number using active cases,0,10.1101/2020.06.28.20142158,6/29/20,medrxiv,0,7,"bayes, bayesian model",0.002130701,0.002130661,0.002130811,0.989346505,0.002130661,0.00213066,Epidemiology,0.15515354,FALSE,26.42857143,0.387160616,3.285714286,0.203973776,8,0.799987654,1454,0.12689622,0.379504566
13798,Protocol for the development and evaluation of a tool for predicting risk of short-term adverse outcomes due to COVID-19 in the general UK population,0,10.1101/2020.06.28.20141986,6/29/20,medrxiv,0,24,dataset,0.000830636,0.000830662,0.12977873,0.275017611,0.245510966,0.348031395,Clinics,0.32774588,FALSE,93.5,0.85317583,128.75,0.862322719,7,0.785110192,1798,0.221526607,0.680533837
13799,Whole-Genome Sequences of the Severe Acute Respiratory Syndrome Coronavirus-2 obtained from Romanian patients between March and June of 2020,0,10.1101/2020.06.28.175802,6/29/20,biorxiv,0,5,"whole-genome, genome sequences, genomes",0.369609944,0.5777978,0.001254653,0.001254679,0.001254637,0.048828286,Genomics,0.22562328,FALSE,20,0.298163152,18.6,0.465881723,0,0.403234768,3361,0.525403323,0.423170741
13800,A mobile genetic element in the SARS-CoV-2 genome is shared with multiple insect species,0,10.1101/2020.06.29.177030,6/29/20,biorxiv,10.1099/jgv.0.001551,3,genomes,0.003101693,0.984490975,0.003101831,0.003102196,0.003101758,0.003101547,Genomics,0.77605987,TRUE,67.66666667,0.75137609,258.6666667,0.940928552,3,0.667819001,3070,0.487358536,0.711870545
13801,Memory-Dependent Model for the Dynamics of COVID-19 Pandemic,0,10.1101/2020.06.26.20141242,6/28/20,medrxiv,0,3,"model simulation, mathematical model",0.002996488,0.002996459,0.002996519,0.985017169,0.002996968,0.002996397,Epidemiology,0.3461443,FALSE,31,0.445111015,14.66666667,0.420323789,1,0.537564047,817,0.005056586,0.352013859
13802,Forecasting COVID-19 Dynamics and Endpoint in Bangladesh: A Data-driven Approach,0,10.1101/2020.06.26.20140905,6/28/20,medrxiv,0,7,"prediction model, lstm",0.001126796,0.001126798,0.019120371,0.976372387,0.001126815,0.001126834,Epidemiology,0.45651427,FALSE,5,0.070752675,0.571428571,0.088038534,1,0.537564047,1509,0.139898868,0.209063531
13803,Predicting the disease outcome in COVID-19 positive patients through Machine Learning: a retrospective cohort study with Brazilian data,0,10.1101/2020.06.26.20140764,6/28/20,medrxiv,0,5,"machine learning, prediction model, dataset",0.001538133,0.001538121,0.461659218,0.149774668,0.001538224,0.383951636,Clinics,0.24133864,FALSE,19.8,0.293957573,7.6,0.308536259,8,0.799987654,2555,0.406934746,0.452354058
13804,Secondary pneumonia in critically ill ventilated patients with COVID-19,0,10.1101/2020.06.26.20139873,6/28/20,medrxiv,0,20,microbiom,0.001717202,0.14083334,0.038572669,0.001717231,0.001717195,0.815442363,Clinics,0.28727648,FALSE,9,0.135320675,11.4,0.375970029,0,0.403234768,3231,0.507825668,0.355587785
13805,Critical Sequence Hot-spots for Binding of nCOV-2019 to ACE2 as Evaluated by Molecular Simulations,0,10.1101/2020.06.27.175448,6/27/20,biorxiv,10.1021/acs.jpcb.0c05994,3,in-silico,0.67492346,0.287309174,0.034589235,0.001059433,0.001059352,0.001059345,Drug discovery,0.562573,TRUE,166.3333333,0.955655885,463.3333333,0.976986888,6,0.764429903,2206,0.325307007,0.755594921
13806,Binding Ability Prediction between Spike Protein and Human ACE2 Reveals the Adaptive Strategy of SARS-CoV-2 in Humans,0,10.1101/2020.06.25.170704,6/27/20,biorxiv,10.1038/s41598-021-82938-2,17,genome sequences,0.485095286,0.509956157,0.00123708,0.001237156,0.001237169,0.001237152,Genomics,0.55563265,TRUE,77.77777778,0.797451914,,,0,0.403234768,2190,0.320732001,0.507139561
13807,Contact Tracing Evaluation for COVID-19 Transmission during the Reopening Phase in a Rural College Town,0,10.1101/2020.06.24.20139204,6/26/20,medrxiv,10.1038/s41598-021-83722-y,2,network model,0.000786351,0.000786352,0.000786385,0.974388072,0.012059178,0.011193663,Epidemiology,0.06484422,FALSE,3.5,0.044344115,0,0.055525823,0,0.403234768,1874,0.239345052,0.185612439
13808,Phylogenomics and phylodynamics of SARS-CoV-2 retrieved genomes from India,0,10.1101/2020.06.23.20138222,6/26/20,medrxiv,10.2217/fvl-2020-0243,6,"bayes, sequencing, phylogenom, whole genome, genomes",0.022943959,0.372765505,0.001371267,0.600176712,0.001371264,0.001371294,Epidemiology,0.3602637,FALSE,19.16666667,0.28641227,17,0.451097137,0,0.403234768,1463,0.125210691,0.316488716
13809,"Ethnic variation in outcome of people hospitalised with Covid-19 in Wales (UK): A rapid analysis of surveillance data using Onomap, a name-based ethnicity classification tool",0,10.1101/2020.06.22.20136036,6/26/20,medrxiv,0,10,classifier,0.0047752,0.004775592,0.143571726,0.00477547,0.004775353,0.837326659,Clinics,0.1741269,FALSE,7.6,0.109283196,1.4,0.133864062,0,0.403234768,1084,0.036600048,0.170745519
13810,A Multi-Task Pipeline with Specialized Streams forClassification and Segmentation of InfectionManifestations in COVID-19 Scans,0,10.1101/2020.06.24.20139238,6/26/20,medrxiv,10.7717/peerj-cs.303,3,"machine learning, computational, neural network, deep model, network model, dataset",0.000846525,0.000846529,0.995767318,0.000846567,0.000846528,0.000846532,Imaging,0.016797304,FALSE,13,0.197352959,2,0.164302917,2,0.618927094,1356,0.096797496,0.269345117
13811,Determination of Robust Regional CT Radiomics Features for COVID-19,0,10.1101/2020.06.24.20139410,6/26/20,medrxiv,0,1,radiom,0.001072272,0.113324791,0.746733707,0.001072264,0.001072222,0.136724744,Imaging,0.385408,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,987,0.02311582,0.129792499
13812,Mathematical modeling of the COVID-19 prevalence in Saudi Arabia,0,10.1101/2020.06.25.20138602,6/26/20,medrxiv,0,2,mathematical model,0.013458165,0.000846595,0.011976118,0.938814327,0.034058261,0.000846533,Epidemiology,0.40954363,FALSE,5,0.070752675,1,0.122023013,2,0.618927094,1735,0.198169998,0.252468195
13813,Swarm Learning as a privacy-preserving machine learning approach for disease classification,0,10.1101/2020.06.25.171009,6/26/20,biorxiv,0,33,"machine learning, classifier, transcriptom,  omics",0.236995122,0.002130776,0.516407584,0.132269198,0.002130723,0.110066597,Drug discovery,0.3289647,FALSE,82.125,0.814954543,123.71875,0.856234948,2,0.618927094,3999,0.599084999,0.722300396
13814,Virus-Receptor Interactions of Glycosylated SARS-CoV-2 Spike and Human ACE2 Receptor,0,10.1101/2020.06.25.172403,6/26/20,biorxiv,10.1016/j.chom.2020.08.004,16,"molecular dynamics simulation, bioinformatic, proteom, glycomics, glycoproteom",0.782152284,0.165260022,0.0017866,0.047228018,0.001786588,0.001786488,Drug discovery,0.36689654,FALSE,84.375,0.82318016,104.9375,0.830947284,53,0.959195012,10747,0.848784012,0.865526617
13815,Covid-19 Pandemic Data Analysis and Forecasting using Machine Learning Algorithms,0,10.1101/2020.06.25.20140004,6/26/20,medrxiv,0,3,"model fit, machine learning, deep learning, forecasting model, lstm",0.001171547,0.001171554,0.2097498,0.760103875,0.026631623,0.001171602,Epidemiology,0.025393844,FALSE,1.5,0.015523533,0,0.055525823,2,0.618927094,6628,0.762099687,0.363019034
13816,Elucidation of the antiviral mechanism of cystine and theanine through transcriptome analysis of mice and comparison with COVID-19 gene set data,0,10.1101/2020.06.25.149427,6/26/20,biorxiv,0,6,transcriptom,0.732858162,0.001156333,0.172277193,0.001156241,0.091395788,0.001156281,Drug discovery,0.45161298,FALSE,44.83333333,0.584389882,18.83333333,0.468624565,0,0.403234768,3554,0.548278353,0.501131892
13817,Modelling COVID-19 using the Fundamentals of Fluid Dynamics,0,10.1101/2020.06.24.20139071,6/25/20,medrxiv,0,5,mathematical model,0.002238494,0.002238505,0.002238462,0.988807517,0.002238471,0.002238551,Epidemiology,0.36353663,FALSE,15.75,0.237862577,5.25,0.260971367,1,0.537564047,2370,0.357332049,0.34843251
13818,Cellular exocytosis gene (EXOC6/6B): a potential molecular link for the susceptibility and mortality of COVID-19 in diabetic patients,0,10.1101/2020.06.25.170936,6/25/20,biorxiv,0,5,"transcriptom, whole genome",0.557168649,0.159506968,0.001593501,0.001593541,0.001593493,0.278543848,Drug discovery,0.46204698,FALSE,37.8,0.516667697,36.2,0.612322719,0,0.403234768,1877,0.238141103,0.442591572
13819,Chasing the origin of SARS-CoV-2 in Canada's COVID-19 cases: A genomics study,0,10.1101/2020.06.25.171744,6/25/20,biorxiv,0,0,"genome sequences, genomes",0.001310375,0.83061893,0.001310436,0.149427702,0.001310348,0.016022209,Genomics,0.42918763,FALSE,44.75,0.583771414,66,0.742574257,0,0.403234768,3055,0.478930893,0.552127833
13820,Molecular evolution of SARS-CoV-2 structural genes: evidence of positive selection in spike glycoprotein,0,10.1101/2020.06.25.170688,6/25/20,biorxiv,0,9,structural model,0.295962977,0.700682993,0.000838496,0.00083852,0.000838503,0.00083851,Genomics,0.22668287,FALSE,37.11111111,0.509122395,21.77777778,0.500668986,4,0.707574542,2330,0.351312304,0.517169557
13821,The discovery of potential natural products for targeting SARS-CoV-2 spike protein by virtual screening,0,10.1101/2020.06.25.170639,6/25/20,biorxiv,0,6,"virtual screening, in silico",0.993143804,0.001371292,0.001371231,0.001371228,0.001371228,0.001371218,Drug discovery,0.7639229,TRUE,27.33333333,0.399529965,8.166666667,0.321915975,1,0.537564047,1952,0.257885866,0.379223963
13822,Using Machine Learning of Clinical Data to Diagnose COVID-19,0,10.1101/2020.06.24.20138859,6/24/20,medrxiv,0,19,"machine learning, computational, dataset",0.043707244,0.001486541,0.434013724,0.001486493,0.107727509,0.411578489,Clinics,0.30991042,FALSE,15.68421053,0.236625642,5.947368421,0.273815895,3,0.667819001,1950,0.256200337,0.358615219
13823,SARS-CoV-2 infection and replication in human fetal and pediatric gastric organoids,0,10.1101/2020.06.24.167049,6/24/20,biorxiv,0,22,transcriptom,0.58761577,0.128910648,0.00109884,0.001098891,0.164397108,0.116878743,Drug discovery,0.291163,FALSE,48.27272727,0.616426495,48.13636364,0.674203907,3,0.667819001,2932,0.458945341,0.604348686
13824,CoronaHiT: large scale multiplexing of SARS-CoV-2 genomes using Nanopore sequencing,0,10.1101/2020.06.24.162156,6/24/20,biorxiv,0,28,"sequencing, whole genome, genomes",0.001350354,0.961776771,0.032821701,0.001350423,0.001350382,0.001350369,Genomics,0.20383424,FALSE,33.24137931,0.468427237,55.96551724,0.705980733,8,0.799987654,6777,0.766915483,0.685327777
13825,In silico identification of conserved cis-acting RNA elements in the SARS-CoV-2 genome,0,10.1101/2020.06.23.167916,6/24/20,biorxiv,10.2217/fvl-2020-0163,1,"computational, bioinformatic, in silico",0.445031806,0.545537079,0.002357819,0.002357806,0.002357737,0.002357753,Genomics,0.7247789,TRUE,5,0.070752675,7,0.299973241,4,0.707574542,1132,0.042860583,0.28029026
13826,Comparative transcriptome analyses reveal genes associated with SARS-CoV-2 infection of human lung epithelial cells,0,10.1101/2020.06.24.169268,6/24/20,biorxiv,0,3,transcriptom,0.901068941,0.092318703,0.001653077,0.001653096,0.001653088,0.001653095,Drug discovery,0.7429295,TRUE,138,0.930546107,426.6666667,0.973240567,2,0.618927094,2947,0.461353239,0.746016752
13827,Lung expression of genes encoding SARS-CoV-2 cell entry molecules and antiviral restriction factors: interindividual differences are associated with age and germline variants,0,10.1101/2020.06.24.168534,6/24/20,biorxiv,0,4,genome-wide,0.741428824,0.175369933,0.000988367,0.000988352,0.000988359,0.080236165,Drug discovery,0.5290704,TRUE,58.25,0.691384749,71.75,0.758295424,1,0.537564047,1442,0.117264628,0.526127212
13828,N-glycosylation network construction and analysis to modify glycans on the spike S glycoprotein of SARS-CoV-2.,0,10.1101/2020.06.23.167791,6/24/20,biorxiv,0,2,network analysis,0.844727185,0.001254683,0.001254655,0.110254082,0.041254729,0.001254667,Drug discovery,0.56461036,TRUE,31,0.445111015,9,0.337904736,0,0.403234768,1463,0.123043583,0.327323525
13829,Intestinal receptor of SARS-CoV-2 in inflamed IBD tissue is downregulated by HNF4A in ileum and upregulated by interferon regulating factors in colon,0,10.1101/2020.06.24.169383,6/24/20,biorxiv,10.1093/ecco-jcc/jjaa185,10,sequencing,0.58916287,0.216597948,0.001751177,0.001751212,0.001751269,0.188985524,Drug discovery,0.9633398,TRUE,65,0.734801163,79.2,0.77642494,0,0.403234768,2389,0.360703106,0.568790994
13830,Modelling donor screening strategies to reduce the risk of SARS-CoV-2 via fecal microbiota transplantation,0,10.1101/2020.06.24.169094,6/24/20,biorxiv,10.1093/ofid/ofaa499,4,mathematical model,0.010372434,0.010372931,0.010372955,0.948136467,0.010372694,0.010372518,Epidemiology,0.6410713,TRUE,26,0.382398417,25,0.529435376,0,0.403234768,1809,0.215266073,0.382583658
13831,Integration of viral transcriptome sequencing with structure and sequence motifs predicts novel regulatory elements in SARS-CoV-2,0,10.1101/2020.06.24.169144,6/24/20,biorxiv,0,1,"sequencing, transcriptom",0.134523269,0.857528151,0.001987109,0.001987137,0.001987164,0.001987171,Genomics,0.13740578,FALSE,243,0.983053992,449,0.97544822,0,0.403234768,1526,0.139176499,0.62522837
13832,A model of COVID-19 transmission and control on university campuses,0,10.1101/2020.06.23.20138677,6/24/20,medrxiv,0,7,probabilistic,0.000956324,0.066198298,0.000956392,0.616857263,0.314075383,0.000956341,Epidemiology,0.24742845,FALSE,9.5,0.143051518,21.5,0.498260637,16,0.881782826,4709,0.658800867,0.545473962
13833,Analysis and Prediction of COVID-19 Characteristics Using a Birth-and-Death Model,0,10.1101/2020.06.23.20138719,6/24/20,medrxiv,0,1,mathematical model,0.08257883,0.002238484,0.002238491,0.908467223,0.002238453,0.002238519,Epidemiology,0.29227686,FALSE,7,0.10179974,1,0.122023013,0,0.403234768,913,0.012280279,0.15983445
13834,How Previous Epidemics Enable Timelier COVID-19 Responses: A Cross-Sectional Study Using Organizational Memory Theory,0,10.1101/2020.06.23.20138479,6/24/20,medrxiv,10.1136/bmjgh-2020-003228,1,dataset,0.001156275,0.019810055,0.0011563,0.868217046,0.108504031,0.001156293,Epidemiology,0.34623024,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,1141,0.044305321,0.131301762
13835,Analysis and Forecast of COVID-19 Pandemic in Pakistan,0,10.1101/2020.06.24.20138800,6/24/20,medrxiv,0,1,mathematical model,0.005047678,0.005047621,0.005047547,0.974761892,0.005047681,0.005047581,Epidemiology,0.47422063,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,1309,0.081146159,0.140511971
13836,Deep convolutional approaches for the analysis of Covid-19 using chest X-Ray images from portable devices,0,10.1101/2020.06.18.20134593,6/23/20,medrxiv,10.1109/access.2020.3033762,8,"deep learning, dataset",0.001371354,0.00137129,0.920310279,0.001371321,0.074204445,0.001371311,Imaging,0.5217291,TRUE,17.375,0.261549879,3.5,0.213607172,7,0.785110192,1195,0.055863231,0.329032619
13837,"COVID-19 lockdown: if, when and how",0,10.1101/2020.06.20.20136325,6/23/20,medrxiv,0,7,mathematical model,0.001010935,0.001010956,0.025648276,0.945749136,0.00101098,0.025569717,Epidemiology,0.08840209,FALSE,5.285714286,0.073041004,0.142857143,0.057398983,2,0.618927094,1638,0.169516013,0.229720774
13838,Chest X-ray classification using Deep learning for automated COVID-19 screening,0,10.1101/2020.06.21.20136598,6/23/20,medrxiv,0,7,"deep learning, artificial intelligence",0.001291286,0.001291268,0.868177313,0.001291254,0.001291257,0.126657622,Imaging,0.43849596,FALSE,2.857142857,0.031541839,0.285714286,0.066296495,5,0.739490092,2548,0.397303154,0.308657895
13839,Modelling the impact of lockdown easing measures on cumulative COVID-19 cases and deaths in England,0,10.1101/2020.06.21.20136853,6/23/20,medrxiv,0,3,"bayes, bayesian model",0.00137123,0.001371251,0.001371227,0.993143768,0.001371265,0.001371258,Epidemiology,0.15136251,FALSE,32,0.455810502,81.33333333,0.781709928,3,0.667819001,2199,0.315675415,0.555253711
13840,Bayesian approach for modelling the dynamic of COVID-19 outbreak on the Diamond Princess Cruise Ship,0,10.1101/2020.06.21.20136465,6/23/20,medrxiv,0,6,"bayes, bayesian model",0.001203458,0.001203421,0.001203421,0.977227113,0.001203429,0.017959158,Epidemiology,0.44309682,FALSE,9.833333333,0.147628177,6,0.280037463,2,0.618927094,1015,0.025764508,0.268089311
13841,The COVID-19 Spread Patterns in Italy and India: A Comparison of the Current Situations,0,10.1101/2020.06.21.20136630,6/23/20,medrxiv,10.22457/jmi.v19a02176,1,mathematical model,0.004530635,0.004530781,0.004530641,0.977346607,0.004530628,0.004530708,Epidemiology,0.26862225,FALSE,7,0.10179974,0,0.055525823,4,0.707574542,1205,0.058993499,0.230973401
13842,Using Machine Learning to assess Covid-19 risks,0,10.1101/2020.06.23.20137950,6/23/20,medrxiv,0,4,"machine learning, supervised learning, unsupervised learning, dataset",0.001126824,0.304140167,0.229254747,0.001126874,0.158043336,0.306308052,Clinics,0.16082332,FALSE,2.25,0.023439916,0.5,0.087101953,2,0.618927094,1907,0.243197688,0.243166663
13843,A Statistical and Dynamical Model for Forecasting COVID-19 Deaths based on a Hybrid Asymmetric Gaussian and SEIR Construct,0,10.1101/2020.06.21.20136937,6/23/20,medrxiv,0,1,forecasting model,0.00071059,0.011728904,0.000710592,0.985428753,0.000710578,0.000710582,Epidemiology,0.024231762,FALSE,45,0.587049292,42,0.644902328,1,0.537564047,831,0.004815796,0.443582866
13844,"Derivation and Validation of Clinical Prediction Rule for COVID-19 Mortality in Ontario, Canada",0,10.1101/2020.06.21.20136929,6/23/20,medrxiv,10.1093/ofid/ofaa463,3,logistic regression,0.001203472,0.00120343,0.195872369,0.07122399,0.001203444,0.729293295,Clinics,0.3153019,FALSE,18.66666667,0.279670975,51.33333333,0.686513246,2,0.618927094,1823,0.217673971,0.450696322
13845,The impact of asymptomatic COVID-19 infections on future pandemic waves,0,10.1101/2020.06.22.20137489,6/23/20,medrxiv,0,7,model fit,0.002898266,0.002898382,0.002898247,0.760573594,0.002898642,0.22783287,Epidemiology,0.2381661,FALSE,7.714285714,0.11225184,55.28571429,0.703037196,6,0.764429903,2116,0.293522755,0.468310423
13846,"Comorbidities might be a risk factor for the incidence of COVID-19: Evidence from a web-based survey of 780,961 participants",0,10.1101/2020.06.22.20137422,6/23/20,medrxiv,10.2196/21779,3,logistic regression,0.001272724,0.04246578,0.001272643,0.001272701,0.240411514,0.713304638,Clinics,0.45347932,FALSE,3.666666667,0.045952131,0,0.055525823,2,0.618927094,1271,0.074163255,0.198642076
13847,4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection,0,10.1101/2020.06.22.20137547,6/23/20,medrxiv,0,3,"supervised learning, neural network, transfer learning, dataset",0.000916702,0.000916696,0.957295067,0.000916685,0.039038139,0.000916711,Imaging,0.11152077,FALSE,5.666666667,0.079473066,0.333333333,0.073187048,5,0.739490092,1623,0.164218637,0.264092211
13848,Loss of pH switch unique to SARS-CoV2 supports unfamiliar virus pathology,0,10.1101/2020.06.16.155457,6/23/20,biorxiv,0,3,sequence alignment,0.534108047,0.197862278,0.000880227,0.199318691,0.066950496,0.00088026,Drug discovery,0.16220456,FALSE,7,0.10179974,1.333333333,0.13252609,0,0.403234768,1677,0.179388394,0.204237248
13849,Climatic-niche evolution of SARS CoV-2,0,10.1101/2020.06.18.147074,6/23/20,biorxiv,0,2,genomes,0.024007588,0.737330566,0.001538221,0.159210775,0.001538166,0.076374685,Genomics,0.7348253,TRUE,2,0.022141134,0,0.055525823,5,0.739490092,3953,0.590898146,0.352013799
13850,Detailed phylogenetic analysis of SARS-CoV-2 reveals latent capacity to bind human ACE2 receptor,0,10.1101/2020.06.22.165787,6/23/20,biorxiv,0,3,molecular dynamics simulation,0.364468239,0.565777857,0.001653034,0.064794821,0.001653042,0.001653006,Genomics,0.24952736,FALSE,40.33333333,0.542890717,11.33333333,0.375501739,1,0.537564047,9506,0.829280039,0.571309135
13851,"Accommodating individual travel history, global mobility, and unsampled diversity in phylogeography: a SARS-CoV-2 case study.",0,10.1101/2020.06.22.165464,6/23/20,biorxiv,0,12,"bayes, genomes",0.001461852,0.544199505,0.052335755,0.399079097,0.001461914,0.001461877,Genomics,0.21092504,FALSE,116.9230769,0.902220298,1842.538462,0.997926144,11,0.840175319,4579,0.647724536,0.847011575
13852,Modeling the effect of COVID-19 disease on the cardiac function: a computational study,0,10.1101/2020.06.23.166421,6/23/20,biorxiv,0,9,computational,0.001126851,0.001126826,0.001126896,0.315320031,0.001126823,0.680172573,Clinics,0.7515049,TRUE,165.1,0.954913724,149.6,0.883195076,0,0.403234768,2040,0.27570431,0.629261969
13853,"What association do political interventions, environmental and health variables have with the number of Covid-19 cases and deaths? A linear modeling approach",0,10.1101/2020.06.18.20135012,6/22/20,medrxiv,0,2,model fit,0.001141327,0.00114133,0.001141326,0.493642774,0.199045614,0.30388763,Epidemiology,0.25418633,FALSE,31,0.445111015,15,0.42594327,0,0.403234768,2655,0.41391765,0.422051676
13854,COVID-19 severity is associated with immunopathology and multi-organ damage,0,10.1101/2020.06.19.20134379,6/22/20,medrxiv,0,27,multi-omics,0.519861115,0.00175127,0.001751212,0.001751212,0.001751243,0.473133948,Drug discovery,0.63731974,TRUE,32.66666667,0.462922877,,,0,0.403234768,2569,0.399711052,0.421956232
13855,Transcriptomic profiling of disease severity in patients with COVID-19 reveals role of blood clotting and vasculature related genes,0,10.1101/2020.06.18.20132571,6/22/20,medrxiv,0,11,transcriptom,0.599780308,0.001751302,0.001751183,0.027264428,0.001751264,0.367701514,Drug discovery,0.3890936,FALSE,3.181818182,0.038035747,0.272727273,0.065828204,0,0.403234768,1441,0.113171202,0.15506748
13856,Covid19 infection spread in Greece: Ensemble forecasting models with statistically calibrated parameters and stochastic noise,0,10.1101/2020.06.18.20132977,6/22/20,medrxiv,0,2,forecasting model,0.001350357,0.001350338,0.044041119,0.950557439,0.001350351,0.001350396,Epidemiology,0.2388387,FALSE,15.5,0.234028078,26.5,0.542012309,0,0.403234768,1143,0.043342162,0.305654329
13857,An interpretable mortality prediction model for COVID-19 patients - alternative approach,0,10.1101/2020.06.14.20130732,6/22/20,medrxiv,0,1,"neural network, classifier, prediction model",0.001112638,0.001112681,0.733741245,0.133831873,0.001112714,0.129088848,Epidemiology,0.045549214,FALSE,45,0.587049292,17,0.451097137,3,0.667819001,1691,0.181796292,0.47194043
13858,Type and frequency of ocular and other known symptoms experienced by people who self diagnosed as suffering from COVID-19 in the UK,0,10.1101/2020.06.20.20134817,6/22/20,medrxiv,0,5,logistic regression,0.001098879,0.099904284,0.001098879,0.001098912,0.563410146,0.3333889,Healthcare,0.26414382,FALSE,11.6,0.175211825,4.8,0.249331014,1,0.537564047,1724,0.188297616,0.287601126
13859,Polymorphisms in the ACE2 Locus Associate with Severity of COVID-19 Infection,0,10.1101/2020.06.18.20135152,6/22/20,medrxiv,0,5,logistic regression,0.366518582,0.134778833,0.002422287,0.002422304,0.002422297,0.491435696,Clinics,0.38734347,FALSE,6,0.086028821,1.6,0.140687717,6,0.764429903,3125,0.484709848,0.368964072
13860,Functional characterization of SARS-CoV-2 infection suggests a complex inflammatory response and metabolic alterations,0,10.1101/2020.06.22.164384,6/22/20,biorxiv,0,12,probabilistic,0.713373421,0.00118733,0.001187355,0.001187372,0.026586847,0.256477675,Drug discovery,0.34958816,FALSE,87.75,0.836229822,95.25,0.81321916,0,0.403234768,2143,0.29857934,0.587815773
13861,Mathematical modeling explains differential SARS CoV-2 kinetics in lung and nasal passages in remdesivir treated rhesus macaques,0,10.1101/2020.06.21.163550,6/22/20,biorxiv,0,4,mathematical model,0.513857247,0.224666813,0.0013713,0.073329675,0.001371249,0.185403716,Drug discovery,0.40094167,FALSE,29,0.41993939,13,0.400521809,5,0.739490092,2039,0.273537202,0.458372123
13862,Endemic human coronaviruses induce distinct antibody repertoires in adults and children,0,10.1101/2020.06.21.163394,6/22/20,biorxiv,10.1172/jci.insight.144499,25,sequencing,0.361208374,0.500481775,0.001371281,0.001371274,0.134195958,0.001371338,Genomics,0.5809207,TRUE,24.875,0.366256417,10.375,0.360650254,2,0.618927094,4986,0.676619311,0.505613269
13863,"Pathogenicity, tissue tropism and potential vertical transmission of SARSr-CoV-2 in Malayan pangolins",0,10.1101/2020.06.22.164442,6/22/20,biorxiv,0,3,transcriptom,0.55920823,0.130430343,0.25521067,0.003101577,0.048947597,0.003101582,Drug discovery,0.3523496,FALSE,77.20689655,0.795534665,203.1724138,0.918718223,2,0.618927094,3200,0.49410065,0.706820158
13864,"In vivo antiviral host response to SARS-CoV-2 by viral load, sex, and age",0,10.1101/2020.06.22.165225,6/22/20,biorxiv,10.1371/journal.pbio.3000849,15,sequencing,0.540588006,0.236612121,0.000977421,0.000977448,0.000977462,0.219867541,Drug discovery,0.55167764,TRUE,38.93333333,0.528047498,38.2,0.622758898,22,0.908142478,8739,0.815073441,0.718505579
13865,Companion vaccine Bioinformatic design tool reveals limited functional genomic variability of SARS-Cov-2 Spike Receptor Binding Domain,0,10.1101/2020.06.22.133355,6/22/20,biorxiv,0,7,"bioinformatic, dataset",0.309550421,0.667416573,0.001141398,0.001141397,0.019608878,0.001141333,Genomics,0.388615,FALSE,85,0.825035562,57.42857143,0.712536794,0,0.403234768,2216,0.317120154,0.564481819
13866,An Analysis of SARS-CoV-2 Using ViReport,0,10.1101/2020.06.20.163162,6/21/20,biorxiv,0,2,genome sequences,0.003927606,0.821555925,0.003927461,0.162734079,0.003927454,0.003927476,Genomics,0.34507123,FALSE,12.5,0.189436576,3,0.199424672,1,0.537564047,1862,0.226101613,0.288131727
13867,"Genomic diversity and hotspot mutations in 30,983 SARS-CoV-2 genomes: moving toward a universal vaccine for the ""confined virus""?",0,10.1101/2020.06.20.163188,6/21/20,biorxiv,10.3390/pathogens9100829,24,genomes,0.145678721,0.848660815,0.001415089,0.001415128,0.001415143,0.001415104,Genomics,0.6709041,TRUE,17.20833333,0.259385243,3.416666667,0.208656676,13,0.858880178,6366,0.748374669,0.518824192
13868,Characterizing transcriptional regulatory sequences in coronaviruses and their role in recombination,0,10.1101/2020.06.21.163410,6/21/20,biorxiv,10.1093/molbev/msaa281,4,genomes,0.00242244,0.987888344,0.002422298,0.00242235,0.002422275,0.002422293,Genomics,0.44082686,FALSE,26,0.382398417,79.25,0.776759433,4,0.707574542,2308,0.338309656,0.551260512
13869,"Structural variability, expression profile and pharmacogenetics properties of TMPRSS2 gene as a potential target for COVID-19 therapy",0,10.1101/2020.06.20.156224,6/20/20,biorxiv,10.3390/genes12010019,16,"sequencing, pharmacogenom, genome-wide",0.67535073,0.319164241,0.001371268,0.001371253,0.001371235,0.001371272,Drug discovery,0.88536537,TRUE,19.1875,0.286721504,34.625,0.602555526,1,0.537564047,2763,0.430291356,0.464283108
13870,"SARS-CoV-2 growth, furin-cleavage-site adaptation and neutralization using serum from acutely infected, hospitalized COVID-19 patients",0,10.1101/2020.06.19.154930,6/20/20,biorxiv,10.1099/jgv.0.001481,15,sequencing,0.567659422,0.332416023,0.001653124,0.001653162,0.001653446,0.094964823,Drug discovery,0.6382307,TRUE,57.8,0.68736471,99.46666667,0.821380787,14,0.866658436,4574,0.64411269,0.754879156
13871,COVID-19 and first trimester spontaneous abortion: a case-control study of 225 pregnant patients,0,10.1101/2020.06.19.20135749,6/20/20,medrxiv,0,11,logistic regression,0.002490454,0.250732391,0.002490489,0.221048764,0.21861815,0.304619752,Clinics,0.73411024,TRUE,128.6363636,0.919722927,60.63636364,0.72310677,7,0.785110192,,,0.809313296
13872,Half of children entitled to free school meals do not have access to the scheme during the COVID-19 lockdown in the UK.,0,10.1101/2020.06.19.20135392,6/20/20,medrxiv,10.1016/j.puhe.2020.08.019,4,logistic regression,0.001330022,0.001330036,0.001330024,0.00133006,0.993349822,0.001330036,Healthcare,0.12651399,FALSE,48.75,0.62032284,30.5,0.573187048,4,0.707574542,1860,0.223212136,0.531074141
13873,Transcriptional response of signalling pathways to SARS-CoV-2 infection in normal human bronchial epithelial cells,0,10.1101/2020.06.20.163006,6/20/20,biorxiv,0,2,transcriptom,0.95565695,0.001310365,0.001310376,0.001310346,0.001310343,0.03910162,Drug discovery,0.52822673,TRUE,9,0.135320675,14,0.412898047,2,0.618927094,1906,0.237659523,0.351201335
13874,Analysis of SARS-CoV-2 specific T-cell receptors in ImmuneCode reveals cross-reactivity to immunodominant Influenza M1 epitope,0,10.1101/2020.06.20.160499,6/20/20,biorxiv,0,2,sequencing,0.505504028,0.480066021,0.003607348,0.003607366,0.003607656,0.003607581,Drug discovery,0.11517912,FALSE,79,0.802585194,42.5,0.647578271,5,0.739490092,3928,0.584637611,0.693572792
13875,"Multi-Omics and Integrated Network Approach to Unveil Evolutionary Patterns, Mutational Hotspots, Functional Crosstalk and Regulatory Interactions in SARS-CoV-2",0,10.1101/2020.06.20.162560,6/20/20,biorxiv,0,13,"phylogenom, genomes, multi-omics, network analysis",0.363618963,0.600318416,0.000846549,0.000846576,0.000846545,0.03352295,Genomics,0.73418295,TRUE,17.41666667,0.261797266,,,2,0.618927094,1977,0.256441127,0.379055163
13876,ApoE e4e4 genotype and mortality with COVID-19 in UK Biobank,0,10.1101/2020.06.19.20134908,6/20/20,medrxiv,10.1093/gerona/glaa169,7,logistic regression,0.002490785,0.19347295,0.002490481,0.00249077,0.08444517,0.714609844,Clinics,0.25499773,FALSE,9.857142857,0.147875564,9,0.337904736,19,0.89561084,1348,0.087165904,0.367139261
13877,Modelling for prediction of the spread and severity of COVID-19 and its association with socioeconomic factors and virus types,0,10.1101/2020.06.18.20134874,6/20/20,medrxiv,0,4,lstm,0.001565313,0.001565315,0.13839418,0.804773498,0.001565373,0.05213632,Epidemiology,0.31322032,FALSE,6.666666667,0.094996598,1.333333333,0.13252609,3,0.667819001,1470,0.120635685,0.253994344
13878,Incubation period and serial interval of Covid-19 in a chain of infections in Bahia Blanca (Argentina),0,10.1101/2020.06.18.20134825,6/20/20,medrxiv,0,4,dataset,0.000683142,0.072656128,0.000683148,0.581212724,0.053446856,0.291318002,Epidemiology,0.22007203,FALSE,20,0.298163152,0.5,0.087101953,6,0.764429903,1081,0.033710571,0.295851395
13879,COVID-19 preventive behaviors among people with anxiety and depression: Findings from Japan,0,10.1101/2020.06.19.20135293,6/20/20,medrxiv,10.1016/j.puhe.2020.09.017,4,logistic regression,0.002996431,0.00299643,0.045191732,0.002996475,0.942822514,0.002996418,Healthcare,0.31457618,FALSE,10,0.15214299,3.5,0.213607172,7,0.785110192,1449,0.112448832,0.315827296
13880,Simulation of COVID-19 Incubation Period and the Effect of Probability Distribution Functionon Model Training Using MIMANSA,0,10.1101/2020.06.18.20134460,6/20/20,medrxiv,0,4,mathematical model,0.00186175,0.001861695,0.119076246,0.797691193,0.001861843,0.077647274,Epidemiology,0.15326461,FALSE,14,0.213494959,6.25,0.283315494,0,0.403234768,1264,0.06886588,0.242227775
13881,Bayesian nowcasting with adjustment for delayed and incomplete reporting to estimate COVID-19 infections in the United States,0,10.1101/2020.06.17.20133983,6/20/20,medrxiv,0,11,bayes,0.001861697,0.048818363,0.001861686,0.943734717,0.001861705,0.001861831,Epidemiology,0.11888075,FALSE,28.44444444,0.41140454,24.11111111,0.522009633,6,0.764429903,3551,0.536961233,0.558701327
13882,The influence of comorbidity on the severity of COVID-19 disease: systematic review and analysis,0,10.1101/2020.06.18.20134478,6/20/20,medrxiv,0,4,dataset,0.000916699,0.00091669,0.074601616,0.225233986,0.000916719,0.697414291,Clinics,0.3538108,FALSE,3.5,0.044344115,0.25,0.065493712,2,0.618927094,2692,0.417047917,0.28645321
13883,"Cardiometabolic traits, sepsis and severe covid-19 with respiratory failure: a Mendelian randomization investigation",0,10.1101/2020.06.18.20134676,6/20/20,medrxiv,10.1161/circulationaha.120.050753,21,genome-wide,0.001438192,0.134652372,0.001438288,0.001438199,0.088286275,0.772746673,Clinics,0.19337869,FALSE,35.76190476,0.494341023,60.95238095,0.723909553,7,0.785110192,2398,0.355405731,0.589691625
13884,Epidemiological characterization of asymptomatic carriers of COVID-19 in Colombia,0,10.1101/2020.06.18.20134734,6/20/20,medrxiv,0,12,logistic regression,0.002238551,0.002238575,0.0022386,0.4569124,0.242503672,0.293868203,Epidemiology,0.28047517,FALSE,5.833333333,0.081514008,0.666666667,0.096200161,5,0.739490092,2026,0.268480616,0.296421219
13885,Racial and ethnic determinants of Covid-19 risk,0,10.1101/2020.06.18.20134742,6/20/20,medrxiv,0,30,logistic regression,0.001901706,0.001901714,0.001901813,0.001901799,0.844362228,0.14803074,Healthcare,0.3235491,FALSE,48.2,0.615808028,72.3,0.759834092,6,0.764429903,2051,0.273296412,0.603342109
13886,Clinical Outcomes of Patients with COVID-19 and Chronic Inflammatory and Autoimmune Rheumatic Diseases: A Multicentric Matched-Cohort Study,0,10.1101/2020.06.18.20133645,6/20/20,medrxiv,0,16,"immunome, logistic regression",0.001310328,0.001310343,0.054189787,0.001310317,0.00131034,0.940568885,Clinics,0.40309823,FALSE,92.375,0.849897953,75.9375,0.768798501,1,0.537564047,2480,0.375632073,0.632973144
13887,A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales,0,10.1101/2020.06.17.20133959,6/20/20,medrxiv,0,11,"bayes, dataset",0.00299646,0.002996566,0.002996675,0.985017291,0.0029965,0.002996508,Epidemiology,0.32276815,FALSE,8.272727273,0.121467005,9.454545455,0.343925609,2,0.618927094,1296,0.076330364,0.290162518
13888,"Identification of a critical horseshoe-shaped region in the nsp5 (Mpro, 3CLpro) protease interdomain loop (IDL) of coronavirus mouse hepatitis virus (MHV)",0,10.1101/2020.06.18.160671,6/19/20,biorxiv,0,9,structural model,0.690214179,0.306722068,0.000765934,0.000765944,0.000765941,0.000765935,Drug discovery,0.41888446,FALSE,23.44444444,0.347083926,75.33333333,0.767326733,0,0.403234768,1640,0.162773898,0.420104831
13889,Cytosine deamination in SARS-CoV-2 leads to progressive CpG depletion.,0,10.1101/2020.06.19.161687,6/19/20,biorxiv,10.1093/jmcb/mjab011,4,"genomes, dataset",0.210665651,0.550731204,0.002080597,0.232361404,0.002080602,0.002080542,Genomics,0.30271375,FALSE,46.25,0.598800173,127.75,0.861051646,3,0.667819001,2806,0.435347941,0.64075469
13890,An in-silico based clinical insight on the effect of noticeable CD4 conserved residues of SARS-CoV-2 on the CD4-MHC-I interactions,0,10.1101/2020.06.19.161802,6/19/20,biorxiv,0,4,in-silico,0.941084877,0.001059361,0.00105938,0.001059357,0.054677665,0.00105936,Drug discovery,0.2699539,FALSE,12.75,0.191786752,1.75,0.148381054,1,0.537564047,1826,0.211654226,0.27234652
13891,Multi-pronged human protein mimicry by SARS-CoV-2 reveals bifurcating potential for MHC detection and immune evasion,0,10.1101/2020.06.19.161620,6/19/20,biorxiv,10.1038/s41420-020-00321-y,6,proteom,0.784073296,0.180009933,0.02981844,0.00203278,0.002032797,0.002032754,Drug discovery,0.10469228,FALSE,161.8333333,0.952563548,900.1666667,0.992507359,0,0.403234768,3419,0.51721647,0.716380536
13892,Potential involvement of monoamine oxidase activity in SARS-COV2 infection and delirium onset,0,10.1101/2020.06.16.20128660,6/19/20,medrxiv,0,11,"computational, metabolom",0.611483444,0.002032889,0.002032812,0.067248597,0.002032877,0.315169381,Drug discovery,0.8447778,TRUE,32.63636364,0.462366256,43,0.649785925,0,0.403234768,1959,0.248976643,0.441090898
13893,A high-throughput strategy for COVID-19 testing based on next-generation sequencing,0,10.1101/2020.06.12.20129718,6/19/20,medrxiv,0,2,sequencing,0.001786568,0.381850468,0.359650548,0.173688976,0.081236728,0.001786712,Genomics,0.4797172,FALSE,112.5,0.895479003,81.5,0.782044421,3,0.667819001,1771,0.196484469,0.635456723
13894,A Counterfactual Graphical Model Reveals Economic and Sociodemographic Variables as Key Determinants of Country-Wise COVID-19 Burden,0,10.1101/2020.06.16.20132563,6/19/20,medrxiv,0,5,"bayes, probabilistic",0.001461872,0.001461872,0.001461867,0.844723111,0.14942932,0.001461958,Epidemiology,0.7117508,TRUE,7,0.10179974,0.6,0.09011239,1,0.537564047,2018,0.26390561,0.248345447
13895,Two distinct immunopathological profiles in lungs of lethal COVID-19,0,10.1101/2020.06.17.20133637,6/19/20,medrxiv,10.1038/s41467-020-18854-2,20,transcriptom,0.459251182,0.133804104,0.001486498,0.001486521,0.001486447,0.402485248,Drug discovery,0.2420013,FALSE,42.8,0.565279238,52.85,0.692601017,3,0.667819001,4072,0.59764026,0.630834879
13896,Essential epidemiological parameters of COVID-19 for clinical and mathematical modeling purposes: a rapid review and meta-analysis,0,10.1101/2020.06.17.20133587,6/19/20,medrxiv,0,6,mathematical model,0.006539736,0.006539684,0.006539659,0.801642945,0.172198107,0.006539868,Epidemiology,0.3021838,FALSE,7.333333333,0.105572392,1.333333333,0.13252609,4,0.707574542,1684,0.173609439,0.279820616
13897,"Higher risk of COVID-19 hospitalization for unemployed: an analysis of 1,298,416 health insured individuals in Germany",0,10.1101/2020.06.17.20133918,6/19/20,medrxiv,10.1007/s00103-021-03280-6,5,logistic regression,0.001653044,0.001653045,0.001653109,0.619538924,0.001653164,0.373848714,Epidemiology,0.2189554,FALSE,29.8,0.428474241,28.6,0.559071448,7,0.785110192,3712,0.556224416,0.582220074
13898,Analysis of Genetic Host Response Risk Factors in Severe COVID-19 Patients,0,10.1101/2020.06.17.20134015,6/19/20,medrxiv,0,10,dataset,0.642847886,0.112898542,0.00069935,0.000699363,0.000699362,0.242155497,Drug discovery,0.11715293,FALSE,7.7,0.111880759,0.9,0.104361788,6,0.764429903,11305,0.856248495,0.459230236
13899,Leveraging wearable technology to predict the risk of COVID-19 infection.,0,10.1101/2020.06.18.20131417,6/19/20,medrxiv,0,7,dataset,0.001461884,0.001461972,0.364445474,0.125599028,0.221386517,0.285645125,Clinics,0.35417473,FALSE,42,0.558537943,46,0.66416912,12,0.850299401,24108,0.937876234,0.752720674
13900,Mathematical Modeling of Coronavirus Reproduction Rate with Policy and Behavioral Effects,0,10.1101/2020.06.16.20133330,6/18/20,medrxiv,0,2,mathematical model,0.034265752,0.002296537,0.002296569,0.956548054,0.002296556,0.002296531,Epidemiology,0.2572664,FALSE,3,0.037293586,0,0.055525823,2,0.618927094,2095,0.280279316,0.248006455
13901,"Introduction to and spread of COVID-19 in care homes in Norfolk, UK",0,10.1101/2020.06.17.20133629,6/18/20,medrxiv,0,4,dataset,0.000999558,0.000999564,0.035550894,0.136657581,0.726459521,0.099332883,Healthcare,0.15086368,FALSE,110.75,0.891149731,237.5,0.933101418,5,0.739490092,2094,0.280038526,0.710944942
13902,Unexpected free fatty acid binding pocket in the cryo-EM structure of SARS-CoV-2 spike protein,0,10.1101/2020.06.18.158584,6/18/20,biorxiv,10.1126/science.abd3255,11,metabolom,0.990282501,0.001943544,0.001943506,0.001943514,0.001943472,0.001943463,Drug discovery,0.5873784,TRUE,79.45454545,0.804069516,100.0909091,0.82265186,5,0.739490092,9015,0.817962918,0.796043596
13903,Bcr-Abl tyrosine kinase inhibitor imatinib as a potential drug for COVID-19,0,10.1101/2020.06.18.158196,6/18/20,biorxiv,0,6,in silico,0.972866554,0.02173209,0.00135033,0.001350412,0.001350311,0.001350302,Drug discovery,0.37069896,FALSE,35.33333333,0.490259138,44.66666667,0.65754616,4,0.707574542,4361,0.621960029,0.619334967
13904,Variant analysis of SARS-CoV-2 strains in Middle Eastern countries,0,10.1101/2020.06.18.156810,6/18/20,biorxiv,10.1016/j.micpath.2021.104741,2,"genomes, sequence alignment",0.00165302,0.991734828,0.001653018,0.001653072,0.001653041,0.001653022,Genomics,0.19992217,FALSE,17,0.257467994,11,0.371287129,3,0.667819001,1827,0.209968697,0.376635705
13905,in-silica Analysis of SARS-CoV-2 viral strain using Reverse Vaccinology Approach: A Case Study for USA,0,10.1101/2020.06.16.154559,6/17/20,biorxiv,10.28991/scimedj-2020-02-si-9,1,bioinformatic,0.201338209,0.367170068,0.001098854,0.42819516,0.001098844,0.001098865,Epidemiology,0.70159435,TRUE,114,0.897767333,23,0.513513514,0,0.403234768,1960,0.246327956,0.515210892
13906,Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets,0,10.1101/2020.06.15.20130328,6/17/20,medrxiv,10.2196/21547,36,"prediction model, dataset",0.001112692,0.001112735,0.386305124,0.073820363,0.074899787,0.462749299,Clinics,0.57763344,TRUE,44.30555556,0.57987507,277.75,0.948019802,1,0.537564047,2168,0.296412232,0.590467788
13907,Risk of Depression in Family Caregivers: Unintended Consequence of COVID-19,0,10.1101/2020.06.15.20131532,6/17/20,medrxiv,10.1192/bjo.2020.99,2,dataset,0.031137996,0.001861719,0.001861971,0.001861774,0.961414806,0.001861735,Healthcare,0.7818346,TRUE,2,0.022141134,0,0.055525823,3,0.667819001,1919,0.235733205,0.245304791
13908,Artificial Intelligence for COVID-19 Risk Classification in Kidney Disease: Can Technology Unmask an Unseen Disease?,0,10.1101/2020.06.15.20131680,6/17/20,medrxiv,0,16,"machine learning, artificial intelligence, dataset",0.001254609,0.001254626,0.098146079,0.451509581,0.001254698,0.446580407,Epidemiology,0.45622116,FALSE,76.625,0.792504175,33.75,0.597337436,4,0.707574542,1881,0.223452926,0.58021727
13909,"Delirium is a presenting symptom of COVID-19 in frail, older adults: a cohort study of 322 hospitalised and 535 community-based older adults",0,10.1101/2020.06.15.20131722,6/17/20,medrxiv,10.1093/ageing/afaa223,20,logistic regression,0.001126808,0.001126869,0.001126894,0.001126842,0.436589062,0.558903525,Clinics,0.7363763,TRUE,14.7,0.221225802,7.75,0.312215681,3,0.667819001,3489,0.523477005,0.431184372
13910,Human food consumption patterns concerning COVID-19 pandemic,0,10.1101/2020.06.16.20132464,6/17/20,medrxiv,10.22034/JZD.2021.43762.1097,2,bayes,0.000956347,0.222829829,0.000956327,0.464596896,0.309704284,0.000956316,Epidemiology,0.950233,TRUE,16,0.243552477,4,0.231469093,0,0.403234768,3397,0.513363833,0.347905043
13911,Mobility network modeling explains higher SARS-CoV-2 infection rates among disadvantaged groups and informs reopening strategies,0,10.1101/2020.06.15.20131979,6/17/20,medrxiv,10.1038/s41586-020-2923-3,7,network model,0.024190482,0.001203438,0.001203461,0.906360548,0.065838605,0.001203466,Epidemiology,0.149154,FALSE,6.714285714,0.095182139,1.142857143,0.123628579,12,0.850299401,6225,0.737298339,0.451602114
13912,Predictive Modeling on the Number of Covid-19 Death Toll in the United States Considering the Effects of Coronavirus-Related Changes and Covid-19 Recovered Cases,0,10.1101/2020.06.15.20132357,6/17/20,medrxiv,10.33889/IJMEMS.2020.5.6.087,1,"mathematical model, predictive model",0.000612262,0.000612251,0.000612249,0.963243514,0.000612273,0.03430745,Epidemiology,0.06710246,FALSE,2,0.022141134,1,0.122023013,2,0.618927094,1924,0.237418733,0.250127494
13913,High-Accuracy Multiplexed SARS-CoV-2 Antibody Assay with Avidity and Saliva Capability on a Nano-Plasmonic Platform,0,10.1101/2020.06.16.155580,6/17/20,biorxiv,0,15,correlation analysis,0.15704746,0.478664211,0.224739894,0.001786585,0.001786583,0.135975267,Genomics,0.44495428,FALSE,25.86666667,0.378873152,,,2,0.618927094,2310,0.33325307,0.443684439
13914,Pervasive RNA secondary structure in the genomes of SARS-CoV-2 and other coronaviruses - an endeavour to understand its biological purpose,0,10.1101/2020.06.17.155200,6/17/20,biorxiv,10.1128/mbio.01661-20,1,genomes,0.250560755,0.744490835,0.00123707,0.001237121,0.001237101,0.001237118,Genomics,0.32322812,FALSE,11,0.167171748,7,0.299973241,3,0.667819001,2020,0.261256923,0.349055228
13915,Multi-level proteomics reveals host-perturbation strategies of SARS-CoV-2 and SARS-CoV,0,10.1101/2020.06.17.156455,6/17/20,biorxiv,0,30,"transcriptom, proteom,  omics, interactom, phosphoproteom, multi-omics, dataset",0.944571613,0.001593583,0.049054149,0.001593621,0.001593535,0.001593499,Drug discovery,0.3917602,FALSE,49.5,0.628362917,136.5333333,0.871420926,71,0.96950429,17854,0.915001204,0.846072334
13916,Covid-19 rapid test by combining a random forest based web system and blood tests,0,10.1101/2020.06.12.20129866,6/16/20,medrxiv,0,11,"computational, artificial intelligence",0.00115624,0.001156265,0.676114867,0.166713137,0.001156273,0.153703218,Epidemiology,0.5677504,TRUE,7.545454545,0.108602882,1.181818182,0.124765855,2,0.618927094,1378,0.089573802,0.235467408
13917,Low and high infection dose transmission of SARS-CoV-2 in the first COVID-19 clusters in Northern Germany,0,10.1101/2020.06.11.20127332,6/16/20,medrxiv,0,17,"sequencing, metagenom",0.001171554,0.865982225,0.001171537,0.001171622,0.072557135,0.057945927,Genomics,0.20305064,FALSE,41.17647059,0.549817552,86.29411765,0.793550977,5,0.739490092,4093,0.595473152,0.669582943
13918,Comparison of Mental Health Symptoms Prior to and During COVID-19 among Patients with Systemic Sclerosis from Four Countries: A Scleroderma Patient-centered Intervention Network (SPIN) Cohort Study,0,10.1101/2020.06.13.20128694,6/16/20,medrxiv,10.1016/j.jpsychores.2020.110262,14,logistic regression,0.000518917,0.000518921,0.000518929,0.07058245,0.863172896,0.064687887,Healthcare,0.3724507,FALSE,68.71428571,0.757684458,72.42857143,0.760034787,1,0.537564047,1249,0.061882976,0.529291567
13919,Spreading Analysis of COVID-19 Epidemic in Bangladesh by Dynamical Mathematical Modelling,0,10.1101/2020.06.12.20130047,6/16/20,medrxiv,0,3,mathematical model,0.001901703,0.001901724,0.001901714,0.990491409,0.001901748,0.001901701,Epidemiology,0.10665232,FALSE,7.666666667,0.111633373,0,0.055525823,0,0.403234768,1139,0.038526366,0.152230082
13920,"Risk factors for critical-ill events of patients with COVID-19 in Wuhan, China: a retrospective cohort study",0,10.1101/2020.06.14.20130765,6/16/20,medrxiv,0,10,logistic regression,0.001034556,0.001034582,0.001034564,0.001034572,0.001034573,0.994827152,Clinics,0.9050548,TRUE,5.7,0.079596759,,,4,0.707574542,1048,0.026486877,0.271219393
13921,Modeling the dynamics of COVID19 spread during and after social distancing,0,10.1101/2020.06.13.20130625,6/16/20,medrxiv,0,2,network model,0.001310354,0.001310353,0.001310341,0.993448275,0.001310347,0.001310329,Epidemiology,0.19339597,FALSE,135.5,0.927206383,192,0.913633931,1,0.537564047,1627,0.156272574,0.633669234
13922,Seroprevalence against COVID-19 and follow-up of suspected cases in primary health care in Spain,0,10.1101/2020.06.13.20130575,6/16/20,medrxiv,0,13,logistic regression,0.001034569,0.122684502,0.001034584,0.001034643,0.270072433,0.604139268,Clinics,0.6333331,TRUE,15.38461538,0.231183128,9.461538462,0.344059406,7,0.785110192,2498,0.371779437,0.433033041
13923,Impact of weather indicators on the COVID-19 outbreak: A multi-state study in India,0,10.1101/2020.06.14.20130666,6/16/20,medrxiv,0,2,dataset,0.002032755,0.00203276,0.002032763,0.989836095,0.002032788,0.00203284,Epidemiology,0.48771217,FALSE,6.5,0.093512277,0.5,0.087101953,4,0.707574542,1207,0.052492174,0.235170236
13924,Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app,0,10.1101/2020.06.12.20129056,6/16/20,medrxiv,0,33,dataset,0.034529881,0.002296719,0.327420433,0.002296721,0.142360397,0.491095848,Clinics,0.5659324,TRUE,25.18181818,0.369967221,17.33333333,0.45424137,31,0.931971109,84169,0.986756562,0.685734066
13925,"Explore the Possible Impact of BCG Vaccination Policy on the Morbidity, Mortality, and Recovery Rates due to COVID-19 Infection.",0,10.1101/2020.06.14.20131268,6/16/20,medrxiv,0,2,logistic regression,0.002032777,0.002032772,0.002032735,0.321595278,0.326554015,0.345752423,Clinics,0.18121627,FALSE,17,0.257467994,6.5,0.288132192,0,0.403234768,2132,0.286058271,0.308723306
13926,Proteomics Uncovers Immunosuppression in COVID-19 Patients with Long Disease Course,0,10.1101/2020.06.14.20131078,6/16/20,medrxiv,0,29,"machine learning, proteom",0.381180531,0.001717228,0.122802256,0.00171726,0.088845123,0.403737603,Clinics,0.36989444,FALSE,6.807692308,0.096357227,1.692307692,0.145169922,0,0.403234768,3441,0.516253311,0.290253807
13927,Cumulative effect of aging and SARS-CoV2 infection on poor prognosis in the elderly: Insights from transcriptomic analysis of lung and blood,0,10.1101/2020.06.15.151761,6/16/20,biorxiv,10.1007/s12041-020-01233-7,2,"transcriptom, dataset",0.444043776,0.001098841,0.001098873,0.001098846,0.001098849,0.551560815,Clinics,0.49373943,FALSE,34.5,0.482404601,24.5,0.525086968,0,0.403234768,2113,0.281964845,0.423172795
13928,"AGE IS ASSOCIATED WITH INCREASED EXPRESSION OF PATTERN RECOGNITION RECEPTOR GENES AND ACE2, THE RECEPTOR FOR SARS-COV-2: IMPLICATIONS FOR THE EPIDEMIOLOGY OF COVID-19 DISEASE",0,10.1101/2020.06.15.134403,6/16/20,biorxiv,0,12,"genome-wide, dataset",0.639357814,0.076535012,0.002996595,0.002996497,0.002996673,0.275117408,Drug discovery,0.31408912,FALSE,84.83333333,0.824107861,82.58333333,0.784586567,1,0.537564047,2325,0.334697809,0.620239071
13929,Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection.,0,10.1101/2020.06.16.155267,6/16/20,biorxiv,0,3,"bioinformatic, genome-wide",0.774179322,0.220940463,0.001220046,0.001220088,0.001220031,0.00122005,Drug discovery,0.1964992,FALSE,83.66666667,0.819716742,118.3333333,0.849344394,1,0.537564047,2536,0.379966289,0.646647868
13930,Machine Learning Models Identify Inhibitors of SARS-CoV-2,0,10.1101/2020.06.16.154765,6/16/20,biorxiv,0,17,"bayes, machine learning, predictive model",0.716520648,0.001565362,0.236941182,0.041842104,0.001565345,0.001565359,Drug discovery,0.6868738,TRUE,44.52941176,0.582039706,38.76470588,0.625903131,2,0.618927094,2763,0.423067662,0.562484398
13931,Comparing library preparation methods for SARS-CoV-2 multiplex amplicon sequencing on the Illumina MiSeq platform,0,10.1101/2020.06.16.154286,6/16/20,biorxiv,0,20,"sequencing, genomes",0.002130743,0.98934647,0.002130709,0.002130762,0.002130678,0.002130639,Genomics,0.06490582,FALSE,37.35,0.511658111,23.7,0.518129516,3,0.667819001,2459,0.361425476,0.514758026
13932,Dissecting the common and compartment-specific features of COVID-19 severity in the lung and periphery with single-cell resolution,0,10.1101/2020.06.15.147470,6/15/20,biorxiv,0,3,"sequencing, network analysis",0.576606765,0.049626108,0.000863091,0.000863078,0.000863072,0.371177886,Drug discovery,0.2508191,FALSE,7.333333333,0.105572392,2.333333333,0.173401124,1,0.537564047,3129,0.472911149,0.322362178
13933,Genome-wide mapping of therapeutically-relevant SARS-CoV-2 RNA structures,0,10.1101/2020.06.15.151647,6/15/20,biorxiv,10.1093/nar/gkaa1053,11,"genome-wide, genomes",0.366184455,0.628935483,0.001220024,0.001220043,0.001220004,0.001219992,Genomics,0.63373655,TRUE,56.09090909,0.675490135,82.63636364,0.784787263,15,0.874313229,6632,0.753672044,0.772065668
13934,Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera,0,10.1101/2020.06.15.152587,6/15/20,biorxiv,0,27,"image analysis, proteom",0.002032843,0.749842822,0.242025863,0.002032882,0.002032845,0.002032745,Genomics,0.5093427,TRUE,48.03703704,0.61500402,82.07407407,0.783181697,8,0.799987654,9954,0.832169516,0.757585722
13935,Interfacial Water Molecules Make RBD of SPIKE Protein and Human ACE2 to Stick Together,0,10.1101/2020.06.15.152892,6/15/20,biorxiv,0,4,"molecular dynamics simulation, computational, in-silico",0.944905653,0.000977477,0.000977437,0.05118454,0.000977458,0.000977436,Drug discovery,0.67359596,TRUE,11.25,0.169274538,1.75,0.148381054,3,0.667819001,2077,0.271610884,0.314271369
13936,Genome sequencing of the first SARS-CoV-2 reported from patients with COVID-19 in Ecuador.,0,10.1101/2020.06.11.20128330,6/14/20,medrxiv,0,17,"sequencing, metagenom, whole genome, genomes",0.001330041,0.909836005,0.001330118,0.001330098,0.001330091,0.084843647,Genomics,0.19919631,FALSE,3.941176471,0.04824046,0.470588235,0.077200963,8,0.799987654,3535,0.524199374,0.362407113
13937,Global research trend in the treatment of the new Coronavirus diseases (COVID-19) : bibliometric analysis.,0,10.1101/2020.06.13.20122762,6/14/20,medrxiv,0,2,network analysis,0.412878645,0.001171593,0.001171612,0.482871554,0.001171602,0.100734994,Epidemiology,0.63965905,TRUE,7.5,0.108355495,0.5,0.087101953,0,0.403234768,2484,0.363592584,0.2405712
13938,Multiplex Isothermal Amplification Coupled with Nanopore Sequencing for Rapid Detection and Mutation Surveillance of SARS-CoV-2,0,10.1101/2020.06.12.20129247,6/14/20,medrxiv,0,15,sequencing,0.002490635,0.749750076,0.240287532,0.002490579,0.002490478,0.0024907,Genomics,0.54979306,TRUE,17.18181818,0.258952316,24.27272727,0.523481402,1,0.537564047,3538,0.524921743,0.461229877
13939,COVID-19 Deaths: Which Explanatory Variables Matter the Most?,0,10.1101/2020.06.11.20129007,6/14/20,medrxiv,0,4,"machine learning, dataset",0.001291217,0.001291227,0.001291331,0.815339142,0.179495793,0.001291289,Epidemiology,0.27488512,FALSE,15.25,0.229884347,2.75,0.187583623,0,0.403234768,3587,0.532145437,0.338212044
13940,"Single-cell screening of SARS-CoV-2 target cells in pets, livestock, poultry and wildlife",0,10.1101/2020.06.13.149690,6/14/20,biorxiv,0,52,transcriptom,0.614691898,0.37915552,0.001538133,0.001538172,0.001538149,0.001538126,Drug discovery,0.68116903,TRUE,58.59615385,0.693363844,,,9,0.814309525,5322,0.687695642,0.73178967
13941,In silico multi-epitope vaccine against covid19 showing effective interaction with HLA-B*15:03,0,10.1101/2020.06.10.143545,6/14/20,biorxiv,0,20,in silico,0.884476354,0.110282174,0.001310359,0.001310382,0.001310373,0.001310359,Drug discovery,0.43028197,FALSE,16.15,0.244294638,3.05,0.199491571,0,0.403234768,3054,0.463038767,0.327514936
13942,Genotypic and antigenic study of SARS-CoV-2 from an Indian isolate.,0,10.1101/2020.06.10.140657,6/14/20,biorxiv,0,14,"bioinformatic, in-silico",0.327129267,0.637497749,0.001461936,0.001461925,0.001461965,0.030987157,Genomics,0.5612718,TRUE,61.85714286,0.714268044,38.42857143,0.624163768,0,0.403234768,1826,0.203226583,0.486223291
13943,Dynamics of the ACE2 - SARS-CoV/SARS-CoV-2 spike protein interface reveal unique mechanisms,0,10.1101/2020.06.10.143990,6/14/20,biorxiv,10.1038/s41598-020-71188-3,2,molecular dynamics simulation,0.993636696,0.001272661,0.001272662,0.001272705,0.001272653,0.001272624,Drug discovery,0.52118593,TRUE,16.5,0.249366071,6,0.280037463,0,0.403234768,2400,0.347459668,0.320024492
13944,Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images,0,10.1101/2020.06.12.20129643,6/14/20,medrxiv,0,3,"deep learning, neural network, adversarial network",0.001751138,0.00175115,0.991244026,0.001751221,0.001751203,0.001751262,Imaging,0.90571904,TRUE,12.66666667,0.191044592,1.333333333,0.13252609,4,0.707574542,1292,0.070069829,0.275303763
13945,High Incidence of Venous Thrombosis in Patients with Moderate to Severe COVID-19,0,10.1101/2020.06.12.20129536,6/14/20,medrxiv,10.1007/s12185-020-03061-y,4,logistic regression,0.000926291,0.000926311,0.071708637,0.000926298,0.000926306,0.924586157,Clinics,0.50539553,TRUE,12.75,0.191786752,0,0.055525823,2,0.618927094,2790,0.42475319,0.322748215
13946,How much leeway is there to relax COVID-19 control measures?,0,10.1101/2020.06.12.20129833,6/14/20,medrxiv,10.1016/j.epidem.2021.100453,10,bayes,0.002638985,0.002639067,0.002638966,0.986804862,0.002639124,0.002638997,Epidemiology,0.13565475,FALSE,25.7,0.376584823,28.9,0.561279101,4,0.707574542,4810,0.65494823,0.575096674
13947,WHOLE-GENOME SEQUENCING AND DE NOVO ASSEMBLY OF A 2019 NOVEL CORONAVIRUS (SARS-COV-2) STRAIN ISOLATED IN VIETNAM,0,10.1101/2020.06.12.149377,6/13/20,biorxiv,10.15625/1811-4989/18/2/15082,16,"sequencing, whole-genome, whole genome",0.002296538,0.818411531,0.002296677,0.134155708,0.002296606,0.040542939,Genomics,0.2609564,FALSE,24.25,0.358092646,6.4375,0.28632593,1,0.537564047,3104,0.466650614,0.412158309
13948,Single-cell transcriptomic analysis of SARS-CoV-2 reactive CD4+ T cells,0,10.1101/2020.06.12.148916,6/13/20,biorxiv,10.1016/j.cell.2020.10.001,14,transcriptom,0.824280896,0.00171724,0.001717169,0.001717205,0.00171726,0.16885023,Drug discovery,0.65188897,TRUE,32,0.455810502,61.5,0.726585496,33,0.936045435,11022,0.845412954,0.740963597
13949,"Multi-epitope Based Peptide Vaccine Design Using Three Structural Proteins (S, E, and M) of SARS-CoV-2: An In Silico Approach",0,10.1101/2020.06.13.149880,6/13/20,biorxiv,0,8,"bioinformatic, in silico",0.938057248,0.056095275,0.001461877,0.001461877,0.001461873,0.001461851,Drug discovery,0.6496284,TRUE,2.5,0.027459954,0,0.055525823,1,0.537564047,2021,0.255477968,0.219006948
13950,DeepEMhacer: a deep learning solution for cryo-EM volume post-processing,0,10.1101/2020.06.12.148296,6/13/20,biorxiv,0,6,"deep learning, dataset",0.236696385,0.173633223,0.584518712,0.001717292,0.001717219,0.001717169,Drug discovery,0.06733045,FALSE,16.16666667,0.244480178,22,0.503746321,12,0.850299401,8237,0.796051047,0.598644237
13951,Analysis of SARS-CoV-2 Genomes from Southern California Reveals Community Transmission Pathways in the Early Stage of the US COVID-19 Pandemic,0,10.1101/2020.06.12.20129999,6/13/20,medrxiv,0,9,"sequencing, genomes",0.001310368,0.862937932,0.00131039,0.001310413,0.001310374,0.131820522,Genomics,0.31886888,FALSE,8.444444444,0.124868576,3.777777778,0.220029435,1,0.537564047,2636,0.397543944,0.320001501
13952,Sentinel surveillance of SARS-CoV-2 in wastewater anticipates the occurrence of COVID-19 cases,0,10.1101/2020.06.13.20129627,6/13/20,medrxiv,0,8,genomes,0.007673905,0.596531237,0.007674102,0.372772716,0.007674074,0.007673966,Genomics,0.2244857,FALSE,7.25,0.104149917,5.5,0.267259834,47,0.95493549,134221,0.992535516,0.579720189
13953,Nearly Perfect Forecasting of the Total COVID-19 Cases in India: A Numerical Approach,0,10.1101/2020.06.13.20130096,6/13/20,medrxiv,0,1,computational,0.0033353,0.003335251,0.003335397,0.983323583,0.003335242,0.003335227,Epidemiology,0.3128913,FALSE,7,0.10179974,0,0.055525823,6,0.764429903,878,0.006742114,0.232124395
13954,ARB/ACEI use and severe COVID-19: a nationwide case-control study,0,10.1101/2020.06.12.20129916,6/13/20,medrxiv,0,8,logistic regression,0.121999094,0.000728122,0.000728108,0.000728124,0.084011518,0.791805034,Clinics,0.54180646,TRUE,14.125,0.213804193,7.875,0.314557131,6,0.764429903,1810,0.197447628,0.372559714
13955,The BCG dilemma: linear versus non-linear correlation models over the time of the COVID-19 pandemic,0,10.1101/2020.06.13.20129569,6/13/20,medrxiv,0,3,dataset,0.006540035,0.00654009,0.424136104,0.006540123,0.006539773,0.549703875,Clinics,0.26122308,FALSE,119.6666667,0.906796957,184,0.908817233,0,0.403234768,1187,0.04599085,0.566209952
13956,COVID-19: Dying is Bad--Losing Life is Worse,0,10.1101/2020.06.08.20050559,6/12/20,medrxiv,0,3,machine learning,0.001593509,0.001593474,0.001593634,0.736948965,0.001593557,0.256676862,Epidemiology,0.13778329,FALSE,8.333333333,0.123693488,6.666666667,0.290607439,0,0.403234768,15046,0.891403804,0.427234875
13957,A Fully Automated Deep Learning-based Network ForDetecting COVID-19 from a New And Large Lung CT ScanDataset,0,10.1101/2020.06.08.20121541,6/12/20,medrxiv,10.1016/j.bspc.2021.102588,3,"deep learning, image processing, dataset",0.001171552,0.001171567,0.994142138,0.00117159,0.001171559,0.001171594,Imaging,0.42489776,FALSE,13.66666667,0.207310285,3.333333333,0.206515922,1,0.537564047,3852,0.562484951,0.378468801
13958,A Comparative Study of Target Reconstruction of Ultra-High-Resolution CT for Patients with Corona-Virus Disease 2019 (COVID-19),0,10.1101/2020.06.04.20119206,6/12/20,medrxiv,0,6,logistic regression,0.06209687,0.00156538,0.660047523,0.001565335,0.001565338,0.273159554,Imaging,0.30936486,FALSE,11.16666667,0.168161296,1.666666667,0.145036125,1,0.537564047,882,0.006982904,0.214436093
13959,Oscillations in USA COVID-19 Incidence and Mortality Data reflect societal factors,0,10.1101/2020.06.08.20123786,6/12/20,medrxiv,10.1128/msystems.00544-20,4,dataset,0.040192296,0.001653107,0.001653161,0.844088128,0.001653085,0.110760223,Epidemiology,0.2902145,FALSE,273.75,0.988310965,391.5,0.970230131,2,0.618927094,2234,0.303876716,0.720336227
13960,Predictors of household food insecurity in the United States during the COVID-19 pandemic,0,10.1101/2020.06.10.20122275,6/12/20,medrxiv,10.1017/s1368980021000355,6,logistic regression,0.001330028,0.001330028,0.001330016,0.001330029,0.968886999,0.0257929,Healthcare,0.49806973,FALSE,30.66666667,0.440225122,13.66666667,0.407412363,4,0.707574542,2236,0.305080665,0.465073173
13961,The arrival and spread of SARS-CoV2 in Colombia,0,10.1101/2020.06.11.20125799,6/12/20,medrxiv,0,23,"phylogenom, genomes",0.005352806,0.973235081,0.005352947,0.005353451,0.005352853,0.005352863,Genomics,0.36057144,FALSE,40.56521739,0.544993506,45.86956522,0.663031844,2,0.618927094,2167,0.288225379,0.528794456
13962,Successful contact tracing systems for COVID-19 rely on effective quarantine and isolation,0,10.1101/2020.06.10.20125013,6/12/20,medrxiv,0,8,mathematical model,0.00054781,0.000547847,0.006062922,0.960186145,0.032107467,0.00054781,Epidemiology,0.024998426,FALSE,35.625,0.493227782,34.125,0.599611988,3,0.667819001,2268,0.312785938,0.518361177
13963,Interregional SARS-CoV-2 spread from a single introduction outbreak in a meat-packing plant in northeast Iowa,0,10.1101/2020.06.08.20125534,6/12/20,medrxiv,0,5,genomes,0.001653074,0.477151043,0.001653069,0.36776302,0.150126734,0.001653059,Genomics,0.7688024,TRUE,54.6,0.66553281,59.8,0.719962537,4,0.707574542,3241,0.485673007,0.644685724
13964,Covid-19 vs BCG: a Statistical Significance Analysis,0,10.1101/2020.06.08.20125542,6/12/20,medrxiv,0,1,dataset,0.005047892,0.005047663,0.005047836,0.974760857,0.005047821,0.005047931,Epidemiology,0.689646,TRUE,24,0.35574247,2,0.164302917,1,0.537564047,1916,0.224656875,0.320566577
13965,A unified activities-based approach to the modelling of viral epidemics and COVID-19 as an illustrative example,0,10.1101/2020.06.10.20127597,6/12/20,medrxiv,0,2,mathematical model,0.13372013,0.002806583,0.002806485,0.797544776,0.060315124,0.002806903,Epidemiology,0.17724136,FALSE,3.5,0.044344115,3.5,0.213607172,0,0.403234768,894,0.007464484,0.167162634
13966,Ethnicity and outcomes in patients hospitalised with COVID-19 infection in East London: an observational cohort study,0,10.1101/2020.06.10.20127621,6/12/20,medrxiv,10.1136/bmjopen-2020-042140,7,dataset,0.000377312,0.000377321,0.000377315,0.123925124,0.170113296,0.704829632,Clinics,0.31839728,FALSE,82.57142857,0.816500711,103.7142857,0.828605834,21,0.903944688,8156,0.794124729,0.83579399
13967,Tracking and Classifying Global COVID-19 Cases by using 1D Deep Convolution Neural Network,0,10.1101/2020.06.09.20126565,6/12/20,medrxiv,0,1,"artificial intelligence, neural network",0.001786602,0.069000503,0.380480851,0.545158652,0.00178688,0.001786512,Epidemiology,0.519655,TRUE,12,0.183190055,9,0.337904736,2,0.618927094,1159,0.039489526,0.294877853
13968,"Even one metre seems generous. A reanalysis of data in: Chu et al. (2020) Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19.",0,10.1101/2020.06.11.20127415,6/12/20,medrxiv,0,1,dataset,0.004530739,0.00453079,0.00453092,0.977346216,0.004530661,0.004530676,Epidemiology,0.19067249,FALSE,11,0.167171748,0,0.055525823,3,0.667819001,1835,0.201541055,0.273014407
13969,The Computational Patient has Diabetes and a COVID,0,10.1101/2020.06.10.20127183,6/12/20,medrxiv,0,2,computational,0.33205111,0.021604038,0.001291301,0.429969932,0.001291301,0.213792318,Epidemiology,0.4109991,FALSE,24,0.35574247,62.5,0.730867006,2,0.618927094,1572,0.134842283,0.460094713
13970,On the sensitivity of non-pharmaceutical intervention models for SARS-CoV-2 spread estimation,0,10.1101/2020.06.10.20127324,6/12/20,medrxiv,0,13,prediction model,0.001823359,0.001823359,0.001823492,0.99088309,0.001823347,0.001823353,Epidemiology,0.42949602,FALSE,45.69230769,0.592924733,27.46153846,0.549170458,5,0.739490092,1898,0.219600289,0.525296393
13971,Meso-scale modeling of COVID-19 spatio-temporal outbreak dynamics in Germany,0,10.1101/2020.06.10.20126771,6/12/20,medrxiv,0,8,network model,0.00218323,0.002183249,0.084370961,0.906896135,0.002183224,0.002183202,Epidemiology,0.15482372,FALSE,21.875,0.323025543,7.625,0.309071448,7,0.785110192,1991,0.245846376,0.41576339
13972,"Angiotensin-converting enzyme (ACE1, ACE2) gene variants are associated with COVID19 severity depending on the hypertension status.",0,10.1101/2020.06.11.20128033,6/12/20,medrxiv,0,15,"sequencing, logistic regression",0.300363168,0.15702784,0.001786492,0.001786506,0.001786576,0.537249419,Clinics,0.425292,FALSE,18.93333333,0.282330385,10.86666667,0.367674605,2,0.618927094,1547,0.129063328,0.349498853
13973,"Associations between wearing masks, washing hands, and social distancing practices, and risk of COVID-19 infection in public: a cohort-based case-control study in Thailand",0,10.1101/2020.06.11.20128900,6/12/20,medrxiv,10.3201/eid2611.203003,21,logistic regression,0.001511791,0.001511823,0.00151189,0.437481343,0.475623637,0.082359516,Healthcare,0.74349463,TRUE,8.3,0.121776238,10.2,0.357706717,6,0.764429903,8753,0.808331327,0.513061046
13974,Comorbidity and Sociodemographic determinants in COVID-19 Mortality in an US Urban Healthcare System,0,10.1101/2020.06.11.20128926,6/12/20,medrxiv,0,3,logistic regression,0.001653032,0.001653047,0.001653034,0.001653135,0.124144225,0.869243527,Clinics,0.3680908,FALSE,4,0.054734368,9,0.337904736,14,0.866658436,2191,0.292559595,0.387964284
13975,Differential impacts of contact tracing and lockdowns on outbreak size in COVID-19 model applied to China,0,10.1101/2020.06.10.20127860,6/12/20,medrxiv,0,3,mathematical model,0.001291228,0.00129123,0.001291232,0.993543797,0.001291297,0.001291216,Epidemiology,0.4317559,FALSE,7,0.10179974,2.333333333,0.173401124,3,0.667819001,1585,0.13797255,0.270248104
13976,Optimizing the COVID-19 Intervention Policy in Scotland and the Case for Testing and Tracing,0,10.1101/2020.06.11.20128173,6/12/20,medrxiv,0,2,optimization model,0.001254599,0.001254613,0.001254607,0.993726988,0.001254611,0.001254583,Epidemiology,0.037477642,FALSE,34.5,0.482404601,46,0.66416912,0,0.403234768,1265,0.061160607,0.402742274
13977,Assessing the influence of parental anxiety on childhood anxiety during the COVID-19 pandemic in the United Arab Emirates,0,10.1101/2020.06.11.20128371,6/12/20,medrxiv,0,10,logistic regression,0.001112659,0.001112656,0.001112632,0.001112689,0.994436743,0.001112621,Healthcare,0.8207295,TRUE,51.8,0.645061538,44.7,0.657679957,3,0.667819001,2074,0.266554298,0.559278698
13978,Estimating the Impact of Control Measures to Prevent Outbreaks of COVID-19 Associated with Air Travel into a COVID-19-free country: A Simulation Modelling Study,0,10.1101/2020.06.10.20127977,6/12/20,medrxiv,0,3,simulation model,0.001486542,0.048410597,0.001486476,0.688993747,0.258136165,0.001486473,Epidemiology,0.17524967,FALSE,13,0.197352959,3.666666667,0.217621086,7,0.785110192,2335,0.329882013,0.382491563
13979,Performance and health economic evaluation of the Mount Sinai COVID-19 serological assay identifies modification of thresholding as necessary to maximise specificity of the assay,0,10.1101/2020.06.11.20128306,6/12/20,medrxiv,0,18,probabilistic,0.001350416,0.321442881,0.186564315,0.095759766,0.001350406,0.393532216,Clinics,0.265914,FALSE,18.05555556,0.271630899,21,0.492239765,1,0.537564047,1689,0.165422586,0.366714324
13980,An international assessment of the COVID-19 pandemic using ensemble data assimilation,0,10.1101/2020.06.11.20128777,6/12/20,medrxiv,0,12,model simulation,0.001622729,0.001622709,0.089969826,0.879192312,0.001622727,0.025969697,Epidemiology,0.035888076,FALSE,49,0.624281032,167.9166667,0.897979663,3,0.667819001,3848,0.561762581,0.687960569
13981,Hidden in plain sight: The effects of BCG vaccination in COVID-19 pandemic,0,10.1101/2020.06.09.142760,6/12/20,biorxiv,10.1002/jmv.26707,8,"bioinformatic, transcriptom, dataset",0.716113394,0.002130689,0.002130701,0.002130693,0.131861721,0.145632802,Drug discovery,0.52576274,TRUE,22,0.326056033,7.25,0.302983677,1,0.537564047,3835,0.559595473,0.431549808
13982,Interactions of SARS-CoV-2 infection with chronic obesity inflammation: a complex network phenomenon,0,10.1101/2020.06.12.148577,6/12/20,biorxiv,0,5,network model,0.221748337,0.001350338,0.092614583,0.197883315,0.00135036,0.485053067,Clinics,0.717721,TRUE,15,0.227596017,3.2,0.202100615,0,0.403234768,2598,0.385745244,0.304669161
13983,TMPRSS2 variants and their susceptibility to COVID-19: focus in East Asian and European populations.,0,10.1101/2020.06.09.20126680,6/11/20,medrxiv,0,16,exom,0.234891043,0.602770194,0.002032824,0.002032891,0.002033009,0.156240039,Genomics,0.4026255,FALSE,20,0.298163152,5.2,0.259700294,3,0.667819001,2388,0.338791235,0.391118421
13984,Mathematical estimation of COVID-19 prevalence in Latin America,0,10.1101/2020.06.09.20126326,6/11/20,medrxiv,0,5,mathematical model,0.002720077,0.002720174,0.002720158,0.948638871,0.00272018,0.040480539,Epidemiology,0.1869604,FALSE,10.2,0.153627312,7.2,0.302047097,0,0.403234768,1290,0.065735613,0.231161197
13985,Impact of public health measures to control SARS-CoV-2Outbreak: a data-driven analysis,0,10.1101/2020.06.10.20126870,6/11/20,medrxiv,10.3389/fpubh.2020.583401,6,bayes,0.001461882,0.001461912,0.001461871,0.992690532,0.001461884,0.001461919,Epidemiology,0.13094544,FALSE,8.2,0.120477457,0,0.055525823,0,0.403234768,2013,0.249217433,0.20711387
13986,Bayesian investigation of SARS-CoV-2-related mortality in France,0,10.1101/2020.06.09.20126862,6/11/20,medrxiv,0,3,"bayes, bayesian model",0.001438154,0.024425165,0.001438118,0.944833609,0.001438229,0.026426725,Epidemiology,0.07885939,FALSE,30,0.432432432,152,0.885001338,2,0.618927094,2282,0.314471466,0.562708083
13987,Changes in Solo and Partnered Sexual Behaviors during the COVID-19 Pandemic: Findings from a U.S. Probability Survey,0,10.1101/2020.06.09.20125609,6/11/20,medrxiv,0,5,logistic regression,0.016823248,0.000956319,0.00095633,0.06229465,0.918013118,0.000956335,Healthcare,0.2583561,FALSE,66.8,0.745809883,59,0.717554188,5,0.739490092,3893,0.566337587,0.692297938
13988,Initial Study of Human Genetic Contribution to COVID-19 Severity and Susceptibility,0,10.1101/2020.06.09.20126607,6/11/20,medrxiv,10.1038/s41421-020-00231-4,27,sequencing,0.117952799,0.53034878,0.001046848,0.00104686,0.001046839,0.348557874,Genomics,0.58780015,TRUE,16.11538462,0.243985404,9.769230769,0.349812684,2,0.618927094,4007,0.579099446,0.447956157
13989,Trajectories of depressive symptoms among vulnerable groups in the UK during the COVID-19 pandemic,0,10.1101/2020.06.09.20126300,6/11/20,medrxiv,10.1001/jamanetworkopen.2020.26064,4,logistic regression,0.001237146,0.001237133,0.001237107,0.125310121,0.826694034,0.044284458,Healthcare,0.8386723,TRUE,75.75,0.789473684,96.25,0.815694407,5,0.739490092,2419,0.346014929,0.672668278
13990,ROBOCOV: An affordable open-source robotic platform for COVID-19 testing by RT-qPCR,0,10.1101/2020.06.11.140285,6/11/20,biorxiv,0,7,bioinformatic,0.001593561,0.361308424,0.334478453,0.299432367,0.00159354,0.001593654,Genomics,0.26029474,FALSE,22.71428571,0.335889665,23.14285714,0.513981804,0,0.403234768,3307,0.492415122,0.43638034
13991,Hijacking SARS-Cov-2/ACE2 receptor interaction by natural and semi-synthetic steroidal agents acting on functional pockets on receptor binding region,0,10.1101/2020.06.10.144964,6/11/20,biorxiv,10.3389/fchem.2020.572885,13,virtual screening,0.745120198,0.001415226,0.00141516,0.249218887,0.001415257,0.001415273,Drug discovery,0.7918883,TRUE,73.69230769,0.78044406,66.46153846,0.743778432,7,0.785110192,8537,0.803033951,0.778091659
13992,COVID-19 Variants Database: A repository for Human SARS-CoV-2 Polymorphism Data,0,10.1101/2020.06.10.145292,6/11/20,biorxiv,0,6,genomes,0.043850785,0.920323974,0.001943561,0.001943618,0.029994591,0.001943472,Genomics,0.28568745,FALSE,27.83333333,0.405219865,22.5,0.507492641,0,0.403234768,3963,0.57476523,0.472678126
13993,Comparative analysis of non structural protein 1 of SARS-COV2 with SARS-COV1 and MERS-COV: An in silico study,0,10.1101/2020.06.09.142570,6/10/20,biorxiv,0,1,"molecular dynamics simulation, in silico, sequence alignment, structural model",0.91788636,0.07671221,0.001350334,0.001350414,0.001350344,0.001350338,Drug discovery,0.7402447,TRUE,29,0.41993939,0,0.055525823,3,0.667819001,1951,0.231639778,0.343730998
13994,Unravelling the debate on heme effects in COVID-19 infections,0,10.1101/2020.06.09.142125,6/10/20,biorxiv,0,8,knowledge graph,0.785152721,0.111555858,0.002422384,0.0024224,0.002422318,0.096024319,Drug discovery,0.75263506,TRUE,36.5,0.503123261,13.75,0.408348943,3,0.667819001,3007,0.452444016,0.507933805
13995,Differential expression of COVID-19-related genes in European Americans and African Americans,0,10.1101/2020.06.09.143271,6/10/20,biorxiv,0,2,sequencing,0.357238267,0.291146706,0.001565391,0.00156541,0.194070128,0.154414097,Drug discovery,0.44376954,FALSE,107.5,0.884593976,220.5,0.926812952,1,0.537564047,4113,0.588249458,0.734305108
13996,"SARS-CoV-2 sequence typing, evolution and signatures of selection using CoVa, a Python-based command-line utility",0,10.1101/2020.06.09.082834,6/10/20,biorxiv,0,3,"genome sequences, genomes",0.001291484,0.904935376,0.001291287,0.089899378,0.001291223,0.001291252,Genomics,0.16443864,FALSE,25,0.369286907,20.66666667,0.487958255,1,0.537564047,2634,0.389838671,0.44616197
13997,The Distal Polybasic Cleavage Sites of SARS-CoV-2 Spike Protein Enhance Spike Protein-ACE2 Binding,0,10.1101/2020.06.09.142877,6/10/20,biorxiv,10.1021/acsnano.0c04798,2,molecular dynamics simulation,0.944772737,0.050413598,0.001203402,0.001203451,0.001203424,0.00120339,Drug discovery,0.71272135,TRUE,41.5,0.553219123,6,0.280037463,2,0.618927094,2347,0.329159644,0.445335831
13998,Identification of multiple large deletions in ORF7a resulting in in-frame gene fusions in clinical SARS-CoV-2 isolates,0,10.1101/2020.06.08.20125856,6/9/20,medrxiv,10.1016/j.jcv.2020.104523,8,genomes,0.162681767,0.817128172,0.005047668,0.00504756,0.005047428,0.005047405,Genomics,0.33026916,FALSE,39.125,0.531263529,48.375,0.675341183,12,0.850299401,1521,0.118950157,0.543963567
13999,Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans,0,10.1101/2020.06.08.20125963,6/9/20,medrxiv,0,9,"deep learning, neural network, dataset",0.000889047,0.000889149,0.977601341,0.018842349,0.000889061,0.000889054,Imaging,0.51915795,TRUE,13.44444444,0.203104707,4.111111111,0.232004282,9,0.814309525,1959,0.232843727,0.37056556
14000,Examine the impact of weather and ambient air pollutant parameters on daily case of COVID-19 in India.,0,10.1101/2020.06.08.20125401,6/9/20,medrxiv,0,2,dataset,0.001010925,0.001010971,0.001010971,0.825737763,0.001010994,0.170218375,Epidemiology,0.035981417,FALSE,21,0.312016822,10.5,0.363459995,3,0.667819001,1520,0.118709367,0.365501296
14001,"Sequence analysis of travel-related SARS-CoV-2 cases in the Greater Geelong region, Australia",0,10.1101/2020.06.08.20125898,6/9/20,medrxiv,0,10,sequencing,0.002638993,0.986804577,0.002639055,0.002639189,0.002639129,0.002639057,Genomics,0.3951456,FALSE,16.6,0.250046385,20.7,0.48822585,1,0.537564047,1367,0.079701421,0.338884426
14002,"Longitudinal Surveillance for SARS-CoV-2 RNA Among Asymptomatic Staff in Five Colorado Skilled Nursing Facilities: Epidemiologic, Virologic and Sequence Analysis.",0,10.1101/2020.06.08.20125989,6/9/20,medrxiv,0,12,"genomic epidemiology, genomes",0.001486425,0.487177051,0.001486433,0.156893242,0.300013959,0.052942889,Genomics,0.20829117,FALSE,23.88888889,0.352155359,45,0.658817233,16,0.881782826,10324,0.835299783,0.6820138
14003,An analysis of SARS-CoV-2 viral load by patient age,0,10.1101/2020.06.08.20125484,6/9/20,medrxiv,0,10,bayes,0.000683226,0.486165731,0.000683156,0.180550758,0.217985821,0.113931309,Genomics,0.19153988,FALSE,10.9,0.161914775,18.9,0.469092855,186,0.990246312,21382,0.929207802,0.637615436
14004,Does susceptibility to novel coronavirus (COVID-19) infection differ by age?: Insights from mathematical modelling,0,10.1101/2020.06.08.20126003,6/9/20,medrxiv,10.1038/s41598-020-73777-8,3,"mathematical model, dataset",0.001717225,0.001717286,0.001717176,0.799604888,0.00171721,0.193526215,Epidemiology,0.44695643,FALSE,15,0.227596017,3.333333333,0.206515922,2,0.618927094,2074,0.260293764,0.328333199
14005,Molecular modelling predicts SARS-CoV-2 ORF8 protein and human complement Factor 1 catalytic domain sharing common binding site on complement C3b,0,10.1101/2020.06.08.107011,6/9/20,biorxiv,0,4,computational,0.920774688,0.072964106,0.001565321,0.001565315,0.001565292,0.001565278,Drug discovery,0.4507915,FALSE,75.5,0.78860783,71.5,0.757626438,2,0.618927094,2757,0.411028172,0.644047384
14006,Rapid whole genome sequence typing reveals multiple waves of SARS-CoV-2 spread,0,10.1101/2020.06.08.139055,6/9/20,biorxiv,0,0,whole genome,0.002296643,0.988516692,0.002296789,0.002296694,0.002296577,0.002296605,Genomics,0.19309795,FALSE,62,0.715814212,95,0.813085363,3,0.667819001,6886,0.756320732,0.738259827
14007,"Predictive usefulness of PCR testing in different patterns of Covid-19 symptomatology - Analysis of a French cohort of 12,810 outpatients",0,10.1101/2020.06.07.20124438,6/9/20,medrxiv,0,14,machine learning,0.002130628,0.002130835,0.476768358,0.002130698,0.002130807,0.514708674,Clinics,0.30121565,FALSE,29.5,0.426000371,,,0,0.403234768,1702,0.164459427,0.331231522
14008,Hydroxychloroquine inhibits trained immunity - implications for COVID-19,0,10.1101/2020.06.08.20122143,6/9/20,medrxiv,0,19,lipidom,0.870710177,0.003760709,0.114247522,0.003760541,0.003760551,0.003760499,Drug discovery,0.41067404,FALSE,91.21052632,0.846681922,140.2105263,0.874364463,3,0.667819001,10145,0.831928726,0.805198528
14009,D936Y and Other Mutations in the Fusion Core of the SARS-Cov-2 Spike Protein Heptad Repeat 1 Undermine the Post-Fusion Assembly,0,10.1101/2020.06.08.140152,6/9/20,biorxiv,0,2,dataset,0.312295433,0.680699596,0.001751284,0.001751303,0.00175122,0.001751163,Genomics,0.47239554,FALSE,245.5,0.98367246,36.5,0.614329676,4,0.707574542,3191,0.472429569,0.694501562
14010,Households at Higher Risk of Losing at Least One Individual in India: if COVID-19 is a new normal,0,10.1101/2020.06.08.20125203,6/9/20,medrxiv,0,3,logistic regression,0.001593509,0.00159351,0.001593736,0.241162401,0.695589434,0.058467411,Healthcare,0.030394703,FALSE,7.666666667,0.111633373,2.666666667,0.185442869,0,0.403234768,925,0.008427643,0.177184663
14011,Genomic epidemiology of SARS-CoV-2 spread in Scotland highlights the role of European travel in COVID-19 emergence,0,10.1101/2020.06.08.20124834,6/9/20,medrxiv,10.1038/s41564-020-00838-z,42,"sequencing, genomic epidemiology, genome sequences",0.000683136,0.479251842,0.000683158,0.468266588,0.000683173,0.050432102,Genomics,0.5410107,TRUE,19.45238095,0.289875688,230.5952381,0.929622692,19,0.89561084,8247,0.795087888,0.727549277
14012,Effects of Tocilizumab on Mortality in Hospitalized Patients with COVID-19: A Multicenter Cohort Study,0,10.1101/2020.06.08.20125245,6/9/20,medrxiv,10.1016/j.cmi.2020.09.021,8,structural model,0.11528633,0.001330077,0.001330095,0.090764084,0.001330081,0.789959334,Clinics,0.75496185,TRUE,64.42857143,0.731152205,74.85714286,0.765921862,21,0.903944688,7491,0.778232603,0.79481284
14013,Covid-19 Incidence Rate Evolution Modeling using Dual Wave Gaussian-Lorentzian Composite Functions,0,10.1101/2020.06.07.20124966,6/9/20,medrxiv,0,1,"mathematical model, probabilistic",0.00256264,0.002562674,0.002562573,0.987186965,0.00256255,0.002562598,Epidemiology,0.19065952,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,1205,0.046713219,0.129445328
14014,Estimating weekly excess mortality at subnational level in Italy during the COVID-19 pandemic,0,10.1101/2020.06.08.20125211,6/9/20,medrxiv,10.1371/journal.pone.0240286,6,probabilistic,0.001237125,0.001237117,0.001237052,0.794798263,0.001237133,0.20025331,Epidemiology,0.29547355,FALSE,13.66666667,0.207310285,1.166666667,0.124565159,2,0.618927094,2318,0.318083313,0.317221463
14015,"Who dies from COVID-19? Post-hoc explanations of mortality prediction models using coalitional game theory, surrogate trees, and partial dependence plots",0,10.1101/2020.06.07.20124933,6/9/20,medrxiv,0,1,"machine learning, prediction model",0.002032762,0.002032809,0.373928428,0.106057916,0.002032783,0.513915302,Clinics,0.15734375,FALSE,20,0.298163152,2,0.164302917,1,0.537564047,1299,0.065976403,0.26650163
14016,An optimal lockdown relaxation strategy for minimizing the economic effects of covid-19 outbreak in Sri Lanka,0,10.1101/2020.06.08.20125583,6/9/20,medrxiv,10.1155/2021/6684271,3,optimization model,0.002639054,0.00263899,0.002639042,0.986804822,0.002639076,0.002639016,Epidemiology,0.52648044,TRUE,9.666666667,0.144968767,0.666666667,0.096200161,1,0.537564047,1384,0.083554057,0.215571758
14017,Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates,0,10.1101/2020.06.06.20124149,6/8/20,medrxiv,0,3,machine learning,0.00203274,0.032469705,0.137752568,0.823679388,0.002032794,0.002032805,Epidemiology,0.14565682,FALSE,51.33333333,0.641288886,27.66666667,0.550909821,11,0.840175319,1858,0.201059475,0.558358375
14018,Early Detection of Coronavirus Cases Using Chest X-ray Images Employing Machine Learning and Deep Learning Approaches,0,10.1101/2020.06.07.20124594,6/8/20,medrxiv,0,8,"bayes, machine learning, deep learning, neural network, classifier, logistic regression, dataset",0.001272629,0.001272654,0.993636724,0.001272643,0.001272654,0.001272696,Imaging,0.49726152,FALSE,4.6,0.062588905,0,0.055525823,2,0.618927094,1931,0.221767397,0.239702305
14019,A Machine Learning Explanation of Incidence Inequalities of SARS-CoV-2 Across 88 Days in 157 Countries,0,10.1101/2020.06.06.20124529,6/8/20,medrxiv,0,1,machine learning,0.001593644,0.025862216,0.13744906,0.708335873,0.125165592,0.001593615,Epidemiology,0.09708056,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,1562,0.127137009,0.149551275
14020,Prevention of household transmission crucial to stop the catastrophic spread of COVID-19 in cities,0,10.1101/2020.06.05.20123711,6/7/20,medrxiv,0,11,mathematical model,0.000946125,0.042539386,0.000946122,0.879591187,0.075031034,0.000946148,Epidemiology,0.38409507,FALSE,6.363636364,0.090048859,4.636363636,0.244447418,2,0.618927094,1854,0.198651577,0.288018737
14021,On the Spread of Coronavirus Infection. A Mechanistic Model to Rate Strategies for Disease Management,0,10.1101/2020.06.05.20123356,6/7/20,medrxiv,0,2,"machine learning, mathematical model",0.002183297,0.08210861,0.002183443,0.909157989,0.002183361,0.002183299,Epidemiology,0.26299047,FALSE,6.5,0.093512277,0,0.055525823,0,0.403234768,1382,0.08066458,0.158234362
14022,Multiscale dynamics of COVID-19 and model-based recommendations for 105 countries,0,10.1101/2020.06.05.20123547,6/7/20,medrxiv,0,3,computational,0.002639151,0.002639056,0.002639017,0.986804717,0.002639081,0.002638979,Epidemiology,0.1338349,FALSE,15,0.227596017,0.333333333,0.073187048,1,0.537564047,2062,0.255718758,0.273516468
14023,"Losing ground at the wrong time: Trends in self-reported influenza vaccination uptake in Switzerland, Heath Survey 2007-2017",0,10.1101/2020.06.05.20123026,6/7/20,medrxiv,10.1136/bmjopen-2020-041354,5,logistic regression,0.000956322,0.000956327,0.00095632,0.000956368,0.995218297,0.000956366,Healthcare,0.7080864,TRUE,179.2,0.963757808,812.4,0.990232807,1,0.537564047,1019,0.017336865,0.627222882
14024,Pollen Explains Flu-Like and COVID-19 Seasonality,0,10.1101/2020.06.05.20123133,6/7/20,medrxiv,10.1016/j.scitotenv.2020.143182,3,predictive model,0.031452643,0.052978128,0.001565331,0.74618177,0.001565461,0.166256667,Epidemiology,0.23973352,FALSE,31.66666667,0.451976003,24.33333333,0.524016591,1,0.537564047,25612,0.940043342,0.613399996
14025,ScRNA-seq discover cell cluster change under OAB: ACE2 expression reveal possible alternation of 2019-nCoV infectious pathway,0,10.1101/2020.06.05.137380,6/6/20,biorxiv,0,8,"sequencing, dataset",0.84230709,0.151201682,0.001622831,0.001622795,0.001622776,0.001622827,Drug discovery,0.554687,TRUE,331.75,0.992702084,211.75,0.922932834,0,0.403234768,1663,0.150975199,0.617461221
14026,"Belief of Previous COVID-19 Infection and Unclear Government Policy are Associated with Reduced Willingness to Participate in App-Based Contact Tracing: A UK-Wide Observational Study of 13,000 Patients",0,10.1101/2020.06.03.20120337,6/6/20,medrxiv,0,4,logistic regression,0.000688454,0.00068847,0.000688484,0.463847128,0.521731479,0.012355986,Healthcare,0.44713092,FALSE,162,0.952749088,95,0.813085363,2,0.618927094,3757,0.542017818,0.731694841
14027,Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis,0,10.1101/2020.06.03.20067793,6/5/20,medrxiv,0,3,mathematical model,0.000482156,0.000482162,0.00048214,0.813826328,0.184245072,0.000482142,Epidemiology,0.0739924,FALSE,8.333333333,0.123693488,2.666666667,0.185442869,1,0.537564047,1229,0.048880327,0.223895183
14028,Tocilizumab for patients with COVID-19 pneumonia. The TOCIVID-19 phase 2 trial,0,10.1101/2020.06.01.20119149,6/5/20,medrxiv,10.1186/s12967-020-02573-9,36,"logistic regression, dataset",0.35265116,0.002080616,0.002080775,0.002080742,0.002080605,0.639026103,Clinics,0.8628042,TRUE,68.63888889,0.757004144,64.38888889,0.736352689,16,0.881782826,9291,0.81579581,0.797733867
14029,Suppressive myeloid cells are a hallmark of severe COVID-19,0,10.1101/2020.06.03.20119818,6/5/20,medrxiv,10.1016/j.cell.2020.08.001,63,"sequencing, proteom",0.36672609,0.096519049,0.001392841,0.001392865,0.001392868,0.532576287,Clinics,0.584884,TRUE,28.86046512,0.416166739,14.74418605,0.420658282,22,0.908142478,8159,0.790272092,0.633809898
14030,Covid-19 Epidemiological Factor Analysis: Identifying Principal Factors with Machine Learning,0,10.1101/2020.06.01.20119560,6/5/20,medrxiv,0,1,machine learning,0.007061949,0.00706239,0.007061792,0.832490215,0.139261433,0.007062221,Epidemiology,0.43876338,FALSE,10,0.15214299,2,0.164302917,2,0.618927094,1142,0.03467373,0.242511683
14031,Reparations for Black American Descendants of Persons Enslaved in the U.S. and Their Estimated Impact on SARS-CoV-2 Transmission,0,10.1101/2020.06.04.20112011,6/5/20,medrxiv,10.1016/j.socscimed.2021.113741,10,artificial intelligence,0.036769864,0.0016531,0.001653177,0.699373438,0.182957074,0.077593347,Epidemiology,0.26610073,FALSE,33.9,0.475168532,46.2,0.664704308,2,0.618927094,4669,0.631591621,0.597597889
14032,"Calculated grades, predicted grades, forecasted grades and actual A-level grades: Reliability, correlations and predictive validity in medical school applicants, undergraduates, and postgraduates in a time of COVID-19",0,10.1101/2020.06.02.20116830,6/5/20,medrxiv,0,7,dataset,0.000746583,0.000746547,0.233435058,0.403110689,0.361214547,0.000746577,Epidemiology,0.001229495,FALSE,34.14285714,0.478137176,59.85714286,0.720230131,6,0.764429903,1763,0.17288707,0.53392107
14033,Estimating excess visual loss in people with neovascular age-related macular degeneration during the COVID-19 pandemic,0,10.1101/2020.06.02.20120642,6/5/20,medrxiv,0,15,"simulation model, dataset",0.001901683,0.001901694,0.001901706,0.990491174,0.001901824,0.00190192,Epidemiology,0.33738583,FALSE,5.333333333,0.074339786,2.2,0.16838373,3,0.667819001,2239,0.291837226,0.300594936
14034,COVID-19 DYNAMICS CONSIDERING THE INFLUENCE OF HOSPITAL INFRASTRUCTURE: AN INVESTIGATION OF BRAZILIAN SCENARIOS,0,10.1101/2020.06.03.20121608,6/5/20,medrxiv,10.1007/s11071-021-06323-4,3,mathematical model,0.001943485,0.00194348,0.001943515,0.990282491,0.001943461,0.001943569,Epidemiology,0.06152284,FALSE,4.333333333,0.057950399,0,0.055525823,1,0.537564047,934,0.008186853,0.16480678
14035,Development and implementation of a customised rapid syndromic diagnostic test for severe pneumonia,0,10.1101/2020.06.02.20118489,6/5/20,medrxiv,0,22,"sequencing, metagenom",0.000518952,0.182428384,0.091213826,0.417890157,0.000518913,0.307429769,Epidemiology,0.6567197,TRUE,5.476190476,0.075143794,2.238095238,0.168718223,2,0.618927094,1693,0.158439682,0.255307198
14036,Modeling COVID-19 dynamics in Illinois under non-pharmaceutical interventions,0,10.1101/2020.06.03.20120691,6/5/20,medrxiv,10.1103/physrevx.10.041033,6,bayes,0.001220007,0.001220037,0.001220026,0.921546615,0.001220035,0.073573281,Epidemiology,0.2308602,FALSE,80.83333333,0.81025419,114.1666667,0.843925609,9,0.814309525,1988,0.233566097,0.675513855
14037,Identification of spatial variations in COVID-19 epidemiological data using K-Means clustering algorithm: a global perspective,0,10.1101/2020.06.03.20121194,6/5/20,medrxiv,0,1,dataset,0.001511818,0.001511888,0.001511925,0.922853685,0.071098764,0.00151192,Epidemiology,0.31799483,FALSE,2,0.022141134,0,0.055525823,3,0.667819001,1222,0.047676379,0.198290584
14038,Clinical Management and Mortality among COVID-19 Cases in Sub-Saharan Africa: A retrospective study from Burkina Faso and simulated case data analysis,0,10.1101/2020.06.04.20119784,6/5/20,medrxiv,10.1016/j.ijid.2020.09.1432,14,"logistic regression, probabilistic",0.001011006,0.001010973,0.00101096,0.301010941,0.001010997,0.694945123,Clinics,0.3236018,FALSE,29.66666667,0.42773208,24.66666667,0.526090447,2,0.618927094,1849,0.194558151,0.441826943
14039,Deep Learning and Holt-Trend Algorithms for predicting COVID-19  pandemic,0,10.1101/2020.06.03.20121590,6/5/20,medrxiv,10.32604/cmc.2021.014498,5,"deep learning, lstm",0.001291227,0.001291224,0.220493623,0.721050095,0.001291247,0.054582584,Epidemiology,0.25389856,FALSE,7,0.10179974,0.4,0.075796093,4,0.707574542,1330,0.069829039,0.238749854
14040,The impact of school reopening on the spread of COVID-19 in England,0,10.1101/2020.06.04.20121434,6/5/20,medrxiv,0,11,model fit,0.001156238,0.001156276,0.001156271,0.515080601,0.480294347,0.001156266,Epidemiology,0.18990114,FALSE,11.63636364,0.175459212,5.181818182,0.258830613,21,0.903944688,12197,0.861305081,0.549884898
14041,Predicting individual risk for COVID19 complications using EMR data,0,10.1101/2020.06.03.20121574,6/5/20,medrxiv,0,7,dataset,0.000946104,0.000946125,0.274381465,0.149651594,0.045730715,0.528343995,Clinics,0.48305282,FALSE,8.714285714,0.128764921,9,0.337904736,1,0.537564047,1320,0.066698772,0.267733119
14042,Dynamics of COVID-19 under social distancing measures are driven by transmission network structure,0,10.1101/2020.06.04.20121673,6/5/20,medrxiv,10.1371/journal.pcbi.1008684,5,mathematical model,0.000779444,0.000779418,0.000779423,0.89653373,0.100348543,0.000779442,Epidemiology,0.33484578,FALSE,36,0.498299215,27.6,0.550441531,15,0.874313229,3246,0.474837467,0.59947286
14043,Characterization of the substitution hotspots in SARS-CoV-2 genome using BioAider and detection of a SR-rich region in N protein providing further evidence of its animal origin,0,10.1101/2020.06.04.135293,6/5/20,biorxiv,0,3,"sequencing, genomes",0.139001139,0.856249791,0.001187265,0.001187268,0.001187275,0.001187262,Genomics,0.5554238,TRUE,22.33333333,0.330261612,16.33333333,0.440460262,1,0.537564047,1691,0.157235733,0.366380414
14044,Mega-phylogeny sheds light on SARS-CoV-2 spatial phylogenetic structure,0,10.1101/2020.06.05.135954,6/5/20,biorxiv,0,2,genomes,0.003101705,0.884113822,0.003101559,0.103479664,0.003101704,0.003101546,Genomics,0.33493298,FALSE,56.5,0.678891706,32,0.585763982,1,0.537564047,2183,0.279556947,0.520444171
14045,CoVID-19 in Singapore: Impact of Contact Tracing and Self-awareness on Healthcare Demand,0,10.1101/2020.06.04.20122879,6/5/20,medrxiv,0,6,mathematical model,0.001237041,0.001237055,0.001237053,0.851644285,0.143407497,0.001237069,Epidemiology,0.23884451,FALSE,35.66666667,0.493908096,12.5,0.392761573,0,0.403234768,1398,0.082590898,0.343123834
14046,"Characteristics and risk factors for COVID-19 diagnosis and adverse outcomes in Mexico: an analysis of 89,756 laboratory-confirmed COVID-19 cases",0,10.1101/2020.06.04.20122481,6/5/20,medrxiv,10.1183/13993003.02144-2020,5,"logistic regression, dataset",0.001156295,0.001156372,0.001156303,0.096912965,0.001156423,0.898461642,Clinics,0.7382848,TRUE,6.4,0.090667326,1.4,0.133864062,31,0.931971109,5793,0.703828558,0.465082764
14047,Optimal Control Measures to Combat COVID19 Spread in Sri Lanka: A Mathematical ModelConsidering the Heterogeneity of Cases,0,10.1101/2020.06.04.20122382,6/5/20,medrxiv,0,2,mathematical model,0.069080252,0.001438123,0.001438112,0.92516714,0.001438153,0.00143822,Epidemiology,0.71862376,TRUE,5.5,0.077246583,0,0.055525823,1,0.537564047,1050,0.021430291,0.172941686
14048,Neuropilin-1 is a host factor for SARS-CoV-2 infection,0,10.1101/2020.06.05.134114,6/5/20,biorxiv,10.1126/science.abd3072,19,structural model,0.873831441,0.118561623,0.001901704,0.001901783,0.001901708,0.001901741,Drug discovery,0.31008625,FALSE,101.7894737,0.872843095,275.7368421,0.947217019,175,0.989628989,28793,0.947748615,0.93935943
14049,Class I HLA allele restricted antigenic coverage for Spike and N proteins is associated with divergent outcomes for COVID-19,0,10.1101/2020.06.03.20121301,6/5/20,medrxiv,0,6,in silico,0.276419525,0.514198249,0.00146188,0.134155325,0.001461919,0.072303102,Genomics,0.11038238,FALSE,16,0.243552477,4.333333333,0.237958255,0,0.403234768,3280,0.480134842,0.341220086
14050,"An exploration of the SARS-CoV-2 spike receptor binding domain (RBD), a complex palette of evolutionary and structural features",0,10.1101/2020.05.31.126615,6/4/20,biorxiv,0,3,network analysis,0.564200886,0.220720203,0.211088706,0.001330071,0.001330088,0.001330045,Drug discovery,0.66240585,TRUE,255.5,0.985218628,445.5,0.974846133,3,0.667819001,2912,0.429568986,0.764363187
14051,Substitutions in Spike and Nucleocapsid proteins of SARS-CoV-2 circulating in Colombia,0,10.1101/2020.06.02.20120782,6/4/20,medrxiv,10.1016/j.meegid.2020.104557,15,"sequencing, genome sequences, genomes",0.155758503,0.826660401,0.015664652,0.000638762,0.000638759,0.000638923,Genomics,0.7321294,TRUE,8,0.118683901,1.133333333,0.12309339,4,0.707574542,2494,0.350349145,0.324925245
14052,Estimating the parameters of SIR model of COVID-19 cases in India during lock down periods,0,10.1101/2020.06.03.20120899,6/4/20,medrxiv,0,4,mathematical model,0.001786574,0.033304928,0.001786542,0.93272405,0.028611362,0.001786544,Epidemiology,0.479873,FALSE,6,0.086028821,0,0.055525823,1,0.537564047,1091,0.026727667,0.176461589
14053,Assessing capacity to social distance and neighborhood-level health disparities during the COVID-19 pandemic,0,10.1101/2020.06.02.20120790,6/4/20,medrxiv,0,7,bayes,0.03576744,0.002639132,0.002639062,0.725195829,0.163906533,0.069852003,Epidemiology,0.28322053,FALSE,52.85714286,0.652544994,54.28571429,0.698621889,10,0.828199272,2947,0.434866362,0.653558129
14054,"The Relationship Between COVID-19 Infection and Risk Perception, Knowledge, Attitude As Well As Four Non-pharmaceutical Interventions (NPIs) During the Late Period Of The COVID-19 Epidemic In China An Online Cross-sectional Survey of 8158 Adults",0,10.1101/2020.06.02.20120808,6/4/20,medrxiv,10.2196/21372,13,logistic regression,0.001141309,0.00114131,0.001141316,0.037584405,0.957850337,0.001141323,Healthcare,0.160671,FALSE,7.538461538,0.108479189,5.538461538,0.267326733,1,0.537564047,1535,0.11461594,0.256996477
14055,"Assembly of an integrated human lung cell atlas reveals that SARS-CoV-2 receptor is co-expressed with key elements of the kinin-kallikrein, renin-angiotensin and coagulation systems in alveolar cells",0,10.1101/2020.06.02.20120634,6/4/20,medrxiv,10.1038/s41598-020-76488-2,7,transcriptom,0.884621291,0.002720149,0.002720107,0.002720113,0.002720136,0.104498204,Drug discovery,0.88532484,TRUE,50,0.632073721,21.57142857,0.498528231,0,0.403234768,2403,0.330845172,0.466170473
14056,A prediction model based on machine learning for diagnosing the early COVID-19 patients,0,10.1101/2020.06.03.20120881,6/4/20,medrxiv,0,7,"machine learning, deep learning, predictive model, logistic regression, prediction model, dataset",0.001203432,0.001203517,0.953467961,0.001203489,0.041718114,0.001203486,Healthcare,0.37762836,FALSE,10,0.15214299,0.142857143,0.057398983,5,0.739490092,1195,0.040934264,0.247491582
14057,Early phylodynamics analysis of the COVID-19 epidemics in France,0,10.1101/2020.06.03.20119925,6/4/20,medrxiv,0,18,genomes,0.002183302,0.226412172,0.002183212,0.764854816,0.002183255,0.002183244,Epidemiology,0.18856648,FALSE,49.61111111,0.628795844,53.16666667,0.694005887,8,0.799987654,3204,0.467372983,0.647540592
14058,"Swab Tests and COVID 19, Italy case studied using Artificial Intelligence, Statistical Analysis and MLR",0,10.1101/2020.06.02.20120394,6/4/20,medrxiv,0,4,artificial intelligence,0.001622725,0.117908214,0.038094339,0.508389373,0.047248289,0.286737059,Epidemiology,0.48957705,FALSE,7.666666667,0.111633373,0.333333333,0.073187048,0,0.403234768,1450,0.093426439,0.170370407
14059,Temporal evolution and adaptation of SARS-COV 2 codon usage,0,10.1101/2020.05.29.123976,6/3/20,biorxiv,0,6,"whole-genome, genomes",0.001392928,0.993035731,0.001392826,0.00139286,0.001392824,0.00139283,Genomics,0.35300583,FALSE,50.2,0.632444802,57,0.711198823,4,0.707574542,2360,0.316638575,0.591964185
14060,COVID-19 Public Sentiment Insights and MachineLearning for Tweets Classification,0,10.1101/2020.06.01.20119347,6/3/20,medrxiv,10.3390/info11060314,5,"bayes, machine learning, logistic regression",0.001901734,0.00190174,0.290644314,0.599510482,0.104139988,0.001901741,Epidemiology,0.53319764,TRUE,2.8,0.031047065,0.2,0.061145304,0,0.403234768,1206,0.041415844,0.134210745
14061,Detection of lung hypoperfusion in Covid-19 patients during recovery by digital imaging quantification,0,10.1101/2020.05.29.20117143,6/3/20,medrxiv,0,6,image processing,0.001203484,0.001203445,0.481319845,0.001203468,0.001203478,0.51386628,Clinics,0.60018975,TRUE,59.33333333,0.698497124,67.33333333,0.746320578,1,0.537564047,1496,0.10305803,0.521359945
14062,"Model Based Covid-19 Case Studies in the UK, the USA and India",0,10.1101/2020.05.31.20118760,6/3/20,medrxiv,0,1,mathematical model,0.006089673,0.006089872,0.006089587,0.969551495,0.006089685,0.006089688,Epidemiology,0.24839276,FALSE,5,0.070752675,1,0.122023013,2,0.618927094,2629,0.377317602,0.297255096
14063,COVID-SGIS: A smart tool for dynamic monitoring and temporal forecasting of Covid-19,0,10.1101/2020.05.30.20117945,6/3/20,medrxiv,10.3389/fpubh.2020.580815,15,machine learning,0.00099952,0.000999519,0.1286503,0.820391397,0.000999539,0.047959724,Epidemiology,0.5751099,TRUE,5.466666667,0.075081947,0.666666667,0.096200161,3,0.667819001,1490,0.101854081,0.235238798
14064,Kalman Filter Based Short Term Prediction Model for COVID-19 Spread,0,10.1101/2020.05.30.20117416,6/3/20,medrxiv,10.1007/s10489-020-01948-1,4,"machine learning, ensemble learning, prediction model",0.001717196,0.001717201,0.497622034,0.495509136,0.001717229,0.001717205,Epidemiology,0.63187927,TRUE,3.5,0.044344115,0,0.055525823,5,0.739490092,1303,0.061401397,0.225190357
14065,The impact of the undetected COVID-19 cases on its transmission dynamics,0,10.1101/2020.05.30.20117838,6/3/20,medrxiv,0,2,mathematical model,0.002032779,0.002032956,0.002032844,0.989835877,0.002032791,0.002032752,Epidemiology,0.4295755,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,977,0.0117987,0.123175106
14066,Clinical Features and Predictors for Patients with Severe SARS-CoV-2 Pneumonia: a retrospective multicenter cohort study,0,10.1101/2020.06.01.20119032,6/3/20,medrxiv,0,11,logistic regression,0.079936327,0.000688493,0.00068849,0.087258156,0.000688478,0.830740056,Clinics,0.081319064,FALSE,14.72727273,0.221596883,7.636363636,0.309138346,0,0.403234768,950,0.008909222,0.235719805
14067,The COVID-19 Pandemic in Africa: Predictions using the SIR Model Indicate the Cases are Falling,0,10.1101/2020.06.01.20118893,6/3/20,medrxiv,0,6,mathematical model,0.002422247,0.002422421,0.002422292,0.987888364,0.002422361,0.002422315,Epidemiology,0.16025043,FALSE,7.333333333,0.105572392,0.666666667,0.096200161,3,0.667819001,2597,0.369130749,0.309680576
14068,Modelling Singapore COVID-19 pandemic with a SEIR multiplex network model,0,10.1101/2020.05.31.20118372,6/3/20,medrxiv,0,2,network model,0.145966546,0.002238503,0.002238582,0.752805189,0.09451275,0.00223843,Epidemiology,0.28808266,FALSE,18.5,0.278001113,2.5,0.180826866,2,0.618927094,1659,0.144473874,0.305557237
14069,"When ""Shelter-in-Place"" Isn't Shelter That's Safe: A Rapid Analysis of Domestic Violence Case Differences During the COVID-19 Pandemic and Stay-at-Home Orders",0,10.1101/2020.05.29.20117366,6/3/20,medrxiv,10.1007/s10896-020-00225-6,1,logistic regression,0.001486408,0.00148648,0.001486412,0.565374651,0.428679567,0.001486482,Epidemiology,0.13743359,FALSE,5,0.070752675,0,0.055525823,3,0.667819001,2697,0.39032025,0.296104437
14070,It's the very time to learn a pandemic lesson: why have predictive techniques been ineffective when describing long-term events?,0,10.1101/2020.06.01.20118869,6/3/20,medrxiv,0,2,mathematical model,0.002806393,0.002806497,0.002806452,0.985967646,0.002806409,0.002806602,Epidemiology,0.18689999,FALSE,15.5,0.234028078,0,0.055525823,1,0.537564047,904,0.006260535,0.208344621
14071,Uncertainty Quantification in Epidemiological Models for COVID-19 Pandemic,0,10.1101/2020.05.30.20117754,6/3/20,medrxiv,10.1016/j.compbiomed.2020.104011,3,"bayes, computational",0.001684505,0.001684492,0.001684561,0.991577425,0.001684528,0.001684488,Epidemiology,0.3653543,FALSE,3,0.037293586,0.666666667,0.096200161,3,0.667819001,1265,0.052973754,0.213571626
14072,Sensitivity and specifity of prediction models based on gustatory disorders in diagnosing COVID-19 patients: a case-control study.,0,10.1101/2020.05.31.20118380,6/3/20,medrxiv,0,12,prediction model,0.000907303,0.062242552,0.418934218,0.000907327,0.209053477,0.307955122,Clinics,0.9671799,TRUE,13.66666667,0.207310285,0.583333333,0.088172331,3,0.667819001,2363,0.317842523,0.320286035
14073,Dynamics and Prediction of the COVID-19 Epidemics in the US:a Compartmental Model with Deep Learning Enhancement,0,10.1101/2020.05.31.20118414,6/3/20,medrxiv,10.2196/21173,1,deep learning,0.001272633,0.001272649,0.158775312,0.83613406,0.001272658,0.001272688,Epidemiology,0.51199204,TRUE,5,0.070752675,1,0.122023013,2,0.618927094,1226,0.045268481,0.214242816
14074,Rapid detection of SARS-CoV-2 and other respiratory viruses by using LAMP method with Nanopore Flongle workflow,0,10.1101/2020.06.03.131474,6/3/20,biorxiv,0,9,sequencing,0.039161517,0.663752683,0.290535812,0.002183397,0.002183298,0.002183293,Genomics,0.5105548,TRUE,47.88888889,0.613457851,36.22222222,0.612523415,3,0.667819001,2843,0.416084758,0.577471256
14075,In silico design and characterization of multiepitopes vaccine for SARS-CoV from its Spike proteins,0,10.1101/2020.06.03.131755,6/3/20,biorxiv,0,1,"bioinformatic, in silico",0.905210786,0.087642903,0.001786573,0.001786579,0.001786631,0.001786527,Drug discovery,0.39494073,FALSE,4,0.054734368,0,0.055525823,2,0.618927094,2656,0.382374187,0.277890368
14076,Analyzing hCov genome sequences: Applying Machine Intelligence and beyond,0,10.1101/2020.06.03.131987,6/3/20,biorxiv,0,8,"deep learning, genome sequences, machine intelligence",0.002357772,0.601360899,0.389208008,0.002357815,0.002357775,0.002357731,Genomics,0.59566164,TRUE,25.5,0.37435834,22.875,0.510971367,0,0.403234768,1900,0.204912112,0.373369147
14077,Compliance and containment in social distancing: mathematical modeling of COVID-19 across townships,0,10.1101/2020.06.01.20119073,6/2/20,medrxiv,10.1080/13658816.2021.1873999,5,mathematical model,0.002080719,0.002080573,0.002080585,0.989596923,0.002080661,0.002080539,Epidemiology,0.31862813,FALSE,2.6,0.028263962,0,0.055525823,0,0.403234768,764,0.000963159,0.121996928
14078,"Greater risk of severe COVID-19 in non-White ethnicities is not explained by cardiometabolic, socioeconomic, or behavioural factors, or by 25(OH)-vitamin D status: study of 1,326 cases from the UK Biobank",0,10.1101/2020.06.01.20118943,6/2/20,medrxiv,10.1093/pubmed/fdaa095,9,logistic regression,0.001486552,0.089281453,0.001486441,0.001486475,0.472227212,0.434031867,Healthcare,0.52860576,TRUE,51.33333333,0.641288886,215.5555556,0.925274284,10,0.828199272,2398,0.325066217,0.679957165
14079,Diagnostic accuracy of a host response point-of-care test for identifying COVID-19,0,10.1101/2020.05.27.20114512,6/2/20,medrxiv,0,9,predictive model,0.00139303,0.176766726,0.289600939,0.001392913,0.001392912,0.52945348,Clinics,0.5494991,TRUE,16.44444444,0.248129136,9.555555556,0.345999465,0,0.403234768,1912,0.20683843,0.30105045
14080,A Time-Dependent SEIRD Model for Forecasting the COVID-19 Transmission Dynamics,0,10.1101/2020.05.29.20113571,6/2/20,medrxiv,0,4,mathematical model,0.00162275,0.001622763,0.001622736,0.991886245,0.001622744,0.001622762,Epidemiology,0.25092834,FALSE,2.75,0.030304904,0,0.055525823,5,0.739490092,2286,0.297375391,0.280674053
14081,SARS-CoV-2 genetic variations associated with COVID-19 severity,0,10.1101/2020.05.27.20114546,6/2/20,medrxiv,0,5,"genome-wide, genomes",0.001901731,0.767801184,0.001901689,0.001901728,0.001901751,0.224591916,Genomics,0.3625602,FALSE,72,0.77302245,45.6,0.661292481,7,0.785110192,5104,0.661690344,0.720278867
14082,Association between NSAIDs use and adverse clinical outcomes among adults hospitalised with COVID-19 in South Korea: A nationwide study,0,10.1101/2020.06.01.20119768,6/2/20,medrxiv,10.1093/cid/ciaa1056,6,logistic regression,0.142624396,0.001461876,0.001461882,0.158286307,0.001461986,0.694703553,Clinics,0.3983584,FALSE,4.833333333,0.065372008,0.333333333,0.073187048,1,0.537564047,5727,0.696845654,0.343242189
14083,The ABO blood group locus and a chromosome 3 gene cluster associate with SARS-CoV-2 respiratory failure in an Italian-Spanish genome-wide association analysis,0,10.1101/2020.05.31.20114991,6/2/20,medrxiv,0,128,genome-wide,0.088200341,0.300845564,0.001461894,0.001461894,0.001461937,0.606568371,Clinics,0.35542208,FALSE,36.93023256,0.507328839,66.60465116,0.744581215,36,0.941169208,462465,0.999277631,0.798089223
14084,"A Simple, SIR-like but Individual-Based l-i AIR Model: Application in Comparison of COVID-19 in New York City and Wuhan",0,10.1101/2020.05.28.20115121,6/2/20,medrxiv,10.1016/j.rinp.2020.103712,1,mathematical model,0.001220072,0.025114495,0.001220027,0.869461466,0.001220098,0.101763842,Epidemiology,0.52170575,TRUE,2,0.022141134,0,0.055525823,1,0.537564047,1287,0.05754876,0.168194941
14085,The Risk of Lifting COVID-19 Confinement in Mexico,0,10.1101/2020.05.28.20115063,6/2/20,medrxiv,0,2,mathematical model,0.001237059,0.001237108,0.001237086,0.827123983,0.082063232,0.087101532,Epidemiology,0.27526534,FALSE,17,0.257467994,4.5,0.242708055,2,0.618927094,1127,0.031061883,0.287541257
14086,G6PD variant distribution in sub-Saharan Africa and potential risks ofusing chloroquine/hydroxychloroquine based treatments forCOVID-19,0,10.1101/2020.05.27.20114066,6/2/20,medrxiv,0,13,whole-genome,0.163980776,0.70854799,0.003607478,0.003607335,0.003607331,0.11664909,Genomics,0.29729488,FALSE,10.53846154,0.15746181,10.23076923,0.358175007,1,0.537564047,2868,0.418733446,0.367983577
14087,"The COVID-19 Critical Care Consortium observational study: Design and rationale of a prospective, international, multicenter, observational study",0,10.1101/2020.05.29.20115253,6/2/20,medrxiv,10.1136/bmjopen-2020-041417,16,sequencing,0.000898123,0.078499267,0.000898119,0.283011163,0.0008981,0.635795227,Clinics,0.5079869,TRUE,15.5625,0.234646546,9.25,0.340379984,1,0.537564047,1893,0.200337106,0.328231921
14088,Estimated Sp02/Fio2 ratio to predict mortality in patients with suspected COVID-19 in the Emergency Department: a prospective cohort study,0,10.1101/2020.05.28.20116194,6/2/20,medrxiv,0,4,"logistic regression, prediction model",0.001022634,0.001022629,0.001022651,0.001022701,0.018373307,0.977536078,Clinics,0.46230045,FALSE,9.75,0.146267549,9,0.337904736,2,0.618927094,1084,0.025042138,0.282035379
14089,Rapid Epidemiological Analysis of Comorbidities andTreatments as risk factors for COVID-19 in Scotland(REACT-SCOT): a population-based case-control study,0,10.1101/2020.05.28.20115394,6/2/20,medrxiv,10.1371/journal.pmed.1003374,17,"classifier, logistic regression",0.049717127,0.000722197,0.022236007,0.081655954,0.000722224,0.84494649,Clinics,0.19684601,FALSE,14.29411765,0.215845136,9.294117647,0.340781375,3,0.667819001,4268,0.594269203,0.454678679
14090,INSIGHT: a scalable isothermal NASBA-based platform for COVID-19 diagnosis,0,10.1101/2020.06.01.127019,6/2/20,biorxiv,0,6,sequencing,0.001272686,0.615942259,0.254750507,0.125489061,0.001272716,0.001272772,Genomics,0.07259029,FALSE,28.33333333,0.410724225,44,0.654401927,3,0.667819001,5845,0.703106188,0.609012835
14091,HiDRA-seq: High-Throughput SARS-CoV-2 Detection by RNA Barcoding and Amplicon Sequencing,0,10.1101/2020.06.02.130484,6/2/20,biorxiv,0,9,sequencing,0.001310366,0.398014032,0.143434185,0.416687039,0.001310465,0.039243913,Epidemiology,0.25967512,FALSE,30.44444444,0.437132785,55.88888889,0.70564624,7,0.785110192,3914,0.555502047,0.620847816
14092,System Dynamics Modeling Of Within-Host Viral Kinetics Of Coronavirus (SARS CoV-2),0,10.1101/2020.06.02.129312,6/2/20,biorxiv,0,1,mathematical model,0.54759394,0.002183405,0.002183221,0.364313573,0.002183331,0.08154253,Drug discovery,0.7415821,TRUE,53,0.654400396,55,0.702167514,0,0.403234768,1655,0.141825187,0.475406966
14093,Optimizing the molecular diagnosis of Covid-19 by combining RT-PCR and a pseudo-convolutional machine learning approach to characterize virus DNA sequences,0,10.1101/2020.06.02.129775,6/2/20,biorxiv,0,10,"machine learning, classifier, sequence alignment",0.001461887,0.485151023,0.509001123,0.001462038,0.00146195,0.001461978,Genomics,0.109015405,FALSE,5.777777778,0.081204775,0.777777778,0.099946481,3,0.667819001,2609,0.369853118,0.304705844
14094,"Changes in Reproductive Rate of SARS-CoV-2 Due to Non-pharmaceutical Interventions in 1,417 U.S. Counties",0,10.1101/2020.05.31.20118687,6/1/20,medrxiv,0,7,bayes,0.001751159,0.001751172,0.001751148,0.99124419,0.001751181,0.00175115,Epidemiology,0.06883949,FALSE,20.14285714,0.298967159,9.428571429,0.343591116,2,0.618927094,997,0.013243438,0.318682202
14095,"Determining the optimal strategy for reopening schools, work and society in the UK: balancing earlier opening and the impact of test and trace strategies with the risk of occurrence of a secondary COVID-19 pandemic wave",0,10.1101/2020.06.01.20100461,6/1/20,medrxiv,0,7,mathematical model,0.000394101,0.000394108,0.000394114,0.766981278,0.218573605,0.013262793,Epidemiology,0.13929224,FALSE,14.42857143,0.217886078,11.42857143,0.37643832,10,0.828199272,4138,0.578617867,0.500285384
14096,Smoking and the risk of COVID-19 in a large observational population study,0,10.1101/2020.06.01.20118877,6/1/20,medrxiv,0,5,logistic regression,0.00178669,0.001786666,0.001786598,0.001786682,0.585159588,0.407693777,Healthcare,0.20231047,FALSE,21.6,0.319438432,7.2,0.302047097,32,0.933699611,55417,0.977606549,0.633197922
14097,Pediatric COVID-19 in Southern California: clinical features and viral genetic diversity,0,10.1101/2020.05.28.20104539,6/1/20,medrxiv,0,13,genomes,0.002898331,0.744456936,0.002898389,0.00289834,0.113606784,0.13324122,Genomics,0.33532748,FALSE,45.76923077,0.593481353,147.1538462,0.880920524,2,0.618927094,1515,0.104502769,0.549457935
14098,Targeted Intracellular Degradation of SARS-CoV-2 RBD via Computationally-Optimized Peptide Fusions,0,10.1101/2020.06.01.127829,6/1/20,biorxiv,10.1038/s42003-020-01470-7,3,computational,0.985507377,0.002898426,0.002898356,0.002898492,0.002898702,0.002898647,Drug discovery,0.7556502,TRUE,33.33333333,0.469911559,36,0.611118544,1,0.537564047,2495,0.34529256,0.490971678
14099,coronapp: a Web Application to Annotate and Monitor SARS-CoV-2 Mutations,0,10.1101/2020.05.31.124966,6/1/20,biorxiv,10.1002/jmv.26678,5,dataset,0.002357763,0.840170475,0.002357846,0.150398281,0.002357861,0.002357774,Genomics,0.61204493,TRUE,23.2,0.343496815,,,21,0.903944688,3450,0.496749338,0.581396947
14100,Impact of emerging mutations on the dynamic properties the SARS-CoV-2 main protease: an in silico investigation,0,10.1101/2020.05.29.123190,6/1/20,biorxiv,10.1021/acs.jcim.0c00634,3,"molecular dynamics simulation, in silico",0.890529798,0.102323709,0.001786522,0.001786595,0.001786613,0.001786763,Drug discovery,0.80365485,TRUE,54.33333333,0.663677407,33.33333333,0.594995986,2,0.618927094,1926,0.208764748,0.521591309
14101,Assignment of coronavirus spike protein site-specific glycosylation using GlycReSoft,0,10.1101/2020.05.31.125302,5/31/20,biorxiv,0,2,"proteom, glycoproteom",0.562818308,0.003607458,0.248957419,0.17740235,0.003607232,0.003607233,Drug discovery,0.584774,TRUE,90,0.843094811,55,0.702167514,3,0.667819001,1390,0.076571153,0.57241312
14102,A distinct phylogenetic cluster of Indian SARS-CoV-2 isolates,0,10.1101/2020.05.31.126136,5/31/20,biorxiv,10.1093/ofid/ofaa434,11,"sequencing, whole-genome, whole genome, genomes",0.001059366,0.994703156,0.001059385,0.001059398,0.001059358,0.001059337,Genomics,0.75734377,TRUE,41.54545455,0.553404663,26.90909091,0.544955847,24,0.914439163,8159,0.787623405,0.700105769
14103,CoViD-19 in Italy: a mathematical model to analyze the epidemic containment strategy and the economic impacts,0,10.1101/2020.05.28.20115790,5/30/20,medrxiv,10.47473/2020rmm0013,3,"mathematical model, probabilistic, dataset",0.001987112,0.001987134,0.001987165,0.990064349,0.001987139,0.001987101,Epidemiology,0.3396129,FALSE,3.666666667,0.045952131,0.333333333,0.073187048,2,0.618927094,1336,0.065013243,0.200769879
14104,COVID-3D: An online resource to explore the structural distribution of genetic variation in SARS-CoV-2 and its implication on therapeutic development,0,10.1101/2020.05.29.124610,5/30/20,biorxiv,0,8,sequencing,0.259485513,0.63975634,0.003927458,0.088975943,0.003927396,0.003927349,Genomics,0.22792557,FALSE,32.55555556,0.461871482,14.33333333,0.415975381,5,0.739490092,2825,0.407416326,0.50618832
14105,Genomic analysis of early SARS-CoV-2 strains introduced in Mexico,0,10.1101/2020.05.27.120402,5/30/20,biorxiv,0,31,phylogenom,0.000926298,0.995368457,0.000926291,0.000926344,0.000926323,0.000926288,Genomics,0.18625271,FALSE,25.5483871,0.374667574,26.48387097,0.541075729,0,0.403234768,4814,0.634962678,0.488485187
14106,Medical students perceptions and motivations in time of COVID-19 pandemic,0,10.1101/2020.05.28.20115956,5/30/20,medrxiv,0,10,logistic regression,0.000946105,0.020202476,0.088449977,0.000946136,0.888509134,0.000946172,Healthcare,0.060626417,FALSE,10.1,0.152390377,2.1,0.164771207,2,0.618927094,2347,0.308933301,0.311255495
14107,Remarkable variability in SARS-CoV-2 antibodies across Brazilian regions: nationwide serological household survey in 27 states,0,10.1101/2020.05.30.20117531,5/30/20,medrxiv,0,15,probabilistic,0.001438103,0.131717666,0.001438109,0.653535032,0.210432929,0.001438161,Epidemiology,0.39743078,FALSE,117.2,0.903147999,247.3333333,0.937316029,50,0.957775171,20942,0.924632796,0.930717999
14108,The effectiveness and perceived burden of nonpharmaceutical interventions against COVID-19 transmission: a modelling study with 41 countries,0,10.1101/2020.05.28.20116129,5/30/20,medrxiv,10.1126/science.abd9338,19,bayes,0.001112672,0.001112653,0.001112708,0.994436575,0.001112683,0.001112709,Epidemiology,0.41048282,FALSE,11.41176471,0.171377327,55.29411765,0.703104094,30,0.930057411,53458,0.975921021,0.695114963
14109,When to relax a lockdown? A modelling-based study of testing-led strategies coupled with sero-surveillance against SARS-CoV-2 infection in India,0,10.1101/2020.05.29.20117010,5/30/20,medrxiv,0,4,mathematical model,0.001751147,0.105388703,0.001751234,0.849123345,0.040234274,0.001751296,Epidemiology,0.20101547,FALSE,4,0.054734368,0.5,0.087101953,1,0.537564047,1744,0.161569949,0.21024258
14110,COVID-19 higher morbidity and mortality in Chinese regions with lower air quality,0,10.1101/2020.05.28.20115832,5/30/20,medrxiv,10.3389/fpubh.2020.597753,2,dataset,0.002183293,0.038562686,0.002183321,0.799723032,0.002183317,0.155164351,Epidemiology,0.27221572,FALSE,3,0.037293586,1.5,0.138747659,6,0.764429903,1768,0.167348904,0.276955013
14111,Automatic Detection of COVID-19 and Pneumonia from Chest X-Ray using Transfer Learning,0,10.1101/2020.05.27.20100297,5/29/20,medrxiv,0,2,"deep learning, transfer learning, dataset",0.001415204,0.061920239,0.841839771,0.001415164,0.001415141,0.091994482,Imaging,0.45070827,FALSE,3,0.037293586,5.5,0.267259834,3,0.667819001,1478,0.093185649,0.266389518
14112,Estimating the Size of High-risk Populations for COVID-19 Mortality across 442 US Cities,0,10.1101/2020.05.27.20115170,5/29/20,medrxiv,10.1038/s41591-020-01191-8,7,forecasting model,0.001684654,0.001684569,0.00168456,0.696417777,0.150410666,0.148117773,Epidemiology,0.032525092,FALSE,9.25,0.137918239,0.5,0.087101953,2,0.618927094,11877,0.851191909,0.423784799
14113,Belief in Conspiracy Theory about COVID-19 Predicts Mental Health and Well-being -- A Study of Healthcare Staff in Ecuador,0,10.1101/2020.05.26.20113258,5/29/20,medrxiv,10.2196/20737,7,logistic regression,0.001461883,0.001461917,0.001461941,0.042905446,0.951246966,0.001461847,Healthcare,0.7554499,TRUE,27.42857143,0.400643206,6.285714286,0.283649987,3,0.667819001,3690,0.521309896,0.468355523
14114,Determinants of intent to uptake Coronavirus vaccination among respondents in Saudi Arabia: a web-based national survey,0,10.1101/2020.05.27.20114413,5/29/20,medrxiv,0,2,logistic regression,0.001046835,0.001046827,0.001046809,0.001046851,0.994765863,0.001046814,Healthcare,0.42186725,FALSE,2,0.022141134,0,0.055525823,5,0.739490092,4231,0.58584156,0.350749652
14115,Modelling the thermal inactivation of viruses from the Coronaviridae family in suspensions or on surfaces with various relative humidities.,0,10.1101/2020.05.26.20114025,5/29/20,medrxiv,10.1128/AEM.01244-20,11,"mathematical model, dataset",0.001786585,0.279741934,0.001786555,0.713111725,0.001786511,0.001786689,Epidemiology,0.36909515,FALSE,25.18181818,0.369967221,25.45454545,0.532445812,2,0.618927094,2631,0.36744522,0.472196337
14116,SARS-CoV-2 SEROPREVALENCE AMONG ALL WORKERS IN A TEACHING HOSPITAL IN SPAIN: UNMASKING THE RISK.,0,10.1101/2020.05.29.20116731,5/29/20,medrxiv,0,9,logistic regression,0.002080559,0.120616377,0.002080659,0.002080638,0.429672437,0.44346933,Clinics,0.39095768,FALSE,16.33333333,0.247015895,10.33333333,0.36038266,17,0.887338725,2820,0.406212377,0.475237414
14117,Rapid and Inexpensive Whole-Genome Sequencing of SARS-CoV2 using 1200 bp Tiled Amplicons and Oxford Nanopore Rapid Barcoding,0,10.1101/2020.05.28.122648,5/29/20,biorxiv,10.1093/biomethods/bpaa014,4,"sequencing, whole-genome, genomes",0.002183286,0.842206585,0.002183381,0.119649239,0.002183324,0.031594184,Genomics,0.25110704,FALSE,20.5,0.304966294,34.25,0.600280974,7,0.785110192,7348,0.765229954,0.613896853
14118,SARS-CoV-2 transmission chains from genetic data: a Danish case study,0,10.1101/2020.05.29.123612,5/29/20,biorxiv,10.1371/journal.pone.0241405,8,genome sequences,0.001310351,0.371390351,0.001310368,0.62336822,0.001310373,0.001310337,Epidemiology,0.40723008,FALSE,37.125,0.509369782,42.25,0.645838908,12,0.850299401,5349,0.672285095,0.669448297
14119,Molecules inhibit the enzyme activity of 3-chymotrypsin-like cysteine protease of SARS-CoV-2 virus,0,10.1101/2020.05.28.120642,5/29/20,biorxiv,0,7,computational,0.924152402,0.04381299,0.001486431,0.00148646,0.001486508,0.02757521,Drug discovery,0.22497714,FALSE,29.42857143,0.423835735,8.428571429,0.326665775,2,0.618927094,2106,0.251143752,0.405143089
14120,Mental health outcomes and associations during the coronavirus disease 2019 pandemic: A cross-sectional survey of the US general population,0,10.1101/2020.05.26.20114140,5/28/20,medrxiv,10.3389/fpsyt.2020.569083,2,logistic regression,0.000863024,0.000863049,0.000863042,0.000863059,0.914261626,0.0822862,Healthcare,0.7851522,TRUE,4.5,0.061784897,0,0.055525823,6,0.764429903,1548,0.109318565,0.247764797
14121,"Ambient air pollutants, meteorological factors and their interactions affect confirmed cases of COVID-19 in 120 Chinese cities",0,10.1101/2020.05.27.20111542,5/28/20,medrxiv,0,4,correlation analysis,0.06800159,0.002296569,0.002296567,0.48212851,0.002296675,0.442980089,Epidemiology,0.71077436,TRUE,6,0.086028821,11.66666667,0.38065293,1,0.537564047,1929,0.202745004,0.3017477
14122,COVID-19 (SARS-CoV-2) Ventilator Resource Management Using a Network Optimization Model and Predictive System Demand,0,10.1101/2020.05.26.20113886,5/28/20,medrxiv,0,4,"network model, optimization model",0.025131149,0.001392937,0.145296313,0.730045933,0.001392917,0.096740752,Epidemiology,0.1872333,FALSE,3.5,0.044344115,0.5,0.087101953,5,0.739490092,1462,0.089092222,0.240007096
14123,Safety Stock: Predicting demand on the supply chain in Brazilian hospitals during the COVID-19 pandemic,0,10.1101/2020.05.27.20114330,5/28/20,medrxiv,0,12,predictive model,0.000946088,0.000946078,0.000946133,0.573728907,0.314616245,0.108816549,Epidemiology,0.10117805,FALSE,14.36363636,0.216958377,3.818181818,0.221568103,0,0.403234768,2889,0.414640019,0.314100317
14124,On the true numbers of COVID-19 infections: behind the available data,0,10.1101/2020.05.26.20114074,5/28/20,medrxiv,0,3,probabilistic,0.004310057,0.004310376,0.00431024,0.978449131,0.004310114,0.004310083,Epidemiology,0.3797649,FALSE,39.66666667,0.536211268,4.333333333,0.237958255,0,0.403234768,2626,0.363833373,0.385309416
14125,Phylogenetic clustering of the Indian SARS-CoV-2 genomes reveals the presence of distinct clades of viral haplotypes among states,0,10.1101/2020.05.28.122143,5/28/20,biorxiv,0,2,"genome-wide, whole genome, genome sequences, genomes, network analysis",0.001291257,0.932136359,0.00129134,0.001291305,0.00129122,0.06269852,Genomics,0.3958266,FALSE,10,0.15214299,6.5,0.288132192,7,0.785110192,2478,0.335420178,0.390201388
14126,SARS-CoV-2 genomics beyond the consensus sequence: evidence for circulating mixed viral populations,0,10.1101/2020.05.28.118992,5/28/20,biorxiv,0,36,"sequencing, whole genome",0.045697319,0.949553574,0.001187271,0.001187297,0.001187275,0.001187264,Genomics,0.22656155,FALSE,50.82142857,0.63665038,152.4285714,0.885335831,26,0.920859312,20377,0.91957621,0.840605433
14127,Single-cell RNA-seq and V(D)J profiling of immune cells in COVID-19 patients,0,10.1101/2020.05.24.20101238,5/27/20,medrxiv,0,18,transcriptom,0.529881509,0.100831491,0.000966813,0.000966833,0.00096681,0.366386544,Drug discovery,0.22810033,FALSE,14.88888889,0.223452285,8.722222222,0.331683168,7,0.785110192,2705,0.37948471,0.429932589
14128,COVID-Net: A deep learning based and interpretable predication model for the county-wise trajectories of COVID-19 in the United States,0,10.1101/2020.05.26.20113787,5/27/20,medrxiv,0,6,"deep learning, lstm",0.001219991,0.001219994,0.10362167,0.891498232,0.001220014,0.001220099,Epidemiology,0.044489264,FALSE,14,0.213494959,2.333333333,0.173401124,6,0.764429903,1774,0.163977847,0.328825958
14129,Infoveillance to Analyze Covid19 Impact on Central America Population,0,10.1101/2020.05.26.20113514,5/27/20,medrxiv,0,2,text mining,0.00333526,0.003335401,0.00333539,0.983323404,0.003335303,0.003335241,Epidemiology,0.15079239,FALSE,3,0.037293586,0,0.055525823,1,0.537564047,1079,0.020948712,0.162833042
14130,A chest radiography-based artificial intelligence deep-learning model to predict severe Covid-19 patient outcomes: the CAPE (Covid-19 AI Predictive Engine) Model,0,10.1101/2020.05.25.20113084,5/27/20,medrxiv,0,4,"artificial intelligence, deep-learning",0.001237042,0.001237044,0.658782533,0.001237058,0.001237044,0.336269279,Imaging,0.75521225,TRUE,3,0.037293586,0.25,0.065493712,3,0.667819001,1330,0.060197448,0.207700937
14131,"Knowledge, attitude, and practice regarding COVID-19 outbreak in Bangladeshi people: An online-based cross-sectional study",0,10.1101/2020.05.26.20105700,5/27/20,medrxiv,10.1371/journal.pone.0239254,6,logistic regression,0.001059331,0.001059351,0.029519456,0.00105939,0.966243133,0.001059339,Healthcare,0.9169723,TRUE,9.6,0.143608139,6.4,0.286058336,11,0.840175319,2568,0.352034674,0.405469117
14132,"Assessment of ACE2, CXCL10 and Their co-expressed Genes: An In-silico Approach to Evaluate the Susceptibility and Fatality of Lung Cancer Patients towards COVID-19 Infection",0,10.1101/2020.05.27.119610,5/27/20,biorxiv,0,5,in-silico,0.59071278,0.15312955,0.001156261,0.001156276,0.001156245,0.252688888,Drug discovery,0.5789474,TRUE,4.8,0.065248315,1,0.122023013,1,0.537564047,1642,0.132434385,0.21431744
14133,"Scrutinising the COVID-19 data on 590.000 cases. A retrospective, population-based descriptive study for data quality surveillance and a review at 4.540.000 cases.",0,10.1101/2020.05.26.20113316,5/27/20,medrxiv,0,1,dataset,0.000476952,0.000476982,0.054025182,0.681649857,0.040370419,0.223000608,Epidemiology,0.4385562,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,1136,0.029617144,0.125171309
14134,Differentiating COVID-19 from other types of pneumonia with convolutional neural networks,0,10.1101/2020.05.26.20113761,5/27/20,medrxiv,0,3,neural network,0.001072194,0.094094799,0.901616382,0.001072174,0.001072202,0.001072249,Imaging,0.5048695,TRUE,16.33333333,0.247015895,2.666666667,0.185442869,2,0.618927094,1630,0.129544907,0.295232691
14135,Benefit-risk analysis of health benefits of routine childhood immunisation against the excess risk of SARS-CoV-2 infections during the Covid-19 pandemic in Africa,0,10.1101/2020.05.19.20106278,5/26/20,medrxiv,10.1016/S2214-109X(20)30308-9,11,probabilistic,0.000414415,0.045605706,0.000414411,0.355620465,0.59753057,0.000414434,Healthcare,0.58142054,TRUE,39.63636364,0.535159874,96.09090909,0.815226117,9,0.814309525,2823,0.40211895,0.641703617
14136,Strict Physical Distancing May Be More Efficient: A Mathematical Argument for Making Lockdowns Count,0,10.1101/2020.05.19.20107045,5/26/20,medrxiv,0,6,mathematical model,0.001987143,0.001987124,0.00198713,0.830815424,0.161236033,0.001987146,Epidemiology,0.34248734,FALSE,14.66666667,0.221163956,10.5,0.363459995,2,0.618927094,7638,0.772212858,0.493940976
14137,Ethnic and regional variation in hospital mortality from COVID-19 in Brazil,0,10.1101/2020.05.19.20107094,5/26/20,medrxiv,10.1016/S2214-109X(20)30285-0,5,dataset,0.000946091,0.000946122,0.00094609,0.584349882,0.125641985,0.287169831,Epidemiology,0.24231875,FALSE,11,0.167171748,3.4,0.208589778,3,0.667819001,2842,0.405490007,0.362267634
14138,COVID-19 Datasets: A Survey and Future Challenges,0,10.1101/2020.05.19.20107532,5/26/20,medrxiv,10.1007/s10489-020-01862-6,4,"artificial intelligence, dataset",0.001112649,0.001112637,0.347082344,0.568740179,0.080839556,0.001112635,Epidemiology,0.42914176,FALSE,10.5,0.157338116,3.25,0.203572384,4,0.707574542,2898,0.412713701,0.370299686
14139,Clinical Utility of SARS-CoV-2 Whole Genome Sequencing in Deciphering Source of Infection,0,10.1101/2020.05.21.20107599,5/26/20,medrxiv,10.1016/j.jhin.2020.10.014,16,"sequencing, whole genome",0.002422281,0.844921361,0.002422413,0.00242242,0.002422408,0.145389118,Genomics,0.2756374,FALSE,32.5625,0.461933329,84.6875,0.78960396,3,0.667819001,1485,0.09222249,0.502894695
14140,"Knowledge, attitude, and practices of Community pharmacists about COVID-19: A cross-sectional survey in two provinces of Pakistan.",0,10.1101/2020.05.22.20108290,5/26/20,medrxiv,10.1017/dmp.2021.54,13,logistic regression,0.001022632,0.001022635,0.001022619,0.001022659,0.994886842,0.001022613,Healthcare,0.8866781,TRUE,4.692307692,0.063516606,0.153846154,0.05753278,2,0.618927094,2242,0.27642668,0.25410079
14141,BCG vaccination and socioeconomic variables vs Covid-19 global features: clearing up a controversial issue,0,10.1101/2020.05.20.20107755,5/26/20,medrxiv,10.1111/all.14524,3,dataset,0.001593503,0.001593538,0.001593507,0.938351148,0.001593571,0.055274731,Epidemiology,0.4517996,FALSE,29.33333333,0.423093574,5.666666667,0.270203372,2,0.618927094,1757,0.159402841,0.36790672
14142,Investigational treatments for COVID-19 may increase ventricular arrhythmia risk through drug interactions,0,10.1101/2020.05.21.20109397,5/26/20,medrxiv,10.1002/psp4.12573,7,mathematical model,0.462063002,0.001511817,0.001511876,0.193211456,0.001511853,0.340189997,Drug discovery,0.60763735,TRUE,29.28571429,0.421918486,28.42857143,0.557265186,5,0.739490092,2172,0.261738502,0.495103067
14143,"Estimation of the Final Size of the COVID-19 Epidemic inBalochistan, Pakistan.",0,10.1101/2020.05.22.20110064,5/26/20,medrxiv,0,1,prediction model,0.006539551,0.006539784,0.00653967,0.967301132,0.006539887,0.006539976,Epidemiology,0.14802998,FALSE,4,0.054734368,1,0.122023013,12,0.850299401,853,0.003130267,0.257546762
14144,SARS-CoV-2 entry related genes are comparably expressed in children's lung as adults,0,10.1101/2020.05.25.20110890,5/26/20,medrxiv,0,15,sequencing,0.518335581,0.086187906,0.003214147,0.003214114,0.38583411,0.003214142,Drug discovery,0.42101684,FALSE,17.71428571,0.266621312,28,0.554321648,1,0.537564047,1704,0.145196244,0.375925813
14145,Estimating Household Transmission of SARS-CoV-2,0,10.1101/2020.05.23.20111559,5/26/20,medrxiv,0,4,bayes,0.000740302,0.000740345,0.00074035,0.715110386,0.281928298,0.000740319,Epidemiology,0.16961506,FALSE,5.75,0.080957388,2,0.164302917,14,0.866658436,4857,0.633517939,0.43635917
14146,Explainable machine learning models to understand determinants of COVID-19 mortality in the United States,0,10.1101/2020.05.23.20110189,5/26/20,medrxiv,0,8,machine learning,0.001141325,0.001141357,0.221652782,0.483630767,0.00114142,0.291292349,Epidemiology,0.113681614,FALSE,25.25,0.370709382,8.375,0.325729195,2,0.618927094,1888,0.189501565,0.376216809
14147,"Prevalence and risk factors for mortality related to COVID-19 in a severely affected area of Madrid, Spain",0,10.1101/2020.05.25.20112912,5/26/20,medrxiv,0,15,logistic regression,0.001310399,0.001310542,0.001310394,0.0387809,0.00131037,0.955977395,Clinics,0.55471367,TRUE,5.2,0.071927763,1,0.122023013,2,0.618927094,1712,0.147844931,0.2401807
14148,SARS-CoV-2 detection in nasopharyngeal throat swabs by metagenomics,0,10.1101/2020.05.24.20110205,5/26/20,medrxiv,0,22,metagenom,0.004775104,0.913010225,0.004775398,0.00477517,0.004775042,0.067889061,Genomics,0.33242735,FALSE,12.95454545,0.193209228,5.863636364,0.273481402,0,0.403234768,1263,0.047917168,0.229460641
14149,No evidence for allelic association between Covid-19 and ACE2 genetic variants by direct exome sequencing in 99 SARS-CoV-2 positive patients,0,10.1101/2020.05.23.20111310,5/26/20,medrxiv,0,14,"sequencing, in silico, exom",0.001310477,0.726262888,0.001310343,0.001310362,0.001310433,0.268495496,Genomics,0.17168951,FALSE,36.5,0.503123261,29.57142857,0.566363393,0,0.403234768,1697,0.143269925,0.403997837
14150,Bayesian Network Analysis of Covid-19 data reveals higher Infection Prevalence Rates and lower Fatality Rates than widely reported,0,10.1101/2020.05.25.20112466,5/26/20,medrxiv,10.1080/13669877.2020.1778771,4,"bayes, network analysis",0.001622737,0.001622763,0.001622774,0.797610617,0.195898356,0.001622752,Epidemiology,0.32805187,FALSE,5.25,0.072855464,0.25,0.065493712,9,0.814309525,4556,0.60943896,0.390524415
14151,Estimation and monitoring of COVID-19 transmissibility from publicly available data,0,10.1101/2020.05.24.20112128,5/26/20,medrxiv,10.3389/fams.2020.565336,2,mathematical model,0.093118811,0.041578068,0.001901819,0.76573571,0.001901751,0.09576384,Epidemiology,0.39081356,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,1574,0.112930412,0.152246147
14152,A data first approach to modelling Covid-19,0,10.1101/2020.05.22.20110171,5/26/20,medrxiv,0,1,"bayes, model fit",0.001392896,0.045456617,0.001392862,0.948971919,0.001392841,0.001392866,Epidemiology,0.20201686,FALSE,2,0.022141134,0,0.055525823,1,0.537564047,1587,0.117505418,0.183184106
14153,Psychological impact of Covid-19 lockdown in India: Different strokes for different folks,0,10.1101/2020.05.25.20111716,5/26/20,medrxiv,10.1371/journal.pone.0238761,2,logistic regression,0.001511807,0.001511862,0.001511863,0.220903492,0.773049137,0.001511839,Healthcare,0.90562475,TRUE,6.5,0.093512277,0,0.055525823,5,0.739490092,5044,0.649650855,0.384544762
14154,Utility of Pan-Family Assays for Rapid Viral Screening: Reducing Delays in Public Health Responses During Pandemics,0,10.1101/2020.05.24.20112318,5/26/20,medrxiv,10.1093/cid/ciaa1028,5,genomes,0.000966782,0.902584703,0.000966824,0.000966796,0.058799586,0.035715308,Genomics,0.1624332,FALSE,16.2,0.244974952,6,0.280037463,1,0.537564047,1020,0.013484228,0.269015173
14155,COVID-19 in Latin America: Contrasting phylodynamic inference with epidemiological surveillance.,0,10.1101/2020.05.23.20111443,5/26/20,medrxiv,0,7,genomes,0.001291234,0.524624712,0.001291265,0.470210339,0.001291211,0.001291239,Genomics,0.28358644,FALSE,18.8,0.281278991,4,0.231469093,3,0.667819001,1528,0.101131712,0.320424699
14156,The novel Coronavirus enigma: Phylogeny and mutation analyses of SARS-CoV-2 viruses circulating in India during early 2020.,0,10.1101/2020.05.25.114199,5/26/20,biorxiv,10.2196/20735,6,genome sequences,0.001141388,0.994293216,0.001141322,0.001141344,0.001141345,0.001141385,Genomics,0.88772696,TRUE,54.83333333,0.667017132,45.5,0.660957988,5,0.739490092,2899,0.412954491,0.620104925
14157,Alveolar early progenitors in the aged human lung have increased expression of ACE2 accompanied with genes involved in beta-amyloid clearance: Indication of SARS-CoV-2 also using soluble ACE2 in aged-lungs to enter ACE2-negative cells,0,10.1101/2020.05.25.115774,5/26/20,biorxiv,0,1,dataset,0.754633509,0.081686059,0.00190187,0.001901794,0.001901805,0.157974963,Drug discovery,0.06189826,FALSE,13,0.197352959,10,0.355632861,0,0.403234768,2247,0.278352998,0.308643396
14158,Worldwide and Regional Forecasting of Coronavirus (Covid-19) Spread using a Deep Learning Model,0,10.1101/2020.05.23.20111039,5/26/20,medrxiv,0,2,"deep learning, artificial intelligence, network model, lstm",0.000815329,0.000815353,0.222935665,0.76187858,0.000815367,0.012739706,Epidemiology,0.26668048,FALSE,20.5,0.304966294,8,0.320511105,5,0.739490092,1208,0.037563207,0.350632674
14159,"Structure, function and variants analysis of the androgen-regulated TMPRSS2, a drug target candidate for COVID-19 infection",0,10.1101/2020.05.26.116608,5/26/20,biorxiv,0,5,"bioinformatic, structural model",0.545154896,0.321455428,0.001187301,0.001187451,0.001187312,0.129827613,Drug discovery,0.32727355,FALSE,56.6,0.679324634,178,0.905004014,0,0.403234768,2558,0.348663617,0.584056758
14160,Study of cell to cell transmission of SARS CoV 2 virus particle using gene network from micro array data,0,10.1101/2020.05.26.116780,5/26/20,biorxiv,0,3,sequencing,0.827612887,0.119291862,0.001059365,0.001059383,0.025193919,0.025782584,Drug discovery,0.37991598,FALSE,148.3333333,0.942668069,202,0.91838373,2,0.618927094,2047,0.231398989,0.67784447
14161,Risk of infection and hospitalization by Covid-19 in Mexico: a case-control study,0,10.1101/2020.05.24.20104414,5/26/20,medrxiv,0,12,logistic regression,0.001350317,0.001350326,0.001350337,0.086716238,0.171805271,0.737427511,Clinics,0.29119223,FALSE,48.66666667,0.619333292,16,0.437316029,9,0.814309525,2541,0.34481098,0.553942457
14162,MACHINE LEARNING PREDICTION FOR COVID 19 PANDEMIC IN INDIA,0,10.1101/2020.05.20.20107847,5/26/20,medrxiv,0,2,"machine learning, neural network, dataset",0.001461895,0.001461909,0.243818713,0.75033358,0.001461974,0.001461929,Epidemiology,0.70105267,TRUE,10.5,0.157338116,0,0.055525823,6,0.764429903,2341,0.29833855,0.318908098
14163,"Mechanistic rationale of drugs, Primary endpoints, Geographical distribution of clinical trials against Severe acute respiratory syndrome-related coronavirus-2: A Systematic Review",0,10.1101/2020.05.24.20112169,5/26/20,medrxiv,10.1002/jmv.26338,6,mathematical model,0.165567484,0.000815367,0.0224928,0.480958225,0.030845206,0.299320917,Epidemiology,0.4414817,FALSE,10.66666667,0.159131672,0.5,0.087101953,1,0.537564047,1686,0.141102817,0.231225122
14164,Performance evaluation of the point-of-care SAMBA II SARS-CoV-2 Test for detection of SARS-CoV-2,0,10.1101/2020.05.24.20100990,5/26/20,medrxiv,10.1128/JCM.01262-20,9,in silico,0.001653154,0.355628864,0.516151883,0.001653074,0.001653025,0.123260001,Genomics,0.4653125,FALSE,13.88888889,0.209660461,6.111111111,0.280438855,11,0.840175319,3543,0.501083554,0.457839547
14165,Modelling of Covid-19 cases in India using Regression and Time Series models,0,10.1101/2020.05.20.20107540,5/25/20,medrxiv,10.1007/s41403-020-00165-z,2,model fit,0.036861151,0.001987237,0.001987143,0.920874585,0.001987178,0.036302706,Epidemiology,0.36647767,FALSE,40.5,0.544746119,8.5,0.329141022,11,0.840175319,2646,0.362629425,0.519172971
14166,Spread dynamics of SARS-CoV-2 epidemic in China: a phylogenetic analysis,0,10.1101/2020.05.20.20107854,5/25/20,medrxiv,0,3,bayes,0.001751192,0.182513212,0.001751166,0.810482084,0.001751172,0.001751176,Epidemiology,0.5380477,TRUE,7.666666667,0.111633373,0.333333333,0.073187048,1,0.537564047,1472,0.087888274,0.202568185
14167,Prevalence of SARS-CoV-2 infection among asymptomatic healthcare workers in greater Houston: a cross-sectional analysis of surveillance data from a large healthcare system,0,10.1101/2020.05.21.20107581,5/25/20,medrxiv,0,10,logistic regression,0.000722169,0.037896372,0.01884621,0.000722213,0.596263323,0.345549713,Healthcare,0.6534864,TRUE,60.1,0.703754097,36.4,0.613593792,8,0.799987654,3270,0.464965085,0.645575157
14168,Risk factors affecting COVID-19 case fatality rate: A quantitative analysis of top 50 affected countries,0,10.1101/2020.05.20.20108449,5/25/20,medrxiv,10.36877/pmmb.a0000171,8,dataset,0.001272662,0.001272673,0.001272698,0.179457228,0.132087322,0.684637418,Clinics,0.7368626,TRUE,7.625,0.109530583,1,0.122023013,5,0.739490092,2990,0.426920299,0.349490997
14169,Age- and sex-specific total mortality impacts of the early weeks of the Covid-19 pandemic in England and Wales: Application of a Bayesian model ensemble to mortality statistics,0,10.1101/2020.05.20.20107680,5/25/20,medrxiv,0,9,"bayes, bayesian model, probabilistic",0.000752883,0.000752895,0.000752907,0.513306051,0.077760517,0.406674747,Epidemiology,0.034212917,FALSE,20,0.298163152,27,0.546026224,4,0.707574542,2404,0.313026728,0.466197661
14170,Influence of countries adopted policies for COVID-19 reduction under the view of the airborne transmission framework,0,10.1101/2020.05.20.20107763,5/25/20,medrxiv,10.2196/20699,1,dataset,0.001098805,0.001098815,0.00109882,0.994505879,0.001098874,0.001098806,Epidemiology,0.51584536,TRUE,19,0.285793803,0,0.055525823,2,0.618927094,1639,0.128822538,0.272267314
14171,A Modelling Analysis of Strategies for Relaxing COVID-19 Social Distancing,0,10.1101/2020.05.19.20107425,5/25/20,medrxiv,0,3,simulation model,0.065835672,0.000966763,0.000966758,0.930297279,0.000966771,0.000966758,Epidemiology,0.1327287,FALSE,11.33333333,0.170635166,1.333333333,0.13252609,4,0.707574542,1636,0.127618589,0.284588597
14172,Modelling information-dependent social behaviors in response to lockdowns: the case of COVID-19 epidemic in Italy,0,10.1101/2020.05.20.20107573,5/25/20,medrxiv,10.1098/rsos.201635,2,mathematical model,0.002130692,0.002130692,0.002130726,0.903178864,0.08829815,0.002130877,Epidemiology,0.092368394,FALSE,49,0.624281032,13,0.400521809,1,0.537564047,1964,0.20732001,0.442421724
14173,Covid19 Surveillance in Peru on April using Text Mining,0,10.1101/2020.05.24.20112193,5/25/20,medrxiv,0,2,text mining,0.003101456,0.003101517,0.003101489,0.984492362,0.003101721,0.003101455,Epidemiology,0.32543612,FALSE,9.5,0.143051518,0.5,0.087101953,3,0.667819001,910,0.004575006,0.22563687
14174,AIDCOV: An Interpretable Artificial Intelligence Model for Detection of COVID-19 from Chest Radiography Images,0,10.1101/2020.05.24.20111922,5/25/20,medrxiv,0,4,"artificial intelligence, neural network, network model, dataset",0.001126806,0.080606793,0.889798126,0.001126855,0.026214566,0.001126853,Imaging,0.30538216,FALSE,4,0.054734368,0,0.055525823,6,0.764429903,1732,0.152419937,0.256777508
14175,Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States,0,10.1101/2020.05.24.20111989,5/25/20,medrxiv,0,6,machine learning,0.001901688,0.026592446,0.075240722,0.892461773,0.001901688,0.001901683,Epidemiology,0.12030676,FALSE,5,0.070752675,0.333333333,0.073187048,29,0.928020248,3904,0.542258608,0.403554645
14176,COVID-19 Outcomes in 4712 consecutively confirmed SARS-CoV2 cases in the city of Madrid.,0,10.1101/2020.05.22.20109850,5/25/20,medrxiv,0,26,"artificial intelligence, neural network, logistic regression",0.00139295,0.001392945,0.239642418,0.089471208,0.001392876,0.666707603,Clinics,0.40432182,FALSE,8.576923077,0.126847671,2.192307692,0.16697886,8,0.799987654,7399,0.763544426,0.464339653
14177,CLINICAL CHARACTERISTICS AND PROGNOSTIC FACTORS FOR ICU ADMISSION OF PATIENTS WITH COVID-19 USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING,0,10.1101/2020.05.22.20109959,5/25/20,medrxiv,10.2196/21801,4,"machine learning, predictive model",0.001237125,0.001237084,0.211372677,0.001237169,0.001237137,0.783678808,Clinics,0.8113582,TRUE,226,0.980394582,329,0.961934707,5,0.739490092,4704,0.62123766,0.82576426
14178,Avoiding COVID-19: Aerosol Guidelines,0,10.1101/2020.05.21.20108894,5/25/20,medrxiv,0,1,mathematical model,0.005353403,0.00535354,0.005353276,0.77664204,0.005353278,0.201944463,Epidemiology,0.41017443,FALSE,35,0.488032655,33,0.593256623,13,0.858880178,11367,0.842282687,0.695613036
14179,Is there an airborne component to the transmission of COVID-19? : a quantitative analysis study,0,10.1101/2020.05.22.20109991,5/25/20,medrxiv,0,1,dataset,0.001272665,0.001272703,0.001272733,0.798844687,0.001272734,0.196064478,Epidemiology,0.06268504,FALSE,6,0.086028821,0,0.055525823,17,0.887338725,5470,0.677100891,0.426498565
14180,Modelling the impact of Plasma Therapy and Immunotherapy for Recovery of COVID-19 Infected Individuals,0,10.1101/2020.05.23.20110973,5/24/20,medrxiv,0,5,mathematical model,0.001565378,0.134320811,0.001565305,0.635830349,0.001565415,0.225152743,Epidemiology,0.32236928,FALSE,9.4,0.140021028,0.2,0.061145304,1,0.537564047,1087,0.020467132,0.189799378
14181,A systems approach to inflammation identifies therapeutic targets in SARS-CoV-2 infection,0,10.1101/2020.05.23.20110916,5/24/20,medrxiv,0,39,proteom,0.397830859,0.001011027,0.001010989,0.001010993,0.001011003,0.598125129,Clinics,0.80048263,TRUE,45.22857143,0.588781001,61.48571429,0.725983409,10,0.828199272,3354,0.4717072,0.65366772
14182,C-Reactive protein and SOFA score as early predictors of critical care requirement in patients with COVID-19 pneumonia in Spain.,0,10.1101/2020.05.22.20110429,5/24/20,medrxiv,0,11,logistic regression,0.02563245,0.001565304,0.001565378,0.00156543,0.001565367,0.968106072,Clinics,0.8822911,TRUE,7.636363636,0.10959243,3.545454545,0.213740969,7,0.785110192,1535,0.100650132,0.302273431
14183,Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France,0,10.1101/2020.05.22.20110593,5/24/20,medrxiv,0,7,mathematical model,0.002357751,0.002357773,0.00235779,0.877969203,0.002357734,0.112599749,Epidemiology,0.38721508,FALSE,24.85714286,0.365947183,59.57142857,0.718825261,19,0.89561084,4282,0.583674452,0.641014434
14184,In silico Proteome analysis of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2),0,10.1101/2020.05.23.104919,5/24/20,biorxiv,0,3,"in silico, proteom",0.803812249,0.168955239,0.00095631,0.000956358,0.024363522,0.000956323,Drug discovery,0.42581707,FALSE,17.66666667,0.266373925,4,0.231469093,2,0.618927094,2843,0.40284132,0.379902858
14185,COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks,0,10.1101/2020.05.22.20110817,5/24/20,medrxiv,0,3,"deep learning, neural network, transfer learning, dataset",0.00178652,0.001786594,0.923213286,0.069640378,0.001786607,0.001786615,Imaging,0.6625308,TRUE,76,0.790586926,38.33333333,0.62356168,8,0.799987654,2975,0.422586082,0.659180585
14186,Conditions for a second wave of COVID-19 due to interactions between disease dynamics and social processes,0,10.1101/2020.05.22.20110502,5/24/20,medrxiv,10.3389/fphy.2020.574514,6,mathematical model,0.002032952,0.002032834,0.002032751,0.989835822,0.002032885,0.002032754,Epidemiology,0.22299609,FALSE,36.66666667,0.505102356,47.16666667,0.669253412,9,0.814309525,2623,0.3561281,0.586198348
14187,SARS-CoV-2 lethality decreased over time in two Italian Provinces,0,10.1101/2020.05.23.20110882,5/24/20,medrxiv,0,8,"logistic regression, dataset",0.001098815,0.001098869,0.001098903,0.247344343,0.001098861,0.74826021,Clinics,0.3263343,FALSE,80.85714286,0.810377884,72,0.7594327,5,0.739490092,8019,0.78160366,0.772726084
14188,Development and clinical application of a rapid and sensitive loop-mediated isothermalamplification test for SARS-CoV-2 infection,0,10.1101/2020.05.20.20108530,5/23/20,medrxiv,10.1128/mSphere.00808-20,15,sequencing,0.001330042,0.623341058,0.111430998,0.001330101,0.001330101,0.2612377,Genomics,0.5516033,TRUE,23.5,0.348506401,13.71428571,0.407679957,1,0.537564047,2035,0.222008187,0.378939648
14189,The estimations of the COVID-19 incubation period: a systematic review of the literature,0,10.1101/2020.05.20.20108340,5/23/20,medrxiv,10.1016/j.jiph.2021.01.019,2,dataset,0.001156325,0.001156271,0.001156291,0.806899712,0.001156362,0.188475039,Epidemiology,0.14600709,FALSE,3.5,0.044344115,0,0.055525823,6,0.764429903,4405,0.594028413,0.364582064
14190,Pathways of the COVID-19 Pandemic with Human Mobility across Countries,0,10.1101/2020.05.21.20108589,5/23/20,medrxiv,10.1007/s40305-020-00317-6,3,dataset,0.059602113,0.002183293,0.002183251,0.931664388,0.002183575,0.002183381,Epidemiology,0.48041335,FALSE,4,0.054734368,0,0.055525823,0,0.403234768,1711,0.142547556,0.164010629
14191,A score-based risk model for predicting severe COVID-19 infection as a key component of lockdown exit strategy,0,10.1101/2020.05.20.20108571,5/23/20,medrxiv,0,6,prediction model,0.000880237,0.000880212,0.292814963,0.280964389,0.10223367,0.322226528,Clinics,0.12160593,FALSE,81.16666667,0.811800359,71,0.756556061,4,0.707574542,1858,0.180592343,0.614130826
14192,Forecasting trajectories of an emerging epidemic with mathematical modeling in an online dashboard: The case of COVID-19,0,10.1101/2020.05.21.20108753,5/23/20,medrxiv,0,2,mathematical model,0.006539648,0.006539702,0.006539847,0.967300784,0.006540234,0.006539785,Epidemiology,0.65464646,TRUE,14,0.213494959,11,0.371287129,1,0.537564047,1102,0.02287503,0.286305291
14193,A pandemic at the Tunisian scale. Mathematical modelling of reported and unreported COVID-19 infected cases,0,10.1101/2020.05.21.20108621,5/23/20,medrxiv,0,1,mathematical model,0.00123712,0.001237081,0.001237064,0.952227882,0.04282369,0.001237163,Epidemiology,0.40528643,FALSE,8,0.118683901,0,0.055525823,1,0.537564047,1440,0.078979051,0.197688206
14194,"Demographic and Clinical Characteristics of the Severe Covid-19 Infections: First Report from Mashhad University of Medical Sciences, Iran",0,10.1101/2020.05.20.20108068,5/23/20,medrxiv,0,6,logistic regression,0.001415233,0.100850156,0.001415139,0.096010405,0.075990954,0.724318113,Clinics,0.60741425,TRUE,37.66666667,0.515430763,14.5,0.418450629,1,0.537564047,1325,0.056826391,0.382067957
14195,Longitudinal laboratory testing tied to PCR diagnostics in COVID-19 patients reveals temporal evolution of distinctive coagulopathy signatures,0,10.1101/2020.05.21.20109439,5/23/20,medrxiv,10.7554/eLife.59209,16,neural network,0.001350368,0.055784787,0.162757463,0.382923608,0.001350386,0.395833388,Clinics,0.52732766,TRUE,56.8125,0.680376028,38.5625,0.624832754,4,0.707574542,2822,0.398025524,0.602702212
14196,The connection of growth and medication of COVID-19 affected people after 30 days of lock down in India,0,10.1101/2020.05.21.20107946,5/23/20,medrxiv,0,3,mathematical model,0.001392859,0.001392859,0.001392864,0.993035708,0.001392867,0.001392844,Epidemiology,0.021930844,FALSE,5,0.070752675,0.666666667,0.096200161,0,0.403234768,880,0.003611847,0.143449862
14197,Data driven inference of the reproduction number (R0) for COVID-19 before and after interventions for 51 European countries,0,10.1101/2020.05.21.20109314,5/23/20,medrxiv,10.4414/smw.2020.20313,7,bayes,0.001684493,0.001684541,0.001684569,0.991577379,0.001684531,0.001684487,Epidemiology,0.07639247,FALSE,57.28571429,0.683715752,23.71428571,0.518263313,2,0.618927094,2218,0.267999037,0.522226299
14198,Quantification of SARS-CoV-2 and cross-assembly phage (crAssphage) from wastewater to monitor coronavirus transmission within communities,0,10.1101/2020.05.21.20109181,5/23/20,medrxiv,0,9,virom,0.002639077,0.563299305,0.002639181,0.426144276,0.002639134,0.002639027,Genomics,0.24174601,FALSE,25.33333333,0.37188447,70.22222222,0.754013915,30,0.930057411,3503,0.491211173,0.636791742
14199,Clustering method for spread pattern analysis of corona-virus (COVID-19) infection in Iran,0,10.1101/2020.05.22.20109942,5/23/20,medrxiv,10.35877/454ri.asci31109,3,data mining,0.00767418,0.007674157,0.205884224,0.763419286,0.007673991,0.007674163,Epidemiology,0.80661523,TRUE,2.5,0.027459954,0,0.055525823,4,0.707574542,1236,0.039248736,0.207452264
14200,Flattening the curve and the effect of atypical events on mitigation measures in Mexico: a modeling perspective,0,10.1101/2020.05.21.20109678,5/23/20,medrxiv,0,3,mathematical model,0.001786495,0.00178653,0.001786525,0.99106736,0.00178655,0.00178654,Epidemiology,0.1328148,FALSE,11.33333333,0.170635166,11.66666667,0.38065293,1,0.537564047,967,0.007946063,0.274199552
14201,Identifying Explosive Cases with Unsupervised Machine Learning,0,10.1101/2020.05.17.20104661,5/22/20,medrxiv,0,1,"machine learning, dataset",0.004775074,0.004775238,0.274391056,0.706508272,0.004775175,0.004775185,Epidemiology,0.6764375,TRUE,10,0.15214299,2,0.164302917,0,0.403234768,990,0.009150012,0.182207672
14202,COVID-19 in China: Risk Factors and R0 Revisited,0,10.1101/2020.05.18.20104703,5/22/20,medrxiv,10.1016/j.actatropica.2020.105731,8,"machine learning, computational, dataset",0.001511861,0.001511884,0.11238013,0.881572365,0.001511841,0.001511918,Epidemiology,0.39980412,FALSE,3.625,0.045086276,,,0,0.403234768,1481,0.086925114,0.178415386
14203,An Interpretable Machine Learning Framework for Accurate Severe vs Non-severe COVID-19 Clinical Type Classification,0,10.1101/2020.05.18.20105841,5/22/20,medrxiv,0,11,machine learning,0.00089813,0.000898097,0.581402788,0.000898102,0.000898112,0.415004771,Clinics,0.56246513,TRUE,4.727272727,0.063763993,0.454545455,0.077000268,4,0.707574542,1244,0.040452685,0.222197872
14204,High seroreactivity against SARS-CoV-2 Spike epitopes in a pre SARS-CoV-2 cohort: implications for antibody testing and vaccine design,0,10.1101/2020.05.18.20105189,5/22/20,medrxiv,0,20,bioinformatic,0.497138258,0.336601935,0.001987156,0.001987151,0.001987269,0.160298231,Drug discovery,0.4307277,FALSE,85.55,0.826705424,127.9,0.861252341,3,0.667819001,3924,0.539609921,0.723846672
14205,"Trends of SARS-CoV-2 infection worldwide: Role of population density, age structure, and climate on transmission and case fatality",0,10.1101/2020.05.20.20104257,5/22/20,medrxiv,0,2,model fit,0.000936081,0.000936137,0.00093608,0.76146845,0.00093613,0.234787122,Epidemiology,0.11401823,FALSE,3.5,0.044344115,1.5,0.138747659,0,0.403234768,2190,0.258849025,0.211293892
14206,SARS-CoV-2 mutations and where to find them: An in silico perspective of structural changes and antigenicity of the Spike protein,0,10.1101/2020.05.21.108563,5/22/20,biorxiv,10.1080/07391102.2020.1844052,10,in silico,0.582177344,0.409874045,0.001987222,0.001987155,0.001987104,0.00198713,Drug discovery,0.595456,TRUE,11.8,0.177994929,7.2,0.302047097,1,0.537564047,3516,0.491933542,0.377384904
14207,Susceptibility-adjusted herd immunity threshold model and potential R0 distribution fitting the observed Covid-19 data in Stockholm,0,10.1101/2020.05.19.20104596,5/22/20,medrxiv,0,2,network model,0.257246823,0.002357883,0.002357771,0.733322011,0.002357783,0.002357728,Epidemiology,0.08645174,FALSE,12,0.183190055,7,0.299973241,6,0.764429903,2227,0.268721406,0.379078651
14208,Contributions of Latin American researchers in the understanding the novel coronavirus outbreak: A literature review,0,10.1101/2020.05.16.20104422,5/22/20,medrxiv,10.7717/peerj.9332,2,bioinformatic,0.001861818,0.042930311,0.001861727,0.949622701,0.001861743,0.0018617,Epidemiology,0.506272,TRUE,3.5,0.044344115,0,0.055525823,5,0.739490092,1224,0.037803997,0.219291007
14209,"Evaluating epidemiological scenarios of isolation and further releases considering protection actions to control transmission of CoViD-19 in Sao Paulo State, Brazil.",0,10.1101/2020.05.19.20099309,5/22/20,medrxiv,0,4,mathematical model,0.001717191,0.00171719,0.001717162,0.829567463,0.163563753,0.00171724,Epidemiology,0.094492555,FALSE,234,0.981693364,455,0.976317902,2,0.618927094,1633,0.123525163,0.675115881
14210,MosMedData: Chest CT Scans with COVID-19 Related Findings,0,10.1101/2020.05.20.20100362,5/22/20,medrxiv,0,11,dataset,0.004109953,0.004109971,0.893336638,0.004110172,0.004109985,0.090223282,Imaging,0.5617618,TRUE,13.3,0.200507143,0.5,0.087101953,36,0.941169208,4982,0.640019263,0.467199392
14211,Consensus study of risk factors and symptoms of SARS-CoV-2 (COVID-19) using biomedical literature and social media data,0,10.1101/2020.05.17.20104729,5/22/20,medrxiv,0,4,dataset,0.001901796,0.001901829,0.001901781,0.537636571,0.340957452,0.115700571,Epidemiology,0.6662197,TRUE,8,0.118683901,1,0.122023013,0,0.403234768,1720,0.143751505,0.196923297
14212,COVID-19 case forecasting model for Sri Lanka based on Stringency Index,0,10.1101/2020.05.20.20103887,5/22/20,medrxiv,0,8,"prediction model, forecasting model",0.001786557,0.001786573,0.001786578,0.991067152,0.00178657,0.00178657,Epidemiology,0.13252512,FALSE,11.625,0.175397365,23.5,0.516791544,2,0.618927094,1229,0.038285577,0.337350395
14213,Higher serum levels of Chemokine CCL19 are associated with poor SARS-CoV-2 acute respiratory distress syndrome (ARDS) outcomes.,0,10.1101/2020.05.21.20051300,5/22/20,medrxiv,10.1152/ajpregu.00324.2020,11,transcriptom,0.309759118,0.001751306,0.001751158,0.001751162,0.001751176,0.683236079,Clinics,0.5592764,TRUE,90.28571429,0.843404045,105.4285714,0.831883864,4,0.707574542,2213,0.265109559,0.661993002
14214,Prevalence Threshold and Temporal Interpretation of Screening Tests: The Example of the SARS-CoV-2 (COVID-19) Pandemic,0,10.1101/2020.05.17.20104927,5/22/20,medrxiv,0,4,mathematical model,0.001786648,0.001786593,0.152864057,0.548107863,0.293668229,0.001786611,Epidemiology,0.16266912,FALSE,27.75,0.404292164,7.75,0.312215681,1,0.537564047,2075,0.233084517,0.371789102
14215,Who should we test for COVID-19?A triage model built from national symptom surveys,0,10.1101/2020.05.18.20105569,5/21/20,medrxiv,10.1016/j.medj.2020.10.002,25,dataset,0.001203537,0.13704512,0.30580607,0.001203511,0.510549245,0.044192516,Healthcare,0.5812852,TRUE,8.304347826,0.121838085,4.782608696,0.248327535,10,0.828199272,3104,0.437515049,0.408969985
14216,How many patients will need ventilators tomorrow?,0,10.1101/2020.05.18.20105783,5/21/20,medrxiv,0,4,simulation model,0.001717213,0.001717205,0.108257873,0.575131979,0.001717208,0.311458521,Epidemiology,0.50875175,TRUE,6.5,0.093512277,0,0.055525823,0,0.403234768,1116,0.024078979,0.144087962
14217,A Machine Learning Solution Framework for Combatting COVID-19 in Smart Cities from Multiple Dimensions,0,10.1101/2020.05.18.20105577,5/21/20,medrxiv,0,9,"machine learning, mathematical model",0.028604588,0.00141514,0.381316792,0.585833101,0.001415125,0.001415254,Epidemiology,0.51650614,TRUE,21.77777778,0.321726761,10.22222222,0.35804121,5,0.739490092,2168,0.253551649,0.418202428
14218,Impact of Superspreaders on dissemination and mitigation of COVID-19,0,10.1101/2020.05.17.20104745,5/21/20,medrxiv,0,3,model fit,0.000815344,0.000815356,0.00081532,0.950547682,0.046190954,0.000815345,Epidemiology,0.16492999,FALSE,68,0.753973653,38,0.622223709,14,0.866658436,7700,0.77004575,0.753225387
14219,"Development and Validation of Two In-house, Low-Cost SARS-CoV-2 Detection Assays",0,10.1101/2020.05.18.20105510,5/21/20,medrxiv,0,8,genome sequences,0.000946093,0.587890921,0.408324622,0.000946097,0.000946098,0.00094617,Genomics,0.29703546,FALSE,5.375,0.074525326,2.875,0.190259566,0,0.403234768,1449,0.079219841,0.186809875
14220,Prevalence of facemask use among general public when visiting wet market during Covid-19 pandemic: An observational study.,0,10.1101/2020.05.17.20105023,5/21/20,medrxiv,0,5,logistic regression,0.001187379,0.097170288,0.001187272,0.001187331,0.848935443,0.050332287,Healthcare,0.58179617,TRUE,5,0.070752675,0,0.055525823,2,0.618927094,2271,0.27546352,0.255167278
14221,Mechanistic insights into ventricular arrhythmogenesis of hydroxychloroquine and azithromycin for the treatment of COVID-19,0,10.1101/2020.05.21.108605,5/21/20,biorxiv,0,19,"in silico, in-silico",0.610002647,0.001622788,0.00162274,0.16322245,0.001622755,0.22190662,Drug discovery,0.18085411,FALSE,90.8,0.845135754,54.4,0.699090179,2,0.618927094,6485,0.723091741,0.721561192
14222,Analyzing the impact of SARS CoV-2 on the human proteome,0,10.1101/2020.05.21.107912,5/21/20,biorxiv,10.1063/5.0015626,1,proteom,0.934131649,0.00299671,0.002996559,0.002996606,0.002996413,0.053882063,Drug discovery,0.5256279,TRUE,290,0.989918981,305,0.955913835,0,0.403234768,2092,0.234770046,0.645959407
14223,Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering associated with negative feelings and contagion fear,0,10.1101/2020.05.19.20107078,5/21/20,medrxiv,10.1371/journal.pone.0242903,13,computational,0.00272024,0.002720259,0.103829062,0.445552023,0.442458286,0.00272013,Epidemiology,0.92301714,TRUE,33.07692308,0.466881069,22.07692308,0.503880118,1,0.537564047,4519,0.600048158,0.527093348
14224,ICU Bed Availability Monitoring and analysis in the Grand Est region of France during the COVID-19 epidemic,0,10.1101/2020.05.18.20091264,5/21/20,medrxiv,0,2,predictive model,0.000652921,0.000652922,0.051485092,0.726698562,0.000652928,0.219857573,Epidemiology,0.059533745,FALSE,51,0.63875317,192,0.913633931,2,0.618927094,1704,0.13845413,0.577442081
14225,When Can Elimination of SARS-CoV-2 Infection be Assumed? Simulation Modelling in a Case Study Island Nation,0,10.1101/2020.05.16.20104240,5/20/20,medrxiv,0,6,simulation model,0.001987084,0.001987223,0.001987202,0.990064166,0.001987178,0.001987148,Epidemiology,0.19385558,FALSE,22.83333333,0.337868761,13.16666667,0.401792882,4,0.707574542,2516,0.330363593,0.444399944
14226,COVID-19 Infection Forecasting based on Deep Learning in Iran,0,10.1101/2020.05.16.20104182,5/20/20,medrxiv,0,3,"deep learning, artificial intelligence, neural network, lstm, dataset",0.001751151,0.00175116,0.18467279,0.808322539,0.001751172,0.001751188,Epidemiology,0.35360903,FALSE,3.5,0.044344115,0,0.055525823,11,0.840175319,1610,0.11485673,0.263725497
14227,Predicting the COVID-19 positive cases in India with concern to Lockdown by using Mathematical and Machine Learning based Models,0,10.1101/2020.05.16.20104133,5/20/20,medrxiv,0,3,machine learning,0.001717174,0.00171719,0.001717282,0.991413976,0.00171719,0.001717188,Epidemiology,0.25633007,FALSE,5,0.070752675,0.333333333,0.073187048,7,0.785110192,1143,0.026246087,0.238824001
14228,"Evaluate the timing of resumption of business for the states of New York, New Jersey and California via a pre-symptomatic and asymptomatic transmission model of COVID-19",0,10.1101/2020.05.16.20103747,5/20/20,medrxiv,0,5,simulation model,0.001823336,0.001823368,0.00182335,0.990883184,0.00182343,0.001823331,Epidemiology,0.34866762,FALSE,10,0.15214299,0,0.055525823,3,0.667819001,2127,0.242234529,0.279430586
14229,"A Real-Time Statistical Model for Tracking and Forecasting COVID-19 Deaths, Prevalence and Incidence",0,10.1101/2020.05.16.20104430,5/20/20,medrxiv,0,1,forecasting model,0.001126807,0.001126819,0.001126796,0.92983093,0.065661804,0.001126845,Epidemiology,0.04445082,FALSE,45,0.587049292,42,0.644902328,1,0.537564047,2214,0.261979292,0.50787374
14230,The potential impact of the COVID-19 pandemic on tuberculosis: a modelling analysis,0,10.1101/2020.05.16.20104075,5/20/20,medrxiv,10.1016/j.eclinm.2020.100603,11,mathematical model,0.001538077,0.00153815,0.001538173,0.992309226,0.001538177,0.001538196,Epidemiology,0.047546536,FALSE,34.45454545,0.481353207,36.63636364,0.614797966,12,0.850299401,2701,0.366241271,0.578172961
14231,Prediction of the virus incubation period for COVID-19 and future outbreaks,0,10.1101/2020.05.19.104513,5/20/20,biorxiv,10.1186/s12915-020-00919-9,4,predictive model,0.00535297,0.558310225,0.005353445,0.420277036,0.005352991,0.005353332,Genomics,0.6914034,TRUE,361.25,0.994557487,2809.5,0.999464811,3,0.667819001,2471,0.318564893,0.745101548
14232,CD8+ T cell cross-reactivity against SARS-CoV-2 conferred by other coronavirus strains and influenza virus,0,10.1101/2020.05.20.107292,5/20/20,biorxiv,10.3389/fimmu.2020.579480,8,"in silico, proteom",0.707310034,0.246635622,0.001823358,0.001823345,0.040584218,0.001823422,Drug discovery,0.51272094,TRUE,54.875,0.667450059,119.25,0.850548568,4,0.707574542,4306,0.580303395,0.701469141
14233,Intra-host Variation and Evolutionary Dynamics of SARS-CoV-2 Population in COVID-19 Patients,0,10.1101/2020.05.20.103549,5/20/20,biorxiv,10.1186/s13073-021-00847-5,35,"sequencing, genomes, deep sequencing",0.126308775,0.760492901,0.034752617,0.001291287,0.001291229,0.07586319,Genomics,0.22686812,FALSE,59.69444444,0.700785454,,,10,0.828199272,3054,0.430050566,0.653011764
14234,"Ostavimir is ineffective against COVID-19: in silico assessment, in vitro and retrospective study",0,10.1101/2020.05.15.20102392,5/20/20,medrxiv,0,2,in silico,0.441574653,0.001538199,0.092084431,0.10799823,0.296550094,0.060254393,Drug discovery,0.55406064,TRUE,4,0.054734368,0,0.055525823,4,0.707574542,1981,0.203948953,0.255445921
14235,Modeling the Effects of Non-PharmaceuticalInterventions on COVID-19 Spread in Kenya,0,10.1101/2020.05.14.20102087,5/20/20,medrxiv,0,4,mathematical model,0.002562678,0.002562595,0.002562595,0.948640687,0.002562968,0.041108477,Epidemiology,0.36750954,FALSE,6.75,0.095862453,0.5,0.087101953,0,0.403234768,1085,0.018059234,0.151064602
14236,COVID-19 death rates by age and sex and the resulting mortality vulnerability of countries and regions in the world,0,10.1101/2020.05.17.20097410,5/20/20,medrxiv,0,1,dataset,0.00047188,0.000471888,0.000471869,0.556563045,0.000471888,0.44154943,Epidemiology,0.26894796,FALSE,18,0.271569052,27,0.546026224,17,0.887338725,3526,0.490007224,0.548735306
14237,Quantifying antibody kinetics and RNA shedding during early-phase SARS-CoV-2 infection,0,10.1101/2020.05.15.20103275,5/20/20,medrxiv,0,8,mathematical model,0.001392861,0.722714865,0.001392882,0.212237973,0.032135913,0.030125507,Genomics,0.17183775,FALSE,30.75,0.441090977,61.75,0.727588975,22,0.908142478,4701,0.613532386,0.672588704
14238,"Early assessment of knowledge, attitudes, anxiety and behavioral adaptations of Connecticut residents to COVID-19",0,10.1101/2020.05.18.20082073,5/20/20,medrxiv,0,4,logistic regression,0.00190171,0.001901708,0.030660873,0.001901824,0.96173215,0.001901736,Healthcare,0.90600216,TRUE,16,0.243552477,21,0.492239765,1,0.537564047,1401,0.070310619,0.335916727
14239,Mathematical Modeling and Simulation of SIR Model for COVID-2019 Epidemic Outbreak: A Case Study of India,0,10.1101/2020.05.15.20103077,5/20/20,medrxiv,0,1,mathematical model,0.001987153,0.001987125,0.00198716,0.990064377,0.001987088,0.001987098,Epidemiology,0.21870518,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,3213,0.451240067,0.233035448
14240,A Human-Pathogen SEIR-P Model for COVID-19 Outbreak under different intervention scenarios in Kenya,0,10.1101/2020.05.15.20102954,5/20/20,medrxiv,0,5,mathematical model,0.002357903,0.002357818,0.00235772,0.988211112,0.002357738,0.002357709,Epidemiology,0.5241204,TRUE,9.8,0.147071557,1,0.122023013,1,0.537564047,1107,0.021911871,0.207142622
14241,"AI-based multi-modal integration of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients",0,10.1101/2020.05.14.20101972,5/19/20,medrxiv,0,48,deep learning,0.001786586,0.00178658,0.678624926,0.001786525,0.001786524,0.314228859,Imaging,0.36472642,FALSE,18,0.271569052,13.48888889,0.404803318,3,0.667819001,4125,0.558391524,0.475645724
14242,Stepping out of lockdown should start with school re-openings while maintaining distancing measures. Insights from mixing matrices and mathematical models.,0,10.1101/2020.05.12.20099036,5/19/20,medrxiv,0,5,mathematical model,0.001203412,0.001203445,0.001203427,0.886740279,0.108446015,0.001203423,Epidemiology,0.23865193,FALSE,19.4,0.289257221,1.8,0.150120417,6,0.764429903,2167,0.249458223,0.363316441
14243,COVID-19 management in a UK NHS Foundation Trust with a High Consequence Infectious Diseases centre: a detailed descriptive analysis,0,10.1101/2020.05.14.20100834,5/19/20,medrxiv,10.3390/medsci9010006,27,logistic regression,0.00148642,0.001486435,0.001486479,0.182915441,0.05063639,0.761988835,Clinics,0.39676896,FALSE,16,0.243552477,14.18518519,0.413834627,12,0.850299401,2852,0.396099205,0.475946428
14244,"Testing, tracing and isolation in compartmental models",0,10.1101/2020.05.14.20101808,5/19/20,medrxiv,10.1371/journal.pcbi.1008633,5,"computational, mathematical model, probabilistic",0.001987165,0.001987127,0.001987169,0.990064296,0.001987162,0.001987081,Epidemiology,0.09710148,FALSE,19.4,0.289257221,1.8,0.150120417,9,0.814309525,2195,0.257163496,0.377712665
14245,Development of a Predictive Score for COVID-19 Diagnosis based on Demographics and Symptoms in Patients Attended at a Dedicated Screening Unit,0,10.1101/2020.05.14.20101931,5/19/20,medrxiv,0,8,logistic regression,0.052316197,0.001486449,0.36586973,0.001486474,0.001486536,0.577354614,Clinics,0.39518842,FALSE,54.375,0.663801101,31.5,0.581883864,0,0.403234768,1002,0.010113171,0.414758226
14246,Transmission onset distribution of COVID-19 in South Korea,0,10.1101/2020.05.13.20101246,5/19/20,medrxiv,10.1016/j.ijid.2020.07.075,3,bayes,0.001310314,0.001310361,0.001310327,0.652532884,0.001310426,0.342225688,Epidemiology,0.38873515,FALSE,11,0.167171748,5.333333333,0.262911426,3,0.667819001,2910,0.406453166,0.376088836
14247,Projecting Demand-Supply Gap of Hospital Capacity in India in the face of COVID-19 pandemic using Age-Structured Deterministic SEIR model,0,10.1101/2020.05.14.20100537,5/19/20,medrxiv,0,5,mathematical model,0.001010963,0.001011003,0.001010943,0.879799062,0.001010958,0.11615707,Epidemiology,0.25374895,FALSE,71.8,0.772156596,97.8,0.818169655,1,0.537564047,1778,0.155550205,0.570860126
14248,Blacks/African Americans are 5 Times More Likely to Develop COVID-19: Spatial Modeling of New York City ZIP Code-level Testing Results,0,10.1101/2020.05.14.20101691,5/19/20,medrxiv,10.1016/j.annepidem.2020.08.012,4,bayes,0.001392816,0.001392894,0.001392851,0.26952675,0.450432507,0.275862181,Healthcare,0.50904197,TRUE,2.75,0.030304904,0,0.055525823,7,0.785110192,5283,0.658319287,0.382315052
14249,Risk factors for hospital admission related to COVID-19 in inflammatory rheumatic diseases,0,10.1101/2020.05.14.20101584,5/19/20,medrxiv,10.1136/annrheumdis-2020-217984,10,logistic regression,0.001330023,0.001330006,0.001330016,0.001330015,0.001330068,0.993349873,Clinics,0.97878563,TRUE,45.9,0.595151215,26,0.53819909,7,0.785110192,1796,0.159643631,0.519526032
14250,Is there a link between temperatures and COVID-19 contagions? Evidence from Italy,0,10.1101/2020.05.13.20101261,5/19/20,medrxiv,0,2,"bayes, bayesian model",0.001511823,0.035767084,0.00151188,0.958185416,0.001511915,0.001511881,Epidemiology,0.36037052,FALSE,12,0.183190055,0.5,0.087101953,4,0.707574542,2925,0.407657115,0.346380916
14251,The impact of super-spreaders in COVID-19: mapping genome variation worldwide,0,10.1101/2020.05.19.097410,5/19/20,biorxiv,10.1101/gr.266221.120,5,genomes,0.002422322,0.987888194,0.002422318,0.002422383,0.002422385,0.002422399,Genomics,0.41837636,FALSE,73.8,0.781124374,74.2,0.764851485,4,0.707574542,12805,0.860582711,0.778533278
14252,Reciprocal association between participation to a national election and the epidemic spread of COVID-19 in France: nationwide observational and dynamic modeling study.,0,10.1101/2020.05.14.20090100,5/19/20,medrxiv,0,6,model fit,0.002296647,0.002296753,0.002296554,0.347867346,0.002296626,0.642946073,Clinics,0.75241303,TRUE,16.5,0.249366071,24.66666667,0.526090447,4,0.707574542,3246,0.453166386,0.484049361
14253,Upper airway gene expression differentiates COVID-19 from other acute respiratory illnesses and reveals suppression of innate immune responses by SARS-CoV-2,0,10.1101/2020.05.18.20105171,5/19/20,medrxiv,10.1038/s41467-020-19587-y,23,"machine learning, sequencing, classifier, metagenom",0.398330358,0.268952442,0.162962504,0.001486429,0.001486434,0.166781834,Drug discovery,0.41316745,FALSE,34.60869565,0.483146762,41.60869565,0.642627776,14,0.866658436,5856,0.691066699,0.670874918
14254,"SARS-CoV2 infection in farmed mink, Netherlands, April 2020",0,10.1101/2020.05.18.101493,5/18/20,biorxiv,0,19,genomes,0.004310121,0.658366913,0.004310126,0.004310028,0.167829811,0.160873001,Genomics,0.42051846,FALSE,49.05263158,0.624466572,82.68421053,0.784987958,31,0.931971109,12216,0.849265591,0.797672808
14255,Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery,0,10.1101/2020.05.08.20090464,5/18/20,medrxiv,0,2,dataset,0.002490512,0.002490578,0.002490592,0.987547038,0.002490632,0.002490647,Epidemiology,0.5112899,TRUE,109,0.888304781,25.5,0.533382392,7,0.785110192,4870,0.625812666,0.708152508
14256,Substantial underestimation of SARS-CoV-2 infection in the United States due to incomplete testing and imperfect test accuracy,0,10.1101/2020.05.12.20091744,5/18/20,medrxiv,0,13,"bayes, probabilistic",0.001943461,0.0019435,0.190476563,0.718786463,0.084906425,0.001943587,Epidemiology,0.33619946,FALSE,60.30769231,0.704681799,115.5384615,0.845196682,4,0.707574542,2257,0.269202986,0.631664002
14257,Quantitative Analysis of the Effectiveness of Public Health Measures on COVID-19 Transmission,0,10.1101/2020.05.15.20102988,5/18/20,medrxiv,0,3,network analysis,0.00151182,0.001511831,0.001511854,0.992440867,0.001511821,0.001511807,Epidemiology,0.1239008,FALSE,64,0.729173109,24.33333333,0.524016591,4,0.707574542,14387,0.877197207,0.709490362
14258,Report 21: Estimating COVID-19 cases and reproduction number in Brazil,0,10.1101/2020.05.09.20096701,5/18/20,medrxiv,0,63,bayes,0.002422282,0.002422286,0.002422262,0.987888542,0.002422312,0.002422316,Epidemiology,0.45658493,FALSE,22.70454545,0.335765972,71.81818182,0.758629917,26,0.920859312,2904,0.403563689,0.604704723
14259,Mathematical Model Based COVID-19 Prediction in India and its Different States,0,10.1101/2020.05.16.20104232,5/18/20,medrxiv,0,3,mathematical model,0.035105652,0.002296753,0.002296593,0.790592061,0.002296634,0.167412306,Epidemiology,0.3855833,FALSE,6.333333333,0.089863319,1,0.122023013,7,0.785110192,3294,0.457741392,0.363684479
14260,How well can we forecast the COVID-19 pandemic with curve fitting and recurrent neural networks?,0,10.1101/2020.05.14.20102541,5/18/20,medrxiv,0,3,"neural network, lstm",0.003335276,0.003335295,0.339768914,0.646889862,0.003335279,0.003335374,Epidemiology,0.21569842,FALSE,2,0.022141134,0,0.055525823,4,0.707574542,1200,0.033228991,0.204617622
14261,Heg.IA: An intelligent system to support diagnosis of Covid-19 based on blood tests,0,10.1101/2020.05.14.20102533,5/18/20,medrxiv,10.1007/s42600-020-00112-5,7,"bayes, machine learning, computational, sequencing, classifier",0.002080676,0.180426793,0.716736446,0.002080687,0.002080584,0.096594815,Genomics,0.32234538,FALSE,8.857142857,0.129940009,1.428571429,0.134265454,7,0.785110192,1817,0.162533109,0.302962191
14262,Traces of SARS-CoV-2 RNA in the Blood of COVID-19 Patients,0,10.1101/2020.05.10.20097055,5/18/20,medrxiv,0,2,computational,0.115101843,0.533250942,0.001565367,0.001565398,0.001565366,0.346951083,Genomics,0.495336,FALSE,2.5,0.027459954,0,0.055525823,5,0.739490092,4975,0.632795569,0.36381786
14263,ALTERED MOLECULAR PATHWAYS OBSERVED IN NASO-OROPHARYNGEAL SAMPLES OF SARS-CoV-2 PATIENTS,0,10.1101/2020.05.14.20102558,5/18/20,medrxiv,0,9,proteom,0.315473055,0.422588364,0.00186173,0.001861746,0.001861733,0.256353372,Genomics,0.95032704,TRUE,26,0.382398417,10.11111111,0.356301846,5,0.739490092,1584,0.104743559,0.395733478
14264,Feasibility of SARS-CoV-2 virus detection from consumer-grade cotton swabs,0,10.1101/2020.05.12.20073577,5/18/20,medrxiv,10.1186/s40168-020-00960-4,14,microbiom,0.074356169,0.557952794,0.059848283,0.163226872,0.026061792,0.11855409,Genomics,0.7912543,TRUE,14.41666667,0.217700538,14.16666667,0.41370083,4,0.707574542,1956,0.194317361,0.383323318
14265,Study of Non-Pharmacological Interventions on COVID-19 Spread,0,10.1101/2020.05.10.20096974,5/18/20,medrxiv,10.32604/cmes.2020.011601,3,mathematical model,0.002422436,0.00242231,0.207518784,0.782791545,0.002422379,0.002422546,Epidemiology,0.4238121,FALSE,5,0.070752675,0,0.055525823,5,0.739490092,2386,0.295689863,0.290364613
14266,Progression of COVID-19 in Indian States - Forecasting Endpoints Using SIR and Logistic Growth Models,0,10.1101/2020.05.15.20103028,5/18/20,medrxiv,0,2,"mathematical model, logistic regression",0.002357708,0.002357817,0.002357745,0.988211041,0.002357845,0.002357844,Epidemiology,0.35187483,FALSE,2.5,0.027459954,0,0.055525823,5,0.739490092,1322,0.052251385,0.218681813
14267,Early transmission of COVID-19 has an optimal temperature but late transmission decreases in warm climate,0,10.1101/2020.05.14.20102459,5/18/20,medrxiv,0,3,dataset,0.001653121,0.11774104,0.001653064,0.875646404,0.001653237,0.001653135,Epidemiology,0.18930611,FALSE,4.333333333,0.057950399,0.333333333,0.073187048,5,0.739490092,2386,0.295689863,0.291579351
14268,COVID-19 genomic susceptibility: Definition of ACE2 variants relevant to human infection with SARS-CoV-2 in the context of ACMG/AMP Guidance,0,10.1101/2020.05.12.20098160,5/18/20,medrxiv,0,2,"computational, exom, genomes, genomic structure",0.286757953,0.528369943,0.043466084,0.001059378,0.042538188,0.097808455,Genomics,0.36729664,FALSE,84,0.821881378,100.5,0.823454643,3,0.667819001,1839,0.168793643,0.620487166
14269,Forecasting Covid-19 dynamics in Brazil: a data driven approach,0,10.1101/2020.05.11.20098392,5/18/20,medrxiv,10.3390/ijerph17145115,6,lstm,0.001565308,0.001565332,0.144123357,0.8496154,0.001565303,0.001565301,Epidemiology,0.070391506,FALSE,34,0.477766096,6.166666667,0.281977522,15,0.874313229,1157,0.027450036,0.415376721
14270,Fusing a Bayesian case velocity model with random forest for predicting COVID-19 in the U.S.,0,10.1101/2020.05.15.20102608,5/18/20,medrxiv,0,10,"bayes, model fit, machine learning, mathematical model",0.000677929,0.000677914,0.184828483,0.812459837,0.00067791,0.000677927,Epidemiology,0.15076545,FALSE,49.2,0.625579813,892.7,0.992239765,4,0.707574542,1125,0.023597399,0.58724788
14271,Spread of the Novel Coronavirus (SARS-CoV-2): Modeling and Simulation of Control Strategies,0,10.1101/2020.05.11.20098418,5/18/20,medrxiv,0,1,"model fit, mathematical model",0.001684494,0.001684536,0.001684553,0.903750753,0.001684586,0.089511078,Epidemiology,0.5326905,TRUE,2,0.022141134,1,0.122023013,2,0.618927094,1488,0.084758006,0.211962312
14272,Covid-19 Detection using CNN Transfer Learning from X-ray Images,0,10.1101/2020.05.12.20098954,5/18/20,medrxiv,0,4,"machine learning, neural network, transfer learning",0.0010594,0.001059416,0.994703044,0.001059415,0.001059338,0.001059388,Imaging,0.290062,FALSE,2,0.022141134,0,0.055525823,13,0.858880178,2233,0.262942451,0.299872397
14273,SARS-CoV-2 seroprevalence trends in healthy blood donors during the COVID-19 Milan outbreak,0,10.1101/2020.05.11.20098442,5/18/20,medrxiv,0,16,logistic regression,0.000907329,0.235047335,0.000907296,0.234713574,0.000907333,0.527517132,Clinics,0.19365984,FALSE,41.25,0.5508071,38,0.622223709,55,0.960800049,38386,0.962436793,0.774066913
14274,Understanding the indoor pre-symptomatic transmission mechanism of COVID-19,0,10.1101/2020.05.12.20099085,5/17/20,medrxiv,0,3,mathematical model,0.002422376,0.00242245,0.00242233,0.987888172,0.002422384,0.002422288,Epidemiology,0.46323428,FALSE,7.666666667,0.111633373,2,0.164302917,5,0.739490092,5765,0.684324585,0.424937741
14275,Epitope-Based Peptide Vaccine Against Severe Acute Respiratory Syndrome-Coronavirus-2 Nucleocapsid Protein: An in silico Approach,0,10.1101/2020.05.16.100206,5/17/20,biorxiv,0,8,"computational, in silico",0.68909525,0.304413696,0.001622732,0.001622777,0.00162278,0.001622765,Drug discovery,0.58838207,TRUE,7.875,0.114045396,0.25,0.065493712,1,0.537564047,2856,0.393450518,0.277638418
14276,REMBRANDT: A high-throughput barcoded sequencing approach for COVID-19 screening.,0,10.1101/2020.05.16.099747,5/17/20,biorxiv,0,5,"bioinformatic, sequencing",0.001350381,0.547512463,0.00135041,0.102880217,0.116297721,0.230608808,Genomics,0.267364,FALSE,27.4,0.400395819,53,0.693738293,6,0.764429903,4065,0.549963882,0.602131974
14277,Computational Study of Ions and Water Permeation and Transportation Mechanisms of the SARS-CoV-2 Pentameric E Protein Channel,0,10.1101/2020.05.17.099143,5/17/20,biorxiv,10.3389/fmolb.2020.565797,9,"computational, in silico",0.930663701,0.002296635,0.002296705,0.060149765,0.002296621,0.002296573,Drug discovery,0.77130294,TRUE,102.1111111,0.873708949,29.66666667,0.567099277,1,0.537564047,2161,0.245364797,0.555934268
14278,Multilevel Integrated Model with a Novel Systems Approach (MIMANSA) for Simulating the Spread of COVID-19,0,10.1101/2020.05.12.20099291,5/16/20,medrxiv,0,4,mathematical model,0.001272731,0.001272666,0.099647053,0.850693753,0.001272696,0.045841101,Epidemiology,0.24243528,FALSE,13,0.197352959,6.25,0.283315494,1,0.537564047,2167,0.245124007,0.315839127
14279,Forecasting undetected COVID-19 cases in Small Island Developing States using Bayesian approach,0,10.1101/2020.05.13.20100545,5/16/20,medrxiv,0,1,bayes,0.003335331,0.003335338,0.00333547,0.983323241,0.003335362,0.003335259,Epidemiology,0.124028236,FALSE,11,0.167171748,0,0.055525823,0,0.403234768,1010,0.009631592,0.158890983
14280,The impact of containment measures and air temperature on mitigating the transmission of COVID-19: a novel data-based comprehensive modeling analysis,0,10.1101/2020.05.12.20099267,5/16/20,medrxiv,0,8,model simulation,0.002357771,0.002357861,0.002357952,0.898213531,0.002357979,0.092354907,Epidemiology,0.124399364,FALSE,3,0.037293586,,,0,0.403234768,1596,0.105465928,0.181998094
14281,The Evaluation of Deep Neural Networks and X-Ray as a Practical Alternative for Diagnosis and Management of COVID-19,0,10.1101/2020.05.12.20099481,5/16/20,medrxiv,0,5,"deep learning, neural network",0.002238478,0.002238427,0.988806326,0.002238488,0.002239332,0.00223895,Imaging,0.27446246,FALSE,7.8,0.113550622,0.6,0.09011239,3,0.667819001,1075,0.015410547,0.22172314
14282,A Sparse Gaussian Network Model for Prediction the Growth Trend of COVID-19 Overseas Import Case: When can Hong Kong Lift the International Traffic Blockad?,0,10.1101/2020.05.13.20099978,5/16/20,medrxiv,0,3,network model,0.00159351,0.001593507,0.001593541,0.992032403,0.001593493,0.001593546,Epidemiology,0.2969098,FALSE,8,0.118683901,0.333333333,0.073187048,0,0.403234768,774,0.00048158,0.148896824
14283,COVID-19 In Shang Hai: It is Worth Learning from the Successful Experience in Preventing and Controlling the Overseas Epidemic Situation,0,10.1101/2020.05.13.20100164,5/16/20,medrxiv,0,3,prediction model,0.001461873,0.001461887,0.001461925,0.992690583,0.001461878,0.001461854,Epidemiology,0.26621634,FALSE,8.666666667,0.12839384,0.333333333,0.073187048,0,0.403234768,791,0.000722369,0.151384506
14284,Fast and accurate diagnostics from highly multiplexed sequencing assays,0,10.1101/2020.05.13.20100131,5/16/20,medrxiv,10.1038/s41598-020-78942-7,9,"computational, sequencing",0.00229662,0.261190983,0.729622716,0.002296627,0.002296529,0.002296525,Genomics,0.38393557,FALSE,39.55555556,0.53447956,692.5555556,0.987088574,5,0.739490092,3904,0.52877438,0.697458151
14285,SARS-CoV-2 Detection in Istanbul Wastewater Treatment Plant Sludges,0,10.1101/2020.05.12.20099358,5/16/20,medrxiv,0,6,dataset,0.220554114,0.442006717,0.104346923,0.195102104,0.000722221,0.037267922,Genomics,0.36937287,FALSE,15.66666667,0.236563795,10.33333333,0.36038266,44,0.952157541,5556,0.671562726,0.55516668
14286,DERIVATION OF A SCORE TO PREDICT ADMISSION TO INTENSIVE CARE UNIT IN PATIENTS WITH COVID-19: THE ABC-GOALS SCORE,0,10.1101/2020.05.12.20099416,5/16/20,medrxiv,10.21149/11684,6,"predictive model, logistic regression",0.00103457,0.001034575,0.192452119,0.02058519,0.001034606,0.783858938,Clinics,0.806435,TRUE,34,0.477766096,8.5,0.329141022,6,0.764429903,13347,0.866602456,0.609484869
14287,Modeling COVID 19 in the Basque Country: from introduction to control measure response,0,10.1101/2020.05.10.20086504,5/15/20,medrxiv,10.1038/s41598-020-74386-1,5,artificial intelligence,0.001187272,0.00118728,0.040313978,0.915064417,0.001187306,0.041059746,Epidemiology,0.08162752,FALSE,18.5,0.278001113,5,0.257024351,5,0.739490092,2919,0.40187816,0.419098429
14288,Depression and anxiety during 2019 coronavirus disease pandemic in Saudi Arabia: a cross-sectional study,0,10.1101/2020.05.09.20096677,5/15/20,medrxiv,0,7,logistic regression,0.001901667,0.001901719,0.001901709,0.001901783,0.968134855,0.024258267,Healthcare,0.55842257,TRUE,11.33333333,0.170635166,1.166666667,0.124565159,8,0.799987654,2405,0.294485914,0.347418473
14289,Upregulation of Human Endogenous Retroviruses in Bronchoalveolar Lavage Fluid of COVID-19 Patients,0,10.1101/2020.05.10.20096958,5/15/20,medrxiv,0,14,transcriptom,0.625614807,0.061273464,0.001823396,0.001823364,0.001823495,0.307641474,Drug discovery,0.7435572,TRUE,76.71428571,0.792875255,129.2142857,0.862857907,0,0.403234768,2424,0.29882013,0.589447015
14290,Estimation of the incubation period of SARS-CoV-2 in Vietnam,0,10.1101/2020.05.09.20096800,5/15/20,medrxiv,0,10,bayes,0.001861668,0.001861674,0.024534464,0.904126752,0.00186188,0.065753562,Epidemiology,0.39097416,FALSE,8.2,0.120477457,1,0.122023013,2,0.618927094,1624,0.111485673,0.243228309
14291,Risk factors for clinical progression in patients with COVID-19: a retrospective study of electronic health record data in the United Kingdom,0,10.1101/2020.05.11.20093096,5/15/20,medrxiv,0,13,logistic regression,0.054811449,0.000740312,0.00074032,0.000740321,0.031143844,0.911823754,Clinics,0.639622,TRUE,44.30769231,0.579936916,57.69230769,0.713540273,2,0.618927094,3167,0.438478209,0.587720623
14292,"NON-WHITE ETHNICITY, MALE SEX, AND HIGHER BODY MASS INDEX, BUT NOT MEDICATIONS ACTING ON THE RENIN-ANGIOTENSIN SYSTEM ARE ASSOCIATED WITH CORONAVIRUS DISEASE 2019 (COVID-19) HOSPITALISATION: REVIEW OF THE FIRST 669 CASES FROM THE UK BIOBANK",0,10.1101/2020.05.10.20096925,5/15/20,medrxiv,0,8,logistic regression,0.043764115,0.001330022,0.001330039,0.001330043,0.424870716,0.527375065,Clinics,0.53749233,TRUE,18.125,0.272063826,3.75,0.21982874,13,0.858880178,2764,0.370816277,0.430397255
14293,Analysis of SARS-CoV-2 RNA-Sequences by Interpretable Machine Learning Models,0,10.1101/2020.05.15.097741,5/15/20,biorxiv,0,7,"machine learning, computational, classifier, sequence alignment",0.00077947,0.488601578,0.447561577,0.06149847,0.000779485,0.00077942,Genomics,0.02461049,FALSE,66.42857143,0.74321232,34.28571429,0.60048167,3,0.667819001,2202,0.251384541,0.565724383
14294,Time is of the essence: impact of delays on effectiveness of contact tracing for COVID-19,0,10.1101/2020.05.09.20096289,5/15/20,medrxiv,10.1016/S2468-2667(20)30157-2,6,mathematical model,0.000321156,0.00032117,0.080993823,0.846309369,0.07173331,0.000321172,Epidemiology,0.12895644,FALSE,11.5,0.17416043,9.333333333,0.342253144,13,0.858880178,5068,0.637370575,0.503166082
14295,On the Generation of Medical Dialogues for COVID-19,0,10.1101/2020.05.08.20095810,5/15/20,medrxiv,0,12,"transfer learning, dataset",0.001943473,0.001943485,0.696109266,0.001943638,0.296116583,0.001943555,Healthcare,0.2823332,FALSE,80.5,0.809202795,271.5833333,0.94621354,4,0.707574542,7304,0.753190465,0.804045336
14296,"Deep phylogenetic analysis of Orthocoronavirinae genomes traces the origin, evolution and transmission route of 2019 novel coronavirus",0,10.1101/2020.05.12.091199,5/15/20,biorxiv,0,2,genomes,0.07016171,0.868336123,0.057987389,0.001171625,0.001171605,0.001171548,Genomics,0.6449784,TRUE,5.5,0.077246583,0,0.055525823,0,0.403234768,2724,0.361666265,0.22441836
14297,Recommendations for sample pooling on the Cepheid GeneXpert(R) system using the Cepheid Xpert(R) Xpress SARS-CoV-2 assay,0,10.1101/2020.05.14.097287,5/15/20,biorxiv,10.1371/journal.pone.0241959,6,dataset,0.001486473,0.551169993,0.185560915,0.258809675,0.001486491,0.001486454,Genomics,0.41776356,FALSE,30.16666667,0.433731214,49.83333333,0.680893765,7,0.785110192,3622,0.49458223,0.59857935
14298,"Distribution of ACE2, CD147, cyclophilins, CD26 and other SARS-CoV-2 associated molecules in human tissues and immune cells in health and disease",0,10.1101/2020.05.14.090332,5/15/20,biorxiv,10.1111/all.14429,17,sequencing,0.660068146,0.022530311,0.00088905,0.000889046,0.063865912,0.251757535,Drug discovery,0.8430468,TRUE,31.16666667,0.445976869,31.72222222,0.582753546,5,0.739490092,4977,0.630869251,0.59977244
14299,Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease,0,10.1101/2020.05.15.097493,5/15/20,biorxiv,10.1021/acs.biochem.0c00462,8,network analysis,0.564854329,0.427371714,0.001943501,0.001943515,0.001943461,0.001943479,Drug discovery,0.7587472,TRUE,42,0.558537943,80.5,0.780572652,1,0.537564047,2374,0.289670118,0.54158619
14300,Transcriptional profiling reveals TRPM5-expressing cells involved in viral infection in the olfactory epithelium,0,10.1101/2020.05.14.096016,5/15/20,biorxiv,0,10,transcriptom,0.96330752,0.002183389,0.027959183,0.002183221,0.002183215,0.002183474,Drug discovery,0.28284657,FALSE,38.75,0.526748717,55,0.702167514,2,0.618927094,3643,0.497471707,0.586328758
14301,SARS-CoV2 (COVID-19) Structural/Evolution Dynamicome: Insights into functional evolution and human genomics.,0,10.1101/2020.05.15.098616,5/15/20,biorxiv,0,22,proteom,0.404921631,0.541745525,0.035317686,0.000946163,0.000946124,0.016122872,Genomics,0.5245448,TRUE,24.04545455,0.35592801,81.90909091,0.782847204,6,0.764429903,4239,0.565856008,0.617265281
14302,Rapid implementation of real-time SARS-CoV-2 sequencing to investigate healthcare-associated COVID-19 infections,0,10.1101/2020.05.08.20095687,5/14/20,medrxiv,0,28,"sequencing, genomes",0.001203423,0.694292049,0.001203441,0.0012035,0.146818721,0.155278866,Genomics,0.14860946,FALSE,24.53571429,0.362050838,94.35714286,0.811479797,9,0.814309525,5967,0.692029858,0.669967504
14303,Early estimation of the risk factors for hospitalisation and mortality by COVID-19 in Mexico,0,10.1101/2020.05.11.20098145,5/14/20,medrxiv,0,5,logistic regression,0.001486495,0.001486645,0.001486436,0.001486509,0.001486515,0.9925674,Clinics,0.34807497,FALSE,44.6,0.582658173,22.8,0.509967889,12,0.850299401,2124,0.231158199,0.543520915
14304,ACE2-Variants Indicate Potential SARS-CoV-2-Susceptibility in Animals: An Extensive Molecular Dynamics Study,0,10.1101/2020.05.14.092767,5/14/20,biorxiv,0,6,simulation model,0.730535039,0.157129084,0.002562549,0.104647862,0.002562692,0.002562774,Drug discovery,0.375948,FALSE,82.5,0.816129631,92.33333333,0.806529302,4,0.707574542,3305,0.453407176,0.695910162
14305,Online COVID-19 diagnosis with chest CT images: Lesion-attention deep neural networks,0,10.1101/2020.05.11.20097907,5/14/20,medrxiv,0,5,"neural network, dataset",0.001126788,0.001126801,0.994365983,0.001126811,0.001126814,0.001126803,Imaging,0.53115135,TRUE,3.4,0.040942544,0,0.055525823,5,0.739490092,2285,0.269443776,0.276350559
14306,COVID-19 Pandemic in Pakistan: Stages and Recommendations,0,10.1101/2020.05.11.20098004,5/14/20,medrxiv,0,1,mathematical model,0.002562914,0.07950847,0.002562808,0.910240591,0.002562668,0.00256255,Epidemiology,0.31449112,FALSE,3,0.037293586,1,0.122023013,1,0.537564047,2313,0.273777992,0.24266466
14307,Tracing two causative SNPs reveals SARS-CoV-2 transmission in North America population,0,10.1101/2020.05.12.092056,5/14/20,biorxiv,0,19,"genome-wide, genome sequences",0.001943477,0.815541794,0.001943445,0.084132369,0.001943542,0.094495374,Genomics,0.28802162,FALSE,140.1578947,0.933885831,35.63157895,0.6085095,3,0.667819001,2476,0.311341199,0.630388883
14308,Machine learning model estimating number of COVID-19 infection cases over coming 24 days in every province of South Korea (XGBoost and MultiOutputRegressor),0,10.1101/2020.05.10.20097527,5/14/20,medrxiv,0,2,machine learning,0.001565295,0.001565314,0.414009853,0.579728692,0.001565482,0.001565364,Epidemiology,0.1566697,FALSE,5,0.070752675,0,0.055525823,8,0.799987654,2061,0.214543703,0.285202464
14309,Deep Transfer Learning-based COVID-19 prediction using Chest X-rays,0,10.1101/2020.05.12.20099937,5/14/20,medrxiv,0,3,"machine learning, deep learning, transfer learning",0.066751315,0.00171721,0.926379837,0.001717282,0.001717193,0.001717163,Imaging,0.765118,TRUE,8.666666667,0.12839384,0.333333333,0.073187048,5,0.739490092,1676,0.124247532,0.266329628
14310,Spread of COVID-19 in India: A Simple Algebraic Study,0,10.1101/2020.05.10.20097691,5/14/20,medrxiv,0,1,mathematical model,0.041391198,0.002422299,0.002422391,0.882817547,0.002422332,0.068524234,Epidemiology,0.5688424,TRUE,20,0.298163152,24,0.521808938,1,0.537564047,1514,0.086202745,0.36093472
14311,"Modeling the Impact of Nationwide BCG Vaccine Recommendations on COVID-19 Transmission, Severity, and Mortality",0,10.1101/2020.05.10.20097121,5/14/20,medrxiv,0,7,mathematical model,0.001486476,0.001486441,0.001486447,0.72567906,0.075030964,0.194830612,Epidemiology,0.066042215,FALSE,9.142857143,0.13637207,0.714285714,0.096936045,1,0.537564047,1924,0.18348182,0.238588496
14312,Automatic Detection of COVID-19 Infection from Chest X-ray using Deep Learning,0,10.1101/2020.05.10.20097063,5/14/20,medrxiv,0,3,"deep learning, artificial intelligence, neural network",0.001943446,0.001943447,0.990282453,0.001943496,0.001943552,0.001943606,Imaging,0.5930636,TRUE,4,0.054734368,0,0.055525823,10,0.828199272,1662,0.121598844,0.265014577
14313,"Meta-analysis of transcriptomes of SARS-Cov2 infected human lung epithelial cells identifies transmembrane serine proteases co-expressed with ACE2 and biological processes related to viral entry, immunity, inflammation and cellular stress.",0,10.1101/2020.05.12.091314,5/13/20,biorxiv,10.1038/s41598-020-78402-2,2,"sequencing, transcriptom, dataset",0.860497785,0.028807907,0.001254606,0.106930237,0.001254702,0.001254763,Drug discovery,0.86132467,TRUE,145.5,0.939884965,160,0.891824993,2,0.618927094,2481,0.311100409,0.690434365
14314,Origin of Novel Coronavirus (COVID-19): A Computational Biology Study using Artificial Intelligence,0,10.1101/2020.05.12.091397,5/13/20,biorxiv,0,6,"computational, artificial intelligence, whole genome, genome sequences, genomes",0.001987146,0.823546439,0.168504741,0.001987304,0.001987161,0.001987208,Genomics,0.50845027,TRUE,43.33333333,0.571154679,19.5,0.475983409,8,0.799987654,5319,0.654225861,0.625337901
14315,Defining the Pandemic at the State Level: Sequence-Based Epidemiology of the SARS-CoV-2 virus by the Arizona COVID-19 Genomics Union (ACGU),0,10.1101/2020.05.08.20095935,5/13/20,medrxiv,0,23,genomes,0.001486435,0.612083418,0.001486424,0.381970611,0.001486523,0.001486588,Genomics,0.14105538,FALSE,39.30434783,0.53237677,426.6086957,0.973173669,5,0.739490092,1890,0.17240549,0.604361505
14316,Mathematical model of COVID-19 spread in Turkey and South Africa,0,10.1101/2020.05.08.20095588,5/13/20,medrxiv,10.1186/s13662-020-03095-w,2,mathematical model,0.000936077,0.000936081,0.000936117,0.995319508,0.000936091,0.000936126,Epidemiology,0.5247606,TRUE,20,0.298163152,2,0.164302917,15,0.874313229,1748,0.139417289,0.369049147
14317,On the Front (Phone) Lines: Results of a COVID-19 Hotline in Northeast Ohio,0,10.1101/2020.05.08.20095745,5/13/20,medrxiv,10.3122/jabfm.2021.s1.200237,9,logistic regression,0.000800592,0.049841451,0.000800603,0.000800609,0.702858906,0.244897839,Healthcare,0.9873756,TRUE,19.22222222,0.287525512,8.666666667,0.331415574,3,0.667819001,1448,0.073440886,0.340050243
14318,The timing of contact restrictions and pro-active testing balances the socio-economic impact of a lockdown with the control of infections,0,10.1101/2020.05.08.20095596,5/13/20,medrxiv,0,2,mathematical model,0.001593497,0.001593519,0.001593515,0.955661803,0.037964148,0.001593517,Epidemiology,0.47559816,FALSE,144,0.938524337,90,0.801445009,1,0.537564047,1866,0.168312064,0.611461364
14319,SARS-CoV-2 activates lung epithelia cell proinflammatory signaling and leads to immune dysregulation in COVID-19 patients by single-cell sequencing,0,10.1101/2020.05.08.20096024,5/13/20,medrxiv,0,5,"sequencing, transcriptom",0.760784459,0.014754503,0.000830643,0.000830639,0.000830637,0.221969118,Drug discovery,0.4454838,FALSE,6.4,0.090667326,1,0.122023013,7,0.785110192,3171,0.43703347,0.3587085
14320,Timing of non-pharmaceutical interventions to mitigate COVID-19 transmission and their effects on mobility: A cross-country analysis,0,10.1101/2020.05.09.20096420,5/13/20,medrxiv,0,2,dataset,0.001254583,0.001254618,0.001254618,0.993726874,0.001254691,0.001254616,Epidemiology,0.5945043,TRUE,9.5,0.143051518,0.5,0.087101953,1,0.537564047,1881,0.171201541,0.234729765
14321,COVID-Classifier: An efficient machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images,0,10.1101/2020.05.09.20096560,5/13/20,medrxiv,0,3,"machine learning, classifier, dataset",0.00146189,0.00146188,0.992690479,0.001461956,0.001461861,0.001461934,Imaging,0.58392733,TRUE,16,0.243552477,1,0.122023013,22,0.908142478,2403,0.291114857,0.391208206
14322,Characterization of SARS-CoV-2 viral diversity within and across hosts,0,10.1101/2020.05.07.083410,5/13/20,biorxiv,0,4,sequencing,0.001684522,0.846179555,0.001684707,0.125814008,0.001684493,0.022952714,Genomics,0.23866525,FALSE,33.25,0.468860165,39.25,0.629114263,18,0.891474782,4869,0.619070551,0.65212994
14323,Genome Analysis of SARS-CoV-2 Isolate from Bangladesh,0,10.1101/2020.05.13.094441,5/13/20,biorxiv,0,4,sequencing,0.001786532,0.965623152,0.001786491,0.001786536,0.027230715,0.001786575,Genomics,0.36236286,FALSE,10.25,0.154245779,0.5,0.087101953,6,0.764429903,4083,0.546833614,0.388152812
14324,Development and validation of the COVID-19 severity index (CSI): a prognostic tool for early respiratory decompensation,0,10.1101/2020.05.07.20094573,5/12/20,medrxiv,10.1016/j.annemergmed.2020.07.022,8,predictive model,0.000786334,0.00078634,0.120714103,0.000786364,0.000786354,0.876140505,Clinics,0.7600771,TRUE,23,0.34225988,10.625,0.364396575,11,0.840175319,6446,0.712015411,0.564711796
14325,Genetic drift and environmental spreading dynamics of COVID-19,0,10.1101/2020.05.08.20095448,5/12/20,medrxiv,0,4,dataset,0.00146187,0.308691276,0.001461916,0.685461094,0.001461867,0.001461977,Epidemiology,0.27661836,FALSE,11,0.167171748,6,0.280037463,1,0.537564047,3095,0.424271611,0.352261217
14326,Evolution of the COVID Pandemic: A Technique for Mathematical Analysis of Data,0,10.1101/2020.05.08.20095273,5/12/20,medrxiv,0,4,mathematical model,0.003214104,0.003214199,0.003214389,0.891272459,0.003214165,0.095870684,Epidemiology,0.16975412,FALSE,9.25,0.137918239,0.25,0.065493712,0,0.403234768,1462,0.074885625,0.170383086
14327,"Clinical Course and Risk Factors for Recurrence of Positive SARS-CoV-2 RNA: A Retrospective Cohort Study from Wuhan, China",0,10.1101/2020.05.08.20095018,5/12/20,medrxiv,10.18632/aging.103795,13,logistic regression,0.051754752,0.087734988,0.087953049,0.000625231,0.000625239,0.77130674,Clinics,0.77379096,TRUE,8.307692308,0.121961779,1.384615385,0.133061279,10,0.828199272,2267,0.261497712,0.33618001
14328,Estimation of the number of general anesthesia cases based on a series of nationwide surveys on Twitter during COVID-19 in Japan: A statistical analysis,0,10.1101/2020.05.08.20094979,5/12/20,medrxiv,10.3390/medicina57020153,3,mathematical model,0.001943461,0.00194348,0.001943483,0.775387259,0.216838834,0.001943483,Epidemiology,0.84944695,TRUE,11,0.167171748,3.333333333,0.206515922,0,0.403234768,3039,0.415362389,0.298071207
14329,Predicting Long-term Evolution of COVID-19 by On-going Data using Bayesian Susceptible-Infected-Removed Model,0,10.1101/2020.05.08.20094953,5/12/20,medrxiv,0,2,"bayes, probabilistic",0.00182332,0.001823401,0.001823377,0.990883213,0.001823356,0.001823333,Epidemiology,0.46270716,FALSE,2.5,0.027459954,0,0.055525823,1,0.537564047,1316,0.046954009,0.166875958
14330,"COVID-19: Predictive Mathematical Models for the Number of Deaths in South Korea, Italy, Spain, France, UK, Germany, and USA",0,10.1101/2020.05.08.20095489,5/12/20,medrxiv,0,3,mathematical model,0.003101462,0.00310157,0.003101436,0.984492505,0.00310145,0.003101577,Epidemiology,0.067249745,FALSE,148,0.942606222,160.3333333,0.892226385,5,0.739490092,2125,0.228268721,0.700647855
14331,"A putative new SARS-CoV protein, 3a*, encoded in an ORF overlapping ORF3a",0,10.1101/2020.05.12.088088,5/12/20,biorxiv,10.1099/jgv.0.001469,1,computational,0.536033674,0.455643965,0.002080676,0.002080621,0.002080532,0.002080531,Drug discovery,0.6202534,TRUE,148,0.942606222,409,0.972103291,3,0.667819001,2278,0.263424031,0.711488136
14332,Children's Hospital Los Angeles COVID-19 Analysis Research Database (CARD) - A Resource for Rapid SARS-CoV-2 Genome Identification Using Interactive Online Phylogenetic Tools,0,10.1101/2020.05.11.089763,5/12/20,biorxiv,0,0,"bioinformatic, sequencing, genomic epidemiology, genome sequences, genomes",0.000889114,0.931663862,0.000889104,0.000889116,0.042903293,0.022765511,Genomics,0.14605439,FALSE,81.21428571,0.811862205,173.5,0.901993578,8,0.799987654,2843,0.381892608,0.723934011
14333,Mutation landscape of SARS-CoV-2 reveals three mutually exclusive clusters of leading and trailing single nucleotide substitutions,0,10.1101/2020.05.07.082768,5/12/20,biorxiv,0,9,genomes,0.000946114,0.978042278,0.00094613,0.000946124,0.000946096,0.018173259,Genomics,0.24804878,FALSE,70,0.764178366,52,0.689523682,12,0.850299401,4502,0.587767879,0.722942332
14334,"The characteristics and death risk factors of 132 COVID-19 pneumonia patients with comorbidities: a retrospective single center analysis in Wuhan, China",0,10.1101/2020.05.07.20092882,5/12/20,medrxiv,0,8,logistic regression,0.000629686,0.000629671,0.00062967,0.000629672,0.000629668,0.996851633,Clinics,0.8534831,TRUE,7,0.10179974,0.375,0.073789136,5,0.739490092,1334,0.050565856,0.241411206
14335,Text Mining Approach to Analyze Coronavirus Impact: Mexico City as Case of Study,0,10.1101/2020.05.07.20094466,5/12/20,medrxiv,0,2,text mining,0.002490581,0.034240698,0.002490505,0.955797288,0.002490518,0.00249041,Epidemiology,0.120793104,FALSE,7.5,0.108355495,0,0.055525823,3,0.667819001,2088,0.21815555,0.262463967
14336,A Droplet Digital PCR Assay to Detect SARS-CoV-2 RNA,0,10.1101/2020.05.06.20090449,5/11/20,medrxiv,0,4,genomes,0.123922573,0.485219946,0.184930304,0.002996784,0.058249255,0.144681139,Genomics,0.31440306,FALSE,27,0.3960047,24.75,0.526491838,0,0.403234768,1693,0.124488322,0.362554907
14337,Monitoring the Covid-19 epidemics in Italy from mortality data,0,10.1101/2020.05.07.20092775,5/11/20,medrxiv,0,2,dataset,0.0065398,0.006539972,0.006540089,0.500087913,0.006540212,0.473752014,Epidemiology,0.36658585,FALSE,416.5,0.996103655,818,0.990366604,3,0.667819001,1177,0.026968457,0.670314429
14338,Spread of Covid-19 in the United States is controlled,0,10.1101/2020.05.04.20091272,5/11/20,medrxiv,0,7,artificial intelligence,0.002638987,0.002638963,0.002639303,0.946690659,0.042753146,0.002638942,Epidemiology,0.3015231,FALSE,13.28571429,0.200321603,39.85714286,0.632258496,2,0.618927094,1735,0.134119913,0.396406777
14339,Multi-omics study revealing tissue-dependent putative mechanisms of SARS-CoV-2 drug targets on viral infections and complex diseases,0,10.1101/2020.05.07.20093286,5/11/20,medrxiv,0,9,multi-omics,0.944086555,0.048767096,0.001786498,0.001786688,0.001786581,0.001786582,Drug discovery,0.11368567,FALSE,98.88888889,0.86665842,335.2222222,0.96320578,4,0.707574542,2895,0.390079461,0.731879551
14340,Smoking and the risk of COVID-19 infection in the UK Biobank Prospective Study,0,10.1101/2020.05.05.20092445,5/11/20,medrxiv,0,3,logistic regression,0.004530663,0.004531429,0.004531295,0.004530915,0.977344615,0.004531084,Healthcare,0.2579705,FALSE,245.3333333,0.983425073,892,0.992172866,10,0.828199272,2554,0.32217674,0.781493487
14341,Deep learning models for COVID-19 infected area segmentation in CT images,0,10.1101/2020.05.08.20094664,5/11/20,medrxiv,0,5,"deep learning, neural network, dataset",0.001538122,0.001538088,0.96039006,0.001538101,0.033457468,0.001538161,Imaging,0.5966128,TRUE,38.6,0.525078855,8.4,0.326598876,17,0.887338725,2240,0.254274019,0.498322619
14342,COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach,0,10.1101/2020.05.07.20093948,5/11/20,medrxiv,10.1038/s41746-020-00372-6,2,"machine learning, prediction model",0.00203276,0.002032771,0.568500345,0.254361666,0.171039612,0.002032846,Epidemiology,0.2927441,FALSE,9,0.135320675,11.5,0.378378378,7,0.785110192,3682,0.496508548,0.448829448
14343,A Mathematical Model Approach for Prevention and Intervention Measures of the COVID-19 Pandemic in Uganda,0,10.1101/2020.05.08.20095067,5/11/20,medrxiv,0,7,mathematical model,0.00156536,0.001565337,0.001565348,0.969757245,0.001565327,0.023981383,Epidemiology,0.3079144,FALSE,10.57142857,0.15764735,0.857142857,0.103224512,2,0.618927094,1444,0.071032988,0.237707986
14344,"Characteristics of 1,573 healthcare workers who underwent nasopharyngeal swab for SARS-CoV-2 in Milano, Lombardy, Italy",0,10.1101/2020.05.07.20094276,5/11/20,medrxiv,10.1016/j.cmi.2020.06.013,15,logistic regression,0.000830636,0.118816204,0.000830648,0.159453027,0.476131774,0.243937711,Healthcare,0.6492418,TRUE,72.26666667,0.774197538,56.2,0.707586299,12,0.850299401,2079,0.214302914,0.636596538
14345,Serological signatures of SARS-CoV-2 infection: Implications for antibody-based diagnostics,0,10.1101/2020.05.07.20093963,5/11/20,medrxiv,0,17,mathematical model,0.000863073,0.499881802,0.189709176,0.083877369,0.146914534,0.078754045,Genomics,0.22444838,FALSE,68.33333333,0.755643515,42.16666667,0.645370618,25,0.918019631,4254,0.561040212,0.720018494
14346,Age-stratified model of the COVID-19 epidemic to analyze the impact of relaxing lockdown measures: nowcasting and forecasting for Switzerland,0,10.1101/2020.05.08.20095059,5/11/20,medrxiv,10.1038/s41598-020-77420-4,2,computational,0.001653033,0.001653038,0.001653056,0.852247604,0.001653075,0.141140195,Epidemiology,0.08059195,FALSE,30.5,0.438493413,173.5,0.901993578,9,0.814309525,10611,0.825908981,0.745176374
14347,Transmission in Latent Period Causes A Large Number of Infected People in the United States,0,10.1101/2020.05.07.20094086,5/11/20,medrxiv,0,11,bayes,0.001943461,0.001943526,0.00194354,0.895132302,0.097093565,0.001943605,Epidemiology,0.23079512,FALSE,28.54545455,0.412765168,15.18181818,0.42687985,1,0.537564047,2798,0.371057067,0.437066533
14348,Effect of underlying comorbidities on the infection and severity of COVID-19 in South Korea,0,10.1101/2020.05.08.20095174,5/11/20,medrxiv,10.3346/jkms.2020.35.e237,13,logistic regression,0.001622712,0.00162276,0.001622815,0.080662221,0.146395308,0.768074184,Clinics,0.7379746,TRUE,3.615384615,0.044900736,0.384615385,0.073856034,8,0.799987654,3079,0.419455815,0.33455006
14349,"Evidence for strong mutation bias towards, and selection against, T/U content in SARS-CoV2: implications for attenuated vaccine design.",0,10.1101/2020.05.11.088112,5/11/20,biorxiv,0,10,"whole-genome, genome sequences",0.128752812,0.865761956,0.001371335,0.00137137,0.001371273,0.001371254,Genomics,0.15455729,FALSE,28.5,0.412579628,100,0.822584961,7,0.785110192,3110,0.42547556,0.611437585
14350,Structural insight into the putative role of Novel Coronavirus-2 E protein in viral infection via in silico approach: a potential target for LAV development and other therapeutic strategies,0,10.1101/2020.05.11.088781,5/11/20,biorxiv,0,2,"bioinformatic, in silico, structural model",0.726441063,0.20083618,0.001415135,0.068477274,0.001415237,0.00141511,Drug discovery,0.5791244,TRUE,23,0.34225988,3,0.199424672,0,0.403234768,3316,0.452925596,0.349461229
14351,Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing,0,10.1101/2020.05.06.20091900,5/11/20,medrxiv,10.1016/j.iot.2020.100222,4,"machine learning, mathematical model",0.001861684,0.001861691,0.223304062,0.769249146,0.001861707,0.001861711,Epidemiology,0.51910424,TRUE,22.33333333,0.330261612,4.333333333,0.237958255,58,0.963022409,2097,0.21887792,0.437530049
14352,"Mathematical Modeling & the Transmission Dynamics of SARS-CoV-2 in Cali, Colombia: Implications to a 2020 Outbreak & public health preparedness",0,10.1101/2020.05.06.20093526,5/10/20,medrxiv,0,5,mathematical model,0.001461849,0.001461872,0.001461859,0.872813634,0.001461878,0.121338908,Epidemiology,0.32628793,FALSE,18.2,0.273053374,7,0.299973241,4,0.707574542,1382,0.05730797,0.334477282
14353,TAMING COVID-19 EPIDEMIC IN SAO PAULO WITH ALOGISTIC MODEL AND NON-PHARMACEUTICAL MEASURES,0,10.1101/2020.05.06.20093666,5/10/20,medrxiv,0,1,mathematical model,0.003335223,0.003335259,0.003335288,0.721617682,0.003335294,0.265041253,Epidemiology,0.12159073,FALSE,15,0.227596017,2,0.164302917,0,0.403234768,1154,0.023838189,0.204742973
14354,"Multiple introductions, regional spread and local differentiation during the first week of COVID-19 epidemic in Montevideo, Uruguay",0,10.1101/2020.05.09.086223,5/10/20,biorxiv,0,7,"sequencing, whole-genome, genomic epidemiology, dataset",0.002080672,0.955431478,0.00208069,0.002080699,0.002080572,0.03624589,Genomics,0.17298016,FALSE,26.57142857,0.389881873,19.14285714,0.472036393,6,0.764429903,3025,0.410546593,0.50922369
14355,Prediction analysis of SARS-COV-2 entry in Livestock and Wild animals,0,10.1101/2020.05.08.084327,5/10/20,biorxiv,0,14,logistic regression,0.54547196,0.325652811,0.00091669,0.126125125,0.000916689,0.000916725,Drug discovery,0.5565386,TRUE,57.85714286,0.687797637,45.78571429,0.662362858,1,0.537564047,4159,0.550445461,0.609542501
14356,How to Flatten the post-lockdown epidemic trajectory,0,10.1101/2020.05.06.20093104,5/10/20,medrxiv,0,2,mathematical model,0.001461852,0.001461848,0.001461863,0.992690692,0.001461869,0.001461876,Epidemiology,0.0349634,FALSE,5,0.070752675,0,0.055525823,0,0.403234768,1175,0.026005297,0.138879641
14357,Associations between ambient air pollutants exposure and case fatality rate of COVID-19: a multi-city ecological study in China.,0,10.1101/2020.05.06.20088682,5/10/20,medrxiv,0,5,model fit,0.001291197,0.001291218,0.001291216,0.916072826,0.078762253,0.00129129,Epidemiology,0.17781377,FALSE,4.6,0.062588905,0.4,0.075796093,3,0.667819001,1491,0.077775102,0.220994775
14358,Recurrence of SARS-CoV-2 PCR positivity in COVID-19 patients: a single center experience and potential implications,0,10.1101/2020.05.06.20089573,5/10/20,medrxiv,0,42,prediction model,0.000746545,0.053926768,0.076788148,0.179615778,0.000746562,0.688176199,Clinics,0.9199235,TRUE,37.19047619,0.509617169,22.5,0.507492641,15,0.874313229,5693,0.674692993,0.641529008
14359,A single-cell RNA expression map of human coronavirus entry factors,0,10.1101/2020.05.08.084806,5/9/20,biorxiv,10.1016/j.celrep.2020.108175,3,sequencing,0.868306793,0.001653149,0.001653038,0.00165306,0.001653116,0.125080844,Drug discovery,0.56508964,TRUE,14.33333333,0.216772837,4.666666667,0.246721969,39,0.945737391,10129,0.816999759,0.556557989
14360,"Awareness, knowledge, attitude and practice towards measures for prevention of the spread of COVID-19 in the Ugandans: A nationwide online cross-sectional Survey",0,10.1101/2020.05.05.20092247,5/9/20,medrxiv,10.3389/fpubh.2020.618731,7,logistic regression,0.00141509,0.001415103,0.001415109,0.001415171,0.992924434,0.001415091,Healthcare,0.50842613,TRUE,16.85714286,0.253633496,10.14285714,0.356636339,7,0.785110192,2819,0.372020226,0.441850063
14361,Screening for SARS-CoV-2 infections with colorimetric RT-LAMP and LAMP sequencing,0,10.1101/2020.05.05.20092288,5/9/20,medrxiv,10.1126/scitranslmed.abc7075,17,sequencing,0.00139286,0.692316553,0.302111934,0.001392893,0.001392857,0.001392903,Genomics,0.6275476,TRUE,10.23529412,0.153812852,377.8235294,0.968758362,17,0.887338725,7057,0.738020708,0.686982662
14362,IKONOS: An intelligent tool to support diagnosis of Covid-19 by texture analysis of x-ray images,0,10.1101/2020.05.05.20092346,5/9/20,medrxiv,10.1007/s42600-020-00091-7,8,"computational, classifier",0.002130672,0.00213076,0.989346543,0.002130684,0.002130686,0.002130656,Imaging,0.7561119,TRUE,7,0.10179974,1.25,0.127776291,9,0.814309525,2335,0.271851673,0.328934308
14363,"Comprehensive Testing Highlights Racial, Ethnic, and Age Disparities in the COVID-19 Outbreak",0,10.1101/2020.05.05.20092031,5/9/20,medrxiv,0,16,logistic regression,0.001237064,0.001237105,0.001237127,0.143023155,0.515852154,0.337413395,Healthcare,0.2203565,FALSE,14.375,0.217205764,4.1875,0.233275355,6,0.764429903,2476,0.300987238,0.378974565
14364,A Computational Model for Estimating the Evolution of COVID-19 in Rondenia-Brazil,0,10.1101/2020.05.05.20091942,5/9/20,medrxiv,0,2,computational,0.002898269,0.002898631,0.002898339,0.628501104,0.185173531,0.177630126,Epidemiology,0.25737584,FALSE,1.5,0.015523533,0,0.055525823,0,0.403234768,1387,0.05778955,0.133018418
14365,Using viral genomics to estimate undetected infections and extent of superspreading events for COVID-19,0,10.1101/2020.05.05.20092098,5/9/20,medrxiv,0,2,"bayes, computational, genomes",0.000999527,0.301759433,0.000999624,0.675988102,0.000999588,0.019253727,Epidemiology,0.16325817,FALSE,6.5,0.093512277,5.5,0.267259834,0,0.403234768,3983,0.527570431,0.322894327
14366,Containing Covid-19 outbreaks with spatiallytargeted short-term lockdowns and mass-testing,0,10.1101/2020.05.05.20092221,5/9/20,medrxiv,0,3,network model,0.048076387,0.000728139,0.000728123,0.916409359,0.03332987,0.000728123,Epidemiology,0.080782175,FALSE,8.5,0.126662131,5.75,0.271742039,1,0.537564047,2020,0.197206838,0.283293764
14367,Anxiety and Depression in Health Workers and General Population During COVID-19 Epidemic in IRAN: A Web-Based Cross-Sectional Study,0,10.1101/2020.05.05.20089292,5/9/20,medrxiv,0,8,logistic regression,0.001415076,0.001415085,0.001415092,0.001415109,0.992924486,0.001415152,Healthcare,0.8734652,TRUE,16.5,0.249366071,4.5,0.242708055,12,0.850299401,2608,0.332289911,0.418665859
14368,CoVID-19 prediction for India from the existing data and SIR(D) model study,0,10.1101/2020.05.05.20085902,5/8/20,medrxiv,0,5,mathematical model,0.002720105,0.002720193,0.002720125,0.986399398,0.002720103,0.002720075,Epidemiology,0.10608247,FALSE,2.6,0.028263962,0.2,0.061145304,10,0.828199272,1505,0.078256682,0.248966305
14369,Estimative of real number of infections by COVID-19 on Brazil and possible scenarios,0,10.1101/2020.05.03.20052779,5/8/20,medrxiv,10.1016/j.idm.2020.09.004,2,mathematical model,0.002720158,0.002720159,0.00272016,0.954963516,0.002720139,0.034155868,Epidemiology,0.6682894,TRUE,13.5,0.205393036,4,0.231469093,10,0.828199272,2488,0.302913556,0.391993739
14370,Real-time tracking and forecasting of the of COVID-19 outbreak in Kuwait: a mathematical modeling study,0,10.1101/2020.05.03.20089771,5/8/20,medrxiv,0,9,mathematical model,0.001538081,0.001538145,0.001538154,0.992309382,0.001538146,0.001538093,Epidemiology,0.25877962,FALSE,3.444444444,0.041189931,0,0.055525823,6,0.764429903,5710,0.673007464,0.38353828
14371,AI based Chest X-Ray (CXR) Scan Texture Analysis Algorithm for Digital Test of COVID-19 Patients,0,10.1101/2020.05.05.20091561,5/8/20,medrxiv,0,4,"machine learning, dataset",0.001310333,0.001310423,0.889801087,0.034325527,0.001310404,0.071942226,Imaging,0.2907437,FALSE,9,0.135320675,2.5,0.180826866,3,0.667819001,1587,0.096556706,0.270130812
14372,Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection,0,10.1101/2020.05.04.20090803,5/8/20,medrxiv,0,2,"deep learning, neural network",0.001098873,0.105157703,0.869529881,0.001098898,0.022015782,0.001098862,Imaging,0.22477517,FALSE,165.5,0.955284804,134,0.867808402,12,0.850299401,1802,0.145918613,0.704827805
14373,Deep Learning for Screening COVID-19 using Chest X-Ray Images,0,10.1101/2020.05.04.20090423,5/8/20,medrxiv,0,3,"machine learning, deep learning, neural network, transfer learning, dataset",0.025891763,0.001059399,0.941061102,0.02986897,0.001059366,0.0010594,Imaging,0.4840032,FALSE,53.33333333,0.656255798,89.33333333,0.800307733,38,0.944132354,1968,0.18444498,0.646285216
14374,Global genetic diversity patterns and transmissions of SARS-CoV-2,0,10.1101/2020.05.05.20091413,5/8/20,medrxiv,0,5,genome sequences,0.001310344,0.575672819,0.001310356,0.00131043,0.109010751,0.311385299,Genomics,0.47575316,FALSE,13.2,0.199270208,17.2,0.452568906,7,0.785110192,2193,0.238863472,0.418953195
14375,Longitudinal peripheral blood transcriptional analysis of COVID-19 patients captures disease progression and reveals potential biomarkers,0,10.1101/2020.05.05.20091355,5/8/20,medrxiv,0,24,transcriptom,0.433652902,0.001751208,0.001751226,0.001751252,0.033937866,0.527155547,Clinics,0.62968963,TRUE,7.19047619,0.103160369,3,0.199424672,9,0.814309525,2826,0.371538647,0.372108303
14376,"Phenomenological Modelling of COVID-19 epidemics in Sri Lanka, Italy and Hebei Province of China",0,10.1101/2020.05.04.20091132,5/8/20,medrxiv,10.1155/2020/6397063,3,mathematical model,0.001291214,0.001291215,0.001291233,0.993543898,0.001291221,0.001291219,Epidemiology,0.1449556,FALSE,6.666666667,0.094996598,0,0.055525823,2,0.618927094,1150,0.022393451,0.197960742
14377,A Noel Intervention Recurrent autoencoder for real time forecasting and non-pharmaceutical intervention selection to curb the spread of Covid-19 in the world,0,10.1101/2020.05.05.20091827,5/8/20,medrxiv,10.4310/sii.2021.v14.n1.a10,6,neural network,0.002080567,0.002080563,0.099922598,0.891755185,0.002080549,0.002080539,Epidemiology,0.82359314,TRUE,3.833333333,0.047745686,0.5,0.087101953,1,0.537564047,1378,0.054177703,0.181647347
14378,Forecasting the spread of COVID-19 in Nigeria using Box-Jenkins Modeling Procedure,0,10.1101/2020.05.05.20091686,5/8/20,medrxiv,0,2,prediction model,0.001943513,0.001943538,0.193722738,0.798503252,0.001943473,0.001943485,Epidemiology,0.4619261,FALSE,3.5,0.044344115,0,0.055525823,5,0.739490092,1068,0.012761859,0.213030472
14379,Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study,0,10.1101/2020.05.05.20091736,5/8/20,medrxiv,0,3,mathematical model,0.22444198,0.001538108,0.001538144,0.769405516,0.001538103,0.001538149,Epidemiology,0.34934944,FALSE,4,0.054734368,0,0.055525823,6,0.764429903,1721,0.127377799,0.250516973
14380,A modified ACE2 peptide mimic to block SARS-CoV2 entry,0,10.1101/2020.05.07.082230,5/8/20,biorxiv,0,2,molecular dynamics simulation,0.941927177,0.052501425,0.001392872,0.001392887,0.001392823,0.001392816,Drug discovery,0.092273176,FALSE,1.5,0.015523533,0,0.055525823,1,0.537564047,3158,0.428846617,0.259365005
14381,Massive Multiplexing Can Deliver a $1 Test for COVID-19,0,10.1101/2020.05.05.079400,5/8/20,biorxiv,0,5,sequencing,0.001901882,0.550064237,0.44232847,0.001901797,0.001901891,0.001901722,Genomics,0.49287164,FALSE,54.6,0.66553281,240.6,0.934238694,4,0.707574542,6183,0.696364074,0.75092753
14382,"The transcriptomic profiling of COVID-19 compared to SARS, MERS, Ebola, and H1N1",0,10.1101/2020.05.06.080960,5/8/20,biorxiv,10.1371/journal.pone.0243270,2,transcriptom,0.99324804,0.001350487,0.001350315,0.001350374,0.001350339,0.001350446,Drug discovery,0.8040203,TRUE,44,0.578390748,8,0.320511105,8,0.799987654,4105,0.541054659,0.559986041
14383,Health Care WorkersMental Health During the First Weeks of the SARS-CoV-2 Pandemic in Switzerland: A Cross-Sectional Study,0,10.1101/2020.05.04.20088625,5/8/20,medrxiv,10.3389/fpsyt.2021.594340,9,network analysis,0.001751204,0.001751203,0.053484461,0.001751277,0.918490126,0.022771729,Healthcare,0.58956355,TRUE,13.33333333,0.201558538,11.55555556,0.378645973,7,0.785110192,2286,0.259089815,0.406101129
14384,SARS-CoV-2 proteins exploit host's genetic and epigenetic mediators for the annexation of key host signaling pathways that confers its immune evasion and disease pathophysiology,0,10.1101/2020.05.06.050260,5/8/20,biorxiv,10.3389/fmolb.2020.598583,2,"computational, interactom",0.941384307,0.000898091,0.00089807,0.000898084,0.000898065,0.055023383,Drug discovery,0.8740863,TRUE,12,0.183190055,9,0.337904736,6,0.764429903,3123,0.422104503,0.426907299
14385,MantisCOVID: Rapid X-Ray Chest Radiograph and Mortality Rate Evaluation With Artificial Intelligence For COVID-19,0,10.1101/2020.05.04.20090779,5/8/20,medrxiv,0,4,"machine learning, artificial intelligence, dataset",0.002130666,0.002130686,0.864910042,0.002130773,0.002130784,0.12656705,Imaging,0.50935346,TRUE,4.25,0.056527924,0,0.055525823,2,0.618927094,1442,0.067421141,0.199600496
14386,ProgNet: Covid-19 prognosis using recurrent andconvolutional neural networks,0,10.1101/2020.05.06.20092874,5/8/20,medrxiv,10.2174/1874347102012010011,4,"deep learning, artificial intelligence, neural network",0.034233884,0.001751327,0.721599996,0.238912204,0.00175124,0.001751349,Imaging,0.7330643,TRUE,8.5,0.126662131,1,0.122023013,5,0.739490092,1819,0.150012039,0.284546819
14387,"Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK",0,10.1101/2020.05.05.20083436,5/8/20,medrxiv,0,6,machine learning,0.0047752,0.004775281,0.17944463,0.801454556,0.004775286,0.004775047,Epidemiology,0.6575419,TRUE,14.33333333,0.216772837,4,0.231469093,9,0.814309525,1142,0.020707922,0.320814844
14388,Estimation of the Potential Impact of COVID-19 Responses on the HIV Epidemic: Analysis using the Goals Model,0,10.1101/2020.05.04.20090399,5/8/20,medrxiv,0,6,simulation model,0.003760559,0.003760481,0.003760695,0.625796401,0.359160868,0.003760995,Epidemiology,0.30328882,FALSE,56,0.675304595,70.83333333,0.755619481,2,0.618927094,2034,0.198410787,0.562065489
14389,Laboratory findings associated with mechanical ventilation requirement and mortality among hospitalized individuals with Covid-19 in Eastern Massachusetts,0,10.1101/2020.05.04.20090555,5/8/20,medrxiv,10.1001/jamanetworkopen.2020.23934,3,prediction model,0.000966768,0.000966771,0.000966822,0.000966797,0.041474467,0.954658376,Clinics,0.39233196,FALSE,135.3333333,0.926897149,380,0.969025957,7,0.785110192,1969,0.184926559,0.716489964
14390,Multivariable prediction model of intensive care unit transfer and death: a French prospective cohort study of COVID-19 patients.,0,10.1101/2020.05.04.20090118,5/8/20,medrxiv,10.1371/journal.pone.0240711,12,prediction model,0.001022618,0.001022663,0.045610933,0.00102265,0.001022698,0.950298438,Clinics,0.61013633,TRUE,238,0.982497372,95,0.813085363,10,0.828199272,2236,0.248495064,0.718069267
14391,IDentif.AI: Artificial Intelligence Pinpoints Remdesivir in Combination with Ritonavir and Lopinavir as an Optimal Regimen Against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2),0,10.1101/2020.05.04.20088104,5/8/20,medrxiv,0,16,artificial intelligence,0.611992313,0.001511892,0.181918068,0.201553937,0.001511891,0.0015119,Drug discovery,0.4194443,FALSE,8.8125,0.129692622,3.375,0.207051111,3,0.667819001,5903,0.683120636,0.421920842
14392,Weather Conditions and COVID-19 Transmission: Estimates and Projections,0,10.1101/2020.05.05.20092627,5/8/20,medrxiv,0,7,dataset,0.000999512,0.000999607,0.000999571,0.91510132,0.080900419,0.000999571,Epidemiology,0.32349113,FALSE,23,0.34225988,21.5,0.498260637,13,0.858880178,6016,0.688899591,0.597075071
14393,Segmentation and shielding of the most vulnerable members of the population as elements of an exit strategy from COVID-19 lockdown,0,10.1101/2020.05.04.20090597,5/8/20,medrxiv,0,16,mathematical model,0.001046847,0.001046825,0.10560247,0.718013076,0.173243941,0.001046841,Epidemiology,0.34477076,FALSE,40.125,0.540355,54.4375,0.699290875,19,0.89561084,5073,0.629665302,0.691230504
14394,Safe Blues: A Method for Estimation and Control in the Fight Against COVID-19,0,10.1101/2020.05.04.20090258,5/8/20,medrxiv,0,8,neural network,0.000966833,0.017618052,0.128385558,0.801254186,0.050808525,0.000966847,Epidemiology,0.13541731,FALSE,63.5,0.72496753,56.25,0.707853893,6,0.764429903,2350,0.273055622,0.617576737
14395,The intensity of COVID-19 outbreaks is modulated by SARS-CoV-2 free-living survival and environmental transmission,0,10.1101/2020.05.04.20090092,5/8/20,medrxiv,0,6,mathematical model,0.182174174,0.001622851,0.001622733,0.811334593,0.001622703,0.001622945,Epidemiology,0.5101644,TRUE,28.5,0.412579628,16.33333333,0.440460262,13,0.858880178,4334,0.566819167,0.569684809
14396,Development of a Multivariate Prediction Network Model for epidemic progression in order to study the effects of lockdown time and coverage on a closed community of non-immune individuals.,0,10.1101/2020.05.04.20090712,5/7/20,medrxiv,0,2,network model,0.0010722,0.001072178,0.001072188,0.994638925,0.001072237,0.001072271,Epidemiology,0.19337088,FALSE,10,0.15214299,0.5,0.087101953,1,0.537564047,2259,0.25307007,0.257469765
14397,OpenSAFELY: factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients.,0,10.1101/2020.05.06.20092999,5/7/20,medrxiv,0,31,dataset,0.000946095,0.000946111,0.000946106,0.307908468,0.000946139,0.688307081,Clinics,0.1616332,FALSE,20.96774194,0.308862638,24.5483871,0.525287664,439,0.997345515,207573,0.996147363,0.706910795
14398,Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study,0,10.1101/2020.05.01.20053413,5/7/20,medrxiv,10.1183/13993003.01104-2020,28,"machine learning, dataset",0.00079338,0.000793387,0.346482096,0.000793404,0.00079344,0.650344293,Clinics,0.9389405,TRUE,12.64285714,0.190487971,5.5,0.267259834,15,0.874313229,2177,0.234047676,0.391527178
14399,The SARS-CoV-2-like virus found in captive pangolins from Guangdong should be better sequenced.,0,10.1101/2020.05.07.077016,5/7/20,biorxiv,0,0,"sequencing, genomes, dataset",0.123372785,0.726442374,0.00125466,0.001254632,0.001254644,0.146420905,Genomics,0.40881708,FALSE,97,0.861586987,511,0.979595933,4,0.707574542,5427,0.65518902,0.80098662
14400,Cooperative virus propagation underlies COVID-19 transmission dynamics,0,10.1101/2020.05.05.20092361,5/7/20,medrxiv,0,2,mathematical model,0.002080656,0.260281608,0.002080749,0.643809421,0.089666832,0.002080733,Epidemiology,0.3646907,FALSE,118,0.904632321,289.5,0.951230934,0,0.403234768,2086,0.209727908,0.617206483
14401,Predictive mathematical models for the number of individuals infected with COVID-19,0,10.1101/2020.05.02.20088591,5/6/20,medrxiv,0,3,mathematical model,0.375671048,0.001653146,0.001653156,0.617716465,0.001653074,0.001653111,Epidemiology,0.1915907,FALSE,148,0.942606222,160.3333333,0.892226385,8,0.799987654,2019,0.192391043,0.706802826
14402,Automatic Detection of COVID-19 Using X-ray Images with Deep Convolutional Neural Networks and Machine Learning,0,10.1101/2020.05.01.20088211,5/6/20,medrxiv,0,5,"machine learning, neural network, transfer learning, dataset",0.001593492,0.001593517,0.992032482,0.001593498,0.001593486,0.001593524,Imaging,0.4731437,FALSE,15,0.227596017,1.8,0.150120417,17,0.887338725,7847,0.764026005,0.507270291
14403,Applying Lexical Link Analysis to DiscoverInsights from Public Information on COVID-19,0,10.1101/2020.05.06.079798,5/6/20,biorxiv,0,2,"information mining, data mining, genomes, dataset",0.001330097,0.347088542,0.001330082,0.61850595,0.030415285,0.001330044,Epidemiology,0.121002555,FALSE,49,0.624281032,26,0.53819909,1,0.537564047,1685,0.116542259,0.454146607
14404,FACTORS INFLUENCING MENTAL HEALTH DURING COVID-19 OUTBREAK: AN EXPLORATORY SURVEY AMONG INDIAN POPULATION,0,10.1101/2020.05.03.20081380,5/6/20,medrxiv,10.17532/jhsci.2020.950,3,logistic regression,0.00156528,0.001565449,0.001565421,0.133561092,0.860177437,0.00156532,Healthcare,0.9579356,TRUE,5,0.070752675,1.333333333,0.13252609,11,0.840175319,3878,0.513123044,0.389144282
14405,"Repeated seroprevalence of anti-SARS-CoV-2 IgG antibodies in a population-based sample from Geneva, Switzerland",0,10.1101/2020.05.02.20088898,5/6/20,medrxiv,10.1016/s0140-6736(20)31304-0,25,bayes,0.001350332,0.194871654,0.020511684,0.283197399,0.498718479,0.001350452,Healthcare,0.15238845,FALSE,47.76,0.612282763,68.84,0.749933101,57,0.962281622,38137,0.960269685,0.821191793
14406,A note on COVID-19 seroprevalence studies: a meta-analysis using hierarchical modelling,0,10.1101/2020.05.03.20089201,5/6/20,medrxiv,0,2,bayes,0.002130703,0.349620762,0.002130746,0.509802373,0.134184716,0.0021307,Epidemiology,0.1547567,FALSE,15,0.227596017,0.5,0.087101953,15,0.874313229,4668,0.596195521,0.44630168
14407,The importance of the timing of quarantine measures before symptom onset to prevent COVID-19 outbreaks - illustrated by Hong Kong's intervention model,0,10.1101/2020.05.03.20089482,5/6/20,medrxiv,0,7,model fit,0.001203403,0.001203439,0.001203429,0.855461665,0.139724588,0.001203475,Epidemiology,0.17786312,FALSE,3.428571429,0.041066238,2,0.164302917,2,0.618927094,1946,0.177462076,0.250439581
14408,"Depression, Anxiety and Depression-anxiety comorbidity amid COVID-19 Pandemic: An online survey conducted during lockdown in Nepal",0,10.1101/2020.04.30.20086926,5/6/20,medrxiv,0,7,logistic regression,0.001461867,0.001461864,0.001461891,0.00146192,0.957582585,0.036569874,Healthcare,0.56701696,TRUE,5.285714286,0.073041004,1.714285714,0.14577201,13,0.858880178,3295,0.444497953,0.380547786
14409,Fully automatic deep convolutional approaches for the analysis of Covid-19 using chest X-ray images,0,10.1101/2020.05.01.20087254,5/6/20,medrxiv,0,3,dataset,0.00139285,0.001392914,0.936774698,0.057653722,0.001392889,0.001392927,Imaging,0.23899949,FALSE,48,0.614942173,10.33333333,0.36038266,4,0.707574542,1588,0.093667229,0.444141651
14410,Effect of Temperature on the Transmission of COVID-19: A Machine Learning Case Study in Spain,0,10.1101/2020.05.01.20087759,5/6/20,medrxiv,0,2,machine learning,0.003335375,0.003335293,0.003335661,0.983322719,0.003335428,0.003335525,Epidemiology,0.52470964,TRUE,5.5,0.077246583,1,0.122023013,7,0.785110192,2306,0.260775343,0.311288783
14411,Frequency of routine testing for SARS-CoV-2 to reduce transmissionamong workers,0,10.1101/2020.04.30.20087015,5/6/20,medrxiv,10.1093/cid/ciaa1383,6,simulation model,0.005697711,0.175700911,0.005698,0.403869805,0.403335978,0.005697595,Epidemiology,0.4562438,FALSE,18.66666667,0.279670975,9.333333333,0.342253144,1,0.537564047,7944,0.767637852,0.481781505
14412,Accounting for underreporting in mathematical modelling of transmission and control of COVID-19 in Iran,0,10.1101/2020.05.02.20087270,5/6/20,medrxiv,10.3389/fphy.2020.00289,8,mathematical model,0.013212302,0.000946113,0.000946091,0.983003204,0.000946145,0.000946145,Epidemiology,0.5066307,TRUE,92.875,0.851073041,108.125,0.836366069,4,0.707574542,1790,0.140380448,0.633848525
14413,COVID-19 Utilization and Resource Visualization Engine (CURVE) to Forecast In-Hospital Resources,0,10.1101/2020.05.01.20087973,5/6/20,medrxiv,0,10,forecasting model,0.001126813,0.00112681,0.001126831,0.935711772,0.001126827,0.059780947,Epidemiology,0.1316888,FALSE,13,0.197352959,7.2,0.302047097,0,0.403234768,1273,0.036118469,0.234688323
14414,Impact of policy interventions and social distancing on SARS-CoV-2 transmission in the United States,0,10.1101/2020.05.01.20088179,5/6/20,medrxiv,0,12,logistic regression,0.0010468,0.001046814,0.001046815,0.744891912,0.250920781,0.001046878,Epidemiology,0.27989107,FALSE,32.16666667,0.45717113,10.5,0.363459995,10,0.828199272,2332,0.267276667,0.479026766
14415,Correlation of coagulation parameters with clinical outcomes in Coronavirus-19 affected minorities in United States: Observational cohort,0,10.1101/2020.05.01.20087932,5/6/20,medrxiv,0,8,"logistic regression, dataset",0.001310366,0.001310368,0.001310421,0.001310401,0.039914148,0.954844296,Clinics,0.34827456,FALSE,24.625,0.362669305,12.5,0.392761573,4,0.707574542,2515,0.305803034,0.442202114
14416,Impact of non-pharmaceutical interventions against COVID-19 in Europe: a quasi-experimental study,0,10.1101/2020.05.01.20088260,5/6/20,medrxiv,0,4,bayes,0.001901679,0.001901733,0.00190171,0.990491045,0.001901987,0.001901845,Epidemiology,0.2288278,FALSE,110,0.89003649,237.5,0.933101418,12,0.850299401,37733,0.959788105,0.908306354
14417,COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach,0,10.1101/2020.05.02.20088427,5/6/20,medrxiv,10.3390/math8060890,5,"machine learning, prediction model",0.001461892,0.001461875,0.42619717,0.567955213,0.001461891,0.001461958,Epidemiology,0.6275317,TRUE,111.4,0.8926959,85,0.790406743,33,0.936045435,1812,0.145437033,0.691146278
14418,Agent-Level Pandemic Simulation (ALPS) for Analyzing Effects of Lockdown Measures,0,10.1101/2020.04.29.20084699,5/5/20,medrxiv,0,1,simulation model,0.003214391,0.003214173,0.003214172,0.983929005,0.003214162,0.003214097,Epidemiology,0.31032854,FALSE,67,0.748160059,39,0.62784319,0,0.403234768,1208,0.028653985,0.451973
14419,"Mathematical model describing CoViD-19 in Sao Paulo State, Brazil - Evaluating isolation as control mechanism and forecasting epidemiological scenarios of release",0,10.1101/2020.04.29.20084830,5/5/20,medrxiv,10.1017/S0950268820001600,4,mathematical model,0.002639064,0.002639112,0.002638991,0.76182186,0.227621922,0.002639051,Epidemiology,0.39570206,FALSE,8.5,0.126662131,0.25,0.065493712,5,0.739490092,1629,0.101372502,0.258254609
14420,Epidemic Models for Personalised COVID-19 Isolation and Exit Policies Using Clinical Risk Predictions,0,10.1101/2020.04.29.20084707,5/5/20,medrxiv,0,6,prediction model,0.000704906,0.00070491,0.000704943,0.906243695,0.000704937,0.090936609,Epidemiology,0.18512335,FALSE,43,0.567629414,125.6666667,0.8585095,7,0.785110192,1765,0.133397544,0.586161662
14421,"How many lives can be saved? A global view on the impact of testing, herd immunity and demographics on COVID-19 fatality rates",0,10.1101/2020.04.29.20084400,5/5/20,medrxiv,0,4,bayes,0.00229655,0.002296594,0.002296568,0.988517193,0.002296549,0.002296545,Epidemiology,0.08363807,FALSE,54.25,0.663120787,59.75,0.719761841,2,0.618927094,2527,0.307247773,0.577264374
14422,Cross-talk between the airway epithelium and activated immune cells defines severity in COVID-19,0,10.1101/2020.04.29.20084327,5/5/20,medrxiv,10.1038/s41587-020-0602-4,32,transcriptom,0.674783345,0.02891562,0.001187257,0.001187277,0.001187302,0.292739198,Drug discovery,0.6432216,TRUE,49.15625,0.62527058,107.09375,0.834693604,14,0.866658436,8152,0.772453648,0.774769067
14423,Mathematical Model with Social Distancing Parameter for Early Estimation of COVID-19 Spread,0,10.1101/2020.04.30.20086611,5/5/20,medrxiv,0,3,mathematical model,0.044139364,0.002806387,0.002806527,0.903062001,0.002806456,0.044379265,Epidemiology,0.20225352,FALSE,6,0.086028821,0,0.055525823,7,0.785110192,1625,0.100409343,0.256768545
14424,Hindsight is 2020 vision: Characterisation of the global response to the COVID-19 pandemic,0,10.1101/2020.04.30.20085662,5/5/20,medrxiv,10.1186/s12889-020-09972-z,6,bayes,0.000822934,0.000822933,0.000822936,0.995885356,0.000822932,0.000822909,Epidemiology,0.04678613,FALSE,56.4,0.677902158,43.2,0.650321113,10,0.828199272,3007,0.401155791,0.639394584
14425,TRACKING AND PREDICTING COVID-19 RADIOLOGICAL TRAJECTORY USING DEEP LEARNING ON CHEST X-RAYS: INITIAL ACCURACY TESTING,0,10.1101/2020.05.01.20086207,5/5/20,medrxiv,0,17,"deep learning, dataset",0.015471768,0.038973157,0.772347362,0.000854764,0.000854758,0.171498193,Imaging,0.45879206,FALSE,16.11764706,0.244047251,8.470588235,0.327334761,6,0.764429903,1945,0.174331808,0.377535931
14426,Modelling and simulation of COVID-19 propagation in a large population with specific reference to India,0,10.1101/2020.04.30.20086306,5/5/20,medrxiv,0,4,mathematical model,0.007061558,0.143070989,0.007061648,0.828682636,0.00706169,0.007061479,Epidemiology,0.4581902,FALSE,27.25,0.398169336,0,0.055525823,7,0.785110192,2560,0.313989887,0.38819881
14427,A non-parametric mathematical model to investigate the dynamic of a pandemic,0,10.1101/2020.04.30.20086199,5/5/20,medrxiv,0,2,mathematical model,0.002898496,0.002898426,0.002898367,0.985508035,0.002898343,0.002898333,Epidemiology,0.27232283,FALSE,7,0.10179974,0.5,0.087101953,2,0.618927094,1146,0.019263183,0.206772993
14428,Presence and vitality of SARS-CoV-2 virus in wastewaters and rivers,0,10.1101/2020.05.01.20086009,5/5/20,medrxiv,10.1016/j.scitotenv.2020.140911,14,"sequencing, whole genome",0.001593558,0.744975484,0.001593536,0.10427599,0.041832482,0.105728952,Genomics,0.7307752,TRUE,42.85714286,0.565959552,35.71428571,0.608710195,72,0.970121612,8763,0.786660246,0.732862901
14429,Alpha 1 Antitrypsin is an Inhibitor of the SARS-CoV2-Priming Protease TMPRSS2,0,10.1101/2020.05.04.077826,5/5/20,biorxiv,0,7,structural model,0.993543643,0.001291322,0.001291222,0.001291325,0.001291228,0.00129126,Drug discovery,0.72632664,TRUE,107.25,0.883728122,169,0.898514851,22,0.908142478,7760,0.759210209,0.862398915
14430,COVIDier: A Deep-learning Tool For Coronaviruses Genome And Virulence Proteins Classification,0,10.1101/2020.05.03.075549,5/5/20,biorxiv,0,5,"deep learning, classifier, genomes, deep-learning",0.022147877,0.390682639,0.553897857,0.001220092,0.001220053,0.030831481,Imaging,0.1801137,FALSE,24.8,0.365328715,17.2,0.452568906,3,0.667819001,2548,0.311581989,0.449324653
14431,Geospatial Correlation Between COVID-19 Health Misinformation on Social Media and Poisoning with Household Cleaners,0,10.1101/2020.04.30.20079657,5/5/20,medrxiv,10.1080/15563650.2020.1811297,5,network analysis,0.114307709,0.002238555,0.002238736,0.748649999,0.002238599,0.130326401,Epidemiology,0.9468798,TRUE,13,0.197352959,0.4,0.075796093,4,0.707574542,2806,0.361184686,0.33547707
14432,Quantifying and mitigating the impact of the COVID-19 pandemic on outcomes in colorectal cancer,0,10.1101/2020.04.28.20083170,5/5/20,medrxiv,0,21,model fit,0.000898168,0.000898178,0.053754018,0.345671,0.000898122,0.597880515,Clinics,0.5128255,TRUE,35.04761905,0.488094502,49.76190476,0.680358576,6,0.764429903,2082,0.20659764,0.534870155
14433,ai-corona: Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans,0,10.1101/2020.05.04.20082081,5/5/20,medrxiv,0,13,"deep learning, artificial intelligence, dataset",0.001538089,0.001538096,0.930020462,0.001538118,0.02448976,0.040875475,Imaging,0.50249696,TRUE,17.875,0.268724102,6,0.280037463,5,0.739490092,2160,0.225379244,0.378407725
14434,Global prediction of unreported SARS-CoV2 infection from observed COVID-19 cases,0,10.1101/2020.04.29.20083485,5/5/20,medrxiv,0,4,bayes,0.001486401,0.03048405,0.0014865,0.92574321,0.00148644,0.039313399,Epidemiology,0.120488435,FALSE,25,0.369286907,145.25,0.879448756,13,0.858880178,5640,0.665542981,0.693289705
14435,Modeling and predicting the spread of COVID-19 in Lebanon: A Bayesian perspective,0,10.1101/2020.04.29.20082263,5/5/20,medrxiv,10.3389/fams.2020.00040,1,bayes,0.002639011,0.002638987,0.002639053,0.986804941,0.002638982,0.002639026,Epidemiology,0.089524955,FALSE,6,0.086028821,0,0.055525823,1,0.537564047,1168,0.02335661,0.175618825
14436,Risk of symptomatic Covid-19 among frontline healthcare workers,0,10.1101/2020.04.29.20084111,5/5/20,medrxiv,10.1016/S2468-2667(20)30164-X,30,classifier,0.000667681,0.01690812,0.014701426,0.147368129,0.776999334,0.04335531,Healthcare,0.27486596,FALSE,273.7,0.988249119,929.4,0.992841852,50,0.957775171,15327,0.880086684,0.954738207
14437,Warmer weather and global trends in the coronavirus COVID-19,0,10.1101/2020.04.28.20084004,5/5/20,medrxiv,0,6,prediction model,0.002639007,0.002639041,0.002639177,0.986804686,0.002639073,0.002639015,Epidemiology,0.12423509,FALSE,5.4,0.074710866,0.2,0.061145304,2,0.618927094,2825,0.365278112,0.280015344
14438,RSI model: COVID-19 in Germany Alternating quarantine episodes and normal episodes,0,10.1101/2020.05.01.20075754,5/5/20,medrxiv,0,1,mathematical model,0.001593511,0.001593498,0.001593568,0.742808957,0.149379598,0.103030868,Epidemiology,0.20735735,FALSE,18,0.271569052,0,0.055525823,0,0.403234768,1142,0.018540814,0.187217614
14439,Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention,0,10.1101/2020.05.05.079194,5/5/20,biorxiv,10.1016/j.isci.2021.102151,23,"sequencing, transcriptom",0.902231669,0.093019189,0.001187261,0.001187338,0.001187269,0.001187275,Drug discovery,0.39119518,FALSE,1.608695652,0.01577092,0.043478261,0.055592721,42,0.949503056,14692,0.875270889,0.474034396
14440,Pre-Existing Characteristics Associated with Covid-19 Illness Severity,0,10.1101/2020.04.29.20084533,5/5/20,medrxiv,10.1371/journal.pone.0236240,29,logistic regression,0.001254623,0.001254612,0.001254595,0.001254629,0.001254705,0.993726836,Clinics,0.720255,TRUE,70.5862069,0.766404849,140.0344828,0.874163768,9,0.814309525,4016,0.525884902,0.745190761
14441,Periodic COVID-19 Testing in Emergency Department Staff,0,10.1101/2020.04.28.20084053,5/4/20,medrxiv,10.2196/20260,2,mathematical model,0.001272642,0.001272695,0.001272671,0.270000386,0.663184411,0.062997196,Healthcare,0.32025564,FALSE,9.5,0.143051518,0,0.055525823,3,0.667819001,2347,0.267758247,0.283538647
14442,Missense variants in ACE2 are predicted to encourage and inhibit interaction with SARS-CoV-2 Spike and contribute to genetic risk in COVID-19,0,10.1101/2020.05.03.074781,5/4/20,biorxiv,0,2,in silico,0.457715436,0.36416962,0.035123782,0.036333485,0.030346778,0.0763109,Drug discovery,0.32689908,FALSE,104.5,0.878471148,839,0.991102489,11,0.840175319,5137,0.630628461,0.835094354
14443,The UCSC SARS-CoV-2 Genome Browser,0,10.1101/2020.05.04.075945,5/4/20,biorxiv,10.1038/s41588-020-0700-8,18,"sequencing, genomes",0.119467243,0.73843866,0.021374478,0.118764687,0.000977477,0.000977455,Genomics,0.41062188,FALSE,70.05555556,0.764302059,1319.5,0.996186781,25,0.918019631,6423,0.705514086,0.846005639
14444,Complexity in SARS-CoV-2 genome data: Price theory of mutant isolates,0,10.1101/2020.05.04.077511,5/4/20,biorxiv,0,5,genomes,0.1250782,0.646855995,0.002130708,0.221673272,0.00213115,0.002130674,Genomics,0.42898005,FALSE,15.8,0.238109964,3.4,0.208589778,2,0.618927094,1987,0.18372261,0.312337361
14445,Computational methods to develop potential neutralizing antibody Fab region against SARS-CoV-2 as therapeutic and diagnostic tool,0,10.1101/2020.05.02.071506,5/4/20,biorxiv,0,2,"computational, whole genome",0.464401179,0.530357328,0.001310386,0.001310366,0.001310334,0.001310407,Genomics,0.1430066,FALSE,15,0.227596017,3.5,0.213607172,0,0.403234768,1749,0.129785697,0.243555913
14446,Computational analysis on the ACE2-derived peptides for neutralizing the ACE2 binding to the spike protein of SARS-CoV-2,0,10.1101/2020.05.03.075473,5/4/20,biorxiv,0,5,"molecular dynamics simulation, computational",0.99028278,0.001943468,0.001943435,0.001943467,0.001943425,0.001943424,Drug discovery,0.5538412,TRUE,23.8,0.351227658,13.4,0.404602622,3,0.667819001,2928,0.384059716,0.451927249
14447,Estimation of SARS-CoV-2 emissions from non-symptomatic cases,0,10.1101/2020.04.27.20081398,5/3/20,medrxiv,10.1001/jamanetworkopen.2020.13807,2,mathematical model,0.001291285,0.224527981,0.001291275,0.591377862,0.001291258,0.180220338,Epidemiology,0.10127035,FALSE,3.5,0.044344115,0.5,0.087101953,2,0.618927094,10732,0.823501084,0.393468562
14448,Brazilian Modeling of COVID-19 (BRAM-COD): a Bayesian Monte Carlo approach for COVID-19 spread in a limited data set context,0,10.1101/2020.04.29.20081174,5/3/20,medrxiv,0,6,bayes,0.000846516,0.000846531,0.000846537,0.910337577,0.000846535,0.086276304,Epidemiology,0.15922466,FALSE,16.83333333,0.253509803,21.33333333,0.495450896,12,0.850299401,29268,0.94269203,0.635488033
14449,Is this beginning or the end of COVID-19 battle in India? A data-driven mathematical model-based analysis of outbreak,0,10.1101/2020.04.27.20081422,5/3/20,medrxiv,0,3,mathematical model,0.002238471,0.002238516,0.002238486,0.988807522,0.002238542,0.002238464,Epidemiology,0.30147892,FALSE,20,0.298163152,1,0.122023013,1,0.537564047,2963,0.389357091,0.336776826
14450,Distinguishing L and H phenotypes of COVID-19 using a single x-ray image,0,10.1101/2020.04.27.20081984,5/3/20,medrxiv,0,2,machine learning,0.001684642,0.001684569,0.448240447,0.001684579,0.001684532,0.545021232,Clinics,0.4359443,FALSE,93.5,0.85317583,22,0.503746321,2,0.618927094,7859,0.761858897,0.684427036
14451,Genome-wide variations of SARS-CoV-2 infer evolution relationship and transmission route,0,10.1101/2020.04.27.20081349,5/3/20,medrxiv,0,7,genome-wide,0.002422278,0.171137813,0.002422296,0.819172926,0.002422419,0.002422267,Epidemiology,0.36926714,FALSE,27.33333333,0.399529965,149.6666667,0.883261975,5,0.739490092,4207,0.547074404,0.642339109
14452,Large scale genomic analysis of 3067 SARS-CoV-2 genomes reveals a clonal geodistribution and a rich genetic variations of hotspots mutations,0,10.1101/2020.05.03.074567,5/3/20,biorxiv,10.3390/pathogens9100829,19,"whole-genome, genomes",0.035790601,0.958724251,0.001371253,0.001371327,0.001371294,0.001371274,Genomics,0.7559694,TRUE,18.68421053,0.279856516,3.842105263,0.22217019,38,0.944132354,18342,0.902961714,0.587280193
14453,Mental Health Status of the General Population during the COVID-19 Pandemic: A Cross-sectional National Survey in Japan,0,10.1101/2020.04.28.20082453,5/3/20,medrxiv,0,4,logistic regression,0.001943462,0.001943492,0.001943457,0.001943501,0.990282603,0.001943484,Healthcare,0.66480696,TRUE,10,0.15214299,3.5,0.213607172,15,0.874313229,3250,0.434625572,0.418672241
14454,The Impact of Social Distancing on COVID19 Spread: State of Georgia Case Study,0,10.1101/2020.04.29.20084764,5/3/20,medrxiv,10.1371/journal.pone.0239798,5,simulation model,0.000652899,0.000652894,0.0006529,0.968234399,0.000652919,0.029153989,Epidemiology,0.1984705,FALSE,4.8,0.065248315,3.8,0.221501204,13,0.858880178,4965,0.615458705,0.4402721
14455,"Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK",0,10.1101/2020.04.28.20082222,5/3/20,medrxiv,0,15,"logistic regression, prediction model, dataset",0.000385533,0.00038553,0.157274041,0.045703349,0.071674073,0.724577475,Clinics,0.14290237,FALSE,22.53333333,0.333663183,3.666666667,0.217621086,20,0.900117291,2779,0.355164941,0.451641625
14456,Predicting community mortality risk due to CoVID-19 using machine learning and development of a prediction tool,0,10.1101/2020.04.27.20081794,5/3/20,medrxiv,10.7717/peerj.10083,3,"machine learning, logistic regression",0.001254587,0.001254625,0.711526155,0.137853767,0.001254684,0.146856182,Clinics,0.63037,TRUE,5.333333333,0.074339786,0.666666667,0.096200161,6,0.764429903,2100,0.207560799,0.285632662
14457,Clinical classifiers of COVID-19 infection from novel ultra-high-throughput proteomics,0,10.1101/2020.04.27.20081810,5/3/20,medrxiv,10.1016/j.cels.2020.05.012,32,"classifier, proteom",0.288225221,0.162414489,0.378542224,0.001622816,0.001622754,0.167572496,Drug discovery,0.80673516,TRUE,80.90625,0.810687117,109.25,0.837904736,7,0.785110192,10295,0.8160366,0.812434661
14458,Failure of the cobas(R) SARS-CoV-2 (Roche) E-gene assay is associated with a C-to-T transition at position 26340 of the SARS-CoV-2 genome,0,10.1101/2020.04.28.20083337,5/3/20,medrxiv,10.1128/JCM.01598-20,11,"sequencing, whole genome, genomes",0.001593519,0.952731612,0.001593552,0.001593524,0.040894226,0.001593567,Genomics,0.6772922,TRUE,18.2,0.273053374,7.2,0.302047097,7,0.785110192,5270,0.638574524,0.499696297
14459,Ocular toxicity and Hydroxychloroquine: A Rapid Meta-Analysis,0,10.1101/2020.04.28.20083378,5/3/20,medrxiv,0,6,artificial intelligence,0.230137864,0.002422309,0.17098439,0.545126161,0.0489068,0.002422476,Epidemiology,0.35134795,FALSE,15.5,0.234028078,6.5,0.288132192,0,0.403234768,1609,0.095593547,0.255247146
14460,"Modeling and Dynamics in Epidemiology, COVID19 withLockdown and Isolation Effect : Application to Moroccan Case",0,10.1101/2020.04.29.20084871,5/2/20,medrxiv,0,1,mathematical model,0.003465909,0.003465884,0.003465887,0.982670581,0.003465905,0.003465834,Epidemiology,0.4391474,FALSE,40,0.539860226,9,0.337904736,0,0.403234768,851,0.001203949,0.32055092
14461,Using Supervised Machine Learning and Empirical Bayesian Kriging to reveal Correlates and Patterns of COVID-19 Disease outbreak in sub-Saharan Africa: Exploratory Data Analysis,0,10.1101/2020.04.27.20082057,5/2/20,medrxiv,0,10,"bayes, machine learning, predictive model",0.001156265,0.001156286,0.176948115,0.690216285,0.129366731,0.001156318,Epidemiology,0.27052847,FALSE,20.2,0.300327788,15.9,0.434506288,6,0.764429903,1963,0.175054178,0.418579539
14462,Ecologic correlation between underlying population level morbidities and COVID-19 case fatality rate among countries infected with SARS-CoV-2,0,10.1101/2020.04.28.20082370,5/2/20,medrxiv,0,6,dataset,0.00107218,0.001072193,0.001072189,0.38462276,0.513482293,0.098678385,Healthcare,0.264022,FALSE,8.5,0.126662131,8,0.320511105,1,0.537564047,1710,0.118709367,0.275861663
14463,Functional pathways in respiratory tract microbiome separate COVID-19 from community-acquired pneumonia patients,0,10.1101/2020.05.01.073171,5/2/20,biorxiv,10.1038/s41598-021-85750-0,4,"sequencing, microbiom",0.542709149,0.308223023,0.001291338,0.001291258,0.001291223,0.145194011,Drug discovery,0.70686054,TRUE,21,0.312016822,5,0.257024351,3,0.667819001,5351,0.644835059,0.470423808
14464,Release of potential pro-inflammatory peptides from SARS-CoV-2 spike glycoproteins in neutrophil-extracellular traps,0,10.1101/2020.05.02.072439,5/2/20,biorxiv,0,2,in silico,0.840310216,0.00165306,0.001653045,0.001653074,0.001653055,0.153077549,Drug discovery,0.28857273,FALSE,10.5,0.157338116,7.5,0.307867273,2,0.618927094,1891,0.159884421,0.311004226
14465,An artificial intelligence system reveals liquiritin inhibits SARS-CoV-2 by mimicking type I interferon,0,10.1101/2020.05.02.074021,5/2/20,biorxiv,0,9,artificial intelligence,0.805223847,0.036839968,0.001461878,0.001461928,0.001461915,0.153550464,Drug discovery,0.55924904,TRUE,58,0.689714887,51.77777778,0.688252609,8,0.799987654,6759,0.717553576,0.723877181
14466,"Structural and Functional Implications of Non-synonymous Mutations in the Spike protein of 2,954 SARS-CoV-2 Genomes",0,10.1101/2020.05.02.071811,5/2/20,biorxiv,0,14,"bioinformatic, in silico, genomes",0.755162621,0.242017714,0.000704921,0.000704926,0.000704906,0.000704913,Drug discovery,0.29349178,FALSE,10.35714286,0.155111633,11.64285714,0.379783249,2,0.618927094,4536,0.578858656,0.433170158
14467,CoV-Seq: SARS-CoV-2 Genome Analysis and Visualization,0,10.1101/2020.05.01.071050,5/2/20,biorxiv,10.2196/22299,6,"bioinformatic, genomes, dataset",0.00242237,0.773448643,0.216861687,0.002422471,0.002422368,0.002422461,Genomics,0.2682646,FALSE,38.16666667,0.520378502,3.666666667,0.217621086,5,0.739490092,3562,0.468576932,0.486516653
14468,Coding variants in ACE2 and TMPRSS2 are not major drivers of COVID-19 severity in UK Biobank subjects.,0,10.1101/2020.05.01.20085860,5/2/20,medrxiv,10.1159/000515200,1,exom,0.00203287,0.659529332,0.002032838,0.00203288,0.002032898,0.332339183,Genomics,0.6180952,TRUE,11,0.167171748,0,0.055525823,4,0.707574542,3382,0.449554539,0.344956663
14469,"CovidCounties - an interactive, real-time tracker of the COVID-19 pandemic at the level of US counties",0,10.1101/2020.04.28.20083279,5/2/20,medrxiv,0,9,dataset,0.002562905,0.002562797,0.118988949,0.87076005,0.002562658,0.00256264,Epidemiology,0.06702551,FALSE,31.66666667,0.451976003,52.66666667,0.691798234,2,0.618927094,2134,0.214062124,0.494190864
14470,Identification of Three Endotypes in Pediatric Acute Respiratory Distress Syndrome by Nasal Transcriptomic Profiling,0,10.1101/2020.04.28.20083451,5/2/20,medrxiv,0,11,transcriptom,0.254603123,0.225248402,0.089072315,0.00088914,0.000889075,0.429297946,Clinics,0.30277956,FALSE,21.66666667,0.320737213,7,0.299973241,0,0.403234768,1235,0.030821093,0.263691579
14471,"COVID-19 in healthcare workers in three hospitals in the South of the Netherlands, March 2020",0,10.1101/2020.04.26.20079418,5/1/20,medrxiv,0,28,"sequencing, whole genome, genome sequences",0.001538121,0.623182246,0.001538121,0.001538172,0.202725269,0.169478071,Genomics,0.5923236,TRUE,80.78571429,0.80988311,590.1785714,0.983342253,7,0.785110192,2926,0.37900313,0.739334671
14472,MODELLING OF COVID-19 OUTBREAK INDICATORS IN CHINA BETWEEN JANUARY AND APRIL,0,10.1101/2020.04.26.20080465,5/1/20,medrxiv,10.1017/dmp.2020.323,3,"bayes, mathematical model",0.001684496,0.001684486,0.001684518,0.991577468,0.001684501,0.001684533,Epidemiology,0.6038958,TRUE,93.33333333,0.852433669,19.33333333,0.473842655,1,0.537564047,888,0.001926318,0.466441672
14473,Correlation analysis of risk factors and GSI score of a medical team assisting Wuhan city during the epidemic of COVID-19 in China -A cohort study,0,10.1101/2020.04.27.20070466,5/1/20,medrxiv,0,9,correlation analysis,0.00075936,0.000759356,0.000759361,0.00075938,0.996203146,0.000759397,Healthcare,0.11481771,FALSE,13.71428571,0.207619519,0.428571429,0.076665775,0,0.403234768,1094,0.013002649,0.175130677
14474,Effectiveness of the strategies implemented in SriLanka for controlling the COVID-19 outbreak,0,10.1101/2020.04.27.20082479,5/1/20,medrxiv,10.1155/2020/2954519,4,network model,0.003760488,0.00376051,0.003760604,0.981197282,0.003760594,0.003760522,Epidemiology,0.28346702,FALSE,10.75,0.160244913,0.75,0.099411292,1,0.537564047,2719,0.339754394,0.284243662
14475,Mathematical Model to Study Early COVID-19 Transmission Dynamics in Sri Lanka,0,10.1101/2020.04.27.20082537,5/1/20,medrxiv,0,4,"mathematical model, dataset",0.00129124,0.001291229,0.04739974,0.947435306,0.00129125,0.001291236,Epidemiology,0.26698363,FALSE,13.25,0.199950523,1.75,0.148381054,2,0.618927094,1196,0.025282928,0.2481354
14476,"High rate of increased level of plasma Angiotensin II and its gender difference in COVID-19: an analysis of 55 hospitalized patients with COVID-19 in a single hospital, WuHan, China",0,10.1101/2020.04.27.20080432,5/1/20,medrxiv,0,4,logistic regression,0.337137884,0.001112676,0.001112681,0.026035001,0.001112685,0.633489073,Clinics,0.21751109,FALSE,17.25,0.260189251,51,0.685509767,10,0.828199272,1628,0.097038286,0.467734144
14477,Fitting SIR model to COVID-19 pandemic data and comparative forecasting with machine learning,0,10.1101/2020.04.26.20081042,5/1/20,medrxiv,0,1,"model fit, machine learning",0.006539687,0.006539731,0.164167197,0.809674226,0.006539607,0.006539553,Epidemiology,0.16836494,FALSE,8,0.118683901,0,0.055525823,10,0.828199272,1820,0.141343607,0.285938151
14478,Risk assessment via layered mobile contact tracing for epidemiological intervention,0,10.1101/2020.04.26.20080648,5/1/20,medrxiv,0,3,probabilistic,0.001254662,0.001254675,0.001254693,0.913664197,0.048154321,0.034417453,Epidemiology,0.08000982,FALSE,21,0.312016822,29.66666667,0.567099277,5,0.739490092,1250,0.032506622,0.412778203
14479,Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques,0,10.1101/2020.04.26.20080655,5/1/20,medrxiv,0,4,machine learning,0.001310333,0.001310354,0.20382548,0.674489111,0.117754315,0.001310407,Epidemiology,0.4561318,FALSE,28,0.408312202,3,0.199424672,4,0.707574542,990,0.005538165,0.330212395
14480,Spatial-temporal variations of atmospheric factors contribute to SARS-CoV-2 outbreak,0,10.1101/2020.04.26.20080846,5/1/20,medrxiv,10.3390/v12060588,4,"neural network, classifier",0.001653045,0.001653174,0.156395294,0.705229733,0.001653158,0.133415595,Epidemiology,0.82072735,TRUE,59,0.696579875,31.25,0.579542414,11,0.840175319,1674,0.106910667,0.555802069
14481,"Molecular Architecture of Early Dissemination and Evolution of the SARS-CoV-2 Virus in Metropolitan Houston, Texas",0,10.1101/2020.05.01.072652,5/1/20,biorxiv,10.1128/mbio.02707-20,17,genomes,0.001220087,0.851084358,0.001220056,0.001220071,0.06681957,0.078435857,Genomics,0.36816603,FALSE,68.23529412,0.754839508,153.7058824,0.886406208,11,0.840175319,5052,0.620033711,0.775363686
14482,COVID-19 mathematical model reopening scenarios for Sao Paulo - Brazil,0,10.1101/2020.04.26.20081208,5/1/20,medrxiv,0,11,mathematical model,0.002422273,0.031823382,0.002422355,0.958487317,0.002422419,0.002422254,Epidemiology,0.31760794,FALSE,11.09090909,0.167357289,1.272727273,0.12784319,4,0.707574542,1697,0.112689622,0.27886616
14483,Electrostatic Characteristics of SARS-CoV-2 Spike and Human ACE2 Protein Variations Predict Mutable Binding Efficacy,0,10.1101/2020.04.30.071175,5/1/20,biorxiv,0,2,computational,0.645649076,0.347612866,0.001684504,0.001684543,0.001684529,0.001684483,Drug discovery,0.12101507,FALSE,22.5,0.333539489,14,0.412898047,0,0.403234768,2513,0.296653022,0.361581331
14484,Dysregulation in mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells,0,10.1101/2020.04.30.070383,5/1/20,biorxiv,10.1080/22221751.2020.1799723,14,transcriptom,0.925007221,0.00235783,0.002357775,0.065561376,0.002357838,0.00235796,Drug discovery,0.64336365,TRUE,70.78571429,0.76733255,221.2857143,0.92687985,7,0.785110192,3026,0.399470262,0.719698214
14485,SARS-CoV-2 genomes recovered by long amplicon tiling multiplex approach using nanopore sequencing and applicable to other sequencing platforms,0,10.1101/2020.04.30.069039,5/1/20,biorxiv,0,16,"sequencing, genomes",0.003335312,0.983322839,0.003335543,0.00333549,0.003335485,0.00333533,Genomics,0.15918958,FALSE,26.9375,0.39334529,20,0.481000803,16,0.881782826,5680,0.664579822,0.605177185
14486,On spatial molecular arrangements of SARS-CoV2 genomes of Indian patients,0,10.1101/2020.05.01.071985,5/1/20,biorxiv,0,6,genomes,0.001901765,0.9904911,0.001901833,0.001901828,0.001901785,0.001901689,Genomics,0.37505913,FALSE,40.33333333,0.542890717,14.33333333,0.415975381,4,0.707574542,1776,0.132193595,0.449658559
14487,Whole Genome Comparison of Pakistani Corona Virus with Chinese and US Strains along with its Predictive Severity of COVID-19,0,10.1101/2020.05.01.072942,5/1/20,biorxiv,0,5,"whole genome, genome sequences",0.178710321,0.702355099,0.001415118,0.082236961,0.001415106,0.033867395,Genomics,0.5124706,TRUE,11,0.167171748,0,0.055525823,0,0.403234768,3612,0.474115098,0.275011859
14488,Rampant C->U hypermutation in the genomes of SARS-CoV-2 and other coronaviruses - causes and consequences for their short and long evolutionary trajectories,0,10.1101/2020.05.01.072330,5/1/20,biorxiv,10.1128/msphere.00408-20,1,"genomes, dataset",0.001220051,0.993899616,0.001220088,0.001220111,0.001220044,0.001220091,Genomics,0.48069337,FALSE,11,0.167171748,7,0.299973241,26,0.920859312,3748,0.492174332,0.470044658
14489,Tuberculosis and COVID-19 in 2020: lessons from the past viral outbreaks and possible future outcomes,0,10.1101/2020.04.28.20082917,5/1/20,medrxiv,10.1155/2020/1401053,7,"data mining, dataset",0.037920278,0.291231364,0.106508864,0.561366389,0.001486471,0.001486635,Epidemiology,0.7986691,TRUE,14.28571429,0.215783289,1.142857143,0.123628579,5,0.739490092,2873,0.368889959,0.36194798
14490,Curbing the AI-induced enthusiasm in diagnosing COVID-19 on chest X-Rays: the present and the near-future,0,10.1101/2020.04.28.20082776,5/1/20,medrxiv,0,8,"machine learning, deep learning, artificial intelligence, neural network, image processing, dataset",0.000907311,0.0009073,0.771424787,0.224945999,0.000907317,0.000907286,Imaging,0.9103912,TRUE,16.625,0.250231925,0.875,0.103826599,8,0.799987654,1458,0.064531664,0.30464446
14491,Comparison of the Clinical Implications among Two Different Nutritional Indices in Hospitalized Patients with COVID-19,0,10.1101/2020.04.28.20082644,5/1/20,medrxiv,0,10,logistic regression,0.001203395,0.001203413,0.001203443,0.00120344,0.00120344,0.99398287,Clinics,0.4786904,FALSE,21.6,0.319438432,7.4,0.305392026,4,0.707574542,1415,0.056344811,0.347187452
14492,"Nonpharmaceutical interventions for pandemic COVID-19: A cross-sectional investigation of US general public beliefs, attitudes, and actions",0,10.1101/2020.04.26.20078618,5/1/20,medrxiv,10.3389/fmed.2020.00384,2,logistic regression,0.001684622,0.001684524,0.030408064,0.120131433,0.844406853,0.001684504,Healthcare,0.78823906,TRUE,4.5,0.061784897,0,0.055525823,5,0.739490092,1142,0.017096075,0.218474222
14493,Genetic structure of SARS-CoV-2 in Western Germany reflects clonal superspreading and multiple independent introduction events,0,10.1101/2020.04.25.20079517,4/30/20,medrxiv,0,17,whole-genome,0.003214238,0.983928955,0.003214131,0.003214463,0.003214134,0.003214079,Genomics,0.25258744,FALSE,10.11764706,0.15251407,11.76470588,0.381924003,2,0.618927094,3506,0.460871659,0.403559207
14494,"Evaluation of ""stratify and shield"" as a policy option for ending the COVID-19 lockdown in the UK",0,10.1101/2020.04.25.20079913,4/30/20,medrxiv,0,2,classifier,0.000977452,0.000977468,0.144357442,0.668027395,0.000977521,0.184682721,Epidemiology,0.07087594,FALSE,25.5,0.37435834,33,0.593256623,12,0.850299401,7916,0.761377318,0.64482292
14495,Empowering Virus Sequences Research through Conceptual Modeling,0,10.1101/2020.04.29.067637,4/30/20,biorxiv,0,4,"sequencing, genomes",0.001823482,0.754157039,0.00182344,0.204040539,0.001823521,0.03633198,Genomics,0.36536276,FALSE,49.5,0.628362917,26,0.53819909,5,0.739490092,2104,0.204189742,0.52756046
14496,Spike mutation pipeline reveals the emergence of a more transmissible form of SARS-CoV-2,0,10.1101/2020.04.29.069054,4/30/20,biorxiv,10.1016/j.cell.2020.06.043,16,structural model,0.144031241,0.78523151,0.001823335,0.065267236,0.001823359,0.001823318,Genomics,0.37325546,FALSE,12.625,0.190426124,40,0.633395772,321,0.995678746,516788,0.99951842,0.704754766
14497,Origin of imported SARS-CoV-2 strains in The Gambia identified from Whole Genome Sequences.,0,10.1101/2020.04.30.070771,4/30/20,biorxiv,0,17,"sequencing, whole genome, genome sequences, genomes",0.001861824,0.990691372,0.001861739,0.001861718,0.001861668,0.00186168,Genomics,0.4345442,FALSE,61.33333333,0.710680933,97,0.816697886,1,0.537564047,2166,0.21839634,0.570834801
14498,"ACE2 and TMPRSS2 expression by clinical, HLA, immune, and microbial correlates across 34 human cancers and matched normal tissues: implications for SARS-COV-2 COVID-19",0,10.1101/2020.04.29.20082867,4/30/20,medrxiv,10.1136/jitc-2020-001020,4,"sequencing, microbiom",0.648037129,0.123598562,0.00080789,0.000807902,0.000807912,0.225940606,Drug discovery,0.49488497,FALSE,3,0.037293586,0,0.055525823,4,0.707574542,2502,0.292078016,0.273117992
14499,"Assessment of the outbreak risk, mapping and infestation behavior of COVID-19: Application of the autoregressive and moving average (ARMA) and polynomial models",0,10.1101/2020.04.28.20083998,4/30/20,medrxiv,10.1371/journal.pone.0236238,7,machine learning,0.000966814,0.000966806,0.20919301,0.786939794,0.000966777,0.000966799,Epidemiology,0.8478349,TRUE,29.71428571,0.427979467,,,2,0.618927094,1474,0.066457982,0.371121514
14500,COVID-19 in Africa -- outbreak despite interventions?,0,10.1101/2020.04.24.20077891,4/29/20,medrxiv,0,7,"computational, predictive model",0.000521974,0.000521972,0.000521971,0.952743521,0.01290105,0.032789512,Epidemiology,0.06401029,FALSE,4.285714286,0.056837158,1.571428571,0.139416644,3,0.667819001,2137,0.210209487,0.268570573
14501,Countries are Clustered but Number of Tests is not Vital to Predict Global COVID-19 Confirmed Cases: A Machine Learning Approach,0,10.1101/2020.04.24.20078238,4/29/20,medrxiv,10.3389/frai.2020.561801,2,machine learning,0.001310364,0.055291816,0.001310443,0.88629224,0.00131038,0.054484756,Epidemiology,0.20159924,FALSE,7.5,0.108355495,4,0.231469093,9,0.814309525,1557,0.08042379,0.308639476
14502,COVID-19 data analysis and modeling in Palestine,0,10.1101/2020.04.24.20078279,4/29/20,medrxiv,0,1,dataset,0.002806379,0.002806393,0.002806373,0.985967917,0.002806399,0.002806538,Epidemiology,0.23112601,FALSE,8,0.118683901,0,0.055525823,2,0.618927094,1870,0.151456778,0.236148399
14503,In silico analysis of RT-qPCR designs recommended by WHO for detection of SARS-CoV-2 and a commercial kit validated following UNE/EN ISO 17025:2005 and two reference laboratories,0,10.1101/2020.04.27.065383,4/29/20,biorxiv,10.1111/jam.14781,6,in silico,0.001593625,0.300767556,0.481755222,0.143323398,0.001593536,0.070966665,Genomics,0.5355972,TRUE,21.16666667,0.31319191,40.83333333,0.638546963,0,0.403234768,2802,0.353720202,0.427173461
14504,"Estimating COVID-19 Antibody Seroprevalence in Santa Clara County, California. A re-analysis of Bendavid et al.",0,10.1101/2020.04.24.20078824,4/29/20,medrxiv,0,2,"bayes, bayesian model",0.007061463,0.007061603,0.182217638,0.789535703,0.007061862,0.007061731,Epidemiology,0.24202496,FALSE,102.5,0.87445111,722.5,0.987891357,23,0.91129082,4104,0.528052011,0.825421324
14505,Automated Diagnosis of COVID-19 Using Deep Learning and Data Augmentation on Chest CT,0,10.1101/2020.04.24.20078998,4/29/20,medrxiv,0,6,"deep learning, dataset",0.001085339,0.001085358,0.942013081,0.018711194,0.036019631,0.001085397,Imaging,0.12486258,FALSE,3.833333333,0.047745686,0.166666667,0.058736955,8,0.799987654,2160,0.215506863,0.280494289
14506,EXPLAINABLE-BY-DESIGN APPROACH FOR COVID-19 CLASSIFICATION VIA CT-SCAN,0,10.1101/2020.04.24.20078584,4/29/20,medrxiv,0,5,"deep learning, dataset",0.027078836,0.00186181,0.818163081,0.149172669,0.001861721,0.001861883,Imaging,0.36602834,FALSE,0,0.006432061,,,42,0.949503056,10053,0.808572117,0.588169078
14507,Cell-based therapies for COVID-19: A living systematic review,0,10.1101/2020.04.24.20078667,4/29/20,medrxiv,10.5867/medwave.2020.11.8078,3,artificial intelligence,0.235011445,0.000759385,0.000759426,0.615604205,0.000759418,0.147106121,Epidemiology,0.27219802,FALSE,8.333333333,0.123693488,0.666666667,0.096200161,0,0.403234768,1729,0.119913316,0.185760433
14508,Reconstructed diagnostic sensitivity and specificity of the RT-PCR test for COVID-19,0,10.1101/2020.04.24.20078949,4/29/20,medrxiv,0,1,bayes,0.001684595,0.199506759,0.630718103,0.164721369,0.001684564,0.001684611,Imaging,0.56265163,TRUE,71,0.769064259,38,0.622223709,12,0.850299401,13075,0.852636648,0.773556004
14509,Mathematical Analysis of a COVID-19 Epidemic Model by using Data Driven Epidemiological Parameters of Diseases Spread in India,0,10.1101/2020.04.25.20079111,4/29/20,medrxiv,0,4,mathematical model,0.002238483,0.002238528,0.002238498,0.98880747,0.002238611,0.002238411,Epidemiology,0.11215547,FALSE,22.25,0.328344363,9.25,0.340379984,8,0.799987654,2330,0.256922706,0.431408677
14510,Intra-host site-specific polymorphisms of SARS-CoV-2 is consistent across multiple samples and methodologies,0,10.1101/2020.04.24.20078691,4/29/20,medrxiv,0,7,sequencing,0.000977462,0.959281695,0.015894959,0.000977467,0.00097747,0.021890947,Genomics,0.29559124,FALSE,19.28571429,0.288020286,,,16,0.881782826,1698,0.110281724,0.426694945
14511,A fractional-order SEIHDR model for COVID-19 with inter-city networked coupling effects,0,10.1101/2020.04.25.20079806,4/29/20,medrxiv,10.1007/s11071-020-05848-4,7,mathematical model,0.001187293,0.001187288,0.001187318,0.931456872,0.001187273,0.063793957,Epidemiology,0.53393704,TRUE,7.857142857,0.113921702,2.142857143,0.165640888,20,0.900117291,1688,0.108114616,0.321948624
14512,COVID-19 Outcomes in Saudi Arabia and the UK: A Tale of Two Kingdoms,0,10.1101/2020.04.25.20079640,4/29/20,medrxiv,0,7,mathematical model,0.001565302,0.001565316,0.001565311,0.992173404,0.001565325,0.001565343,Epidemiology,0.34529418,FALSE,3.142857143,0.037912054,0.142857143,0.057398983,3,0.667819001,3982,0.514808572,0.319484653
14513,Integrated analysis of bulk multi omic and single-cell sequencing data confirms the molecular origin of hemodynamic changes in Covid-19 infection explaining coagulopathy and higher geriatric mortality,0,10.1101/2020.04.26.20081182,4/29/20,medrxiv,0,3,"sequencing, interactom",0.63751299,0.076329014,0.001085329,0.001085343,0.001085379,0.282901944,Drug discovery,0.3118527,FALSE,2,0.022141134,0.333333333,0.073187048,1,0.537564047,3266,0.432699254,0.266397871
14514,A gene locus that controls expression of ACE2 in virus infection,0,10.1101/2020.04.26.20080408,4/29/20,medrxiv,0,24,genome-wide,0.625198638,0.20679664,0.002032757,0.002032837,0.002032827,0.161906302,Drug discovery,0.43893406,FALSE,30.95652174,0.442389758,72.52173913,0.760503077,4,0.707574542,5420,0.646279798,0.639186794
14515,"Controlling the SARS-CoV-2 outbreak, insights from large scale whole genome sequences generated across the world",0,10.1101/2020.04.28.066977,4/29/20,biorxiv,0,6,"sequencing, whole genome, genome sequences, genomes",0.09099918,0.827513291,0.02048685,0.058747051,0.001126819,0.00112681,Genomics,0.1976654,FALSE,119.6666667,0.906796957,266,0.94434038,22,0.908142478,16973,0.889236696,0.912129128
14516,Noncanonical junctions in subgenomic RNAs of SARS-CoV-2 lead to variant open reading frames.,0,10.1101/2020.04.28.066951,4/29/20,biorxiv,10.1186/s13073-020-00802-w,3,"sequencing, transcriptom",0.114159867,0.881153852,0.001171553,0.001171597,0.001171568,0.001171564,Genomics,0.1531809,FALSE,76.33333333,0.791576473,730,0.98822585,5,0.739490092,4207,0.5400915,0.764845979
14517,"Analyses of spike protein from first deposited sequences of SARS-CoV2 from West Bengal, India",0,10.1101/2020.04.28.066985,4/29/20,biorxiv,10.12688/f1000research.23805.1,7,sequencing,0.310542201,0.684643932,0.001203501,0.001203517,0.001203422,0.001203427,Genomics,0.26193815,FALSE,15.42857143,0.231801596,4.571428571,0.24337704,7,0.785110192,2648,0.322658319,0.395736787
14518,Mass spectrometry analysis of newly emerging coronavirus HCoV-19 spike S protein and human ACE2 reveals camouflaging glycans and unique post-translational modifications,0,10.1101/2020.04.29.068098,4/29/20,biorxiv,10.1016/j.eng.2020.07.014,9,structural model,0.989596604,0.00208065,0.002080655,0.002080658,0.002080708,0.002080725,Drug discovery,0.61330116,TRUE,91.22222222,0.846743769,86.77777778,0.794688253,6,0.764429903,4107,0.52901517,0.733719274
14519,"Genomics of Indian SARS-CoV-2: Implications in genetic diversity, possible origin and spread of virus",0,10.1101/2020.04.25.20079475,4/29/20,medrxiv,0,3,"genome sequences, genomes",0.000889057,0.995554728,0.000889044,0.000889067,0.00088905,0.000889053,Genomics,0.23053315,FALSE,2,0.022141134,0,0.055525823,15,0.874313229,2739,0.340717554,0.323174435
14520,Strong effect of socioeconomic levels on the spread and treatment of the 2019 novel coronavirus (COVID-19) in China,0,10.1101/2020.04.25.20079400,4/29/20,medrxiv,0,3,mathematical model,0.000541109,0.000541128,0.000541163,0.953449709,0.000541132,0.044385758,Epidemiology,0.22513169,FALSE,6.5,0.093512277,1,0.122023013,3,0.667819001,1628,0.095834337,0.244797157
14521,"Key predictors of attending hospital with COVID19: An association study from the COVID Symptom Tracker App in 2,618,948 individuals",0,10.1101/2020.04.25.20079251,4/29/20,medrxiv,0,25,logistic regression,0.001126863,0.001126798,0.001126861,0.142658354,0.364000582,0.489960542,Clinics,0.2740208,FALSE,23.92,0.352526439,19,0.471367407,16,0.881782826,11498,0.831447147,0.634280955
14522,COVID-19 Growth Rate Decreases with Social Capital,0,10.1101/2020.04.23.20077321,4/29/20,medrxiv,0,2,correlation analysis,0.000926274,0.000926284,0.00092627,0.938424342,0.000926305,0.057870525,Epidemiology,0.24060199,FALSE,18.5,0.278001113,14,0.412898047,7,0.785110192,2811,0.354924151,0.457733376
14523,Epidemiological and clinical characteristics of the early phase of the COVID-19 epidemic in Brazil,0,10.1101/2020.04.25.20077396,4/29/20,medrxiv,0,38,logistic regression,0.001461906,0.277604004,0.00146191,0.180938598,0.239698545,0.298835036,Clinics,0.51701224,TRUE,19.55263158,0.291298163,21.73684211,0.499866203,18,0.891474782,5539,0.653744281,0.584095857
14524,Hypertension and Renin-Angiotensin-Aldosterone System Inhibitors in Patients with Covid-19,0,10.1101/2020.04.24.20077388,4/29/20,medrxiv,0,7,logistic regression,0.290184357,0.001593521,0.001593532,0.001593547,0.092627797,0.612407246,Clinics,0.30613458,FALSE,150.2857143,0.944214237,156,0.888747659,27,0.92443978,3005,0.39128341,0.787171271
14525,A new design of an adaptive model of infectious diseases based on artificial intelligence approach: monitoring and forecasting of COVID-19 epidemic cases,0,10.1101/2020.04.23.20077677,4/29/20,medrxiv,0,4,artificial intelligence,0.001684562,0.028466539,0.001684656,0.964795259,0.001684485,0.001684499,Epidemiology,0.12712276,FALSE,16,0.243552477,0,0.055525823,3,0.667819001,1220,0.027209246,0.248526637
14526,Detection and isolation of asymptomatic individuals can make the difference in COVID-19 epidemic management,0,10.1101/2020.04.23.20077255,4/29/20,medrxiv,10.1186/s12889-020-09843-7,7,mathematical model,0.001415118,0.00141523,0.001415212,0.963553229,0.001415178,0.030786033,Epidemiology,0.14004204,FALSE,46,0.596882924,44.57142857,0.656944073,5,0.739490092,2481,0.28726222,0.570144827
14527,Several countries in one: a mathematical modeling analysis for COVID-19 in inner Brazil,0,10.1101/2020.04.23.20077438,4/29/20,medrxiv,0,4,mathematical model,0.001901722,0.138171157,0.024730419,0.83139318,0.001901774,0.001901747,Epidemiology,0.2840446,FALSE,3.25,0.039148989,0.75,0.099411292,2,0.618927094,2389,0.269684565,0.256792985
14528,A model for 2019-nCoV infection with treatment,0,10.1101/2020.04.24.20077958,4/29/20,medrxiv,10.1155/2020/1352982,2,mathematical model,0.762604998,0.08226581,0.002562604,0.147441369,0.002562657,0.002562562,Drug discovery,0.8244146,TRUE,11.5,0.17416043,1,0.122023013,2,0.618927094,2060,0.191909463,0.276755
14529,Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients,0,10.1101/2020.04.26.20073411,4/28/20,medrxiv,10.2196/24018,49,machine learning,0.001156244,0.001156257,0.294971651,0.168934845,0.00115626,0.532624745,Clinics,0.7475795,TRUE,99.18181818,0.867276888,292.3636364,0.952033717,20,0.900117291,5590,0.656633759,0.844015414
14530,The incubation period of COVID-19: A rapid systematic review and meta-analysis of observational research,0,10.1101/2020.04.24.20073957,4/28/20,medrxiv,10.1136/bmjopen-2020-039652,13,mathematical model,0.000988357,0.000988352,0.000988364,0.977991214,0.018055324,0.000988389,Epidemiology,0.24984547,FALSE,85,0.825035562,114.0714286,0.84385871,72,0.970121612,5779,0.66698772,0.826500901
14531,Racial and Ethnic Disparities in SARS-CoV-2 Pandemic: Analysis of a COVID-19 Observational Registry for a Diverse U.S. Metropolitan Population,0,10.1101/2020.04.24.20073148,4/28/20,medrxiv,10.1136/bmjopen-2020-039849,11,logistic regression,0.044219127,0.000740374,0.000740325,0.09118702,0.625426543,0.237686611,Healthcare,0.5871829,TRUE,53.63636364,0.6581112,29.18181818,0.563620551,16,0.881782826,3852,0.500361185,0.650968941
14532,"Regional differences in reported Covid-19 cases show genetic correlations with higher socio-economic status and better health, potentially confounding studies on the genetics of disease susceptibility",0,10.1101/2020.04.24.20075333,4/28/20,medrxiv,0,1,genome-wide,0.044323041,0.5912315,0.001330044,0.306012642,0.055772689,0.001330084,Genomics,0.08089924,FALSE,17,0.257467994,2,0.164302917,4,0.707574542,4091,0.524440164,0.413446404
14533,Evaluation of WHO listed COVID-19 qPCR primers and probe in silico with 375 SERS-CoV-2 full genome sequences,0,10.1101/2020.04.22.20075697,4/28/20,medrxiv,0,3,"in silico, genome sequences",0.002130728,0.868085189,0.123391992,0.002130718,0.002130726,0.002130647,Genomics,0.34047472,FALSE,20,0.298163152,15,0.42594327,7,0.785110192,2419,0.272574043,0.445447664
14534,COVID-19 spreading: a model,0,10.1101/2020.04.23.20076562,4/28/20,medrxiv,10.2196/21468,1,mathematical model,0.005047663,0.078413146,0.005047543,0.901396667,0.005047535,0.005047447,Epidemiology,0.2644111,FALSE,73,0.778464964,8,0.320511105,0,0.403234768,2212,0.227546352,0.432439297
14535,Pharmacokinetic bases of the hydroxychloroquine response in COVID-19: implications for therapy and prevention,0,10.1101/2020.04.23.20076471,4/28/20,medrxiv,10.1007/s13318-020-00640-6,2,mathematical model,0.628372879,0.067025785,0.001486524,0.300141804,0.001486468,0.001486541,Drug discovery,0.8570559,TRUE,3,0.037293586,0,0.055525823,9,0.814309525,4726,0.592102095,0.374807757
14536,"Performing risk stratification for COVID-19 when individual level data is not available, the experience of a large healthcare organization",0,10.1101/2020.04.23.20076976,4/28/20,medrxiv,10.1038/s41467-020-18297-9,15,prediction model,0.001486456,0.001486417,0.442946995,0.15839437,0.001486494,0.394199268,Clinics,0.38103345,FALSE,15.73333333,0.236996722,50.66666667,0.683569708,8,0.799987654,2661,0.323862268,0.511104088
14537,A simple mathematical model for Coronavirus (COVID-19),0,10.1101/2020.04.23.20076919,4/28/20,medrxiv,0,3,mathematical model,0.004110027,0.047994113,0.187485407,0.752190616,0.004109915,0.004109922,Epidemiology,0.6717126,TRUE,29,0.41993939,0,0.055525823,0,0.403234768,2664,0.324103058,0.30070076
14538,Dataset on the COVID-19 Pandemic Situation in Tunisia with application to SIR Model,0,10.1101/2020.04.23.20076802,4/28/20,medrxiv,0,5,"mathematical model, dataset",0.001291227,0.001291224,0.001291241,0.784150676,0.001291277,0.210684355,Epidemiology,0.15614417,FALSE,28.2,0.409734677,4.2,0.234211935,0,0.403234768,2226,0.230917409,0.319524697
14539,COVID-19 in Iran: A Deeper Look Into The Future,0,10.1101/2020.04.24.20078477,4/27/20,medrxiv,0,9,predictive model,0.001653014,0.001653038,0.001653173,0.991734689,0.001653072,0.001653014,Epidemiology,0.609023,TRUE,14.11111111,0.213618653,4,0.231469093,13,0.858880178,2130,0.206116061,0.377520996
14540,Role of 1'-Ribose Cyano Substitution for Remdesivir to Effectively Inhibit both Nucleotide Addition and Proofreading in SARS-CoV-2 Viral RNA Replication,0,10.1101/2020.04.27.063859,4/27/20,biorxiv,10.1039/d0cp05948j,10,molecular dynamics simulation,0.930995542,0.001461992,0.001461877,0.001461943,0.001461923,0.063156723,Drug discovery,0.48203272,FALSE,55.9,0.673943967,39.3,0.62924806,4,0.707574542,4842,0.599325789,0.652523089
14541,Emergence of multiple variants of SARS-CoV-2 with signature structural changes,0,10.1101/2020.04.26.062471,4/27/20,biorxiv,0,5,"phylogenom, whole genome, genome sequences, genomes, structural model",0.002357928,0.907120487,0.00235785,0.083448121,0.002357808,0.002357806,Genomics,0.65168643,TRUE,3.8,0.047374606,0,0.055525823,8,0.799987654,4401,0.557669155,0.365139309
14542,"Coronavirus, as a source of pandemic pathogens.",0,10.1101/2020.04.26.063032,4/27/20,biorxiv,0,1,sequencing,0.137651036,0.852917674,0.002357766,0.002357807,0.002357921,0.002357795,Genomics,0.41042352,FALSE,2,0.022141134,0,0.055525823,0,0.403234768,3209,0.419696605,0.225149582
14543,Quality control of low-frequency variants in SARS-CoV-2 genomes,0,10.1101/2020.04.26.062422,4/27/20,biorxiv,0,2,genomes,0.002996481,0.985017364,0.0029965,0.002996749,0.002996482,0.002996425,Genomics,0.58483315,TRUE,19,0.285793803,19.5,0.475983409,9,0.814309525,3510,0.458463761,0.508637625
14544,COVID-19 serial interval estimates based on confirmed cases in public reports from 86 Chinese cities,0,10.1101/2020.04.23.20075796,4/27/20,medrxiv,0,6,dataset,0.004775146,0.004775282,0.004775246,0.509604898,0.471294038,0.00477539,Epidemiology,0.43342587,FALSE,61.5,0.712350795,180.6666667,0.90660958,8,0.799987654,2500,0.288706959,0.676913747
14545,Modeling geographical spread of COVID-19 in India using network-based approach,0,10.1101/2020.04.23.20076489,4/27/20,medrxiv,0,1,mathematical model,0.001310415,0.001310413,0.001310371,0.959579769,0.035178647,0.001310384,Epidemiology,0.105323255,FALSE,26,0.382398417,11,0.371287129,7,0.785110192,4735,0.591379725,0.532543866
14546,How fast does the SARS-Cov-2 virus really mutate in heterogeneous populations?,0,10.1101/2020.04.23.20076075,4/27/20,medrxiv,0,4,"computational, bioinformatic, sequencing, genomes",0.000710609,0.636052233,0.092343885,0.20464615,0.03514696,0.031100164,Genomics,0.114738464,FALSE,64,0.729173109,93.75,0.810409419,1,0.537564047,3307,0.434384782,0.627882839
14547,Organising outpatient dialysis services during the COVID-19 pandemic. A simulation and mathematical modelling study.,0,10.1101/2020.04.22.20075457,4/27/20,medrxiv,10.1371/journal.pone.0237628,6,"simulation model, mathematical model",0.000838562,0.000838553,0.000838518,0.523642046,0.068216657,0.405625664,Epidemiology,0.7327131,TRUE,2.333333333,0.024800544,0,0.055525823,0,0.403234768,2080,0.193354202,0.169228834
14548,Research on CNN-based Models Optimized by Genetic Algorithm and Application in the Diagnosis of Pneumonia and COVID-19,0,10.1101/2020.04.21.20072637,4/26/20,medrxiv,0,3,"deep learning, artificial intelligence, neural network, network model",0.002296594,0.034762489,0.956051319,0.002296575,0.002296511,0.002296511,Genomics,0.47497177,FALSE,3,0.037293586,0,0.055525823,2,0.618927094,1998,0.175535757,0.221820565
14549,Forecasting the impact of the first wave of the COVID-19 pandemic on hospital demand and deaths for the USA and European Economic Area countries,0,10.1101/2020.04.21.20074732,4/26/20,medrxiv,0,2,simulation model,0.000580125,0.000580138,0.000580137,0.90209287,0.000580146,0.095586584,Epidemiology,0.34601784,FALSE,132,0.923990352,1658,0.997324057,100,0.978270264,61589,0.97688418,0.969117213
14550,Characterization of intra-host SARS-CoV-2 variants improves phylogenomic reconstruction and may reveal functionally convergent mutations,0,10.1101/2020.04.22.044404,4/26/20,biorxiv,10.1016/j.patter.2021.100212,7,"sequencing, phylogenom, genomes",0.00151187,0.801228118,0.001511973,0.192724421,0.001511819,0.0015118,Genomics,0.38097945,FALSE,71.57142857,0.770981508,66.71428571,0.744848809,3,0.667819001,7679,0.751504936,0.733788564
14551,Open Access and Altmetrics in the pandemic age: Forescast analysis on COVID-19 related literature,0,10.1101/2020.04.23.057307,4/26/20,biorxiv,0,3,dataset,0.001486441,0.001486514,0.001486497,0.904795571,0.089258485,0.001486492,Epidemiology,0.084092945,FALSE,86.33333333,0.830354382,31,0.578204442,8,0.799987654,4216,0.536479653,0.686256533
14552,"How did SARS-CoV-19 spread in India from Italy, Iran and China? Genetic surveillance of early cases and virus demography",0,10.1101/2020.04.26.062406,4/26/20,biorxiv,0,8,"sequencing, genomes",0.003214232,0.668032845,0.003214152,0.22268643,0.099638246,0.003214096,Genomics,0.5589236,TRUE,480.75,0.99715505,191.875,0.913299438,0,0.403234768,3054,0.396339995,0.677507313
14553,"Expression of ACE2 and TMPRSS2, the SARS2-CoV-2 receptor and co-receptor, in prostate epithelial cells",0,10.1101/2020.04.24.056259,4/25/20,biorxiv,10.1016/j.eururo.2020.04.065,4,"sequencing, dataset",0.591448794,0.07251737,0.001392915,0.001392944,0.00139297,0.331855006,Drug discovery,0.73268473,TRUE,198.5,0.972168965,235.5,0.932499331,24,0.914439163,5586,0.652299542,0.86785175
14554,The coronavirus proofreading exoribonuclease mediates extensive viral recombination,0,10.1101/2020.04.23.057786,4/25/20,biorxiv,10.1371/journal.ppat.1009226,9,genomes,0.500226413,0.490586933,0.002296679,0.002296649,0.002296699,0.002296628,Drug discovery,0.3760887,FALSE,42.77777778,0.564908158,91.11111111,0.804120953,22,0.908142478,11729,0.833132675,0.777576066
14555,CoV2ID: Detection and Therapeutics Oligo Database for SARS-CoV-2,0,10.1101/2020.04.19.048991,4/25/20,biorxiv,0,4,sequencing,0.001461963,0.794273029,0.199879345,0.001461941,0.001461864,0.001461858,Genomics,0.59129804,TRUE,61,0.709196611,38.5,0.624765855,4,0.707574542,3524,0.456537443,0.624518613
14556,Rapid SARS-CoV-2 whole genome sequencing for informed public health decision making in the Netherlands,0,10.1101/2020.04.21.050633,4/25/20,biorxiv,0,24,"sequencing, whole genome, genome sequences",0.00453065,0.574424489,0.004530714,0.407452812,0.004530671,0.004530664,Genomics,0.28695333,FALSE,79.52,0.804997217,618.44,0.984814022,23,0.91129082,8560,0.776306285,0.869352086
14557,"A rapid, low cost, and highly sensitive SARS-CoV-2 diagnostic based on whole genome sequencing",0,10.1101/2020.04.25.061499,4/25/20,biorxiv,0,15,"sequencing, whole genome",0.097944351,0.470516237,0.381015698,0.00122005,0.048083547,0.001220117,Genomics,0.2700359,FALSE,24.46666667,0.36087575,438.6,0.974110249,18,0.891474782,14749,0.870695882,0.774289166
14558,A Library of Nucleotide Analogues Terminate RNA Synthesis Catalyzed by Polymerases of Coronaviruses Causing SARS and COVID-19,0,10.1101/2020.04.23.058776,4/25/20,biorxiv,10.1016/j.antiviral.2020.104857,9,genomes,0.486004399,0.453386526,0.001538198,0.055994557,0.001538154,0.001538165,Drug discovery,0.63935757,TRUE,44.33333333,0.580679077,167.2222222,0.89717688,6,0.764429903,3303,0.431976884,0.668565686
14559,Gut microbiota may underlie the predisposition of healthy individuals to COVID-19,0,10.1101/2020.04.22.20076091,4/25/20,medrxiv,0,22,"machine learning, proteom, metabolom",0.210289453,0.003101593,0.140682022,0.003101487,0.003101778,0.639723667,Clinics,0.5200076,TRUE,8.181818182,0.119920836,12.59090909,0.393698154,60,0.964503982,38318,0.957861787,0.60899619
14560,Association between rRT-PCR test results upon admission and outcome in hospitalized chest CT-Positive COVID-19 patients; a provincial retrospective cohort with active follow-up,0,10.1101/2020.04.21.20074641,4/25/20,medrxiv,0,9,logistic regression,0.001461844,0.001461966,0.109651353,0.001461996,0.224047693,0.661915148,Clinics,0.8175646,TRUE,4.555555556,0.061970437,0.333333333,0.073187048,3,0.667819001,1455,0.059234288,0.215552694
14561,Knowledge and Beliefs of General Public of India on COVID-19: A Web-based Cross-sectional Survey,0,10.1101/2020.04.22.20075267,4/25/20,medrxiv,10.36349/easjpp.2020.v02i05.004,4,logistic regression,0.001156234,0.0011563,0.001156355,0.129821113,0.865553767,0.001156232,Healthcare,0.34702662,FALSE,17,0.257467994,4,0.231469093,2,0.618927094,2859,0.355646521,0.365877676
14562,Experience with Hydroxychloroquine and Azithromycin in the COVID-19 Pandemic: Implications for QT Interval Monitoring,0,10.1101/2020.04.22.20075671,4/25/20,medrxiv,10.1161/jaha.120.017144,11,logistic regression,0.190972307,0.001310348,0.001310356,0.001310373,0.001310374,0.803786243,Clinics,0.70546937,TRUE,53.09090909,0.654524089,74,0.764182499,33,0.936045435,5450,0.644353479,0.749776376
14563,"Estimating the number of COVID-19-related infections, deaths and hospitalizations in Iran under different physical distancing and isolation scenarios: A compartmental mathematical modeling",0,10.1101/2020.04.22.20075440,4/25/20,medrxiv,0,13,mathematical model,0.000846624,0.000846648,0.000846521,0.821486699,0.109475041,0.066498467,Epidemiology,0.13863668,FALSE,29.76923077,0.428288701,9.384615385,0.342788333,3,0.667819001,2182,0.214784493,0.413420132
14564,The anti-HIV Drug Nelfinavir Mesylate (Viracept) is a Potent Inhibitor of Cell Fusion Caused by the SARS-CoV-2 Spike (S) Glycoprotein Warranting further Evaluation as an Antiviral against COVID-19 infections,0,10.1101/2020.04.24.060376,4/24/20,biorxiv,10.1002/jmv.25985,6,"computational, in silico",0.89265247,0.055450553,0.000793411,0.000793416,0.000793457,0.049516691,Drug discovery,0.1470901,FALSE,43.33333333,0.571154679,46.66666667,0.667045759,10,0.828199272,4120,0.522754635,0.647288586
14565,Introductions and early spread of SARS-CoV-2 in France,0,10.1101/2020.04.24.059576,4/24/20,biorxiv,0,14,"sequencing, genomes",0.002422285,0.915732523,0.002422314,0.00242241,0.002422355,0.074578114,Genomics,0.40791532,FALSE,14.78571429,0.222648278,20.5,0.486218892,28,0.926168282,38498,0.958102576,0.648284507
14566,Higher mortality in men from COVID19 infection-understanding the factors that drive the differences between the biological sexes.,0,10.1101/2020.04.19.20062174,4/24/20,medrxiv,0,7,dataset,0.002130803,0.183377328,0.040643541,0.480114934,0.002130837,0.291602556,Epidemiology,0.35654265,FALSE,10.85714286,0.161729235,32.14285714,0.58629917,2,0.618927094,3536,0.457259812,0.456053828
14567,"Genomic, geographic and temporal distributions of SARS-CoV-2 mutations",0,10.1101/2020.04.22.055863,4/24/20,biorxiv,0,9,"sequencing, genomes",0.029728264,0.780630915,0.000936091,0.163952109,0.023816479,0.000936143,Genomics,0.54096293,TRUE,63,0.721998887,149.2222222,0.882927482,10,0.828199272,4846,0.59739947,0.757631278
14568,Evolution and molecular characteristics of SARS-CoV-2 genome,0,10.1101/2020.04.24.058933,4/24/20,biorxiv,0,10,genomes,0.002357827,0.68047043,0.002357738,0.310098418,0.002357786,0.0023578,Genomics,0.518877,TRUE,35.5,0.492547467,11.2,0.373026492,12,0.850299401,6188,0.685287744,0.600290276
14569,ACE2 polymorphisms and individual susceptibility to SARS-CoV-2 infection: insights from an in silico study,0,10.1101/2020.04.23.057042,4/24/20,biorxiv,0,6,in silico,0.603194096,0.261530237,0.002422262,0.002422571,0.002422431,0.128008403,Drug discovery,0.52407897,TRUE,156.1666667,0.948543509,174.3333333,0.902328071,21,0.903944688,6357,0.693233807,0.862012519
14570,Understanding the Collective Responses of Populations to the COVID-19 Pandemic in Mainland China,0,10.1101/2020.04.20.20068676,4/24/20,medrxiv,0,9,correlation analysis,0.001901708,0.001901728,0.001901705,0.990491283,0.001901824,0.001901752,Epidemiology,0.4277636,FALSE,5.777777778,0.081204775,1.777777778,0.148849344,4,0.707574542,2708,0.328196484,0.316456286
14571,A web-based Diagnostic Tool for COVID-19 Using Machine Learning on Chest Radiographs (CXR),0,10.1101/2020.04.21.20063263,4/24/20,medrxiv,0,4,"machine learning, transfer learning",0.002422388,0.002422409,0.98788818,0.002422424,0.002422309,0.00242229,Imaging,0.3031159,FALSE,5,0.070752675,1,0.122023013,8,0.799987654,2081,0.190464724,0.295807016
14572,No Clear Benefit to the Use of Corticosteroid as Treatment in Adult Patients with Coronavirus Disease 2019 : A Retrospective Cohort Study,0,10.1101/2020.04.21.20066258,4/24/20,medrxiv,0,9,logistic regression,0.001653074,0.001653082,0.001653086,0.051389636,0.001653102,0.941998021,Clinics,0.8650336,TRUE,63.88888889,0.7274414,16.66666667,0.444674873,16,0.881782826,3287,0.428124248,0.620505837
14573,Immune defects and cardiovascular risk in X chromosome monosomy mosaicism mediated by loss of chromosome Y. A risk factor for SARS-CoV-2 vulnerability in elderly men?,0,10.1101/2020.04.19.20071357,4/24/20,medrxiv,0,4,transcriptom,0.457440887,0.001371309,0.061532639,0.001371343,0.001371338,0.476912483,Clinics,0.597233,TRUE,14.5,0.219617787,5.25,0.260971367,1,0.537564047,2166,0.209487118,0.30691008
14574,Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing,0,10.1101/2020.04.21.20073890,4/24/20,medrxiv,0,2,forecasting model,0.001823457,0.001823379,0.001823371,0.990883105,0.001823339,0.001823349,Epidemiology,0.19070151,FALSE,3,0.037293586,0,0.055525823,15,0.874313229,1511,0.069588249,0.259180222
14575,MATHEMATICAL MODELING FOR TRANSMISSIBILITY OF COVID-19 VIA MOTORCYCLES,0,10.1101/2020.04.18.20070797,4/24/20,medrxiv,0,1,mathematical model,0.005697614,0.005697627,0.005697544,0.971512172,0.005697473,0.00569757,Epidemiology,0.5999841,TRUE,12,0.183190055,0,0.055525823,1,0.537564047,2563,0.296171442,0.268112842
14576,Risk of COVID-19 is associated with long-term exposure to air pollution,0,10.1101/2020.04.21.20073700,4/24/20,medrxiv,0,12,dataset,0.001593515,0.001593533,0.001593503,0.792716689,0.00159364,0.20090912,Epidemiology,0.36911553,FALSE,53.91666667,0.660275836,115.5,0.845129783,5,0.739490092,4657,0.580784975,0.706420172
14577,The impact of current and future control measures on the spread of COVID-19 in Germany,0,10.1101/2020.04.18.20069955,4/24/20,medrxiv,0,7,mathematical model,0.002032769,0.002032803,0.002032828,0.795644177,0.002032801,0.196224622,Epidemiology,0.22849101,FALSE,7.714285714,0.11225184,1.285714286,0.128512176,7,0.785110192,3055,0.392968938,0.354710786
14578,Biased and unbiased estimation of the average lengths of stay in intensive care units in the COVID-19 pandemic,0,10.1101/2020.04.21.20073916,4/24/20,medrxiv,10.1186/s13613-020-00749-6,6,forecasting model,0.000977439,0.000977441,0.000977434,0.584215413,0.00097744,0.411874833,Epidemiology,0.4573054,FALSE,98.66666667,0.866163646,108.6666667,0.837235751,5,0.739490092,1736,0.114134361,0.639255962
14579,Variational-LSTM Autoencoder to forecast the spread of coronavirus across the globe,0,10.1101/2020.04.20.20070938,4/24/20,medrxiv,10.1371/journal.pone.0246120,6,lstm,0.001330036,0.001330045,0.212070859,0.782608913,0.001330072,0.001330076,Epidemiology,0.49592808,FALSE,8,0.118683901,2.833333333,0.189523682,9,0.814309525,1609,0.086684325,0.302300358
14580,Population modeling of early COVID-19 epidemic dynamics in French regions and estimation of the lockdown impact on infection rate,0,10.1101/2020.04.21.20073536,4/24/20,medrxiv,0,8,dataset,0.001823325,0.001823348,0.001823374,0.990883318,0.001823331,0.001823304,Epidemiology,0.16159204,FALSE,82,0.814769002,87.83333333,0.796494514,11,0.840175319,3213,0.416325548,0.716941096
14581,Effect of a one-month lockdown on the epidemic dynamics of COVID-19 in France,0,10.1101/2020.04.21.20074054,4/24/20,medrxiv,10.3389/fmed.2020.00274,5,probabilistic,0.001461857,0.001461866,0.001461857,0.955030505,0.001461946,0.039121969,Epidemiology,0.2003439,FALSE,27,0.3960047,51.6,0.68771742,10,0.828199272,1626,0.090055382,0.500494193
14582,"An Adaptive, Interacting, Cluster-Based Model Accurately Predicts the Transmission Dynamics of COVID-19",0,10.1101/2020.04.21.20074211,4/24/20,medrxiv,10.1016/j.heliyon.2020.e05722,8,"mathematical model, predictive model",0.00171725,0.001717243,0.001717346,0.966505649,0.00171718,0.026625332,Epidemiology,0.2587951,FALSE,4.75,0.064506154,0.125,0.056729997,2,0.618927094,2329,0.248254274,0.24710438
14583,"Rapid, sensitive, full genome sequencing of Severe Acute Respiratory Syndrome Virus Coronavirus 2 (SARS-CoV-2)",0,10.1101/2020.04.22.055897,4/24/20,biorxiv,10.3201/eid2610.201800,7,"sequencing, genomes",0.004775335,0.976123021,0.004775396,0.004775367,0.004775456,0.004775424,Genomics,0.34253532,FALSE,49.71428571,0.629476158,85.71428571,0.792146107,10,0.828199272,4508,0.565615218,0.703859189
14584,"Estimated surge in hospitalization and intensive care due to the novel coronavirus pandemic in the Greater Toronto Area, Canada: a mathematical modeling study with application at two local area hospitals",0,10.1101/2020.04.20.20073023,4/23/20,medrxiv,0,16,mathematical model,0.00108532,0.001085351,0.023694983,0.699792259,0.001085361,0.273256726,Epidemiology,0.45433995,FALSE,19.5,0.29117447,11.8125,0.382726786,0,0.403234768,2135,0.199855526,0.319247887
14585,"An improved mathematical prediction of the time evolution of the Covid-19 Pandemic in Italy, with Monte Carlo simulations and error analyses",0,10.1101/2020.04.20.20073155,4/23/20,medrxiv,10.1140/epjp/s13360-020-00488-4,2,mathematical prediction,0.001291219,0.272372476,0.001291288,0.722462179,0.00129147,0.001291367,Epidemiology,0.28408858,FALSE,26,0.382398417,0.5,0.087101953,1,0.537564047,1653,0.094630388,0.275423701
14586,Characteristics of scientific articles on COVID-19 published during the initial three months of the pandemic: a meta-epidemiological study,0,10.1101/2020.04.20.20073130,4/23/20,medrxiv,10.1007/s11192-020-03632-0,2,in silico,0.034315153,0.063567962,0.0006939,0.844015512,0.000693923,0.05671355,Epidemiology,0.7518573,TRUE,27,0.3960047,8,0.320511105,1,0.537564047,1602,0.084517216,0.334649267
14587,Impact of control strategies on COVID-19 pandemic and the SIR model based forecasting in Bangladesh.,0,10.1101/2020.04.19.20071415,4/23/20,medrxiv,10.5455/fsh.2021.9,5,mathematical model,0.002422241,0.002422275,0.002422271,0.987888631,0.002422281,0.0024223,Epidemiology,0.35313523,FALSE,7.4,0.106190859,0.8,0.101351351,9,0.814309525,2238,0.225620034,0.311867942
14588,Population genomics insights into the recent evolution of SARS-CoV-2,0,10.1101/2020.04.21.054122,4/23/20,biorxiv,0,5,"bayes, computational, genomes",0.001371336,0.759242235,0.001371247,0.220390874,0.001371272,0.016253036,Genomics,0.267224,FALSE,38.4,0.523285299,89.8,0.801110516,6,0.764429903,7159,0.728629906,0.704363906
14589,The genomic variation landscape of globally-circulating clades of SARS-CoV-2 defines a genetic barcoding scheme,0,10.1101/2020.04.21.054221,4/23/20,biorxiv,10.1016/j.ijid.2020.08.052,6,"sequencing, classifier, genomes",0.057447945,0.880682165,0.057938739,0.001310421,0.001310365,0.001310364,Genomics,0.3189081,FALSE,55.83333333,0.673572886,97,0.816697886,14,0.866658436,3411,0.442812425,0.699935408
14590,TARGETED PROTEOMICS FOR THE DETECTION OF SARS-COV-2 PROTEINS.,0,10.1101/2020.04.23.057810,4/23/20,biorxiv,0,4,proteom,0.002898476,0.985507914,0.00289853,0.002898401,0.00289835,0.002898329,Genomics,0.31517285,FALSE,49,0.624281032,106.5,0.833823923,20,0.900117291,6942,0.718516735,0.769184745
14591,Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2,0,10.1101/2020.04.21.054387,4/23/20,biorxiv,0,15,"deep learning, artificial intelligence",0.781394002,0.002296647,0.11690258,0.002296569,0.002296563,0.094813639,Drug discovery,0.1764231,FALSE,15.46666667,0.232110829,18,0.46180091,26,0.920859312,24785,0.929448591,0.636054911
14592,Distinguish Coronavirus Disease 2019 Patients in General Surgery Emergency by CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China,0,10.1101/2020.04.18.20071019,4/23/20,medrxiv,0,10,"logistic regression, prediction model",0.000898114,0.000898087,0.318160745,0.057790091,0.00089811,0.621354853,Clinics,0.62260467,TRUE,42.1,0.558661636,,,2,0.618927094,1907,0.152901517,0.443496749
14593,A single-cell atlas of the peripheral immune response to severe COVID-19,0,10.1101/2020.04.17.20069930,4/23/20,medrxiv,10.1038/s41591-020-0944-y,15,sequencing,0.547591795,0.0720826,0.002032806,0.002032803,0.002032747,0.374227248,Drug discovery,0.26143473,FALSE,15.53333333,0.234151772,22.86666667,0.510703773,65,0.966911538,18247,0.89694197,0.652177263
14594,An empirical estimate of the infection fatality rate of COVID-19 from the first Italian outbreak,0,10.1101/2020.04.18.20070912,4/23/20,medrxiv,0,2,"bayes, model fit, bayesian model",0.001622688,0.001622715,0.001622765,0.787406087,0.001622825,0.206102921,Epidemiology,0.08401397,FALSE,3,0.037293586,3.5,0.213607172,27,0.92443978,23704,0.925114375,0.525113728
14595,COVID-19 Asymptomatic Infection Estimation,0,10.1101/2020.04.19.20068072,4/23/20,medrxiv,0,7,"machine learning, prediction model, lstm",0.001059422,0.001059363,0.254478382,0.705352304,0.001059394,0.036991135,Epidemiology,0.25777426,FALSE,7.714285714,0.11225184,0.571428571,0.088038534,12,0.850299401,9225,0.789549723,0.460034874
14596,COVID-19. Transport of respiratory droplets in a microclimatologic urban scenario,0,10.1101/2020.04.17.20064394,4/23/20,medrxiv,0,3,"simulation model, computational",0.002296595,0.002296586,0.002296576,0.988516931,0.002296727,0.002296586,Epidemiology,0.079637825,FALSE,1.333333333,0.01366813,,,3,0.667819001,4533,0.567541536,0.416342889
14597,"Reduced expression of COVID-19 host receptor, ACE2 is associated with small bowel inflammation, more severe disease, and response to anti-TNF therapy in Crohn's disease",0,10.1101/2020.04.19.20070995,4/23/20,medrxiv,0,16,transcriptom,0.564048224,0.002490498,0.002490512,0.00249046,0.002490524,0.425989782,Drug discovery,0.5705525,TRUE,152.375,0.946069639,288.1875,0.95096334,15,0.874313229,3394,0.440163737,0.802877486
14598,Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis,0,10.1101/2020.04.19.20067660,4/23/20,medrxiv,10.7554/eLife.58227,31,neural network,0.001461928,0.207286076,0.482746846,0.001461978,0.001461954,0.305581217,Clinics,0.7090019,TRUE,79.62962963,0.805244604,105.4814815,0.83208456,16,0.881782826,10186,0.807368168,0.831620039
14599,Impaired type I interferon activity and exacerbated inflammatory responses in severe Covid-19 patients,0,10.1101/2020.04.19.20068015,4/23/20,medrxiv,10.1126/science.abc6027,30,transcriptom,0.634950272,0.001291336,0.001291288,0.001291258,0.001291251,0.359884595,Drug discovery,0.5684069,TRUE,63.2,0.722679201,59.66666667,0.71936045,182,0.990061115,30510,0.943655189,0.843938989
14600,Estimates of COVID-19 case-fatality risk from individual-level data,0,10.1101/2020.04.16.20067751,4/22/20,medrxiv,0,3,dataset,0.00453063,0.004530753,0.004530758,0.746365149,0.004530779,0.23551193,Epidemiology,0.15870103,FALSE,27.33333333,0.399529965,16.33333333,0.440460262,2,0.618927094,2359,0.25282928,0.42793665
14601,Impact of blood analysis and immune function on the prognosis of patients with COVID-19,0,10.1101/2020.04.16.20067587,4/22/20,medrxiv,10.1371/journal.pone.0240751,6,logistic regression,0.001330201,0.001330038,0.001330023,0.001330033,0.001330027,0.993349679,Clinics,0.85531384,TRUE,48,0.614942173,,,6,0.764429903,2411,0.262701661,0.547357913
14602,"Modeling projections for COVID-19 pandemic by combining epidemiological, statistical, and neural network approaches",0,10.1101/2020.04.17.20059535,4/22/20,medrxiv,0,4,neural network,0.001901728,0.001901732,0.188753535,0.803639298,0.001901791,0.001901915,Epidemiology,0.310381,FALSE,54.5,0.665161729,108.25,0.836566765,11,0.840175319,2112,0.194317361,0.634055294
14603,A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter,0,10.1101/2020.04.19.20069948,4/22/20,medrxiv,0,6,machine learning,0.005047593,0.005047665,0.205428892,0.77438072,0.005047702,0.005047428,Epidemiology,0.21866983,FALSE,17.66666667,0.266373925,7.666666667,0.310877709,12,0.850299401,2484,0.277149049,0.426175021
14604,CAN-NPI: A Curated Open Dataset of Canadian Non-Pharmaceutical Interventions in Response to the Global COVID-19 Pandemic,0,10.1101/2020.04.17.20068460,4/22/20,medrxiv,0,11,dataset,0.001538244,0.001538175,0.124621231,0.869226034,0.001538141,0.001538174,Epidemiology,0.310894,FALSE,29.1,0.42012493,,,4,0.707574542,2574,0.294967493,0.474222322
14605,COVID-19 Outbreak Prediction with Machine Learning,0,10.1101/2020.04.17.20070094,4/22/20,medrxiv,10.3390/a13100249,8,"machine learning, prediction model",0.001823378,0.001823356,0.381304864,0.611401599,0.001823422,0.001823381,Epidemiology,0.34204778,FALSE,99.125,0.867215041,120.25,0.851685844,67,0.967960985,4329,0.545629665,0.808122884
14606,Estimating the impact of COVID-19 control measures using a Bayesian model of physical distancing,0,10.1101/2020.04.17.20070086,4/22/20,medrxiv,10.1371/journal.pcbi.1008274,12,"bayes, bayesian model",0.001219996,0.001220013,0.00122,0.797856185,0.197263717,0.001220089,Epidemiology,0.18474656,FALSE,30.33333333,0.436266931,30.83333333,0.576264383,27,0.92443978,10004,0.803756321,0.685181854
14607,"AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia",0,10.1101/2020.04.17.20069187,4/22/20,medrxiv,10.1016/j.media.2020.101860,35,"deep learning, artificial intelligence",0.206134678,0.001392859,0.553752103,0.001392946,0.00139285,0.235934565,Imaging,0.5777172,TRUE,34.80645161,0.485064011,44.29032258,0.655472304,15,0.874313229,6126,0.680712738,0.673890571
14608,Forecasting of COVID-19 Cases and Deaths Using ARIMA Models,0,10.1101/2020.04.17.20069237,4/22/20,medrxiv,0,2,forecasting model,0.001823383,0.001823385,0.001823403,0.912599535,0.001823393,0.080106901,Epidemiology,0.30856588,FALSE,4.5,0.061784897,0,0.055525823,9,0.814309525,3187,0.410305803,0.335481512
14609,Multivariate Analysis of Factors Affecting COVID-19 Case and Death Rate in U.S. Counties: The Significant Effects of Black Race and Temperature,0,10.1101/2020.04.17.20069708,4/22/20,medrxiv,10.1016/j.amjms.2020.06.015,11,logistic regression,0.000946138,0.00094611,0.000946095,0.439076952,0.176027886,0.382056818,Epidemiology,0.13526371,FALSE,34.18181818,0.47881749,11.45454545,0.376772812,14,0.866658436,5607,0.650854804,0.593275886
14610,"protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses",0,10.1101/2020.04.21.053967,4/22/20,biorxiv,10.1093/bioinformatics/btaa646,2,computational,0.449511943,0.190013031,0.002130721,0.156063097,0.002130736,0.200150472,Drug discovery,0.18513331,FALSE,64,0.729173109,34.5,0.602221033,1,0.537564047,1807,0.128581748,0.499384984
14611,Evaluating the different control policies for COVID-19 between mainland China and European countries by a mathematical model in the confirmed cases,0,10.1101/2020.04.17.20068775,4/22/20,medrxiv,0,6,mathematical model,0.001486408,0.018666766,0.001486439,0.943134375,0.001486438,0.033739575,Epidemiology,0.447905,FALSE,5,0.070752675,1.666666667,0.145036125,0,0.403234768,1218,0.024319769,0.160835834
14612,Projections for first-wave COVID-19 deaths across the US using social-distancing measures derived from mobile phones,0,10.1101/2020.04.16.20068163,4/22/20,medrxiv,0,8,"bayes, bayesian model",0.002080531,0.002080676,0.172685581,0.818992019,0.002080598,0.002080594,Epidemiology,0.28297698,FALSE,54.625,0.665842043,196.625,0.915975381,51,0.958392493,4932,0.601011317,0.785305309
14613,Mathematical modeling of COVID-19 containment strategies with considerations for limited medical resources,0,10.1101/2020.04.17.20068585,4/22/20,medrxiv,0,4,"machine learning, mathematical model",0.001272668,0.00127266,0.001272727,0.948304744,0.046604478,0.001272724,Epidemiology,0.28494513,FALSE,12.75,0.191786752,3.75,0.21982874,0,0.403234768,2336,0.247050325,0.265475146
14614,Structural Basis of SARS-CoV-2 Spike Protein Priming by TMPRSS2,0,10.1101/2020.04.21.052639,4/22/20,biorxiv,10.3934/microbiol.2020021,7,structural model,0.994505956,0.00109885,0.001098796,0.001098813,0.001098788,0.001098797,Drug discovery,0.5385185,TRUE,14.85714286,0.223143052,5.285714286,0.26130586,18,0.891474782,5841,0.664820612,0.510186076
14615,A Novel Heuristic Global Algorithm to Predict the COVID-19 Pandemic Trend,0,10.1101/2020.04.16.20068445,4/22/20,medrxiv,0,9,mathematical model,0.002183212,0.002183239,0.148764067,0.842503033,0.002183206,0.002183245,Epidemiology,0.12485257,FALSE,54.77777778,0.666707898,75.55555556,0.767795023,0,0.403234768,1765,0.120394895,0.489533146
14616,REVEALING COVID-19 TRANSMISSION BY SARS-CoV-2 GENOME SEQUENCING AND AGENT BASED MODELLING,0,10.1101/2020.04.19.048751,4/22/20,biorxiv,0,24,"computational, sequencing, genomes",0.002296598,0.42613619,0.002296638,0.466874409,0.050765445,0.051630719,Epidemiology,0.33589733,FALSE,82.41666667,0.815696704,167.7916667,0.897712069,16,0.881782826,5707,0.657596918,0.813197129
14617,Impact of Social Distancing Measures on COVID-19 Healthcare Demand in Central Texas,0,10.1101/2020.04.16.20068403,4/22/20,medrxiv,0,9,mathematical model,0.020514451,0.000612288,0.000612242,0.826782786,0.000612264,0.150865969,Epidemiology,0.32081333,FALSE,57.55555556,0.685818542,80.66666667,0.780773348,4,0.707574542,5620,0.651336383,0.706375704
14618,A multivariate spatiotemporal spread model of COVID-19 using ensemble of ConvLSTM networks,0,10.1101/2020.04.17.20069898,4/22/20,medrxiv,10.1007/s40031-020-00517-x,3,"machine learning, lstm",0.001901825,0.001901729,0.418565553,0.573827018,0.001902016,0.001901859,Epidemiology,0.47224456,FALSE,17.33333333,0.261178799,4,0.231469093,7,0.785110192,2220,0.219841079,0.374399791
14619,Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence,0,10.1101/2020.04.17.20069666,4/22/20,medrxiv,0,11,"artificial intelligence, neural network, prediction model, dataset",0.001943525,0.001943508,0.357882091,0.63434384,0.001943495,0.001943542,Epidemiology,0.23126787,FALSE,8.4,0.124435648,2.4,0.174872893,11,0.840175319,3754,0.480857212,0.405085268
14620,A Path to the End of COIVD-19 - a Mathematical Model,0,10.1101/2020.04.17.20069443,4/22/20,medrxiv,0,2,mathematical model,0.001156279,0.001156272,0.012145149,0.983229711,0.001156327,0.001156262,Epidemiology,0.19575211,FALSE,141,0.934999072,95.5,0.813821247,6,0.764429903,2719,0.327233325,0.710120887
14621,America Addresses Two Epidemics: Cannabis and Coronavirus and their Interactions: An Ecological Geospatial Study,0,10.1101/2020.04.17.20069021,4/22/20,medrxiv,0,2,dataset,0.071228318,0.000603905,0.000603897,0.69630594,0.230654019,0.00060392,Epidemiology,0.112523496,FALSE,65.5,0.737646113,40.5,0.637342788,0,0.403234768,3138,0.404286058,0.545627432
14622,Mortality from COVID-19 in 12 countries and 6 states of the United States,0,10.1101/2020.04.17.20069161,4/22/20,medrxiv,0,3,bayes,0.000587836,0.000587846,0.000587845,0.803905449,0.000587849,0.193743175,Epidemiology,0.17022592,FALSE,51.5,0.643267982,43.5,0.651725983,13,0.858880178,3046,0.386708404,0.635145637
14623,"Predicting the impact of asymptomatic transmission,non-pharmaceutical intervention and testing on the spread of COVID19COVID19",0,10.1101/2020.04.16.20068387,4/22/20,medrxiv,0,4,"mathematical model, predictive model",0.002080546,0.002080583,0.002080559,0.989597204,0.002080561,0.002080548,Epidemiology,0.14800403,FALSE,101,0.871296926,40.25,0.634934439,6,0.764429903,2602,0.301228028,0.642972324
14624,Identification of super-transmitters of SARS-CoV-2,0,10.1101/2020.04.19.20071399,4/22/20,medrxiv,0,4,"genome sequences, genomes",0.001220017,0.993899876,0.001220022,0.001220067,0.001220001,0.001220017,Genomics,0.46512318,FALSE,35,0.488032655,123.5,0.855833556,5,0.739490092,4983,0.604863954,0.672055064
14625,"More prevalent, less deadly? Bayesian inference of the COVID19 Infection Fatality Ratio from mortality data",0,10.1101/2020.04.19.20071811,4/22/20,medrxiv,0,6,"bayes, model fit",0.003466013,0.198089776,0.003465951,0.674106963,0.003466321,0.117404977,Epidemiology,0.16302484,FALSE,37.5,0.513946441,40.83333333,0.638546963,3,0.667819001,2911,0.360943896,0.545314075
14626,"A Bayesian analysis of the total number of cases of the COVID 19 when only a few data is available. A case study in the state of Goias, Brazil",0,10.1101/2020.04.19.20071852,4/22/20,medrxiv,0,4,bayes,0.003335285,0.003335311,0.003335396,0.983323327,0.003335344,0.003335337,Epidemiology,0.50283253,TRUE,2.75,0.030304904,0,0.055525823,6,0.764429903,1580,0.079460631,0.232430315
14627,"COVID-19: An Update on the Epidemiological, Genomic Origin, Phylogenetic study, Indiacentric to Worldwide current status",0,10.1101/2020.04.17.20070284,4/21/20,medrxiv,0,2,"whole genome, genome sequences",0.001943532,0.465642419,0.001943463,0.526583485,0.001943519,0.001943584,Epidemiology,0.25733668,FALSE,29.5,0.426000371,3,0.199424672,13,0.858880178,1915,0.151938358,0.409060895
14628,Enisamium is a small molecule inhibitor of the influenza A virus and SARS-CoV-2 RNA polymerases,0,10.1101/2020.04.21.053017,4/21/20,biorxiv,0,7,genomes,0.662523198,0.315461798,0.000926278,0.000926295,0.000926281,0.019236151,Drug discovery,0.22125092,FALSE,76.42857143,0.792133094,152.2857143,0.885068237,6,0.764429903,5464,0.640260053,0.770472822
14629,Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs,0,10.1101/2020.04.21.052084,4/21/20,biorxiv,10.1038/s41598-020-78758-5,11,"artificial intelligence, proteom",0.576355746,0.234251638,0.081411752,0.106147261,0.000916764,0.000916838,Drug discovery,0.1668753,FALSE,79.90909091,0.806481539,46,0.66416912,3,0.667819001,8266,0.766433903,0.726225891
14630,Bioinformatics Study on Structural Protein of Severe Acute Respiratory Syndrome Coronavirus 2 (Sars-Cov-2) For Better Understanding the Vaccine Development,0,10.1101/2020.04.21.053199,4/21/20,biorxiv,10.31632/ijalsr.20.v03i04.003,2,"computational, bioinformatic",0.991885943,0.001622777,0.001622843,0.001622855,0.00162275,0.001622832,Drug discovery,0.68209463,TRUE,4.5,0.061784897,0,0.055525823,0,0.403234768,2571,0.292800385,0.203336468
14631,Elucidating the differences in the molecular mechanism of receptor binding between 2019-nCoV and the SARS-CoV viruses using computational tools,0,10.1101/2020.04.21.053009,4/21/20,biorxiv,0,5,"computational, genomes",0.822670489,0.173624201,0.000926323,0.00092633,0.000926332,0.000926325,Drug discovery,0.25783983,FALSE,21.6,0.319438432,5.2,0.259700294,5,0.739490092,3092,0.395136046,0.428441216
14632,Diagnosis and Prediction Model for COVID19 Patients Response to Treatment based on Convolutional Neural Networks and Whale Optimization Algorithm Using CT Images,0,10.1101/2020.04.16.20063990,4/21/20,medrxiv,0,2,"deep learning, neural network, prediction model, dataset",0.000936187,0.000936153,0.927982727,0.000936174,0.00093617,0.06827259,Imaging,0.69835544,TRUE,2,0.022141134,0,0.055525823,16,0.881782826,2508,0.280520106,0.309992472
14633,"Epidemiological and Genomic Analysis of SARS-CoV-2 in Ten Patients from a Mid-sized City outside of Hubei, China",0,10.1101/2020.04.16.20058560,4/21/20,medrxiv,10.3389/fpubh.2020.567621,11,"sequencing, metagenom, genomes",0.001141345,0.62041716,0.041883597,0.240248908,0.001141397,0.095167593,Genomics,0.556817,TRUE,25.27272727,0.370771229,,,4,0.707574542,2531,0.285095112,0.454480294
14634,Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation,0,10.1101/2020.04.16.20064709,4/21/20,medrxiv,10.1016/j.compbiomed.2020.104037,3,"deep learning, dataset",0.001461886,0.001461931,0.938668645,0.001461931,0.001461986,0.055483621,Imaging,0.74789095,TRUE,39.66666667,0.536211268,10.33333333,0.36038266,35,0.939317242,4084,0.515771731,0.587920725
14635,Social interventions can lower COVID-19 deaths in middle-income countries,0,10.1101/2020.04.16.20063727,4/21/20,medrxiv,0,8,simulation model,0.001141321,0.001141346,0.001141315,0.914027832,0.001141414,0.081406772,Epidemiology,0.21958953,FALSE,18.375,0.275279857,10,0.355632861,4,0.707574542,2893,0.35588731,0.423593642
14636,Identification and enrichment of SECReTE cis-acting RNA elements in the Coronaviridae and other (+) single-strand RNA viruses,0,10.1101/2020.04.20.050088,4/20/20,biorxiv,0,4,genomes,0.35473568,0.640813677,0.00111267,0.001112642,0.001112634,0.001112696,Genomics,0.23713076,FALSE,49,0.624281032,148,0.881924003,0,0.403234768,2294,0.235010836,0.536112659
14637,"Host, Viral, and Environmental Transcriptome Profiles of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)",0,10.1101/2020.04.20.048066,4/20/20,biorxiv,0,52,"transcriptom, metatranscriptom",0.360056603,0.566152968,0.001987272,0.001987246,0.001987136,0.067828774,Genomics,0.47078645,FALSE,54.65384615,0.665965737,134.1346154,0.868009098,60,0.964503982,31208,0.943895979,0.860593699
14638,"Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells",0,10.1101/2020.04.19.049254,4/20/20,biorxiv,0,103,dataset,0.829362889,0.066671347,0.000838528,0.000838521,0.000838555,0.101450161,Drug discovery,0.7835002,TRUE,23.37373737,0.345908838,38.84848485,0.626170725,154,0.987530094,56865,0.973753913,0.733340893
14639,Leverging Deep Learning to Simulate Coronavirus Spike proteins has the potential to predict future Zoonotic sequences,0,10.1101/2020.04.20.046920,4/20/20,biorxiv,0,1,"deep learning, neural network, dataset",0.265651418,0.383906665,0.347373796,0.001022675,0.001022722,0.001022724,Genomics,0.45954525,FALSE,80,0.807532933,325,0.960797431,2,0.618927094,2126,0.19503973,0.645574297
14640,Limited SARS-CoV-2 diversity within hosts and following passage in cell culture,0,10.1101/2020.04.20.051011,4/20/20,biorxiv,0,10,metagenom,0.139654625,0.818184378,0.014358494,0.000693885,0.00069387,0.026414749,Genomics,0.34205696,FALSE,126.1818182,0.915517348,273,0.946481135,8,0.799987654,4204,0.526848062,0.797208549
14641,Phylodynamics of SARS-CoV-2 transmission in Spain,0,10.1101/2020.04.20.050039,4/20/20,biorxiv,0,14,"bayes, whole-genome, whole genome, genome sequences",0.001392976,0.968353003,0.00139282,0.001392956,0.001392848,0.026075398,Genomics,0.6751226,TRUE,58,0.689714887,61.35714286,0.725782713,25,0.918019631,26128,0.932338069,0.816463825
14642,Interplay of host regulatory network on SARS-CoV-2 binding and replication machinery,0,10.1101/2020.04.20.050138,4/20/20,biorxiv,0,6,interactom,0.882753374,0.001684563,0.001684539,0.001684544,0.001684493,0.110508488,Drug discovery,0.39170608,FALSE,35,0.488032655,10.66666667,0.365333155,4,0.707574542,2812,0.341439923,0.475595069
14643,Proteome-wide analysis of differentially-expressed SARS-CoV-2 antibodies in early COVID-19 infection,0,10.1101/2020.04.14.20064535,4/20/20,medrxiv,0,12,proteom,0.159198757,0.51066903,0.052517457,0.001861739,0.00186172,0.273891296,Genomics,0.36853987,FALSE,82,0.814769002,42.5,0.647578271,10,0.828199272,5045,0.607271852,0.724454599
14644,TOWARD A COVID-19 SCORE-RISK ASSESSMENTS AND REGISTRY,0,10.1101/2020.04.15.20066860,4/20/20,medrxiv,0,7,"prediction model, dataset",0.000648222,0.000648132,0.358381906,0.151692807,0.000648135,0.487980799,Clinics,0.36764598,FALSE,62.28571429,0.71680376,191,0.912630452,6,0.764429903,2517,0.281242475,0.668776648
14645,A dynamic nomenclature proposal for SARS-CoV-2 to assist genomic epidemiology,0,10.1101/2020.04.17.046086,4/19/20,biorxiv,10.1038/s41564-020-0770-5,8,"genomic epidemiology, genome sequences",0.08678482,0.901621198,0.002898709,0.002898572,0.00289836,0.002898341,Genomics,0.33388487,FALSE,176.125,0.962706414,1792.75,0.997725448,158,0.988270881,18471,0.897182759,0.961471376
14646,Exploring Conformational Transition of 2019 Novel Coronavirus Spike Glycoprotein Between Its Closed and Open States Using Molecular Dynamics Simulations,0,10.1101/2020.04.17.047324,4/19/20,biorxiv,10.1063/5.0011141,6,molecular dynamics simulation,0.897782059,0.00101102,0.001010951,0.098174041,0.001010965,0.001010964,Drug discovery,0.17824733,FALSE,2.166666667,0.022450368,0,0.055525823,3,0.667819001,4369,0.546352035,0.323036807
14647,Serial interval and generation interval for respectively the imported and local infectors estimated using reported contact-tracing data of COVID-19 in China,0,10.1101/2020.04.15.20065946,4/19/20,medrxiv,10.3389/fpubh.2020.577431,5,bayes,0.002296552,0.206948049,0.002296578,0.783865643,0.002296566,0.002296613,Epidemiology,0.610588,TRUE,67.6,0.750572082,30,0.570176612,5,0.739490092,1639,0.088369853,0.53715216
14648,Early prediction of mortality risk among severe COVID-19 patients using machine learning,0,10.1101/2020.04.13.20064329,4/19/20,medrxiv,10.1093/ije/dyaa171,2,"machine learning, phenomics, predictive model, logistic regression",0.001237117,0.001237062,0.252815209,0.001237085,0.001237053,0.742236475,Clinics,0.6037801,TRUE,25,0.369286907,8.5,0.329141022,23,0.91129082,4038,0.509751987,0.529867684
14649,Investigating the genomic landscape in novel coronavirus (2019-nCoV) genomes to identify non-synonymous mutations for use in diagnosis and drug design,0,10.1101/2020.04.16.043273,4/18/20,biorxiv,10.1016/j.jcv.2020.104441,2,genomes,0.201146468,0.731480971,0.001085389,0.001085365,0.001085382,0.064116425,Genomics,0.8148471,TRUE,7.5,0.108355495,0,0.055525823,13,0.858880178,3949,0.498434866,0.380299091
14650,An in silico map of the SARS-CoV-2 RNA Structurome,0,10.1101/2020.04.17.045161,4/18/20,biorxiv,0,8,"bioinformatic, in silico, transcriptom",0.845334284,0.147796895,0.001717197,0.001717266,0.001717205,0.001717153,Drug discovery,0.45383382,FALSE,94.625,0.855773394,64.875,0.738292748,39,0.945737391,5376,0.630146882,0.792487604
14651,Distinct Structural Flexibility within SARS-CoV-2 Spike Protein Reveals Potential Therapeutic Targets,0,10.1101/2020.04.17.047548,4/18/20,biorxiv,0,5,"molecular dynamics simulation, artificial intelligence",0.874231879,0.001901847,0.118161103,0.001901767,0.001901699,0.001901705,Drug discovery,0.380378,FALSE,29.6,0.426495145,6.8,0.292881991,7,0.785110192,3773,0.478208524,0.495673963
14652,Transcriptional Difference between SARS-COV-2 and other Human Coronaviruses Revealed by Sub-genomic RNA Profiling,0,10.1101/2020.04.16.043224,4/18/20,biorxiv,0,7,transcriptom,0.47815928,0.437279445,0.001187275,0.001187292,0.001187266,0.080999441,Drug discovery,0.16029489,FALSE,41.14285714,0.549570165,65,0.739028633,2,0.618927094,3454,0.442090055,0.587403987
14653,Coronavirus Infection and PARP Expression Dysregulate the NAD Metabolome: A Potentially Actionable Component of Innate Immunity,0,10.1101/2020.04.17.047480,4/18/20,biorxiv,10.1074/jbc.RA120.015138,10,metabolom,0.990491055,0.001901848,0.001901787,0.001901816,0.001901786,0.001901708,Drug discovery,0.3250228,FALSE,57.2,0.683097285,90.8,0.802782981,8,0.799987654,28470,0.936672285,0.805635051
14654,Regulation of angiotensin converting enzyme 2 (ACE2) in obesity: implications for COVID-19,0,10.1101/2020.04.17.046938,4/18/20,biorxiv,10.3389/fphys.2020.555039,6,transcriptom,0.713741147,0.001371288,0.001371257,0.001371247,0.00137123,0.280773832,Drug discovery,0.92663383,TRUE,146,0.940688973,164.2857143,0.895638212,27,0.92443978,9052,0.784493137,0.886315026
14655,Significant expression of FURIN and ACE2 on oral epithelial cells may facilitate the efficiency of 2019-nCov entry,0,10.1101/2020.04.18.047951,4/18/20,biorxiv,0,13,"bioinformatic, dataset",0.877639105,0.000977486,0.000977473,0.095893106,0.00097747,0.02353536,Drug discovery,0.95398265,TRUE,34.83333333,0.485496939,2.666666667,0.185442869,19,0.89561084,5635,0.648446906,0.553749388
14656,Modeling COVID-19 latent prevalence to assess a public health intervention at a state and regional scale,0,10.1101/2020.04.14.20063420,4/18/20,medrxiv,10.2196/19353,8,predictive model,0.000740293,0.00074033,0.016513665,0.929483126,0.051782255,0.00074033,Epidemiology,0.17585844,FALSE,17.75,0.267672707,12,0.386740701,3,0.667819001,2342,0.243438478,0.391417722
14657,Analysis of the mitigation strategies for COVID-19: from mathematical modelling perspective,0,10.1101/2020.04.15.20066308,4/18/20,medrxiv,10.1016/j.chaos.2020.109968,3,mathematical model,0.002032948,0.002032933,0.002032832,0.901646459,0.002032834,0.090221994,Epidemiology,0.38440317,FALSE,8.333333333,0.123693488,0.666666667,0.096200161,14,0.866658436,1988,0.163014688,0.312391693
14658,"Revealing variants in SARS-CoV-2 interaction domain of ACE2 and loss of function intolerance through analysis of >200,000 exomes",0,10.1101/2020.04.07.030544,4/17/20,biorxiv,0,4,exom,0.294748981,0.555820652,0.0717935,0.075637662,0.000999588,0.000999618,Genomics,0.121167004,FALSE,36,0.498299215,127.75,0.861051646,4,0.707574542,4057,0.508548038,0.64386836
14659,Single-cell analysis of human lung epithelia reveals concomitant expression of the SARS-CoV-2 receptor ACE2 with multiple virus receptors and scavengers in alveolar type II cells,0,10.1101/2020.04.16.045617,4/17/20,biorxiv,0,32,"sequencing, transcriptom, dataset",0.850133108,0.063487714,0.00122008,0.001220026,0.001220009,0.082719064,Drug discovery,0.40463483,FALSE,153.375,0.946873647,234.375,0.931830345,2,0.618927094,4969,0.598844209,0.774118824
14660,Shotgun proteomics of SARS-CoV-2 infected cells and its application to the optimisation of whole viral particle antigen production for vaccines,0,10.1101/2020.04.17.046193,4/17/20,biorxiv,10.1080/22221751.2020.1791737,20,proteom,0.586810801,0.1688286,0.00289844,0.235665308,0.002898437,0.002898413,Drug discovery,0.65408033,TRUE,19.95,0.294823428,11.25,0.374364463,5,0.739490092,3783,0.477726944,0.471601232
14661,Analysis of COVID-19 spread in South Korea using the SIR model with time-dependent parameters and deep learning,0,10.1101/2020.04.13.20063412,4/17/20,medrxiv,0,4,"deep learning, neural network, mathematical model",0.030417518,0.001901685,0.061531387,0.902346021,0.001901689,0.0019017,Epidemiology,0.34886187,FALSE,27,0.3960047,11.5,0.378378378,16,0.881782826,4356,0.54033229,0.549124549
14662,Applying the SEIR Model in Forecasting The COVID-19 Trend in Malaysia: A Preliminary Study,0,10.1101/2020.04.14.20065607,4/17/20,medrxiv,0,2,mathematical model,0.001861651,0.001861668,0.001861687,0.990691508,0.001861743,0.001861742,Epidemiology,0.57917404,TRUE,7,0.10179974,0,0.055525823,2,0.618927094,2410,0.256681917,0.258233644
14663,The socio-economic determinants of the coronavirus disease (COVID-19) pandemic,0,10.1101/2020.04.15.20066068,4/17/20,medrxiv,0,5,"bayes, bayesian model",0.003101675,0.003101595,0.003101572,0.984491995,0.003101627,0.003101535,Epidemiology,0.64009714,TRUE,28.6,0.413321789,9.4,0.343256623,31,0.931971109,3386,0.433662413,0.530552983
14664,CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images,0,10.1101/2020.04.14.20065722,4/17/20,medrxiv,0,3,"supervised learning, deep learning, artificial intelligence, image processing, classifier, dataset",0.000625237,0.000625239,0.978257279,0.01924178,0.000625234,0.000625231,Imaging,0.2500345,FALSE,11,0.167171748,2.333333333,0.173401124,20,0.900117291,2369,0.246809535,0.371874925
14665,Estimating the last day for COVID-19 outbreak in mainland China,0,10.1101/2020.04.14.20064824,4/17/20,medrxiv,0,3,mathematical model,0.002296541,0.002296554,0.00229661,0.988517204,0.00229654,0.002296551,Epidemiology,0.15947536,FALSE,24,0.35574247,20,0.481000803,5,0.739490092,1554,0.071514568,0.411936983
14666,Novel Coronavirus in Nigeria: Epidemiological analysis of the first 45 days of the pandemic,0,10.1101/2020.04.14.20064949,4/17/20,medrxiv,10.3390/ijerph17093054,3,bayes,0.001593463,0.001593524,0.001593485,0.808331143,0.00159358,0.185294804,Epidemiology,0.38451895,FALSE,28.66666667,0.414125796,4.333333333,0.237958255,4,0.707574542,1410,0.044546111,0.351051176
14667,Countries should aim to lower the reproduction number R close to 1.0 for the short-term mitigation of COVID-19 outbreaks,0,10.1101/2020.04.14.20065268,4/17/20,medrxiv,0,1,mathematical model,0.001943475,0.001943508,0.001943513,0.990282437,0.001943505,0.001943562,Epidemiology,0.16162738,FALSE,3,0.037293586,0,0.055525823,0,0.403234768,1406,0.044064532,0.135029677
14668,Time course quantitative detection of SARS-CoV-2 in Parisian wastewaters correlates with COVID-19 confirmed cases,0,10.1101/2020.04.12.20062679,4/17/20,medrxiv,0,8,genomes,0.001684545,0.477872521,0.001684524,0.445916813,0.071156964,0.001684633,Genomics,0.5452126,TRUE,32.5,0.461685942,31,0.578204442,147,0.986418915,37440,0.95352757,0.744959217
14669,Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning,0,10.1101/2020.04.11.20054643,4/17/20,medrxiv,0,5,"deep learning, transfer learning, dataset",0.001717194,0.00171727,0.916539119,0.076592094,0.001717169,0.001717154,Imaging,0.33490935,FALSE,27.8,0.404848785,6,0.280037463,14,0.866658436,2266,0.222971346,0.443629007
14670,Monitoring and predicting viral dynamics in SARS-CoV-2-infected Patients,0,10.1101/2020.04.14.20060491,4/17/20,medrxiv,0,2,mathematical model,0.002639051,0.438101984,0.002639192,0.324012076,0.002639165,0.229968532,Genomics,0.27482972,FALSE,7.5,0.108355495,,,0,0.403234768,1763,0.111726463,0.207772242
14671,Coronavirus epidemic: prediction and controlling measures,0,10.1101/2020.04.11.20062125,4/17/20,medrxiv,0,2,mathematical model,0.00360725,0.003607272,0.003607436,0.981962713,0.00360735,0.003607978,Epidemiology,0.09077552,FALSE,1,0.012307502,0,0.055525823,0,0.403234768,1754,0.109800144,0.145217059
14672,Distinct early IgA profile may determine severity of COVID-19 symptoms: an immunological case series,0,10.1101/2020.04.14.20059733,4/17/20,medrxiv,0,12,proteom,0.398960724,0.401781537,0.001653085,0.001653079,0.001653092,0.194298482,Genomics,0.16085798,FALSE,126.9090909,0.916630589,131,0.864597271,25,0.918019631,10497,0.811943174,0.877797666
14673,COVID-19 pandemic: A Hill type mathematical model predicts the US death number and the reopening date,0,10.1101/2020.04.12.20062893,4/17/20,medrxiv,0,1,mathematical model,0.003760478,0.003760572,0.003760653,0.981196892,0.00376073,0.003760674,Epidemiology,0.057491034,FALSE,58,0.689714887,7,0.299973241,6,0.764429903,2942,0.359258367,0.528344099
14674,"Knowledge, Attitude and Practice among Healthcare Professionals regarding COVID-19: A cross-sectional survey from Pakistan",0,10.1101/2020.04.13.20063198,4/17/20,medrxiv,10.1016/j.jhin.2020.05.007,8,logistic regression,0.001072216,0.0010722,0.0010722,0.001072208,0.994638999,0.001072177,Healthcare,0.78017545,TRUE,4.5,0.061784897,1.625,0.141222906,12,0.850299401,4075,0.510955935,0.391065785
14675,Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19),0,10.1101/2020.04.12.20062661,4/17/20,medrxiv,0,28,"artificial intelligence, sequencing",0.001112614,0.061326038,0.783220969,0.00111263,0.001112669,0.15211508,Imaging,0.5754751,TRUE,46.82142857,0.603871606,80.28571429,0.77970297,5,0.739490092,4620,0.568023116,0.672771946
14676,SimCOVID: An Open-Source Simulation Program for theCOVID-19 Outbreak,0,10.1101/2020.04.13.20063354,4/17/20,medrxiv,0,1,mathematical model,0.001486475,0.001486443,0.193754449,0.800299734,0.001486469,0.001486429,Epidemiology,0.5730146,TRUE,15,0.227596017,1,0.122023013,5,0.739490092,3887,0.490488803,0.394899481
14677,A Predictive Model for the Evolution of COVID-19,0,10.1101/2020.04.13.20063271,4/17/20,medrxiv,10.1007/s41403-020-00130-w,1,predictive model,0.002562531,0.002562632,0.002562562,0.987187122,0.002562561,0.002562592,Epidemiology,0.293657,FALSE,22,0.326056033,15,0.42594327,14,0.866658436,1748,0.108355406,0.431753286
14678,Machine Learning Analysis of Chest CT Scan Images as a Complementary Digital Test of Coronavirus (COVID-19) Patients,0,10.1101/2020.04.13.20063479,4/17/20,medrxiv,0,4,machine learning,0.002032766,0.039670012,0.952198823,0.00203279,0.002032777,0.002032833,Imaging,0.42721906,FALSE,9,0.135320675,2.5,0.180826866,18,0.891474782,3478,0.444257163,0.412969872
14679,Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers,0,10.1101/2020.04.13.20063461,4/17/20,medrxiv,0,9,"machine learning, deep learning, classifier",0.00146185,0.001461912,0.992690482,0.001461915,0.001461901,0.001461941,Imaging,0.5339824,TRUE,11.66666667,0.176510607,1.444444444,0.134399251,28,0.926168282,3898,0.491692752,0.432192723
14680,Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans,0,10.1101/2020.04.13.20063941,4/17/20,medrxiv,0,7,"supervised learning, deep learning, transfer learning, dataset",0.00133001,0.001330024,0.99334974,0.001330079,0.001330091,0.001330056,Imaging,0.14525571,FALSE,9.857142857,0.147875564,10.14285714,0.356636339,76,0.971664918,9139,0.785697086,0.565468477
14681,Identifying common pharmacotherapies associated with reduced COVID-19 morbidity using electronic health records,0,10.1101/2020.04.11.20061994,4/16/20,medrxiv,0,4,in silico,0.081575239,0.001187313,0.00118731,0.001187344,0.148124025,0.766738769,Clinics,0.7705414,TRUE,146,0.940688973,344.5,0.964744447,11,0.840175319,9006,0.782326029,0.881983692
14682,A Mathematical prediction of the time evolution of the Covid-19 pandemic in some countries of the European Union using Monte Carlo simulations,0,10.1101/2020.04.10.20061051,4/16/20,medrxiv,10.1140/epjp/s13360-020-00488-4,2,mathematical prediction,0.002639005,0.096029786,0.002639047,0.893414152,0.002638984,0.002639027,Epidemiology,0.3420388,FALSE,26,0.382398417,0.5,0.087101953,3,0.667819001,1750,0.107392247,0.311177904
14683,A High-Coverage SARS-CoV-2 Genome Sequence Acquired by Target Capture Sequencing,0,10.1101/2020.04.11.20061507,4/15/20,medrxiv,10.1002/jmv.26116,2,"sequencing, metagenom",0.063834286,0.849695608,0.082596248,0.0012913,0.001291295,0.001291262,Genomics,0.51431245,TRUE,29,0.41993939,6,0.280037463,2,0.618927094,1936,0.148567301,0.366867812
14684,Covid-19 clinical data analysis using Ball Mapper,0,10.1101/2020.04.10.20061374,4/15/20,medrxiv,0,2,dataset,0.005352888,0.005353023,0.248880426,0.005353204,0.005352822,0.729707638,Clinics,0.37779522,FALSE,18.5,0.278001113,2,0.164302917,0,0.403234768,2150,0.193113412,0.259663052
14685,Cardiac or Infectious? Transfer Learning with Chest X-Rays for ER Patient Classification,0,10.1101/2020.04.11.20062091,4/15/20,medrxiv,0,12,"deep-learning, transfer learning",0.001350435,0.001350346,0.833432002,0.001350397,0.076192433,0.086324387,Imaging,0.6730922,TRUE,66.25,0.742346465,91.83333333,0.805525823,2,0.618927094,1771,0.111004093,0.569450869
14686,Challenges in control of Covid-19: short doubling time and long delay to effect of interventions,0,10.1101/2020.04.12.20059972,4/15/20,medrxiv,0,17,mathematical model,0.002183209,0.002183287,0.002183337,0.989083677,0.002183262,0.002183228,Epidemiology,0.16319185,FALSE,19.17647059,0.286474117,12.52941176,0.392828472,49,0.956787456,4213,0.521791476,0.53947038
14687,Phylogenetic Analysis of the Novel Coronavirus Reveals Important Variants in Indian Strains,0,10.1101/2020.04.14.041301,4/15/20,biorxiv,0,2,"whole genome, genome sequences, sequence alignment",0.018161826,0.807710807,0.000728129,0.14937294,0.000728142,0.023298156,Genomics,0.075128436,FALSE,12,0.183190055,2,0.164302917,7,0.785110192,6140,0.676137732,0.452185224
14688,"Analysis of SARS-CoV-2-controlled autophagy reveals spermidine, MK-2206, and niclosamide as putative antiviral therapeutics",0,10.1101/2020.04.15.997254,4/15/20,biorxiv,0,15,metabolom,0.950399329,0.00213072,0.002130638,0.041077938,0.002130752,0.002130623,Drug discovery,0.3463573,FALSE,38.26666667,0.520873276,120.9333333,0.853023816,54,0.959812334,33743,0.948230195,0.820484905
14689,Multidrug treatment with nelfinavir and cepharanthine against COVID-19,0,10.1101/2020.04.14.039925,4/15/20,biorxiv,0,27,"mathematical model, in silico",0.925456455,0.001987252,0.001987086,0.066594997,0.001987093,0.001987117,Drug discovery,0.29537123,FALSE,156.1851852,0.948605356,159.2222222,0.891222906,31,0.931971109,15687,0.875993258,0.911948157
14690,Glycosaminoglycan binding motif at S1/S2 proteolytic cleavage site on spike glycoprotein may facilitate novel coronavirus (SARS-CoV-2) host cell entry,0,10.1101/2020.04.14.041459,4/15/20,biorxiv,0,11,computational,0.990883258,0.001823382,0.001823346,0.001823349,0.001823354,0.001823311,Drug discovery,0.59748524,TRUE,260.1818182,0.986084483,191.9090909,0.913366337,30,0.930057411,7645,0.742114134,0.892905591
14691,Genome based Evolutionary study of SARS-CoV-2 towards the Prediction of Epitope Based Chimeric Vaccine,0,10.1101/2020.04.15.036285,4/15/20,biorxiv,10.1016/j.meegid.2020.104517,9,"in silico, whole genome, sequence alignment",0.751481622,0.148222529,0.000838537,0.097780239,0.000838556,0.000838516,Drug discovery,0.7572307,TRUE,13.88888889,0.209660461,14.11111111,0.413098742,8,0.799987654,3328,0.420418974,0.460791458
14692,"Relevance of enriched expression of SARS-CoV-2 binding receptor ACE2 in gastrointestinal tissue with pathogenesis of digestive symptoms, diabetes-associated mortality, and disease recurrence in COVID-19 patients",0,10.1101/2020.04.14.040204,4/15/20,biorxiv,10.1016/j.mehy.2020.110271,15,"sequencing, transcriptom, proteom",0.826805145,0.043306583,0.000759381,0.000759369,0.000759396,0.127610125,Drug discovery,0.9215319,TRUE,25.71428571,0.376894057,6.571428571,0.288734279,7,0.785110192,6073,0.669877197,0.530153931
14693,De novo 3D models of SARS-CoV-2 RNA elements and small-molecule-binding RNAs to guide drug discovery,0,10.1101/2020.04.14.041962,4/15/20,biorxiv,0,9,"computational, dataset",0.694732958,0.161616958,0.139599094,0.001350374,0.001350308,0.001350308,Drug discovery,0.08752927,FALSE,25.5,0.37435834,15.25,0.427682633,15,0.874313229,5166,0.610402119,0.57168908
14694,Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics during Epidemic Outbreaks,0,10.1101/2020.04.09.20059634,4/14/20,medrxiv,10.1162/99608f92.7656c213,4,bayes,0.233677179,0.001751363,0.075448258,0.598915402,0.001751255,0.088456544,Epidemiology,0.38072312,FALSE,60.25,0.704372565,527.25,0.980933904,0,0.403234768,1570,0.072959307,0.540375136
14695,Analysis of factors associated early diagnosis in coronavirus disease 2019 (COVID-19),0,10.1101/2020.04.09.20059352,4/14/20,medrxiv,0,16,logistic regression,0.00072812,0.000728151,0.384924534,0.068247363,0.000728128,0.544643704,Clinics,0.3577454,FALSE,21.66666667,0.320737213,9,0.337904736,2,0.618927094,3563,0.450036118,0.431901291
14696,"COVID-19 in India: Predictions, Reproduction Number and Public Health Preparedness",0,10.1101/2020.04.09.20059261,4/14/20,medrxiv,0,3,mathematical model,0.001717198,0.001717216,0.001717196,0.991414007,0.001717213,0.00171717,Epidemiology,0.29409355,FALSE,15.66666667,0.236563795,4.333333333,0.237958255,8,0.799987654,5533,0.636648206,0.477789477
14697,Standardization and Age-Distribution of COVID-19: Implications for Variability in Case Fatality and Outbreak Identification,0,10.1101/2020.04.09.20059832,4/14/20,medrxiv,0,3,mathematical model,0.001237147,0.001237111,0.001237167,0.816239442,0.001237132,0.178812001,Epidemiology,0.20489645,FALSE,14,0.213494959,38.33333333,0.62356168,0,0.403234768,3675,0.462557188,0.425712149
14698,Forecasting Covid-19 Outbreak Progression in Italian Regions: A model based on neural network training from Chinese data,0,10.1101/2020.04.09.20059055,4/14/20,medrxiv,0,5,"neural network, network model",0.001126791,0.001126815,0.108146395,0.887346357,0.001126824,0.001126818,Epidemiology,0.19512358,FALSE,22.2,0.327787742,4.6,0.244246722,9,0.814309525,2752,0.320009632,0.426588405
14699,A novel high specificity COVID-19 screening method based on simple blood exams and artificial intelligence,0,10.1101/2020.04.10.20061036,4/14/20,medrxiv,0,1,"machine learning, artificial intelligence, classifier, dataset",0.000561722,0.014318666,0.634010238,0.096322039,0.000561763,0.254225572,Clinics,0.31439114,FALSE,45,0.587049292,32.28571429,0.587704041,11,0.840175319,5884,0.660245606,0.668793564
14700,Optimal Control applied to a SEIR model of 2019-nCoV with social distancing,0,10.1101/2020.04.10.20061069,4/14/20,medrxiv,0,1,mathematical model,0.002806518,0.035317765,0.002806464,0.953456444,0.002806447,0.002806362,Epidemiology,0.24103713,FALSE,4,0.054734368,1,0.122023013,3,0.667819001,2481,0.266795088,0.277842868
14701,Risk Assessment of nCOVID-19 Pandemic In India: A Mathematical Model And Simulation,0,10.1101/2020.04.10.20060830,4/14/20,medrxiv,0,2,mathematical model,0.001272683,0.00127271,0.0012727,0.993636562,0.001272688,0.001272658,Epidemiology,0.22746241,FALSE,22,0.326056033,2,0.164302917,1,0.537564047,2324,0.233806887,0.315432471
14702,"ACE inhibition and cardiometabolic risk factors, lung ACE2 and TMPRSS2 gene expression, and plasma ACE2 levels: a Mendelian randomization study",0,10.1101/2020.04.10.20059121,4/14/20,medrxiv,10.1098/rsos.200958,32,genome-wide,0.511774254,0.174774885,0.00071632,0.000716344,0.045807197,0.266211,Drug discovery,0.44307023,FALSE,94.59375,0.855402313,198.59375,0.917112657,11,0.840175319,5103,0.605345533,0.804508956
14703,Next weeks of SARS-CoV-2: Projection model to predict time evolution scenarios of accumulated cases in Spain,0,10.1101/2020.04.09.20059881,4/14/20,medrxiv,0,2,predictive model,0.001461906,0.05794288,0.001461908,0.936209463,0.001461915,0.001461929,Epidemiology,0.18859899,FALSE,15,0.227596017,1.5,0.138747659,0,0.403234768,1426,0.04647243,0.204012718
14704,"The Epidemiological Implications of Incarceration Dynamics in Jails for Community, Corrections Officer, and Incarcerated Population Risks from COVID-19",0,10.1101/2020.04.08.20058842,4/14/20,medrxiv,0,6,mathematical model,0.002720128,0.002720124,0.002720272,0.754907395,0.234211848,0.002720234,Epidemiology,0.5679002,TRUE,44.6,0.582658173,31.6,0.582218357,10,0.828199272,7607,0.739465447,0.683135312
14705,Recovery Ratios Reliably Anticipate COVID-19 Pandemic Progression,0,10.1101/2020.04.09.20059824,4/14/20,medrxiv,0,3,predictive model,0.002183268,0.002183338,0.002183302,0.951823676,0.002183287,0.039443128,Epidemiology,0.13511369,FALSE,10,0.15214299,0.333333333,0.073187048,0,0.403234768,2920,0.352997833,0.24539066
14706,COVID-19 pandemic and lockdown measures impact on mental health among the general population in Italy. An N=18147 web-based survey.,0,10.1101/2020.04.09.20057802,4/14/20,medrxiv,10.3389/fpsyt.2020.00790,10,logistic regression,0.001538092,0.001538103,0.001538083,0.001538108,0.99230952,0.001538094,Healthcare,0.95693886,TRUE,38.7,0.526068402,28.8,0.560543216,57,0.962281622,12168,0.834818204,0.720927861
14707,ACE 2 Coding Variants: A Potential X-linked Risk Factor for COVID-19 Disease,0,10.1101/2020.04.05.026633,4/14/20,biorxiv,0,2,"sequencing, exom, genome-wide",0.154407927,0.593553567,0.03193137,0.000746594,0.080866044,0.138494499,Genomics,0.36109364,FALSE,61,0.709196611,202,0.91838373,22,0.908142478,7165,0.719720684,0.813860876
14708,Improving COVID-19 Testing Efficiency using Guided Agglomerative Sampling,0,10.1101/2020.04.13.039792,4/14/20,biorxiv,0,8,machine learning,0.002080589,0.134223418,0.461763667,0.397771063,0.002080661,0.002080603,Epidemiology,0.041547596,FALSE,126.625,0.916259509,277.25,0.947886005,1,0.537564047,2870,0.343366241,0.686268951
14709,Global COVID-19 transmission rate is influenced by precipitation seasonality and the speed of climate temperature warming,0,10.1101/2020.04.10.20060459,4/14/20,medrxiv,0,2,dataset,0.001943503,0.001943533,0.00194357,0.931019738,0.001943539,0.061206118,Epidemiology,0.3726938,FALSE,5,0.070752675,1.5,0.138747659,15,0.874313229,5063,0.602215266,0.421507207
14710,Application of COVID-19 pneumonia diffusion data to predict epidemic situation,0,10.1101/2020.04.11.20061432,4/14/20,medrxiv,0,1,mathematical model,0.001438119,0.001438134,0.045212506,0.871837889,0.001438127,0.078635225,Epidemiology,0.13539106,FALSE,9,0.135320675,3,0.199424672,0,0.403234768,1991,0.160125211,0.224526331
14711,Single Nucleus Multiomic Profiling Reveals Age-Dynamic Regulation of Host Genes Associated with SARS-CoV-2 Infection,0,10.1101/2020.04.12.037580,4/14/20,biorxiv,0,22,"multiom, dataset",0.652789383,0.090024773,0.002238655,0.002238548,0.250469942,0.002238699,Drug discovery,0.24183348,FALSE,28,0.408312202,38.5,0.624765855,18,0.891474782,10006,0.800144474,0.681174328
14712,"Estimating required lockdown cycles before immunity to SARS-CoV-2: Model-based analyses of susceptible population sizes, S0, in seven European countries including the UK and Ireland",0,10.1101/2020.04.10.20060426,4/14/20,medrxiv,10.12688/wellcomeopenres.15886.1,9,"bayes, bayesian model",0.011388767,0.000800594,0.00080057,0.98540888,0.000800596,0.000800593,Epidemiology,0.16269752,FALSE,401.1111111,0.995732575,,,14,0.866658436,2786,0.328437274,0.730276095
14713,CoV Genome Tracker: tracing genomic footprints of Covid-19 pandemic,0,10.1101/2020.04.10.036343,4/14/20,biorxiv,0,7,"genome sequences, genomes",0.001823398,0.563434153,0.001823428,0.42927225,0.001823418,0.001823352,Genomics,0.4517156,FALSE,17.42857143,0.261982807,15.57142857,0.430960664,8,0.799987654,4523,0.554538888,0.511867503
14714,Comparative in vitro transcriptomic analyses of COVID-19 candidate therapy hydroxychloroquine suggest limited immunomodulatory evidence of SARS-CoV-2 host response genes.,0,10.1101/2020.04.13.039263,4/14/20,biorxiv,0,5,transcriptom,0.848106559,0.001350364,0.001350337,0.001350337,0.022791261,0.125051142,Drug discovery,0.388523,FALSE,55.4,0.670542396,86.4,0.794019267,7,0.785110192,3960,0.49434144,0.686003324
14715,"Hydroxychloroquine in patients with COVID-19: an open-label, randomized, controlled trial",0,10.1101/2020.04.10.20060558,4/14/20,medrxiv,0,24,dataset,0.020480265,0.000422318,0.000422319,0.192550927,0.126959811,0.65916436,Clinics,0.6879764,TRUE,98.41666667,0.865668873,120.0416667,0.85141825,154,0.987530094,186061,0.994943414,0.924890158
14716,Comparison of SARS-CoV-2 infections among 3 species of non-human primates,0,10.1101/2020.04.08.031807,4/12/20,biorxiv,0,33,genomes,0.21468489,0.405312549,0.055829334,0.12121412,0.001392921,0.201566186,Genomics,0.55194694,TRUE,48.61764706,0.618591131,42.79411765,0.648514851,52,0.958701154,14069,0.857934024,0.77093529
14717,BIP4COVID19: Releasing impact metrics data for articles relevant to COVID-19,0,10.1101/2020.04.11.037093,4/12/20,biorxiv,0,5,dataset,0.002639123,0.002639042,0.002639313,0.986804068,0.002639411,0.002639044,Epidemiology,0.3867489,FALSE,25.4,0.372626631,79.2,0.77642494,1,0.537564047,5787,0.651817963,0.584608395
14718,Immuno-informatics Characterization SARS-CoV-2 Spike Glycoprotein for Prioritization of Epitope based Multivalent Peptide Vaccine,0,10.1101/2020.04.05.026005,4/12/20,biorxiv,10.1016/j.molliq.2020.113612,3,molecular dynamics simulation,0.994218491,0.001156352,0.001156282,0.001156329,0.001156307,0.001156239,Drug discovery,0.86989105,TRUE,42.33333333,0.560888119,8,0.320511105,7,0.785110192,3244,0.406693956,0.518300843
14719,Use of a Single Ventilator to Support Multiple Patients: Modeling Tidal Volume Response to Heterogeneous Lung Mechanics,0,10.1101/2020.04.07.20056671,4/11/20,medrxiv,0,5,computational,0.001254669,0.001254648,0.442660853,0.225267682,0.00125465,0.328307499,Clinics,0.12221348,FALSE,74.6,0.785020719,30.6,0.573655339,1,0.537564047,1583,0.072718517,0.492239655
14720,"Simplified model of the number of Covid-19 patients in the ICU: update April 6, 2020",0,10.1101/2020.04.07.20056226,4/11/20,medrxiv,0,7,mathematical model,0.001371233,0.001371294,0.00137129,0.381299879,0.001371259,0.613215045,Clinics,0.27452236,FALSE,74.28571429,0.782979776,25.28571429,0.530840246,0,0.403234768,1508,0.058752709,0.443951875
14721,"Prediction of the clinical outcome of COVID-19 patients using T lymphocyte subsets with 340 cases from Wuhan, China: a retrospective cohort study and a web visualization tool",0,10.1101/2020.04.06.20056127,4/11/20,medrxiv,0,10,logistic regression,0.132081401,0.000946126,0.000946132,0.000946155,0.237575222,0.627504963,Clinics,0.84973127,TRUE,33.5,0.471890655,29.6,0.566630987,9,0.814309525,2920,0.348904406,0.550433894
14722,INFEKTA: A General Agent-based Model for Transmission of Infectious Diseases: Studying the COVID-19 Propagation in Bogota - Colombia,0,10.1101/2020.04.06.20056119,4/11/20,medrxiv,10.1371/journal.pone.0245787,4,computational,0.0024906,0.0024905,0.0024905,0.987547451,0.002490467,0.002490483,Epidemiology,0.55390453,TRUE,19.5,0.29117447,3.75,0.21982874,14,0.866658436,3389,0.42499398,0.450663906
14723,A first study on the impact of current and future control measures on the spread of COVID-19 in Germany,0,10.1101/2020.04.08.20056630,4/11/20,medrxiv,0,8,mathematical model,0.007061611,0.007061669,0.007061649,0.964691983,0.007061532,0.007061556,Epidemiology,0.43671525,FALSE,39.125,0.531263529,24,0.521808938,16,0.881782826,5285,0.615217915,0.637518302
14724,Adjuvant corticosteroid therapy for critically ill patients with COVID-19,0,10.1101/2020.04.07.20056390,4/11/20,medrxiv,10.1186/s13054-020-02964-w,7,logistic regression,0.001593656,0.001593534,0.001593519,0.001593541,0.001593612,0.992032139,Clinics,0.737637,TRUE,35.85714286,0.495330571,,,64,0.966479412,6141,0.670358777,0.71072292
14725,Multi-route respiratory infection: when a transmission route may dominate,0,10.1101/2020.04.06.20055228,4/11/20,medrxiv,10.1016/j.scitotenv.2020.141856,7,mathematical model,0.001538211,0.055526502,0.001538103,0.838351097,0.101507901,0.001538186,Epidemiology,0.61796457,TRUE,66,0.741356918,91.71428571,0.805325127,4,0.707574542,2900,0.34457019,0.649706694
14726,Early Stage Prediction of US County Vulnerability to the COVID-19 Pandemic,0,10.1101/2020.04.06.20055285,4/11/20,medrxiv,0,4,classifier,0.000926276,0.000926299,0.060400206,0.82769929,0.052648731,0.057399197,Epidemiology,0.094879,FALSE,42.75,0.564598924,94,0.810944608,0,0.403234768,2867,0.338068866,0.529211791
14727,Analysis of the mutation dynamics of SARS-CoV-2 reveals the spread history and emergence of RBD mutant with lower ACE2 binding affinity,0,10.1101/2020.04.09.034942,4/11/20,biorxiv,0,11,"in silico, genome sequences",0.366098264,0.629714283,0.001046853,0.001046855,0.001046892,0.001046853,Genomics,0.19583142,FALSE,42.36363636,0.560949966,,,62,0.965553429,33617,0.947267036,0.824590144
14728,Brief Analysis of the ARIMA model on the COVID-19 in Italy,0,10.1101/2020.04.08.20058636,4/11/20,medrxiv,0,4,mathematical model,0.001823344,0.001823354,0.001823469,0.898097667,0.001823416,0.094608749,Epidemiology,0.07099089,FALSE,25.25,0.370709382,4.75,0.248260637,2,0.618927094,2386,0.242956899,0.370213503
14729,De novo design of high-affinity antibody variable regions (scFv) against the SARS-CoV-2 spike protein,0,10.1101/2020.04.09.034868,4/11/20,biorxiv,0,3,computational,0.585575556,0.309862362,0.00097748,0.101629646,0.000977476,0.00097748,Drug discovery,0.35310963,FALSE,87,0.833694106,204.6666667,0.919454108,1,0.537564047,4593,0.556946785,0.711914762
14730,Classification of COVID-19 in intensive care patients: towards rational and effective clinical triage,0,10.1101/2020.04.09.20058909,4/11/20,medrxiv,0,6,bayes,0.001511836,0.001511857,0.370569155,0.102207562,0.001511925,0.522687665,Clinics,0.71957886,TRUE,18.83333333,0.281588224,,,0,0.403234768,2613,0.287984589,0.324269194
14731,"Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: Case study of COVID-19 in Alberta, Canada and Philadelphia, USA",0,10.1101/2020.04.08.20057661,4/11/20,medrxiv,10.1186/s12874-020-01037-4,3,bayes,0.001392846,0.077368677,0.305356594,0.563453355,0.001392946,0.051035582,Epidemiology,0.23131928,FALSE,43.66666667,0.57492733,26.66666667,0.543216484,8,0.799987654,2265,0.213821334,0.5329882
14732,Characterizing key attributes of the epidemiology of COVID-19 in China: Model-based estimations,0,10.1101/2020.04.08.20058214,4/11/20,medrxiv,0,7,mathematical model,0.001461901,0.1385138,0.00146185,0.723598505,0.133502029,0.001461915,Epidemiology,0.3995586,FALSE,12.85714286,0.192714454,5.571428571,0.267527428,19,0.89561084,2897,0.3443294,0.425045531
14733,"In-depth Bioinformatic Analyses of Human SARS-CoV-2, SARS-CoV, MERS-CoV, and Other Nidovirales Suggest Important Roles of Noncanonical Nucleic Acid Structures in Their Lifecycles",0,10.1101/2020.04.09.031252,4/11/20,biorxiv,10.3389/fmicb.2020.01583,11,"bioinformatic, genomes",0.446589663,0.54691948,0.001622724,0.001622754,0.001622701,0.001622678,Genomics,0.8559261,TRUE,13.36363636,0.201805925,4.454545455,0.23963072,0,0.403234768,7373,0.727185167,0.392964145
14734,Understanding Economic and Health Factors Impacting the Spread of COVID-19 Disease,0,10.1101/2020.04.10.20058222,4/11/20,medrxiv,0,4,"bayes, correlation analysis, dataset",0.000772638,0.023372081,0.088153541,0.886156457,0.000772639,0.000772643,Epidemiology,0.4921839,FALSE,18.5,0.278001113,2.25,0.170925876,6,0.764429903,5241,0.611846858,0.456300938
14735,Positive selection of ORF3a and ORF8 genes drives the evolution of SARS-CoV-2 during the 2020 COVID-19 pandemic,0,10.1101/2020.04.10.035964,4/11/20,biorxiv,10.3389/fmicb.2020.550674,6,genomes,0.310980643,0.67661281,0.00310143,0.003101781,0.00310176,0.003101577,Genomics,0.47946227,FALSE,54.5,0.665161729,65.66666667,0.741236286,14,0.866658436,6858,0.705754876,0.744702832
14736,COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms,0,10.1101/2020.04.08.20057679,4/11/20,medrxiv,0,3,"machine learning, deep learning, artificial intelligence, mathematical model",0.002130687,0.002130718,0.346841421,0.644635667,0.002130874,0.002130632,Epidemiology,0.5747953,TRUE,50.33333333,0.63380543,19,0.471367407,47,0.95493549,13826,0.853599807,0.728427034
14737,"Factors associated with hospitalization and critical illness among 4,103 patients with COVID-19 disease in New York City",0,10.1101/2020.04.08.20057794,4/11/20,medrxiv,10.1136/bmj.m1966,9,logistic regression,0.001371286,0.001371302,0.063615438,0.001371286,0.001371267,0.930899421,Clinics,0.8224355,TRUE,87,0.833694106,103.1,0.827535456,278,0.994567566,136047,0.989886829,0.911420989
14738,COVID-19 epidemic in Malaysia: Impact of lock-down on infection dynamics,0,10.1101/2020.04.08.20057463,4/11/20,medrxiv,0,10,forecasting model,0.001098808,0.001098822,0.001098816,0.994505932,0.001098815,0.001098807,Epidemiology,0.445705,FALSE,32.1,0.456305276,10.3,0.358710195,14,0.866658436,7636,0.737539128,0.604803259
14739,HOW THE COVID-19 PANDEMIA IS SPREADING IN ITALY,0,10.1101/2020.04.07.20056846,4/11/20,medrxiv,0,1,mathematical model,0.003607396,0.0036073,0.003607435,0.923987033,0.003607337,0.0615835,Epidemiology,0.24349892,FALSE,54,0.661574618,5,0.257024351,0,0.403234768,2432,0.2523477,0.393545359
14740,Infoveillance based on Social Sensors to Analyze the impact of Covid19 in South American Population,0,10.1101/2020.04.06.20055749,4/11/20,medrxiv,0,1,text mining,0.002490429,0.00249047,0.002490486,0.987547642,0.002490503,0.00249047,Epidemiology,0.40758792,FALSE,5,0.070752675,0,0.055525823,5,0.739490092,1903,0.136046232,0.250453705
14741,The Framework for the Prediction of the Critical Turning Period for Outbreak of COVID-19 Spread in China based on the iSEIR Model,0,10.1101/2020.04.05.20054346,4/11/20,medrxiv,0,5,mathematical model,0.001112617,0.001112642,0.001112669,0.994436754,0.00111265,0.001112669,Epidemiology,0.124537796,FALSE,21.2,0.313686684,6,0.280037463,1,0.537564047,1505,0.05803034,0.297329634
14742,Textile Masks and Surface Covers - A 'Universal Droplet Reduction Model'Against Respiratory Pandemics,0,10.1101/2020.04.07.20045617,4/10/20,medrxiv,10.3389/fmed.2020.00260,5,simulation model,0.001272713,0.001272733,0.090163455,0.795019592,0.110998764,0.001272741,Epidemiology,0.57586837,TRUE,99,0.867029501,119.6,0.850816163,9,0.814309525,5300,0.615699494,0.786963671
14743,"Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, Italy",0,10.1101/2020.04.08.20040907,4/10/20,medrxiv,0,10,"artificial intelligence, neural network, dataset",0.000854689,0.000854703,0.914589998,0.000854704,0.000854723,0.081991184,Imaging,0.2644394,FALSE,6,0.086028821,1.1,0.1226251,33,0.936045435,4035,0.497953287,0.410663161
14744,Is the impact of social distancing on coronavirus growth rates effective across different settings? A non-parametric and local regression approach to test and compare the growth rate,0,10.1101/2020.04.07.20049049,4/10/20,medrxiv,0,1,mathematical model,0.001901712,0.00190171,0.00190182,0.990491333,0.001901728,0.001901697,Epidemiology,0.059050024,FALSE,11,0.167171748,0,0.055525823,3,0.667819001,3065,0.372501806,0.315754595
14745,The Use of Adjuvant Therapy in Preventing Progression to Severe Pneumonia in Patients with Coronavirus Disease 2019: A Multicenter Data Analysis,0,10.1101/2020.04.08.20057539,4/10/20,medrxiv,0,15,logistic regression,0.165427983,0.00070492,0.000704919,0.000704925,0.000704917,0.831752337,Clinics,0.84501964,TRUE,53.93333333,0.660337683,46.86666667,0.667982339,30,0.930057411,15729,0.874789309,0.783291685
14746,Robust Estimation of Infection Fatality Rates during the Early Phase of a Pandemic,0,10.1101/2020.04.08.20057729,4/10/20,medrxiv,0,1,dataset,0.001486566,0.06820702,0.001486516,0.623750974,0.001486495,0.303582428,Epidemiology,0.2605715,FALSE,55,0.668686994,2,0.164302917,3,0.667819001,3074,0.374187334,0.468749062
14747,Purely data-driven exploration of COVID-19 pandemic after three months of the outbreak,0,10.1101/2020.04.08.20057638,4/10/20,medrxiv,0,2,dataset,0.0026391,0.002639058,0.002639027,0.986804754,0.002639058,0.002639003,Epidemiology,0.45534545,FALSE,16,0.243552477,4,0.231469093,2,0.618927094,1761,0.10402119,0.299492463
14748,Understanding the B and T cells epitopes of spike protein of severe respiratory syndrome coronavirus-2: A computational way to predict the immunogens,0,10.1101/2020.04.08.013516,4/10/20,biorxiv,10.1016/j.meegid.2020.104382,3,computational,0.809636741,0.179137507,0.002806455,0.002806492,0.002806356,0.002806449,Drug discovery,0.8081429,TRUE,64.33333333,0.730781124,36.33333333,0.613393096,10,0.828199272,2996,0.357572839,0.632486583
14749,BioLaboro: A bioinformatics system for detecting molecular assay signature erosion and designing new assays in response to emerging and reemerging pathogens,0,10.1101/2020.04.08.031963,4/10/20,biorxiv,0,10,"bioinformatic, sequencing, in silico, whole genome, genome sequences, genomes",0.12784299,0.816424283,0.053143465,0.00086311,0.000863086,0.000863067,Genomics,0.2022016,FALSE,6.2,0.087574989,7,0.299973241,4,0.707574542,3332,0.415603178,0.377681487
14750,Rapid in silico design of antibodies targeting SARS-CoV-2 using machine learning and supercomputing,0,10.1101/2020.04.03.024885,4/10/20,biorxiv,0,5,"machine learning, computational, bioinformatic, in silico, structural model",0.7113429,0.152621083,0.133634011,0.000800688,0.000800609,0.00080071,Drug discovery,0.2633915,FALSE,31.8,0.452965551,60.6,0.722972973,7,0.785110192,5038,0.59715868,0.639551849
14751,ACE2 fragment as a decoy for novel SARS-Cov-2 virus,0,10.1101/2020.04.06.028647,4/10/20,biorxiv,0,2,"molecular dynamics simulation, computational",0.986399544,0.002720138,0.00272009,0.002720099,0.002720064,0.002720064,Drug discovery,0.40347168,FALSE,17,0.257467994,10,0.355632861,2,0.618927094,4161,0.512159884,0.436046958
14752,Human ACE2 receptor polymorphisms predict SARS-CoV-2 susceptibility,0,10.1101/2020.04.07.024752,4/10/20,biorxiv,0,20,dataset,0.422106492,0.539168256,0.00109882,0.001098832,0.001098852,0.035428748,Genomics,0.28335422,FALSE,44.1,0.578514441,151.3,0.884131656,94,0.976726958,30380,0.939802552,0.844793902
14753,Data model to predict prevalence of COVID-19 in Pakistan,0,10.1101/2020.04.06.20055244,4/10/20,medrxiv,0,5,mathematical model,0.002490444,0.046270446,0.002490522,0.943767515,0.002490553,0.00249052,Epidemiology,0.11784333,FALSE,25,0.369286907,3.4,0.208589778,4,0.707574542,6727,0.697808813,0.49581501
14754,Nonmedical Masks in Public for Respiratory Pandemics: Droplet Retention by two-layer Textile Barrier Fully Protects Germ-free Mice from Bacteria in Droplets,0,10.1101/2020.04.06.028688,4/10/20,biorxiv,0,3,simulation model,0.121087074,0.085141723,0.064816102,0.513811143,0.213392688,0.001751269,Epidemiology,0.25592247,FALSE,93.33333333,0.852433669,90,0.801445009,2,0.618927094,3936,0.487599326,0.690101275
14755,Single-cell atlas of a non-human primate reveals new pathogenic mechanisms of COVID-19,0,10.1101/2020.04.10.022103,4/10/20,biorxiv,0,40,"transcriptom, correlation analysis",0.921636974,0.001330138,0.001330048,0.001330107,0.001330045,0.073042687,Drug discovery,0.4207487,FALSE,78.3,0.79943101,165.25,0.895972705,12,0.850299401,11002,0.816758969,0.840615521
14756,Confronting the COVID-19 Pandemic with Systems Biology,0,10.1101/2020.04.06.028712,4/10/20,biorxiv,0,11,"transcriptom, proteom",0.682410434,0.120755385,0.002898294,0.002898344,0.002898549,0.188138995,Drug discovery,0.55777895,TRUE,24.125,0.356484631,49.875,0.681161359,4,0.707574542,19552,0.902720925,0.661985364
14757,Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium,0,10.1101/2020.04.09.034454,4/10/20,biorxiv,10.1038/s41467-020-18781-2,26,transcriptom,0.636078398,0.133261368,0.002562637,0.00256278,0.222972113,0.002562703,Drug discovery,0.6874596,TRUE,102.5925926,0.874698497,736.7777778,0.988627241,50,0.957775171,9131,0.78208524,0.900796537
14758,A Highly Scalable and Rapidly Deployable RNA Extraction-Free COVID-19 Assay by Quantitative Sanger Sequencing,0,10.1101/2020.04.07.029199,4/10/20,biorxiv,0,4,sequencing,0.001717211,0.68383417,0.16847357,0.142540533,0.001717276,0.001717241,Genomics,0.16759187,FALSE,5,0.070752675,0.75,0.099411292,9,0.814309525,5589,0.635925837,0.405099832
14759,Decoding the lethal effect of SARS-CoV-2 (novel coronavirus) strains from global perspective: molecular pathogenesis and evolutionary divergence,0,10.1101/2020.04.06.027854,4/9/20,biorxiv,0,4,"sequencing, whole genome, sequence alignment",0.087478665,0.908965066,0.000889051,0.000889083,0.000889051,0.000889085,Genomics,0.44149056,FALSE,14.25,0.215535902,5,0.257024351,7,0.785110192,9409,0.786419456,0.511022475
14760,Gene expression and in situ protein profiling of candidate SARS-CoV-2 receptors in human airway epithelial cells and lung tissue,0,10.1101/2020.04.07.030742,4/9/20,biorxiv,10.1183/13993003.01123-2020,22,"sequencing, dataset",0.869902747,0.125808319,0.001072222,0.00107227,0.001072227,0.001072215,Drug discovery,0.49053925,FALSE,73.59090909,0.780196673,71.36363636,0.757225047,34,0.937156615,7817,0.743799663,0.804594499
14761,The spatial and cell-type distribution of SARS-CoV-2 receptor ACE2 in human and mouse brain,0,10.1101/2020.04.07.030650,4/9/20,biorxiv,10.3389/fneur.2020.573095,8,"transcriptom, dataset",0.67900477,0.001901911,0.001901813,0.282775098,0.032514617,0.00190179,Drug discovery,0.6602456,TRUE,56.125,0.675737522,36.75,0.615466952,94,0.976726958,12453,0.836262942,0.776048594
14762,3D Models of glycosylated SARS-CoV-2 spike protein suggest challenges and opportunities for vaccine development,0,10.1101/2020.04.07.030445,4/9/20,biorxiv,0,4,glycomics,0.990883012,0.00182342,0.001823373,0.001823393,0.001823437,0.001823365,Drug discovery,0.14641547,FALSE,79.25,0.803512895,111,0.839844795,29,0.928020248,14262,0.859378762,0.857689175
14763,SARS-CoV-2 might manipulate against its host the immunity RNAi/Dicer/Ago system,0,10.1101/2020.04.08.031856,4/9/20,biorxiv,0,2,"computational, transcriptom, genomes",0.359347096,0.634278829,0.001593585,0.001593544,0.001593468,0.001593477,Genomics,0.15881646,FALSE,45.5,0.591440411,53.5,0.695477656,2,0.618927094,5319,0.615940284,0.630446361
14764,Characterizing geographical and temporal dynamics of novel coronavirus SARS-CoV-2 using informative subtype markers,0,10.1101/2020.04.07.030759,4/9/20,biorxiv,10.1371/journal.pcbi.1008269,5,genomes,0.000693882,0.833917007,0.000693881,0.163307513,0.000693858,0.000693859,Genomics,0.20138931,FALSE,47,0.606407323,45.66666667,0.661760771,9,0.814309525,16395,0.879605105,0.740520681
14765,"Acute kidney injury in patients hospitalized with COVID-19 in Wuhan, China: A single-center retrospective observational study",0,10.1101/2020.04.06.20055194,4/8/20,medrxiv,0,10,logistic regression,0.001126817,0.001126791,0.015285978,0.001126799,0.001126799,0.980206815,Clinics,0.5285796,TRUE,36.9,0.507205146,28.5,0.558402462,24,0.914439163,6967,0.707681194,0.671931991
14766,Use Crow-AMSAA Method to predict the cases of the Coronavirus 19 in Michigan and U.S.A,0,10.1101/2020.04.03.20052845,4/8/20,medrxiv,10.1016/j.idm.2020.07.001,1,model fit,0.002422309,0.002422283,0.00242229,0.987888429,0.002422276,0.002422412,Epidemiology,0.444441,FALSE,0,0.006432061,,,0,0.403234768,2113,0.178666025,0.196110951
14767,Optimize Clinical Laboratory Diagnosis of COVID-19 from Suspect Cases by Likelihood Ratio of SARS-CoV-2 IgM and IgG antibody,0,10.1101/2020.04.07.20053660,4/8/20,medrxiv,0,1,sequencing,0.001538257,0.664448988,0.203302055,0.001538126,0.001538109,0.127634465,Genomics,0.45375538,FALSE,12,0.183190055,0,0.055525823,3,0.667819001,4141,0.506380929,0.353228952
14768,LAMP-Seq: Population-Scale COVID-19 Diagnostics Using a Compressed Barcode Space,0,10.1101/2020.04.06.025635,4/8/20,biorxiv,0,25,sequencing,0.001565331,0.463827972,0.001565392,0.529910652,0.001565357,0.001565296,Epidemiology,0.27844223,FALSE,62.32,0.716989301,391.92,0.97029703,43,0.9507377,51080,0.968697327,0.901680339
14769,Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses,0,10.1101/2020.04.05.026450,4/8/20,biorxiv,10.1073/pnas.2008176117,6,machine learning,0.001943621,0.830316829,0.161908887,0.001943661,0.001943532,0.001943471,Genomics,0.5016096,TRUE,261.8333333,0.986455563,2112.166667,0.998795826,78,0.972405704,14241,0.858174813,0.953957977
14770,Sequence-based prediction of vaccine targets for inducing T cell responses to SARS-CoV-2 utilizing the bioinformatics predictor RECON,0,10.1101/2020.04.06.027805,4/8/20,biorxiv,10.1186/s13073-020-00767-w,6,"bioinformatic, proteom, genomes",0.738246455,0.227395993,0.000926315,0.000926316,0.000926325,0.031578596,Drug discovery,0.28169116,FALSE,40.16666667,0.540602387,128.5,0.86178753,17,0.887338725,4246,0.51673489,0.701615883
14771,Plasma Metabolomic and Lipidomic Alterations Associated with COVID-19,0,10.1101/2020.04.05.20053819,4/7/20,medrxiv,10.1093/nsr/nwaa086,23,"metabolom, lipidom",0.3484286,0.001751325,0.001751216,0.00175126,0.001751343,0.644566257,Clinics,0.9860829,TRUE,13.14285714,0.198218814,,,42,0.949503056,23790,0.920780159,0.689500676
14772,Feasibility of Controlling COVID-19 Outbreaks in the UK by Rolling Interventions,0,10.1101/2020.04.05.20054429,4/7/20,medrxiv,0,8,mathematical model,0.00045962,0.008383155,0.007939284,0.969581977,0.013176328,0.000459636,Epidemiology,0.09960052,FALSE,41,0.549013544,19.75,0.477990367,6,0.764429903,2820,0.323139899,0.528643428
14773,Generic probabilistic modelling and non-homogeneity issues for the UK epidemic of COVID-19,0,10.1101/2020.04.04.20053462,4/7/20,medrxiv,0,2,"computational, probabilistic",0.000793459,0.000793418,0.000793454,0.911907842,0.000793436,0.084918392,Epidemiology,0.17193758,FALSE,69.5,0.761271569,31,0.578204442,9,0.814309525,1826,0.11533831,0.567280962
14774,Autocatalytic Model for Covid-19 Progression in a Country,0,10.1101/2020.04.03.20052985,4/7/20,medrxiv,0,1,computational,0.002806525,0.002806471,0.147579265,0.841194873,0.002806393,0.002806472,Epidemiology,0.19486257,FALSE,3,0.037293586,0,0.055525823,2,0.618927094,1773,0.10353961,0.203821528
14775,Corona Epidemic in Indian context: Predictive Mathematical Modelling,0,10.1101/2020.04.03.20047175,4/7/20,medrxiv,0,3,mathematical model,0.001371282,0.001371282,0.001371403,0.931819032,0.034460544,0.029606458,Epidemiology,0.43229553,FALSE,14.66666667,0.221163956,0.666666667,0.096200161,8,0.799987654,3995,0.490248013,0.401899946
14776,Loss of smell and taste in combination with other symptoms is a strong predictor of COVID-19 infection,0,10.1101/2020.04.05.20048421,4/7/20,medrxiv,10.1038/s41591-020-0916-2,13,mathematical model,0.001022633,0.098341499,0.2748172,0.029477951,0.595318025,0.001022693,Healthcare,0.66936934,TRUE,28.38461538,0.410847919,44.38461538,0.655806797,118,0.982159393,52248,0.969419697,0.754558451
14777,A globally available COVID-19 - Template for clinical imaging studies,0,10.1101/2020.04.02.20048793,4/7/20,medrxiv,0,5,radiom,0.143588122,0.001010995,0.573600132,0.00101102,0.086214945,0.194574786,Imaging,0.716646,TRUE,88,0.837095677,47,0.668718223,2,0.618927094,2193,0.192631832,0.579343207
14778,A Bayesian Logistic Growth Model for the Spread of COVID-19 in New York,0,10.1101/2020.04.05.20054577,4/7/20,medrxiv,0,1,bayes,0.003607309,0.003607205,0.003607408,0.981963569,0.003607266,0.003607243,Epidemiology,0.0718129,FALSE,7,0.10179974,2,0.164302917,7,0.785110192,2855,0.331085962,0.345574703
14779,Study of Epidemiological Characteristics and In-silico Analysis of the Effect of Interventions in the SARS-CoV-2 Epidemic in India,0,10.1101/2020.04.05.20053884,4/7/20,medrxiv,0,8,in-silico,0.002080545,0.00208056,0.002080538,0.788344115,0.002080606,0.203333635,Epidemiology,0.31703755,FALSE,9.625,0.143917373,0.75,0.099411292,2,0.618927094,3112,0.377076812,0.309833143
14780,"COVID-19 pandemic: Impact of lockdown, contact and non-contact transmissions on infection dynamics",0,10.1101/2020.04.04.20050328,4/7/20,medrxiv,0,1,model simulation,0.001034584,0.001034599,0.001034599,0.99482704,0.001034587,0.001034592,Epidemiology,0.1216037,FALSE,20,0.298163152,24,0.521808938,7,0.785110192,7872,0.744522032,0.587401078
14781,Fast spread of COVID-19 in Europe and the US and its implications: even modest public health goals require comprehensive intervention,0,10.1101/2020.04.04.20050427,4/7/20,medrxiv,0,4,mathematical model,0.001901691,0.001901729,0.001901706,0.990491466,0.0019017,0.001901708,Epidemiology,0.13482136,FALSE,49.5,0.628362917,74.5,0.765252877,17,0.887338725,3507,0.4363111,0.679316405
14782,COVID-19 diagnosis prediction in emergency care patients: a machine learning approach,0,10.1101/2020.04.04.20052092,4/7/20,medrxiv,0,4,"machine learning, neural network, logistic regression",0.001486467,0.00148647,0.784091848,0.001486528,0.060835208,0.15061348,Clinics,0.7024892,TRUE,17.5,0.263776362,5.5,0.267259834,29,0.928020248,7406,0.724777269,0.545958428
14783,Increased Detection coupled with Social Distancing and Health Capacity Planning Reduce the Burden of COVID-19 Cases and Fatalities: A Proof of Concept Study using a Stochastic Computational Simulation Model,0,10.1101/2020.04.05.20054775,4/7/20,medrxiv,0,5,"simulation model, computational",0.001237067,0.001237111,0.034025419,0.855484718,0.034308083,0.073707602,Epidemiology,0.29780918,FALSE,14.2,0.214793741,0.8,0.101351351,5,0.739490092,3103,0.374909704,0.357636222
14784,Investigating the likely association between genetic ancestry and COVID-19 manifestation,0,10.1101/2020.04.05.20054627,4/7/20,medrxiv,0,2,genomes,0.06336914,0.601634508,0.00071634,0.114197455,0.000716377,0.21936618,Genomics,0.3434412,FALSE,6.5,0.093512277,1.5,0.138747659,5,0.739490092,8111,0.751986516,0.430934136
14785,Explaining the Bomb-Like Dynamics of COVID-19 with Modeling and the Implications for Policy,0,10.1101/2020.04.05.20054338,4/7/20,medrxiv,0,14,bayes,0.001901727,0.001901849,0.00190178,0.898215211,0.029292426,0.066787007,Epidemiology,0.19226757,FALSE,27.3,0.398602264,41.2,0.640620819,11,0.840175319,4894,0.581748134,0.615286634
14786,A mechanistic population balance model to evaluate the impact of interventions on infectious disease outbreaks: Case for COVID19,0,10.1101/2020.04.04.20053017,4/7/20,medrxiv,0,4,model simulation,0.001098854,0.001098842,0.001098887,0.921227146,0.074377385,0.001098885,Epidemiology,0.14223775,FALSE,40,0.539860226,14.25,0.415038801,3,0.667819001,3305,0.407897905,0.507653983
14787,Adaptive cyclic exit strategies from lockdown to suppress COVID-19 and allow economic activity,0,10.1101/2020.04.04.20053579,4/7/20,medrxiv,0,12,mathematical model,0.002032771,0.032490409,0.002032764,0.959378467,0.002032829,0.00203276,Epidemiology,0.12578419,FALSE,12.08333333,0.183313749,,,58,0.963022409,20636,0.907055141,0.684463766
14788,Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia,0,10.1101/2020.04.03.20052068,4/6/20,medrxiv,0,13,logistic regression,0.001371355,0.001371397,0.445741643,0.00137129,0.001371255,0.548773059,Clinics,0.66685045,TRUE,70.69230769,0.766961469,26.61538462,0.542547498,8,0.799987654,1996,0.151697568,0.565298547
14789,Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning,0,10.1101/2020.04.03.20052084,4/6/20,medrxiv,0,2,"machine learning, neural network, network model",0.001438144,0.031951485,0.115278467,0.848455646,0.00143812,0.001438138,Epidemiology,0.20580313,FALSE,21,0.312016822,2.5,0.180826866,52,0.958701154,83578,0.982903925,0.608612192
14790,CovProfile: profiling the viral genome and gene expressions of SARS-COV2,0,10.1101/2020.04.05.026146,4/6/20,biorxiv,0,8,computational,0.2648633,0.591041678,0.058841791,0.002238473,0.002238425,0.080776333,Genomics,0.7234796,TRUE,31.375,0.448512586,20.625,0.487222371,0,0.403234768,4867,0.577413918,0.47909591
14791,Classification of Coronavirus Images using Shrunken Features,0,10.1101/2020.04.03.20048868,4/6/20,medrxiv,0,3,"machine learning, dataset",0.001254613,0.00125464,0.891777468,0.103204044,0.001254613,0.001254622,Imaging,0.37823415,FALSE,7.333333333,0.105572392,1.333333333,0.13252609,8,0.799987654,2958,0.347941247,0.346506846
14792,Optimal COVID-19 epidemic control until vaccine deployment,0,10.1101/2020.04.02.20049189,4/6/20,medrxiv,0,5,mathematical model,0.00151186,0.001511836,0.001511848,0.992440801,0.001511841,0.001511813,Epidemiology,0.20736232,FALSE,47.8,0.612777537,94,0.810944608,49,0.956787456,6000,0.659523236,0.760008209
14793,A Model for Supply-Chain Decisions for Resource Sharing with an Application to Ventilator Allocation to Combat COVID-19,0,10.1101/2020.04.02.20051078,4/6/20,medrxiv,10.1002/nav.21905,5,optimization model,0.002238461,0.002238474,0.002238654,0.819082584,0.002238476,0.171963352,Epidemiology,0.20360416,FALSE,11.2,0.168594224,3.2,0.202100615,20,0.900117291,2768,0.311822779,0.395658727
14794,Acute liver injury and its association with death risk of patients with COVID-19: a hospital-based prospective case-cohort study,0,10.1101/2020.04.02.20050997,4/6/20,medrxiv,0,13,logistic regression,0.001622769,0.001622777,0.001622705,0.001622856,0.001622729,0.991886164,Clinics,0.69143116,TRUE,85.84615385,0.828189746,45.53846154,0.661024886,16,0.881782826,2840,0.326029376,0.674256709
14795,Reduction of lymphocyte at early stage elevates severity and death risk of COVID-19 patients: a hospital-based case-cohort study,0,10.1101/2020.04.02.20050955,4/6/20,medrxiv,10.5114/aoms.2020.99006,9,logistic regression,0.000907325,0.000907278,0.063653037,0.000907323,0.000907284,0.932717753,Clinics,0.9744556,TRUE,112,0.894180221,43.88888889,0.653465347,7,0.785110192,2596,0.276908259,0.652416005
14796,A Contribution to the Mathematical Modeling of the Corona/COVID-19 Pandemic,0,10.1101/2020.04.01.20050229,4/6/20,medrxiv,0,1,mathematical model,0.00171718,0.001717182,0.001717186,0.991414042,0.001717206,0.001717205,Epidemiology,0.7416161,TRUE,78,0.798812543,9,0.337904736,8,0.799987654,2388,0.236696364,0.543350324
14797,Explaining national differences in the mortality of Covid-19: individual patient simulation model to investigate the effects of testing policy and other factors on apparent mortality.,0,10.1101/2020.04.02.20050633,4/6/20,medrxiv,0,2,simulation model,0.001291312,0.001291235,0.001291367,0.587535669,0.039932376,0.36865804,Epidemiology,0.4413274,FALSE,23,0.34225988,16.5,0.44293551,2,0.618927094,2552,0.270647725,0.418692552
14798,Estimation of the Final Size of the COVID-19 Epidemic in Pakistan,0,10.1101/2020.04.01.20050369,4/6/20,medrxiv,0,2,mathematical model,0.003607258,0.003607229,0.003607195,0.981963778,0.003607316,0.003607223,Epidemiology,0.20772931,FALSE,8.5,0.126662131,3.5,0.213607172,56,0.961540836,4388,0.529737539,0.457886919
14799,Flattening the curve before it flattens us: hospital critical care capacity limits and mortality from novel coronavirus (SARS-CoV2) cases in US counties,0,10.1101/2020.04.01.20049759,4/6/20,medrxiv,0,11,dataset,0.000648122,0.000648117,0.000648121,0.909968852,0.000648144,0.087438645,Epidemiology,0.3740055,FALSE,117.5454545,0.90370462,123.9090909,0.856435644,32,0.933699611,2856,0.330122803,0.755990669
14800,Pre-outbreak determinants of perceived risks of corona infection and preventive measures taken. A prospective population-based study,0,10.1101/2020.04.01.20049957,4/6/20,medrxiv,10.1371/journal.pone.0234600,4,logistic regression,0.001461867,0.001461893,0.001461907,0.00146195,0.814585443,0.17956694,Healthcare,0.8305099,TRUE,8.5,0.126662131,4.25,0.235750602,1,0.537564047,1971,0.144714664,0.261172861
14801,COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care: computer simulation study,0,10.1101/2020.04.02.20050898,4/6/20,medrxiv,10.1007/s10729-020-09511-7,5,simulation model,0.000988363,0.00098836,0.000988351,0.775549869,0.000988376,0.220496681,Epidemiology,0.60279167,TRUE,25,0.369286907,2.6,0.18256623,2,0.618927094,2786,0.314953046,0.371433319
14802,Inferring COVID-19 spreading rates and potential change points for case number forecasts,0,10.1101/2020.04.02.20050922,4/6/20,medrxiv,0,7,bayes,0.000266107,0.000266111,0.000266106,0.956154704,0.042780851,0.000266121,Epidemiology,0.065331936,FALSE,31.71428571,0.452285237,44.71428571,0.657813754,46,0.954009507,4342,0.523958584,0.647016771
14803,"Frequency of testing for COVID 19 infection and the presence of higher number of available beds per country predict outcomes with the infection, not the GDP of the country - A descriptive statistical analysis",0,10.1101/2020.04.01.20047373,4/6/20,medrxiv,0,4,dataset,0.001112637,0.001112647,0.001112676,0.657398034,0.001112718,0.338151288,Epidemiology,0.10325146,FALSE,18.25,0.273609994,5,0.257024351,6,0.764429903,3860,0.471947989,0.44175306
14804,ACE2 variants underlie interindividual variability and susceptibility to COVID-19 in Italian population,0,10.1101/2020.04.03.20047977,4/6/20,medrxiv,10.1038/s41431-020-0691-z,26,"sequencing, exom, genomes",0.342338919,0.521730153,0.001330079,0.001330142,0.001330135,0.131940572,Genomics,0.69784176,TRUE,29.65384615,0.427051766,30.34615385,0.57218357,32,0.933699611,27958,0.934264387,0.716799833
14805,Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results,0,10.1101/2020.04.02.20051136,4/6/20,medrxiv,0,14,machine learning,0.001486675,0.001486577,0.565548142,0.173053427,0.001486497,0.256938682,Imaging,0.6006207,TRUE,22.07142857,0.32611788,4.857142857,0.249933101,26,0.920859312,4711,0.56296653,0.514969206
14806,How to quit confinement? French scenarios face to COVID-19,0,10.1101/2020.04.02.20051342,4/6/20,medrxiv,0,1,mathematical model,0.008402482,0.008402815,0.008402569,0.957987194,0.008402484,0.008402457,Epidemiology,0.22892189,FALSE,45,0.587049292,17,0.451097137,3,0.667819001,2607,0.279075367,0.496260199
14807,Estimating the presymptomatic transmission of COVID19 using incubation period and serial interval data,0,10.1101/2020.04.02.20051318,4/6/20,medrxiv,0,1,mathematical model,0.00767379,0.007673948,0.007673866,0.850897566,0.118406376,0.007674454,Epidemiology,0.36303002,FALSE,30,0.432432432,1,0.122023013,14,0.866658436,4144,0.504213821,0.481331926
14808,Structural basis of RNA recognition by the SARS-CoV-2 nucleocapsid phosphoprotein,0,10.1101/2020.04.02.022194,4/5/20,biorxiv,10.1371/journal.ppat.1009100,5,genomes,0.530226598,0.465087222,0.001171544,0.001171553,0.001171547,0.001171535,Drug discovery,0.49745116,FALSE,45.8,0.593852434,49.8,0.680559272,39,0.945737391,7357,0.721165423,0.73532863
14809,Computational analysis suggests putative intermediate animal hosts of the SARS-CoV-2,0,10.1101/2020.04.04.025080,4/5/20,biorxiv,0,8,"molecular dynamics simulation, computational",0.350212596,0.642494024,0.001823379,0.001823362,0.001823315,0.001823324,Genomics,0.3584108,FALSE,54.5,0.665161729,30.375,0.572317367,5,0.739490092,11482,0.821093186,0.699515593
14810,"A snapshot of SARS-CoV-2 genome availability up to 30th March, 2020 and its implications",0,10.1101/2020.04.01.020594,4/5/20,biorxiv,0,4,"genome sequences, genomes, dataset",0.001486431,0.517885119,0.001486482,0.452668991,0.024986545,0.001486432,Genomics,0.08877581,FALSE,67.25,0.748964067,94.75,0.812349478,10,0.828199272,4023,0.490729593,0.720060602
14811,Increasing testing throughput and case detection with a pooled-sample Bayesian approach in the context of COVID-19,0,10.1101/2020.04.03.024216,4/5/20,biorxiv,0,2,bayes,0.001653111,0.456452797,0.218187619,0.320400183,0.001653164,0.001653126,Genomics,0.36437088,FALSE,43,0.567629414,43,0.649785925,11,0.840175319,3697,0.453888755,0.627869853
14812,In-silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs,0,10.1101/2020.03.31.018499,4/4/20,biorxiv,0,4,"in-silico, genome sequences, genomes",0.493237294,0.501814408,0.001237068,0.001237073,0.001237055,0.001237102,Genomics,0.52714914,TRUE,9.25,0.137918239,1.75,0.148381054,3,0.667819001,3628,0.446424272,0.350135641
14813,Feasibility Study of Mitigation and Suppression Intervention Strategies for Controlling COVID-19 Outbreaks in London and Wuhan,0,10.1101/2020.04.01.20043794,4/4/20,medrxiv,10.1371/journal.pone.0236857,7,"mathematical model, dataset",0.000672779,0.000672774,0.023010373,0.974298484,0.000672787,0.000672802,Epidemiology,0.15390599,FALSE,40.85714286,0.547158142,18.57142857,0.465681028,5,0.739490092,3601,0.443294004,0.548905817
14814,Modeling the COVID-19 outbreaks and the effectiveness of the containment measures adopted across countries,0,10.1101/2020.04.02.20046375,4/4/20,medrxiv,0,3,bayes,0.002490442,0.002490466,0.002490579,0.836261068,0.002490492,0.153776954,Epidemiology,0.24831626,FALSE,24,0.35574247,17,0.451097137,21,0.903944688,9219,0.780158921,0.622735804
14815,"Structural basis to design multi-epitope vaccines against Novel Coronavirus 19 (COVID19) infection, the ongoing pandemic emergency: an in silico approach",0,10.1101/2020.04.01.019299,4/3/20,biorxiv,10.2196/19371,9,"molecular dynamics simulation, in silico",0.995416459,0.000916725,0.000916692,0.000916734,0.000916684,0.000916707,Drug discovery,0.70905757,TRUE,38,0.519327107,14.33333333,0.415975381,10,0.828199272,3795,0.463279557,0.556695329
14816,In silico approach for designing of a multi-epitope based vaccine against novel Coronavirus (SARS-COV-2),0,10.1101/2020.03.31.017459,4/3/20,biorxiv,0,2,"computational, in silico",0.992440324,0.001511866,0.001511929,0.001511943,0.001511928,0.001512009,Drug discovery,0.50572425,TRUE,2.5,0.027459954,0,0.055525823,14,0.866658436,3061,0.360221527,0.327466435
14817,Reply to Gautret et al. 2020: A Bayesian reanalysis of the effects of hydroxychloroquine and azithromycin on viral carriage in patients with COVID-19,0,10.1101/2020.03.31.20048777,4/3/20,medrxiv,10.1371/journal.pone.0245048,9,bayes,0.091105137,0.122835253,0.000871581,0.43161063,0.082004533,0.271572867,Epidemiology,0.18291059,FALSE,158.5555556,0.950213371,223.2222222,0.927415039,13,0.858880178,16169,0.875752468,0.903065264
14818,"Meteorological factors correlate with transmission of 2019-nCoV: Proof of incidence of novel coronavirus pneumonia in Hubei Province, China",0,10.1101/2020.04.01.20050526,4/3/20,medrxiv,0,6,correlation analysis,0.001392866,0.019769292,0.00139285,0.757717377,0.001392867,0.218334747,Epidemiology,0.6696607,TRUE,16.5,0.249366071,,,5,0.739490092,3569,0.437996629,0.475617597
14819,Modelling the COVID-19 epidemics in Brasil: Parametric identification and public health measures influence,0,10.1101/2020.03.31.20049130,4/3/20,medrxiv,10.3390/biology9080220,3,"bayes, dataset",0.000898105,0.000898105,0.00089812,0.995509469,0.00089809,0.00089811,Epidemiology,0.06477061,FALSE,30.33333333,0.436266931,26,0.53819909,8,0.799987654,4965,0.583915242,0.589592229
14820,Perceptions and behavioural responses of the general public during the COVID-19 pandemic: A cross-sectional survey of UK Adults,0,10.1101/2020.04.01.20050039,4/3/20,medrxiv,10.1136/bmjopen-2020-043577,7,"mathematical model, logistic regression",0.001059346,0.001059431,0.001059348,0.277973186,0.717789349,0.00105934,Healthcare,0.9155757,TRUE,32.28571429,0.458531758,25.42857143,0.532178218,76,0.971664918,19729,0.901276186,0.71591277
14821,Importance of suppression and mitigation measures in managing COVID-19 outbreaks,0,10.1101/2020.03.31.20048835,4/2/20,medrxiv,0,1,mathematical model,0.001330046,0.083430397,0.001330076,0.834115134,0.001330055,0.078464292,Epidemiology,0.12362382,FALSE,3,0.037293586,0,0.055525823,6,0.764429903,4654,0.552612569,0.35246547
14822,ACE2 and TMPRSS2 variants and expression as candidates to sex and country differences in COVID-19 severity in Italy,0,10.1101/2020.03.30.20047878,4/2/20,medrxiv,10.18632/aging.103415,4,exom,0.213688862,0.384898433,0.001350375,0.001350423,0.001350388,0.39736152,Clinics,0.546514,TRUE,43,0.567629414,53.25,0.694674873,115,0.981665535,17103,0.881531423,0.781375311
14823,Comparative genomics suggests limited variability and similar evolutionary patterns between major clades of SARS-Cov-2,0,10.1101/2020.03.30.016790,4/2/20,biorxiv,0,3,"phylogenom, genome sequences, genomes",0.001203433,0.993982829,0.001203414,0.001203439,0.001203449,0.001203436,Genomics,0.44729704,FALSE,136.25,0.928134084,522.25,0.980398716,24,0.914439163,5796,0.64363111,0.866650768
14824,Virus-host interactome and proteomic survey of PMBCs from COVID-19 patients reveal potential virulence factors influencing SARS-CoV-2 pathogenesis,0,10.1101/2020.03.31.019216,4/2/20,biorxiv,10.1016/j.medj.2020.07.002,16,"proteom, interactom",0.843250682,0.001461977,0.001461903,0.108149973,0.001461949,0.044213516,Drug discovery,0.44624615,FALSE,65.375,0.736471025,30.875,0.57639818,46,0.954009507,9150,0.777510234,0.761097236
14825,SARS-CoV-2 receptor and entry genes are expressed by sustentacular cells in the human olfactory neuroepithelium,0,10.1101/2020.03.31.013268,4/2/20,biorxiv,10.1016/j.isci.2020.101839,11,dataset,0.987547161,0.002490551,0.002490597,0.002490558,0.002490609,0.002490524,Drug discovery,0.7779366,TRUE,23.18181818,0.343311275,21.09090909,0.492373562,66,0.967652324,8997,0.772694438,0.6440079
14826,deepMINE - Natural Language Processing based Automatic Literature Mining and Research Summarization for Early Stage Comprehension in Pandemic Situations specifically for COVID-19,0,10.1101/2020.03.30.014555,4/2/20,biorxiv,0,4,"deep learning, literature mining",0.035978996,0.09552345,0.281109429,0.584364391,0.001511901,0.001511833,Epidemiology,0.3014385,FALSE,3.75,0.046817985,0.25,0.065493712,4,0.707574542,2294,0.208042379,0.256982154
14827,Insights into The Codon Usage Bias of 13 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Isolates from Different Geo-locations,0,10.1101/2020.04.01.019463,4/2/20,biorxiv,0,2,genome-wide,0.001861804,0.990691141,0.001861758,0.001861845,0.00186173,0.001861721,Genomics,0.93764174,TRUE,1.5,0.015523533,0,0.055525823,1,0.537564047,4405,0.525644113,0.283564379
14828,A SARS-CoV-2 Vaccination Strategy Focused on Population-Scale Immunity,0,10.1101/2020.03.31.018978,4/2/20,biorxiv,10.1016/j.xcrm.2020.100036,1,proteom,0.767622426,0.224930528,0.001861745,0.001861776,0.001861855,0.001861671,Drug discovery,0.11458996,FALSE,167.25,0.956521739,307.5,0.956649719,1,0.537564047,5760,0.640500843,0.772809087
14829,Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network,0,10.1101/2020.03.30.20047456,4/1/20,medrxiv,10.1007/s10489-020-01829-7,3,"neural network, transfer learning, dataset",0.001171558,0.001171535,0.994142282,0.001171541,0.001171539,0.001171545,Imaging,0.41074133,FALSE,87.33333333,0.834621807,55.66666667,0.705244849,141,0.985246003,11673,0.822297135,0.836852448
14830,Early behavior of Madrid Covid-19 disease outbreak: A mathematical model,0,10.1101/2020.03.30.20047019,4/1/20,medrxiv,0,2,"simulation model, mathematical model",0.002806449,0.002806436,0.002806483,0.985967808,0.002806389,0.002806435,Epidemiology,0.33831054,FALSE,11.5,0.17416043,3,0.199424672,2,0.618927094,1484,0.048157958,0.260167539
14831,COVID-19 Epidemic in Switzerland: Growth Prediction and Containment Strategy Using Artificial Intelligence and Big Data,0,10.1101/2020.03.30.20047472,4/1/20,medrxiv,0,3,artificial intelligence,0.019157495,0.001156267,0.001156299,0.976217437,0.001156262,0.00115624,Epidemiology,0.14565831,FALSE,76,0.790586926,16.33333333,0.440460262,14,0.866658436,4009,0.485191428,0.645724263
14832,Perception of emergent epidemic of COVID-2019 / SARS CoV-2 on the Polish Internet,0,10.1101/2020.03.29.20046789,4/1/20,medrxiv,0,3,"data mining, network analysis",0.000936127,0.00093614,0.000936102,0.908678299,0.08757724,0.000936091,Epidemiology,0.11674625,FALSE,34,0.477766096,9,0.337904736,7,0.785110192,1691,0.081627739,0.420602191
14833,Transmission Dynamics of COVID-19 and Impact on Public Health Policy,0,10.1101/2020.03.29.20047035,4/1/20,medrxiv,0,5,mathematical model,0.001565307,0.001565339,0.001565502,0.911170187,0.001565443,0.082568222,Epidemiology,0.17000479,FALSE,144.4,0.938895417,155.6,0.888413166,10,0.828199272,3175,0.378039971,0.758386956
14834,Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making,0,10.1101/2020.03.30.20047308,4/1/20,medrxiv,10.1016/j.smhl.2020.100178,2,"machine learning, artificial intelligence, neural network, classifier, predictive model, logistic regression, dataset",0.001684472,0.001684511,0.729960119,0.001684539,0.001684526,0.263301834,Clinics,0.75415206,TRUE,32.5,0.461685942,6.5,0.288132192,32,0.933699611,6728,0.691548278,0.593766506
14835,Stochastic Compartmental Modelling of SARS-CoV-2 with Approximate Bayesian Computation,0,10.1101/2020.03.29.20046862,4/1/20,medrxiv,0,1,bayes,0.003927352,0.003927368,0.003927619,0.980362969,0.003927342,0.003927349,Epidemiology,0.392458,FALSE,6,0.086028821,0,0.055525823,1,0.537564047,1502,0.050806646,0.182481334
14836,Understanding COVID-19 spreading through simulation modeling and scenarios comparison: preliminary results.,0,10.1101/2020.03.30.20047043,4/1/20,medrxiv,0,4,simulation model,0.001415176,0.031071407,0.00141513,0.963267915,0.001415148,0.001415224,Epidemiology,0.25895625,FALSE,6.25,0.088564537,1.75,0.148381054,6,0.764429903,5155,0.596436311,0.399452951
14837,Molecular Docking Analysis Of Some Phytochemicals On Two SARS-CoV-2 Targets,0,10.1101/2020.03.31.017657,4/1/20,biorxiv,0,10,virtual screening,0.991413879,0.001717258,0.001717193,0.001717199,0.001717266,0.001717204,Drug discovery,0.97674495,TRUE,14.2,0.214793741,4,0.231469093,5,0.739490092,4781,0.563929689,0.437420654
14838,Scrutinizing the SARS-CoV-2 protein information for the designing an effective vaccine encompassing both the T-cell and B-cell epitopes,0,10.1101/2020.03.26.009209,4/1/20,biorxiv,10.1016/j.meegid.2020.104648,4,"in silico, proteom",0.680083008,0.179140025,0.001126818,0.137396527,0.001126833,0.001126789,Drug discovery,0.46551237,FALSE,21.5,0.318263343,7.25,0.302983677,2,0.618927094,3182,0.37924392,0.404854509
14839,The first three months of the COVID-19 epidemic: Epidemiological evidence for two separate strains of SARS-CoV-2 viruses spreading and implications for prevention strategies,0,10.1101/2020.03.28.20036715,3/31/20,medrxiv,0,1,genome-wide,0.001059359,0.112035027,0.001059361,0.788346169,0.001059419,0.096440665,Epidemiology,0.13237974,FALSE,35,0.488032655,5,0.257024351,6,0.764429903,159268,0.991331568,0.625204619
14840,The SARS-CoV-2 epidemic outbreak: a review of plausible scenarios of containment and mitigation for Mexico,0,10.1101/2020.03.28.20046276,3/31/20,medrxiv,0,5,mathematical model,0.000977478,0.019833327,0.000977512,0.952034483,0.025199765,0.000977436,Epidemiology,0.18797484,FALSE,26.6,0.390191106,13.6,0.406208188,14,0.866658436,2994,0.345774139,0.502207967
14841,Predicting the evolution and control of COVID-19 pandemic in Portugal.,0,10.1101/2020.03.28.20046250,3/31/20,medrxiv,10.12688/f1000research.23401.1,2,mathematical model,0.003760473,0.003760675,0.003760438,0.981197427,0.00376054,0.003760447,Epidemiology,0.39355892,FALSE,12.5,0.189436576,16,0.437316029,5,0.739490092,2505,0.252106911,0.404587402
14842,Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020 through ARIMA Model with Machine Learning Approach,0,10.1101/2020.03.30.20046227,3/31/20,medrxiv,0,8,machine learning,0.00263905,0.002639168,0.002639017,0.986804402,0.002639044,0.002639318,Epidemiology,0.51387095,TRUE,22,0.326056033,4.375,0.238426545,33,0.936045435,4589,0.544425716,0.511238432
14843,The diagnostic evaluation of Convolutional NeuralNetwork (CNN) for the assessment of chest X-ray ofpatients infected with COVID-19,0,10.1101/2020.03.26.20044610,3/31/20,medrxiv,0,4,neural network,0.000830674,0.00083071,0.932154519,0.0008307,0.000830707,0.064522689,Imaging,0.5105122,TRUE,6.75,0.095862453,0,0.055525823,25,0.918019631,2866,0.320972791,0.347595174
14844,"A Territory-wide study of COVID-19 cases and clusters with unknown source in Hong Kong community: A clinical, epidemiological and phylogenomic investigation",0,10.1101/2020.03.30.20045740,3/31/20,medrxiv,0,26,"sequencing, phylogenom, whole-genome",0.001786507,0.644669762,0.001786538,0.270761722,0.001786519,0.079208952,Genomics,0.22646561,FALSE,23.11538462,0.342754654,27.07692308,0.546293819,16,0.881782826,5888,0.646761377,0.604398169
14845,Computational Design of Peptides to Block Binding of the SARS-CoV-2 Spike Protein to Human ACE2,0,10.1101/2020.03.28.013607,3/31/20,biorxiv,10.18632/aging.103416,3,computational,0.936142623,0.001046848,0.031588909,0.001046876,0.001046857,0.029127887,Drug discovery,0.67800635,TRUE,58,0.689714887,325.6666667,0.960998127,16,0.881782826,6056,0.657115338,0.797402794
14846,Sequence variation among SARS-CoV-2 isolates in Taiwan,0,10.1101/2020.03.29.014290,3/31/20,biorxiv,10.1080/22221751.2020.1782271,16,"sequencing, genomes",0.002638946,0.986804819,0.002638984,0.00263909,0.002639154,0.002639007,Genomics,0.18197435,FALSE,63.375,0.723730596,56.375,0.708322184,2,0.618927094,3422,0.414880809,0.616465171
14847,Structural analysis of SARS-CoV-2 andprediction of the human interactome,0,10.1101/2020.03.28.013789,3/31/20,biorxiv,10.1093/nar/gkaa864,9,"interactom, genomes",0.726837086,0.26847657,0.001171602,0.001171579,0.001171611,0.001171551,Drug discovery,0.36424908,FALSE,11.33333333,0.170635166,5.777777778,0.272009633,7,0.785110192,7499,0.722128582,0.487470893
14848,Atlas of ACE2 gene expression in mammals reveals novel insights in transmission of SARS-Cov-2,0,10.1101/2020.03.30.015644,3/31/20,biorxiv,10.1016/j.heliyon.2020.e05850,4,"transcriptom, dataset",0.491203232,0.482533923,0.001486463,0.001486528,0.021803384,0.00148647,Drug discovery,0.44553354,FALSE,35.5,0.492547467,30.25,0.57171528,37,0.942712513,10636,0.80447869,0.702863488
14849,Computational Prediction of the Comprehensive SARS-CoV-2 vs. Human Interactome to Guide the Design of Therapeutics,0,10.1101/2020.03.29.014381,3/31/20,biorxiv,0,3,"computational, interactom",0.922294618,0.001059432,0.044432728,0.001059429,0.001059427,0.030094365,Drug discovery,0.12149143,FALSE,36,0.498299215,18.66666667,0.466818303,2,0.618927094,3964,0.478449314,0.515623482
14850,Sequence analysis of SARS-CoV-2 genome reveals features important for vaccine design,0,10.1101/2020.03.30.016832,3/31/20,biorxiv,10.1038/s41598-020-72533-2,9,"in silico, transcriptom",0.354581346,0.637811619,0.001901799,0.001901739,0.001901782,0.001901715,Genomics,0.55787206,TRUE,43.22222222,0.56923743,83.77777778,0.787864597,16,0.881782826,4773,0.560558632,0.699860871
14851,"Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2 etiologic agent of global pandemic COVID-19: an in silico approach",0,10.1101/2020.03.30.015164,3/31/20,biorxiv,10.7717/peerj.9572,10,"bioinformatic, in silico",0.959000156,0.034508596,0.001622782,0.001622832,0.001622796,0.001622837,Drug discovery,0.7620958,TRUE,18.6,0.278990661,5.9,0.273548301,27,0.92443978,5431,0.614013966,0.522748177
14852,Biophysical characterization of the SARS-CoV2 spike protein binding with the ACE2 receptor explains increased COVID-19 pathogenesis,0,10.1101/2020.03.30.015891,3/31/20,biorxiv,0,2,computational,0.880026217,0.115883187,0.001022635,0.001022727,0.001022628,0.001022606,Drug discovery,0.10094422,FALSE,129.5,0.921269095,307,0.956449023,11,0.840175319,9563,0.785456297,0.875837434
14853,Failed detection of the full-length genome of SARS-CoV-2 by ultra-deep sequencing from the recovered and discharged patients retested viral PCR positive,0,10.1101/2020.03.27.20043299,3/30/20,medrxiv,0,6,"sequencing, deep sequencing",0.00608977,0.610138653,0.006089614,0.006089712,0.006089771,0.365502481,Genomics,0.52134323,TRUE,41.33333333,0.551549261,,,8,0.799987654,5801,0.639778473,0.663771796
14854,"Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Release",0,10.1101/2020.03.25.20043505,3/30/20,medrxiv,0,2,"machine learning, neural network, deep-learning, lstm, dataset",0.001461867,0.001461859,0.727184203,0.266968246,0.001461883,0.001461943,Epidemiology,0.6930187,TRUE,179.5,0.964005195,41,0.639751137,34,0.937156615,3388,0.409824223,0.737684293
14855,"A Multi-hospital Study in Wuhan, China：Protective Effects of Non-menopause and Female Hormones on SARS-CoV-2 infection",0,10.1101/2020.03.26.20043943,3/30/20,medrxiv,0,17,"logistic regression, correlation analysis",0.000916768,0.000916696,0.000916678,0.0525495,0.000916741,0.943783618,Clinics,0.9835768,TRUE,49.70588235,0.629290618,17.94117647,0.45992775,18,0.891474782,7741,0.732482543,0.678293923
14856,Changing transmission dynamics of COVID-19 in China: a nationwide population-based piecewise mathematical modelling study,0,10.1101/2020.03.27.20045757,3/30/20,medrxiv,0,16,mathematical model,0.000652903,0.000652924,0.009601711,0.987786637,0.000652899,0.000652926,Epidemiology,0.24583262,FALSE,74.4375,0.783845631,33.1875,0.593724913,7,0.785110192,1974,0.13773176,0.575103124
14857,Genetic Profiles in Pharmacogenes Indicate Personalized Drug Therapy for COVID-19,0,10.1101/2020.03.23.20041350,3/30/20,medrxiv,0,8,"sequencing, exom",0.85395006,0.11145444,0.001593503,0.001593522,0.029814903,0.001593572,Drug discovery,0.5664783,TRUE,5.625,0.07786505,,,5,0.739490092,4261,0.508307248,0.441887463
14858,Stabilization of the coronavirus pandemic in Italy and global prospects,0,10.1101/2020.03.28.20045898,3/30/20,medrxiv,0,1,mathematical model,0.002490478,0.00249049,0.002490504,0.987547615,0.002490441,0.002490472,Epidemiology,0.38602495,FALSE,16,0.243552477,13,0.400521809,8,0.799987654,2744,0.295208283,0.434817556
14859,Scaling analysis of COVID-19 spreading based on Belgian hospitalization data,0,10.1101/2020.03.29.20046730,3/30/20,medrxiv,0,3,model fit,0.002238502,0.002238548,0.084159617,0.906886237,0.002238428,0.002238668,Epidemiology,0.2925892,FALSE,63.33333333,0.723545055,42.33333333,0.6462403,2,0.618927094,2803,0.309174091,0.574471635
14860,Evaluating the effectiveness of social distancing interventions against COVID-19,0,10.1101/2020.03.27.20044891,3/30/20,medrxiv,10.3201/eid2608.201093,2,mathematical model,0.001987074,0.00198711,0.001987095,0.627420288,0.364631206,0.001987228,Epidemiology,0.2493538,FALSE,4.5,0.061784897,0,0.055525823,21,0.903944688,8597,0.760414158,0.445417392
14861,Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT Scan Images,0,10.1101/2020.03.28.20046045,3/30/20,medrxiv,0,10,"machine learning, deep learning, artificial intelligence, neural network, dataset",0.001034614,0.001034638,0.994826925,0.001034605,0.001034574,0.001034645,Imaging,0.64626515,TRUE,11.9,0.178984476,27,0.546026224,19,0.89561084,2844,0.316156995,0.484194634
14862,A Machine Learning Model Reveals Older Age and Delayed Hospitalization as Predictors of Mortality in Patients with COVID-19,0,10.1101/2020.03.25.20043331,3/30/20,medrxiv,0,2,"machine learning, dataset",0.002357722,0.002357736,0.355654271,0.002357862,0.072708659,0.56456375,Clinics,0.85435903,TRUE,20.5,0.304966294,2,0.164302917,21,0.903944688,3547,0.431495305,0.451177301
14863,Comparative Genomic Analysis of Rapidly Evolving SARS CoV-2 Viruses Reveal Mosaic Pattern of Phylogeographical Distribution,0,10.1101/2020.03.25.006213,3/30/20,biorxiv,10.1128/msystems.00505-20,17,"interactom, genomes",0.129517478,0.865241061,0.001310371,0.001310388,0.001310351,0.00131035,Genomics,0.3621214,FALSE,19.52941176,0.291236316,7.647058824,0.309272143,5,0.739490092,7611,0.726462798,0.516615337
14864,In-host Modelling of COVID-19 Kinetics in Humans,0,10.1101/2020.03.26.20044487,3/30/20,medrxiv,0,2,mathematical model,0.353962097,0.080708158,0.001565437,0.498800764,0.001565431,0.063398113,Epidemiology,0.21779567,FALSE,11.5,0.17416043,16.5,0.44293551,21,0.903944688,5124,0.592342885,0.528345878
14865,Orthogonal genome-wide screenings in bat cells identify MTHFD1 as a target of broad antiviral therapy,0,10.1101/2020.03.29.014209,3/30/20,biorxiv,0,17,"sequencing, genome-wide",0.535514536,0.458111462,0.001593486,0.001593505,0.00159352,0.001593492,Drug discovery,0.5644486,TRUE,40.76470588,0.546725215,92.35294118,0.8065962,8,0.799987654,9967,0.790994462,0.736075883
14866,Analysis and Prediction of False Negative Results for SARS-CoV-2 Detection with Pharyngeal Swab Specimen in COVID-19 Patients: A Retrospective Study,0,10.1101/2020.03.26.20043042,3/30/20,medrxiv,0,11,logistic regression,0.000772619,0.125998752,0.452108749,0.000772632,0.100238875,0.320108372,Imaging,0.39876175,FALSE,45.81818182,0.593976127,11.54545455,0.378445277,12,0.850299401,3112,0.362870214,0.546397755
14867,Development and external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19,0,10.1101/2020.03.28.20045997,3/30/20,medrxiv,0,15,"logistic regression, prediction model, dataset",0.00056531,0.000565318,0.106595953,0.043725235,0.000565333,0.847982851,Clinics,0.4255929,FALSE,56.26666667,0.676850764,,,67,0.967960985,4597,0.543462557,0.729424769
14868,Prognostic factors for COVID-19 pneumonia progression to severe symptom based on the earlier clinical features: a retrospective analysis,0,10.1101/2020.03.28.20045989,3/30/20,medrxiv,10.3389/fmed.2020.557453,11,logistic regression,0.001171606,0.044497518,0.124020926,0.001171644,0.001171624,0.827966682,Clinics,0.8762721,TRUE,34.36363636,0.480611046,24.90909091,0.527428419,24,0.914439163,3235,0.385263665,0.576935573
14869,Forecasting the Worldwide Spread of COVID-19 based on Logistic Model and SEIR Model,0,10.1101/2020.03.26.20044289,3/30/20,medrxiv,0,12,mathematical model,0.001072168,0.001072197,0.023912384,0.971798809,0.001072233,0.00107221,Epidemiology,0.16767243,FALSE,45,0.587049292,20,0.481000803,31,0.931971109,5587,0.624367927,0.656097283
14870,Temperature dependence of COVID-19 transmission,0,10.1101/2020.03.26.20044529,3/30/20,medrxiv,10.1016/j.scitotenv.2020.144390,1,dataset,0.001059396,0.001059392,0.001059381,0.927004594,0.001059399,0.068757837,Epidemiology,0.22314656,FALSE,73,0.778464964,80,0.779033984,50,0.957775171,12093,0.82711293,0.835596763
14871,Trend Analysis and Forecasting of COVID-19 outbreak in India,0,10.1101/2020.03.26.20044511,3/30/20,medrxiv,0,2,forecasting model,0.001156235,0.001156261,0.001156312,0.975198889,0.001156274,0.020176029,Epidemiology,0.07154712,FALSE,75.5,0.78860783,18,0.46180091,49,0.956787456,6053,0.6556706,0.715716699
14872,Government Responses Matter: Predicting Covid-19 cases in US under an empirical Bayesian time series framework,0,10.1101/2020.03.28.20044578,3/30/20,medrxiv,0,2,bayes,0.002296577,0.002296679,0.002296595,0.988516989,0.002296589,0.002296571,Epidemiology,0.16192165,FALSE,36.5,0.503123261,56.5,0.709124967,4,0.707574542,2122,0.170238382,0.522515288
14873,Improved deep learning model for differentiating novel coronavirus pneumonia and influenza pneumonia,0,10.1101/2020.03.24.20043117,3/30/20,medrxiv,0,18,"deep learning, image analysis",0.001254623,0.05931575,0.750518122,0.001254651,0.021354641,0.166302212,Imaging,0.473707,FALSE,17.66666667,0.266373925,21.61111111,0.498862724,9,0.814309525,3126,0.365759692,0.486326467
14874,Understand Research Hotspots Surrounding COVID-19 and Other Coronavirus Infections Using Topic Modeling,0,10.1101/2020.03.26.20044164,3/30/20,medrxiv,0,6,dataset,0.106906102,0.198149279,0.053062708,0.638158354,0.001861757,0.001861801,Epidemiology,0.45545095,FALSE,9.833333333,0.147628177,2.166666667,0.166845063,7,0.785110192,2870,0.320250421,0.354958463
14875,Clinical and Paraclinical Characteristics of COVID-19 patients: A systematic review and meta-analysis,0,10.1101/2020.03.26.20044057,3/30/20,medrxiv,0,10,sequencing,0.001486448,0.203011654,0.249778237,0.134258128,0.070729524,0.340736009,Clinics,0.6632855,TRUE,20.9,0.308491558,5,0.257024351,13,0.858880178,3493,0.423549242,0.461986332
14876,Site-specific N-glycosylation Characterization of Recombinant SARS-CoV-2 Spike Proteins using High-Resolution Mass Spectrometry,0,10.1101/2020.03.28.013276,3/29/20,biorxiv,10.1074/mcp.ra120.002295,12,genome sequences,0.81174298,0.181995706,0.001565324,0.001565388,0.00156531,0.001565292,Drug discovery,0.56318,TRUE,83.91666667,0.82089183,67.75,0.747524752,32,0.933699611,11117,0.813147123,0.828815829
14877,Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors,0,10.1101/2020.03.24.005702,3/29/20,biorxiv,10.7554/eLife.58040,18,"neural network, sequencing",0.647821334,0.001462015,0.127155852,0.220636956,0.001461938,0.001461906,Drug discovery,0.7112221,TRUE,15.44444444,0.231987136,17.05555556,0.451364731,43,0.9507377,5554,0.621719239,0.563952201
14878,Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proteins by Machine Learning and Physics-Based Refinement,0,10.1101/2020.03.25.008904,3/28/20,biorxiv,0,2,"molecular dynamics simulation, machine learning, proteom",0.729031737,0.001653073,0.221749509,0.044259466,0.001653123,0.001653092,Drug discovery,0.27990782,FALSE,116,0.900735976,293,0.952167514,23,0.91129082,5128,0.589212617,0.838351732
14879,SARS-CoV-2 and ORF3a: Non-Synonymous Mutations and Polyproline Regions,0,10.1101/2020.03.27.012013,3/28/20,biorxiv,10.1128/mSystems.00266-20,5,genomes,0.316276961,0.67855804,0.001291277,0.001291298,0.001291218,0.001291206,Genomics,0.44174665,FALSE,22.2,0.327787742,12,0.386740701,11,0.840175319,4784,0.558150734,0.528213624
14880,Gastrointestinal tract symptoms in coronavirus disease 2019: Analysis of clinical symptoms in adult patients,0,10.1101/2020.03.23.20040279,3/27/20,medrxiv,0,1,logistic regression,0.001717193,0.043881923,0.001717225,0.001717285,0.26211898,0.688847394,Clinics,0.7348973,TRUE,15,0.227596017,11,0.371287129,9,0.814309525,3068,0.353238623,0.441607823
14881,Modeling for Corona Virus Outbreak in IRAN,0,10.1101/2020.03.24.20041095,3/27/20,medrxiv,0,6,"predictive model, dataset",0.000946084,0.000946102,0.000946095,0.995269474,0.00094615,0.000946096,Epidemiology,0.020177454,FALSE,6.2,0.087574989,2.4,0.174872893,4,0.707574542,2931,0.329641223,0.324915912
14882,Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection,0,10.1101/2020.03.24.20041020,3/27/20,medrxiv,10.1136/bmj.m1328,16,"machine learning, prediction model",0.00048481,0.000484809,0.404646234,0.024817042,0.073333483,0.496233622,Clinics,0.19431663,FALSE,132.8125,0.925103593,204.5,0.919387209,34,0.937156615,14184,0.849987961,0.907908844
14883,Factors associated with prolonged viral shedding and impact of Lopinavir/Ritonavir treatment in patients with SARS-CoV-2 infection,0,10.1101/2020.03.22.20040832,3/27/20,medrxiv,10.1183/13993003.00799-2020,7,logistic regression,0.001237173,0.248852655,0.00123711,0.001237158,0.001237175,0.746198728,Clinics,0.9471191,TRUE,52.85714286,0.652544994,12,0.386740701,34,0.937156615,5052,0.582711293,0.639788401
14884,Haplotype networks of SARS-CoV-2 infections in the Diamond Princess cruise ship outbreak,0,10.1101/2020.03.23.20041970,3/27/20,medrxiv,10.1073/pnas.2006824117,17,"sequencing, whole genome, network analysis",0.002898335,0.573032976,0.002898377,0.415373707,0.002898331,0.002898274,Genomics,0.53908324,TRUE,66.35294118,0.742964933,72,0.7594327,23,0.91129082,5922,0.645316639,0.764751273
14885,Computational simulations reveal the binding dynamics between human ACE2 and the receptor binding domain of SARS-CoV-2 spike protein,0,10.1101/2020.03.24.005561,3/27/20,biorxiv,0,7,computational,0.904517436,0.089108484,0.001593518,0.001593563,0.001593466,0.001593534,Drug discovery,0.67947716,TRUE,71.28571429,0.769868266,21.14285714,0.492841852,16,0.881782826,5002,0.577654707,0.680536913
14886,Automatic Identification of SARS Coronavirus using Compression-Complexity Measures,0,10.1101/2020.03.24.006007,3/27/20,biorxiv,0,2,"computational, metagenom, sequence alignment",0.026362775,0.523344918,0.249095781,0.1987224,0.001237064,0.001237061,Genomics,0.42711926,FALSE,20.5,0.304966294,4.5,0.242708055,0,0.403234768,3157,0.367204431,0.329528387
14887,In-Silico evidence for two receptors based strategy of SARS-CoV-2,0,10.1101/2020.03.24.006197,3/27/20,biorxiv,0,6,"in-silico, dataset",0.7176608,0.001943526,0.27456489,0.001943706,0.001943536,0.001943541,Drug discovery,0.6896174,TRUE,16,0.243552477,5,0.257024351,38,0.944132354,5680,0.627979774,0.518172239
14888,Non-neural expression of SARS-CoV-2 entry genes in the olfactory epithelium suggests mechanisms underlying anosmia in COVID-19 patients,0,10.1101/2020.03.25.009084,3/27/20,biorxiv,10.1126/sciadv.abc5801,23,sequencing,0.816807138,0.078533994,0.00272009,0.002720138,0.096498504,0.002720136,Drug discovery,0.92911166,TRUE,51.4,0.642092894,340.84,0.96414236,276,0.994444101,80020,0.981218396,0.895474438
14889,A Simple Mathematical Model for Estimating the Inflection Points of COVID-19 Outbreaks,0,10.1101/2020.03.25.20043893,3/27/20,medrxiv,0,1,"mathematical model, dataset",0.00146191,0.11134406,0.001461974,0.882808296,0.00146188,0.001461881,Epidemiology,0.08966863,FALSE,7,0.10179974,1,0.122023013,2,0.618927094,4401,0.518420419,0.340292567
14890,Global profiling of SARS-CoV-2 specific IgG/ IgM responses of convalescents using a proteome microarray,0,10.1101/2020.03.20.20039495,3/27/20,medrxiv,10.1038/s41467-020-17488-8,9,proteom,0.324047887,0.461262729,0.021196234,0.001350352,0.001350409,0.190792388,Genomics,0.7581754,TRUE,55.77777778,0.672954419,27.44444444,0.54903666,48,0.955614544,11995,0.824705033,0.750577664
14891,COVID-19 outbreak response: a first assessment of mobility changes in Italy following national lockdown,0,10.1101/2020.03.22.20039933,3/27/20,medrxiv,10.1038/s41597-020-00575-2,7,dataset,0.00203275,0.002032861,0.002032828,0.98983598,0.002032823,0.002032757,Epidemiology,0.70765287,TRUE,10.71428571,0.159688292,12,0.386740701,54,0.959812334,8208,0.74717072,0.563353012
14892,A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coasts,0,10.1101/2020.03.24.20043026,3/27/20,medrxiv,10.1017/exp.2020.45,3,computational,0.001901921,0.097594703,0.001901927,0.894797864,0.001901859,0.001901726,Epidemiology,0.47046834,FALSE,2.333333333,0.024800544,0,0.055525823,6,0.764429903,4471,0.524680954,0.342359306
14893,Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China,0,10.1101/2020.03.23.20041608,3/27/20,medrxiv,0,4,"deep learning, neural network, network model, forecasting model",0.002183225,0.002183228,0.583520519,0.407746552,0.002183226,0.002183251,Epidemiology,0.77370334,TRUE,21.25,0.314614386,8.25,0.323789136,38,0.944132354,4000,0.476763785,0.514824915
14894,Modelling SARS-CoV-2 Dynamics: Implications for Therapy,0,10.1101/2020.03.23.20040493,3/27/20,medrxiv,0,11,mathematical model,0.496458917,0.236177386,0.000946127,0.178381389,0.000946123,0.087090058,Drug discovery,0.0845269,FALSE,51.63636364,0.643639062,35.90909091,0.609579877,14,0.866658436,3675,0.443053215,0.640732647
14895,A Social Network Model of the COVID-19 Pandemic,0,10.1101/2020.03.23.20041798,3/27/20,medrxiv,0,1,network model,0.002130723,0.118518038,0.002130682,0.872959188,0.002130716,0.002130652,Epidemiology,0.16326043,FALSE,14,0.213494959,3,0.199424672,5,0.739490092,4164,0.495063809,0.411868383
14896,"Risk Factors Associated with Clinical Outcomes in 323 COVID-19 Patients in Wuhan, China",0,10.1101/2020.03.25.20037721,3/26/20,medrxiv,0,20,logistic regression,0.001751256,0.001751205,0.001751235,0.272798565,0.0017513,0.720196439,Clinics,0.49135855,FALSE,60.2,0.704125178,80.85,0.780974043,49,0.956787456,10257,0.793883939,0.808942654
14897,An artificial intelligence based first-line defence against COVID-19: digitally screeningcitizens for risks via a chatbot,0,10.1101/2020.03.25.008805,3/26/20,biorxiv,10.1038/s41598-020-75912-x,6,"artificial intelligence, digital health",0.002806454,0.002806438,0.540178826,0.24062071,0.210781076,0.002806496,Epidemiology,0.72793394,TRUE,15.66666667,0.236563795,7.166666667,0.301311212,20,0.900117291,7358,0.711774621,0.53744173
14898,"Mathematical modeling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada",0,10.1101/2020.03.24.20042705,3/26/20,medrxiv,10.1503/cmaj.200476,3,mathematical model,0.001220003,0.001219992,0.001219994,0.93477475,0.025639272,0.035925989,Epidemiology,0.3169917,FALSE,84.33333333,0.823056466,109.3333333,0.838038534,20,0.900117291,20282,0.901998555,0.865802712
14899,Coast-to-coast spread of SARS-CoV-2 in the United States revealed by genomic epidemiology,0,10.1101/2020.03.25.20043828,3/26/20,medrxiv,0,36,"genomic epidemiology, genomes",0.001861679,0.707003874,0.001861714,0.261280132,0.001861711,0.026130891,Genomics,0.44465983,FALSE,46.63888889,0.602016204,101.7777778,0.825394702,25,0.918019631,19077,0.892607753,0.809509572
14900,Acute kidney injury at early stage as a negative prognostic indicator of patients with COVID-19: a hospital-based retrospective analysis,0,10.1101/2020.03.24.20042408,3/26/20,medrxiv,0,14,logistic regression,0.001901698,0.001901755,0.001901691,0.001901782,0.001901708,0.990491365,Clinics,0.988037,TRUE,18.64285714,0.279052508,12.85714286,0.39717688,36,0.941169208,4567,0.533108596,0.537626798
14901,Human leukocyte antigen susceptibility map for SARS-CoV-2,0,10.1101/2020.03.22.20040600,3/26/20,medrxiv,10.1128/JVI.00510-20,7,"in silico, proteom",0.371797877,0.622716776,0.001371307,0.001371312,0.001371385,0.001371344,Genomics,0.3507602,FALSE,14.71428571,0.221473189,16.57142857,0.443336901,95,0.976912155,40745,0.956176258,0.649474626
14902,Modes of contact and risk of transmission in COVID-19 among close contacts,0,10.1101/2020.03.24.20042606,3/26/20,medrxiv,0,26,logistic regression,0.001272639,0.001272822,0.00127267,0.660793533,0.157262673,0.178125662,Epidemiology,0.2928533,FALSE,36.42307692,0.501515245,17.53846154,0.456181429,74,0.970924131,37361,0.950397303,0.719754527
14903,"Estimating the ascertainment rate of SARS-CoV-2 infection in Wuhan, China: implications for management of the global outbreak",0,10.1101/2020.03.24.20042218,3/26/20,medrxiv,0,3,bayes,0.005047779,0.00504781,0.005047515,0.884616686,0.005047674,0.095192536,Epidemiology,0.25528222,FALSE,104.6666667,0.878594842,114,0.843791812,5,0.739490092,6234,0.662412714,0.781072365
14904,Computational analysis of SARS-CoV-2 S1 protein O-glycosylation and phosphorylation modifications and identifying potential target positions against CD209L-mannose interaction to inhibit initial binding of the virus,0,10.1101/2020.03.25.007898,3/25/20,biorxiv,0,2,computational,0.885124987,0.07751647,0.032744139,0.001538178,0.001538128,0.001538099,Drug discovery,0.94875324,TRUE,6,0.086028821,0.5,0.087101953,6,0.764429903,3444,0.410787383,0.337087015
14905,FEP-based screening prompts drug repositioning against COVID-19,0,10.1101/2020.03.23.004580,3/25/20,biorxiv,0,14,"virtual screening, computational",0.899838411,0.000830635,0.096838924,0.000830687,0.000830636,0.000830709,Drug discovery,0.8053217,TRUE,81.29411765,0.812233286,32.35294118,0.588306128,19,0.89561084,7482,0.716108837,0.753064773
14906,Social distancing strategies for curbing the COVID-19 epidemic,0,10.1101/2020.03.22.20041079,3/24/20,medrxiv,0,4,mathematical model,0.001861679,0.022308811,0.001861689,0.822731972,0.001861897,0.149373952,Epidemiology,0.2463117,FALSE,165.5,0.955284804,333.25,0.962804389,145,0.986048522,62794,0.974717072,0.969713697
14907,Mechanistic-statistical SIR modelling for early estimation of the actual number of cases and mortality rate from COVID-19,0,10.1101/2020.03.22.20040915,3/24/20,medrxiv,10.3390/biology9050097,5,probabilistic,0.051320053,0.002357783,0.002357894,0.723246675,0.002357896,0.218359699,Epidemiology,0.41445395,FALSE,13.8,0.208980147,6.4,0.286058336,48,0.955614544,11880,0.821333975,0.567996751
14908,Epidemiological parameters of coronavirus disease 2019: a pooled analysis of publicly reported individual data of 1155 cases from seven countries,0,10.1101/2020.03.21.20040329,3/24/20,medrxiv,0,9,dataset,0.000629666,0.000629683,0.000629693,0.866628624,0.092292204,0.03919013,Epidemiology,0.35001582,FALSE,14.44444444,0.217947925,12.33333333,0.389550442,69,0.968763504,15133,0.86178666,0.609512133
14909,"A New, Simple Projection Model for COVID-19 Pandemic",0,10.1101/2020.03.21.20039867,3/24/20,medrxiv,0,1,prediction model,0.000863041,0.000863066,0.000863072,0.995684696,0.000863062,0.000863063,Epidemiology,0.18355104,FALSE,138,0.930546107,11,0.371287129,6,0.764429903,19041,0.891163015,0.739356538
14910,MATHEMATICAL PREDICTIONS FOR COVID-19 AS A GLOBAL PANDEMIC,0,10.1101/2020.03.19.20038794,3/24/20,medrxiv,0,1,mathematical prediction,0.004109987,0.004109838,0.004109799,0.979450688,0.004109884,0.004109804,Epidemiology,0.34107554,FALSE,11,0.167171748,1,0.122023013,27,0.92443978,14158,0.847098483,0.515183256
14911,Characterisation of the transcriptome and proteome of SARS-CoV-2 using direct RNA sequencing and tandem mass spectrometry reveals evidence for a cell passage induced in-frame deletion in the spike glycoprotein that removes the furin-like cleavage site.,0,10.1101/2020.03.22.002204,3/24/20,biorxiv,10.1186/s13073-020-00763-0,11,"sequencing, transcriptom, proteom",0.485420431,0.510072167,0.001126823,0.001126885,0.001126798,0.001126896,Genomics,0.5714724,TRUE,51.45454545,0.642278434,70.90909091,0.755887075,60,0.964503982,20165,0.900794606,0.815866024
14912,AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks,0,10.1101/2020.03.19.20039354,3/23/20,medrxiv,10.1016/j.asoc.2020.106897,30,"artificial intelligence, dataset",0.0015118,0.001511834,0.814231051,0.179721463,0.001511926,0.001511925,Imaging,0.65801036,TRUE,27.16666667,0.397303482,7.833333333,0.314088841,103,0.978949318,6819,0.688177221,0.594629716
14913,The Effectiveness of Social Distancing in Mitigating COVID-19 Spread: a modelling analysis,0,10.1101/2020.03.20.20040055,3/23/20,medrxiv,0,2,simulation model,0.001085332,0.001085349,0.001085348,0.944710147,0.050948464,0.00108536,Epidemiology,0.30677783,FALSE,19,0.285793803,3.5,0.213607172,36,0.941169208,28162,0.932097279,0.593166865
14914,"Assessing the potential impacts of COVID-19 in Brasil: Mobility, Morbidity and Impact to the Health System",0,10.1101/2020.03.19.20039131,3/23/20,medrxiv,0,10,probabilistic,0.002032779,0.002032843,0.002032808,0.940312788,0.002032874,0.051555909,Epidemiology,0.1963996,FALSE,29.4,0.423650195,52.2,0.689724378,17,0.887338725,9171,0.771972068,0.693171341
14915,Covid-19 health care demand and mortality in Sweden in response to non-pharmaceutical (NPIs) mitigation and suppression scenarios,0,10.1101/2020.03.20.20039594,3/23/20,medrxiv,0,1,model fit,0.000580129,0.000580131,0.000580129,0.886279294,0.000580167,0.11140015,Epidemiology,0.17065722,FALSE,14,0.213494959,18,0.46180091,17,0.887338725,22812,0.913074886,0.61892737
14916,Supervised Machine Learning for the Early Prediction of Acute Respiratory Distress Syndrome (ARDS),0,10.1101/2020.03.19.20038364,3/23/20,medrxiv,10.1016/j.jcrc.2020.07.019,7,"machine learning, classifier",0.00213064,0.002130653,0.532857425,0.00213078,0.002130743,0.45861976,Clinics,0.5115409,TRUE,16.85714286,0.253633496,7.142857143,0.300709125,5,0.739490092,2552,0.250421382,0.386063524
14917,Covid-19 dynamics in Albania: first estimates and projections,0,10.1101/2020.03.20.20038141,3/23/20,medrxiv,0,1,"bayes, mathematical model",0.001291321,0.021185563,0.001291287,0.973649393,0.001291219,0.001291218,Epidemiology,0.17946333,FALSE,6,0.086028821,2,0.164302917,5,0.739490092,2477,0.235973995,0.306448956
14918,Predicting COVID-19 malignant progression with AI techniques,0,10.1101/2020.03.20.20037325,3/23/20,medrxiv,0,15,"deep learning, logistic regression, prediction model",0.001141358,0.00114133,0.37518365,0.001141356,0.001141332,0.620250974,Clinics,0.7891996,TRUE,48.7,0.619518832,136.7,0.871554723,8,0.799987654,7724,0.725499639,0.754140212
14919,Development and Evaluation of an AI System for COVID-19,0,10.1101/2020.03.20.20039834,3/23/20,medrxiv,0,11,"artificial intelligence, neural network, dataset",0.001156276,0.023467021,0.958717751,0.001156286,0.001156258,0.014346409,Imaging,0.45336485,FALSE,56.3,0.677036304,,,13,0.858880178,6220,0.658560077,0.731492186
14920,COVID-19 outbreak in Algeria: A mathematical model to predict the incidence,0,10.1101/2020.03.20.20039891,3/23/20,medrxiv,10.30935/jconseph/8451,1,"model fit, mathematical model",0.001272656,0.001272661,0.019132088,0.975777218,0.001272662,0.001272715,Epidemiology,0.20187497,FALSE,15,0.227596017,8,0.320511105,13,0.858880178,3444,0.408379485,0.453841696
14921,A mathematical model for the spatiotemporal epidemic spreading of COVID19,0,10.1101/2020.03.21.20040022,3/23/20,medrxiv,10.1103/PhysRevX.10.041055,8,mathematical model,0.001059339,0.001059375,0.00105939,0.961687463,0.034075038,0.001059395,Epidemiology,0.30061394,FALSE,34.875,0.485620632,53.25,0.694674873,94,0.976726958,14298,0.848543222,0.751391421
14922,COVID-19 Myocarditis and Severity Factors： An Adult Cohort Study,0,10.1101/2020.03.19.20034124,3/23/20,medrxiv,0,18,logistic regression,0.017802982,0.066006206,0.197886622,0.046189033,0.000625233,0.671489924,Clinics,0.9134412,TRUE,40.55555556,0.544931659,,,45,0.953206988,6830,0.688418011,0.72885222
14923,Modelling the Potential Health Impact of the COVID-19 Pandemic on a Hypothetical European Country,0,10.1101/2020.03.20.20039776,3/23/20,medrxiv,0,6,simulation model,0.007673918,0.007673966,0.007673782,0.961630381,0.007674069,0.007673883,Epidemiology,0.14754069,FALSE,77.83333333,0.797822995,76,0.769132994,22,0.908142478,3478,0.412232121,0.721832647
14924,Comparative analyses of SAR-CoV2 genomes from different geographical locations and other coronavirus family genomes reveals unique features potentially consequential to host-virus interaction and pathogenesis,0,10.1101/2020.03.21.001586,3/21/20,biorxiv,10.1016/j.heliyon.2020.e04658,4,"computational, genomes",0.264396656,0.729450918,0.001538152,0.001538115,0.001538079,0.001538079,Genomics,0.6189797,TRUE,23,0.34225988,2.75,0.187583623,63,0.965985555,56986,0.971827595,0.616914163
14925,Molecular characterization of SARS-CoV-2 in the first COVID-19 cluster in France reveals an amino-acid deletion in nsp2 (Asp268Del),0,10.1101/2020.03.19.998179,3/21/20,biorxiv,10.1016/j.cmi.2020.03.020,14,"sequencing, metagenom, whole genome, genome sequences",0.002806342,0.891978833,0.03990935,0.002806456,0.002806351,0.059692669,Genomics,0.7097418,TRUE,62.92857143,0.720638258,45.21428571,0.659151726,26,0.920859312,10710,0.800866843,0.775379035
14926,COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning,0,10.1101/2020.03.20.000141,3/21/20,biorxiv,10.3389/fimmu.2020.01581,4,"machine learning, proteom",0.723318029,0.173191274,0.059917309,0.001330035,0.040913248,0.001330106,Drug discovery,0.6057972,TRUE,47.5,0.610551054,36,0.611118544,79,0.972899562,16449,0.872862991,0.766858038
14927,Development and utilization of an intelligent application for aiding COVID-19 diagnosis,0,10.1101/2020.03.18.20035816,3/21/20,medrxiv,0,13,machine learning,0.003335319,0.00333557,0.909888388,0.076769746,0.003335532,0.003335446,Epidemiology,0.57809633,TRUE,25.53846154,0.374605727,21.92307692,0.501672465,21,0.903944688,4675,0.538887551,0.579777608
14928,The first-in-class peptide binder to the SARS-CoV-2 spike protein,0,10.1101/2020.03.19.999318,3/20/20,biorxiv,0,6,molecular dynamics simulation,0.83939548,0.076921853,0.02283327,0.058915748,0.000966837,0.000966812,Drug discovery,0.5393451,TRUE,29.8,0.428474241,13.8,0.408884132,61,0.964936107,37165,0.949193354,0.687871958
14929,GENOMIC CHARACTERISATION AND PHYLOGENETIC ANALYSIS OF SARS-COV-2 IN ITALY,0,10.1101/2020.03.15.20032870,3/20/20,medrxiv,10.1002/jmv.25794,13,genomes,0.006540085,0.883097511,0.006539628,0.006540235,0.006539943,0.090742598,Genomics,0.59830225,TRUE,78.53846154,0.800606098,53.30769231,0.694942467,14,0.866658436,25325,0.922706477,0.82122837
14930,Roles of meteorological conditions in COVID-19 transmission on a worldwide scale,0,10.1101/2020.03.16.20037168,3/20/20,medrxiv,0,9,dataset,0.001291295,0.058101486,0.00129124,0.837271626,0.001291252,0.100753102,Epidemiology,0.50211537,TRUE,15.77777778,0.237924423,6.666666667,0.290607439,70,0.969072165,35553,0.947026246,0.611157568
14931,Regaining perspective on SARS-CoV-2 molecular tracing and its implications,0,10.1101/2020.03.16.20034470,3/20/20,medrxiv,0,6,genome sequences,0.002357761,0.390619992,0.002357794,0.559595169,0.042711531,0.002357753,Epidemiology,0.17076397,FALSE,43.5,0.573381161,66.5,0.744246722,15,0.874313229,4240,0.49482302,0.671691033
14932,Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics,0,10.1101/2020.03.17.20037309,3/20/20,medrxiv,0,1,machine learning,0.002996513,0.002996497,0.092268967,0.757988349,0.002996496,0.140753179,Epidemiology,0.27668133,FALSE,16,0.243552477,4,0.231469093,12,0.850299401,5148,0.583192873,0.477128461
14933,"Pandemic dynamics of COVID-19 using epidemic stage, instantaneous reproductive number and pathogen genome identity (GENI) score: modeling molecular epidemiology",0,10.1101/2020.03.17.20037481,3/20/20,medrxiv,0,2,"sequencing, whole genome, genome sequences",0.000323506,0.361826388,0.000323517,0.602344301,0.000323515,0.034858774,Epidemiology,0.13122109,FALSE,98,0.864741171,126,0.859245384,5,0.739490092,9440,0.775824705,0.809825338
14934,A Novel Triage Tool of Artificial Intelligence Assisted Diagnosis Aid System for Suspected COVID-19 pneumonia In Fever Clinics,0,10.1101/2020.03.19.20039099,3/20/20,medrxiv,10.21037/atm-20-3073,23,artificial intelligence,0.000916731,0.000916701,0.448712894,0.000916721,0.000916753,0.547620199,Clinics,0.59785473,TRUE,10.875,0.161791082,,,35,0.939317242,5578,0.612810017,0.571306113
14935,"Fear, Access, and the Real-Time Estimation of Etiological Parameters for Outbreaks of Novel Pathogens",0,10.1101/2020.03.19.20038729,3/20/20,medrxiv,0,6,"model fit, predictive model",0.001684491,0.001684552,0.084011866,0.909249999,0.001684568,0.001684523,Epidemiology,0.075086445,FALSE,56.5,0.678891706,43.83333333,0.653063955,1,0.537564047,2414,0.215988442,0.521377038
14936,Estimating the Risks from COVID-19 Infection in Adult Chemotherapy Patients,0,10.1101/2020.03.18.20038067,3/20/20,medrxiv,0,9,computational,0.000977446,0.000977443,0.00097761,0.329790152,0.000977488,0.666299862,Clinics,0.4014418,FALSE,11.5,0.17416043,6,0.280037463,12,0.850299401,5770,0.627016614,0.482878477
14937,Estimating Preventable COVID19 Infections Related to Elective Outpatient Surgery in Washington State: A Quantitative Model,0,10.1101/2020.03.18.20037952,3/20/20,medrxiv,0,2,mathematical model,0.00299643,0.002996547,0.002996557,0.444470511,0.394806614,0.15173334,Epidemiology,0.2942325,FALSE,3,0.037293586,0,0.055525823,3,0.667819001,2545,0.245605586,0.251560999
14938,A framework for identifying regional outbreak and spread of COVID-19 from one-minute population-wide surveys,0,10.1101/2020.03.19.20038844,3/20/20,medrxiv,10.1038/s41591-020-0857-9,10,predictive model,0.001438109,0.001438215,0.001438245,0.809065478,0.123825659,0.062794294,Epidemiology,0.76161546,TRUE,35.5,0.492547467,92.7,0.807666577,1,0.537564047,12134,0.823741873,0.665379991
14939,Predicting the epidemic trend of COVID-19 in China and across the world using the machine learning approach,0,10.1101/2020.03.18.20038117,3/20/20,medrxiv,0,7,machine learning,0.001987083,0.00198712,0.001987133,0.990064341,0.001987107,0.001987216,Epidemiology,0.2315625,FALSE,30.71428571,0.440534356,7.285714286,0.30331817,5,0.739490092,4844,0.555261257,0.509650969
14940,Investigating the Impact of Asymptomatic Carriers on COVID-19 Transmission,0,10.1101/2020.03.18.20037994,3/20/20,medrxiv,0,4,mathematical model,0.001717208,0.001717219,0.001717196,0.955327044,0.037804058,0.001717274,Epidemiology,0.46301156,FALSE,14.5,0.219617787,4.5,0.242708055,60,0.964503982,38750,0.951119673,0.594487374
14941,AAEDM: Theoretical Dynamic Epidemic Diffusion Model and Covid-19 Korea Pandemic Cases,0,10.1101/2020.03.17.20037838,3/20/20,medrxiv,0,1,"simulation model, machine learning, prediction model, dataset",0.001786531,0.001786523,0.283423551,0.709430291,0.001786568,0.001786536,Epidemiology,0.21309474,FALSE,56,0.675304595,7,0.299973241,5,0.739490092,4640,0.533349386,0.562029328
14942,Short-range airborne route dominates exposure of respiratory infection during close contact,0,10.1101/2020.03.16.20037291,3/20/20,medrxiv,10.1016/j.buildenv.2020.106859,5,mathematical model,0.210641935,0.106633547,0.016738479,0.534196179,0.000554718,0.131235142,Epidemiology,0.6233226,TRUE,37.4,0.512585812,41,0.639751137,57,0.962281622,6962,0.692270648,0.701722305
14943,Estimating unobserved SARS-CoV-2 infections in the United States,0,10.1101/2020.03.15.20036582,3/18/20,medrxiv,10.1073/pnas.2005476117,6,simulation model,0.001098808,0.100275247,0.00109886,0.895329406,0.001098839,0.00109884,Epidemiology,0.4277675,FALSE,18.66666667,0.279670975,16.33333333,0.440460262,37,0.942712513,7706,0.720202263,0.595761504
14944,Effect of large-scale testing platform in prevention and control of the COVID-19 pandemic: an empirical study with a novel numerical model,0,10.1101/2020.03.15.20036624,3/18/20,medrxiv,0,21,"mathematical model, sequencing",0.000966754,0.164447285,0.218856247,0.613796094,0.000966795,0.000966826,Epidemiology,0.83553225,TRUE,31.76190476,0.452470777,29.19047619,0.56368745,5,0.739490092,3596,0.421622923,0.544317811
14945,Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models,0,10.1101/2020.03.13.20035345,3/18/20,medrxiv,0,3,prediction model,0.002296615,0.002296567,0.002296563,0.988516758,0.002296699,0.002296797,Epidemiology,0.23072019,FALSE,16,0.243552477,2.666666667,0.185442869,36,0.941169208,4711,0.538405972,0.477142631
14946,RBD mutations from circulating SARS-CoV-2 strains enhance the structure stability and infectivity of the spike protein,0,10.1101/2020.03.15.991844,3/17/20,biorxiv,0,13,"molecular dynamics simulation, in silico, genomes",0.514090885,0.483133668,0.000693854,0.000693876,0.000693856,0.000693861,Drug discovery,0.494571,FALSE,33.4,0.47046818,32.1,0.586031576,19,0.89561084,24146,0.916927522,0.71725953
14947,Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label,0,10.1101/2020.03.12.20027185,3/17/20,medrxiv,10.1109/TMI.2020.2995965,13,"deep learning, neural network",0.001034566,0.001034575,0.994827082,0.001034604,0.001034567,0.001034605,Imaging,0.3838919,FALSE,71.875,0.772280289,169.375,0.898782446,192,0.990740169,8575,0.751745726,0.853387158
14948,Impact of city and residential unit lockdowns on prevention and control of COVID-19,0,10.1101/2020.03.13.20035253,3/17/20,medrxiv,0,1,computational,0.001901772,0.001901716,0.001901737,0.864200292,0.001901719,0.128192763,Epidemiology,0.691316,TRUE,31,0.445111015,7,0.299973241,6,0.764429903,3457,0.403804479,0.478329659
14949,Impacts of social and economic factors on the transmission of coronavirus disease (COVID-19) in China,0,10.1101/2020.03.13.20035238,3/17/20,medrxiv,10.1007/s00148-020-00778-2,3,machine learning,0.002183255,0.002183299,0.002183385,0.989083542,0.002183295,0.002183224,Epidemiology,0.60157204,TRUE,208,0.974766529,154.3333333,0.887342788,120,0.98265325,16961,0.876234048,0.930249154
14950,Evidence of the Recombinant Origin and Ongoing Mutations in Severe Acute Respiratory Syndrome 2 (SARS-COV-2),0,10.1101/2020.03.16.993816,3/17/20,biorxiv,0,5,"whole genome, genomes",0.112721288,0.878092316,0.002296755,0.002296609,0.002296506,0.002296527,Genomics,0.42209187,FALSE,56.4,0.677902158,24.2,0.522946214,13,0.858880178,9459,0.774861546,0.708647524
14951,Recapitulation of SARS-CoV-2 Infection and Cholangiocyte Damage with Human Liver Organoids,0,10.1101/2020.03.16.990317,3/17/20,biorxiv,10.1007/s13238-020-00718-6,16,sequencing,0.681498483,0.079626056,0.0015935,0.001593573,0.001593491,0.234094896,Drug discovery,0.5475549,TRUE,143,0.937596636,,,13,0.858880178,5680,0.617385023,0.804620612
14952,Multiple approaches for massively parallel sequencing of HCoV-19 genomes directly from clinical samples,0,10.1101/2020.03.16.993584,3/17/20,biorxiv,10.1186/s13073-020-00751-4,34,"sequencing, transcriptom, genomes, metatranscriptom",0.002080576,0.788175094,0.203502508,0.002080718,0.002080559,0.002080546,Genomics,0.6294341,TRUE,61.26470588,0.710309852,,,11,0.840175319,6234,0.653985071,0.734823414
14953,Blood single cell immune profiling reveals the interferon-MAPK pathway mediated adaptive immune response for COVID-19,0,10.1101/2020.03.15.20033472,3/17/20,medrxiv,0,17,transcriptom,0.792452292,0.002032943,0.002032753,0.002032766,0.002033062,0.199416183,Drug discovery,0.5153768,TRUE,54.47058824,0.664234028,49.82352941,0.680693069,33,0.936045435,8669,0.754635203,0.758901934
14954,Multi-city modeling of epidemics using spatial networks: Application to 2019-nCov (COVID-19) coronavirus in India,0,10.1101/2020.03.13.20035386,3/17/20,medrxiv,0,2,computational,0.002080663,0.043152838,0.002080702,0.948524544,0.002080681,0.002080572,Epidemiology,0.2084409,FALSE,20.5,0.304966294,3,0.199424672,27,0.92443978,4235,0.491451962,0.480070677
14955,"Influence factors of death risk among COVID-19 patients in Wuhan, China: a hospital-based case-cohort study",0,10.1101/2020.03.13.20035329,3/16/20,medrxiv,0,12,logistic regression,0.000946134,0.000946113,0.000946073,0.000946124,0.000946096,0.99526946,Clinics,0.54761124,TRUE,86.33333333,0.830354382,34.5,0.602221033,55,0.960800049,5483,0.602937635,0.749078275
14956,Rational evaluation of various epidemic models based on the COVID-19 data of China,0,10.1101/2020.03.12.20034595,3/16/20,medrxiv,0,5,"bayes, dataset",0.000966776,0.000966758,0.072813716,0.923319211,0.000966779,0.00096676,Epidemiology,0.12657183,FALSE,26.8,0.392417589,13.8,0.408884132,23,0.91129082,2648,0.259330604,0.492980786
14957,High sensitivity detection of SARS-CoV-2 using multiplex PCR and a multiplex-PCR-based metagenomic method,0,10.1101/2020.03.12.988246,3/14/20,biorxiv,0,16,"sequencing, metagenom",0.027357133,0.894052857,0.07405444,0.001511928,0.001511811,0.001511832,Genomics,0.62323105,TRUE,16.36,0.247077741,65.68,0.741303184,7,0.785110192,12235,0.822056345,0.648886866
14958,Accurate Identification of SARS-CoV-2 from Viral Genome Sequences using Deep Learning,0,10.1101/2020.03.13.990242,3/14/20,biorxiv,10.1038/s41598-020-80363-5,9,"deep learning, artificial intelligence, neural network, classifier, genome sequences, dataset",0.000752905,0.391991813,0.604996509,0.000752919,0.000752955,0.000752899,Genomics,0.21327391,FALSE,103,0.875378811,,,11,0.840175319,9756,0.780881291,0.83214514
14959,SARS-CoV-2 receptor ACE2 and TMPRSS2 are predominantly expressed in a transient secretory cell type in subsegmental bronchial branches,0,10.1101/2020.03.13.991455,3/14/20,biorxiv,10.15252/embj.20105114,14,sequencing,0.989346513,0.002130793,0.002130689,0.002130726,0.002130638,0.002130641,Drug discovery,0.5478976,TRUE,107.2857143,0.883851815,162.2857143,0.894032647,30,0.930057411,13919,0.841801108,0.887435745
14960,Development of CRISPR as a prophylactic strategy to combat novel coronavirus and influenza,0,10.1101/2020.03.13.991307,3/14/20,biorxiv,0,15,bioinformatic,0.57168768,0.274330822,0.002422285,0.002422487,0.146714227,0.0024225,Drug discovery,0.22223112,FALSE,26.2,0.383820892,71.06666667,0.75662296,18,0.891474782,44122,0.960510474,0.748107277
14961,Dark proteome of Newly Emerged SARS-CoV-2 in Comparison with Human and Bat Coronaviruses,0,10.1101/2020.03.13.990598,3/14/20,biorxiv,0,8,"computational, proteom, sequence alignment",0.638598583,0.269024042,0.000898116,0.000898108,0.063566909,0.027014242,Drug discovery,0.7492014,TRUE,12,0.183190055,2,0.164302917,9,0.814309525,9739,0.779677342,0.48536996
14962,Virus strain of a mild COVID-19 patient in Hangzhou representing a new trend in SARS-CoV-2 evolution related to Furin cleavage site,0,10.1101/2020.03.10.20033944,3/13/20,medrxiv,10.1080/22221751.2020.1781551,0,"sequencing, sequence alignment",0.15842526,0.580298127,0.001237083,0.001237097,0.001237153,0.25756528,Genomics,0.6126092,TRUE,46.27777778,0.598985713,39.02777778,0.627910088,16,0.881782826,16732,0.871899831,0.745144615
14963,Genomic epidemiology of a densely sampled COVID19 outbreak in China,0,10.1101/2020.03.09.20033365,3/13/20,medrxiv,0,14,"bayes, genomic epidemiology, genomes, bayesian model",0.0028983,0.534402182,0.002898413,0.454004338,0.002898359,0.002898408,Genomics,0.2098616,FALSE,20.53846154,0.305089987,32.61538462,0.589710998,11,0.840175319,7039,0.69010354,0.606269961
14964,A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19,0,10.1101/2020.03.11.986836,3/12/20,biorxiv,0,29,"machine learning, in silico, transcriptom, knowledge graph",0.823317333,0.001085354,0.172341201,0.001085381,0.001085355,0.001085376,Drug discovery,0.7846083,TRUE,79.31034483,0.803636589,32.65517241,0.589844795,78,0.972405704,19439,0.889959066,0.813961538
14965,"Rigidity, normal modes and flexible motion of a SARS-CoV-2 (COVID-19) protease structure.",0,10.1101/2020.03.10.986190,3/12/20,biorxiv,0,1,"computational, network model",0.988516796,0.00229679,0.002296593,0.002296726,0.002296561,0.002296534,Drug discovery,0.552031,TRUE,113,0.89609747,39,0.62784319,3,0.667819001,4470,0.509511197,0.675317715
14966,Effects of Chinese strategies for controlling the diffusion and deterioration of novel coronavirus-infected pneumonia in China,0,10.1101/2020.03.10.20032755,3/12/20,medrxiv,0,8,model fit,0.024658099,0.00146189,0.019620498,0.951335646,0.001461908,0.001461959,Epidemiology,0.7701323,TRUE,17.125,0.258148308,5.125,0.257760235,3,0.667819001,2223,0.169756802,0.338371087
14967,"The effect of control strategies that reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China",0,10.1101/2020.03.09.20033050,3/12/20,medrxiv,10.1016/s2468-2667(20)30073-6,9,model fit,0.001272651,0.017379092,0.001272635,0.953240563,0.025562403,0.001272656,Epidemiology,0.29837084,FALSE,104.875,0.878904076,205.25,0.919855499,10,0.828199272,11511,0.810498435,0.85936432
14968,First 12 patients with coronavirus disease 2019 (COVID-19) in the United States,0,10.1101/2020.03.09.20032896,3/12/20,medrxiv,0,178,"sequencing, whole genome",0.001203501,0.39990484,0.023462979,0.001203486,0.001203463,0.573021731,Clinics,0.3061501,FALSE,37.11235955,0.509184241,53.7247191,0.696079743,99,0.977961603,59733,0.972549964,0.788943888
14969,In silico approach to accelerate the development of mass spectrometry-based proteomics methods for detection of viral proteins: Application to COVID-19,0,10.1101/2020.03.08.980383,3/10/20,biorxiv,0,5,"deep learning, in silico, proteom, microbiom",0.415422618,0.467375504,0.114821565,0.000793437,0.000793415,0.000793461,Genomics,0.21181431,FALSE,9,0.135320675,0,0.055525823,12,0.850299401,15143,0.855044546,0.474047611
14970,A proposal of an alternative primer for the ARTIC Network's multiplex PCR to improve coverage of SARS-CoV-2 genome sequencing,0,10.1101/2020.03.10.985150,3/10/20,biorxiv,10.1371/journal.pone.0239403,5,"sequencing, whole-genome",0.08471197,0.853231518,0.056225876,0.00194357,0.001943548,0.001943518,Genomics,0.36909828,FALSE,72.2,0.773950152,49.8,0.680559272,24,0.914439163,15009,0.852877438,0.805456506
14971,In silico Design of novel Multi-epitope recombinant Vaccine based on Coronavirus surface glycoprotein,0,10.1101/2020.03.10.985499,3/10/20,biorxiv,0,1,"bioinformatic, in silico",0.9930357,0.001392845,0.001392868,0.001392896,0.001392855,0.001392836,Drug discovery,0.6458166,TRUE,23,0.34225988,7,0.299973241,10,0.828199272,4447,0.503973032,0.493601356
14972,Aerosol and surface stability of HCoV-19 (SARS-CoV-2) compared to SARS-CoV-1,0,10.1101/2020.03.09.20033217,3/10/20,medrxiv,10.1056/NEJMc2004973,13,bayes,0.347961967,0.004775411,0.004775254,0.632936934,0.004775214,0.004775221,Epidemiology,0.37441796,FALSE,47.38461538,0.608881192,257.9230769,0.940727857,158,0.988270881,473868,0.998796051,0.884168995
14973,A deterministic epidemic model for the emergence of COVID-19 in China,0,10.1101/2020.03.08.20032854,3/10/20,medrxiv,0,2,mathematical model,0.003465891,0.003465991,0.045242875,0.940893142,0.003465989,0.003466112,Epidemiology,0.19838947,FALSE,26.5,0.389325252,14.5,0.418450629,9,0.814309525,3631,0.417288707,0.509843528
14974,Amplicon based MinION sequencing of SARS-CoV-2 and metagenomic characterisation of nasopharyngeal swabs from patients with COVID-19,0,10.1101/2020.03.05.20032011,3/8/20,medrxiv,0,30,"sequencing, metagenom, microbiom",0.00654066,0.534073166,0.006539653,0.006539846,0.006539783,0.439766892,Genomics,0.3200182,FALSE,68.46666667,0.755952749,101.4,0.824792614,37,0.942712513,13559,0.836744522,0.8400506
14975,COVID-19 early warning score: a multi-parameter screening tool to identify highly suspected patients,0,10.1101/2020.03.05.20031906,3/8/20,medrxiv,0,4,"logistic regression, dataset",0.001310456,0.001310348,0.376235691,0.052320818,0.001310384,0.567512303,Clinics,0.3587459,FALSE,30.5,0.438493413,13.75,0.408348943,68,0.968393111,23383,0.911870937,0.681776601
14976,A mathematical model for estimating the age-specific transmissibility of a novel coronavirus,0,10.1101/2020.03.05.20031849,3/8/20,medrxiv,0,18,"model fit, mathematical model",0.001330012,0.001330096,0.001330022,0.616958166,0.151440152,0.227611553,Epidemiology,0.37388602,FALSE,40.38888889,0.543199951,,,19,0.89561084,4949,0.550686251,0.663165681
14977,"Exploring diseases/traits and blood proteins causally related to expression of ACE2, the putative receptor of 2019-nCov: A Mendelian Randomization analysis",0,10.1101/2020.03.04.20031237,3/8/20,medrxiv,10.2337/dc20-0643,3,"proteom, dataset",0.763514667,0.001220053,0.001220112,0.001220078,0.001220114,0.231604976,Drug discovery,0.7121539,TRUE,8.666666667,0.12839384,5.333333333,0.262911426,31,0.931971109,6684,0.668673248,0.497987406
14978,Direct RNA sequencing and early evolution of SARS-CoV-2,0,10.1101/2020.03.05.976167,3/7/20,biorxiv,0,15,"sequencing, transcriptom, genomic epidemiology",0.003335473,0.983323277,0.003335365,0.003335407,0.00333525,0.003335229,Genomics,0.43596795,FALSE,49.33333333,0.626507514,53.53333333,0.695544554,72,0.970121612,24102,0.915241994,0.801853919
14979,"In silico study of the spike protein from SARS-CoV-2 interaction with ACE2: similarity with SARS-CoV, hot-spot analysis and effect of the receptor polymorphism",0,10.1101/2020.03.04.976027,3/7/20,biorxiv,10.1016/j.bbrc.2020.05.028,8,in silico,0.761068324,0.234306662,0.001156258,0.00115625,0.00115626,0.001156245,Drug discovery,0.550344,TRUE,30.125,0.433174593,15.625,0.431495852,46,0.954009507,9139,0.760895738,0.644893923
14980,Novel Immunoglobulin Domain Proteins Provide Insights into Evolution and Pathogenesis Mechanisms of SARS-Related Coronaviruses,0,10.1101/2020.03.04.977736,3/7/20,biorxiv,10.1128/mbio.00760-20,5,genomes,0.412659729,0.578817437,0.002130807,0.002130684,0.002130676,0.002130667,Genomics,0.5958506,TRUE,96.8,0.860659286,738.6,0.988761038,11,0.840175319,5072,0.559836263,0.812357977
14981,Genome-wide data inferring the evolution and population demography of the novel pneumonia coronavirus (SARS-CoV-2),0,10.1101/2020.03.04.976662,3/7/20,biorxiv,0,12,"genome-wide, genomes",0.002422334,0.774139999,0.002422402,0.216170716,0.002422287,0.002422262,Genomics,0.3038954,FALSE,60,0.703444864,169.25,0.898648649,11,0.840175319,8609,0.746448351,0.797179296
14982,Projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period,0,10.1101/2020.03.04.20031112,3/6/20,medrxiv,10.1126/science.abb5793,5,mathematical model,0.003101586,0.151005714,0.003101623,0.836588052,0.003101507,0.003101518,Epidemiology,0.16595158,FALSE,141.4,0.935741233,276.2,0.947551512,113,0.981171677,80519,0.980496027,0.961240112
14983,"Adjusted age-specific case fatality ratio during the COVID-19 epidemic in Hubei, China, January and February 2020",0,10.1101/2020.03.04.20031104,3/6/20,medrxiv,10.1371/journal.pmed.1003189,7,mathematical model,0.000512877,0.000512891,0.000512885,0.789451279,0.000512897,0.208497171,Epidemiology,0.3586254,FALSE,41.25,0.5508071,61.75,0.727588975,57,0.962281622,53305,0.967734168,0.802102966
14984,Immunodepletion with Hypoxemia: A Potential High Risk Subtype of Coronavirus Disease 2019,0,10.1101/2020.03.03.20030650,3/6/20,medrxiv,0,13,sequencing,0.354777305,0.028426796,0.001034629,0.001034624,0.001034624,0.613692022,Clinics,0.5371286,TRUE,39.38461538,0.532809698,,,26,0.920859312,12049,0.81627739,0.7566488
14985,Modelling-based evaluation of the effect of quarantine control by the Chinese government in the coronavirus disease 2019 outbreak,0,10.1101/2020.03.03.20030445,3/6/20,medrxiv,10.1007/s11427-020-1717-9,9,model fit,0.000509959,0.000510019,0.000509933,0.997450186,0.000509942,0.000509962,Epidemiology,0.09371954,FALSE,31,0.445111015,21.55555556,0.498327535,11,0.840175319,3170,0.341680713,0.531323646
14986,Transmission interval estimates suggest pre-symptomatic spread of COVID-19,0,10.1101/2020.03.03.20029983,3/6/20,medrxiv,10.7554/eLife.57149,11,dataset,0.002032728,0.002032815,0.002032748,0.942869389,0.048999478,0.002032843,Epidemiology,0.4550152,FALSE,15.63636364,0.235574247,31.72727273,0.582820444,159,0.988394345,48637,0.963881531,0.692667642
14987,Risk estimation and prediction by modeling the transmission of the novel coronavirus (COVID-19) in mainland China excluding Hubei province,0,10.1101/2020.03.01.20029629,3/6/20,medrxiv,10.1186/s40249-020-00683-6,3,mathematical model,0.000838532,0.000838515,0.000838506,0.995807382,0.000838553,0.000838511,Epidemiology,0.211254,FALSE,17.66666667,0.266373925,15.66666667,0.432365534,18,0.891474782,3858,0.439682157,0.5074741
14988,Nanopore target sequencing for accurate and comprehensive detection of SARS-CoV-2 and other respiratory viruses,0,10.1101/2020.03.04.20029538,3/6/20,medrxiv,10.1002/smll.202002169,15,sequencing,0.001862023,0.734285955,0.188143591,0.001861816,0.001861718,0.071984897,Genomics,0.7035789,TRUE,77.53333333,0.7967716,66.6,0.744514316,27,0.92443978,8101,0.726944378,0.798167519
14989,Partial RdRp sequences offer a robust method for Coronavirus subgenus classification,0,10.1101/2020.03.02.974311,3/6/20,biorxiv,0,4,"bayes, genomes",0.000889063,0.865074168,0.131369542,0.000889115,0.000889063,0.000889049,Genomics,0.08680889,FALSE,26,0.382398417,27.25,0.547497993,0,0.403234768,2827,0.279316157,0.403111834
14990,A novel bat coronavirus reveals natural insertions at the S1/S2 cleavage site of the Spike protein and a possible recombinant origin of HCoV-19,0,10.1101/2020.03.02.974139,3/5/20,biorxiv,0,13,metagenom,0.261391324,0.731870503,0.001684528,0.00168457,0.001684562,0.001684513,Genomics,0.3319224,FALSE,282.3846154,0.98899128,437.6923077,0.973909553,35,0.939317242,22007,0.905610402,0.951957119
14991,The spatiotemporal estimation of the dynamic risk and the international transmission of 2019 Novel Coronavirus (COVID-19) outbreak: A global perspective,0,10.1101/2020.02.29.20029413,3/3/20,medrxiv,0,4,"simulation model, prediction model",0.001156257,0.001156274,0.019720933,0.975653975,0.001156273,0.001156288,Epidemiology,0.37011334,FALSE,10.75,0.160244913,1.5,0.138747659,2,0.618927094,3091,0.323621478,0.310385286
14992,Highly ACE2 Expression in Pancreas May Cause Pancreas Damage After SARS-CoV-2 Infection,0,10.1101/2020.02.28.20029181,3/3/20,medrxiv,0,10,dataset,0.405654958,0.002238577,0.129094927,0.002238611,0.002238507,0.45853442,Clinics,0.56690735,TRUE,33,0.466757375,16.7,0.444741771,49,0.956787456,9378,0.765470744,0.658439337
14993,Multi-epitope vaccine design using an immunoinformatics approach for 2019 novel coronavirus in China (SARS-CoV-2),0,10.1101/2020.03.03.962332,3/3/20,biorxiv,0,14,in silico,0.826028447,0.067524289,0.001034588,0.08902838,0.001034579,0.015349717,Drug discovery,0.35807818,FALSE,209.9285714,0.975508689,237.6428571,0.933168317,7,0.785110192,13841,0.837466891,0.882813522
14994,The level of plasma C-reactive protein is closely related to the liver injury in patients with COVID-19,0,10.1101/2020.02.28.20028514,3/3/20,medrxiv,0,9,logistic regression,0.001415142,0.001415162,0.035974722,0.001415147,0.001415141,0.958364686,Clinics,0.9529506,TRUE,55.44444444,0.670727936,20.77777778,0.488961734,32,0.933699611,5742,0.607994221,0.675345876
14995,Evidence for RNA editing in the transcriptome of 2019 Novel Coronavirus,0,10.1101/2020.03.02.973255,3/3/20,biorxiv,10.1126/sciadv.abb5813,5,"transcriptom, genomes",0.280069991,0.52153925,0.0013301,0.153596055,0.001330027,0.042134576,Genomics,0.34555906,FALSE,23.6,0.349124869,46.2,0.664704308,71,0.96950429,12269,0.817722129,0.700263899
14996,Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study,0,10.1101/2020.02.29.20029603,3/3/20,medrxiv,10.21037/atm-20-3026,21,"machine learning, radiom, logistic regression, dataset",0.001141306,0.001141315,0.551173351,0.001141362,0.00114132,0.444261345,Imaging,0.7673517,TRUE,33.33333333,0.469911559,,,55,0.960800049,5852,0.614977125,0.681896245
14997,Detectable serum SARS-CoV-2 viral load (RNAaemia) is closely associated with drastically elevated interleukin 6 (IL-6) level in critically ill COVID-19 patients,0,10.1101/2020.02.29.20029520,3/3/20,medrxiv,0,12,prediction model,0.001059422,0.190496294,0.049409574,0.001059378,0.001059388,0.756915943,Clinics,0.72902274,TRUE,53.75,0.659348135,30.83333333,0.576264383,146,0.986110254,17110,0.871418252,0.773285256
14998,Association of Cardiovascular Manifestations with In-hospital Outcomes in Patients with COVID-19: A Hospital Staff Data,0,10.1101/2020.02.29.20029348,3/3/20,medrxiv,0,8,logistic regression,0.000956308,0.061726374,0.000956363,0.000956399,0.140277381,0.795127176,Clinics,0.5834574,TRUE,60,0.703444864,24.375,0.524150388,27,0.92443978,6144,0.633758729,0.69644844
14999,CRISPR-based COVID-19 surveillance using a genomically-comprehensive machine learning approach,0,10.1101/2020.02.26.967026,3/2/20,biorxiv,0,5,machine learning,0.00190177,0.905862096,0.086531,0.001901742,0.001901684,0.001901708,Genomics,0.40514296,FALSE,50.2,0.632444802,315.8,0.958389082,60,0.964503982,21061,0.898868288,0.863551538
15000,Molecular Dynamics Simulations Indicate the COVID-19 Mpro Is Not a Viable Target for Small-Molecule Inhibitors Design,0,10.1101/2020.02.27.968008,3/2/20,biorxiv,10.3390/ijms21093099,6,molecular dynamics simulation,0.866375034,0.12701263,0.001653095,0.001653166,0.001653065,0.001653009,Drug discovery,0.8032259,TRUE,33.28571429,0.469107551,5,0.257024351,16,0.881782826,11180,0.799181315,0.601774011
15001,Predictions for the binding domain and potential new drug targets of 2019-nCoV,0,10.1101/2020.02.26.961938,3/2/20,biorxiv,0,3,prediction model,0.686719297,0.207840466,0.002639072,0.09752317,0.002639014,0.002638981,Drug discovery,0.52010024,TRUE,112.3333333,0.894736842,48.66666667,0.676143966,0,0.403234768,2546,0.220563448,0.548669756
15002,Strategies for vaccine design for corona virus using Immunoinformatics techniques,0,10.1101/2020.02.27.967422,3/2/20,biorxiv,0,3,vaccinom,0.868293643,0.09682402,0.00127264,0.031064425,0.00127265,0.001272621,Drug discovery,0.44228363,FALSE,141.3333333,0.93561754,205.6666667,0.920189992,2,0.618927094,4858,0.533590176,0.7520812
15003,Prediction of survival for severe Covid-19 patients with three clinical features: development of a machine learning-based prognostic model with clinical data in Wuhan,0,10.1101/2020.02.27.20028027,3/1/20,medrxiv,0,0,machine learning,0.00146198,0.001461946,0.501355267,0.001461979,0.001462018,0.49279681,Clinics,0.53418535,TRUE,46.05263158,0.596944771,18.42105263,0.464811346,103,0.978949318,29189,0.93089333,0.742899691
15004,Prediction of the Epidemic of COVID-19 Based on Quarantined Surveillance in China,0,10.1101/2020.02.27.20027169,2/29/20,medrxiv,0,7,mathematical model,0.001901691,0.00190177,0.001901813,0.905616406,0.001901749,0.086776572,Epidemiology,0.41370124,FALSE,33.14285714,0.46762323,1.857142857,0.151525288,2,0.618927094,4164,0.465205875,0.425820372
15005,Estimate the incubation period of coronavirus 2019 (COVID-19),0,10.1101/2020.02.24.20027474,2/29/20,medrxiv,0,1,machine learning,0.000898111,0.000898134,0.108287457,0.660657498,0.067977413,0.161281388,Epidemiology,0.3994584,FALSE,38,0.519327107,13,0.400521809,26,0.920859312,17240,0.871659042,0.678091818
15006,Prediction of receptorome for human-infecting virome,0,10.1101/2020.02.27.967885,2/28/20,biorxiv,10.1007/s12250-020-00259-6,5,virom,0.816301916,0.001565459,0.177436736,0.001565314,0.001565287,0.001565287,Drug discovery,0.53778523,TRUE,18.2,0.273053374,36.2,0.612322719,1,0.537564047,2996,0.300746448,0.430921647
15007,Correlation Analysis Between Disease Severity and Inflammation-related Parameters in Patients with COVID-19 Pneumonia,0,10.1101/2020.02.25.20025643,2/27/20,medrxiv,0,11,correlation analysis,0.104082794,0.001350315,0.001350344,0.001350346,0.001350304,0.890515898,Clinics,0.83760166,TRUE,107.0909091,0.883418888,83.45454545,0.786727321,149,0.986912772,14058,0.838189261,0.873812061
15008,"Spread and control of COVID-19 in China and their associations with population movement, public health emergency measures, and medical resources",0,10.1101/2020.02.24.20027623,2/27/20,medrxiv,0,21,correlation analysis,0.001486423,0.00148644,0.001486444,0.797277391,0.001486508,0.196776795,Epidemiology,0.4340429,FALSE,41.14285714,0.549570165,21.23809524,0.493778432,12,0.850299401,2868,0.27618589,0.542458472
15009,Spike protein binding prediction with neutralizing antibodies of SARS-CoV-2,0,10.1101/2020.02.22.951178,2/27/20,biorxiv,0,10,bioinformatic,0.645954543,0.331162767,0.00108536,0.019626686,0.001085324,0.00108532,Drug discovery,0.5159561,TRUE,45.7,0.593048426,27.4,0.548969762,17,0.887338725,10386,0.784974717,0.703582907
15010,"Structure-based drug design, virtual screening and high-throughput screening rapidly identify antiviral leads targeting COVID-19",0,10.1101/2020.02.26.964882,2/27/20,biorxiv,10.1038/s41586-020-2223-y,31,virtual screening,0.994063611,0.001187293,0.001187313,0.001187266,0.001187259,0.001187257,Drug discovery,0.6725586,TRUE,71.64516129,0.771290742,286.6129032,0.95049505,222,0.993024261,42874,0.95400915,0.9172048
15011,Genomic variations of COVID-19 suggest multiple outbreak sources of transmission,0,10.1101/2020.02.25.20027953,2/26/20,medrxiv,0,4,genomes,0.087147737,0.817803508,0.001823407,0.001823418,0.001823351,0.08957858,Genomics,0.3416342,FALSE,16.75,0.252458408,17.5,0.45611453,27,0.92443978,23496,0.909463039,0.635618939
15012,Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study in 27 patients,0,10.1101/2020.02.25.20021568,2/26/20,medrxiv,0,18,"deep learning, dataset",0.00115623,0.00115626,0.69050301,0.063384069,0.001156285,0.242644146,Imaging,0.7885112,TRUE,34.55555556,0.482590142,,,157,0.987900488,37380,0.946063087,0.805517905
15013,The landscape of lung bronchoalveolar immune cells in COVID-19 revealed by single-cell RNA sequencing,0,10.1101/2020.02.23.20026690,2/26/20,medrxiv,10.1038/s41591-020-0901-9,13,sequencing,0.510925236,0.001141422,0.001141342,0.001141378,0.067722694,0.417927928,Drug discovery,0.6472087,TRUE,72.84615385,0.776918795,56.84615385,0.710061547,125,0.983270572,44781,0.957620997,0.856967978
15014,Can routine laboratory tests discriminate 2019 novel coronavirus infected pneumonia from other community-acquired pneumonia?,0,10.1101/2020.02.25.20024711,2/25/20,medrxiv,0,14,logistic regression,0.038259565,0.001438171,0.275153506,0.001438179,0.001438146,0.682272432,Clinics,0.44242316,FALSE,42.28571429,0.560207805,73.92857143,0.763580412,2,0.618927094,2914,0.283168794,0.556471026
15015,Characterizing the transmission and identifying the control strategy for COVID-19 through epidemiological modeling,0,10.1101/2020.02.24.20026773,2/25/20,medrxiv,0,6,predictive model,0.002562599,0.002562717,0.002562614,0.987186704,0.002562595,0.00256277,Epidemiology,0.31552082,FALSE,11.83333333,0.178304162,5.166666667,0.25849612,9,0.814309525,3297,0.349385986,0.400123948
15016,Effectiveness of intervention strategies for Coronavirus Disease 2019 and an estimation of its peak time,0,10.1101/2020.02.19.20025387,2/23/20,medrxiv,0,8,mathematical model,0.00057635,0.000576369,0.000576352,0.958326928,0.000576376,0.039367625,Epidemiology,0.38877237,FALSE,38.125,0.519821881,,,19,0.89561084,4505,0.492655911,0.636029544
15017,Development and Evaluation of A CRISPR-based Diagnostic For 2019-novel Coronavirus,0,10.1101/2020.02.22.20025460,2/23/20,medrxiv,0,15,"sequencing, metagenom",0.001593545,0.7876078,0.206018134,0.001593547,0.001593495,0.001593479,Genomics,0.729226,TRUE,34.86666667,0.485558785,39.13333333,0.628445277,43,0.9507377,6419,0.644594269,0.677334008
15018,Early Prediction of Disease Progression in 2019 Novel Coronavirus Pneumonia Patients Outside Wuhan with CT and Clinical Characteristics,0,10.1101/2020.02.19.20025296,2/23/20,medrxiv,0,23,"logistic regression, correlation analysis",0.000871543,0.000871549,0.233075715,0.00087156,0.000871552,0.763438081,Clinics,0.36647388,FALSE,74.91304348,0.786072113,28.56521739,0.558536259,42,0.949503056,4046,0.44786901,0.68549511
15019,Novel Coronavirus 2019 (Covid-19) epidemic scale estimation: topological network-based infection dynamic model,0,10.1101/2020.02.20.20023572,2/23/20,medrxiv,0,3,network model,0.002490512,0.002490485,0.045158317,0.944879657,0.00249051,0.002490519,Epidemiology,0.21652305,FALSE,25.66666667,0.376461129,9.666666667,0.348274017,5,0.739490092,3970,0.439441368,0.475916651
15020,Early Phylogenetic Estimate Of The Effective Reproduction Number Of 2019-nCoV,0,10.1101/2020.02.19.20024851,2/23/20,medrxiv,10.1002/jmv.25723,5,genomes,0.003335306,0.317675742,0.003335231,0.668983139,0.003335263,0.003335319,Epidemiology,0.5279821,TRUE,50.4,0.634547591,49.8,0.680559272,7,0.785110192,6235,0.6320732,0.683072564
15021,A Note on NCP Diagnosis Number Prediction Model,0,10.1101/2020.02.19.20025262,2/23/20,medrxiv,0,0,"mathematical model, prediction model",0.001751147,0.001751181,0.142262871,0.677262192,0.052585469,0.124387141,Epidemiology,0.26284975,FALSE,17.83333333,0.268476715,,,2,0.618927094,2685,0.239585842,0.375663217
15022,"Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 epidemic in China: a web-based cross-sectional survey",0,10.1101/2020.02.19.20025395,2/23/20,medrxiv,10.1016/j.psychres.2020.112954,2,logistic regression,0.001371233,0.001371361,0.022855924,0.001371358,0.971658834,0.00137129,Healthcare,0.7498436,TRUE,26.5,0.389325252,40.5,0.637342788,97,0.977467745,11002,0.79267999,0.699203944
15023,"Epidemiological characteristics of 1212 COVID-19 patients in Henan, China",0,10.1101/2020.02.21.20026112,2/23/20,medrxiv,0,6,network analysis,0.00263904,0.002639076,0.002639018,0.556111352,0.002639095,0.433332419,Epidemiology,0.2827322,FALSE,31,0.445111015,8.333333333,0.325662296,15,0.874313229,5825,0.605104744,0.562547821
15024,Profiling ACE2 expression in colon tissue of healthy adults and colorectal cancer patients by single-cell transcriptome analysis,0,10.1101/2020.02.15.20023457,2/23/20,medrxiv,0,16,"transcriptom, dataset",0.53923758,0.136860331,0.031155995,0.001220054,0.001220051,0.290305989,Drug discovery,0.6509581,TRUE,59.6875,0.700723607,39.0625,0.627976987,13,0.858880178,2965,0.289429328,0.619252525
15025,COVID-19 in Wuhan: Immediate Psychological Impact on 5062 Health Workers,0,10.1101/2020.02.20.20025338,2/23/20,medrxiv,10.1016/j.eclinm.2020.100443,15,logistic regression,0.001171541,0.001171557,0.001171542,0.001171565,0.906362415,0.08895138,Healthcare,0.94254756,TRUE,129,0.920588781,57.4,0.712469896,104,0.979134514,11511,0.802070792,0.853565996
15026,A Multiscale and Comparative Model for Receptor Binding of 2019 Novel Coronavirus and the Implication of its Life Cycle in Host Cells,0,10.1101/2020.02.20.958272,2/21/20,biorxiv,0,2,"computational, mathematical model, structural model",0.816446994,0.001622777,0.001622742,0.177061829,0.001622807,0.001622852,Drug discovery,0.42092416,FALSE,17,0.257467994,2.5,0.180826866,11,0.840175319,5665,0.593787623,0.468064451
15027,Potential T-cell and B-cell Epitopes of 2019-nCoV,0,10.1101/2020.02.19.955484,2/21/20,biorxiv,0,3,"machine learning, computational, genomes",0.646804373,0.326878876,0.023978451,0.000779425,0.00077943,0.000779445,Drug discovery,0.17034096,FALSE,201.3333333,0.972725586,390,0.970096334,60,0.964503982,13842,0.832651096,0.934994249
15028,Isolation and Characterization of 2019-nCoV-like Coronavirus from Malayan Pangolins,0,10.1101/2020.02.17.951335,2/20/20,biorxiv,0,26,genomes,0.100713588,0.892417578,0.001717198,0.001717245,0.001717202,0.00171719,Genomics,0.5313887,TRUE,37.15384615,0.509431628,70.5,0.755351887,149,0.986912772,63780,0.972790754,0.80612176
15029,Are pangolins the intermediate host of the 2019 novel coronavirus (2019-nCoV) ?,0,10.1101/2020.02.18.954628,2/20/20,biorxiv,10.1371/journal.ppat.1008421,9,genomes,0.002806494,0.985967874,0.002806462,0.002806426,0.002806338,0.002806406,Genomics,0.42815545,FALSE,35.22222222,0.488960356,26.88888889,0.54482205,57,0.962281622,14315,0.83867084,0.708683717
15030,Pangolin homology associated with 2019-nCoV,0,10.1101/2020.02.19.950253,2/20/20,biorxiv,0,3,whole genome,0.20315226,0.785967238,0.002720136,0.002720139,0.002720103,0.002720123,Genomics,0.46395218,FALSE,20,0.298163152,24.33333333,0.524016591,69,0.968763504,21572,0.897664339,0.672151896
15031,Estimation of the epidemic properties of the 2019 novel coronavirus: A mathematical modeling study,0,10.1101/2020.02.18.20024315,2/20/20,medrxiv,0,13,mathematical model,0.001098802,0.001098829,0.001098804,0.911230461,0.00109887,0.084374234,Epidemiology,0.5416689,TRUE,31.23076923,0.446780877,,,21,0.903944688,8134,0.715145678,0.688623748
15032,"Psychological responses, behavioral changes and public perceptions during the early phase of the COVID-19 outbreak in China: a population based cross-sectional survey",0,10.1101/2020.02.18.20024448,2/20/20,medrxiv,0,7,logistic regression,0.001291282,0.001291286,0.001291327,0.097583138,0.897251685,0.001291281,Healthcare,0.49009845,FALSE,42.42857143,0.561692127,125.1428571,0.857840514,59,0.963516266,10502,0.784252348,0.791825314
15033,Association of Population Migration and Coronavirus Disease 2019 Epidemic Control,0,10.1101/2020.02.18.20024661,2/20/20,medrxiv,10.1007/s11427-020-1695-5,7,correlation analysis,0.001272662,0.001272696,0.001272667,0.715706804,0.001272714,0.279202457,Epidemiology,0.20112261,FALSE,73.28571429,0.778959738,48.71428571,0.676277763,5,0.739490092,3240,0.334457019,0.632296153
15034,Fractal kinetics of COVID-19 pandemic,0,10.1101/2020.02.16.20023820,2/20/20,medrxiv,10.18562/ijee.053,2,network model,0.001272635,0.00127265,0.001272657,0.99363667,0.001272677,0.001272712,Epidemiology,0.23389414,FALSE,15,0.227596017,7.5,0.307867273,83,0.974381135,20700,0.891644594,0.600372255
15035,Structure of dimeric full-length human ACE2 in complex with B0AT1,0,10.1101/2020.02.17.951848,2/18/20,biorxiv,0,5,structural model,0.930945162,0.002296684,0.002296549,0.059868488,0.002296571,0.002296546,Drug discovery,0.5163986,TRUE,30.2,0.433916754,98,0.819106235,34,0.937156615,20868,0.891885384,0.770516247
15036,Identification of 2019-nCoV related coronaviruses in Malayan pangolins in southern China,0,10.1101/2020.02.13.945485,2/18/20,biorxiv,10.1038/s41586-020-2169-0,14,"sequencing, metagenom",0.135918713,0.854894839,0.002296686,0.002296633,0.002296558,0.00229657,Genomics,0.68266976,TRUE,111.3571429,0.892448513,360.6428571,0.967286594,153,0.987344898,60723,0.970864435,0.95448611
15037,Recombination and convergent evolution led to the emergence of 2019 Wuhan coronavirus,0,10.1101/2020.02.10.942748,2/18/20,biorxiv,0,7,structural model,0.486566434,0.482079625,0.02860384,0.000916736,0.000916687,0.000916678,Drug discovery,0.39220086,FALSE,68.5,0.756571217,91,0.803987155,16,0.881782826,22822,0.905128823,0.836867505
15038,Structural modeling of 2019-novel coronavirus (nCoV) spike protein reveals a proteolytically-sensitive activation loop as a distinguishing feature compared to SARS-CoV and related SARS-like coronaviruses,0,10.1101/2020.02.10.942185,2/18/20,biorxiv,10.1016/j.jmb.2020.04.009,4,structural model,0.618598359,0.374108149,0.00182334,0.001823442,0.001823363,0.001823347,Drug discovery,0.5068961,TRUE,57.25,0.683344672,152,0.885001338,28,0.926168282,15404,0.846857693,0.835342996
15039,Can Search Query Forecast successfully in China's 2019-nCov pneumonia?,0,10.1101/2020.02.12.20022400,2/18/20,medrxiv,0,3,"predictive model, prediction model",0.002720155,0.098695768,0.277999641,0.61514394,0.002720165,0.002720332,Epidemiology,0.30945992,FALSE,33.66666667,0.47312759,1.333333333,0.13252609,4,0.707574542,2389,0.176017337,0.37231139
15040,A model simulation study on effects of intervention measures in Wuhan COVID-19 epidemic,0,10.1101/2020.02.14.20023168,2/18/20,medrxiv,0,2,"simulation model, model simulation",0.001392849,0.001392853,0.001392901,0.755188883,0.077453247,0.163179268,Epidemiology,0.27168253,FALSE,9.5,0.143051518,0,0.055525823,7,0.785110192,3894,0.426438719,0.352531563
15041,"Evaluating new evidence in the early dynamics of the novel coronavirus COVID-19 outbreak in Wuhan, China with real time domestic traffic and potential asymptomatic transmissions",0,10.1101/2020.02.15.20023440,2/18/20,medrxiv,0,1,dataset,0.001461866,0.103351325,0.00146191,0.890800992,0.001461998,0.001461909,Epidemiology,0.29381394,FALSE,17,0.257467994,3,0.199424672,25,0.918019631,4027,0.440645317,0.453889403
15042,"Optimizing diagnostic strategy for novel coronavirus pneumonia, a multi-center study in Eastern China",0,10.1101/2020.02.13.20022673,2/17/20,medrxiv,0,19,"sequencing, metagenom",0.001203417,0.4165524,0.299857569,0.001203482,0.001203474,0.279979659,Genomics,0.50311065,TRUE,31.21052632,0.446595337,25.68421053,0.53492106,43,0.9507377,7677,0.696604864,0.65721474
15043,The Efficacy of Contact Tracing for the Containment of the 2019 Novel Coronavirus (COVID-19).,0,10.1101/2020.02.14.20023036,2/17/20,medrxiv,10.1136/jech-2020-214051,3,predictive model,0.002130662,0.002130691,0.002130649,0.900658193,0.09081912,0.002130687,Epidemiology,0.2667691,FALSE,173,0.960603624,493,0.97832486,133,0.984196555,24252,0.909703829,0.958207217
15044,"Structural genomics and interactomics of 2019 Wuhan novel coronavirus, 2019-nCoV, indicate evolutionary conserved functional regions of viral proteins",0,10.1101/2020.02.10.942136,2/14/20,biorxiv,0,9,"computational, interactom",0.40165636,0.388477758,0.002422381,0.202598782,0.002422437,0.002422281,Drug discovery,0.27723294,FALSE,115.5555556,0.899870122,66.11111111,0.742708055,9,0.814309525,8038,0.709366723,0.791563606
15045,Identification of a pangolin niche for a 2019-nCoV-like coronavirus through an extensive meta-metagenomic search,0,10.1101/2020.02.08.939660,2/14/20,biorxiv,0,7,"sequencing, metagenom, virom, deep sequencing, dataset",0.001254645,0.719908454,0.137607603,0.138720078,0.001254618,0.001254602,Genomics,0.31548795,FALSE,28.14285714,0.408868823,242.1428571,0.934573187,27,0.92443978,11954,0.80496027,0.768210515
15046,Estimating underdetection of internationally imported COVID-19 cases,0,10.1101/2020.02.13.20022707,2/14/20,medrxiv,0,4,"bayes, mathematical model, bayesian model",0.001237067,0.257850353,0.001237141,0.737201132,0.001237184,0.001237123,Epidemiology,0.24362394,FALSE,139.5,0.93295813,298.25,0.95377308,66,0.967652324,40760,0.949915724,0.951074814
15047,Potentially highly potent drugs for 2019-nCoV,0,10.1101/2020.02.05.936013,2/13/20,biorxiv,0,5,"machine learning, dataset",0.849371392,0.00223862,0.141674276,0.002238594,0.00223866,0.002238458,Drug discovery,0.6217073,TRUE,40.8,0.546972602,11.2,0.373026492,33,0.936045435,9275,0.751023357,0.651766971
15048,Evidence of recombination in coronaviruses implicating pangolin origins of nCoV-2019,0,10.1101/2020.02.07.939207,2/13/20,biorxiv,0,4,"metagenom, genomes, dataset",0.103199242,0.88394352,0.003214388,0.003214278,0.003214211,0.00321436,Genomics,0.5721503,TRUE,113.5,0.896715938,282.25,0.949491571,119,0.982344589,35949,0.94221045,0.942690637
15049,Statistics based predictions of coronavirus 2019-nCoV spreading in mainland China,0,10.1101/2020.02.12.20021931,2/13/20,medrxiv,10.20535/ibb.2020.4.1.195074,1,mathematical model,0.001538149,0.050374931,0.001538118,0.921922779,0.023087933,0.001538089,Epidemiology,0.5922071,TRUE,16,0.243552477,13,0.400521809,41,0.948144947,5324,0.556705996,0.537231307
15050,ACE2 Expression in Kidney and Testis May Cause Kidney and Testis Damage After 2019-nCoV Infection,0,10.1101/2020.02.12.20022418,2/13/20,medrxiv,10.3389/fmed.2020.563893,5,dataset,0.532713105,0.001371285,0.001371386,0.001371336,0.025721434,0.437451453,Drug discovery,0.93255836,TRUE,48.2,0.615808028,33.2,0.593791812,212,0.991974813,381219,0.998073682,0.799912084
15051,Serial interval of novel coronavirus (2019-nCoV) infections,0,10.1101/2020.02.03.20019497,2/13/20,medrxiv,10.1016/j.ijid.2020.02.060,3,dataset,0.001653061,0.001653128,0.001653054,0.740264945,0.001653063,0.253122749,Epidemiology,0.26512223,FALSE,138.3333333,0.931102727,301.6666667,0.954776559,99,0.977961603,18119,0.87382615,0.93441676
15052,"Early epidemiological assessment of the transmission potential and virulence of 2019 Novel Coronavirus in Wuhan City: China, 2019-2020",0,10.1101/2020.02.12.20022434,2/13/20,medrxiv,10.1186/s12916-020-01691-x,3,dataset,0.000977423,0.000977458,0.000977431,0.927461502,0.000977475,0.068628711,Epidemiology,0.22274682,FALSE,108.3333333,0.886573072,231.6666667,0.930358576,69,0.968763504,199731,0.993498676,0.944798457
15053,"Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCoV, in the nasal tissue",0,10.1101/2020.02.11.20022228,2/13/20,medrxiv,0,4,dataset,0.421051269,0.572574522,0.001593566,0.001593579,0.001593533,0.001593531,Genomics,0.48911813,FALSE,58.25,0.691384749,60.25,0.721969494,42,0.949503056,8342,0.719239104,0.770524101
15054,Facemask shortage and the novel coronavirus (2019-nCoV) outbreak: Reflection on public health measures,0,10.1101/2020.02.11.20020735,2/12/20,medrxiv,10.1016/j.eclinm.2020.100329,5,mathematical model,0.001438098,0.001438199,0.041707588,0.825155603,0.128822359,0.001438153,Epidemiology,0.42665523,FALSE,26.2,0.383820892,8,0.320511105,10,0.828199272,8611,0.728870696,0.565350491
15055,Characteristics of lymphocyte subsets and cytokines in peripheral blood of 123 hospitalized patients with 2019 novel coronavirus pneumonia (NCP),0,10.1101/2020.02.10.20021832,2/12/20,medrxiv,0,14,correlation analysis,0.126937714,0.027967958,0.001187396,0.001187301,0.00118727,0.841532362,Clinics,0.979105,TRUE,43.57142857,0.573814089,34.28571429,0.60048167,335,0.995863942,18161,0.87358536,0.760936265
15056,"Tobacco-use disparity in gene expression of ACE2, the receptor of 2019-nCov",0,10.1101/2020.02.05.20020107,2/11/20,medrxiv,0,1,"transcriptom, dataset",0.690565263,0.00135039,0.001350389,0.001350405,0.304033137,0.001350416,Drug discovery,0.6341025,TRUE,27,0.3960047,54,0.697952903,0,0.403234768,46834,0.958824946,0.614004329
15057,"The Novel Coronavirus, 2019-nCoV, is Highly Contagious and More Infectious Than Initially Estimated",0,10.1101/2020.02.07.20021154,2/11/20,medrxiv,10.3201/eid2607.200282,6,mathematical model,0.00249053,0.067905884,0.002490439,0.922132243,0.002490464,0.002490439,Epidemiology,0.22545296,FALSE,46.16666667,0.597625085,66.66666667,0.744781911,139,0.98487561,213306,0.994702625,0.830496307
15058,Diarrhea may be underestimated: a missing link in 2019 novel coronavirus,0,10.1101/2020.02.03.20020289,2/11/20,medrxiv,10.1136/gutjnl-2020-320832,7,sequencing,0.421968527,0.293026337,0.001751256,0.001751304,0.001751295,0.27975128,Drug discovery,0.5142364,TRUE,7.5,0.108355495,10.66666667,0.365333155,16,0.881782826,8649,0.729833855,0.521326333
15059,Transmission Dynamics of 2019-nCoV in Malaysia,0,10.1101/2020.02.07.20021188,2/11/20,medrxiv,0,2,mathematical model,0.003927488,0.003927535,0.003927481,0.928043681,0.003927338,0.056246477,Epidemiology,0.12212774,FALSE,45.5,0.591440411,54,0.697952903,11,0.840175319,3566,0.373464965,0.6257584
15060,Feasibility of controlling 2019-nCoV outbreaks by isolation of cases and contacts,0,10.1101/2020.02.08.20021162,2/11/20,medrxiv,10.1016/s2214-109x(20)30074-7,12,mathematical model,0.000572626,0.058442139,0.000572627,0.819413294,0.120426673,0.00057264,Epidemiology,0.19505101,FALSE,80.83333333,0.81025419,209.5833333,0.921996254,67,0.967960985,8730,0.732241753,0.858113295
15061,Exploring the coronavirus epidemic using the new WashU Virus Genome Browser,0,10.1101/2020.02.07.939124,2/11/20,biorxiv,10.1038/s41588-020-0697-z,8,genome sequences,0.001717203,0.991413637,0.001717333,0.001717346,0.001717307,0.001717174,Genomics,0.22127506,FALSE,17.75,0.267672707,16.75,0.446012845,4,0.707574542,4638,0.492896701,0.478539199
15062,Design of multi epitope-based peptide vaccine against E protein of human 2019-nCoV: An immunoinformatics approach,0,10.1101/2020.02.04.934232,2/11/20,biorxiv,10.1155/2020/2683286,7,sequencing,0.668266784,0.187061039,0.00148645,0.140212803,0.001486497,0.001486427,Drug discovery,0.73464763,TRUE,15,0.227596017,0,0.055525823,27,0.92443978,9867,0.764989164,0.493137696
15063,The Essential Facts of Wuhan Novel Coronavirus Outbreak in China and Epitope-based Vaccine Designing against 2019-nCoV,0,10.1101/2020.02.05.935072,2/11/20,biorxiv,0,5,"molecular dynamics simulation, computational, in silico",0.872280113,0.00194363,0.001943548,0.119945695,0.001943517,0.001943496,Drug discovery,0.7796708,TRUE,17.2,0.259323397,0.8,0.101351351,15,0.874313229,7859,0.699494341,0.48362058
15064,The transmembrane serine protease inhibitors are potential antiviral drugs for 2019-nCoV targeting the insertion sequence-induced viral infectivity enhancement,0,10.1101/2020.02.08.926006,2/11/20,biorxiv,0,17,"bioinformatic, transcriptom",0.993035616,0.001392975,0.001392881,0.001392848,0.001392812,0.001392868,Drug discovery,0.8509557,TRUE,89.88235294,0.842290803,95.58823529,0.813888146,56,0.961540836,13207,0.821815555,0.859883835
15065,Analysis of the epidemic growth of the early 2019-nCoV outbreak using internationally confirmed cases,0,10.1101/2020.02.06.20020941,2/9/20,medrxiv,0,3,bayes,0.001010943,0.065091972,0.00101094,0.891403808,0.040471347,0.00101099,Epidemiology,0.15171462,FALSE,167.6666667,0.956707279,183,0.908348943,16,0.881782826,8403,0.717794366,0.866158353
15066,Protein structure and sequence re-analysis of 2019-nCoV genome does not indicate snakes as its intermediate host or the unique similarity between its spike protein insertions and HIV-1,0,10.1101/2020.02.04.933135,2/8/20,biorxiv,10.1021/acs.jproteome.0c00129,6,"computational, bioinformatic, dataset",0.207464711,0.665137553,0.1203243,0.002357905,0.00235779,0.002357741,Genomics,0.5467225,TRUE,58.83333333,0.694910013,173,0.901659085,22,0.908142478,10611,0.779918131,0.821157427
15067,"ACE2 expression by colonic epithelial cells is associated with viral infection, immunity and energy metabolism",0,10.1101/2020.02.05.20020545,2/7/20,medrxiv,0,18,"sequencing, microbiom",0.677143704,0.276923848,0.001823369,0.001823456,0.001823374,0.04046225,Drug discovery,0.7200135,TRUE,36.6875,0.50522605,66.3125,0.743176345,64,0.966479412,10876,0.784733927,0.749903934
15068,A database resource for Genome-wide dynamics analysis of Coronaviruses on a historical and global scale,0,10.1101/2020.02.05.920009,2/7/20,biorxiv,10.1093/database/baaa070,3,"proteom, genome-wide, genomes",0.002238593,0.794729974,0.002238668,0.196315533,0.002238795,0.002238436,Genomics,0.44578195,FALSE,17.33333333,0.261178799,20,0.481000803,1,0.537564047,4601,0.485913797,0.441414362
15069,Risk assessment of novel coronavirus 2019-nCoVoutbreaks outside China,0,10.1101/2020.02.04.20020503,2/5/20,medrxiv,10.3390/jcm9020571,6,computational,0.131219389,0.001861834,0.001861744,0.861333485,0.001861815,0.001861733,Epidemiology,0.2780463,FALSE,21.16666667,0.31319191,5.5,0.267259834,46,0.954009507,8890,0.733204912,0.566916541
15070,Network-based Drug Repurposing for Human Coronavirus,0,10.1101/2020.02.03.20020263,2/5/20,medrxiv,0,6,"computational, transcriptom, interactom, whole genome, genomes",0.796081967,0.200092663,0.000956341,0.000956351,0.000956328,0.00095635,Drug discovery,0.78234625,TRUE,137.3333333,0.929680252,83.16666667,0.785723843,13,0.858880178,10123,0.768360222,0.835661124
15071,Integrative Bioinformatics Analysis Provides Insight into the Molecular Mechanisms of 2019-nCoV,0,10.1101/2020.02.03.20020206,2/5/20,medrxiv,0,8,bioinformatic,0.80137388,0.087423425,0.002080645,0.002080647,0.002080738,0.104960664,Drug discovery,0.5263588,TRUE,26.375,0.386418455,43.75,0.652863259,13,0.858880178,5126,0.530459908,0.60715545
15072,Human-to-human transmission of 2019-novel coronavirus (2019-nCoV),0,10.1101/2020.02.03.20019141,2/5/20,medrxiv,0,34,"sequencing, whole genome, genomes",0.001187269,0.961617717,0.001187366,0.001187341,0.033632996,0.001187311,Genomics,0.3142176,FALSE,44.55882353,0.582287093,,,12,0.850299401,8440,0.715868047,0.716151514
15073,Early epidemiological analysis of the 2019-nCoV outbreak based on a crowdsourced data,0,10.1101/2020.01.31.20019935,2/4/20,medrxiv,0,3,dataset,0.001392882,0.072748783,0.001392887,0.773778311,0.090400302,0.060286835,Epidemiology,0.27048624,FALSE,130.3333333,0.92213495,398,0.970966016,18,0.891474782,8048,0.70142066,0.871499102
15074,Phylogenomic analysis of the 2019-nCoV coronavirus,0,10.1101/2020.02.02.931162,2/4/20,biorxiv,10.1002/jmv.25700,2,"proteom, phylogenom, genomes",0.001902017,0.990490961,0.001901717,0.001901806,0.001901756,0.001901744,Genomics,0.714414,TRUE,6.5,0.093512277,25,0.529435376,2,0.618927094,15666,0.844690585,0.521641333
15075,Machine intelligence design of 2019-nCoV drugs,0,10.1101/2020.01.30.927889,2/4/20,biorxiv,0,4,machine intelligence,0.653094415,0.159959773,0.084169001,0.097498731,0.002639037,0.002639043,Drug discovery,0.8046693,TRUE,40.25,0.541715629,11.25,0.374364463,30,0.930057411,7429,0.679027209,0.631291178
15076,Machine learning-based analysis of genomes suggests associations between Wuhan 2019-nCoV and bat Betacoronaviruses,0,10.1101/2020.02.03.932350,2/4/20,biorxiv,10.1371/journal.pone.0232391,6,"machine learning, whole-genome, genome sequences, genomes, dataset",0.001010962,0.478006605,0.367356974,0.15160355,0.001010942,0.001010967,Genomics,0.63242936,TRUE,60,0.703444864,37.16666667,0.617005619,81,0.973331687,11940,0.799662894,0.773361266
15077,Early dynamics of transmission and control of 2019-nCoV: a mathematical modelling study,0,10.1101/2020.01.31.20019901,2/2/20,medrxiv,10.1016/S1473-3099(20)30144-4,8,mathematical model,0.001486401,0.001486482,0.001486414,0.96342175,0.001486538,0.030632414,Epidemiology,0.34433758,FALSE,99.71428571,0.868204589,144.2857143,0.878378378,274,0.994382369,35981,0.939320973,0.920071577
15078,"Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model",0,10.1101/2020.01.31.929547,2/2/20,biorxiv,10.1016/j.csbj.2020.03.025,5,deep learning,0.867395845,0.001684573,0.076334109,0.05121641,0.001684512,0.001684551,Drug discovery,0.9680795,TRUE,26,0.382398417,21,0.492239765,81,0.973331687,23681,0.904888033,0.688214475
15079,The digestive system is a potential route of 2019-nCov infection: a bioinformatics analysis based on single-cell transcriptomes,0,10.1101/2020.01.30.927806,1/31/20,biorxiv,0,12,"bioinformatic, transcriptom, dataset",0.663542997,0.120490827,0.002032913,0.209867424,0.00203285,0.002032989,Drug discovery,0.62136877,TRUE,74.41666667,0.783721937,91.33333333,0.804656141,384,0.996542996,63472,0.971105225,0.889006575
15080,Evolution and variation of 2019-novel coronavirus,0,10.1101/2020.01.30.926477,1/30/20,biorxiv,0,4,"bayes, whole genome, genomes",0.020497403,0.895402937,0.00115624,0.001156283,0.001156269,0.080630869,Genomics,0.2852825,FALSE,23,0.34225988,15.5,0.430157881,26,0.920859312,18736,0.87406694,0.641836003
15081,Potential inhibitors for 2019-nCoV coronavirus M protease from clinically approved medicines,0,10.1101/2020.01.29.924100,1/29/20,biorxiv,10.1016/j.jgg.2020.02.001,2,computational,0.855071734,0.084368149,0.003101507,0.003101576,0.051255489,0.003101544,Drug discovery,0.8380666,TRUE,56.5,0.678891706,19,0.471367407,83,0.974381135,22246,0.894534072,0.75479358
15082,"Breaking down of the healthcare system: Mathematical modelling for controlling the novel coronavirus (2019-nCoV) outbreak in Wuhan, China",0,10.1101/2020.01.27.922443,1/28/20,biorxiv,0,3,mathematical model,0.002422386,0.036870241,0.093360626,0.862501934,0.002422352,0.002422461,Epidemiology,0.5517984,TRUE,21.33333333,0.31548024,3,0.199424672,58,0.963022409,21597,0.889718276,0.591911399
15083,Beware of asymptomatic transmission: Study on 2019-nCoV prevention and control measures based on extended SEIR model,0,10.1101/2020.01.28.923169,1/28/20,biorxiv,0,0,computational,0.002238528,0.158604857,0.036206919,0.798472704,0.002238498,0.002238495,Epidemiology,0.23948696,FALSE,4,0.054734368,0.5,0.087101953,28,0.926168282,8360,0.709125933,0.444282634
15084,Full-genome evolutionary analysis of the novel corona virus (2019-nCoV) rejects the hypothesis of emergence as a result of a recent recombination event,0,10.1101/2020.01.26.920249,1/27/20,biorxiv,10.1016/j.meegid.2020.104212,5,bayes,0.001203445,0.993982809,0.001203426,0.001203449,0.001203441,0.001203429,Genomics,0.50302374,TRUE,63.4,0.723977983,59,0.717554188,19,0.89561084,85731,0.979773658,0.829229167
15085,Origin time and epidemic dynamics of the 2019 novel coronavirus,0,10.1101/2020.01.25.919688,1/26/20,biorxiv,0,2,bayes,0.00333533,0.332410762,0.003335333,0.654247889,0.0033353,0.003335387,Epidemiology,0.47574097,FALSE,34.5,0.482404601,4.5,0.242708055,19,0.89561084,14424,0.829761618,0.612621279
15086,"Complete genome characterisation of a novel coronavirus associated with severe human respiratory disease in Wuhan, China",0,10.1101/2020.01.24.919183,1/25/20,biorxiv,0,19,"sequencing, metagenom",0.001486448,0.727086403,0.00148642,0.001486483,0.06752958,0.200924665,Genomics,0.3246135,FALSE,141.1052632,0.935184613,280.1578947,0.948822585,82,0.973578616,30729,0.928485432,0.946517811
15087,The 2019-new Coronavirus epidemic: evidence for virus evolution,0,10.1101/2020.01.24.915157,1/24/20,biorxiv,10.1002/jmv.25688,6,"whole genome, genome sequences",0.001751256,0.991244028,0.001751181,0.00175126,0.00175114,0.001751134,Genomics,0.38569352,FALSE,101.3333333,0.87160616,73.16666667,0.762443136,1,0.537564047,22600,0.895256441,0.766717446
15088,Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm,0,10.1101/2020.01.21.914044,1/24/20,biorxiv,0,9,deep learning,0.001220103,0.763962827,0.231157033,0.001220044,0.001220001,0.001219993,Genomics,0.33962753,FALSE,19.22222222,0.287525512,59.77777778,0.71982874,81,0.973331687,40650,0.946544667,0.731807651
15089,Discovery of a novel coronavirus associated with the recent pneumonia outbreak in humans and its potential bat origin,0,10.1101/2020.01.22.914952,1/23/20,biorxiv,10.1038/s41586-020-2012-7,29,"whole genome, genome sequences",0.13755731,0.721297493,0.001943467,0.001943578,0.001943455,0.135314697,Genomics,0.5094936,TRUE,50.37931034,0.634114664,,,508,0.997901105,339063,0.997110523,0.87637543
15090,"Functional assessment of cell entry and receptor usage for lineage B β-coronaviruses, including 2019-nCoV",0,10.1101/2020.01.22.915660,1/22/20,biorxiv,10.1038/s41564-020-0688-y,2,virom,0.38878661,0.605552893,0.001415104,0.001415136,0.001415125,0.001415132,Genomics,0.27100673,FALSE,23,0.34225988,99.5,0.821581482,115,0.981665535,64937,0.971346015,0.779213228
15091,A mathematical model for simulating the transmission of Wuhan novel Coronavirus,0,10.1101/2020.01.19.911669,1/19/20,biorxiv,10.1186/s40249-020-00640-3,6,"mathematical model, network model",0.003101598,0.53276927,0.00310189,0.454824178,0.003101499,0.003101565,Genomics,0.7416408,TRUE,20.16666667,0.299585627,15,0.42594327,68,0.968393111,37931,0.94196966,0.658972917